diff --git a/.env.example b/.env.example index d9ddfd2..ed9b342 100644 --- a/.env.example +++ b/.env.example @@ -28,6 +28,10 @@ LLM_PROVIDER=openai LLM_MODEL_NAME=gpt-4o-mini LLM_TEMPERATURE=0.0 LLM_API_KEY=your_openai_api_key_here +# LLM_CACHE_ENABLED=true +# LLM_CACHE_READ_ONLY=false +# LLM_MAX_INFLIGHT=16 +# MAX_CONCURRENT_PROCESSES=4 # Ollama Configuration (Alternative) # LLM_PROVIDER=ollama diff --git a/CHANGELOG.md b/CHANGELOG.md index 3bc1324..a36c224 100644 --- a/CHANGELOG.md +++ b/CHANGELOG.md @@ -8,10 +8,19 @@ and this project adheres to [Semantic Versioning](https://semver.org/spec/v2.0.0 ## [Unreleased] ### Added +- **Facts precision/recall/F1** on `POST /match/evaluate` (`fact_precision`, `fact_recall`, `fact_f1` and counts): relational triples only, excluding schema predicates and triples with ontological class/concept nodes in subject or object position. - **Anthropic (Claude) and Google (Gemini) LLM providers** via `LLM_PROVIDER=anthropic|google`, with `ClaudeModel` and `GeminiModel` config enums. - **Token usage reporting** in `BudgetTracker` when providers return `usage_metadata` on LLM responses (character counts remain the universal fallback). +- **LLM disk cache controls** on `LLMConfig`: `LLM_CACHE_ENABLED` (default on), `LLM_CACHE_READ_ONLY`, and in-memory plus on-disk stats via `LLMTool.get_cache_stats()`; `GET /info` exposes `llm_cache`. +- **Global LLM in-flight limit** (`LLM_MAX_INFLIGHT`, default 16) — shared semaphore caps concurrent provider requests across parallel unit workers. +- **Optional process concurrency cap** (`MAX_CONCURRENT_PROCESSES`) — limits simultaneous `/process` and `/process_unit` handlers (additional requests wait for a slot). +- **OpenAI Batch API helpers** (`ontocast.tool.llm_batch`) to export chat batch JSONL and import completed results into the LLM disk cache for offline benchmark pre-warming. +- **`BudgetTracker.cache_hits`** — disk-cache hits count toward character totals but not `calls_count`; included in budget summaries when non-zero. +- **Structured-document preprocessing** for heading-structured text (papers, reports): optional **Tag Sections** node detects academic-style headings, assigns **section-aligned labels** to semantic chunks via character-span overlap, and `target_sections` filters units before extraction. +- **Optional chunk summarization** — `summarize_sections` and `summary_max_sentences` on `/process` and CLI (`--summarize-sections`, `--summary-max-sentences`) run a **Summarize Chunks** graph node; ontology/facts render and critic prompts use `ContentUnit.extraction_text` (summary when present, else full chunk text). ### Changed +- **LLM caching path** — `complete`, `extract`, `__call__`, and `acall` share one `_invoke_cached` implementation with consistent cache keys (normalized prompt text), optional disable/read-only modes, and provider calls gated by the global in-flight semaphore. - **Facts extraction prompts** (`facts_guidelines.py`): clearer two-namespace contract — domain ontology is read-only schema plus optional **reference individuals**; all text-derived occurrences use `cd:` with `lowercase_snake_case` local names. New rules separate **classes** from **instances** (no PascalCase class IRIs in subject/object slots), forbid typing `cd:` entities as `rdfs:Class` / `rdf:Property`, and add a final structural validation checklist before output. ### Fixed @@ -20,6 +29,8 @@ and this project adheres to [Semantic Versioning](https://semver.org/spec/v2.0.0 ### Documentation - User guide: facts two-namespace model (`concepts.md`), facts guidelines vs `facts_user_instruction` (`user_instructions.md`), entity alignment and evaluate semantics (`aggregation.md`, `api.md`, `workflow.md`). +- User guide: LLM cache configuration, in-flight/process limits, batch pre-warming, and `/info` cache stats (`llm_caching.md`, `configuration.md`, `api.md`, `concepts.md`, `workflow.md`). +- User guide: structured documents — section tagging, section-aligned chunk labels, `target_sections` / `summarize_sections` (`concepts.md`, `workflow.md`, `api.md`, `configuration.md`). ## [0.4.0] - 2026-05-26 diff --git a/docs/assets/graph.lr.png b/docs/assets/graph.lr.png index 8afdb4d..fe15a16 100644 Binary files a/docs/assets/graph.lr.png and b/docs/assets/graph.lr.png differ diff --git a/docs/assets/graph.lr.svg b/docs/assets/graph.lr.svg index 10ce342..11c8fcc 100644 --- a/docs/assets/graph.lr.svg +++ b/docs/assets/graph.lr.svg @@ -1,5 +1,5 @@ - + @@ -39,282 +39,328 @@ - + - + - + - + - + - + - + - + - + - + - + - + - + - + - + - + - + - + - + - - + + - - - - - - - + + + + + + + - - + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + - - - - - - - - - - + + + + + + + + + + - - - - - + + + + + + + + + - - - - - - - - + + + + + + + + + + - - - - - - + + + + + + - - - - - - - - - - - - + + + + + + + + + + + + - - - - - - + + + + + + - - - - - - - - - + + + + + + + + + + + + + - - - - - - - - - + + + + + + + + + - - - - - - - - + + + + + + + + - - - - - - - - - - - + + + + + + + + + + + - - - - - + + + + + - - - - - - - - - + + + + + + + + + - - - - - - - - - - - + + + + + + + + + + + - - - - - - - - - - - - + + + + + + + + + + + + - - - - - - - - - - + + + + + + + + + + - - - - - - - - - + + + + + + + + + - - - - - - - - - - - + + + + + + + + + + + - - - - - - - - - - + + + + + + + + + + - - - - - - - - - - - - - - - - + + + + + + + + + + + + + + + + diff --git a/docs/assets/graph.png b/docs/assets/graph.png index 50a4805..fe2bfcd 100644 Binary files a/docs/assets/graph.png and b/docs/assets/graph.png differ diff --git a/docs/assets/graph.preview.png b/docs/assets/graph.preview.png index ef2fe0b..2838e81 100644 Binary files a/docs/assets/graph.preview.png and b/docs/assets/graph.preview.png differ diff --git a/docs/assets/graph.svg b/docs/assets/graph.svg index 0edf5b3..921b4c0 100644 --- a/docs/assets/graph.svg +++ b/docs/assets/graph.svg @@ -1,5 +1,5 @@ - + @@ -53,52 +53,52 @@ - + - + - + - + - + - + - + - + - + - + - + - + - + - + - + - + @@ -107,232 +107,281 @@ + + + - - + + + + - - - - - + + + + + - + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + - - - - - - - - - - - - - - - - - - - - - - - + + + + + + + + + + + + + + + + - - - - - - - - - - - - - - + + + + + + + + + + + + + + + + + + + + - - - - - - - - - - - - - - - - - - - + + + + + + + + + + + + + + + + + + + - - - - - - - - - - - - - - - - + + + + + + + + + + + + + + + + + + + + - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + - - - - - - - - - - - - - - - + + + + + + + + + + + + + + + - - - - - - - - - - - - - - - - - - - - - - - - + + + + + + + + + + + + + + + + + + + + + + + + - - - - - - - - - - - - - - - - - - - - + + + + + + + + + + + + + + + + + + + + - - - - - - - - - - - - - - - - - - - - - - + + + + + + + + + + + + + + + + + + + + + + - - - - - - - - - - - - - + + + + + + + + + + + + + - - - - - - - - - - + + + + + + + + + + diff --git a/docs/index.md b/docs/index.md index d7001e2..2e86fbf 100644 --- a/docs/index.md +++ b/docs/index.md @@ -28,7 +28,8 @@ OntoCast extracts semantic triples from documents using an agentic, ontology-dri - **Triple store integration** — Fuseki, Neo4j (n10s), or filesystem fallback - **Tenancy** — partition datasets/collections by tenant and project - **REST API** — document processing, ontology catalog management, graph matching -- **Automatic LLM caching** — built-in response caching +- **Automatic LLM caching** — disk cache with optional read-only mode, global in-flight limiting, and OpenAI Batch API pre-warming for benchmarks +- **Structured documents** — optional section tagging, section-aligned chunk labels, section filtering, and LLM summarization before extraction --- @@ -101,7 +102,7 @@ Document-level pipeline (regenerated via `uv run plot-graph`): Landscape variant: [graph.lr.png](assets/graph.lr.png). Per-unit render/critic loops are documented in [Workflow](user_guide/workflow.md#per-unit-atomic-loop). -1. Convert → chunk document +1. Convert → optional tag sections → chunk (semantic) → optional summarize chunks 2. Parallel ontology render per unit → normalize → optional consolidate → validate 3. Parallel facts render per unit → merge with disambiguation 4. Serialize to triple store; return Turtle in API response diff --git a/docs/user_guide/aggregation.md b/docs/user_guide/aggregation.md index 0aa8069..b0c67e1 100644 --- a/docs/user_guide/aggregation.md +++ b/docs/user_guide/aggregation.md @@ -44,7 +44,9 @@ For evaluation against ground truth, use the match endpoints (see [API Endpoints - Align entities across multiple graphs globally - Derive pairwise predicted↔GT mappings -- Compute triple and entity precision/recall/F1 +- Compute triple, facts, and entity precision/recall/F1 + +**Facts vs triple metrics:** triple-level scores count typing and taxonomy (`rdf:type`, `rdfs:subClassOf`, …). **Facts** scores measure only instance-to-instance relations (e.g. book → character via an ontology property), excluding schema predicates and triples that touch class/concept nodes in subject or object position. Relation property IRIs in predicate position still count toward facts. Entity match payloads accept IRI strings or `URIRef` values; evaluation normalizes to `URIRef` for projection. **Entity false positives/negatives** count unmatched entities in each graph (set difference), so a shared ontology vocabulary IRI matched once is not also counted as an extra false positive on the other side. diff --git a/docs/user_guide/api.md b/docs/user_guide/api.md index 9b1aeda..c67d4fa 100644 --- a/docs/user_guide/api.md +++ b/docs/user_guide/api.md @@ -10,7 +10,17 @@ Returns service health. Use for load balancers and readiness probes. ### `GET /info` -Returns version, configuration summary, and active backend information. +Returns service metadata, including: + +| Field | Description | +|-------|-------------| +| `version` | Package version | +| `llm_cache` | When the LLM tool is initialized: in-memory hit/miss counters plus on-disk cache file stats (`cache_hits`, `cache_misses`, `disk`) | +| `max_concurrent_processes` | Configured cap on simultaneous `/process` handlers, if `MAX_CONCURRENT_PROCESSES` is set | + +```bash +curl http://localhost:8999/info +``` --- @@ -18,7 +28,7 @@ Returns version, configuration summary, and active backend information. ### `POST /process` -Runs the full document pipeline: convert → chunk → ontology map/reduce → facts map/reduce → serialize. +Runs the full document pipeline: convert → [tag sections] → chunk → [summarize chunks] → ontology map/reduce → facts map/reduce → serialize. Bracketed stages run only when structured-document parameters are set. **Content types:** @@ -40,6 +50,18 @@ Runs the full document pipeline: convert → chunk → ontology map/reduce → f | `ontology_user_instruction` | Guide ontology extraction | | `ontology_selection_user_instruction` | Guide catalog ontology selection | | `facts_user_instruction` | Guide facts extraction | +| `target_sections` | Comma-separated or JSON list; keep only these sections (enables section tagging) | +| `summarize_sections` | Sections to summarize before extraction; omit to skip. `*` or empty = all chunks | +| `summary_max_sentences` | Max sentences per summary when summarization runs (default `5`) | + +**CLI file processing** (`ontocast --input-path …`) accepts the same structured-document flags: + +```bash +ontocast --input-path ./papers/ \ + --target-sections results,methods \ + --summarize-sections results \ + --summary-max-sentences 5 +``` **Examples:** @@ -60,9 +82,18 @@ curl -X POST "http://localhost:8999/process?strip_provenance=true" \ # Multi-tenant request curl -X POST "http://localhost:8999/process?tenant=acme&project=reports" \ -F "file=@document.pdf" + +# Structured paper: keep Results/Methods, summarize Results only +curl -X POST "http://localhost:8999/process?target_sections=results,methods&summarize_sections=results&summary_max_sentences=5" \ + -F "file=@paper.pdf" + +# JSON body with section lists +curl -X POST http://localhost:8999/process \ + -H "Content-Type: application/json" \ + -d '{"text": "# Introduction\n...\n## Results\n...", "target_sections": ["results"], "summarize_sections": ["*"], "summary_max_sentences": 5}' ``` -**Response:** JSON with `data.facts` (Turtle), `data.ontology_artifacts` (list of ontology TTL payloads), and `metadata` (status, chunk counts, budget). +**Response:** JSON with `data.facts` (Turtle), `data.ontology_artifacts` (list of ontology TTL payloads), and `metadata` (status, chunk counts, budget including `cache_hits` when applicable). --- @@ -154,9 +185,13 @@ Derive 1:1 predicted↔ground-truth entity matches for one graph pair from align ### `POST /match/evaluate` -Compute triple and entity precision/recall/F1 given graphs and entity matches. Label triples (`rdfs:label`) are excluded from triple metrics. +Compute triple, **facts**, and entity precision/recall/F1 given graphs and entity matches. + +- **Triple metrics** — all triples except `rdfs:label` (includes `rdf:type` and other schema assertions). +- **Facts metrics** — relational assertions only: excludes schema predicates (`rdf:type`, `rdfs:subClassOf`, `rdfs:comment`) and any triple whose subject or object is an ontological (class/concept) URIRef. Ontology **relation** IRIs used only as predicates (e.g. `.../relations#P674`) are not treated as ontological entities. +- **Entity metrics** — true positives = number of accepted entity matches; false positives = predicted entities not in the matched set; false negatives = ground-truth entities not in the matched set (set-based, so correctly matched shared vocabulary IRIs are not double-penalized). -Entity metrics: true positives = number of accepted entity matches; false positives = predicted entities not in the matched set; false negatives = ground-truth entities not in the matched set (set-based, so correctly matched shared vocabulary IRIs are not double-penalized). +Response fields: `precision` / `recall` / `f1` (triples), `fact_precision` / `fact_recall` / `fact_f1` (facts), `entity_precision` / `entity_recall` / `entity_f1` (entities), plus TP/FP/FN counts for each tier. **Standalone CLI:** @@ -178,6 +213,9 @@ match-dirs \ | `400` | Invalid parameters (e.g. missing fixed ontology id) | | `409` | Vector store unavailable when vector ontology mode requested | | `500` | Processing or store errors | +| `503` | Health check: LLM not initialized or health probe failure | + +When `MAX_CONCURRENT_PROCESSES` is set, additional `/process` and `/process_unit` requests **wait** until a handler slot is free (they are not rejected with 503). Vector mode unavailable: @@ -193,5 +231,7 @@ Vector mode unavailable: ## Related - [Configuration](configuration.md) — server and tool settings +- [LLM Caching](llm_caching.md) — disk cache, in-flight limits, batch pre-warming - [User Instructions](user_instructions.md) — guiding extraction - [Workflow](workflow.md) — what happens inside `/process` +- [Structured documents](concepts.md#structured-documents-optional) — section tagging and summarization diff --git a/docs/user_guide/concepts.md b/docs/user_guide/concepts.md index 72f1248..dc378fc 100644 --- a/docs/user_guide/concepts.md +++ b/docs/user_guide/concepts.md @@ -31,6 +31,30 @@ OntoCast uses **pyoxigraph** for RDF 1.2 quoted-triple syntax and separates prov See [Workflow](workflow.md#4-ontology-reduce-document-level). +## Structured documents (optional) + +For papers and other heading-structured Markdown text, `/process` and `ontocast --input-path` accept optional parameters. When both `target_sections` and `summarize_sections` are omitted, the pipeline stays `convert → chunk → extract` with no extra graph nodes. + +### Section tagging and section-aligned chunks + +1. **Tag Sections** (when `target_sections` or `summarize_sections` is set) scans converted text for academic-style headings (`introduction`, `methods`, `results`, `discussion`, `conclusion`, `future_work`, `limitations`, `related_work`, `background`, and numbered variants). +2. **Chunk** still uses the semantic `ChunkerTool`; each content unit then gets a `section_label` by **maximum character-span overlap** with detected section ranges (section-aligned labeling, not a separate chunker mode). +3. **`target_sections`** drops units whose label is not in the allowlist (case-insensitive). + +Recognized labels match normalized heading text (underscore form), e.g. `results`, `future_work`. + +### Optional summarization + +When `summarize_sections` is present (including empty or `*` for all units), the **Summarize Chunks** node runs an LLM pass per selected unit (bounded by `PARALLEL_WORKERS`). Summaries are stored on `ContentUnit.summary`; render and critic agents read `extraction_text`, which prefers the summary over the raw chunk. + +| Parameter | Default | Effect | +|-----------|---------|--------| +| `target_sections` | omitted | Enable tagging; keep only listed sections (e.g. `results,methods`) | +| `summarize_sections` | omitted | Enable tagging + summarization node; omit to skip summaries. `*` or empty = all chunks | +| `summary_max_sentences` | `5` | Max sentences per summary when summarization runs | + +Section lists accept comma-separated values or a JSON array in query, form, or JSON body fields. + ## Parallel Map/Reduce Document processing uses a **parallel map/reduce** architecture: @@ -96,11 +120,12 @@ Details: [Tenancy](tenancy.md). ## Budget Tracking -- **LLM Statistics**: API calls, characters sent/received +- **LLM Statistics**: API calls, characters sent/received; optional token counts when the provider reports usage metadata +- **Cache hits**: Disk-cache hits increment `cache_hits` and character totals but **not** `calls_count` (no provider tokens) - **Triple Metrics**: Ontology and facts triples per operation - **Summary Reports**: Logged at end of processing: ``` - LLM: X calls, Y sent, Z received | Triples: A ontology, B facts + LLM: X calls, Y sent, Z received, N cache hits | Triples: A ontology, B facts ``` - **BudgetTracker** lives on `AgentState` and per-unit states; merged at reduce stages @@ -114,7 +139,7 @@ Details: [Tenancy](tenancy.md). | `UnitOntologyState` / `UnitFactsState` | Per-unit loop state | | `ToolBox` | LLM, triple store, chunking, vector store, cache | | `GraphUpdate` | Structured SPARQL operations from the LLM | -| `ContentUnit` | One chunk's ontology/facts outputs | +| `ContentUnit` | One chunk's text, optional `section_label` / `summary`, and ontology/facts outputs (`extraction_text` for LLM prompts) | ## Next Steps diff --git a/docs/user_guide/configuration.md b/docs/user_guide/configuration.md index a0ecfb1..0c95ddf 100644 --- a/docs/user_guide/configuration.md +++ b/docs/user_guide/configuration.md @@ -51,6 +51,14 @@ LLM_BASE_URL=http://localhost:11434 # optional (ollama; anthropic proxy URL) OntoCast uses `LLM_API_KEY` for all cloud providers (not `ANTHROPIC_API_KEY` / `GOOGLE_API_KEY`). +**Disk cache and provider concurrency** (see [LLM Caching](llm_caching.md)): + +```bash +LLM_CACHE_ENABLED=true # read/write disk cache (default true) +LLM_CACHE_READ_ONLY=false # use cache without writing new entries +LLM_MAX_INFLIGHT=16 # max concurrent provider requests (all documents) +``` + ```bash # Anthropic Claude LLM_PROVIDER=anthropic @@ -79,6 +87,7 @@ PARALLEL_WORKERS=4 PARALLEL_FACTS_RETRIES=3 PARALLEL_ONTOLOGY_RETRIES=3 ENABLE_ONTOLOGY_CONSOLIDATION=false +# MAX_CONCURRENT_PROCESSES=4 # optional cap on simultaneous /process handlers ``` ### Chunking @@ -90,6 +99,27 @@ CHUNK_MIN_SIZE=3000 CHUNK_MAX_SIZE=12000 ``` +Semantic chunking is configured here. **Section-aligned labels** and filtering are not chunker settings: they run when `/process` or CLI file mode passes `target_sections` and/or `summarize_sections` (see [Structured documents](concepts.md#structured-documents-optional)). + +### Structured documents (per request) + +No environment variables. Pass on `POST /process`, multipart form, JSON body, or CLI batch mode: + +| Parameter | CLI flag | Description | +|-----------|----------|-------------| +| `target_sections` | `--target-sections` | Comma-separated or JSON list; enables tagging and keeps only these sections | +| `summarize_sections` | `--summarize-sections` | Enables tagging + summarization; `*` or empty = all chunks | +| `summary_max_sentences` | `--summary-max-sentences` | Max sentences per summary (default `5`) | + +```bash +ontocast --input-path ./papers/ \ + --target-sections results,methods \ + --summarize-sections results \ + --summary-max-sentences 5 +``` + +Details: [API Endpoints](api.md#post-process), [Workflow](workflow.md#2-chunking-and-optional-structured-preprocessing). + ### Triple Stores ```bash @@ -247,6 +277,8 @@ Entity alignment and evaluation endpoints are documented in [API Endpoints](api. - `MAX_VISITS` is supported as an alias for `max_visits_per_node`. - `RECURSION_LIMIT` was renamed to `BASE_RECURSION_LIMIT`. - `WEB_SEARCH_ALLOWED_DOMAINS` and `WEB_SEARCH_BLOCKED_DOMAINS` accept comma-separated values. +- `LLM_CACHE_ENABLED` and `LLM_CACHE_READ_ONLY` control disk cache read/write behavior. +- `LLM_MAX_INFLIGHT` must be ≥ 1; `MAX_CONCURRENT_PROCESSES` must be ≥ 1 when set. ## Recommended Workflow diff --git a/docs/user_guide/llm_caching.md b/docs/user_guide/llm_caching.md index fd19fc5..7a054a4 100644 --- a/docs/user_guide/llm_caching.md +++ b/docs/user_guide/llm_caching.md @@ -11,7 +11,37 @@ The LLM caching system automatically caches responses from language model provid - **Performance**: Cached responses return instantly - **Cost Reduction**: Avoids duplicate API calls - **Offline Testing**: Tests can run without API access -- **Transparency**: No configuration required - works automatically +- **Transparency**: Enabled by default; optional env vars control read-only mode, concurrency, and observability + +--- + +## Configuration + +| Setting | Env | Default | Description | +|---------|-----|---------|-------------| +| `cache_enabled` | `LLM_CACHE_ENABLED` | `true` | Read/write disk cache | +| `cache_read_only` | `LLM_CACHE_READ_ONLY` | `false` | Use cache without writing new entries | +| `llm_max_inflight` | `LLM_MAX_INFLIGHT` | `16` | Max concurrent provider requests (all documents) | + +Server-wide process concurrency (separate from LLM in-flight limit): + +| Setting | Env | Description | +|---------|-----|-------------| +| `max_concurrent_processes` | `MAX_CONCURRENT_PROCESSES` | Cap simultaneous `/process` and `/process_unit` handlers | + +Cache statistics are exposed on `GET /info` under `llm_cache`. Processing budget summaries include `cache_hits` when responses are served from disk (see [Budget Tracking](concepts.md#budget-tracking)). + +### Concurrency layers + +Three independent knobs affect parallelism: + +| Layer | Setting | What it limits | +|-------|---------|----------------| +| Unit workers | `PARALLEL_WORKERS` | Concurrent ontology/facts loops per document | +| Provider calls | `LLM_MAX_INFLIGHT` | Concurrent LLM HTTP requests **across all units and documents** | +| HTTP handlers | `MAX_CONCURRENT_PROCESSES` | Simultaneous `/process` and `/process_unit` pipelines | + +`LLM_MAX_INFLIGHT` prevents rate-limit storms when `PARALLEL_WORKERS` is high. `MAX_CONCURRENT_PROCESSES` is optional; when set, extra clients **wait** for a handler slot rather than starting unbounded full pipelines. --- @@ -63,15 +93,7 @@ response1 = llm_tool("What is the capital of France?") response2 = llm_tool("What is the capital of France?") ``` -### Cache Key Generation - -Cache keys are generated based on: -- LLM provider and model -- Prompt text -- Temperature and other parameters -- API endpoint URL - -This ensures that different configurations or parameters result in separate cache entries. +Cache keys hash **normalized prompt text** (LangChain prompt values use `to_string()`) together with provider, model, temperature, base URL, and schema-specific fields (for structured `extract` calls). Different configurations never share an entry. --- @@ -209,11 +231,13 @@ from ontocast.tool.llm import LLMTool # Check cache directory llm_tool = LLMTool.create(config=llm_config) -print(f"Cache directory: {llm_tool.cache.tool_cache_dir}") +cache_dir = llm_tool.cache.shared_cacher.cache_dir / "llm" +print(f"Cache directory: {cache_dir}") # List cached files -cache_files = list(llm_tool.cache.tool_cache_dir.glob("**/*.json")) +cache_files = list(cache_dir.glob("*.json")) print(f"Cached responses: {len(cache_files)}") +print(f"Stats: {llm_tool.get_cache_stats()}") ``` ### Clear Cache @@ -254,12 +278,14 @@ class CustomLLMTool(LLMTool): ```python from ontocast.tool.llm import LLMTool -# Get cache statistics llm_tool = LLMTool.create(config=llm_config) -stats = llm_tool.cache.get_cache_stats() +stats = llm_tool.get_cache_stats() +# {"cache_hits": 12, "cache_misses": 3, "disk": {"total_files": 42, ...}} print(f"Cache stats: {stats}") ``` +On a running server, the same counters are available from `GET /info` (`llm_cache` field). + --- ## Integration with Other Tools @@ -410,14 +436,30 @@ converted = tools.converter(document_file) chunks = tools.chunker(text) ``` -### Cache Key Generation +### OpenAI Batch API (offline benchmarks) -Cache keys are generated based on: -- **Content hash**: SHA256 hash of the input content -- **Configuration**: All relevant parameters that affect the output -- **Tool-specific parameters**: Model names, chunking modes, etc. +For large first-pass benchmark runs, you can pre-fill the disk cache using the provider Batch API (~50% lower cost, hours of latency). See `ontocast.tool.llm_batch`: -This ensures that different configurations produce different cache entries, even for the same input content. +```python +from pathlib import Path +from ontocast.config import LLMConfig +from ontocast.tool.cache import Cacher +from ontocast.tool.llm_batch import ( + import_openai_batch_output_jsonl, + write_openai_chat_batch_jsonl, +) + +# 1. Build requests (custom_id -> prompt text mapping for import) +write_openai_chat_batch_jsonl(requests, Path("batch_input.jsonl")) +# 2. Submit batch_input.jsonl via OpenAI dashboard or API; download output JSONL +# 3. Import into the same cache directory the server will use +import_openai_batch_output_jsonl( + Path("batch_output.jsonl"), + shared_cache=Cacher(cache_dir="/path/to/cache"), + llm_config=LLMConfig(), + custom_id_to_cache_key={"req-1": "full prompt text used as cache key"}, +) +``` ### Best Practices diff --git a/docs/user_guide/workflow.md b/docs/user_guide/workflow.md index 7c63d53..e3306a0 100644 --- a/docs/user_guide/workflow.md +++ b/docs/user_guide/workflow.md @@ -1,13 +1,13 @@ # OntoCast Workflow -This document describes the document processing pipeline implemented in `stategraph/create.py`. +This document describes the document processing pipeline implemented in `stategraph/create.py`. After changing optional nodes (e.g. Tag Sections, Summarize Chunks), regenerate workflow diagrams with `uv run plot-graph`. ## Overview OntoCast transforms input documents into RDF ontology and facts graphs through a **parallel map/reduce** pipeline: 1. **Document conversion** — PDF, DOCX, TXT, MD, or JSON → Markdown -2. **Semantic chunking** — split into content units (optionally limited with `--head-chunks`) +2. **Chunking** — semantic split into content units (`--head-chunks` limits count for testing). Optionally: **Tag Sections** → chunk → assign section-aligned labels → filter by `target_sections` → **Summarize Chunks** (see [Structured documents](concepts.md#structured-documents-optional)) 3. **Ontology map/reduce** (when `render_mode` includes ontology): - Per-unit context assembly (catalog selection or vector retrieval) - Render/critic loops with optional web evidence @@ -91,11 +91,21 @@ Implementation: [`stategraph/atomic.py`](../../ontocast/stategraph/atomic.py). - Accepts text, JSON (`text` field), or file uploads via `/process` - Converts supported formats to Markdown while preserving structure -### 2. Chunking +### 2. Chunking (and optional structured preprocessing) -- Semantic chunking splits the document into **content units** -- Units are processed **in parallel** up to `PARALLEL_WORKERS` +Default path: **Convert** → **Chunk** → extraction. + +When `target_sections` and/or `summarize_sections` are set on `/process` or CLI (`--target-sections`, `--summarize-sections`): + +| Node | When | What it does | +|------|------|----------------| +| **Tag Sections** | Either parameter set | Detect heading lines; store `section_spans` on `AgentState` | +| **Chunk** | Always | Semantic `ChunkerTool` split; assign `section_label` per unit via span overlap; filter by `target_sections` if set | +| **Summarize Chunks** | `summarize_sections` set | LLM compresses selected units; prompts use `extraction_text` | + +- Units are processed **in parallel** up to `PARALLEL_WORKERS` (summarization respects the same cap) - Use `--head-chunks N` on the CLI to process only the first N units (testing) +- Without section parameters, the graph skips Tag Sections and Summarize Chunks ### 3. Per-Unit Ontology Loop @@ -134,7 +144,7 @@ Facts output uses the **`cd:` namespace** for text-derived instances; domain ont - Ontology and facts serialized to the configured triple store - API returns Turtle (optionally with `strip_provenance=true` to omit reification scaffolding) -- Budget summary logged (LLM calls, characters, triple counts) +- Budget summary logged (LLM calls, cache hits, characters, triple counts) ## Configuration @@ -142,11 +152,14 @@ Facts output uses the **`cd:` namespace** for text-derived instances; domain ont |---------------------|--------| | `RENDER_MODE` | `ontology`, `facts`, or `ontology_and_facts` | | `PARALLEL_WORKERS` | Max concurrent unit workers | +| `LLM_MAX_INFLIGHT` | Max concurrent provider LLM requests (shared across units) | +| `MAX_CONCURRENT_PROCESSES` | Optional cap on simultaneous `/process` pipelines | | `MAX_VISITS` / `max_visits` | Render/critic retry budget per loop | | `ENABLE_ONTOLOGY_CONSOLIDATION` | Optional post-normalization consolidation | | `ONTOLOGY_CONTEXT_MODE` | How per-unit ontology context is sourced | | `LLM_GRAPH_FORMAT` | `turtle` or `jsonld` LLM wire encoding | | `--head-chunks` | CLI limit on units processed | +| `target_sections` / `summarize_sections` / `summary_max_sentences` | Per-request structured-document preprocessing (not env vars) | Full reference: [Configuration System](configuration.md). diff --git a/graph.mmd b/graph.mmd index fcea1e2..8e7644b 100644 --- a/graph.mmd +++ b/graph.mmd @@ -17,7 +17,9 @@ config: graph TD; __start__([

__start__

]):::first Convert\20to\20Markdown(Convert to Markdown) + Tag\20Sections(Tag Sections) Chunk\20Text(Chunk Text) + Summarize\20Chunks(Summarize Chunks) Update\20Ontology(Update Ontology) Normalize\20Ontology\20Updates(Normalize Ontology Updates) Consolidate\20Ontology(Consolidate Ontology) @@ -28,15 +30,20 @@ graph TD; Serialize(Serialize) __end__([

__end__

]):::last Chunk\20Text -.-> Render\20Facts; + Chunk\20Text -.-> Summarize\20Chunks; Chunk\20Text -.-> Update\20Ontology; Consistency\20Critic -.-> Render\20Facts; Consistency\20Critic -.-> Serialize; Consolidate\20Ontology --> Structural\20Check; - Convert\20to\20Markdown --> Chunk\20Text; + Convert\20to\20Markdown -.-> Chunk\20Text; + Convert\20to\20Markdown -.-> Tag\20Sections; Merge\20Facts --> Serialize; Normalize\20Ontology\20Updates --> Consolidate\20Ontology; Render\20Facts --> Merge\20Facts; Structural\20Check --> Consistency\20Critic; + Summarize\20Chunks -.-> Render\20Facts; + Summarize\20Chunks -.-> Update\20Ontology; + Tag\20Sections --> Chunk\20Text; Update\20Ontology --> Normalize\20Ontology\20Updates; __start__ --> Convert\20to\20Markdown; Serialize --> __end__; diff --git a/ontocast/_version.py b/ontocast/_version.py new file mode 100644 index 0000000..dc7f9a7 --- /dev/null +++ b/ontocast/_version.py @@ -0,0 +1,8 @@ +"""Installed package version (from ``pyproject.toml`` project metadata).""" + +from importlib.metadata import PackageNotFoundError, version + +try: + __version__ = version("ontocast") +except PackageNotFoundError: # pragma: no cover + __version__ = "0.0.0+unknown" diff --git a/ontocast/agent/__init__.py b/ontocast/agent/__init__.py index f4288c7..17cf975 100644 --- a/ontocast/agent/__init__.py +++ b/ontocast/agent/__init__.py @@ -13,10 +13,14 @@ from .render_facts import render_facts, render_facts_fresh from .render_ontology import render_ontology, render_ontology_fresh from .serialize import serialize +from .summarize_chunks import summarize_chunk +from .tag_sections import tag_sections __all__ = [ "chunk_text", "convert_document", + "tag_sections", + "summarize_chunk", "criticise_facts", "criticise_ontology", "render_facts", diff --git a/ontocast/agent/chunk_text.py b/ontocast/agent/chunk_text.py index 83923cc..1e659b9 100644 --- a/ontocast/agent/chunk_text.py +++ b/ontocast/agent/chunk_text.py @@ -9,6 +9,7 @@ from ontocast.onto.content_unit import ContentUnit from ontocast.onto.enum import Status +from ontocast.onto.section import assign_section_labels, filter_units_by_target_sections from ontocast.onto.state import AgentState from ontocast.toolbox import ToolBox @@ -50,6 +51,28 @@ def chunk_text(state: AgentState, tools: ToolBox) -> AgentState: ) ) + if state.section_spans: + assign_section_labels( + state.content_units, + state.input_text, + state.section_spans, + ) + + if state.target_sections is not None: + before = len(state.content_units) + state.content_units = filter_units_by_target_sections( + state.content_units, + state.target_sections, + ) + logger.info( + "Section filter %s: kept %s/%s chunks", + state.target_sections, + len(state.content_units), + before, + ) + for index, unit in enumerate(state.content_units): + unit.index = index + logger.info( "Created " f"{len(state.content_units)} content units for processing: " diff --git a/ontocast/agent/criticise_facts.py b/ontocast/agent/criticise_facts.py index 0f8e3da..a62252c 100644 --- a/ontocast/agent/criticise_facts.py +++ b/ontocast/agent/criticise_facts.py @@ -77,7 +77,7 @@ async def criticise_facts( state.content_unit.graph ) + _build_quarantine_chapter(state) - text_chapter = text_template.format(text=state.content_unit.text) + text_chapter = text_template.format(text=state.content_unit.extraction_text) user_instruction = ( user_template.format(user_instruction=state.facts_user_instruction) diff --git a/ontocast/agent/criticise_ontology.py b/ontocast/agent/criticise_ontology.py index bc29944..ef3fd3f 100644 --- a/ontocast/agent/criticise_ontology.py +++ b/ontocast/agent/criticise_ontology.py @@ -67,7 +67,7 @@ async def criticise_ontology( ontology_chapter = profile.format_ontology_chapter(current.graph) - text_chapter = text_template.format(text=state.content_unit.text) + text_chapter = text_template.format(text=state.content_unit.extraction_text) user_instruction = state.ontology_user_instruction external_evidence = state.external_evidence_text diff --git a/ontocast/agent/external_evidence.py b/ontocast/agent/external_evidence.py index 24eeed9..e37769d 100644 --- a/ontocast/agent/external_evidence.py +++ b/ontocast/agent/external_evidence.py @@ -98,7 +98,7 @@ def _resolve_user_instruction(state: UnitFactsState | UnitOntologyState) -> str: def _resolve_content_text(state: UnitFactsState | UnitOntologyState) -> str: - return state.content_unit.text + return state.content_unit.extraction_text def _resolve_search_request( diff --git a/ontocast/agent/render_facts.py b/ontocast/agent/render_facts.py index 78e9adb..9dcd871 100644 --- a/ontocast/agent/render_facts.py +++ b/ontocast/agent/render_facts.py @@ -102,7 +102,7 @@ def _prepare_prompt_data( domain_ontologies_clause=format_ontologies_clause(domain_pairs), ) - text_chapter = text_template.format(text=state.content_unit.text) + text_chapter = text_template.format(text=state.content_unit.extraction_text) fact_chapter = "" diff --git a/ontocast/agent/render_ontology.py b/ontocast/agent/render_ontology.py index 183bc78..ce2b1de 100644 --- a/ontocast/agent/render_ontology.py +++ b/ontocast/agent/render_ontology.py @@ -73,7 +73,7 @@ def _prepare_ontology_common_prompt_layers( general_ontology_instruction_str = general_ontology_instruction.format( domain_ontologies_clause=format_ontologies_clause(domain_pairs) ) - text_chapter = text_template.format(text=state.content_unit.text) + text_chapter = text_template.format(text=state.content_unit.extraction_text) external_evidence = state.external_evidence_text if external_evidence: state.mark_external_evidence_used(WorkflowNode.TEXT_TO_ONTOLOGY) diff --git a/ontocast/agent/summarize_chunks.py b/ontocast/agent/summarize_chunks.py new file mode 100644 index 0000000..a755f7d --- /dev/null +++ b/ontocast/agent/summarize_chunks.py @@ -0,0 +1,53 @@ +"""LLM summarization of content units before extraction.""" + +import logging + +from langchain_core.prompts import ChatPromptTemplate + +from ontocast.onto.content_unit import ContentUnit +from ontocast.toolbox import ToolBox + +logger = logging.getLogger(__name__) + +_SUMMARIZE_PROMPT = ChatPromptTemplate.from_messages( + [ + ( + "system", + "You are a knowledge extraction assistant. Compress the user's text to at " + "most {max_sentences} sentences. Retain all facts, named entities, and " + "epistemic markers (hedging words, attribution phrases, modal verbs). " + "Do not interpret or infer — only compress. Output plain text only.", + ), + ( + "human", + "Section: {section_label}\n\n{text}", + ), + ] +) + + +async def summarize_chunk( + unit: ContentUnit, + tools: ToolBox, + *, + max_sentences: int, +) -> str: + """Compress a content unit for downstream extraction.""" + section_label = unit.section_label or "unclassified" + prompt = _SUMMARIZE_PROMPT.format_prompt( + max_sentences=max_sentences, + section_label=section_label, + text=unit.text, + ) + response = await tools.llm(prompt) + summary = (response.content or "").strip() + if not summary: + raise ValueError("Summarization returned empty text") + logger.debug( + "Summarized unit %s (%s): %s -> %s chars", + unit.index, + section_label, + len(unit.text), + len(summary), + ) + return summary diff --git a/ontocast/agent/tag_sections.py b/ontocast/agent/tag_sections.py new file mode 100644 index 0000000..570c3a9 --- /dev/null +++ b/ontocast/agent/tag_sections.py @@ -0,0 +1,34 @@ +"""Section tagging agent for structured documents.""" + +import logging + +from ontocast.onto.enum import Status +from ontocast.onto.section import detect_section_spans +from ontocast.onto.state import AgentState +from ontocast.toolbox import ToolBox + +logger = logging.getLogger(__name__) + + +def tag_sections(state: AgentState, tools: ToolBox) -> AgentState: + """Detect section headings in converted document text.""" + _ = tools + if not state.use_section_tagging: + return state + + if not state.input_text: + state.status = Status.FAILED + return state + + spans = detect_section_spans(state.input_text) + state.section_spans = spans + if spans: + logger.info( + "Tagged %s section(s): %s", + len(spans), + [span.label for span in spans], + ) + else: + logger.info("No section headings detected; section_spans left empty") + state.status = Status.SUCCESS + return state diff --git a/ontocast/api/schemas.py b/ontocast/api/schemas.py index b1deb64..1ac9726 100644 --- a/ontocast/api/schemas.py +++ b/ontocast/api/schemas.py @@ -2,10 +2,12 @@ from pydantic import BaseModel, Field +from ontocast._version import __version__ + class HealthOkResponse(BaseModel): status: str = "healthy" - version: str = "0.1.1" + version: str = __version__ llm_provider: str | None = None @@ -16,7 +18,7 @@ class HealthErrorResponse(BaseModel): class InfoResponse(BaseModel): name: str = "ontocast" - version: str = "0.1.1" + version: str = __version__ description: str = ( "Agentic ontology assisted framework for semantic triple extraction" ) @@ -27,6 +29,14 @@ class InfoResponse(BaseModel): default_factory=lambda: ["text", "json", "pdf", "markdown"] ) output_types: list[str] = Field(default_factory=lambda: ["turtle", "json"]) + llm_cache: dict | None = Field( + default=None, + description="In-memory and on-disk LLM cache statistics when available.", + ) + max_concurrent_processes: int | None = Field( + default=None, + description="Configured cap on concurrent /process handlers, if any.", + ) class FlushOkResponse(BaseModel): diff --git a/ontocast/cli/http_parse.py b/ontocast/cli/http_parse.py index 8678808..6d8c927 100644 --- a/ontocast/cli/http_parse.py +++ b/ontocast/cli/http_parse.py @@ -1,5 +1,6 @@ """Shared HTTP query/body parsing for CLI server routes.""" +import json import logging from ontocast.onto.enum import LLMGraphFormat, OntologyContextMode, RenderMode @@ -99,6 +100,36 @@ def parse_strip_provenance_param(value: str | None) -> bool: return False +def parse_sections_list_param(value: str | list[str] | None) -> list[str]: + """Parse a section list from comma-separated text or JSON array.""" + if value is None: + return [] + if isinstance(value, list): + return [str(item).strip() for item in value if str(item).strip()] + raw = str(value).strip() + if not raw: + return [] + if raw.startswith("["): + parsed = json.loads(raw) + if not isinstance(parsed, list): + raise ValueError("section list JSON must be an array") + return [str(item).strip() for item in parsed if str(item).strip()] + return [part.strip() for part in raw.split(",") if part.strip()] + + +def parse_summary_max_sentences_param(value: str | int | None, default: int) -> int: + """Parse optional summary_max_sentences (positive integer).""" + if value is None: + return default + try: + parsed = int(str(value).strip()) + except (TypeError, ValueError) as exc: + raise ValueError("summary_max_sentences must be a positive integer") from exc + if parsed < 1: + raise ValueError("summary_max_sentences must be a positive integer") + return parsed + + def parse_max_visits_param(value: str | int | None, default: int) -> int: """Parse optional ``max_visits`` override from query/form/json metadata.""" if value is None: diff --git a/ontocast/cli/process_request.py b/ontocast/cli/process_request.py index 6bde60d..0074b89 100644 --- a/ontocast/cli/process_request.py +++ b/ontocast/cli/process_request.py @@ -14,7 +14,9 @@ parse_max_visits_param, parse_ontology_context_mode_param, parse_render_mode_param, + parse_sections_list_param, parse_strip_provenance_param, + parse_summary_max_sentences_param, resolve_ontology_context_mode, ) from ontocast.cli.http_responses import missing_fixed_catalog_ontology_id_response @@ -39,6 +41,9 @@ class ParsedProcessRequest: render_mode: str | None llm_graph_format: str | None ontology_context_mode_value: OntologyContextMode + target_sections: list[str] | None + summarize_sections: list[str] | None + summary_max_sentences: int async def load_parsed_process_request( @@ -78,6 +83,23 @@ async def load_parsed_process_request( ) ) + target_sections: list[str] | None = None + if "target_sections" in request.query_params: + target_sections = parse_sections_list_param( + request.query_params.get("target_sections") + ) + + summarize_sections: list[str] | None = None + if "summarize_sections" in request.query_params: + summarize_sections = parse_sections_list_param( + request.query_params.get("summarize_sections") + ) + + summary_max_sentences = parse_summary_max_sentences_param( + request.query_params.get("summary_max_sentences"), + default=5, + ) + if content_type.startswith("application/json"): bytes_data = await request.body() logger.debug("%s JSON body length: %s", log_label, len(bytes_data)) @@ -95,6 +117,19 @@ async def load_parsed_process_request( body_format = parsed_obj.get("llm_graph_format") if body_format is not None: llm_graph_format = body_format + if "target_sections" in parsed_obj: + target_sections = parse_sections_list_param( + parsed_obj.get("target_sections") + ) + if "summarize_sections" in parsed_obj: + summarize_sections = parse_sections_list_param( + parsed_obj.get("summarize_sections") + ) + if "summary_max_sentences" in parsed_obj: + summary_max_sentences = parse_summary_max_sentences_param( + parsed_obj.get("summary_max_sentences"), + summary_max_sentences, + ) except (json.JSONDecodeError, UnicodeDecodeError): logger.debug( "%s JSON body could not be decoded for ontology id preview", @@ -120,6 +155,15 @@ async def load_parsed_process_request( max_visits = parse_max_visits_param(str(value), max_visits) elif key == "llm_graph_format" and value: llm_graph_format = str(value) + elif key == "target_sections" and value is not None: + target_sections = parse_sections_list_param(str(value)) + elif key == "summarize_sections" and value is not None: + summarize_sections = parse_sections_list_param(str(value)) + elif key == "summary_max_sentences" and value: + summary_max_sentences = parse_summary_max_sentences_param( + str(value), + summary_max_sentences, + ) if not files_dict: return JSONResponse( status_code=400, @@ -158,6 +202,9 @@ async def load_parsed_process_request( render_mode=render_mode, llm_graph_format=llm_graph_format, ontology_context_mode_value=ontology_context_mode_value, + target_sections=target_sections, + summarize_sections=summarize_sections, + summary_max_sentences=summary_max_sentences, ) @@ -192,4 +239,7 @@ def build_agent_state_from_parsed( ontology_selection_user_instruction=parsed.ontology_selection_user_instruction, facts_user_instruction=parsed.facts_user_instruction, ontology_context_fixed_ontology_id=parsed.ontology_context_fixed_ontology_id, + target_sections=parsed.target_sections, + summarize_sections=parsed.summarize_sections, + summary_max_sentences=parsed.summary_max_sentences, ) diff --git a/ontocast/cli/server.py b/ontocast/cli/server.py index 6e4b288..52afc01 100644 --- a/ontocast/cli/server.py +++ b/ontocast/cli/server.py @@ -39,13 +39,17 @@ # Process with chunk limit ontocast --head-chunks 5 + + # Structured document: filter sections and summarize before extraction + ontocast --input-path ./paper.pdf \\ + --target-sections results,methods \\ + --summarize-sections results --summary-max-sentences 5 """ import asyncio import logging import logging.config import pathlib -from importlib import metadata import click import uvicorn @@ -55,6 +59,7 @@ from langgraph.graph.state import CompiledStateGraph from pydantic import BaseModel, ConfigDict, Field +from ontocast._version import __version__ from ontocast.agent.serialize import serialize as serialize_agent_state from ontocast.api.ontologies import build_ontology_router from ontocast.api.schemas import ( @@ -73,6 +78,10 @@ resolve_tenant_project, stores_use_tenancy_partitions, ) +from ontocast.cli.http_parse import ( + parse_sections_list_param, + parse_summary_max_sentences_param, +) from ontocast.cli.http_responses import ( invalid_max_visits_response, ontology_context_config_error_response, @@ -273,6 +282,9 @@ def expand_input_to_states( ontology_context_mode_value: OntologyContextMode, tenant: str | None, project: str | None, + target_sections: list[str] | None = None, + summarize_sections: list[str] | None = None, + summary_max_sentences: int = 5, ) -> list[AgentState]: """Expand a local input file into one ``AgentState`` per logical record. @@ -292,6 +304,9 @@ def expand_input_to_states( ), "tenant": tenant, "project": project, + "target_sections": target_sections, + "summarize_sections": summarize_sections, + "summary_max_sentences": summary_max_sentences, } if file_path.suffix.lower() != ".jsonl": @@ -368,6 +383,9 @@ async def _process_files_input( ontology_context_mode_value: OntologyContextMode, tenant: str | None, project: str | None, + target_sections: list[str] | None = None, + summarize_sections: list[str] | None = None, + summary_max_sentences: int = 5, ) -> None: recursion_limit = calculate_recursion_limit(head_chunks, config.server) for file_path in files: @@ -379,6 +397,9 @@ async def _process_files_input( ontology_context_mode_value=ontology_context_mode_value, tenant=tenant, project=project, + target_sections=target_sections, + summarize_sections=summarize_sections, + summary_max_sentences=summary_max_sentences, ) for state in states: if use_unit_pipeline: @@ -418,7 +439,7 @@ def create_app( when the request omits ``tenant`` / ``project`` query parameters. """ - app = FastAPI(title="ontocast", version=metadata.version("ontocast")) + app = FastAPI(title="ontocast", version=__version__) app.include_router( build_ontology_router( tools, @@ -430,6 +451,10 @@ def create_app( workflow: CompiledStateGraph = create_agent_graph(tools) + process_semaphore: asyncio.Semaphore | None = None + if server_config.max_concurrent_processes is not None: + process_semaphore = asyncio.Semaphore(server_config.max_concurrent_processes) + @app.get("/health") async def health_check(): try: @@ -441,7 +466,7 @@ async def health_check(): ).model_dump(), ) return HealthOkResponse( - llm_provider=tools.llm_provider, version=metadata.version("ontocast") + llm_provider=tools.llm_provider, version=__version__ ) except Exception as e: logger.error("Health check failed: %s", e) @@ -452,7 +477,14 @@ async def health_check(): @app.get("/info", response_model=InfoResponse) async def info(): - return InfoResponse(version=metadata.version("ontocast")) + llm_cache = None + if tools.llm is not None: + llm_cache = tools.llm.get_cache_stats() + return InfoResponse( + version=__version__, + llm_cache=llm_cache, + max_concurrent_processes=server_config.max_concurrent_processes, + ) @app.post("/match/entities", response_model=AlignEntitiesResponse) async def align_entities(request: AlignEntitiesRequest): @@ -570,6 +602,8 @@ async def flush( @app.post("/process") async def process(request: Request): workflow_state: dict | None = None + if process_semaphore is not None: + await process_semaphore.acquire() try: loaded = await load_parsed_process_request( request, server_config, log_label="process" @@ -708,6 +742,9 @@ async def process(request: Request): error_details=error_details, ).model_dump(), ) + finally: + if process_semaphore is not None: + process_semaphore.release() @app.post("/process_unit") async def process_unit(request: Request): @@ -719,6 +756,8 @@ async def process_unit(request: Request): ontology. Accepts the same content types and query parameters as ``/process`` (including ``strip_provenance``). """ + if process_semaphore is not None: + await process_semaphore.acquire() try: loaded = await load_parsed_process_request( request, server_config, log_label="process_unit" @@ -860,6 +899,9 @@ async def process_unit(request: Request): error_details=None, ).model_dump(), ) + finally: + if process_semaphore is not None: + process_semaphore.release() return app @@ -893,12 +935,40 @@ async def process_unit(request: Request): f"(default {DEFAULT_PROJECT!r} when omitted; not read from .env)." ), ) +@click.option( + "--target-sections", + type=str, + default=None, + help=( + "Comma-separated section labels to keep when chunking (e.g. results,methods). " + "Enables section tagging in the workflow graph." + ), +) +@click.option( + "--summarize-sections", + type=str, + default=None, + help=( + "Comma-separated section labels to summarize before extraction, or '*' / empty " + "for all chunks. When set, runs the summarize_chunks graph node." + ), +) +@click.option( + "--summary-max-sentences", + type=int, + default=5, + show_default=True, + help="Max sentences per chunk summary when --summarize-sections is set.", +) def run( input_path: pathlib.Path | None, head_chunks: int | None, use_unit_pipeline: bool, tenant: str | None, project: str | None, + target_sections: str | None, + summarize_sections: str | None, + summary_max_sentences: int, ): """ Main entry point for the OntoCast server/CLI. @@ -958,6 +1028,21 @@ def run( ) validate_ontology_context_mode(ontology_context_mode_value, tools) + parsed_target_sections = ( + parse_sections_list_param(target_sections) + if target_sections is not None + else None + ) + parsed_summarize_sections = ( + parse_sections_list_param(summarize_sections) + if summarize_sections is not None + else None + ) + parsed_summary_max_sentences = parse_summary_max_sentences_param( + summary_max_sentences, + default=5, + ) + workflow: CompiledStateGraph = create_agent_graph(tools) if input_path is not None: @@ -979,6 +1064,9 @@ def run( ontology_context_mode_value=ontology_context_mode_value, tenant=t_res, project=p_res, + target_sections=parsed_target_sections, + summarize_sections=parsed_summarize_sections, + summary_max_sentences=parsed_summary_max_sentences, ) ) else: diff --git a/ontocast/config.py b/ontocast/config.py index dc313e2..31125de 100644 --- a/ontocast/config.py +++ b/ontocast/config.py @@ -161,6 +161,21 @@ class LLMConfig(BaseSettings): default=None, description="LLM base URL (for ollama, etc.)" ) api_key: str | None = Field(default=None, description="API key for LLM provider") + cache_enabled: bool = Field( + default=True, + description="When true, read and write LLM response disk cache entries.", + ) + cache_read_only: bool = Field( + default=False, + description="When true, use cached responses but do not write new entries.", + ) + llm_max_inflight: int = Field( + default=16, + ge=1, + description=( + "Maximum concurrent provider LLM requests shared across all documents." + ), + ) model_config = SettingsConfigDict( env_prefix="LLM_", @@ -281,6 +296,14 @@ class ServerConfig(BaseSettings): default=False, description="Run optional ontology consolidation pass after normalization", ) + max_concurrent_processes: int | None = Field( + default=None, + ge=1, + description=( + "When set, limit concurrent /process and /process_unit handlers; " + "additional requests receive HTTP 503 until a slot is free." + ), + ) model_config = SettingsConfigDict( case_sensitive=False, diff --git a/ontocast/onto/content_unit.py b/ontocast/onto/content_unit.py index dab7312..215e51a 100644 --- a/ontocast/onto/content_unit.py +++ b/ontocast/onto/content_unit.py @@ -41,6 +41,14 @@ class SourceUnit(BaseModel): type: OutputType = Field( default=OutputType.FACTS, description="Type of content unit" ) + section_label: str | None = Field( + default=None, + description="Section label assigned by tag_sections (e.g. results, methods)", + ) + summary: str | None = Field( + default=None, + description="LLM-compressed summary of this chunk used for extraction prompts", + ) _hid: str = PrivateAttr(default="") @field_validator("doc_iri", mode="before") @@ -89,6 +97,13 @@ def namespace(self): def __len__(self): return len(self.text) + @property + def extraction_text(self) -> str: + """Text fed to extraction and critique LLM prompts.""" + if self.summary: + return self.summary + return self.text + class ContentUnit(SourceUnit): """A processing unit that extends source data with mutable output fields.""" diff --git a/ontocast/onto/enum.py b/ontocast/onto/enum.py index a33a116..ba17e61 100644 --- a/ontocast/onto/enum.py +++ b/ontocast/onto/enum.py @@ -83,7 +83,9 @@ class WorkflowNode(StrEnum): """Enumeration of workflow nodes in the processing pipeline.""" CONVERT_TO_MD = "Convert to Markdown" + TAG_SECTIONS = "Tag Sections" CHUNK = "Chunk Text" + SUMMARIZE_CHUNKS = "Summarize Chunks" TEXT_TO_ONTOLOGY = "Text to Ontology" TEXT_TO_FACTS = "Text to Facts" CRITICISE_ONTOLOGY = "Criticise Ontology" diff --git a/ontocast/onto/model.py b/ontocast/onto/model.py index ec611c4..9fdc80b 100644 --- a/ontocast/onto/model.py +++ b/ontocast/onto/model.py @@ -16,8 +16,8 @@ def __init__(self, **kwargs): """Initialize the model with given keyword arguments.""" super().__init__(**kwargs) - def serialize(self, file_path: str | pathlib.Path) -> None: - """Serialize the state to a JSON file. + def save_json(self, file_path: str | pathlib.Path) -> None: + """Write model state to a JSON file. Args: file_path: Path to save the JSON file. @@ -358,8 +358,15 @@ def to_markdown(self) -> str: def _coerce_critique_score(v: object) -> float: """Coerce LLM score output (may be a JSON string) to float.""" + if isinstance(v, (int, float)): + return float(v) + if isinstance(v, str): + try: + return float(v) + except ValueError: + return 0.0 try: - return float(v) # type: ignore[arg-type] + return float(str(v)) except (TypeError, ValueError): return 0.0 diff --git a/ontocast/onto/rdfgraph.py b/ontocast/onto/rdfgraph.py index f12df6b..da57c64 100644 --- a/ontocast/onto/rdfgraph.py +++ b/ontocast/onto/rdfgraph.py @@ -6,7 +6,7 @@ from collections.abc import Iterable, Mapping from contextvars import ContextVar from decimal import Decimal, InvalidOperation -from typing import Any, Union +from typing import Any, Union, cast from pydantic import BaseModel, ConfigDict, GetCoreSchemaHandler from pydantic_core import core_schema @@ -21,6 +21,15 @@ logger = logging.getLogger(__name__) + +def _oxigraph_inner_store(rdflib_store: object) -> object: + """Return the underlying pyoxigraph ``Store`` from an ``OxigraphStore``.""" + inner = getattr(rdflib_store, "_inner", None) + if inner is None: + raise RuntimeError("Expected an OxigraphStore with a pyoxigraph _inner store") + return inner + + PREFIX_PATTERN = re.compile(r"@prefix\s+(\w+):\s+<[^>]+>\s+\.") PREFIX_DECLARATION_PATTERN = re.compile(r"@prefix\s+(\w+):\s+<([^>]+)>\s+\.") # Pattern to match prefix usage: prefix:something (not in @prefix declarations) @@ -1041,7 +1050,7 @@ def serialize_turtle_star(self) -> str: "pyoxigraph / oxrdflib must be installed for Turtle-star serialisation" ) from exc - inner_store: ox.Store = self.store._inner # type: ignore[attr-defined] + inner_store = cast(ox.Store, _oxigraph_inner_store(self.store)) graph_ctx_raw = to_ox(self.identifier) assert isinstance( graph_ctx_raw, @@ -1094,11 +1103,13 @@ def _collect_used_iris(term: Any) -> None: for namespace, prefix in namespace_to_prefix.items() if any(iri.startswith(namespace) for iri in used_iri_terms) } - raw: bytes = tmp.dump( + raw = tmp.dump( format=ox.RdfFormat.TURTLE, from_graph=ox.DefaultGraph(), prefixes=prefixes or None, - ) # type: ignore[assignment] + ) + if raw is None: + raise RuntimeError("pyoxigraph dump returned no data") return raw.decode() def serialize_canonical_turtle(self) -> str: diff --git a/ontocast/onto/section.py b/ontocast/onto/section.py new file mode 100644 index 0000000..680ceeb --- /dev/null +++ b/ontocast/onto/section.py @@ -0,0 +1,166 @@ +"""Document section span models and helpers for structured-document preprocessing.""" + +from __future__ import annotations + +import re + +from pydantic import Field + +from ontocast.onto.content_unit import ContentUnit +from ontocast.onto.model import BasePydanticModel + +# Normalised section labels and heading line patterns (after stripping markdown #). +_SECTION_HEADING_PATTERNS: tuple[tuple[str, re.Pattern[str]], ...] = ( + ("introduction", re.compile(r"^(?:\d+(?:\.\d+)*[.)]\s*)?introduction\s*$", re.I)), + ("related_work", re.compile(r"^(?:\d+(?:\.\d+)*[.)]\s*)?related\s+work\s*$", re.I)), + ("background", re.compile(r"^(?:\d+(?:\.\d+)*[.)]\s*)?background\s*$", re.I)), + ("methods", re.compile(r"^(?:\d+(?:\.\d+)*[.)]\s*)?methods?\s*$", re.I)), + ("methods", re.compile(r"^(?:\d+(?:\.\d+)*[.)]\s*)?methodology\s*$", re.I)), + ("results", re.compile(r"^(?:\d+(?:\.\d+)*[.)]\s*)?results?\s*$", re.I)), + ("discussion", re.compile(r"^(?:\d+(?:\.\d+)*[.)]\s*)?discussion\s*$", re.I)), + ("conclusion", re.compile(r"^(?:\d+(?:\.\d+)*[.)]\s*)?conclusions?\s*$", re.I)), + ("future_work", re.compile(r"^(?:\d+(?:\.\d+)*[.)]\s*)?future\s+work\s*$", re.I)), + ("limitations", re.compile(r"^(?:\d+(?:\.\d+)*[.)]\s*)?limitations?\s*$", re.I)), +) + +_MAX_HEADING_LINE_LEN = 120 + + +class SectionSpan(BasePydanticModel): + """Character span of a document section with a normalised label.""" + + label: str = Field( + description="Normalised section label, e.g. results, future_work" + ) + start: int = Field(description="Start character offset in input_text (inclusive)") + end: int = Field(description="End character offset in input_text (exclusive)") + + +def _normalise_heading_line(line: str) -> str: + stripped = line.strip() + if stripped.startswith("#"): + stripped = stripped.lstrip("#").strip() + return stripped + + +def _match_section_label(heading_line: str) -> str | None: + normalised = _normalise_heading_line(heading_line) + if not normalised or len(normalised) > _MAX_HEADING_LINE_LEN: + return None + for label, pattern in _SECTION_HEADING_PATTERNS: + if pattern.match(normalised): + return label + return None + + +def detect_section_spans(text: str) -> list[SectionSpan]: + """Detect academic-style section headings and return character spans.""" + if not text: + return [] + + heading_starts: list[tuple[int, str]] = [] + offset = 0 + for line in text.splitlines(keepends=True): + label = _match_section_label(line) + if label is not None: + heading_starts.append((offset, label)) + offset += len(line) + + if not heading_starts: + return [] + + spans: list[SectionSpan] = [] + for index, (start, label) in enumerate(heading_starts): + end = ( + heading_starts[index + 1][0] + if index + 1 < len(heading_starts) + else len(text) + ) + if end > start: + spans.append(SectionSpan(label=label, start=start, end=end)) + return spans + + +def _chunk_char_range( + chunk_text: str, document_text: str, search_from: int +) -> tuple[int, int]: + """Locate chunk in document_text; return (start, end) or (0, 0) if not found.""" + if not chunk_text: + return 0, 0 + position = document_text.find(chunk_text, search_from) + if position < 0: + position = document_text.find(chunk_text) + if position < 0: + return 0, 0 + return position, position + len(chunk_text) + + +def resolve_section_label( + chunk_text: str, + document_text: str, + spans: list[SectionSpan], + search_from: int = 0, +) -> tuple[str | None, int]: + """Return section label with max overlap; second value is next search offset.""" + start, end = _chunk_char_range(chunk_text, document_text, search_from) + if end <= start or not spans: + return None, end + + best_label: str | None = None + best_overlap = 0 + for span in spans: + overlap_start = max(start, span.start) + overlap_end = min(end, span.end) + overlap = max(0, overlap_end - overlap_start) + if overlap > best_overlap: + best_overlap = overlap + best_label = span.label + return best_label, end + + +def assign_section_labels( + units: list[ContentUnit], + document_text: str, + spans: list[SectionSpan], +) -> None: + """Set section_label on each content unit from section spans.""" + search_from = 0 + for unit in units: + label, search_from = resolve_section_label( + unit.text, document_text, spans, search_from + ) + unit.section_label = label + + +def filter_units_by_target_sections( + units: list[ContentUnit], + target_sections: list[str] | None, +) -> list[ContentUnit]: + """Drop units whose section_label is not in target_sections.""" + if target_sections is None: + return units + allowed = { + section.strip().lower() for section in target_sections if section.strip() + } + if not allowed: + return units + return [ + unit + for unit in units + if unit.section_label is not None and unit.section_label.lower() in allowed + ] + + +def should_summarize_unit( + unit: ContentUnit, + summarize_sections: list[str] | None, +) -> bool: + """Whether a unit should be passed through the summarization node.""" + if summarize_sections is None: + return False + if not summarize_sections or "*" in summarize_sections: + return True + if unit.section_label is None: + return False + allowed = {section.strip().lower() for section in summarize_sections} + return unit.section_label.lower() in allowed diff --git a/ontocast/onto/sparql_models.py b/ontocast/onto/sparql_models.py index b4c1634..d2916ed 100644 --- a/ontocast/onto/sparql_models.py +++ b/ontocast/onto/sparql_models.py @@ -65,7 +65,9 @@ class TripleOp(BaseModel): def normalize_op_type(cls, v: object) -> str: if isinstance(v, str) and v.lower() == "update": return "insert" - return v # type: ignore[return-value] + if isinstance(v, str): + return v + raise TypeError(f"TripleOp.type must be a string, got {type(v).__name__}") graph: LLMGraphWire = Field( default_factory=RDFGraph, diff --git a/ontocast/onto/state.py b/ontocast/onto/state.py index 3a74d62..1c07390 100644 --- a/ontocast/onto/state.py +++ b/ontocast/onto/state.py @@ -24,6 +24,7 @@ from ontocast.onto.model import BasePydanticModel, Suggestions from ontocast.onto.ontology import Ontology from ontocast.onto.rdfgraph import RDFGraph +from ontocast.onto.section import SectionSpan from ontocast.onto.sparql_models import GraphUpdate, TripleOp from ontocast.util import render_text_hash @@ -39,6 +40,10 @@ class BudgetTracker(BasePydanticModel): default=0, description="Total characters received from LLM" ) calls_count: int = Field(default=0, description="Total number of LLM API calls") + cache_hits: int = Field( + default=0, + description="LLM calls satisfied from disk cache (no provider tokens)", + ) input_tokens: int = Field( default=0, description="Total input tokens (when reported by provider)" ) @@ -77,6 +82,12 @@ def add_usage( if output_tokens is not None: self.output_tokens += output_tokens + def add_cache_hit(self, chars_sent: int, chars_received: int) -> None: + """Record a disk-cache hit (does not increment calls_count).""" + self.cache_hits += 1 + self.chars_sent += chars_sent + self.chars_received += chars_received + def add_ontology_update(self, num_operations: int, num_triples: int) -> None: """Add ontology update statistics. @@ -102,6 +113,7 @@ def merge_from(self, other: BudgetTracker) -> None: self.chars_sent += other.chars_sent self.chars_received += other.chars_received self.calls_count += other.calls_count + self.cache_hits += other.cache_hits self.input_tokens += other.input_tokens self.output_tokens += other.output_tokens self.ontology_triples_generated += other.ontology_triples_generated @@ -116,6 +128,8 @@ def get_summary(self) -> str: f"{self.chars_sent:,} sent, " f"{self.chars_received:,} received", ] + if self.cache_hits > 0: + parts.append(f"{self.cache_hits:,} cache hits") if self.input_tokens > 0 or self.output_tokens > 0: parts.append( @@ -326,6 +340,22 @@ class AgentState(BasePydanticModel): default=3, description="Maximum number of visits allowed per node" ) max_chunks: int | None = None + section_spans: list[SectionSpan] = Field( + default_factory=list, + description="Section spans detected in input_text by tag_sections.", + ) + target_sections: list[str] | None = Field( + default=None, + description="Sections to include when chunking. None = no filter.", + ) + summarize_sections: list[str] | None = Field( + default=None, + description="Sections to summarize. None = skip summarization node.", + ) + summary_max_sentences: int = Field( + default=5, + description="Max sentences per chunk summary when summarization is enabled.", + ) model_config = ConfigDict(arbitrary_types_allowed=True, populate_by_name=True) render_mode: RenderMode = Field( default=RenderMode.ONTOLOGY_AND_FACTS, @@ -381,6 +411,16 @@ def get_node_status(self, node: WorkflowNode) -> Status: """Get the status of a workflow node, returning NOT_VISITED if not set.""" return self.statuses.get(node, Status.NOT_VISITED) + @property + def use_section_tagging(self) -> bool: + """Whether the tag_sections node should run.""" + return self.target_sections is not None or self.summarize_sections is not None + + @property + def use_summarization(self) -> bool: + """Whether the summarize_chunks node should run.""" + return self.summarize_sections is not None + @property def render_ontology(self) -> bool: """Whether ontology rendering should run.""" diff --git a/ontocast/stategraph/create.py b/ontocast/stategraph/create.py index b40ecde..2e80737 100644 --- a/ontocast/stategraph/create.py +++ b/ontocast/stategraph/create.py @@ -4,7 +4,7 @@ from langgraph.graph import StateGraph from langgraph.graph.state import CompiledStateGraph -from ontocast.agent import chunk_text, convert_document +from ontocast.agent import chunk_text, convert_document, tag_sections from ontocast.agent.serialize import serialize from ontocast.onto.enum import WorkflowNode from ontocast.onto.state import AgentState @@ -16,15 +16,20 @@ make_render_facts_node, make_render_ontology_node, make_structural_check_node, + make_summarize_chunks_node, +) +from ontocast.stategraph.routing import ( + route_after_chunk, + route_after_chunk_pre, + route_after_convert, ) -from ontocast.stategraph.routing import route_after_chunk from ontocast.toolbox import ToolBox def create_agent_graph(tools: ToolBox) -> CompiledStateGraph: """Create the parallel map/reduce agent graph. - Flow: CONVERT -> CHUNK -> (conditional on render_ontology) + Flow: CONVERT -> [TAG_SECTIONS] -> CHUNK -> [SUMMARIZE_CHUNKS] -> (conditional) - Ontology map-reduce: RENDER_ONTOLOGY_UPDATE -> NORMALIZE -> CONSOLIDATE -> STRUCTURAL_CHECK -> CONSISTENCY_CRITIC -> (optional facts) -> ... - Facts-only: RENDER_FACTS -> MERGE_FACTS -> SERIALIZE @@ -37,9 +42,11 @@ def create_agent_graph(tools: ToolBox) -> CompiledStateGraph: workflow = StateGraph(AgentState) convert_document_node = partial(convert_document, tools=tools) + tag_sections_node = partial(tag_sections, tools=tools) chunk_text_node = partial(chunk_text, tools=tools) serialize_node = partial(serialize, tools=tools) + summarize_chunks_node = make_summarize_chunks_node(tools) render_ontology_node = make_render_ontology_node(tools) normalize_ontology_node = make_normalize_ontology_node(tools) consolidate_ontology_node = make_consolidate_ontology_node(tools) @@ -49,7 +56,9 @@ def create_agent_graph(tools: ToolBox) -> CompiledStateGraph: consistency_critic_node = make_consistency_critic_node(tools) workflow.add_node(WorkflowNode.CONVERT_TO_MD, convert_document_node) + workflow.add_node(WorkflowNode.TAG_SECTIONS, tag_sections_node) workflow.add_node(WorkflowNode.CHUNK, chunk_text_node) + workflow.add_node(WorkflowNode.SUMMARIZE_CHUNKS, summarize_chunks_node) workflow.add_node(WorkflowNode.RENDER_ONTOLOGY_UPDATE, render_ontology_node) workflow.add_node(WorkflowNode.NORMALIZE_ONTOLOGY_UPDATES, normalize_ontology_node) workflow.add_node(WorkflowNode.CONSOLIDATE_ONTOLOGY, consolidate_ontology_node) @@ -59,9 +68,26 @@ def create_agent_graph(tools: ToolBox) -> CompiledStateGraph: workflow.add_node(WorkflowNode.CONSISTENCY_CRITIC, consistency_critic_node) workflow.add_node(WorkflowNode.SERIALIZE, serialize_node) workflow.add_edge(START, WorkflowNode.CONVERT_TO_MD) - workflow.add_edge(WorkflowNode.CONVERT_TO_MD, WorkflowNode.CHUNK) + workflow.add_conditional_edges( + WorkflowNode.CONVERT_TO_MD, + route_after_convert, + { + WorkflowNode.TAG_SECTIONS: WorkflowNode.TAG_SECTIONS, + WorkflowNode.CHUNK: WorkflowNode.CHUNK, + }, + ) + workflow.add_edge(WorkflowNode.TAG_SECTIONS, WorkflowNode.CHUNK) workflow.add_conditional_edges( WorkflowNode.CHUNK, + route_after_chunk_pre, + { + WorkflowNode.SUMMARIZE_CHUNKS: WorkflowNode.SUMMARIZE_CHUNKS, + WorkflowNode.RENDER_ONTOLOGY_UPDATE: WorkflowNode.RENDER_ONTOLOGY_UPDATE, + WorkflowNode.RENDER_FACTS: WorkflowNode.RENDER_FACTS, + }, + ) + workflow.add_conditional_edges( + WorkflowNode.SUMMARIZE_CHUNKS, route_after_chunk, { WorkflowNode.RENDER_ONTOLOGY_UPDATE: WorkflowNode.RENDER_ONTOLOGY_UPDATE, diff --git a/ontocast/stategraph/node_factories.py b/ontocast/stategraph/node_factories.py index a914fdc..e5f7f60 100644 --- a/ontocast/stategraph/node_factories.py +++ b/ontocast/stategraph/node_factories.py @@ -6,13 +6,20 @@ from ontocast.agent.normalize_ontology import normalize_ontology_units from ontocast.agent.render_ontology import render_ontology_update +from ontocast.agent.summarize_chunks import summarize_chunk from ontocast.onto.content_unit import ContentUnit, OutputType, SourceUnit -from ontocast.onto.enum import OntologyAssemblyMode, OntologyContextMode, Status +from ontocast.onto.enum import ( + OntologyAssemblyMode, + OntologyContextMode, + Status, + WorkflowNode, +) from ontocast.onto.iri_policy import split_namespace_local from ontocast.onto.null import NULL_ONTOLOGY from ontocast.onto.ontology import Ontology from ontocast.onto.ontology_access import document_ontology_access from ontocast.onto.rdfgraph import RDFGraph +from ontocast.onto.section import should_summarize_unit from ontocast.onto.state import AgentState, BudgetTracker from ontocast.onto.unit_states import UnitFactsState, UnitOntologyState from ontocast.stategraph.atomic import facts_loop, ontology_loop @@ -537,3 +544,53 @@ def consistency_critic(state: AgentState) -> AgentState: return state return consistency_critic + + +def make_summarize_chunks_node(tools: ToolBox): + async def summarize_chunks(state: AgentState) -> AgentState: + if not state.content_units or not state.use_summarization: + state.status = Status.SUCCESS + return state + + worker_limit = max(1, tools.config.server.parallel_workers) + semaphore = asyncio.Semaphore(worker_limit) + + async def process_unit(unit_index: int) -> tuple[int, str | None]: + async with semaphore: + unit = state.content_units[unit_index] + if not should_summarize_unit(unit, state.summarize_sections): + return unit_index, None + try: + summary = await summarize_chunk( + unit, + tools, + max_sentences=state.summary_max_sentences, + ) + return unit_index, summary + except Exception as exc: + logger.warning( + "Summarization failed for unit %s: %s", + unit_index, + exc, + ) + return unit_index, None + + tasks = [process_unit(i) for i in range(len(state.content_units))] + raw_results = await asyncio.gather(*tasks) + summarized_count = 0 + for unit_index, summary in sorted(raw_results, key=lambda item: item[0]): + if summary is None: + continue + state.content_units[unit_index].summary = summary + summarized_count += 1 + + logger.info( + "Summarized %s/%s content unit(s)", + summarized_count, + len(state.content_units), + ) + state.set_node_status(WorkflowNode.SUMMARIZE_CHUNKS, Status.SUCCESS) + state.status = Status.SUCCESS + return state + + return summarize_chunks diff --git a/ontocast/stategraph/routing.py b/ontocast/stategraph/routing.py index 1bbc6d4..d9fcc55 100644 --- a/ontocast/stategraph/routing.py +++ b/ontocast/stategraph/routing.py @@ -9,6 +9,20 @@ def route_after_chunk(state: AgentState) -> str: return WorkflowNode.RENDER_ONTOLOGY_UPDATE +def route_after_convert(state: AgentState) -> str: + """Route after document conversion: optional section tagging.""" + if state.use_section_tagging: + return WorkflowNode.TAG_SECTIONS + return WorkflowNode.CHUNK + + +def route_after_chunk_pre(state: AgentState) -> str: + """Route after chunking: optional summarization or extraction.""" + if state.use_summarization: + return WorkflowNode.SUMMARIZE_CHUNKS + return route_after_chunk(state) + + def route_after_ontology_consolidation(state: AgentState) -> str: """Route after ontology stage to the ontology-only structural check.""" return WorkflowNode.STRUCTURAL_CHECK diff --git a/ontocast/tool/agg/aggregate.py b/ontocast/tool/agg/aggregate.py index 05657d6..a196c42 100644 --- a/ontocast/tool/agg/aggregate.py +++ b/ontocast/tool/agg/aggregate.py @@ -18,7 +18,6 @@ from difflib import SequenceMatcher from enum import StrEnum from itertools import combinations -from typing import cast import numpy as np from pydantic import BaseModel, ConfigDict, Field @@ -450,7 +449,7 @@ def union(left: URIRef, right: URIRef) -> None: grouped.setdefault(find(entity), []).append(entity) for group in grouped.values(): - sorted_group = cast(list[URIRef], sorted(group, key=str)) + sorted_group = sorted(group, key=str) validated_clusters.append(sorted_group) return validated_clusters, rejected_merges diff --git a/ontocast/tool/agg/match_common.py b/ontocast/tool/agg/match_common.py index 57cf6c3..31d4d7d 100644 --- a/ontocast/tool/agg/match_common.py +++ b/ontocast/tool/agg/match_common.py @@ -6,7 +6,7 @@ from itertools import product import numpy as np -from rdflib import RDFS, XSD, URIRef +from rdflib import RDF, RDFS, XSD, URIRef from rdflib.term import Literal, Node from ontocast.onto.iri_policy import split_namespace_local @@ -81,6 +81,61 @@ def prepare_metric_triples( } +SCHEMA_PREDICATES = frozenset({RDF.type, RDFS.subClassOf, RDFS.label, RDFS.comment}) + + +def _is_generic_vocab_entity(entity: URIRef) -> bool: + namespace, _ = split_namespace_local(str(entity)) + return namespace is not None and namespace in GENERIC_NAMESPACES + + +def collect_ontology_entities( + triples: set[tuple[Node, Node, Node]], +) -> frozenset[URIRef]: + """URIRefs that are class/concept/schema nodes (s/o), not relation predicates.""" + ontology_entities: set[URIRef] = set() + for subject, predicate, obj in triples: + if predicate == RDF.type and isinstance(obj, URIRef): + ontology_entities.add(obj) + elif predicate == RDFS.subClassOf: + if isinstance(subject, URIRef): + ontology_entities.add(subject) + if isinstance(obj, URIRef): + ontology_entities.add(obj) + if isinstance(subject, URIRef) and _is_generic_vocab_entity(subject): + ontology_entities.add(subject) + if isinstance(obj, URIRef) and _is_generic_vocab_entity(obj): + ontology_entities.add(obj) + return frozenset(ontology_entities) + + +def is_fact_triple( + triple: tuple[Node, Node, Node], + ontology_entities: frozenset[URIRef], +) -> bool: + if not is_informative_triple(triple): + return False + subject, predicate, obj = triple + if predicate in SCHEMA_PREDICATES: + return False + if isinstance(subject, URIRef) and subject in ontology_entities: + return False + if isinstance(obj, URIRef) and obj in ontology_entities: + return False + return True + + +def prepare_fact_triples( + triples: set[tuple[Node, Node, Node]], + ontology_entities: frozenset[URIRef], +) -> set[tuple[Node, Node, Node]]: + return { + normalize_triple(triple) + for triple in triples + if is_fact_triple(triple, ontology_entities) + } + + def is_domain_entity(entity: URIRef) -> bool: namespace, _ = split_namespace_local(str(entity)) return namespace is not None and namespace not in GENERIC_NAMESPACES diff --git a/ontocast/tool/agg/match_models.py b/ontocast/tool/agg/match_models.py index 5e4acf5..809c39e 100644 --- a/ontocast/tool/agg/match_models.py +++ b/ontocast/tool/agg/match_models.py @@ -70,6 +70,14 @@ class MatchMetrics(BaseModel): entity_false_positives: int entity_false_negatives: int domain_entity_matches: int + fact_precision: float + fact_recall: float + fact_f1: float + fact_true_positives: int + fact_false_positives: int + fact_false_negatives: int + fact_predicted_count: int + fact_ground_truth_count: int class TaggedGraph(BaseModel): diff --git a/ontocast/tool/agg/rewriter.py b/ontocast/tool/agg/rewriter.py index 1a7793a..6c604aa 100644 --- a/ontocast/tool/agg/rewriter.py +++ b/ontocast/tool/agg/rewriter.py @@ -22,6 +22,7 @@ import logging from collections import defaultdict +from typing import cast import pyoxigraph as ox from oxrdflib._converter import to_ox @@ -352,7 +353,9 @@ def _add_reified_provenance( """ # Access the underlying pyoxigraph Store and graph context so # that triples added here are visible through the rdflib API. - ox_store: ox.Store = graph.store._inner # type: ignore[attr-defined] + from ontocast.onto.rdfgraph import _oxigraph_inner_store + + ox_store = cast(ox.Store, _oxigraph_inner_store(graph.store)) graph_ctx_raw = to_ox(graph.identifier) assert isinstance(graph_ctx_raw, (ox.NamedNode, ox.BlankNode, ox.DefaultGraph)) graph_ctx: ox.NamedNode | ox.BlankNode | ox.DefaultGraph = graph_ctx_raw diff --git a/ontocast/tool/agg/triple_evaluator.py b/ontocast/tool/agg/triple_evaluator.py index 66cfbc9..69460e6 100644 --- a/ontocast/tool/agg/triple_evaluator.py +++ b/ontocast/tool/agg/triple_evaluator.py @@ -5,9 +5,11 @@ from ontocast.onto.rdfgraph import RDFGraph from .match_common import ( + collect_ontology_entities, compute_prf, count_domain_entity_matches, extract_entities, + prepare_fact_triples, prepare_metric_triples, project_triples, ) @@ -43,6 +45,18 @@ def evaluate( len(ground_truth), ) + ontology_entities = collect_ontology_entities(predicted | ground_truth) + predicted_facts = prepare_fact_triples(predicted, ontology_entities) + ground_truth_facts = prepare_fact_triples(ground_truth, ontology_entities) + fact_true_positives = len(predicted_facts & ground_truth_facts) + fact_false_positives = len(predicted_facts - ground_truth_facts) + fact_false_negatives = len(ground_truth_facts - predicted_facts) + fact_precision, fact_recall, fact_f1 = compute_prf( + fact_true_positives, + len(predicted_facts), + len(ground_truth_facts), + ) + predicted_entities = set(extract_entities(predicted_graph)) gt_entities = set(extract_entities(gt_graph)) matched_predicted = { @@ -75,4 +89,12 @@ def evaluate( entity_false_positives=entity_false_positives, entity_false_negatives=entity_false_negatives, domain_entity_matches=domain_entity_matches, + fact_precision=fact_precision, + fact_recall=fact_recall, + fact_f1=fact_f1, + fact_true_positives=fact_true_positives, + fact_false_positives=fact_false_positives, + fact_false_negatives=fact_false_negatives, + fact_predicted_count=len(predicted_facts), + fact_ground_truth_count=len(ground_truth_facts), ) diff --git a/ontocast/tool/cache.py b/ontocast/tool/cache.py index f91ee09..5bffaab 100644 --- a/ontocast/tool/cache.py +++ b/ontocast/tool/cache.py @@ -228,8 +228,9 @@ def clear(self, subdirectory: str | None = None) -> None: logger.info(f"Cleared cache directory: {tool_cache_dir}") def get_cache_stats( - self, subdirectory: str | None = None - ) -> dict[str, int | dict[str, int]]: + self, + subdirectory: str | None = None, + ) -> dict[str, int | dict[str, int] | dict[str, dict[str, int]]]: """Get cache statistics. Args: @@ -339,7 +340,9 @@ def clear(self) -> None: """Clear cached results for this tool.""" self.shared_cacher.clear(subdirectory=self.subdirectory) - def get_cache_stats(self) -> dict[str, int | dict[str, int]]: + def get_cache_stats( + self, + ) -> dict[str, int | dict[str, int] | dict[str, dict[str, int]]]: """Get cache statistics for this tool. Returns: diff --git a/ontocast/tool/llm.py b/ontocast/tool/llm.py index 8d16e78..be5d43b 100644 --- a/ontocast/tool/llm.py +++ b/ontocast/tool/llm.py @@ -52,7 +52,7 @@ from langchain_google_genai import ChatGoogleGenerativeAI from langchain_ollama import ChatOllama from langchain_openai import ChatOpenAI -from pydantic import BaseModel, Field, SecretStr +from pydantic import BaseModel, Field, PrivateAttr, SecretStr from ontocast.config import LLMConfig, LLMProvider @@ -63,6 +63,15 @@ logger = logging.getLogger(__name__) +# Shared across all LLMTool instances with the same max_inflight setting. +_inflight_semaphores: dict[int, asyncio.Semaphore] = {} + + +def _inflight_semaphore(max_inflight: int) -> asyncio.Semaphore: + if max_inflight not in _inflight_semaphores: + _inflight_semaphores[max_inflight] = asyncio.Semaphore(max_inflight) + return _inflight_semaphores[max_inflight] + def _usage_from_llm_result(result: Any) -> tuple[int | None, int | None]: """Extract token usage from an LLM response when the provider reports it.""" @@ -92,7 +101,7 @@ def _chars_received_from_result(result: Any) -> int: def track_llm_usage(func: Callable) -> Callable: - """Decorator to track LLM usage automatically.""" + """Decorator to track LLM usage for methods that always hit the provider.""" def _record_usage(tool: LLMTool, prompt_str: str, result: Any) -> None: bt = tool.budget_tracker @@ -106,14 +115,6 @@ def _record_usage(tool: LLMTool, prompt_str: str, result: Any) -> None: output_tokens=output_tokens, ) - @wraps(func) - def wrapper(self: LLMTool, *args, **kwargs): - prompt = args[0] if args else "" - prompt_str = self._prompt_to_string(prompt) - result = func(self, *args, **kwargs) - _record_usage(self, prompt_str, result) - return result - @wraps(func) async def async_wrapper(self: LLMTool, *args, **kwargs): prompt = args[0] if args else "" @@ -122,7 +123,7 @@ async def async_wrapper(self: LLMTool, *args, **kwargs): _record_usage(self, prompt_str, result) return result - return async_wrapper if asyncio.iscoroutinefunction(func) else wrapper + return async_wrapper class LLMTool(Tool): @@ -141,6 +142,8 @@ class LLMTool(Tool): config: LLMConfig = Field(default_factory=LLMConfig) cache: Any = Field(default=None, exclude=True) budget_tracker: Any = Field(default=None, exclude=True) + _cache_hits: int = PrivateAttr(default=0) + _cache_misses: int = PrivateAttr(default=0) def __init__( self, @@ -230,12 +233,12 @@ async def setup(self): f"model {self.config.model_name}" ) self._llm = ChatOpenAI( - model=self.config.model_name, # type: ignore + model=self.config.model_name, temperature=self.config.temperature, - base_url=self.config.base_url, # type: ignore + base_url=self.config.base_url, api_key=( SecretStr(self.config.api_key) if self.config.api_key else None - ), # type: ignore + ), ) elif self.config.provider == LLMProvider.OLLAMA: self._llm = ChatOllama( @@ -262,105 +265,96 @@ async def setup(self): else: raise ValueError(f"Unsupported provider: {self.config.provider}") - @track_llm_usage - async def __call__(self, *args: Any, **kwds: Any) -> Any: - """Call the language model directly (asynchronous). - - Args: - *args: Positional arguments passed to the LLM. - **kwds: Keyword arguments passed to the LLM. - - Returns: - Any: The LLM's response. - """ - # Extract prompt from args (first argument is typically the prompt) - prompt = args[0] if args else "" - - # Prepare configuration for caching - config_dict = { + def _cache_config_dict(self, **extra: Any) -> dict[str, Any]: + config_dict: dict[str, Any] = { "provider": self.config.provider, "model_name": self.config.model_name, "temperature": self.config.temperature, "base_url": self.config.base_url, } - - # Check cache first - cached_response = self.cache.get(prompt, config=config_dict, **kwds) - - if cached_response is not None: - prompt_str = self._prompt_to_string(prompt) - logger.debug(f"Cache hit for __call__: {prompt_str[:50]}...") - # Return a mock BaseMessage object with the cached content - content = cached_response["content"] - content_str = content if isinstance(content, str) else str(content) - return AIMessage(content=content_str) - - # Generate new response - prompt_str = self._prompt_to_string(prompt) - logger.debug( - f"Cache miss, calling LLM for __call__, prompt size {len(prompt_str[:50])}..." + config_dict.update(extra) + return config_dict + + def _cache_key_content(self, *args: Any) -> str: + """Stable string for disk cache keys from invoke arguments.""" + if not args: + return "" + primary = self._prompt_to_string(args[0]) + if len(args) == 1: + return primary + extra = [self._prompt_to_string(arg) for arg in args[1:]] + return primary + "\n---\n" + "\n---\n".join(extra) + + def _record_cache_hit(self, prompt_str: str, content_str: str) -> None: + self._cache_hits += 1 + bt = self.budget_tracker + if bt is not None: + bt.add_cache_hit(len(prompt_str), len(content_str)) + + def _record_api_usage(self, prompt_str: str, result: Any) -> None: + self._cache_misses += 1 + bt = self.budget_tracker + if bt is None: + return + input_tokens, output_tokens = _usage_from_llm_result(result) + bt.add_usage( + len(prompt_str), + _chars_received_from_result(result), + input_tokens=input_tokens, + output_tokens=output_tokens, ) - response = await self.llm.ainvoke(*args, **kwds) - - # Cache the response - response_data = { - "content": response.content, - "prompt": self._prompt_to_string(prompt), - "kwargs": kwds, - } - self.cache.set(prompt, response_data, config=config_dict, **kwds) - - return response - - @track_llm_usage - async def acall(self, *args: Any, **kwds: Any) -> Any: - """Call the language model directly (asynchronous). - - Args: - *args: Positional arguments passed to the LLM. - **kwds: Keyword arguments passed to the LLM. - - Returns: - Any: The LLM's response. - """ - # Extract prompt from args (first argument is typically the prompt) - prompt = args[0] if args else "" - - # Prepare configuration for caching - config_dict = { - "provider": self.config.provider, - "model_name": self.config.model_name, - "temperature": self.config.temperature, - "base_url": self.config.base_url, + def get_cache_stats( + self, + ) -> dict[str, int | dict[str, int | dict[str, int] | dict[str, dict[str, int]]]]: + """Return in-memory hit/miss counters and on-disk cache file stats.""" + disk_stats = self.cache.get_cache_stats() + return { + "cache_hits": self._cache_hits, + "cache_misses": self._cache_misses, + "disk": disk_stats, } - # Check cache first - cached_response = self.cache.get(prompt, config=config_dict, **kwds) - - if cached_response is not None: - prompt_str = self._prompt_to_string(prompt) - logger.debug(f"Cache hit for acall: {prompt_str[:50]}...") - # Return a mock BaseMessage object with the cached content - content = cached_response["content"] - content_str = content if isinstance(content, str) else str(content) - return AIMessage(content=content_str) - - # Generate new response - prompt_str = self._prompt_to_string(prompt) - logger.debug(f"Cache miss, calling LLM for acall: {prompt_str[:50]}...") + async def _invoke_cached(self, *args: Any, **kwds: Any) -> Any: + """Invoke the LLM with optional disk cache and global in-flight limiting.""" + prompt_key = self._cache_key_content(*args) + prompt_str = self._prompt_to_string(args[0]) if args else "" + config_dict = self._cache_config_dict() + + if self.config.cache_enabled: + cached_response = self.cache.get(prompt_key, config=config_dict, **kwds) + if cached_response is not None: + logger.debug("Cache hit: %s...", prompt_str[:50]) + content = cached_response["content"] + content_str = content if isinstance(content, str) else str(content) + self._record_cache_hit(prompt_str, content_str) + return AIMessage(content=content_str) + + logger.debug("Cache miss, calling LLM: %s...", prompt_str[:50]) + + max_inflight = max(1, self.config.llm_max_inflight) + async with _inflight_semaphore(max_inflight): + response = await self.llm.ainvoke(*args, **kwds) + + self._record_api_usage(prompt_str, response) + + if self.config.cache_enabled and not self.config.cache_read_only: + response_data = { + "content": response.content, + "prompt": prompt_str, + "kwargs": kwds, + } + self.cache.set(prompt_key, response_data, config=config_dict, **kwds) - response = await self.llm.ainvoke(*args, **kwds) + return response - # Cache the response - response_data = { - "content": response.content, - "prompt": self._prompt_to_string(prompt), - "kwargs": kwds, - } - self.cache.set(prompt, response_data, config=config_dict, **kwds) + async def __call__(self, *args: Any, **kwds: Any) -> Any: + """Call the language model directly (asynchronous).""" + return await self._invoke_cached(*args, **kwds) - return response + async def acall(self, *args: Any, **kwds: Any) -> Any: + """Alias for :meth:`__call__`.""" + return await self._invoke_cached(*args, **kwds) @property def llm(self) -> BaseChatModel: @@ -400,100 +394,76 @@ def _prompt_to_string(self, prompt) -> str: return str(content_attr) return str(prompt) - @track_llm_usage async def complete(self, prompt: str, **kwargs) -> Any: - """Generate a completion for the given prompt. - - Args: - prompt: The input prompt for generation. - **kwargs: Additional keyword arguments for generation. - - Returns: - Any: The generated completion. - """ - # Prepare configuration for caching - config_dict = { - "provider": self.config.provider, - "model_name": self.config.model_name, - "temperature": self.config.temperature, - "base_url": self.config.base_url, - } - - # Check cache first - cached_response = self.cache.get(prompt, config=config_dict, **kwargs) - - if cached_response is not None: - logger.debug(f"Cache hit for prompt: {prompt[:50]}...") - content = cached_response["content"] - return content if isinstance(content, str) else str(content) - - # Generate new response - logger.debug(f"Cache miss, calling LLM for prompt: {prompt[:50]}...") - - response = await self.llm.ainvoke(prompt, **kwargs) - - # Cache the response - response_data = { - "content": response.content, - "prompt": self._prompt_to_string(prompt), - "kwargs": kwargs, - } - self.cache.set(prompt, response_data, config=config_dict, **kwargs) + """Generate a completion for the given prompt.""" + config_dict = self._cache_config_dict() + prompt_key = self._prompt_to_string(prompt) + + if self.config.cache_enabled: + cached_response = self.cache.get(prompt_key, config=config_dict, **kwargs) + if cached_response is not None: + logger.debug("Cache hit for prompt: %s...", prompt_key[:50]) + content = cached_response["content"] + content_str = content if isinstance(content, str) else str(content) + self._record_cache_hit(prompt_key, content_str) + return content_str + + logger.debug("Cache miss, calling LLM for prompt: %s...", prompt_key[:50]) + + max_inflight = max(1, self.config.llm_max_inflight) + async with _inflight_semaphore(max_inflight): + response = await self.llm.ainvoke(prompt, **kwargs) + + self._record_api_usage(prompt_key, response) + + if self.config.cache_enabled and not self.config.cache_read_only: + response_data = { + "content": response.content, + "prompt": prompt_key, + "kwargs": kwargs, + } + self.cache.set(prompt_key, response_data, config=config_dict, **kwargs) - return response.content + content = response.content + return content if isinstance(content, str) else str(content) - @track_llm_usage async def extract(self, prompt: str, output_schema: Type[T], **kwargs) -> T: - """Extract structured data from the prompt according to a schema. - - Args: - prompt: The input prompt for extraction. - output_schema: The Pydantic model class defining the output structure. - **kwargs: Additional keyword arguments for extraction. - - Returns: - T: The extracted data conforming to the output schema. - """ + """Extract structured data from the prompt according to a schema.""" parser = PydanticOutputParser(pydantic_object=output_schema) format_instructions = parser.get_format_instructions() full_prompt = f"{prompt}\n\n{format_instructions}" + config_dict = self._cache_config_dict( + output_schema=output_schema.__name__, + ) - # Prepare configuration for caching - config_dict = { - "provider": self.config.provider, - "model_name": self.config.model_name, - "temperature": self.config.temperature, - "base_url": self.config.base_url, - "output_schema": output_schema.__name__, - } - - # Check cache first - cached_response = self.cache.get(full_prompt, config=config_dict, **kwargs) - - if cached_response is not None: - logger.debug(f"Cache hit for extraction: {prompt[:50]}...") - # Parse the cached content - content = cached_response["content"] - if isinstance(content, str): - return parser.parse(content) - else: - # Fallback: convert to string if it's not already + if self.config.cache_enabled: + cached_response = self.cache.get(full_prompt, config=config_dict, **kwargs) + if cached_response is not None: + logger.debug("Cache hit for extraction: %s...", prompt[:50]) + content = cached_response["content"] + content_str = content if isinstance(content, str) else str(content) + self._record_cache_hit(full_prompt, content_str) + if isinstance(content, str): + return parser.parse(content) return parser.parse(str(content)) - # Generate new response - logger.debug(f"Cache miss, calling LLM for extraction: {prompt[:50]}...") + logger.debug("Cache miss, calling LLM for extraction: %s...", prompt[:50]) - response = await self.llm.ainvoke(full_prompt, **kwargs) + max_inflight = max(1, self.config.llm_max_inflight) + async with _inflight_semaphore(max_inflight): + response = await self.llm.ainvoke(full_prompt, **kwargs) - # Cache the response - response_data = { - "content": response.content, - "prompt": self._prompt_to_string(full_prompt), - "output_schema": output_schema.__name__, - "kwargs": kwargs, - } - self.cache.set(full_prompt, response_data, config=config_dict, **kwargs) + self._record_api_usage(full_prompt, response) + + if self.config.cache_enabled and not self.config.cache_read_only: + response_data = { + "content": response.content, + "prompt": full_prompt, + "output_schema": output_schema.__name__, + "kwargs": kwargs, + } + self.cache.set(full_prompt, response_data, config=config_dict, **kwargs) content = response.content return parser.parse(content if isinstance(content, str) else str(content)) diff --git a/ontocast/tool/llm_batch.py b/ontocast/tool/llm_batch.py new file mode 100644 index 0000000..78687ed --- /dev/null +++ b/ontocast/tool/llm_batch.py @@ -0,0 +1,113 @@ +"""Provider Batch API helpers for offline benchmark pre-warming. + +OpenAI and Anthropic offer asynchronous batch endpoints (~50% lower cost, +multi-hour latency). This module supports exporting pending LLM prompts to +batch JSONL and importing completed results into the OntoCast disk cache so +subsequent server runs hit :class:`~ontocast.tool.llm.LLMTool` cache entries. + +This is intended for validation / benchmark workflows, not interactive +``/process`` traffic. +""" + +from __future__ import annotations + +import json +import logging +from pathlib import Path +from typing import Any + +from ontocast.config import LLMConfig +from ontocast.tool.cache import Cacher, ToolCacher + +logger = logging.getLogger(__name__) + + +def _llm_cache_config_dict( + llm_config: LLMConfig, +) -> dict[str, str | int | float | bool]: + """Build a cache config dict without optional None values.""" + cfg: dict[str, str | int | float | bool] = { + "provider": llm_config.provider, + "model_name": llm_config.model_name, + "temperature": llm_config.temperature, + } + if llm_config.base_url is not None: + cfg["base_url"] = llm_config.base_url + return cfg + + +def write_openai_chat_batch_jsonl( + requests: list[dict[str, Any]], + output_path: Path, +) -> None: + """Write OpenAI Batch API input JSONL (one request object per line). + + Each item in ``requests`` should include: + - ``custom_id``: stable id (e.g. cache key prefix) + - ``body``: chat completions body with ``model``, ``messages``, etc. + """ + output_path.parent.mkdir(parents=True, exist_ok=True) + with output_path.open("w", encoding="utf-8") as handle: + for item in requests: + line = { + "custom_id": item["custom_id"], + "method": "POST", + "url": "/v1/chat/completions", + "body": item["body"], + } + handle.write(json.dumps(line, ensure_ascii=False) + "\n") + logger.info("Wrote %s OpenAI batch request(s) to %s", len(requests), output_path) + + +def import_openai_batch_output_jsonl( + output_path: Path, + *, + shared_cache: Cacher, + llm_config: LLMConfig, + custom_id_to_cache_key: dict[str, str], +) -> int: + """Import OpenAI batch result JSONL lines into the LLM disk cache. + + Args: + output_path: Path to the batch output JSONL from the provider. + shared_cache: Shared :class:`Cacher` instance (same as the server uses). + llm_config: LLM settings used when the batch was submitted. + custom_id_to_cache_key: Maps each ``custom_id`` to the normalized prompt + string used as cache content (see :meth:`LLMTool._cache_key_content`). + + Returns: + Number of cache entries written. + """ + tool_cache = ToolCacher(shared_cache, "llm") + config_dict = _llm_cache_config_dict(llm_config) + written = 0 + with output_path.open("r", encoding="utf-8") as handle: + for raw_line in handle: + line = raw_line.strip() + if not line: + continue + record = json.loads(line) + custom_id = record.get("custom_id") + if custom_id is None: + continue + cache_key = custom_id_to_cache_key.get(custom_id) + if cache_key is None: + logger.warning("No cache key mapping for custom_id=%s", custom_id) + continue + response = record.get("response", {}) + body = response.get("body", {}) + choices = body.get("choices", []) + if not choices: + logger.warning("Empty choices for custom_id=%s", custom_id) + continue + content = choices[0].get("message", {}).get("content", "") + response_data = { + "content": content, + "prompt": cache_key[:200], + "kwargs": {}, + "source": "openai_batch", + } + tool_cache.set(cache_key, response_data, config=config_dict) + written += 1 + logger.info("Imported %s batch result(s) into LLM cache", written) + return written diff --git a/ontocast/tool/triple_manager/core.py b/ontocast/tool/triple_manager/core.py index dfae73f..107850f 100644 --- a/ontocast/tool/triple_manager/core.py +++ b/ontocast/tool/triple_manager/core.py @@ -7,7 +7,7 @@ import abc import asyncio import os -from typing import ClassVar +from typing import Any, ClassVar from pydantic import Field from rdflib import Graph @@ -60,7 +60,7 @@ async def afetch_ontologies(self) -> list[Ontology]: return await asyncio.to_thread(self.fetch_ontologies) @abc.abstractmethod - def serialize_graph(self, graph: Graph, **kwargs) -> bool | None: + def serialize_graph(self, graph: Graph, **kwargs) -> bool | dict[str, Any] | None: """Store an RDF graph in the triple store. This method should store the given RDF graph in the triple store. @@ -77,7 +77,9 @@ def serialize_graph(self, graph: Graph, **kwargs) -> bool | None: pass @abc.abstractmethod - def serialize(self, o: Ontology | RDFGraph, **kwargs) -> bool | None: # type: ignore[override] + def serialize( + self, o: Ontology | RDFGraph, **kwargs + ) -> bool | dict[str, Any] | None: """Store an RDF graph in the triple store. This method should store the given RDF graph in the triple store. @@ -93,7 +95,9 @@ def serialize(self, o: Ontology | RDFGraph, **kwargs) -> bool | None: # type: i """ pass - async def aserialize(self, o: Ontology | RDFGraph, **kwargs) -> bool | None: + async def aserialize( + self, o: Ontology | RDFGraph, **kwargs + ) -> bool | dict[str, Any] | None: """Async serialize helper for backends without native async I/O.""" return await asyncio.to_thread(self.serialize, o, **kwargs) diff --git a/ontocast/tool/triple_manager/filesystem_manager.py b/ontocast/tool/triple_manager/filesystem_manager.py index 67d607a..de438fc 100644 --- a/ontocast/tool/triple_manager/filesystem_manager.py +++ b/ontocast/tool/triple_manager/filesystem_manager.py @@ -7,6 +7,7 @@ import logging import pathlib +from typing import Any from rdflib import Graph @@ -144,7 +145,10 @@ def _write_turtle( serialization_format = "turtle" if is_oxigraph else "longturtle" graph.serialize(format=serialization_format, destination=output_path) - def serialize(self, o: Ontology | RDFGraph, graph_uri: str | None = None): # type: ignore[override] + def serialize( + self, o: Ontology | RDFGraph, **kwargs: Any + ) -> bool | dict[str, Any] | None: + graph_uri = kwargs.get("graph_uri") if isinstance(o, Ontology): graph = o.graph fname = f"ontology_{o.ontology_id}_{o.version}.ttl" diff --git a/ontocast/tool/triple_manager/fuseki.py b/ontocast/tool/triple_manager/fuseki.py index 8f5b4df..6632f91 100644 --- a/ontocast/tool/triple_manager/fuseki.py +++ b/ontocast/tool/triple_manager/fuseki.py @@ -681,6 +681,8 @@ async def fetch_single_ontology(graph_uri: str) -> Ontology | None: ontology_dict = defaultdict(list) for onto in all_ontologies: + if not isinstance(onto, Ontology): + continue ontology_dict[onto.iri].append(onto) # Build set of all parent hashes to identify terminal ontologies @@ -688,6 +690,8 @@ async def fetch_single_ontology(graph_uri: str) -> Ontology | None: all_parent_hashes = set() for onto in all_ontologies: + if not isinstance(onto, Ontology): + continue if onto.hash: # Collect all parent hashes for parent_hash in onto.parent_hashes: diff --git a/ontocast/tool/triple_manager/mock.py b/ontocast/tool/triple_manager/mock.py index 08e03b4..a50cbcd 100644 --- a/ontocast/tool/triple_manager/mock.py +++ b/ontocast/tool/triple_manager/mock.py @@ -385,7 +385,7 @@ def fetch_ontologies(self) -> List[Ontology]: """ return self.ontologies.copy() - def serialize_graph(self, graph: Graph, **kwargs) -> Dict[str, Any] | None: # type: ignore[override] + def serialize_graph(self, graph: Graph, **kwargs) -> Dict[str, Any] | None: """Store an RDF graph in the mock store. Args: @@ -437,7 +437,7 @@ def serialize_graph(self, graph: Graph, **kwargs) -> Dict[str, Any] | None: # t "labels_added": 1, } - def serialize(self, o: Ontology | RDFGraph, **kwargs) -> Dict[str, Any] | None: # type: ignore[override] + def serialize(self, o: Ontology | RDFGraph, **kwargs) -> Dict[str, Any] | None: """Store an Ontology or RDFGraph in the mock store. Args: diff --git a/ontocast/tool/vector_store/atomizer.py b/ontocast/tool/vector_store/atomizer.py index 568323c..314e7c7 100644 --- a/ontocast/tool/vector_store/atomizer.py +++ b/ontocast/tool/vector_store/atomizer.py @@ -8,7 +8,7 @@ from collections import defaultdict, deque from datetime import datetime, timezone -from typing import Protocol, cast +from typing import Protocol from pydantic import Field from rdflib import DCTERMS, OWL, RDF, RDFS, SKOS, BNode, Literal, URIRef @@ -279,10 +279,7 @@ def _collect_focal_entities( if not any(str(e).startswith(p) for p in excluded_vocab_prefixes) } - return cast( - list[URIRef], - sorted(entities, key=lambda entity: str(entity)), - ) + return sorted(entities, key=lambda entity: str(entity)) def _parent_resource_phrase(self, graph: RDFGraph, parent: URIRef) -> str: """Local name plus optional label gloss when it adds information.""" @@ -401,14 +398,13 @@ def _append_property_domain_range_clues_for_subject_resource( key=str, )[:max_properties] for prop in props_with_domain: - prop_ref = cast(URIRef, prop) - prop_verb = self._normalize_uri(prop_ref) # bare verb for SPO + prop_verb = self._normalize_uri(prop) # bare verb for SPO ranges = self._collect_range_labels( - prop_ref, graph, max_items=endpoint_label_cap + prop, graph, max_items=endpoint_label_cap ) for r_label in ranges or ["something"]: clues.append(f"it {prop_verb} {r_label}") - self._append_inverse_of_clues_for_property(prop_ref, graph, clues) + self._append_inverse_of_clues_for_property(prop, graph, clues) props_with_range = sorted( { @@ -419,14 +415,13 @@ def _append_property_domain_range_clues_for_subject_resource( key=str, )[:max_properties] for prop in props_with_range: - prop_ref = cast(URIRef, prop) - prop_verb = self._normalize_uri(prop_ref) # bare verb for SPO + prop_verb = self._normalize_uri(prop) # bare verb for SPO domains = self._collect_domain_labels( - prop_ref, graph, max_items=endpoint_label_cap + prop, graph, max_items=endpoint_label_cap ) for d_label in domains or ["something"]: clues.append(f"{d_label} {prop_verb} it") - self._append_inverse_of_clues_for_property(prop_ref, graph, clues) + self._append_inverse_of_clues_for_property(prop, graph, clues) def _build_minimal_representation(self, entity: URIRef) -> str: """IRI local name as keyword-oriented tokens: split camelCase/PascalCase, etc. @@ -515,20 +510,18 @@ def _collect_structural_clues( if isinstance(o, URIRef) ] for par in sorted(set(parents), key=str): - par_ref = cast(URIRef, par) siblings = sorted( ( sib - for sib in parent_to_children.get(par_ref, set[URIRef]()) + for sib in parent_to_children.get(par, set[URIRef]()) if sib != entity ), key=str, ) for sib in siblings[:6]: - sib_ref = cast(URIRef, sib) clues.append( - f"{self._normalize_uri(sib_ref)} is also a kind of " - f"{self._parent_resource_phrase(graph, par_ref)}" + f"{self._normalize_uri(sib)} is also a kind of " + f"{self._parent_resource_phrase(graph, par)}" ) self._append_property_domain_range_clues_for_subject_resource( diff --git a/ontocast/tool/vector_store/embedding.py b/ontocast/tool/vector_store/embedding.py index 487ed86..85d75ee 100644 --- a/ontocast/tool/vector_store/embedding.py +++ b/ontocast/tool/vector_store/embedding.py @@ -103,8 +103,8 @@ def _build_embedder(self) -> Embeddings: ) return OpenAIEmbeddings( model=self.config.model_name, - openai_api_key=api_key, - openai_api_base=self.config.base_url, + api_key=api_key, + base_url=self.config.base_url, ) diff --git a/ontocast/toolbox.py b/ontocast/toolbox.py index 20af757..7f8218e 100644 --- a/ontocast/toolbox.py +++ b/ontocast/toolbox.py @@ -213,20 +213,16 @@ def __init__(self, config: Config): self.ontology_manager.register_vector_store(self.patch_retriever) async def get_llm_tool(self, budget_tracker): - """Get an LLM tool instance with a specific budget tracker. + """Return the shared LLM tool with the given budget tracker attached. Args: budget_tracker: The budget tracker instance to use. Returns: - LLMTool: LLM tool with the specified budget tracker. + LLMTool: Shared LLM tool with the specified budget tracker. """ - # Create a new LLM tool with the budget tracker - return await LLMTool.acreate( - config=self.config.tool_config.llm_config, - cache=self.shared_cache, - budget_tracker=budget_tracker, - ) + self.llm.budget_tracker = budget_tracker + return self.llm def require_triple_store_manager(self) -> TripleStoreManager: """Return the configured triple store manager or raise a clear error.""" @@ -573,6 +569,8 @@ async def render_ontology_summary(ontology: Ontology, llm_tool) -> OntologyPrope Returns: OntologyProperties: A structured summary containing only the missing properties. """ + from typing import Any, cast + from pydantic import create_model # Sample the graph intelligently (first 100 sections) @@ -602,7 +600,7 @@ async def render_ontology_summary(ontology: Ontology, llm_tool) -> OntologyPrope return OntologyProperties() # Create a dynamic model with only unset fields - DynamicProps = create_model("DynamicOntologyProps", **unset_fields) + DynamicProps = create_model("DynamicOntologyProps", **cast(Any, unset_fields)) # Define the output parser parser = PydanticOutputParser(pydantic_object=DynamicProps) diff --git a/test/test_budget_tracker.py b/test/test_budget_tracker.py index 5ae0a31..91c7703 100644 --- a/test/test_budget_tracker.py +++ b/test/test_budget_tracker.py @@ -31,6 +31,24 @@ def test_merge_from_accumulates_tokens() -> None: assert left.chars_sent == 30 +def test_add_cache_hit_does_not_increment_calls_count() -> None: + tracker = BudgetTracker() + tracker.add_cache_hit(100, 50) + assert tracker.cache_hits == 1 + assert tracker.calls_count == 0 + assert tracker.chars_sent == 100 + assert tracker.chars_received == 50 + + +def test_merge_from_accumulates_cache_hits() -> None: + left = BudgetTracker() + left.add_cache_hit(10, 5) + right = BudgetTracker() + right.add_cache_hit(20, 15) + left.merge_from(right) + assert left.cache_hits == 2 + + def test_get_summary_includes_tokens_when_present() -> None: tracker = BudgetTracker() tracker.add_usage(100, 50, input_tokens=1000, output_tokens=250) diff --git a/test/test_cli_server.py b/test/test_cli_server.py index e3765b3..b3c3494 100644 --- a/test/test_cli_server.py +++ b/test/test_cli_server.py @@ -106,6 +106,9 @@ def test_build_agent_state_from_parsed_sets_max_visits() -> None: render_mode=None, llm_graph_format=None, ontology_context_mode_value=OntologyContextMode.FIXED_SINGLE_ONTOLOGY, + target_sections=None, + summarize_sections=None, + summary_max_sentences=5, ) state = build_agent_state_from_parsed( parsed, @@ -317,6 +320,14 @@ def evaluate(self, **_kwargs): entity_false_positives=0, entity_false_negatives=0, domain_entity_matches=1, + fact_precision=1.0, + fact_recall=1.0, + fact_f1=1.0, + fact_true_positives=1, + fact_false_positives=0, + fact_false_negatives=0, + fact_predicted_count=1, + fact_ground_truth_count=1, ) monkeypatch.setattr(server_module, "EntityAligner", _FakeAligner) diff --git a/test/test_graph_update.py b/test/test_graph_update.py index 3920bb2..b2cf11d 100644 --- a/test/test_graph_update.py +++ b/test/test_graph_update.py @@ -67,7 +67,7 @@ def test_graph_update_with_language_tags(): triple_operations=[ TripleOp( type="insert", - graph=triples, # type: ignore[arg-type] + graph=RDFGraph._from_turtle_str(triples), prefixes={"ex": "http://example.org/"}, ) ] @@ -116,7 +116,7 @@ def test_graph_update_insert_operation(): triple_operations=[ TripleOp( type="insert", - graph=triples, # type: ignore[arg-type] + graph=RDFGraph._from_turtle_str(triples), prefixes={"ex": "http://example.org/"}, ) ] @@ -161,8 +161,8 @@ def test_graph_update_extract_insert_graph() -> None: """ gu = GraphUpdate( triple_operations=[ - TripleOp(type="insert", graph=insert_ttl), # type: ignore[arg-type] - TripleOp(type="delete", graph=delete_ttl), # type: ignore[arg-type] + TripleOp(type="insert", graph=RDFGraph._from_turtle_str(insert_ttl)), + TripleOp(type="delete", graph=RDFGraph._from_turtle_str(delete_ttl)), ] ) insert_graph = gu.extract_insert_graph() @@ -213,7 +213,7 @@ def test_graph_update_delete_operation(): triple_operations=[ TripleOp( type="delete", - graph=triples, # type: ignore[arg-type] + graph=RDFGraph._from_turtle_str(triples), prefixes={"ex": "http://example.org/"}, ) ] @@ -276,7 +276,7 @@ def test_graph_update_with_prefixes(): triple_operations=[ TripleOp( type="insert", - graph=triples, # type: ignore[arg-type] + graph=RDFGraph._from_turtle_str(triples), prefixes={ "ex": "http://example.org/", "schema": "https://schema.org/", @@ -357,7 +357,7 @@ def test_graph_update_mixed_operations_ordered(): # First: Insert new person with custom schema prefix TripleOp( type="insert", - graph=insert_jane, # type: ignore[arg-type] + graph=RDFGraph._from_turtle_str(insert_jane), prefixes={ "ex": "http://example.org/", "schema": "https://schema.org/", @@ -366,13 +366,13 @@ def test_graph_update_mixed_operations_ordered(): # Second: Delete John's label TripleOp( type="delete", - graph=delete_john_label, # type: ignore[arg-type] + graph=RDFGraph._from_turtle_str(delete_john_label), prefixes={"ex": "http://example.org/"}, ), # Third: Insert new label for John TripleOp( type="insert", - graph=insert_john_label, # type: ignore[arg-type] + graph=RDFGraph._from_turtle_str(insert_john_label), prefixes={"ex": "http://example.org/"}, ), ] diff --git a/test/test_llm_batch.py b/test/test_llm_batch.py new file mode 100644 index 0000000..de80c80 --- /dev/null +++ b/test/test_llm_batch.py @@ -0,0 +1,65 @@ +"""Tests for OpenAI batch cache import helpers.""" + +import json + +from ontocast.config import LLMConfig, LLMProvider, OpenAIModel +from ontocast.tool.cache import Cacher +from ontocast.tool.llm_batch import ( + _llm_cache_config_dict, + import_openai_batch_output_jsonl, + write_openai_chat_batch_jsonl, +) + + +def test_write_and_import_openai_batch_jsonl(tmp_path) -> None: + input_path = tmp_path / "batch_in.jsonl" + output_path = tmp_path / "batch_out.jsonl" + cache_dir = tmp_path / "cache" + + write_openai_chat_batch_jsonl( + [ + { + "custom_id": "req-1", + "body": { + "model": "gpt-4o-mini", + "messages": [{"role": "user", "content": "hello"}], + }, + } + ], + input_path, + ) + assert input_path.exists() + + output_path.write_text( + json.dumps( + { + "custom_id": "req-1", + "response": { + "body": { + "choices": [{"message": {"content": "cached batch reply"}}] + } + }, + } + ) + + "\n", + encoding="utf-8", + ) + + llm_config = LLMConfig( + provider=LLMProvider.OPENAI, + model_name=OpenAIModel.GPT4_O_MINI, + temperature=0.0, + ) + shared = Cacher(cache_dir=cache_dir) + written = import_openai_batch_output_jsonl( + output_path, + shared_cache=shared, + llm_config=llm_config, + custom_id_to_cache_key={"req-1": "hello"}, + ) + assert written == 1 + tool_cache = shared.get( + "hello", subdirectory="llm", config=_llm_cache_config_dict(llm_config) + ) + assert isinstance(tool_cache, dict) + assert tool_cache["content"] == "cached batch reply" diff --git a/test/test_llm_cache.py b/test/test_llm_cache.py new file mode 100644 index 0000000..22cff08 --- /dev/null +++ b/test/test_llm_cache.py @@ -0,0 +1,142 @@ +"""Tests for LLMTool disk cache behavior.""" + +from __future__ import annotations + +import asyncio +from unittest.mock import AsyncMock, MagicMock, patch + +import pytest +from langchain_core.messages.ai import AIMessage +from langchain_core.prompt_values import StringPromptValue + +from ontocast.config import LLMConfig, LLMProvider, OpenAIModel +from ontocast.onto.state import BudgetTracker +from ontocast.tool.cache import Cacher +from ontocast.tool.llm import LLMTool + + +@pytest.fixture +def cache_dir(tmp_path): + return tmp_path / "cache" + + +@pytest.fixture +def llm_config(): + return LLMConfig( + provider=LLMProvider.OPENAI, + model_name=OpenAIModel.GPT4_O_MINI, + temperature=0.0, + cache_enabled=True, + cache_read_only=False, + ) + + +async def _make_tool( + llm_config: LLMConfig, + cache_dir, + budget_tracker: BudgetTracker | None = None, +) -> LLMTool: + shared = Cacher(cache_dir=cache_dir) + with patch("ontocast.tool.llm.ChatOpenAI") as mock_cls: + mock_llm = MagicMock() + mock_llm.ainvoke = AsyncMock(return_value=AIMessage(content='{"answer": 42}')) + mock_cls.return_value = mock_llm + tool = await LLMTool.acreate( + config=llm_config, + cache=shared, + budget_tracker=budget_tracker or BudgetTracker(), + ) + return tool + + +def test_cache_hit_skips_provider_and_counts_cache_hit(llm_config, cache_dir) -> None: + tracker = BudgetTracker() + + async def run() -> None: + tool = await _make_tool(llm_config, cache_dir, tracker) + await tool.complete("What is 2+2?") + assert tracker.calls_count == 1 + assert tracker.cache_hits == 0 + + await tool.complete("What is 2+2?") + assert tracker.calls_count == 1 + assert tracker.cache_hits == 1 + assert tool.get_cache_stats()["cache_hits"] == 1 + + asyncio.run(run()) + + +def test_cache_read_only_does_not_write(llm_config, cache_dir) -> None: + llm_config.cache_read_only = True + + async def run() -> None: + tool = await _make_tool(llm_config, cache_dir) + await tool.complete("unique prompt alpha") + assert tool.get_cache_stats()["cache_misses"] == 1 + + tool2 = await _make_tool(llm_config, cache_dir) + await tool2.complete("unique prompt alpha") + assert tool2.get_cache_stats()["cache_misses"] == 1 + + asyncio.run(run()) + + +def test_cache_disabled_always_calls_provider(llm_config, cache_dir) -> None: + llm_config.cache_enabled = False + + async def run() -> None: + tool = await _make_tool(llm_config, cache_dir) + await tool.complete("no cache 1") + await tool.complete("no cache 1") + assert tool.get_cache_stats()["cache_misses"] == 2 + + asyncio.run(run()) + + +def test_prompt_value_uses_to_string_for_cache_key(llm_config, cache_dir) -> None: + tracker = BudgetTracker() + + async def run() -> None: + tool = await _make_tool(llm_config, cache_dir, tracker) + prompt_text = "Explain ontologies briefly." + prompt_value = StringPromptValue(text=prompt_text) + + await tool(prompt_value) + assert tracker.calls_count == 1 + + await tool(prompt_value) + assert tracker.calls_count == 1 + assert tracker.cache_hits == 1 + + asyncio.run(run()) + + +def test_config_change_invalidates_cache(llm_config, cache_dir) -> None: + tracker = BudgetTracker() + + async def run() -> None: + tool = await _make_tool(llm_config, cache_dir, tracker) + await tool.complete("same question") + assert tracker.calls_count == 1 + + tool.config.temperature = 0.5 + await tool.complete("same question") + assert tracker.calls_count == 2 + + asyncio.run(run()) + + +def test_get_cache_stats_includes_disk(llm_config, cache_dir) -> None: + async def run() -> None: + tool = await _make_tool(llm_config, cache_dir) + await tool.complete("stats probe") + stats = tool.get_cache_stats() + assert stats["cache_misses"] == 1 + assert "disk" in stats + disk = stats["disk"] + assert isinstance(disk, dict) + total_files = disk.get("total_files") + assert isinstance(total_files, int) + assert total_files >= 1 + + asyncio.run(run()) diff --git a/test/test_rdfgraph_turtle_parsing.py b/test/test_rdfgraph_turtle_parsing.py index a6acb91..aa5fe2c 100644 --- a/test/test_rdfgraph_turtle_parsing.py +++ b/test/test_rdfgraph_turtle_parsing.py @@ -253,7 +253,7 @@ def test_content_unit_sanitize_coerces_plain_graph() -> None: index=0, doc_iri=URIRef("https://example.org/doc/1"), ) - unit.graph = plain # type: ignore[assignment] + object.__setattr__(unit, "graph", plain) unit.sanitize() diff --git a/test/test_section_pipeline.py b/test/test_section_pipeline.py new file mode 100644 index 0000000..06d0cad --- /dev/null +++ b/test/test_section_pipeline.py @@ -0,0 +1,143 @@ +"""Tests for section tagging, filtering, and optional graph routing.""" + +import json +from pathlib import Path + +from rdflib import URIRef + +from ontocast.cli.server import expand_input_to_states +from ontocast.config import Config +from ontocast.onto.content_unit import ContentUnit +from ontocast.onto.enum import OntologyContextMode, RenderMode, WorkflowNode +from ontocast.onto.section import ( + assign_section_labels, + detect_section_spans, + filter_units_by_target_sections, + should_summarize_unit, +) +from ontocast.onto.state import AgentState +from ontocast.stategraph.routing import ( + route_after_chunk_pre, + route_after_convert, +) + +_SAMPLE_DOC = """# Introduction +We survey prior work. + +## Methods +We used a benchmark. + +## Results +Accuracy improved by 10%. + +## Future Work +We may extend the model. +""" + + +def test_detect_section_spans_finds_headings() -> None: + spans = detect_section_spans(_SAMPLE_DOC) + labels = [span.label for span in spans] + assert "introduction" in labels + assert "methods" in labels + assert "results" in labels + assert "future_work" in labels + + +def test_assign_section_labels_on_units() -> None: + spans = detect_section_spans(_SAMPLE_DOC) + units = [ + ContentUnit( + text="Accuracy improved by 10%.", + index=0, + doc_iri=URIRef("http://example.org/doc"), + ) + ] + assign_section_labels(units, _SAMPLE_DOC, spans) + assert units[0].section_label == "results" + + +def test_filter_units_by_target_sections() -> None: + units = [ + ContentUnit( + text="a", + index=0, + doc_iri=URIRef("http://example.org/doc"), + section_label="results", + ), + ContentUnit( + text="b", + index=1, + doc_iri=URIRef("http://example.org/doc"), + section_label="introduction", + ), + ] + filtered = filter_units_by_target_sections(units, ["results"]) + assert len(filtered) == 1 + assert filtered[0].section_label == "results" + + +def test_should_summarize_unit_wildcard_and_named() -> None: + unit = ContentUnit( + text="x", + index=0, + doc_iri=URIRef("http://example.org/doc"), + section_label="results", + ) + assert should_summarize_unit(unit, []) is True + assert should_summarize_unit(unit, ["*"]) is True + assert should_summarize_unit(unit, ["results"]) is True + assert should_summarize_unit(unit, ["methods"]) is False + assert should_summarize_unit(unit, None) is False + + +def test_agent_state_optional_routing_flags() -> None: + default = AgentState() + assert default.use_section_tagging is False + assert default.use_summarization is False + assert route_after_convert(default) == WorkflowNode.CHUNK + assert route_after_chunk_pre(default) == WorkflowNode.RENDER_ONTOLOGY_UPDATE + + tagged = AgentState(target_sections=["results"]) + assert tagged.use_section_tagging is True + assert route_after_convert(tagged) == WorkflowNode.TAG_SECTIONS + + summarized = AgentState( + summarize_sections=["results"], render_mode=RenderMode.FACTS + ) + assert summarized.use_summarization is True + assert route_after_chunk_pre(summarized) == WorkflowNode.SUMMARIZE_CHUNKS + + +def test_expand_input_to_states_passes_section_params(tmp_path: Path) -> None: + input_file = tmp_path / "doc.json" + input_file.write_text(json.dumps({"text": "hello"}), encoding="utf-8") + config = Config() + states = expand_input_to_states( + input_file, + config=config, + head_chunks=2, + ontology_context_mode_value=OntologyContextMode.SELECTED_SINGLE_ONTOLOGY, + tenant="t", + project="p", + target_sections=["results"], + summarize_sections=["*"], + summary_max_sentences=3, + ) + assert len(states) == 1 + state = states[0] + assert state.target_sections == ["results"] + assert state.summarize_sections == ["*"] + assert state.summary_max_sentences == 3 + assert state.use_section_tagging is True + assert state.use_summarization is True + + +def test_content_unit_extraction_text_prefers_summary() -> None: + unit = ContentUnit( + text="original long text", + index=0, + doc_iri=URIRef("http://example.org/doc"), + summary="short summary", + ) + assert unit.extraction_text == "short summary" diff --git a/test/test_toolbox_sync.py b/test/test_toolbox_sync.py index f657f57..4461634 100644 --- a/test/test_toolbox_sync.py +++ b/test/test_toolbox_sync.py @@ -107,13 +107,14 @@ def should_initialize_vector_store(self, ontology_context_mode): cast(ToolBox, self), ontology_context_mode ) + _synchronize_ontologies = fake_sync + _materialize_ontology = fake_mat + st = Stub() - st._synchronize_ontologies = fake_sync.__get__(st, Stub) # type: ignore[method-assign] - st._materialize_ontology = fake_mat.__get__(st, Stub) # type: ignore[method-assign] st.ontology_manager.add_ontology = MagicMock(side_effect=fake_add) async def main(): - await ToolBox.initialize(st) # type: ignore[arg-type] + await ToolBox.initialize(cast(ToolBox, st)) asyncio.run(main()) @@ -184,7 +185,7 @@ def should_initialize_vector_store(self, ontology_context_mode): st = Stub() asyncio.run( ToolBox.initialize( - st, # type: ignore[arg-type] + cast(ToolBox, st), ontology_context_mode=OntologyContextMode.SELECTED_SINGLE_ONTOLOGY, fail_on_vector_store_error=False, ) @@ -229,7 +230,7 @@ def should_initialize_vector_store(self, ontology_context_mode): st = Stub() asyncio.run( ToolBox.initialize( - st, # type: ignore[arg-type] + cast(ToolBox, st), ontology_context_mode=OntologyContextMode.SELECTED_VECTOR_SEARCH_ONTOLOGY, fail_on_vector_store_error=False, ) @@ -274,7 +275,7 @@ def __init__(self) -> None: stub = Stub() with pytest.raises(ValueError, match="already bound to IRI"): - asyncio.run(ToolBox.ingest_ontology_ttl(stub, incoming_ttl)) # type: ignore[arg-type] + asyncio.run(ToolBox.ingest_ontology_ttl(cast(ToolBox, stub), incoming_ttl)) stub._materialize_ontology.assert_not_awaited() assert list(od.glob("*.ttl")) == [] diff --git a/test/test_triple_evaluator.py b/test/test_triple_evaluator.py index 6a00164..af34259 100644 --- a/test/test_triple_evaluator.py +++ b/test/test_triple_evaluator.py @@ -200,3 +200,145 @@ def test_evaluate_json_validated_matches_produce_true_positives() -> None: entity_matches=entity_matches, ) assert metrics.true_positives >= 1 + + +def test_type_only_overlap_has_triple_metrics_but_no_fact_tp() -> None: + person_type = URIRef("https://types/Person") + entity = URIRef("https://example.org/Alpha") + graph = RDFGraph() + graph.add((entity, RDF.type, person_type)) + + metrics = TripleSetEvaluator().evaluate( + predicted_graph=graph, + gt_graph=graph, + entity_matches=[ + EntityMatch(predicted_entity=entity, gt_entity=entity, similarity=1.0), + EntityMatch( + predicted_entity=person_type, gt_entity=person_type, similarity=1.0 + ), + EntityMatch(predicted_entity=RDF.type, gt_entity=RDF.type, similarity=1.0), + ], + ) + assert metrics.true_positives == 1 + assert metrics.precision == 1.0 + assert metrics.fact_true_positives == 0 + assert metrics.fact_predicted_count == 0 + assert metrics.fact_ground_truth_count == 0 + + +def test_relational_triple_perfect_fact_metrics() -> None: + book = URIRef("http://text2kg.bench/prisoners_of_the_sun") + person = URIRef("http://text2kg.bench/captain_haddock") + characters = URIRef("https://bench.example/relations/P674") + + graph = RDFGraph() + graph.add((book, characters, person)) + + metrics = TripleSetEvaluator().evaluate( + predicted_graph=graph, + gt_graph=graph, + entity_matches=[ + EntityMatch(predicted_entity=book, gt_entity=book, similarity=1.0), + EntityMatch(predicted_entity=person, gt_entity=person, similarity=1.0), + EntityMatch( + predicted_entity=characters, gt_entity=characters, similarity=1.0 + ), + ], + ) + assert metrics.fact_true_positives == 1 + assert metrics.fact_precision == 1.0 + assert metrics.fact_recall == 1.0 + assert metrics.fact_f1 == 1.0 + + +def test_subclass_axiom_excluded_from_facts() -> None: + class_a = URIRef("https://ontology.example/ClassA") + class_b = URIRef("https://ontology.example/ClassB") + graph = RDFGraph() + graph.add((class_a, RDFS.subClassOf, class_b)) + + metrics = TripleSetEvaluator().evaluate( + predicted_graph=graph, + gt_graph=graph, + entity_matches=[ + EntityMatch(predicted_entity=class_a, gt_entity=class_a, similarity=1.0), + EntityMatch(predicted_entity=class_b, gt_entity=class_b, similarity=1.0), + EntityMatch( + predicted_entity=RDFS.subClassOf, + gt_entity=RDFS.subClassOf, + similarity=1.0, + ), + ], + ) + assert metrics.true_positives == 1 + assert metrics.fact_true_positives == 0 + + +def test_extra_relation_lowers_fact_precision() -> None: + book = URIRef("http://text2kg.bench/book") + person = URIRef("http://text2kg.bench/person") + extra = URIRef("http://text2kg.bench/extra") + p674 = URIRef("https://bench.example/relations/P674") + p50 = URIRef("https://bench.example/relations/P50") + + gt_graph = RDFGraph() + gt_graph.add((book, p674, person)) + + predicted_graph = RDFGraph() + predicted_graph.add((book, p674, person)) + predicted_graph.add((book, p50, extra)) + + entity_matches = [ + EntityMatch(predicted_entity=book, gt_entity=book, similarity=1.0), + EntityMatch(predicted_entity=person, gt_entity=person, similarity=1.0), + EntityMatch(predicted_entity=extra, gt_entity=extra, similarity=1.0), + EntityMatch(predicted_entity=p674, gt_entity=p674, similarity=1.0), + EntityMatch(predicted_entity=p50, gt_entity=p50, similarity=1.0), + ] + + metrics = TripleSetEvaluator().evaluate( + predicted_graph=predicted_graph, + gt_graph=gt_graph, + entity_matches=entity_matches, + ) + assert metrics.fact_true_positives == 1 + assert metrics.fact_predicted_count == 2 + assert metrics.fact_precision == 0.5 + assert metrics.fact_recall == 1.0 + + +def test_wrong_type_excluded_from_facts_but_counts_in_triple_metrics() -> None: + book = URIRef("http://text2kg.bench/book") + person = URIRef("http://text2kg.bench/person") + correct_type = URIRef("https://ontology.example/concepts/Q95074") + wrong_type = URIRef("https://ontology.example/concepts/Q5") + p674 = URIRef("https://bench.example/relations/P674") + + gt_graph = RDFGraph() + gt_graph.add((book, p674, person)) + gt_graph.add((person, RDF.type, correct_type)) + + predicted_graph = RDFGraph() + predicted_graph.add((book, p674, person)) + predicted_graph.add((person, RDF.type, wrong_type)) + + metrics = TripleSetEvaluator().evaluate( + predicted_graph=predicted_graph, + gt_graph=gt_graph, + entity_matches=[ + EntityMatch(predicted_entity=book, gt_entity=book, similarity=1.0), + EntityMatch(predicted_entity=person, gt_entity=person, similarity=1.0), + EntityMatch( + predicted_entity=correct_type, gt_entity=correct_type, similarity=1.0 + ), + EntityMatch( + predicted_entity=wrong_type, gt_entity=wrong_type, similarity=1.0 + ), + EntityMatch(predicted_entity=p674, gt_entity=p674, similarity=1.0), + EntityMatch(predicted_entity=RDF.type, gt_entity=RDF.type, similarity=1.0), + ], + ) + assert metrics.fact_true_positives == 1 + assert metrics.fact_f1 == 1.0 + assert metrics.true_positives == 1 + assert metrics.false_positives == 1 diff --git a/uv.lock b/uv.lock index bcb6189..dcb8f63 100644 --- a/uv.lock +++ b/uv.lock @@ -33,11 +33,11 @@ wheels = [ [[package]] name = "aiohappyeyeballs" -sdist = {hash = "sha256:c3f9d0113123803ccadfdf3f0faa505bc78e6a72d1cc4806cbd719826e943558", size = 22760, upload-time = "2025-03-12T01:42:48.764Z", url = "https://files.pythonhosted.org/packages/26/30/f84a107a9c4331c14b2b586036f40965c128aa4fee4dda5d3d51cb14ad54/aiohappyeyeballs-2.6.1.tar.gz"} +sdist = {hash = "sha256:e202810ee718bd01fc6ef49e8ea53d023d5cb6b581076d7925aa499fa55dbe64", size = 22591, upload-time = "2026-05-20T15:12:24.631Z", url = "https://files.pythonhosted.org/packages/33/c6/61a2d7b7572279226bb2e7f61d7a19ca7c90da0329c93fa0d560cbf288d8/aiohappyeyeballs-2.6.2.tar.gz"} source = {registry = "https://pypi.org/simple"} -version = "2.6.1" +version = "2.6.2" wheels = [ - {hash = 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