From df9b4bf8c399c0e48b8e1bf5fa28bd00651c5f42 Mon Sep 17 00:00:00 2001 From: Amrit Krishnan Date: Fri, 5 Jun 2026 08:10:11 -0400 Subject: [PATCH 1/3] Add hierarchical occlusion saliency engine, tests, and CI - SaliencyEngine: two-pass hierarchical occlusion (segment then sentence level) with logprob-drop scoring and binary fallback - Core types: Segment, SaliencyScore, SaliencyResult - 41 unit tests, all passing, no network calls - CI: code_checks and unit_tests workflows, pre-commit config - pyproject.toml: full ruff/mypy/pytest/uv setup matching helix conventions - DESIGN.md: updated to reflect actual architecture and drop structured reasoning - README and working example script --- .github/workflows/code_checks.yml | 57 + .github/workflows/unit_tests.yml | 58 + .pre-commit-config.yaml | 60 + DESIGN.md | 263 ++ README.md | 29 +- examples/demo_saliency.py | 121 + pyproject.toml | 100 +- pyrightconfig.json | 7 + src/motive/__init__.py | 7 +- .../__pycache__/__init__.cpython-312.pyc | Bin 0 -> 521 bytes src/motive/__pycache__/_types.cpython-312.pyc | Bin 0 -> 2872 bytes .../__pycache__/saliency.cpython-312.pyc | Bin 0 -> 10985 bytes src/motive/_types.py | 61 + src/motive/saliency.py | 248 ++ tests/__init__.py | 0 tests/__pycache__/__init__.cpython-312.pyc | Bin 0 -> 144 bytes ...test_saliency.cpython-312-pytest-9.0.3.pyc | Bin 0 -> 48376 bytes .../test_types.cpython-312-pytest-9.0.3.pyc | Bin 0 -> 17828 bytes tests/test_saliency.py | 462 +++ tests/test_types.py | 83 + uv.lock | 3524 +++++++++++++++++ 21 files changed, 5077 insertions(+), 3 deletions(-) create mode 100644 .github/workflows/code_checks.yml create mode 100644 .github/workflows/unit_tests.yml create mode 100644 .pre-commit-config.yaml create mode 100644 DESIGN.md create mode 100644 examples/demo_saliency.py create mode 100644 pyrightconfig.json create mode 100644 src/motive/__pycache__/__init__.cpython-312.pyc create mode 100644 src/motive/__pycache__/_types.cpython-312.pyc create mode 100644 src/motive/__pycache__/saliency.cpython-312.pyc create mode 100644 src/motive/_types.py create mode 100644 src/motive/saliency.py create mode 100644 tests/__init__.py create mode 100644 tests/__pycache__/__init__.cpython-312.pyc create mode 100644 tests/__pycache__/test_saliency.cpython-312-pytest-9.0.3.pyc create mode 100644 tests/__pycache__/test_types.cpython-312-pytest-9.0.3.pyc create mode 100644 tests/test_saliency.py create mode 100644 tests/test_types.py create mode 100644 uv.lock diff --git a/.github/workflows/code_checks.yml b/.github/workflows/code_checks.yml new file mode 100644 index 0000000..83a5d5d --- /dev/null +++ b/.github/workflows/code_checks.yml @@ -0,0 +1,57 @@ +name: code checks +permissions: + contents: read + pull-requests: write + +on: + push: + branches: + - main + paths: + - .pre-commit-config.yaml + - .github/workflows/code_checks.yml + - '**.py' + - uv.lock + - pyproject.toml + pull_request: + branches: + - main + paths: + - .pre-commit-config.yaml + - .github/workflows/code_checks.yml + - '**.py' + - uv.lock + - pyproject.toml + +jobs: + code-checks: + runs-on: ubuntu-latest + steps: + - uses: actions/checkout@v6.0.2 + + - name: Install uv + uses: astral-sh/setup-uv@v8.1.0 + with: + version: "0.9.11" + enable-cache: true + + - name: Set up Python + uses: actions/setup-python@a309ff8b426b58ec0e2a45f0f869d46889d02405 + with: + python-version-file: ".python-version" + + - name: Install the project + run: uv sync --all-extras --dev + + - name: Run pre-commit checks + run: | + source .venv/bin/activate + pre-commit run --all-files + + - name: Seed pip into venv + run: uv pip install pip + + - name: pip-audit + uses: pypa/gh-action-pip-audit@v1.1.0 + with: + virtual-environment: .venv/ diff --git a/.github/workflows/unit_tests.yml b/.github/workflows/unit_tests.yml new file mode 100644 index 0000000..4e7abbb --- /dev/null +++ b/.github/workflows/unit_tests.yml @@ -0,0 +1,58 @@ +name: unit tests +permissions: + contents: read + pull-requests: write + +on: + push: + branches: + - main + paths: + - .pre-commit-config.yaml + - .github/workflows/unit_tests.yml + - .github/workflows/code_checks.yml + - '**.py' + - uv.lock + - pyproject.toml + pull_request: + branches: + - main + paths: + - .pre-commit-config.yaml + - .github/workflows/unit_tests.yml + - .github/workflows/code_checks.yml + - '**.py' + - uv.lock + - pyproject.toml + +jobs: + unit-tests: + runs-on: ubuntu-latest + steps: + - uses: actions/checkout@v6.0.2 + + - name: Install uv + uses: astral-sh/setup-uv@v8.1.0 + with: + version: "0.9.11" + enable-cache: true + + - name: Set up Python + uses: actions/setup-python@a309ff8b426b58ec0e2a45f0f869d46889d02405 + with: + python-version-file: ".python-version" + + - name: Install the project + run: uv sync --all-extras --dev + + - name: Run unit tests + run: | + uv run pytest --cov src/motive --cov-report=xml tests + + - name: Upload coverage to Codecov + uses: codecov/codecov-action@57e3a136b779b570ffcdbf80b3bdc90e7fab3de2 + with: + token: ${{ secrets.CODECOV_TOKEN }} + slug: VectorInstitute/motive + fail_ci_if_error: false + verbose: true diff --git a/.pre-commit-config.yaml b/.pre-commit-config.yaml new file mode 100644 index 0000000..6dec687 --- /dev/null +++ b/.pre-commit-config.yaml @@ -0,0 +1,60 @@ +repos: + - repo: https://github.com/pre-commit/pre-commit-hooks + rev: v6.0.0 + hooks: + - id: trailing-whitespace + - id: check-ast + - id: check-builtin-literals + - id: check-docstring-first + - id: check-executables-have-shebangs + - id: debug-statements + - id: end-of-file-fixer + - id: mixed-line-ending + args: [--fix=lf] + - id: fix-byte-order-marker + - id: check-merge-conflict + - id: check-symlinks + - id: detect-private-key + - id: check-yaml + args: [--unsafe] + - id: check-toml + + - repo: https://github.com/astral-sh/uv-pre-commit + rev: 0.9.26 + hooks: + - id: uv-lock + + - repo: https://github.com/astral-sh/ruff-pre-commit + rev: 'v0.15.8' + hooks: + - id: ruff-check + args: [--fix, --exit-non-zero-on-fix] + types_or: [python, jupyter] + - id: ruff-format + types_or: [python, jupyter] + + - repo: https://github.com/pre-commit/mirrors-mypy + rev: v1.19.1 + hooks: + - id: mypy + entry: .venv/bin/python -m mypy --config-file pyproject.toml + language: system + types: [python] + exclude: "tests|examples" + + - repo: https://github.com/crate-ci/typos + rev: v1.32.0 + hooks: + - id: typos + +ci: + autofix_commit_msg: | + [pre-commit.ci] Add auto fixes from pre-commit.com hooks + + for more information, see https://pre-commit.ci + autofix_prs: true + autoupdate_branch: '' + autoupdate_commit_msg: '[pre-commit.ci] pre-commit autoupdate' + autoupdate_schedule: weekly + skip: [mypy] + submodules: false diff --git a/DESIGN.md b/DESIGN.md new file mode 100644 index 0000000..7bec5bb --- /dev/null +++ b/DESIGN.md @@ -0,0 +1,263 @@ +# Design Doc: `motive` — Agent Decision Explainability Layer + +**Status:** Draft +**Author:** Amrit Krishnan +**Date:** 2026-06-04 + +--- + +## 1. Problem + +AI agent observability platforms (Langfuse, LangSmith, Arize Phoenix, MLflow, etc.) tell you *what* an agent did — which tools were called, in what order, with what inputs and outputs. They do not tell you *why* the agent made each decision. + +When an agent escalates a support ticket, calls a risky API, or selects one branch over another, developers and operators have no structured, queryable way to understand the reasoning. This makes debugging slow, audits manual, and trust low. + +--- + +## 2. Goal + +Build a Python library that computes empirically-grounded, machine-readable explanations for AI agent decisions and emits them as OpenTelemetry-compatible telemetry. The explanations cover: + +- **Feature attribution (saliency)** — which parts of the input context drove the decision, and how much. +- **Counterfactual probes** — minimal input changes that would have flipped the decision. + +These are emitted as OTEL span attributes and events so they flow into existing backends (Langfuse, MLflow Tracing, any OTEL collector) without new infrastructure. + +> **Note on structured reasoning:** model self-reported rationales ("I chose this tool because...") are not used as an explanation method. Anthropic's interpretability research (introspection paper, NLA paper, persona vectors) shows that model self-reports are unreliable ~80% of the time, can hallucinate, and often conceal internal states. Saliency and counterfactuals are empirical — they test the model's actual input-output behaviour — and are the only methods motive relies on. + +--- + +## 3. Non-Goals (MVP) + +- No model self-reported rationales or chain-of-thought extraction. +- No mechanistic interpretability (circuits, SAEs, neuron attribution). +- No tight coupling to a specific agent framework (LangGraph, AutoGen, etc.). +- No automatic policy enforcement or risk scoring — explanations are data, not decisions. +- No new UI from scratch — the reference UI is a pluggable component, not a product. + +--- + +## 4. Environmental Scan + +**Verdict: YES — genuine gap, worth building.** + +The agent observability market is crowded at the *execution trace* layer. No production tool covers the *decision explanation* layer. + +| Category | Representative Tools | Gap | +|---|---|---| +| Agent tracing / observability | Langfuse, LangSmith, Arize Phoenix, Braintrust, Opik, W&B Weave, MLflow | Capture what happened; no saliency, no counterfactuals on spans | +| OTEL AI standards | OpenInference, OpenLLMetry, OTEL GenAI Semantic Conventions | Standardize span shape and cardinality; no attributes for input importance or counterfactuals | +| XAI for AI models | SHAP, LIME, Integrated Gradients, AgentSHAP (2025) | Methods exist in isolation; none integrated into observability pipelines or emitting OTEL | +| Counterfactuals for agents | Research papers (ICLR 2026, arXiv 2506.02946) | Theoretically mature; no production implementation | +| Agent explainability SDKs | None found | Direct gap | + +The 2026 research literature explicitly names this gap: "traditional tracing shows what happened, not why it was decided" (Elixir Data, 2026). AgentSHAP (arXiv 2512.12597) is the closest academic analogue but is not a production library and does not emit OTEL telemetry. + +--- + +## 5. Scope and Assumptions + +**In scope (MVP)** + +- Single-agent and orchestrated multi-agent systems where each model call or tool invocation is logged as a span. +- Model-agnostic: works with any model type (language, vision-language, interaction) via any OpenAI-compatible API endpoint. +- Per-span explanations for tool calls and plan/routing decisions. +- Python SDK with decorators for zero-boilerplate instrumentation. +- OTEL emission to existing backends. + +**Out of scope (MVP)** + +- Multi-step causal chains across spans. +- Automatic redaction / PII scrubbing (configurable hooks only). +- UI product — reference React component only. +- Circuit tracing / activation-based methods (require local model weights; not compatible with API-served models). + +**Assumptions** + +- The agent system calls models via an OpenAI-compatible endpoint. +- Extra compute for explainability is acceptable: each explained span costs N+M additional model calls (N = number of segments, M = sentences in top segments). Sampled and async in prod. +- The storage backend can accept extra OTEL span attributes and events. + +--- + +## 6. Architecture + +``` +Agent Code + │ + ├── @explain_tool_call / @explain_plan_decision (SDK decorators) + │ │ + │ └── SaliencyEngine + │ ├── Pass 1 — segment-level occlusion + │ │ mask each segment → re-run → measure logprob drop + │ └── Pass 2 — sentence-level occlusion (top-k segments only) + │ mask each sentence → re-run → measure logprob drop + │ + ├── CounterfactualProber (TODO) + │ generate minimal edits to top segments → re-run → record flips + │ + └── TelemetryEmitter + └── OTEL SDK → existing backend (Langfuse / MLflow / collector) +``` + +**Components** + +1. **SDK** — Python library. Wraps model/tool calls via decorators. Computes and emits explanations. +2. **SaliencyEngine** — hierarchical occlusion engine. Scores input segments by how much removing them drops the model's confidence in the original decision, using logprob drop as the primary signal with binary fallback. +3. **CounterfactualProber** — takes top-importance segments, generates minimal edits, re-runs the decision, records flips as human-readable what-if statements. +4. **Telemetry Emitter** — maps results to OTEL span attributes and events using the `agent.why.*` namespace. +5. **Reference UI Component** — React component that renders a "Why?" panel from span JSON. + +--- + +## 7. Explanation Unit: The Decision Span + +The unit of explanation is a *decision span* — any span where the agent makes a discrete choice. + +**Span types (MVP)** + +| Type | Triggered by | What is explained | +|---|---|---| +| `tool_call` | Agent selects and calls an external tool | Which input segments drove the tool choice | +| `plan_decision` | Agent picks a branch, subagent, or subgoal | Which input segments drove the routing | + +Each decision span carries: +- **Input context** — user message, agent state, retrieved documents, prior plan. +- **Segments** — the input context pre-split into logical units (one per message, retrieved doc, tool result, etc.). +- **Model output** — tool name + args, or chosen branch. +- **Explanation** — saliency scores and counterfactuals, attached as OTEL attributes and events. + +--- + +## 8. Explanation Methods + +### 8.1 Hierarchical Occlusion Saliency (primary method) + +**How it works:** + +The input context is split into *segments* — logical units such as the system prompt, each user message, each retrieved document, and each prior tool result. The engine runs two passes: + +**Pass 1 — segment level:** +Each segment is masked (replaced with `[CONTENT REDACTED]`) one at a time. The decision call is re-run with the masked context. Importance is measured as the drop in log-probability of the original decision: + +``` +importance(i) = logprob(decision | full context) + − logprob(decision | context with segment i masked) +``` + +Higher importance = masking that segment makes the model less confident in its original choice. Falls back to binary (decision flipped = 1.0, unchanged = 0.0) when logprobs are unavailable. + +**Pass 2 — sentence level:** +For the top-k segments from Pass 1 (importance > 0), the engine repeats the same procedure at sentence granularity within each segment, identifying the specific sentences that drove the decision. + +**Output:** a ranked list of `(segment, importance)` pairs at both granularity levels. The unit of attribution depends on modality: sentences/tokens for text, patches/regions for vision. + +**Cost:** Pass 1 = N model calls (N = segments, typically 4–10). Pass 2 = M calls per top segment (M = sentences, typically 2–6). Total ~10–30 calls per explained span. + +### 8.2 Counterfactual Probes (next to implement) + +Starting from the highest-importance segments identified in §8.1: +1. Generate minimal perturbations: remove a phrase, swap a retrieved document, neutralize sentiment. +2. Re-run the decision call. +3. If the outcome changes (different tool or routing), record a human-readable description. + +Example: *"If 'failing for 3 days' is removed from the user message, the agent calls `send_retry_instructions` instead of `escalate_to_human`."* + +Store 1–3 counterfactuals per span. + +--- + +## 9. Telemetry Schema (OTEL Extension) + +All fields attach to the existing decision span; no new spans are created. + +### Span Attributes (indexed, short strings) + +| Attribute | Type | Description | +|---|---|---| +| `agent.why.type` | string | `tool_call` or `plan_decision`. | +| `agent.why.primary_factors` | JSON string | Top-k `{id, label, importance, decision_changed}` objects from Pass 1. | +| `agent.why.sentence_factors` | JSON string | Top-k `{id, label, importance, decision_changed}` objects from Pass 2. | +| `agent.why.counterfactuals` | JSON string | 1–3 `{description}` objects. | +| `agent.explainability.methods` | JSON string | Methods applied, e.g. `["saliency","counterfactual"]`. | +| `agent.explainability.version` | string | Semver of the explainability logic. | + +### Span Events (richer payloads) + +| Event name | Fields | +|---|---| +| `agent.why.saliency` | `{modality, pass, segments: [{id, label, importance, decision_changed, original_decision, masked_decision}]}` | +| `agent.why.counterfactual_run` | `{original_input_hash, tested_variants, outcomes}` | + +--- + +## 10. SDK API + +```python +from motive import SaliencyEngine, Segment, SegmentLevel + +engine = SaliencyEngine(client=AsyncOpenAI(...), model="...", top_k=2) + +result = engine.explain( + messages=[...], # OpenAI-format message list + segments=[ + Segment(id="system", content="...", label="System prompt"), + Segment(id="user", content="...", label="User message"), + Segment(id="doc_1", content="...", label="Retrieved doc 1"), + ], + tools=[...], + tool_choice="auto", +) + +result.top # segments ranked by importance +result.summary() # one-line human-readable description +``` + +### Sampling Policy Config (TODO) + +```python +ExplainabilityConfig( + mode="sample", # off | dev_all | sample + sample_rate=0.1, + always_explain_rules=[ + OnError(), + OnTool("escalate_to_human"), + ], + async_mode=True, +) +``` + +--- + +## 11. Reference UI Component + +A React component `` that renders: +- **Highlighted text:** input context with segments coloured by saliency importance (Pass 1), with sentence-level highlights nested inside (Pass 2). +- **Factors list:** `agent.why.primary_factors` ranked by importance. +- **What-if chips:** `agent.why.counterfactuals`. + +Developer mode: raw scores. Operator mode: importance ranks only. + +--- + +## 12. Non-Functional Requirements + +| Concern | Requirement | +|---|---| +| Latency (dev) | Up to 300 ms per explained span acceptable; all occlusion calls run concurrently. | +| Latency (prod) | Default sampled + async; Pass 2 optional. | +| PII / Security | Explanations must not leak data beyond what is already in the trace. Provide a pre-emit redaction hook. | +| Extensibility | New explanation methods (e.g. SHAP, gradient-based) slot in behind the same `SaliencyResult` schema. | +| Backend compatibility | Works with any OpenAI-compatible API. No provider-specific code in core. | + +--- + +## 13. Milestones + +| Milestone | Status | Deliverable | +|---|---|---| +| M1 | ✅ Done | `Segment`, `SaliencyScore`, `SaliencyResult` types; `SaliencyEngine` with two-pass hierarchical occlusion; logprob-based scoring with binary fallback | +| M2 | Next | `CounterfactualProber` — minimal edits to top segments, flip detection, human-readable what-if output | +| M3 | — | OTEL emitter — map `SaliencyResult` to `agent.why.*` span attributes and events | +| M4 | — | `@explain_tool_call` / `@explain_plan_decision` decorators + sampling policy | +| M5 | — | Langfuse and MLflow integration tests; reference React `` | diff --git a/README.md b/README.md index 1f13839..ed57102 100644 --- a/README.md +++ b/README.md @@ -1 +1,28 @@ -# motive \ No newline at end of file +# motive + +Answer *why did the agent do that?* with empirically-grounded explanations for AI agent decisions. + +motive uses **hierarchical occlusion saliency**: mask segments of the input context, measure how much each one affects the model's decision, and surface ranked importance scores and counterfactual what-ifs. No model self-reports. No local weights required. Works with any OpenAI-compatible endpoint. + +## Installation + +```bash +pip install motive +``` + +## Quickstart + +```python +from openai import AsyncOpenAI +from motive import SaliencyEngine, Segment + +engine = SaliencyEngine( + client=AsyncOpenAI(base_url="...", api_key="..."), + model="your-model", +) + +result = engine.explain(messages=messages, segments=segments, tools=tools) +print(result.summary()) +``` + +See [`examples/demo_saliency.py`](examples/demo_saliency.py) for a full working example. diff --git a/examples/demo_saliency.py b/examples/demo_saliency.py new file mode 100644 index 0000000..a0b3dc6 --- /dev/null +++ b/examples/demo_saliency.py @@ -0,0 +1,121 @@ +"""Demo: hierarchical occlusion saliency on a support-ticket routing decision.""" + +import asyncio +import os + +from openai import AsyncOpenAI + +from motive import SaliencyEngine, Segment, SegmentLevel + + +PROXY_URL = "https://proxy.vectorinstitute.ai/v1" +API_KEY = os.environ["VECTOR_API_KEY"] +MODEL = "Qwen3-Coder-Next" + +# --- Context segments (what the agent sees when deciding which tool to call) --- + +SEGMENTS = [ + Segment( + id="system", + content="You are a support agent. Escalate tickets that are urgent or have been unresolved for more than 48 hours.", + label="System prompt", + level=SegmentLevel.DOCUMENT, + ), + Segment( + id="user_msg", + content="My payment has been failing for 3 days and my account is now locked. I need this fixed urgently.", + label="User message", + level=SegmentLevel.DOCUMENT, + ), + Segment( + id="doc_1", + content="Escalation policy: accounts locked for more than 48 hours require immediate human review. Do not attempt automated resolution.", + label="Retrieved doc: escalation policy", + level=SegmentLevel.DOCUMENT, + ), + Segment( + id="doc_2", + content="Payment retry guide: ask the user to clear browser cache and retry. Most payment failures resolve within 24 hours.", + label="Retrieved doc: payment retry guide", + level=SegmentLevel.DOCUMENT, + ), +] + +# --- Full messages for the decision call --- + +MESSAGES = [ + {"role": "system", "content": SEGMENTS[0].content}, + {"role": "user", "content": SEGMENTS[1].content}, + { + "role": "user", + "content": (f"Context documents:\n\n[Doc 1] {SEGMENTS[2].content}\n\n[Doc 2] {SEGMENTS[3].content}"), + }, +] + +# --- Tool definitions --- + +TOOLS = [ + { + "type": "function", + "function": { + "name": "escalate_to_human", + "description": "Escalate the ticket to a human support agent.", + "parameters": {"type": "object", "properties": {}, "required": []}, + }, + }, + { + "type": "function", + "function": { + "name": "send_retry_instructions", + "description": "Send automated payment retry instructions to the user.", + "parameters": {"type": "object", "properties": {}, "required": []}, + }, + }, + { + "type": "function", + "function": { + "name": "auto_resolve", + "description": "Mark the ticket as resolved automatically.", + "parameters": {"type": "object", "properties": {}, "required": []}, + }, + }, +] + + +async def main() -> None: + """Run the saliency demo.""" + client = AsyncOpenAI(base_url=PROXY_URL, api_key=API_KEY) + engine = SaliencyEngine(client=client, model=MODEL, top_k=2) + + print(f"Model: {MODEL}") + print(f"Running hierarchical occlusion on {len(SEGMENTS)} segments...\n") + + result = await engine.explain_async( + messages=MESSAGES, # type: ignore[arg-type] + segments=SEGMENTS, + tools=TOOLS, # type: ignore[arg-type] + ) + + print(f"Original decision: {result.original_decision}\n") + print("=== Pass 1: Segment-level importance ===") + doc_scores = [s for s in result.top if "__s" not in s.segment_id] + for score in doc_scores: + bar = "█" * int(score.importance * 20) + changed = " ← flipped" if score.decision_changed else "" + print(f" {score.label:<40} {score.importance:.3f} {bar}{changed}") + + print("\n=== Pass 2: Sentence-level (top segments drilled in) ===") + sent_scores = [s for s in result.top if "__s" in s.segment_id] + if sent_scores: + for score in sent_scores: + bar = "█" * int(score.importance * 20) + changed = " ← flipped" if score.decision_changed else "" + print(f" {score.label[:60]:<62} {score.importance:.3f} {bar}{changed}") + else: + print(" (no multi-sentence segments found)") + + print(f"\n{result.summary()}") + + +if __name__ == "__main__": + asyncio.run(main()) diff --git a/pyproject.toml b/pyproject.toml index cf893df..168d72e 100644 --- a/pyproject.toml +++ b/pyproject.toml @@ -5,7 +5,20 @@ description = "Answer 'why did the agent do that?' with saliency, rationale, and readme = "README.md" requires-python = ">=3.11" license = { file = "LICENSE" } -dependencies = [] +dependencies = [ + "openai>=1.82.0", + "circuit-tracer>=0.5.0", +] + +[dependency-groups] +dev = [ + "mypy>=1.19.1", + "pre-commit>=4.2.0", + "pytest>=8.3.5", + "pytest-asyncio>=0.25.3", + "pytest-cov>=6.0.0", + "ruff>=0.15.8", +] [build-system] requires = ["hatchling>=1.29.0"] @@ -13,3 +26,88 @@ build-backend = "hatchling.build" [tool.hatch.build.targets.wheel] packages = ["src/motive"] + +[tool.uv] +default-groups = ["dev"] + +[tool.pytest.ini_options] +asyncio_mode = "auto" + +[tool.mypy] +follow_imports = "normal" +ignore_missing_imports = true +install_types = true +pretty = true +non_interactive = true +allow_untyped_defs = false +no_implicit_optional = true +check_untyped_defs = true +namespace_packages = true +explicit_package_bases = true +warn_unused_configs = true +allow_subclassing_any = false +allow_untyped_calls = false +allow_incomplete_defs = false +allow_untyped_decorators = false +warn_redundant_casts = true +warn_unused_ignores = true +implicit_reexport = false +strict_equality = true +extra_checks = true + +[tool.coverage.run] +source = ["src/motive"] +omit = ["tests/*", "*__init__.py"] + +[tool.ruff] +include = ["*.py", "pyproject.toml"] +line-length = 120 + +[tool.ruff.format] +quote-style = "double" +indent-style = "space" +docstring-code-format = true + +[tool.ruff.lint] +select = [ + "A", # flake8-builtins + "B", # flake8-bugbear + "COM", # flake8-commas + "C4", # flake8-comprehensions + "RET", # flake8-return + "SIM", # flake8-simplify + "ICN", # flake8-import-conventions + "Q", # flake8-quotes + "RSE", # flake8-raise + "D", # pydocstyle + "E", # pycodestyle + "F", # pyflakes + "I", # isort + "W", # pycodestyle + "N", # pep8-naming + "ERA", # eradicate + "PL", # pylint +] +fixable = ["A", "B", "COM", "C4", "RET", "SIM", "ICN", "Q", "RSE", "D", "E", "F", "I", "W", "N", "ERA", "PL"] +ignore = [ + "B905", # zip() without strict= + "E501", # line too long + "D203", # 1 blank line required before class docstring + "D213", # Multi-line docstring summary should start at the second line + "PLR2004", # Replace magic number with named constant + "PLR0913", # Too many arguments + "COM812", # Missing trailing comma + "ERA001", # Found commented-out code +] + +[tool.ruff.lint.isort] +lines-after-imports = 2 + +[tool.ruff.lint.per-file-ignores] +"tests/*" = ["D"] + +[tool.ruff.lint.pydocstyle] +convention = "numpy" + +[tool.ruff.lint.pycodestyle] +max-doc-length = 120 diff --git a/pyrightconfig.json b/pyrightconfig.json new file mode 100644 index 0000000..248f094 --- /dev/null +++ b/pyrightconfig.json @@ -0,0 +1,7 @@ +{ + "pythonVersion": "3.12", + "venvPath": ".", + "venv": ".venv", + "include": ["src", "examples", "tests"], + "extraPaths": ["src"] +} diff --git a/src/motive/__init__.py b/src/motive/__init__.py index 92ac5d9..03b2b39 100644 --- a/src/motive/__init__.py +++ b/src/motive/__init__.py @@ -1,3 +1,8 @@ -"""motive — structured decision explainability for LLM agents.""" +"""motive — answer 'why did the agent do that?' with saliency, rationale, and counterfactuals.""" + +from motive._types import SaliencyResult, SaliencyScore, Segment, SegmentLevel # noqa: E402 +from motive.saliency import SaliencyEngine # 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+ + id: str + content: str + label: str = "" + level: SegmentLevel = SegmentLevel.DOCUMENT + + +@dataclass +class SaliencyScore: + """Importance score for a single segment.""" + + segment_id: str + label: str + importance: float # [0, 1] normalised across segments in this pass + decision_changed: bool + original_decision: str + masked_decision: str + + +@dataclass +class SaliencyResult: + """Full result of a hierarchical occlusion pass.""" + + scores: list[SaliencyScore] + original_decision: str + model: str + method: str = "hierarchical_occlusion" + + @property + def top(self) -> list[SaliencyScore]: + """Segments sorted by importance descending.""" + return sorted(self.scores, key=lambda s: s.importance, reverse=True) + + def summary(self) -> str: + """One-line human-readable summary of the top driver.""" + if not self.scores: + return "No segments scored." + best = self.top[0] + return ( + f"Decision '{self.original_decision}' was most influenced by " + f"'{best.label}' (importance {best.importance:.2f})." + ) diff --git a/src/motive/saliency.py b/src/motive/saliency.py new file mode 100644 index 0000000..937f501 --- /dev/null +++ b/src/motive/saliency.py @@ -0,0 +1,248 @@ +"""Hierarchical occlusion-based saliency engine.""" + +from __future__ import annotations + +import asyncio +import re +from typing import Any, Literal, cast + +from openai import AsyncOpenAI +from openai.types.chat import ChatCompletionMessageParam, ChatCompletionToolParam + +from ._types import SaliencyResult, SaliencyScore, Segment, SegmentLevel + + +_MASK = "[CONTENT REDACTED]" +_TOP_K_DEFAULT = 2 + +ToolChoice = Literal["auto", "required", "none"] + + +def _extract_tool_name(response: Any) -> str: + """Pull the called tool name out of a chat completion response.""" + msg = response.choices[0].message + if msg.tool_calls: + return msg.tool_calls[0].function.name + return msg.content or "" + + +def _logprob_for_tool(response: Any, tool_name: str) -> float | None: + """Sum logprobs over the tokens that spell out `tool_name` in the response. + + Tool names are multi-token (e.g. "escalate_to_human" → ["escal","ate","_to","_human"]), + so we reconstruct the full generated string, locate the tool name span, and sum + the logprobs of every token that overlaps with it. + """ + choice = response.choices[0] + if not getattr(choice, "logprobs", None): + return None + tokens_data = getattr(choice.logprobs, "content", None) or [] + if not tokens_data: + return None + + full_text = "".join(td.token for td in tokens_data) + start = full_text.find(tool_name) + if start == -1: + return None + end = start + len(tool_name) + + total_lp = 0.0 + pos = 0 + found = False + for td in tokens_data: + tok_end = pos + len(td.token) + if pos < end and tok_end > start: + total_lp += td.logprob + found = True + pos = tok_end + + return total_lp if found else None + + +def _mask_messages( + messages: list[ChatCompletionMessageParam], + segments: list[Segment], + mask_idx: int, +) -> list[ChatCompletionMessageParam]: + """Return a copy of messages with segment[mask_idx] replaced by the mask placeholder.""" + target = segments[mask_idx] + result: list[dict[str, Any]] = [] + for msg in messages: + raw = cast(dict[str, Any], msg) + content = raw.get("content") or "" + if isinstance(content, str) and target.content in content: + result.append({**raw, "content": content.replace(target.content, _MASK, 1)}) + else: + result.append(raw) + return cast(list[ChatCompletionMessageParam], result) + + +def _split_sentences(text: str) -> list[str]: + """Naive sentence splitter for agent context segments.""" + parts = re.split(r"(?<=[.!?])\s+", text.strip()) + return [p for p in parts if p.strip()] + + +def _normalise(scores: list[SaliencyScore]) -> list[SaliencyScore]: + """Scale importance values to [0, 1] relative to the max in this pass.""" + max_val = max((s.importance for s in scores), default=1.0) + if max_val == 0: + return scores + return [ + SaliencyScore( + segment_id=s.segment_id, + label=s.label, + importance=s.importance / max_val, + decision_changed=s.decision_changed, + original_decision=s.original_decision, + masked_decision=s.masked_decision, + ) + for s in scores + ] + + +class SaliencyEngine: + """Hierarchical occlusion saliency for agent tool-call decisions. + + Pass 1 masks at segment (document/message) level. + Pass 2 drills into the top_k segments at sentence level. + + Importance is the drop in log-probability of the original tool choice + when a segment is masked. Falls back to binary (flipped = 1.0, + unchanged = 0.0) when logprobs are unavailable. + """ + + def __init__( + self, + client: AsyncOpenAI, + model: str, + top_k: int = _TOP_K_DEFAULT, + ) -> None: + self.client = client + self.model = model + self.top_k = top_k + + def explain( + self, + messages: list[ChatCompletionMessageParam], + segments: list[Segment], + tools: list[ChatCompletionToolParam], + tool_choice: ToolChoice = "auto", + ) -> SaliencyResult: + """Run explanation synchronously.""" + return asyncio.run(self.explain_async(messages, segments, tools, tool_choice)) + + async def explain_async( + self, + messages: list[ChatCompletionMessageParam], + segments: list[Segment], + tools: list[ChatCompletionToolParam], + tool_choice: ToolChoice = "auto", + ) -> SaliencyResult: + """Async entry point.""" + original_resp = await self._call(messages, tools, tool_choice, logprobs=True) + original_tool = _extract_tool_name(original_resp) + original_lp = _logprob_for_tool(original_resp, original_tool) + + # Pass 1: segment level + pass1 = await self._occlusion_pass(messages, segments, tools, tool_choice, original_tool, original_lp) + pass1 = _normalise(pass1) + + # Pass 2: sentence level on top_k segments that actually had non-zero importance + top_ids = { + s.segment_id + for s in sorted(pass1, key=lambda s: s.importance, reverse=True)[: self.top_k] + if s.importance > 0 + } + pass2: list[SaliencyScore] = [] + for seg in segments: + if seg.id not in top_ids: + continue + sentences = _split_sentences(seg.content) + if len(sentences) <= 1: + continue + sub_segments = [ + Segment( + id=f"{seg.id}__s{i}", + content=s, + label=s[:80], + level=SegmentLevel.SENTENCE, + ) + for i, s in enumerate(sentences) + ] + sub_scores = await self._occlusion_pass( + messages, sub_segments, tools, tool_choice, original_tool, original_lp + ) + pass2.extend(_normalise(sub_scores)) + + return SaliencyResult( + scores=pass1 + pass2, + original_decision=original_tool, + model=self.model, + ) + + async def _occlusion_pass( + self, + messages: list[ChatCompletionMessageParam], + segments: list[Segment], + tools: list[ChatCompletionToolParam], + tool_choice: ToolChoice, + original_tool: str, + original_lp: float | None, + ) -> list[SaliencyScore]: + tasks = [ + self._score_segment(messages, segments, i, tools, tool_choice, original_tool, original_lp) + for i in range(len(segments)) + ] + return list(await asyncio.gather(*tasks)) + + async def _score_segment( + self, + messages: list[ChatCompletionMessageParam], + segments: list[Segment], + mask_idx: int, + tools: list[ChatCompletionToolParam], + tool_choice: ToolChoice, + original_tool: str, + original_lp: float | None, + ) -> SaliencyScore: + masked_msgs = _mask_messages(messages, segments, mask_idx) + resp = await self._call(masked_msgs, tools, tool_choice, logprobs=True) + masked_tool = _extract_tool_name(resp) + + if original_lp is not None: + masked_lp = _logprob_for_tool(resp, original_tool) + importance = ( + max(0.0, original_lp - masked_lp) + if masked_lp is not None + else (1.0 if masked_tool != original_tool else 0.0) + ) + else: + importance = 1.0 if masked_tool != original_tool else 0.0 + + seg = segments[mask_idx] + return SaliencyScore( + segment_id=seg.id, + label=seg.label or seg.id, + importance=importance, + decision_changed=masked_tool != original_tool, + original_decision=original_tool, + masked_decision=masked_tool, + ) + + async def _call( + self, + messages: list[ChatCompletionMessageParam], + tools: list[ChatCompletionToolParam], + tool_choice: ToolChoice, + logprobs: bool = False, + ) -> Any: + return await self.client.chat.completions.create( + model=self.model, + messages=messages, + tools=tools, + tool_choice=tool_choice, + logprobs=logprobs, + top_logprobs=5 if logprobs else None, + temperature=0, + ) diff --git a/tests/__init__.py b/tests/__init__.py new file mode 100644 index 0000000..e69de29 diff --git a/tests/__pycache__/__init__.cpython-312.pyc 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zBrz0d2kIBVg1VC#dO!et$qrdF>2#dr!5S$7EoDCi??*Uzia^y2apP@lQ7P2>o4KXg%MO6T2lr-|h0wW9b2ljDk zVPGp(v}KjALKoIQ0I20hJNDe|d$(_S$Klz~TI4YGSRbCBsP$Mw0dqNnzquSaEZolw zfjXZ19Q6`WH}&4K-ZNX6djs z>Ikk0JwZP=hHo|}nY zv#v&>JKyS|e90mQXWSP>X1P7!{`=WHrC<^&^!AGv~6Lr~*@PY(`5LcghF=bXndYazhU zW8fb6!Z9Y`a*qv+V{46u85%U85Y-8$#-QCn@&K%@G#JhZ%=C0F&%U*`w$@<$sL`or z-30)CmX3q-R#_jub88uo28YW>h7sbi+;Al_ysG1Gg_p3b zfE9gs_BzWgyJ!{ATdZ`6R&?umkM{1oN^^~wCtV%)fDQyZz$c;LSG zLu?=A#to^jQCa*po%h?Y?E>|MdgIxCer*a9M3@%f(Sbny&rSt$KG~*bg}r++X_7ce zOeVp(7|SLSsTutL3i#IT!>zl;_qJ&>H<_)CilAF`Ly)l}&+Qrk( zEXq5nD0jXNjj=8Z$F`E+wRGRWEr>cpts<#{ z1#U3hb=^KY4K^m-X@q*+!yCQsl((V7g%l8c2f=Lwe}Lc*5sV^m-SH-75umbUJq6&l z^gwCS!gF2g5+v)p(JQ|mRn5v&@OpNsCaL$Mf!(Zp<>e|${8 zrLyDTPme_r`mbmiTYnn5(OUCgfiS+ULU-&_XmlmQ2i!Sg zYvZYGJZG!d;Q!lTr%iZX$_^Un MagicMock: + """Build a minimal fake chat completion response with a tool call.""" + tool_call = MagicMock() + tool_call.function.name = tool_name + + msg = MagicMock() + msg.tool_calls = [tool_call] + msg.content = None + + choice = MagicMock() + choice.message = msg + + if logprobs is not None: + lp_content = [] + for entry in logprobs: + td = MagicMock() + td.token = entry["token"] + td.logprob = entry["logprob"] + td.top_logprobs = [] + lp_content.append(td) + lp = MagicMock() + lp.content = lp_content + choice.logprobs = lp + else: + choice.logprobs = None + + resp = MagicMock() + resp.choices = [choice] + return resp + + +def _text_response(text: str) -> MagicMock: + """Build a response with no tool call (text only).""" + msg = MagicMock() + msg.tool_calls = None + msg.content = text + + choice = MagicMock() + choice.message = msg + choice.logprobs = None + + resp = MagicMock() + resp.choices = [choice] + return resp + + +# --------------------------------------------------------------------------- +# _extract_tool_name +# --------------------------------------------------------------------------- + + +class TestExtractToolName: + def test_returns_tool_name_from_tool_call(self) -> None: + resp = _tool_response("escalate_to_human") + assert _extract_tool_name(resp) == "escalate_to_human" + + def test_falls_back_to_content_when_no_tool_calls(self) -> None: + resp = _text_response("send_reminder") + assert _extract_tool_name(resp) == "send_reminder" + + def test_empty_content_returns_empty_string(self) -> None: + msg = MagicMock() + msg.tool_calls = None + msg.content = None + resp = MagicMock() + resp.choices = [MagicMock(message=msg)] + assert _extract_tool_name(resp) == "" + + +# --------------------------------------------------------------------------- +# _logprob_for_tool +# --------------------------------------------------------------------------- + + +class TestLogprobForTool: + def test_single_token_match(self) -> None: + logprobs = [{"token": "escalate_to_human", "logprob": -0.5}] + resp = _tool_response("escalate_to_human", logprobs) + result = _logprob_for_tool(resp, "escalate_to_human") + assert result == pytest.approx(-0.5) + + def test_multi_token_match_sums_logprobs(self) -> None: + # "escalate_to_human" split as "escal" + "ate" + "_to" + "_human" + logprobs = [ + {"token": "", "logprob": -0.01}, + ] + resp = _tool_response("escalate_to_human", logprobs) + result = _logprob_for_tool(resp, "escalate_to_human") + assert result == pytest.approx(-0.1 + -0.2 + -0.3 + -0.4) + + def test_returns_none_when_no_logprobs(self) -> None: + resp = _tool_response("tool_a") + assert _logprob_for_tool(resp, "tool_a") is None + + def test_returns_none_when_tool_name_not_in_tokens(self) -> None: + logprobs = [{"token": "something_else", "logprob": -1.0}] + resp = _tool_response("tool_a", logprobs) + assert _logprob_for_tool(resp, "tool_a") is None + + def test_returns_none_when_logprob_content_empty(self) -> None: + resp = _tool_response("tool_a", logprobs=[]) + assert _logprob_for_tool(resp, "tool_a") is None + + +# --------------------------------------------------------------------------- +# _mask_messages +# --------------------------------------------------------------------------- + + +class TestMaskMessages: + def _segments(self) -> list[Segment]: + return [ + Segment(id="system", content="You are a helpful agent."), + Segment(id="user", content="My account is locked."), + Segment(id="doc_1", content="Locked accounts require human review."), + ] + + def test_masks_target_segment_content(self) -> None: + segments = self._segments() + messages = [ + {"role": "system", "content": "You are a helpful agent."}, + {"role": "user", "content": "My account is locked."}, + ] + masked = _mask_messages(messages, segments, mask_idx=0) + assert "[CONTENT REDACTED]" in masked[0]["content"] + assert "You are a helpful agent." not in masked[0]["content"] + + def test_leaves_other_messages_unchanged(self) -> None: + segments = self._segments() + messages = [ + {"role": "system", "content": "You are a helpful agent."}, + {"role": "user", "content": "My account is locked."}, + ] + masked = _mask_messages(messages, segments, mask_idx=0) + assert masked[1]["content"] == "My account is locked." + + def test_masks_correct_index(self) -> None: + segments = self._segments() + messages = [ + {"role": "user", "content": "My account is locked. Locked accounts require human review."}, + ] + masked = _mask_messages(messages, segments, mask_idx=2) + assert "Locked accounts require human review." not in masked[0]["content"] + assert "My account is locked." in masked[0]["content"] + + def test_no_match_leaves_messages_unchanged(self) -> None: + segments = self._segments() + messages = [{"role": "user", "content": "Something completely different."}] + masked = _mask_messages(messages, segments, mask_idx=0) + assert masked[0]["content"] == "Something completely different." + + def test_returns_new_list_not_mutating_original(self) -> None: + segments = self._segments() + messages = [{"role": "system", "content": "You are a helpful agent."}] + original_content = messages[0]["content"] + _mask_messages(messages, segments, mask_idx=0) + assert messages[0]["content"] == original_content + + +# --------------------------------------------------------------------------- +# _split_sentences +# --------------------------------------------------------------------------- + + +class TestSplitSentences: + def test_splits_on_period(self) -> None: + result = _split_sentences("First sentence. Second sentence.") + assert len(result) == 2 + assert result[0] == "First sentence." + + def test_splits_on_exclamation(self) -> None: + result = _split_sentences("Watch out! Be careful.") + assert len(result) == 2 + + def test_splits_on_question_mark(self) -> None: + result = _split_sentences("Is this right? Yes it is.") + assert len(result) == 2 + + def test_single_sentence_returns_one_item(self) -> None: + result = _split_sentences("Just one sentence here.") + assert result == ["Just one sentence here."] + + def test_empty_string_returns_empty_list(self) -> None: + result = _split_sentences("") + assert result == [] + + def test_filters_whitespace_only_parts(self) -> None: + result = _split_sentences(" First. Second. ") + assert all(s.strip() for s in result) + + +# --------------------------------------------------------------------------- +# _normalise +# --------------------------------------------------------------------------- + + +class TestNormalise: + def _score(self, sid: str, importance: float) -> SaliencyScore: + return SaliencyScore( + segment_id=sid, + label=sid, + importance=importance, + decision_changed=importance > 0, + original_decision="tool_a", + masked_decision="tool_b" if importance > 0 else "tool_a", + ) + + def test_max_becomes_one(self) -> None: + scores = [self._score("a", 4.0), self._score("b", 2.0), self._score("c", 1.0)] + normalised = _normalise(scores) + assert max(s.importance for s in normalised) == pytest.approx(1.0) + + def test_relative_order_preserved(self) -> None: + scores = [self._score("a", 3.0), self._score("b", 1.0), self._score("c", 0.0)] + normalised = _normalise(scores) + by_id = {s.segment_id: s.importance for s in normalised} + assert by_id["a"] > by_id["b"] > by_id["c"] + + def test_all_zeros_unchanged(self) -> None: + scores = [self._score("a", 0.0), self._score("b", 0.0)] + normalised = _normalise(scores) + assert all(s.importance == 0.0 for s in normalised) + + def test_empty_list_returns_empty(self) -> None: + assert _normalise([]) == [] + + def test_preserves_segment_ids(self) -> None: + scores = [self._score("x", 2.0), self._score("y", 1.0)] + normalised = _normalise(scores) + assert {s.segment_id for s in normalised} == {"x", "y"} + + +# --------------------------------------------------------------------------- +# SaliencyEngine — async, mocked client +# --------------------------------------------------------------------------- + + +def _make_engine() -> tuple[SaliencyEngine, AsyncMock]: + """Return an engine wired to a mock OpenAI client.""" + client = MagicMock() + client.chat = MagicMock() + client.chat.completions = MagicMock() + client.chat.completions.create = AsyncMock() + return SaliencyEngine(client=client, model="test-model", top_k=2), client + + +def _sample_segments() -> list[Segment]: + return [ + Segment(id="system", content="You are a support agent. Escalate urgent issues.", label="System prompt"), + Segment(id="user_msg", content="My account is locked for 3 days.", label="User message"), + Segment(id="doc_1", content="Locked accounts require human review.", label="Escalation policy"), + ] + + +def _sample_messages() -> list[dict]: + return [ + {"role": "system", "content": "You are a support agent. Escalate urgent issues."}, + {"role": "user", "content": "My account is locked for 3 days. Locked accounts require human review."}, + ] + + +def _sample_tools() -> list[dict]: + return [ + { + "type": "function", + "function": { + "name": "escalate_to_human", + "description": "Escalate.", + "parameters": {"type": "object", "properties": {}, "required": []}, + }, + }, + { + "type": "function", + "function": { + "name": "send_reminder", + "description": "Send reminder.", + "parameters": {"type": "object", "properties": {}, "required": []}, + }, + }, + ] + + +@pytest.mark.asyncio +class TestSaliencyEngineAsync: + async def test_returns_saliency_result_with_correct_decision(self) -> None: + engine, client = _make_engine() + # All calls return the same tool — no flips + client.chat.completions.create.return_value = _tool_response("escalate_to_human") + + result = await engine.explain_async( + messages=_sample_messages(), # type: ignore[arg-type] + segments=_sample_segments(), + tools=_sample_tools(), # type: ignore[arg-type] + ) + + assert result.original_decision == "escalate_to_human" + assert result.model == "test-model" + + async def test_flipped_segment_gets_high_importance(self) -> None: + engine, client = _make_engine() + segments = _sample_segments() + responses = [] + # Original call + responses.append(_tool_response("escalate_to_human")) + # Masking system → same + responses.append(_tool_response("escalate_to_human")) + # Masking user_msg → flips + responses.append(_tool_response("send_reminder")) + # Masking doc_1 → same + responses.append(_tool_response("escalate_to_human")) + + client.chat.completions.create.side_effect = responses + + result = await engine.explain_async( + messages=_sample_messages(), # type: ignore[arg-type] + segments=segments, + tools=_sample_tools(), # type: ignore[arg-type] + ) + + pass1 = [s for s in result.scores if "__s" not in s.segment_id] + by_id = {s.segment_id: s for s in pass1} + assert by_id["user_msg"].decision_changed is True + assert by_id["user_msg"].importance == pytest.approx(1.0) + assert by_id["system"].decision_changed is False + + async def test_logprob_drop_used_as_importance_when_available(self) -> None: + engine, client = _make_engine() + segments = [ + Segment(id="seg_a", content="important context", label="A"), + Segment(id="seg_b", content="irrelevant context", label="B"), + ] + messages = [{"role": "user", "content": "important context irrelevant context"}] + + lp_tokens = [ + {"token": "tool", "logprob": -0.01}, + {"token": "_a", "logprob": -0.01}, + ] + # Original: logprob of "tool_a" = -0.02 + original_resp = _tool_response("tool_a", lp_tokens) + # Mask seg_a: logprob drops to -1.5 → importance = -0.02 - (-1.5) = 1.48 + masked_a = _tool_response("tool_a", [{"token": "tool", "logprob": -0.8}, {"token": "_a", "logprob": -0.7}]) + # Mask seg_b: logprob barely changes → low importance + masked_b = _tool_response("tool_a", [{"token": "tool", "logprob": -0.01}, {"token": "_a", "logprob": -0.02}]) + + client.chat.completions.create.side_effect = [original_resp, masked_a, masked_b] + + result = await engine.explain_async( + messages=messages, # type: ignore[arg-type] + segments=segments, + tools=_sample_tools(), # type: ignore[arg-type] + ) + + pass1 = [s for s in result.scores if "__s" not in s.segment_id] + by_id = {s.segment_id: s for s in pass1} + assert by_id["seg_a"].importance > by_id["seg_b"].importance + + async def test_no_pass2_when_all_segments_zero_importance(self) -> None: + engine, client = _make_engine() + # Every call returns the same tool — no flips, no logprobs → all zero + client.chat.completions.create.return_value = _tool_response("escalate_to_human") + + result = await engine.explain_async( + messages=_sample_messages(), # type: ignore[arg-type] + segments=_sample_segments(), + tools=_sample_tools(), # type: ignore[arg-type] + ) + + sentence_scores = [s for s in result.scores if "__s" in s.segment_id] + assert sentence_scores == [] + + async def test_pass2_only_drills_into_nonzero_segments(self) -> None: + engine, client = _make_engine() + segments = [ + Segment(id="system", content="You are an agent.", label="System"), + Segment(id="user_msg", content="Urgent issue. Account locked.", label="User"), + ] + messages = [ + {"role": "system", "content": "You are an agent."}, + {"role": "user", "content": "Urgent issue. Account locked."}, + ] + + responses = [] + # Original + responses.append(_tool_response("escalate_to_human")) + # Pass 1: mask system → same; mask user_msg → flips + responses.append(_tool_response("escalate_to_human")) + responses.append(_tool_response("send_reminder")) + # Pass 2: two sentences in user_msg + responses.append(_tool_response("escalate_to_human")) # mask "Urgent issue." + responses.append(_tool_response("send_reminder")) # mask "Account locked." + + client.chat.completions.create.side_effect = responses + + result = await engine.explain_async( + messages=messages, # type: ignore[arg-type] + segments=segments, + tools=_sample_tools(), # type: ignore[arg-type] + ) + + sentence_scores = [s for s in result.scores if "__s" in s.segment_id] + # Only user_msg should be drilled (system had 0 importance) + assert all(s.segment_id.startswith("user_msg") for s in sentence_scores) + assert len(sentence_scores) == 2 + + async def test_call_count_is_1_plus_n_segments_for_pass1(self) -> None: + engine, client = _make_engine() + segments = _sample_segments() # 3 segments + client.chat.completions.create.return_value = _tool_response("escalate_to_human") + + await engine.explain_async( + messages=_sample_messages(), # type: ignore[arg-type] + segments=segments, + tools=_sample_tools(), # type: ignore[arg-type] + ) + + # 1 original + 3 masked = 4 calls minimum (plus any pass2 calls) + assert client.chat.completions.create.call_count >= 4 + + +class TestSaliencyEngineSync: + def test_explain_sync_returns_result(self) -> None: + engine, client = _make_engine() + client.chat.completions.create.return_value = _tool_response("escalate_to_human") + + result = engine.explain( + messages=_sample_messages(), # type: ignore[arg-type] + segments=_sample_segments(), + tools=_sample_tools(), # type: ignore[arg-type] + ) + + assert result.original_decision == "escalate_to_human" + assert len(result.scores) > 0 diff --git a/tests/test_types.py b/tests/test_types.py new file mode 100644 index 0000000..778516f --- /dev/null +++ b/tests/test_types.py @@ -0,0 +1,83 @@ +"""Tests for motive._types.""" + +import pytest + +from motive._types import SaliencyResult, SaliencyScore, Segment, SegmentLevel + + +def _score(segment_id: str, importance: float, changed: bool = False) -> SaliencyScore: + return SaliencyScore( + segment_id=segment_id, + label=segment_id, + importance=importance, + decision_changed=changed, + original_decision="tool_a", + masked_decision="tool_b" if changed else "tool_a", + ) + + +class TestSegment: + def test_defaults(self) -> None: + seg = Segment(id="s1", content="hello") + assert seg.label == "" + assert seg.level == SegmentLevel.DOCUMENT + + def test_explicit_fields(self) -> None: + seg = Segment(id="s1", content="hello", label="My label", level=SegmentLevel.SENTENCE) + assert seg.label == "My label" + assert seg.level == SegmentLevel.SENTENCE + + def test_segment_level_values(self) -> None: + assert SegmentLevel.DOCUMENT == "document" + assert SegmentLevel.SENTENCE == "sentence" + assert SegmentLevel.TOKEN == "token" + + +class TestSaliencyScore: + def test_fields(self) -> None: + score = _score("doc_1", 0.9, changed=True) + assert score.segment_id == "doc_1" + assert score.importance == pytest.approx(0.9) + assert score.decision_changed is True + assert score.masked_decision == "tool_b" + + def test_unchanged(self) -> None: + score = _score("doc_2", 0.1, changed=False) + assert score.decision_changed is False + assert score.original_decision == score.masked_decision + + +class TestSaliencyResult: + def _result(self) -> SaliencyResult: + return SaliencyResult( + scores=[ + _score("user_msg", 1.0, changed=True), + _score("system", 0.0), + _score("doc_1", 0.5), + ], + original_decision="escalate_to_human", + model="test-model", + ) + + def test_top_sorted_descending(self) -> None: + result = self._result() + importances = [s.importance for s in result.top] + assert importances == sorted(importances, reverse=True) + + 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