diff --git a/.github/workflows/python.yml b/.github/workflows/python.yml index b3eee50a8b..83d7d91f82 100644 --- a/.github/workflows/python.yml +++ b/.github/workflows/python.yml @@ -155,19 +155,32 @@ jobs: run: ./py/bin/generate_schema_typing --ci - name: Run serial tests - if: matrix.test-type == 'serial' - run: uv run --python ${{ matrix.python-version }} --active --isolated --directory py pytest -xvs --log-level=DEBUG . + # TODO: Re-enable serial tests for Python 3.14 when onnxruntime adds wheels. + # Track: https://github.com/microsoft/onnxruntime/issues/21292 + # Skip serial tests for Python 3.14 - chromadb doesn't support it yet. + # Nox tests handle Python 3.14 with proper exclusions. + if: matrix.test-type == 'serial' && matrix.python-version != '3.14' + run: | + uv run --python ${{ matrix.python-version }} --active --isolated --directory py pytest -xvs --log-level=DEBUG . - name: Run tox tests if: matrix.test-type == 'tox' run: | clean_version=$(echo "${{ matrix.python-version }}" | tr -d '.') - uv run --directory py tox -e "py$clean_version" + # TODO: Remove --python 3.12 when onnxruntime adds Python 3.14 wheels. + # Track: https://github.com/microsoft/onnxruntime/issues/21292 + # Run tox with Python 3.12 to avoid workspace compatibility issues + uv run --python 3.12 --directory py tox -e "py$clean_version" - name: Run nox tests if: matrix.test-type == 'nox' run: | - uv run --directory py nox -s "tests-${{ matrix.python-version }}" + # TODO: Remove --python 3.12 when onnxruntime adds Python 3.14 wheels. + # Track: https://github.com/microsoft/onnxruntime/issues/21292 + # Run nox with Python 3.12 to avoid workspace compatibility issues + # with chromadb (which doesn't support Python 3.14 yet). + # Nox will still create the test environment with the target Python version. + uv run --python 3.12 --directory py nox -s "tests-${{ matrix.python-version }}" python-build: name: Build Distributions diff --git a/py/GEMINI.md b/py/GEMINI.md index 9a60824169..9c3cccabcc 100644 --- a/py/GEMINI.md +++ b/py/GEMINI.md @@ -58,8 +58,28 @@ # pyrefly: ignore[unexpected-keyword] - Pydantic populate_by_name=True allows schema_ schema_=options.output.json_schema if options.output else None, ``` -* Move imports to the top of the file and avoid using imports inside function - definitions. +* **Import Location**: Always place all imports at the top of the file. + Do not use imports inside functions, methods, or class definitions. + This ensures: + * Faster module loading (imports are resolved once at module load time) + * Clearer dependency visibility (all dependencies visible at a glance) + * Easier refactoring and testing + * Better IDE support for auto-imports and navigation + + **Wrong**: + ```python + async def my_function(): + from genkit.ai import Genkit # Don't import inside functions + ... + ``` + + **Correct**: + ```python + from genkit.ai import Genkit # Import at module top + + async def my_function(): + ... + ``` ## Generated Files & Data Model @@ -94,6 +114,39 @@ * **Imports**: Import types like `Callable`, `Awaitable` from `collections.abc`, not standard library `typing`. * **Enums**: Use `StrEnum` instead of `(str, Enum)`. +* **Pydantic Models**: When defining Pydantic `BaseModel` classes that need + camelCase JSON serialization (for API compatibility with JavaScript): + * Use `alias_generator=to_camel` from `pydantic.alias_generators` in `model_config` + * Include `populate_by_name=True` to allow instantiation with Python snake_case names + * Do **NOT** use manual `Field(alias='camelCase')` for individual fields + + This approach: + * Ensures type checkers understand the model correctly + * Allows Pythonic constructor calls with snake_case field names + * Automatically handles camelCase serialization/deserialization for JSON + + **Correct:** + ```python + from pydantic import BaseModel, ConfigDict + from pydantic.alias_generators import to_camel + + class MyConfig(BaseModel): + model_config = ConfigDict(alias_generator=to_camel, populate_by_name=True) + + my_field: str | None = None + another_field: int = 0 + ``` + + **Wrong** (causes type checker errors): + ```python + from pydantic import BaseModel, ConfigDict, Field + + class MyConfig(BaseModel): + model_config = ConfigDict(populate_by_name=True) + + my_field: str | None = Field(None, alias='myField') # Don't use manual aliases + another_field: int = Field(0, alias='anotherField') + ``` * **Strictness**: Apply type hints strictly, including `-> None` for void functions. * **Design**: * Code against interfaces, not implementations. @@ -101,7 +154,27 @@ * **Comments**: * Use proper punctuation. * Avoid comments explaining obvious code. - * Use `TODO: Fix this later.` format for stubs. + * **TODO Format**: All TODOs **MUST** include a GitHub issue link for tracking. + Plain `# TODO: description` comments are **NOT** allowed. + + **Correct format:** + ```python + # TODO(https://github.com/firebase/genkit/issues/1234): Implement caching + ``` + + **Short format (also accepted):** + ```python + # TODO(#1234): Implement caching + ``` + + **Wrong format (will fail CI):** + ```python + # TODO: Implement caching + # TODO: Fix this later. + ``` + + Before adding a TODO, create a GitHub issue first, then reference it. + This ensures all technical debt is tracked and prioritized. * **Do not add section marker comments** (e.g., `# ============` banners). Keep code clean and let structure speak for itself. * Ensure that `bin/lint` passes without errors. @@ -201,6 +274,12 @@ * Update the roadmap.md file as and when features are implemented. * When a plugin such as a model provider is updated or changes, please also update relevant documentation and samples. +* **Keep Directory READMEs Updated**: When adding or modifying plugins or samples, + always update the corresponding directory README files: + * **`py/plugins/README.md`**: Update when adding a new plugin. Add it to the + appropriate category table with package name, description, and features. + * **`py/samples/README.md`**: Update when adding a new sample. Add it to the + appropriate category table with description and requirements. * Try to make running the sample flows a one-click operation by always defining default input values. * **IMPORTANT**: For default values to appear in the Dev UI input fields, use @@ -314,6 +393,36 @@ Include the Apache 2.0 license header at the top of each file (update year as ne # SPDX-License-Identifier: Apache-2.0 ``` +## Plugin Package Naming Convention + +All Genkit plugins **MUST** follow the naming convention `genkit-plugin-{name}`: + +| Pattern | Example | Status | +|---------|---------|--------| +| `genkit-plugin-{name}` | `genkit-plugin-google-genai` | ✅ Correct | +| `genkit-{name}-plugin` | `genkit-google-genai-plugin` | ❌ Wrong | +| `{name}-genkit-plugin` | `google-genai-genkit-plugin` | ❌ Wrong | + +**Requirements:** +* Package name in `pyproject.toml` must be `genkit-plugin-{name}` +* Use lowercase with hyphens (not underscores) in package names +* The `{name}` should match the plugin directory name (e.g., `plugins/google-genai/` → `genkit-plugin-google-genai`) +* Set `requires-python = ">=3.10"` for CI compatibility +* Include Python 3.10, 3.11, and 3.12 in classifiers + +**Example pyproject.toml:** +```toml +[project] +name = "genkit-plugin-example" +version = "0.1.0" +requires-python = ">=3.10" +classifiers = [ + "Programming Language :: Python :: 3.10", + "Programming Language :: Python :: 3.11", + "Programming Language :: Python :: 3.12", +] +``` + ## Dependency Management When updating dependencies for the Python SDK, ensure consistency across both files: diff --git a/py/bin/generate_schema_typing b/py/bin/generate_schema_typing index 7861be1418..2e84ca6f5d 100755 --- a/py/bin/generate_schema_typing +++ b/py/bin/generate_schema_typing @@ -48,7 +48,11 @@ if [[ $CI_ENABLED == "true" ]] && [[ -f $TYPING_FILE ]]; then fi # Generate types using configuration from pyproject.toml. -uv run --directory "${TOP_DIR}/py" datamodel-codegen \ +# TODO(https://github.com/microsoft/onnxruntime/issues/21292): Remove --python 3.12 +# when onnxruntime adds Python 3.14 wheels. +# Note: We use --python 3.12 to avoid compatibility issues with chromadb +# which doesn't support Python 3.14 yet (onnxruntime lacks 3.14 wheels). +uv run --python 3.12 --directory "${TOP_DIR}/py" datamodel-codegen \ --formatters=ruff-check \ --formatters=ruff-format @@ -56,11 +60,11 @@ uv run --directory "${TOP_DIR}/py" datamodel-codegen \ python3 "${TOP_DIR}/py/bin/sanitize_schema_typing.py" "${TYPING_FILE}" # Checks and formatting. -uv run --directory "${TOP_DIR}/py" \ +uv run --python 3.12 --directory "${TOP_DIR}/py" \ ruff format "${TOP_DIR}" -uv run --directory "${TOP_DIR}/py" \ +uv run --python 3.12 --directory "${TOP_DIR}/py" \ ruff check --fix "${TYPING_FILE}" -uv run --directory "${TOP_DIR}/py" \ +uv run --python 3.12 --directory "${TOP_DIR}/py" \ ruff format "${TOP_DIR}" # We want to detect and raise an error when this file changes in our hooks or in diff --git a/py/engdoc/parity-analysis/roadmap.md b/py/engdoc/parity-analysis/roadmap.md index c98a4e6160..e90bff9747 100644 --- a/py/engdoc/parity-analysis/roadmap.md +++ b/py/engdoc/parity-analysis/roadmap.md @@ -7,7 +7,7 @@ This document organizes the identified gaps into executable milestones with depe ## Current Status (Updated 2026-01-30) > [!IMPORTANT] -> **Overall Parity: ~99% Complete** - Nearly all milestones done! +> **Overall Parity: ~99% Complete** - Core functionality is feature-complete! ### Completed Milestones ✅ @@ -20,44 +20,104 @@ This document organizes the identified gaps into executable milestones with depe | **M4: Telemetry** | ✅ Complete | RealtimeSpanProcessor, flushTracing, AdjustingTraceExporter, GCP parity | | **M5: Advanced** | ✅ Complete | embed_many ✅, define_simple_retriever ✅, define_background_model ✅ | | **M6: Media Models** | ✅ Complete | Veo, Lyria, TTS, Gemini Image models | +| **M7: Vector Stores** | ✅ Complete | Chroma, Pinecone plugins with samples | +| **M8: Safety** | ✅ Complete | Checks plugin (guardrails, middleware) | +| **M9: DAP & Testing** | ✅ Complete | Dynamic Action Providers, test_models() suite | + +### Plugin Comparison (JS vs Python) + +| Plugin | JS | Python | Status | Notes | +|--------|-----|--------|--------|-------| +| **anthropic** | ✅ | ✅ | ✅ Complete | Claude models, ThinkingConfig | +| **google-genai** | ✅ | ✅ | ✅ Complete | Gemini, Imagen, Veo, Lyria, TTS | +| **vertexai** | ✅ | ✅ | ✅ Complete | Model Garden, Vector Search | +| **compat-oai** | ✅ | ✅ | ✅ Complete | OpenAI-compatible endpoints | +| **ollama** | ✅ | ✅ | ✅ Complete | Local models, embeddings | +| **deepseek** | ✅ | ✅ | ✅ Complete | DeepSeek models | +| **xai** | ✅ | ✅ | ✅ Complete | Grok models | +| **pinecone** | ✅ | ✅ | ✅ Complete | Vector store (indexer/retriever) | +| **chroma** | ✅ | ✅ | ✅ Complete | Vector store (indexer/retriever) | +| **dev-local-vectorstore** | ✅ | ✅ | ✅ Complete | Local development store | +| **evaluators** | ✅ | ✅ | ✅ Complete | Built-in evaluators | +| **checks** | ✅ | ✅ | ✅ Complete | Guardrails, safety checks | +| **mcp** | ✅ | ✅ | ✅ Complete | MCP client/server/host | +| **firebase** | ✅ | ✅ | ⚠️ Partial | Missing: Firestore session store, stream managers | +| **google-cloud** | ✅ | ✅ | ✅ Complete | Telemetry, tracing, metrics | +| **flask** | N/A | ✅ | N/A | Python-specific (Express equiv.) | +| **express** | ✅ | N/A | N/A | JS-specific | +| **next** | ✅ | N/A | N/A | JS-specific | +| **cloud-sql-pg** | ✅ | ✅ | ✅ Complete | Cloud SQL PostgreSQL vectorstore | +| **langchain** | ✅ | ❌ | ⚠️ Gap | LangChain integration | ### Remaining Work | Priority | Task | Effort | Status | |----------|------|--------|--------| -| **P0** | Testing Infrastructure (`genkit.testing`) | S | ✅ Complete | -| **P0** | Context Caching (google-genai) | M | ✅ Complete | -| **P1** | `define_background_model()` | M | ✅ Complete | -| **P1** | Veo support in google-genai plugin | M | ✅ Complete | -| **P1** | TTS (Text-to-Speech) models | S | ✅ Complete | -| **P1** | Gemini Image models | S | ✅ Complete | -| **P1** | Lyria audio generation (Vertex AI) | S | ✅ Complete | -| **P1** | Live/Realtime API | L | ❌ Not Started | +| **P1** | Live/Realtime API (google-genai) | L | ❌ Not Started | +| **P2** | Durable Streaming (StreamManager) | M | ❌ Not Started | +| **P2** | `cloud-sql-pg` plugin | M | ✅ Complete | +| **P2** | `langchain` plugin | M | ❌ Not Started | | **P2** | Multi-agent sample | M | ❌ Not Started | -| **P2** | MCP sample | M | ❌ Not Started | +| **P3** | `session.run()` method | S | ❌ Not Started | +| **P3** | `stopServers()` method | S | ❌ Not Started | +| **P3** | Firestore session store | S | ❌ Not Started | --- ## Remaining Gaps (Prioritized) > [!NOTE] -> Most original gaps have been addressed. These are the remaining items. - -| Gap | Description | Priority | Status | -|-----|-------------|----------|--------| -| **Testing Infrastructure** | JS has `echoModel`, `ProgrammableModel`, `TestAction` for unit testing. | **P0** | ✅ Complete | -| **Context Caching** | `ai.cacheContent()`, `cachedContent` option in generate | **P0** | ✅ Complete | -| **define_background_model** | Core API for background models (Veo, etc.) | **P1** | ✅ Complete | -| **Veo plugin support** | Add `veo.py` to google-genai plugin (JS has `veo.ts`) | **P1** | ✅ Complete | -| **TTS models** | Text-to-speech Gemini models (gemini-*-tts) | **P1** | ✅ Complete | -| **Gemini Image models** | Native image generation (gemini-*-image) | **P1** | ✅ Complete | -| **Lyria audio generation** | Audio generation via Vertex AI (lyria-002) | **P1** | ✅ Complete | -| **Live/Realtime API** | Google GenAI Live API for real-time streaming | **P1** | ❌ Not Started | -| **CLI/Tooling Parity** | `genkit` CLI commands and Python project behavior | Medium | ⚠️ Mostly Working | -| **Error Types** | Python error hierarchy parity check | Low | ⚠️ Needs Review | -| **Auth/Security Patterns** | Auth context flow through actions | Medium | ⚠️ Needs Review | -| **Performance Benchmarks** | Streaming latency, memory usage | Low | ❌ Not Started | -| **Migration Guide** | Documentation for JS to Python migration | Low | ❌ Not Started | +> Core functionality is complete. These are enhancement items. + +### P1 - Important (User-Facing Features) + +| Gap | Description | Effort | Status | +|-----|-------------|--------|--------| +| **Live/Realtime API** | Google GenAI Live API for real-time bidirectional streaming | L | ❌ Not Started | + +### P2 - Nice to Have (Ecosystem Completeness) + +| Gap | Description | Effort | Status | +|-----|-------------|--------|--------| +| **Durable Streaming** | `StreamManager` interface, reconnection support | M | ❌ Not Started | +| **cloud-sql-pg plugin** | Cloud SQL PostgreSQL vector store | M | ✅ Complete | +| **langchain plugin** | LangChain integration for existing chains | M | ❌ Not Started | + +### P3 - Low Priority (Minor Enhancements) + +| Gap | Description | Effort | Status | +|-----|-------------|--------|--------| +| **session.run()** | Execute functions within session context | S | ❌ Not Started | +| **stopServers()** | Graceful server shutdown API | S | ❌ Not Started | +| **Firestore session store** | Persistent session storage via Firestore | S | ❌ Not Started | +| **MCP host as DAP** | Register MCP host as Dynamic Action Provider | S | ❌ Not Started | + +### Python 3.14 Compatibility + +> [!WARNING] +> The Chroma plugin is **not compatible with Python 3.14** due to `onnxruntime` lacking 3.14 wheels. +> Track: https://github.com/microsoft/onnxruntime/issues/21292 + +| Component | Compatibility | Notes | +|-----------|---------------|-------| +| Core Genkit | ✅ Python 3.10-3.14 | Full support | +| All plugins except Chroma | ✅ Python 3.10-3.14 | Full support | +| Chroma plugin | ⚠️ Python 3.10-3.13 only | Waiting on onnxruntime | +| rag-chroma sample | ⚠️ Python 3.10-3.13 only | Uses Chroma | + +**CI Workarounds Applied:** +- noxfile uses `--no-project` for Python 3.14 sessions +- tox.ini has py314-specific config skipping chroma +- GitHub Actions workflow skips serial tests for 3.14 +- All workarounds have TODO comments referencing the tracking issue + +### Recent Bug Fixes (2026-01-30) + +| Bug | Component | Fix | +|-----|-----------|-----| +| `data_type()` method not called | Chroma, Pinecone | Added `()` to call method instead of accessing as property | +| Plugin `init()` signature | All plugins | Standardized to accept optional `registry: Registry | None` | +| RAG samples require manual indexing | rag-chroma, rag-pinecone | Added auto-indexing at startup in `main()` | --- @@ -152,6 +212,171 @@ This document organizes the identified gaps into executable milestones with depe --- +## Comprehensive JS vs Python Feature Parity (2026-01-30) + +> [!NOTE] +> Complete audit of all JS features compared to Python implementation. + +### Core Genkit Class Methods + +| Method | JS | Python | Status | Notes | +|--------|-----|--------|--------|-------| +| `generate()` | ✅ | ✅ | ✅ Complete | String, Part[], or options overloads | +| `generateStream()` | ✅ | ✅ | ✅ Complete | Returns iterator + future | +| `generateOperation()` | ✅ | ✅ | ✅ Complete | Operation-tracked generation | +| `checkOperation()` | ✅ | ✅ | ✅ Complete | Background op status | +| `cancelOperation()` | ✅ | ✅ | ✅ Complete | Cancel background op | +| `embed()` | ✅ | ✅ | ✅ Complete | Single embedding | +| `embedMany()` | ✅ | ✅ | ✅ Complete | Batch embeddings | +| `evaluate()` | ✅ | ✅ | ✅ Complete | Dataset evaluation | +| `rerank()` | ✅ | ✅ | ✅ Complete | Document reranking | +| `index()` | ✅ | ✅ | ✅ Complete | Document indexing | +| `retrieve()` | ✅ | ✅ | ✅ Complete | Document retrieval | +| `run()` | ✅ | ✅ | ✅ Complete | Traced sub-steps | +| `currentContext()` | ✅ | ✅ | ✅ Complete | Static method | +| `flushTracing()` | ✅ | ✅ | ✅ Complete | Flush trace exporters | +| `stopServers()` | ✅ | ❌ | ⚠️ Gap | Server cleanup | + +### Action Definition Methods + +| Method | JS | Python | Status | Notes | +|--------|-----|--------|--------|-------| +| `defineFlow()` / `@flow()` | ✅ | ✅ | ✅ Complete | Decorator pattern in Python | +| `defineTool()` / `@tool()` | ✅ | ✅ | ✅ Complete | Decorator pattern in Python | +| `dynamicTool()` | ✅ | ✅ | ✅ Complete | Unregistered tools | +| `defineModel()` | ✅ | ✅ | ✅ Complete | Custom models | +| `defineBackgroundModel()` | ✅ | ✅ | ✅ Complete | Long-running operations | +| `definePrompt()` | ✅ | ✅ | ✅ Complete | Programmatic prompts | +| `prompt()` | ✅ | ✅ | ✅ Complete | Prompt lookup | +| `defineRetriever()` | ✅ | ✅ | ✅ Complete | Custom retrievers | +| `defineSimpleRetriever()` | ✅ | ✅ | ✅ Complete | Simple retriever helper | +| `defineIndexer()` | ✅ | ✅ | ✅ Complete | Custom indexers | +| `defineEvaluator()` | ✅ | ✅ | ✅ Complete | Custom evaluators | +| `defineBatchEvaluator()` | ❌ | ✅ | 🎉 Python extra | Python has batch evaluator | +| `defineEmbedder()` | ✅ | ✅ | ✅ Complete | Custom embedders | +| `defineReranker()` | ✅ | ✅ | ✅ Complete | Custom rerankers | +| `defineSchema()` | ✅ | ✅ | ✅ Complete | Schema registration | +| `defineJsonSchema()` | ✅ | ✅ | ✅ Complete | JSON schema registration | +| `defineHelper()` | ✅ | ✅ | ✅ Complete | Handlebars helpers | +| `definePartial()` | ✅ | ✅ | ✅ Complete | Handlebars partials | +| `defineResource()` | ✅ | ✅ | ✅ Complete | Resource actions | +| `defineFormat()` | ✅ | ✅ | ✅ Complete | Custom formats | +| `defineDynamicActionProvider()` | ✅ | ✅ | ✅ Complete | Dynamic action providers | + +### Session & Chat APIs + +| Feature | JS | Python | Status | Notes | +|---------|-----|--------|--------|-------| +| `Session` class | ✅ | ✅ | ✅ Complete | Stateful execution | +| `session.chat()` | ✅ | ✅ | ✅ Complete | Create chat instances | +| `chat.send()` | ✅ | ✅ | ✅ Complete | Send messages | +| `chat.sendStream()` | ✅ | ✅ | ✅ Complete | Streaming messages | +| `session.updateState()` | ✅ | ✅ | ✅ Complete | State management | +| `session.updateMessages()` | ✅ | ✅ | ✅ Complete | Message management | +| `session.run()` | ✅ | ❌ | ⚠️ Gap | Execute within session | +| `SessionStore` interface | ✅ | ✅ | ✅ Complete | Persistence protocol | +| `InMemorySessionStore` | ✅ | ✅ | ✅ Complete | In-memory impl | +| Thread support | ✅ | ✅ | ✅ Complete | Multiple threads | + +### Streaming & Durable Streaming + +| Feature | JS | Python | Status | Notes | +|---------|-----|--------|--------|-------| +| Basic streaming | ✅ | ✅ | ✅ Complete | `generate_stream()` | +| `on_chunk` callback | ✅ | ✅ | ✅ Complete | Per-chunk callback | +| `StreamManager` interface | ✅ | ❌ | ⚠️ Gap | Durable streaming | +| `InMemoryStreamManager` | ✅ | ❌ | ⚠️ Gap | In-memory impl | +| `FirestoreStreamManager` | ✅ | ❌ | ⚠️ Gap | Firestore backend | +| `RtdbStreamManager` | ✅ | ❌ | ⚠️ Gap | RTDB backend | +| Stream reconnection | ✅ | ❌ | ⚠️ Gap | HTTP reconnection | + +### Interrupts (Tool Pause/Resume) + +| Feature | JS | Python | Status | Notes | +|---------|-----|--------|--------|-------| +| `ctx.interrupt()` | ✅ | ✅ | ✅ Complete | In `ToolRunContext` | +| `ToolInterruptError` | ✅ | ✅ | ✅ Complete | Exception class | +| `tool_response()` helper | ✅ | ✅ | ✅ Complete | Resume helper | +| `response.interrupts` | ✅ | ✅ | ✅ Complete | Interrupt list on response | +| Interrupt handling in generate loop | ✅ | ✅ | ✅ Complete | Auto-handles interrupts | +| `defineInterrupt()` | ✅ | ❌ | ⚠️ Gap | Standalone interrupt tool | +| `tool.respond()` | ✅ | ✅ | ⚠️ Partial | In prompt.py docstring | +| `tool.restart()` | ✅ | ❌ | ⚠️ Gap | Restart from interrupt | + +### MCP (Model Context Protocol) + +| Feature | JS | Python | Status | Notes | +|---------|-----|--------|--------|-------| +| `createMcpClient()` | ✅ | ✅ | ✅ Complete | Single MCP client | +| `createMcpHost()` | ✅ | ✅ | ✅ Complete | Multi-server host | +| `createMcpServer()` | ✅ | ✅ | ✅ Complete | Expose as MCP server | +| `defineMcpHost()` as DAP | ✅ | ❌ | ⚠️ Gap | Register host as DAP | +| `defineMcpClient()` as DAP | ✅ | ❌ | ⚠️ Gap | Register client as DAP | +| Stdio transport | ✅ | ✅ | ✅ Complete | | +| HTTP/SSE transport | ✅ | ✅ | ✅ Complete | | + +### Testing Infrastructure + +| Feature | JS | Python | Status | Notes | +|---------|-----|--------|--------|-------| +| `EchoModel` | ✅ | ✅ | ✅ Complete | Echo input | +| `ProgrammableModel` | ✅ | ✅ | ✅ Complete | Configurable responses | +| `StaticResponseModel` | ❌ | ✅ | 🎉 Python extra | Fixed response | +| `testModels()` suite | ✅ | ✅ | ✅ Complete | Model test suite | +| Request inspection | ✅ | ✅ | ✅ Complete | `last_request` | +| Streaming test support | ✅ | ✅ | ✅ Complete | Countdown chunks | +| `skip()` helper | ✅ | ✅ | ✅ Complete | Skip test cases | + +### Tracing & Telemetry + +| Feature | JS | Python | Status | Notes | +|---------|-----|--------|--------|-------| +| OpenTelemetry integration | ✅ | ✅ | ✅ Complete | | +| `runInNewSpan()` | ✅ | ✅ | ✅ Complete | Traced spans | +| `RealtimeSpanProcessor` | ✅ | ✅ | ✅ Complete | Live trace export | +| `flushTracing()` | ✅ | ✅ | ✅ Complete | Flush exporters | +| `AdjustingTraceExporter` | ✅ | ✅ | ✅ Complete | PII redaction | +| Logging instrumentation | ✅ | ✅ | ✅ Complete | Log correlation | + +### Framework Integration + +| Feature | JS | Python | Status | Notes | +|---------|-----|--------|--------|-------| +| Express integration | ✅ | N/A | N/A | JS-specific | +| Next.js integration | ✅ | N/A | N/A | JS-specific | +| Flask integration | N/A | ✅ | N/A | Python-specific | +| HTTP endpoints | ✅ | ✅ | ✅ Complete | Flow endpoints | +| Durable streaming HTTP | ✅ | ❌ | ⚠️ Gap | Stream reconnection | + +### Priority Gaps Summary + +**P0 - Critical:** +- None identified - core functionality complete ✅ + +**P1 - Important:** +| Gap | Description | Effort | +|-----|-------------|--------| +| **Live/Realtime API** | Google GenAI Live API for bidirectional streaming | L | + +**P2 - Nice to Have:** +| Gap | Description | Effort | +|-----|-------------|--------| +| **Durable Streaming** | StreamManager, reconnection | M | +| `stopServers()` | Server cleanup | S | +| `defineMcpHost()` as DAP | MCP host as DAP | S | +| **langchain** plugin | LangChain integration | M | +| **cloud-sql-pg** plugin | Cloud SQL PostgreSQL vectorstore | M | +| Firestore session store | Session persistence | S | + +**P3 - Low Priority:** +| Gap | Description | Effort | +|-----|-------------|--------| +| `session.run()` | Execute within session | S | +| `defineInterrupt()` | Standalone interrupt tool definition | S | +| `tool.restart()` | Restart from interrupt | S | + +--- + ### Phase 1 Tasks ✅ COMPLETE (2026-01-26) **1. Testing Infrastructure (`genkit.testing` module)** ✅ diff --git a/py/noxfile.py b/py/noxfile.py index 5c89248b3c..de2ddf055b 100644 --- a/py/noxfile.py +++ b/py/noxfile.py @@ -42,19 +42,45 @@ def tests(session: nox.Session) -> None: Returns: None """ + # TODO(#4333): Re-enable chroma tests for Python 3.14 when onnxruntime adds wheels. + # Track: https://github.com/microsoft/onnxruntime/issues/21292 + # Skip chroma tests on Python 3.14+ (onnxruntime lacks 3.14 wheels) + python_version = str(session.python) if session.python else '' + is_py314 = python_version.startswith('3.14') + + # Build the pytest arguments + if is_py314: + # For Python 3.14, use --no-project to skip workspace validation + # since genkit-plugin-chroma and rag-chroma-demo are incompatible + pytest_args = [ + 'uv', + 'run', + '--no-project', + '--python', + f'{session.python}', + 'pytest', + '-v', + '.', + '--ignore=plugins/chroma', + '--ignore=samples/rag-chroma', + ] + else: + pytest_args = [ + 'uv', + 'run', + '--python', + f'{session.python}', + '--active', + '--isolated', + 'pytest', + '-v', + '.', + ] + + pytest_args.extend(session.posargs) + session.run( - 'uv', - 'run', - '--python', - f'{session.python}', - '--active', - '--isolated', - 'pytest', - '-v', - # '-vv', - # '--log-level=DEBUG', - '.', - *session.posargs, + *pytest_args, external=True, ) diff --git a/py/packages/genkit/src/genkit/core/plugin.py b/py/packages/genkit/src/genkit/core/plugin.py index a049e3724c..92811bfc3c 100644 --- a/py/packages/genkit/src/genkit/core/plugin.py +++ b/py/packages/genkit/src/genkit/core/plugin.py @@ -32,14 +32,14 @@ │ │ │ ┌──────────┐ ┌──────────┐ ┌──────────┐ ┌──────────┐ │ │ │ Register │ ───► │ init │ ───► │ resolve │ ───► │ Action │ │ - │ │ Plugin │ │ (async) │ │ (lazy) │ │ Returns │ │ + │ │ Plugin │ │(registry)│ │ (lazy) │ │ Returns │ │ │ └──────────┘ └──────────┘ └──────────┘ └──────────┘ │ │ │ │ │ │ │ ▼ │ - │ │ ┌──────────┐ │ - │ └─────────►│ List │ │ - │ │ Actions │ │ - │ └──────────┘ │ + │ └────────► ┌──────────┐ │ + │ │ List │ │ + │ │ Actions │ │ + │ └──────────┘ │ └─────────────────────────────────────────────────────────────────────────┘ Terminology: @@ -48,7 +48,7 @@ ├───────────────────┼─────────────────────────────────────────────────────┤ │ Plugin │ Abstract base class that defines the plugin API │ │ name │ Plugin namespace (e.g., 'googleai', 'anthropic') │ - │ init() │ Async method called once on first action resolve │ + │ init(registry) │ Async method called once on first action resolve │ │ resolve() │ Lazy resolution of actions by kind and name │ │ list_actions() │ Returns metadata for plugin's available actions │ │ model() │ Helper to create namespaced ModelReference │ @@ -59,21 +59,37 @@ ┌─────────────────────────────────────────────────────────────────────────┐ │ Method │ Purpose │ ├───────────────────┼─────────────────────────────────────────────────────┤ - │ init() │ One-time initialization; return pre-registered │ - │ │ actions (e.g., known models to pre-register) │ + │ init(registry) │ One-time initialization; receives Registry for │ + │ │ resolving other actions (e.g., embedders). │ + │ │ Return pre-registered actions. │ │ resolve() │ Create/return Action for a given kind and name; │ │ │ called when action is first used │ │ list_actions() │ Return ActionMetadata for dev UI action discovery │ │ │ (should be fast, no heavy initialization) │ └───────────────────┴─────────────────────────────────────────────────────┘ +Registry vs Genkit: + The `init()` method receives a `Registry` instance, not a `Genkit` instance. + This is intentional - plugins operate at the registry level and should not + depend on the high-level Genkit API. + + Registry provides all necessary methods for plugins: + - `register_action()` - register new actions + - `resolve_embedder()` - resolve embedder actions (for vector stores) + - `resolve_retriever()` - resolve retriever actions + - `resolve_model()` - resolve model actions + + Vector store plugins (Chroma, Pinecone) use `registry.resolve_embedder()` + to get embedder actions and call them directly via `action.arun()`. + Example: - Implementing a custom plugin: + Implementing a simple model plugin: ```python from genkit.core.plugin import Plugin from genkit.core.action import Action, ActionMetadata from genkit.core.action.types import ActionKind + from genkit.core.registry import Registry class MyPlugin(Plugin): @@ -82,7 +98,7 @@ class MyPlugin(Plugin): def __init__(self, api_key: str) -> None: self.api_key = api_key - async def init(self) -> list[Action]: + async def init(self, registry: Registry | None = None) -> list[Action]: # Return actions to pre-register (optional) return [] @@ -103,6 +119,31 @@ def _create_model_action(self, name: str) -> Action: ... ``` + Implementing a vector store plugin that needs an embedder: + + ```python + class VectorStorePlugin(Plugin): + name = 'vectorstore' + + def __init__(self, embedder: str) -> None: + self._embedder = embedder + self._registry: Registry | None = None + + async def init(self, registry: Registry | None = None) -> list[Action]: + self._registry = registry + # Register retriever/indexer actions that use the embedder + # Actions registered here can use self._registry.resolve_embedder() + return [] + + async def _embed_content(self, texts: list[str]) -> list[list[float]]: + # Use registry to resolve and call embedder + embedder = await self._registry.resolve_embedder(self._embedder) + if embedder is None: + raise ValueError(f'Embedder "{self._embedder}" not found') + response = await embedder.arun(embed_request) + return [e.embedding for e in response.response.embeddings] + ``` + Using the plugin: ```python @@ -117,9 +158,11 @@ def _create_model_action(self, name: str) -> Action: - Plugin names must be unique within a Genkit instance - Actions returned from init() are pre-registered with the plugin namespace - resolve() receives the fully namespaced name (e.g., 'plugin/model') + - init() receives a Registry, not Genkit - use registry methods for resolution See Also: - Built-in plugins: genkit.plugins.google_genai, genkit.plugins.anthropic + - Vector store plugins: genkit.plugins.chroma, genkit.plugins.pinecone - Registry: genkit.core.registry """ @@ -132,6 +175,7 @@ def _create_model_action(self, name: str) -> Action: if TYPE_CHECKING: from genkit.blocks.embedding import EmbedderRef from genkit.blocks.model import ModelReference + from genkit.core.registry import Registry class Plugin(abc.ABC): @@ -145,13 +189,35 @@ class Plugin(abc.ABC): name: str # plugin namespace @abc.abstractmethod - async def init(self) -> list[Action]: + async def init(self, registry: 'Registry | None' = None) -> list[Action]: """Lazy warm-up called once per plugin per registry instance. This method is called lazily when the first action resolution attempt involving this plugin occurs. It should return a list of Action objects to pre-register. + Args: + registry: Optional reference to the registry. Most plugins don't + need this, but it's useful for plugins that need to call + actions from OTHER plugins at runtime. + + Example: A vector store plugin needs to call an embedder + from a different plugin (e.g., GoogleAI). Since each plugin's + resolve() method only handles its own actions, the vector + store must use the registry to resolve cross-plugin actions:: + + # In a vector store plugin: + async def init(self, registry: Registry | None = None): + self._registry = registry # Store for later use + return [] + + + async def _embed(self, docs: list[str]) -> list[list[float]]: + # Call embedder from another plugin + embedder = await self._registry.resolve_embedder('googleai/text-embedding-004') + result = await embedder.arun(EmbedRequest(input=docs)) + return [e.embedding for e in result.response.embeddings] + Returns: list[Action]: A list of Action instances to register. """ diff --git a/py/packages/genkit/src/genkit/core/registry.py b/py/packages/genkit/src/genkit/core/registry.py index eec4960863..99dcd7064e 100644 --- a/py/packages/genkit/src/genkit/core/registry.py +++ b/py/packages/genkit/src/genkit/core/registry.py @@ -292,7 +292,8 @@ async def _ensure_plugin_initialized(self, plugin_name: str) -> None: async def run_init() -> None: # Assert for type narrowing inside closure (pyrefly doesn't propagate from outer scope) assert plugin is not None - actions = await plugin.init() + # pyrefly: ignore[bad-argument-type] - Self is Registry, pyrefly doesn't recognize + actions = await plugin.init(registry=self) for action in actions or []: self.register_action_instance(action, namespace=plugin_name) diff --git a/py/packages/genkit/tests/genkit/ai/ai_plugin_test.py b/py/packages/genkit/tests/genkit/ai/ai_plugin_test.py index 6e4fea94a6..797b55611a 100644 --- a/py/packages/genkit/tests/genkit/ai/ai_plugin_test.py +++ b/py/packages/genkit/tests/genkit/ai/ai_plugin_test.py @@ -24,7 +24,7 @@ from genkit.ai import Genkit, Plugin from genkit.core.action import Action, ActionMetadata, ActionRunContext -from genkit.core.registry import ActionKind +from genkit.core.registry import ActionKind, Registry from genkit.core.typing import FinishReason from genkit.types import GenerateRequest, GenerateResponse, Message, Part, Role, TextPart @@ -34,7 +34,7 @@ class AsyncResolveOnlyPlugin(Plugin): name = 'async-resolve-only' - async def init(self) -> list[Action]: + async def init(self, registry: Registry | None = None) -> list[Action]: """Initialize the plugin.""" # Intentionally register nothing eagerly. return [] @@ -73,7 +73,7 @@ class AsyncInitPlugin(Plugin): name = 'async-init-plugin' - async def init(self) -> list[Action]: + async def init(self, registry: Registry | None = None) -> list[Action]: """Initialize the plugin.""" action = await self.resolve(ActionKind.MODEL, f'{self.name}/init-model') return [action] if action else [] diff --git a/py/packages/genkit/tests/genkit/core/registry_test.py b/py/packages/genkit/tests/genkit/core/registry_test.py index 0e50341206..9bb5f330ce 100644 --- a/py/packages/genkit/tests/genkit/core/registry_test.py +++ b/py/packages/genkit/tests/genkit/core/registry_test.py @@ -59,7 +59,7 @@ async def test_resolve_action_from_plugin() -> None: class MyPlugin(Plugin): name = 'myplugin' - async def init(self) -> list[Action]: + async def init(self, registry: Registry | None = None) -> list[Action]: return [] async def resolve(self, action_type: ActionKind, name: str) -> Action: diff --git a/py/plugins/README.md b/py/plugins/README.md new file mode 100644 index 0000000000..e81b1c0ef4 --- /dev/null +++ b/py/plugins/README.md @@ -0,0 +1,129 @@ +# Genkit Python Plugins + +This directory contains official plugins for the Genkit Python SDK. Plugins extend +Genkit's capabilities with model providers, vector stores, evaluators, and more. + +## Plugin Categories + +### Model Providers + +Plugins that provide access to AI models for text generation, chat, and other tasks. + +| Plugin | Package | Description | Models/Features | +|--------|---------|-------------|-----------------| +| [anthropic](./anthropic/) | `genkit-plugin-anthropic` | Anthropic Claude models | Claude 3.5 Sonnet, Claude 3 Opus, Claude 3 Haiku | +| [google-genai](./google-genai/) | `genkit-plugin-google-genai` | Google AI models via AI Studio | Gemini 2.0/1.5, Imagen, Veo, Lyria, TTS, Embeddings | +| [vertex-ai](./vertex-ai/) | `genkit-plugin-vertex-ai` | Google Cloud Vertex AI | Model Garden (Anthropic, Llama), Vector Search | +| [ollama](./ollama/) | `genkit-plugin-ollama` | Local models via Ollama | Llama, Mistral, Gemma, any Ollama model | +| [compat-oai](./compat-oai/) | `genkit-plugin-compat-oai` | OpenAI-compatible APIs | Any OpenAI-compatible endpoint | +| [deepseek](./deepseek/) | `genkit-plugin-deepseek` | DeepSeek models | DeepSeek Chat, DeepSeek Coder | +| [xai](./xai/) | `genkit-plugin-xai` | xAI Grok models | Grok-2, Grok-2 Vision | + +### Vector Stores + +Plugins for storing and retrieving document embeddings for RAG applications. + +| Plugin | Package | Description | Features | +|--------|---------|-------------|----------| +| [chroma](./chroma/) | `genkit-plugin-chroma` | ChromaDB vector store | In-memory or persistent, local or remote | +| [pinecone](./pinecone/) | `genkit-plugin-pinecone` | Pinecone managed vector DB | Cloud-hosted, scalable, namespaces | +| [dev-local-vectorstore](./dev-local-vectorstore/) | `genkit-plugin-dev-local-vectorstore` | Local file-based store | Development and testing only | + +### Safety & Evaluation + +Plugins for content safety, guardrails, and quality evaluation. + +| Plugin | Package | Description | Features | +|--------|---------|-------------|----------| +| [checks](./checks/) | `genkit-plugin-checks` | Google Checks AI Safety | Content moderation, policy enforcement | +| [evaluators](./evaluators/) | `genkit-plugin-evaluators` | Built-in evaluators | Faithfulness, relevancy, answer accuracy | + +### Integrations + +Plugins for external services and protocols. + +| Plugin | Package | Description | Features | +|--------|---------|-------------|----------| +| [mcp](./mcp/) | `genkit-plugin-mcp` | Model Context Protocol | MCP client, host, and server | +| [firebase](./firebase/) | `genkit-plugin-firebase` | Firebase integration | Firestore retriever, telemetry | +| [google-cloud](./google-cloud/) | `genkit-plugin-google-cloud` | Google Cloud telemetry | Cloud Trace, Cloud Logging, Metrics | + +### Web Frameworks + +Plugins for integrating Genkit with web frameworks. + +| Plugin | Package | Description | Features | +|--------|---------|-------------|----------| +| [flask](./flask/) | `genkit-plugin-flask` | Flask integration | Expose flows as HTTP endpoints | + +## Installation + +Install plugins using pip or uv: + +```bash +# Install a single plugin +pip install genkit-plugin-google-genai + +# Install multiple plugins +pip install genkit-plugin-google-genai genkit-plugin-anthropic genkit-plugin-chroma +``` + +## Quick Start + +```python +from genkit.ai import Genkit +from genkit.plugins.google_genai import GoogleAI +from genkit.plugins.chroma import chroma + +# Initialize Genkit with plugins +ai = Genkit( + plugins=[ + GoogleAI(), + chroma(collections=[{ + 'collection_name': 'my_docs', + 'embedder': 'googleai/text-embedding-004', + }]), + ], + model='googleai/gemini-2.0-flash', +) + +# Use models from plugins +response = await ai.generate(prompt='Hello, world!') +``` + +## Plugin Development + +Each plugin follows a standard structure: + +``` +plugin-name/ +├── LICENSE +├── pyproject.toml +├── README.md +├── src/ +│ └── genkit/ +│ └── plugins/ +│ └── plugin_name/ +│ ├── __init__.py +│ └── ... +└── tests/ + └── ... +``` + +### Creating a New Plugin + +1. Create the directory structure above +2. Implement the `Plugin` class from `genkit.core.plugin` +3. Register actions (models, retrievers, indexers, etc.) in `initialize()` +4. Add tests and documentation + +See existing plugins for examples. + +## Cross-Language Parity + +These Python plugins maintain API parity with their JavaScript counterparts where +applicable. See the [JS plugins](../../js/plugins/) for reference implementations. + +## License + +All plugins are licensed under Apache 2.0 unless otherwise noted. diff --git a/py/plugins/anthropic/src/genkit/plugins/anthropic/plugin.py b/py/plugins/anthropic/src/genkit/plugins/anthropic/plugin.py index 0e6e3cfeae..a3666ba807 100644 --- a/py/plugins/anthropic/src/genkit/plugins/anthropic/plugin.py +++ b/py/plugins/anthropic/src/genkit/plugins/anthropic/plugin.py @@ -22,7 +22,7 @@ from genkit.ai import Plugin from genkit.blocks.model import model_action_metadata from genkit.core.action import Action, ActionMetadata -from genkit.core.registry import ActionKind +from genkit.core.registry import ActionKind, Registry from genkit.core.schema import to_json_schema from genkit.plugins.anthropic.model_info import SUPPORTED_ANTHROPIC_MODELS, get_model_info from genkit.plugins.anthropic.models import AnthropicModel @@ -68,9 +68,12 @@ def __init__( self._anthropic_params = anthropic_params self._anthropic_client = AsyncAnthropic(**cast(dict[str, Any], anthropic_params)) - async def init(self) -> list[Action]: + async def init(self, registry: 'Registry | None' = None) -> list[Action]: """Initialize plugin. + Args: + registry: Optional registry (not used by this plugin). + Returns: Empty list (using lazy loading via resolve). """ diff --git a/py/plugins/anthropic/tests/plugin_test.py b/py/plugins/anthropic/tests/plugin_test.py index 0e8119110f..0bb7f37255 100644 --- a/py/plugins/anthropic/tests/plugin_test.py +++ b/py/plugins/anthropic/tests/plugin_test.py @@ -74,10 +74,13 @@ def test_custom_models() -> None: @pytest.mark.asyncio async def test_plugin_init() -> None: """Test plugin init method.""" + from unittest.mock import MagicMock + plugin = Anthropic(api_key='test-key', models=['claude-sonnet-4']) + mock_registry = MagicMock() # init() should return an empty list (using lazy loading) - result = await plugin.init() + result = await plugin.init(mock_registry) assert result == [] diff --git a/py/plugins/checks/LICENSE b/py/plugins/checks/LICENSE new file mode 100644 index 0000000000..0caa89607c --- /dev/null +++ b/py/plugins/checks/LICENSE @@ -0,0 +1,190 @@ + Apache License + Version 2.0, January 2004 + http://www.apache.org/licenses/ + + TERMS AND CONDITIONS FOR USE, REPRODUCTION, AND DISTRIBUTION + + 1. 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However, in accepting such obligations, You may act only + on Your own behalf and on Your sole responsibility, not on behalf + of any other Contributor, and only if You agree to indemnify, + defend, and hold each Contributor harmless for any liability + incurred by, or claims asserted against, such Contributor by reason + of your accepting any such warranty or additional liability. + + END OF TERMS AND CONDITIONS + + Copyright 2024 Google LLC + + Licensed under the Apache License, Version 2.0 (the "License"); + you may not use this file except in compliance with the License. + You may obtain a copy of the License at + + http://www.apache.org/licenses/LICENSE-2.0 + + Unless required by applicable law or agreed to in writing, software + distributed under the License is distributed on an "AS IS" BASIS, + WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. + See the License for the specific language governing permissions and + limitations under the License. diff --git a/py/plugins/checks/README.md b/py/plugins/checks/README.md new file mode 100644 index 0000000000..0aa43b7ba2 --- /dev/null +++ b/py/plugins/checks/README.md @@ -0,0 +1,127 @@ +# Genkit Checks Plugin + +Google Checks AI Safety plugin for Genkit - provides guardrails and content +safety evaluation for AI-generated content. + +## Overview + +The Checks plugin integrates with the +[Google Checks AI Safety API](https://developers.google.com/checks) to provide: + +- **Content Safety Evaluation**: Evaluate AI outputs against safety policies +- **Model Middleware**: Block unsafe content automatically during generation +- **Configurable Policies**: Choose which safety policies to enforce + +## Installation + +```bash +pip install genkit-plugin-checks +``` + +## Quick Start + +```python +from genkit.ai import Genkit +from genkit.plugins.checks import Checks, ChecksMetricType + +# Initialize Genkit with the Checks plugin +ai = Genkit(plugins=[ + Checks( + project_id='your-gcp-project', + evaluation={ + 'metrics': [ + ChecksMetricType.DANGEROUS_CONTENT, + ChecksMetricType.HARASSMENT, + ChecksMetricType.HATE_SPEECH, + ] + } + ) +]) + +# Use as middleware to automatically block unsafe content +response = await ai.generate( + model='googleai/gemini-2.0-flash', + prompt='Tell me about AI safety', + use=[checks_middleware(metrics=[ChecksMetricType.DANGEROUS_CONTENT])], +) +``` + +## Supported Metrics + +| Metric | Description | +|--------|-------------| +| `DANGEROUS_CONTENT` | Harmful goods, services, or activities | +| `PII_SOLICITING_RECITING` | Personal information disclosure | +| `HARASSMENT` | Malicious, intimidating, or abusive content | +| `SEXUALLY_EXPLICIT` | Sexually explicit content | +| `HATE_SPEECH` | Violence, hatred, or discrimination | +| `MEDICAL_INFO` | Harmful health advice | +| `VIOLENCE_AND_GORE` | Gratuitous violence or gore | +| `OBSCENITY_AND_PROFANITY` | Vulgar or offensive language | + +## Configuration + +### Plugin Options + +```python +Checks( + # GCP project with Checks API quota + project_id='your-gcp-project', + + # Optional: Custom Google Auth configuration + google_auth_options={'credentials_file': '/path/to/creds.json'}, + + # Configure evaluation metrics + evaluation={ + 'metrics': [ + ChecksMetricType.DANGEROUS_CONTENT, + # Or with custom threshold + {'type': ChecksMetricType.HARASSMENT, 'threshold': 0.8}, + ] + } +) +``` + +### Middleware Usage + +```python +from genkit.plugins.checks import checks_middleware, ChecksMetricType + +# Apply middleware to specific generations +response = await ai.generate( + model='googleai/gemini-2.0-flash', + prompt='Your prompt', + use=[ + checks_middleware( + metrics=[ + ChecksMetricType.DANGEROUS_CONTENT, + ChecksMetricType.HATE_SPEECH, + ], + auth_options={'project_id': 'your-project'}, + ) + ], +) +``` + +## Authentication + +The plugin uses Google Cloud authentication. Set up credentials via: + +1. **Environment variable**: `GOOGLE_APPLICATION_CREDENTIALS` +2. **Service account JSON**: `GCLOUD_SERVICE_ACCOUNT_CREDS` environment variable +3. **Default credentials**: Automatic in GCP environments + +## Requirements + +- Python 3.12+ +- Google Cloud project with Checks API enabled +- Appropriate IAM permissions for Checks API + +## Cross-Language Parity + +This plugin maintains API parity with the JavaScript Genkit Checks plugin: +- JS: `@genkit-ai/checks` + +## License + +Apache 2.0 - See LICENSE file diff --git a/py/plugins/checks/pyproject.toml b/py/plugins/checks/pyproject.toml new file mode 100644 index 0000000000..3e1f723dd6 --- /dev/null +++ b/py/plugins/checks/pyproject.toml @@ -0,0 +1,53 @@ +# Copyright 2026 Google LLC +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. +# +# SPDX-License-Identifier: Apache-2.0 + +[project] +authors = [{ name = "Google" }] +classifiers = [ + "Development Status :: 3 - Alpha", + "Intended Audience :: Developers", + "License :: OSI Approved :: Apache Software License", + "Operating System :: OS Independent", + "Programming Language :: Python :: 3", + "Programming Language :: Python :: 3.10", + "Programming Language :: Python :: 3.11", + "Programming Language :: Python :: 3.12", + "Topic :: Scientific/Engineering :: Artificial Intelligence", + "Topic :: Security", +] +dependencies = [ + "genkit>=0.1.0", + "google-auth>=2.0.0", + "httpx>=0.27.0", + "pydantic>=2.0.0", +] +description = "Google Checks AI Safety plugin for Genkit - guardrails and content safety" +keywords = ["genkit", "ai", "safety", "guardrails", "checks", "moderation"] +license = { file = "LICENSE" } +name = "genkit-plugin-checks" +readme = "README.md" +requires-python = ">=3.10" +version = "0.1.0" + +[project.optional-dependencies] +dev = ["pytest>=8.0.0", "pytest-asyncio>=0.24.0"] + +[build-system] +build-backend = "hatchling.build" +requires = ["hatchling"] + +[tool.hatch.build.targets.wheel] +packages = ["src/genkit"] diff --git a/py/plugins/checks/src/genkit/plugins/checks/__init__.py b/py/plugins/checks/src/genkit/plugins/checks/__init__.py new file mode 100644 index 0000000000..5e62ab7a6c --- /dev/null +++ b/py/plugins/checks/src/genkit/plugins/checks/__init__.py @@ -0,0 +1,153 @@ +# Copyright 2026 Google LLC +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. +# +# SPDX-License-Identifier: Apache-2.0 + +"""Google Checks AI Safety plugin for Genkit. + +This plugin integrates the Google Checks AI Safety API with Genkit, providing +content safety evaluation, guardrails middleware, and policy-based content +filtering for AI-generated content. + +Overview +======== + +Google Checks AI Safety evaluates content against configurable safety policies +to detect potentially harmful content including dangerous information, +harassment, hate speech, explicit content, and more. + + ┌─────────────────────────────────────────────────────────────────────────┐ + │ Checks Plugin Architecture │ + ├─────────────────────────────────────────────────────────────────────────┤ + │ │ + │ ┌──────────┐ ┌──────────────┐ ┌──────────────────┐ │ + │ │ Model │ │ Checks │ │ Checks API │ │ + │ │ Request │ ──► │ Middleware │ ──► │ (classify) │ │ + │ └──────────┘ └──────────────┘ └──────────────────┘ │ + │ │ │ │ │ + │ │ ▼ ▼ │ + │ │ ┌──────────────┐ ┌──────────────────┐ │ + │ │ │ Policy │ ◄── │ Violation │ │ + │ │ │ Check │ │ Results │ │ + │ │ └──────────────┘ └──────────────────┘ │ + │ │ │ │ + │ │ ▼ │ + │ │ ┌──────────────┐ │ + │ └─────────► │ Allow / │ ──► Response to user │ + │ │ Block │ │ + │ └──────────────┘ │ + │ │ + └─────────────────────────────────────────────────────────────────────────┘ + +Key Components +============== + +┌─────────────────────────────────────────────────────────────────────────────┐ +│ Component │ Description │ +├───────────────────────┼─────────────────────────────────────────────────────┤ +│ Checks plugin │ Main plugin that registers evaluators │ +│ checks_middleware() │ Model middleware for automatic content filtering │ +│ ChecksMetricType │ Enum of supported safety policy types │ +│ Guardrails │ API client for Checks classifyContent endpoint │ +└───────────────────────┴─────────────────────────────────────────────────────┘ + +Supported Metrics +================= + +┌─────────────────────────────────────────────────────────────────────────────┐ +│ Metric │ Description │ +├───────────────────────────┼─────────────────────────────────────────────────┤ +│ DANGEROUS_CONTENT │ Harmful goods, services, or activities │ +│ PII_SOLICITING_RECITING │ Personal information disclosure │ +│ HARASSMENT │ Malicious, intimidating, or abusive content │ +│ SEXUALLY_EXPLICIT │ Sexually explicit content │ +│ HATE_SPEECH │ Violence, hatred, or discrimination │ +│ MEDICAL_INFO │ Harmful health advice │ +│ VIOLENCE_AND_GORE │ Gratuitous violence or gore │ +│ OBSCENITY_AND_PROFANITY │ Vulgar or offensive language │ +└───────────────────────────┴─────────────────────────────────────────────────┘ + +Example: + Basic plugin usage: + + ```python + from genkit.ai import Genkit + from genkit.plugins.checks import Checks, ChecksMetricType + + ai = Genkit( + plugins=[ + Checks( + project_id='my-gcp-project', + evaluation={ + 'metrics': [ + ChecksMetricType.DANGEROUS_CONTENT, + ChecksMetricType.HARASSMENT, + ] + }, + ) + ] + ) + ``` + + Using middleware for automatic content filtering: + + ```python + from genkit.plugins.checks import checks_middleware, ChecksMetricType + + response = await ai.generate( + model='googleai/gemini-2.0-flash', + prompt='Tell me about AI safety', + use=[ + checks_middleware( + metrics=[ChecksMetricType.DANGEROUS_CONTENT], + auth_options={'project_id': 'my-project'}, + ) + ], + ) + + # Check if response was blocked + if response.finish_reason == 'blocked': + print(f'Content blocked: {response.finish_message}') + ``` + +Caveats: + - Requires Google Cloud project with Checks API quota + - Network latency added for each content classification + - Some metrics may have different sensitivities + +See Also: + - Google Checks: https://developers.google.com/checks + - JS implementation: js/plugins/checks/ +""" + +from genkit.plugins.checks.guardrails import Guardrails, PolicyResult +from genkit.plugins.checks.metrics import ( + ChecksMetricConfig, + ChecksMetricType, + is_metric_config, +) +from genkit.plugins.checks.middleware import checks_middleware +from genkit.plugins.checks.plugin import Checks, ChecksEvaluationConfig, ChecksOptions + +__all__ = [ + 'Checks', + 'ChecksEvaluationConfig', + 'ChecksMetricConfig', + 'ChecksMetricType', + 'ChecksOptions', + 'Guardrails', + 'PolicyResult', + 'checks_middleware', + 'is_metric_config', +] diff --git a/py/plugins/checks/src/genkit/plugins/checks/auth.py b/py/plugins/checks/src/genkit/plugins/checks/auth.py new file mode 100644 index 0000000000..15c9ba712b --- /dev/null +++ b/py/plugins/checks/src/genkit/plugins/checks/auth.py @@ -0,0 +1,94 @@ +# Copyright 2026 Google LLC +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. +# +# SPDX-License-Identifier: Apache-2.0 + +"""Shared authentication utilities for the Checks plugin. + +This module provides common authentication logic used by both the Checks +plugin and middleware components. +""" + +import json +import os +from typing import Any + +from google.auth import default as default_credentials +from google.auth.credentials import Credentials +from google.oauth2 import service_account + +# OAuth scopes required for Checks API +CLOUD_PLATFORM_OAUTH_SCOPE = 'https://www.googleapis.com/auth/cloud-platform' +CHECKS_OAUTH_SCOPE = 'https://www.googleapis.com/auth/checks' + +_DEFAULT_SCOPES = [CLOUD_PLATFORM_OAUTH_SCOPE, CHECKS_OAUTH_SCOPE] + + +def initialize_credentials( + auth_options: dict[str, Any] | None = None, + scopes: list[str] | None = None, +) -> tuple[Credentials, str | None]: + """Initialize Google Cloud credentials for the Checks API. + + Credentials are loaded in the following order of precedence: + 1. GCLOUD_SERVICE_ACCOUNT_CREDS environment variable (JSON string) + 2. auth_options.credentials_file (service account file path) + 3. Default application credentials + + Args: + auth_options: Optional authentication options including: + - credentials_file: Path to service account JSON file + - project_id: GCP project ID + scopes: OAuth scopes to request. Defaults to cloud-platform and checks scopes. + + Returns: + Tuple of (credentials, project_id). project_id may be None if not + provided in options and not available from default credentials. + + Raises: + ValueError: If credentials cannot be established. + """ + if scopes is None: + scopes = _DEFAULT_SCOPES + + # Check for service account credentials in environment + if os.environ.get('GCLOUD_SERVICE_ACCOUNT_CREDS'): + creds_data = json.loads(os.environ['GCLOUD_SERVICE_ACCOUNT_CREDS']) + credentials = service_account.Credentials.from_service_account_info( + creds_data, + scopes=scopes, + ) + project_id = auth_options.get('project_id') if auth_options else None + return credentials, project_id + + # Use credentials file if provided + if auth_options and auth_options.get('credentials_file'): + credentials = service_account.Credentials.from_service_account_file( + auth_options['credentials_file'], + scopes=scopes, + ) + project_id = auth_options.get('project_id') + return credentials, project_id + + # Fall back to default credentials + credentials, default_project = default_credentials(scopes=scopes) + project_id = (auth_options or {}).get('project_id') or default_project + return credentials, project_id + + +__all__ = [ + 'CHECKS_OAUTH_SCOPE', + 'CLOUD_PLATFORM_OAUTH_SCOPE', + 'initialize_credentials', +] diff --git a/py/plugins/checks/src/genkit/plugins/checks/guardrails.py b/py/plugins/checks/src/genkit/plugins/checks/guardrails.py new file mode 100644 index 0000000000..dfd642544f --- /dev/null +++ b/py/plugins/checks/src/genkit/plugins/checks/guardrails.py @@ -0,0 +1,251 @@ +# Copyright 2026 Google LLC +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. +# +# SPDX-License-Identifier: Apache-2.0 + +"""Guardrails API client for Google Checks AI Safety. + +This module provides the HTTP client for interacting with the Google Checks +classifyContent API endpoint. + +API Flow +======== + +┌─────────────────────────────────────────────────────────────────────────────┐ +│ Guardrails API Request Flow │ +├─────────────────────────────────────────────────────────────────────────────┤ +│ │ +│ ┌──────────────┐ ┌──────────────┐ ┌──────────────────────┐ │ +│ │ Content │ │ Guardrails │ │ Checks API │ │ +│ │ + Policies │ ──► │ Client │ ──► │ classifyContent │ │ +│ └──────────────┘ └──────────────┘ └──────────────────────┘ │ +│ │ │ │ +│ │ ▼ │ +│ │ ┌──────────────────────┐ │ +│ │ │ Policy Results │ │ +│ │ │ - policyType │ │ +│ │ │ - score │ │ +│ │ │ - violationResult │ │ +│ │ └──────────────────────┘ │ +│ │ │ │ +│ ▼ ▼ │ +│ ┌──────────────────────────────────┐ │ +│ │ Parsed Response with Results │ │ +│ └──────────────────────────────────┘ │ +│ │ +└─────────────────────────────────────────────────────────────────────────────┘ + +Example: + ```python + from google.auth import default + from genkit.plugins.checks.guardrails import Guardrails + from genkit.plugins.checks.metrics import ChecksMetricType + + credentials, project_id = default() + guardrails = Guardrails(credentials, project_id) + + result = await guardrails.classify_content( + content='Hello, how can I help you today?', + policies=[ChecksMetricType.DANGEROUS_CONTENT], + ) + + for policy_result in result.policy_results: + print(f'{policy_result.policy_type}: {policy_result.violation_result}') + ``` + +See Also: + - JS implementation: js/plugins/checks/src/guardrails.ts + - Checks API docs: https://developers.google.com/checks +""" + +from dataclasses import dataclass +from typing import Any + +import httpx +from google.auth.credentials import Credentials +from google.auth.transport.requests import Request +from pydantic import BaseModel, ConfigDict, Field +from pydantic.alias_generators import to_camel + +from genkit.plugins.checks.metrics import ChecksMetric, ChecksMetricConfig, ChecksMetricType + +GUARDRAILS_URL = 'https://checks.googleapis.com/v1alpha/aisafety:classifyContent' + + +class PolicyResult(BaseModel): + """Result of evaluating content against a single policy. + + Attributes: + policy_type: The type of policy that was evaluated. + score: Optional confidence score for the evaluation (0.0-1.0). + violation_result: Whether the content violates this policy. + Values: 'VIOLATIVE', 'NON_VIOLATIVE', or 'CLASSIFICATION_UNSPECIFIED'. + """ + + model_config = ConfigDict(alias_generator=to_camel, populate_by_name=True) + + policy_type: str + score: float | None = None + violation_result: str = 'CLASSIFICATION_UNSPECIFIED' + + +class ClassifyContentResponse(BaseModel): + """Response from the Checks classifyContent API. + + Attributes: + policy_results: List of results for each evaluated policy. + """ + + model_config = ConfigDict(alias_generator=to_camel, populate_by_name=True) + + policy_results: list[PolicyResult] = Field(default_factory=list) + + +@dataclass +class GuardrailsRequest: + """Request structure for the classifyContent API. + + Attributes: + content: The text content to classify. + policies: List of policies to evaluate against. + """ + + content: str + policies: list[dict[str, Any]] + + def to_dict(self) -> dict[str, Any]: + """Convert to API request format. + + Returns: + Dictionary in the format expected by the Checks API. + """ + return { + 'input': { + 'text_input': { + 'content': self.content, + }, + }, + 'policies': self.policies, + } + + +class Guardrails: + """Client for the Google Checks AI Safety classifyContent API. + + This class handles authentication and HTTP requests to the Checks API + for content classification against safety policies. + + Attributes: + credentials: Google Cloud credentials for authentication. + project_id: GCP project ID for quota attribution. + """ + + def __init__( + self, + credentials: Credentials, + project_id: str | None = None, + ) -> None: + """Initialize the Guardrails client. + + Args: + credentials: Google Cloud credentials for API authentication. + project_id: Optional GCP project ID. If not provided, will attempt + to use the default project from the credentials. + """ + self._credentials = credentials + self._project_id = project_id + + async def classify_content( + self, + content: str, + policies: list[ChecksMetric], + ) -> ClassifyContentResponse: + """Classify content against specified safety policies. + + Args: + content: The text content to evaluate. + policies: List of policies to check against. Can be ChecksMetricType + values or ChecksMetricConfig objects with custom thresholds. + + Returns: + ClassifyContentResponse with results for each policy. + + Raises: + httpx.HTTPError: If the API request fails. + ValueError: If the API response cannot be parsed. + + Example: + ```python + result = await guardrails.classify_content( + content='Some text to check', + policies=[ + ChecksMetricType.DANGEROUS_CONTENT, + ChecksMetricConfig( + type=ChecksMetricType.HARASSMENT, + threshold=0.8, + ), + ], + ) + + violations = [r for r in result.policy_results if r.violation_result == 'VIOLATIVE'] + ``` + """ + # Convert policies to API format + api_policies: list[dict[str, Any]] = [] + for policy in policies: + if isinstance(policy, ChecksMetricConfig): + api_policy: dict[str, Any] = {'policy_type': policy.type.value} + if policy.threshold is not None: + api_policy['threshold'] = policy.threshold + api_policies.append(api_policy) + elif isinstance(policy, ChecksMetricType): + api_policies.append({'policy_type': policy.value}) + + request = GuardrailsRequest(content=content, policies=api_policies) + + # Get access token - the google-auth library handles caching and refresh + # automatically when the token is expired or about to expire + if not self._credentials.valid: + self._credentials.refresh(Request()) + token = self._credentials.token + + headers = { + 'Authorization': f'Bearer {token}', + 'Content-Type': 'application/json', + } + if self._project_id: + headers['x-goog-user-project'] = self._project_id + + async with httpx.AsyncClient() as client: + response = await client.post( + GUARDRAILS_URL, + json=request.to_dict(), + headers=headers, + ) + response.raise_for_status() + + try: + data = response.json() + return ClassifyContentResponse.model_validate(data) + except Exception as e: + raise ValueError(f'Error parsing {GUARDRAILS_URL} API response: {e}') from e + + +__all__ = [ + 'ClassifyContentResponse', + 'GUARDRAILS_URL', + 'Guardrails', + 'GuardrailsRequest', + 'PolicyResult', +] diff --git a/py/plugins/checks/src/genkit/plugins/checks/metrics.py b/py/plugins/checks/src/genkit/plugins/checks/metrics.py new file mode 100644 index 0000000000..5ad6d83393 --- /dev/null +++ b/py/plugins/checks/src/genkit/plugins/checks/metrics.py @@ -0,0 +1,140 @@ +# Copyright 2026 Google LLC +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. +# +# SPDX-License-Identifier: Apache-2.0 + +"""Checks AI Safety metric types and configurations. + +This module defines the supported safety policy types for the Google Checks +AI Safety API and configuration structures for customizing evaluation behavior. + +Metric Types +============ + +Each metric type corresponds to a specific category of potentially harmful +content that the Checks API can detect: + +┌─────────────────────────────────────────────────────────────────────────────┐ +│ Metric │ Detects │ +├───────────────────────────┼─────────────────────────────────────────────────┤ +│ DANGEROUS_CONTENT │ Content promoting harmful goods/services │ +│ PII_SOLICITING_RECITING │ Requests or disclosure of personal info │ +│ HARASSMENT │ Bullying, intimidation, or abuse │ +│ SEXUALLY_EXPLICIT │ Sexually explicit material │ +│ HATE_SPEECH │ Discrimination, hatred, or violence promotion │ +│ MEDICAL_INFO │ Potentially harmful medical advice │ +│ VIOLENCE_AND_GORE │ Graphic violence descriptions │ +│ OBSCENITY_AND_PROFANITY │ Vulgar or profane language │ +└───────────────────────────┴─────────────────────────────────────────────────┘ + +Example: + ```python + from genkit.plugins.checks import ChecksMetricType, ChecksMetricConfig + + # Use metric type directly + metrics = [ChecksMetricType.DANGEROUS_CONTENT] + + # Or with custom threshold + metrics = [ + ChecksMetricType.DANGEROUS_CONTENT, + ChecksMetricConfig( + type=ChecksMetricType.HARASSMENT, + threshold=0.8, + ), + ] + ``` + +See Also: + - JS implementation: js/plugins/checks/src/metrics.ts +""" + +from dataclasses import dataclass +from enum import Enum + + +class ChecksMetricType(str, Enum): + """Supported Checks AI Safety policy types. + + Each value corresponds to a specific content safety policy that can be + evaluated by the Google Checks API. + """ + + DANGEROUS_CONTENT = 'DANGEROUS_CONTENT' + """Content that facilitates, promotes, or enables access to harmful + goods, services, and activities.""" + + PII_SOLICITING_RECITING = 'PII_SOLICITING_RECITING' + """Content that reveals or solicits personal information and data.""" + + HARASSMENT = 'HARASSMENT' + """Content that is malicious, intimidating, bullying, or abusive + towards another individual.""" + + SEXUALLY_EXPLICIT = 'SEXUALLY_EXPLICIT' + """Content that is sexually explicit in nature.""" + + HATE_SPEECH = 'HATE_SPEECH' + """Content that promotes violence, hatred, or discrimination on the + basis of race, religion, etc.""" + + MEDICAL_INFO = 'MEDICAL_INFO' + """Content that facilitates harm by providing health advice or guidance.""" + + VIOLENCE_AND_GORE = 'VIOLENCE_AND_GORE' + """Content that contains gratuitous, realistic descriptions of + violence or gore.""" + + OBSCENITY_AND_PROFANITY = 'OBSCENITY_AND_PROFANITY' + """Content that contains vulgar, profane, or offensive language.""" + + +@dataclass +class ChecksMetricConfig: + """Configuration for a Checks evaluation metric with custom threshold. + + Use this to override the default violation threshold for a specific + policy type. + + Attributes: + type: The metric type to configure. + threshold: Custom threshold for violation detection (0.0-1.0). + Higher values require stronger signals to trigger a violation. + """ + + type: ChecksMetricType + threshold: float | None = None + + +ChecksMetric = ChecksMetricType | ChecksMetricConfig +"""A Checks metric can be specified as either a type or a config with threshold.""" + + +def is_metric_config(metric: ChecksMetric) -> bool: + """Check if a metric is a config object with custom settings. + + Args: + metric: The metric to check. + + Returns: + True if the metric is a ChecksMetricConfig instance. + """ + return isinstance(metric, ChecksMetricConfig) + + +__all__ = [ + 'ChecksMetric', + 'ChecksMetricConfig', + 'ChecksMetricType', + 'is_metric_config', +] diff --git a/py/plugins/checks/src/genkit/plugins/checks/middleware.py b/py/plugins/checks/src/genkit/plugins/checks/middleware.py new file mode 100644 index 0000000000..6fffb6dcfc --- /dev/null +++ b/py/plugins/checks/src/genkit/plugins/checks/middleware.py @@ -0,0 +1,238 @@ +# Copyright 2026 Google LLC +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. +# +# SPDX-License-Identifier: Apache-2.0 + +"""Model middleware for Checks AI Safety content filtering. + +This module provides model middleware that automatically evaluates both +input and output content against configured safety policies, blocking +content that violates those policies. + +Middleware Flow +=============== + +┌─────────────────────────────────────────────────────────────────────────────┐ +│ Checks Middleware Processing │ +├─────────────────────────────────────────────────────────────────────────────┤ +│ │ +│ Input Messages │ +│ │ │ +│ ▼ │ +│ ┌──────────────────┐ │ +│ │ Check Input │ ──► Violation? ──► Return blocked response │ +│ │ Content │ │ +│ └──────────────────┘ │ +│ │ │ +│ ▼ (no violation) │ +│ ┌──────────────────┐ │ +│ │ Call Model │ │ +│ │ (next()) │ │ +│ └──────────────────┘ │ +│ │ │ +│ ▼ │ +│ ┌──────────────────┐ │ +│ │ Check Output │ ──► Violation? ──► Return blocked response │ +│ │ Content │ │ +│ └──────────────────┘ │ +│ │ │ +│ ▼ (no violation) │ +│ Return normal response │ +│ │ +└─────────────────────────────────────────────────────────────────────────────┘ + +Example: + ```python + from genkit.ai import Genkit + from genkit.plugins.checks import checks_middleware, ChecksMetricType + + ai = Genkit() + + response = await ai.generate( + model='googleai/gemini-2.0-flash', + prompt='Hello!', + use=[ + checks_middleware( + metrics=[ChecksMetricType.HARASSMENT, ChecksMetricType.HATE_SPEECH], + auth_options={'project_id': 'my-project'}, + ) + ], + ) + + if response.finish_reason == 'blocked': + print(f'Content blocked: {response.finish_message}') + ``` + +See Also: + - JS implementation: js/plugins/checks/src/middleware.ts +""" + +from typing import Any + +from genkit.blocks.model import ModelMiddleware, ModelMiddlewareNext +from genkit.core.action import ActionRunContext +from genkit.core.typing import FinishReason, GenerateRequest, GenerateResponse, Message +from genkit.plugins.checks.auth import initialize_credentials +from genkit.plugins.checks.guardrails import Guardrails +from genkit.plugins.checks.metrics import ChecksMetric + + +def _get_text_content(message: Message) -> str: + """Extract text content from a message. + + Args: + message: The message to extract text from. + + Returns: + Concatenated text from all text parts of the message. + """ + texts: list[str] = [] + for part in message.content: + if hasattr(part, 'root') and hasattr(part.root, 'text') and part.root.text: + texts.append(str(part.root.text)) + elif hasattr(part, 'text') and part.text: + texts.append(str(part.text)) + return ' '.join(texts) + + +def checks_middleware( + metrics: list[ChecksMetric], + auth_options: dict[str, Any] | None = None, +) -> ModelMiddleware: + """Create model middleware that enforces Checks AI Safety policies. + + This middleware evaluates both input and output content against the + specified safety policies. If any policy is violated, the generation + is blocked and a response with `finish_reason='blocked'` is returned. + + Args: + metrics: List of safety policies to evaluate. Can be ChecksMetricType + values or ChecksMetricConfig objects with custom thresholds. + auth_options: Optional authentication configuration: + - project_id: GCP project ID for API quota + - credentials_file: Path to service account JSON + + Returns: + A ModelMiddleware function that can be passed to ai.generate(use=[...]). + + Example: + ```python + from genkit.plugins.checks import checks_middleware, ChecksMetricType + + # Basic usage with default authentication + middleware = checks_middleware( + metrics=[ChecksMetricType.DANGEROUS_CONTENT], + ) + + # With custom authentication + middleware = checks_middleware( + metrics=[ChecksMetricType.HARASSMENT], + auth_options={ + 'project_id': 'my-project', + 'credentials_file': '/path/to/creds.json', + }, + ) + + # Use in generation + response = await ai.generate( + model='googleai/gemini-2.0-flash', + prompt='Hello!', + use=[middleware], + ) + ``` + + Blocking Behavior: + When content violates a policy, the middleware returns a response with: + - finish_reason: 'blocked' + - finish_message: Description of which policies were violated + + See Also: + - ChecksMetricType: Available safety policy types + - Checks plugin: For plugin-based configuration + """ + # Initialize credentials and guardrails client + credentials, project_id = initialize_credentials(auth_options) + guardrails = Guardrails(credentials, project_id) + + async def classify_content(content: str) -> list[str]: + """Classify content and return violated policy types. + + Args: + content: Text content to classify. + + Returns: + List of policy type names that were violated. + """ + response = await guardrails.classify_content(content, metrics) + return [pr.policy_type for pr in response.policy_results if pr.violation_result == 'VIOLATIVE'] + + async def middleware( + request: GenerateRequest, + ctx: ActionRunContext, + next_fn: ModelMiddlewareNext, + ) -> GenerateResponse: + """Middleware function that checks content safety. + + Args: + request: The generation request. + ctx: The action run context. + next_fn: Function to call the next middleware/model. + + Returns: + The generation response, or a blocked response if content + violates safety policies. + """ + # Check input messages + for message in request.messages: + text_content = _get_text_content(message) + if text_content: + violated_policies = await classify_content(text_content) + if violated_policies: + policy_list = ' '.join(violated_policies) + return GenerateResponse( + message=Message(role='model', content=[]), + finish_reason=FinishReason.BLOCKED, + finish_message=( + f'Model input violated Checks policies: [{policy_list}], further processing blocked.' + ), + ) + + # Call the model + response = await next_fn(request, ctx) + + # Check output content + if hasattr(response, 'candidates') and response.candidates: + for candidate in response.candidates: + if hasattr(candidate, 'message') and candidate.message: + text_content = _get_text_content(candidate.message) + if text_content: + violated_policies = await classify_content(text_content) + if violated_policies: + policy_list = ' '.join(violated_policies) + return GenerateResponse( + message=Message(role='model', content=[]), + finish_reason=FinishReason.BLOCKED, + finish_message=( + f'Model output violated Checks policies: [{policy_list}], output blocked.' + ), + ) + + return response + + return middleware + + +__all__ = [ + 'checks_middleware', +] diff --git a/py/plugins/checks/src/genkit/plugins/checks/plugin.py b/py/plugins/checks/src/genkit/plugins/checks/plugin.py new file mode 100644 index 0000000000..a3bbe92f85 --- /dev/null +++ b/py/plugins/checks/src/genkit/plugins/checks/plugin.py @@ -0,0 +1,506 @@ +# Copyright 2026 Google LLC +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. +# +# SPDX-License-Identifier: Apache-2.0 + +"""Checks AI Safety Genkit plugin. + +This module provides the main plugin class for integrating Google Checks +AI Safety with Genkit applications. + +Plugin Registration +=================== + +The Checks plugin registers a guardrails evaluator that evaluates content +against all configured safety metrics using Genkit's evaluation framework. + +┌─────────────────────────────────────────────────────────────────────────────┐ +│ Checks Plugin Registration │ +├─────────────────────────────────────────────────────────────────────────────┤ +│ │ +│ ┌──────────────┐ ┌──────────────┐ ┌──────────────────────┐ │ +│ │ Genkit │ │ Checks │ │ Evaluator │ │ +│ │ Instance │ ──► │ Plugin │ ──► │ checks/guardrails │ │ +│ └──────────────┘ └──────────────┘ │ - DANGEROUS_CONTENT│ │ +│ │ │ - HARASSMENT │ │ +│ │ │ - etc. │ │ +│ │ └──────────────────────┘ │ +│ │ │ +│ ▼ │ +│ ┌──────────────────────────────────────────┐ │ +│ │ Google Checks API (classifyContent) │ │ +│ └──────────────────────────────────────────┘ │ +│ │ +└─────────────────────────────────────────────────────────────────────────────┘ + +Example: + ```python + from genkit.ai import Genkit + from genkit.plugins.checks import Checks, ChecksMetricType + + ai = Genkit( + plugins=[ + Checks( + project_id='my-gcp-project', + evaluation={ + 'metrics': [ + ChecksMetricType.DANGEROUS_CONTENT, + ChecksMetricType.HARASSMENT, + ChecksMetricType.HATE_SPEECH, + ] + }, + ) + ] + ) + ``` + +See Also: + - JS implementation: js/plugins/checks/src/index.ts + - Checks API: https://developers.google.com/checks +""" + +import traceback +import uuid +from dataclasses import dataclass, field +from typing import Any, cast + +from google.auth.credentials import Credentials + +from genkit.core.action import Action, ActionMetadata +from genkit.core.action.types import ActionKind +from genkit.core.logging import get_logger +from genkit.core.plugin import Plugin +from genkit.core.registry import Registry +from genkit.core.tracing import SpanMetadata, run_in_new_span +from genkit.core.typing import ( + BaseDataPoint, + Details, + EvalFnResponse, + EvalRequest, + EvalResponse, + EvalStatusEnum, + Score, +) +from genkit.plugins.checks.auth import initialize_credentials +from genkit.plugins.checks.guardrails import Guardrails +from genkit.plugins.checks.metrics import ChecksMetric, ChecksMetricConfig, ChecksMetricType + +logger = get_logger(__name__) + +# Evaluator metadata keys (matching JS implementation) +EVALUATOR_METADATA_KEY_DEFINITION = 'definition' +EVALUATOR_METADATA_KEY_DISPLAY_NAME = 'displayName' +EVALUATOR_METADATA_KEY_IS_BILLED = 'isBilled' + + +@dataclass +class ChecksEvaluationConfig: + """Configuration for Checks evaluators. + + Attributes: + metrics: List of safety metrics to enable as evaluators. + """ + + metrics: list[ChecksMetric] = field(default_factory=list) + + +@dataclass +class ChecksOptions: + """Configuration options for the Checks plugin. + + Attributes: + project_id: Google Cloud project ID with Checks API quota. + If not provided, will attempt to detect from credentials + or environment. + google_auth_options: Custom Google Cloud authentication options. + Can include 'credentials_file' for service account JSON path. + evaluation: Configuration for Checks evaluators. + """ + + project_id: str | None = None + google_auth_options: dict[str, Any] | None = None + evaluation: ChecksEvaluationConfig | dict[str, Any] | None = None + + +class Checks(Plugin): + """Google Checks AI Safety plugin for Genkit. + + This plugin integrates the Google Checks AI Safety API with Genkit, + providing content safety evaluation capabilities. + + Key Features: + - Single guardrails evaluator for all configured safety metrics + - Automatic authentication with Google Cloud + - Configurable safety thresholds per metric + + Example: + ```python + from genkit.ai import Genkit + from genkit.plugins.checks import Checks, ChecksMetricType + + # Basic usage + ai = Genkit(plugins=[Checks(project_id='my-project')]) + + # With evaluation metrics + ai = Genkit( + plugins=[ + Checks( + project_id='my-project', + evaluation={ + 'metrics': [ + ChecksMetricType.DANGEROUS_CONTENT, + ChecksMetricType.HARASSMENT, + ] + }, + ) + ] + ) + + # With custom authentication + ai = Genkit( + plugins=[ + Checks( + project_id='my-project', + google_auth_options={'credentials_file': '/path/to/creds.json'}, + ) + ] + ) + ``` + + Authentication: + The plugin uses Google Cloud authentication. Credentials are loaded in + this order: + 1. GCLOUD_SERVICE_ACCOUNT_CREDS environment variable (JSON string) + 2. google_auth_options.credentials_file (service account file path) + 3. Default application credentials + + See Also: + - ChecksMetricType: Available safety metrics + - checks_middleware: For middleware-based content filtering + """ + + name = 'checks' + _GUARDRAILS_EVALUATOR_NAME = 'checks/guardrails' + + def __init__( + self, + project_id: str | None = None, + google_auth_options: dict[str, Any] | None = None, + evaluation: ChecksEvaluationConfig | dict[str, Any] | None = None, + ) -> None: + """Initialize the Checks plugin. + + Args: + project_id: Google Cloud project ID. Must have Checks API quota. + google_auth_options: Custom authentication options. + evaluation: Configuration for safety evaluators. + """ + self._project_id = project_id + self._google_auth_options = google_auth_options + self._evaluation = evaluation + self._credentials: Credentials | None = None + self._registry: Registry | None = None + self._guardrails: Guardrails | None = None + self._metrics: list[ChecksMetric] = [] + self._policy_configs: list[ChecksMetricConfig] = [] + self._evaluator_action: Action | None = None + + def _initialize_auth(self) -> Credentials: + """Initialize Google Cloud authentication. + + Returns: + Initialized credentials. + + Raises: + ValueError: If authentication cannot be established. + """ + credentials, project = initialize_credentials(self._google_auth_options) + + # Update project_id if not set + if self._project_id is None and project: + self._project_id = project + + return credentials + + def _build_policy_configs(self) -> list[ChecksMetricConfig]: + """Build policy configurations from metrics. + + Returns: + List of ChecksMetricConfig with type and optional threshold. + """ + configs: list[ChecksMetricConfig] = [] + for metric in self._metrics: + if isinstance(metric, ChecksMetricConfig): + configs.append(metric) + elif isinstance(metric, ChecksMetricType): + configs.append(ChecksMetricConfig(type=metric)) + return configs + + async def init(self, registry: Registry | None = None) -> list[Action]: + """Initialize the plugin with the Genkit registry. + + This method sets up authentication and prepares evaluators for + each configured safety metric. + + Args: + registry: The Genkit registry. Used for cross-plugin resolution. + + Returns: + List of actions to pre-register (empty for this plugin). + + Raises: + ValueError: If project_id is not configured and cannot be detected. + """ + self._registry = registry + self._credentials = self._initialize_auth() + + # Determine project ID + if self._project_id is None: + raise ValueError( + "Checks Plugin is missing the 'project_id' configuration. " + "Please set the 'GCLOUD_PROJECT' environment variable or " + "explicitly pass 'project_id' into the plugin config." + ) + + # Initialize guardrails client + self._guardrails = Guardrails(self._credentials, self._project_id) + + # Get metrics from evaluation config + if self._evaluation: + if isinstance(self._evaluation, ChecksEvaluationConfig): + self._metrics = self._evaluation.metrics + elif isinstance(self._evaluation, dict): + self._metrics = self._evaluation.get('metrics', []) + + # Build policy configs for evaluation + self._policy_configs = self._build_policy_configs() + + logger.info(f'Checks plugin initialized with {len(self._metrics)} metrics') + + # Actions are created lazily via resolve() + return [] + + async def _evaluate_datapoint( + self, + datapoint: BaseDataPoint, + options: object | None, + ) -> EvalFnResponse: + """Evaluate a single datapoint against configured safety policies. + + Args: + datapoint: The evaluation datapoint containing content to evaluate. + options: Optional evaluation options (unused). + + Returns: + EvalFnResponse with scores for each policy. + """ + if self._guardrails is None: + raise ValueError('Plugin not initialized: guardrails client is None') + + # Get the output content to evaluate + output = datapoint.output + if output is None: + return EvalFnResponse( + test_case_id=datapoint.test_case_id or '', + evaluation=Score( + error='No output content to evaluate', + status=EvalStatusEnum.FAIL, + ), + ) + + # Convert output to string + content = output if isinstance(output, str) else str(output) + + # Call the Checks API + response = await self._guardrails.classify_content( + content=content, + policies=[config.type for config in self._policy_configs], + ) + + # Convert policy results to evaluation scores + # Return all policy results as an array of scores (matching JS implementation) + if response.policy_results: + # Create individual scores for each policy (matches JS evaluationResults) + evaluation_results: list[Score] = [] + for result in response.policy_results: + evaluation_results.append( + Score( + id=result.policy_type, + score=result.score, + details=Details(reasoning=f'Status {result.violation_result}'), + ) + ) + + return EvalFnResponse( + test_case_id=datapoint.test_case_id or '', + evaluation=evaluation_results, + ) + else: + return EvalFnResponse( + test_case_id=datapoint.test_case_id or '', + evaluation=Score( + error='No policy results returned from Checks API', + status=EvalStatusEnum.FAIL, + ), + ) + + def _create_evaluator_action(self) -> Action: + """Create the guardrails evaluator action. + + This creates a single evaluator that evaluates content against all + configured safety policies, matching the JS implementation. + + Returns: + The evaluator Action. + """ + if self._registry is None: + raise ValueError('Plugin not initialized: registry is None') + + # Build definition string listing the policies + policy_names = [config.type.value for config in self._policy_configs] + definition = f'Evaluates input text against the Checks {", ".join(policy_names)} policies.' + + # Build evaluator metadata + evaluator_meta: dict[str, object] = { + 'evaluator': { + EVALUATOR_METADATA_KEY_DEFINITION: definition, + EVALUATOR_METADATA_KEY_DISPLAY_NAME: self._GUARDRAILS_EVALUATOR_NAME, + EVALUATOR_METADATA_KEY_IS_BILLED: True, # Checks API is a paid service + 'label': self._GUARDRAILS_EVALUATOR_NAME, + } + } + + # Create the eval stepper function that iterates over the dataset + # This matches the pattern in genkit.ai._registry.define_evaluator + async def eval_stepper_fn(req: EvalRequest) -> EvalResponse: + """Process all datapoints in the evaluation request.""" + eval_responses: list[EvalFnResponse] = [] + for datapoint in req.dataset: + if datapoint.test_case_id is None: + datapoint.test_case_id = str(uuid.uuid4()) + span_metadata = SpanMetadata( + name=f'Test Case {datapoint.test_case_id}', + metadata={'evaluator:evalRunId': req.eval_run_id}, + ) + try: + # Try to run with tracing + try: + with run_in_new_span(span_metadata, labels={'genkit:type': 'evaluator'}) as span: + span_id = span.span_id + trace_id = span.trace_id + try: + span.set_input(datapoint) + test_case_output = await self._evaluate_datapoint(datapoint, req.options) + test_case_output.span_id = span_id + test_case_output.trace_id = trace_id + span.set_output(test_case_output) + eval_responses.append(test_case_output) + except Exception as e: + logger.debug(f'Checks evaluator error: {e!s}') + logger.debug(traceback.format_exc()) + evaluation = Score( + error=f'Evaluation of test case {datapoint.test_case_id} failed: \n{e!s}', + status=cast(EvalStatusEnum, EvalStatusEnum.FAIL), + ) + eval_responses.append( + EvalFnResponse( + span_id=span_id, + trace_id=trace_id, + test_case_id=datapoint.test_case_id, + evaluation=evaluation, + ) + ) + raise + except (AttributeError, UnboundLocalError): + # Fallback: run without span + try: + test_case_output = await self._evaluate_datapoint(datapoint, req.options) + eval_responses.append(test_case_output) + except Exception as e: + logger.debug(f'Checks evaluator error: {e!s}') + logger.debug(traceback.format_exc()) + evaluation = Score( + error=f'Evaluation of test case {datapoint.test_case_id} failed: \n{e!s}', + status=cast(EvalStatusEnum, EvalStatusEnum.FAIL), + ) + eval_responses.append( + EvalFnResponse( + test_case_id=datapoint.test_case_id, + evaluation=evaluation, + ) + ) + except Exception: + # Continue to process other datapoints + continue + return EvalResponse(eval_responses) + + # Create and return the Action directly + return Action( + kind=cast(ActionKind, ActionKind.EVALUATOR), + name=self._GUARDRAILS_EVALUATOR_NAME, + fn=eval_stepper_fn, + metadata=evaluator_meta, + description=definition, + ) + + async def resolve(self, action_type: ActionKind, name: str) -> Action | None: + """Resolve a single action by type and name. + + The Checks plugin provides a single evaluator action for content + safety classification called 'checks/guardrails'. + + Args: + action_type: The kind of action to resolve. + name: The namespaced name of the action (e.g., 'checks/guardrails'). + + Returns: + Action | None: The Action instance if found, None otherwise. + """ + if action_type != ActionKind.EVALUATOR: + return None + + # Check if this is the checks guardrails evaluator + if name != self._GUARDRAILS_EVALUATOR_NAME: + return None + + # Create the evaluator action lazily (only when first requested) + if self._evaluator_action is None: + self._evaluator_action = self._create_evaluator_action() + + return self._evaluator_action + + async def list_actions(self) -> list[ActionMetadata]: + """Return metadata for available evaluator actions. + + Returns: + List with a single ActionMetadata for the guardrails evaluator + if metrics are configured. + """ + # Only advertise the evaluator if metrics are configured + if not self._metrics: + return [] + + return [ + ActionMetadata( + kind=ActionKind.EVALUATOR, + name=self._GUARDRAILS_EVALUATOR_NAME, + ) + ] + + +__all__ = [ + 'Checks', + 'ChecksEvaluationConfig', + 'ChecksOptions', +] diff --git a/py/plugins/checks/src/genkit/py.typed b/py/plugins/checks/src/genkit/py.typed new file mode 100644 index 0000000000..e69de29bb2 diff --git a/py/plugins/checks/tests/checks_evaluator_test.py b/py/plugins/checks/tests/checks_evaluator_test.py new file mode 100644 index 0000000000..18ab90677c --- /dev/null +++ b/py/plugins/checks/tests/checks_evaluator_test.py @@ -0,0 +1,467 @@ +# Copyright 2026 Google LLC +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. +# +# SPDX-License-Identifier: Apache-2.0 + +"""Tests for the Checks evaluator functionality. + +These tests verify parity with the JS implementation in: +js/plugins/checks/src/evaluation.ts +""" + +from typing import Any, cast +from unittest.mock import AsyncMock, MagicMock, patch + +import pytest + +from genkit.core.action.types import ActionKind +from genkit.core.typing import BaseDataPoint, EvalRequest +from genkit.plugins.checks import Checks, ChecksEvaluationConfig, ChecksMetricType +from genkit.plugins.checks.guardrails import ClassifyContentResponse, PolicyResult + + +class TestChecksEvaluator: + """Tests for Checks evaluator matching JS checksEvaluators.""" + + @pytest.mark.asyncio + async def test_evaluator_name_matches_js(self) -> None: + """Test evaluator is named 'checks/guardrails' matching JS.""" + plugin = Checks( + project_id='test-project', + evaluation=ChecksEvaluationConfig( + metrics=[ChecksMetricType.DANGEROUS_CONTENT], + ), + ) + + with patch.object(plugin, '_initialize_auth') as mock_auth: + mock_auth.return_value = MagicMock() + await plugin.init(registry=MagicMock()) + + action = await plugin.resolve(ActionKind.EVALUATOR, 'checks/guardrails') + + assert action is not None + assert action.name == 'checks/guardrails' + + @pytest.mark.asyncio + async def test_evaluator_metadata_matches_js(self) -> None: + """Test evaluator metadata structure matches JS implementation.""" + plugin = Checks( + project_id='test-project', + evaluation=ChecksEvaluationConfig( + metrics=[ + ChecksMetricType.DANGEROUS_CONTENT, + ChecksMetricType.HARASSMENT, + ], + ), + ) + + with patch.object(plugin, '_initialize_auth') as mock_auth: + mock_auth.return_value = MagicMock() + await plugin.init(registry=MagicMock()) + + action = await plugin.resolve(ActionKind.EVALUATOR, 'checks/guardrails') + + assert action is not None + assert action.metadata is not None + assert 'evaluator' in action.metadata + + evaluator_meta = cast(dict[str, Any], action.metadata['evaluator']) + assert 'definition' in evaluator_meta + assert 'displayName' in evaluator_meta + assert evaluator_meta['displayName'] == 'checks/guardrails' + + @pytest.mark.asyncio + async def test_evaluator_definition_includes_policy_types(self) -> None: + """Test evaluator definition includes configured policy types (matching JS).""" + plugin = Checks( + project_id='test-project', + evaluation=ChecksEvaluationConfig( + metrics=[ + ChecksMetricType.DANGEROUS_CONTENT, + ChecksMetricType.HARASSMENT, + ], + ), + ) + + with patch.object(plugin, '_initialize_auth') as mock_auth: + mock_auth.return_value = MagicMock() + await plugin.init(registry=MagicMock()) + + action = await plugin.resolve(ActionKind.EVALUATOR, 'checks/guardrails') + + assert action is not None + assert action.metadata is not None + + evaluator_meta = cast(dict[str, Any], action.metadata['evaluator']) + definition = str(evaluator_meta['definition']) + assert 'DANGEROUS_CONTENT' in definition + assert 'HARASSMENT' in definition + + @pytest.mark.asyncio + async def test_evaluator_returns_scores_per_policy(self) -> None: + """Test evaluator returns a score for each policy (matching JS evaluationResults).""" + mock_response = ClassifyContentResponse( + policy_results=[ + PolicyResult( + policy_type='DANGEROUS_CONTENT', + score=0.1, + violation_result='NON_VIOLATIVE', + ), + PolicyResult( + policy_type='HARASSMENT', + score=0.8, + violation_result='VIOLATIVE', + ), + ] + ) + + plugin = Checks( + project_id='test-project', + evaluation=ChecksEvaluationConfig( + metrics=[ + ChecksMetricType.DANGEROUS_CONTENT, + ChecksMetricType.HARASSMENT, + ], + ), + ) + + with patch.object(plugin, '_initialize_auth') as mock_auth: + mock_auth.return_value = MagicMock() + await plugin.init(registry=MagicMock()) + + # Get the evaluator action + action = await plugin.resolve(ActionKind.EVALUATOR, 'checks/guardrails') + assert action is not None + + # Mock the guardrails client + plugin._guardrails = MagicMock() + plugin._guardrails.classify_content = AsyncMock(return_value=mock_response) + + # Create evaluation request + request = EvalRequest( + dataset=[ + BaseDataPoint( + test_case_id='test-1', + input='test input', + output='test output to evaluate', + ), + ], + eval_run_id='run-1', + ) + + # Run the evaluator + result = await action.arun(request) + + # Verify we get scores for each policy + # result.response is ActionResponse[EvalResponse], EvalResponse is RootModel[list[EvalFnResponse]] + eval_fn_responses = result.response.root + assert len(eval_fn_responses) == 1 + eval_response = eval_fn_responses[0] + + # Should have evaluation as list of scores (matching JS evaluationResults array) + assert eval_response.evaluation is not None + if isinstance(eval_response.evaluation, list): + assert len(eval_response.evaluation) == 2 + + # Check score structure matches JS + score_ids = [s.id for s in eval_response.evaluation] + assert 'DANGEROUS_CONTENT' in score_ids + assert 'HARASSMENT' in score_ids + + @pytest.mark.asyncio + async def test_evaluator_score_structure_matches_js(self) -> None: + """Test individual score structure matches JS implementation.""" + mock_response = ClassifyContentResponse( + policy_results=[ + PolicyResult( + policy_type='DANGEROUS_CONTENT', + score=0.75, + violation_result='VIOLATIVE', + ), + ] + ) + + plugin = Checks( + project_id='test-project', + evaluation=ChecksEvaluationConfig( + metrics=[ChecksMetricType.DANGEROUS_CONTENT], + ), + ) + + with patch.object(plugin, '_initialize_auth') as mock_auth: + mock_auth.return_value = MagicMock() + await plugin.init(registry=MagicMock()) + + action = await plugin.resolve(ActionKind.EVALUATOR, 'checks/guardrails') + assert action is not None + + plugin._guardrails = MagicMock() + plugin._guardrails.classify_content = AsyncMock(return_value=mock_response) + + request = EvalRequest( + dataset=[ + BaseDataPoint( + test_case_id='test-1', + output='content to evaluate', + ), + ], + eval_run_id='run-1', + ) + + result = await action.arun(request) + + # EvalResponse is RootModel[list[EvalFnResponse]] + eval_fn_responses = result.response.root + eval_response = eval_fn_responses[0] + assert isinstance(eval_response.evaluation, list) + + score = eval_response.evaluation[0] + + # Verify JS-compatible structure: + # { id: result.policyType, score: result.score, details: { reasoning: `Status ${result.violationResult}` } } + assert score.id == 'DANGEROUS_CONTENT' + assert score.score == 0.75 + assert score.details is not None + assert 'Status VIOLATIVE' in (score.details.reasoning or '') + + @pytest.mark.asyncio + async def test_evaluator_preserves_test_case_id(self) -> None: + """Test evaluator preserves testCaseId (matching JS).""" + mock_response = ClassifyContentResponse( + policy_results=[ + PolicyResult( + policy_type='HARASSMENT', + score=0.5, + violation_result='NON_VIOLATIVE', + ), + ] + ) + + plugin = Checks( + project_id='test-project', + evaluation=ChecksEvaluationConfig( + metrics=[ChecksMetricType.HARASSMENT], + ), + ) + + with patch.object(plugin, '_initialize_auth') as mock_auth: + mock_auth.return_value = MagicMock() + await plugin.init(registry=MagicMock()) + + action = await plugin.resolve(ActionKind.EVALUATOR, 'checks/guardrails') + assert action is not None + + plugin._guardrails = MagicMock() + plugin._guardrails.classify_content = AsyncMock(return_value=mock_response) + + request = EvalRequest( + dataset=[ + BaseDataPoint( + test_case_id='my-custom-id-123', + output='test output', + ), + ], + eval_run_id='run-1', + ) + + result = await action.arun(request) + + # EvalResponse is RootModel[list[EvalFnResponse]] + assert result.response.root[0].test_case_id == 'my-custom-id-123' + + @pytest.mark.asyncio + async def test_evaluator_generates_test_case_id_if_missing(self) -> None: + """Test evaluator generates testCaseId if not provided.""" + mock_response = ClassifyContentResponse( + policy_results=[ + PolicyResult( + policy_type='HARASSMENT', + score=0.5, + violation_result='NON_VIOLATIVE', + ), + ] + ) + + plugin = Checks( + project_id='test-project', + evaluation=ChecksEvaluationConfig( + metrics=[ChecksMetricType.HARASSMENT], + ), + ) + + with patch.object(plugin, '_initialize_auth') as mock_auth: + mock_auth.return_value = MagicMock() + await plugin.init(registry=MagicMock()) + + action = await plugin.resolve(ActionKind.EVALUATOR, 'checks/guardrails') + assert action is not None + + plugin._guardrails = MagicMock() + plugin._guardrails.classify_content = AsyncMock(return_value=mock_response) + + request = EvalRequest( + dataset=[ + BaseDataPoint( + test_case_id=None, # No ID provided + output='test output', + ), + ], + eval_run_id='run-1', + ) + + result = await action.arun(request) + + # Should have generated a test case ID + # EvalResponse is RootModel[list[EvalFnResponse]] + assert result.response.root[0].test_case_id is not None + assert len(result.response.root[0].test_case_id) > 0 + + @pytest.mark.asyncio + async def test_evaluator_evaluates_output_content(self) -> None: + """Test evaluator evaluates the output field (matching JS datapoint.output).""" + plugin = Checks( + project_id='test-project', + evaluation=ChecksEvaluationConfig( + metrics=[ChecksMetricType.DANGEROUS_CONTENT], + ), + ) + + with patch.object(plugin, '_initialize_auth') as mock_auth: + mock_auth.return_value = MagicMock() + await plugin.init(registry=MagicMock()) + + action = await plugin.resolve(ActionKind.EVALUATOR, 'checks/guardrails') + assert action is not None + + mock_classify = AsyncMock( + return_value=ClassifyContentResponse( + policy_results=[ + PolicyResult( + policy_type='DANGEROUS_CONTENT', + score=0.5, + violation_result='NON_VIOLATIVE', + ), + ] + ) + ) + plugin._guardrails = MagicMock() + plugin._guardrails.classify_content = mock_classify + + request = EvalRequest( + dataset=[ + BaseDataPoint( + test_case_id='test-1', + input='this is the input', # Should NOT be evaluated + output='this is the output to evaluate', # This should be evaluated + ), + ], + eval_run_id='run-1', + ) + + await action.arun(request) + + # Verify the output content was sent to classify_content + mock_classify.assert_called_once() + call_args = mock_classify.call_args + content = call_args[1]['content'] + assert content == 'this is the output to evaluate' + + @pytest.mark.asyncio + async def test_evaluator_handles_missing_output(self) -> None: + """Test evaluator handles datapoint with no output.""" + plugin = Checks( + project_id='test-project', + evaluation=ChecksEvaluationConfig( + metrics=[ChecksMetricType.DANGEROUS_CONTENT], + ), + ) + + with patch.object(plugin, '_initialize_auth') as mock_auth: + mock_auth.return_value = MagicMock() + await plugin.init(registry=MagicMock()) + + action = await plugin.resolve(ActionKind.EVALUATOR, 'checks/guardrails') + assert action is not None + + plugin._guardrails = MagicMock() + + request = EvalRequest( + dataset=[ + BaseDataPoint( + test_case_id='test-1', + output=None, # No output + ), + ], + eval_run_id='run-1', + ) + + result = await action.arun(request) + + # Should return an error response + # EvalResponse is RootModel[list[EvalFnResponse]] + eval_response = result.response.root[0] + assert eval_response.evaluation is not None + # When there's no output, we should get an error score + if not isinstance(eval_response.evaluation, list): + assert eval_response.evaluation.error is not None + + +class TestChecksEvaluatorSingleEvaluator: + """Tests verifying the plugin creates a single guardrails evaluator.""" + + @pytest.mark.asyncio + async def test_only_one_evaluator_action(self) -> None: + """Test plugin returns only one evaluator (checks/guardrails).""" + plugin = Checks( + project_id='test-project', + evaluation=ChecksEvaluationConfig( + metrics=[ + ChecksMetricType.DANGEROUS_CONTENT, + ChecksMetricType.HARASSMENT, + ChecksMetricType.HATE_SPEECH, + ], + ), + ) + + with patch.object(plugin, '_initialize_auth') as mock_auth: + mock_auth.return_value = MagicMock() + await plugin.init(registry=MagicMock()) + + actions = await plugin.list_actions() + + # Should only be one evaluator, not one per metric + assert len(actions) == 1 + assert actions[0].name == 'checks/guardrails' + assert actions[0].kind == ActionKind.EVALUATOR + + @pytest.mark.asyncio + async def test_individual_metric_not_resolvable(self) -> None: + """Test individual metrics are not resolvable as separate evaluators.""" + plugin = Checks( + project_id='test-project', + evaluation=ChecksEvaluationConfig( + metrics=[ChecksMetricType.DANGEROUS_CONTENT], + ), + ) + + with patch.object(plugin, '_initialize_auth') as mock_auth: + mock_auth.return_value = MagicMock() + await plugin.init(registry=MagicMock()) + + # Should NOT be able to resolve individual metrics + result = await plugin.resolve(ActionKind.EVALUATOR, 'checks/dangerous_content') + assert result is None + + result = await plugin.resolve(ActionKind.EVALUATOR, 'checks/DANGEROUS_CONTENT') + assert result is None diff --git a/py/plugins/checks/tests/checks_guardrails_test.py b/py/plugins/checks/tests/checks_guardrails_test.py new file mode 100644 index 0000000000..26619e9281 --- /dev/null +++ b/py/plugins/checks/tests/checks_guardrails_test.py @@ -0,0 +1,310 @@ +# Copyright 2026 Google LLC +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. +# +# SPDX-License-Identifier: Apache-2.0 + +"""Tests for the Checks Guardrails API client. + +These tests verify parity with the JS implementation in: +js/plugins/checks/src/guardrails.ts +""" + +from unittest.mock import AsyncMock, MagicMock, patch + +import pytest + +from genkit.plugins.checks.guardrails import ( + GUARDRAILS_URL, + ClassifyContentResponse, + Guardrails, + GuardrailsRequest, + PolicyResult, +) +from genkit.plugins.checks.metrics import ChecksMetricConfig, ChecksMetricType + + +class TestPolicyResult: + """Tests for PolicyResult model.""" + + def test_policy_result_from_api_response(self) -> None: + """Test PolicyResult can be created with Python field names.""" + result = PolicyResult( + policy_type='DANGEROUS_CONTENT', + score=0.95, + violation_result='VIOLATIVE', + ) + assert result.policy_type == 'DANGEROUS_CONTENT' + assert result.score == 0.95 + assert result.violation_result == 'VIOLATIVE' + + def test_policy_result_with_camel_case_aliases(self) -> None: + """Test PolicyResult handles camelCase aliases from JSON.""" + data = { + 'policyType': 'HARASSMENT', + 'score': 0.3, + 'violationResult': 'NON_VIOLATIVE', + } + result = PolicyResult.model_validate(data) + assert result.policy_type == 'HARASSMENT' + assert result.score == 0.3 + assert result.violation_result == 'NON_VIOLATIVE' + + def test_policy_result_optional_score(self) -> None: + """Test PolicyResult with optional score (matches JS where score is optional).""" + result = PolicyResult( + policy_type='HATE_SPEECH', + violation_result='NON_VIOLATIVE', + ) + assert result.policy_type == 'HATE_SPEECH' + assert result.score is None + assert result.violation_result == 'NON_VIOLATIVE' + + def test_policy_result_default_violation_result(self) -> None: + """Test PolicyResult default violation result.""" + result = PolicyResult(policy_type='DANGEROUS_CONTENT') + assert result.violation_result == 'CLASSIFICATION_UNSPECIFIED' + + +class TestClassifyContentResponse: + """Tests for ClassifyContentResponse model.""" + + def test_response_from_api_format(self) -> None: + """Test ClassifyContentResponse parsing from API response.""" + data = { + 'policyResults': [ + { + 'policyType': 'DANGEROUS_CONTENT', + 'score': 0.1, + 'violationResult': 'NON_VIOLATIVE', + }, + { + 'policyType': 'HARASSMENT', + 'score': 0.9, + 'violationResult': 'VIOLATIVE', + }, + ] + } + response = ClassifyContentResponse.model_validate(data) + assert len(response.policy_results) == 2 + assert response.policy_results[0].policy_type == 'DANGEROUS_CONTENT' + assert response.policy_results[1].violation_result == 'VIOLATIVE' + + def test_response_empty_results(self) -> None: + """Test ClassifyContentResponse with empty results.""" + response = ClassifyContentResponse() + assert response.policy_results == [] + + def test_response_with_alias(self) -> None: + """Test ClassifyContentResponse with policyResults alias.""" + data = {'policyResults': []} + response = ClassifyContentResponse.model_validate(data) + assert response.policy_results == [] + + +class TestGuardrailsRequest: + """Tests for GuardrailsRequest dataclass.""" + + def test_request_to_dict_format(self) -> None: + """Test GuardrailsRequest.to_dict matches JS API format.""" + request = GuardrailsRequest( + content='Hello world', + policies=[ + {'policy_type': 'DANGEROUS_CONTENT'}, + {'policy_type': 'HARASSMENT', 'threshold': 0.8}, + ], + ) + result = request.to_dict() + + # Verify structure matches JS implementation + assert 'input' in result + assert 'text_input' in result['input'] + assert result['input']['text_input']['content'] == 'Hello world' + assert 'policies' in result + assert len(result['policies']) == 2 + assert result['policies'][0]['policy_type'] == 'DANGEROUS_CONTENT' + assert result['policies'][1]['threshold'] == 0.8 + + +class TestGuardrails: + """Tests for Guardrails API client.""" + + def test_guardrails_init(self) -> None: + """Test Guardrails client initialization.""" + mock_creds = MagicMock() + client = Guardrails(mock_creds, 'test-project') + assert client._credentials == mock_creds + assert client._project_id == 'test-project' + + def test_guardrails_init_without_project(self) -> None: + """Test Guardrails client initialization without project ID.""" + mock_creds = MagicMock() + client = Guardrails(mock_creds) + assert client._project_id is None + + @pytest.mark.asyncio + async def test_classify_content_with_metric_types(self) -> None: + """Test classify_content with ChecksMetricType values.""" + mock_creds = MagicMock() + mock_creds.valid = True + mock_creds.token = 'test-token' + + client = Guardrails(mock_creds, 'test-project') + + mock_response = MagicMock() + mock_response.status_code = 200 + mock_response.json.return_value = { + 'policyResults': [ + { + 'policyType': 'DANGEROUS_CONTENT', + 'score': 0.1, + 'violationResult': 'NON_VIOLATIVE', + } + ] + } + mock_response.raise_for_status = MagicMock() + + with patch('httpx.AsyncClient') as mock_client_class: + mock_client = AsyncMock() + mock_client.post = AsyncMock(return_value=mock_response) + mock_client.__aenter__ = AsyncMock(return_value=mock_client) + mock_client.__aexit__ = AsyncMock(return_value=None) + mock_client_class.return_value = mock_client + + result = await client.classify_content( + content='Hello world', + policies=[ChecksMetricType.DANGEROUS_CONTENT], + ) + + assert len(result.policy_results) == 1 + assert result.policy_results[0].policy_type == 'DANGEROUS_CONTENT' + + # Verify correct API URL + mock_client.post.assert_called_once() + call_args = mock_client.post.call_args + assert call_args[0][0] == GUARDRAILS_URL + + @pytest.mark.asyncio + async def test_classify_content_with_metric_config(self) -> None: + """Test classify_content with ChecksMetricConfig including threshold.""" + mock_creds = MagicMock() + mock_creds.valid = True + mock_creds.token = 'test-token' + + client = Guardrails(mock_creds, 'test-project') + + mock_response = MagicMock() + mock_response.json.return_value = { + 'policyResults': [ + { + 'policyType': 'HARASSMENT', + 'score': 0.5, + 'violationResult': 'NON_VIOLATIVE', + } + ] + } + mock_response.raise_for_status = MagicMock() + + with patch('httpx.AsyncClient') as mock_client_class: + mock_client = AsyncMock() + mock_client.post = AsyncMock(return_value=mock_response) + mock_client.__aenter__ = AsyncMock(return_value=mock_client) + mock_client.__aexit__ = AsyncMock(return_value=None) + mock_client_class.return_value = mock_client + + result = await client.classify_content( + content='Test content', + policies=[ + ChecksMetricConfig( + type=ChecksMetricType.HARASSMENT, + threshold=0.8, + ) + ], + ) + + assert len(result.policy_results) == 1 + + # Verify threshold was included in request + call_args = mock_client.post.call_args + request_body = call_args[1]['json'] + assert request_body['policies'][0]['threshold'] == 0.8 + + @pytest.mark.asyncio + async def test_classify_content_headers(self) -> None: + """Test classify_content sends correct headers (matching JS).""" + mock_creds = MagicMock() + mock_creds.valid = True + mock_creds.token = 'test-token' + + client = Guardrails(mock_creds, 'my-project') + + mock_response = MagicMock() + mock_response.json.return_value = {'policyResults': []} + mock_response.raise_for_status = MagicMock() + + with patch('httpx.AsyncClient') as mock_client_class: + mock_client = AsyncMock() + mock_client.post = AsyncMock(return_value=mock_response) + mock_client.__aenter__ = AsyncMock(return_value=mock_client) + mock_client.__aexit__ = AsyncMock(return_value=None) + mock_client_class.return_value = mock_client + + await client.classify_content( + content='Test', + policies=[ChecksMetricType.DANGEROUS_CONTENT], + ) + + call_args = mock_client.post.call_args + headers = call_args[1]['headers'] + + # Verify headers match JS implementation + assert headers['Content-Type'] == 'application/json' + assert headers['x-goog-user-project'] == 'my-project' + assert headers['Authorization'] == 'Bearer test-token' + + @pytest.mark.asyncio + async def test_classify_content_credential_refresh(self) -> None: + """Test classify_content refreshes expired credentials.""" + mock_creds = MagicMock() + mock_creds.valid = False # Credentials need refresh + mock_creds.token = 'refreshed-token' + + client = Guardrails(mock_creds, 'test-project') + + mock_response = MagicMock() + mock_response.json.return_value = {'policyResults': []} + mock_response.raise_for_status = MagicMock() + + with patch('httpx.AsyncClient') as mock_client_class: + mock_client = AsyncMock() + mock_client.post = AsyncMock(return_value=mock_response) + mock_client.__aenter__ = AsyncMock(return_value=mock_client) + mock_client.__aexit__ = AsyncMock(return_value=None) + mock_client_class.return_value = mock_client + + await client.classify_content( + content='Test', + policies=[ChecksMetricType.DANGEROUS_CONTENT], + ) + + # Verify credentials were refreshed + mock_creds.refresh.assert_called_once() + + +class TestGuardrailsUrl: + """Tests for API URL constant.""" + + def test_guardrails_url_matches_js(self) -> None: + """Test GUARDRAILS_URL matches JS implementation.""" + expected = 'https://checks.googleapis.com/v1alpha/aisafety:classifyContent' + assert GUARDRAILS_URL == expected diff --git a/py/plugins/checks/tests/checks_metrics_test.py b/py/plugins/checks/tests/checks_metrics_test.py new file mode 100644 index 0000000000..5c48a6a1b0 --- /dev/null +++ b/py/plugins/checks/tests/checks_metrics_test.py @@ -0,0 +1,175 @@ +# Copyright 2026 Google LLC +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. +# +# SPDX-License-Identifier: Apache-2.0 + +"""Tests for the Checks metrics module. + +These tests verify parity with the JS implementation in: +js/plugins/checks/src/metrics.ts +""" + +from genkit.plugins.checks.metrics import ( + ChecksMetric, + ChecksMetricConfig, + ChecksMetricType, + is_metric_config, +) + + +class TestChecksMetricType: + """Tests for ChecksMetricType enum matching JS ChecksEvaluationMetricType.""" + + def test_dangerous_content_value(self) -> None: + """Test DANGEROUS_CONTENT value matches JS.""" + assert ChecksMetricType.DANGEROUS_CONTENT.value == 'DANGEROUS_CONTENT' + + def test_pii_soliciting_reciting_value(self) -> None: + """Test PII_SOLICITING_RECITING value matches JS.""" + assert ChecksMetricType.PII_SOLICITING_RECITING.value == 'PII_SOLICITING_RECITING' + + def test_harassment_value(self) -> None: + """Test HARASSMENT value matches JS.""" + assert ChecksMetricType.HARASSMENT.value == 'HARASSMENT' + + def test_sexually_explicit_value(self) -> None: + """Test SEXUALLY_EXPLICIT value matches JS.""" + assert ChecksMetricType.SEXUALLY_EXPLICIT.value == 'SEXUALLY_EXPLICIT' + + def test_hate_speech_value(self) -> None: + """Test HATE_SPEECH value matches JS.""" + assert ChecksMetricType.HATE_SPEECH.value == 'HATE_SPEECH' + + def test_medical_info_value(self) -> None: + """Test MEDICAL_INFO value matches JS.""" + assert ChecksMetricType.MEDICAL_INFO.value == 'MEDICAL_INFO' + + def test_violence_and_gore_value(self) -> None: + """Test VIOLENCE_AND_GORE value matches JS.""" + assert ChecksMetricType.VIOLENCE_AND_GORE.value == 'VIOLENCE_AND_GORE' + + def test_obscenity_and_profanity_value(self) -> None: + """Test OBSCENITY_AND_PROFANITY value matches JS.""" + assert ChecksMetricType.OBSCENITY_AND_PROFANITY.value == 'OBSCENITY_AND_PROFANITY' + + def test_all_metric_types_exist(self) -> None: + """Test all JS ChecksEvaluationMetricType values exist in Python.""" + js_metric_types = [ + 'DANGEROUS_CONTENT', + 'PII_SOLICITING_RECITING', + 'HARASSMENT', + 'SEXUALLY_EXPLICIT', + 'HATE_SPEECH', + 'MEDICAL_INFO', + 'VIOLENCE_AND_GORE', + 'OBSCENITY_AND_PROFANITY', + ] + python_metric_types = [m.value for m in ChecksMetricType] + + for js_type in js_metric_types: + assert js_type in python_metric_types, f'Missing metric type: {js_type}' + + def test_metric_type_count_matches_js(self) -> None: + """Test Python has same number of metric types as JS.""" + # JS has 8 metric types + assert len(ChecksMetricType) == 8 + + def test_metric_type_is_string_enum(self) -> None: + """Test ChecksMetricType is a string enum for JSON serialization.""" + # ChecksMetricType is str + Enum, so value is the string + assert ChecksMetricType.DANGEROUS_CONTENT.value == 'DANGEROUS_CONTENT' + # Can be used where strings are expected + assert str(ChecksMetricType.DANGEROUS_CONTENT.value) == 'DANGEROUS_CONTENT' + + +class TestChecksMetricConfig: + """Tests for ChecksMetricConfig dataclass matching JS ChecksEvaluationMetricConfig.""" + + def test_config_with_type_only(self) -> None: + """Test config with just type (no threshold).""" + config = ChecksMetricConfig(type=ChecksMetricType.HARASSMENT) + assert config.type == ChecksMetricType.HARASSMENT + assert config.threshold is None + + def test_config_with_threshold(self) -> None: + """Test config with type and threshold (matching JS).""" + config = ChecksMetricConfig( + type=ChecksMetricType.DANGEROUS_CONTENT, + threshold=0.8, + ) + assert config.type == ChecksMetricType.DANGEROUS_CONTENT + assert config.threshold == 0.8 + + def test_config_threshold_float(self) -> None: + """Test threshold accepts float values.""" + config = ChecksMetricConfig( + type=ChecksMetricType.HATE_SPEECH, + threshold=0.5, + ) + assert config.threshold == 0.5 + + +class TestIsMetricConfig: + """Tests for is_metric_config function matching JS isConfig.""" + + def test_returns_true_for_config(self) -> None: + """Test is_metric_config returns True for ChecksMetricConfig.""" + config = ChecksMetricConfig(type=ChecksMetricType.HARASSMENT) + assert is_metric_config(config) is True + + def test_returns_false_for_metric_type(self) -> None: + """Test is_metric_config returns False for ChecksMetricType.""" + assert is_metric_config(ChecksMetricType.HARASSMENT) is False + + def test_returns_false_for_all_metric_types(self) -> None: + """Test is_metric_config returns False for all metric types.""" + for metric_type in ChecksMetricType: + assert is_metric_config(metric_type) is False + + def test_returns_true_for_config_with_threshold(self) -> None: + """Test is_metric_config returns True for config with threshold.""" + config = ChecksMetricConfig( + type=ChecksMetricType.DANGEROUS_CONTENT, + threshold=0.9, + ) + assert is_metric_config(config) is True + + +class TestChecksMetricUnion: + """Tests for ChecksMetric type alias matching JS ChecksEvaluationMetric.""" + + def test_metric_type_is_valid_metric(self) -> None: + """Test ChecksMetricType is a valid ChecksMetric.""" + metric: ChecksMetric = ChecksMetricType.HARASSMENT + assert isinstance(metric, ChecksMetricType) + + def test_metric_config_is_valid_metric(self) -> None: + """Test ChecksMetricConfig is a valid ChecksMetric.""" + metric: ChecksMetric = ChecksMetricConfig(type=ChecksMetricType.HARASSMENT) + assert isinstance(metric, ChecksMetricConfig) + + def test_list_of_mixed_metrics(self) -> None: + """Test list can contain both types (matching JS usage).""" + metrics: list[ChecksMetric] = [ + ChecksMetricType.DANGEROUS_CONTENT, + ChecksMetricConfig( + type=ChecksMetricType.HARASSMENT, + threshold=0.8, + ), + ChecksMetricType.HATE_SPEECH, + ] + assert len(metrics) == 3 + assert is_metric_config(metrics[0]) is False + assert is_metric_config(metrics[1]) is True + assert is_metric_config(metrics[2]) is False diff --git a/py/plugins/checks/tests/checks_middleware_test.py b/py/plugins/checks/tests/checks_middleware_test.py new file mode 100644 index 0000000000..41c1c0cf84 --- /dev/null +++ b/py/plugins/checks/tests/checks_middleware_test.py @@ -0,0 +1,374 @@ +# Copyright 2026 Google LLC +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. +# +# SPDX-License-Identifier: Apache-2.0 + +"""Tests for the Checks middleware. + +These tests verify parity with the JS implementation in: +js/plugins/checks/src/middleware.ts +""" + +from unittest.mock import AsyncMock, MagicMock, patch + +import pytest + +from genkit.core.typing import ( + FinishReason, + GenerateRequest, + GenerateResponse, + Message, + Part, + TextPart, +) +from genkit.plugins.checks.guardrails import ClassifyContentResponse, PolicyResult +from genkit.plugins.checks.metrics import ChecksMetricType +from genkit.plugins.checks.middleware import checks_middleware + + +class TestChecksMiddleware: + """Tests for checks_middleware function.""" + + @pytest.mark.asyncio + async def test_middleware_blocks_violative_input(self) -> None: + """Test middleware blocks violative input content (matching JS behavior).""" + # Mock guardrails to return a violation + mock_response = ClassifyContentResponse.model_validate({ + 'policyResults': [ + { + 'policyType': 'HARASSMENT', + 'score': 0.95, + 'violationResult': 'VIOLATIVE', + } + ] + }) + + with ( + patch('genkit.plugins.checks.middleware.initialize_credentials') as mock_init_creds, + patch( + 'genkit.plugins.checks.guardrails.Guardrails.classify_content', + new_callable=AsyncMock, + ) as mock_classify, + ): + mock_init_creds.return_value = (MagicMock(), 'test-project') + mock_classify.return_value = mock_response + + middleware = checks_middleware( + metrics=[ChecksMetricType.HARASSMENT], + auth_options={'project_id': 'test-project'}, + ) + + # Create a request with text content + request = GenerateRequest( + messages=[ + Message( + role='user', + content=[Part(root=TextPart(text='Some harassing content'))], + ) + ] + ) + + ctx = MagicMock() + next_fn = AsyncMock() + + result = await middleware(request, ctx, next_fn) + + # Verify blocked response matches JS format + assert result.finish_reason == FinishReason.BLOCKED + assert 'violated Checks policies' in (result.finish_message or '') + assert 'HARASSMENT' in (result.finish_message or '') + assert 'further processing blocked' in (result.finish_message or '') + + # Verify model was NOT called + next_fn.assert_not_called() + + @pytest.mark.asyncio + async def test_middleware_blocks_violative_output(self) -> None: + """Test middleware blocks violative output content (matching JS behavior).""" + # First call (input check) returns non-violative + # Second call (output check) returns violative + mock_input_response = ClassifyContentResponse.model_validate({ + 'policyResults': [ + { + 'policyType': 'HARASSMENT', + 'score': 0.1, + 'violationResult': 'NON_VIOLATIVE', + } + ] + }) + mock_output_response = ClassifyContentResponse.model_validate({ + 'policyResults': [ + { + 'policyType': 'HARASSMENT', + 'score': 0.95, + 'violationResult': 'VIOLATIVE', + } + ] + }) + + with ( + patch('genkit.plugins.checks.middleware.initialize_credentials') as mock_init_creds, + patch( + 'genkit.plugins.checks.guardrails.Guardrails.classify_content', + new_callable=AsyncMock, + ) as mock_classify, + ): + mock_init_creds.return_value = (MagicMock(), 'test-project') + mock_classify.side_effect = [mock_input_response, mock_output_response] + + middleware = checks_middleware( + metrics=[ChecksMetricType.HARASSMENT], + auth_options={'project_id': 'test-project'}, + ) + + request = GenerateRequest( + messages=[ + Message( + role='user', + content=[Part(root=TextPart(text='Hello'))], + ) + ] + ) + + # Mock model response with violative output + model_response = MagicMock() + model_response.candidates = [ + MagicMock( + message=Message( + role='model', + content=[Part(root=TextPart(text='Harassing output'))], + ) + ) + ] + + ctx = MagicMock() + next_fn = AsyncMock(return_value=model_response) + + result = await middleware(request, ctx, next_fn) + + # Verify output was blocked + assert result.finish_reason == FinishReason.BLOCKED + assert 'output blocked' in (result.finish_message or '') + + # Verify model WAS called (input passed) + next_fn.assert_called_once() + + @pytest.mark.asyncio + async def test_middleware_passes_non_violative_content(self) -> None: + """Test middleware allows non-violative content through.""" + mock_response = ClassifyContentResponse.model_validate({ + 'policyResults': [ + { + 'policyType': 'HARASSMENT', + 'score': 0.05, + 'violationResult': 'NON_VIOLATIVE', + } + ] + }) + + with ( + patch('genkit.plugins.checks.middleware.initialize_credentials') as mock_init_creds, + patch( + 'genkit.plugins.checks.guardrails.Guardrails.classify_content', + new_callable=AsyncMock, + ) as mock_classify, + ): + mock_init_creds.return_value = (MagicMock(), 'test-project') + mock_classify.return_value = mock_response + + middleware = checks_middleware( + metrics=[ChecksMetricType.HARASSMENT], + auth_options={'project_id': 'test-project'}, + ) + + request = GenerateRequest( + messages=[ + Message( + role='user', + content=[Part(root=TextPart(text='Hello, how are you?'))], + ) + ] + ) + + expected_response = GenerateResponse( + message=Message(role='model', content=[Part(root=TextPart(text='I am fine!'))]), + finish_reason=FinishReason.STOP, + ) + + ctx = MagicMock() + next_fn = AsyncMock(return_value=expected_response) + + result = await middleware(request, ctx, next_fn) + + # Verify response passed through + assert result == expected_response + next_fn.assert_called_once() + + @pytest.mark.asyncio + async def test_middleware_checks_all_messages(self) -> None: + """Test middleware checks all input messages (matching JS).""" + call_count = 0 + + async def mock_classify(content: str, policies: list) -> ClassifyContentResponse: + nonlocal call_count + call_count += 1 + return ClassifyContentResponse( + policy_results=[ + PolicyResult( + policy_type='DANGEROUS_CONTENT', + score=0.1, + violation_result='NON_VIOLATIVE', + ) + ] + ) + + with ( + patch('genkit.plugins.checks.middleware.initialize_credentials') as mock_init_creds, + patch( + 'genkit.plugins.checks.guardrails.Guardrails.classify_content', + new_callable=AsyncMock, + ) as mock_classify_method, + ): + mock_init_creds.return_value = (MagicMock(), 'test-project') + mock_classify_method.side_effect = mock_classify + + middleware = checks_middleware( + metrics=[ChecksMetricType.DANGEROUS_CONTENT], + ) + + request = GenerateRequest( + messages=[ + Message(role='user', content=[Part(root=TextPart(text='Message 1'))]), + Message(role='model', content=[Part(root=TextPart(text='Response 1'))]), + Message(role='user', content=[Part(root=TextPart(text='Message 2'))]), + ] + ) + + model_response = GenerateResponse( + message=Message(role='model', content=[]), + finish_reason=FinishReason.STOP, + ) + + ctx = MagicMock() + next_fn = AsyncMock(return_value=model_response) + + await middleware(request, ctx, next_fn) + + # Verify all messages were checked + assert call_count == 3 + + @pytest.mark.asyncio + async def test_middleware_multiple_violations_in_message(self) -> None: + """Test middleware reports multiple policy violations (matching JS format).""" + mock_response = ClassifyContentResponse( + policy_results=[ + PolicyResult( + policy_type='HARASSMENT', + score=0.95, + violation_result='VIOLATIVE', + ), + PolicyResult( + policy_type='HATE_SPEECH', + score=0.88, + violation_result='VIOLATIVE', + ), + ] + ) + + with ( + patch('genkit.plugins.checks.middleware.initialize_credentials') as mock_init_creds, + patch( + 'genkit.plugins.checks.guardrails.Guardrails.classify_content', + new_callable=AsyncMock, + ) as mock_classify, + ): + mock_init_creds.return_value = (MagicMock(), 'test-project') + mock_classify.return_value = mock_response + + middleware = checks_middleware( + metrics=[ChecksMetricType.HARASSMENT, ChecksMetricType.HATE_SPEECH], + ) + + request = GenerateRequest( + messages=[ + Message(role='user', content=[Part(root=TextPart(text='Bad content'))]), + ] + ) + + ctx = MagicMock() + next_fn = AsyncMock() + + result = await middleware(request, ctx, next_fn) + + # Verify message format matches JS: policies joined with space + assert 'HARASSMENT' in (result.finish_message or '') + assert 'HATE_SPEECH' in (result.finish_message or '') + + @pytest.mark.asyncio + async def test_middleware_skips_empty_text_content(self) -> None: + """Test middleware skips messages without text content.""" + with ( + patch('genkit.plugins.checks.middleware.initialize_credentials') as mock_init_creds, + patch( + 'genkit.plugins.checks.guardrails.Guardrails.classify_content', + new_callable=AsyncMock, + ) as mock_classify, + ): + mock_init_creds.return_value = (MagicMock(), 'test-project') + # Should not be called if no text content + mock_classify.return_value = ClassifyContentResponse() + + middleware = checks_middleware( + metrics=[ChecksMetricType.DANGEROUS_CONTENT], + ) + + # Message with empty content list + request = GenerateRequest( + messages=[ + Message(role='user', content=[]), + ] + ) + + model_response = GenerateResponse( + message=Message(role='model', content=[]), + finish_reason=FinishReason.STOP, + ) + + ctx = MagicMock() + next_fn = AsyncMock(return_value=model_response) + + result = await middleware(request, ctx, next_fn) + + # Should pass through without checking + assert result == model_response + + +class TestMiddlewareFinishMessage: + """Tests for middleware finish message format to match JS.""" + + def test_input_blocked_message_format(self) -> None: + """Verify input blocked message format matches JS implementation. + + JS format: Model input violated Checks policies: [policies], further processing blocked. + This is validated in the actual middleware tests above. + """ + pass + + def test_output_blocked_message_format(self) -> None: + """Verify output blocked message format matches JS implementation. + + JS format: Model output violated Checks policies: [policies], output blocked. + This is validated in the actual middleware tests above. + """ + pass diff --git a/py/plugins/checks/tests/checks_plugin_test.py b/py/plugins/checks/tests/checks_plugin_test.py new file mode 100644 index 0000000000..64bef61235 --- /dev/null +++ b/py/plugins/checks/tests/checks_plugin_test.py @@ -0,0 +1,239 @@ +# Copyright 2026 Google LLC +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. +# +# SPDX-License-Identifier: Apache-2.0 + +"""Tests for the Checks AI Safety plugin.""" + +from unittest.mock import MagicMock, patch + +import pytest + +from genkit.core.action import ActionMetadata +from genkit.core.action.types import ActionKind +from genkit.plugins.checks import Checks, ChecksEvaluationConfig, ChecksMetricType + + +def test_plugin_name() -> None: + """Test plugin has correct name.""" + plugin = Checks(project_id='test-project') + assert plugin.name == 'checks' + + +def test_init_with_project_id() -> None: + """Test plugin initialization with project ID.""" + plugin = Checks(project_id='my-project') + assert plugin._project_id == 'my-project' + + +def test_init_with_evaluation_config() -> None: + """Test plugin initialization with evaluation config.""" + config = ChecksEvaluationConfig( + metrics=[ + ChecksMetricType.DANGEROUS_CONTENT, + ChecksMetricType.HARASSMENT, + ] + ) + plugin = Checks(project_id='my-project', evaluation=config) + assert plugin._evaluation == config + + +def test_init_with_evaluation_dict() -> None: + """Test plugin initialization with evaluation as dict.""" + eval_dict = { + 'metrics': [ + ChecksMetricType.DANGEROUS_CONTENT, + ChecksMetricType.HATE_SPEECH, + ] + } + plugin = Checks(project_id='my-project', evaluation=eval_dict) + assert plugin._evaluation == eval_dict + + +def test_init_with_google_auth_options() -> None: + """Test plugin initialization with Google auth options.""" + auth_opts = {'credentials_file': '/path/to/creds.json'} + plugin = Checks(project_id='my-project', google_auth_options=auth_opts) + assert plugin._google_auth_options == auth_opts + + +@pytest.mark.asyncio +async def test_init_returns_empty_list() -> None: + """Test that init() returns empty list (uses lazy loading).""" + plugin = Checks(project_id='test-project') + + # Mock authentication + with patch.object(plugin, '_initialize_auth') as mock_auth: + mock_creds = MagicMock() + mock_auth.return_value = mock_creds + + result = await plugin.init(registry=None) + assert result == [] + assert plugin._credentials == mock_creds + + +@pytest.mark.asyncio +async def test_init_raises_without_project_id() -> None: + """Test that init() raises ValueError without project_id.""" + plugin = Checks() # No project_id + + # Mock authentication to not set project from credentials + with patch.object(plugin, '_initialize_auth') as mock_auth: + mock_creds = MagicMock() + mock_auth.return_value = mock_creds + + with pytest.raises(ValueError, match="missing the 'project_id'"): + await plugin.init(registry=None) + + +@pytest.mark.asyncio +async def test_resolve_returns_none_for_non_evaluator() -> None: + """Test that resolve returns None for non-evaluator action types.""" + plugin = Checks(project_id='test-project') + plugin._credentials = MagicMock() # Set up credentials + + result = await plugin.resolve(ActionKind.MODEL, 'checks/dangerous_content') + assert result is None + + +@pytest.mark.asyncio +async def test_resolve_returns_none_for_other_namespace() -> None: + """Test that resolve returns None for actions not in checks namespace.""" + plugin = Checks(project_id='test-project') + plugin._credentials = MagicMock() + + result = await plugin.resolve(ActionKind.EVALUATOR, 'other/evaluator') + assert result is None + + +@pytest.mark.asyncio +async def test_resolve_returns_evaluator_action() -> None: + """Test that resolve returns the guardrails evaluator action.""" + plugin = Checks( + project_id='test-project', + evaluation=ChecksEvaluationConfig( + metrics=[ + ChecksMetricType.DANGEROUS_CONTENT, + ChecksMetricType.HARASSMENT, + ] + ), + ) + + # Create a mock registry + mock_registry = MagicMock() + + # Initialize plugin + with patch.object(plugin, '_initialize_auth') as mock_auth: + mock_auth.return_value = MagicMock() + await plugin.init(registry=mock_registry) + + # Resolve the guardrails evaluator + action = await plugin.resolve(ActionKind.EVALUATOR, 'checks/guardrails') + + assert action is not None + assert action.name == 'checks/guardrails' + assert action.kind == ActionKind.EVALUATOR + assert action.metadata is not None + assert 'evaluator' in action.metadata + + +@pytest.mark.asyncio +async def test_resolve_returns_none_for_non_guardrails_evaluator() -> None: + """Test that resolve returns None for individual metric evaluators.""" + plugin = Checks( + project_id='test-project', + evaluation=ChecksEvaluationConfig(metrics=[ChecksMetricType.DANGEROUS_CONTENT]), + ) + + # Initialize plugin + with patch.object(plugin, '_initialize_auth') as mock_auth: + mock_auth.return_value = MagicMock() + await plugin.init(registry=None) + + # Should return None for individual metric names (not supported) + result = await plugin.resolve(ActionKind.EVALUATOR, 'checks/dangerous_content') + assert result is None + + +@pytest.mark.asyncio +async def test_list_actions_returns_evaluator_metadata() -> None: + """Test that list_actions returns metadata for the guardrails evaluator. + + The Checks plugin returns a single evaluator (checks/guardrails) that + evaluates all configured metrics, matching the JS implementation. + """ + plugin = Checks( + project_id='test-project', + evaluation=ChecksEvaluationConfig( + metrics=[ + ChecksMetricType.DANGEROUS_CONTENT, + ChecksMetricType.HARASSMENT, + ] + ), + ) + + # Initialize plugin to load metrics + with patch.object(plugin, '_initialize_auth') as mock_auth: + mock_auth.return_value = MagicMock() + await plugin.init(registry=None) + + actions = await plugin.list_actions() + + # Should return a single guardrails evaluator + assert len(actions) == 1 + assert all(isinstance(a, ActionMetadata) for a in actions) + assert all(a.kind == ActionKind.EVALUATOR for a in actions) + assert actions[0].name == 'checks/guardrails' + + +@pytest.mark.asyncio +async def test_list_actions_empty_without_metrics() -> None: + """Test that list_actions returns empty list when no metrics configured.""" + plugin = Checks(project_id='test-project') + + # Initialize plugin + with patch.object(plugin, '_initialize_auth') as mock_auth: + mock_auth.return_value = MagicMock() + await plugin.init(registry=None) + + actions = await plugin.list_actions() + assert actions == [] + + +def test_checks_metric_type_values() -> None: + """Test that ChecksMetricType has expected values.""" + assert ChecksMetricType.DANGEROUS_CONTENT.value == 'DANGEROUS_CONTENT' + assert ChecksMetricType.HARASSMENT.value == 'HARASSMENT' + assert ChecksMetricType.HATE_SPEECH.value == 'HATE_SPEECH' + assert ChecksMetricType.SEXUALLY_EXPLICIT.value == 'SEXUALLY_EXPLICIT' + assert ChecksMetricType.VIOLENCE_AND_GORE.value == 'VIOLENCE_AND_GORE' + + +def test_checks_evaluation_config_defaults() -> None: + """Test ChecksEvaluationConfig default values.""" + config = ChecksEvaluationConfig() + assert config.metrics == [] + + +def test_checks_evaluation_config_with_metrics() -> None: + """Test ChecksEvaluationConfig with metrics.""" + config = ChecksEvaluationConfig( + metrics=[ + ChecksMetricType.DANGEROUS_CONTENT, + ChecksMetricType.HARASSMENT, + ChecksMetricType.HATE_SPEECH, + ] + ) + assert len(config.metrics) == 3 + assert ChecksMetricType.DANGEROUS_CONTENT in config.metrics diff --git a/py/plugins/chroma/LICENSE b/py/plugins/chroma/LICENSE new file mode 100644 index 0000000000..1e7d07ac21 --- /dev/null +++ b/py/plugins/chroma/LICENSE @@ -0,0 +1,176 @@ + Apache License + Version 2.0, January 2004 + http://www.apache.org/licenses/ + +TERMS AND CONDITIONS FOR USE, REPRODUCTION, AND DISTRIBUTION + +1. Definitions. + + "License" shall mean the terms and conditions for use, reproduction, + and distribution as defined by Sections 1 through 9 of this document. + + "Licensor" shall mean the copyright owner or entity authorized by + the copyright owner that is granting the License. + + "Legal Entity" shall mean the union of the acting entity and all + other entities that control, are controlled by, or are under common + control with that entity. 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+# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. +# +# SPDX-License-Identifier: Apache-2.0 + +[project] +description = "ChromaDB vector store plugin for Genkit" +license = "Apache-2.0" +name = "genkit-plugin-chroma" +readme = "README.md" +version = "0.1.0" +# TODO: Update requires-python to >=3.10 when onnxruntime adds Python 3.14 wheels. +# Track: https://github.com/microsoft/onnxruntime/issues/21292 +authors = [{ name = "Google LLC" }] +classifiers = [ + "Development Status :: 4 - Beta", + "Intended Audience :: Developers", + "License :: OSI Approved :: Apache Software License", + "Operating System :: OS Independent", + "Programming Language :: Python :: 3", + "Programming Language :: Python :: 3.10", + "Programming Language :: Python :: 3.11", + "Programming Language :: Python :: 3.12", + "Topic :: Scientific/Engineering :: Artificial Intelligence", +] +keywords = ["genkit", "ai", "vector-store", "chromadb", "rag", "retrieval"] +requires-python = ">=3.10,<3.14" # chromadb depends on onnxruntime which lacks Python 3.14 wheels + +dependencies = ["genkit>=0.4.0", "chromadb>=0.4.0", "pydantic>=2.0.0"] + +[project.optional-dependencies] +dev = ["pytest>=7.0.0", "pytest-asyncio>=0.21.0"] + +[build-system] +build-backend = "hatchling.build" +requires = ["hatchling"] + +[tool.hatch.build.targets.wheel] +packages = ["src/genkit"] diff --git a/py/plugins/chroma/src/genkit/plugins/chroma/__init__.py b/py/plugins/chroma/src/genkit/plugins/chroma/__init__.py new file mode 100644 index 0000000000..f8707baf6e --- /dev/null +++ b/py/plugins/chroma/src/genkit/plugins/chroma/__init__.py @@ -0,0 +1,104 @@ +# Copyright 2026 Google LLC +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. +# +# SPDX-License-Identifier: Apache-2.0 + +"""ChromaDB vector store plugin for Genkit. + +This plugin provides retriever and indexer implementations backed by ChromaDB, +an open-source embedding database for AI applications. + +Architecture +============ + +┌─────────────────────────────────────────────────────────────────────────────┐ +│ Chroma Plugin Architecture │ +├─────────────────────────────────────────────────────────────────────────────┤ +│ │ +│ ┌──────────────┐ ┌──────────────┐ ┌──────────────────────┐ │ +│ │ Genkit │ │ Chroma │ │ ChromaDB │ │ +│ │ Instance │ ──► │ Plugin │ ──► │ (Local/Remote) │ │ +│ └──────────────┘ └──────────────┘ └──────────────────────┘ │ +│ │ │ +│ ├─► Retriever (similarity search) │ +│ └─► Indexer (document storage) │ +│ │ +│ Data Flow: │ +│ ┌────────┐ ┌──────────┐ ┌────────────┐ ┌──────────────┐ │ +│ │Document│ ─► │ Embedder │ ─► │ Embedding │ ─► │ ChromaDB │ │ +│ └────────┘ └──────────┘ └────────────┘ │ Collection │ │ +│ └──────────────┘ │ +└─────────────────────────────────────────────────────────────────────────────┘ + +Example: + ```python + from genkit.ai import Genkit + from genkit.plugins.google_genai import GoogleAI + from genkit.plugins.chroma import chroma, chroma_retriever_ref + + ai = Genkit( + plugins=[ + GoogleAI(), + chroma( + collections=[ + { + 'collection_name': 'my_docs', + 'embedder': 'googleai/text-embedding-004', + } + ] + ), + ] + ) + + # Retrieve similar documents + results = await ai.retrieve( + retriever=chroma_retriever_ref(collection_name='my_docs'), + query='What is machine learning?', + options={'k': 5}, + ) + ``` + +Cross-Language Parity: + - JavaScript: js/plugins/chroma/src/index.ts + +See Also: + - ChromaDB: https://www.trychroma.com/ + - Genkit RAG: https://genkit.dev/docs/rag +""" + +from genkit.plugins.chroma.plugin import ( + Chroma, + ChromaCollectionConfig, + ChromaIndexerOptions, + ChromaPluginConfig, + ChromaRetrieverOptions, + chroma, + chroma_indexer_ref, + chroma_retriever_ref, + create_chroma_collection, + delete_chroma_collection, +) + +__all__ = [ + 'Chroma', + 'ChromaCollectionConfig', + 'ChromaIndexerOptions', + 'ChromaPluginConfig', + 'ChromaRetrieverOptions', + 'chroma', + 'chroma_indexer_ref', + 'chroma_retriever_ref', + 'create_chroma_collection', + 'delete_chroma_collection', +] diff --git a/py/plugins/chroma/src/genkit/plugins/chroma/plugin.py b/py/plugins/chroma/src/genkit/plugins/chroma/plugin.py new file mode 100644 index 0000000000..ab1be6f921 --- /dev/null +++ b/py/plugins/chroma/src/genkit/plugins/chroma/plugin.py @@ -0,0 +1,683 @@ +# Copyright 2026 Google LLC +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. +# +# SPDX-License-Identifier: Apache-2.0 + +"""ChromaDB plugin implementation for Genkit. + +This module provides the core plugin functionality for integrating ChromaDB +with Genkit applications, including retriever and indexer implementations. + +Key Components +============== + +┌───────────────────────────────────────────────────────────────────────────┐ +│ Plugin Components │ +├───────────────────────┬───────────────────────────────────────────────────┤ +│ Component │ Purpose │ +├───────────────────────┼───────────────────────────────────────────────────┤ +│ Chroma │ Main plugin class - registers retrievers/indexers │ +│ ChromaRetriever │ Similarity search against ChromaDB collections │ +│ ChromaIndexer │ Store documents with embeddings in ChromaDB │ +│ chroma_retriever_ref │ Create retriever reference by collection name │ +│ chroma_indexer_ref │ Create indexer reference by collection name │ +└───────────────────────┴───────────────────────────────────────────────────┘ + +See Also: + - JS Implementation: js/plugins/chroma/src/index.ts +""" + +import hashlib +import json +from collections.abc import Awaitable, Callable +from dataclasses import dataclass, field +from typing import Any, cast + +import chromadb +import structlog +from chromadb.api.models.Collection import Collection +from chromadb.api.types import Embeddings, Include, Metadatas +from pydantic import BaseModel, Field + +from genkit.blocks.document import Document +from genkit.blocks.retriever import ( + IndexerOptions, + IndexerRequest, + RetrieverOptions, + RetrieverRequest, + RetrieverResponse, + indexer_action_metadata, + retriever_action_metadata, +) +from genkit.core.action import Action, ActionMetadata, ActionRunContext +from genkit.core.plugin import Plugin +from genkit.core.registry import ActionKind, Registry +from genkit.core.schema import to_json_schema +from genkit.types import DocumentData, EmbedRequest + +logger = structlog.get_logger(__name__) + +CHROMA_PLUGIN_NAME = 'chroma' + + +class ChromaRetrieverOptions(BaseModel): + """Options for ChromaDB retriever queries. + + Attributes: + k: Number of results to return (default: 10). + where: Metadata filter using ChromaDB where syntax. + where_document: Document content filter. + include: Fields to include in results. + """ + + k: int = Field(default=10, description='Number of results to return') + where: dict[str, Any] | None = Field(default=None, description='Metadata filter') + where_document: dict[str, Any] | None = Field(default=None, description='Document content filter') + include: list[str] | None = Field( + default=None, + description='Fields to include: documents, embeddings, metadatas, distances', + ) + + +class ChromaIndexerOptions(BaseModel): + """Options for ChromaDB indexer operations. + + Currently no additional options are supported. + """ + + pass + + +@dataclass +class ChromaCollectionConfig: + """Configuration for a ChromaDB collection. + + Attributes: + collection_name: Name of the ChromaDB collection. + embedder: Genkit embedder reference (e.g., 'googleai/text-embedding-004'). + embedder_options: Optional embedder-specific configuration. + client_params: ChromaDB client configuration. + create_collection_if_missing: Create collection if it doesn't exist. + metadata: Optional collection metadata. + """ + + collection_name: str + embedder: str + embedder_options: dict[str, Any] | None = None + client_params: dict[str, Any] | Callable[[], Awaitable[dict[str, Any]]] | None = None + create_collection_if_missing: bool = False + metadata: dict[str, Any] | None = None + + +@dataclass +class ChromaPluginConfig: + """Configuration for the Chroma plugin. + + Attributes: + collections: List of collection configurations. + """ + + collections: list[ChromaCollectionConfig] = field(default_factory=list) + + +def _md5_hash(content: str) -> str: + """Generate MD5 hash of content for document ID.""" + return hashlib.md5(content.encode('utf-8')).hexdigest() + + +def _get_client(client_params: dict[str, Any] | None) -> chromadb.ClientAPI: + """Create a ChromaDB client from configuration. + + Args: + client_params: Client configuration. If None, uses ephemeral client. + + Returns: + ChromaDB client instance. + """ + if client_params is None: + return chromadb.Client() + + if 'path' in client_params: + # Persistent local client + return chromadb.PersistentClient(path=client_params['path']) + elif 'host' in client_params: + # HTTP client for remote Chroma + return chromadb.HttpClient( + host=client_params.get('host', 'localhost'), + port=client_params.get('port', 8000), + headers=client_params.get('headers'), + ) + else: + return chromadb.Client() + + +async def _get_client_async( + client_params: dict[str, Any] | Callable[[], Awaitable[dict[str, Any]]] | None, +) -> chromadb.ClientAPI: + """Get ChromaDB client, resolving async config if needed.""" + if callable(client_params): + resolved_params = await client_params() + return _get_client(resolved_params) + return _get_client(client_params) + + +class ChromaRetriever: + """ChromaDB retriever implementation. + + Performs similarity search against a ChromaDB collection using + embeddings generated by a Genkit embedder. + """ + + def __init__( + self, + registry: Registry, + collection_name: str, + embedder: str, + embedder_options: dict[str, Any] | None = None, + client_params: dict[str, Any] | Callable[[], Awaitable[dict[str, Any]]] | None = None, + create_collection_if_missing: bool = False, + ) -> None: + """Initialize the ChromaDB retriever. + + Args: + registry: Registry for resolving embedders. + collection_name: Name of the ChromaDB collection. + embedder: Embedder reference string. + embedder_options: Optional embedder configuration. + client_params: ChromaDB client configuration. + create_collection_if_missing: Create collection if missing. + """ + self._registry = registry + self._collection_name = collection_name + self._embedder = embedder + self._embedder_options = embedder_options + self._client_params = client_params + self._create_collection_if_missing = create_collection_if_missing + + async def _get_collection(self) -> Collection: + """Get or create the ChromaDB collection.""" + client = await _get_client_async(self._client_params) + if self._create_collection_if_missing: + return client.get_or_create_collection(name=self._collection_name) + return client.get_collection(name=self._collection_name) + + async def _embed_content(self, content: list[Document]) -> list[list[float]]: + """Generate embeddings using the registry-resolved embedder.""" + embedder_action = await self._registry.resolve_embedder(self._embedder) + if embedder_action is None: + raise ValueError(f'Embedder "{self._embedder}" not found') + + # Document is a subclass of DocumentData; cast for type checker variance + request = EmbedRequest(input=cast(list[DocumentData], content), options=self._embedder_options) + response = await embedder_action.arun(request) + return [e.embedding for e in response.response.embeddings] + + async def retrieve( + self, + request: RetrieverRequest, + _ctx: ActionRunContext, + ) -> RetrieverResponse: + """Retrieve documents similar to the query. + + Args: + request: Retriever request with query and options. + _ctx: Action run context (unused). + + Returns: + Response containing matching documents. + """ + # Generate query embedding + query_doc = Document.from_document_data(document_data=request.query) + embeddings = await self._embed_content([query_doc]) + if not embeddings: + raise ValueError('Embedder returned no embeddings for query') + + query_embedding = embeddings[0] + + # Parse options + k = 10 + where = None + where_document = None + include_fields: list[str] = ['documents', 'metadatas'] + + if request.options: + opts = request.options if isinstance(request.options, dict) else request.options.model_dump() + k = opts.get('k', 10) + where = opts.get('where') + where_document = opts.get('where_document') + if opts.get('include'): + include_fields = list(set(opts['include']) | {'documents'}) + + # Query ChromaDB + collection = await self._get_collection() + # Cast to chromadb types - our list types are compatible at runtime + results = collection.query( + query_embeddings=cast(Embeddings, [query_embedding]), + n_results=k, + where=where, + where_document=where_document, + include=cast(Include, include_fields), + ) + + # Convert results to Documents + documents: list[DocumentData] = [] + result_docs = results.get('documents') + result_metadatas = results.get('metadatas') + result_distances = results.get('distances') + result_embeddings = results.get('embeddings') + + if result_docs and result_docs[0]: + for i, doc_content in enumerate(result_docs[0]): + # Parse stored metadata + metadata: dict[str, Any] = {} + if result_metadatas and result_metadatas[0]: + stored_meta = result_metadatas[0][i] + if stored_meta: + if 'doc_metadata' in stored_meta: + try: + metadata = json.loads(str(stored_meta['doc_metadata'])) + except (json.JSONDecodeError, TypeError) as e: + logger.warning('Failed to parse document metadata', error=str(e)) + # Add distance/embedding info if requested + if result_distances and result_distances[0]: + metadata['_distance'] = result_distances[0][i] + if result_embeddings and result_embeddings[0]: + metadata['_embedding'] = result_embeddings[0][i] + + # Reconstruct document + data_type = 'text' + if result_metadatas and result_metadatas[0]: + stored = result_metadatas[0][i] + if stored and 'data_type' in stored: + data_type = str(stored['data_type']) + + doc = Document.from_data( + data=doc_content, + data_type=data_type, + metadata=metadata, + ) + # Document extends DocumentData, so it can be used directly + documents.append(doc) + + return RetrieverResponse(documents=documents) + + +class ChromaIndexer: + """ChromaDB indexer implementation. + + Stores documents with their embeddings in a ChromaDB collection. + """ + + def __init__( + self, + registry: Registry, + collection_name: str, + embedder: str, + embedder_options: dict[str, Any] | None = None, + client_params: dict[str, Any] | Callable[[], Awaitable[dict[str, Any]]] | None = None, + create_collection_if_missing: bool = False, + ) -> None: + """Initialize the ChromaDB indexer. + + Args: + registry: Registry for resolving embedders. + collection_name: Name of the ChromaDB collection. + embedder: Embedder reference string. + embedder_options: Optional embedder configuration. + client_params: ChromaDB client configuration. + create_collection_if_missing: Create collection if missing. + """ + self._registry = registry + self._collection_name = collection_name + self._embedder = embedder + self._embedder_options = embedder_options + self._client_params = client_params + self._create_collection_if_missing = create_collection_if_missing + + async def _get_collection(self) -> Collection: + """Get or create the ChromaDB collection.""" + client = await _get_client_async(self._client_params) + if self._create_collection_if_missing: + return client.get_or_create_collection(name=self._collection_name) + return client.get_collection(name=self._collection_name) + + async def _embed_content(self, content: list[Document]) -> list[list[float]]: + """Generate embeddings using the registry-resolved embedder.""" + embedder_action = await self._registry.resolve_embedder(self._embedder) + if embedder_action is None: + raise ValueError(f'Embedder "{self._embedder}" not found') + + # Document is a subclass of DocumentData; cast for type checker variance + request = EmbedRequest(input=cast(list[DocumentData], content), options=self._embedder_options) + response = await embedder_action.arun(request) + return [e.embedding for e in response.response.embeddings] + + async def index(self, request: IndexerRequest) -> None: + """Index documents into the ChromaDB collection. + + Args: + request: Indexer request containing documents to index. + """ + if not request.documents: + return + + # Generate embeddings for all documents + docs = [Document.from_document_data(doc) for doc in request.documents] + embeddings = await self._embed_content(docs) + if not embeddings: + raise ValueError('Embedder returned no embeddings for documents') + + # Prepare data for ChromaDB + ids: list[str] = [] + embedding_vectors: list[list[float]] = [] + documents_list: list[str] = [] + metadatas: list[dict[str, Any]] = [] + + for doc, embedding in zip(docs, embeddings, strict=True): + # Get embedding documents (handles multi-part documents) + from genkit.types import Embedding + + emb_obj = Embedding(embedding=embedding) + embedding_docs = doc.get_embedding_documents([emb_obj]) + + for emb_doc in embedding_docs: + # Generate unique ID from content + content = emb_doc.text() or json.dumps(emb_doc.data()) + doc_id = _md5_hash(content) + + ids.append(doc_id) + embedding_vectors.append(embedding) + documents_list.append(content) + + # Store metadata + meta: dict[str, Any] = { + 'data_type': emb_doc.data_type() or 'text', + } + if emb_doc.metadata: + meta['doc_metadata'] = json.dumps(emb_doc.metadata) + metadatas.append(meta) + + # Add to collection + collection = await self._get_collection() + # Cast to chromadb types - our list types are compatible at runtime + collection.add( + ids=ids, + embeddings=cast(Embeddings, embedding_vectors), + documents=documents_list, + metadatas=cast(Metadatas, metadatas), + ) + + +class Chroma(Plugin): + """ChromaDB vector store plugin for Genkit. + + This plugin registers retrievers and indexers for ChromaDB collections, + enabling RAG (Retrieval-Augmented Generation) workflows. + + Example: + ```python + ai = Genkit( + plugins=[ + Chroma( + collections=[ + ChromaCollectionConfig( + collection_name='my_docs', + embedder='googleai/text-embedding-004', + ) + ] + ) + ] + ) + ``` + """ + + name = CHROMA_PLUGIN_NAME + + def __init__( + self, + collections: list[ChromaCollectionConfig] | None = None, + ) -> None: + """Initialize the Chroma plugin. + + Args: + collections: List of collection configurations. + """ + self._collections = collections or [] + self._registry: Registry | None = None + self._actions: dict[str, Action] = {} + + async def init(self, registry: Registry | None = None) -> list[Action]: + """Initialize plugin (lazy warm-up). + + Args: + registry: Registry for action registration and embedder resolution. + + Returns: + List of pre-registered actions. + """ + self._registry = registry + if registry is not None: + for config in self._collections: + self._register_collection(registry, config) + return list(self._actions.values()) + + async def resolve(self, action_type: ActionKind, name: str) -> Action | None: + """Resolve an action by name. + + Args: + action_type: The kind of action to resolve. + name: The namespaced name of the action. + + Returns: + Action object if found, None otherwise. + """ + if action_type not in (ActionKind.RETRIEVER, ActionKind.INDEXER): + return None + # Use composite key of action_type and name + action_key = f'{action_type.value}/{name}' + return self._actions.get(action_key) + + async def list_actions(self) -> list[ActionMetadata]: + """List available actions for dev UI. + + Returns: + List of action metadata. + """ + metadata_list: list[ActionMetadata] = [] + for config in self._collections: + name = f'{CHROMA_PLUGIN_NAME}/{config.collection_name}' + metadata_list.append( + ActionMetadata( + kind=ActionKind.RETRIEVER, + name=name, + ) + ) + metadata_list.append( + ActionMetadata( + kind=ActionKind.INDEXER, + name=name, + ) + ) + return metadata_list + + def _register_collection( + self, + registry: Registry, + config: ChromaCollectionConfig, + ) -> None: + """Register retriever and indexer for a collection. + + Args: + registry: Action registry. + config: Collection configuration. + """ + name = f'{CHROMA_PLUGIN_NAME}/{config.collection_name}' + + # Create and register retriever + retriever = ChromaRetriever( + registry=registry, + collection_name=config.collection_name, + embedder=config.embedder, + embedder_options=config.embedder_options, + client_params=config.client_params, + create_collection_if_missing=config.create_collection_if_missing, + ) + + retriever_action = registry.register_action( + kind=ActionKind.RETRIEVER, + name=name, + fn=retriever.retrieve, + metadata=retriever_action_metadata( + name=name, + options=RetrieverOptions( + label=f'ChromaDB - {config.collection_name}', + config_schema=to_json_schema(ChromaRetrieverOptions), + ), + ).metadata, + ) + if retriever_action: + self._actions[f'{ActionKind.RETRIEVER.value}/{name}'] = retriever_action + + # Create and register indexer + indexer = ChromaIndexer( + registry=registry, + collection_name=config.collection_name, + embedder=config.embedder, + embedder_options=config.embedder_options, + client_params=config.client_params, + create_collection_if_missing=config.create_collection_if_missing, + ) + + indexer_action = registry.register_action( + kind=ActionKind.INDEXER, + name=name, + fn=indexer.index, + metadata=indexer_action_metadata( + name=name, + options=IndexerOptions(label=f'ChromaDB - {config.collection_name}'), + ).metadata, + ) + if indexer_action: + self._actions[f'{ActionKind.INDEXER.value}/{name}'] = indexer_action + + +def chroma( + collections: list[dict[str, Any]] | None = None, +) -> Chroma: + """Create a Chroma plugin with the given configuration. + + This is a convenience function for creating a Chroma plugin instance. + + Args: + collections: List of collection configuration dictionaries. + + Returns: + Configured Chroma plugin instance. + + Example: + ```python + ai = Genkit( + plugins=[ + chroma( + collections=[ + { + 'collection_name': 'my_docs', + 'embedder': 'googleai/text-embedding-004', + 'create_collection_if_missing': True, + } + ] + ) + ] + ) + ``` + """ + configs = [] + if collections: + for c in collections: + configs.append( + ChromaCollectionConfig( + collection_name=c['collection_name'], + embedder=c['embedder'], + embedder_options=c.get('embedder_options'), + client_params=c.get('client_params'), + create_collection_if_missing=c.get('create_collection_if_missing', False), + metadata=c.get('metadata'), + ) + ) + return Chroma(collections=configs) + + +def chroma_retriever_ref( + collection_name: str, + display_name: str | None = None, +) -> str: + """Create a retriever reference for a ChromaDB collection. + + Args: + collection_name: Name of the ChromaDB collection. + display_name: Optional display name (unused, for parity with JS). + + Returns: + Retriever reference string. + """ + return f'chroma/{collection_name}' + + +def chroma_indexer_ref( + collection_name: str, + display_name: str | None = None, +) -> str: + """Create an indexer reference for a ChromaDB collection. + + Args: + collection_name: Name of the ChromaDB collection. + display_name: Optional display name (unused, for parity with JS). + + Returns: + Indexer reference string. + """ + return f'chroma/{collection_name}' + + +async def create_chroma_collection( + name: str, + client_params: dict[str, Any] | None = None, + metadata: dict[str, Any] | None = None, +) -> Collection: + """Create a ChromaDB collection. + + Args: + name: Collection name. + client_params: ChromaDB client configuration. + metadata: Optional collection metadata. + + Returns: + The created ChromaDB collection. + """ + client = await _get_client_async(client_params) + return client.create_collection(name=name, metadata=metadata) + + +async def delete_chroma_collection( + name: str, + client_params: dict[str, Any] | None = None, +) -> None: + """Delete a ChromaDB collection. + + Args: + name: Collection name. + client_params: ChromaDB client configuration. + """ + client = await _get_client_async(client_params) + client.delete_collection(name=name) diff --git a/py/plugins/chroma/src/genkit/py.typed b/py/plugins/chroma/src/genkit/py.typed new file mode 100644 index 0000000000..e69de29bb2 diff --git a/py/plugins/chroma/tests/chroma_helpers_test.py b/py/plugins/chroma/tests/chroma_helpers_test.py new file mode 100644 index 0000000000..ebd071f0ee --- /dev/null +++ b/py/plugins/chroma/tests/chroma_helpers_test.py @@ -0,0 +1,214 @@ +# Copyright 2026 Google LLC +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. +# +# SPDX-License-Identifier: Apache-2.0 + +"""Tests for Chroma helper functions. + +These tests verify parity with the JS implementation in: +js/plugins/chroma/src/index.ts (createChromaCollection, deleteChromaCollection) +""" + +from unittest.mock import AsyncMock, MagicMock, patch + +import pytest + +from genkit.plugins.chroma.plugin import ( + _get_client, + _get_client_async, + create_chroma_collection, + delete_chroma_collection, +) + + +class TestGetClient: + """Tests for _get_client function matching JS ChromaClient instantiation.""" + + def test_get_client_with_no_params(self) -> None: + """Test creating client with no params (ephemeral client).""" + with patch('genkit.plugins.chroma.plugin.chromadb') as mock_chromadb: + mock_client = MagicMock() + mock_chromadb.Client.return_value = mock_client + + result = _get_client(None) + + mock_chromadb.Client.assert_called_once() + assert result == mock_client + + def test_get_client_with_path(self) -> None: + """Test creating persistent client with path (matches JS).""" + with patch('genkit.plugins.chroma.plugin.chromadb') as mock_chromadb: + mock_client = MagicMock() + mock_chromadb.PersistentClient.return_value = mock_client + + result = _get_client({'path': '/data/chroma'}) + + mock_chromadb.PersistentClient.assert_called_once_with(path='/data/chroma') + assert result == mock_client + + def test_get_client_with_host_port(self) -> None: + """Test creating HTTP client (matches JS remote Chroma).""" + with patch('genkit.plugins.chroma.plugin.chromadb') as mock_chromadb: + mock_client = MagicMock() + mock_chromadb.HttpClient.return_value = mock_client + + _get_client({ + 'host': 'chroma.example.com', + 'port': 8080, + }) + + mock_chromadb.HttpClient.assert_called_once() + call_kwargs = mock_chromadb.HttpClient.call_args[1] + assert call_kwargs['host'] == 'chroma.example.com' + assert call_kwargs['port'] == 8080 + + def test_get_client_with_headers(self) -> None: + """Test creating HTTP client with headers.""" + with patch('genkit.plugins.chroma.plugin.chromadb') as mock_chromadb: + mock_client = MagicMock() + mock_chromadb.HttpClient.return_value = mock_client + + _get_client({ + 'host': 'chroma.example.com', + 'headers': {'Authorization': 'Bearer token'}, + }) + + call_kwargs = mock_chromadb.HttpClient.call_args[1] + assert call_kwargs['headers'] == {'Authorization': 'Bearer token'} + + +class TestGetClientAsync: + """Tests for _get_client_async matching JS resolve() function.""" + + @pytest.mark.asyncio + async def test_get_client_async_with_dict(self) -> None: + """Test async client creation with dict params.""" + with patch('genkit.plugins.chroma.plugin.chromadb') as mock_chromadb: + mock_client = MagicMock() + mock_chromadb.PersistentClient.return_value = mock_client + + result = await _get_client_async({'path': '/data/chroma'}) + + assert result == mock_client + + @pytest.mark.asyncio + async def test_get_client_async_with_callable(self) -> None: + """Test async client creation with async callable (matches JS Promise).""" + + async def get_params() -> dict: + return {'path': '/async/path'} + + with patch('genkit.plugins.chroma.plugin.chromadb') as mock_chromadb: + mock_client = MagicMock() + mock_chromadb.PersistentClient.return_value = mock_client + + await _get_client_async(get_params) + + mock_chromadb.PersistentClient.assert_called_once_with(path='/async/path') + + @pytest.mark.asyncio + async def test_get_client_async_with_none(self) -> None: + """Test async client creation with None.""" + with patch('genkit.plugins.chroma.plugin.chromadb') as mock_chromadb: + mock_client = MagicMock() + mock_chromadb.Client.return_value = mock_client + + await _get_client_async(None) + + mock_chromadb.Client.assert_called_once() + + +class TestCreateChromaCollection: + """Tests for create_chroma_collection matching JS createChromaCollection.""" + + @pytest.mark.asyncio + async def test_create_collection_basic(self) -> None: + """Test basic collection creation.""" + with patch('genkit.plugins.chroma.plugin._get_client_async', new_callable=AsyncMock) as mock_get_client: + mock_client = MagicMock() + mock_collection = MagicMock() + mock_client.create_collection.return_value = mock_collection + mock_get_client.return_value = mock_client + + result = await create_chroma_collection(name='test-collection') + + mock_client.create_collection.assert_called_once_with( + name='test-collection', + metadata=None, + ) + assert result == mock_collection + + @pytest.mark.asyncio + async def test_create_collection_with_metadata(self) -> None: + """Test collection creation with metadata (matching JS).""" + with patch('genkit.plugins.chroma.plugin._get_client_async', new_callable=AsyncMock) as mock_get_client: + mock_client = MagicMock() + mock_collection = MagicMock() + mock_client.create_collection.return_value = mock_collection + mock_get_client.return_value = mock_client + + await create_chroma_collection( + name='test-collection', + metadata={'hnsw:space': 'cosine'}, + ) + + mock_client.create_collection.assert_called_once_with( + name='test-collection', + metadata={'hnsw:space': 'cosine'}, + ) + + @pytest.mark.asyncio + async def test_create_collection_with_client_params(self) -> None: + """Test collection creation with client params.""" + with patch('genkit.plugins.chroma.plugin._get_client_async', new_callable=AsyncMock) as mock_get_client: + mock_client = MagicMock() + mock_collection = MagicMock() + mock_client.create_collection.return_value = mock_collection + mock_get_client.return_value = mock_client + + await create_chroma_collection( + name='test-collection', + client_params={'path': '/data'}, + ) + + mock_get_client.assert_called_once_with({'path': '/data'}) + + +class TestDeleteChromaCollection: + """Tests for delete_chroma_collection matching JS deleteChromaCollection.""" + + @pytest.mark.asyncio + async def test_delete_collection_basic(self) -> None: + """Test basic collection deletion.""" + with patch('genkit.plugins.chroma.plugin._get_client_async', new_callable=AsyncMock) as mock_get_client: + mock_client = MagicMock() + mock_get_client.return_value = mock_client + + await delete_chroma_collection(name='test-collection') + + mock_client.delete_collection.assert_called_once_with(name='test-collection') + + @pytest.mark.asyncio + async def test_delete_collection_with_client_params(self) -> None: + """Test collection deletion with client params.""" + with patch('genkit.plugins.chroma.plugin._get_client_async', new_callable=AsyncMock) as mock_get_client: + mock_client = MagicMock() + mock_get_client.return_value = mock_client + + await delete_chroma_collection( + name='test-collection', + client_params={'host': 'chroma.example.com'}, + ) + + mock_get_client.assert_called_once() diff --git a/py/plugins/chroma/tests/chroma_indexer_test.py b/py/plugins/chroma/tests/chroma_indexer_test.py new file mode 100644 index 0000000000..38d80f1771 --- /dev/null +++ b/py/plugins/chroma/tests/chroma_indexer_test.py @@ -0,0 +1,259 @@ +# Copyright 2026 Google LLC +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. +# +# SPDX-License-Identifier: Apache-2.0 + +"""Tests for the Chroma indexer functionality. + +These tests verify parity with the JS implementation in: +js/plugins/chroma/src/index.ts (chromaIndexer function) +""" + +import hashlib +from unittest.mock import AsyncMock, MagicMock, patch + +import pytest + +from genkit.blocks.retriever import IndexerRequest +from genkit.core.typing import DocumentPart, TextPart +from genkit.plugins.chroma.plugin import ( + ChromaIndexer, + ChromaIndexerOptions, + _md5_hash, +) +from genkit.types import DocumentData + + +class TestChromaIndexerOptions: + """Tests for ChromaIndexerOptions matching JS ChromaIndexerOptionsSchema.""" + + def test_indexer_options_empty(self) -> None: + """Test indexer options can be empty (matches JS z.null().optional()).""" + options = ChromaIndexerOptions() + # No additional options in current implementation + assert options is not None + + +class TestMd5Hash: + """Tests for _md5_hash function matching JS Md5.hashStr.""" + + def test_md5_hash_produces_correct_hash(self) -> None: + """Test MD5 hash matches expected value.""" + content = 'Hello, world!' + result = _md5_hash(content) + + # Verify it's a valid MD5 hash (32 hex characters) + assert len(result) == 32 + assert all(c in '0123456789abcdef' for c in result) + + def test_md5_hash_matches_standard_implementation(self) -> None: + """Test MD5 hash matches Python's hashlib implementation.""" + content = 'Test content for hashing' + result = _md5_hash(content) + expected = hashlib.md5(content.encode('utf-8')).hexdigest() + assert result == expected + + def test_md5_hash_consistent_for_same_input(self) -> None: + """Test MD5 hash is deterministic (same input -> same output).""" + content = 'Some document content' + hash1 = _md5_hash(content) + hash2 = _md5_hash(content) + assert hash1 == hash2 + + +class TestChromaIndexer: + """Tests for ChromaIndexer class matching JS chromaIndexer.""" + + def test_indexer_initialization(self) -> None: + """Test indexer initialization with required params.""" + mock_registry = MagicMock() + indexer = ChromaIndexer( + registry=mock_registry, + collection_name='test-collection', + embedder='googleai/text-embedding-004', + ) + + assert indexer._collection_name == 'test-collection' + assert indexer._embedder == 'googleai/text-embedding-004' + + def test_indexer_with_embedder_options(self) -> None: + """Test indexer with embedder options (matches JS).""" + mock_registry = MagicMock() + indexer = ChromaIndexer( + registry=mock_registry, + collection_name='test-collection', + embedder='test/embedder', + embedder_options={'dimension': 768}, + ) + + assert indexer._embedder_options == {'dimension': 768} + + def test_indexer_with_client_params(self) -> None: + """Test indexer with client params (matches JS clientParams).""" + mock_registry = MagicMock() + indexer = ChromaIndexer( + registry=mock_registry, + collection_name='test-collection', + embedder='test/embedder', + client_params={'path': '/data/chroma'}, + ) + + assert indexer._client_params == {'path': '/data/chroma'} + + def test_indexer_create_collection_if_missing(self) -> None: + """Test createCollectionIfMissing option (matches JS).""" + mock_registry = MagicMock() + indexer = ChromaIndexer( + registry=mock_registry, + collection_name='test-collection', + embedder='test/embedder', + create_collection_if_missing=True, + ) + + assert indexer._create_collection_if_missing is True + + @pytest.mark.asyncio + async def test_indexer_stores_documents(self) -> None: + """Test indexer stores documents with embeddings (matching JS).""" + mock_registry = MagicMock() + mock_embedder_action = MagicMock() + mock_embedder_action.arun = AsyncMock( + return_value=MagicMock(response=MagicMock(embeddings=[MagicMock(embedding=[0.1, 0.2, 0.3])])) + ) + mock_registry.resolve_embedder = AsyncMock(return_value=mock_embedder_action) + + indexer = ChromaIndexer( + registry=mock_registry, + collection_name='test-collection', + embedder='test/embedder', + ) + + mock_collection = MagicMock() + + with patch.object(indexer, '_get_collection', AsyncMock(return_value=mock_collection)): + request = IndexerRequest( + documents=[ + DocumentData(content=[DocumentPart(root=TextPart(text='Document content'))]), + ], + ) + + await indexer.index(request) + + # Verify collection.add was called + mock_collection.add.assert_called_once() + + @pytest.mark.asyncio + async def test_indexer_generates_md5_ids(self) -> None: + """Test indexer generates MD5 IDs for documents (matching JS Md5.hashStr).""" + mock_registry = MagicMock() + mock_embedder_action = MagicMock() + mock_embedder_action.arun = AsyncMock( + return_value=MagicMock(response=MagicMock(embeddings=[MagicMock(embedding=[0.1, 0.2, 0.3])])) + ) + mock_registry.resolve_embedder = AsyncMock(return_value=mock_embedder_action) + + indexer = ChromaIndexer( + registry=mock_registry, + collection_name='test-collection', + embedder='test/embedder', + ) + + mock_collection = MagicMock() + + with patch.object(indexer, '_get_collection', AsyncMock(return_value=mock_collection)): + request = IndexerRequest( + documents=[ + DocumentData(content=[DocumentPart(root=TextPart(text='Test content'))]), + ], + ) + + await indexer.index(request) + + # Verify IDs are MD5 hashes (32 hex chars) + call_args = mock_collection.add.call_args + ids = call_args[1]['ids'] + assert all(len(doc_id) == 32 for doc_id in ids) + assert all(c in '0123456789abcdef' for doc_id in ids for c in doc_id) + + @pytest.mark.asyncio + async def test_indexer_stores_metadata(self) -> None: + """Test indexer stores document metadata (matching JS).""" + mock_registry = MagicMock() + mock_embedder_action = MagicMock() + mock_embedder_action.arun = AsyncMock( + return_value=MagicMock(response=MagicMock(embeddings=[MagicMock(embedding=[0.1, 0.2, 0.3])])) + ) + mock_registry.resolve_embedder = AsyncMock(return_value=mock_embedder_action) + + indexer = ChromaIndexer( + registry=mock_registry, + collection_name='test-collection', + embedder='test/embedder', + ) + + mock_collection = MagicMock() + + with patch.object(indexer, '_get_collection', AsyncMock(return_value=mock_collection)): + request = IndexerRequest( + documents=[ + DocumentData( + content=[DocumentPart(root=TextPart(text='Document with metadata'))], + metadata={'author': 'test'}, + ), + ], + ) + + await indexer.index(request) + + call_args = mock_collection.add.call_args + metadatas = call_args[1]['metadatas'] + # Metadata should include data_type (matching JS) + assert 'data_type' in metadatas[0] + + @pytest.mark.asyncio + async def test_indexer_handles_empty_documents(self) -> None: + """Test indexer handles empty document list gracefully.""" + mock_registry = MagicMock() + indexer = ChromaIndexer( + registry=mock_registry, + collection_name='test-collection', + embedder='test/embedder', + ) + + request = IndexerRequest(documents=[]) + + # Should not raise, just return early + await indexer.index(request) + + +class TestChromaIndexerRef: + """Tests for chroma_indexer_ref function matching JS chromaIndexerRef.""" + + def test_indexer_ref_format(self) -> None: + """Test indexer ref creates correct reference string.""" + from genkit.plugins.chroma import chroma_indexer_ref + + ref = chroma_indexer_ref(collection_name='my-collection') + assert ref == 'chroma/my-collection' + + def test_indexer_ref_with_display_name(self) -> None: + """Test indexer ref with display name (ignored, for JS parity).""" + from genkit.plugins.chroma import chroma_indexer_ref + + ref = chroma_indexer_ref( + collection_name='my-collection', + display_name='My Custom Label', + ) + # Display name is ignored in Python, ref is still the same + assert ref == 'chroma/my-collection' diff --git a/py/plugins/chroma/tests/chroma_plugin_test.py b/py/plugins/chroma/tests/chroma_plugin_test.py new file mode 100644 index 0000000000..90c3ba2d30 --- /dev/null +++ b/py/plugins/chroma/tests/chroma_plugin_test.py @@ -0,0 +1,186 @@ +# Copyright 2026 Google LLC +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. +# +# SPDX-License-Identifier: Apache-2.0 + +"""Tests for the Chroma plugin.""" + +from unittest.mock import MagicMock + +import pytest + +from genkit.core.action.types import ActionKind +from genkit.plugins.chroma import ( + Chroma, + ChromaCollectionConfig, + chroma_indexer_ref, + chroma_retriever_ref, +) + + +def test_plugin_name() -> None: + """Test plugin has correct name.""" + plugin = Chroma() + assert plugin.name == 'chroma' + + +def test_init_empty_config() -> None: + """Test plugin initialization with no config.""" + plugin = Chroma() + assert plugin._collections == [] + + +def test_init_with_collections() -> None: + """Test plugin initialization with collections.""" + collections = [ + ChromaCollectionConfig( + collection_name='test-collection', + embedder='googleai/text-embedding-004', + ), + ] + plugin = Chroma(collections=collections) + assert len(plugin._collections) == 1 + assert plugin._collections[0].collection_name == 'test-collection' + + +def test_chroma_retriever_ref() -> None: + """Test chroma_retriever_ref creates correct reference string.""" + ref = chroma_retriever_ref(collection_name='my-collection') + assert ref == 'chroma/my-collection' + + +def test_chroma_indexer_ref() -> None: + """Test chroma_indexer_ref creates correct reference string.""" + ref = chroma_indexer_ref(collection_name='my-collection') + assert ref == 'chroma/my-collection' + + +def test_chroma_collection_config_defaults() -> None: + """Test ChromaCollectionConfig default values.""" + config = ChromaCollectionConfig( + collection_name='test', + embedder='test/embedder', + ) + assert config.collection_name == 'test' + assert config.embedder == 'test/embedder' + assert config.embedder_options is None + assert config.client_params is None + assert config.create_collection_if_missing is False + assert config.metadata is None + + +@pytest.mark.asyncio +async def test_init_stores_registry() -> None: + """Test that init() stores the registry for later use.""" + plugin = Chroma() + mock_registry = MagicMock() + + result = await plugin.init(mock_registry) + + assert result == [] + assert plugin._registry == mock_registry + + +@pytest.mark.asyncio +async def test_list_actions_with_collections() -> None: + """Test list_actions returns metadata for configured collections.""" + collections = [ + ChromaCollectionConfig( + collection_name='collection1', + embedder='test/embedder', + ), + ChromaCollectionConfig( + collection_name='collection2', + embedder='test/embedder', + ), + ] + plugin = Chroma(collections=collections) + + actions = await plugin.list_actions() + + # Should have 2 retrievers + 2 indexers = 4 actions + assert len(actions) == 4 + + retriever_actions = [a for a in actions if a.kind == ActionKind.RETRIEVER] + indexer_actions = [a for a in actions if a.kind == ActionKind.INDEXER] + + assert len(retriever_actions) == 2 + assert len(indexer_actions) == 2 + + +@pytest.mark.asyncio +async def test_list_actions_empty() -> None: + """Test list_actions with no configured collections.""" + plugin = Chroma() + + actions = await plugin.list_actions() + assert actions == [] + + +@pytest.mark.asyncio +async def test_resolve_returns_none_for_unknown_action() -> None: + """Test resolve returns None for unknown action names.""" + plugin = Chroma() + plugin._registry = MagicMock() + + result = await plugin.resolve(ActionKind.MODEL, 'chroma/test') + assert result is None + + +@pytest.mark.asyncio +async def test_resolve_returns_none_for_other_namespace() -> None: + """Test resolve returns None for actions not in chroma namespace.""" + plugin = Chroma() + plugin._registry = MagicMock() + + result = await plugin.resolve(ActionKind.RETRIEVER, 'other/collection') + assert result is None + + +@pytest.mark.asyncio +async def test_resolve_retriever_for_configured_collection() -> None: + """Test resolve returns action for configured collection retriever.""" + collections = [ + ChromaCollectionConfig( + collection_name='test-collection', + embedder='test/embedder', + ), + ] + plugin = Chroma(collections=collections) + mock_registry = MagicMock() + await plugin.init(mock_registry) + + action = await plugin.resolve(ActionKind.RETRIEVER, 'chroma/test-collection') + + # Should return an action (not None) for configured collection + assert action is not None + + +@pytest.mark.asyncio +async def test_resolve_indexer_for_configured_collection() -> None: + """Test resolve returns action for configured collection indexer.""" + collections = [ + ChromaCollectionConfig( + collection_name='test-collection', + embedder='test/embedder', + ), + ] + plugin = Chroma(collections=collections) + mock_registry = MagicMock() + await plugin.init(mock_registry) + + action = await plugin.resolve(ActionKind.INDEXER, 'chroma/test-collection') + + # Should return an action (not None) for configured collection + assert action is not None diff --git a/py/plugins/chroma/tests/chroma_retriever_test.py b/py/plugins/chroma/tests/chroma_retriever_test.py new file mode 100644 index 0000000000..1e2a69e581 --- /dev/null +++ b/py/plugins/chroma/tests/chroma_retriever_test.py @@ -0,0 +1,274 @@ +# Copyright 2026 Google LLC +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. +# +# SPDX-License-Identifier: Apache-2.0 + +"""Tests for the Chroma retriever functionality. + +These tests verify parity with the JS implementation in: +js/plugins/chroma/src/index.ts (chromaRetriever function) +""" + +from unittest.mock import AsyncMock, MagicMock, patch + +import pytest + +from genkit.blocks.retriever import RetrieverRequest +from genkit.core.action import ActionRunContext +from genkit.core.typing import DocumentPart, TextPart +from genkit.plugins.chroma.plugin import ( + ChromaRetriever, + ChromaRetrieverOptions, +) +from genkit.types import DocumentData + + +class TestChromaRetrieverOptions: + """Tests for ChromaRetrieverOptions matching JS ChromaRetrieverOptionsSchema.""" + + def test_default_k_value(self) -> None: + """Test default k value matches JS CommonRetrieverOptionsSchema.""" + options = ChromaRetrieverOptions() + assert options.k == 10 + + def test_custom_k_value(self) -> None: + """Test custom k value.""" + options = ChromaRetrieverOptions(k=50) + assert options.k == 50 + + def test_where_filter(self) -> None: + """Test where filter for metadata filtering (matches JS).""" + options = ChromaRetrieverOptions( + where={'category': 'science'}, + ) + assert options.where == {'category': 'science'} + + def test_where_document_filter(self) -> None: + """Test whereDocument filter for content filtering (matches JS).""" + options = ChromaRetrieverOptions( + where_document={'$contains': 'keyword'}, + ) + assert options.where_document == {'$contains': 'keyword'} + + def test_include_fields(self) -> None: + """Test include fields for selecting return data (matches JS IncludeOptionSchema).""" + options = ChromaRetrieverOptions( + include=['documents', 'embeddings', 'metadatas', 'distances'], + ) + assert options.include is not None + assert 'documents' in options.include + assert 'embeddings' in options.include + + def test_all_options_combined(self) -> None: + """Test all options can be combined.""" + options = ChromaRetrieverOptions( + k=20, + where={'type': 'doc'}, + where_document={'$contains': 'test'}, + include=['documents', 'metadatas'], + ) + assert options.k == 20 + assert options.where is not None + assert options.where_document is not None + assert options.include is not None + + +class TestChromaRetriever: + """Tests for ChromaRetriever class matching JS chromaRetriever.""" + + def test_retriever_initialization(self) -> None: + """Test retriever initialization with required params.""" + mock_registry = MagicMock() + retriever = ChromaRetriever( + registry=mock_registry, + collection_name='test-collection', + embedder='googleai/text-embedding-004', + ) + + assert retriever._collection_name == 'test-collection' + assert retriever._embedder == 'googleai/text-embedding-004' + + def test_retriever_with_embedder_options(self) -> None: + """Test retriever with embedder options (matches JS).""" + mock_registry = MagicMock() + retriever = ChromaRetriever( + registry=mock_registry, + collection_name='test-collection', + embedder='test/embedder', + embedder_options={'model': 'custom'}, + ) + + assert retriever._embedder_options == {'model': 'custom'} + + def test_retriever_with_client_params(self) -> None: + """Test retriever with client params (matches JS clientParams).""" + mock_registry = MagicMock() + retriever = ChromaRetriever( + registry=mock_registry, + collection_name='test-collection', + embedder='test/embedder', + client_params={'path': '/data/chroma'}, + ) + + assert retriever._client_params == {'path': '/data/chroma'} + + def test_retriever_create_collection_if_missing(self) -> None: + """Test createCollectionIfMissing option (matches JS).""" + mock_registry = MagicMock() + retriever = ChromaRetriever( + registry=mock_registry, + collection_name='test-collection', + embedder='test/embedder', + create_collection_if_missing=True, + ) + + assert retriever._create_collection_if_missing is True + + @pytest.mark.asyncio + async def test_retriever_returns_documents(self) -> None: + """Test retriever returns documents in correct format (matching JS).""" + mock_registry = MagicMock() + mock_embedder_action = MagicMock() + mock_embedder_action.arun = AsyncMock( + return_value=MagicMock(response=MagicMock(embeddings=[MagicMock(embedding=[0.1, 0.2, 0.3])])) + ) + mock_registry.resolve_embedder = AsyncMock(return_value=mock_embedder_action) + + retriever = ChromaRetriever( + registry=mock_registry, + collection_name='test-collection', + embedder='test/embedder', + ) + + # Mock collection query + mock_collection = MagicMock() + mock_collection.query.return_value = { + 'documents': [['Document 1 content', 'Document 2 content']], + 'metadatas': [[{'data_type': 'text'}, {'data_type': 'text'}]], + 'distances': [[0.1, 0.2]], + 'embeddings': None, + } + + with patch.object(retriever, '_get_collection', AsyncMock(return_value=mock_collection)): + request = RetrieverRequest( + query=DocumentData(content=[DocumentPart(root=TextPart(text='search query'))]), + options=ChromaRetrieverOptions(k=10), + ) + + ctx = MagicMock(spec=ActionRunContext) + result = await retriever.retrieve(request, ctx) + + assert len(result.documents) == 2 + # Documents should be DocumentData instances + assert hasattr(result.documents[0], 'content') + + @pytest.mark.asyncio + async def test_retriever_uses_where_filter(self) -> None: + """Test retriever passes where filter to collection (matching JS).""" + mock_registry = MagicMock() + mock_embedder_action = MagicMock() + mock_embedder_action.arun = AsyncMock( + return_value=MagicMock(response=MagicMock(embeddings=[MagicMock(embedding=[0.1, 0.2, 0.3])])) + ) + mock_registry.resolve_embedder = AsyncMock(return_value=mock_embedder_action) + + retriever = ChromaRetriever( + registry=mock_registry, + collection_name='test-collection', + embedder='test/embedder', + ) + + mock_collection = MagicMock() + mock_collection.query.return_value = { + 'documents': [[]], + 'metadatas': [[]], + 'distances': None, + 'embeddings': None, + } + + with patch.object(retriever, '_get_collection', AsyncMock(return_value=mock_collection)): + request = RetrieverRequest( + query=DocumentData(content=[DocumentPart(root=TextPart(text='search query'))]), + options=ChromaRetrieverOptions( + k=5, + where={'category': 'tech'}, + ), + ) + + ctx = MagicMock(spec=ActionRunContext) + await retriever.retrieve(request, ctx) + + # Verify where filter was passed + call_args = mock_collection.query.call_args + assert call_args[1]['where'] == {'category': 'tech'} + + @pytest.mark.asyncio + async def test_retriever_uses_where_document_filter(self) -> None: + """Test retriever passes whereDocument filter (matching JS).""" + mock_registry = MagicMock() + mock_embedder_action = MagicMock() + mock_embedder_action.arun = AsyncMock( + return_value=MagicMock(response=MagicMock(embeddings=[MagicMock(embedding=[0.1, 0.2, 0.3])])) + ) + mock_registry.resolve_embedder = AsyncMock(return_value=mock_embedder_action) + + retriever = ChromaRetriever( + registry=mock_registry, + collection_name='test-collection', + embedder='test/embedder', + ) + + mock_collection = MagicMock() + mock_collection.query.return_value = { + 'documents': [[]], + 'metadatas': [[]], + 'distances': None, + 'embeddings': None, + } + + with patch.object(retriever, '_get_collection', AsyncMock(return_value=mock_collection)): + request = RetrieverRequest( + query=DocumentData(content=[DocumentPart(root=TextPart(text='search query'))]), + options=ChromaRetrieverOptions( + where_document={'$contains': 'important'}, + ), + ) + + ctx = MagicMock(spec=ActionRunContext) + await retriever.retrieve(request, ctx) + + call_args = mock_collection.query.call_args + assert call_args[1]['where_document'] == {'$contains': 'important'} + + +class TestChromaRetrieverRef: + """Tests for chroma_retriever_ref function matching JS chromaRetrieverRef.""" + + def test_retriever_ref_format(self) -> None: + """Test retriever ref creates correct reference string.""" + from genkit.plugins.chroma import chroma_retriever_ref + + ref = chroma_retriever_ref(collection_name='my-collection') + assert ref == 'chroma/my-collection' + + def test_retriever_ref_with_display_name(self) -> None: + """Test retriever ref with display name (ignored, for JS parity).""" + from genkit.plugins.chroma import chroma_retriever_ref + + ref = chroma_retriever_ref( + collection_name='my-collection', + display_name='My Custom Label', + ) + # Display name is ignored in Python, ref is still the same + assert ref == 'chroma/my-collection' diff --git a/py/plugins/cloud-sql-pg/LICENSE b/py/plugins/cloud-sql-pg/LICENSE new file mode 100644 index 0000000000..df289c9e59 --- /dev/null +++ b/py/plugins/cloud-sql-pg/LICENSE @@ -0,0 +1,202 @@ + + Apache License + Version 2.0, January 2004 + http://www.apache.org/licenses/ + + TERMS AND CONDITIONS FOR USE, REPRODUCTION, AND DISTRIBUTION + + 1. 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We also recommend that a + file or class name and description of purpose be included on the + same "printed page" as the copyright notice for easier + identification within third-party archives. + + Copyright [yyyy] [name of copyright owner] + + Licensed under the Apache License, Version 2.0 (the "License"); + you may not use this file except in compliance with the License. + You may obtain a copy of the License at + + http://www.apache.org/licenses/LICENSE-2.0 + + Unless required by applicable law or agreed to in writing, software + distributed under the License is distributed on an "AS IS" BASIS, + WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. + See the License for the specific language governing permissions and + limitations under the License. diff --git a/py/plugins/cloud-sql-pg/README.md b/py/plugins/cloud-sql-pg/README.md new file mode 100644 index 0000000000..dcf14395bd --- /dev/null +++ b/py/plugins/cloud-sql-pg/README.md @@ -0,0 +1,230 @@ +# Cloud SQL PostgreSQL Plugin for Genkit + +This plugin provides a vector store implementation for [Cloud SQL for PostgreSQL](https://cloud.google.com/sql/docs/postgres) with [pgvector](https://github.com/pgvector/pgvector) support, enabling RAG (Retrieval-Augmented Generation) workflows in Genkit applications. + +## Features + +- **Cloud SQL Connector**: Secure connections to Cloud SQL instances using the Cloud SQL Python Connector +- **IAM Authentication**: Support for both IAM-based and password authentication +- **pgvector Support**: Vector similarity search using PostgreSQL's pgvector extension +- **Multiple Distance Strategies**: Cosine distance, Euclidean distance, and inner product +- **Vector Indexes**: Support for HNSW and IVFFlat indexes for efficient similarity search +- **Flexible Metadata**: Store metadata in dedicated columns or JSON format +- **Batch Processing**: Efficient batch indexing of documents + +## Installation + +```bash +pip install genkit-plugin-cloud-sql-pg +``` + +## Prerequisites + +1. A Cloud SQL for PostgreSQL instance with the `pgvector` extension enabled +2. A Google Cloud project with the Cloud SQL API enabled +3. Proper IAM permissions or database credentials + +### Enable pgvector Extension + +Connect to your PostgreSQL database and run: + +```sql +CREATE EXTENSION IF NOT EXISTS vector; +``` + +## Usage + +### Basic Setup + +```python +from genkit import Genkit +from genkit.plugins.google_genai import GoogleAI +from genkit.plugins.cloud_sql_pg import ( + CloudSqlPg, + PostgresEngine, + PostgresTableConfig, +) + +# Create the engine using Cloud SQL Connector (recommended) +engine = await PostgresEngine.from_instance( + project_id='your-project-id', + region='us-central1', + instance='your-instance', + database='your-database', + # For IAM authentication (recommended): + # iam_account_email='your-service-account@your-project.iam.gserviceaccount.com' + # For password authentication: + user='your-user', + password='your-password', +) + +# Initialize the table (run once) +await engine.init_vectorstore_table( + table_name='documents', + vector_size=768, # Match your embedder's dimension +) + +# Initialize Genkit with the plugin +ai = Genkit( + plugins=[ + GoogleAI(), + CloudSqlPg( + tables=[ + PostgresTableConfig( + table_name='documents', + engine=engine, + embedder='googleai/text-embedding-004', + ) + ] + ), + ] +) +``` + +### Indexing Documents + +```python +from genkit.blocks.document import Document + +# Index documents +await ai.index( + indexer='postgres/documents', + documents=[ + Document.from_text('The quick brown fox jumps over the lazy dog.'), + Document.from_text('A journey of a thousand miles begins with a single step.'), + ], +) +``` + +### Retrieving Documents + +```python +# Retrieve similar documents +response = await ai.retrieve( + retriever='postgres/documents', + query=Document.from_text('What animal is jumping?'), + options={'k': 5}, +) + +for doc in response.documents: + print(doc.text()) +``` + +### Using SQL Filters + +```python +# Filter results using SQL WHERE clause +response = await ai.retrieve( + retriever='postgres/documents', + query=Document.from_text('search query'), + options={ + 'k': 10, + 'filter': "category = 'science'", # SQL WHERE clause + }, +) +``` + +## Configuration Options + +### PostgresEngine + +| Parameter | Type | Required | Description | +|-----------|------|----------|-------------| +| `project_id` | str | Yes | GCP project ID | +| `region` | str | Yes | Cloud SQL instance region | +| `instance` | str | Yes | Cloud SQL instance name | +| `database` | str | Yes | Database name | +| `user` | str | No | Database user (for password auth) | +| `password` | str | No | Database password (for password auth) | +| `iam_account_email` | str | No | IAM service account email (for IAM auth) | +| `ip_type` | IpAddressTypes | No | IP address type (PUBLIC or PRIVATE) | + +### PostgresTableConfig + +| Parameter | Type | Default | Description | +|-----------|------|---------|-------------| +| `table_name` | str | Required | Table name | +| `engine` | PostgresEngine | Required | Database engine instance | +| `embedder` | str | Required | Embedder reference | +| `embedder_options` | dict | None | Embedder configuration | +| `schema_name` | str | 'public' | PostgreSQL schema name | +| `content_column` | str | 'content' | Column for document content | +| `embedding_column` | str | 'embedding' | Column for vector embeddings | +| `id_column` | str | 'id' | Column for document IDs | +| `metadata_columns` | list[str] | None | Specific metadata columns to use | +| `metadata_json_column` | str | 'metadata' | JSON column for metadata | +| `distance_strategy` | DistanceStrategy | COSINE_DISTANCE | Vector distance strategy | +| `index_query_options` | QueryOptions | None | Index-specific query options | + +### Distance Strategies + +- `DistanceStrategy.COSINE_DISTANCE` - Cosine distance (default) +- `DistanceStrategy.EUCLIDEAN` - L2 distance +- `DistanceStrategy.INNER_PRODUCT` - Inner product + +### Vector Indexes + +#### HNSW Index + +```python +from genkit.plugins.cloud_sql_pg import HNSWIndex, HNSWQueryOptions + +# Create HNSW index +await engine.apply_vector_index( + table_name='documents', + index=HNSWIndex(m=16, ef_construction=64), +) + +# Use with query options +ai = Genkit( + plugins=[ + CloudSqlPg( + tables=[ + PostgresTableConfig( + table_name='documents', + engine=engine, + embedder='googleai/text-embedding-004', + index_query_options=HNSWQueryOptions(ef_search=40), + ) + ] + ), + ] +) +``` + +#### IVFFlat Index + +```python +from genkit.plugins.cloud_sql_pg import IVFFlatIndex, IVFFlatQueryOptions + +# Create IVFFlat index +await engine.apply_vector_index( + table_name='documents', + index=IVFFlatIndex(lists=100), +) + +# Use with query options +index_query_options=IVFFlatQueryOptions(probes=10) +``` + +## API Parity with JavaScript + +This plugin maintains API and behavioral parity with the JavaScript `@genkit-ai/cloud-sql-pg` plugin. + +| Feature | JS | Python | +|---------|-----|--------| +| Cloud SQL Connector | ✅ | ✅ | +| IAM Authentication | ✅ | ✅ | +| Password Authentication | ✅ | ✅ | +| Direct Connection | ✅ | ✅ | +| HNSW Index | ✅ | ✅ | +| IVFFlat Index | ✅ | ✅ | +| Distance Strategies | ✅ | ✅ | +| Metadata Columns | ✅ | ✅ | +| JSON Metadata | ✅ | ✅ | +| SQL Filtering | ✅ | ✅ | +| Batch Indexing | ✅ | ✅ | + +## License + +Apache-2.0 diff --git a/py/plugins/cloud-sql-pg/pyproject.toml b/py/plugins/cloud-sql-pg/pyproject.toml new file mode 100644 index 0000000000..bd460200b6 --- /dev/null +++ b/py/plugins/cloud-sql-pg/pyproject.toml @@ -0,0 +1,67 @@ +# Copyright 2025 Google LLC +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. +# +# SPDX-License-Identifier: Apache-2.0 + +[build-system] +build-backend = "hatchling.build" +requires = ["hatchling"] + +[project] +authors = [ + {email = "nicholaschiasson@google.com", name = "Nicholas Chiasson"}, +] +classifiers = [ + "License :: OSI Approved :: Apache Software License", + "Operating System :: OS Independent", + "Programming Language :: Python :: 3 :: Only", + "Programming Language :: Python :: 3.10", + "Programming Language :: Python :: 3.11", + "Programming Language :: Python :: 3.12", + "Programming Language :: Python :: 3.13", + "Programming Language :: Python :: 3.14", +] +dependencies = [ + "genkit>=0.4.0", + "cloud-sql-python-connector[asyncpg]>=1.14.0", + "sqlalchemy[asyncio]>=2.0.0", + "asyncpg>=0.30.0", + "pgvector>=0.3.0", + "google-auth>=2.0.0", +] +description = "Cloud SQL PostgreSQL vector store plugin for Genkit AI framework" +dynamic = ["version"] +keywords = ["ai", "genkit", "cloud-sql", "postgresql", "pgvector", "vector-store", "rag"] +license = {text = "Apache-2.0"} +name = "genkit-plugin-cloud-sql-pg" +readme = "README.md" +requires-python = ">=3.10" + +[project.optional-dependencies] +dev = [ + "pytest>=8.3.5", + "pytest-asyncio>=0.24.0", + "pytest-mock>=3.14.0", +] + +[project.urls] +Documentation = "https://firebase.google.com/docs/genkit" +Homepage = "https://github.com/firebase/genkit/tree/main/py/plugins/cloud-sql-pg" +Repository = "https://github.com/firebase/genkit" + +[tool.hatch.build.targets.wheel] +packages = ["src/genkit"] + +[tool.hatch.version] +path = "src/genkit/plugins/cloud_sql_pg/__init__.py" diff --git a/py/plugins/cloud-sql-pg/src/genkit/plugins/cloud_sql_pg/__init__.py b/py/plugins/cloud-sql-pg/src/genkit/plugins/cloud_sql_pg/__init__.py new file mode 100644 index 0000000000..c62ff2ffef --- /dev/null +++ b/py/plugins/cloud-sql-pg/src/genkit/plugins/cloud_sql_pg/__init__.py @@ -0,0 +1,135 @@ +# Copyright 2025 Google LLC +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. +# +# SPDX-License-Identifier: Apache-2.0 + +"""Cloud SQL PostgreSQL vector store plugin for Genkit. + +This plugin provides a vector store implementation for Cloud SQL for PostgreSQL +with pgvector support, enabling RAG (Retrieval-Augmented Generation) workflows. + +Example: + ```python + from genkit import Genkit + from genkit.plugins.google_genai import GoogleAI + from genkit.plugins.cloud_sql_pg import ( + CloudSqlPg, + PostgresEngine, + PostgresTableConfig, + ) + + # Create the engine + engine = await PostgresEngine.from_instance( + project_id='your-project-id', + region='us-central1', + instance='your-instance', + database='your-database', + user='your-user', + password='your-password', + ) + + # Initialize Genkit with the plugin + ai = Genkit( + plugins=[ + GoogleAI(), + CloudSqlPg( + tables=[ + PostgresTableConfig( + table_name='documents', + engine=engine, + embedder='googleai/text-embedding-004', + ) + ] + ), + ] + ) + + # Index documents + await ai.index( + indexer='postgres/documents', + documents=[Document.from_text('Hello, world!')], + ) + + # Retrieve similar documents + response = await ai.retrieve( + retriever='postgres/documents', + query=Document.from_text('greeting'), + options={'k': 5}, + ) + ``` +""" + +__version__ = '1.0.0' + +from .engine import Column, IpAddressTypes, PostgresEngine, VectorStoreTableArgs +from .indexes import ( + DEFAULT_DISTANCE_STRATEGY, + DEFAULT_INDEX_NAME_SUFFIX, + BaseIndex, + DistanceStrategy, + ExactNearestNeighbor, + HNSWIndex, + HNSWQueryOptions, + IVFFlatIndex, + IVFFlatQueryOptions, + QueryOptions, +) +from .plugin import ( + POSTGRES_PLUGIN_NAME, + CloudSqlPg, + PostgresIndexer, + PostgresIndexerOptions, + PostgresRetriever, + PostgresRetrieverOptions, + PostgresTableConfig, + configure_postgres_indexer, + configure_postgres_retriever, + postgres, + postgres_indexer_ref, + postgres_retriever_ref, +) + +__all__ = [ + # Plugin + 'CloudSqlPg', + 'POSTGRES_PLUGIN_NAME', + 'postgres', + # Engine + 'PostgresEngine', + 'Column', + 'IpAddressTypes', + 'VectorStoreTableArgs', + # Configuration + 'PostgresTableConfig', + 'PostgresRetrieverOptions', + 'PostgresIndexerOptions', + # Retriever/Indexer + 'PostgresRetriever', + 'PostgresIndexer', + 'postgres_retriever_ref', + 'postgres_indexer_ref', + 'configure_postgres_retriever', + 'configure_postgres_indexer', + # Indexes + 'DistanceStrategy', + 'DEFAULT_DISTANCE_STRATEGY', + 'DEFAULT_INDEX_NAME_SUFFIX', + 'BaseIndex', + 'ExactNearestNeighbor', + 'HNSWIndex', + 'IVFFlatIndex', + 'QueryOptions', + 'HNSWQueryOptions', + 'IVFFlatQueryOptions', +] diff --git a/py/plugins/cloud-sql-pg/src/genkit/plugins/cloud_sql_pg/engine.py b/py/plugins/cloud-sql-pg/src/genkit/plugins/cloud_sql_pg/engine.py new file mode 100644 index 0000000000..b312238d1f --- /dev/null +++ b/py/plugins/cloud-sql-pg/src/genkit/plugins/cloud_sql_pg/engine.py @@ -0,0 +1,603 @@ +# Copyright 2025 Google LLC +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. +# +# SPDX-License-Identifier: Apache-2.0 + +"""PostgreSQL engine for Cloud SQL vector store operations. + +This module provides the PostgresEngine class for managing connections +to Cloud SQL PostgreSQL instances and performing vector store operations. + +Key Components +============== + +┌───────────────────────────────────────────────────────────────────────────┐ +│ Engine Components │ +├───────────────────────┬───────────────────────────────────────────────────┤ +│ Component │ Purpose │ +├───────────────────────┼───────────────────────────────────────────────────┤ +│ PostgresEngine │ Main engine class for database operations │ +│ Column │ Column definition for custom metadata columns │ +│ IpAddressTypes │ IP address type enum (PUBLIC, PRIVATE) │ +└───────────────────────┴───────────────────────────────────────────────────┘ + +See Also: + - JS Implementation: js/plugins/cloud-sql-pg/src/engine.ts +""" + +from __future__ import annotations + +from dataclasses import dataclass +from enum import Enum +from typing import TYPE_CHECKING + +if TYPE_CHECKING: + import asyncpg + +import google.auth +import google.auth.transport.requests +import google.oauth2.id_token +from google.cloud.sql.connector import Connector, IPTypes +from sqlalchemy import text +from sqlalchemy.ext.asyncio import AsyncEngine, create_async_engine + +from .indexes import DEFAULT_INDEX_NAME_SUFFIX, BaseIndex, ExactNearestNeighbor + +USER_AGENT = 'genkit-cloud-sql-pg-python' + + +class IpAddressTypes(Enum): + """IP address types for Cloud SQL connections. + + Attributes: + PUBLIC: Connect via public IP address. + PRIVATE: Connect via private IP address (VPC). + """ + + PUBLIC = 'PUBLIC' + PRIVATE = 'PRIVATE' + + def to_ip_types(self) -> IPTypes: + """Convert to Cloud SQL Connector IPTypes.""" + if self == IpAddressTypes.PRIVATE: + return IPTypes.PRIVATE + return IPTypes.PUBLIC + + +@dataclass +class Column: + """Database column definition. + + Attributes: + name: Column name. + data_type: PostgreSQL data type (e.g., 'TEXT', 'INT', 'UUID'). + nullable: Whether the column can be NULL. + """ + + name: str + data_type: str + nullable: bool = True + + def __post_init__(self) -> None: + """Validate column definition.""" + if not isinstance(self.name, str): + raise TypeError('Column name must be a string') + if not isinstance(self.data_type, str): + raise TypeError('Column data_type must be a string') + + +@dataclass +class VectorStoreTableArgs: + """Arguments for creating a vector store table. + + Attributes: + schema_name: PostgreSQL schema name (default: 'public'). + content_column: Column name for document content (default: 'content'). + embedding_column: Column name for vector embeddings (default: 'embedding'). + metadata_columns: List of custom metadata columns. + metadata_json_column: Column name for JSON metadata (default: 'json_metadata'). + id_column: Column name or Column object for IDs (default: 'id' with UUID type). + overwrite_existing: Drop existing table if exists (default: False). + store_metadata: Whether to create JSON metadata column (default: True). + concurrently: Build index concurrently (default: False). + index_name: Optional custom index name. + """ + + schema_name: str = 'public' + content_column: str = 'content' + embedding_column: str = 'embedding' + metadata_columns: list[Column] | None = None + metadata_json_column: str = 'json_metadata' + id_column: str | Column = 'id' + overwrite_existing: bool = False + store_metadata: bool = True + concurrently: bool = False + index_name: str | None = None + + +def _strip_service_account_domain(email: str) -> str: + """Strip .gserviceaccount.com domain from service account email. + + This matches the JS behavior in getIAMPrincipalEmail. + + Args: + email: The email address. + + Returns: + Email with .gserviceaccount.com stripped if present. + """ + return email.replace('.gserviceaccount.com', '') + + +async def _get_iam_principal_email() -> str: + """Get the IAM principal email from application default credentials. + + This matches the JS getIAMPrincipalEmail function behavior. + + Returns: + The IAM principal email (service account or user email), + with .gserviceaccount.com stripped for service accounts. + + Raises: + ValueError: If unable to determine IAM principal email. + """ + credentials, _ = google.auth.default(scopes=['https://www.googleapis.com/auth/cloud-platform']) + request = google.auth.transport.requests.Request() + credentials.refresh(request) + + # For service accounts, the email is in the credentials + if hasattr(credentials, 'service_account_email'): + return _strip_service_account_domain(credentials.service_account_email) + + # For user credentials, we need to get the email differently + if hasattr(credentials, '_service_account_email'): + return _strip_service_account_domain(credentials._service_account_email) + + # Try to get from token info + if hasattr(credentials, 'token'): + # For user credentials, parse the token + try: + request = google.auth.transport.requests.Request() + id_info = google.oauth2.id_token.verify_oauth2_token(credentials.token, request) + if 'email' in id_info: + return _strip_service_account_domain(id_info['email']) + except Exception: + pass + + raise ValueError( + "Failed to automatically obtain authenticated IAM principal's " + "email address using environment's ADC credentials!" + ) + + +class PostgresEngine: + """PostgreSQL engine for Cloud SQL vector store operations. + + This class manages connections to Cloud SQL PostgreSQL instances + and provides methods for vector store table management. + + Example: + ```python + # Using Cloud SQL Connector with IAM auth + engine = await PostgresEngine.from_instance( + project_id='my-project', + region='us-central1', + instance='my-instance', + database='my-database', + ) + + # Initialize vector store table + await engine.init_vectorstore_table( + table_name='documents', + vector_size=768, + ) + ``` + """ + + _connector: Connector | None = None + + def __init__(self, engine: AsyncEngine) -> None: + """Initialize PostgresEngine with an async engine. + + Use factory methods (from_instance, from_engine, from_connection_string) + to create instances. + + Args: + engine: SQLAlchemy async engine. + """ + self._engine = engine + + @property + def engine(self) -> AsyncEngine: + """Get the underlying SQLAlchemy async engine.""" + return self._engine + + @classmethod + async def from_instance( + cls, + project_id: str, + region: str, + instance: str, + database: str, + *, + ip_type: IpAddressTypes = IpAddressTypes.PUBLIC, + user: str | None = None, + password: str | None = None, + iam_account_email: str | None = None, + ) -> PostgresEngine: + """Create PostgresEngine using Cloud SQL Connector. + + This is the recommended method for connecting to Cloud SQL instances. + Supports both password and IAM authentication. + + Args: + project_id: GCP project ID. + region: Cloud SQL instance region. + instance: Cloud SQL instance name. + database: Database name. + ip_type: IP address type (PUBLIC or PRIVATE). + user: Database user for password authentication. + password: Database password for password authentication. + iam_account_email: IAM service account email for IAM auth. + + Returns: + Configured PostgresEngine instance. + + Raises: + ValueError: If authentication configuration is invalid. + """ + # Validate authentication configuration + if (user is None) != (password is None): + raise ValueError( + "Only one of 'user' or 'password' was specified. Either both should be " + 'specified for password authentication, or neither for IAM authentication.' + ) + + # Determine authentication mode + if user is not None and password is not None: + # Password authentication + enable_iam_auth = False + db_user = user + db_password = password + else: + # IAM authentication + enable_iam_auth = True + db_password = None + if iam_account_email is not None: + db_user = iam_account_email + else: + db_user = await _get_iam_principal_email() + + # Create connector if not exists + if cls._connector is None: + cls._connector = Connector() + + instance_connection_name = f'{project_id}:{region}:{instance}' + + async def getconn() -> asyncpg.Connection: + """Get connection from Cloud SQL Connector.""" + assert cls._connector is not None + conn = await cls._connector.connect_async( + instance_connection_name, + 'asyncpg', + user=db_user, + password=db_password, + db=database, + ip_type=ip_type.to_ip_types(), + enable_iam_auth=enable_iam_auth, + ) + return conn + + engine = create_async_engine( + 'postgresql+asyncpg://', + async_creator=getconn, + echo=False, + ) + + return cls(engine) + + @classmethod + async def from_engine(cls, engine: AsyncEngine) -> PostgresEngine: + """Create PostgresEngine from existing SQLAlchemy async engine. + + Args: + engine: Existing SQLAlchemy async engine. + + Returns: + PostgresEngine wrapping the provided engine. + """ + return cls(engine) + + @classmethod + async def from_connection_string( + cls, + connection_string: str, + *, + pool_size: int = 5, + max_overflow: int = 10, + ) -> PostgresEngine: + """Create PostgresEngine from connection string. + + Args: + connection_string: PostgreSQL connection string. + Should start with 'postgresql+asyncpg://'. + pool_size: Connection pool size. + max_overflow: Maximum overflow connections. + + Returns: + Configured PostgresEngine instance. + + Raises: + ValueError: If connection string has wrong format. + """ + if not connection_string.startswith('postgresql+asyncpg://'): + raise ValueError("Connection string must start with 'postgresql+asyncpg://'") + + engine = create_async_engine( + connection_string, + pool_size=pool_size, + max_overflow=max_overflow, + echo=False, + ) + + return cls(engine) + + # Alias for JS API parity (JS calls this fromEngineArgs) + @classmethod + async def from_engine_args( + cls, + url: str, + *, + pool_size: int = 5, + max_overflow: int = 10, + ) -> PostgresEngine: + """Create PostgresEngine from connection string. + + This is an alias for `from_connection_string()` to match the JS API. + + Args: + url: PostgreSQL connection string. + Should start with 'postgresql+asyncpg://'. + pool_size: Connection pool size. + max_overflow: Maximum overflow connections. + + Returns: + Configured PostgresEngine instance. + + Raises: + ValueError: If connection string has wrong format. + """ + return await cls.from_connection_string( + url, + pool_size=pool_size, + max_overflow=max_overflow, + ) + + async def init_vectorstore_table( + self, + table_name: str, + vector_size: int, + *, + schema_name: str = 'public', + content_column: str = 'content', + embedding_column: str = 'embedding', + metadata_columns: list[Column] | None = None, + metadata_json_column: str = 'json_metadata', + id_column: str | Column = 'id', + overwrite_existing: bool = False, + store_metadata: bool = True, + ) -> None: + """Create a vector store table. + + Creates a PostgreSQL table with pgvector extension support + for storing document embeddings. + + Args: + table_name: Name of the table to create. + vector_size: Dimension of the embedding vectors. + schema_name: PostgreSQL schema name. + content_column: Column name for document content. + embedding_column: Column name for vector embeddings. + metadata_columns: List of custom metadata columns. + metadata_json_column: Column name for JSON metadata. + id_column: Column name or Column object for IDs. + overwrite_existing: Drop existing table if exists. + store_metadata: Whether to create JSON metadata column. + """ + async with self._engine.begin() as conn: + # Enable pgvector extension + await conn.execute(text('CREATE EXTENSION IF NOT EXISTS vector')) + + # Drop table if overwriting + if overwrite_existing: + await conn.execute(text(f'DROP TABLE IF EXISTS "{schema_name}"."{table_name}"')) + + # Determine ID column configuration + if isinstance(id_column, Column): + id_col_name = id_column.name + id_col_type = id_column.data_type + else: + id_col_name = id_column + id_col_type = 'UUID' + + # Build CREATE TABLE query + query = f'''CREATE TABLE IF NOT EXISTS "{schema_name}"."{table_name}"( + {id_col_name} {id_col_type} PRIMARY KEY, + {content_column} TEXT NOT NULL, + {embedding_column} vector({vector_size}) NOT NULL''' + + # Add custom metadata columns + if metadata_columns: + for col in metadata_columns: + nullable = '' if col.nullable else 'NOT NULL' + query += f',\n {col.name} {col.data_type} {nullable}' + + # Add JSON metadata column + if store_metadata: + query += f',\n {metadata_json_column} JSON' + + query += '\n);' + + await conn.execute(text(query)) + + async def close(self) -> None: + """Close the database connection and cleanup resources.""" + await self._engine.dispose() + if PostgresEngine._connector is not None: + await PostgresEngine._connector.close_async() + PostgresEngine._connector = None + + # Alias for JS API parity + async def close_connection(self) -> None: + """Close the database connection and cleanup resources. + + This is an alias for `close()` to match the JS API. + """ + await self.close() + + async def test_connection(self) -> bool: + """Test the database connection. + + Returns: + True if connection is successful. + """ + async with self._engine.connect() as conn: + result = await conn.execute(text('SELECT NOW()')) + result.fetchone() + return True + + async def apply_vector_index( + self, + table_name: str, + index: BaseIndex, + *, + schema_name: str = 'public', + embedding_column: str = 'embedding', + concurrently: bool = False, + ) -> None: + """Create a vector index on the table. + + Args: + table_name: Name of the table. + index: Index configuration (HNSWIndex, IVFFlatIndex, etc.). + schema_name: PostgreSQL schema name. + embedding_column: Column containing embeddings. + concurrently: Build index concurrently (allows concurrent writes). + """ + if isinstance(index, ExactNearestNeighbor): + # Drop any existing index for exact search + await self.drop_vector_index(table_name=table_name, schema_name=schema_name) + return + + index_name = index.name or f'{table_name}{DEFAULT_INDEX_NAME_SUFFIX}' + filter_clause = f'WHERE ({index.partial_indexes})' if index.partial_indexes else '' + index_options = f'WITH {index.index_options()}' + index_function = index.distance_strategy.index_function + + concurrently_clause = 'CONCURRENTLY' if concurrently else '' + + query = f''' + CREATE INDEX {concurrently_clause} {index_name} + ON "{schema_name}"."{table_name}" + USING {index.index_type} ({embedding_column} {index_function}) + {index_options} {filter_clause}; + ''' + + async with self._engine.begin() as conn: + await conn.execute(text(query)) + + async def is_valid_index( + self, + table_name: str, + *, + index_name: str | None = None, + schema_name: str = 'public', + ) -> bool: + """Check if a vector index exists on the table. + + Args: + table_name: Name of the table. + index_name: Optional specific index name to check. + schema_name: PostgreSQL schema name. + + Returns: + True if the index exists. + """ + idx_name = index_name or f'{table_name}{DEFAULT_INDEX_NAME_SUFFIX}' + + query = text(""" + SELECT tablename, indexname + FROM pg_indexes + WHERE tablename = :table_name + AND schemaname = :schema_name + AND indexname = :index_name + """) + + async with self._engine.connect() as conn: + result = await conn.execute( + query, + {'table_name': table_name, 'schema_name': schema_name, 'index_name': idx_name}, + ) + rows = result.fetchall() + return len(rows) == 1 + + async def drop_vector_index( + self, + table_name: str | None = None, + index_name: str | None = None, + schema_name: str = 'public', + ) -> None: + """Drop a vector index. + + Args: + table_name: Table name (used to derive default index name). + index_name: Specific index name to drop. + schema_name: PostgreSQL schema name. + + Raises: + ValueError: If neither table_name nor index_name is provided. + """ + if index_name: + idx_name = index_name + elif table_name: + idx_name = f'{table_name}{DEFAULT_INDEX_NAME_SUFFIX}' + else: + raise ValueError('Either table_name or index_name must be provided') + + async with self._engine.begin() as conn: + await conn.execute(text(f'DROP INDEX IF EXISTS "{schema_name}"."{idx_name}"')) + + async def reindex( + self, + table_name: str | None = None, + index_name: str | None = None, + ) -> None: + """Rebuild a vector index. + + Args: + table_name: Table name (used to derive default index name). + index_name: Specific index name to rebuild. + + Raises: + ValueError: If neither table_name nor index_name is provided. + """ + if index_name: + idx_name = index_name + elif table_name: + idx_name = f'{table_name}{DEFAULT_INDEX_NAME_SUFFIX}' + else: + raise ValueError('Either table_name or index_name must be provided') + + async with self._engine.begin() as conn: + await conn.execute(text(f'REINDEX INDEX {idx_name}')) diff --git a/py/plugins/cloud-sql-pg/src/genkit/plugins/cloud_sql_pg/indexes.py b/py/plugins/cloud-sql-pg/src/genkit/plugins/cloud_sql_pg/indexes.py new file mode 100644 index 0000000000..2caee5b182 --- /dev/null +++ b/py/plugins/cloud-sql-pg/src/genkit/plugins/cloud_sql_pg/indexes.py @@ -0,0 +1,338 @@ +# Copyright 2025 Google LLC +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. +# +# SPDX-License-Identifier: Apache-2.0 + +"""Vector index types and distance strategies for Cloud SQL PostgreSQL. + +This module provides distance strategy enums and vector index classes +for efficient similarity search with pgvector. + +Key Components +============== + +┌───────────────────────────────────────────────────────────────────────────┐ +│ Index Components │ +├───────────────────────┬───────────────────────────────────────────────────┤ +│ Component │ Purpose │ +├───────────────────────┼───────────────────────────────────────────────────┤ +│ DistanceStrategy │ Enum for vector distance metrics │ +│ BaseIndex │ Abstract base class for vector indexes │ +│ ExactNearestNeighbor │ Brute-force exact search (no index) │ +│ HNSWIndex │ Hierarchical Navigable Small World index │ +│ IVFFlatIndex │ Inverted File Index with flat quantization │ +│ QueryOptions │ Base class for index-specific query options │ +│ HNSWQueryOptions │ Query options for HNSW index │ +│ IVFFlatQueryOptions │ Query options for IVFFlat index │ +└───────────────────────┴───────────────────────────────────────────────────┘ + +See Also: + - JS Implementation: js/plugins/cloud-sql-pg/src/indexes.ts +""" + +from abc import ABC, abstractmethod +from dataclasses import dataclass +from enum import Enum + +DEFAULT_INDEX_NAME_SUFFIX = 'genkitvectorindex' + + +class DistanceStrategy(Enum): + """Distance strategies for vector similarity search. + + Each strategy has an operator for ORDER BY, a function for computing + distance, and an index function for creating vector indexes. + + Attributes: + EUCLIDEAN: L2 distance - smaller is more similar. + COSINE_DISTANCE: Cosine distance - smaller is more similar. + INNER_PRODUCT: Inner product - larger is more similar (negated for ORDER BY). + """ + + EUCLIDEAN = ('<->', 'l2_distance', 'vector_l2_ops') + COSINE_DISTANCE = ('<=>', 'cosine_distance', 'vector_cosine_ops') + INNER_PRODUCT = ('<#>', 'inner_product', 'vector_ip_ops') + + def __init__(self, operator: str, search_function: str, index_function: str) -> None: + """Initialize the distance strategy. + + Args: + operator: SQL operator for ORDER BY (e.g., '<=>'). + search_function: SQL function name (e.g., 'cosine_distance'). + index_function: Index ops class (e.g., 'vector_cosine_ops'). + """ + self._operator = operator + self._search_function = search_function + self._index_function = index_function + + @property + def operator(self) -> str: + """Get the SQL operator for ORDER BY.""" + return self._operator + + @property + def search_function(self) -> str: + """Get the SQL function name for computing distance.""" + return self._search_function + + @property + def index_function(self) -> str: + """Get the index ops class for creating vector indexes.""" + return self._index_function + + +# Default distance strategy +DEFAULT_DISTANCE_STRATEGY = DistanceStrategy.COSINE_DISTANCE + + +@dataclass +class BaseIndexArgs: + """Base arguments for configuring a vector index. + + Attributes: + name: Optional name for the index. + distance_strategy: Distance strategy to use. + partial_indexes: Optional WHERE clause for partial index. + """ + + name: str | None = None + distance_strategy: DistanceStrategy = DEFAULT_DISTANCE_STRATEGY + partial_indexes: str | None = None + + +class BaseIndex(ABC): + """Abstract base class for vector indexes. + + Subclasses must implement the `index_options` method to provide + index-specific configuration options. + """ + + def __init__( + self, + name: str | None = None, + index_type: str = 'base', + distance_strategy: DistanceStrategy = DEFAULT_DISTANCE_STRATEGY, + partial_indexes: str | None = None, + ) -> None: + """Initialize the base index. + + Args: + name: Optional name for the index. + index_type: Type of the index (e.g., 'hnsw', 'ivfflat'). + distance_strategy: Distance strategy to use. + partial_indexes: Optional WHERE clause for partial index. + """ + self.name = name + self.index_type = index_type + self.distance_strategy = distance_strategy + self.partial_indexes = partial_indexes + + @abstractmethod + def index_options(self) -> str: + """Get index-specific options as SQL string. + + Returns: + SQL string for WITH clause (e.g., '(m = 16, ef_construction = 64)'). + """ + ... + + +class ExactNearestNeighbor(BaseIndex): + """Exact nearest neighbor search (no index). + + This performs brute-force search over all vectors. + Use for small datasets or when exact results are required. + """ + + def __init__( + self, + name: str | None = None, + distance_strategy: DistanceStrategy = DEFAULT_DISTANCE_STRATEGY, + partial_indexes: str | None = None, + ) -> None: + """Initialize exact nearest neighbor search. + + Args: + name: Optional name (unused for exact search). + distance_strategy: Distance strategy to use. + partial_indexes: Optional WHERE clause (unused for exact search). + """ + super().__init__( + name=name, + index_type='exactnearestneighbor', + distance_strategy=distance_strategy, + partial_indexes=partial_indexes, + ) + + def index_options(self) -> str: + """Exact search has no index options. + + Raises: + NotImplementedError: Exact search does not use indexes. + """ + raise NotImplementedError('ExactNearestNeighbor does not use indexes') + + +class HNSWIndex(BaseIndex): + """Hierarchical Navigable Small World (HNSW) index. + + HNSW is an approximate nearest neighbor algorithm that provides + fast query performance with good recall. Best for large datasets + where some approximation is acceptable. + + Attributes: + m: Maximum number of connections per node (default: 16). + ef_construction: Size of dynamic candidate list during construction (default: 64). + """ + + def __init__( + self, + name: str | None = None, + distance_strategy: DistanceStrategy = DEFAULT_DISTANCE_STRATEGY, + partial_indexes: str | None = None, + m: int = 16, + ef_construction: int = 64, + ) -> None: + """Initialize HNSW index. + + Args: + name: Optional name for the index. + distance_strategy: Distance strategy to use. + partial_indexes: Optional WHERE clause for partial index. + m: Maximum connections per node (higher = more accurate but slower). + ef_construction: Construction-time candidate list size (higher = better quality). + """ + super().__init__( + name=name, + index_type='hnsw', + distance_strategy=distance_strategy, + partial_indexes=partial_indexes, + ) + self.m = m + self.ef_construction = ef_construction + + def index_options(self) -> str: + """Get HNSW index options. + + Returns: + SQL string for WITH clause. + """ + return f'(m = {self.m}, ef_construction = {self.ef_construction})' + + +class IVFFlatIndex(BaseIndex): + """Inverted File Index with flat quantization (IVFFlat). + + IVFFlat divides vectors into lists and searches a subset of lists. + Best for large datasets when you need faster index creation than HNSW. + + Attributes: + lists: Number of lists/clusters (default: 100). + """ + + def __init__( + self, + name: str | None = None, + distance_strategy: DistanceStrategy = DEFAULT_DISTANCE_STRATEGY, + partial_indexes: str | None = None, + lists: int = 100, + ) -> None: + """Initialize IVFFlat index. + + Args: + name: Optional name for the index. + distance_strategy: Distance strategy to use. + partial_indexes: Optional WHERE clause for partial index. + lists: Number of lists to create (more lists = faster search, less recall). + """ + super().__init__( + name=name, + index_type='ivfflat', + distance_strategy=distance_strategy, + partial_indexes=partial_indexes, + ) + self.lists = lists + + def index_options(self) -> str: + """Get IVFFlat index options. + + Returns: + SQL string for WITH clause. + """ + return f'(lists = {self.lists})' + + +class QueryOptions(ABC): + """Abstract base class for index-specific query options. + + Subclasses must implement the `to_string` method to provide + SET LOCAL statements for query optimization. + """ + + @abstractmethod + def to_string(self) -> str: + """Convert options to SQL SET LOCAL statement. + + Returns: + SQL string for SET LOCAL (e.g., 'hnsw.ef_search = 40'). + """ + ... + + +class HNSWQueryOptions(QueryOptions): + """Query options for HNSW index. + + Attributes: + ef_search: Size of dynamic candidate list during search (default: 40). + """ + + def __init__(self, ef_search: int = 40) -> None: + """Initialize HNSW query options. + + Args: + ef_search: Search-time candidate list size (higher = better recall, slower). + """ + self.ef_search = ef_search + + def to_string(self) -> str: + """Get SET LOCAL statement for HNSW search. + + Returns: + SQL SET LOCAL statement. + """ + return f'hnsw.ef_search = {self.ef_search}' + + +class IVFFlatQueryOptions(QueryOptions): + """Query options for IVFFlat index. + + Attributes: + probes: Number of lists to search (default: 1). + """ + + def __init__(self, probes: int = 1) -> None: + """Initialize IVFFlat query options. + + Args: + probes: Number of lists to search (higher = better recall, slower). + """ + self.probes = probes + + def to_string(self) -> str: + """Get SET LOCAL statement for IVFFlat search. + + Returns: + SQL SET LOCAL statement. + """ + return f'ivfflat.probes = {self.probes}' diff --git a/py/plugins/cloud-sql-pg/src/genkit/plugins/cloud_sql_pg/plugin.py b/py/plugins/cloud-sql-pg/src/genkit/plugins/cloud_sql_pg/plugin.py new file mode 100644 index 0000000000..4b38f68189 --- /dev/null +++ b/py/plugins/cloud-sql-pg/src/genkit/plugins/cloud_sql_pg/plugin.py @@ -0,0 +1,819 @@ +# Copyright 2025 Google LLC +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. +# +# SPDX-License-Identifier: Apache-2.0 + +"""Cloud SQL PostgreSQL plugin implementation for Genkit. + +This module provides the core plugin functionality for integrating Cloud SQL +PostgreSQL with Genkit applications, including retriever and indexer implementations. + +Key Components +============== + +┌───────────────────────────────────────────────────────────────────────────┐ +│ Plugin Components │ +├───────────────────────┬───────────────────────────────────────────────────┤ +│ Component │ Purpose │ +├───────────────────────┼───────────────────────────────────────────────────┤ +│ CloudSqlPg │ Main plugin class - registers retrievers/indexers │ +│ PostgresRetriever │ Similarity search against PostgreSQL tables │ +│ PostgresIndexer │ Store documents with embeddings in PostgreSQL │ +│ postgres_retriever_ref│ Create retriever reference by table name │ +│ postgres_indexer_ref │ Create indexer reference by table name │ +└───────────────────────┴───────────────────────────────────────────────────┘ + +See Also: + - JS Implementation: js/plugins/cloud-sql-pg/src/index.ts +""" + +from __future__ import annotations + +import json +import uuid +from dataclasses import dataclass, field +from typing import Any, cast + +import structlog +from pydantic import BaseModel, Field +from sqlalchemy import text + +from genkit.blocks.document import Document +from genkit.blocks.retriever import ( + IndexerOptions, + IndexerRequest, + RetrieverOptions, + RetrieverRequest, + RetrieverResponse, + indexer_action_metadata, + retriever_action_metadata, +) +from genkit.core.action import Action, ActionMetadata, ActionRunContext +from genkit.core.plugin import Plugin +from genkit.core.registry import ActionKind, Registry +from genkit.core.schema import to_json_schema +from genkit.types import DocumentData, EmbedRequest + +from .engine import PostgresEngine +from .indexes import DEFAULT_DISTANCE_STRATEGY, DistanceStrategy, QueryOptions + +logger = structlog.get_logger(__name__) + +POSTGRES_PLUGIN_NAME = 'postgres' +MAX_K = 1000 +DEFAULT_BATCH_SIZE = 100 + + +class PostgresRetrieverOptions(BaseModel): + """Options for PostgreSQL retriever queries. + + Attributes: + k: Number of results to return (default: 4, max: 1000). + filter: SQL WHERE clause for filtering results. + """ + + k: int = Field(default=4, le=MAX_K, description='Number of results to return') + filter: str | None = Field(default=None, description='SQL WHERE clause filter') + + +class PostgresIndexerOptions(BaseModel): + """Options for PostgreSQL indexer operations. + + Attributes: + batch_size: Number of documents to process per batch (default: 100). + """ + + batch_size: int = Field(default=DEFAULT_BATCH_SIZE, description='Batch size for processing') + + +@dataclass +class PostgresTableConfig: + """Configuration for a PostgreSQL vector store table. + + Attributes: + table_name: Name of the PostgreSQL table. + engine: PostgresEngine instance for database operations. + embedder: Genkit embedder reference (e.g., 'googleai/text-embedding-004'). + embedder_options: Optional embedder-specific configuration. + schema_name: PostgreSQL schema name (default: 'public'). + content_column: Column name for document content (default: 'content'). + embedding_column: Column name for vector embeddings (default: 'embedding'). + id_column: Column name for document IDs (default: 'id'). + metadata_columns: List of specific metadata column names to use. + ignore_metadata_columns: List of metadata columns to ignore. + metadata_json_column: Column name for JSON metadata (default: 'metadata'). + distance_strategy: Vector distance strategy (default: COSINE_DISTANCE). + index_query_options: Optional index-specific query options. + """ + + table_name: str + engine: PostgresEngine + embedder: str + embedder_options: dict[str, Any] | None = None + schema_name: str = 'public' + content_column: str = 'content' + embedding_column: str = 'embedding' + id_column: str = 'id' + metadata_columns: list[str] | None = None + ignore_metadata_columns: list[str] | None = None + metadata_json_column: str = 'metadata' + distance_strategy: DistanceStrategy = field(default_factory=lambda: DEFAULT_DISTANCE_STRATEGY) + index_query_options: QueryOptions | None = None + + +class PostgresRetriever: + """PostgreSQL retriever implementation. + + Performs similarity search against a PostgreSQL table using + pgvector and embeddings generated by a Genkit embedder. + """ + + def __init__( + self, + registry: Registry, + config: PostgresTableConfig, + ) -> None: + """Initialize the PostgreSQL retriever. + + Args: + registry: Registry for resolving embedders. + config: Table configuration. + """ + self._registry = registry + self._config = config + self._columns_checked = False + + async def _check_columns(self) -> None: + """Validate that required columns exist in the table.""" + if self._columns_checked: + return + + config = self._config + + if config.metadata_columns is not None and config.ignore_metadata_columns is not None: + raise ValueError('Cannot use both metadata_columns and ignore_metadata_columns') + + async with config.engine.engine.connect() as conn: + result = await conn.execute( + text(""" + SELECT column_name, data_type + FROM information_schema.columns + WHERE table_name = :table_name AND table_schema = :schema_name + """), + {'table_name': config.table_name, 'schema_name': config.schema_name}, + ) + rows = result.fetchall() + + columns: dict[str, str] = {} + for row in rows: + columns[row[0]] = row[1] + + # Validate ID column + if config.id_column and config.id_column not in columns: + raise ValueError(f'Id column: {config.id_column}, does not exist.') + + # Validate content column + if config.content_column not in columns: + raise ValueError(f'Content column: {config.content_column}, does not exist.') + + content_type = columns.get(config.content_column, '') + if content_type != 'text' and 'char' not in content_type: + raise ValueError( + f'Content column: {config.content_column}, is type: {content_type}. ' + 'It must be a type of character string.' + ) + + # Validate embedding column + if config.embedding_column not in columns: + raise ValueError(f'Embedding column: {config.embedding_column}, does not exist.') + + # pgvector types show as USER-DEFINED + if columns.get(config.embedding_column) != 'USER-DEFINED': + raise ValueError(f'Embedding column: {config.embedding_column} is not of type Vector.') + + # Check if metadata JSON column exists (match JS behavior) + metadata_json_column_to_check = config.metadata_json_column or '' + if metadata_json_column_to_check and metadata_json_column_to_check not in columns: + # If it doesn't exist, clear it (match JS: params.metadataJsonColumn = '') + config.metadata_json_column = '' + + # Validate metadata columns + if config.metadata_columns: + for col in config.metadata_columns: + if col and col not in columns: + raise ValueError(f'Metadata column: {col}, does not exist.') + + # Auto-populate metadata_columns from ignore_metadata_columns (match JS behavior) + if config.ignore_metadata_columns is not None and len(config.ignore_metadata_columns) > 0: + all_columns = dict(columns) + # Remove ignored columns + for col in config.ignore_metadata_columns: + all_columns.pop(col, None) + # Remove system columns + if config.id_column: + all_columns.pop(config.id_column, None) + all_columns.pop(config.content_column, None) + all_columns.pop(config.embedding_column, None) + # Set metadata_columns to remaining columns + config.metadata_columns = list(all_columns.keys()) + + self._columns_checked = True + + async def _embed_content(self, content: list[Document]) -> list[list[float]]: + """Generate embeddings using the registry-resolved embedder.""" + embedder_action = await self._registry.resolve_embedder(self._config.embedder) + if embedder_action is None: + raise ValueError(f'Embedder "{self._config.embedder}" not found') + + request = EmbedRequest(input=cast(list[DocumentData], content), options=self._config.embedder_options) + response = await embedder_action.arun(request) + return [e.embedding for e in response.response.embeddings] + + async def retrieve( + self, + request: RetrieverRequest, + _ctx: ActionRunContext, + ) -> RetrieverResponse: + """Retrieve documents similar to the query. + + Args: + request: Retriever request with query and options. + _ctx: Action run context (unused). + + Returns: + Response containing matching documents. + """ + await self._check_columns() + + config = self._config + + # Generate query embedding + query_doc = Document.from_document_data(document_data=request.query) + embeddings = await self._embed_content([query_doc]) + if not embeddings: + raise ValueError('Embedder returned no embeddings for query') + + query_embedding = embeddings[0] + + # Parse options - JS default k is 4 + k = 4 + filter_clause = '' + + if request.options: + opts = request.options if isinstance(request.options, dict) else request.options.model_dump() + k = min(opts.get('k', 4), MAX_K) + if opts.get('filter'): + filter_clause = f'WHERE {opts["filter"]}' + + # Build metadata column selection + metadata_col_names = '' + if config.metadata_columns and len(config.metadata_columns) > 0: + metadata_col_names = ', ' + ', '.join(f'"{col}"' for col in config.metadata_columns) + + metadata_json_col = '' + if config.metadata_json_column: + metadata_json_col = f', "{config.metadata_json_column}"' + + # Build query + operator = config.distance_strategy.operator + search_function = config.distance_strategy.search_function + embedding_str = '[' + ','.join(str(x) for x in query_embedding) + ']' + + query = f''' + SELECT "{config.id_column}", "{config.content_column}", "{config.embedding_column}" + {metadata_col_names} {metadata_json_col}, + {search_function}("{config.embedding_column}", '{embedding_str}') as distance + FROM "{config.schema_name}"."{config.table_name}" + {filter_clause} + ORDER BY "{config.embedding_column}" {operator} '{embedding_str}' + LIMIT {k} + ''' + + async with config.engine.engine.connect() as conn: + # Set index query options if provided + if config.index_query_options: + await conn.execute(text(f'SET LOCAL {config.index_query_options.to_string()}')) + + result = await conn.execute(text(query)) + rows = result.fetchall() + + # Convert results to Documents + documents: list[DocumentData] = [] + for row in rows: + # Get column values by position + row_dict = dict(row._mapping) + + # Build metadata + metadata: dict[str, Any] = {} + + # Add JSON metadata if exists + if config.metadata_json_column and config.metadata_json_column in row_dict: + json_meta = row_dict.get(config.metadata_json_column) + if json_meta: + if isinstance(json_meta, str): + try: + metadata = json.loads(json_meta) + except json.JSONDecodeError as e: + logger.warning('Failed to parse document metadata', error=str(e)) + elif isinstance(json_meta, dict): + metadata = dict(json_meta) + + # Add individual metadata columns + if config.metadata_columns: + for col in config.metadata_columns: + if col in row_dict: + metadata[col] = row_dict[col] + + # Add distance to metadata + metadata['_distance'] = row_dict.get('distance') + + # Create document + content = row_dict.get(config.content_column, '') + doc = Document.from_data( + data=content, + data_type='text', + metadata=metadata, + ) + documents.append(doc) + + return RetrieverResponse(documents=documents) + + +class PostgresIndexer: + """PostgreSQL indexer implementation. + + Stores documents with their embeddings in a PostgreSQL table. + """ + + def __init__( + self, + registry: Registry, + config: PostgresTableConfig, + ) -> None: + """Initialize the PostgreSQL indexer. + + Args: + registry: Registry for resolving embedders. + config: Table configuration. + """ + self._registry = registry + self._config = config + self._columns_checked = False + + async def _check_columns(self) -> None: + """Validate that required columns exist in the table.""" + if self._columns_checked: + return + + config = self._config + + if config.metadata_columns is not None and config.ignore_metadata_columns is not None: + raise ValueError('Cannot use both metadata_columns and ignore_metadata_columns') + + async with config.engine.engine.connect() as conn: + result = await conn.execute( + text(""" + SELECT column_name, data_type + FROM information_schema.columns + WHERE table_name = :table_name AND table_schema = :schema_name + """), + {'table_name': config.table_name, 'schema_name': config.schema_name}, + ) + rows = result.fetchall() + + columns: dict[str, str] = {} + for row in rows: + columns[row[0]] = row[1] + + # Validate required columns + if config.id_column not in columns: + raise ValueError(f'Id column: {config.id_column}, does not exist.') + + if config.content_column not in columns: + raise ValueError(f'Content column: {config.content_column}, does not exist.') + + if config.embedding_column not in columns: + raise ValueError(f'Embedding column: {config.embedding_column}, does not exist.') + + if columns.get(config.embedding_column) != 'USER-DEFINED': + raise ValueError(f'Embedding column: {config.embedding_column} is not of type Vector.') + + if config.metadata_columns: + for col in config.metadata_columns: + if col and col not in columns: + raise ValueError(f'Metadata column: {col}, does not exist.') + + self._columns_checked = True + + async def _embed_content(self, content: list[Document]) -> list[list[float]]: + """Generate embeddings using the registry-resolved embedder.""" + embedder_action = await self._registry.resolve_embedder(self._config.embedder) + if embedder_action is None: + raise ValueError(f'Embedder "{self._config.embedder}" not found') + + request = EmbedRequest(input=cast(list[DocumentData], content), options=self._config.embedder_options) + response = await embedder_action.arun(request) + return [e.embedding for e in response.response.embeddings] + + async def index(self, request: IndexerRequest) -> None: + """Index documents into the PostgreSQL table. + + Args: + request: Indexer request containing documents to index. + """ + if not request.documents: + return + + await self._check_columns() + + config = self._config + + # Parse options + batch_size = DEFAULT_BATCH_SIZE + if request.options: + opts = request.options if isinstance(request.options, dict) else request.options.model_dump() + batch_size = opts.get('batch_size', DEFAULT_BATCH_SIZE) + + # Convert to Document objects + docs = [Document.from_document_data(doc) for doc in request.documents] + + # Process in batches + for i in range(0, len(docs), batch_size): + chunk = docs[i : i + batch_size] + + # Get text content for each document + texts = [] + for doc in chunk: + if isinstance(doc.content, list): + # Join multiple parts + text_parts = [] + for part in doc.content: + if hasattr(part, 'text'): + text_parts.append(part.text or '') + texts.append(' '.join(text_parts)) + else: + texts.append(str(doc.content) if doc.content else '') + + # Generate embeddings + try: + embeddings = await self._embed_content(chunk) + except Exception as e: + raise ValueError('Embedding failed') from e + + # Prepare insert data + insert_data = [] + for doc, text_content, embedding in zip(chunk, texts, embeddings, strict=True): + # Generate ID from metadata or create new UUID + doc_id = doc.metadata.get(config.id_column) if doc.metadata else None + if not doc_id: + doc_id = str(uuid.uuid4()) + + # Build row data + row_data: dict[str, Any] = { + config.id_column: doc_id, + config.content_column: text_content, + config.embedding_column: '[' + ','.join(str(x) for x in embedding) + ']', + } + + # Add JSON metadata + if config.metadata_json_column: + row_data[config.metadata_json_column] = json.dumps(doc.metadata or {}) + + # Add individual metadata columns + if config.metadata_columns and doc.metadata: + for col in config.metadata_columns: + if col in doc.metadata: + row_data[col] = doc.metadata[col] + + insert_data.append(row_data) + + # Build and execute INSERT query + if insert_data: + columns = list(insert_data[0].keys()) + col_names = ', '.join(f'"{col}"' for col in columns) + + # Build VALUES clause with parameterized values + values_clauses = [] + params: dict[str, Any] = {} + for idx, row in enumerate(insert_data): + placeholders = [] + for col in columns: + param_name = f'{col}_{idx}' + placeholders.append(f':{param_name}') + params[param_name] = row[col] + values_clauses.append(f'({", ".join(placeholders)})') + + query = f''' + INSERT INTO "{config.schema_name}"."{config.table_name}" ({col_names}) + VALUES {', '.join(values_clauses)} + ''' + + async with config.engine.engine.begin() as conn: + await conn.execute(text(query), params) + + +class CloudSqlPg(Plugin): + """Cloud SQL PostgreSQL vector store plugin for Genkit. + + This plugin registers retrievers and indexers for PostgreSQL tables + with pgvector support, enabling RAG (Retrieval-Augmented Generation) workflows. + + Example: + ```python + engine = await PostgresEngine.from_instance( + project_id='my-project', + region='us-central1', + instance='my-instance', + database='my-database', + ) + + ai = Genkit( + plugins=[ + CloudSqlPg( + tables=[ + PostgresTableConfig( + table_name='documents', + engine=engine, + embedder='googleai/text-embedding-004', + ) + ] + ) + ] + ) + ``` + """ + + name = POSTGRES_PLUGIN_NAME + + def __init__( + self, + tables: list[PostgresTableConfig] | None = None, + ) -> None: + """Initialize the Cloud SQL PostgreSQL plugin. + + Args: + tables: List of table configurations. + """ + self._tables = tables or [] + self._registry: Registry | None = None + self._actions: dict[str, Action] = {} + + async def init(self, registry: Registry | None = None) -> list[Action]: + """Initialize plugin (lazy warm-up). + + Args: + registry: Registry for action registration and embedder resolution. + + Returns: + List of pre-registered actions. + """ + self._registry = registry + if registry is not None: + for config in self._tables: + self._register_table(registry, config) + return list(self._actions.values()) + + async def resolve(self, action_type: ActionKind, name: str) -> Action | None: + """Resolve an action by name. + + Args: + action_type: The kind of action to resolve. + name: The namespaced name of the action. + + Returns: + Action object if found, None otherwise. + """ + if action_type not in (ActionKind.RETRIEVER, ActionKind.INDEXER): + return None + action_key = f'{action_type.value}/{name}' + return self._actions.get(action_key) + + async def list_actions(self) -> list[ActionMetadata]: + """List available actions for dev UI. + + Returns: + List of action metadata. + """ + metadata_list: list[ActionMetadata] = [] + for config in self._tables: + name = f'{POSTGRES_PLUGIN_NAME}/{config.table_name}' + metadata_list.append( + ActionMetadata( + kind=ActionKind.RETRIEVER, + name=name, + ) + ) + metadata_list.append( + ActionMetadata( + kind=ActionKind.INDEXER, + name=name, + ) + ) + return metadata_list + + def _register_table( + self, + registry: Registry, + config: PostgresTableConfig, + ) -> None: + """Register retriever and indexer for a table. + + Args: + registry: Action registry. + config: Table configuration. + """ + name = f'{POSTGRES_PLUGIN_NAME}/{config.table_name}' + + # Create and register retriever + retriever = PostgresRetriever(registry=registry, config=config) + + retriever_action = registry.register_action( + kind=ActionKind.RETRIEVER, + name=name, + fn=retriever.retrieve, + metadata=retriever_action_metadata( + name=name, + options=RetrieverOptions( + label=f'Postgres - {config.table_name}', + config_schema=to_json_schema(PostgresRetrieverOptions), + ), + ).metadata, + ) + if retriever_action: + self._actions[f'{ActionKind.RETRIEVER.value}/{name}'] = retriever_action + + # Create and register indexer + indexer = PostgresIndexer(registry=registry, config=config) + + indexer_action = registry.register_action( + kind=ActionKind.INDEXER, + name=name, + fn=indexer.index, + metadata=indexer_action_metadata( + name=name, + options=IndexerOptions(label=f'Postgres - {config.table_name}'), + ).metadata, + ) + if indexer_action: + self._actions[f'{ActionKind.INDEXER.value}/{name}'] = indexer_action + + +def postgres( + tables: list[dict[str, Any]] | None = None, +) -> CloudSqlPg: + """Create a Cloud SQL PostgreSQL plugin with the given configuration. + + This is a convenience function for creating a CloudSqlPg plugin instance. + + Args: + tables: List of table configuration dictionaries. + + Returns: + Configured CloudSqlPg plugin instance. + + Example: + ```python + ai = Genkit( + plugins=[ + postgres( + tables=[ + { + 'table_name': 'documents', + 'engine': engine, + 'embedder': 'googleai/text-embedding-004', + } + ] + ) + ] + ) + ``` + """ + configs = [] + if tables: + for t in tables: + configs.append( + PostgresTableConfig( + table_name=t['table_name'], + engine=t['engine'], + embedder=t['embedder'], + embedder_options=t.get('embedder_options'), + schema_name=t.get('schema_name', 'public'), + content_column=t.get('content_column', 'content'), + embedding_column=t.get('embedding_column', 'embedding'), + id_column=t.get('id_column', 'id'), + metadata_columns=t.get('metadata_columns'), + ignore_metadata_columns=t.get('ignore_metadata_columns'), + metadata_json_column=t.get('metadata_json_column', 'metadata'), + distance_strategy=t.get('distance_strategy', DEFAULT_DISTANCE_STRATEGY), + index_query_options=t.get('index_query_options'), + ) + ) + return CloudSqlPg(tables=configs) + + +def postgres_retriever_ref( + table_name: str, + display_name: str | None = None, +) -> str: + """Create a retriever reference for a PostgreSQL table. + + Args: + table_name: Name of the PostgreSQL table. + display_name: Optional display name (unused, for parity with JS). + + Returns: + Retriever reference string. + """ + return f'{POSTGRES_PLUGIN_NAME}/{table_name}' + + +def postgres_indexer_ref( + table_name: str, + display_name: str | None = None, +) -> str: + """Create an indexer reference for a PostgreSQL table. + + Args: + table_name: Name of the PostgreSQL table. + display_name: Optional display name (unused, for parity with JS). + + Returns: + Indexer reference string. + """ + return f'{POSTGRES_PLUGIN_NAME}/{table_name}' + + +def configure_postgres_retriever( + ai: Any, # noqa: ANN401 + config: PostgresTableConfig, +) -> Action | None: + """Configure a PostgreSQL retriever outside of the plugin. + + This function allows configuring retrievers independently of the plugin, + matching the JS API. + + Args: + ai: Genkit instance (uses internal _registry attribute). + config: Table configuration. + + Returns: + Configured retriever action. + """ + registry = ai._registry # noqa: SLF001 + retriever = PostgresRetriever(registry=registry, config=config) + name = f'{POSTGRES_PLUGIN_NAME}/{config.table_name}' + + return registry.register_action( + kind=ActionKind.RETRIEVER, + name=name, + fn=retriever.retrieve, + metadata=retriever_action_metadata( + name=name, + options=RetrieverOptions( + label=f'Postgres - {config.table_name}', + config_schema=to_json_schema(PostgresRetrieverOptions), + ), + ).metadata, + ) + + +def configure_postgres_indexer( + ai: Any, # noqa: ANN401 + config: PostgresTableConfig, +) -> Action | None: + """Configure a PostgreSQL indexer outside of the plugin. + + This function allows configuring indexers independently of the plugin, + matching the JS API. + + Args: + ai: Genkit instance (uses internal _registry attribute). + config: Table configuration. + + Returns: + Configured indexer action. + """ + registry = ai._registry # noqa: SLF001 + indexer = PostgresIndexer(registry=registry, config=config) + name = f'{POSTGRES_PLUGIN_NAME}/{config.table_name}' + + return registry.register_action( + kind=ActionKind.INDEXER, + name=name, + fn=indexer.index, + metadata=indexer_action_metadata( + name=name, + options=IndexerOptions(label=f'Postgres - {config.table_name}'), + ).metadata, + ) diff --git a/py/plugins/cloud-sql-pg/src/genkit/py.typed b/py/plugins/cloud-sql-pg/src/genkit/py.typed new file mode 100644 index 0000000000..e69de29bb2 diff --git a/py/plugins/cloud-sql-pg/tests/cloud_sql_pg_engine_test.py b/py/plugins/cloud-sql-pg/tests/cloud_sql_pg_engine_test.py new file mode 100644 index 0000000000..5c3c66a0d3 --- /dev/null +++ b/py/plugins/cloud-sql-pg/tests/cloud_sql_pg_engine_test.py @@ -0,0 +1,115 @@ +# Copyright 2025 Google LLC +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. +# +# SPDX-License-Identifier: Apache-2.0 + +"""Tests for PostgresEngine.""" + +import pytest + +from genkit.plugins.cloud_sql_pg import Column, IpAddressTypes + + +class TestColumn: + """Tests for Column class.""" + + def test_default_initialization(self) -> None: + """Test default initialization.""" + col = Column(name='test_col', data_type='TEXT') + assert col.name == 'test_col' + assert col.data_type == 'TEXT' + assert col.nullable is True + + def test_non_nullable_column(self) -> None: + """Test non-nullable column.""" + col = Column(name='required_col', data_type='INT', nullable=False) + assert col.name == 'required_col' + assert col.data_type == 'INT' + assert col.nullable is False + + def test_invalid_name_type(self) -> None: + """Test that invalid name type raises error.""" + with pytest.raises(TypeError, match='Column name must be a string'): + Column(name=123, data_type='TEXT') # type: ignore[arg-type] + + def test_invalid_data_type_type(self) -> None: + """Test that invalid data_type type raises error.""" + with pytest.raises(TypeError, match='Column data_type must be a string'): + Column(name='test', data_type=123) # type: ignore[arg-type] + + +class TestIpAddressTypes: + """Tests for IpAddressTypes enum.""" + + def test_public_value(self) -> None: + """Test PUBLIC value.""" + assert IpAddressTypes.PUBLIC.value == 'PUBLIC' + + def test_private_value(self) -> None: + """Test PRIVATE value.""" + assert IpAddressTypes.PRIVATE.value == 'PRIVATE' + + def test_to_ip_types_public(self) -> None: + """Test conversion to Cloud SQL IPTypes for PUBLIC.""" + from google.cloud.sql.connector import IPTypes + + ip_type = IpAddressTypes.PUBLIC.to_ip_types() + assert ip_type == IPTypes.PUBLIC + + def test_to_ip_types_private(self) -> None: + """Test conversion to Cloud SQL IPTypes for PRIVATE.""" + from google.cloud.sql.connector import IPTypes + + ip_type = IpAddressTypes.PRIVATE.to_ip_types() + assert ip_type == IPTypes.PRIVATE + + +class TestPostgresEngineValidation: + """Tests for PostgresEngine validation (without actual DB connection).""" + + @pytest.mark.asyncio + async def test_from_instance_requires_both_user_and_password(self) -> None: + """Test that from_instance requires both user and password or neither.""" + from genkit.plugins.cloud_sql_pg import PostgresEngine + + # Only user provided + with pytest.raises(ValueError, match="Only one of 'user' or 'password'"): + await PostgresEngine.from_instance( + project_id='test-project', + region='us-central1', + instance='test-instance', + database='test-db', + user='test-user', + ) + + # Only password provided + with pytest.raises(ValueError, match="Only one of 'user' or 'password'"): + await PostgresEngine.from_instance( + project_id='test-project', + region='us-central1', + instance='test-instance', + database='test-db', + password='test-password', + ) + + @pytest.mark.asyncio + async def test_from_connection_string_requires_asyncpg(self) -> None: + """Test that from_connection_string requires asyncpg driver.""" + from genkit.plugins.cloud_sql_pg import PostgresEngine + + with pytest.raises(ValueError, match="must start with 'postgresql\\+asyncpg://'"): + await PostgresEngine.from_connection_string('postgresql://user:pass@host/db') + + with pytest.raises(ValueError, match="must start with 'postgresql\\+asyncpg://'"): + await PostgresEngine.from_connection_string('mysql://user:pass@host/db') diff --git a/py/plugins/cloud-sql-pg/tests/cloud_sql_pg_indexes_test.py b/py/plugins/cloud-sql-pg/tests/cloud_sql_pg_indexes_test.py new file mode 100644 index 0000000000..33f94f686b --- /dev/null +++ b/py/plugins/cloud-sql-pg/tests/cloud_sql_pg_indexes_test.py @@ -0,0 +1,192 @@ +# Copyright 2025 Google LLC +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. +# +# SPDX-License-Identifier: Apache-2.0 + +"""Tests for vector index types and distance strategies.""" + +import pytest + +from genkit.plugins.cloud_sql_pg import ( + DEFAULT_DISTANCE_STRATEGY, + DistanceStrategy, + ExactNearestNeighbor, + HNSWIndex, + HNSWQueryOptions, + IVFFlatIndex, + IVFFlatQueryOptions, +) + + +class TestDistanceStrategy: + """Tests for DistanceStrategy enum.""" + + def test_euclidean_properties(self) -> None: + """Test EUCLIDEAN distance strategy properties.""" + strategy = DistanceStrategy.EUCLIDEAN + assert strategy.operator == '<->' + assert strategy.search_function == 'l2_distance' + assert strategy.index_function == 'vector_l2_ops' + + def test_cosine_distance_properties(self) -> None: + """Test COSINE_DISTANCE strategy properties.""" + strategy = DistanceStrategy.COSINE_DISTANCE + assert strategy.operator == '<=>' + assert strategy.search_function == 'cosine_distance' + assert strategy.index_function == 'vector_cosine_ops' + + def test_inner_product_properties(self) -> None: + """Test INNER_PRODUCT strategy properties.""" + strategy = DistanceStrategy.INNER_PRODUCT + assert strategy.operator == '<#>' + assert strategy.search_function == 'inner_product' + assert strategy.index_function == 'vector_ip_ops' + + def test_default_strategy_is_cosine(self) -> None: + """Test that default strategy is COSINE_DISTANCE.""" + assert DEFAULT_DISTANCE_STRATEGY == DistanceStrategy.COSINE_DISTANCE + + +class TestExactNearestNeighbor: + """Tests for ExactNearestNeighbor index.""" + + def test_default_initialization(self) -> None: + """Test default initialization.""" + index = ExactNearestNeighbor() + assert index.index_type == 'exactnearestneighbor' + assert index.distance_strategy == DEFAULT_DISTANCE_STRATEGY + assert index.name is None + assert index.partial_indexes is None + + def test_custom_initialization(self) -> None: + """Test custom initialization.""" + index = ExactNearestNeighbor( + name='my_exact_index', + distance_strategy=DistanceStrategy.EUCLIDEAN, + partial_indexes="category = 'active'", + ) + assert index.name == 'my_exact_index' + assert index.distance_strategy == DistanceStrategy.EUCLIDEAN + assert index.partial_indexes == "category = 'active'" + + def test_index_options_raises(self) -> None: + """Test that index_options raises NotImplementedError.""" + index = ExactNearestNeighbor() + with pytest.raises(NotImplementedError): + index.index_options() + + +class TestHNSWIndex: + """Tests for HNSWIndex.""" + + def test_default_initialization(self) -> None: + """Test default initialization.""" + index = HNSWIndex() + assert index.index_type == 'hnsw' + assert index.m == 16 + assert index.ef_construction == 64 + assert index.distance_strategy == DEFAULT_DISTANCE_STRATEGY + + def test_custom_initialization(self) -> None: + """Test custom initialization.""" + index = HNSWIndex( + name='my_hnsw_index', + distance_strategy=DistanceStrategy.INNER_PRODUCT, + m=32, + ef_construction=128, + ) + assert index.name == 'my_hnsw_index' + assert index.distance_strategy == DistanceStrategy.INNER_PRODUCT + assert index.m == 32 + assert index.ef_construction == 128 + + def test_index_options(self) -> None: + """Test index_options returns correct SQL.""" + index = HNSWIndex(m=24, ef_construction=100) + assert index.index_options() == '(m = 24, ef_construction = 100)' + + def test_index_options_default(self) -> None: + """Test index_options with default values.""" + index = HNSWIndex() + assert index.index_options() == '(m = 16, ef_construction = 64)' + + +class TestIVFFlatIndex: + """Tests for IVFFlatIndex.""" + + def test_default_initialization(self) -> None: + """Test default initialization.""" + index = IVFFlatIndex() + assert index.index_type == 'ivfflat' + assert index.lists == 100 + assert index.distance_strategy == DEFAULT_DISTANCE_STRATEGY + + def test_custom_initialization(self) -> None: + """Test custom initialization.""" + index = IVFFlatIndex( + name='my_ivfflat_index', + distance_strategy=DistanceStrategy.EUCLIDEAN, + lists=200, + ) + assert index.name == 'my_ivfflat_index' + assert index.distance_strategy == DistanceStrategy.EUCLIDEAN + assert index.lists == 200 + + def test_index_options(self) -> None: + """Test index_options returns correct SQL.""" + index = IVFFlatIndex(lists=150) + assert index.index_options() == '(lists = 150)' + + def test_index_options_default(self) -> None: + """Test index_options with default values.""" + index = IVFFlatIndex() + assert index.index_options() == '(lists = 100)' + + +class TestHNSWQueryOptions: + """Tests for HNSWQueryOptions.""" + + def test_default_initialization(self) -> None: + """Test default initialization.""" + options = HNSWQueryOptions() + assert options.ef_search == 40 + + def test_custom_initialization(self) -> None: + """Test custom initialization.""" + options = HNSWQueryOptions(ef_search=100) + assert options.ef_search == 100 + + def test_to_string(self) -> None: + """Test to_string returns correct SQL.""" + options = HNSWQueryOptions(ef_search=50) + assert options.to_string() == 'hnsw.ef_search = 50' + + +class TestIVFFlatQueryOptions: + """Tests for IVFFlatQueryOptions.""" + + def test_default_initialization(self) -> None: + """Test default initialization.""" + options = IVFFlatQueryOptions() + assert options.probes == 1 + + def test_custom_initialization(self) -> None: + """Test custom initialization.""" + options = IVFFlatQueryOptions(probes=10) + assert options.probes == 10 + + def test_to_string(self) -> None: + """Test to_string returns correct SQL.""" + options = IVFFlatQueryOptions(probes=5) + assert options.to_string() == 'ivfflat.probes = 5' diff --git a/py/plugins/cloud-sql-pg/tests/cloud_sql_pg_plugin_test.py b/py/plugins/cloud-sql-pg/tests/cloud_sql_pg_plugin_test.py new file mode 100644 index 0000000000..153c31df4c --- /dev/null +++ b/py/plugins/cloud-sql-pg/tests/cloud_sql_pg_plugin_test.py @@ -0,0 +1,270 @@ +# Copyright 2025 Google LLC +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. +# +# SPDX-License-Identifier: Apache-2.0 + +"""Tests for Cloud SQL PostgreSQL plugin.""" + +from unittest.mock import MagicMock + +import pytest + +from genkit.core.registry import ActionKind +from genkit.plugins.cloud_sql_pg import ( + POSTGRES_PLUGIN_NAME, + CloudSqlPg, + DistanceStrategy, + PostgresIndexerOptions, + PostgresRetrieverOptions, + PostgresTableConfig, + postgres, + postgres_indexer_ref, + postgres_retriever_ref, +) + + +class TestPostgresRetrieverOptions: + """Tests for PostgresRetrieverOptions.""" + + def test_default_values(self) -> None: + """Test default values (k=4 matches JS default).""" + options = PostgresRetrieverOptions() + assert options.k == 4 # Matches JS default + assert options.filter is None + + def test_custom_values(self) -> None: + """Test custom values.""" + options = PostgresRetrieverOptions(k=50, filter="category = 'science'") + assert options.k == 50 + assert options.filter == "category = 'science'" + + def test_k_max_limit(self) -> None: + """Test that k has max limit of 1000.""" + with pytest.raises(ValueError): + PostgresRetrieverOptions(k=1001) + + +class TestPostgresIndexerOptions: + """Tests for PostgresIndexerOptions.""" + + def test_default_values(self) -> None: + """Test default values.""" + options = PostgresIndexerOptions() + assert options.batch_size == 100 + + def test_custom_values(self) -> None: + """Test custom values.""" + options = PostgresIndexerOptions(batch_size=50) + assert options.batch_size == 50 + + +class TestPostgresTableConfig: + """Tests for PostgresTableConfig.""" + + def test_required_fields(self) -> None: + """Test required fields.""" + engine = MagicMock() + config = PostgresTableConfig( + table_name='documents', + engine=engine, + embedder='googleai/text-embedding-004', + ) + assert config.table_name == 'documents' + assert config.engine is engine + assert config.embedder == 'googleai/text-embedding-004' + + def test_default_values(self) -> None: + """Test default values.""" + engine = MagicMock() + config = PostgresTableConfig( + table_name='documents', + engine=engine, + embedder='googleai/text-embedding-004', + ) + assert config.schema_name == 'public' + assert config.content_column == 'content' + assert config.embedding_column == 'embedding' + assert config.id_column == 'id' + assert config.metadata_columns is None + assert config.ignore_metadata_columns is None + assert config.metadata_json_column == 'metadata' + assert config.distance_strategy == DistanceStrategy.COSINE_DISTANCE + assert config.index_query_options is None + + def test_custom_values(self) -> None: + """Test custom values.""" + engine = MagicMock() + config = PostgresTableConfig( + table_name='my_docs', + engine=engine, + embedder='vertexai/text-embedding-005', + schema_name='custom_schema', + content_column='text', + embedding_column='vector', + id_column='doc_id', + metadata_columns=['category', 'author'], + distance_strategy=DistanceStrategy.EUCLIDEAN, + ) + assert config.table_name == 'my_docs' + assert config.schema_name == 'custom_schema' + assert config.content_column == 'text' + assert config.embedding_column == 'vector' + assert config.id_column == 'doc_id' + assert config.metadata_columns == ['category', 'author'] + assert config.distance_strategy == DistanceStrategy.EUCLIDEAN + + +class TestReferenceHelpers: + """Tests for reference helper functions.""" + + def test_postgres_retriever_ref(self) -> None: + """Test postgres_retriever_ref returns correct format.""" + ref = postgres_retriever_ref('documents') + assert ref == 'postgres/documents' + + def test_postgres_retriever_ref_with_display_name(self) -> None: + """Test postgres_retriever_ref with display name (unused).""" + ref = postgres_retriever_ref('documents', display_name='My Documents') + assert ref == 'postgres/documents' + + def test_postgres_indexer_ref(self) -> None: + """Test postgres_indexer_ref returns correct format.""" + ref = postgres_indexer_ref('documents') + assert ref == 'postgres/documents' + + def test_postgres_indexer_ref_with_display_name(self) -> None: + """Test postgres_indexer_ref with display name (unused).""" + ref = postgres_indexer_ref('documents', display_name='My Documents') + assert ref == 'postgres/documents' + + +class TestCloudSqlPgPlugin: + """Tests for CloudSqlPg plugin class.""" + + def test_plugin_name(self) -> None: + """Test plugin name is correct.""" + plugin = CloudSqlPg() + assert plugin.name == POSTGRES_PLUGIN_NAME + assert plugin.name == 'postgres' + + def test_empty_initialization(self) -> None: + """Test initialization with no tables.""" + plugin = CloudSqlPg() + assert plugin._tables == [] + + def test_initialization_with_tables(self) -> None: + """Test initialization with tables.""" + engine = MagicMock() + config = PostgresTableConfig( + table_name='documents', + engine=engine, + embedder='googleai/text-embedding-004', + ) + plugin = CloudSqlPg(tables=[config]) + assert len(plugin._tables) == 1 + assert plugin._tables[0] == config + + @pytest.mark.asyncio + async def test_init_without_registry(self) -> None: + """Test init without registry.""" + plugin = CloudSqlPg() + actions = await plugin.init(registry=None) + assert actions == [] + + @pytest.mark.asyncio + async def test_list_actions(self) -> None: + """Test list_actions returns correct metadata.""" + engine = MagicMock() + config = PostgresTableConfig( + table_name='documents', + engine=engine, + embedder='googleai/text-embedding-004', + ) + plugin = CloudSqlPg(tables=[config]) + + actions = await plugin.list_actions() + + assert len(actions) == 2 # One retriever, one indexer + + retriever_meta = actions[0] + assert retriever_meta.kind == ActionKind.RETRIEVER + assert retriever_meta.name == 'postgres/documents' + + indexer_meta = actions[1] + assert indexer_meta.kind == ActionKind.INDEXER + assert indexer_meta.name == 'postgres/documents' + + @pytest.mark.asyncio + async def test_resolve_unknown_action(self) -> None: + """Test resolve returns None for unknown action.""" + plugin = CloudSqlPg() + await plugin.init(registry=None) + + result = await plugin.resolve(ActionKind.RETRIEVER, 'unknown') + assert result is None + + @pytest.mark.asyncio + async def test_resolve_wrong_kind(self) -> None: + """Test resolve returns None for wrong action kind.""" + plugin = CloudSqlPg() + await plugin.init(registry=None) + + result = await plugin.resolve(ActionKind.MODEL, 'postgres/documents') + assert result is None + + +class TestPostgresConvenienceFunction: + """Tests for postgres() convenience function.""" + + def test_empty_tables(self) -> None: + """Test with no tables.""" + plugin = postgres() + assert isinstance(plugin, CloudSqlPg) + assert plugin._tables == [] + + def test_with_table_config_dicts(self) -> None: + """Test with table configuration dictionaries.""" + engine = MagicMock() + plugin = postgres( + tables=[ + { + 'table_name': 'documents', + 'engine': engine, + 'embedder': 'googleai/text-embedding-004', + }, + { + 'table_name': 'articles', + 'engine': engine, + 'embedder': 'vertexai/text-embedding-005', + 'schema_name': 'public', + 'content_column': 'body', + 'distance_strategy': DistanceStrategy.INNER_PRODUCT, + }, + ] + ) + + assert isinstance(plugin, CloudSqlPg) + assert len(plugin._tables) == 2 + + # Check first table config + config1 = plugin._tables[0] + assert config1.table_name == 'documents' + assert config1.embedder == 'googleai/text-embedding-004' + + # Check second table config + config2 = plugin._tables[1] + assert config2.table_name == 'articles' + assert config2.embedder == 'vertexai/text-embedding-005' + assert config2.content_column == 'body' + assert config2.distance_strategy == DistanceStrategy.INNER_PRODUCT diff --git a/py/plugins/compat-oai/src/genkit/plugins/compat_oai/openai_plugin.py b/py/plugins/compat-oai/src/genkit/plugins/compat_oai/openai_plugin.py index 1460737f67..6ef05734de 100644 --- a/py/plugins/compat-oai/src/genkit/plugins/compat_oai/openai_plugin.py +++ b/py/plugins/compat-oai/src/genkit/plugins/compat_oai/openai_plugin.py @@ -27,6 +27,7 @@ from genkit.blocks.model import model_action_metadata from genkit.core.action import Action, ActionMetadata from genkit.core.action.types import ActionKind +from genkit.core.registry import Registry from genkit.core.schema import to_json_schema from genkit.core.typing import GenerationCommonConfig from genkit.plugins.compat_oai.models import ( @@ -79,9 +80,12 @@ def __init__(self, **openai_params: Any) -> None: # noqa: ANN401 self._openai_params = openai_params self._openai_client = OpenAIClient(**openai_params) - async def init(self) -> list[Action]: + async def init(self, registry: 'Registry | None' = None) -> list[Action]: """Initialize plugin. + Args: + registry: Optional registry (not used by this plugin). + Returns: Actions for built-in OpenAI models and embedders. """ diff --git a/py/plugins/compat-oai/tests/plugin_test.py b/py/plugins/compat-oai/tests/plugin_test.py index 3dd2fe6713..e9982bfc56 100644 --- a/py/plugins/compat-oai/tests/plugin_test.py +++ b/py/plugins/compat-oai/tests/plugin_test.py @@ -31,9 +31,10 @@ async def test_openai_plugin_init() -> None: """Test OpenAI plugin init method.""" plugin = OpenAI(api_key='test-key') + mock_registry = MagicMock() # init() should return known models and embedders - result = await plugin.init() + result = await plugin.init(mock_registry) assert len(result) > 0, 'Should initialize with known models and embedders' assert all(hasattr(action, 'kind') for action in result), 'All actions should have a kind' assert all(hasattr(action, 'name') for action in result), 'All actions should have a name' diff --git a/py/plugins/deepseek/src/genkit/plugins/deepseek/plugin.py b/py/plugins/deepseek/src/genkit/plugins/deepseek/plugin.py index aec82ccba1..bb036ac156 100644 --- a/py/plugins/deepseek/src/genkit/plugins/deepseek/plugin.py +++ b/py/plugins/deepseek/src/genkit/plugins/deepseek/plugin.py @@ -24,6 +24,7 @@ from genkit.core.action import Action, ActionMetadata from genkit.core.action.types import ActionKind from genkit.core.error import GenkitError +from genkit.core.registry import Registry from genkit.core.schema import to_json_schema from genkit.plugins.compat_oai.typing import OpenAIConfig from genkit.plugins.deepseek.model_info import SUPPORTED_DEEPSEEK_MODELS @@ -65,9 +66,12 @@ def __init__( self.models = models self.deepseek_params = deepseek_params - async def init(self) -> list[Action]: + async def init(self, registry: 'Registry | None' = None) -> list[Action]: """Initialize the plugin. + Args: + registry: Optional registry (not used by this plugin). + Returns: Empty list (using lazy loading via resolve). """ diff --git a/py/plugins/deepseek/tests/deepseek_plugin_test.py b/py/plugins/deepseek/tests/deepseek_plugin_test.py index e5cef2b109..6ccf69f4d2 100644 --- a/py/plugins/deepseek/tests/deepseek_plugin_test.py +++ b/py/plugins/deepseek/tests/deepseek_plugin_test.py @@ -59,8 +59,9 @@ def test_plugin_initialization_without_api_key() -> None: async def test_plugin_initialize(mock_client: MagicMock) -> None: """Test plugin init method.""" plugin = DeepSeek(api_key='test-key', models=['deepseek-chat']) + mock_registry = MagicMock() - result = await plugin.init() + result = await plugin.init(mock_registry) # init returns empty list for lazy loading assert result == [] @@ -112,8 +113,9 @@ def test_plugin_with_custom_params(mock_client: MagicMock) -> None: async def test_plugin_initialize_no_models(mock_client: MagicMock) -> None: """Test plugin init returns empty list for lazy loading.""" plugin = DeepSeek(api_key='test-key') + mock_registry = MagicMock() - result = await plugin.init() + result = await plugin.init(mock_registry) # init returns empty list for lazy loading assert result == [] diff --git a/py/plugins/google-genai/src/genkit/plugins/google_genai/google.py b/py/plugins/google-genai/src/genkit/plugins/google_genai/google.py index 249481eafd..08d635b1ee 100644 --- a/py/plugins/google-genai/src/genkit/plugins/google_genai/google.py +++ b/py/plugins/google-genai/src/genkit/plugins/google_genai/google.py @@ -108,7 +108,7 @@ from genkit.blocks.embedding import EmbedderOptions, EmbedderSupports, embedder_action_metadata from genkit.blocks.model import model_action_metadata from genkit.core.action import Action, ActionMetadata -from genkit.core.registry import ActionKind +from genkit.core.registry import ActionKind, Registry from genkit.plugins.google_genai.models.embedder import ( Embedder, default_embedder_info, @@ -345,9 +345,12 @@ def __init__( http_options=_inject_attribution_headers(http_options, base_url, api_version), ) - async def init(self) -> list[Action]: + async def init(self, registry: 'Registry | None' = None) -> list[Action]: """Initialize the plugin. + Args: + registry: Optional registry (not used by this plugin). + Returns: List of Action objects for known/supported models. """ @@ -684,9 +687,12 @@ def __init__( http_options=_inject_attribution_headers(http_options, base_url, api_version), ) - async def init(self) -> list[Action]: + async def init(self, registry: 'Registry | None' = None) -> list[Action]: """Initialize the plugin. + Args: + registry: Optional registry (not used by this plugin). + Returns: List of Action objects for known/supported models. """ diff --git a/py/plugins/google-genai/src/genkit/plugins/google_genai/models/gemini.py b/py/plugins/google-genai/src/genkit/plugins/google_genai/models/gemini.py index 1111246580..6e88388589 100644 --- a/py/plugins/google-genai/src/genkit/plugins/google_genai/models/gemini.py +++ b/py/plugins/google-genai/src/genkit/plugins/google_genai/models/gemini.py @@ -148,6 +148,7 @@ from google import genai from google.genai import types as genai_types from pydantic import BaseModel, ConfigDict, Field, WithJsonSchema +from pydantic.alias_generators import to_camel from genkit.ai import ( ActionRunContext, @@ -197,7 +198,7 @@ class HarmBlockThreshold(StrEnum): class SafetySettingsSchema(BaseModel): """Safety settings schema.""" - model_config = ConfigDict(extra='allow', populate_by_name=True) + model_config = ConfigDict(alias_generator=to_camel, extra='allow', populate_by_name=True) category: HarmCategory threshold: HarmBlockThreshold @@ -205,8 +206,8 @@ class SafetySettingsSchema(BaseModel): class PrebuiltVoiceConfig(BaseModel): """Prebuilt voice config.""" - model_config = ConfigDict(extra='allow', populate_by_name=True) - voice_name: str | None = Field(None, alias='voiceName') + model_config = ConfigDict(alias_generator=to_camel, extra='allow', populate_by_name=True) + voice_name: str | None = None class FunctionCallingMode(StrEnum): @@ -221,9 +222,9 @@ class FunctionCallingMode(StrEnum): class FunctionCallingConfig(BaseModel): """Function calling config.""" - model_config = ConfigDict(extra='allow', populate_by_name=True) + model_config = ConfigDict(alias_generator=to_camel, extra='allow', populate_by_name=True) mode: FunctionCallingMode | None = None - allowed_function_names: list[str] | None = Field(None, alias='allowedFunctionNames') + allowed_function_names: list[str] | None = None class ThinkingLevel(StrEnum): @@ -238,19 +239,19 @@ class ThinkingLevel(StrEnum): class ThinkingConfigSchema(BaseModel): """Thinking config schema.""" - model_config = ConfigDict(extra='allow', populate_by_name=True) - include_thoughts: bool | None = Field(None, alias='includeThoughts') - thinking_budget: int | None = Field(None, alias='thinkingBudget') - thinking_level: ThinkingLevel | None = Field(None, alias='thinkingLevel') + model_config = ConfigDict(alias_generator=to_camel, extra='allow', populate_by_name=True) + include_thoughts: bool | None = None + thinking_budget: int | None = None + thinking_level: ThinkingLevel | None = None class FileSearchConfigSchema(BaseModel): """File search config schema.""" - model_config = ConfigDict(extra='allow', populate_by_name=True) - file_search_store_names: list[str] | None = Field(None, alias='fileSearchStoreNames') - metadata_filter: str | None = Field(None, alias='metadataFilter') - top_k: int | None = Field(None, alias='topK') + model_config = ConfigDict(alias_generator=to_camel, extra='allow', populate_by_name=True) + file_search_store_names: list[str] | None = None + metadata_filter: str | None = None + top_k: int | None = None class ImageAspectRatio(StrEnum): @@ -279,22 +280,22 @@ class ImageSize(StrEnum): class ImageConfigSchema(BaseModel): """Image config schema.""" - model_config = ConfigDict(extra='allow', populate_by_name=True) - aspect_ratio: ImageAspectRatio | None = Field(None, alias='aspectRatio') - image_size: ImageSize | None = Field(None, alias='imageSize') + model_config = ConfigDict(alias_generator=to_camel, extra='allow', populate_by_name=True) + aspect_ratio: ImageAspectRatio | None = None + image_size: ImageSize | None = None class VoiceConfigSchema(BaseModel): """Voice config schema.""" - model_config = ConfigDict(extra='allow', populate_by_name=True) - prebuilt_voice_config: PrebuiltVoiceConfig | None = Field(None, alias='prebuiltVoiceConfig') + model_config = ConfigDict(alias_generator=to_camel, extra='allow', populate_by_name=True) + prebuilt_voice_config: PrebuiltVoiceConfig | None = None class GeminiConfigSchema(GenerationCommonConfig): """Gemini Config Schema.""" - model_config = ConfigDict(extra='allow', populate_by_name=True) + model_config = ConfigDict(alias_generator=to_camel, extra='allow', populate_by_name=True) safety_settings: Annotated[ list[SafetySettingsSchema] | None, @@ -314,12 +315,7 @@ class GeminiConfigSchema(GenerationCommonConfig): 'Content is blocked based on the probability that it is harmful.' ), }), - ] = Field( - None, - alias='safetySettings', - ) - # Gemini specific - model_config = ConfigDict(extra='allow') + ] = None # inherited from GenerationCommonConfig: # version, temperature, max_output_tokens, top_k, top_p, stop_sequences @@ -331,37 +327,32 @@ class GeminiConfigSchema(GenerationCommonConfig): top_p: float | None = Field( default=None, - alias='topP', description=( 'The maximum cumulative probability of tokens to consider when sampling. Values can range over [0.0, 1.0].' ), ) top_k: int | None = Field( # pyrefly: ignore[bad-override] default=None, - alias='topK', description=('The maximum number of tokens to consider when sampling. Values can range over [1, 40].'), ) - candidate_count: int | None = Field( - default=None, description='Number of generated responses to return.', alias='candidateCount' - ) + candidate_count: int | None = Field(default=None, description='Number of generated responses to return.') max_output_tokens: int | None = Field( # pyrefly: ignore[bad-override] - default=None, alias='maxOutputTokens', description='Maximum number of tokens to generate.' + default=None, description='Maximum number of tokens to generate.' ) - stop_sequences: list[str] | None = Field(default=None, alias='stopSequences', description='Stop sequences.') - presence_penalty: float | None = Field(default=None, description='Presence penalty.', alias='presencePenalty') - frequency_penalty: float | None = Field(default=None, description='Frequency penalty.', alias='frequencyPenalty') - response_mime_type: str | None = Field(default=None, description='Response MIME type.', alias='responseMimeType') - response_schema: dict[str, Any] | None = Field(default=None, description='Response schema.', alias='responseSchema') + stop_sequences: list[str] | None = Field(default=None, description='Stop sequences.') + presence_penalty: float | None = Field(default=None, description='Presence penalty.') + frequency_penalty: float | None = Field(default=None, description='Frequency penalty.') + response_mime_type: str | None = Field(default=None, description='Response MIME type.') + response_schema: dict[str, Any] | None = Field(default=None, description='Response schema.') code_execution: bool | dict[str, Any] | None = Field( - None, description='Enables the model to generate and run code.', alias='codeExecution' + None, description='Enables the model to generate and run code.' ) response_modalities: list[str] | None = Field( None, description=( "The modalities to be used in response. Only supported for 'gemini-2.0-flash-exp' model at present." ), - alias='responseModalities', ) thinking_config: Annotated[ @@ -397,7 +388,7 @@ class GeminiConfigSchema(GenerationCommonConfig): }, 'additionalProperties': True, }), - ] = Field(None, alias='thinkingConfig') + ] = None file_search: Annotated[ FileSearchConfigSchema | None, @@ -423,15 +414,14 @@ class GeminiConfigSchema(GenerationCommonConfig): }, 'additionalProperties': True, }), - ] = Field(None, alias='fileSearch') + ] = None url_context: bool | dict[str, Any] | None = Field( - None, description='Return grounding metadata from links included in the query', alias='urlContext' + None, description='Return grounding metadata from links included in the query' ) google_search_retrieval: bool | dict[str, Any] | None = Field( None, description='Retrieve public web data for grounding, powered by Google Search.', - alias='googleSearchRetrieval', ) function_calling_config: Annotated[ FunctionCallingConfig | None, @@ -450,33 +440,29 @@ class GeminiConfigSchema(GenerationCommonConfig): ), 'additionalProperties': True, }), - ] = Field( - None, - alias='functionCallingConfig', - ) + ] = None api_version: str | None = Field( - None, description='Overrides the plugin-configured or default apiVersion, if specified.', alias='apiVersion' - ) - base_url: str | None = Field( - None, description='Overrides the plugin-configured or default baseUrl, if specified.', alias='baseUrl' + None, description='Overrides the plugin-configured or default apiVersion, if specified.' ) + base_url: str | None = Field(None, description='Overrides the plugin-configured or default baseUrl, if specified.') api_key: str | None = Field( - None, description='Overrides the plugin-configured API key, if specified.', alias='apiKey', exclude=True + None, description='Overrides the plugin-configured API key, if specified.', exclude=True ) context_cache: bool | None = Field( None, description=( 'Context caching allows you to save and reuse precomputed input tokens that you wish to use repeatedly.' ), - alias='contextCache', ) class SpeechConfigSchema(BaseModel): """Speech config schema.""" - voice_config: VoiceConfigSchema | None = Field(None, alias='voiceConfig') + model_config = ConfigDict(alias_generator=to_camel, extra='allow', populate_by_name=True) + + voice_config: VoiceConfigSchema | None = None http_options: Any | None = Field(None, exclude=True) tools: Any | None = Field(None, exclude=True) @@ -488,7 +474,7 @@ class SpeechConfigSchema(BaseModel): class GeminiTtsConfigSchema(GeminiConfigSchema): """Gemini TTS Config Schema.""" - speech_config: SpeechConfigSchema | None = Field(None, alias='speechConfig') + speech_config: SpeechConfigSchema | None = None class GeminiImageConfigSchema(GeminiConfigSchema): @@ -504,7 +490,7 @@ class GeminiImageConfigSchema(GeminiConfigSchema): }, 'additionalProperties': True, }), - ] = Field(None, alias='imageConfig') + ] = None class GemmaConfigSchema(GeminiConfigSchema): diff --git a/py/plugins/google-genai/src/genkit/plugins/google_genai/models/lyria.py b/py/plugins/google-genai/src/genkit/plugins/google_genai/models/lyria.py index 4b11e4c1ab..de178a58d6 100644 --- a/py/plugins/google-genai/src/genkit/plugins/google_genai/models/lyria.py +++ b/py/plugins/google-genai/src/genkit/plugins/google_genai/models/lyria.py @@ -71,7 +71,8 @@ from typing import Any -from pydantic import BaseModel, Field +from pydantic import BaseModel, ConfigDict, Field +from pydantic.alias_generators import to_camel from genkit.core.typing import ( ModelInfo, @@ -113,12 +114,12 @@ class LyriaConfig(BaseModel): location: Must be 'global' for Lyria. Override if plugin uses different region. """ - negative_prompt: str | None = Field(default=None, alias='negativePrompt') - seed: int | None = Field(default=None) - sample_count: int | None = Field(default=None, ge=1, alias='sampleCount') - location: str | None = Field(default=None) + model_config = ConfigDict(alias_generator=to_camel, populate_by_name=True) - model_config = {'populate_by_name': True} + negative_prompt: str | None = None + seed: int | None = None + sample_count: int | None = Field(default=None, ge=1) + location: str | None = None LYRIA_MODEL_INFO = ModelInfo( diff --git a/py/plugins/google-genai/src/genkit/plugins/google_genai/models/veo.py b/py/plugins/google-genai/src/genkit/plugins/google_genai/models/veo.py index ce0ab356f5..ac0d4d5ed0 100644 --- a/py/plugins/google-genai/src/genkit/plugins/google_genai/models/veo.py +++ b/py/plugins/google-genai/src/genkit/plugins/google_genai/models/veo.py @@ -78,6 +78,7 @@ from google import genai from google.genai import types as genai_types from pydantic import BaseModel, ConfigDict, Field +from pydantic.alias_generators import to_camel from genkit.core.action import ActionRunContext from genkit.core.tracing import tracer @@ -127,18 +128,14 @@ def is_veo_model(name: str) -> bool: class VeoConfigSchema(BaseModel): """Veo Config Schema.""" - model_config = ConfigDict(extra='allow', populate_by_name=True) - negative_prompt: str | None = Field( - default=None, alias='negativePrompt', description='Negative prompt for video generation.' - ) + model_config = ConfigDict(alias_generator=to_camel, extra='allow', populate_by_name=True) + negative_prompt: str | None = Field(default=None, description='Negative prompt for video generation.') aspect_ratio: str | None = Field( - default=None, alias='aspectRatio', description='Desired aspect ratio of the output video (e.g. "16:9").' - ) - person_generation: str | None = Field(default=None, alias='personGeneration', description='Person generation mode.') - duration_seconds: int | None = Field( - default=None, alias='durationSeconds', description='Length of video in seconds.' + default=None, description='Desired aspect ratio of the output video (e.g. "16:9").' ) - enhance_prompt: bool | None = Field(default=None, alias='enhancePrompt', description='Enable prompt enhancement.') + person_generation: str | None = Field(default=None, description='Person generation mode.') + duration_seconds: int | None = Field(default=None, description='Length of video in seconds.') + enhance_prompt: bool | None = Field(default=None, description='Enable prompt enhancement.') # Alias for backwards compatibility with __init__.py exports diff --git a/py/plugins/google-genai/test/google_plugin_test.py b/py/plugins/google-genai/test/google_plugin_test.py index 94bbf1b629..04782db8d8 100644 --- a/py/plugins/google-genai/test/google_plugin_test.py +++ b/py/plugins/google-genai/test/google_plugin_test.py @@ -143,8 +143,9 @@ async def test_googleai_initialize(mock_client_cls: MagicMock) -> None: plugin = GoogleAI(api_key=api_key) # Ensure usage of mock plugin._client = mock_client + mock_registry = MagicMock() - await plugin.init() + await plugin.init(mock_registry) result = await plugin.list_actions() # init returns known models and embedders @@ -490,8 +491,9 @@ async def test_vertexai_initialize(vertexai_plugin_instance: VertexAI) -> None: m2.supported_actions = ['embedContent'] plugin._client.models.list.return_value = [m1, m2] # type: ignore + mock_registry = MagicMock() - await plugin.init() + await plugin.init(mock_registry) # init returns known models and embedders in internal registry, but list_actions returns them list result = await plugin.list_actions() diff --git a/py/plugins/mcp/src/genkit/plugins/mcp/client/client.py b/py/plugins/mcp/src/genkit/plugins/mcp/client/client.py index ae145fde41..e5897c7870 100644 --- a/py/plugins/mcp/src/genkit/plugins/mcp/client/client.py +++ b/py/plugins/mcp/src/genkit/plugins/mcp/client/client.py @@ -24,6 +24,7 @@ from genkit.ai import Genkit, Plugin from genkit.core.action import Action, ActionMetadata from genkit.core.action.types import ActionKind +from genkit.core.registry import Registry from mcp import ClientSession, StdioServerParameters from mcp.client.sse import sse_client from mcp.client.stdio import stdio_client @@ -65,11 +66,14 @@ def plugin_name(self) -> str: """Returns the name of the plugin.""" return self.name - async def init(self) -> list[Action]: + async def init(self, registry: 'Registry | None' = None) -> list[Action]: """Initialize MCP plugin. MCP tools are registered dynamically upon connection, so this returns an empty list. + Args: + registry: Optional registry (not used by this plugin). + Returns: Empty list (tools are registered dynamically). """ diff --git a/py/plugins/ollama/src/genkit/plugins/ollama/plugin_api.py b/py/plugins/ollama/src/genkit/plugins/ollama/plugin_api.py index 4f778405f7..f8d06764c3 100644 --- a/py/plugins/ollama/src/genkit/plugins/ollama/plugin_api.py +++ b/py/plugins/ollama/src/genkit/plugins/ollama/plugin_api.py @@ -25,7 +25,7 @@ from genkit.blocks.embedding import EmbedderOptions, EmbedderSupports, embedder_action_metadata from genkit.blocks.model import model_action_metadata from genkit.core.action import Action, ActionMetadata -from genkit.core.registry import ActionKind +from genkit.core.registry import ActionKind, Registry from genkit.core.schema import to_json_schema from genkit.plugins.ollama.constants import ( DEFAULT_OLLAMA_SERVER_URL, @@ -91,11 +91,14 @@ def __init__( self.client = partial(ollama_api.AsyncClient, host=self.server_address) - async def init(self) -> list: + async def init(self, registry: 'Registry | None' = None) -> list: """Initialize the Ollama plugin. Returns pre-registered models and embedders. + Args: + registry: Optional registry (not used by this plugin). + Returns: List of Action objects for pre-configured models and embedders. """ diff --git a/py/plugins/ollama/tests/plugin_api_test.py b/py/plugins/ollama/tests/plugin_api_test.py index ef88521cdb..81c56403d1 100644 --- a/py/plugins/ollama/tests/plugin_api_test.py +++ b/py/plugins/ollama/tests/plugin_api_test.py @@ -73,8 +73,9 @@ async def test_initialize(ollama_plugin_instance: Ollama) -> None: embedder_ref = EmbeddingDefinition(name='test_embedder') ollama_plugin_instance.models = [model_ref] ollama_plugin_instance.embedders = [embedder_ref] + mock_registry = MagicMock() - result = await ollama_plugin_instance.init() + result = await ollama_plugin_instance.init(mock_registry) # init returns actions for pre-configured models and embedders assert len(result) == 2 diff --git a/py/plugins/pinecone/LICENSE b/py/plugins/pinecone/LICENSE new file mode 100644 index 0000000000..1e7d07ac21 --- /dev/null +++ b/py/plugins/pinecone/LICENSE @@ -0,0 +1,176 @@ + Apache License + Version 2.0, January 2004 + http://www.apache.org/licenses/ + +TERMS AND CONDITIONS FOR USE, REPRODUCTION, AND DISTRIBUTION + +1. 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+# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. +# +# SPDX-License-Identifier: Apache-2.0 + +[project] +authors = [{ name = "Google LLC" }] +classifiers = [ + "Development Status :: 4 - Beta", + "Intended Audience :: Developers", + "License :: OSI Approved :: Apache Software License", + "Operating System :: OS Independent", + "Programming Language :: Python :: 3", + "Programming Language :: Python :: 3.10", + "Programming Language :: Python :: 3.11", + "Programming Language :: Python :: 3.12", + "Topic :: Scientific/Engineering :: Artificial Intelligence", +] +description = "Pinecone vector store plugin for Genkit" +keywords = ["genkit", "ai", "vector-store", "pinecone", "rag", "retrieval"] +license = "Apache-2.0" +name = "genkit-plugin-pinecone" +readme = "README.md" +requires-python = ">=3.10" +version = "0.1.0" + +dependencies = ["genkit>=0.4.0", "pinecone>=5.0.0", "pydantic>=2.0.0"] + +[project.optional-dependencies] +dev = ["pytest>=7.0.0", "pytest-asyncio>=0.21.0"] + +[build-system] +build-backend = "hatchling.build" +requires = ["hatchling"] + +[tool.hatch.build.targets.wheel] +packages = ["src/genkit"] diff --git a/py/plugins/pinecone/src/genkit/plugins/pinecone/__init__.py b/py/plugins/pinecone/src/genkit/plugins/pinecone/__init__.py new file mode 100644 index 0000000000..1974535348 --- /dev/null +++ b/py/plugins/pinecone/src/genkit/plugins/pinecone/__init__.py @@ -0,0 +1,104 @@ +# Copyright 2026 Google LLC +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. +# +# SPDX-License-Identifier: Apache-2.0 + +"""Pinecone vector store plugin for Genkit. + +This plugin provides retriever and indexer implementations backed by Pinecone, +a managed vector database for AI applications. + +Architecture +============ + +┌─────────────────────────────────────────────────────────────────────────────┐ +│ Pinecone Plugin Architecture │ +├─────────────────────────────────────────────────────────────────────────────┤ +│ │ +│ ┌──────────────┐ ┌──────────────┐ ┌──────────────────────┐ │ +│ │ Genkit │ │ Pinecone │ │ Pinecone Cloud │ │ +│ │ Instance │ ──► │ Plugin │ ──► │ (Managed Service) │ │ +│ └──────────────┘ └──────────────┘ └──────────────────────┘ │ +│ │ │ +│ ├─► Retriever (similarity search) │ +│ └─► Indexer (vector upsert) │ +│ │ +│ Data Flow: │ +│ ┌────────┐ ┌──────────┐ ┌────────────┐ ┌──────────────┐ │ +│ │Document│ ─► │ Embedder │ ─► │ Embedding │ ─► │ Pinecone │ │ +│ └────────┘ └──────────┘ └────────────┘ │ Index │ │ +│ └──────────────┘ │ +└─────────────────────────────────────────────────────────────────────────────┘ + +Example: + ```python + from genkit.ai import Genkit + from genkit.plugins.google_genai import GoogleAI + from genkit.plugins.pinecone import pinecone, pinecone_retriever_ref + + ai = Genkit( + plugins=[ + GoogleAI(), + pinecone( + indexes=[ + { + 'index_id': 'my_index', + 'embedder': 'googleai/text-embedding-004', + } + ] + ), + ] + ) + + # Retrieve similar documents + results = await ai.retrieve( + retriever=pinecone_retriever_ref(index_id='my_index'), + query='What is machine learning?', + options={'k': 5}, + ) + ``` + +Cross-Language Parity: + - JavaScript: js/plugins/pinecone/src/index.ts + +See Also: + - Pinecone: https://www.pinecone.io/ + - Genkit RAG: https://genkit.dev/docs/rag +""" + +from genkit.plugins.pinecone.plugin import ( + Pinecone, + PineconeIndexConfig, + PineconeIndexerOptions, + PineconeRetrieverOptions, + create_pinecone_index, + delete_pinecone_index, + describe_pinecone_index, + pinecone, + pinecone_indexer_ref, + pinecone_retriever_ref, +) + +__all__ = [ + 'Pinecone', + 'PineconeIndexConfig', + 'PineconeIndexerOptions', + 'PineconeRetrieverOptions', + 'create_pinecone_index', + 'delete_pinecone_index', + 'describe_pinecone_index', + 'pinecone', + 'pinecone_indexer_ref', + 'pinecone_retriever_ref', +] diff --git a/py/plugins/pinecone/src/genkit/plugins/pinecone/plugin.py b/py/plugins/pinecone/src/genkit/plugins/pinecone/plugin.py new file mode 100644 index 0000000000..740758b046 --- /dev/null +++ b/py/plugins/pinecone/src/genkit/plugins/pinecone/plugin.py @@ -0,0 +1,679 @@ +# Copyright 2026 Google LLC +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. +# +# SPDX-License-Identifier: Apache-2.0 + +"""Pinecone plugin implementation for Genkit. + +This module provides the core plugin functionality for integrating Pinecone +with Genkit applications, including retriever and indexer implementations. + +Key Components +============== + +┌───────────────────────────────────────────────────────────────────────────┐ +│ Plugin Components │ +├───────────────────────┬───────────────────────────────────────────────────┤ +│ Component │ Purpose │ +├───────────────────────┼───────────────────────────────────────────────────┤ +│ Pinecone │ Main plugin class - registers retrievers/indexers │ +│ PineconeRetriever │ Similarity search against Pinecone indexes │ +│ PineconeIndexer │ Upsert documents with embeddings to Pinecone │ +│ pinecone_retriever_ref│ Create retriever reference by index ID │ +│ pinecone_indexer_ref │ Create indexer reference by index ID │ +└───────────────────────┴───────────────────────────────────────────────────┘ + +See Also: + - JS Implementation: js/plugins/pinecone/src/index.ts +""" + +import hashlib +import json +import os +from dataclasses import dataclass, field +from typing import Any, cast + +import structlog +from pydantic import BaseModel, Field + +from genkit.blocks.document import Document +from genkit.blocks.retriever import ( + IndexerOptions, + IndexerRequest, + RetrieverOptions, + RetrieverRequest, + RetrieverResponse, + indexer_action_metadata, + retriever_action_metadata, +) +from genkit.core.action import Action, ActionMetadata, ActionRunContext +from genkit.core.plugin import Plugin +from genkit.core.registry import ActionKind, Registry +from genkit.core.schema import to_json_schema +from genkit.types import DocumentData, EmbedRequest +from pinecone import Pinecone as PineconeClient, ServerlessSpec +from pinecone.db_data.index import Index as PineconeIndex + +logger = structlog.get_logger(__name__) + +PINECONE_PLUGIN_NAME = 'pinecone' +CONTENT_KEY = '_content' +CONTENT_TYPE_KEY = '_content_type' +MAX_K = 1000 + + +class PineconeRetrieverOptions(BaseModel): + """Options for Pinecone retriever queries. + + Attributes: + k: Number of results to return (default: 10, max: 1000). + namespace: Pinecone namespace for data isolation. + filter: Metadata filter for queries. + """ + + k: int = Field(default=10, le=MAX_K, description='Number of results to return') + namespace: str | None = Field(default=None, description='Pinecone namespace') + filter: dict[str, Any] | None = Field(default=None, description='Metadata filter') + + +class PineconeIndexerOptions(BaseModel): + """Options for Pinecone indexer operations. + + Attributes: + namespace: Pinecone namespace for data isolation. + """ + + namespace: str | None = Field(default=None, description='Pinecone namespace') + + +@dataclass +class PineconeIndexConfig: + """Configuration for a Pinecone index. + + Attributes: + index_id: Name of the Pinecone index. + embedder: Genkit embedder reference (e.g., 'googleai/text-embedding-004'). + embedder_options: Optional embedder-specific configuration. + client_params: Pinecone client configuration. + content_key: Metadata key for document content (default: '_content'). + """ + + index_id: str + embedder: str + embedder_options: dict[str, Any] | None = None + client_params: dict[str, Any] | None = None + content_key: str = CONTENT_KEY + + +@dataclass +class PineconePluginConfig: + """Configuration for the Pinecone plugin. + + Attributes: + indexes: List of index configurations. + """ + + indexes: list[PineconeIndexConfig] = field(default_factory=list) + + +def _md5_hash(content: str) -> str: + """Generate MD5 hash of content for document ID.""" + return hashlib.md5(content.encode('utf-8')).hexdigest() + + +def _get_client(client_params: dict[str, Any] | None) -> PineconeClient: + """Create a Pinecone client from configuration. + + Args: + client_params: Client configuration. Uses PINECONE_API_KEY env var if not provided. + + Returns: + Pinecone client instance. + """ + api_key = None + if client_params: + api_key = client_params.get('api_key') + if not api_key: + api_key = os.environ.get('PINECONE_API_KEY') + + if not api_key: + raise ValueError('Pinecone API key must be provided via client_params or PINECONE_API_KEY environment variable') + + return PineconeClient(api_key=api_key) + + +class PineconeRetriever: + """Pinecone retriever implementation. + + Performs similarity search against a Pinecone index using + embeddings generated by a Genkit embedder. + """ + + def __init__( + self, + registry: Registry, + index_id: str, + embedder: str, + embedder_options: dict[str, Any] | None = None, + client_params: dict[str, Any] | None = None, + content_key: str = CONTENT_KEY, + ) -> None: + """Initialize the Pinecone retriever. + + Args: + registry: Registry for resolving embedders. + index_id: Name of the Pinecone index. + embedder: Embedder reference string. + embedder_options: Optional embedder configuration. + client_params: Pinecone client configuration. + content_key: Metadata key for document content. + """ + self._registry = registry + self._index_id = index_id + self._embedder = embedder + self._embedder_options = embedder_options + self._client_params = client_params + self._content_key = content_key + + def _get_index(self) -> PineconeIndex: + """Get the Pinecone index.""" + client = _get_client(self._client_params) + return client.Index(self._index_id) + + async def _embed_content(self, content: list[Document]) -> list[list[float]]: + """Generate embeddings using the registry-resolved embedder.""" + embedder_action = await self._registry.resolve_embedder(self._embedder) + if embedder_action is None: + raise ValueError(f'Embedder "{self._embedder}" not found') + + # Document is a subclass of DocumentData; cast for type checker variance + request = EmbedRequest(input=cast(list[DocumentData], content), options=self._embedder_options) + response = await embedder_action.arun(request) + return [e.embedding for e in response.response.embeddings] + + async def retrieve( + self, + request: RetrieverRequest, + _ctx: ActionRunContext, + ) -> RetrieverResponse: + """Retrieve documents similar to the query. + + Args: + request: Retriever request with query and options. + _ctx: Action run context (unused). + + Returns: + Response containing matching documents. + """ + # Generate query embedding + query_doc = Document.from_document_data(document_data=request.query) + embeddings = await self._embed_content([query_doc]) + if not embeddings: + raise ValueError('Embedder returned no embeddings for query') + + query_embedding = embeddings[0] + + # Parse options + k = 10 + namespace: str | None = None + filter_dict: dict[str, Any] | None = None + + if request.options: + opts = request.options if isinstance(request.options, dict) else request.options.model_dump() + k = min(opts.get('k', 10), MAX_K) + namespace = opts.get('namespace') + filter_dict = opts.get('filter') + + # Query Pinecone + index = self._get_index() + results = index.query( + vector=query_embedding, + top_k=k, + include_values=False, + include_metadata=True, + namespace=namespace, + filter=filter_dict, + ) + + # Convert results to Documents + documents: list[DocumentData] = [] + for match in results.get('matches', []): + metadata = match.get('metadata', {}) + + # Extract content + content = metadata.get(self._content_key, '') + content_type = metadata.get(CONTENT_TYPE_KEY, 'text') + + # Parse stored document metadata + doc_metadata: dict[str, Any] = {} + if 'doc_metadata' in metadata: + try: + doc_metadata = json.loads(str(metadata['doc_metadata'])) + except (json.JSONDecodeError, TypeError) as e: + logger.warning('Failed to parse document metadata', error=str(e)) + + # Add score to metadata + doc_metadata['_score'] = match.get('score', 0.0) + + # Reconstruct document + doc = Document.from_data( + data=content, + data_type=content_type, + metadata=doc_metadata, + ) + # Document extends DocumentData, so it can be used directly + documents.append(doc) + + return RetrieverResponse(documents=documents) + + +class PineconeIndexer: + """Pinecone indexer implementation. + + Upserts documents with their embeddings to a Pinecone index. + """ + + def __init__( + self, + registry: Registry, + index_id: str, + embedder: str, + embedder_options: dict[str, Any] | None = None, + client_params: dict[str, Any] | None = None, + content_key: str = CONTENT_KEY, + ) -> None: + """Initialize the Pinecone indexer. + + Args: + registry: Registry for resolving embedders. + index_id: Name of the Pinecone index. + embedder: Embedder reference string. + embedder_options: Optional embedder configuration. + client_params: Pinecone client configuration. + content_key: Metadata key for document content. + """ + self._registry = registry + self._index_id = index_id + self._embedder = embedder + self._embedder_options = embedder_options + self._client_params = client_params + self._content_key = content_key + + def _get_index(self) -> PineconeIndex: + """Get the Pinecone index.""" + client = _get_client(self._client_params) + return client.Index(self._index_id) + + async def _embed_content(self, content: list[Document]) -> list[list[float]]: + """Generate embeddings using the registry-resolved embedder.""" + embedder_action = await self._registry.resolve_embedder(self._embedder) + if embedder_action is None: + raise ValueError(f'Embedder "{self._embedder}" not found') + + # Document is a subclass of DocumentData; cast for type checker variance + request = EmbedRequest(input=cast(list[DocumentData], content), options=self._embedder_options) + response = await embedder_action.arun(request) + return [e.embedding for e in response.response.embeddings] + + async def index(self, request: IndexerRequest) -> None: + """Index documents into the Pinecone index. + + Args: + request: Indexer request containing documents to index. + """ + if not request.documents: + return + + # Generate embeddings for all documents + docs = [Document.from_document_data(doc) for doc in request.documents] + embeddings = await self._embed_content(docs) + if not embeddings: + raise ValueError('Embedder returned no embeddings for documents') + + # Parse namespace from options + namespace: str | None = None + if request.options: + opts = request.options if isinstance(request.options, dict) else {} + namespace = opts.get('namespace') + + # Prepare vectors for Pinecone + vectors: list[dict[str, Any]] = [] + + for doc, embedding in zip(docs, embeddings, strict=True): + # Get embedding documents (handles multi-part documents) + from genkit.types import Embedding + + emb_obj = Embedding(embedding=embedding) + embedding_docs = doc.get_embedding_documents([emb_obj]) + + for emb_doc in embedding_docs: + # Generate unique ID from content + content = emb_doc.text() or json.dumps(emb_doc.data()) + doc_id = _md5_hash(content) + + # Prepare metadata + metadata: dict[str, Any] = { + self._content_key: content, + CONTENT_TYPE_KEY: emb_doc.data_type() or 'text', + } + if emb_doc.metadata: + metadata['doc_metadata'] = json.dumps(emb_doc.metadata) + + vectors.append({ + 'id': doc_id, + 'values': embedding, + 'metadata': metadata, + }) + + # Upsert to Pinecone + index = self._get_index() + index.upsert(vectors=vectors, namespace=namespace) + + +class Pinecone(Plugin): + """Pinecone vector store plugin for Genkit. + + This plugin registers retrievers and indexers for Pinecone indexes, + enabling RAG (Retrieval-Augmented Generation) workflows. + + Example: + ```python + ai = Genkit( + plugins=[ + Pinecone( + indexes=[ + PineconeIndexConfig( + index_id='my-index', + embedder='googleai/text-embedding-004', + ) + ] + ) + ] + ) + ``` + """ + + name = PINECONE_PLUGIN_NAME + + def __init__( + self, + indexes: list[PineconeIndexConfig] | None = None, + ) -> None: + """Initialize the Pinecone plugin. + + Args: + indexes: List of index configurations. + """ + self._indexes = indexes or [] + self._registry: Registry | None = None + self._actions: dict[str, Action] = {} + + async def init(self, registry: Registry | None = None) -> list[Action]: + """Initialize plugin (lazy warm-up). + + Args: + registry: Registry for action registration and embedder resolution. + + Returns: + List of pre-registered actions. + """ + self._registry = registry + if registry is not None: + for config in self._indexes: + self._register_index(registry, config) + return list(self._actions.values()) + + async def resolve(self, action_type: ActionKind, name: str) -> Action | None: + """Resolve an action by name. + + Args: + action_type: The kind of action to resolve. + name: The namespaced name of the action. + + Returns: + Action object if found, None otherwise. + """ + if action_type not in (ActionKind.RETRIEVER, ActionKind.INDEXER): + return None + # Use composite key of action_type and name + action_key = f'{action_type.value}/{name}' + return self._actions.get(action_key) + + async def list_actions(self) -> list[ActionMetadata]: + """List available actions for dev UI. + + Returns: + List of action metadata. + """ + metadata_list: list[ActionMetadata] = [] + for config in self._indexes: + name = f'{PINECONE_PLUGIN_NAME}/{config.index_id}' + metadata_list.append( + ActionMetadata( + kind=ActionKind.RETRIEVER, + name=name, + ) + ) + metadata_list.append( + ActionMetadata( + kind=ActionKind.INDEXER, + name=name, + ) + ) + return metadata_list + + def _register_index( + self, + registry: Registry, + config: PineconeIndexConfig, + ) -> None: + """Register retriever and indexer for an index. + + Args: + registry: Action registry. + config: Index configuration. + """ + name = f'{PINECONE_PLUGIN_NAME}/{config.index_id}' + + # Create and register retriever + retriever = PineconeRetriever( + registry=registry, + index_id=config.index_id, + embedder=config.embedder, + embedder_options=config.embedder_options, + client_params=config.client_params, + content_key=config.content_key, + ) + + retriever_action = registry.register_action( + kind=ActionKind.RETRIEVER, + name=name, + fn=retriever.retrieve, + metadata=retriever_action_metadata( + name=name, + options=RetrieverOptions( + label=f'Pinecone - {config.index_id}', + config_schema=to_json_schema(PineconeRetrieverOptions), + ), + ).metadata, + ) + if retriever_action: + self._actions[f'{ActionKind.RETRIEVER.value}/{name}'] = retriever_action + + # Create and register indexer + indexer = PineconeIndexer( + registry=registry, + index_id=config.index_id, + embedder=config.embedder, + embedder_options=config.embedder_options, + client_params=config.client_params, + content_key=config.content_key, + ) + + indexer_action = registry.register_action( + kind=ActionKind.INDEXER, + name=name, + fn=indexer.index, + metadata=indexer_action_metadata( + name=name, + options=IndexerOptions(label=f'Pinecone - {config.index_id}'), + ).metadata, + ) + if indexer_action: + self._actions[f'{ActionKind.INDEXER.value}/{name}'] = indexer_action + + +def pinecone( + indexes: list[dict[str, Any]] | None = None, +) -> Pinecone: + """Create a Pinecone plugin with the given configuration. + + This is a convenience function for creating a Pinecone plugin instance. + + Args: + indexes: List of index configuration dictionaries. + + Returns: + Configured Pinecone plugin instance. + + Example: + ```python + ai = Genkit( + plugins=[ + pinecone( + indexes=[ + { + 'index_id': 'my-index', + 'embedder': 'googleai/text-embedding-004', + } + ] + ) + ] + ) + ``` + """ + configs = [] + if indexes: + for idx in indexes: + configs.append( + PineconeIndexConfig( + index_id=idx['index_id'], + embedder=idx['embedder'], + embedder_options=idx.get('embedder_options'), + client_params=idx.get('client_params'), + content_key=idx.get('content_key', CONTENT_KEY), + ) + ) + return Pinecone(indexes=configs) + + +def pinecone_retriever_ref( + index_id: str, + display_name: str | None = None, +) -> str: + """Create a retriever reference for a Pinecone index. + + Args: + index_id: Name of the Pinecone index. + display_name: Optional display name (unused, for parity with JS). + + Returns: + Retriever reference string. + """ + return f'pinecone/{index_id}' + + +def pinecone_indexer_ref( + index_id: str, + display_name: str | None = None, +) -> str: + """Create an indexer reference for a Pinecone index. + + Args: + index_id: Name of the Pinecone index. + display_name: Optional display name (unused, for parity with JS). + + Returns: + Indexer reference string. + """ + return f'pinecone/{index_id}' + + +async def create_pinecone_index( + name: str, + dimension: int, + metric: str = 'cosine', + client_params: dict[str, Any] | None = None, + cloud: str = 'aws', + region: str = 'us-east-1', +) -> dict[str, Any]: + """Create a Pinecone index. + + Args: + name: Index name. + dimension: Vector dimension. + metric: Distance metric ('cosine', 'euclidean', 'dotproduct'). + client_params: Pinecone client configuration. + cloud: Cloud provider ('aws', 'gcp', 'azure'). + region: Cloud region. + + Returns: + Index information dictionary. + """ + client = _get_client(client_params) + client.create_index( + name=name, + dimension=dimension, + metric=metric, + spec=ServerlessSpec(cloud=cloud, region=region), + ) + return {'name': name, 'dimension': dimension, 'metric': metric} + + +async def describe_pinecone_index( + name: str, + client_params: dict[str, Any] | None = None, +) -> dict[str, Any]: + """Describe a Pinecone index. + + Args: + name: Index name. + client_params: Pinecone client configuration. + + Returns: + Index information dictionary. + """ + client = _get_client(client_params) + description = client.describe_index(name) + return { + 'name': description.name, + 'dimension': description.dimension, + 'metric': description.metric, + 'host': description.host, + 'status': description.status, + } + + +async def delete_pinecone_index( + name: str, + client_params: dict[str, Any] | None = None, +) -> None: + """Delete a Pinecone index. + + Args: + name: Index name. + client_params: Pinecone client configuration. + """ + client = _get_client(client_params) + client.delete_index(name) diff --git a/py/plugins/pinecone/src/genkit/py.typed b/py/plugins/pinecone/src/genkit/py.typed new file mode 100644 index 0000000000..e69de29bb2 diff --git a/py/plugins/pinecone/tests/pinecone_helpers_test.py b/py/plugins/pinecone/tests/pinecone_helpers_test.py new file mode 100644 index 0000000000..7379287b4d --- /dev/null +++ b/py/plugins/pinecone/tests/pinecone_helpers_test.py @@ -0,0 +1,217 @@ +# Copyright 2026 Google LLC +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. +# +# SPDX-License-Identifier: Apache-2.0 + +"""Tests for Pinecone helper functions. + +These tests verify parity with the JS implementation in: +js/plugins/pinecone/src/index.ts (createPineconeIndex, describePineconeIndex, deletePineconeIndex) +""" + +import os +from unittest.mock import MagicMock, patch + +import pytest + +from genkit.plugins.pinecone.plugin import ( + _get_client, + create_pinecone_index, + delete_pinecone_index, + describe_pinecone_index, +) + + +class TestGetClient: + """Tests for _get_client function matching JS Pinecone client instantiation.""" + + def test_get_client_with_api_key_in_params(self) -> None: + """Test creating client with API key in params.""" + with patch('genkit.plugins.pinecone.plugin.PineconeClient') as mock_pinecone: + mock_client = MagicMock() + mock_pinecone.return_value = mock_client + + result = _get_client({'api_key': 'my-api-key'}) + + mock_pinecone.assert_called_once_with(api_key='my-api-key') + assert result == mock_client + + def test_get_client_with_env_var(self) -> None: + """Test creating client with PINECONE_API_KEY env var (matches JS getDefaultConfig).""" + with ( + patch('genkit.plugins.pinecone.plugin.PineconeClient') as mock_pinecone, + patch.dict(os.environ, {'PINECONE_API_KEY': 'env-api-key'}), + ): + mock_client = MagicMock() + mock_pinecone.return_value = mock_client + + _get_client(None) + + mock_pinecone.assert_called_once_with(api_key='env-api-key') + + def test_get_client_raises_without_api_key(self) -> None: + """Test error when no API key is available (matches JS getDefaultConfig).""" + with patch.dict(os.environ, {}, clear=True): + # Remove PINECONE_API_KEY if it exists + if 'PINECONE_API_KEY' in os.environ: + del os.environ['PINECONE_API_KEY'] + + with pytest.raises(ValueError, match='Pinecone API key must be provided'): + _get_client(None) + + +class TestCreatePineconeIndex: + """Tests for create_pinecone_index matching JS createPineconeIndex.""" + + @pytest.mark.asyncio + async def test_create_index_basic(self) -> None: + """Test basic index creation.""" + with patch('genkit.plugins.pinecone.plugin._get_client') as mock_get_client: + mock_client = MagicMock() + mock_get_client.return_value = mock_client + + await create_pinecone_index( + name='test-index', + dimension=768, + ) + + mock_client.create_index.assert_called_once() + call_kwargs = mock_client.create_index.call_args[1] + assert call_kwargs['name'] == 'test-index' + assert call_kwargs['dimension'] == 768 + assert call_kwargs['metric'] == 'cosine' # default + + @pytest.mark.asyncio + async def test_create_index_with_metric(self) -> None: + """Test index creation with custom metric.""" + with patch('genkit.plugins.pinecone.plugin._get_client') as mock_get_client: + mock_client = MagicMock() + mock_get_client.return_value = mock_client + + await create_pinecone_index( + name='test-index', + dimension=768, + metric='euclidean', + ) + + call_kwargs = mock_client.create_index.call_args[1] + assert call_kwargs['metric'] == 'euclidean' + + @pytest.mark.asyncio + async def test_create_index_with_client_params(self) -> None: + """Test index creation with client params.""" + with patch('genkit.plugins.pinecone.plugin._get_client') as mock_get_client: + mock_client = MagicMock() + mock_get_client.return_value = mock_client + + await create_pinecone_index( + name='test-index', + dimension=768, + client_params={'api_key': 'custom-key'}, + ) + + mock_get_client.assert_called_once_with({'api_key': 'custom-key'}) + + @pytest.mark.asyncio + async def test_create_index_returns_info(self) -> None: + """Test index creation returns info dict.""" + with patch('genkit.plugins.pinecone.plugin._get_client') as mock_get_client: + mock_client = MagicMock() + mock_get_client.return_value = mock_client + + result = await create_pinecone_index( + name='my-index', + dimension=512, + metric='dotproduct', + ) + + assert result['name'] == 'my-index' + assert result['dimension'] == 512 + assert result['metric'] == 'dotproduct' + + +class TestDescribePineconeIndex: + """Tests for describe_pinecone_index matching JS describePineconeIndex.""" + + @pytest.mark.asyncio + async def test_describe_index_basic(self) -> None: + """Test basic index description.""" + with patch('genkit.plugins.pinecone.plugin._get_client') as mock_get_client: + mock_client = MagicMock() + mock_description = MagicMock() + mock_description.name = 'test-index' + mock_description.dimension = 768 + mock_description.metric = 'cosine' + mock_description.host = 'test-index.svc.pinecone.io' + mock_description.status = {'ready': True} + mock_client.describe_index.return_value = mock_description + mock_get_client.return_value = mock_client + + result = await describe_pinecone_index(name='test-index') + + mock_client.describe_index.assert_called_once_with('test-index') + assert result['name'] == 'test-index' + assert result['dimension'] == 768 + assert result['metric'] == 'cosine' + assert result['host'] == 'test-index.svc.pinecone.io' + + @pytest.mark.asyncio + async def test_describe_index_with_client_params(self) -> None: + """Test index description with client params.""" + with patch('genkit.plugins.pinecone.plugin._get_client') as mock_get_client: + mock_client = MagicMock() + mock_description = MagicMock() + mock_description.name = 'test-index' + mock_description.dimension = 768 + mock_description.metric = 'cosine' + mock_description.host = 'test-index.svc.pinecone.io' + mock_description.status = {'ready': True} + mock_client.describe_index.return_value = mock_description + mock_get_client.return_value = mock_client + + await describe_pinecone_index( + name='test-index', + client_params={'api_key': 'custom-key'}, + ) + + mock_get_client.assert_called_once_with({'api_key': 'custom-key'}) + + +class TestDeletePineconeIndex: + """Tests for delete_pinecone_index matching JS deletePineconeIndex.""" + + @pytest.mark.asyncio + async def test_delete_index_basic(self) -> None: + """Test basic index deletion.""" + with patch('genkit.plugins.pinecone.plugin._get_client') as mock_get_client: + mock_client = MagicMock() + mock_get_client.return_value = mock_client + + await delete_pinecone_index(name='test-index') + + mock_client.delete_index.assert_called_once_with('test-index') + + @pytest.mark.asyncio + async def test_delete_index_with_client_params(self) -> None: + """Test index deletion with client params.""" + with patch('genkit.plugins.pinecone.plugin._get_client') as mock_get_client: + mock_client = MagicMock() + mock_get_client.return_value = mock_client + + await delete_pinecone_index( + name='test-index', + client_params={'api_key': 'custom-key'}, + ) + + mock_get_client.assert_called_once_with({'api_key': 'custom-key'}) diff --git a/py/plugins/pinecone/tests/pinecone_indexer_test.py b/py/plugins/pinecone/tests/pinecone_indexer_test.py new file mode 100644 index 0000000000..c650707de7 --- /dev/null +++ b/py/plugins/pinecone/tests/pinecone_indexer_test.py @@ -0,0 +1,322 @@ +# Copyright 2026 Google LLC +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. +# +# SPDX-License-Identifier: Apache-2.0 + +"""Tests for the Pinecone indexer functionality. + +These tests verify parity with the JS implementation in: +js/plugins/pinecone/src/index.ts (configurePineconeIndexer function) +""" + +import hashlib +from unittest.mock import AsyncMock, MagicMock, patch + +import pytest + +from genkit.blocks.retriever import IndexerRequest +from genkit.core.typing import DocumentPart, TextPart +from genkit.plugins.pinecone.plugin import ( + CONTENT_KEY, + CONTENT_TYPE_KEY, + PineconeIndexer, + PineconeIndexerOptions, + _md5_hash, +) +from genkit.types import DocumentData + + +class TestPineconeIndexerOptions: + """Tests for PineconeIndexerOptions matching JS PineconeIndexerOptionsSchema.""" + + def test_default_namespace_is_none(self) -> None: + """Test default namespace is None (matches JS optional).""" + options = PineconeIndexerOptions() + assert options.namespace is None + + def test_namespace_option(self) -> None: + """Test namespace option (matches JS).""" + options = PineconeIndexerOptions(namespace='my-namespace') + assert options.namespace == 'my-namespace' + + +class TestMd5Hash: + """Tests for _md5_hash function matching JS Md5.hashStr.""" + + def test_md5_hash_produces_correct_hash(self) -> None: + """Test MD5 hash matches expected value.""" + content = 'Hello, world!' + result = _md5_hash(content) + + # Verify it's a valid MD5 hash (32 hex characters) + assert len(result) == 32 + assert all(c in '0123456789abcdef' for c in result) + + def test_md5_hash_matches_standard_implementation(self) -> None: + """Test MD5 hash matches Python's hashlib implementation.""" + content = 'Test content for hashing' + result = _md5_hash(content) + expected = hashlib.md5(content.encode('utf-8')).hexdigest() + assert result == expected + + def test_md5_hash_consistent_for_same_input(self) -> None: + """Test MD5 hash is deterministic (same input -> same output).""" + content = 'Some document content' + hash1 = _md5_hash(content) + hash2 = _md5_hash(content) + assert hash1 == hash2 + + +class TestContentKeys: + """Tests for content key constants matching JS.""" + + def test_content_key_value(self) -> None: + """Test CONTENT_KEY matches JS.""" + assert CONTENT_KEY == '_content' + + def test_content_type_key_value(self) -> None: + """Test CONTENT_TYPE_KEY matches JS CONTENT_TYPE.""" + # JS uses '_contentType', Python uses '_content_type' + # Both are valid as they're internal storage keys + assert CONTENT_TYPE_KEY == '_content_type' + + +class TestPineconeIndexer: + """Tests for PineconeIndexer class matching JS configurePineconeIndexer.""" + + def test_indexer_initialization(self) -> None: + """Test indexer initialization with required params.""" + mock_registry = MagicMock() + indexer = PineconeIndexer( + registry=mock_registry, + index_id='test-index', + embedder='googleai/text-embedding-004', + ) + + assert indexer._index_id == 'test-index' + assert indexer._embedder == 'googleai/text-embedding-004' + + def test_indexer_with_embedder_options(self) -> None: + """Test indexer with embedder options (matches JS).""" + mock_registry = MagicMock() + indexer = PineconeIndexer( + registry=mock_registry, + index_id='test-index', + embedder='test/embedder', + embedder_options={'dimension': 768}, + ) + + assert indexer._embedder_options == {'dimension': 768} + + def test_indexer_with_client_params(self) -> None: + """Test indexer with client params (matches JS clientParams).""" + mock_registry = MagicMock() + indexer = PineconeIndexer( + registry=mock_registry, + index_id='test-index', + embedder='test/embedder', + client_params={'api_key': 'test-key'}, + ) + + assert indexer._client_params == {'api_key': 'test-key'} + + def test_indexer_custom_content_key(self) -> None: + """Test contentKey option (matches JS).""" + mock_registry = MagicMock() + indexer = PineconeIndexer( + registry=mock_registry, + index_id='test-index', + embedder='test/embedder', + content_key='custom_content', + ) + + assert indexer._content_key == 'custom_content' + + def test_indexer_default_content_key(self) -> None: + """Test default contentKey is '_content' (matches JS).""" + mock_registry = MagicMock() + indexer = PineconeIndexer( + registry=mock_registry, + index_id='test-index', + embedder='test/embedder', + ) + + assert indexer._content_key == CONTENT_KEY + + @pytest.mark.asyncio + async def test_indexer_upserts_vectors(self) -> None: + """Test indexer upserts vectors to Pinecone (matching JS).""" + mock_registry = MagicMock() + mock_embedder_action = MagicMock() + mock_embedder_action.arun = AsyncMock( + return_value=MagicMock(response=MagicMock(embeddings=[MagicMock(embedding=[0.1, 0.2, 0.3])])) + ) + mock_registry.resolve_embedder = AsyncMock(return_value=mock_embedder_action) + + indexer = PineconeIndexer( + registry=mock_registry, + index_id='test-index', + embedder='test/embedder', + client_params={'api_key': 'test-key'}, + ) + + mock_index = MagicMock() + + with patch.object(indexer, '_get_index', return_value=mock_index): + request = IndexerRequest( + documents=[ + DocumentData(content=[DocumentPart(root=TextPart(text='Document content'))]), + ], + ) + + await indexer.index(request) + + # Verify upsert was called (matches JS) + mock_index.upsert.assert_called_once() + + @pytest.mark.asyncio + async def test_indexer_generates_md5_ids(self) -> None: + """Test indexer generates MD5 IDs for vectors (matching JS Md5.hashStr).""" + mock_registry = MagicMock() + mock_embedder_action = MagicMock() + mock_embedder_action.arun = AsyncMock( + return_value=MagicMock(response=MagicMock(embeddings=[MagicMock(embedding=[0.1, 0.2, 0.3])])) + ) + mock_registry.resolve_embedder = AsyncMock(return_value=mock_embedder_action) + + indexer = PineconeIndexer( + registry=mock_registry, + index_id='test-index', + embedder='test/embedder', + client_params={'api_key': 'test-key'}, + ) + + mock_index = MagicMock() + + with patch.object(indexer, '_get_index', return_value=mock_index): + request = IndexerRequest( + documents=[ + DocumentData(content=[DocumentPart(root=TextPart(text='Test content'))]), + ], + ) + + await indexer.index(request) + + # Verify IDs are MD5 hashes (32 hex chars) + call_args = mock_index.upsert.call_args + vectors = call_args[1]['vectors'] + assert all(len(v['id']) == 32 for v in vectors) + assert all(c in '0123456789abcdef' for v in vectors for c in v['id']) + + @pytest.mark.asyncio + async def test_indexer_stores_content_in_metadata(self) -> None: + """Test indexer stores content in metadata (matching JS).""" + mock_registry = MagicMock() + mock_embedder_action = MagicMock() + mock_embedder_action.arun = AsyncMock( + return_value=MagicMock(response=MagicMock(embeddings=[MagicMock(embedding=[0.1, 0.2, 0.3])])) + ) + mock_registry.resolve_embedder = AsyncMock(return_value=mock_embedder_action) + + indexer = PineconeIndexer( + registry=mock_registry, + index_id='test-index', + embedder='test/embedder', + client_params={'api_key': 'test-key'}, + ) + + mock_index = MagicMock() + + with patch.object(indexer, '_get_index', return_value=mock_index): + request = IndexerRequest( + documents=[ + DocumentData(content=[DocumentPart(root=TextPart(text='Document content'))]), + ], + ) + + await indexer.index(request) + + call_args = mock_index.upsert.call_args + vectors = call_args[1]['vectors'] + # Verify content is stored in metadata under content_key + assert CONTENT_KEY in vectors[0]['metadata'] + + @pytest.mark.asyncio + async def test_indexer_uses_namespace(self) -> None: + """Test indexer uses namespace option (matching JS).""" + mock_registry = MagicMock() + mock_embedder_action = MagicMock() + mock_embedder_action.arun = AsyncMock( + return_value=MagicMock(response=MagicMock(embeddings=[MagicMock(embedding=[0.1, 0.2, 0.3])])) + ) + mock_registry.resolve_embedder = AsyncMock(return_value=mock_embedder_action) + + indexer = PineconeIndexer( + registry=mock_registry, + index_id='test-index', + embedder='test/embedder', + client_params={'api_key': 'test-key'}, + ) + + mock_index = MagicMock() + + with patch.object(indexer, '_get_index', return_value=mock_index): + request = IndexerRequest( + documents=[ + DocumentData(content=[DocumentPart(root=TextPart(text='Document content'))]), + ], + options={'namespace': 'my-namespace'}, + ) + + await indexer.index(request) + + call_args = mock_index.upsert.call_args + assert call_args[1]['namespace'] == 'my-namespace' + + @pytest.mark.asyncio + async def test_indexer_handles_empty_documents(self) -> None: + """Test indexer handles empty document list gracefully.""" + mock_registry = MagicMock() + indexer = PineconeIndexer( + registry=mock_registry, + index_id='test-index', + embedder='test/embedder', + ) + + request = IndexerRequest(documents=[]) + + # Should not raise, just return early + await indexer.index(request) + + +class TestPineconeIndexerRef: + """Tests for pinecone_indexer_ref function matching JS pineconeIndexerRef.""" + + def test_indexer_ref_format(self) -> None: + """Test indexer ref creates correct reference string.""" + from genkit.plugins.pinecone import pinecone_indexer_ref + + ref = pinecone_indexer_ref(index_id='my-index') + assert ref == 'pinecone/my-index' + + def test_indexer_ref_with_display_name(self) -> None: + """Test indexer ref with display name (ignored, for JS parity).""" + from genkit.plugins.pinecone import pinecone_indexer_ref + + ref = pinecone_indexer_ref( + index_id='my-index', + display_name='My Custom Label', + ) + # Display name is ignored in Python, ref is still the same + assert ref == 'pinecone/my-index' diff --git a/py/plugins/pinecone/tests/pinecone_plugin_test.py b/py/plugins/pinecone/tests/pinecone_plugin_test.py new file mode 100644 index 0000000000..842c2f0902 --- /dev/null +++ b/py/plugins/pinecone/tests/pinecone_plugin_test.py @@ -0,0 +1,183 @@ +# Copyright 2026 Google LLC +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. +# +# SPDX-License-Identifier: Apache-2.0 + +"""Tests for the Pinecone plugin.""" + +from unittest.mock import MagicMock + +import pytest + +from genkit.core.action.types import ActionKind +from genkit.plugins.pinecone import ( + Pinecone, + PineconeIndexConfig, + PineconeIndexerOptions, + PineconeRetrieverOptions, + pinecone, + pinecone_indexer_ref, + pinecone_retriever_ref, +) + + +def test_plugin_name() -> None: + """Test plugin has correct name.""" + plugin = Pinecone() + assert plugin.name == 'pinecone' + + +def test_init_empty_config() -> None: + """Test plugin initialization with no config.""" + plugin = Pinecone() + assert plugin._indexes == [] + + +def test_init_with_indexes() -> None: + """Test plugin initialization with index configs.""" + config = PineconeIndexConfig( + index_id='test-index', + embedder='googleai/text-embedding-004', + ) + plugin = Pinecone(indexes=[config]) + assert len(plugin._indexes) == 1 + assert plugin._indexes[0].index_id == 'test-index' + + +def test_pinecone_factory_function() -> None: + """Test pinecone() factory function creates plugin correctly.""" + plugin = pinecone( + indexes=[ + { + 'index_id': 'my-index', + 'embedder': 'test/embedder', + } + ] + ) + assert isinstance(plugin, Pinecone) + assert len(plugin._indexes) == 1 + + +def test_pinecone_retriever_ref() -> None: + """Test pinecone_retriever_ref creates correct reference string.""" + ref = pinecone_retriever_ref(index_id='my-index') + assert ref == 'pinecone/my-index' + + +def test_pinecone_indexer_ref() -> None: + """Test pinecone_indexer_ref creates correct reference string.""" + ref = pinecone_indexer_ref(index_id='my-index') + assert ref == 'pinecone/my-index' + + +def test_pinecone_index_config_defaults() -> None: + """Test PineconeIndexConfig default values.""" + config = PineconeIndexConfig( + index_id='test', + embedder='test/embedder', + ) + assert config.index_id == 'test' + assert config.embedder == 'test/embedder' + assert config.embedder_options is None + + +def test_pinecone_retriever_options_defaults() -> None: + """Test PineconeRetrieverOptions default values.""" + options = PineconeRetrieverOptions() + assert options.k == 10 + assert options.namespace is None + assert options.filter is None + + +def test_pinecone_indexer_options_defaults() -> None: + """Test PineconeIndexerOptions default values.""" + options = PineconeIndexerOptions() + assert options.namespace is None + + +@pytest.mark.asyncio +async def test_init_stores_registry() -> None: + """Test that init() stores the registry for later use.""" + plugin = Pinecone() + mock_registry = MagicMock() + + result = await plugin.init(mock_registry) + + assert result == [] + assert plugin._registry == mock_registry + + +@pytest.mark.asyncio +async def test_list_actions_with_indexes() -> None: + """Test list_actions returns metadata for configured indexes.""" + config1 = PineconeIndexConfig( + index_id='index1', + embedder='test/embedder', + ) + config2 = PineconeIndexConfig( + index_id='index2', + embedder='test/embedder', + ) + plugin = Pinecone(indexes=[config1, config2]) + + actions = await plugin.list_actions() + + # Should have 2 retrievers + 2 indexers = 4 actions + assert len(actions) == 4 + + retriever_actions = [a for a in actions if a.kind == ActionKind.RETRIEVER] + indexer_actions = [a for a in actions if a.kind == ActionKind.INDEXER] + + assert len(retriever_actions) == 2 + assert len(indexer_actions) == 2 + + +@pytest.mark.asyncio +async def test_list_actions_empty() -> None: + """Test list_actions with no configured indexes.""" + plugin = Pinecone() + + actions = await plugin.list_actions() + assert actions == [] + + +@pytest.mark.asyncio +async def test_resolve_returns_none_for_unknown_action() -> None: + """Test resolve returns None for unknown action names.""" + plugin = Pinecone() + plugin._registry = MagicMock() + + result = await plugin.resolve(ActionKind.MODEL, 'pinecone/test') + assert result is None + + +@pytest.mark.asyncio +async def test_resolve_returns_none_for_other_namespace() -> None: + """Test resolve returns None for actions not in pinecone namespace.""" + plugin = Pinecone() + plugin._registry = MagicMock() + + result = await plugin.resolve(ActionKind.RETRIEVER, 'other/index') + assert result is None + + +@pytest.mark.asyncio +async def test_resolve_returns_none_for_unconfigured_index() -> None: + """Test resolve returns None for indexes that aren't configured.""" + plugin = Pinecone() + mock_registry = MagicMock() + await plugin.init(mock_registry) + + result = await plugin.resolve(ActionKind.RETRIEVER, 'pinecone/nonexistent') + assert result is None diff --git a/py/plugins/pinecone/tests/pinecone_retriever_test.py b/py/plugins/pinecone/tests/pinecone_retriever_test.py new file mode 100644 index 0000000000..9782f119e7 --- /dev/null +++ b/py/plugins/pinecone/tests/pinecone_retriever_test.py @@ -0,0 +1,266 @@ +# Copyright 2026 Google LLC +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. +# +# SPDX-License-Identifier: Apache-2.0 + +"""Tests for the Pinecone retriever functionality. + +These tests verify parity with the JS implementation in: +js/plugins/pinecone/src/index.ts (configurePineconeRetriever function) +""" + +from unittest.mock import AsyncMock, MagicMock, patch + +import pytest + +from genkit.blocks.retriever import RetrieverRequest +from genkit.core.action import ActionRunContext +from genkit.core.typing import DocumentPart, TextPart +from genkit.plugins.pinecone.plugin import ( + CONTENT_KEY, + MAX_K, + PineconeRetriever, + PineconeRetrieverOptions, +) +from genkit.types import DocumentData + + +class TestPineconeRetrieverOptions: + """Tests for PineconeRetrieverOptions matching JS PineconeRetrieverOptionsSchema.""" + + def test_default_k_value(self) -> None: + """Test default k value matches JS CommonRetrieverOptionsSchema.""" + options = PineconeRetrieverOptions() + assert options.k == 10 + + def test_k_max_value(self) -> None: + """Test k max value is 1000 (matching JS .max(1000)).""" + assert MAX_K == 1000 + # Pydantic should enforce max + options = PineconeRetrieverOptions(k=1000) + assert options.k == 1000 + + def test_namespace_option(self) -> None: + """Test namespace option (matches JS schema).""" + options = PineconeRetrieverOptions(namespace='my-namespace') + assert options.namespace == 'my-namespace' + + def test_filter_option(self) -> None: + """Test filter option for metadata filtering (matches JS).""" + options = PineconeRetrieverOptions( + filter={'category': 'science'}, + ) + assert options.filter == {'category': 'science'} + + def test_all_options_combined(self) -> None: + """Test all options can be combined.""" + options = PineconeRetrieverOptions( + k=50, + namespace='test-namespace', + filter={'type': 'doc'}, + ) + assert options.k == 50 + assert options.namespace == 'test-namespace' + assert options.filter is not None + + +class TestPineconeRetriever: + """Tests for PineconeRetriever class matching JS configurePineconeRetriever.""" + + def test_retriever_initialization(self) -> None: + """Test retriever initialization with required params.""" + mock_registry = MagicMock() + retriever = PineconeRetriever( + registry=mock_registry, + index_id='test-index', + embedder='googleai/text-embedding-004', + ) + + assert retriever._index_id == 'test-index' + assert retriever._embedder == 'googleai/text-embedding-004' + + def test_retriever_with_embedder_options(self) -> None: + """Test retriever with embedder options (matches JS).""" + mock_registry = MagicMock() + retriever = PineconeRetriever( + registry=mock_registry, + index_id='test-index', + embedder='test/embedder', + embedder_options={'model': 'custom'}, + ) + + assert retriever._embedder_options == {'model': 'custom'} + + def test_retriever_with_client_params(self) -> None: + """Test retriever with client params (matches JS clientParams/PineconeConfiguration).""" + mock_registry = MagicMock() + retriever = PineconeRetriever( + registry=mock_registry, + index_id='test-index', + embedder='test/embedder', + client_params={'api_key': 'test-key'}, + ) + + assert retriever._client_params == {'api_key': 'test-key'} + + def test_retriever_custom_content_key(self) -> None: + """Test contentKey option (matches JS).""" + mock_registry = MagicMock() + retriever = PineconeRetriever( + registry=mock_registry, + index_id='test-index', + embedder='test/embedder', + content_key='custom_content', + ) + + assert retriever._content_key == 'custom_content' + + def test_retriever_default_content_key(self) -> None: + """Test default contentKey is '_content' (matches JS CONTENT_KEY).""" + mock_registry = MagicMock() + retriever = PineconeRetriever( + registry=mock_registry, + index_id='test-index', + embedder='test/embedder', + ) + + assert retriever._content_key == CONTENT_KEY + assert retriever._content_key == '_content' + + @pytest.mark.asyncio + async def test_retriever_uses_namespace(self) -> None: + """Test retriever uses namespace option (matching JS).""" + mock_registry = MagicMock() + mock_embedder_action = MagicMock() + mock_embedder_action.arun = AsyncMock( + return_value=MagicMock(response=MagicMock(embeddings=[MagicMock(embedding=[0.1, 0.2, 0.3])])) + ) + mock_registry.resolve_embedder = AsyncMock(return_value=mock_embedder_action) + + retriever = PineconeRetriever( + registry=mock_registry, + index_id='test-index', + embedder='test/embedder', + client_params={'api_key': 'test-key'}, + ) + + mock_index = MagicMock() + mock_index.query.return_value = {'matches': []} + + with patch.object(retriever, '_get_index', return_value=mock_index): + request = RetrieverRequest( + query=DocumentData(content=[DocumentPart(root=TextPart(text='search query'))]), + options=PineconeRetrieverOptions( + k=10, + namespace='my-namespace', + ), + ) + + ctx = MagicMock(spec=ActionRunContext) + await retriever.retrieve(request, ctx) + + # Verify namespace was passed + call_args = mock_index.query.call_args + assert call_args[1]['namespace'] == 'my-namespace' + + @pytest.mark.asyncio + async def test_retriever_uses_filter(self) -> None: + """Test retriever uses filter option (matching JS).""" + mock_registry = MagicMock() + mock_embedder_action = MagicMock() + mock_embedder_action.arun = AsyncMock( + return_value=MagicMock(response=MagicMock(embeddings=[MagicMock(embedding=[0.1, 0.2, 0.3])])) + ) + mock_registry.resolve_embedder = AsyncMock(return_value=mock_embedder_action) + + retriever = PineconeRetriever( + registry=mock_registry, + index_id='test-index', + embedder='test/embedder', + client_params={'api_key': 'test-key'}, + ) + + mock_index = MagicMock() + mock_index.query.return_value = {'matches': []} + + with patch.object(retriever, '_get_index', return_value=mock_index): + request = RetrieverRequest( + query=DocumentData(content=[DocumentPart(root=TextPart(text='search query'))]), + options=PineconeRetrieverOptions( + filter={'category': 'tech'}, + ), + ) + + ctx = MagicMock(spec=ActionRunContext) + await retriever.retrieve(request, ctx) + + call_args = mock_index.query.call_args + assert call_args[1]['filter'] == {'category': 'tech'} + + @pytest.mark.asyncio + async def test_retriever_query_params_match_js(self) -> None: + """Test retriever query params match JS implementation.""" + mock_registry = MagicMock() + mock_embedder_action = MagicMock() + mock_embedder_action.arun = AsyncMock( + return_value=MagicMock(response=MagicMock(embeddings=[MagicMock(embedding=[0.1, 0.2, 0.3])])) + ) + mock_registry.resolve_embedder = AsyncMock(return_value=mock_embedder_action) + + retriever = PineconeRetriever( + registry=mock_registry, + index_id='test-index', + embedder='test/embedder', + client_params={'api_key': 'test-key'}, + ) + + mock_index = MagicMock() + mock_index.query.return_value = {'matches': []} + + with patch.object(retriever, '_get_index', return_value=mock_index): + request = RetrieverRequest( + query=DocumentData(content=[DocumentPart(root=TextPart(text='search query'))]), + options=PineconeRetrieverOptions(k=20), + ) + + ctx = MagicMock(spec=ActionRunContext) + await retriever.retrieve(request, ctx) + + call_args = mock_index.query.call_args + # Match JS: includeValues: false, includeMetadata: true + assert call_args[1]['include_values'] is False + assert call_args[1]['include_metadata'] is True + assert call_args[1]['top_k'] == 20 + + +class TestPineconeRetrieverRef: + """Tests for pinecone_retriever_ref function matching JS pineconeRetrieverRef.""" + + def test_retriever_ref_format(self) -> None: + """Test retriever ref creates correct reference string.""" + from genkit.plugins.pinecone import pinecone_retriever_ref + + ref = pinecone_retriever_ref(index_id='my-index') + assert ref == 'pinecone/my-index' + + def test_retriever_ref_with_display_name(self) -> None: + """Test retriever ref with display name (ignored, for JS parity).""" + from genkit.plugins.pinecone import pinecone_retriever_ref + + ref = pinecone_retriever_ref( + index_id='my-index', + display_name='My Custom Label', + ) + # Display name is ignored in Python, ref is still the same + assert ref == 'pinecone/my-index' diff --git a/py/plugins/vertex-ai/src/genkit/plugins/vertex_ai/model_garden/modelgarden_plugin.py b/py/plugins/vertex-ai/src/genkit/plugins/vertex_ai/model_garden/modelgarden_plugin.py index a07ab5c455..7d4ea07875 100644 --- a/py/plugins/vertex-ai/src/genkit/plugins/vertex_ai/model_garden/modelgarden_plugin.py +++ b/py/plugins/vertex-ai/src/genkit/plugins/vertex_ai/model_garden/modelgarden_plugin.py @@ -23,6 +23,7 @@ from genkit.blocks.model import model_action_metadata from genkit.core.action import Action, ActionMetadata from genkit.core.action.types import ActionKind +from genkit.core.registry import Registry from genkit.core.schema import to_json_schema from genkit.plugins.compat_oai.models import SUPPORTED_OPENAI_COMPAT_MODELS from genkit.plugins.compat_oai.typing import OpenAIConfig @@ -77,9 +78,12 @@ def __init__( self.models = models self.model_locations = model_locations or {} - async def init(self) -> list[Action]: + async def init(self, registry: 'Registry | None' = None) -> list[Action]: """Initialize plugin. + Args: + registry: Optional registry (not used by this plugin). + Returns: Empty list (using lazy loading via resolve). """ diff --git a/py/plugins/xai/src/genkit/plugins/xai/plugin.py b/py/plugins/xai/src/genkit/plugins/xai/plugin.py index a6e2c6f1df..9c9aa2d10d 100644 --- a/py/plugins/xai/src/genkit/plugins/xai/plugin.py +++ b/py/plugins/xai/src/genkit/plugins/xai/plugin.py @@ -25,7 +25,7 @@ from genkit.blocks.model import model_action_metadata from genkit.core.action import Action from genkit.core.error import GenkitError -from genkit.core.registry import ActionKind +from genkit.core.registry import ActionKind, Registry from genkit.core.schema import to_json_schema from genkit.plugins.xai.model_info import SUPPORTED_XAI_MODELS, get_model_info from genkit.plugins.xai.models import XAIConfig, XAIModel @@ -67,9 +67,12 @@ def __init__( self._xai_params = xai_params self._xai_client = XAIClient(api_key=api_key, **cast(dict[str, Any], xai_params)) - async def init(self) -> list: + async def init(self, registry: 'Registry | None' = None) -> list: """Initialize plugin. + Args: + registry: Optional registry (not used by this plugin). + Returns: List of Action objects for pre-configured models. """ diff --git a/py/plugins/xai/tests/xai_plugin_test.py b/py/plugins/xai/tests/xai_plugin_test.py index 9d49e8ffce..6f4101aefb 100644 --- a/py/plugins/xai/tests/xai_plugin_test.py +++ b/py/plugins/xai/tests/xai_plugin_test.py @@ -64,8 +64,11 @@ def test_custom_models() -> None: @pytest.mark.asyncio async def test_plugin_initialize() -> None: """Test plugin initialization.""" + from unittest.mock import MagicMock + plugin = XAI(api_key='test-key') - actions = await plugin.init() + mock_registry = MagicMock() + actions = await plugin.init(mock_registry) assert len(actions) == len(SUPPORTED_XAI_MODELS) diff --git a/py/pyproject.toml b/py/pyproject.toml index f9a323f6cb..b626658fb1 100644 --- a/py/pyproject.toml +++ b/py/pyproject.toml @@ -20,6 +20,11 @@ dependencies = [ "dotpromptz==0.1.5", "genkit", "genkit-plugin-anthropic", + # TODO: Re-enable genkit-plugin-chroma for Python 3.14 when onnxruntime adds wheels. + # Track: https://github.com/microsoft/onnxruntime/issues/21292 + "genkit-plugin-chroma; python_version < '3.14'", + "genkit-plugin-checks", + "genkit-plugin-cloud-sql-pg", "genkit-plugin-compat-oai", "genkit-plugin-deepseek", "genkit-plugin-dev-local-vectorstore", @@ -29,6 +34,7 @@ dependencies = [ "genkit-plugin-google-cloud", "genkit-plugin-google-genai", "genkit-plugin-ollama", + "genkit-plugin-pinecone", "genkit-plugin-vertex-ai", "genkit-plugin-xai", "genkit-plugin-mcp", @@ -37,6 +43,10 @@ dependencies = [ "mcp>=1.25.0", "python-multipart>=0.0.22", "strenum>=0.4.15; python_version < '3.11'", + # TODO: Re-enable chromadb for Python 3.14 when onnxruntime adds wheels. + # Track: https://github.com/microsoft/onnxruntime/issues/21292 + "chromadb>=1.4.1; python_version < '3.14'", + "pinecone>=7.0.1", ] description = "Workspace for Genkit packages" license = "Apache-2.0" @@ -121,6 +131,8 @@ default-groups = ["dev", "lint"] evaluator-demo = { workspace = true } genkit = { workspace = true } genkit-plugin-anthropic = { workspace = true } +genkit-plugin-checks = { workspace = true } +genkit-plugin-chroma = { workspace = true } genkit-plugin-compat-oai = { workspace = true } genkit-plugin-deepseek = { workspace = true } genkit-plugin-dev-local-vectorstore = { workspace = true } @@ -131,13 +143,18 @@ genkit-plugin-google-cloud = { workspace = true } genkit-plugin-google-genai = { workspace = true } genkit-plugin-mcp = { workspace = true } genkit-plugin-msfoundry = { workspace = true } +genkit-plugin-cloud-sql-pg = { workspace = true } genkit-plugin-ollama = { workspace = true } +genkit-plugin-pinecone = { workspace = true } genkit-plugin-vertex-ai = { workspace = true } genkit-plugin-xai = { workspace = true } google-genai-hello = { workspace = true } google-genai-image = { workspace = true } msfoundry-hello = { workspace = true } prompt-demo = { workspace = true } +rag-chroma-demo = { workspace = true } +rag-cloud-sql-pg-sample = { workspace = true } +rag-pinecone-demo = { workspace = true } [tool.uv.workspace] exclude = ["*/shared"] @@ -255,15 +272,20 @@ use-unique-items-as-set = true [tool.liccheck] authorized_licenses = [ + "0bsd", "3-clause bsd", "apache 2.0", + "apache license, version 2.0", "apache software license", "apache software", "apache", "apache-2.0", "bsd license", + "bsd-2-clause", "bsd-3-clause", + "bsd-3-clause and 0bsd and mit and zlib and cc0-1.0", "bsd", + "cc0-1.0", "cmu license (mit-cmu)", "isc license (iscl)", "isc license", @@ -275,10 +297,10 @@ authorized_licenses = [ "psf-2.0", "python software foundation license", "simplified bsd", - "the unlicense (unlicense)", # TODO: verify. - "bsd-2-clause", + "the unlicense (unlicense)", # TODO: verify. "apache license 2.0", "apache-2.0 and mit", + "zlib", ] dependencies = true unauthorized_licenses = [ @@ -296,8 +318,11 @@ certifi = "2026.1.4" # TODO: Verify. dependencies = true dotpromptz-handlebars = "0.1.8" # Apache-2.0 "https://github.com/google/dotprompt/blob/main/LICENSE" google-crc32c = "1.8.0" # Apache-2.0 +greenlet = ">=3.0.0" # MIT AND Python-2.0 (transitive dep of sqlalchemy) multidict = "6.7.0" # Apache-2.0 +numpy = ">=2.0.0" # BSD-3-Clause AND 0BSD AND MIT AND Zlib AND CC0-1.0 (transitive dep of chromadb) ollama = "0.5.1" # MIT "https://github.com/ollama/ollama-python/blob/main/LICENSE" +overrides = ">=7.0.0" # Apache License, Version 2.0 (transitive dep of chromadb) pyasn1 = "0.6.2" # BSD-2-Clause # Ty (Astral/Ruff) type checking configuration. @@ -308,6 +333,8 @@ root = [ "packages/genkit/src", # Plugins "plugins/anthropic/src", + "plugins/checks/src", + "plugins/chroma/src", "plugins/compat-oai/src", "plugins/deepseek/src", "plugins/dev-local-vectorstore/src", @@ -318,6 +345,8 @@ root = [ "plugins/google-genai/src", "plugins/mcp/src", "plugins/ollama/src", + "plugins/pinecone/src", + "plugins/cloud-sql-pg/src", "plugins/vertex-ai/src", "plugins/xai/src", "plugins/msfoundry/src", diff --git a/py/samples/README.md b/py/samples/README.md new file mode 100644 index 0000000000..c043f3a4d3 --- /dev/null +++ b/py/samples/README.md @@ -0,0 +1,184 @@ +# Genkit Python Samples + +This directory contains example applications demonstrating various Genkit features +and integrations. Each sample is self-contained and can be run independently. + +## Quick Start + +All samples follow a consistent pattern: + +```bash +cd sample-name/ +./run.sh +# Open http://localhost:4000 in your browser +``` + +## Sample Categories + +### Getting Started + +Basic examples to get started with different model providers. + +| Sample | Description | Required API Key | +|--------|-------------|------------------| +| [google-genai-hello](./google-genai-hello/) | Basic Gemini usage with flows, tools, and streaming | `GOOGLE_API_KEY` | +| [anthropic-hello](./anthropic-hello/) | Claude models with tools and thinking mode | `ANTHROPIC_API_KEY` | +| [ollama-hello](./ollama-hello/) | Local models via Ollama | None (local) | +| [xai-hello](./xai-hello/) | xAI Grok models | `XAI_API_KEY` | +| [deepseek-hello](./deepseek-hello/) | DeepSeek models | `DEEPSEEK_API_KEY` | +| [compat-oai-hello](./compat-oai-hello/) | OpenAI-compatible endpoints | Varies | + +### Google Cloud & Vertex AI + +Samples using Google Cloud services and Vertex AI. + +| Sample | Description | Required Setup | +|--------|-------------|----------------| +| [google-genai-vertexai-hello](./google-genai-vertexai-hello/) | Vertex AI Gemini models | GCP Project + ADC | +| [google-genai-vertexai-image](./google-genai-vertexai-image/) | Vertex AI Imagen | GCP Project + ADC | +| [model-garden](./model-garden/) | Vertex AI Model Garden (Anthropic, Llama) | GCP Project + ADC | +| [vertex-ai-vector-search-bigquery](./vertex-ai-vector-search-bigquery/) | Vector Search with BigQuery | GCP Project + ADC | +| [vertex-ai-vector-search-firestore](./vertex-ai-vector-search-firestore/) | Vector Search with Firestore | GCP Project + ADC | +| [firestore-retreiver](./firestore-retreiver/) | Firestore document retrieval | GCP Project + ADC | + +### RAG (Retrieval-Augmented Generation) + +Samples demonstrating RAG patterns with different vector stores. + +| Sample | Description | Vector Store | +|--------|-------------|--------------| +| [rag-chroma](./rag-chroma/) | RAG with ChromaDB - cat knowledge base | ChromaDB (in-memory) | +| [rag-pinecone](./rag-pinecone/) | RAG with Pinecone - cat knowledge base | Pinecone (cloud) | +| [dev-local-vectorstore-hello](./dev-local-vectorstore-hello/) | Local file-based vector store | Local files | +| [ollama-simple-embed](./ollama-simple-embed/) | Embeddings with Ollama | Ollama | + +### Media Generation + +Samples for generating images, audio, and video. + +| Sample | Description | Models Used | +|--------|-------------|-------------| +| [media-models-demo](./media-models-demo/) | All media generation (TTS, Image, Video) | TTS, Imagen, Veo, Lyria | +| [google-genai-image](./google-genai-image/) | Image generation with Gemini | Gemini Vision | + +### Advanced Features + +Samples demonstrating advanced Genkit capabilities. + +| Sample | Description | Features | +|--------|-------------|----------| +| [checks-demo](./checks-demo/) | Google Checks AI safety guardrails | Content moderation, policy filtering | +| [menu](./menu/) | Restaurant menu assistant | Multi-case flows, RAG, chat | +| [evaluator-demo](./evaluator-demo/) | Evaluation and testing | Built-in evaluators, custom metrics | +| [prompt_demo](./prompt_demo/) | Dotprompt templates | Prompt files, partials, variants | +| [format-demo](./format-demo/) | Output formatting | JSON, structured output | +| [tool-interrupts](./tool-interrupts/) | Tool interrupt/resume pattern | Human-in-the-loop | +| [short-n-long](./short-n-long/) | Short and long-running operations | Background actions | +| [google-genai-context-caching](./google-genai-context-caching/) | Context caching for efficiency | Cached contexts | +| [google-genai-code-execution](./google-genai-code-execution/) | Code execution sandbox | Code interpreter | + +### Web Frameworks + +Samples showing framework integrations. + +| Sample | Description | Framework | +|--------|-------------|-----------| +| [flask-hello](./flask-hello/) | Flask web app with Genkit | Flask | +| [multi-server](./multi-server/) | Multiple servers architecture | Multi-process | + +### Observability + +Samples demonstrating tracing and monitoring. + +| Sample | Description | Features | +|--------|-------------|----------| +| [realtime-tracing-demo](./realtime-tracing-demo/) | Real-time trace visualization | OpenTelemetry | + +## Running Samples + +### Prerequisites + +1. **Python 3.10+** and **uv** package manager +2. **Genkit CLI**: `npm install -g genkit` +3. **API keys** for the providers you want to use + +### Environment Variables + +Set the required environment variables for your chosen sample: + +```bash +# Google AI (Gemini) +export GOOGLE_API_KEY=your-key + +# Anthropic (Claude) +export ANTHROPIC_API_KEY=your-key + +# xAI (Grok) +export XAI_API_KEY=your-key + +# DeepSeek +export DEEPSEEK_API_KEY=your-key + +# OpenAI-compatible +export OPENAI_API_KEY=your-key +export OPENAI_BASE_URL=https://api.example.com/v1 + +# Google Cloud (ADC) +gcloud auth application-default login +``` + +### Using run.sh + +Each sample includes a `run.sh` script that: +- Installs dependencies via `uv sync` +- Starts the Genkit Dev UI +- Watches for file changes and auto-reloads + +```bash +./run.sh # Start the sample +./run.sh --help # Show help (if available) +``` + +### Manual Execution + +```bash +cd sample-name/ +uv sync +genkit start -- uv run src/main.py +``` + +## Sample Structure + +Each sample follows a standard structure: + +``` +sample-name/ +├── README.md # Sample documentation +├── pyproject.toml # Dependencies +├── run.sh # Run script with hot-reload +├── src/ +│ └── main.py # Main application code +├── data/ # Sample data (if needed) +└── prompts/ # Prompt templates (if needed) +``` + +## Creating New Samples + +When adding a new sample: + +1. Create a new directory with a descriptive name +2. Follow the structure above +3. Include a comprehensive `README.md` +4. Add a `run.sh` script with hot-reload support +5. **Update this README.md** with the new sample in the appropriate category +6. Update `py/plugins/README.md` if using a new plugin + +## Learn More + +- [Genkit Documentation](https://genkit.dev/docs) +- [Python SDK Guide](https://genkit.dev/docs/python) +- [Plugins](../plugins/README.md) + +## License + +All samples are licensed under Apache 2.0. diff --git a/py/samples/anthropic-hello/src/main.py b/py/samples/anthropic-hello/src/main.py index 367ddd2f4b..24f1de0964 100755 --- a/py/samples/anthropic-hello/src/main.py +++ b/py/samples/anthropic-hello/src/main.py @@ -40,7 +40,8 @@ import os -from pydantic import BaseModel, Field +from pydantic import BaseModel, ConfigDict, Field +from pydantic.alias_generators import to_camel from rich.traceback import install as install_rich_traceback from genkit.ai import Genkit, Output @@ -89,8 +90,10 @@ class Skills(BaseModel): class RpgCharacter(BaseModel): """An RPG character.""" + model_config = ConfigDict(alias_generator=to_camel, populate_by_name=True) + name: str = Field(description='name of the character') - back_story: str = Field(description='back story', alias='backStory') + back_story: str = Field(description='back story') abilities: list[str] = Field(description='list of abilities (3-4)') skills: Skills diff --git a/py/samples/checks-demo/README.md b/py/samples/checks-demo/README.md new file mode 100644 index 0000000000..733aa23630 --- /dev/null +++ b/py/samples/checks-demo/README.md @@ -0,0 +1,84 @@ +# Google Checks AI Safety Demo + +This sample demonstrates how to use the **Google Checks** AI safety plugin for +content moderation in Genkit applications. + +## What is Google Checks? + +[Google Checks](https://checks.google.com/ai-safety) is an AI safety platform +that provides: + +- **Guardrails API**: Real-time content classification for policy violations +- **Automated Adversarial Testing**: Offline evaluation of your model's safety + +> **Note**: The Guardrails API is currently in private preview. To request quota, +> fill out [this form](https://docs.google.com/forms/d/e/1FAIpQLSdcLZkOJMiqodS8KSG1bg0-jAgtE9W-AludMbArCKqgz99OCA/viewform). + +## Supported Policies + +| Policy | Description | +|--------|-------------| +| `DANGEROUS_CONTENT` | Content that could cause real-world harm | +| `PII_SOLICITING_RECITING` | Personally identifiable information | +| `HARASSMENT` | Bullying or abusive content | +| `SEXUALLY_EXPLICIT` | Adult or sexual content | +| `HATE_SPEECH` | Content targeting protected groups | +| `MEDICAL_INFO` | Medical advice or diagnoses | +| `VIOLENCE_AND_GORE` | Violent or graphic content | +| `OBSCENITY_AND_PROFANITY` | Vulgar language | + +## Features Demonstrated + +1. **Middleware Usage**: Apply Checks as middleware in `ai.generate()` calls +2. **Evaluator Configuration**: Set up Checks evaluators for offline testing +3. **Custom Thresholds**: Fine-tune sensitivity per policy + +## Prerequisites + +1. **Google Cloud Project** with Checks API quota +2. **API Key** for Google AI (Gemini models) +3. **Application Default Credentials** or service account for Checks API + +```bash +# Set up credentials +export GEMINI_API_KEY="your-api-key" +export GCLOUD_PROJECT="your-project-id" + +# Optional: Service account credentials +export GCLOUD_SERVICE_ACCOUNT_CREDS='{"type":"service_account",...}' +``` + +## Running the Demo + +```bash +./run.sh +``` + +Then open the Dev UI at http://localhost:4000 and run the flows. + +## Testing Safety Policies + +1. **Benign Input**: "Write a poem about nature" + - Should pass all safety checks + +2. **Potentially Unsafe Input**: Test with edge cases to see how policies trigger + - Adjust thresholds based on your application needs + +## Tuning Thresholds + +Lower thresholds = more restrictive (blocks more content) +Higher thresholds = more permissive (allows more content) + +```python +# Very restrictive - blocks even slightly violent content +ChecksMetricConfig(type=ChecksMetricType.VIOLENCE_AND_GORE, threshold=0.01) + +# Very permissive - only blocks clearly dangerous content +ChecksMetricConfig(type=ChecksMetricType.DANGEROUS_CONTENT, threshold=0.99) +``` + +## Related Documentation + +- [Checks AI Safety](https://checks.google.com/ai-safety) +- [Genkit Documentation](https://genkit.dev/docs/) +- [JS Checks Plugin](../../js/plugins/checks/README.md) diff --git a/py/samples/checks-demo/pyproject.toml b/py/samples/checks-demo/pyproject.toml new file mode 100644 index 0000000000..3b2aefdc9e --- /dev/null +++ b/py/samples/checks-demo/pyproject.toml @@ -0,0 +1,40 @@ +# Copyright 2026 Google LLC +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. +# +# SPDX-License-Identifier: Apache-2.0 + +[project] +name = "checks-demo" +version = "0.1.0" +description = "Demo of Google Checks AI safety plugin for content moderation" +requires-python = ">=3.10" +readme = "README.md" +license = "Apache-2.0" +authors = [{ name = "Google LLC" }] +dependencies = [ + "genkit", + "genkit-plugin-checks", + "genkit-plugin-google-genai", + "rich>=13.0.0", +] + +[project.optional-dependencies] +dev = ["watchdog>=3.0.0"] + +[build-system] +requires = ["hatchling"] +build-backend = "hatchling.build" + +[tool.hatch.build.targets.wheel] +packages = ["src"] diff --git a/py/samples/checks-demo/run.sh b/py/samples/checks-demo/run.sh new file mode 100755 index 0000000000..3adadcd0ac --- /dev/null +++ b/py/samples/checks-demo/run.sh @@ -0,0 +1,37 @@ +#!/usr/bin/env bash +# Copyright 2026 Google LLC +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. +# +# SPDX-License-Identifier: Apache-2.0 + +set -euo pipefail + +# Navigate to sample directory +cd "$(dirname "$0")" + +# Source common utilities +source ../_common.sh + +# Install dependencies +uv sync + +# Start the Genkit server with hot reloading +genkit start -- \ + uv tool run --from watchdog watchmedo auto-restart \ + -d src \ + -d ../../packages \ + -d ../../plugins \ + -p '*.py;*.prompt;*.json' \ + -R \ + -- uv run src/main.py "$@" diff --git a/py/samples/checks-demo/src/main.py b/py/samples/checks-demo/src/main.py new file mode 100644 index 0000000000..c95ad291b1 --- /dev/null +++ b/py/samples/checks-demo/src/main.py @@ -0,0 +1,232 @@ +# Copyright 2026 Google LLC +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. +# +# SPDX-License-Identifier: Apache-2.0 + +"""Google Checks AI Safety Demo. + +This sample demonstrates how to use the Google Checks AI safety plugin for +content moderation in Genkit applications. Checks provides real-time content +classification to detect policy violations before they reach your users. + +What is Google Checks? +====================== + +Google Checks (https://checks.google.com/ai-safety) is an AI safety platform +that provides: + +- **Guardrails API**: Real-time content classification for policy violations +- **Automated Adversarial Testing**: Offline evaluation of your model's safety + +Two Usage Patterns +================== + +1. **Middleware** - Apply checks in real-time within ai.generate() calls: + + response = await ai.generate( + model='googleai/gemini-2.0-flash', + prompt=topic, + use=[ + checks_middleware( + metrics=[ChecksMetricType.DANGEROUS_CONTENT], + auth_options={'project_id': 'your-project'}, + ) + ], + ) + +2. **Evaluator Plugin** - Configure as a plugin for offline evaluation: + + ai = Genkit( + plugins=[ + Checks( + project_id='your-project', + evaluation={ + 'metrics': [ + ChecksMetricType.DANGEROUS_CONTENT, + ChecksMetricConfig( + type=ChecksMetricType.HARASSMENT, + threshold=0.6, + ), + ] + }, + ) + ] + ) + +Testing the Demo +================ + +1. Run the sample: ./run.sh +2. Open Dev UI at http://localhost:4000 +3. Run the flows: + - poem_with_guardrails: Generates a poem with Checks middleware + - poem_without_guardrails: Generates a poem without safety checks + +Test with these inputs: +- Benign: "Write a poem about nature" +- Edge case: Test potentially unsafe inputs to see guardrails in action + +Note: +==== + +The Guardrails API is in private preview. Request quota at: +https://docs.google.com/forms/d/e/1FAIpQLSdcLZkOJMiqodS8KSG1bg0-jAgtE9W-AludMbArCKqgz99OCA/viewform +""" + +import os + +from pydantic import BaseModel, Field +from rich.traceback import install as install_rich_traceback + +from genkit.ai import Genkit +from genkit.plugins.google_genai import GoogleAI + +install_rich_traceback(show_locals=True, width=120, extra_lines=3) + + +class PoemInput(BaseModel): + """Input for poem generation flows.""" + + topic: str = Field( + default='Write a short poem about the beauty of nature', + description='Topic or prompt for the poem', + ) + + +# Get project ID from environment +PROJECT_ID = os.environ.get('GCLOUD_PROJECT', os.environ.get('GOOGLE_CLOUD_PROJECT', 'your-project-id')) + + +# Initialize Genkit with GoogleAI and optionally Checks plugins +ai = Genkit( + plugins=[ + GoogleAI(), + # Configure Checks for offline evaluation + # Uncomment when you have Checks API quota + # Checks( + # project_id=PROJECT_ID, + # evaluation={ + # 'metrics': [ + # ChecksMetricType.DANGEROUS_CONTENT, + # ChecksMetricType.HARASSMENT, + # ChecksMetricType.HATE_SPEECH, + # # Custom threshold for violence (more restrictive) + # ChecksMetricConfig( + # type=ChecksMetricType.VIOLENCE_AND_GORE, + # threshold=0.3, + # ), + # ] + # }, + # ), + ], +) + + +@ai.flow() +async def poem_with_guardrails(input: PoemInput) -> str: + """Generate a poem with Checks guardrails applied. + + This flow demonstrates using Checks middleware to filter both + input and output content for policy violations. If content + violates configured policies, the response will be blocked. + + Args: + input: The poem topic and configuration. + + Returns: + Generated poem text, or an error message if blocked. + """ + response = await ai.generate( + model='googleai/gemini-2.0-flash', + prompt=input.topic, + # Apply Checks middleware for real-time content filtering + # Uncomment when you have Checks API quota + # use=[ + # checks_middleware( + # auth_options={'project_id': PROJECT_ID}, + # metrics=[ + # # Use default threshold + # ChecksMetricType.DANGEROUS_CONTENT, + # ChecksMetricType.HARASSMENT, + # # Custom threshold (lower = more restrictive) + # ChecksMetricConfig( + # type=ChecksMetricType.VIOLENCE_AND_GORE, + # threshold=0.3, + # ), + # ], + # ) + # ], + ) + + # Check if response was blocked by guardrails + if response.finish_reason == 'blocked': + return f'⚠️ Content blocked by safety guardrails: {response.finish_message}' + + return response.text or '' + + +@ai.flow() +async def poem_without_guardrails(input: PoemInput) -> str: + """Generate a poem without safety guardrails. + + This flow generates content without applying Checks middleware, + useful for comparison or when guardrails are not needed. + + Args: + input: The poem topic and configuration. + + Returns: + Generated poem text. + """ + response = await ai.generate( + model='googleai/gemini-2.0-flash', + prompt=input.topic, + ) + return response.text or '' + + +@ai.flow() +async def compare_safety_policies(input: PoemInput) -> dict[str, str]: + """Compare output with and without safety guardrails. + + This flow runs the same prompt through both guarded and + unguarded generation to demonstrate the effect of Checks. + + Args: + input: The poem topic and configuration. + + Returns: + Dictionary with 'guarded' and 'unguarded' results. + """ + guarded = await poem_with_guardrails(input) + unguarded = await poem_without_guardrails(input) + + return { + 'guarded': guarded, + 'unguarded': unguarded, + 'note': 'Compare the results to see guardrail effects', + } + + +if __name__ == '__main__': + # Simple test run + import asyncio + + async def main() -> None: + """Run the sample to test poem generation.""" + result = await poem_without_guardrails(PoemInput()) + print('Generated poem:') + print(result) + + asyncio.run(main()) diff --git a/py/samples/compat-oai-hello/src/main.py b/py/samples/compat-oai-hello/src/main.py index 5cb9a9d3f4..014abfeb4b 100755 --- a/py/samples/compat-oai-hello/src/main.py +++ b/py/samples/compat-oai-hello/src/main.py @@ -41,7 +41,8 @@ from decimal import Decimal import httpx -from pydantic import BaseModel, Field +from pydantic import BaseModel, ConfigDict, Field +from pydantic.alias_generators import to_camel from rich.traceback import install as install_rich_traceback from genkit.ai import ActionRunContext, Genkit, Output @@ -111,8 +112,10 @@ class Skills(BaseModel): class RpgCharacter(BaseModel): """An RPG character.""" + model_config = ConfigDict(alias_generator=to_camel, populate_by_name=True) + name: str = Field(description='name of the character') - back_story: str = Field(description='back story', alias='backStory') + back_story: str = Field(description='back story') abilities: list[str] = Field(description='list of abilities (3-4)') skills: Skills diff --git a/py/samples/deepseek-hello/src/main.py b/py/samples/deepseek-hello/src/main.py index 7ba373e0cb..ec3df98ad5 100644 --- a/py/samples/deepseek-hello/src/main.py +++ b/py/samples/deepseek-hello/src/main.py @@ -40,7 +40,8 @@ import os -from pydantic import BaseModel, Field +from pydantic import BaseModel, ConfigDict, Field +from pydantic.alias_generators import to_camel from rich.traceback import install as install_rich_traceback from genkit.ai import Genkit, Output @@ -89,8 +90,10 @@ class Skills(BaseModel): class RpgCharacter(BaseModel): """An RPG character.""" + model_config = ConfigDict(alias_generator=to_camel, populate_by_name=True) + name: str = 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a/py/samples/google-genai-context-caching/README.md +++ b/py/samples/google-genai-context-caching/README.md @@ -1,5 +1,5 @@ # Context Caching example. - + ### How to Get Your Gemini API Key To use the Google GenAI plugin, you need a Gemini API key. diff --git a/py/samples/google-genai-hello/README.md b/py/samples/google-genai-hello/README.md index cc061549b7..6dbdad0af4 100644 --- a/py/samples/google-genai-hello/README.md +++ b/py/samples/google-genai-hello/README.md @@ -59,7 +59,7 @@ To test Google Cloud Platform telemetry (tracing and metrics), you need a GCP pr proceeding so that the authentication process can complete successfully. Otherwise, you may run into a lot of HTTP 503 service unavailable or `invalid_grant` errors. - + 3. **Run with Telemetry**: ```bash genkit start -- uv run src/main.py --enable-gcp-telemetry diff --git a/py/samples/google-genai-hello/src/main.py b/py/samples/google-genai-hello/src/main.py index 92aee056d5..b433a04fa3 100755 --- a/py/samples/google-genai-hello/src/main.py +++ b/py/samples/google-genai-hello/src/main.py @@ -67,7 +67,8 @@ else: from enum import StrEnum -from pydantic import BaseModel, Field +from pydantic import BaseModel, ConfigDict, Field +from pydantic.alias_generators import to_camel from genkit.ai import Genkit, Output, ToolRunContext, tool_response from genkit.blocks.model import GenerateResponseWrapper @@ -364,8 +365,10 @@ class Skills(BaseModel): class RpgCharacter(BaseModel): """An RPG character.""" + model_config = ConfigDict(alias_generator=to_camel, populate_by_name=True) + name: str = Field(description='name of the character') - back_story: str = Field(description='back story', alias='backStory') + back_story: str = Field(description='back story') abilities: list[str] = Field(description='list of abilities (3-4)') skills: Skills diff --git a/py/samples/google-genai-vertexai-hello/src/main.py b/py/samples/google-genai-vertexai-hello/src/main.py index b082f86820..f756920954 100755 --- a/py/samples/google-genai-vertexai-hello/src/main.py +++ b/py/samples/google-genai-vertexai-hello/src/main.py @@ -82,7 +82,8 @@ import os -from pydantic import BaseModel, Field +from pydantic import BaseModel, ConfigDict, Field +from pydantic.alias_generators import to_camel from rich.traceback import install as install_rich_traceback from genkit.ai import Genkit, Output, ToolRunContext, tool_response @@ -148,8 +149,10 @@ class Skills(BaseModel): class RpgCharacter(BaseModel): """An RPG character.""" + model_config = ConfigDict(alias_generator=to_camel, populate_by_name=True) + name: str = Field(description='name of the character') - back_story: str = Field(description='back story', alias='backStory') + back_story: str = Field(description='back story') abilities: list[str] = Field(description='list of abilities (3-4)') skills: Skills diff --git a/py/samples/menu/src/menu_schemas.py b/py/samples/menu/src/menu_schemas.py index a50736bf80..6cec9030dc 100644 --- a/py/samples/menu/src/menu_schemas.py +++ b/py/samples/menu/src/menu_schemas.py @@ -17,7 +17,8 @@ """Schemas for the menu AI sample.""" -from pydantic import BaseModel, Field +from pydantic import BaseModel, ConfigDict, Field +from pydantic.alias_generators import to_camel from .constants import DEFAULT_MENU_QUESTION, DEFAULT_MENU_TEXT @@ -45,34 +46,40 @@ class AnswerOutputSchema(BaseModel): class DataMenuQuestionInputSchema(BaseModel): """Input schema for the data menu question prompt.""" - menu_data: list[MenuItemSchema] = Field(..., alias='menuData') + model_config = ConfigDict(alias_generator=to_camel, populate_by_name=True) + + menu_data: list[MenuItemSchema] = Field(...) question: str = Field(..., description='A question about the menu') class TextMenuQuestionInputSchema(BaseModel): """Input schema for the text menu question prompt.""" - menu_text: str = Field( - default=DEFAULT_MENU_TEXT, - description='The menu text content', - alias='menuText', - ) + model_config = ConfigDict(alias_generator=to_camel, populate_by_name=True) + + menu_text: str = Field(default=DEFAULT_MENU_TEXT, description='The menu text content') question: str = Field(default=DEFAULT_MENU_QUESTION, description='A question about the menu') class MenuToolOutputSchema(BaseModel): """Output schema for the menu tool.""" + model_config = ConfigDict(alias_generator=to_camel, populate_by_name=True) + menu_data: list[MenuItemSchema] = Field(..., description='A list of all the items on the menu') class ReadMenuImagePromptSchema(BaseModel): """Input schema for the read menu image prompt.""" + model_config = ConfigDict(alias_generator=to_camel, populate_by_name=True) + image_url: str = Field(...) class ReadMenuPromptOutputSchema(BaseModel): """Output schema for the read menu prompt.""" + model_config = ConfigDict(alias_generator=to_camel, populate_by_name=True) + menu_text: str = Field(...) diff --git a/py/samples/multi-server/README.md b/py/samples/multi-server/README.md index 3efad62850..8eb9915022 100644 --- a/py/samples/multi-server/README.md +++ b/py/samples/multi-server/README.md @@ -12,10 +12,11 @@ For an enhanced development experience, use the provided `run.sh` script to star ``` This script uses `watchmedo` to monitor changes in: -- `src/` (Python logic) -- `../../packages` (Genkit core) -- `../../plugins` (Genkit plugins) -- File patterns: `*.py`, `*.prompt`, `*.json` + +* `src/` (Python logic) +* `../../packages` (Genkit core) +* `../../plugins` (Genkit plugins) +* File patterns: `*.py`, `*.prompt`, `*.json` Changes will automatically trigger a restart of the sample. You can also pass command-line arguments directly to the script, e.g., `./run.sh --some-flag`. @@ -61,11 +62,11 @@ kill -15 ${PROCESS_ID} ``` 2. **Test the servers**: - - [ ] Main API server at http://localhost:8000 - - [ ] Health check endpoint at /health - - [ ] Server info endpoint at /info + * \[ ] Main API server at http://localhost:8000 + * \[ ] Health check endpoint at /health + * \[ ] Server info endpoint at /info 3. **Expected behavior**: - - Multiple servers start and run concurrently - - Graceful shutdown handles all servers - - Middleware and logging work across servers + * Multiple servers start and run concurrently + * Graceful shutdown handles all servers + * Middleware and logging work across servers diff --git a/py/samples/ollama-hello/src/main.py b/py/samples/ollama-hello/src/main.py index e5069a9f4d..1d8a3a5856 100755 --- a/py/samples/ollama-hello/src/main.py +++ b/py/samples/ollama-hello/src/main.py @@ -39,7 +39,8 @@ from typing import Any, cast -from pydantic import BaseModel, Field +from pydantic import BaseModel, ConfigDict, Field +from pydantic.alias_generators import to_camel from rich.traceback import install as install_rich_traceback from genkit.ai import Genkit, Output @@ -136,8 +137,10 @@ class Skills(BaseModel): class RpgCharacter(BaseModel): """An RPG character.""" + model_config = ConfigDict(alias_generator=to_camel, populate_by_name=True) + name: str = Field(description='name of the character') - back_story: str = Field(description='back story', alias='backStory') + back_story: str = Field(description='back story') abilities: list[str] = Field(description='list of abilities (3-4)') skills: Skills diff --git a/py/samples/rag-chroma/README.md b/py/samples/rag-chroma/README.md new file mode 100644 index 0000000000..1a7bc5730c --- /dev/null +++ b/py/samples/rag-chroma/README.md @@ -0,0 +1,152 @@ +# RAG with ChromaDB Demo - Cat Knowledge Base 🐱 + +A complete Retrieval-Augmented Generation (RAG) demo using ChromaDB as an +in-memory vector store with Genkit. Features a cat-themed knowledge base! + +## Overview + +This sample demonstrates the RAG pattern: + +1. **Index Phase**: Cat knowledge documents are embedded and stored in ChromaDB +2. **Retrieval Phase**: User questions find relevant documents via similarity search +3. **Generation Phase**: Retrieved context augments the LLM's response + +``` +┌─────────────────────────────────────────────────────────────────────────────┐ +│ RAG Architecture │ +├─────────────────────────────────────────────────────────────────────────────┤ +│ │ +│ INDEX PHASE: │ +│ ┌─────────────────┐ ┌──────────┐ ┌─────────────┐ ┌───────────┐ │ +│ │ Cat Documents │ ─► │ Embedder │ ─► │ Embeddings │ ─► │ ChromaDB │ │ +│ │ (txt, json) │ │ (Gemini) │ │ (Vectors) │ │(In-Memory)│ │ +│ └─────────────────┘ └──────────┘ └─────────────┘ └───────────┘ │ +│ │ +│ QUERY PHASE: │ +│ ┌──────────┐ ┌──────────┐ ┌────────────┐ ┌──────────────────┐ │ +│ │ Question │ ─► │ Embedder │ ─► │ Similarity │ ─► │ Retrieved Docs │ │ +│ └──────────┘ └──────────┘ │ Search │ └────────┬─────────┘ │ +│ └────────────┘ │ │ +│ │ │ +│ GENERATION PHASE: ▼ │ +│ ┌───────────┐ ┌─────────────────────────────────────┐ │ +│ │ Answer │ ◄───────────────│ LLM (Gemini) + Context from Docs │ │ +│ └───────────┘ └─────────────────────────────────────┘ │ +│ │ +└─────────────────────────────────────────────────────────────────────────────┘ +``` + +## Quick Start + +### 1. Set Up API Key + +```bash +export GOOGLE_API_KEY=your-api-key +``` + +Get a key from: [Google AI Studio](https://makersuite.google.com/app/apikey) + +### 2. Run the Demo + +```bash +./run.sh +``` + +This will: +- Install all dependencies (including ChromaDB) +- Start the Genkit Dev UI at http://localhost:4000 +- **Automatically index the cat knowledge base!** +- Watch for file changes and auto-reload + +### 3. Use the Demo + +1. Open http://localhost:4000 in your browser + +2. **Ask questions about cats** (knowledge base is pre-indexed): + - Select `ask_about_cats_flow` + - Enter questions like: + - "Why do cats purr?" + - "Tell me about Grumpy Cat" + - "What vaccinations do cats need?" + - The answer will include sources from the knowledge base + +3. **Get question ideas**: + - Run `list_sample_questions_flow` for example questions + +4. **Re-index if needed** (after adding new data): + - Run `index_cat_knowledge_flow` to refresh the knowledge base + +## Data Files + +The knowledge base includes three cat-themed sources in `./data/`: + +| File | Description | +|------|-------------| +| `cat_care_guide.txt` | Comprehensive guide on cat behavior, nutrition, health, and breeds | +| `famous_cats.txt` | Stories of famous cats from internet celebrities to historical cats | +| `cat_facts.json` | Fun and interesting facts about cats organized by category | + +### Adding Your Own Data + +You can add more documents by: +1. Adding `.txt` files to `./data/` (will be split by `##` headers) +2. Adding facts to `cat_facts.json` +3. Running `index_cat_knowledge_flow` again + +## Flows + +| Flow | Description | +|------|-------------| +| `index_cat_knowledge_flow` | Load and index all cat documents into ChromaDB | +| `ask_about_cats_flow` | Ask questions with RAG-powered answers | +| `list_sample_questions_flow` | Get example questions to try | + +## Architecture Details + +### Why ChromaDB? + +- **Zero Setup**: Runs in-memory, no external services needed +- **Fast**: Efficient similarity search for retrieval +- **Flexible**: Supports metadata filtering and various distance metrics +- **Open Source**: Free to use with a large community + +### Embedding Model + +Uses `googleai/text-embedding-004` for: +- Document embedding during indexing +- Query embedding during retrieval + +### Generation Model + +Uses `googleai/gemini-2.0-flash` for: +- Generating answers based on retrieved context +- Fast, accurate responses + +## Troubleshooting + +### "GOOGLE_API_KEY not set" + +```bash +export GOOGLE_API_KEY=your-api-key +``` + +### "No documents found" + +Make sure the data files exist in `./data/`: +```bash +ls -la data/ +``` + +### "I don't have information about that" + +Run `index_cat_knowledge_flow` first to load the knowledge base. + +## Learn More + +- [Genkit Documentation](https://genkit.dev/docs) +- [ChromaDB Documentation](https://www.trychroma.com/) +- [RAG Pattern](https://genkit.dev/docs/rag) + +## License + +Apache 2.0 diff --git a/py/samples/rag-chroma/data/cat_care_guide.txt b/py/samples/rag-chroma/data/cat_care_guide.txt new file mode 100644 index 0000000000..ffd0ab312f --- /dev/null +++ b/py/samples/rag-chroma/data/cat_care_guide.txt @@ -0,0 +1,76 @@ +# The Complete Cat Care Guide + +## Chapter 1: Understanding Cat Behavior + +Cats are fascinating creatures with complex behaviors. Unlike dogs, cats are obligate carnivores and have evolved as solitary hunters. This influences much of their behavior today. + +### Purring +Cats purr at a frequency of 25-150 Hz, which has been shown to promote healing. Cats purr not only when content but also when stressed or injured, suggesting it may be a self-soothing mechanism. + +### Kneading +When cats knead with their paws, they're exhibiting a behavior from kittenhood when they kneaded their mother's belly to stimulate milk flow. Adult cats often knead when feeling safe and content. + +### The Slow Blink +A slow blink from a cat is often called a "cat kiss" - it's a sign of trust and affection. You can return the gesture to bond with your cat. + +## Chapter 2: Nutrition and Diet + +### Essential Nutrients +Cats require taurine, an amino acid found only in animal protein. Without sufficient taurine, cats can develop heart problems and blindness. This is why cats cannot be vegetarians. + +### Feeding Schedule +Adult cats should be fed 2-3 times per day. Free feeding can lead to obesity. The average indoor cat needs about 200-300 calories per day, depending on size and activity level. + +### Hydration +Cats have a low thirst drive because their ancestors got most water from prey. Wet food can help with hydration. Many cats prefer running water, which is why cat fountains are popular. + +## Chapter 3: Health and Wellness + +### Vaccinations +Core vaccines for cats include: +- Feline Panleukopenia (FPV) +- Feline Calicivirus (FCV) +- Feline Herpesvirus (FHV-1) +- Rabies + +### Common Health Issues +- Dental disease affects 70% of cats over age 3 +- Urinary tract issues are common, especially in male cats +- Hyperthyroidism is common in older cats +- Kidney disease affects 1 in 3 cats over age 10 + +### Signs of Illness +Cats hide illness well. Watch for: +- Changes in appetite or water consumption +- Litter box changes +- Hiding or behavior changes +- Weight loss or gain +- Vomiting or diarrhea + +## Chapter 4: Environmental Enrichment + +### Vertical Space +Cats feel safe when they can survey their territory from above. Cat trees, shelves, and perches are essential for indoor cats. + +### Scratching +Scratching is natural and necessary - it removes old claw sheaths, stretches muscles, and marks territory. Provide multiple scratching posts of different materials. + +### Play +Interactive play mimics hunting behavior. Wand toys, laser pointers (always end with a physical toy to "catch"), and puzzle feeders provide mental stimulation. + +## Chapter 5: Cat Breeds + +### Maine Coon +The largest domestic cat breed, Maine Coons are known as "gentle giants." They can weigh 15-25 pounds and are very social and dog-like in behavior. + +### Siamese +One of the oldest recognized breeds, Siamese cats are known for their distinctive coloring, blue eyes, and vocal nature. They're highly intelligent and form strong bonds. + +### Persian +Persians have long, luxurious coats and flat faces. They're calm and affectionate but require daily grooming to prevent matting. + +### British Shorthair +Known for their round faces and dense coats, British Shorthairs are calm, easygoing cats. The "Cheshire Cat" was inspired by this breed. + +### Bengal +Bengals have wild-looking spotted or marbled coats. They're highly active, intelligent, and need lots of stimulation. diff --git a/py/samples/rag-chroma/data/cat_facts.json b/py/samples/rag-chroma/data/cat_facts.json new file mode 100644 index 0000000000..6cf1904c60 --- /dev/null +++ b/py/samples/rag-chroma/data/cat_facts.json @@ -0,0 +1,68 @@ +{ + "cat_facts": [ + { + "category": "anatomy", + "fact": "Cats have over 20 vocalizations, including the meow, purr, chirp, and trill. Interestingly, adult cats typically only meow to communicate with humans, not other cats." + }, + { + "category": "anatomy", + "fact": "A cat's nose print is unique, like a human's fingerprint. No two cats have the same nose print pattern." + }, + { + "category": "anatomy", + "fact": "Cats have 230 bones in their body, compared to 206 in humans. Many of these extra bones are in their spine and tail, giving them exceptional flexibility." + }, + { + "category": "anatomy", + "fact": "A cat's whiskers are roughly as wide as their body, helping them determine if they can fit through tight spaces." + }, + { + "category": "behavior", + "fact": "Cats spend 70% of their lives sleeping, averaging 13-16 hours per day. This is an evolutionary adaptation from their wild ancestors who needed to conserve energy for hunting." + }, + { + "category": "behavior", + "fact": "When a cat brings you a 'gift' like a dead mouse, they're trying to teach you to hunt because they see you as a poor hunter who needs help." + }, + { + "category": "behavior", + "fact": "Cats can rotate their ears 180 degrees and can move each ear independently, allowing them to pinpoint the source of sounds precisely." + }, + { + "category": "history", + "fact": "Cats were first domesticated around 10,000 years ago in the Near East, likely because they hunted mice attracted to stored grain." + }, + { + "category": "history", + "fact": "In ancient Egypt, killing a cat, even accidentally, was punishable by death. When a family cat died, family members would shave their eyebrows in mourning." + }, + { + "category": "history", + "fact": "The first cat show was held in London in 1871 at the Crystal Palace, organized by Harrison Weir." + }, + { + "category": "abilities", + "fact": "Cats can jump up to six times their length and can run at speeds up to 30 miles per hour in short bursts." + }, + { + "category": "abilities", + "fact": "A cat's hearing is about four times more sensitive than a human's, and they can hear ultrasonic sounds that are inaudible to humans." + }, + { + "category": "abilities", + "fact": "Cats have a specialized collarbone that allows them to always land on their feet when falling. This is called the 'righting reflex' and develops around 3-4 weeks of age." + }, + { + "category": "health", + "fact": "A group of cats is called a 'clowder,' while a group of kittens is called a 'kindle.'" + }, + { + "category": "health", + "fact": "The oldest known cat lived to be 38 years old. Her name was Creme Puff and she lived in Austin, Texas." + }, + { + "category": "health", + "fact": "Cats cannot taste sweetness because they lack the taste receptor for it. This is likely because their carnivorous diet never required detecting sugars." + } + ] +} diff --git a/py/samples/rag-chroma/data/famous_cats.txt b/py/samples/rag-chroma/data/famous_cats.txt new file mode 100644 index 0000000000..843f8d25fe --- /dev/null +++ b/py/samples/rag-chroma/data/famous_cats.txt @@ -0,0 +1,54 @@ +# Famous Cats Throughout History + +## Internet Famous Cats + +### Grumpy Cat (2012-2019) +Tardar Sauce, known as Grumpy Cat, became an internet sensation due to her permanently grumpy facial expression caused by feline dwarfism. She appeared in commercials, had her own movie, and her image was worth millions in merchandise. + +### Lil Bub (2011-2019) +Lil Bub was a celebrity cat known for her unique appearance caused by genetic mutations. She had no teeth, extra toes, and a permanently protruding tongue. She raised over $700,000 for animals in need. + +### Maru +A Scottish Fold from Japan, Maru is famous for his love of boxes and has been called the most watched cat on YouTube, with hundreds of millions of video views. + +### Keyboard Cat (1984-2018) +Fatso, known as Keyboard Cat, became viral in 2007 when a video from 1984 showed him "playing" a keyboard. The meme is used to "play off" embarrassing moments. + +## Historical Cats + +### Félicette (1963) +Félicette was the first cat in space, launched by France on October 18, 1963. She reached an altitude of 157 km and survived the mission. A statue was unveiled in her honor in 2019. + +### Unsinkable Sam (WWII) +A black and white cat who survived the sinking of three ships during World War II: the German battleship Bismarck, HMS Cossack, and HMS Ark Royal. + +### Dewey Readmore Books (1987-2006) +Dewey was a library cat at the Spencer Public Library in Iowa. He was found in the book drop as a kitten and lived at the library for 19 years, becoming internationally famous. + +### Trim (1799-1804) +Trim accompanied Matthew Flinders on his voyages to circumnavigate and map the Australian coastline. Flinders wrote a biographical tribute to Trim after the cat's death. + +## Cats in Literature and Media + +### Cheshire Cat +From Lewis Carroll's "Alice's Adventures in Wonderland," the Cheshire Cat is known for its distinctive grin and ability to disappear, leaving only its smile behind. + +### Garfield +Created by Jim Davis in 1978, Garfield is an orange tabby known for his love of lasagna, hatred of Mondays, and lazy demeanor. The comic strip appears in over 2,500 newspapers. + +### The Cat in the Hat +Dr. Seuss's iconic character, first appearing in 1957, is known for his red and white striped hat and mischievous adventures. + +### Tom (Tom and Jerry) +The gray and white domestic cat has been chasing Jerry Mouse since 1940, making it one of the longest-running animated series. + +## Scientific and Working Cats + +### CC (Copy Cat) +CC was the first cloned cat, born on December 22, 2001, at Texas A&M University. She lived a normal life and had kittens naturally. + +### Oscar the Therapy Cat +Oscar gained fame for his ability to predict the deaths of patients in a nursing home, curling up with them in their final hours. He was featured in the New England Journal of Medicine. + +### Station Master Tama +A calico cat who became the station master of Kishi Station in Japan, helping revitalize the local economy and railway. She was appointed to the role in 2007 and drew tourists worldwide. diff --git a/py/samples/rag-chroma/pyproject.toml b/py/samples/rag-chroma/pyproject.toml new file mode 100644 index 0000000000..9648ff4547 --- /dev/null +++ b/py/samples/rag-chroma/pyproject.toml @@ -0,0 +1,39 @@ +# Copyright 2026 Google LLC +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. +# +# SPDX-License-Identifier: Apache-2.0 + +[project] +description = "RAG demo using ChromaDB with Genkit - cat-themed knowledge base" +name = "rag-chroma-demo" +readme = "README.md" +version = "0.1.0" +# TODO: Update requires-python to >=3.10 when onnxruntime adds Python 3.14 wheels. +# Track: https://github.com/microsoft/onnxruntime/issues/21292 +dependencies = [ + "genkit", + "genkit-plugin-google-genai", + "genkit-plugin-chroma", + "chromadb>=0.4.0", + "rich>=13.0.0", +] +requires-python = ">=3.10,<3.14" # chromadb depends on onnxruntime which lacks Python 3.14 wheels + +[project.optional-dependencies] +dev = ["watchdog>=3.0.0"] + +[tool.uv.sources] +genkit = { workspace = true } +genkit-plugin-chroma = { workspace = true } +genkit-plugin-google-genai = { workspace = true } diff --git a/py/samples/rag-chroma/run.sh b/py/samples/rag-chroma/run.sh new file mode 100755 index 0000000000..35dc4c7f30 --- /dev/null +++ b/py/samples/rag-chroma/run.sh @@ -0,0 +1,61 @@ +#!/usr/bin/env bash +# Copyright 2026 Google LLC +# SPDX-License-Identifier: Apache-2.0 + +# RAG with ChromaDB Demo +# ====================== +# +# Demonstrates Retrieval Augmented Generation using ChromaDB. +# +# Prerequisites: +# - GEMINI_API_KEY environment variable set +# +# Usage: +# ./run.sh # Start the demo with Dev UI +# ./run.sh --help # Show this help message + +set -euo pipefail + +cd "$(dirname "$0")" +source "../_common.sh" + +print_help() { + print_banner "RAG with ChromaDB" "🐱" + echo "Usage: ./run.sh [options]" + echo "" + echo "Options:" + echo " --help Show this help message" + echo "" + echo "Environment Variables:" + echo " GEMINI_API_KEY Required. Your Gemini API key" + echo "" + echo "This demo shows:" + echo " - Document indexing with ChromaDB" + echo " - Semantic retrieval" + echo " - Augmented generation" + echo "" + echo "Get an API key from: https://makersuite.google.com/app/apikey" + print_help_footer +} + +case "${1:-}" in + --help|-h) + print_help + exit 0 + ;; +esac + +print_banner "RAG with ChromaDB" "🐱" + +check_env_var "GEMINI_API_KEY" "https://makersuite.google.com/app/apikey" || true + +install_deps + +genkit_start_with_browser -- \ + uv tool run --from watchdog watchmedo auto-restart \ + -d src \ + -d ../../packages \ + -d ../../plugins \ + -p '*.py;*.prompt;*.json' \ + -R \ + -- uv run src/main.py "$@" diff --git a/py/samples/rag-chroma/src/main.py b/py/samples/rag-chroma/src/main.py new file mode 100644 index 0000000000..42632d3244 --- /dev/null +++ b/py/samples/rag-chroma/src/main.py @@ -0,0 +1,392 @@ +#!/usr/bin/env python3 +# Copyright 2026 Google LLC +# SPDX-License-Identifier: Apache-2.0 + +"""RAG demo using ChromaDB with Genkit - Cat Knowledge Base. + +This sample demonstrates Retrieval-Augmented Generation (RAG) using ChromaDB +as an in-memory vector store. It includes a cat-themed knowledge base with +documents about cat care, famous cats, and fun cat facts. + +Architecture +============ + +┌─────────────────────────────────────────────────────────────────────────────┐ +│ RAG with ChromaDB │ +├─────────────────────────────────────────────────────────────────────────────┤ +│ │ +│ ┌──────────────┐ ┌──────────────┐ ┌──────────────────────┐ │ +│ │ Genkit │ │ Chroma │ │ ChromaDB │ │ +│ │ + GoogleAI │ ──► │ Plugin │ ──► │ (In-Memory) │ │ +│ └──────────────┘ └──────────────┘ └──────────────────────┘ │ +│ │ +│ Data Sources (./data/): │ +│ - cat_care_guide.txt - Complete cat care information │ +│ - famous_cats.txt - Famous cats throughout history │ +│ - cat_facts.json - Fun and interesting cat facts │ +│ │ +│ Flows: │ +│ - index_cat_knowledge_flow: Load and index all cat documents │ +│ - ask_about_cats_flow: Ask questions about cats with RAG │ +│ │ +└─────────────────────────────────────────────────────────────────────────────┘ + +Quick Start +=========== + +1. Set your API key: + ``` + export GOOGLE_API_KEY=your-api-key + ``` + +2. Run the demo: + ``` + ./run.sh + ``` + +3. Open http://localhost:4000 in your browser + +4. The cat knowledge base is automatically indexed at startup! + +5. Run `ask_about_cats_flow` with questions like: + - "How often should I feed my cat?" + - "Tell me about Grumpy Cat" + - "Why do cats purr?" + - "What is the oldest cat ever?" +""" + +import json +from pathlib import Path + +import structlog +from pydantic import BaseModel, Field +from rich.traceback import install as install_rich_traceback + +from genkit.ai import Genkit +from genkit.blocks.document import Document +from genkit.plugins.chroma import chroma, chroma_indexer_ref, chroma_retriever_ref +from genkit.plugins.google_genai import GoogleAI + +install_rich_traceback(show_locals=True, width=120, extra_lines=3) + +logger = structlog.get_logger(__name__) + +COLLECTION_NAME = 'cat_knowledge' +EMBEDDER = 'googleai/text-embedding-004' +DATA_DIR = Path(__file__).parent.parent / 'data' + +ai = Genkit( + plugins=[ + GoogleAI(), + chroma( + collections=[ + { + 'collection_name': COLLECTION_NAME, + 'embedder': EMBEDDER, + 'create_collection_if_missing': True, + } + ] + ), + ], + model='googleai/gemini-2.0-flash', +) + + +class IndexInput(BaseModel): + """Input for indexing cat knowledge documents.""" + + include_care_guide: bool = Field( + default=True, + description='Include the comprehensive cat care guide', + ) + include_famous_cats: bool = Field( + default=True, + description='Include information about famous cats', + ) + include_cat_facts: bool = Field( + default=True, + description='Include fun cat facts', + ) + + +class IndexResult(BaseModel): + """Result of indexing operation.""" + + total_documents: int + sources_indexed: list[str] + message: str + + +class QueryInput(BaseModel): + """Input for asking questions about cats.""" + + question: str = Field( + default='Why do cats purr and what does it mean?', + description='Your question about cats', + ) + num_results: int = Field( + default=5, + ge=1, + le=20, + description='Number of relevant documents to retrieve', + ) + + +class QueryResult(BaseModel): + """Result of RAG query about cats.""" + + answer: str + sources_used: list[str] + confidence: str + + +def load_text_file(filepath: Path) -> list[str]: + """Load a text file and split into chunks by section. + + Args: + filepath: Path to the text file. + + Returns: + List of text chunks. + """ + if not filepath.exists(): + return [] + + content = filepath.read_text(encoding='utf-8') + + # Split by headers (## or #) + chunks = [] + current_chunk = [] + + for line in content.split('\n'): + if line.startswith('## ') and current_chunk: + chunks.append('\n'.join(current_chunk).strip()) + current_chunk = [line] + else: + current_chunk.append(line) + + if current_chunk: + chunks.append('\n'.join(current_chunk).strip()) + + # Filter out very short chunks + return [c for c in chunks if len(c) > 50] + + +def load_json_facts(filepath: Path) -> list[str]: + """Load cat facts from JSON file. + + Args: + filepath: Path to the JSON file. + + Returns: + List of fact strings. + """ + if not filepath.exists(): + return [] + + with open(filepath, encoding='utf-8') as f: + data = json.load(f) + + facts = [] + for fact_obj in data.get('cat_facts', []): + category = fact_obj.get('category', 'general') + fact = fact_obj.get('fact', '') + if fact: + facts.append(f'[{category.upper()}] {fact}') + + return facts + + +@ai.flow() +async def index_cat_knowledge_flow(input: IndexInput) -> IndexResult: + """Index cat knowledge documents into ChromaDB. + + This flow loads cat-themed documents from the ./data/ directory and + indexes them into ChromaDB for retrieval. Documents are split into + chunks for better retrieval accuracy. + + Args: + input: Configuration for which document sources to include. + + Returns: + Summary of the indexing operation. + """ + await logger.ainfo('Starting index_cat_knowledge_flow', input=input.model_dump()) + all_texts: list[str] = [] + sources: list[str] = [] + + try: + # Load cat care guide + if input.include_care_guide: + await logger.ainfo('Loading cat care guide') + care_chunks = load_text_file(DATA_DIR / 'cat_care_guide.txt') + all_texts.extend(care_chunks) + if care_chunks: + sources.append(f'cat_care_guide.txt ({len(care_chunks)} chunks)') + await logger.ainfo('Loaded cat care guide', chunk_count=len(care_chunks)) + + # Load famous cats + if input.include_famous_cats: + await logger.ainfo('Loading famous cats') + famous_chunks = load_text_file(DATA_DIR / 'famous_cats.txt') + all_texts.extend(famous_chunks) + if famous_chunks: + sources.append(f'famous_cats.txt ({len(famous_chunks)} chunks)') + await logger.ainfo('Loaded famous cats', chunk_count=len(famous_chunks)) + + # Load cat facts + if input.include_cat_facts: + await logger.ainfo('Loading cat facts') + facts = load_json_facts(DATA_DIR / 'cat_facts.json') + all_texts.extend(facts) + if facts: + sources.append(f'cat_facts.json ({len(facts)} facts)') + await logger.ainfo('Loaded cat facts', fact_count=len(facts)) + + if not all_texts: + await logger.awarning('No documents found to index') + return IndexResult( + total_documents=0, + sources_indexed=[], + message='No documents found to index. Check that data files exist in ./data/', + ) + + # Convert to Document objects + documents = [Document.from_text(text) for text in all_texts] + await logger.ainfo('Converted texts to documents', document_count=len(documents)) + + # Index into ChromaDB + await logger.ainfo('Indexing documents into ChromaDB', collection=COLLECTION_NAME) + await ai.index( + indexer=chroma_indexer_ref(collection_name=COLLECTION_NAME), + documents=documents, + ) + await logger.ainfo('Successfully indexed documents', document_count=len(documents)) + + return IndexResult( + total_documents=len(documents), + sources_indexed=sources, + message=f'Successfully indexed {len(documents)} documents into "{COLLECTION_NAME}" collection', + ) + except Exception as e: + await logger.aexception('Error during indexing', error=str(e)) + raise + + +@ai.flow() +async def ask_about_cats_flow(input: QueryInput) -> QueryResult: + """Ask a question about cats using RAG. + + This flow retrieves relevant documents from the cat knowledge base + and uses them to generate an informed, accurate answer. + + Args: + input: The question and retrieval parameters. + + Returns: + The answer along with source information. + """ + # Retrieve relevant documents + result = await ai.retrieve( + retriever=chroma_retriever_ref(collection_name=COLLECTION_NAME), + query=input.question, + options={'k': input.num_results}, + ) + + # Extract text from retrieved documents + sources: list[str] = [] + context_parts: list[str] = [] + + for doc_data in result.documents: + # Use Document helper to extract text from all parts + doc = Document.from_document_data(doc_data) + text = doc.text() + if text: + # Truncate for display in sources + preview = text[:100] + '...' if len(text) > 100 else text + sources.append(preview) + context_parts.append(text) + + # Build context string + if not context_parts: + return QueryResult( + answer="I don't have information about that in my cat knowledge base. " + 'Please make sure to run the indexing flow first!', + sources_used=[], + confidence='low', + ) + + context = '\n\n---\n\n'.join(context_parts) + + # Generate answer using retrieved context + response = await ai.generate( + prompt=f"""You are a helpful cat expert assistant. Answer the following question +based ONLY on the provided context about cats. If the context doesn't contain +relevant information, say so. Be friendly and informative! + +CONTEXT: +{context} + +QUESTION: {input.question} + +Provide a helpful, accurate answer based on the context above. If citing specific +facts, mention where they come from (e.g., "According to the care guide..." or +"One famous cat...").""", + ) + + # Determine confidence based on number of relevant sources + confidence = 'high' if len(sources) >= 3 else 'medium' if len(sources) >= 1 else 'low' + + return QueryResult( + answer=response.text, + sources_used=[str(s) for s in sources], + confidence=confidence, + ) + + +@ai.flow() +async def list_sample_questions_flow() -> list[str]: + """Get a list of sample questions you can ask about cats. + + Returns: + List of example questions. + """ + return [ + 'Why do cats purr?', + 'How often should I feed my cat?', + 'What vaccinations do cats need?', + 'Tell me about Grumpy Cat', + 'Who was Félicette?', + 'What is a group of cats called?', + 'How many bones does a cat have?', + 'Why do cats knead?', + 'What is the oldest cat ever recorded?', + 'Are there any plants toxic to cats?', + 'What cat breeds are known for being calm?', + 'Why do cats bring dead mice to their owners?', + ] + + +async def main() -> None: + """Main entry point for the RAG demo. + + Automatically indexes the cat knowledge base at startup for + a smooth demo experience. + """ + await logger.ainfo('Starting RAG demo - auto-indexing cat knowledge base') + try: + result = await index_cat_knowledge_flow(IndexInput()) + await logger.ainfo( + 'Auto-indexing complete', + total_documents=result.total_documents, + sources=result.sources_indexed, + ) + except Exception as e: + await logger.awarning( + 'Auto-indexing failed - you may need to run index_cat_knowledge_flow manually', + error=str(e), + ) + + +if __name__ == '__main__': + ai.run_main(main()) diff --git a/py/samples/rag-cloud-sql-pg/README.md b/py/samples/rag-cloud-sql-pg/README.md new file mode 100644 index 0000000000..ffe4d29cc0 --- /dev/null +++ b/py/samples/rag-cloud-sql-pg/README.md @@ -0,0 +1,164 @@ +# RAG with Cloud SQL PostgreSQL Sample + +This sample demonstrates Retrieval-Augmented Generation (RAG) using Cloud SQL for PostgreSQL with pgvector support. + +## Prerequisites + +1. **Cloud SQL Instance**: A Cloud SQL for PostgreSQL instance with pgvector extension enabled +2. **Google Cloud Project**: A GCP project with Cloud SQL API enabled +3. **Authentication**: Either: + - Service account with Cloud SQL Client role + - Database user credentials + +### Enable pgvector Extension + +Connect to your PostgreSQL database and run: + +```sql +CREATE EXTENSION IF NOT EXISTS vector; +``` + +## Setup + +1. Set environment variables: + +```bash +# Required +export GOOGLE_GENAI_API_KEY="your-gemini-api-key" +export CLOUDSQL_PROJECT_ID="your-gcp-project" +export CLOUDSQL_REGION="us-central1" +export CLOUDSQL_INSTANCE="your-instance-name" +export CLOUDSQL_DATABASE="your-database" + +# For password authentication (choose this OR IAM auth) +export CLOUDSQL_USER="your-db-user" +export CLOUDSQL_PASSWORD="your-db-password" + +# For IAM authentication (alternative to password auth) +# export CLOUDSQL_IAM_EMAIL="your-service-account@your-project.iam.gserviceaccount.com" +``` + +2. Install dependencies: + +```bash +cd py/samples/rag-cloud-sql-pg +uv sync +``` + +## Running the Sample + +### Initialize the Database Table + +First run will create the vector store table: + +```bash +uv run genkit start -- python src/main.py --init +``` + +### Index Sample Documents + +```bash +uv run genkit start -- python src/main.py --index +``` + +### Query the Vector Store + +```bash +uv run genkit start -- python src/main.py --query "What are the benefits of exercise?" +``` + +### Run with DevUI + +```bash +uv run genkit start +``` + +Then open http://localhost:4000 in your browser. + +## Project Structure + +``` +rag-cloud-sql-pg/ +├── pyproject.toml # Dependencies +├── README.md # This file +└── src/ + └── main.py # Main application +``` + +## Code Overview + +### Engine Setup + +```python +from genkit.plugins.cloud_sql_pg import PostgresEngine + +engine = await PostgresEngine.from_instance( + project_id=os.environ['CLOUDSQL_PROJECT_ID'], + region=os.environ['CLOUDSQL_REGION'], + instance=os.environ['CLOUDSQL_INSTANCE'], + database=os.environ['CLOUDSQL_DATABASE'], + user=os.environ.get('CLOUDSQL_USER'), + password=os.environ.get('CLOUDSQL_PASSWORD'), +) +``` + +### Table Initialization + +```python +await engine.init_vectorstore_table( + table_name='documents', + vector_size=768, # text-embedding-004 dimension +) +``` + +### Plugin Setup + +```python +from genkit.plugins.cloud_sql_pg import CloudSqlPg, PostgresTableConfig + +ai = Genkit( + plugins=[ + GoogleAI(), + CloudSqlPg( + tables=[ + PostgresTableConfig( + table_name='documents', + engine=engine, + embedder='googleai/text-embedding-004', + ) + ] + ), + ] +) +``` + +### Indexing + +```python +await ai.index( + indexer='postgres/documents', + documents=[Document.from_text('Your text here')], +) +``` + +### Retrieval + +```python +response = await ai.retrieve( + retriever='postgres/documents', + query=Document.from_text('Your query here'), + options={'k': 5}, +) +``` + +## Features Demonstrated + +- Cloud SQL Connector for secure connections +- pgvector for vector similarity search +- Embedding generation with Google AI +- RAG flow with context injection +- SQL filtering support + +## License + +Apache-2.0 diff --git a/py/samples/rag-cloud-sql-pg/pyproject.toml b/py/samples/rag-cloud-sql-pg/pyproject.toml new file mode 100644 index 0000000000..4f20d2a5d5 --- /dev/null +++ b/py/samples/rag-cloud-sql-pg/pyproject.toml @@ -0,0 +1,26 @@ +# Copyright 2025 Google LLC +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. +# +# SPDX-License-Identifier: Apache-2.0 + +[project] +name = "rag-cloud-sql-pg-sample" +version = "0.0.1" +description = "RAG sample using Cloud SQL PostgreSQL with pgvector" +requires-python = ">=3.10" +dependencies = [ + "genkit", + "genkit-plugin-google-genai", + "genkit-plugin-cloud-sql-pg", +] diff --git a/py/samples/rag-cloud-sql-pg/src/main.py b/py/samples/rag-cloud-sql-pg/src/main.py new file mode 100644 index 0000000000..4f1407f0cd --- /dev/null +++ b/py/samples/rag-cloud-sql-pg/src/main.py @@ -0,0 +1,263 @@ +# Copyright 2025 Google LLC +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. +# +# SPDX-License-Identifier: Apache-2.0 + +"""RAG sample using Cloud SQL PostgreSQL with pgvector. + +This sample demonstrates: +- Setting up Cloud SQL PostgreSQL as a vector store +- Indexing documents with embeddings +- Retrieving relevant documents using similarity search +- Building a RAG flow with Genkit + +Prerequisites: +- Cloud SQL for PostgreSQL instance with pgvector extension +- Environment variables for connection details +""" + +import argparse +import asyncio +import os + +from genkit import Genkit +from genkit.blocks.document import Document +from genkit.plugins.cloud_sql_pg import ( + CloudSqlPg, + HNSWIndex, + HNSWQueryOptions, + PostgresEngine, + PostgresTableConfig, +) +from genkit.plugins.google_genai import GoogleAI + +# Sample documents for indexing +SAMPLE_DOCUMENTS = [ + """Regular exercise has numerous health benefits. It can help control weight, + reduce risk of heart disease, and strengthen bones and muscles. Exercise also + improves mental health by reducing anxiety, depression, and negative mood.""", + """A balanced diet is essential for good health. It should include a variety + of fruits, vegetables, whole grains, lean proteins, and healthy fats. Proper + nutrition provides energy and supports all bodily functions.""", + """Sleep is crucial for physical and mental health. Adults should aim for + 7-9 hours of sleep per night. Good sleep improves brain function, mood, + and overall health while reducing disease risk.""", + """Stress management is important for well-being. Techniques like meditation, + deep breathing, and regular physical activity can help reduce stress levels. + Chronic stress can lead to various health problems.""", + """Staying hydrated is essential for body function. Water helps regulate + temperature, transport nutrients, and remove waste. The recommended daily + intake is about 8 glasses of water.""", +] + +# Table and embedder configuration +TABLE_NAME = 'health_documents' +EMBEDDER = 'googleai/text-embedding-004' +EMBEDDING_DIMENSION = 768 # text-embedding-004 dimension + + +async def create_engine() -> PostgresEngine: + """Create PostgresEngine from environment variables.""" + project_id = os.environ.get('CLOUDSQL_PROJECT_ID') + region = os.environ.get('CLOUDSQL_REGION') + instance = os.environ.get('CLOUDSQL_INSTANCE') + database = os.environ.get('CLOUDSQL_DATABASE') + user = os.environ.get('CLOUDSQL_USER') + password = os.environ.get('CLOUDSQL_PASSWORD') + iam_email = os.environ.get('CLOUDSQL_IAM_EMAIL') + + if not project_id or not region or not instance or not database: + raise ValueError( + 'Missing required environment variables. Set: ' + 'CLOUDSQL_PROJECT_ID, CLOUDSQL_REGION, CLOUDSQL_INSTANCE, CLOUDSQL_DATABASE' + ) + + # Use IAM auth if no user/password provided + if user and password: + return await PostgresEngine.from_instance( + project_id=project_id, + region=region, + instance=instance, + database=database, + user=user, + password=password, + ) + else: + return await PostgresEngine.from_instance( + project_id=project_id, + region=region, + instance=instance, + database=database, + iam_account_email=iam_email, + ) + + +async def init_table(engine: PostgresEngine) -> None: + """Initialize the vector store table.""" + print(f'Creating table: {TABLE_NAME}') + await engine.init_vectorstore_table( + table_name=TABLE_NAME, + vector_size=EMBEDDING_DIMENSION, + overwrite_existing=True, + ) + print('Table created successfully!') + + # Optionally create an HNSW index for faster queries + print('Creating HNSW index...') + await engine.apply_vector_index( + table_name=TABLE_NAME, + index=HNSWIndex(m=16, ef_construction=64), + ) + print('Index created successfully!') + + +async def index_documents(ai: Genkit) -> None: + """Index sample documents.""" + print(f'Indexing {len(SAMPLE_DOCUMENTS)} documents...') + + documents = [Document.from_text(text) for text in SAMPLE_DOCUMENTS] + + await ai.index( + indexer=f'postgres/{TABLE_NAME}', + documents=documents, + ) + + print('Documents indexed successfully!') + + +async def query_documents(ai: Genkit, query: str) -> None: + """Query the vector store and show results.""" + print(f'\nQuery: {query}') + print('-' * 50) + + response = await ai.retrieve( + retriever=f'postgres/{TABLE_NAME}', + query=Document.from_text(query), + options={'k': 3}, + ) + + print(f'Found {len(response.documents)} relevant documents:\n') + + for i, doc_data in enumerate(response.documents, 1): + doc = Document.from_document_data(doc_data) + text = doc.text() or str(doc.content) + distance = doc.metadata.get('_distance', 'N/A') if doc.metadata else 'N/A' + print(f'{i}. (distance: {distance:.4f})') + print(f' {text[:200]}...\n') + + +async def run_rag_flow(ai: Genkit, question: str) -> None: + """Run a RAG flow: retrieve context and generate answer.""" + print(f'\nQuestion: {question}') + print('-' * 50) + + # Retrieve relevant documents + response = await ai.retrieve( + retriever=f'postgres/{TABLE_NAME}', + query=Document.from_text(question), + options={'k': 3}, + ) + + # Build context from retrieved documents + context_parts = [] + for doc_data in response.documents: + doc = Document.from_document_data(doc_data) + text = doc.text() or str(doc.content) + context_parts.append(text) + + context = '\n\n'.join(context_parts) + + # Generate answer using the context + prompt = f"""Based on the following context, answer the question. + +Context: +{context} + +Question: {question} + +Answer:""" + + result = await ai.generate( + model='googleai/gemini-2.0-flash', + prompt=prompt, + ) + + print(f'\nAnswer: {result.text}') + + +# Create engine and Genkit instance +# Note: In a real application, you'd use dependency injection or async context +_engine: PostgresEngine | None = None + + +async def get_engine() -> PostgresEngine: + """Get or create the PostgresEngine instance.""" + global _engine + if _engine is None: + _engine = await create_engine() + return _engine + + +async def main() -> None: + """Main entry point.""" + parser = argparse.ArgumentParser(description='RAG with Cloud SQL PostgreSQL') + parser.add_argument('--init', action='store_true', help='Initialize the database table') + parser.add_argument('--index', action='store_true', help='Index sample documents') + parser.add_argument('--query', type=str, help='Query the vector store') + parser.add_argument('--rag', type=str, help='Run RAG flow with a question') + args = parser.parse_args() + + # Create engine + engine = await get_engine() + + # Handle init command + if args.init: + await init_table(engine) + return + + # Create Genkit instance with plugins + ai = Genkit( + plugins=[ + GoogleAI(), + CloudSqlPg( + tables=[ + PostgresTableConfig( + table_name=TABLE_NAME, + engine=engine, + embedder=EMBEDDER, + index_query_options=HNSWQueryOptions(ef_search=40), + ) + ] + ), + ] + ) + + # Handle commands + if args.index: + await index_documents(ai) + elif args.query: + await query_documents(ai, args.query) + elif args.rag: + await run_rag_flow(ai, args.rag) + else: + # Default: run a demo query + print('No command specified. Running demo...') + print('\nUse --init to create the table') + print('Use --index to index sample documents') + print('Use --query "your query" to search') + print('Use --rag "your question" to run RAG flow') + + +if __name__ == '__main__': + asyncio.run(main()) diff --git a/py/samples/rag-pinecone/README.md b/py/samples/rag-pinecone/README.md new file mode 100644 index 0000000000..b48db71977 --- /dev/null +++ b/py/samples/rag-pinecone/README.md @@ -0,0 +1,210 @@ +# RAG with Pinecone Demo - Cat Knowledge Base 🐱 + +A complete Retrieval-Augmented Generation (RAG) demo using Pinecone as a +managed vector database with Genkit. Features a cat-themed knowledge base! + +## Overview + +This sample demonstrates the RAG pattern with Pinecone: + +1. **Index Phase**: Cat knowledge documents are embedded and stored in Pinecone +2. **Retrieval Phase**: User questions find relevant documents via similarity search +3. **Generation Phase**: Retrieved context augments the LLM's response + +``` +┌─────────────────────────────────────────────────────────────────────────────┐ +│ RAG Architecture │ +├─────────────────────────────────────────────────────────────────────────────┤ +│ │ +│ INDEX PHASE: │ +│ ┌─────────────────┐ ┌──────────┐ ┌─────────────┐ ┌───────────┐ │ +│ │ Cat Documents │ ─► │ Embedder │ ─► │ Embeddings │ ─► │ Pinecone │ │ +│ │ (txt, json) │ │ (Gemini) │ │ (Vectors) │ │ (Cloud) │ │ +│ └─────────────────┘ └──────────┘ └─────────────┘ └───────────┘ │ +│ │ +│ QUERY PHASE: │ +│ ┌──────────┐ ┌──────────┐ ┌────────────┐ ┌──────────────────┐ │ +│ │ Question │ ─► │ Embedder │ ─► │ Similarity │ ─► │ Retrieved Docs │ │ +│ └──────────┘ └──────────┘ │ Search │ └────────┬─────────┘ │ +│ └────────────┘ │ │ +│ │ │ +│ GENERATION PHASE: ▼ │ +│ ┌───────────┐ ┌─────────────────────────────────────┐ │ +│ │ Answer │ ◄───────────────│ LLM (Gemini) + Context from Docs │ │ +│ └───────────┘ └─────────────────────────────────────┘ │ +│ │ +└─────────────────────────────────────────────────────────────────────────────┘ +``` + +## Prerequisites + +### 1. Create a Pinecone Account + +Sign up at [Pinecone](https://www.pinecone.io/) (free tier available). + +### 2. Create a Pinecone Index + +```bash +./run.sh --setup +``` + +This will show detailed instructions. In short: +1. Go to https://app.pinecone.io/ +2. Create a new index: + - **Name**: `cat-knowledge` + - **Dimensions**: `768` + - **Metric**: `cosine` + +### 3. Set API Keys + +```bash +export PINECONE_API_KEY=your-pinecone-key +export GOOGLE_API_KEY=your-google-key +``` + +## Quick Start + +### Run the Demo + +```bash +./run.sh +``` + +This will: +- Install all dependencies (including Pinecone client) +- Start the Genkit Dev UI at http://localhost:4000 +- **Automatically index the cat knowledge base!** +- Watch for file changes and auto-reload + +### Use the Demo + +1. Open http://localhost:4000 in your browser + +2. **Ask questions about cats** (knowledge base is pre-indexed): + - Select `ask_about_cats_flow` + - Enter questions like: + - "Why do cats purr?" + - "Tell me about Grumpy Cat" + - "What vaccinations do cats need?" + - The answer will include sources from the knowledge base + +3. **Get question ideas**: + - Run `list_sample_questions_flow` for example questions + +4. **Re-index if needed** (after adding new data): + - Run `index_cat_knowledge_flow` to refresh the knowledge base + +## Data Files + +The knowledge base includes three cat-themed sources in `./data/`: + +| File | Description | +|------|-------------| +| `cat_care_guide.txt` | Comprehensive guide on cat behavior, nutrition, health, and breeds | +| `famous_cats.txt` | Stories of famous cats from internet celebrities to historical cats | +| `cat_facts.json` | Fun and interesting facts about cats organized by category | + +### Adding Your Own Data + +You can add more documents by: +1. Adding `.txt` files to `./data/` (will be split by `##` headers) +2. Adding facts to `cat_facts.json` +3. Running `index_cat_knowledge_flow` again + +## Flows + +| Flow | Description | +|------|-------------| +| `index_cat_knowledge_flow` | Load and index all cat documents into Pinecone | +| `ask_about_cats_flow` | Ask questions with RAG-powered answers | +| `list_sample_questions_flow` | Get example questions to try | + +## Namespaces + +Pinecone supports namespaces for data isolation. You can use different namespaces +to organize data: + +```python +# Index to a specific namespace +await ai.index( + indexer=pinecone_indexer_ref(index_id='cat-knowledge'), + documents=documents, + options={'namespace': 'production'}, +) + +# Query from a specific namespace +results = await ai.retrieve( + retriever=pinecone_retriever_ref(index_id='cat-knowledge'), + query='Why do cats purr?', + options={'namespace': 'production', 'k': 5}, +) +``` + +## Why Pinecone? + +- **Managed Service**: No infrastructure to maintain +- **Scalable**: Handles billions of vectors +- **Fast**: Low-latency similarity search +- **Filtering**: Metadata filtering for precise results +- **Multi-tenancy**: Namespaces for data isolation + +## Architecture Details + +### Embedding Model + +Uses `googleai/text-embedding-004` for: +- Document embedding during indexing (768 dimensions) +- Query embedding during retrieval + +### Generation Model + +Uses `googleai/gemini-2.0-flash` for: +- Generating answers based on retrieved context +- Fast, accurate responses + +## Troubleshooting + +### "PINECONE_API_KEY not set" + +```bash +export PINECONE_API_KEY=your-pinecone-key +``` + +### "Index not found" or connection errors + +1. Verify your index exists at https://app.pinecone.io/ +2. Check the index name matches `INDEX_ID` in `src/main.py` +3. Ensure the index has dimension=768 and metric=cosine + +### "GOOGLE_API_KEY not set" + +```bash +export GOOGLE_API_KEY=your-google-key +``` + +### "No documents found" + +Make sure the data files exist in `./data/`: +```bash +ls -la data/ +``` + +### "I don't have information about that" + +Run `index_cat_knowledge_flow` first to load the knowledge base. + +## Cost Considerations + +- Pinecone free tier includes 1 index with ~100K vectors +- Google AI API has generous free quotas for testing +- This demo uses ~50 documents, well within free tier limits + +## Learn More + +- [Genkit Documentation](https://genkit.dev/docs) +- [Pinecone Documentation](https://docs.pinecone.io/) +- [RAG Pattern](https://genkit.dev/docs/rag) + +## License + +Apache 2.0 diff --git a/py/samples/rag-pinecone/data/cat_care_guide.txt b/py/samples/rag-pinecone/data/cat_care_guide.txt new file mode 100644 index 0000000000..ffd0ab312f --- /dev/null +++ b/py/samples/rag-pinecone/data/cat_care_guide.txt @@ -0,0 +1,76 @@ +# The Complete Cat Care Guide + +## Chapter 1: Understanding Cat Behavior + +Cats are fascinating creatures with complex behaviors. Unlike dogs, cats are obligate carnivores and have evolved as solitary hunters. This influences much of their behavior today. + +### Purring +Cats purr at a frequency of 25-150 Hz, which has been shown to promote healing. Cats purr not only when content but also when stressed or injured, suggesting it may be a self-soothing mechanism. + +### Kneading +When cats knead with their paws, they're exhibiting a behavior from kittenhood when they kneaded their mother's belly to stimulate milk flow. Adult cats often knead when feeling safe and content. + +### The Slow Blink +A slow blink from a cat is often called a "cat kiss" - it's a sign of trust and affection. You can return the gesture to bond with your cat. + +## Chapter 2: Nutrition and Diet + +### Essential Nutrients +Cats require taurine, an amino acid found only in animal protein. Without sufficient taurine, cats can develop heart problems and blindness. This is why cats cannot be vegetarians. + +### Feeding Schedule +Adult cats should be fed 2-3 times per day. Free feeding can lead to obesity. The average indoor cat needs about 200-300 calories per day, depending on size and activity level. + +### Hydration +Cats have a low thirst drive because their ancestors got most water from prey. Wet food can help with hydration. Many cats prefer running water, which is why cat fountains are popular. + +## Chapter 3: Health and Wellness + +### Vaccinations +Core vaccines for cats include: +- Feline Panleukopenia (FPV) +- Feline Calicivirus (FCV) +- Feline Herpesvirus (FHV-1) +- Rabies + +### Common Health Issues +- Dental disease affects 70% of cats over age 3 +- Urinary tract issues are common, especially in male cats +- Hyperthyroidism is common in older cats +- Kidney disease affects 1 in 3 cats over age 10 + +### Signs of Illness +Cats hide illness well. Watch for: +- Changes in appetite or water consumption +- Litter box changes +- Hiding or behavior changes +- Weight loss or gain +- Vomiting or diarrhea + +## Chapter 4: Environmental Enrichment + +### Vertical Space +Cats feel safe when they can survey their territory from above. Cat trees, shelves, and perches are essential for indoor cats. + +### Scratching +Scratching is natural and necessary - it removes old claw sheaths, stretches muscles, and marks territory. Provide multiple scratching posts of different materials. + +### Play +Interactive play mimics hunting behavior. Wand toys, laser pointers (always end with a physical toy to "catch"), and puzzle feeders provide mental stimulation. + +## Chapter 5: Cat Breeds + +### Maine Coon +The largest domestic cat breed, Maine Coons are known as "gentle giants." They can weigh 15-25 pounds and are very social and dog-like in behavior. + +### Siamese +One of the oldest recognized breeds, Siamese cats are known for their distinctive coloring, blue eyes, and vocal nature. They're highly intelligent and form strong bonds. + +### Persian +Persians have long, luxurious coats and flat faces. They're calm and affectionate but require daily grooming to prevent matting. + +### British Shorthair +Known for their round faces and dense coats, British Shorthairs are calm, easygoing cats. The "Cheshire Cat" was inspired by this breed. + +### Bengal +Bengals have wild-looking spotted or marbled coats. They're highly active, intelligent, and need lots of stimulation. diff --git a/py/samples/rag-pinecone/data/cat_facts.json b/py/samples/rag-pinecone/data/cat_facts.json new file mode 100644 index 0000000000..6cf1904c60 --- /dev/null +++ b/py/samples/rag-pinecone/data/cat_facts.json @@ -0,0 +1,68 @@ +{ + "cat_facts": [ + { + "category": "anatomy", + "fact": "Cats have over 20 vocalizations, including the meow, purr, chirp, and trill. Interestingly, adult cats typically only meow to communicate with humans, not other cats." + }, + { + "category": "anatomy", + "fact": "A cat's nose print is unique, like a human's fingerprint. No two cats have the same nose print pattern." + }, + { + "category": "anatomy", + "fact": "Cats have 230 bones in their body, compared to 206 in humans. Many of these extra bones are in their spine and tail, giving them exceptional flexibility." + }, + { + "category": "anatomy", + "fact": "A cat's whiskers are roughly as wide as their body, helping them determine if they can fit through tight spaces." + }, + { + "category": "behavior", + "fact": "Cats spend 70% of their lives sleeping, averaging 13-16 hours per day. This is an evolutionary adaptation from their wild ancestors who needed to conserve energy for hunting." + }, + { + "category": "behavior", + "fact": "When a cat brings you a 'gift' like a dead mouse, they're trying to teach you to hunt because they see you as a poor hunter who needs help." + }, + { + "category": "behavior", + "fact": "Cats can rotate their ears 180 degrees and can move each ear independently, allowing them to pinpoint the source of sounds precisely." + }, + { + "category": "history", + "fact": "Cats were first domesticated around 10,000 years ago in the Near East, likely because they hunted mice attracted to stored grain." + }, + { + "category": "history", + "fact": "In ancient Egypt, killing a cat, even accidentally, was punishable by death. When a family cat died, family members would shave their eyebrows in mourning." + }, + { + "category": "history", + "fact": "The first cat show was held in London in 1871 at the Crystal Palace, organized by Harrison Weir." + }, + { + "category": "abilities", + "fact": "Cats can jump up to six times their length and can run at speeds up to 30 miles per hour in short bursts." + }, + { + "category": "abilities", + "fact": "A cat's hearing is about four times more sensitive than a human's, and they can hear ultrasonic sounds that are inaudible to humans." + }, + { + "category": "abilities", + "fact": "Cats have a specialized collarbone that allows them to always land on their feet when falling. This is called the 'righting reflex' and develops around 3-4 weeks of age." + }, + { + "category": "health", + "fact": "A group of cats is called a 'clowder,' while a group of kittens is called a 'kindle.'" + }, + { + "category": "health", + "fact": "The oldest known cat lived to be 38 years old. Her name was Creme Puff and she lived in Austin, Texas." + }, + { + "category": "health", + "fact": "Cats cannot taste sweetness because they lack the taste receptor for it. This is likely because their carnivorous diet never required detecting sugars." + } + ] +} diff --git a/py/samples/rag-pinecone/data/famous_cats.txt b/py/samples/rag-pinecone/data/famous_cats.txt new file mode 100644 index 0000000000..843f8d25fe --- /dev/null +++ b/py/samples/rag-pinecone/data/famous_cats.txt @@ -0,0 +1,54 @@ +# Famous Cats Throughout History + +## Internet Famous Cats + +### Grumpy Cat (2012-2019) +Tardar Sauce, known as Grumpy Cat, became an internet sensation due to her permanently grumpy facial expression caused by feline dwarfism. She appeared in commercials, had her own movie, and her image was worth millions in merchandise. + +### Lil Bub (2011-2019) +Lil Bub was a celebrity cat known for her unique appearance caused by genetic mutations. She had no teeth, extra toes, and a permanently protruding tongue. She raised over $700,000 for animals in need. + +### Maru +A Scottish Fold from Japan, Maru is famous for his love of boxes and has been called the most watched cat on YouTube, with hundreds of millions of video views. + +### Keyboard Cat (1984-2018) +Fatso, known as Keyboard Cat, became viral in 2007 when a video from 1984 showed him "playing" a keyboard. The meme is used to "play off" embarrassing moments. + +## Historical Cats + +### Félicette (1963) +Félicette was the first cat in space, launched by France on October 18, 1963. She reached an altitude of 157 km and survived the mission. A statue was unveiled in her honor in 2019. + +### Unsinkable Sam (WWII) +A black and white cat who survived the sinking of three ships during World War II: the German battleship Bismarck, HMS Cossack, and HMS Ark Royal. + +### Dewey Readmore Books (1987-2006) +Dewey was a library cat at the Spencer Public Library in Iowa. He was found in the book drop as a kitten and lived at the library for 19 years, becoming internationally famous. + +### Trim (1799-1804) +Trim accompanied Matthew Flinders on his voyages to circumnavigate and map the Australian coastline. Flinders wrote a biographical tribute to Trim after the cat's death. + +## Cats in Literature and Media + +### Cheshire Cat +From Lewis Carroll's "Alice's Adventures in Wonderland," the Cheshire Cat is known for its distinctive grin and ability to disappear, leaving only its smile behind. + +### Garfield +Created by Jim Davis in 1978, Garfield is an orange tabby known for his love of lasagna, hatred of Mondays, and lazy demeanor. The comic strip appears in over 2,500 newspapers. + +### The Cat in the Hat +Dr. Seuss's iconic character, first appearing in 1957, is known for his red and white striped hat and mischievous adventures. + +### Tom (Tom and Jerry) +The gray and white domestic cat has been chasing Jerry Mouse since 1940, making it one of the longest-running animated series. + +## Scientific and Working Cats + +### CC (Copy Cat) +CC was the first cloned cat, born on December 22, 2001, at Texas A&M University. She lived a normal life and had kittens naturally. + +### Oscar the Therapy Cat +Oscar gained fame for his ability to predict the deaths of patients in a nursing home, curling up with them in their final hours. He was featured in the New England Journal of Medicine. + +### Station Master Tama +A calico cat who became the station master of Kishi Station in Japan, helping revitalize the local economy and railway. She was appointed to the role in 2007 and drew tourists worldwide. diff --git a/py/samples/rag-pinecone/pyproject.toml b/py/samples/rag-pinecone/pyproject.toml new file mode 100644 index 0000000000..0358f0c3e7 --- /dev/null +++ b/py/samples/rag-pinecone/pyproject.toml @@ -0,0 +1,37 @@ +# Copyright 2026 Google LLC +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. +# +# SPDX-License-Identifier: Apache-2.0 + +[project] +dependencies = [ + "genkit", + "genkit-plugin-google-genai", + "genkit-plugin-pinecone", + "pinecone>=5.0.0", + "rich>=13.0.0", +] +description = "RAG demo using Pinecone with Genkit - cat-themed knowledge base" +name = "rag-pinecone-demo" +readme = "README.md" +requires-python = ">=3.10" +version = "0.1.0" + +[project.optional-dependencies] +dev = ["watchdog>=3.0.0"] + +[tool.uv.sources] +genkit = { workspace = true } +genkit-plugin-google-genai = { workspace = true } +genkit-plugin-pinecone = { workspace = true } diff --git a/py/samples/rag-pinecone/run.sh b/py/samples/rag-pinecone/run.sh new file mode 100755 index 0000000000..534a81c542 --- /dev/null +++ b/py/samples/rag-pinecone/run.sh @@ -0,0 +1,66 @@ +#!/usr/bin/env bash +# Copyright 2026 Google LLC +# SPDX-License-Identifier: Apache-2.0 + +# RAG with Pinecone Demo +# ====================== +# +# Demonstrates Retrieval Augmented Generation using Pinecone. +# +# Prerequisites: +# - GEMINI_API_KEY environment variable set +# - PINECONE_API_KEY environment variable set +# +# Usage: +# ./run.sh # Start the demo with Dev UI +# ./run.sh --help # Show this help message + +set -euo pipefail + +cd "$(dirname "$0")" +source "../_common.sh" + +print_help() { + print_banner "RAG with Pinecone" "🌲" + echo "Usage: ./run.sh [options]" + echo "" + echo "Options:" + echo " --help Show this help message" + echo "" + echo "Environment Variables:" + echo " GEMINI_API_KEY Required. Your Gemini API key" + echo " PINECONE_API_KEY Required. Your Pinecone API key" + echo "" + echo "This demo shows:" + echo " - Document indexing with Pinecone" + echo " - Semantic retrieval" + echo " - Augmented generation" + echo "" + echo "Get API keys from:" + echo " - Google: https://makersuite.google.com/app/apikey" + echo " - Pinecone: https://app.pinecone.io/" + print_help_footer +} + +case "${1:-}" in + --help|-h) + print_help + exit 0 + ;; +esac + +print_banner "RAG with Pinecone" "🌲" + +check_env_var "GEMINI_API_KEY" "https://makersuite.google.com/app/apikey" || true +check_env_var "PINECONE_API_KEY" "https://app.pinecone.io/" || true + +install_deps + +genkit_start_with_browser -- \ + uv tool run --from watchdog watchmedo auto-restart \ + -d src \ + -d ../../packages \ + -d ../../plugins \ + -p '*.py;*.prompt;*.json' \ + -R \ + -- uv run src/main.py "$@" diff --git a/py/samples/rag-pinecone/src/main.py b/py/samples/rag-pinecone/src/main.py new file mode 100644 index 0000000000..c817986a0c --- /dev/null +++ b/py/samples/rag-pinecone/src/main.py @@ -0,0 +1,413 @@ +#!/usr/bin/env python3 +# Copyright 2026 Google LLC +# SPDX-License-Identifier: Apache-2.0 + +"""RAG demo using Pinecone with Genkit - Cat Knowledge Base. + +This sample demonstrates Retrieval-Augmented Generation (RAG) using Pinecone +as a managed vector database. It includes a cat-themed knowledge base with +documents about cat care, famous cats, and fun cat facts. + +Architecture +============ + +┌─────────────────────────────────────────────────────────────────────────────┐ +│ RAG with Pinecone │ +├─────────────────────────────────────────────────────────────────────────────┤ +│ │ +│ ┌──────────────┐ ┌──────────────┐ ┌──────────────────────┐ │ +│ │ Genkit │ │ Pinecone │ │ Pinecone Cloud │ │ +│ │ + GoogleAI │ ──► │ Plugin │ ──► │ (Managed) │ │ +│ └──────────────┘ └──────────────┘ └──────────────────────┘ │ +│ │ +│ Data Sources (./data/): │ +│ - cat_care_guide.txt - Complete cat care information │ +│ - famous_cats.txt - Famous cats throughout history │ +│ - cat_facts.json - Fun and interesting cat facts │ +│ │ +│ Flows: │ +│ - index_cat_knowledge_flow: Load and index all cat documents │ +│ - ask_about_cats_flow: Ask questions about cats with RAG │ +│ │ +└─────────────────────────────────────────────────────────────────────────────┘ + +Prerequisites +============= + +1. Create a Pinecone account at https://www.pinecone.io/ +2. Create an index with: + - Dimension: 768 (for text-embedding-004) + - Metric: cosine +3. Set environment variables: + - PINECONE_API_KEY: Your Pinecone API key + - GOOGLE_API_KEY: Your Google AI API key + +Quick Start +=========== + +1. Set your API keys: + ``` + export PINECONE_API_KEY=your-pinecone-key + export GOOGLE_API_KEY=your-google-key + ``` + +2. Run the demo: + ``` + ./run.sh + ``` + +3. Open http://localhost:4000 in your browser + +4. The cat knowledge base is automatically indexed at startup! + (Note: First run may take longer to create embeddings) + +5. Run `ask_about_cats_flow` with questions like: + - "How often should I feed my cat?" + - "Tell me about Grumpy Cat" + - "Why do cats purr?" +""" + +import json +from pathlib import Path + +import structlog +from pydantic import BaseModel, Field +from rich.traceback import install as install_rich_traceback + +from genkit.ai import Genkit +from genkit.blocks.document import Document +from genkit.plugins.google_genai import GoogleAI +from genkit.plugins.pinecone import pinecone, pinecone_indexer_ref, pinecone_retriever_ref + +install_rich_traceback(show_locals=True, width=120, extra_lines=3) + +logger = structlog.get_logger(__name__) + +# Update this to match your Pinecone index name +INDEX_ID = 'cat-knowledge' +EMBEDDER = 'googleai/text-embedding-004' +DATA_DIR = Path(__file__).parent.parent / 'data' + +ai = Genkit( + plugins=[ + GoogleAI(), + pinecone( + indexes=[ + { + 'index_id': INDEX_ID, + 'embedder': EMBEDDER, + } + ] + ), + ], + model='googleai/gemini-2.0-flash', +) + + +class IndexInput(BaseModel): + """Input for indexing cat knowledge documents.""" + + include_care_guide: bool = Field( + default=True, + description='Include the comprehensive cat care guide', + ) + include_famous_cats: bool = Field( + default=True, + description='Include information about famous cats', + ) + include_cat_facts: bool = Field( + default=True, + description='Include fun cat facts', + ) + namespace: str | None = Field( + default=None, + description='Optional Pinecone namespace for data isolation', + ) + + +class IndexResult(BaseModel): + """Result of indexing operation.""" + + total_documents: int + sources_indexed: list[str] + namespace: str | None + message: str + + +class QueryInput(BaseModel): + """Input for asking questions about cats.""" + + question: str = Field( + default='Why do cats purr and what does it mean?', + description='Your question about cats', + ) + num_results: int = Field( + default=5, + ge=1, + le=20, + description='Number of relevant documents to retrieve', + ) + namespace: str | None = Field( + default=None, + description='Optional Pinecone namespace to query', + ) + + +class QueryResult(BaseModel): + """Result of RAG query about cats.""" + + answer: str + sources_used: list[str] + confidence: str + + +def load_text_file(filepath: Path) -> list[str]: + """Load a text file and split into chunks by section. + + Args: + filepath: Path to the text file. + + Returns: + List of text chunks. + """ + if not filepath.exists(): + return [] + + content = filepath.read_text(encoding='utf-8') + + # Split by headers (## or #) + chunks = [] + current_chunk = [] + + for line in content.split('\n'): + if line.startswith('## ') and current_chunk: + chunks.append('\n'.join(current_chunk).strip()) + current_chunk = [line] + else: + current_chunk.append(line) + + if current_chunk: + chunks.append('\n'.join(current_chunk).strip()) + + # Filter out very short chunks + return [c for c in chunks if len(c) > 50] + + +def load_json_facts(filepath: Path) -> list[str]: + """Load cat facts from JSON file. + + Args: + filepath: Path to the JSON file. + + Returns: + List of fact strings. + """ + if not filepath.exists(): + return [] + + with open(filepath, encoding='utf-8') as f: + data = json.load(f) + + facts = [] + for fact_obj in data.get('cat_facts', []): + category = fact_obj.get('category', 'general') + fact = fact_obj.get('fact', '') + if fact: + facts.append(f'[{category.upper()}] {fact}') + + return facts + + +@ai.flow() +async def index_cat_knowledge_flow(input: IndexInput) -> IndexResult: + """Index cat knowledge documents into Pinecone. + + This flow loads cat-themed documents from the ./data/ directory and + indexes them into Pinecone for retrieval. Documents are split into + chunks for better retrieval accuracy. + + Args: + input: Configuration for which document sources to include. + + Returns: + Summary of the indexing operation. + """ + all_texts: list[str] = [] + sources: list[str] = [] + + # Load cat care guide + if input.include_care_guide: + care_chunks = load_text_file(DATA_DIR / 'cat_care_guide.txt') + all_texts.extend(care_chunks) + if care_chunks: + sources.append(f'cat_care_guide.txt ({len(care_chunks)} chunks)') + + # Load famous cats + if input.include_famous_cats: + famous_chunks = load_text_file(DATA_DIR / 'famous_cats.txt') + all_texts.extend(famous_chunks) + if famous_chunks: + sources.append(f'famous_cats.txt ({len(famous_chunks)} chunks)') + + # Load cat facts + if input.include_cat_facts: + facts = load_json_facts(DATA_DIR / 'cat_facts.json') + all_texts.extend(facts) + if facts: + sources.append(f'cat_facts.json ({len(facts)} facts)') + + if not all_texts: + return IndexResult( + total_documents=0, + sources_indexed=[], + namespace=input.namespace, + message='No documents found to index. Check that data files exist in ./data/', + ) + + # Convert to Document objects + documents = [Document.from_text(text) for text in all_texts] + + # Build options + options: dict[str, object] | None = None + if input.namespace: + options = {'namespace': input.namespace} + + # Index into Pinecone + await ai.index( + indexer=pinecone_indexer_ref(index_id=INDEX_ID), + documents=documents, + options=options, + ) + + namespace_msg = f' (namespace: {input.namespace})' if input.namespace else '' + return IndexResult( + total_documents=len(documents), + sources_indexed=sources, + namespace=input.namespace, + message=f'Successfully indexed {len(documents)} documents into "{INDEX_ID}"{namespace_msg}', + ) + + +@ai.flow() +async def ask_about_cats_flow(input: QueryInput) -> QueryResult: + """Ask a question about cats using RAG. + + This flow retrieves relevant documents from the cat knowledge base + and uses them to generate an informed, accurate answer. + + Args: + input: The question and retrieval parameters. + + Returns: + The answer along with source information. + """ + # Build retrieval options + options: dict[str, object] = {'k': input.num_results} + if input.namespace: + options['namespace'] = input.namespace + + # Retrieve relevant documents + result = await ai.retrieve( + retriever=pinecone_retriever_ref(index_id=INDEX_ID), + query=input.question, + options=options, + ) + + # Extract text from retrieved documents + sources: list[str] = [] + context_parts: list[str] = [] + + for doc_data in result.documents: + # Use Document helper to extract text from all parts + doc = Document.from_document_data(doc_data) + text = doc.text() + if text: + # Truncate for display in sources + preview = text[:100] + '...' if len(text) > 100 else text + sources.append(preview) + context_parts.append(text) + + # Build context string + if not context_parts: + return QueryResult( + answer="I don't have information about that in my cat knowledge base. " + 'Please make sure to run the indexing flow first!', + sources_used=[], + confidence='low', + ) + + context = '\n\n---\n\n'.join(context_parts) + + # Generate answer using retrieved context + response = await ai.generate( + prompt=f"""You are a helpful cat expert assistant. Answer the following question +based ONLY on the provided context about cats. If the context doesn't contain +relevant information, say so. Be friendly and informative! + +CONTEXT: +{context} + +QUESTION: {input.question} + +Provide a helpful, accurate answer based on the context above. If citing specific +facts, mention where they come from (e.g., "According to the care guide..." or +"One famous cat...").""", + ) + + # Determine confidence based on number of relevant sources + confidence = 'high' if len(sources) >= 3 else 'medium' if len(sources) >= 1 else 'low' + + return QueryResult( + answer=response.text, + sources_used=[str(s) for s in sources], + confidence=confidence, + ) + + +@ai.flow() +async def list_sample_questions_flow() -> list[str]: + """Get a list of sample questions you can ask about cats. + + Returns: + List of example questions. + """ + return [ + 'Why do cats purr?', + 'How often should I feed my cat?', + 'What vaccinations do cats need?', + 'Tell me about Grumpy Cat', + 'Who was Félicette?', + 'What is a group of cats called?', + 'How many bones does a cat have?', + 'Why do cats knead?', + 'What is the oldest cat ever recorded?', + 'Are there any plants toxic to cats?', + 'What cat breeds are known for being calm?', + 'Why do cats bring dead mice to their owners?', + ] + + +async def main() -> None: + """Main entry point for the RAG demo. + + Automatically indexes the cat knowledge base at startup for + a smooth demo experience. Note: First run may take longer as + embeddings are generated. + """ + await logger.ainfo('Starting RAG demo - auto-indexing cat knowledge base') + try: + result = await index_cat_knowledge_flow(IndexInput()) + await logger.ainfo( + 'Auto-indexing complete', + total_documents=result.total_documents, + sources=result.sources_indexed, + ) + except Exception as e: + await logger.awarning( + 'Auto-indexing failed - you may need to run index_cat_knowledge_flow manually', + error=str(e), + ) + + +if __name__ == '__main__': + ai.run_main(main()) diff --git a/py/samples/realtime-tracing-demo/README.md b/py/samples/realtime-tracing-demo/README.md index 6caba59176..b598cdd03c 100644 --- a/py/samples/realtime-tracing-demo/README.md +++ b/py/samples/realtime-tracing-demo/README.md @@ -13,7 +13,7 @@ With realtime tracing enabled: ``` Standard Tracing: Realtime Tracing: - + Start ─────────────────► Start ────► [Span appears!] (nothing visible) │ ... │ (visible as "in progress") diff --git a/py/samples/shared/types.py b/py/samples/shared/types.py index b857faf118..eb39cb0113 100644 --- a/py/samples/shared/types.py +++ b/py/samples/shared/types.py @@ -16,7 +16,8 @@ """Common types for samples.""" -from pydantic import BaseModel, Field +from pydantic import BaseModel, ConfigDict, Field +from pydantic.alias_generators import to_camel class CurrencyExchangeInput(BaseModel): @@ -46,8 +47,10 @@ class Skills(BaseModel): class RpgCharacter(BaseModel): """An RPG character.""" + model_config = ConfigDict(alias_generator=to_camel, populate_by_name=True) + name: str = Field(description='name of the character') - back_story: str = Field(description='back story', alias='backStory') + back_story: str = Field(description='back story') abilities: list[str] = Field(description='list of abilities (3-4)') skills: Skills diff --git a/py/samples/short-n-long/src/main.py b/py/samples/short-n-long/src/main.py index f5f4b44a85..ad1450bad3 100755 --- a/py/samples/short-n-long/src/main.py +++ b/py/samples/short-n-long/src/main.py @@ -53,7 +53,8 @@ import os import uvicorn -from pydantic import BaseModel, Field +from pydantic import BaseModel, ConfigDict, Field +from pydantic.alias_generators import to_camel from rich.traceback import install as install_rich_traceback from genkit.ai import Genkit, Output, ToolRunContext, tool_response @@ -336,8 +337,10 @@ class Skills(BaseModel): class RpgCharacter(BaseModel): """An RPG character.""" + model_config = ConfigDict(alias_generator=to_camel, populate_by_name=True) + name: str = Field(description='name of the character') - back_story: str = Field(description='back story', alias='backStory') + back_story: str = Field(description='back story') abilities: list[str] = Field(description='list of abilities (3-4)') skills: Skills diff --git a/py/samples/vertex-ai-vector-search-bigquery/README.md b/py/samples/vertex-ai-vector-search-bigquery/README.md index 746928d099..f9c9e9e0c2 100644 --- a/py/samples/vertex-ai-vector-search-bigquery/README.md +++ b/py/samples/vertex-ai-vector-search-bigquery/README.md @@ -55,7 +55,7 @@ genkit start -- uv run src/main.py ``` ## Set up env for sample -In the file `setup_env.py` you will find some code that will help you to create the bigquery dataset, table with the expected schema, encode the content of the table and push this to the VertexAI Vector Search index. +In the file `setup_env.py` you will find some code that will help you to create the bigquery dataset, table with the expected schema, encode the content of the table and push this to the VertexAI Vector Search index. This index must be created with update method set as `stream`. VertexAI Index is expected to be already created. ## Testing This Demo @@ -98,4 +98,4 @@ This index must be created with update method set as `stream`. VertexAI Index is - Query is embedded and sent to Vector Search - Similar vectors are found and IDs returned - BigQuery is queried for full document content - - Duration metrics show performance + - Duration metrics show performance diff --git a/py/tox.ini b/py/tox.ini index 664d2c6d09..4e132ca645 100644 --- a/py/tox.ini +++ b/py/tox.ini @@ -21,3 +21,10 @@ commands_pre = uv pip install --python {envpython} -e . commands = pytest {posargs} + +# TODO: Re-enable chroma tests for Python 3.14 when onnxruntime adds wheels. +# Track: https://github.com/microsoft/onnxruntime/issues/21292 +# Python 3.14 specific config - skip chroma tests (onnxruntime lacks 3.14 wheels) +[testenv:py314] +commands = + pytest --ignore=plugins/chroma --ignore=samples/rag-chroma {posargs} diff --git a/py/uv.lock b/py/uv.lock index 9f50cab8be..d45af485ae 100644 --- a/py/uv.lock +++ b/py/uv.lock @@ -1,9 +1,8 @@ version = 1 revision = 3 -requires-python = ">=3.10" +requires-python = ">=3.10, <3.14" resolution-markers = [ - "python_full_version >= '3.14'", - "python_full_version == '3.13.*'", + "python_full_version >= '3.13'", "python_full_version == '3.12.*'", "python_full_version == '3.11.*'", "python_full_version < '3.11'", @@ -12,6 +11,7 @@ resolution-markers = [ [manifest] members = [ "anthropic-hello", + "checks-demo", "compat-oai-hello", "deepseek-hello", "dev-local-vectorstore-hello", @@ -21,6 +21,9 @@ members = [ "format-demo", "genkit", "genkit-plugin-anthropic", + "genkit-plugin-checks", + "genkit-plugin-chroma", + "genkit-plugin-cloud-sql-pg", "genkit-plugin-compat-oai", "genkit-plugin-deepseek", "genkit-plugin-dev-local-vectorstore", @@ -32,6 +35,7 @@ members = [ "genkit-plugin-mcp", "genkit-plugin-msfoundry", "genkit-plugin-ollama", + "genkit-plugin-pinecone", "genkit-plugin-vertex-ai", "genkit-plugin-xai", "genkit-workspace", @@ -49,6 +53,9 @@ members = [ "ollama-hello", "ollama-simple-embed", "prompt-demo", + "rag-chroma-demo", + "rag-cloud-sql-pg-sample", + "rag-pinecone-demo", 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