diff --git a/.apm/skills/benchmark-qed-autoe/SKILL.md b/.apm/skills/benchmark-qed-autoe/SKILL.md index 4075e87..ed7b9da 100644 --- a/.apm/skills/benchmark-qed-autoe/SKILL.md +++ b/.apm/skills/benchmark-qed-autoe/SKILL.md @@ -32,6 +32,7 @@ uvx --from "git+https://github.com/microsoft/benchmark-qed" benchmark-qed [OPTIONS] ``` -**Config requires**: `reference`, `generated` (list), `criteria`, `score_min`/`score_max`, `trials`, `llm_config` +**Config requires**: `reference`, `generated` (list), `criteria`, `score_min`/`score_max`, `trials`, `llm_config`. Generated configs include a persistent SQLite `cache_config`. Default criteria: `correctness`, `completeness`. Default score range: 1–10. **Output**: `reference_scores-{name}.csv`, `model_usage.json` +**Reference caching**: + +- Each question/criterion/trial is cached separately under the `reference` + namespace. +- Cache identity includes both answers, score range, prompts, model/provider, + call arguments, and score-ID mode. +- Rerun after a failure to reuse completed judgments. +- Use `cache_config: {type: none, storage: null}` for fresh judgments. +- Inspect the default cache with + `benchmark-qed cache inspect .benchmark_qed_cache/reference/reference.sqlite3`. + ### 3. Assertion Scores Evaluate RAG methods using assertion-based scoring. Auto-detects single-RAG vs multi-RAG config. @@ -99,6 +140,20 @@ uvx --from "git+https://github.com/microsoft/benchmark-qed" benchmark-qed autoe **Multi-RAG output**: Per-method scores + significance tests in structured `output_dir/` +**Single-RAG assertion caching**: + +- Generated configs use SQLite at + `.benchmark_qed_cache/assertion/assertion.sqlite3`. +- Each question/assertion/trial is cached independently; the identity also + includes the answer, prompts, model/provider, call arguments, and score-ID + mode. +- Rerun after a failure to reuse completed judgments. +- Inspect with `benchmark-qed cache inspect + .benchmark_qed_cache/assertion/assertion.sqlite3`. +- Use `cache_config: {type: none, storage: null}` for fresh judgments. +- This cache is not used by multi-RAG or hierarchical assertion scoring. + Chunk-assertion scoring has a separate assertion/chunk cache. + ### 4. Hierarchical Assertion Scores Score hierarchical assertions (global assertions with supporting local assertions). @@ -177,6 +232,17 @@ For comparing multiple RAG methods, use multi-RAG config format (include `rag_me - **Config auto-detection**: `assertion-scores` and `hierarchical-assertion-scores` detect single vs multi-RAG based on the `rag_methods` key in YAML. Ensure your config matches your intent. - **Trials must be even**: For pairwise scores, `trials` must be even (for counterbalancing). Use 4 as default. +- **Pairwise cache identity**: Questions, answers and labels, criterion, trial, + prompts, model/provider, call arguments, and score-ID mode determine reuse. + Changing any of them creates a cache miss. +- **Blob storage does not relocate SQLite caches**: Pairwise + `cache_config.storage.base_dir` remains a local path even when input/output + storage uses Azure Blob Storage. +- **Cache storage is independent**: JSON caches use the storage configured + under `cache_config.storage`; they do not inherit input/output blob settings. + `type: file` resolves a relative `base_dir` from the command's working + directory. Use `cache_config.storage.type: blob` with an explicit container + and credentials to store JSON entries in Azure Blob Storage. - **Stale outputs**: Several commands skip existing output files. Use a fresh output directory or delete specific files to force re-evaluation. - **Output is in files**: All scores are written to CSV/JSON files. Parse output files, not CLI stdout. - **Long-running**: Evaluation with many questions and trials can take hours. Use background execution. diff --git a/.apm/skills/benchmark-qed-setup/SKILL.md b/.apm/skills/benchmark-qed-setup/SKILL.md index d3c64a6..2fd56ea 100644 --- a/.apm/skills/benchmark-qed-setup/SKILL.md +++ b/.apm/skills/benchmark-qed-setup/SKILL.md @@ -44,7 +44,8 @@ uvx --from "git+https://github.com/microsoft/benchmark-qed" benchmark-qed init < ``` This walks through: -- Config type selection (autoq, autoe_pairwise, autoe_reference, autoe_assertion) + +- Config type selection (autoq, autoe_pairwise, autoe_differential_pairwise, autoe_reference, autoe_assertion) - LLM provider selection with Azure-specific prompts (endpoint, API version) - Section-by-section customization (press Enter to accept defaults) - Automatic YAML validation before writing @@ -62,6 +63,7 @@ uvx --from "git+https://github.com/microsoft/benchmark-qed" benchmark-qed config |------|---------| | `autoq` | Question generation (includes all prompt templates) | | `autoe_pairwise` | Pairwise comparison evaluation | +| `autoe_differential_pairwise` | Two-stage extract-and-judge pairwise evaluation | | `autoe_reference` | Reference-based scoring | | `autoe_assertion` | Assertion-based scoring | @@ -172,9 +174,18 @@ Ask the same shape of questions for the embedding model: #### Eval-config-specific fields (autoe_*) Only ask the questions relevant to the chosen `config_type`: -- `autoe_pairwise`: `base.name` + `base.answer_base_path`, plus a list of `others` (each with `name` and `answer_base_path`), and `question_sets`. -- `autoe_reference`: `reference.name` + `reference.answer_base_path`, list of `generated`, and `question_sets`. -- `autoe_assertion`: in single-RAG mode, `generated.name` + `generated.answer_base_path` and `assertions.assertions_path`. In multi-RAG mode (`rag_methods` provided), ask for `input_dir`, `output_dir`, `rag_methods` list, and `question_sets`. + +- `autoe_pairwise`: `base.name` + `base.answer_base_path`, plus a list of `others` (each with `name` and `answer_base_path`), `question_sets`, and whether to keep the default persistent SQLite `cache_config` or select `json`, `memory`, or `none`. +- `autoe_differential_pairwise`: the same condition and question-set fields as + pairwise, plus whether to keep the default stage-aware SQLite cache. Explain + that extraction and verdict entries are stored independently. +- `autoe_reference`: `reference.name` + `reference.answer_base_path`, a list of `generated` conditions with `name` and `answer_base_path`, and whether to keep the default persistent SQLite `cache_config` or select another backend. +- `autoe_assertion`: in single-RAG mode, `generated.name` + + `generated.answer_base_path`, `assertions.assertions_path`, and whether to + keep the default persistent SQLite `cache_config` or select `json`, `memory`, + or `none`. Explain that this cache does not apply to multi-RAG or + hierarchical scoring. In multi-RAG mode (`rag_methods` provided), ask for + `input_dir`, `output_dir`, `rag_methods` list, and `question_sets`. #### Storage fields (all config types, optional) @@ -224,6 +235,7 @@ input: ``` **Rules when writing the YAML:** + - Omit `api_key` entirely when `auth_type=azure_managed_identity` — do not leave `${OPENAI_API_KEY}` in place. - Omit `init_args` for non-Azure providers. - Quote `api_version` (it would otherwise be parsed as a date). @@ -244,9 +256,11 @@ After writing `settings.yaml`, **show the user the generated configuration** and Do **not** limit the user to predefined sections — they should be able to modify any field in `settings.yaml` by describing what they want. **Sections the user is most likely to customize** (call these out): + - **autoq**: `num_questions` per type, `num_clusters`, `chunk_size`, assertion settings, `concurrent_requests` -- **autoe_pairwise**: `trials`, `criteria`, `question_sets` -- **autoe_reference**: `score_min`/`score_max`, `trials` +- **autoe_pairwise**: `trials`, `criteria`, `question_sets`, `cache_config` +- **autoe_differential_pairwise**: `trials`, `criteria`, four extract/judge prompts, `cache_config` +- **autoe_reference**: `score_min`/`score_max`, `trials`, `criteria`, `cache_config` - **autoe_assertion**: `pass_threshold`, `trials` For the full set of optional fields and best practices, read [references/config-reference.md](references/config-reference.md). @@ -260,6 +274,7 @@ The benchmark-qed CLI validates `settings.yaml` via pydantic at startup, so any See [references/config-reference.md](references/config-reference.md) for detailed best practices covering LLM configuration, prompts, question generation, assertion generation, evaluation, and retrieval. Key highlights: + - Use `${OPENAI_API_KEY}` env var substitution — never hardcode secrets - Use `benchmark-qed init` (interactive wizard) to avoid manual YAML errors - Pin a specific version of benchmark-qed for reproducibility in CI/CD @@ -273,6 +288,14 @@ Key highlights: - Prompts are copied as `.txt` files using Python `string.Template` syntax (`$variable` or `${variable}`). - **`prompt_config` key**: The runtime expects `prompt_config` (singular) for all autoe config types. Both `benchmark-qed init` and `config init` now generate the correct key. If you hand-edit YAML, ensure you use `prompt_config`, not `prompts_config`. - **`config init --storage-type blob`**: When combined with `--account-url` or `--connection-string`, the command uploads the generated `settings.yaml` and prompt files directly to blob storage. Without those auth options, it only scaffolds the storage YAML sections locally. +- **Pairwise cache location**: The generated SQLite cache path is local even + when pairwise input/output storage uses Azure Blob Storage. Set + `cache_config.type: none` to disable caching or choose another supported + backend explicitly. +- **Assertion cache scope**: The generated `autoe_assertion` SQLite cache + applies to standard single-RAG answer-level scoring only. Multi-RAG and + hierarchical scoring do not use it; chunk-assertion scoring uses a separate + cache. - **Blob URI format**: CLI commands accept `blob:///` for config paths. The CLI downloads the config and all sibling files (prompt templates) to a temp directory so relative paths resolve correctly. Credentials can be passed via `--account-url`/`--connection-string` or the environment variables `AZURE_STORAGE_ACCOUNT_URL`/`AZURE_STORAGE_CONNECTION_STRING`. - **Storage config in YAML**: AutoQ uses `input.storage` (nested under `input`) and `output_storage` (top-level). AutoE uses `input_storage` and `output_storage` (both top-level). When storage is omitted, local filesystem is used. - **graphrag-llm backend**: As of the graphrag-llm migration, built-in providers (OpenAI, Azure OpenAI, Azure AI Inference) are served by `graphrag-llm`'s LiteLLM-backed factory. Custom providers must implement the `graphrag_llm.completion.LLMCompletion` or `graphrag_llm.embedding.LLMEmbedding` interfaces (the older `benchmark_qed.llm.type.base.ChatModel` / `EmbeddingModel` Protocols have been removed). See [references/config-reference.md](references/config-reference.md#custom-llm-providers). diff --git a/.apm/skills/benchmark-qed-setup/references/config-reference.md b/.apm/skills/benchmark-qed-setup/references/config-reference.md index 1a73f86..c797e2f 100644 --- a/.apm/skills/benchmark-qed-setup/references/config-reference.md +++ b/.apm/skills/benchmark-qed-setup/references/config-reference.md @@ -73,6 +73,7 @@ azure_identity_scopes: | `azure_identity_scopes` | `list[str]` | `["https://cognitiveservices.azure.com/.default"]` | OAuth scopes passed to `get_bearer_token_provider`. Only used when `auth_type` is `azure_managed_identity`. | **When to change this:** + - The default scope (`https://cognitiveservices.azure.com/.default`) works for standard Azure OpenAI deployments - Use a custom scope if your Azure resource requires a different audience (e.g., private endpoints, sovereign clouds) - Multiple scopes can be listed if your deployment requires more than one @@ -215,12 +216,58 @@ criteria: # Default: comprehensiveness, diversity, em description: "..." trials: 4 # Must be even (counterbalancing) + +cache_config: + type: sqlite # sqlite, json, memory, or none + storage: + type: file + base_dir: .benchmark_qed_cache/pairwise + database_name: pairwise.sqlite3 + llm_config: ... # Same LLM config structure as above prompt_config: user_prompt: prompts/pairwise_user.txt system_prompt: prompts/pairwise_system.txt ``` +Pairwise caching stores each question/criterion/trial independently. Cache +identity includes the answers and labels, criterion, trial, prompt templates, +model/provider, call arguments, and score-ID mode. The default SQLite cache +supports interruption recovery and cross-process work coordination. Use +`type: none` with `storage: null` for fresh judgments. Existing output CSVs are +separate: remove or relocate them as well when fully recomputing a comparison. +The SQLite `base_dir` remains local when input/output storage is Azure Blob. + +## autoe Differential Pairwise Configuration (`DifferentialPairwiseConfig`) + +Differential pairwise uses the same `base`, `others`, `question_sets`, +`criteria`, and even `trials` fields as standard pairwise, with four prompt +entries for its extract-and-judge stages. + +```yaml +cache_config: + type: sqlite + storage: + type: file + base_dir: .benchmark_qed_cache/differential_pairwise + database_name: differential_pairwise.sqlite3 + +prompt_config: + extract_system_prompt: + prompt: prompts/pairwise_extract_system_prompt.txt + extract_user_prompt: + prompt: prompts/pairwise_extract_user_prompt.txt + judge_system_prompt: + prompt: prompts/pairwise_unique_judge_system_prompt.txt + judge_user_prompt: + prompt: prompts/pairwise_unique_judge_user_prompt.txt +``` + +The cache stores extraction and verdict stages in separate namespaces. Judge +failures reuse completed extraction; criteria and judge-prompt changes +invalidate verdicts only. Trials remain separate. Use `type: none` with +`storage: null` to disable both stages. + ## autoe Reference Configuration (`ReferenceConfig`) ```yaml reference: @@ -238,8 +285,19 @@ criteria: # Default: correctness, completeness score_min: 1 score_max: 10 trials: 4 # Default is 4 (not 3) + +cache_config: + type: sqlite + storage: + type: file + base_dir: .benchmark_qed_cache/reference + database_name: reference.sqlite3 ``` +Reference cache entries are specific to the question, both answers, criterion, +trial/order, score range, prompts, model/provider, call arguments, and score-ID +mode. Use `type: none` with `storage: null` when fresh judgments are required. + ## autoe Assertion Configuration ### Single-RAG (`AssertionConfig`) @@ -253,8 +311,29 @@ assertions: pass_threshold: 0.5 trials: 4 # Default is 4 (not 3) + +cache_config: + type: sqlite + storage: + type: file + base_dir: .benchmark_qed_cache/assertion + database_name: assertion.sqlite3 ``` +The standard single-RAG assertion cache stores each +question/answer/assertion/trial judgment independently. Prompt templates, +model/provider configuration, call arguments, and score-ID mode also affect +identity. Use `type: none` with `storage: null` for fresh judgments. This cache +does not apply to multi-RAG or hierarchical assertion scoring and is distinct +from the chunk-assertion cache. + +Changing an Azure endpoint or API version under `llm_config.init_args`, the +model/provider, call arguments, prompt contents, or judgment inputs produces +new entries. Previous entries are retained and can be reused by restoring the +old configuration. Concurrency, retry settings, `pass_threshold`, `top_k`, and +credentials do not affect identity. Changing the cache path or database selects +a different store; it does not remove the original cache. + ### Multi-RAG (`MultiRAGAssertionConfig`) ```yaml input_dir: ./data # REQUIRED @@ -350,7 +429,8 @@ relevance_threshold: 2 # Min relevance score for a text unit assessor_type: rationale # rationale or bing concurrent_requests: 16 max_questions: null # null = process all questions -cache_dir: null # Optional cache directory +cache_config: + type: none # sqlite, json, memory, or none embedding_config: ... # LLM config for generating embeddings (if needed) @@ -383,7 +463,8 @@ context_id_key: chunk_id # Key for chunk ID in retrieval results context_text_key: text # Key for chunk text in retrieval results cluster_match_by: text # Field to match clusters -cache_dir: null # Optional cache directory +cache_config: + type: none # sqlite, json, memory, or none run_significance_test: true significance_alpha: 0.05 @@ -404,6 +485,7 @@ significance_correction: holm ## Custom LLM Providers Built-in providers are served by `graphrag-llm`'s LiteLLM-backed factory. To register a custom provider, point benchmark-qed at a class implementing the `graphrag_llm` interface: + - Chat: `graphrag_llm.completion.LLMCompletion` - Embedding: `graphrag_llm.embedding.LLMEmbedding` @@ -450,6 +532,12 @@ custom_providers: ### Evaluation (autoe) - Trials must be **even** for pairwise and reference evaluation (counterbalancing) — the config validator rejects odd values +- Keep the default persistent pairwise `cache_config` for resumable evaluation; + use `type: none` only when new judgments are required. Each trial is cached + separately, so cache reuse preserves counterbalancing. +- For a completely fresh pairwise comparison, use a new output directory (or + remove the comparison CSV) and disable, relocate, or clear the judgment + cache. - Assertion and hierarchical evaluation do NOT require even trials - Use `staged` mode for hierarchical assertions (more accurate); `joint` mode is cheaper but risks anchoring bias - Use `holm` correction for significance testing (default) — balances power and error control @@ -459,7 +547,8 @@ custom_providers: - `assessor_type: rationale` (default) provides structured JSON with reasoning; `bing` uses the UMBRELA DNA prompt - Match the assessor type between `generate-retrieval-reference` and `retrieval-scores` to share the cache - `relevance_threshold: 2` on a 0–3 scale is a reasonable default — lower values include marginal matches -- Use `cache_dir` for iterative development to avoid redundant LLM calls across runs +- Use a persistent `cache_config` (`sqlite` or `json`) for iterative development + to avoid redundant LLM calls across runs ### Storage Configuration - Use `connection_string` with `${AZURE_STORAGE_CONNECTION_STRING}` for development; use `account_url` with managed identity for production diff --git a/.gitignore b/.gitignore index 38cc69d..0101f84 100644 --- a/.gitignore +++ b/.gitignore @@ -158,6 +158,7 @@ cache/ # notebook cache docs/notebooks/cache/ +docs/notebooks/.benchmark_qed_cache # extracted example data docs/notebooks/example_answers/retrieval_reference/ diff --git a/.semversioner/next-release/minor-20260911141730000000.json b/.semversioner/next-release/minor-20260911141730000000.json new file mode 100644 index 0000000..6dd5531 --- /dev/null +++ b/.semversioner/next-release/minor-20260911141730000000.json @@ -0,0 +1,4 @@ +{ + "type": "minor", + "description": "Add persistent, concurrency-safe evaluation caches with GraphRAG cache configuration and reproducibility metadata. Existing cache_dir settings and affected Python cache APIs must migrate to cache_config." +} diff --git a/benchmark_qed/__main__.py b/benchmark_qed/__main__.py index 3f5f9c5..979fddd 100644 --- a/benchmark_qed/__main__.py +++ b/benchmark_qed/__main__.py @@ -8,6 +8,7 @@ from benchmark_qed.autoe.cli import app as autoe_cli from benchmark_qed.autoq.cli import app as autoq_cli +from benchmark_qed.cli.cache import app as cache_cli from benchmark_qed.cli.init_config import app as init_cli from benchmark_qed.cli.interactive import interactive_init from benchmark_qed.data.cli import app as data_cli @@ -16,6 +17,7 @@ app.add_typer(autoe_cli, name="autoe", help="Relative scores CLI.") app.add_typer(autoq_cli, help="Question generation CLI.") +app.add_typer(cache_cli, name="cache", help="Local cache inspection CLI.") app.add_typer(init_cli, name="config", help="Configuration initialization CLI.") app.command(name="init", help="Interactively create a benchmark-qed configuration.")( interactive_init diff --git a/benchmark_qed/autoe/assertion/aggregation.py b/benchmark_qed/autoe/assertion/aggregation.py index 9ebfd11..8c9ab9f 100644 --- a/benchmark_qed/autoe/assertion/aggregation.py +++ b/benchmark_qed/autoe/assertion/aggregation.py @@ -359,7 +359,7 @@ def summarize_standard_scores( .agg( score=( "score", - lambda x: int(x.mean() > pass_threshold), + lambda x: int(np.asarray(x, dtype=float).mean() > pass_threshold), ), scores=("score", list), ) @@ -370,8 +370,8 @@ def summarize_standard_scores( summary_by_assertion .groupby(["question"]) .agg( - success=("score", lambda x: (x == 1).sum()), - fail=("score", lambda x: (x == 0).sum()), + success=("score", lambda x: int(np.count_nonzero(np.asarray(x) == 1))), + fail=("score", lambda x: int(np.count_nonzero(np.asarray(x) == 0))), ) .reset_index() ) @@ -386,8 +386,8 @@ def summarize_standard_scores( summary_by_assertion = summary_by_assertion.drop(columns=["scores"]) # Overall metrics - total_success = int(summary_by_question["success"].sum()) - total_fail = int(summary_by_question["fail"].sum()) + total_success = int(summary_by_question["success"].to_numpy(dtype=int).sum()) + total_fail = int(summary_by_question["fail"].to_numpy(dtype=int).sum()) total_assertions = total_success + total_fail overall_accuracy = total_success / total_assertions if total_assertions > 0 else 0.0 @@ -397,7 +397,9 @@ def summarize_standard_scores( summary_by_question["success"] + summary_by_question["fail"] ) num_questions = len(summary_by_question) - avg_question_pass_rate = float(summary_by_question["pass_rate"].mean()) + avg_question_pass_rate = float( + summary_by_question["pass_rate"].to_numpy(dtype=float).mean() + ) eval_stats: dict[str, object] = { "total_assertions": total_assertions, @@ -448,21 +450,29 @@ def compute_hierarchical_eval_summary( - num_questions: number of unique questions """ total_assertions = len(aggregated) - passed_assertions = int((aggregated["global_score"] == 1).sum()) + passed_assertions = int( + np.count_nonzero(aggregated["global_score"].to_numpy() == 1) + ) failed_assertions = total_assertions - passed_assertions - overridden_count = int( - aggregated["global_score_overridden"].sum() + overridden_count = ( + int(aggregated["global_score_overridden"].to_numpy(dtype=int).sum()) if "global_score_overridden" in aggregated.columns else 0 ) - discovery_count = int(aggregated["has_discovery"].sum()) + discovery_count = int(aggregated["has_discovery"].to_numpy(dtype=int).sum()) # Per-question metrics (average per question, then mean across) per_q = aggregated.groupby("question") - avg_global_pass_rate = float(per_q["global_score"].mean().mean()) - avg_support_level = float(per_q["support_level"].mean().mean()) - discovery_rate = float(per_q["has_discovery"].mean().mean()) + avg_global_pass_rate = float( + np.asarray(per_q["global_score"].mean(), dtype=float).mean() + ) + avg_support_level = float( + np.asarray(per_q["support_level"].mean(), dtype=float).mean() + ) + discovery_rate = float( + np.asarray(per_q["has_discovery"].mean(), dtype=float).mean() + ) # Supporting pass rate: count all evaluations per question # (no deduplication, same as multi-RAG pipeline) @@ -488,8 +498,12 @@ def compute_hierarchical_eval_summary( if not passed_df.empty: per_q_passed = passed_df.groupby("question") - support_level_passed = float(per_q_passed["support_level"].mean().mean()) - discovery_rate_passed = float(per_q_passed["has_discovery"].mean().mean()) + support_level_passed = float( + np.asarray(per_q_passed["support_level"].mean(), dtype=float).mean() + ) + discovery_rate_passed = float( + np.asarray(per_q_passed["has_discovery"].mean(), dtype=float).mean() + ) per_q_supp_rates_passed: list[float] = [] for _, group in per_q_passed: diff --git a/benchmark_qed/autoe/assertion/cache.py b/benchmark_qed/autoe/assertion/cache.py new file mode 100644 index 0000000..243d61d --- /dev/null +++ b/benchmark_qed/autoe/assertion/cache.py @@ -0,0 +1,139 @@ +# Copyright (c) 2025 Microsoft Corporation. +"""Content-addressed cache for answer-level assertion judgments.""" + +from __future__ import annotations + +from typing import TYPE_CHECKING, Any + +from benchmark_qed.cache import ( + CachePublishResult, + CacheStore, + redact_sensitive_values, + stable_fingerprint, +) + +if TYPE_CHECKING: + from graphrag_cache import CacheConfig + +_CACHE_NAMESPACE = "assertion" +_CACHE_SCHEMA_VERSION = 1 +_DEFAULT_LEASE_TTL_SECONDS = 120.0 + + +def build_cache_metadata( + *, + model: str, + llm_provider: str, + init_args: dict[str, Any] | None, + call_args: dict[str, Any] | None, + custom_providers: list[dict[str, Any]] | None, + system_prompt: str, + user_prompt: str, + include_score_id_in_prompt: bool, +) -> dict[str, Any]: + """Build credential-safe metadata that identifies judge configuration.""" + return { + "schema_version": _CACHE_SCHEMA_VERSION, + "evaluator": _CACHE_NAMESPACE, + "model": model, + "llm_provider": llm_provider, + "init_args": redact_sensitive_values(init_args or {}), + "call_args": redact_sensitive_values(call_args or {}), + "custom_providers": redact_sensitive_values(custom_providers or []), + "system_prompt": system_prompt, + "user_prompt": user_prompt, + "include_score_id_in_prompt": include_score_id_in_prompt, + } + + +def compute_logical_key( + *, + question: str, + answer: str, + assertion: str, + trial: int, +) -> str: + """Fingerprint one assertion trial independently from judge configuration.""" + return stable_fingerprint({ + "question": question, + "answer": answer, + "assertion": assertion, + "trial": trial, + }) + + +def compute_cache_key(logical_key: str, metadata: dict[str, Any]) -> str: + """Fingerprint an assertion trial and complete judge configuration.""" + return stable_fingerprint({ + "logical_key": logical_key, + "configuration": metadata, + }) + + +class AssertionScoreCache: + """Persistent, concurrency-safe cache for assertion score dictionaries.""" + + def __init__( + self, + cache_config: CacheConfig, + *, + lease_ttl_seconds: float = _DEFAULT_LEASE_TTL_SECONDS, + ) -> None: + self._store = CacheStore( + cache_config, + _CACHE_NAMESPACE, + lease_ttl_seconds=lease_ttl_seconds, + ) + self.lease_ttl_seconds = lease_ttl_seconds + + async def get(self, cache_key: str) -> dict[str, Any] | None: + """Return a cached assertion score.""" + entry = await self._store.get(cache_key) + if entry is None: + return None + if not isinstance(entry[0], dict): + msg = f"Invalid cached assertion result for key {cache_key}" + raise TypeError(msg) + return entry[0] + + async def claim(self, cache_key: str, owner_id: str) -> bool: + """Claim an uncached assertion score for this worker.""" + return await self._store.claim( + cache_key, + owner_id, + ttl_seconds=self.lease_ttl_seconds, + ) + + def renew(self, cache_key: str, owner_id: str) -> bool: + """Renew an active assertion-score lease.""" + return ( + self._store.renew_leases( + [cache_key], + owner_id, + ttl_seconds=self.lease_ttl_seconds, + ) + == 1 + ) + + def release(self, cache_key: str, owner_id: str) -> None: + """Release an assertion-score lease without publishing.""" + self._store.release_leases([cache_key], owner_id) + + async def publish( + self, + cache_key: str, + score: dict[str, Any], + metadata: dict[str, Any], + *, + owner_id: str, + logical_key: str, + ) -> CachePublishResult: + """Publish an assertion score and release its lease.""" + return await self._store.publish( + cache_key, + score, + metadata, + owner_id=owner_id, + logical_key=logical_key, + config_fingerprint=stable_fingerprint(metadata), + ) diff --git a/benchmark_qed/autoe/assertion/hierarchical.py b/benchmark_qed/autoe/assertion/hierarchical.py index 0735b74..e25f188 100644 --- a/benchmark_qed/autoe/assertion/hierarchical.py +++ b/benchmark_qed/autoe/assertion/hierarchical.py @@ -22,6 +22,7 @@ from typing import Any from uuid import uuid4 +import numpy as np import pandas as pd from graphrag_llm.completion import LLMCompletion from rich import print as rich_print @@ -388,7 +389,10 @@ def _get_hierarchical_scores_staged( .groupby(["question_id", "question", "assertion"]) .agg( global_score_mean=("score", "mean"), - global_score=("score", lambda x: int(x.mean() > pass_threshold)), + global_score=( + "score", + lambda x: int(np.asarray(x, dtype=float).mean() > pass_threshold), + ), reasoning_list=("reasoning", list), trial_scores=("score", list), ) @@ -396,10 +400,13 @@ def _get_hierarchical_scores_staged( ) global_aggregated["global_passed"] = global_aggregated["global_score"] == 1 - passed_count = global_aggregated["global_passed"].sum() + passed_count = int(global_aggregated["global_passed"].to_numpy(dtype=int).sum()) total_count = len(global_aggregated) - per_q_pass_rate = ( - global_aggregated.groupby("question")["global_score"].mean().mean() + per_q_pass_rate = float( + np.asarray( + global_aggregated.groupby("question")["global_score"].mean(), + dtype=float, + ).mean() ) rich_print( f" Global assertions: {passed_count}/{total_count} passed " diff --git a/benchmark_qed/autoe/assertion/pipeline.py b/benchmark_qed/autoe/assertion/pipeline.py index 23027ca..6c906cc 100644 --- a/benchmark_qed/autoe/assertion/pipeline.py +++ b/benchmark_qed/autoe/assertion/pipeline.py @@ -8,6 +8,7 @@ from pathlib import Path from typing import Any, cast +import numpy as np import pandas as pd from graphrag_llm.completion import LLMCompletion from graphrag_storage import Storage @@ -606,7 +607,7 @@ def run_hierarchical_assertion_evaluation( if "support_level" in aggregated.columns: rich_print( f" Average support level (per-question avg): " - f"{per_q_support.mean() * 100:.1f}%" + f"{np.asarray(per_q_support, dtype=float).mean() * 100:.1f}%" ) if not all_aggregated: diff --git a/benchmark_qed/autoe/assertion/standard.py b/benchmark_qed/autoe/assertion/standard.py index a01bed7..1d57856 100644 --- a/benchmark_qed/autoe/assertion/standard.py +++ b/benchmark_qed/autoe/assertion/standard.py @@ -6,6 +6,7 @@ """ import asyncio +import contextlib import functools import itertools from collections.abc import Callable @@ -15,9 +16,16 @@ from uuid import uuid4 import pandas as pd +from graphrag_cache import CacheConfig, CacheType from graphrag_llm.completion import LLMCompletion from rich.progress import Progress, TaskID +from benchmark_qed.autoe.assertion.cache import ( + AssertionScoreCache, + build_cache_metadata, + compute_cache_key, + compute_logical_key, +) from benchmark_qed.autoe.data_model.assertion import ( Assertion, AssertionLLMResponse, @@ -44,6 +52,7 @@ def get_assertion_scores( question_id_key: str = "question_id", question_text_key: str = "question_text", answer_text_key: str = "answer", + cache_config: CacheConfig | None = None, ) -> pd.DataFrame: """Score assertions based on the provided answers using a language model. @@ -64,6 +73,8 @@ def get_assertion_scores( question_id_key: Column name for question ID. question_text_key: Column name for question text. answer_text_key: Column name for answer text. + cache_config: GraphRAG cache backend configuration. Caching is disabled + when omitted. Returns ------- @@ -123,6 +134,28 @@ def get_assertion_scores( # If no rank column, just take first k assertions per question pairs = pairs.groupby("question_id").head(top_k).reset_index(drop=True) + assessment_system_prompt = assessment_system_prompt or load_template_file( + ASSERTION_PROMPTS / "assertion_system_prompt.txt" + ) + assessment_user_prompt = assessment_user_prompt or load_template_file( + ASSERTION_PROMPTS / "assertion_user_prompt.txt" + ) + cache = AssertionScoreCache( + cache_config or CacheConfig(type=CacheType.Noop, storage=None) + ) + cache_metadata = build_cache_metadata( + model=llm_config.model, + llm_provider=str(llm_config.llm_provider), + init_args=llm_config.init_args, + call_args=llm_config.call_args, + custom_providers=[ + provider.model_dump(mode="json") for provider in llm_config.custom_providers + ], + system_prompt=assessment_system_prompt.template, + user_prompt=assessment_user_prompt.template, + include_score_id_in_prompt=include_score_id_in_prompt, + ) + with Progress() as progress: def on_complete_callback(progress_task: TaskID) -> None: @@ -130,8 +163,10 @@ def on_complete_callback(progress_task: TaskID) -> None: progress_task = progress.add_task("Scoring...", total=len(pairs) * trials) tasks = [ - evaluate_assertion( + _evaluate_assertion_with_cache( llm_client=llm_client, + cache=cache, + cache_metadata=cache_metadata, assertion=assertion.assertion, question=assertion.question_text, answer=assertion.answer_text, @@ -156,6 +191,87 @@ async def _run_tasks() -> list[dict[str, Any]]: return pd.DataFrame(results) +async def _evaluate_assertion_with_cache( + llm_client: LLMCompletion, + *, + cache: AssertionScoreCache, + cache_metadata: dict[str, Any], + assertion: str, + question: str, + answer: str, + trial: int, + assessment_system_prompt: Template, + assessment_user_prompt: Template, + include_score_id_in_prompt: bool, + additional_call_args: dict[str, Any] | None, + complete_callback: Callable | None, +) -> dict[str, Any]: + """Reuse or compute one assertion score with cross-process coalescing.""" + logical_key = compute_logical_key( + question=question, + answer=answer, + assertion=assertion, + trial=trial, + ) + cache_key = compute_cache_key(logical_key, cache_metadata) + owner_id = uuid4().hex + + while True: + cached = await cache.get(cache_key) + if cached is not None: + if complete_callback: + complete_callback() + return cached + if await cache.claim(cache_key, owner_id): + cached = await cache.get(cache_key) + if cached is not None: + cache.release(cache_key, owner_id) + if complete_callback: + complete_callback() + return cached + break + await asyncio.sleep(0.1) + + async def _heartbeat() -> None: + while True: + await asyncio.sleep(max(0.1, cache.lease_ttl_seconds / 3)) + cache.renew(cache_key, owner_id) + + heartbeat = asyncio.create_task(_heartbeat()) + try: + score = await evaluate_assertion( + llm_client=llm_client, + assertion=assertion, + question=question, + answer=answer, + trial=trial, + assessment_system_prompt=assessment_system_prompt, + assessment_user_prompt=assessment_user_prompt, + include_score_id_in_prompt=include_score_id_in_prompt, + additional_call_args=additional_call_args, + ) + publication = await cache.publish( + cache_key, + score, + cache_metadata, + owner_id=owner_id, + logical_key=logical_key, + ) + if publication.accepted and isinstance(publication.value, dict): + score = publication.value + except (Exception, asyncio.CancelledError): + cache.release(cache_key, owner_id) + raise + finally: + heartbeat.cancel() + with contextlib.suppress(asyncio.CancelledError): + await heartbeat + + if complete_callback: + complete_callback() + return score + + async def evaluate_assertion( llm_client: LLMCompletion, assertion: str, diff --git a/benchmark_qed/autoe/chunk_assertion/__init__.py b/benchmark_qed/autoe/chunk_assertion/__init__.py index d56d47e..d1c0f12 100644 --- a/benchmark_qed/autoe/chunk_assertion/__init__.py +++ b/benchmark_qed/autoe/chunk_assertion/__init__.py @@ -4,7 +4,10 @@ from benchmark_qed.autoe.chunk_assertion.aggregation import summarize_at_k from benchmark_qed.autoe.chunk_assertion.cache import ( ContentAddressedCache, + build_cache_metadata, compute_cache_key, + compute_config_fingerprint, + compute_logical_key, ) from benchmark_qed.autoe.chunk_assertion.scoring import ( run_assertion_eval_chunk_mode, @@ -12,7 +15,10 @@ __all__ = [ "ContentAddressedCache", + "build_cache_metadata", "compute_cache_key", + "compute_config_fingerprint", + "compute_logical_key", "run_assertion_eval_chunk_mode", "summarize_at_k", ] diff --git a/benchmark_qed/autoe/chunk_assertion/cache.py b/benchmark_qed/autoe/chunk_assertion/cache.py index 2cbd790..65d1bb9 100644 --- a/benchmark_qed/autoe/chunk_assertion/cache.py +++ b/benchmark_qed/autoe/chunk_assertion/cache.py @@ -6,93 +6,269 @@ import hashlib import json import logging -from pathlib import Path -from typing import Any +from typing import TYPE_CHECKING, Any + +from benchmark_qed.cache import ( + CachePublishResult, + CacheStore, + changed_configuration_fields, + get_cache_base_dir, + get_sqlite_cache_path, + redact_sensitive_values, + stable_fingerprint, +) log: logging.Logger = logging.getLogger(__name__) +if TYPE_CHECKING: + from pathlib import Path + + from graphrag_cache import CacheConfig + +_CACHE_NAMESPACE = "chunk_assertion" +_CACHE_SCHEMA_VERSION = 1 +_DEFAULT_LEASE_TTL_SECONDS = 120.0 + + +def build_cache_metadata( + *, + model: str = "", + call_args: dict[str, Any] | None = None, + system_prompt: str = "", + user_prompt: str = "", +) -> dict[str, Any]: + """Build inspectable, credential-safe metadata for a cached judgement.""" + return { + "schema_version": _CACHE_SCHEMA_VERSION, + "evaluator": _CACHE_NAMESPACE, + "model": model, + "call_args": redact_sensitive_values(call_args or {}), + "system_prompt": system_prompt, + "user_prompt": user_prompt, + } + + +def compute_logical_key(assertion_text: str, chunk_content: str) -> str: + """Fingerprint the inputs independently from judge configuration.""" + return stable_fingerprint({ + "assertion": assertion_text, + "chunk": chunk_content, + }) + + +def compute_config_fingerprint( + *, + model: str = "", + call_args: dict[str, Any] | None = None, + system_prompt: str = "", + user_prompt: str = "", +) -> str: + """Fingerprint the judge configuration independently from inputs.""" + return stable_fingerprint( + build_cache_metadata( + model=model, + call_args=call_args, + system_prompt=system_prompt, + user_prompt=user_prompt, + ) + ) + class ContentAddressedCache: - r"""Persistent SHA256-based cache for (assertion, chunk) -> grade. + r"""Persistent cache for (assertion, chunk) -> grade. - Key is computed by :func:`compute_cache_key`, which hashes the assertion, - chunk, judge model configuration, and prompt templates so that changes to - the model or prompts invalidate stale entries while identical configurations - reuse cached grades across runs. + The graphrag-cache SQLite backend allows readers and writers in separate + processes to share the cache. Existing JSONL caches are imported once. """ - def __init__(self, cache_path: Path | str) -> None: - """Initialize cache at given path. + def __init__( + self, + cache_config: CacheConfig, + *, + lease_ttl_seconds: float = _DEFAULT_LEASE_TTL_SECONDS, + ) -> None: + """Initialize the cache and import a legacy JSONL cache when present.""" + self.cache_config = cache_config + base_dir = get_cache_base_dir(cache_config) + self.cache_path: Path | None = get_sqlite_cache_path(cache_config) + self.legacy_cache_path: Path | None = None + if self.cache_path is not None: + legacy_path = self.cache_path.with_suffix(".jsonl") + elif base_dir is not None: + legacy_path = base_dir / "chunk_assertions.jsonl" + else: + legacy_path = None + if ( + legacy_path is not None + and legacy_path != self.cache_path + and legacy_path.exists() + ): + self.legacy_cache_path = legacy_path - Args: - cache_path: Path to JSONL cache file. Parent directory is created if needed. - """ - self.cache_path: Path = Path(cache_path) - self.cache_path.parent.mkdir(parents=True, exist_ok=True) - self._data: dict[str, str] = {} + self._store = CacheStore( + cache_config, + _CACHE_NAMESPACE, + lease_ttl_seconds=lease_ttl_seconds, + ) + self.lease_ttl_seconds = lease_ttl_seconds + self._pending: dict[str, tuple[str, dict[str, Any]]] = {} self.new_count: int = 0 - self._load() + self._initialized = False + + async def _ensure_initialized(self) -> None: + if self._initialized: + return + await self._migrate_legacy_cache() + self._initialized = True + + async def _migrate_legacy_cache(self) -> None: + """Import an existing JSONL cache once without modifying the source.""" + if self.legacy_cache_path is None or not self.legacy_cache_path.exists(): + return - def _load(self) -> None: - """Load existing cache from disk.""" - if not self.cache_path.exists(): + migration_name = f"legacy_import:{self.legacy_cache_path.resolve()}" + if await self._store.get_property(migration_name) is not None: return + + records: list[tuple[str, str, dict[str, Any]]] = [] try: - with self.cache_path.open(encoding="utf-8") as f: - for line in f: - stripped = line.strip() - if not stripped: - continue + with self.legacy_cache_path.open(encoding="utf-8") as legacy_file: + for line in legacy_file: try: - record = json.loads(stripped) - cache_key = record.get("key") - grade = record.get("grade") - if cache_key and grade: - self._data[cache_key] = grade + record = json.loads(line) except json.JSONDecodeError: continue - except OSError as exc: - log.warning("Failed to load cache from %s: %s", self.cache_path, exc) - - def get(self, cache_key: str) -> str | None: - """Retrieve grade for cache key. - - Args: - cache_key: SHA256 hash of (assertion + chunk) - - Returns - ------- - Grade string ('full_support', 'partial_support', 'no_support') or None - """ - return self._data.get(cache_key) - - def put(self, cache_key: str, grade: str) -> None: - """Store grade for cache key. - - Args: - cache_key: SHA256 hash of (assertion + chunk) - grade: Grade string ('full_support', 'partial_support', 'no_support') - """ - if cache_key not in self._data: - self._data[cache_key] = grade - self.new_count += 1 - - def flush(self) -> None: - """Write cache contents to disk. - - Rewrites the full JSONL file atomically to avoid duplicating existing - entries when flushing incremental updates. - """ - if self.new_count == 0: - return - tmp_path = self.cache_path.with_suffix(self.cache_path.suffix + ".tmp") - with tmp_path.open("w", encoding="utf-8") as f: - f.writelines( - json.dumps({"key": cache_key, "grade": grade}) + "\n" - for cache_key, grade in self._data.items() + cache_key = record.get("key") + grade = record.get("grade") + if cache_key and grade: + records.append(( + str(cache_key), + str(grade), + { + "schema_version": _CACHE_SCHEMA_VERSION, + "source": "legacy_jsonl", + }, + )) + except (OSError, UnicodeError) as exc: + log.warning( + "Failed to import legacy cache from %s: %s", self.legacy_cache_path, exc ) - tmp_path.replace(self.cache_path) + return + + await self._store.put_many(records) + await self._store.set_property_if_absent(migration_name, "complete") + + async def get(self, cache_key: str) -> str | None: + """Retrieve a grade for a cache key.""" + await self._ensure_initialized() + pending = self._pending.get(cache_key) + if pending is not None: + return pending[0] + entry = await self._store.get(cache_key) + return str(entry[0]) if entry is not None else None + + async def get_many(self, cache_keys: list[str]) -> dict[str, str]: + """Retrieve grades for existing cache keys in one backend operation.""" + await self._ensure_initialized() + entries = await self._store.get_many(cache_keys) + grades = {key: str(entry[0]) for key, entry in entries.items()} + requested_keys = set(cache_keys) + grades.update({ + key: pending[0] + for key, pending in self._pending.items() + if key in requested_keys + }) + return grades + + async def get_metadata(self, cache_key: str) -> dict[str, Any] | None: + """Retrieve the inspectable metadata stored with a cache entry.""" + await self._ensure_initialized() + pending = self._pending.get(cache_key) + if pending is not None: + return pending[1] + entry = await self._store.get(cache_key) + return entry[1] if entry is not None else None + + async def put( + self, + cache_key: str, + grade: str, + metadata: dict[str, Any] | None = None, + ) -> None: + """Stage a grade for insertion on the next flush.""" + await self._ensure_initialized() + if cache_key in self._pending or await self.get(cache_key) is not None: + return + self._pending[cache_key] = (grade, metadata or {}) + self.new_count = len(self._pending) + + async def claim(self, cache_key: str, owner_id: str) -> bool: + """Claim an uncached judgement for this worker.""" + await self._ensure_initialized() + return await self._store.claim( + cache_key, + owner_id, + ttl_seconds=self.lease_ttl_seconds, + ) + + def renew(self, cache_keys: list[str], owner_id: str) -> int: + """Renew active judgement leases.""" + return self._store.renew_leases( + cache_keys, + owner_id, + ttl_seconds=self.lease_ttl_seconds, + ) + + def release(self, cache_keys: list[str], owner_id: str) -> None: + """Release judgement leases without publishing.""" + self._store.release_leases(cache_keys, owner_id) + + async def publish( + self, + cache_key: str, + grade: str, + metadata: dict[str, Any], + *, + owner_id: str, + logical_key: str, + config_fingerprint: str, + ) -> CachePublishResult: + """Publish a grade and release its lease atomically.""" + await self._ensure_initialized() + return await self._store.publish( + cache_key, + grade, + metadata, + owner_id=owner_id, + logical_key=logical_key, + config_fingerprint=config_fingerprint, + ) + + async def find_configuration_mismatches( + self, + identities: list[tuple[str, str]], + current_metadata: dict[str, Any], + ) -> tuple[int, list[str]]: + """Return the mismatch count and changed configuration fields.""" + await self._ensure_initialized() + alternatives = await self._store.find_alternate_configurations(identities) + changed_fields = { + field + for metadata in alternatives + for field in changed_configuration_fields(current_metadata, metadata) + } + return len(alternatives), sorted(changed_fields) + + async def flush(self) -> int: + """Atomically insert staged entries and return the number persisted.""" + await self._ensure_initialized() + inserted_count = await self._store.put_many([ + (cache_key, grade, metadata) + for cache_key, (grade, metadata) in self._pending.items() + ]) + self._pending.clear() self.new_count = 0 + return inserted_count def compute_cache_key( @@ -104,30 +280,12 @@ def compute_cache_key( system_prompt: str = "", user_prompt: str = "", ) -> str: - """Compute stable SHA256 cache key for an (assertion, chunk) judgement. - - The judge model identifier, call arguments (for example temperature and - seed), and the prompt templates are folded into the key so that changing - the model or prompts invalidates stale cache entries instead of returning - grades produced under a different configuration. - - Args: - assertion_text: Assertion statement - chunk_content: Chunk/passage text - model: Judge model identifier (for example ``gpt-4.1``) - call_args: Additional LLM call arguments (for example temperature, seed) - system_prompt: System prompt template used for judging - user_prompt: User prompt template used for judging - - Returns - ------- - SHA256 hexdigest - """ + """Compute a stable SHA256 key for an (assertion, chunk) judgement.""" payload = { "assertion": assertion_text, "chunk": chunk_content, "model": model, - "call_args": call_args or {}, + "call_args": redact_sensitive_values(call_args or {}), "system_prompt": system_prompt, "user_prompt": user_prompt, } diff --git a/benchmark_qed/autoe/chunk_assertion/scoring.py b/benchmark_qed/autoe/chunk_assertion/scoring.py index 6109a08..b5923e6 100644 --- a/benchmark_qed/autoe/chunk_assertion/scoring.py +++ b/benchmark_qed/autoe/chunk_assertion/scoring.py @@ -4,10 +4,12 @@ from __future__ import annotations import asyncio +import contextlib import json import logging import operator import re +import uuid from pathlib import Path from typing import TYPE_CHECKING, Any @@ -16,15 +18,20 @@ from benchmark_qed.autoe.chunk_assertion.aggregation import summarize_at_k from benchmark_qed.autoe.chunk_assertion.cache import ( ContentAddressedCache, + build_cache_metadata, compute_cache_key, + compute_config_fingerprint, + compute_logical_key, ) from benchmark_qed.autoe.data_model.chunk_assertion import ( ChunkAssertionGrade, EvalSummary, grade_to_score, ) +from benchmark_qed.cache import create_default_cache_config if TYPE_CHECKING: + from graphrag_cache import CacheConfig from graphrag_llm.completion import LLMCompletion from graphrag_storage import Storage @@ -33,6 +40,9 @@ log: logging.Logger = logging.getLogger(__name__) +ChunkTarget = tuple[int, int, int] +ChunkWork = tuple[str, str, str, str, str, list[ChunkTarget]] + def _collect_chunks(eval_result: RetrievalResult) -> list[dict[str, Any]]: """Flatten one question's retrieval into an ordered chunk list. @@ -121,7 +131,7 @@ async def _label_chunk_assertion( # Parse response response_text = (response.content or "").strip() - except Exception as e: # noqa: BLE001 - any provider error maps to an error sentinel + except Exception as e: # ruff: ignore[blind-except] - provider error sentinel log.warning("LLM judging failed for an (assertion, chunk) pair: %s", e) return "__error__", str(e) @@ -190,16 +200,18 @@ def _extract_grade(response_text: str) -> str: async def _evaluate_uncached_pairs( - uncached_work: list[tuple[str, str, str, int, int, int]], + uncached_work: list[ChunkWork], *, llm_client: LLMCompletion, llm_config: LLMConfig, system_prompt: str, user_prompt: str, + cache_metadata: dict[str, Any], cache: ContentAddressedCache, + lease_owner: str, per_chunk_grades: dict[tuple[int, int], list[tuple[int, float, str]]], assertion_call_stats: dict[tuple[int, int], dict[str, int]], -) -> int: +) -> tuple[int, int]: """Judge uncached (assertion, chunk) pairs via a bounded worker pool. Uses a shared queue with at most ``concurrent_requests`` worker tasks so @@ -208,43 +220,67 @@ async def _evaluate_uncached_pairs( shared counters and grade maps are mutated safely between awaits. Args: - uncached_work: Work items of (assertion, chunk, cache_key, q_idx, a_idx, rank). + uncached_work: Unique work items with all result targets for each cache key. llm_client: LLM client for judging. llm_config: LLM configuration. system_prompt: System prompt template. user_prompt: User prompt template. + cache_metadata: Reproducibility metadata stored with successful grades. cache: Cache to store new grades in. + lease_owner: Unique owner of the work-item leases. per_chunk_grades: Mutated in place with (rank, score, grade) per (q, a). assertion_call_stats: Mutated in place with successful/failed call counts. Returns ------- - Number of LLM calls that failed. + Number of logical pairs that failed and number of cache entries inserted. """ - n_total = len(uncached_work) + n_total = sum(len(targets) for *_inputs, targets in uncached_work) next_progress_threshold = max(1, n_total // 10) call_errors = 0 + inserted_count = 0 n_done = 0 - work_queue: asyncio.Queue[tuple[str, str, str, int, int, int]] = asyncio.Queue() + work_queue: asyncio.Queue[ChunkWork] = asyncio.Queue() for work_item in uncached_work: work_queue.put_nowait(work_item) - def _record_result( - work_item: tuple[str, str, str, int, int, int], grade: str + async def _record_result( + work_item: ChunkWork, + grade: str, ) -> None: - nonlocal call_errors, n_done, next_progress_threshold - _assertion_text, _chunk_content, cache_key, q_idx, a_idx, rank = work_item - - q_key = (q_idx, a_idx) + nonlocal call_errors, inserted_count, n_done, next_progress_threshold + ( + _assertion_text, + _chunk_content, + cache_key, + logical_key, + config_fingerprint, + targets, + ) = work_item if grade == "__error__": - call_errors += 1 - assertion_call_stats[q_key]["failed"] += 1 + call_errors += len(targets) + cache.release([cache_key], lease_owner) + for q_idx, a_idx, _rank in targets: + assertion_call_stats[(q_idx, a_idx)]["failed"] += 1 else: - cache.put(cache_key, grade) + publication = await cache.publish( + cache_key, + grade, + cache_metadata, + owner_id=lease_owner, + logical_key=logical_key, + config_fingerprint=config_fingerprint, + ) + inserted_count += int(publication.inserted) + canonical_grade = ( + publication.value + if publication.accepted and isinstance(publication.value, str) + else grade + ) mapped_grade = ( - grade - if grade + canonical_grade + if canonical_grade in { ChunkAssertionGrade.FULL_SUPPORT, ChunkAssertionGrade.PARTIAL_SUPPORT, @@ -252,14 +288,16 @@ def _record_result( } else ChunkAssertionGrade.NO_SUPPORT ) - per_chunk_grades[q_key].append(( - rank, - grade_to_score(grade), - mapped_grade, - )) - assertion_call_stats[q_key]["successful"] += 1 - - n_done += 1 + for q_idx, a_idx, rank in targets: + q_key = (q_idx, a_idx) + per_chunk_grades[q_key].append(( + rank, + grade_to_score(canonical_grade), + mapped_grade, + )) + assertion_call_stats[q_key]["successful"] += 1 + + n_done += len(targets) if n_done >= next_progress_threshold: rich_print( f" LLM judging progress: {n_done}/{n_total} pairs ({(100.0 * n_done / n_total):.1f}%)", @@ -272,7 +310,14 @@ async def _worker() -> None: work_item = work_queue.get_nowait() except asyncio.QueueEmpty: return - assertion_text, chunk_content, _cache_key, _q_idx, _a_idx, _rank = work_item + ( + assertion_text, + chunk_content, + _cache_key, + _logical_key, + _config_fingerprint, + _targets, + ) = work_item grade, _reasoning = await _label_chunk_assertion( llm_client, assertion_text, @@ -281,12 +326,98 @@ async def _worker() -> None: user_prompt=user_prompt, additional_call_args=llm_config.call_args, ) - _record_result(work_item, grade) + await _record_result(work_item, grade) - n_workers = max(1, min(llm_config.concurrent_requests, n_total)) - await asyncio.gather(*[asyncio.ensure_future(_worker()) for _ in range(n_workers)]) + async def _heartbeat() -> None: + cache_keys = [work_item[2] for work_item in uncached_work] + while True: + await asyncio.sleep(max(0.1, cache.lease_ttl_seconds / 3)) + cache.renew(cache_keys, lease_owner) - return call_errors + n_workers = max(1, min(llm_config.concurrent_requests, len(uncached_work))) + heartbeat = asyncio.create_task(_heartbeat()) + try: + await asyncio.gather(*[ + asyncio.ensure_future(_worker()) for _ in range(n_workers) + ]) + except (Exception, asyncio.CancelledError): + cache.release([work_item[2] for work_item in uncached_work], lease_owner) + raise + finally: + heartbeat.cancel() + with contextlib.suppress(asyncio.CancelledError): + await heartbeat + + return call_errors, inserted_count + + +async def _evaluate_with_leases( + uncached_work: list[ChunkWork], + *, + llm_client: LLMCompletion, + llm_config: LLMConfig, + system_prompt: str, + user_prompt: str, + cache_metadata: dict[str, Any], + cache: ContentAddressedCache, + per_chunk_grades: dict[tuple[int, int], list[tuple[int, float, str]]], + assertion_call_stats: dict[tuple[int, int], dict[str, int]], +) -> tuple[int, int, int]: + """Evaluate owned work and reuse results published by other processes.""" + lease_owner = uuid.uuid4().hex + owned_work: list[ChunkWork] = [] + waiting_work: dict[str, ChunkWork] = {} + for work_item in uncached_work: + if await cache.claim(work_item[2], lease_owner): + owned_work.append(work_item) + else: + waiting_work[work_item[2]] = work_item + + call_errors = 0 + inserted_count = 0 + concurrent_hits = 0 + while owned_work or waiting_work: + if owned_work: + failed, inserted = await _evaluate_uncached_pairs( + owned_work, + llm_client=llm_client, + llm_config=llm_config, + system_prompt=system_prompt, + user_prompt=user_prompt, + cache_metadata=cache_metadata, + cache=cache, + lease_owner=lease_owner, + per_chunk_grades=per_chunk_grades, + assertion_call_stats=assertion_call_stats, + ) + call_errors += failed + inserted_count += inserted + owned_work = [] + + completed_keys: list[str] = [] + for cache_key, work_item in waiting_work.items(): + cached_grade = await cache.get(cache_key) + if cached_grade is not None: + targets = work_item[5] + for q_idx, a_idx, rank in targets: + q_key = (q_idx, a_idx) + per_chunk_grades[q_key].append(( + rank, + grade_to_score(cached_grade), + cached_grade, + )) + assertion_call_stats[q_key]["successful"] += 1 + concurrent_hits += len(targets) + completed_keys.append(cache_key) + elif await cache.claim(cache_key, lease_owner): + owned_work.append(work_item) + completed_keys.append(cache_key) + for cache_key in completed_keys: + waiting_work.pop(cache_key) + if waiting_work and not owned_work: + await asyncio.sleep(0.1) + + return call_errors, inserted_count, concurrent_hits async def run_assertion_eval_chunk_mode( @@ -297,7 +428,7 @@ async def run_assertion_eval_chunk_mode( llm_config: LLMConfig, output_storage: Storage, pass_threshold: float = 0.5, - cache_path: Path | None = None, + cache_config: CacheConfig | None = None, k_list: list[int] | None = None, system_prompt: str = "", user_prompt: str = "", @@ -315,7 +446,7 @@ async def run_assertion_eval_chunk_mode( llm_config: LLM configuration output_storage: Storage backend for writing debug records (local or blob) pass_threshold: Score threshold for pass (0.5 = partial+ is passing) - cache_path: Path to persistent cache (created if not specified) + cache_config: GraphRAG cache backend configuration. k_list: List of k values to report (e.g., [5, 10, 20, 50]) system_prompt: System prompt template user_prompt: User prompt template @@ -326,14 +457,29 @@ async def run_assertion_eval_chunk_mode( ------- Dict of {label: EvalSummary} keyed by f"k{k}" plus "all" entry """ - if cache_path is None: - cache_path = Path.cwd() / ".benchmark_qed_cache" / "chunk_assertions.jsonl" - cache = ContentAddressedCache(cache_path) + if cache_config is None: + cache_config = create_default_cache_config( + Path.cwd() / ".benchmark_qed_cache", + database_name="chunk_assertions.sqlite3", + ) + cache = ContentAddressedCache(cache_config) + cache_metadata = build_cache_metadata( + model=llm_config.model, + call_args=llm_config.call_args, + system_prompt=system_prompt, + user_prompt=user_prompt, + ) + config_fingerprint = compute_config_fingerprint( + model=llm_config.model, + call_args=llm_config.call_args, + system_prompt=system_prompt, + user_prompt=user_prompt, + ) # Track per-(q, a) chunk grades: (question_idx, assertion_idx) -> [(rank, score, grade), ...] per_chunk_grades: dict[tuple[int, int], list[tuple[int, float, str]]] = {} assertion_call_stats: dict[tuple[int, int], dict[str, int]] = {} - uncached_work: list[tuple[str, str, str, int, int, int]] = [] + uncached_by_key: dict[str, tuple[str, str, str, str, list[ChunkTarget]]] = {} retrieved_chunk_counts: list[int] = [] total_checks = 0 @@ -389,24 +535,31 @@ async def run_assertion_eval_chunk_mode( system_prompt=system_prompt, user_prompt=user_prompt, ) - cached_grade = cache.get(cache_key) - if cached_grade is not None: - cache_hits += 1 - per_chunk_grades[q_key].append(( - rank, - grade_to_score(cached_grade), - cached_grade, - )) - assertion_call_stats[q_key]["successful"] += 1 - else: - uncached_work.append(( + logical_key = compute_logical_key(assertion_text, chunk_content) + existing_work = uncached_by_key.get(cache_key) + if existing_work is None: + uncached_by_key[cache_key] = ( assertion_text, chunk_content, - cache_key, - q_idx, - a_idx, - rank, - )) + logical_key, + config_fingerprint, + [(q_idx, a_idx, rank)], + ) + else: + existing_work[4].append((q_idx, a_idx, rank)) + + cached_grades = await cache.get_many(list(uncached_by_key)) + for cache_key, cached_grade in cached_grades.items(): + work_item = uncached_by_key.pop(cache_key) + for q_idx, a_idx, rank in work_item[4]: + q_key = (q_idx, a_idx) + cache_hits += 1 + per_chunk_grades[q_key].append(( + rank, + grade_to_score(cached_grade), + cached_grade, + )) + assertion_call_stats[q_key]["successful"] += 1 if positional_fallbacks: rich_print( @@ -425,24 +578,63 @@ async def run_assertion_eval_chunk_mode( rich_print(" No assertions or chunks to evaluate.") # Second pass: evaluate uncached pairs - if uncached_work: + if uncached_by_key: + uncached_work = [ + ( + assertion_text, + chunk_content, + cache_key, + logical_key, + work_config_fingerprint, + targets, + ) + for cache_key, ( + assertion_text, + chunk_content, + logical_key, + work_config_fingerprint, + targets, + ) in uncached_by_key.items() + ] + mismatch_count, changed_fields = await cache.find_configuration_mismatches( + [ + (logical_key, work_config_fingerprint) + for ( + _assertion, + _chunk, + _key, + logical_key, + work_config_fingerprint, + _targets, + ) in uncached_work + ], + cache_metadata, + ) + if mismatch_count: + changed_text = ", ".join(changed_fields) or "configuration metadata" + rich_print( + f" [yellow]WARNING: Found {mismatch_count} cached result(s) for " + f"the same inputs with different {changed_text}; they will not " + "be reused.[/yellow]" + ) rich_print( f" Running {len(uncached_work)} LLM judgements for uncached (assertion, chunk) pairs...", ) - call_errors += await _evaluate_uncached_pairs( + failed, inserted_count, concurrent_hits = await _evaluate_with_leases( uncached_work, llm_client=llm_client, llm_config=llm_config, system_prompt=system_prompt, user_prompt=user_prompt, + cache_metadata=cache_metadata, cache=cache, per_chunk_grades=per_chunk_grades, assertion_call_stats=assertion_call_stats, ) - - # Flush cache - cache.flush() - rich_print(f" Cached {len(uncached_work)} new entries to {cache_path.name}") + call_errors += failed + cache_hits += concurrent_hits + cache_target = cache.cache_path.name if cache.cache_path else cache_config.type + rich_print(f" Cached {inserted_count} new entries to {cache_target}") if call_errors: rich_print(f" WARNING: {call_errors} LLM calls failed") diff --git a/benchmark_qed/autoe/cli.py b/benchmark_qed/autoe/cli.py index 6354f62..dc58704 100644 --- a/benchmark_qed/autoe/cli.py +++ b/benchmark_qed/autoe/cli.py @@ -154,7 +154,7 @@ def pairwise_scores( include_score_id_in_prompt: Annotated[ bool, typer.Option( - help="Whether to include the score ID in the evaluation prompt for the LLM (might be useful to avoid cached scores)." + help="Whether to include a unique score ID in provider requests. This can avoid provider-side prompt caching, but does not bypass cache_config." ), ] = True, question_id_key: Annotated[ @@ -232,6 +232,7 @@ def pairwise_scores( trials=config.trials, question_id_key=question_id_key, include_score_id_in_prompt=include_score_id_in_prompt, + cache_config=config.cache_config, ) asyncio.run(_write_csv_df(output_storage, cache_key, result)) @@ -300,7 +301,7 @@ def differential_pairwise_scores( include_score_id_in_prompt: Annotated[ bool, typer.Option( - help="Whether to include the score ID in the evaluation prompt for the LLM (might be useful to avoid cached scores)." + help="Whether to include a unique score ID in provider requests. This can avoid provider-side prompt caching, but does not bypass cache_config." ), ] = True, question_id_key: Annotated[ @@ -374,6 +375,7 @@ def differential_pairwise_scores( trials=config.trials, question_id_key=question_id_key, include_score_id_in_prompt=include_score_id_in_prompt, + cache_config=config.cache_config, ) asyncio.run(_write_csv_df(output_storage, cache_key, result)) @@ -438,7 +440,7 @@ def reference_scores( include_score_id_in_prompt: Annotated[ bool, typer.Option( - help="Whether to include the score ID in the evaluation prompt for the LLM (might be useful to avoid cached scores)." + help="Whether to include a unique score ID in provider requests. This can avoid provider-side prompt caching, but does not bypass cache_config." ), ] = True, question_id_key: Annotated[ @@ -493,6 +495,7 @@ def reference_scores( trials=config.trials, include_score_id_in_prompt=include_score_id_in_prompt, question_id_key=question_id_key, + cache_config=config.cache_config, ) asyncio.run( _write_csv_df( @@ -555,7 +558,7 @@ def assertion_scores( include_score_id_in_prompt: Annotated[ bool, typer.Option( - help="Whether to include the score ID in the prompt (single-RAG mode only)." + help="Whether to include a unique score ID in provider requests (single-RAG only). This can avoid provider-side prompt caching, but does not bypass cache_config." ), ] = True, question_id_key: Annotated[ @@ -696,6 +699,7 @@ def _run_single_rag_assertion_scores( question_id_key=question_id_key, question_text_key=question_text_key, answer_text_key=answer_text_key, + cache_config=config.cache_config, ) asyncio.run(_write_csv_df(output_storage, "assertion_scores.csv", assertion_score)) @@ -1425,8 +1429,7 @@ async def _generate_retrieval_reference_async( relevance_rater = BingRelevanceRater( llm_client=llm_client, llm_config=config.llm_config, - cache_dir=config.cache_dir, - cache_enabled=config.cache_dir is not None, + cache_config=config.cache_config, concurrent_requests=config.concurrent_requests, ) rich_print( @@ -1436,8 +1439,7 @@ async def _generate_retrieval_reference_async( relevance_rater = RationaleRelevanceRater( llm_client=llm_client, llm_config=config.llm_config, - cache_dir=config.cache_dir, - cache_enabled=config.cache_dir is not None, + cache_config=config.cache_config, concurrent_requests=config.concurrent_requests, ) rich_print( @@ -1798,16 +1800,14 @@ def retrieval_scores( relevance_rater = BingRelevanceRater( llm_client=llm_client, llm_config=config.llm_config, - cache_dir=config.cache_dir, - cache_enabled=config.cache_dir is not None, + cache_config=config.cache_config, ) rich_print("Using BingRelevanceRater (UMBRELA DNA prompt)") else: relevance_rater = RationaleRelevanceRater( llm_client=llm_client, llm_config=config.llm_config, - cache_dir=config.cache_dir, - cache_enabled=config.cache_dir is not None, + cache_config=config.cache_config, ) rich_print("Using RationaleRelevanceRater (structured JSON)") @@ -2076,13 +2076,6 @@ def chunk_assertion_scores( # Load prompts (use defaults from package if not configured) system_prompt, user_prompt = _load_chunk_assertion_prompts(config) - # Set cache directory - cache_path = None - if config.cache_dir: - cache_path = Path(config.cache_dir) / "chunk_assertions.jsonl" - else: - cache_path = Path.cwd() / ".benchmark_qed_cache" / "chunk_assertions.jsonl" - # Run chunk-level evaluation rich_print("\n[bold]Running chunk-level assertion evaluation...[/bold]") summaries = asyncio.run( @@ -2093,7 +2086,7 @@ def chunk_assertion_scores( llm_config=config.llm_config, pass_threshold=config.pass_threshold, output_storage=output_storage, - cache_path=cache_path, + cache_config=config.cache_config, k_list=config.k_list, system_prompt=system_prompt, user_prompt=user_prompt, diff --git a/benchmark_qed/autoe/config.py b/benchmark_qed/autoe/config.py index b66f1e9..a53965a 100644 --- a/benchmark_qed/autoe/config.py +++ b/benchmark_qed/autoe/config.py @@ -2,8 +2,9 @@ """Scoring configuration models.""" from pathlib import Path -from typing import Literal, Self +from typing import Any, Literal, Self +from graphrag_cache import CacheConfig, CacheType from graphrag_storage.storage_config import StorageConfig from pydantic import BaseModel, Field, model_validator @@ -11,6 +12,7 @@ from benchmark_qed.autoe.prompts import assertion as assertion_prompts from benchmark_qed.autoe.prompts import pairwise as pairwise_prompts from benchmark_qed.autoe.prompts import reference as reference_prompts +from benchmark_qed.cache import create_default_cache_config from benchmark_qed.config.llm_config import LLMConfig from benchmark_qed.config.model.score import ( Assertions, @@ -124,6 +126,14 @@ class PairwiseConfig(BaseAutoEConfig): description="List of criteria to use for scoring.", ) + cache_config: CacheConfig = Field( + default_factory=lambda: create_default_cache_config( + ".benchmark_qed_cache/pairwise", + database_name="pairwise.sqlite3", + ), + description="GraphRAG cache configuration for pairwise judgements.", + ) + prompt_config: AutoEPromptConfig = Field( default=AutoEPromptConfig( user_prompt=PromptConfig( @@ -196,6 +206,14 @@ class DifferentialPairwiseConfig(BaseModel): description="Number of trials to run for each condition.", ) + cache_config: CacheConfig = Field( + default_factory=lambda: create_default_cache_config( + ".benchmark_qed_cache/differential_pairwise", + database_name="differential_pairwise.sqlite3", + ), + description="GraphRAG cache configuration for extraction and verdict stages.", + ) + input_storage: StorageConfig | None = Field( default=None, description="Optional storage configuration for reading input from Azure Blob Storage. When omitted, reads from local filesystem paths.", @@ -249,6 +267,14 @@ class ReferenceConfig(BaseAutoEConfig): score_min: int = Field(1, description="Minimum score for the criteria.") score_max: int = Field(10, description="Maximum score for the criteria.") + cache_config: CacheConfig = Field( + default_factory=lambda: create_default_cache_config( + ".benchmark_qed_cache/reference", + database_name="reference.sqlite3", + ), + description="GraphRAG cache configuration for reference judgments.", + ) + prompt_config: AutoEPromptConfig = Field( default=AutoEPromptConfig( user_prompt=PromptConfig( @@ -281,6 +307,14 @@ class AssertionConfig(BaseAutoEConfig): description="Threshold for passing the assertion score.", ) + cache_config: CacheConfig = Field( + default_factory=lambda: create_default_cache_config( + ".benchmark_qed_cache/assertion", + database_name="assertion.sqlite3", + ), + description="GraphRAG cache configuration for answer-level assertion judgments.", + ) + prompt_config: AutoEPromptConfig = Field( default=AutoEPromptConfig( user_prompt=PromptConfig( @@ -813,11 +847,20 @@ class RetrievalReferenceConfig(BaseModel): description="Column name mappings for text unit data.", ) - cache_dir: Path | None = Field( - default=None, - description="Directory for caching relevance assessments.", + cache_config: CacheConfig = Field( + default_factory=lambda: CacheConfig(type=CacheType.Noop), + description="GraphRAG cache configuration for relevance assessments.", ) + @model_validator(mode="before") + @classmethod + def reject_legacy_cache_dir(cls, data: Any) -> Any: + """Reject the removed cache_dir setting with actionable guidance.""" + if isinstance(data, dict) and "cache_dir" in data: + msg = "cache_dir was replaced by cache_config" + raise ValueError(msg) + return data + input_storage: StorageConfig | None = Field( default=None, description="Optional storage configuration for reading text units, clusters, and question files from Azure Blob Storage. When omitted, reads from local filesystem paths.", @@ -896,11 +939,20 @@ class RetrievalScoresConfig(BaseModel): description="Minimum relevance score to consider relevant (0-3 scale).", ) - cache_dir: Path | None = Field( - default=None, - description="Directory for caching relevance assessments (shared across RAG methods).", + cache_config: CacheConfig = Field( + default_factory=lambda: CacheConfig(type=CacheType.Noop), + description="GraphRAG cache configuration shared across RAG methods.", ) + @model_validator(mode="before") + @classmethod + def reject_legacy_cache_dir(cls, data: Any) -> Any: + """Reject the removed cache_dir setting with actionable guidance.""" + if isinstance(data, dict) and "cache_dir" in data: + msg = "cache_dir was replaced by cache_config" + raise ValueError(msg) + return data + context_id_key: str = Field( default="chunk_id", description="Key name for chunk ID in retrieval results.", diff --git a/benchmark_qed/autoe/data_model/chunk_assertion.py b/benchmark_qed/autoe/data_model/chunk_assertion.py index 92b66de..297baed 100644 --- a/benchmark_qed/autoe/data_model/chunk_assertion.py +++ b/benchmark_qed/autoe/data_model/chunk_assertion.py @@ -2,11 +2,13 @@ """Data models for chunk-level assertion evaluation.""" from pathlib import Path -from typing import ClassVar +from typing import Any, ClassVar +from graphrag_cache import CacheConfig from graphrag_storage.storage_config import StorageConfig -from pydantic import BaseModel, Field +from pydantic import BaseModel, Field, model_validator +from benchmark_qed.cache import create_default_cache_config from benchmark_qed.config.llm_config import LLMConfig @@ -115,7 +117,22 @@ class ChunkAssertionConfig(BaseModel): "highest-ranked chunks). Useful for reducing LLM call volume during " "testing. None means evaluate all retrieved chunks.", ) - cache_dir: str | None = Field(default=None) + cache_config: CacheConfig = Field( + default_factory=lambda: create_default_cache_config( + ".benchmark_qed_cache/chunk_assertions", + database_name="chunk_assertions.sqlite3", + ) + ) + + @model_validator(mode="before") + @classmethod + def reject_legacy_cache_dir(cls, data: Any) -> Any: + """Reject the removed cache_dir setting with actionable guidance.""" + if isinstance(data, dict) and "cache_dir" in data: + msg = "cache_dir was replaced by cache_config" + raise ValueError(msg) + return data + output_storage: StorageConfig | None = Field(default=None) input_storage: StorageConfig | None = Field(default=None) llm_config: LLMConfig diff --git a/benchmark_qed/autoe/pairwise/cache.py b/benchmark_qed/autoe/pairwise/cache.py new file mode 100644 index 0000000..18bd769 --- /dev/null +++ b/benchmark_qed/autoe/pairwise/cache.py @@ -0,0 +1,144 @@ +# Copyright (c) 2025 Microsoft Corporation. +"""Content-addressed cache for pairwise LLM judgements.""" + +from __future__ import annotations + +from typing import TYPE_CHECKING, Any + +from benchmark_qed.cache import ( + CachePublishResult, + CacheStore, + redact_sensitive_values, + stable_fingerprint, +) + +if TYPE_CHECKING: + from graphrag_cache import CacheConfig + +_CACHE_NAMESPACE = "pairwise" +_CACHE_SCHEMA_VERSION = 1 +_DEFAULT_LEASE_TTL_SECONDS = 120.0 + + +def build_cache_metadata( + *, + model: str, + llm_provider: str, + init_args: dict[str, Any] | None, + call_args: dict[str, Any] | None, + custom_providers: list[dict[str, Any]] | None, + system_prompt: str, + user_prompt: str, + include_score_id_in_prompt: bool, +) -> dict[str, Any]: + """Build credential-safe metadata that identifies judge configuration.""" + return { + "schema_version": _CACHE_SCHEMA_VERSION, + "evaluator": _CACHE_NAMESPACE, + "model": model, + "llm_provider": llm_provider, + "init_args": redact_sensitive_values(init_args or {}), + "call_args": redact_sensitive_values(call_args or {}), + "custom_providers": redact_sensitive_values(custom_providers or []), + "system_prompt": system_prompt, + "user_prompt": user_prompt, + "include_score_id_in_prompt": include_score_id_in_prompt, + } + + +def compute_logical_key( + *, + question: str, + answer_1_name: str, + answer_1: str, + answer_2_name: str, + answer_2: str, + criteria_name: str, + criteria_description: str, + trial: int, +) -> str: + """Fingerprint one trial independently from judge configuration.""" + return stable_fingerprint({ + "question": question, + "answer_1_name": answer_1_name, + "answer_1": answer_1, + "answer_2_name": answer_2_name, + "answer_2": answer_2, + "criteria_name": criteria_name, + "criteria_description": criteria_description, + "trial": trial, + }) + + +def compute_cache_key(logical_key: str, metadata: dict[str, Any]) -> str: + """Fingerprint a trial and the complete judge configuration.""" + return stable_fingerprint({ + "logical_key": logical_key, + "configuration": metadata, + }) + + +class PairwiseScoreCache: + """Persistent, concurrency-safe cache for pairwise score dictionaries.""" + + def __init__( + self, + cache_config: CacheConfig, + *, + lease_ttl_seconds: float = _DEFAULT_LEASE_TTL_SECONDS, + ) -> None: + self._store = CacheStore( + cache_config, + _CACHE_NAMESPACE, + lease_ttl_seconds=lease_ttl_seconds, + ) + self.lease_ttl_seconds = lease_ttl_seconds + + async def get(self, cache_key: str) -> dict[str, Any] | None: + """Return a cached pairwise score.""" + entry = await self._store.get(cache_key) + if entry is None or not isinstance(entry[0], dict): + return None + return entry[0] + + async def claim(self, cache_key: str, owner_id: str) -> bool: + """Claim an uncached score for this worker.""" + return await self._store.claim( + cache_key, + owner_id, + ttl_seconds=self.lease_ttl_seconds, + ) + + def renew(self, cache_key: str, owner_id: str) -> bool: + """Renew an active score lease.""" + return ( + self._store.renew_leases( + [cache_key], + owner_id, + ttl_seconds=self.lease_ttl_seconds, + ) + == 1 + ) + + def release(self, cache_key: str, owner_id: str) -> None: + """Release a score lease without publishing.""" + self._store.release_leases([cache_key], owner_id) + + async def publish( + self, + cache_key: str, + score: dict[str, Any], + metadata: dict[str, Any], + *, + owner_id: str, + logical_key: str, + ) -> CachePublishResult: + """Publish a score and release its lease.""" + return await self._store.publish( + cache_key, + score, + metadata, + owner_id=owner_id, + logical_key=logical_key, + config_fingerprint=stable_fingerprint(metadata), + ) diff --git a/benchmark_qed/autoe/pairwise/differential.py b/benchmark_qed/autoe/pairwise/differential.py index c6a004d..7d10d79 100644 --- a/benchmark_qed/autoe/pairwise/differential.py +++ b/benchmark_qed/autoe/pairwise/differential.py @@ -23,14 +23,22 @@ from typing import Any import pandas as pd +from graphrag_cache import CacheConfig, CacheType from graphrag_llm.completion import LLMCompletion from rich.progress import Progress, TaskID from benchmark_qed.autoe.data_model import ( ConditionPair, + DifferentialCriterionVerdict, DifferentialPairwiseLLMResponse, PairwiseExtractionLLMResponse, ) +from benchmark_qed.autoe.pairwise.differential_cache import ( + DifferentialPairwiseCache, + build_stage_metadata, + compute_cache_key, + compute_logical_key, +) from benchmark_qed.autoe.pairwise.scores import SCORE_MAPPING from benchmark_qed.autoe.prompts import pairwise as pairwise_prompts from benchmark_qed.config.llm_config import LLMConfig @@ -49,6 +57,29 @@ def _format_criteria_block(criteria: list[Criteria]) -> str: ) +def _validate_verdicts( + response: DifferentialPairwiseLLMResponse, + criteria: list[Criteria], +) -> dict[str, DifferentialCriterionVerdict]: + """Validate criterion coverage and winners before a verdict can be cached.""" + verdicts_by_name = { + item.criteria.strip().lower(): item for item in response.verdicts + } + for criterion in criteria: + criterion_name = criterion.name.strip().lower() + criterion_verdict = verdicts_by_name.get(criterion_name) + if criterion_verdict is None: + msg = f"LLM did not return a verdict for criterion '{criterion.name}'." + raise RuntimeError(msg) + if criterion_verdict.winner not in SCORE_MAPPING: + msg = ( + f"LLM returned invalid winner {criterion_verdict.winner} " + f"for criterion '{criterion.name}'; expected 0, 1, or 2." + ) + raise RuntimeError(msg) + return verdicts_by_name + + def get_differential_pairwise_scores( *, llm_client: LLMCompletion, @@ -66,6 +97,7 @@ def get_differential_pairwise_scores( include_score_id_in_prompt: bool = True, question_id_key: str = "question_id", question_text_key: str = "question_text", + cache_config: CacheConfig | None = None, ) -> pd.DataFrame: """Score a pair of conditions with the differential extract-and-judge method. @@ -90,6 +122,8 @@ def get_differential_pairwise_scores( include_score_id_in_prompt: Whether to include score ID in the prompt. question_id_key: The column name for question ID in the DataFrames. question_text_key: The column name for question text in the DataFrames. + cache_config: GraphRAG cache backend configuration. Caching is disabled + when omitted. Returns ------- @@ -115,6 +149,43 @@ def get_differential_pairwise_scores( ) ) pairs = pairs[["question_id", "question_text", "answer_base", "answer_other"]] + extract_system_prompt = extract_system_prompt or load_template_file( + PAIRWISE_PROMPTS_PATH / "pairwise_extract_system_prompt.txt" + ) + extract_user_prompt = extract_user_prompt or load_template_file( + PAIRWISE_PROMPTS_PATH / "pairwise_extract_user_prompt.txt" + ) + judge_system_prompt = judge_system_prompt or load_template_file( + PAIRWISE_PROMPTS_PATH / "pairwise_unique_judge_system_prompt.txt" + ) + judge_user_prompt = judge_user_prompt or load_template_file( + PAIRWISE_PROMPTS_PATH / "pairwise_unique_judge_user_prompt.txt" + ) + cache = DifferentialPairwiseCache( + cache_config or CacheConfig(type=CacheType.Noop, storage=None) + ) + model_metadata = { + "model": llm_config.model, + "llm_provider": str(llm_config.llm_provider), + "init_args": llm_config.init_args, + "call_args": llm_config.call_args, + "custom_providers": [ + provider.model_dump(mode="json") for provider in llm_config.custom_providers + ], + "include_score_id_in_prompt": include_score_id_in_prompt, + } + extraction_cache_metadata = build_stage_metadata( + stage="extraction", + system_prompt=extract_system_prompt.template, + user_prompt=extract_user_prompt.template, + **model_metadata, + ) + verdict_cache_metadata = build_stage_metadata( + stage="verdict", + system_prompt=judge_system_prompt.template, + user_prompt=judge_user_prompt.template, + **model_metadata, + ) with Progress() as progress: @@ -139,6 +210,9 @@ def on_complete_callback(progress_task: TaskID) -> None: extract_user_prompt=extract_user_prompt, judge_system_prompt=judge_system_prompt, judge_user_prompt=judge_user_prompt, + cache=cache, + extraction_cache_metadata=extraction_cache_metadata, + verdict_cache_metadata=verdict_cache_metadata, complete_callback=functools.partial( on_complete_callback, progress_task ), @@ -179,6 +253,9 @@ async def get_differential_pairwise_score( trial: int = 0, include_score_id_in_prompt: bool = True, additional_call_args: dict[str, Any] | None = None, + cache: DifferentialPairwiseCache | None = None, + extraction_cache_metadata: dict[str, Any] | None = None, + verdict_cache_metadata: dict[str, Any] | None = None, ) -> list[dict[str, Any]]: """Extract common/unique content then judge the unique content for one pair. @@ -218,29 +295,60 @@ async def get_differential_pairwise_score( score_id_text = f"Score ID: {score_id}\n" if include_score_id_in_prompt else "" # --- Step 1: extract common and unique content --- - extract_user = extract_user_prompt.substitute( - score_id=score_id_text, - question=question, - answer1=answers_text[answers_order[0]], - answer2=answers_text[answers_order[1]], - ).strip() - extract_system = extract_system_prompt.template - - extraction = ( - await chat( - llm, - messages=[ - {"role": "system", "content": extract_system}, - {"role": "user", "content": extract_user}, - ], - response_format=PairwiseExtractionLLMResponse, - **(additional_call_args or {}), - ) - ).formatted_response + async def _extract() -> dict[str, Any]: + extract_user = extract_user_prompt.substitute( + score_id=score_id_text, + question=question, + answer1=answers_text[answers_order[0]], + answer2=answers_text[answers_order[1]], + ).strip() + extraction_response = ( + await chat( + llm, + messages=[ + {"role": "system", "content": extract_system_prompt.template}, + {"role": "user", "content": extract_user}, + ], + response_format=PairwiseExtractionLLMResponse, + **(additional_call_args or {}), + ) + ).formatted_response + if extraction_response is None: + msg = "LLM did not return a structured PairwiseExtractionLLMResponse." + raise RuntimeError(msg) + return { + "score_id": score_id, + "response": extraction_response.model_dump(mode="json"), + } - if extraction is None: - msg = "LLM did not return a structured PairwiseExtractionLLMResponse." - raise RuntimeError(msg) + extraction_logical_key = compute_logical_key( + "extraction", + { + "question": question, + "answer_1_name": answer_1_name, + "answer_1": answer_1, + "answer_2_name": answer_2_name, + "answer_2": answer_2, + "trial": trial, + }, + ) + if cache is not None and extraction_cache_metadata is not None: + extraction_data = await cache.get_or_compute( + stage="extraction", + cache_key=compute_cache_key( + extraction_logical_key, extraction_cache_metadata + ), + logical_key=extraction_logical_key, + metadata=extraction_cache_metadata, + compute=_extract, + ) + else: + extraction_data = await _extract() + score_id = str(extraction_data["score_id"]) + score_id_text = f"Score ID: {score_id}\n" if include_score_id_in_prompt else "" + extraction = PairwiseExtractionLLMResponse.model_validate( + extraction_data["response"] + ) # --- Step 2: judge the unique content on the requested criteria --- judge_user = judge_user_prompt.substitute( @@ -253,32 +361,52 @@ async def get_differential_pairwise_score( ).strip() judge_system = judge_system_prompt.substitute(criteria=criteria_block) - verdict = ( - await chat( - llm, - messages=[ - {"role": "system", "content": judge_system}, - {"role": "user", "content": judge_user}, - ], - response_format=DifferentialPairwiseLLMResponse, - **(additional_call_args or {}), - ) - ).formatted_response - - if verdict is None: - msg = "LLM did not return a structured DifferentialPairwiseLLMResponse." - raise RuntimeError(msg) + async def _judge() -> dict[str, Any]: + verdict_response = ( + await chat( + llm, + messages=[ + {"role": "system", "content": judge_system}, + {"role": "user", "content": judge_user}, + ], + response_format=DifferentialPairwiseLLMResponse, + **(additional_call_args or {}), + ) + ).formatted_response + if verdict_response is None: + msg = "LLM did not return a structured DifferentialPairwiseLLMResponse." + raise RuntimeError(msg) + _validate_verdicts(verdict_response, criteria) + return {"response": verdict_response.model_dump(mode="json")} - verdicts_by_name = { - item.criteria.strip().lower(): item for item in verdict.verdicts - } + verdict_logical_key = compute_logical_key( + "verdict", + { + "question": question, + "answer_1_name": answer_1_name, + "answer_2_name": answer_2_name, + "trial": trial, + "score_id": score_id, + "extraction": extraction.model_dump(mode="json"), + "criteria": [criterion.model_dump(mode="json") for criterion in criteria], + }, + ) + if cache is not None and verdict_cache_metadata is not None: + verdict_data = await cache.get_or_compute( + stage="verdict", + cache_key=compute_cache_key(verdict_logical_key, verdict_cache_metadata), + logical_key=verdict_logical_key, + metadata=verdict_cache_metadata, + compute=_judge, + ) + else: + verdict_data = await _judge() + verdict = DifferentialPairwiseLLMResponse.model_validate(verdict_data["response"]) + verdicts_by_name = _validate_verdicts(verdict, criteria) rows: list[dict[str, Any]] = [] for criterion in criteria: - criterion_verdict = verdicts_by_name.get(criterion.name.strip().lower()) - if criterion_verdict is None: - msg = f"LLM did not return a verdict for criterion '{criterion.name}'." - raise RuntimeError(msg) + criterion_verdict = verdicts_by_name[criterion.name.strip().lower()] rows.append({ "score_id": score_id, "question": question, diff --git a/benchmark_qed/autoe/pairwise/differential_cache.py b/benchmark_qed/autoe/pairwise/differential_cache.py new file mode 100644 index 0000000..0e855be --- /dev/null +++ b/benchmark_qed/autoe/pairwise/differential_cache.py @@ -0,0 +1,143 @@ +# Copyright (c) 2025 Microsoft Corporation. +"""Stage-aware cache for differential pairwise scoring.""" + +from __future__ import annotations + +import asyncio +import contextlib +import uuid +from typing import TYPE_CHECKING, Any, Literal + +from benchmark_qed.cache import CacheStore, redact_sensitive_values, stable_fingerprint + +if TYPE_CHECKING: + from collections.abc import Awaitable, Callable + + from graphrag_cache import CacheConfig + +DifferentialStage = Literal["extraction", "verdict"] + +_CACHE_NAMESPACE = "differential_pairwise" +_CACHE_SCHEMA_VERSION = 1 +_DEFAULT_LEASE_TTL_SECONDS = 120.0 + + +def build_stage_metadata( + *, + stage: DifferentialStage, + model: str, + llm_provider: str, + init_args: dict[str, Any] | None, + call_args: dict[str, Any] | None, + custom_providers: list[dict[str, Any]] | None, + system_prompt: str, + user_prompt: str, + include_score_id_in_prompt: bool, +) -> dict[str, Any]: + """Build credential-safe metadata for one differential scoring stage.""" + return { + "schema_version": _CACHE_SCHEMA_VERSION, + "evaluator": _CACHE_NAMESPACE, + "stage": stage, + "model": model, + "llm_provider": llm_provider, + "init_args": redact_sensitive_values(init_args or {}), + "call_args": redact_sensitive_values(call_args or {}), + "custom_providers": redact_sensitive_values(custom_providers or []), + "system_prompt": system_prompt, + "user_prompt": user_prompt, + "include_score_id_in_prompt": include_score_id_in_prompt, + } + + +def compute_logical_key(stage: DifferentialStage, payload: dict[str, Any]) -> str: + """Fingerprint stage inputs independently from model configuration.""" + return stable_fingerprint({"stage": stage, "inputs": payload}) + + +def compute_cache_key(logical_key: str, metadata: dict[str, Any]) -> str: + """Fingerprint stage inputs and model configuration.""" + return stable_fingerprint({ + "logical_key": logical_key, + "configuration": metadata, + }) + + +class DifferentialPairwiseCache: + """Persistent cache with independent extraction and verdict namespaces.""" + + def __init__( + self, + cache_config: CacheConfig, + *, + lease_ttl_seconds: float = _DEFAULT_LEASE_TTL_SECONDS, + ) -> None: + self._stores = { + stage: CacheStore( + cache_config, + f"{_CACHE_NAMESPACE}/{stage}", + lease_ttl_seconds=lease_ttl_seconds, + ) + for stage in ("extraction", "verdict") + } + self.lease_ttl_seconds = lease_ttl_seconds + + async def get_or_compute( + self, + *, + stage: DifferentialStage, + cache_key: str, + logical_key: str, + metadata: dict[str, Any], + compute: Callable[[], Awaitable[dict[str, Any]]], + ) -> dict[str, Any]: + """Return a cached stage result or compute and publish it once.""" + store = self._stores[stage] + owner_id = uuid.uuid4().hex + + while True: + entry = await store.get(cache_key) + if entry is not None: + if isinstance(entry[0], dict): + return entry[0] + msg = f"Invalid cached {stage} result for key {cache_key}" + raise RuntimeError(msg) + if await store.claim( + cache_key, + owner_id, + ttl_seconds=self.lease_ttl_seconds, + ): + break + await asyncio.sleep(0.1) + + async def _heartbeat() -> None: + while True: + await asyncio.sleep(max(0.1, self.lease_ttl_seconds / 3)) + store.renew_leases( + [cache_key], + owner_id, + ttl_seconds=self.lease_ttl_seconds, + ) + + heartbeat = asyncio.create_task(_heartbeat()) + try: + result = await compute() + publication = await store.publish( + cache_key, + result, + metadata, + owner_id=owner_id, + logical_key=logical_key, + config_fingerprint=stable_fingerprint(metadata), + ) + if publication.accepted and isinstance(publication.value, dict): + result = publication.value + except (Exception, asyncio.CancelledError): + store.release_leases([cache_key], owner_id) + raise + finally: + heartbeat.cancel() + with contextlib.suppress(asyncio.CancelledError): + await heartbeat + + return result diff --git a/benchmark_qed/autoe/pairwise/scores.py b/benchmark_qed/autoe/pairwise/scores.py index 7ab733c..2d02cf0 100644 --- a/benchmark_qed/autoe/pairwise/scores.py +++ b/benchmark_qed/autoe/pairwise/scores.py @@ -6,6 +6,7 @@ """ import asyncio +import contextlib import functools import itertools import uuid @@ -16,6 +17,7 @@ import numpy as np import pandas as pd +from graphrag_cache import CacheConfig, CacheType from graphrag_llm.completion import LLMCompletion from rich.progress import Progress, TaskID from scipy.stats import shapiro, ttest_rel, wilcoxon @@ -23,6 +25,12 @@ from benchmark_qed.autoe.config import Criteria from benchmark_qed.autoe.data_model import ConditionPair, PairwiseLLMResponse +from benchmark_qed.autoe.pairwise.cache import ( + PairwiseScoreCache, + build_cache_metadata, + compute_cache_key, + compute_logical_key, +) from benchmark_qed.autoe.prompts import pairwise as pairwise_prompts from benchmark_qed.config.llm_config import LLMConfig from benchmark_qed.config.utils import load_template_file @@ -52,6 +60,7 @@ def get_pairwise_scores( include_score_id_in_prompt: bool = True, question_id_key: str = "question_id", question_text_key: str = "question_text", + cache_config: CacheConfig | None = None, ) -> pd.DataFrame: """Score a pair of conditions using the specified criteria. @@ -69,6 +78,8 @@ def get_pairwise_scores( include_score_id_in_prompt: Whether to include score ID in the prompt. question_id_key: The column name for question ID in the DataFrames. question_text_key: The column name for question text in the DataFrames. + cache_config: GraphRAG cache backend configuration. Caching is disabled + when omitted. Returns ------- @@ -92,6 +103,27 @@ def get_pairwise_scores( ) # Select only the columns needed for ConditionPair pairs = pairs[["question_id", "question_text", "answer_base", "answer_other"]] + assessment_system_prompt = assessment_system_prompt or load_template_file( + PAIRWISE_PROMPTS_PATH / "pairwise_system_prompt.txt" + ) + assessment_user_prompt = assessment_user_prompt or load_template_file( + PAIRWISE_PROMPTS_PATH / "pairwise_user_prompt.txt" + ) + cache = PairwiseScoreCache( + cache_config or CacheConfig(type=CacheType.Noop, storage=None) + ) + cache_metadata = build_cache_metadata( + model=llm_config.model, + llm_provider=str(llm_config.llm_provider), + init_args=llm_config.init_args, + call_args=llm_config.call_args, + custom_providers=[ + provider.model_dump(mode="json") for provider in llm_config.custom_providers + ], + system_prompt=assessment_system_prompt.template, + user_prompt=assessment_user_prompt.template, + include_score_id_in_prompt=include_score_id_in_prompt, + ) with Progress() as progress: @@ -105,8 +137,10 @@ def on_complete_callback(progress_task: TaskID) -> None: for criterion in criteria } tasks = [ - get_pairwise_score( + _get_pairwise_score_with_cache( llm=llm_client, + cache=cache, + cache_metadata=cache_metadata, question=pair.question_text, answer_1_name=base_name, answer_1=pair.answer_base, @@ -139,6 +173,93 @@ async def _run_tasks() -> list[dict[str, Any]]: return result +async def _get_pairwise_score_with_cache( + llm: LLMCompletion, + *, + cache: PairwiseScoreCache, + cache_metadata: dict[str, Any], + question: str, + answer_1_name: str, + answer_1: str, + answer_2_name: str, + answer_2: str, + criteria_name: str, + criteria_description: str, + assessment_system_prompt: Template, + assessment_user_prompt: Template, + complete_callback: Callable | None, + trial: int, + include_score_id_in_prompt: bool, + additional_call_args: dict[str, Any] | None, +) -> dict[str, Any]: + """Reuse or compute one pairwise score with cross-process coalescing.""" + logical_key = compute_logical_key( + question=question, + answer_1_name=answer_1_name, + answer_1=answer_1, + answer_2_name=answer_2_name, + answer_2=answer_2, + criteria_name=criteria_name, + criteria_description=criteria_description, + trial=trial, + ) + cache_key = compute_cache_key(logical_key, cache_metadata) + owner_id = uuid.uuid4().hex + + while True: + cached = await cache.get(cache_key) + if cached is not None: + if complete_callback: + complete_callback() + return cached + if await cache.claim(cache_key, owner_id): + break + await asyncio.sleep(0.1) + + async def _heartbeat() -> None: + while True: + await asyncio.sleep(max(0.1, cache.lease_ttl_seconds / 3)) + cache.renew(cache_key, owner_id) + + heartbeat = asyncio.create_task(_heartbeat()) + try: + score = await get_pairwise_score( + llm, + question=question, + answer_1_name=answer_1_name, + answer_1=answer_1, + answer_2_name=answer_2_name, + answer_2=answer_2, + criteria_name=criteria_name, + criteria_description=criteria_description, + assessment_system_prompt=assessment_system_prompt, + assessment_user_prompt=assessment_user_prompt, + trial=trial, + include_score_id_in_prompt=include_score_id_in_prompt, + additional_call_args=additional_call_args, + ) + publication = await cache.publish( + cache_key, + score, + cache_metadata, + owner_id=owner_id, + logical_key=logical_key, + ) + if publication.accepted and isinstance(publication.value, dict): + score = publication.value + except (Exception, asyncio.CancelledError): + cache.release(cache_key, owner_id) + raise + finally: + heartbeat.cancel() + with contextlib.suppress(asyncio.CancelledError): + await heartbeat + + if complete_callback: + complete_callback() + return score + + async def get_pairwise_score( llm: LLMCompletion, *, diff --git a/benchmark_qed/autoe/reference/cache.py b/benchmark_qed/autoe/reference/cache.py new file mode 100644 index 0000000..d4aad11 --- /dev/null +++ b/benchmark_qed/autoe/reference/cache.py @@ -0,0 +1,147 @@ +# Copyright (c) 2025 Microsoft Corporation. +"""Content-addressed cache for reference-based LLM judgments.""" + +from __future__ import annotations + +from typing import TYPE_CHECKING, Any + +from benchmark_qed.cache import ( + CachePublishResult, + CacheStore, + redact_sensitive_values, + stable_fingerprint, +) + +if TYPE_CHECKING: + from graphrag_cache import CacheConfig + +_CACHE_NAMESPACE = "reference" +_CACHE_SCHEMA_VERSION = 1 +_DEFAULT_LEASE_TTL_SECONDS = 120.0 + + +def build_cache_metadata( + *, + model: str, + llm_provider: str, + init_args: dict[str, Any] | None, + call_args: dict[str, Any] | None, + custom_providers: list[dict[str, Any]] | None, + system_prompt: str, + user_prompt: str, + include_score_id_in_prompt: bool, +) -> dict[str, Any]: + """Build credential-safe metadata that identifies judge configuration.""" + return { + "schema_version": _CACHE_SCHEMA_VERSION, + "evaluator": _CACHE_NAMESPACE, + "model": model, + "llm_provider": llm_provider, + "init_args": redact_sensitive_values(init_args or {}), + "call_args": redact_sensitive_values(call_args or {}), + "custom_providers": redact_sensitive_values(custom_providers or []), + "system_prompt": system_prompt, + "user_prompt": user_prompt, + "include_score_id_in_prompt": include_score_id_in_prompt, + } + + +def compute_logical_key( + *, + question: str, + reference_answer: str, + generated_answer: str, + criteria_name: str, + criteria_description: str, + trial: int, + score_min: int, + score_max: int, +) -> str: + """Fingerprint one reference trial independently from judge configuration.""" + return stable_fingerprint({ + "question": question, + "reference_answer": reference_answer, + "generated_answer": generated_answer, + "criteria_name": criteria_name, + "criteria_description": criteria_description, + "trial": trial, + "score_min": score_min, + "score_max": score_max, + }) + + +def compute_cache_key(logical_key: str, metadata: dict[str, Any]) -> str: + """Fingerprint a reference trial and complete judge configuration.""" + return stable_fingerprint({ + "logical_key": logical_key, + "configuration": metadata, + }) + + +class ReferenceScoreCache: + """Persistent, concurrency-safe cache for reference score dictionaries.""" + + def __init__( + self, + cache_config: CacheConfig, + *, + lease_ttl_seconds: float = _DEFAULT_LEASE_TTL_SECONDS, + ) -> None: + self._store = CacheStore( + cache_config, + _CACHE_NAMESPACE, + lease_ttl_seconds=lease_ttl_seconds, + ) + self.lease_ttl_seconds = lease_ttl_seconds + + async def get(self, cache_key: str) -> dict[str, Any] | None: + """Return a cached reference score.""" + entry = await self._store.get(cache_key) + if entry is None: + return None + if not isinstance(entry[0], dict): + msg = f"Invalid cached reference result for key {cache_key}" + raise TypeError(msg) + return entry[0] + + async def claim(self, cache_key: str, owner_id: str) -> bool: + """Claim an uncached reference score for this worker.""" + return await self._store.claim( + cache_key, + owner_id, + ttl_seconds=self.lease_ttl_seconds, + ) + + def renew(self, cache_key: str, owner_id: str) -> bool: + """Renew an active reference-score lease.""" + return ( + self._store.renew_leases( + [cache_key], + owner_id, + ttl_seconds=self.lease_ttl_seconds, + ) + == 1 + ) + + def release(self, cache_key: str, owner_id: str) -> None: + """Release a reference-score lease without publishing.""" + self._store.release_leases([cache_key], owner_id) + + async def publish( + self, + cache_key: str, + score: dict[str, Any], + metadata: dict[str, Any], + *, + owner_id: str, + logical_key: str, + ) -> CachePublishResult: + """Publish a reference score and release its lease.""" + return await self._store.publish( + cache_key, + score, + metadata, + owner_id=owner_id, + logical_key=logical_key, + config_fingerprint=stable_fingerprint(metadata), + ) diff --git a/benchmark_qed/autoe/reference/scores.py b/benchmark_qed/autoe/reference/scores.py index 88f3ba3..09552ff 100644 --- a/benchmark_qed/autoe/reference/scores.py +++ b/benchmark_qed/autoe/reference/scores.py @@ -6,6 +6,7 @@ """ import asyncio +import contextlib import functools import itertools import uuid @@ -16,12 +17,19 @@ import numpy as np import pandas as pd +from graphrag_cache import CacheConfig, CacheType from graphrag_llm.completion import LLMCompletion from rich.progress import Progress, TaskID from benchmark_qed.autoe.config import Criteria from benchmark_qed.autoe.data_model import ConditionPair, ReferenceLLMResponse from benchmark_qed.autoe.prompts import reference as reference_prompts +from benchmark_qed.autoe.reference.cache import ( + ReferenceScoreCache, + build_cache_metadata, + compute_cache_key, + compute_logical_key, +) from benchmark_qed.config.llm_config import LLMConfig from benchmark_qed.config.utils import load_template_file from benchmark_qed.llm import chat @@ -44,6 +52,7 @@ def get_reference_scores( include_score_id_in_prompt: bool = True, question_id_key: str = "question_id", question_text_key: str = "question_text", + cache_config: CacheConfig | None = None, ) -> pd.DataFrame: """Score generated answers against reference answers using specified criteria. @@ -61,6 +70,8 @@ def get_reference_scores( include_score_id_in_prompt: Whether to include score ID in the prompt. question_id_key: The column name for question ID in the DataFrames. question_text_key: The column name for question text in the DataFrames. + cache_config: GraphRAG cache backend configuration. Caching is disabled + when omitted. Returns ------- @@ -84,6 +95,27 @@ def get_reference_scores( ) # Select only the columns needed for ConditionPair pairs = pairs[["question_id", "question_text", "answer_base", "answer_other"]] + assessment_system_prompt = assessment_system_prompt or load_template_file( + REFERENCE_PROMPTS_PATH / "reference_system_prompt.txt" + ) + assessment_user_prompt = assessment_user_prompt or load_template_file( + REFERENCE_PROMPTS_PATH / "reference_user_prompt.txt" + ) + cache = ReferenceScoreCache( + cache_config or CacheConfig(type=CacheType.Noop, storage=None) + ) + cache_metadata = build_cache_metadata( + model=llm_config.model, + llm_provider=str(llm_config.llm_provider), + init_args=llm_config.init_args, + call_args=llm_config.call_args, + custom_providers=[ + provider.model_dump(mode="json") for provider in llm_config.custom_providers + ], + system_prompt=assessment_system_prompt.template, + user_prompt=assessment_user_prompt.template, + include_score_id_in_prompt=include_score_id_in_prompt, + ) with Progress(transient=True) as progress: @@ -98,8 +130,10 @@ def on_complete_callback(progress_task: TaskID) -> None: } tasks = [ - get_reference_score( + _get_reference_score_with_cache( llm_client, + cache=cache, + cache_metadata=cache_metadata, question=pair.question_text, reference_answer=pair.answer_base, generated_answer=pair.answer_other, @@ -129,6 +163,99 @@ async def _run_tasks() -> list[dict[str, Any]]: return pd.DataFrame(results) +async def _get_reference_score_with_cache( + llm: LLMCompletion, + *, + cache: ReferenceScoreCache, + cache_metadata: dict[str, Any], + question: str, + reference_answer: str, + generated_answer: str, + criteria_name: str, + criteria_description: str, + assessment_system_prompt: Template, + assessment_user_prompt: Template, + complete_callback: Callable | None, + trial: int, + score_min: int, + score_max: int, + include_score_id_in_prompt: bool, + additional_call_args: dict[str, Any] | None, +) -> dict[str, Any]: + """Reuse or compute one reference score with cross-process coalescing.""" + logical_key = compute_logical_key( + question=question, + reference_answer=reference_answer, + generated_answer=generated_answer, + criteria_name=criteria_name, + criteria_description=criteria_description, + trial=trial, + score_min=score_min, + score_max=score_max, + ) + cache_key = compute_cache_key(logical_key, cache_metadata) + owner_id = uuid.uuid4().hex + + while True: + cached = await cache.get(cache_key) + if cached is not None: + if complete_callback: + complete_callback() + return cached + if await cache.claim(cache_key, owner_id): + cached = await cache.get(cache_key) + if cached is not None: + cache.release(cache_key, owner_id) + if complete_callback: + complete_callback() + return cached + break + await asyncio.sleep(0.1) + + async def _heartbeat() -> None: + while True: + await asyncio.sleep(max(0.1, cache.lease_ttl_seconds / 3)) + cache.renew(cache_key, owner_id) + + heartbeat = asyncio.create_task(_heartbeat()) + try: + score = await get_reference_score( + llm, + question=question, + reference_answer=reference_answer, + generated_answer=generated_answer, + criteria_name=criteria_name, + criteria_description=criteria_description, + assessment_system_prompt=assessment_system_prompt, + assessment_user_prompt=assessment_user_prompt, + trial=trial, + score_min=score_min, + score_max=score_max, + include_score_id_in_prompt=include_score_id_in_prompt, + additional_call_args=additional_call_args, + ) + publication = await cache.publish( + cache_key, + score, + cache_metadata, + owner_id=owner_id, + logical_key=logical_key, + ) + if publication.accepted and isinstance(publication.value, dict): + score = publication.value + except (Exception, asyncio.CancelledError): + cache.release(cache_key, owner_id) + raise + finally: + heartbeat.cancel() + with contextlib.suppress(asyncio.CancelledError): + await heartbeat + + if complete_callback: + complete_callback() + return score + + async def get_reference_score( llm: LLMCompletion, *, diff --git a/benchmark_qed/autoe/retrieval/scores.py b/benchmark_qed/autoe/retrieval/scores.py index 4f92a60..f93b272 100644 --- a/benchmark_qed/autoe/retrieval/scores.py +++ b/benchmark_qed/autoe/retrieval/scores.py @@ -206,7 +206,7 @@ def load_clusters_from_json( def load_reference_results( reference_dir: Path, - question_set: str, # noqa: ARG001 + question_set: str, # ruff: ignore[unused-function-argument] reference_filename: str = "reference.json", *, input_storage: Storage | None = None, diff --git a/benchmark_qed/autoe/retrieval_metrics/relevance_assessment/base.py b/benchmark_qed/autoe/retrieval_metrics/relevance_assessment/base.py index 65679fe..496a8bf 100644 --- a/benchmark_qed/autoe/retrieval_metrics/relevance_assessment/base.py +++ b/benchmark_qed/autoe/retrieval_metrics/relevance_assessment/base.py @@ -1,44 +1,63 @@ # Copyright (c) 2025 Microsoft Corporation. """Base classes for relevance assessment.""" +import asyncio import contextlib import hashlib import json import logging +import uuid from abc import ABC, abstractmethod from datetime import UTC, datetime -from pathlib import Path from typing import Any +from graphrag_cache import CacheConfig, CacheType + from benchmark_qed.autod.data_model.text_unit import TextUnit from benchmark_qed.autoe.data_model.relevance import ( RelevanceAssessmentItem, RelevanceAssessmentResponse, ) +from benchmark_qed.cache import ( + CacheStore, + changed_configuration_fields, + get_cache_base_dir, + redact_sensitive_values, + stable_fingerprint, +) log: logging.Logger = logging.getLogger(__name__) +_LEASE_TTL_SECONDS = 120.0 class RelevanceRater(ABC): """Abstract base class for rating the relevance of text chunks to queries.""" - def __init__( - self, cache_dir: Path | None = None, cache_enabled: bool = True - ) -> None: + def __init__(self, cache_config: CacheConfig | None = None) -> None: """ Initialize the RelevanceRater with optional caching. Args: - cache_dir: Directory to store cache files. If None, caching is disabled. - cache_enabled: Whether to enable caching functionality. + cache_config: GraphRAG cache backend configuration. None or a + no-op configuration disables caching. """ - self.cache_dir: Path | None = cache_dir - self.cache_enabled: bool = cache_enabled and cache_dir is not None + self.cache_config = cache_config + self.cache_enabled: bool = ( + cache_config is not None and cache_config.type != CacheType.Noop + ) + self._cache_base_dir = ( + get_cache_base_dir(cache_config) if cache_config is not None else None + ) self.cache_hits: int = 0 self.cache_misses: int = 0 + self._cache_store: CacheStore | None = None + self._legacy_cache_migrated = False - if self.cache_enabled and self.cache_dir: - self.cache_dir.mkdir(parents=True, exist_ok=True) + if self.cache_enabled and cache_config is not None: + self._cache_store = CacheStore( + cache_config, + namespace=self.__class__.__name__, + ) async def rate_relevance( self, query: str, text_units: list[TextUnit] @@ -57,51 +76,203 @@ async def rate_relevance( if not self.cache_enabled: # No caching - call implementation directly return await self._rate_relevance_impl(query, text_units) + await self._migrate_legacy_cache_files() # With caching enabled, check each text unit individually - cached_assessments = [] - uncached_text_units = [] - uncached_indices = [] + cached_assessments: list[tuple[int, RelevanceAssessmentItem]] = [] + uncached_by_key: dict[str, tuple[TextUnit, list[int]]] = {} rater_params = self._get_cache_relevant_params() for i, text_unit in enumerate(text_units): cache_key = self._generate_cache_key(query, text_unit, rater_params) - cached_assessment = self._load_from_cache(cache_key) + cached_assessment = await self._load_from_cache(cache_key) if cached_assessment is not None: cached_assessments.append((i, cached_assessment)) self.cache_hits += 1 else: - uncached_text_units.append(text_unit) - uncached_indices.append(i) self.cache_misses += 1 + pending = uncached_by_key.get(cache_key) + if pending is None: + uncached_by_key[cache_key] = (text_unit, [i]) + else: + pending[1].append(i) # Process uncached text units if any - uncached_results = [] - if uncached_text_units: - uncached_response = await self._rate_relevance_impl( - query, uncached_text_units - ) - uncached_results = uncached_response.assessment - - # Cache individual results - for j, text_unit in enumerate(uncached_text_units): - cache_key = self._generate_cache_key(query, text_unit, rater_params) - self._save_to_cache(cache_key, query, uncached_results[j]) + if uncached_by_key: + if self._cache_store is None: + msg = "Caching is enabled but the cache store is unavailable" + raise RuntimeError(msg) + + config_fingerprint = self._generate_config_fingerprint(rater_params) + current_metadata = self._build_cache_metadata(query, rater_params) + alternatives = await self._cache_store.find_alternate_configurations([ + ( + self._generate_logical_key(query, text_unit), + config_fingerprint, + ) + for text_unit, _indices in uncached_by_key.values() + ]) + if alternatives: + changed_fields = sorted({ + field + for metadata in alternatives + for field in changed_configuration_fields( + current_metadata, metadata + ) + }) + log.warning( + "Found %d cached relevance result(s) for the same inputs " + "with different %s; they will not be reused", + len(alternatives), + ", ".join(changed_fields) or "configuration metadata", + ) + + lease_owner = uuid.uuid4().hex + owned = { + cache_key: item + for cache_key, item in uncached_by_key.items() + if await self._cache_store.claim( + cache_key, + lease_owner, + ttl_seconds=_LEASE_TTL_SECONDS, + ) + } + waiting = { + cache_key: item + for cache_key, item in uncached_by_key.items() + if cache_key not in owned + } + + while owned or waiting: + if owned: + await self._evaluate_and_publish_claimed( + query=query, + claimed=owned, + rater_params=rater_params, + config_fingerprint=config_fingerprint, + lease_owner=lease_owner, + cached_assessments=cached_assessments, + ) + owned = {} + + completed_keys: list[str] = [] + recovered: dict[str, tuple[TextUnit, list[int]]] = {} + for cache_key, item in waiting.items(): + cached_assessment = await self._load_from_cache(cache_key) + if cached_assessment is not None: + cached_assessments.extend( + (index, cached_assessment) for index in item[1] + ) + completed_keys.append(cache_key) + elif await self._cache_store.claim( + cache_key, + lease_owner, + ttl_seconds=_LEASE_TTL_SECONDS, + ): + recovered[cache_key] = item + completed_keys.append(cache_key) + for cache_key in completed_keys: + waiting.pop(cache_key) + owned = recovered + if waiting and not owned: + await asyncio.sleep(0.1) # Combine cached and uncached results in original order - all_assessments: list[RelevanceAssessmentItem] = [None] * len(text_units) # type: ignore + assessments_by_index = dict(cached_assessments) + all_assessments = [ + self._attach_text_unit(assessments_by_index[index], text_unit) + for index, text_unit in enumerate(text_units) + ] - # Place cached results - for original_idx, cached_assessment in cached_assessments: - all_assessments[original_idx] = cached_assessment + return RelevanceAssessmentResponse(assessment=all_assessments) - # Place uncached results - for j, original_idx in enumerate(uncached_indices): - all_assessments[original_idx] = uncached_results[j] + async def _evaluate_and_publish_claimed( + self, + *, + query: str, + claimed: dict[str, tuple[TextUnit, list[int]]], + rater_params: dict[str, Any], + config_fingerprint: str, + lease_owner: str, + cached_assessments: list[tuple[int, RelevanceAssessmentItem]], + ) -> None: + """Evaluate claimed units while renewing and atomically releasing leases.""" + if self._cache_store is None: + msg = "Cache store is unavailable" + raise RuntimeError(msg) + cache_store = self._cache_store + cache_keys = list(claimed) + + async def _heartbeat() -> None: + while True: + await asyncio.sleep(max(0.1, _LEASE_TTL_SECONDS / 3)) + cache_store.renew_leases( + cache_keys, + lease_owner, + ttl_seconds=_LEASE_TTL_SECONDS, + ) + + heartbeat = asyncio.create_task(_heartbeat()) + try: + response = await self._rate_relevance_impl( + query, + [text_unit for text_unit, _indices in claimed.values()], + ) + self._validate_result_count(response, len(claimed)) + + for ( + cache_key, + (text_unit, indices), + ), assessment in zip(claimed.items(), response.assessment, strict=True): + publication = await cache_store.publish( + cache_key, + assessment.model_dump(exclude={"text_unit"}), + self._build_cache_metadata(query, rater_params), + owner_id=lease_owner, + logical_key=self._generate_logical_key(query, text_unit), + config_fingerprint=config_fingerprint, + ) + canonical_assessment = ( + RelevanceAssessmentItem(**publication.value) + if publication.accepted and isinstance(publication.value, dict) + else assessment + ) + cached_assessments.extend( + (original_idx, canonical_assessment) for original_idx in indices + ) + except (Exception, asyncio.CancelledError): + cache_store.release_leases(cache_keys, lease_owner) + raise + finally: + heartbeat.cancel() + with contextlib.suppress(asyncio.CancelledError): + await heartbeat + + @staticmethod + def _attach_text_unit( + assessment: RelevanceAssessmentItem, text_unit: TextUnit + ) -> RelevanceAssessmentItem: + """Attach a cached judgment to the input identity it represents.""" + return RelevanceAssessmentItem( + text_unit=text_unit, + reasoning=assessment.reasoning, + score=assessment.score, + ) - return RelevanceAssessmentResponse(assessment=all_assessments) + @staticmethod + def _validate_result_count( + response: RelevanceAssessmentResponse, expected_count: int + ) -> None: + """Raise when a rater violates its one-result-per-input contract.""" + if len(response.assessment) != expected_count: + msg = ( + "Relevance rater returned " + f"{len(response.assessment)} results for " + f"{expected_count} uncached text units" + ) + raise RuntimeError(msg) @abstractmethod async def _rate_relevance_impl( @@ -127,56 +298,95 @@ def _generate_cache_key( "query": query.strip().lower(), "text_content": text_unit.text.strip().lower(), "rater_type": self.__class__.__name__, - "rater_params": rater_params, + "rater_params": redact_sensitive_values(rater_params), } - content_str = json.dumps(cache_data, sort_keys=True) + content_str = json.dumps(cache_data, sort_keys=True, default=str) return hashlib.sha256(content_str.encode()).hexdigest() - def _load_from_cache(self, cache_key: str) -> RelevanceAssessmentItem | None: - """Load cached result for a single text unit if available.""" - if not self.cache_enabled or not self.cache_dir: - return None + def _generate_logical_key(self, query: str, text_unit: TextUnit) -> str: + """Fingerprint query and text independently from rater configuration.""" + return stable_fingerprint({ + "query": query.strip().lower(), + "text_content": text_unit.text.strip().lower(), + "rater_type": self.__class__.__name__, + }) + + @staticmethod + def _generate_config_fingerprint(rater_params: dict[str, Any]) -> str: + """Fingerprint relevance settings independently from query and text.""" + return stable_fingerprint(rater_params) - cache_file = self.cache_dir / f"{cache_key}.json" - if not cache_file.exists(): + def _build_cache_metadata( + self, query: str, rater_params: dict[str, Any] + ) -> dict[str, Any]: + """Build inspectable metadata for a relevance assessment.""" + return { + "timestamp": datetime.now(tz=UTC).isoformat(), + "query": query.strip().lower(), + "rater_type": self.__class__.__name__, + "rater_params": redact_sensitive_values(rater_params), + } + + async def _load_from_cache(self, cache_key: str) -> RelevanceAssessmentItem | None: + """Load cached result for a single text unit if available.""" + if self._cache_store is None: return None try: - with cache_file.open(encoding="utf-8") as f: - data = json.load(f) - return RelevanceAssessmentItem(**data["assessment_item"]) - except (OSError, json.JSONDecodeError, KeyError, TypeError): - # If cache file is corrupted, ignore and continue + entry = await self._cache_store.get(cache_key) + if entry is None: + return None + assessment_data, _metadata = entry + return RelevanceAssessmentItem(**assessment_data) + except (json.JSONDecodeError, TypeError, ValueError) as exc: + log.warning("Ignoring invalid relevance cache entry %s: %s", cache_key, exc) return None - def _save_to_cache( - self, cache_key: str, query: str, assessment_item: RelevanceAssessmentItem - ) -> None: - """Save single text unit assessment result to cache.""" - if not self.cache_enabled or not self.cache_dir: + async def _migrate_legacy_cache_files(self) -> None: + """Import legacy per-key JSON files once without deleting them.""" + if ( + self._legacy_cache_migrated + or self._cache_store is None + or self._cache_base_dir is None + ): + return + migration_name = f"legacy_relevance_import:{self.__class__.__name__}" + if await self._cache_store.get_property(migration_name) is not None: + self._legacy_cache_migrated = True return - cache_file = self.cache_dir / f"{cache_key}.json" - - # Create a copy for serialization to avoid mutating the original - # We exclude the embedding to avoid Pydantic warnings about numpy arrays - assessment_data = assessment_item.model_dump( - exclude={"text_unit": {"text_embedding"}} - ) - - cache_data = { - "timestamp": datetime.now(tz=UTC).isoformat(), - "query": query.strip().lower(), - "assessment_item": assessment_data, - } - - try: - with cache_file.open("w", encoding="utf-8") as f: - json.dump(cache_data, f, indent=2) - except OSError: - # If we can't write cache, continue without it - log.debug("Failed to write cache file: %s", cache_file) + entries: list[tuple[str, Any, dict[str, Any]]] = [] + for cache_file in self._cache_base_dir.glob("*.json"): + try: + with cache_file.open(encoding="utf-8") as file: + data = json.load(file) + assessment_data = data["assessment_item"] + RelevanceAssessmentItem(**assessment_data) + except ( + OSError, + json.JSONDecodeError, + KeyError, + TypeError, + ValueError, + ) as exc: + log.warning( + "Could not import legacy relevance cache file %s: %s", + cache_file, + exc, + ) + continue + metadata = { + "source": "legacy_json", + "timestamp": data.get("timestamp"), + "query": data.get("query"), + "rater_type": self.__class__.__name__, + } + entries.append((cache_file.stem, assessment_data, metadata)) + + await self._cache_store.put_many(entries) + await self._cache_store.set_property_if_absent(migration_name, "complete") + self._legacy_cache_migrated = True def _get_cache_relevant_params(self) -> dict[str, Any]: """ @@ -233,16 +443,12 @@ def get_cache_stats(self) -> dict[str, Any]: total_requests = self.cache_hits + self.cache_misses hit_rate = (self.cache_hits / total_requests * 100) if total_requests > 0 else 0 - # Count cache files cache_files = 0 cache_size_mb = 0 - if self.cache_dir and self.cache_dir.exists(): - all_cache_files = list(self.cache_dir.glob("*.json")) - cache_files = len(all_cache_files) - cache_size_mb = sum(f.stat().st_size for f in all_cache_files) / ( - 1024 * 1024 - ) + if self._cache_store is not None: + cache_files = self._cache_store.count() + cache_size_mb = self._cache_store.size_bytes() / (1024 * 1024) return { "caching_enabled": True, @@ -251,17 +457,29 @@ def get_cache_stats(self) -> dict[str, Any]: "hit_rate_percent": round(hit_rate, 1), "cache_files": cache_files, "cache_size_mb": round(cache_size_mb, 2), - "cache_dir": str(self.cache_dir) if self.cache_dir else None, + "cache_config": ( + self.cache_config.model_dump(mode="json") + if self.cache_config is not None + else None + ), } - def clear_cache(self) -> None: + async def clear_cache(self) -> None: """Clear all cached results.""" - if not self.cache_enabled or not self.cache_dir: + if self._cache_store is None: return - for cache_file in self.cache_dir.glob("*.json"): - with contextlib.suppress(Exception): - cache_file.unlink() + await self._cache_store.clear() + if self._cache_base_dir is not None: + for cache_file in self._cache_base_dir.glob("*.json"): + try: + cache_file.unlink() + except OSError as exc: + log.warning( + "Failed to remove legacy relevance cache file %s: %s", + cache_file, + exc, + ) # Reset statistics self.cache_hits = 0 diff --git a/benchmark_qed/autoe/retrieval_metrics/relevance_assessment/bing_rater.py b/benchmark_qed/autoe/retrieval_metrics/relevance_assessment/bing_rater.py index c2e3726..233b626 100644 --- a/benchmark_qed/autoe/retrieval_metrics/relevance_assessment/bing_rater.py +++ b/benchmark_qed/autoe/retrieval_metrics/relevance_assessment/bing_rater.py @@ -8,6 +8,7 @@ from string import Template from typing import Any +from graphrag_cache import CacheConfig from graphrag_llm.completion import LLMCompletion from benchmark_qed.autod.data_model.text_unit import TextUnit @@ -37,8 +38,7 @@ def __init__( llm_config: LLMConfig, prompt_template: Template | None = None, concurrent_requests: int = 32, - cache_dir: Path | None = None, - cache_enabled: bool = True, + cache_config: CacheConfig | None = None, ) -> None: """ Initialize the BingRelevanceRater. @@ -48,10 +48,9 @@ def __init__( llm_config: The LLM configuration containing call arguments and other settings. prompt_template: UMBRELA prompt template. If None, uses default from file. concurrent_requests: Maximum number of concurrent requests to the LLM. - cache_dir: Directory to store cache files. If None, caching is disabled. - cache_enabled: Whether to enable caching functionality. + cache_config: GraphRAG cache backend configuration. """ - super().__init__(cache_dir=cache_dir, cache_enabled=cache_enabled) + super().__init__(cache_config=cache_config) self.llm_client = llm_client self.llm_config = llm_config self.prompt_template: Template = prompt_template or load_template_file( diff --git a/benchmark_qed/autoe/retrieval_metrics/relevance_assessment/compare_raters.py b/benchmark_qed/autoe/retrieval_metrics/relevance_assessment/compare_raters.py index 78e7aa6..100cfca 100644 --- a/benchmark_qed/autoe/retrieval_metrics/relevance_assessment/compare_raters.py +++ b/benchmark_qed/autoe/retrieval_metrics/relevance_assessment/compare_raters.py @@ -7,6 +7,8 @@ from pathlib import Path from typing import Any +from graphrag_cache import CacheConfig + from benchmark_qed.autod.data_model.text_unit import TextUnit from benchmark_qed.autoe.retrieval_metrics.relevance_assessment.bing_rater import ( BingRelevanceRater, @@ -787,7 +789,7 @@ async def compare_raters( queries: list[str], text_units: list[TextUnit], llm_config: LLMConfig, - cache_dir: Path | None = None, + cache_config: CacheConfig | None = None, ) -> RaterComparisonResult: """Compare Bing and Rationale raters on the same queries and text units. @@ -795,7 +797,7 @@ async def compare_raters( queries: List of queries to assess. text_units: List of text units to assess for each query. llm_config: LLM configuration for both raters. - cache_dir: Optional cache directory for assessments. + cache_config: Optional GraphRAG cache configuration for assessments. Returns ------- @@ -805,22 +807,16 @@ async def compare_raters( # Create LLM client llm_client = ModelFactory.create_chat_model(llm_config) - # Create both raters - bing_cache = cache_dir / "bing" if cache_dir else None - rationale_cache = cache_dir / "rationale" if cache_dir else None - bing_rater = BingRelevanceRater( llm_client=llm_client, llm_config=llm_config, - cache_dir=bing_cache, - cache_enabled=cache_dir is not None, + cache_config=cache_config, ) rationale_rater = RationaleRelevanceRater( llm_client=llm_client, llm_config=llm_config, - cache_dir=rationale_cache, - cache_enabled=cache_dir is not None, + cache_config=cache_config, ) comparison_items: list[RaterComparisonItem] = [] @@ -910,7 +906,7 @@ async def _compare_raters_with_topk_selection( all_text_units: list[TextUnit], llm_config: LLMConfig, max_text_units: int, - cache_dir: Path | None = None, + cache_config: CacheConfig | None = None, embedding_config: LLMConfig | None = None, ) -> RaterComparisonResult: """Compare raters using top-K text units by similarity for each query. @@ -921,7 +917,7 @@ async def _compare_raters_with_topk_selection( all_text_units: All text units to select from. llm_config: LLM configuration for raters. max_text_units: Number of top text units to select per query. - cache_dir: Optional cache directory. + cache_config: Optional GraphRAG cache configuration. embedding_config: LLM config for embedding queries without embeddings. Returns @@ -934,21 +930,16 @@ async def _compare_raters_with_topk_selection( # Create LLM client and raters llm_client = ModelFactory.create_chat_model(llm_config) - bing_cache = cache_dir / "bing" if cache_dir else None - rationale_cache = cache_dir / "rationale" if cache_dir else None - bing_rater = BingRelevanceRater( llm_client=llm_client, llm_config=llm_config, - cache_dir=bing_cache, - cache_enabled=cache_dir is not None, + cache_config=cache_config, ) rationale_rater = RationaleRelevanceRater( llm_client=llm_client, llm_config=llm_config, - cache_dir=rationale_cache, - cache_enabled=cache_dir is not None, + cache_config=cache_config, ) # Embed queries that don't have embeddings @@ -1093,7 +1084,7 @@ async def compare_raters_from_files( llm_config: LLMConfig, max_questions: int | None = None, max_text_units: int | None = None, - cache_dir: Path | None = None, + cache_config: CacheConfig | None = None, output_path: Path | None = None, random_seed: int | None = 42, use_similarity_selection: bool = True, @@ -1110,7 +1101,7 @@ async def compare_raters_from_files( llm_config: LLM configuration for both raters. max_questions: Maximum number of questions to process. max_text_units: Maximum number of text units per question. - cache_dir: Optional cache directory for assessments. + cache_config: Optional GraphRAG cache configuration for assessments. output_path: Optional path to save comparison results as JSON. random_seed: Random seed for reproducible sampling. Set to None for no seed. use_similarity_selection: If True, select top-K text units by similarity @@ -1133,7 +1124,7 @@ async def compare_raters_from_files( if random_seed is not None: random.seed(random_seed) - np.random.seed(random_seed) # noqa: NPY002 + np.random.seed(random_seed) # ruff: ignore[numpy-legacy-random] # Load questions questions_data = json.loads(Path(questions_path).read_text()) @@ -1170,7 +1161,7 @@ async def compare_raters_from_files( all_text_units=all_text_units, llm_config=llm_config, max_text_units=max_text_units, - cache_dir=cache_dir, + cache_config=cache_config, embedding_config=embedding_config, ) else: @@ -1188,7 +1179,7 @@ async def compare_raters_from_files( queries=queries, text_units=all_text_units, llm_config=llm_config, - cache_dir=cache_dir, + cache_config=cache_config, ) # Run judge if requested diff --git a/benchmark_qed/autoe/retrieval_metrics/relevance_assessment/rationale_rater.py b/benchmark_qed/autoe/retrieval_metrics/relevance_assessment/rationale_rater.py index 1377c4c..a9fede8 100644 --- a/benchmark_qed/autoe/retrieval_metrics/relevance_assessment/rationale_rater.py +++ b/benchmark_qed/autoe/retrieval_metrics/relevance_assessment/rationale_rater.py @@ -7,6 +7,7 @@ from string import Template from typing import Any +from graphrag_cache import CacheConfig from graphrag_llm.completion import LLMCompletion from benchmark_qed.autod.data_model.text_unit import TextUnit @@ -40,8 +41,7 @@ def __init__( llm_config: LLMConfig, prompt_template: Template | None = None, concurrent_requests: int = 32, - cache_dir: Path | None = None, - cache_enabled: bool = True, + cache_config: CacheConfig | None = None, ) -> None: """ Initialize the RationaleRelevanceAssessor. @@ -51,10 +51,9 @@ def __init__( llm_config: The LLM configuration containing call arguments and other settings. prompt_template: Rationale prompt template. If None, uses default from file. concurrent_requests: Maximum number of concurrent requests to the LLM. - cache_dir: Directory to store cache files. If None, caching is disabled. - cache_enabled: Whether to enable caching functionality. + cache_config: GraphRAG cache backend configuration. """ - super().__init__(cache_dir=cache_dir, cache_enabled=cache_enabled) + super().__init__(cache_config=cache_config) self.llm_client = llm_client self.llm_config = llm_config self.prompt_template: Template = prompt_template or load_template_file( @@ -147,7 +146,7 @@ async def _assess_unit( ).formatted_response if llm_response is None: msg = "LLM did not return a structured RelevanceAssessmentItem." - raise RuntimeError(msg) # noqa: TRY301 + raise RuntimeError(msg) # ruff: ignore[raise-within-try] # Create the final response with the text unit included return RelevanceAssessmentItem( diff --git a/benchmark_qed/autoe/utils/stats.py b/benchmark_qed/autoe/utils/stats.py index b3851b8..6a8099d 100644 --- a/benchmark_qed/autoe/utils/stats.py +++ b/benchmark_qed/autoe/utils/stats.py @@ -381,7 +381,7 @@ def _run_tukey_posthoc( groups: Mapping[str, Sequence[float]], group_names: list[str], alpha: float, - correction: str, # noqa: ARG001 - kept for API consistency + correction: str, # ruff: ignore[unused-function-argument] - kept for API consistency ) -> PostHocResult: """Run Tukey HSD test for normal data (independent samples). diff --git a/benchmark_qed/autoe/visualization/assertions.py b/benchmark_qed/autoe/visualization/assertions.py index 11e68b6..b43dd9b 100644 --- a/benchmark_qed/autoe/visualization/assertions.py +++ b/benchmark_qed/autoe/visualization/assertions.py @@ -135,7 +135,7 @@ def plot_assertion_accuracy_by_rag_method( def plot_assertion_score_distribution( - results_df: pd.DataFrame, # noqa: ARG001 + results_df: pd.DataFrame, # ruff: ignore[unused-function-argument] output_path: Path | None = None, figsize: tuple[int, int] = (10, 6), title: str = "Assertion Score Distribution by RAG Method", diff --git a/benchmark_qed/autoq/question_gen/data_questions/assertion_gen/base.py b/benchmark_qed/autoq/question_gen/data_questions/assertion_gen/base.py index d38fb8f..6947cd8 100644 --- a/benchmark_qed/autoq/question_gen/data_questions/assertion_gen/base.py +++ b/benchmark_qed/autoq/question_gen/data_questions/assertion_gen/base.py @@ -235,7 +235,7 @@ async def _process_single_question(self, question: Question) -> None: question.text, ) - except Exception as e: # noqa: BLE001 + except Exception as e: # ruff: ignore[blind-except] log.warning( "Failed to generate assertions for question '%s': %s", question.text, diff --git a/benchmark_qed/autoq/question_gen/data_questions/assertion_gen/validator.py b/benchmark_qed/autoq/question_gen/data_questions/assertion_gen/validator.py index 7debbe5..45f5ae1 100644 --- a/benchmark_qed/autoq/question_gen/data_questions/assertion_gen/validator.py +++ b/benchmark_qed/autoq/question_gen/data_questions/assertion_gen/validator.py @@ -224,7 +224,7 @@ async def validate_assertion( return ValidationResult( assertion=assertion, is_valid=is_valid, scores=scores ) - except Exception as e: # noqa: BLE001 + except Exception as e: # ruff: ignore[blind-except] log.warning("Validation failed for assertion: %s", e) return ValidationResult( assertion=assertion, diff --git a/benchmark_qed/autoq/question_gen/data_questions/global_question_gen.py b/benchmark_qed/autoq/question_gen/data_questions/global_question_gen.py index 1527ed4..c4db058 100644 --- a/benchmark_qed/autoq/question_gen/data_questions/global_question_gen.py +++ b/benchmark_qed/autoq/question_gen/data_questions/global_question_gen.py @@ -292,7 +292,9 @@ def __init__( self.claim_extractor_params = claim_extractor_params self.claim_extractor: DataGlobalClaimExtractor = DataGlobalClaimExtractor( - llm=llm, local_questions=local_questions, **claim_extractor_params + llm=llm, + local_questions=local_questions, + claim_extractor_params=claim_extractor_params, ) async def agenerate( @@ -507,7 +509,7 @@ def _weighted_sample_categories( num_questions: int, ) -> dict[str, int]: """Sample categories with probability proportional to log(question_count).""" - rng = random.Random(self.random_seed) # noqa: S311 + rng = random.Random(self.random_seed) # ruff: ignore[suspicious-non-cryptographic-random-usage] # Use log(count + 1) to handle edge cases and compress weights weights = [ math.log(len(category_to_questions[c]) + 1) for c in valid_categories diff --git a/benchmark_qed/autoq/question_gen/data_questions/linked_question_gen.py b/benchmark_qed/autoq/question_gen/data_questions/linked_question_gen.py index 867c133..e1da8ff 100644 --- a/benchmark_qed/autoq/question_gen/data_questions/linked_question_gen.py +++ b/benchmark_qed/autoq/question_gen/data_questions/linked_question_gen.py @@ -1019,7 +1019,7 @@ def _select_related_questions( if len(text_units) < 2: # Fall back to random selection - rng = random.Random(self.random_seed) # noqa: S311 + rng = random.Random(self.random_seed) # ruff: ignore[suspicious-non-cryptographic-random-usage] return rng.sample(questions, min(max_count, len(questions))) # Use MMR sampler - high lambda to start from centroid, then diversify diff --git a/benchmark_qed/cache.py b/benchmark_qed/cache.py new file mode 100644 index 0000000..9d7e7be --- /dev/null +++ b/benchmark_qed/cache.py @@ -0,0 +1,745 @@ +# Copyright (c) 2025 Microsoft Corporation. +"""Shared cache helpers backed by graphrag-cache.""" + +from __future__ import annotations + +import asyncio +import hashlib +import json +import os +import sqlite3 +import tempfile +import threading +import time +from dataclasses import dataclass +from datetime import UTC, datetime +from pathlib import Path +from typing import Any, ClassVar + +from filelock import FileLock +from graphrag_cache import Cache, CacheConfig, CacheType, create_cache +from graphrag_storage import StorageConfig, StorageType + +_SENSITIVE_KEYS = { + "api_key", + "apikey", + "authorization", + "connection_string", + "credential", + "credentials", + "password", + "secret", + "token", +} +_SENSITIVE_KEY_SUFFIXES = ( + "_access_token", + "_api_key", + "_auth_token", + "_connection_string", + "_credential", + "_password", + "_secret", +) +_SQLITE_INITIALIZATION_TIMEOUT_SECONDS = 30.0 +_SQLITE_INITIALIZATION_RETRY_SECONDS = 0.01 + + +@dataclass(frozen=True) +class CachePublishResult: + """Result of publishing a cache entry under a lease.""" + + accepted: bool + inserted: bool + value: Any | None + metadata: dict[str, Any] | None + + +def _is_sensitive_key(key: object) -> bool: + """Return whether a configuration key conventionally contains a secret.""" + normalized = str(key).lower() + return normalized in _SENSITIVE_KEYS or normalized.endswith(_SENSITIVE_KEY_SUFFIXES) + + +def redact_sensitive_values(value: Any) -> Any: + """Remove credentials from configuration persisted in local caches.""" + if isinstance(value, dict): + return { + str(key): ( + "" + if _is_sensitive_key(key) + else redact_sensitive_values(item) + ) + for key, item in value.items() + } + if isinstance(value, (list, tuple)): + return [redact_sensitive_values(item) for item in value] + return value + + +def stable_fingerprint(value: Any) -> str: + """Return a deterministic SHA256 fingerprint for JSON-like data.""" + content = json.dumps( + redact_sensitive_values(value), sort_keys=True, default=str + ).encode() + return hashlib.sha256(content).hexdigest() + + +def changed_configuration_fields( + current: dict[str, Any], cached: dict[str, Any] +) -> list[str]: + """Return top-level configuration fields whose values differ.""" + ignored = {"created_at", "schema_version", "source", "timestamp"} + return sorted( + key + for key in current.keys() | cached.keys() + if key not in ignored and current.get(key) != cached.get(key) + ) + + +def create_default_cache_config( + base_dir: Path | str, + *, + database_name: str, +) -> CacheConfig: + """Create the default persistent SQLite cache configuration.""" + return CacheConfig( + type=CacheType.Sqlite, + storage=StorageConfig( + type=StorageType.File, + base_dir=str(base_dir), + ), + database_name=database_name, + ) + + +def get_cache_base_dir(config: CacheConfig) -> Path | None: + """Return the configured local cache directory, when one exists.""" + storage = config.storage + if ( + config.type not in {CacheType.Json, CacheType.Sqlite} + or storage is None + or storage.type != StorageType.File + or storage.base_dir is None + ): + return None + return Path(storage.base_dir) + + +def get_sqlite_cache_path(config: CacheConfig) -> Path | None: + """Return the configured SQLite database path, when applicable.""" + base_dir = get_cache_base_dir(config) + if config.type != CacheType.Sqlite or base_dir is None: + return None + return base_dir / config.database_name + + +def create_configured_cache(config: CacheConfig, namespace: str) -> Cache: + """Create a namespaced cache from a GraphRAG cache configuration.""" + database_path = get_sqlite_cache_path(config) + if database_path is not None: + database_path.parent.mkdir(parents=True, exist_ok=True) + _migrate_local_cache_schema(database_path) + deadline = time.monotonic() + _SQLITE_INITIALIZATION_TIMEOUT_SECONDS + retry_delay = _SQLITE_INITIALIZATION_RETRY_SECONDS + while True: + try: + return create_cache(config).child(namespace) + except sqlite3.OperationalError as exc: + if "locked" not in str(exc).lower() or time.monotonic() >= deadline: + raise + time.sleep(retry_delay) + retry_delay = min(retry_delay * 2, 0.25) + return create_cache(config).child(namespace) + + +def _migrate_local_cache_schema(path: Path) -> None: + """Convert the retired benchmark-qed schema to graphrag-cache in place.""" + if not path.exists(): + return + connection = sqlite3.connect(path, timeout=30) + try: + columns = { + str(row[1]) + for row in connection.execute("PRAGMA table_info(cache_entries)") + } + if not columns or columns == {"namespace", "key", "value_json"}: + return + required_old_columns = { + "namespace", + "key", + "value_json", + "metadata_json", + } + if not required_old_columns <= columns: + msg = f"Unsupported cache schema in {path}" + raise RuntimeError(msg) + + logical_key_column = ( + "logical_key" if "logical_key" in columns else "NULL AS logical_key" + ) + config_fingerprint_column = ( + "config_fingerprint" + if "config_fingerprint" in columns + else "NULL AS config_fingerprint" + ) + created_at_column = ( + "created_at" if "created_at" in columns else "NULL AS created_at" + ) + rows = connection.execute( + f""" + SELECT namespace, key, value_json, metadata_json, + {logical_key_column}, {config_fingerprint_column}, + {created_at_column} + FROM cache_entries + """ # ruff: ignore[hardcoded-sql-expression] -- fixed expressions only + ).fetchall() + with connection: + connection.execute("DROP TABLE IF EXISTS cache_entries_graphrag") + connection.execute( + """ + CREATE TABLE cache_entries_graphrag ( + namespace TEXT NOT NULL, + key TEXT NOT NULL, + value_json TEXT NOT NULL, + PRIMARY KEY (namespace, key) + ) + """ + ) + for ( + namespace, + key, + value_json, + metadata_json, + logical_key, + config_fingerprint, + created_at, + ) in rows: + metadata = json.loads(metadata_json) + envelope = { + "result": { + "value": json.loads(value_json), + "metadata": metadata, + "logical_key": logical_key, + "config_fingerprint": config_fingerprint, + "created_at": created_at, + } + } + connection.execute( + """ + INSERT INTO cache_entries_graphrag(namespace, key, value_json) + VALUES (?, ?, ?) + """, + (namespace, key, json.dumps(envelope, ensure_ascii=False)), + ) + if logical_key is not None and config_fingerprint is not None: + configuration = { + "result": { + "config_fingerprint": config_fingerprint, + "metadata": metadata, + } + } + connection.execute( + """ + INSERT INTO cache_entries_graphrag( + namespace, key, value_json + ) + VALUES (?, ?, ?) + ON CONFLICT(namespace, key) + DO UPDATE SET value_json = excluded.value_json + """, + ( + f"{namespace}/configurations", + logical_key, + json.dumps(configuration, ensure_ascii=False), + ), + ) + connection.execute("DROP TABLE cache_entries") + connection.execute( + "ALTER TABLE cache_entries_graphrag RENAME TO cache_entries" + ) + connection.execute("DROP TABLE IF EXISTS cache_properties") + connection.execute("DROP TABLE IF EXISTS cache_leases") + finally: + connection.close() + + +class CacheStore: + """Application cache metadata layered over graphrag-cache.""" + + _memory_lease_lock = threading.Lock() + _memory_leases: ClassVar[dict[tuple[str, str], tuple[str, float]]] = {} + + def __init__( + self, + config: CacheConfig, + namespace: str, + *, + lease_ttl_seconds: float = 120.0, + ) -> None: + """Initialize cache namespaces and process-coordination files.""" + self.config = config + self.namespace = namespace + root_cache = create_configured_cache(config, namespace) + self._cache = root_cache + self._properties = create_configured_cache(config, f"{namespace}/properties") + self._configurations = create_configured_cache( + config, f"{namespace}/configurations" + ) + self._lease_ttl_seconds = lease_ttl_seconds + self.database_path: Path | None = get_sqlite_cache_path(config) + self.base_dir: Path | None = get_cache_base_dir(config) + namespace_hash = hashlib.sha256(namespace.encode()).hexdigest()[:16] + config_hash = hashlib.sha256(config.model_dump_json().encode()).hexdigest()[:16] + self._lease_dir = ( + self.base_dir / ".benchmark_qed_cache_leases" / config_hash / namespace_hash + if self.base_dir is not None + else None + ) + self._memory_lease_prefix = (config_hash, namespace) + self._known_keys: set[str] = set() + + async def get(self, key: str) -> tuple[Any, dict[str, Any]] | None: + """Return a decoded value and metadata for a key.""" + entry = await self._cache.get(key) + if not isinstance(entry, dict) or "value" not in entry: + return None + metadata = entry.get("metadata") + if not isinstance(metadata, dict): + return None + self._known_keys.add(key) + return entry["value"], metadata + + async def get_many(self, keys: list[str]) -> dict[str, tuple[Any, dict[str, Any]]]: + """Return decoded values and metadata for existing keys.""" + entries = await self._cache.get_many(keys) + results: dict[str, tuple[Any, dict[str, Any]]] = {} + for key, entry in entries.items(): + if not isinstance(entry, dict) or "value" not in entry: + continue + metadata = entry.get("metadata") + if not isinstance(metadata, dict): + continue + results[key] = (entry["value"], metadata) + self._known_keys.update(results) + return results + + async def put_many( + self, + entries: list[tuple[str, Any, dict[str, Any]]], + *, + identities: dict[str, tuple[str, str]] | None = None, + ) -> int: + """Insert entries with first-observed-writer semantics.""" + if self.config.type == CacheType.Noop: + return 0 + inserted = 0 + for key, value, metadata in entries: + if self._lease_dir is None: + with self._memory_lease_lock: + was_inserted, _ = await self._insert_if_absent( + key, value, metadata, identities + ) + else: + was_inserted, _ = await asyncio.to_thread( + self._insert_file_entry_if_absent, + key, + value, + metadata, + identities, + ) + inserted += int(was_inserted) + return inserted + + def _insert_file_entry_if_absent( + self, + key: str, + value: Any, + metadata: dict[str, Any], + identities: dict[str, tuple[str, str]] | None, + ) -> tuple[bool, tuple[Any, dict[str, Any]]]: + with FileLock(self._lease_mutex_path(key)): + return asyncio.run(self._insert_if_absent(key, value, metadata, identities)) + + def _publish_file_entry( + self, + key: str, + value: Any, + metadata: dict[str, Any], + owner_id: str, + logical_key: str, + config_fingerprint: str, + ) -> CachePublishResult: + with FileLock(self._lease_mutex_path(key)): + lease_path = self._lease_path(key) + try: + lease = json.loads(lease_path.read_text(encoding="utf-8")) + owns_lease = ( + lease.get("owner_id") == owner_id + and float(lease["expires_at"]) > time.time() + ) + except ( + AttributeError, + FileNotFoundError, + KeyError, + TypeError, + ValueError, + ): + owns_lease = False + if not owns_lease: + canonical = asyncio.run(self.get(key)) + return self._rejected_publication(canonical) + inserted, canonical = asyncio.run( + self._insert_if_absent( + key, + value, + metadata, + {key: (logical_key, config_fingerprint)}, + ) + ) + lease_path.unlink(missing_ok=True) + return CachePublishResult( + accepted=True, + inserted=inserted, + value=canonical[0], + metadata=canonical[1], + ) + + async def publish( + self, + key: str, + value: Any, + metadata: dict[str, Any], + *, + owner_id: str, + logical_key: str, + config_fingerprint: str, + ) -> CachePublishResult: + """Publish a claimed result, its configuration, and release the lease.""" + if self._lease_dir is None: + with self._memory_lease_lock: + lease = self._memory_leases.get(self._memory_lease_key(key)) + if lease is None or lease[0] != owner_id or lease[1] <= time.time(): + canonical = await self.get(key) + return self._rejected_publication(canonical) + inserted, canonical = await self._insert_if_absent( + key, + value, + metadata, + {key: (logical_key, config_fingerprint)}, + ) + self._memory_leases.pop(self._memory_lease_key(key), None) + else: + return await asyncio.to_thread( + self._publish_file_entry, + key, + value, + metadata, + owner_id, + logical_key, + config_fingerprint, + ) + + return CachePublishResult( + accepted=True, + inserted=inserted, + value=canonical[0], + metadata=canonical[1], + ) + + async def _insert_if_absent( + self, + key: str, + value: Any, + metadata: dict[str, Any], + identities: dict[str, tuple[str, str]] | None, + ) -> tuple[bool, tuple[Any, dict[str, Any]]]: + canonical = await self.get(key) + if canonical is not None: + return False, canonical + logical_key, config_fingerprint = (identities or {}).get(key, (None, None)) + await self._cache.set( + key, + { + "value": value, + "metadata": metadata, + "logical_key": logical_key, + "config_fingerprint": config_fingerprint, + "created_at": datetime.now(tz=UTC).isoformat(), + }, + ) + self._known_keys.add(key) + if logical_key is not None and config_fingerprint is not None: + await self._configurations.set( + logical_key, + { + "config_fingerprint": config_fingerprint, + "metadata": metadata, + }, + ) + return True, (value, metadata) + + @staticmethod + def _rejected_publication( + canonical: tuple[Any, dict[str, Any]] | None, + ) -> CachePublishResult: + return CachePublishResult( + accepted=False, + inserted=False, + value=canonical[0] if canonical is not None else None, + metadata=canonical[1] if canonical is not None else None, + ) + + async def claim( + self, + key: str, + owner_id: str, + *, + ttl_seconds: float | None = None, + ) -> bool: + """Claim uncached work and close the check-after-publication race.""" + if await self.get(key) is not None: + return False + if not self.try_acquire(key, owner_id, ttl_seconds=ttl_seconds): + return False + if await self.get(key) is None: + return True + self.release_leases([key], owner_id) + return False + + def try_acquire( + self, + key: str, + owner_id: str, + *, + ttl_seconds: float | None = None, + now: float | None = None, + ) -> bool: + """Claim work using an expiring local or in-memory lease.""" + current_time = time.time() if now is None else now + lease_ttl = self._lease_ttl_seconds if ttl_seconds is None else ttl_seconds + if self._lease_dir is None: + lease_key = self._memory_lease_key(key) + with self._memory_lease_lock: + lease = self._memory_leases.get(lease_key) + if lease is not None and lease[1] > current_time: + return False + self._memory_leases[lease_key] = ( + owner_id, + current_time + lease_ttl, + ) + return True + + lease_path = self._lease_path(key) + lease_path.parent.mkdir(parents=True, exist_ok=True) + payload = { + "owner_id": owner_id, + "expires_at": current_time + lease_ttl, + } + + with FileLock(self._lease_mutex_path(key)): + try: + lease = json.loads(lease_path.read_text(encoding="utf-8")) + if float(lease["expires_at"]) > current_time: + return False + except (FileNotFoundError, KeyError, TypeError, ValueError): + pass + self._write_lease(lease_path, payload) + return True + + def renew_leases( + self, keys: list[str], owner_id: str, *, ttl_seconds: float | None = None + ) -> int: + """Extend leases still owned by a worker.""" + lease_ttl = self._lease_ttl_seconds if ttl_seconds is None else ttl_seconds + if self._lease_dir is None: + renewed = 0 + with self._memory_lease_lock: + for key in keys: + lease_key = self._memory_lease_key(key) + lease = self._memory_leases.get(lease_key) + if lease is not None and lease[0] == owner_id: + self._memory_leases[lease_key] = ( + owner_id, + time.time() + lease_ttl, + ) + renewed += 1 + return renewed + + renewed = 0 + for key in keys: + lease_path = self._lease_path(key) + with FileLock(self._lease_mutex_path(key)): + try: + lease = json.loads(lease_path.read_text(encoding="utf-8")) + except (FileNotFoundError, json.JSONDecodeError): + continue + if lease.get("owner_id") != owner_id: + continue + lease["expires_at"] = time.time() + lease_ttl + self._write_lease(lease_path, lease) + renewed += 1 + return renewed + + def release_leases(self, keys: list[str], owner_id: str) -> None: + """Release leases owned by a worker without publishing results.""" + if self._lease_dir is None: + with self._memory_lease_lock: + for key in keys: + lease_key = self._memory_lease_key(key) + lease = self._memory_leases.get(lease_key) + if lease is not None and lease[0] == owner_id: + self._memory_leases.pop(lease_key) + return + + for key in keys: + lease_path = self._lease_path(key) + with FileLock(self._lease_mutex_path(key)): + try: + lease = json.loads(lease_path.read_text(encoding="utf-8")) + except (FileNotFoundError, json.JSONDecodeError): + continue + if lease.get("owner_id") == owner_id: + lease_path.unlink(missing_ok=True) + + async def find_alternate_configurations( + self, requests: list[tuple[str, str]] + ) -> list[dict[str, Any]]: + """Return metadata for matching inputs produced by another configuration.""" + alternatives: list[dict[str, Any]] = [] + for logical_key, config_fingerprint in dict(requests).items(): + cached = await self._configurations.get(logical_key) + if ( + isinstance(cached, dict) + and cached.get("config_fingerprint") != config_fingerprint + and isinstance(cached.get("metadata"), dict) + ): + alternatives.append(cached["metadata"]) + return alternatives + + async def get_property(self, name: str) -> str | None: + """Return a cache property.""" + value = await self._properties.get(name) + return str(value) if value is not None else None + + async def set_property_if_absent(self, name: str, value: str) -> None: + """Set a cache property without replacing an existing value.""" + if await self._properties.get(name) is None: + await self._properties.set(name, value) + + def count(self) -> int: + """Return the number of entries observed by this store instance.""" + return len(self._known_keys) + + async def clear(self) -> None: + """Delete entries and supporting metadata in this namespace.""" + await self._cache.clear() + await self._properties.clear() + await self._configurations.clear() + if self.config.type == CacheType.Json and self.base_dir is not None: + for namespace in ( + self.namespace, + f"{self.namespace}/properties", + f"{self.namespace}/configurations", + ): + (self.base_dir / namespace).mkdir(parents=True, exist_ok=True) + self._known_keys.clear() + if self._lease_dir is not None and self._lease_dir.exists(): + for lease_path in self._lease_dir.glob("*.lock"): + lease_path.unlink() + elif self._lease_dir is None: + with self._memory_lease_lock: + for lease_key in list(self._memory_leases): + if lease_key[0] == ":".join(self._memory_lease_prefix): + self._memory_leases.pop(lease_key) + + def size_bytes(self) -> int: + """Return the size of configured local cache files.""" + if self.database_path is not None: + return sum( + path.stat().st_size + for path in ( + self.database_path, + Path(f"{self.database_path}-wal"), + Path(f"{self.database_path}-shm"), + ) + if path.exists() + ) + if self.base_dir is None or not self.base_dir.exists(): + return 0 + return sum( + path.stat().st_size for path in self.base_dir.rglob("*") if path.is_file() + ) + + def _lease_path(self, key: str) -> Path: + if self._lease_dir is None: + msg = "File lease path requested for a non-file cache" + raise RuntimeError(msg) + key_hash = hashlib.sha256(key.encode()).hexdigest() + return self._lease_dir / f"{key_hash}.lock" + + def _lease_mutex_path(self, key: str) -> str: + return f"{self._lease_path(key)}.mutex" + + @staticmethod + def _write_lease(lease_path: Path, lease: dict[str, Any]) -> None: + descriptor, temporary_name = tempfile.mkstemp( + dir=lease_path.parent, + prefix=f".{lease_path.name}.", + suffix=".tmp", + ) + temporary_path = Path(temporary_name) + try: + with os.fdopen(descriptor, "w", encoding="utf-8") as lease_file: + json.dump(lease, lease_file) + temporary_path.replace(lease_path) + finally: + temporary_path.unlink(missing_ok=True) + + def _memory_lease_key(self, key: str) -> tuple[str, str]: + return (":".join(self._memory_lease_prefix), key) + + +def inspect_cache(database_path: Path | str) -> dict[str, Any]: + """Inspect the graphrag-cache SQLite file without mutating it.""" + path = Path(database_path) + if not path.exists(): + msg = f"Cache database does not exist: {path}" + raise FileNotFoundError(msg) + connection = sqlite3.connect(f"{path.resolve().as_uri()}?mode=ro", uri=True) + try: + columns = { + str(row[1]) + for row in connection.execute("PRAGMA table_info(cache_entries)") + } + if columns != {"namespace", "key", "value_json"}: + msg = f"Not a graphrag-cache SQLite database: {path}" + raise ValueError(msg) + namespaces = [ + {"namespace": namespace, "entries": entry_count} + for namespace, entry_count in connection.execute( + """ + SELECT namespace, COUNT(*) + FROM cache_entries + GROUP BY namespace + ORDER BY namespace + """ + ) + ] + finally: + connection.close() + lease_root = path.parent / ".benchmark_qed_cache_leases" + active_leases = 0 + if lease_root.exists(): + now = time.time() + for lease_path in lease_root.glob("*/*/*.lock"): + try: + lease = json.loads(lease_path.read_text(encoding="utf-8")) + active_leases += int(float(lease["expires_at"]) > now) + except (OSError, KeyError, TypeError, ValueError, json.JSONDecodeError): + continue + return { + "path": str(path), + "backend": "graphrag-cache", + "namespaces": namespaces, + "active_leases": active_leases, + } diff --git a/benchmark_qed/cli/cache.py b/benchmark_qed/cli/cache.py new file mode 100644 index 0000000..00d0df7 --- /dev/null +++ b/benchmark_qed/cli/cache.py @@ -0,0 +1,45 @@ +# Copyright (c) 2025 Microsoft Corporation. +"""Commands for inspecting local evaluation caches.""" + +import json +from pathlib import Path +from typing import Annotated + +import typer + +from benchmark_qed.cache import inspect_cache + +app: typer.Typer = typer.Typer( + pretty_exceptions_show_locals=False, + help="Inspect local evaluation caches.", +) + + +@app.command(name="inspect") +def inspect_command( + cache_path: Annotated[ + Path, + typer.Argument( + exists=True, + dir_okay=False, + readable=True, + help="Path to a benchmark-qed SQLite cache.", + ), + ], + json_output: Annotated[ + bool, + typer.Option("--json", help="Emit machine-readable JSON."), + ] = False, +) -> None: + """Show graphrag-cache namespaces and active evaluation leases.""" + details = inspect_cache(cache_path) + if json_output: + typer.echo(json.dumps(details, indent=2, sort_keys=True)) + return + + typer.echo(f"Cache: {details['path']}") + typer.echo(f"Backend: {details['backend']}") + typer.echo(f"Active leases: {details['active_leases']}") + typer.echo("Namespaces:") + for namespace in details["namespaces"]: + typer.echo(f" {namespace['namespace']}: {namespace['entries']} entries") diff --git a/benchmark_qed/cli/init_config.py b/benchmark_qed/cli/init_config.py index 14feea0..b91417e 100644 --- a/benchmark_qed/cli/init_config.py +++ b/benchmark_qed/cli/init_config.py @@ -279,6 +279,16 @@ class ConfigType(StrEnum): pass_threshold: 0.5 # The threshold for passing the assertion. If the score is above this threshold, the assertion is considered passed. trials: 4 # Number of trials to repeat the scoring process for each question-assertion pair. +## Cache Configuration +# Reuses completed question/assertion/trial judgments across runs. +# Set type: none and storage: null to force fresh LLM judgments. +cache_config: + type: sqlite # Supported: sqlite, json, memory, none + storage: + type: file + base_dir: .benchmark_qed_cache/assertion + database_name: assertion.sqlite3 + ## LLM Configuration llm_config: {CHAT_MODEL_DEFAULTS} @@ -297,15 +307,20 @@ class ConfigType(StrEnum): # {{"question_id", "text", "context": [{{"chunk_id", "text", "rank"}}]}} # "rank" is optional; when absent chunks are assumed pre-sorted by relevance. # This matches the standard retrieval-results schema (e.g. data_local_retrieval_results.json). - retrieval_path: input/retrieval.json + retrieval_path: input/vector_rag_short_context/data_local_retrieval_results.json assertions: - assertions_path: input/assertions.json # Path to assertions file + assertions_path: input/data_local_assertions.json # Path to assertions file ## Chunk Evaluation Configuration k_list: [5, 10, 20, 50] # Report coverage metrics at these k values (plus 'all') pass_threshold: 0.5 # Score threshold: 0.5 = partial_support or full_support counts as pass # max_chunks_per_question: 20 # Optional: cap chunks evaluated per question (keeps highest-ranked). Reduces LLM calls during testing. Omit to evaluate all. -cache_dir: .benchmark_qed_cache/chunk_assertions # Cache directory for (assertion, chunk) pairs +cache_config: + type: sqlite # Supported: sqlite, json, memory, none + storage: + type: file + base_dir: .benchmark_qed_cache/chunk_assertions + database_name: chunk_assertions.sqlite3 ## LLM Configuration llm_config: {CHAT_MODEL_DEFAULTS} @@ -337,6 +352,16 @@ class ConfigType(StrEnum): # description: "criteria description" trials: 4 # Number of trials to repeat the scoring process for each question. Should be an even number to allow for counterbalancing. +## Cache Configuration +# Reuses completed question/criterion/trial judgements across runs. +# Set type: none and storage: null to force fresh LLM judgements. +cache_config: + type: sqlite # Supported: sqlite, json, memory, none + storage: + type: file + base_dir: .benchmark_qed_cache/pairwise + database_name: pairwise.sqlite3 + ## LLM Configuration llm_config: {CHAT_MODEL_DEFAULTS} @@ -371,6 +396,16 @@ class ConfigType(StrEnum): # description: "criteria description" trials: 4 # Number of trials to repeat the scoring process for each question. Should be an even number to allow for counterbalancing. +## Cache Configuration +# Caches extraction and verdict stages independently for interruption recovery. +# Set type: none and storage: null to force fresh LLM calls. +cache_config: + type: sqlite # Supported: sqlite, json, memory, none + storage: + type: file + base_dir: .benchmark_qed_cache/differential_pairwise + database_name: differential_pairwise.sqlite3 + ## LLM Configuration llm_config: {CHAT_MODEL_DEFAULTS} @@ -405,6 +440,16 @@ class ConfigType(StrEnum): # description: "criteria description" trials: 4 # Number of trials to repeat the scoring process for each question. Should be an even number to allow for counterbalancing. +## Cache Configuration +# Reuses completed question/criterion/trial judgments across runs. +# Set type: none and storage: null to force fresh LLM judgments. +cache_config: + type: sqlite # Supported: sqlite, json, memory, none + storage: + type: file + base_dir: .benchmark_qed_cache/reference + database_name: reference.sqlite3 + ## LLM Configuration llm_config: {CHAT_MODEL_DEFAULTS} diff --git a/benchmark_qed/cli/interactive.py b/benchmark_qed/cli/interactive.py index 25cb35e..481406f 100644 --- a/benchmark_qed/cli/interactive.py +++ b/benchmark_qed/cli/interactive.py @@ -671,7 +671,8 @@ def build_autoe_chunk_assertion_config() -> dict[str, Any]: rich_print("\n[bold] Generated condition (retrieval results)[/bold]") name = typer.prompt(" name", default="vector_rag") retrieval_path = typer.prompt( - " retrieval_path (RetrievalResult JSON)", default="input/retrieval.json" + " retrieval_path (RetrievalResult JSON)", + default=("input/vector_rag_short_context/data_local_retrieval_results.json"), ) generated = { "name": name, @@ -680,7 +681,7 @@ def build_autoe_chunk_assertion_config() -> dict[str, Any]: # Assertions path assertions_path = typer.prompt( - "Path to assertions file", default="input/assertions.json" + "Path to assertions file", default="input/data_local_assertions.json" ) # K values @@ -690,10 +691,23 @@ def build_autoe_chunk_assertion_config() -> dict[str, Any]: # Pass threshold pass_threshold = typer.prompt("Pass threshold", default=0.5, type=float) - # Cache directory - cache_dir = typer.prompt( - "Cache directory", default=".benchmark_qed_cache/chunk_assertions" - ) + cache_type = typer.prompt( + "Cache type (sqlite, json, memory, none)", default="sqlite" + ).lower() + cache_config: dict[str, Any] = {"type": cache_type} + if cache_type in {"sqlite", "json"}: + cache_config["storage"] = { + "type": "file", + "base_dir": typer.prompt( + "Cache directory", + default=".benchmark_qed_cache/chunk_assertions", + ), + } + if cache_type == "sqlite": + cache_config["database_name"] = typer.prompt( + "SQLite database name", + default="chunk_assertions.sqlite3", + ) return { "chat_provider": chat_provider, @@ -701,7 +715,7 @@ def build_autoe_chunk_assertion_config() -> dict[str, Any]: "assertions": {"assertions_path": assertions_path}, "k_list": k_list, "pass_threshold": pass_threshold, - "cache_dir": cache_dir, + "cache_config": cache_config, } diff --git a/benchmark_qed/cli/yaml_renderer.py b/benchmark_qed/cli/yaml_renderer.py index 2dd2f4d..733826b 100644 --- a/benchmark_qed/cli/yaml_renderer.py +++ b/benchmark_qed/cli/yaml_renderer.py @@ -310,6 +310,16 @@ def render_autoe_pairwise_yaml(config: dict[str, Any]) -> str: {criteria_lines} trials: {trials} +## Cache Configuration +# Reuses completed question/criterion/trial judgements across runs. +# Set type: none and storage: null to force fresh LLM judgements. +cache_config: + type: sqlite + storage: + type: file + base_dir: .benchmark_qed_cache/pairwise + database_name: pairwise.sqlite3 + ## LLM Configuration llm_config: {llm_section} @@ -362,6 +372,16 @@ def render_autoe_reference_yaml(config: dict[str, Any]) -> str: score_max: {score_max} trials: {trials} +## Cache Configuration +# Reuses completed question/criterion/trial judgments across runs. +# Set type: none and storage: null to force fresh LLM judgments. +cache_config: + type: sqlite + storage: + type: file + base_dir: .benchmark_qed_cache/reference + database_name: reference.sqlite3 + ## LLM Configuration llm_config: {llm_section} @@ -406,6 +426,16 @@ def render_autoe_assertion_yaml(config: dict[str, Any]) -> str: pass_threshold: {pass_threshold} trials: {trials} +## Cache Configuration +# Reuses completed question/assertion/trial judgments across runs. +# Set type: none and storage: null to force fresh LLM judgments. +cache_config: + type: sqlite + storage: + type: file + base_dir: .benchmark_qed_cache/assertion + database_name: assertion.sqlite3 + ## LLM Configuration llm_config: {llm_section} @@ -425,13 +455,27 @@ def render_autoe_chunk_assertion_yaml(config: dict[str, Any]) -> str: ---------- config: Dict with keys ``chat_provider``, ``generated``, ``assertions``, - ``k_list``, ``pass_threshold``, ``cache_dir``. + ``k_list``, ``pass_threshold``, ``cache_config``. """ gen = config["generated"] assertions = config["assertions"] k_list = config.get("k_list", [5, 10, 20, 50]) pass_threshold = config.get("pass_threshold", 0.5) - cache_dir = config.get("cache_dir", ".benchmark_qed_cache/chunk_assertions") + cache_config = config.get( + "cache_config", + { + "type": "sqlite", + "storage": { + "type": "file", + "base_dir": ".benchmark_qed_cache/chunk_assertions", + }, + "database_name": "chunk_assertions.sqlite3", + }, + ) + cache_section = yaml.safe_dump( + {"cache_config": cache_config}, + sort_keys=False, + ).rstrip() llm_section = _render_llm_section(config["chat_provider"]) @@ -441,14 +485,14 @@ def render_autoe_chunk_assertion_yaml(config: dict[str, Any]) -> str: ## Input Configuration generated: name: {gen["name"]} - retrieval_path: {gen.get("retrieval_path", "input/retrieval.json")} # RetrievalResult JSON array + retrieval_path: {gen.get("retrieval_path", "input/vector_rag_short_context/data_local_retrieval_results.json")} # RetrievalResult JSON array assertions: assertions_path: {assertions["assertions_path"]} ## Chunk Evaluation Configuration k_list: [{k_list_str}] # Report coverage metrics at these k values pass_threshold: {pass_threshold} # Score threshold: 0.5 = partial_support or full_support counts as pass -cache_dir: {cache_dir} # Persistent cache for (assertion, chunk) pairs +{cache_section} ## LLM Configuration llm_config: @@ -485,6 +529,7 @@ def render_autoe_chunk_assertion_yaml(config: dict[str, Any]) -> str: "others", "question_sets", "trials", + "cache_config", "llm_config", "prompt_config", ], @@ -494,6 +539,7 @@ def render_autoe_chunk_assertion_yaml(config: dict[str, Any]) -> str: "score_min", "score_max", "trials", + "cache_config", "llm_config", "prompt_config", ], @@ -502,6 +548,7 @@ def render_autoe_chunk_assertion_yaml(config: dict[str, Any]) -> str: "assertions", "pass_threshold", "trials", + "cache_config", "llm_config", "prompt_config", ], @@ -510,7 +557,7 @@ def render_autoe_chunk_assertion_yaml(config: dict[str, Any]) -> str: "assertions", "k_list", "pass_threshold", - "cache_dir", + "cache_config", "llm_config", "prompt_config", ], @@ -560,16 +607,19 @@ def validate_config(yaml_content: str, config_type: str) -> None: _check_type(data, "others", list) _check_type(data, "question_sets", list) _check_type(data, "trials", int) + _check_type(data, "cache_config", dict) _check_type(data, "llm_config", dict) elif config_type == "autoe_reference": _check_type(data, "reference", dict) _check_type(data, "generated", list) _check_type(data, "trials", int) + _check_type(data, "cache_config", dict) _check_type(data, "llm_config", dict) elif config_type == "autoe_assertion": _check_type(data, "generated", dict) _check_type(data, "assertions", dict) _check_type(data, "trials", int) + _check_type(data, "cache_config", dict) _check_type(data, "llm_config", dict) except typer.BadParameter: raise diff --git a/docs/cli/autoe.md b/docs/cli/autoe.md index 038d135..7e086fb 100644 --- a/docs/cli/autoe.md +++ b/docs/cli/autoe.md @@ -20,6 +20,22 @@ To perform pairwise scoring with your configuration file, use: benchmark-qed autoe pairwise-scores ./local/pairwise_test/settings.yaml ./local/pairwise_test/output ``` +The generated configuration enables a persistent SQLite cache by default. +Completed LLM judgments are reused when the same questions, answers, criteria, +trials, prompts, and model configuration are evaluated again. See +[Evaluation Caches](cache.md) for backend choices, inspection, clearing, and +reproducibility guidance. + +Useful pairwise options include: + +| Option | Default | Description | +|---|---|---| +| `--alpha` | `0.05` | P-value threshold used by the significance analysis. | +| `--exclude-criteria` | `[]` | Criterion name to exclude; repeat the option to exclude several. | +| `--print-model-usage` | `false` | Print model usage statistics after scoring. | +| `--include-score-id-in-prompt` / `--no-include-score-id-in-prompt` | enabled | Add a unique score ID to provider requests. This can avoid provider-side prompt caching, but it does not bypass `cache_config`. | +| `--question-id-key` | `question_id` | Input field used to match questions across conditions. | + If your config lives in Azure Blob Storage, pass a `blob://` URI and supply credentials inline with `--account-url` (managed identity) or `--connection-string`: ```sh @@ -87,6 +103,7 @@ Top-level configuration for scoring a set of conditions. | `criteria` | `list[Criteria]` | `pairwise_scores_criteria()` | List of criteria to use for scoring. | | `llm_config` | `LLMConfig` | `LLMConfig()` | Configuration for the LLM used in scoring. | | `trials` | `int` | `4` | Number of trials to run for each condition. | +| `cache_config` | `CacheConfig` | SQLite under `.benchmark_qed_cache/pairwise` | Cache for completed pairwise judgements. Use `type: none` to disable it. | --- @@ -117,6 +134,13 @@ question_sets: trials: 4 +cache_config: + type: sqlite + storage: + type: file + base_dir: .benchmark_qed_cache/pairwise + database_name: pairwise.sqlite3 + llm_config: auth_type: api_key model: gpt-4.1 @@ -132,6 +156,100 @@ OPENAI_API_KEY=your-secret-api-key-here >💡 Note: The api_key field uses an environment variable reference `${OPENAI_API_KEY}`. Make sure to define this variable in a .env file or your environment before running the application. +Each question, criterion, and trial has an independent cache entry. The cache +identity includes both answers and their labels, the trial number, prompts, +model/provider configuration, and call arguments. This preserves +counterbalancing and repeated-trial behavior while allowing interrupted runs to +resume. Changing any of those values causes a fresh judgement. + +### Using the Pairwise Cache + +SQLite is the recommended backend and is enabled in newly generated pairwise +configurations: + +```yaml +cache_config: + type: sqlite + storage: + type: file + base_dir: .benchmark_qed_cache/pairwise + database_name: pairwise.sqlite3 +``` + +The supported backends are: + +- `sqlite`: persistent local database; recommended for resumable runs and + concurrent processes. +- `json`: persistent JSON entries in the configured GraphRAG storage. +- `memory`: process-local only; nothing is available to a later run. +- `none`: disables pairwise judgment caching. + +To temporarily force new LLM judgments: + +```yaml +cache_config: + type: none + storage: null +``` + +Inspect the default SQLite cache with: + +```sh +benchmark-qed cache inspect \ + .benchmark_qed_cache/pairwise/pairwise.sqlite3 +``` + +Pairwise output files and judgment caching are separate. If a comparison CSV +already exists in the output directory, the CLI skips that comparison before +consulting the judgment cache. To fully recompute it, use a new output +directory (or remove that comparison CSV) and also disable, relocate, or clear +the judgment cache. + +The cache remains local when `input_storage` or `output_storage` uses Azure Blob +Storage. For full backend examples and safe clearing instructions, see +[Evaluation Caches](cache.md). + +--- + +## Differential Pairwise Scoring + +Differential pairwise scoring first extracts common and unique content from the +two answers, then judges only the unique content. Generate a configuration and +run it with: + +```sh +benchmark-qed config init autoe_differential_pairwise ./local/differential_test +benchmark-qed autoe differential-pairwise-scores \ + ./local/differential_test/settings.yaml \ + ./local/differential_test/output +``` + +New configurations enable a stage-aware SQLite cache: + +```yaml +cache_config: + type: sqlite + storage: + type: file + base_dir: .benchmark_qed_cache/differential_pairwise + database_name: differential_pairwise.sqlite3 +``` + +Extraction and verdict results are stored independently. Consequently: + +- A judge failure resumes from a successful extraction. +- Changing criteria or judge prompts recomputes verdicts but reuses compatible + extractions. +- Changing answers, trial/order, extraction prompts, model/provider, or call + arguments recomputes extraction and its dependent verdict. +- Every trial has its own entries, preserving stochastic trials and + counterbalancing. + +Set `cache_config.type: none` and `storage: null` to force fresh extraction and +verdict calls. Existing output CSVs still take precedence over the stage cache; +remove or relocate both when fully recomputing a comparison. See +[Evaluation Caches](cache.md) for inspection and clearing instructions. + --- ## Providing Prompts: File or Text @@ -191,6 +309,10 @@ To perform reference-based scoring with your configuration file, run: benchmark-qed autoe reference-scores ./local/reference_test/settings.yaml ./local/reference_test/output ``` +New reference configurations enable a persistent SQLite cache. Successful +question/criterion/trial judgments are saved immediately, so interrupted runs +can resume and unchanged reruns avoid duplicate LLM calls. + For information about the `config init` command, see: [Config Init CLI](config_init.md) --- @@ -229,6 +351,7 @@ Top-level configuration for scoring generated answers against a reference. | `score_max` | `int` | `10` | Maximum score for each criterion. | | `llm_config` | `LLMConfig` | `LLMConfig()` | Configuration for the LLM used in scoring. | | `trials` | `int` | `4` | Number of trials to run for each condition. | +| `cache_config` | `CacheConfig` | SQLite under `.benchmark_qed_cache/reference` | Cache for completed reference judgments. | --- @@ -257,6 +380,13 @@ score_max: 10 trials: 4 +cache_config: + type: sqlite + storage: + type: file + base_dir: .benchmark_qed_cache/reference + database_name: reference.sqlite3 + llm_config: model: "gpt-4.1" auth_type: "api_key" @@ -276,6 +406,30 @@ OPENAI_API_KEY=your-secret-api-key-here >💡 Note: The api_key field uses an environment variable reference `${OPENAI_API_KEY}`. Make sure to define this variable in a .env file or your environment before running the application. +Each question, criterion, and trial is cached independently. The cache identity +also includes the reference and generated answers, score range, prompts, +model/provider, call arguments, and score-ID mode. Trials therefore remain +counterbalanced and stochastic samples are not collapsed. + +To force fresh judgments: + +```yaml +cache_config: + type: none + storage: null +``` + +Inspect the default cache with: + +```sh +benchmark-qed cache inspect .benchmark_qed_cache/reference/reference.sqlite3 +``` + +Reference output CSVs are rewritten on each command invocation; cache hits +avoid the LLM calls used to produce those rows. The SQLite path remains local +when input or output storage uses Azure Blob Storage. See +[Evaluation Caches](cache.md) for all backends and clearing guidance. + --- ## Assertion-Based Scoring Configuration @@ -295,6 +449,7 @@ benchmark-qed autoe assertion-scores config.yaml ``` The command auto-detects the config format: + - **Single-RAG**: Config has `generated` key → requires output argument - **Multi-RAG**: Config has `rag_methods` key → output in config, includes significance testing @@ -347,6 +502,7 @@ Top-level configuration for scoring generated answers against assertions. | `pass_threshold` | `float` | `0.5` | Threshold for passing the assertion score. | | `llm_config` | `LLMConfig` | `LLMConfig()` | Configuration for the LLM used in scoring. | | `trials` | `int` | `4` | Number of trials to run for each assertion. | +| `cache_config` | `CacheConfig` | SQLite under `.benchmark_qed_cache/assertion` | Cache for completed question/assertion/trial judgments. Use `type: none` to disable it. | --- @@ -367,6 +523,13 @@ pass_threshold: 0.5 trials: 4 +cache_config: + type: sqlite + storage: + type: file + base_dir: .benchmark_qed_cache/assertion + database_name: assertion.sqlite3 + llm_config: auth_type: api_key model: gpt-4.1 @@ -382,6 +545,37 @@ OPENAI_API_KEY=your-secret-api-key-here >💡 Note: The api_key field uses an environment variable reference `${OPENAI_API_KEY}`. Make sure to define this variable in a .env file or your environment before running the application. +The cache identity includes the question, answer, assertion, trial, prompts, +model/provider, call arguments, and score-ID mode. Completed trials are +persisted immediately and reused after an interrupted or repeated run. Inspect +the default cache with: + +```sh +benchmark-qed cache inspect \ + .benchmark_qed_cache/assertion/assertion.sqlite3 +``` + +To force fresh judgments, set: + +```yaml +cache_config: + type: none + storage: null +``` + +Changing the model, provider, Azure endpoint, API version, call arguments, +prompt contents, question, answer, assertion, trial, or score-ID mode produces +new entries. Old entries remain stored and become reusable if their previous +configuration is restored. Concurrency, retries, `pass_threshold`, `top_k`, and +credentials do not affect judgment identity. Changing the cache path or +database selects a different store rather than deleting the original entries. +See [Evaluation Caches](cache.md#standard-assertion-score-cache-behavior) for +the complete invalidation and reuse rules. + +This answer-level cache is currently limited to this single-RAG configuration. +It is separate from chunk-assertion caching and is not used by the multi-RAG or +hierarchical assertion paths. + > 📋 Assertions json example: ```json [ @@ -528,6 +722,7 @@ benchmark-qed autoe hierarchical-assertion-scores config.yaml ``` The command auto-detects the config format: + - **Single-RAG**: Config has `generated` key → requires output argument - **Multi-RAG**: Config has `rag_methods` key → output in config, includes significance testing diff --git a/docs/cli/cache.md b/docs/cli/cache.md new file mode 100644 index 0000000..fa31160 --- /dev/null +++ b/docs/cli/cache.md @@ -0,0 +1,378 @@ +# Evaluation Caches + +BenchmarkQED can cache individual LLM evaluations so interrupted or repeated +runs do not need to make the same model calls again. Standard and differential +pairwise scoring, reference scoring, standard single-RAG assertion scoring, +chunk-assertion scoring, and retrieval relevance assessment use the shared +GraphRAG cache configuration format. + +## Migrating existing cache configuration + +`cache_dir` has been replaced by the GraphRAG `cache_config` object. Existing +configurations that contain `cache_dir` are rejected so that BenchmarkQED does +not silently select a different cache backend or location. + +For example, replace a chunk-assertion configuration such as: + +```yaml +cache_dir: .benchmark_qed_cache +``` + +with: + +```yaml +cache_config: + type: sqlite + storage: + type: file + base_dir: .benchmark_qed_cache/chunk_assertions + database_name: chunk_assertions.sqlite3 +``` + +For retrieval relevance caches, use the same `cache_config` structure and +choose a workflow-specific `base_dir` and `database_name`. Use `type: json` +instead of `sqlite` when entries must be stored through a GraphRAG storage +backend such as Azure Blob Storage. + +Existing chunk-assertion JSONL files are imported automatically when they are +adjacent to the configured SQLite database. For example, configuring +`cache.sqlite3` imports an existing `cache.jsonl` once and leaves the source +file unchanged. + +The corresponding Python APIs also changed: + +| Previous API | Current API | +|---|---| +| `ContentAddressedCache(path)` | `ContentAddressedCache(cache_config)` | +| `cache.get(...)`, `put(...)`, `flush()` | `await cache.get(...)`, `await cache.put(...)`, `await cache.flush()` | +| `run_assertion_eval_chunk_mode(cache_path=...)` | `run_assertion_eval_chunk_mode(cache_config=...)` | +| Relevance raters with `cache_dir` and `cache_enabled` | Relevance raters with `cache_config` | + +Use `create_default_cache_config()` to construct the standard local SQLite +configuration from Python: + +```python +from benchmark_qed.cache import create_default_cache_config + +cache_config = create_default_cache_config( + ".benchmark_qed_cache/chunk_assertions", + database_name="chunk_assertions.sqlite3", +) +``` + +Standard pairwise, reference, assertion, and chunk-assertion configurations now +enable persistent SQLite judgment caching by default. Select `type: none` with +`storage: null` when every run must issue fresh provider requests. + +## Cache backends + +Set `cache_config.type` to one of: + +| Type | Persistence | Intended use | +|---|---|---| +| `sqlite` | Persistent, local file | Recommended for normal runs, resumability, and concurrent processes. | +| `json` | Persistent GraphRAG storage | Useful when JSON entries are preferred over a SQLite database. | +| `memory` | Current process only | Deduplicates work within one process but cannot resume a later run. | +| `none` | None | Forces every required LLM evaluation to run. | + +A SQLite configuration has this shape: + +```yaml +cache_config: + type: sqlite + storage: + type: file + base_dir: .benchmark_qed_cache/pairwise + database_name: pairwise.sqlite3 +``` + +`cache_config.storage` is independent of `input_storage` and `output_storage`. +Configuring inputs or outputs in Azure Blob Storage does not automatically move +the cache to blob storage. SQLite requires `cache_config.storage.type: file`, so +its `base_dir` is always a local filesystem path. + +A JSON file cache can be configured as: + +```yaml +cache_config: + type: json + storage: + type: file + base_dir: .benchmark_qed_cache/pairwise-json +``` + +With `storage.type: file`, a relative `base_dir` is resolved from the working +directory where `benchmark-qed` is executed, not from the directory containing +`settings.yaml`. For example, running from `/work/my-evaluation` resolves the +configuration above to: + +```text +/work/my-evaluation/.benchmark_qed_cache/pairwise-json +``` + +The JSON backend writes individual entries under namespaced subdirectories; it +does not create one JSON database file. `database_name` applies only to SQLite +and should be omitted from JSON configurations. + +To store JSON cache entries in Azure Blob Storage, configure the cache storage +itself as blob storage: + +```yaml +cache_config: + type: json + storage: + type: blob + container_name: my-cache-container + base_dir: benchmark-qed/chunk-assertions + account_url: https://.blob.core.windows.net +``` + +Alternatively, replace `account_url` with: + +```yaml + connection_string: ${AZURE_STORAGE_CONNECTION_STRING} +``` + +Entries are then stored below +`my-cache-container/benchmark-qed/chunk-assertions/`. Cache storage does not +inherit the container, credentials, or base directory from input or output +storage; those values must be configured explicitly under +`cache_config.storage`. + +Use `memory` or `none` without a storage block: + +```yaml +cache_config: + type: memory + storage: null +``` + +```yaml +cache_config: + type: none + storage: null +``` + +## Pairwise cache behavior + +Pairwise scoring stores one entry for each question, criterion, and trial. It +does not reuse one stochastic judgment for all trials. The cache identity +includes: + +- The question, both answer labels, and both answer texts +- The trial number, which also preserves counterbalanced answer ordering +- The criterion name and description +- The system and user prompt templates +- The model, provider, initialization arguments, and call arguments +- Whether the score ID is included in the prompt + +Changing any of these values creates a new cache entry. API keys and other +credentials are redacted and do not affect the cache identity. + +Successful judgments are published as soon as they complete. If a run is +interrupted, the next run can reuse completed judgments and evaluate only +missing entries. Expiring work leases prevent concurrent processes sharing the +same persistent cache from evaluating the same entry more than once. + +`--include-score-id-in-prompt` controls whether each new provider request +contains a unique score ID. It can avoid provider-side prompt caching, but it +does **not** bypass BenchmarkQED's `cache_config`. Use `cache_config.type: none` +when you need BenchmarkQED to issue fresh LLM requests. + +## Differential pairwise cache behavior + +Differential pairwise scoring uses two independent namespaces in one configured +cache: + +- `differential_pairwise/extraction` stores the common and unique content + extracted from each answer pair. +- `differential_pairwise/verdict` stores the judgment over the extracted unique + content. + +This stage separation provides two useful forms of reuse: + +- If a judge call fails after extraction succeeds, the next run reuses the + extraction and retries only the verdict. +- If criteria or judge prompts change, extraction remains reusable while the + verdict is recomputed. + +Each trial remains independent. Extraction identity includes the question, +answers and labels, trial/order, extraction prompts, model/provider, call +arguments, and score-ID mode. Verdict identity additionally includes the +extracted content, criteria, and judge prompts. + +The generated differential configuration uses: + +```yaml +cache_config: + type: sqlite + storage: + type: file + base_dir: .benchmark_qed_cache/differential_pairwise + database_name: differential_pairwise.sqlite3 +``` + +Inspect it with: + +```sh +benchmark-qed cache inspect \ + .benchmark_qed_cache/differential_pairwise/differential_pairwise.sqlite3 +``` + +The output lists extraction and verdict namespaces separately, making it +possible to confirm that both stages are being persisted. + +## Reference score cache behavior + +Reference scoring stores one entry for each question, criterion, and trial. Its +identity includes the question, reference and generated answers, criterion, +trial/order, score range, prompts, model/provider configuration, call +arguments, and score-ID mode. Changing any of those values produces a cache +miss. + +The generated reference configuration uses: + +```yaml +cache_config: + type: sqlite + storage: + type: file + base_dir: .benchmark_qed_cache/reference + database_name: reference.sqlite3 +``` + +Inspect it with: + +```sh +benchmark-qed cache inspect \ + .benchmark_qed_cache/reference/reference.sqlite3 +``` + +Reference scoring rewrites its output CSV on every invocation, but cache hits +avoid repeating the underlying LLM calls. Set `type: none` with `storage: null` +when fresh judgments are required. + +## Standard assertion score cache behavior + +Standard single-RAG assertion scoring stores one entry for each question, +assertion, and trial. Its identity includes the question, generated answer, +assertion text, trial, prompt templates, model/provider configuration, call +arguments, and score-ID mode. `top_k` is not part of an entry's identity, so +overlapping assertions remain reusable when that selection limit changes. + +The following changes produce new assertion cache entries: + +- `llm_config.model` or `llm_config.llm_provider` +- Values under `llm_config.init_args`, including `azure_endpoint` and + `api_version` +- Values under `llm_config.call_args`, such as `temperature`, `seed`, or token + limits +- Custom provider configuration +- System or user prompt contents +- Score-ID mode +- Question, generated-answer, or assertion text +- Trial number + +Changing one of these values does not delete or overwrite the old entries. The +new configuration receives a different fingerprint in the same database. If +the previous configuration is restored later, its entries are reusable again. +For example, moving from one Azure OpenAI endpoint to another causes cache +misses, while switching back to the original endpoint makes its prior entries +available again. + +The following settings do not change an assertion judgment's identity: + +- `llm_config.concurrent_requests` +- Retry policy, retry count, delays, and jitter +- `pass_threshold` +- `top_k` +- Input and output paths when the question, answer, and assertion contents are + unchanged +- API keys, access tokens, credentials, and connection strings, which are + redacted before fingerprinting + +Increasing `trials` reuses existing trial numbers and evaluates only the newly +requested trials. Decreasing it simply selects fewer trials. + +Changing the cache backend, `base_dir`, or database name selects a different +cache store rather than invalidating entries in the old store. The old entries +remain available if that original cache configuration is restored. + +The generated `autoe_assertion` configuration uses: + +```yaml +cache_config: + type: sqlite + storage: + type: file + base_dir: .benchmark_qed_cache/assertion + database_name: assertion.sqlite3 +``` + +Inspect it with: + +```sh +benchmark-qed cache inspect \ + .benchmark_qed_cache/assertion/assertion.sqlite3 +``` + +Successful judgments are persisted immediately, so rerunning after an +interruption evaluates only missing entries. Set `type: none` with +`storage: null` for fresh judgments. + +This cache applies only to the standard single-RAG `AssertionConfig` path. +Multi-RAG and hierarchical assertion scoring do not use this cache. The +chunk-assertion workflow has its own cache at assertion/chunk granularity under +`.benchmark_qed_cache/chunk_assertions`. + +## Output files versus cached judgments + +Pairwise scoring has two separate reuse mechanisms: + +1. If an output file such as + `activity_global_vector_rag--lazygraphrag.csv` already exists, the CLI reads + that completed output and skips generation for that comparison. +2. If the output file does not exist, pairwise scoring checks `cache_config` + entry by entry and calls the LLM only for cache misses. + +To recompute a comparison from scratch, use a new output directory (or remove +the specific completed output CSV) **and** either set `cache_config.type: none`, +use a new cache location, or remove the old cache. + +## Inspecting a SQLite cache + +Run: + +```sh +benchmark-qed cache inspect .benchmark_qed_cache/pairwise/pairwise.sqlite3 +``` + +This reports the cache backend, namespace entry counts, and active evaluation +leases. Use JSON output for scripts: + +```sh +benchmark-qed cache inspect .benchmark_qed_cache/pairwise/pairwise.sqlite3 --json +``` + +The inspection command accepts SQLite cache files only. It does not inspect +`json`, `memory`, or `none` backends. + +## Clearing or bypassing a cache + +- **One fresh run:** set `type: none`. +- **Keep old results but start a new cache:** change `base_dir` or + `database_name`. +- **Permanently clear a SQLite cache:** stop active evaluation processes, then + remove the configured database and its adjacent + `.benchmark_qed_cache_leases` directory. + +Do not delete a cache while evaluations are actively using it. + +## Reproducibility guidance + +- Keep the cache with the configuration and outputs used to produce a benchmark. +- Use stable model versions and deterministic call arguments where supported. +- A cache hit reproduces the previously stored judgment; it does not ask the + provider to generate a new sample. +- Use `type: none` when the purpose of a run is to collect new stochastic + judgments rather than reproduce or resume previous work. diff --git a/docs/cli/config_init.md b/docs/cli/config_init.md index d0f7843..ecdc06d 100644 --- a/docs/cli/config_init.md +++ b/docs/cli/config_init.md @@ -10,7 +10,7 @@ The `config init` command generates a starter `settings.yaml` file, prompt templ | Argument | Description | |---|---| -| `config_type` | The type of configuration to generate. One of: `autoq`, `autoe_pairwise`, `autoe_reference`, `autoe_assertion`. | +| `config_type` | The type of configuration to generate. One of: `autoq`, `autoe_pairwise`, `autoe_differential_pairwise`, `autoe_reference`, `autoe_assertion`, `autoe_chunk_assertion`. | | `root` | The path to the root directory where the configuration will be created. | #### Options @@ -77,6 +77,9 @@ benchmark-qed config init autoq ./autoq_project -s blob --base-dir experiments/r # Generate AutoE pairwise config with blob storage benchmark-qed config init autoe_pairwise ./pairwise_project --storage-type blob --base-dir experiments/run1 +# Generate differential pairwise config with stage-aware caching +benchmark-qed config init autoe_differential_pairwise ./differential_project + # Generate AutoE reference config with blob storage benchmark-qed config init autoe_reference ./reference_project -s blob --base-dir experiments/run1 @@ -134,6 +137,43 @@ When `--storage-type blob` is used: Both blocks include placeholders for `container_name`, `connection_string`, and `account_url` (managed identity). +#### Evaluation Cache Configuration + +Generated `autoe_pairwise` settings enable a persistent SQLite cache: + +```yaml +cache_config: + type: sqlite + storage: + type: file + base_dir: .benchmark_qed_cache/pairwise + database_name: pairwise.sqlite3 +``` + +This cache stores completed question/criterion/trial judgments and allows +interrupted or repeated runs to avoid duplicate LLM calls. Set `type: none` and +`storage: null` to disable it. + +The SQLite cache is always local, including when `--storage-type blob` is used +for inputs and outputs. See [Evaluation Caches](cache.md) for supported +backends, cache identity, inspection, and clearing instructions. + +Generated `autoe_differential_pairwise` settings use a separate SQLite database +under `.benchmark_qed_cache/differential_pairwise`. Extraction and verdict +stages are cached independently so judge failures can resume from extraction +and criteria changes need only recompute verdicts. + +Generated `autoe_reference` settings enable a persistent SQLite cache under +`.benchmark_qed_cache/reference`. Each question/criterion/trial judgment is +stored independently. Set `type: none` with `storage: null` to request fresh +judgments. + +Generated `autoe_assertion` settings enable a persistent SQLite cache under +`.benchmark_qed_cache/assertion` for standard single-RAG answer-level scoring. +Each question/assertion/trial judgment is stored independently. This cache is +not used by multi-RAG or hierarchical assertion scoring, and chunk-assertion +scoring uses its own assertion/chunk cache. + ::: mkdocs-typer2 :module: benchmark_qed.cli.init_config :name: config \ No newline at end of file diff --git a/docs/cli/retrieval_scores.md b/docs/cli/retrieval_scores.md index 99f755c..f18ae63 100644 --- a/docs/cli/retrieval_scores.md +++ b/docs/cli/retrieval_scores.md @@ -20,6 +20,7 @@ python -m benchmark_qed autoe retrieval-scores [options] ## Command 1: generate-retrieval-reference This command creates ground truth relevance assessments by: + 1. Clustering text units into semantic groups 2. For each question, assessing which chunks are relevant using LLM-based relevance scoring 3. Saving the reference data for later evaluation @@ -82,8 +83,13 @@ concurrent_requests: 32 # Optional: limit questions for testing max_questions: null -# Cache directory (enables resume on failure) -cache_dir: "./output/retrieval_reference/cache" +# Cache backend (use type: none to disable) +cache_config: + type: sqlite + storage: + type: file + base_dir: "./output/retrieval_reference/cache" + database_name: relevance_cache.sqlite3 # Text unit column mappings text_unit_fields: @@ -215,7 +221,12 @@ significance_correction: "holm" # P-value correction method fidelity_metric: "js" # Cache for relevance assessments -cache_dir: "./output/retrieval_scores/cache" +cache_config: + type: sqlite + storage: + type: file + base_dir: "./output/retrieval_scores/cache" + database_name: relevance_cache.sqlite3 ``` ### Retrieval Results File Format @@ -344,11 +355,43 @@ python -m benchmark_qed autoe retrieval-scores \ ### Caching -Always enable `cache_dir` to: +Configure `cache_config` with `type: sqlite` or `type: json` to: + - Resume interrupted runs - Avoid re-assessing the same query-chunk pairs - Share cache across multiple runs +The supported GraphRAG cache types are: + +- `sqlite`: persistent SQLite storage; requires `storage.type: file` and accepts + `database_name`. +- `json`: JSON entries backed by the configured GraphRAG storage. +- `memory`: process-local, non-persistent caching. +- `none`: disables caching. + +For example, a JSON cache can be configured as: + +```yaml +cache_config: + type: json + storage: + type: file + base_dir: "./output/cache" +``` + +Relevance assessments are stored through the `graphrag-cache` SQLite backend +in `relevance_cache.sqlite3` under this directory. Existing per-assessment JSON +cache files are imported automatically and retained for backward +compatibility. Cross-process work leases prevent duplicate LLM calls and +automatically expire after an interrupted worker exits. + +Use `benchmark-qed cache inspect /relevance_cache.sqlite3` for a +SQLite cache to view +the cache backend, namespace entry counts, and active leases. If the same query +and text were previously evaluated with another model or prompt configuration, +the evaluator logs a warning and computes a new result instead of reusing +incompatible data. + ### Concurrency - Start with `concurrent_requests: 16-32` for OpenAI diff --git a/docs/developing.md b/docs/developing.md index 9b6c3d8..cf632f6 100644 --- a/docs/developing.md +++ b/docs/developing.md @@ -49,6 +49,7 @@ Follow these steps to generate synthetic queries using AutoQ: --base-dir autoq_test ``` This command creates two files in the `./autoq_test` directory: + - `.env`: Stores environment variables for the AutoQ pipeline. Open this file and replace `` with your OpenAI or Azure API key. - `settings.yaml`: Contains pipeline settings. Edit this file as needed for your use case. @@ -110,8 +111,11 @@ Follow these steps to compare RAG answer pairs using the pairwise scoring pipeli --base-dir pairwise_test ``` This command creates two files in the `./pairwise_test` directory: + - `.env`: Contains environment variables for the pairwise comparison tests. Open this file and replace `` with your OpenAI or Azure API key. - - `settings.yaml`: Contains pipeline settings, which you can modify as needed. + - `settings.yaml`: Contains pipeline settings, including a persistent + stage-aware SQLite cache under + `.benchmark_qed_cache/differential_pairwise`. The generated `settings.yaml` includes commented-out `input_storage` and `output_storage` sections for configuring Azure Blob Storage backends. @@ -179,10 +183,24 @@ Follow these steps to run the differential pairwise scoring pipeline: --base-dir differential_pairwise_test ``` This command creates two files in the `./differential_pairwise_test` directory: + - `.env`: Contains environment variables for the pairwise comparison tests. Open this file and replace `` with your OpenAI or Azure API key. - `settings.yaml`: Contains pipeline settings, which you can modify as needed. - The differential command reuses the pairwise configuration structure (`base`, `others`, `question_sets`, `trials`, `llm_config`, `criteria`); when `criteria` is omitted the standard defaults are used. The generated `settings.yaml` wires up the extract-and-judge prompts under `prompt_config` (`extract_system_prompt`, `extract_user_prompt`, `judge_system_prompt`, `judge_user_prompt`); edit those prompt files under `prompts/` or remove the `prompt_config` section to use the built-in defaults. + The differential command reuses the pairwise configuration structure + (`base`, `others`, `question_sets`, `trials`, `llm_config`, `criteria`); + when `criteria` is omitted the standard defaults are used. The generated + `settings.yaml` wires up the extract-and-judge prompts under + `prompt_config` (`extract_system_prompt`, `extract_user_prompt`, + `judge_system_prompt`, `judge_user_prompt`); edit those prompt files under + `prompts/` or remove the `prompt_config` section to use the built-in + defaults. + + The generated `cache_config` caches extraction and verdict stages + independently. A failed judge call can therefore resume from its completed + extraction, and changing criteria invalidates verdicts without repeating + extraction. Set `type: none` with `storage: null` to force both stages to + run again. The generated `settings.yaml` includes commented-out `input_storage` and `output_storage` sections for configuring Azure Blob Storage backends. @@ -199,6 +217,18 @@ Follow these steps to run the differential pairwise scoring pipeline: ``` The results will be saved in the `output` directory. In addition to the per-criterion win scores, each cached comparison CSV also records the extracted `common`, `unique_1`, and `unique_2` content for inspection. + Inspect the default stage cache with: + + ```sh + uv run benchmark-qed cache inspect \ + .benchmark_qed_cache/differential_pairwise/differential_pairwise.sqlite3 + ``` + + Existing comparison CSVs are checked before the stage cache. To fully + recompute a comparison, use a new output directory (or remove its CSV) and + also disable, relocate, or clear the stage cache. See + [Evaluation Caches](cli/cache.md) for backend and clearing guidance. + ## Scoring RAG answers against reference answers Follow these steps to score RAG answers against reference answers using example data from the AP news dataset: @@ -233,10 +263,13 @@ Follow these steps to score RAG answers against reference answers using example --base-dir reference_test ``` This creates two files in the `./reference_test` directory: + - `.env`: Contains environment variables for the reference scoring pipeline. Open this file and replace `` with your OpenAI or Azure API key. - - `settings.yaml`: Contains pipeline settings, which you can modify as needed. + - `settings.yaml`: Contains pipeline settings, including a persistent + SQLite judgment cache under `.benchmark_qed_cache/reference`. The generated `settings.yaml` includes commented-out `input_storage` and `output_storage` sections for configuring Azure Blob Storage backends. + The SQLite cache remains local when blob input/output storage is enabled. 4. **Run the reference scoring:** ```sh @@ -251,6 +284,18 @@ Follow these steps to score RAG answers against reference answers using example ``` The results will be saved in the `output` directory. + Successful question/criterion/trial judgments are cached immediately. If a + request fails, rerun the same command to reuse completed judgments and + evaluate only misses. Inspect the cache with: + + ```sh + uv run benchmark-qed cache inspect \ + .benchmark_qed_cache/reference/reference.sqlite3 + ``` + + Set `cache_config.type: none` with `storage: null` when fresh judgments are + required. + ## Scoring RAG answers against assertions Follow these steps to evaluate RAG answers against per-question assertions using answer-level (LLM-as-a-judge) evaluation: @@ -287,6 +332,7 @@ Follow these steps to evaluate RAG answers against per-question assertions using --base-dir assertion_test ``` This command creates two files in the `./assertion_test` directory: + - `.env`: Contains environment variables for the assertion evaluation pipeline. Open this file and replace `` with your OpenAI or Azure API key. - `settings.yaml`: Contains pipeline settings, which you can modify as needed. @@ -305,9 +351,25 @@ Follow these steps to evaluate RAG answers against per-question assertions using ``` The results will be saved in the `output` directory, including per-assertion scores and per-question summaries. + The generated single-RAG configuration enables a persistent SQLite cache at + `.benchmark_qed_cache/assertion/assertion.sqlite3`. Each + question/assertion/trial judgment is persisted independently, so rerunning + after an interruption evaluates only missing work. Inspect it with: + + ```sh + uv run benchmark-qed cache inspect \ + .benchmark_qed_cache/assertion/assertion.sqlite3 + ``` + + Set `cache_config.type: none` with `storage: null` when fresh judgments are + required. This answer-level cache does not apply to multi-RAG or + hierarchical assertion scoring; chunk-level assertion evaluation uses its + own assertion/chunk cache. + ## Evaluating retrieved chunks against assertions Chunk-level evaluation scores how well retrieved passages (chunks) support assertions, without waiting for answer synthesis. This is useful for: + - **Fast retrieval evaluation**: Assess retrieval quality before generating complete answers - **Efficient iteration**: Fix retriever settings without re-running expensive answer generation - **Persistent caching**: Avoid re-evaluating same (assertion, chunk) pairs across multiple runs @@ -315,57 +377,70 @@ Chunk-level evaluation scores how well retrieved passages (chunks) support asser ### Prerequisites: Where do chunks come from? **Chunks** are the retrieved passages from your RAG system. You obtain them by running your retriever (e.g., vector search, BM25, hybrid) on each question. Chunks should include: + - The passage text - The chunk index (chunk_id) -- The retrieval rank - -You'll provide this data to benchmark-qed in one of two formats (see Step 2 below). +- The retrieval rank when available ### Step-by-step guide 1. **Set up your project directory:** + ```sh mkdir -p ./local/chunk_assertion_test cd ./local/chunk_assertion_test ``` -2. **Prepare your input data:** +2. **Initialize the chunk-assertion project:** + + For a local-filesystem project, run: + ```sh - mkdir ./input + uv run benchmark-qed config init autoe_chunk_assertion . + ``` + + Alternative blob variant (choose this instead of the local command above; do not run both): + + ```sh + uv run benchmark-qed config init autoe_chunk_assertion . \ + --storage-type blob \ + --container-name my-container \ + --account-url https://.blob.core.windows.net \ + --base-dir chunk_assertion_test + ``` + + The command creates the project structure before any settings need to be + edited: + + - `settings.yaml`: Pipeline configuration with input placeholders + - `.env`: Environment-variable placeholders for credentials + - `input/`: Local input directory + - `prompts/`: Default chunk-assertion judge prompts + + Set the required credentials in `.env` before running the evaluation. + +3. **Prepare the retrieval and assertion inputs:** + + You need two JSON files with matching `question_id` values: + + - A **retrieval-results file** produced by your retriever. BenchmarkQED + evaluates the retrieved chunks, not the synthesized answer. + - An **assertions file** containing the per-question assertions to check. + + To try the workflow with bundled example data, download both kinds of files + into the generated `input/` directory: + + ```sh + uv run benchmark-qed data download example_answers input ``` - - You need two files whose `question_id`s line up: - - **Assertion file**: The per-question assertions to check. Set via `assertions_path` - in `settings.yaml`. - - **Retrieval file**: Created by YOUR retrieval system. The tool always evaluates the - retrieved **chunks** (not the synthesized answer) against your assertions. Provide - these chunks as a JSON array of `RetrievalResult` records, set via `retrieval_path` - in `settings.yaml`. - - > **Note:** To try the pipeline end-to-end without wiring up your own retriever, download - > the bundled example data, which ships matching retrieval-results and assertions files - > (same `question_id`s) for both short- and long-context retrievers: - > ```sh - > uv run benchmark-qed data download example_answers input - > ``` - > Then set, for example: - > ```yaml - > retrieval_path: input/vector_rag_short_context/data_local_retrieval_results.json - > assertions_path: input/data_local_assertions.json - > ``` - > - > Swap `data_local` for `data_global` or `data_linked` (and pick - > `vector_rag_short_context` or `vector_rag_long_context`) to evaluate a different - > question set — just keep the retrieval and assertions files on the same set so their - > `question_id`s match. These are the same files used by the AutoE notebook example. - - ### Input Format: RetrievalResult JSON file - - Create a JSON array (e.g., `input/retrieval.json`, or any path you prefer) where each - record holds a question and its retrieved `context`. Each context item carries a - `chunk_id`, `text`, and an optional `rank`. When `rank` is present on every item, - chunks are evaluated in rank order (top-k semantics); otherwise the chunks are assumed - to be already pre-sorted in decreasing order of relevance and list order is used. + + The examples include matching files for `data_local`, `data_global`, and + `data_linked`, with both `vector_rag_short_context` and + `vector_rag_long_context` retrieval results. + + If you use your own retriever, its retrieval-results file must be a JSON + array in this form: + ```json [ { @@ -374,79 +449,115 @@ You'll provide this data to benchmark-qed in one of two formats (see Step 2 belo "context": [ { "chunk_id": "0", - "text": "Photosynthesis is the process by which plants convert sunlight into chemical energy...", + "text": "Photosynthesis converts sunlight into chemical energy.", "rank": 0 }, { "chunk_id": "5", - "text": "In photosynthesis, light energy is captured by chlorophyll molecules..." + "text": "Chlorophyll captures light energy.", + "rank": 1 } ] } ] ``` - This is the same schema produced by the retrieval-results examples (e.g. the bundled - `data_local_retrieval_results.json`), so those files work directly with no conversion. - See the **chunk-level assertion** example in the AutoE notebook - ([notebooks/autoe.ipynb](notebooks/autoe.ipynb)) for end-to-end usage. - -3. **Create a configuration file for chunk-level evaluation:** - ```sh - uv run benchmark-qed config init autoe_chunk_assertion . - ``` - This is the local-filesystem variant. - - Alternative blob variant (choose this instead of the local command above; do not run both): - ```sh - uv run benchmark-qed config init autoe_chunk_assertion . \ - --storage-type blob \ - --container-name my-container \ - --account-url https://.blob.core.windows.net \ - --base-dir chunk_assertion_test - ``` - This command creates two files in the `./chunk_assertion_test` directory: - - `.env`: Contains environment variables. Open this file and replace `` with your OpenAI or Azure API key. - - `settings.yaml`: Contains pipeline settings, including: - - `retrieval_path`: Path to your retrieved chunks JSON (RetrievalResult format above) - - `k_list`: K values to report coverage metrics (e.g., [5, 10, 20, 50]) - - `cache_dir`: Directory for persistent (assertion, chunk) cache - - The generated `settings.yaml` includes commented-out `input_storage` and `output_storage` sections for configuring Azure Blob Storage backends. + Each context item requires `chunk_id` and `text`; `rank` is optional. When + every item has a rank, chunks are evaluated in rank order. Otherwise, their + existing list order is used. See the chunk-level assertion example in the + [AutoE notebook](notebooks/autoe.ipynb) for programmatic usage. -4. **Update settings.yaml for your data:** +4. **Review the generated `settings.yaml`:** - The generated template includes placeholder paths. Update them to match your actual file locations: + The generated configuration already points to the bundled `data_local` + short-context example: ```yaml generated: name: my_retriever - retrieval_path: input/vector_rag_short_context/data_local_retrieval_results.json # Update to your RetrievalResult file path + retrieval_path: input/vector_rag_short_context/data_local_retrieval_results.json + assertions: - assertions_path: input/data_local_assertions.json # Update to your assertions file path + assertions_path: input/data_local_assertions.json ``` + If you downloaded the bundled data in the previous step, these defaults can + be used unchanged. To evaluate another example set, change `data_local` in + both paths to `data_global` or `data_linked`. To use the long-context + retriever, also change `vector_rag_short_context` to + `vector_rag_long_context`. The question set must match in both files. + + Other important generated settings are: + + - `k_list`: Retrieval depths for which coverage metrics are reported + - `max_chunks_per_question`: Optional cap on evaluated chunks + - `cache_config`: Cache backend for assertion/chunk judgments + - `llm_config`: Judge model, provider, credentials, and request settings + - `prompt_config`: Judge prompt files + + The default cache is persistent SQLite: + + ```yaml + cache_config: + type: sqlite + storage: + type: file + base_dir: .benchmark_qed_cache/chunk_assertions + database_name: chunk_assertions.sqlite3 + ``` + + Supported cache types are `sqlite`, `json`, `memory`, and `none`. Existing + local `chunk_assertions.jsonl` caches are imported automatically. Persistent + local caches coordinate concurrent processes so only one process judges + each uncached assertion/chunk pair. + + Cache storage is independent of `input_storage` and `output_storage`. A + JSON cache with `storage.type: file` remains local even when inputs and + outputs use Azure Blob Storage, and its relative `base_dir` is resolved from + the directory where the command is run. To place JSON entries in blob + storage, set `cache_config.storage.type: blob` and provide its container and + credentials explicitly. See [Evaluation Caches](cli/cache.md#cache-backends) + for local and blob JSON examples. + 5. **Run the chunk-level assertion evaluation:** + ```sh uv run benchmark-qed autoe chunk-assertion-scores settings.yaml output ``` - This is the local-filesystem variant. Alternative blob-stored config variant (choose this instead of the local command above; do not run both): + ```sh uv run benchmark-qed autoe chunk-assertion-scores blob://my-container/chunk_assertion_test/settings.yaml output \ --account-url https://.blob.core.windows.net ``` - The results will be saved in the `output` directory, including: + +6. **Review the outputs and cache:** + + The `output/` directory contains: + - `chunk_assertion_results.json`: Coverage metrics at each k - `per_query_metrics_*.json`: Per-question metrics for paired significance testing - `debug/`: Detailed per-question evaluation records -**Chunk-level evaluation benefits:** -- **Efficient caching**: Results cached at (assertion, chunk) granularity using SHA256 content-addressing + Inspect the default SQLite cache, including namespace counts and active work + leases, with: + + ```sh + uv run benchmark-qed cache inspect \ + .benchmark_qed_cache/chunk_assertions/chunk_assertions.sqlite3 + ``` + + The inspection command applies only to SQLite cache files. To force fresh + LLM judgments, set `cache_config.type: none` and `storage: null`. + +### Chunk-level evaluation benefits + +- **Efficient caching**: Results cached at `(assertion, chunk)` granularity using SHA256 content-addressing - **Multi-k reporting**: Coverage, Strict Coverage, and Coverage Strength metrics at each k value - **Reusable cache**: Re-run with different k values or retriever configs with zero LLM cost on overlapping chunks - **Coverage metrics**: + - Coverage: % of assertions with full or partial support in top-k chunks - Strict Coverage: % of assertions with full support only - Coverage Strength: Average score across all assertions @@ -459,5 +570,3 @@ To learn how to use AutoE programmatically, please see the [AutoE Notebook Examp To explore the query synthesis workflow in detail, please see the [AutoQ CLI Documentation](cli/autoq.md) for command-line usage and the [AutoQ Notebook Example](notebooks/autoq.ipynb) for a step-by-step programmatic guide. For a deeper understanding of AutoE evaluation pipelines, please refer to the [AutoE CLI Documentation](cli/autoe.md) for available commands and the [AutoE Notebook Example](notebooks/autoe.ipynb) for hands-on examples. - - diff --git a/docs/index.md b/docs/index.md index ad29f3a..9c8c2b0 100644 --- a/docs/index.md +++ b/docs/index.md @@ -27,6 +27,7 @@ To get started with BenchmarkQED, you have two options: ```sh pip install benchmark-qed ``` + 2. [Use it from source](./developing.md) ## Usage @@ -79,6 +80,7 @@ Please follow these steps to generate synthetic queries from the [AP news datase benchmark-qed config init autoq . ``` This command creates two files in the `./autoq_test` directory: + - `.env`: Contains environment variables for the AutoQ pipeline. Open this file and replace `` with your own OpenAI or Azure API key. - `settings.yaml`: Contains pipeline settings, which you can modify as needed. @@ -128,14 +130,29 @@ Please follow these steps to perform a relative comparison of RAG methods using benchmark-qed config init autoe_pairwise . ``` This command creates two files in the `./pairwise_test` directory: + - `.env`: Contains environment variables for the pairwise comparison tests. Open this file and replace `` with your own OpenAI or Azure API key. - - `settings.yaml`: Contains pipeline settings, which you can modify as needed. + - `settings.yaml`: Contains pipeline settings, including a persistent + SQLite pairwise cache under `.benchmark_qed_cache/pairwise`. The cache + lets interrupted and repeated runs reuse completed LLM judgments. Set + `cache_config.type: none` to disable it. 4. **Run the pairwise comparison:** ```sh benchmark-qed autoe pairwise-scores settings.yaml output ``` + Inspect the default pairwise cache with: + + ```sh + benchmark-qed cache inspect \ + .benchmark_qed_cache/pairwise/pairwise.sqlite3 + ``` + + See [Evaluation Caches](cli/cache.md) for details about cache identity, + backend selection, forcing fresh judgments, and the distinction between + cached judgments and existing output CSVs. + #### Example 2: Scoring of RAG answers against reference answers Please follow these steps to score RAG answers against reference answers using example data from the [AP news dataset](https://github.com/microsoft/benchmark-qed/tree/main/docs/notebooks/example_answers): @@ -157,14 +174,21 @@ Please follow these steps to score RAG answers against reference answers using e benchmark-qed config init autoe_reference . ``` This command creates two files in the `./reference_test` directory: + - `.env`: Contains environment variables for the reference scoring tests. Open this file and replace `` with your own OpenAI or Azure API key. - - `settings.yaml`: Contains pipeline settings, which you can modify as needed. + - `settings.yaml`: Contains pipeline settings and a persistent SQLite cache + under `.benchmark_qed_cache/reference`. 4. **Run the reference scoring:** ```sh benchmark-qed autoe reference-scores settings.yaml output ``` + Interrupted and unchanged reruns reuse completed question/criterion/trial + judgments. Set `cache_config.type: none` with `storage: null` for fresh + judgments. See [Evaluation Caches](cli/cache.md) for inspection and backend + options. + #### Example 3: Assertion-based scoring of RAG answers Please follow these steps to evaluate RAG answers against predefined assertions using example data from the [AP news dataset](https://github.com/microsoft/benchmark-qed/tree/main/docs/notebooks/example_answers): @@ -186,14 +210,22 @@ Please follow these steps to evaluate RAG answers against predefined assertions benchmark-qed config init autoe_assertion . ``` This command creates two files in the `./assertion_test` directory: + - `.env`: Contains environment variables for the assertion scoring tests. Open this file and replace `` with your own OpenAI or Azure API key. - - `settings.yaml`: Contains pipeline settings, which you can modify as needed. + - `settings.yaml`: Contains pipeline settings, including a persistent + single-RAG assertion cache at + `.benchmark_qed_cache/assertion/assertion.sqlite3`. 4. **Run the assertion scoring:** ```sh benchmark-qed autoe assertion-scores settings.yaml output ``` + Repeated or resumed runs reuse completed question/assertion/trial + judgments. Run `benchmark-qed cache inspect + .benchmark_qed_cache/assertion/assertion.sqlite3` to inspect the cache, or + set `cache_config.type: none` with `storage: null` for fresh judgments. + For more details on configuring and running AutoE, see the [AutoE CLI Documentation](cli/autoe.md). If your `settings.yaml` is stored in Azure Blob Storage, see [Running with Blob-Stored Configuration](developing.md#running-with-blob-stored-configuration). diff --git a/docs/notebooks/autoe.ipynb b/docs/notebooks/autoe.ipynb index 30ade1f..bc75f4b 100644 --- a/docs/notebooks/autoe.ipynb +++ b/docs/notebooks/autoe.ipynb @@ -1265,21 +1265,21 @@ ") -> pd.DataFrame:\n", " \"\"\"Wilcoxon signed-rank test on paired joint vs. separate win rates.\"\"\"\n", " keys = [\"question_set\", \"question\", \"criteria\"]\n", - " joint_mean = joint_df.groupby(keys)[base_col].mean().to_frame(\"joint\")\n", - " separate_mean = separate_df.groupby(keys)[base_col].mean().to_frame(\"separate\")\n", + " joint_mean = joint_df.groupby(keys).agg(joint=(base_col, \"mean\"))\n", + " separate_mean = separate_df.groupby(keys).agg(separate=(base_col, \"mean\"))\n", " merged = joint_mean.join(separate_mean, how=\"inner\").dropna().reset_index()\n", "\n", " def _test(sub: pd.DataFrame) -> pd.Series:\n", - " a = sub[\"joint\"].to_numpy()\n", - " b = sub[\"separate\"].to_numpy()\n", + " a = np.asarray(sub[\"joint\"], dtype=float)\n", + " b = np.asarray(sub[\"separate\"], dtype=float)\n", " diff = b - a\n", " if np.allclose(diff, 0.0):\n", " stat, p_value = 0.0, 1.0\n", " else:\n", " try:\n", - " result = stats.wilcoxon(a, b)\n", - " stat = float(result.statistic) # type: ignore[attr-defined]\n", - " p_value = float(result.pvalue) # type: ignore[attr-defined]\n", + " statistic, probability = stats.wilcoxon(a, b)\n", + " stat = float(np.asarray(statistic, dtype=float).item())\n", + " p_value = float(np.asarray(probability, dtype=float).item())\n", " except ValueError:\n", " stat, p_value = 0.0, 1.0\n", " return pd.Series({\n", @@ -1311,9 +1311,9 @@ " \"Joint vs. Separate — Paired Significance (Wilcoxon; p > alpha ⇒ no systematic difference)\",\n", ")\n", "\n", - "overall_p = float(\n", - " significance_df.loc[significance_df[\"criteria\"] == \"ALL\", \"p_value\"].iloc[0]\n", - ")\n", + "overall_p = significance_df.loc[\n", + " significance_df[\"criteria\"] == \"ALL\", \"p_value\"\n", + "].to_numpy(dtype=float)[0]\n", "if overall_p > alpha:\n", " rich_print(\n", " f\"[green]Overall p = {overall_p:.3f} > alpha = {alpha}: no statistically \"\n", @@ -1357,16 +1357,18 @@ "\n", "n_examples = min(3, len(disagreements))\n", "for _, row in disagreements.head(n_examples).iterrows():\n", + " joint_score = float(np.asarray(row[\"joint_score\"], dtype=float).item())\n", + " separate_score = float(np.asarray(row[\"separate_score\"], dtype=float).item())\n", " rich_print(\n", " f\"[bold cyan]{row['question_set']} · {row['criteria']} · trial {row['trial']}[/bold cyan]\"\n", " )\n", " rich_print(f\"[bold]Question:[/bold] {str(row['question'])[:200]}...\")\n", " rich_print(\n", - " f\"[bold]JOINT verdict:[/bold] {verdict_labels[float(row['joint_score'])]}\\n\"\n", + " f\"[bold]JOINT verdict:[/bold] {verdict_labels[joint_score]}\\n\"\n", " f\"{row['joint_reasoning']}\\n\"\n", " )\n", " rich_print(\n", - " f\"[bold]SEPARATE verdict:[/bold] {verdict_labels[float(row['separate_score'])]}\\n\"\n", + " f\"[bold]SEPARATE verdict:[/bold] {verdict_labels[separate_score]}\\n\"\n", " f\"{row['separate_reasoning']}\\n\"\n", " )\n", " rich_print(\"[dim]\" + \"-\" * 80 + \"[/dim]\\n\")" @@ -1503,7 +1505,7 @@ " .agg(agreement_rate=\"mean\", n=\"count\")\n", " .reset_index()\n", " )\n", - " overall_agreement = float(merged_items[\"agree\"].mean())\n", + " overall_agreement = float(merged_items[\"agree\"].to_numpy(dtype=float).mean())\n", " agreement_view = pd.concat(\n", " [\n", " agreement_view,\n", @@ -1549,14 +1551,21 @@ " f\"[{model_name}] Joint vs. Separate — Paired Significance\",\n", " )\n", "\n", - " overall = model_sig[model_sig[\"criteria\"] == \"ALL\"].iloc[0]\n", + " overall = model_sig.loc[\n", + " model_sig[\"criteria\"] == \"ALL\",\n", + " [\"joint_mean_wr\", \"separate_mean_wr\", \"p_value\", \"significant\"],\n", + " ]\n", + " overall_rates = overall[[\"joint_mean_wr\", \"separate_mean_wr\", \"p_value\"]].to_numpy(\n", + " dtype=float\n", + " )[0]\n", + " significant = overall[\"significant\"].to_numpy(dtype=bool)[0]\n", " return {\n", " \"judge_model\": model_name,\n", " \"verdict_agreement\": round(overall_agreement, 3),\n", - " \"joint_mean_wr\": round(float(overall[\"joint_mean_wr\"]), 4),\n", - " \"separate_mean_wr\": round(float(overall[\"separate_mean_wr\"]), 4),\n", - " \"overall_p_value\": round(float(overall[\"p_value\"]), 3),\n", - " \"significant_diff\": bool(overall[\"significant\"]),\n", + " \"joint_mean_wr\": round(float(overall_rates[0]), 4),\n", + " \"separate_mean_wr\": round(float(overall_rates[1]), 4),\n", + " \"overall_p_value\": round(float(overall_rates[2]), 3),\n", + " \"significant_diff\": bool(significant),\n", " }\n", "\n", "\n", @@ -1580,7 +1589,7 @@ ], "metadata": { "kernelspec": { - "display_name": "benchmark-qed (3.13.2.final.0)", + "display_name": "benchmark-qed (3.13.2)", "language": "python", "name": "python3" }, diff --git a/docs/notebooks/cache_parallelism_benchmark.ipynb b/docs/notebooks/cache_parallelism_benchmark.ipynb new file mode 100644 index 0000000..aa790fa --- /dev/null +++ b/docs/notebooks/cache_parallelism_benchmark.ipynb @@ -0,0 +1,1168 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "id": "overview", + "metadata": {}, + "source": [ + "# Cache and parallelism benchmark\n", + "\n", + "This notebook demonstrates the efficiency and reliability improvements of the SQLite/WAL cache compared with the original JSONL snapshot design. It uses deterministic simulated work instead of paid LLM calls.\n", + "\n", + "The demonstrations cover:\n", + "\n", + "1. incremental write scaling;\n", + "2. multiprocess integrity;\n", + "3. duplicate LLM-call avoidance;\n", + "4. interrupted-worker recovery;\n", + "5. configuration mismatch diagnostics; and\n", + "6. provenance inspection.\n", + "\n", + "> Performance varies by filesystem and machine. Run each benchmark several times and report medians, call counts, and integrity results rather than relying on one timing." + ] + }, + { + "cell_type": "code", + "execution_count": 1, + "id": "imports", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Workspace: /var/folders/dn/k4bgd8bs47g6ldk94svck9hw0000gn/T/benchmark-qed-cache-demo-zjdm1vqo\n", + "Multiprocess demonstrations available: True\n" + ] + } + ], + "source": [ + "# Copyright (c) 2025 Microsoft Corporation.\n", + "from __future__ import annotations\n", + "\n", + "import asyncio\n", + "import contextlib\n", + "import json\n", + "import multiprocessing as mp\n", + "import shutil\n", + "import statistics\n", + "import subprocess # ruff: ignore[suspicious-subprocess-import] - invokes the local benchmark-qed CLI only\n", + "import sys\n", + "import tempfile\n", + "import time\n", + "from concurrent.futures import ProcessPoolExecutor\n", + "from pathlib import Path\n", + "from typing import TYPE_CHECKING, Any, TypeVar\n", + "\n", + "import matplotlib.pyplot as plt\n", + "import nest_asyncio\n", + "import pandas as pd\n", + "from IPython.display import display as show\n", + "\n", + "from benchmark_qed.autoe.chunk_assertion.cache import (\n", + " ContentAddressedCache,\n", + " build_cache_metadata,\n", + " compute_cache_key,\n", + " compute_config_fingerprint,\n", + " compute_logical_key,\n", + ")\n", + "from benchmark_qed.cache import CacheStore, create_default_cache_config, inspect_cache\n", + "\n", + "if TYPE_CHECKING:\n", + " from collections.abc import Coroutine\n", + "\n", + " from graphrag_cache import CacheConfig\n", + "\n", + "RUNS = 5\n", + "BATCHES = 30\n", + "BATCH_SIZE = 100\n", + "WORKERS = 4\n", + "SHARED_KEYS = 30\n", + "SIMULATED_CALL_SECONDS = 0.01\n", + "T = TypeVar(\"T\")\n", + "\n", + "\n", + "def cache_config(path: Path | str) -> CacheConfig:\n", + " \"\"\"Create a SQLite cache configuration for a database path.\"\"\"\n", + " database_path = Path(path)\n", + " return create_default_cache_config(\n", + " database_path.parent, database_name=database_path.name\n", + " )\n", + "\n", + "\n", + "def run_async(coroutine: Coroutine[Any, Any, T]) -> T:\n", + " \"\"\"Run an async cache operation from a notebook cell.\"\"\"\n", + " return asyncio.get_event_loop().run_until_complete(coroutine)\n", + "\n", + "\n", + "nest_asyncio.apply()\n", + "workspace = Path(tempfile.mkdtemp(prefix=\"benchmark-qed-cache-demo-\"))\n", + "mp_context = mp.get_context(\"fork\") if \"fork\" in mp.get_all_start_methods() else None\n", + "print(f\"Workspace: {workspace}\")\n", + "print(f\"Multiprocess demonstrations available: {mp_context is not None}\")" + ] + }, + { + "cell_type": "markdown", + "id": "legacy-baseline", + "metadata": {}, + "source": [ + "## Original JSONL baseline\n", + "\n", + "The class below reproduces the original chunk-cache persistence behavior: load the complete JSONL file, keep an in-memory snapshot, write the complete snapshot to one shared `.tmp` file, and replace the cache on every flush." + ] + }, + { + "cell_type": "code", + "execution_count": 2, + "id": "legacy-cache", + "metadata": {}, + "outputs": [], + "source": [ + "class LegacyJSONLCache:\n", + " \"\"\"Reproduce the original full-snapshot JSONL cache behavior.\"\"\"\n", + "\n", + " def __init__(self, path: Path) -> None:\n", + " self.path = path\n", + " self.path.parent.mkdir(parents=True, exist_ok=True)\n", + " self.data: dict[str, str] = {}\n", + " if self.path.exists():\n", + " for line in self.path.read_text(encoding=\"utf-8\").splitlines():\n", + " if not line:\n", + " continue\n", + " try:\n", + " record = json.loads(line)\n", + " except json.JSONDecodeError:\n", + " continue\n", + " if record.get(\"key\") and record.get(\"grade\"):\n", + " self.data[str(record[\"key\"])] = str(record[\"grade\"])\n", + "\n", + " def put(self, key: str, value: str) -> None:\n", + " \"\"\"Store a value in the in-memory snapshot.\"\"\"\n", + " self.data.setdefault(key, value)\n", + "\n", + " def flush(self) -> None:\n", + " \"\"\"Rewrite and atomically replace the complete JSONL snapshot.\"\"\"\n", + " temporary_path = self.path.with_suffix(self.path.suffix + \".tmp\")\n", + " with temporary_path.open(\"w\", encoding=\"utf-8\") as file:\n", + " for key, value in self.data.items():\n", + " file.write(json.dumps({\"key\": key, \"grade\": value}) + \"\\n\")\n", + " temporary_path.replace(self.path)\n", + "\n", + "\n", + "def reset_path(path: Path) -> None:\n", + " \"\"\"Remove one benchmark cache and its SQLite sidecars.\"\"\"\n", + " if path.is_dir():\n", + " shutil.rmtree(path)\n", + " elif path.exists():\n", + " path.unlink()\n", + " for suffix in (\"-wal\", \"-shm\"):\n", + " sidecar = Path(f\"{path}{suffix}\")\n", + " if sidecar.exists():\n", + " sidecar.unlink()" + ] + }, + { + "cell_type": "markdown", + "id": "incremental-heading", + "metadata": {}, + "source": [ + "## 1. Incremental write scaling\n", + "\n", + "This workload adds fixed-size batches and persists after every batch. The original cache rewrites every preceding entry; SQLite inserts only the new rows." + ] + }, + { + "cell_type": "code", + "execution_count": 3, + "id": "incremental-benchmark", + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "
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batch_started)\n", + " else:\n", + " cache = CacheStore(cache_config(path), \"benchmark\")\n", + " for batch in range(BATCHES):\n", + " batch_started = time.perf_counter()\n", + " entries = [\n", + " (f\"key-{batch * BATCH_SIZE + offset}\", \"full_support\", {})\n", + " for offset in range(BATCH_SIZE)\n", + " ]\n", + " await cache.put_many(entries)\n", + " batch_times.append(time.perf_counter() - batch_started)\n", + " return time.perf_counter() - started, batch_times\n", + "\n", + "\n", + "incremental_runs: list[dict[str, Any]] = []\n", + "incremental_curves: dict[str, list[float]] = {}\n", + "for implementation in (\"legacy\", \"sqlite\"):\n", + " totals = []\n", + " curves = []\n", + " for run in range(RUNS):\n", + " total, curve = run_async(benchmark_incremental_writes(implementation, run))\n", + " totals.append(total)\n", + " curves.append(curve)\n", + " incremental_runs.append({\n", + " \"implementation\": implementation,\n", + " \"median_seconds\": statistics.median(totals),\n", + " \"p95_batch_ms\": statistics.quantiles(\n", + " [value for curve in curves for value in curve], n=20\n", + " )[18]\n", + " * 1000,\n", + " })\n", + " incremental_curves[implementation] = [\n", + " statistics.median(curve[index] for curve in curves) for index in range(BATCHES)\n", + " ]\n", + "\n", + "incremental_df = pd.DataFrame(incremental_runs)\n", + "show(incremental_df)" + ] + }, + { + "cell_type": "code", + "execution_count": 4, + "id": "incremental-plot", + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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", 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" + ], + "text/plain": [ + " implementation workers expected_entries surviving_entries write_errors \\\n", + "0 legacy 4 400 100 3 \n", + "1 sqlite 4 400 400 0 \n", + "\n", + " seconds integrity \n", + "0 1.007491 False \n", + "1 1.019867 True " + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "def legacy_parallel_writer(\n", + " path_text: str, worker: int, start_at: float, count: int\n", + ") -> str | None:\n", + " \"\"\"Write disjoint entries through the original snapshot cache.\"\"\"\n", + " cache = LegacyJSONLCache(Path(path_text))\n", + " while time.time() < start_at:\n", + " time.sleep(0.001)\n", + " for index in range(count):\n", + " cache.put(f\"worker-{worker}-key-{index}\", \"full_support\")\n", + " try:\n", + " cache.flush()\n", + " except OSError as exc:\n", + " return str(exc)\n", + " return None\n", + "\n", + "\n", + "def sqlite_parallel_writer(\n", + " path_text: str, worker: int, start_at: float, count: int\n", + ") -> str | None:\n", + " \"\"\"Write disjoint entries through the SQLite cache.\"\"\"\n", + " cache = CacheStore(cache_config(path_text), \"parallel-benchmark\")\n", + " while time.time() < start_at:\n", + " time.sleep(0.001)\n", + " asyncio.run(\n", + " cache.put_many([\n", + " (f\"worker-{worker}-key-{index}\", \"full_support\", {})\n", + " for index in range(count)\n", + " ])\n", + " )\n", + " return None\n", + "\n", + "\n", + "def count_legacy_entries(path: Path) -> int:\n", + " \"\"\"Count valid distinct keys surviving in a JSONL cache.\"\"\"\n", + " if not path.exists():\n", + " return 0\n", + " keys = set()\n", + " for line in path.read_text(encoding=\"utf-8\").splitlines():\n", + " with contextlib.suppress(json.JSONDecodeError, KeyError):\n", + " keys.add(json.loads(line)[\"key\"])\n", + " return len(keys)\n", + "\n", + "\n", + "parallel_rows = []\n", + "if mp_context is None:\n", + " print(\"Skipped: this demonstration requires a platform with the fork start method.\")\n", + "else:\n", + " expected = WORKERS * BATCH_SIZE\n", + " for implementation, writer in (\n", + " (\"legacy\", legacy_parallel_writer),\n", + " (\"sqlite\", sqlite_parallel_writer),\n", + " ):\n", + " path = (\n", + " workspace\n", + " / f\"parallel-{implementation}.{'jsonl' if implementation == 'legacy' else 'sqlite3'}\"\n", + " )\n", + " reset_path(path)\n", + " start_at = time.time() + 1\n", + " started = time.perf_counter()\n", + " with ProcessPoolExecutor(\n", + " max_workers=WORKERS, mp_context=mp_context\n", + " ) as executor:\n", + " futures = [\n", + " executor.submit(writer, str(path), worker, start_at, BATCH_SIZE)\n", + " for worker in range(WORKERS)\n", + " ]\n", + " errors = [future.result(timeout=30) for future in futures]\n", + " elapsed = time.perf_counter() - started\n", + " surviving = (\n", + " count_legacy_entries(path)\n", + " if implementation == \"legacy\"\n", + " else sum(\n", + " int(item[\"entries\"])\n", + " for item in inspect_cache(path)[\"namespaces\"]\n", + " if item[\"namespace\"] == \"parallel-benchmark\"\n", + " )\n", + " )\n", + " parallel_rows.append({\n", + " \"implementation\": implementation,\n", + " \"workers\": WORKERS,\n", + " \"expected_entries\": expected,\n", + " \"surviving_entries\": surviving,\n", + " \"write_errors\": sum(error is not None for error in errors),\n", + " \"seconds\": elapsed,\n", + " \"integrity\": surviving == expected and not any(errors),\n", + " })\n", + "\n", + "show(pd.DataFrame(parallel_rows))" + ] + }, + { + "cell_type": "markdown", + "id": "coalescing-heading", + "metadata": {}, + "source": [ + "## 3. Duplicate expensive-call avoidance\n", + "\n", + "Every process requests the same keys. The legacy baseline performs every simulated call. The leased cache permits only the claim owner to perform each call; other processes reuse its published result." + ] + }, + { + "cell_type": "code", + "execution_count": 6, + "id": "coalescing-benchmark", + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "
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implementationlogical_requestssimulated_llm_callsduplicate_calls_avoidedseconds
0legacy12012000.363362
1sqlite_leases12030900.716646
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" + ], + "text/plain": [ + " implementation logical_requests simulated_llm_calls \\\n", + "0 legacy 120 120 \n", + "1 sqlite_leases 120 30 \n", + "\n", + " duplicate_calls_avoided seconds \n", + "0 0 0.363362 \n", + "1 90 0.716646 " + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "def legacy_duplicate_worker(key_count: int, delay: float) -> int:\n", + " \"\"\"Perform every simulated expensive call without coalescing.\"\"\"\n", + " calls = 0\n", + " for _index in range(key_count):\n", + " time.sleep(delay)\n", + " calls += 1\n", + " return calls\n", + "\n", + "\n", + "def leased_duplicate_worker(\n", + " path_text: str, owner: str, key_count: int, delay: float\n", + ") -> int:\n", + " \"\"\"Perform only simulated calls transactionally claimed by this worker.\"\"\"\n", + "\n", + " async def run() -> int:\n", + " store = CacheStore(cache_config(path_text), \"llm-call-benchmark\")\n", + " calls = 0\n", + " for index in range(key_count):\n", + " key = f\"shared-{index}\"\n", + " while await store.get(key) is None:\n", + " if store.try_acquire(key, owner, ttl_seconds=5):\n", + " await asyncio.sleep(delay)\n", + " await store.publish(\n", + " key,\n", + " \"result\",\n", + " {\"simulated\": True},\n", + " owner_id=owner,\n", + " logical_key=key,\n", + " config_fingerprint=\"demo\",\n", + " )\n", + " calls += 1\n", + " break\n", + " await asyncio.sleep(0.002)\n", + " return calls\n", + "\n", + " return asyncio.run(run())\n", + "\n", + "\n", + "coalescing_rows = []\n", + "if mp_context is None:\n", + " print(\"Skipped: this demonstration requires a platform with the fork start method.\")\n", + "else:\n", + " legacy_started = time.perf_counter()\n", + " with ProcessPoolExecutor(max_workers=WORKERS, mp_context=mp_context) as executor:\n", + " legacy_calls = sum(\n", + " executor.map(\n", + " legacy_duplicate_worker,\n", + " [SHARED_KEYS] * WORKERS,\n", + " [SIMULATED_CALL_SECONDS] * WORKERS,\n", + " )\n", + " )\n", + " legacy_seconds = time.perf_counter() - legacy_started\n", + "\n", + " leased_path = workspace / \"leased-duplicate.sqlite3\"\n", + " reset_path(leased_path)\n", + " leased_started = time.perf_counter()\n", + " with ProcessPoolExecutor(max_workers=WORKERS, mp_context=mp_context) as executor:\n", + " leased_calls = sum(\n", + " executor.map(\n", + " leased_duplicate_worker,\n", + " [str(leased_path)] * WORKERS,\n", + " [f\"worker-{index}\" for index in range(WORKERS)],\n", + " [SHARED_KEYS] * WORKERS,\n", + " [SIMULATED_CALL_SECONDS] * WORKERS,\n", + " )\n", + " )\n", + " leased_seconds = time.perf_counter() - leased_started\n", + "\n", + " logical_requests = WORKERS * SHARED_KEYS\n", + " coalescing_rows = [\n", + " {\n", + " \"implementation\": \"legacy\",\n", + " \"logical_requests\": logical_requests,\n", + " \"simulated_llm_calls\": legacy_calls,\n", + " \"duplicate_calls_avoided\": logical_requests - legacy_calls,\n", + " \"seconds\": legacy_seconds,\n", + " },\n", + " {\n", + " \"implementation\": \"sqlite_leases\",\n", + " \"logical_requests\": logical_requests,\n", + " \"simulated_llm_calls\": leased_calls,\n", + " \"duplicate_calls_avoided\": logical_requests - leased_calls,\n", + " \"seconds\": leased_seconds,\n", + " },\n", + " ]\n", + "\n", + "show(pd.DataFrame(coalescing_rows))" + ] + }, + { + "cell_type": "markdown", + "id": "recovery-heading", + "metadata": {}, + "source": [ + "## 4. Interrupted-worker recovery\n", + "\n", + "A short-lived process claims work and exits without publishing. Another worker cannot steal an active lease, but can claim it after expiration—without deleting lock files manually." + ] + }, + { + "cell_type": "code", + "execution_count": 7, + "id": "recovery-demo", + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "
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original_worker_claimedprotected_before_expiryrecovered_after_expirymanual_cleanup_required
0TrueTrueTrueFalse
\n", + "
" + ], + "text/plain": [ + " original_worker_claimed protected_before_expiry recovered_after_expiry \\\n", + "0 True True True \n", + "\n", + " manual_cleanup_required \n", + "0 False " + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "def claim_and_exit(path_text: str) -> bool:\n", + " \"\"\"Claim work and exit without publishing to simulate interruption.\"\"\"\n", + " return CacheStore(cache_config(path_text), \"recovery-demo\").try_acquire(\n", + " \"abandoned-key\", \"terminated-worker\", ttl_seconds=0.5\n", + " )\n", + "\n", + "\n", + "recovery_path = workspace / \"recovery.sqlite3\"\n", + "reset_path(recovery_path)\n", + "if mp_context is None:\n", + " print(\"Skipped: this demonstration requires a platform with the fork start method.\")\n", + "else:\n", + " with ProcessPoolExecutor(max_workers=1, mp_context=mp_context) as executor:\n", + " original_claim = executor.submit(claim_and_exit, str(recovery_path)).result(\n", + " timeout=30\n", + " )\n", + " recovery_store = CacheStore(cache_config(recovery_path), \"recovery-demo\")\n", + " blocked_before_expiry = not recovery_store.try_acquire(\n", + " \"abandoned-key\", \"recovery-worker\", ttl_seconds=1\n", + " )\n", + " time.sleep(0.55)\n", + " recovered_after_expiry = recovery_store.try_acquire(\n", + " \"abandoned-key\", \"recovery-worker\", ttl_seconds=1\n", + " )\n", + " show(\n", + " pd.DataFrame([\n", + " {\n", + " \"original_worker_claimed\": original_claim,\n", + " \"protected_before_expiry\": blocked_before_expiry,\n", + " \"recovered_after_expiry\": recovered_after_expiry,\n", + " \"manual_cleanup_required\": False,\n", + " }\n", + " ])\n", + " )" + ] + }, + { + "cell_type": "markdown", + "id": "metadata-heading", + "metadata": {}, + "source": [ + "## 5. Configuration mismatch and provenance\n", + "\n", + "The logical input fingerprint is independent from the model configuration. This allows the cache to detect that a result exists for the same input while correctly refusing to reuse it under different settings." + ] + }, + { + "cell_type": "code", + "execution_count": 8, + "id": "metadata-demo", + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "
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matching_inputconfiguration_mismatcheschanged_fieldscredential_persisted
0True1call_args, modelFalse
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" + ], + "text/plain": [ + " matching_input configuration_mismatches changed_fields \\\n", + "0 True 1 call_args, model \n", + "\n", + " credential_persisted \n", + "0 False " + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "provenance_path = workspace / \"provenance.sqlite3\"\n", + "cache = ContentAddressedCache(cache_config(provenance_path))\n", + "assertion = \"The answer is supported by the passage.\"\n", + "chunk = \"A representative passage.\"\n", + "prompt_template = \"{assertion}\\n{chunk}\" # ruff: ignore[missing-f-string-syntax] - literal format template\n", + "first_metadata = build_cache_metadata(\n", + " model=\"model-a\",\n", + " call_args={\"temperature\": 0, \"api_key\": \"not-persisted\"},\n", + " system_prompt=\"Judge support.\",\n", + " user_prompt=prompt_template,\n", + ")\n", + "first_key = compute_cache_key(\n", + " assertion,\n", + " chunk,\n", + " model=\"model-a\",\n", + " call_args={\"temperature\": 0, \"api_key\": \"not-persisted\"},\n", + " system_prompt=\"Judge support.\",\n", + " user_prompt=prompt_template,\n", + ")\n", + "logical_key = compute_logical_key(assertion, chunk)\n", + "first_config = compute_config_fingerprint(\n", + " model=\"model-a\",\n", + " call_args={\"temperature\": 0},\n", + " system_prompt=\"Judge support.\",\n", + " user_prompt=prompt_template,\n", + ")\n", + "run_async(cache.claim(first_key, \"demo-owner\"))\n", + "run_async(\n", + " cache.publish(\n", + " first_key,\n", + " \"full_support\",\n", + " first_metadata,\n", + " owner_id=\"demo-owner\",\n", + " logical_key=logical_key,\n", + " config_fingerprint=first_config,\n", + " )\n", + ")\n", + "\n", + "second_metadata = build_cache_metadata(\n", + " model=\"model-b\",\n", + " call_args={\"temperature\": 0},\n", + " system_prompt=\"Judge support.\",\n", + " user_prompt=prompt_template,\n", + ")\n", + "mismatch_count, changed_fields = run_async(\n", + " cache.find_configuration_mismatches(\n", + " [\n", + " (\n", + " logical_key,\n", + " compute_config_fingerprint(\n", + " model=\"model-b\",\n", + " call_args={\"temperature\": 0},\n", + " system_prompt=\"Judge support.\",\n", + " user_prompt=prompt_template,\n", + " ),\n", + " )\n", + " ],\n", + " second_metadata,\n", + " )\n", + ")\n", + "show(\n", + " pd.DataFrame([\n", + " {\n", + " \"matching_input\": True,\n", + " \"configuration_mismatches\": mismatch_count,\n", + " \"changed_fields\": \", \".join(changed_fields),\n", + " \"credential_persisted\": \"not-persisted\"\n", + " in json.dumps(inspect_cache(provenance_path)),\n", + " }\n", + " ])\n", + ")" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "inspection-demo", + "metadata": {}, + "outputs": [], + "source": [ + "inspection = inspect_cache(provenance_path)\n", + "print(json.dumps(inspection, indent=2, sort_keys=True))\n", + "\n", + "print(\"\\nEquivalent CLI command:\")\n", + "print(f\"uv run benchmark-qed cache inspect {provenance_path} --json\")\n", + "cli_result = subprocess.run( # ruff: ignore[subprocess-without-shell-equals-true] - fixed local module invocation\n", + " [\n", + " sys.executable,\n", + " \"-m\",\n", + " \"benchmark_qed\",\n", + " \"cache\",\n", + " \"inspect\",\n", + " str(provenance_path),\n", + " \"--json\",\n", + " ],\n", + " check=True,\n", + " capture_output=True,\n", + " text=True,\n", + ")\n", + "if json.loads(cli_result.stdout)[\"backend\"] != \"graphrag-cache\":\n", + " message = \"Cache inspection CLI returned an unexpected backend\"\n", + " raise RuntimeError(message)" + ] + }, + { + "cell_type": "markdown", + "id": "reporting-heading", + "metadata": {}, + "source": [ + "## 6. Reporting guidance\n", + "\n", + "When sharing results, report the machine, filesystem, Python version, benchmark constants, median timings, p95 batch latency, surviving entry count, write errors, and actual simulated LLM-call count.\n", + "\n", + "The defensible conclusion is:\n", + "\n", + "> The SQLite/WAL cache is more efficient and reliable for repeated incremental writes and concurrent workloads. It prevents lost updates, coalesces duplicate model requests across processes, supports concurrent readers, warns about incompatible configurations, and recovers automatically from interrupted workers.\n", + "\n", + "Do not claim that SQLite wins every single-key microbenchmark; the primary gains are scaling, integrity, duplicate-call avoidance, and recoverability." + ] + }, + { + "cell_type": "code", + "execution_count": 10, + "id": "summary", + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "
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implementationmedian_secondsp95_batch_msworkersexpected_entriessurviving_entrieswrite_errorssecondsintegritylogical_requestssimulated_llm_callsduplicate_calls_avoided
0legacy0.0484132.816558NaNNaNNaNNaNNaNNaNNaNNaNNaN
1sqlite0.0694073.786323NaNNaNNaNNaNNaNNaNNaNNaNNaN
2legacyNaNNaN4.0400.0100.03.01.007491FalseNaNNaNNaN
3sqliteNaNNaN4.0400.0400.00.01.019867TrueNaNNaNNaN
4legacyNaNNaNNaNNaNNaNNaN0.363362NaN120.0120.00.0
5sqlite_leasesNaNNaNNaNNaNNaNNaN0.716646NaN120.030.090.0
\n", + "
" + ], + "text/plain": [ + " implementation median_seconds p95_batch_ms workers expected_entries \\\n", + "0 legacy 0.048413 2.816558 NaN NaN \n", + "1 sqlite 0.069407 3.786323 NaN NaN \n", + "2 legacy NaN NaN 4.0 400.0 \n", + "3 sqlite NaN NaN 4.0 400.0 \n", + "4 legacy NaN NaN NaN NaN \n", + "5 sqlite_leases NaN NaN NaN NaN \n", + "\n", + " surviving_entries write_errors seconds integrity logical_requests \\\n", + "0 NaN NaN NaN NaN NaN \n", + "1 NaN NaN NaN NaN NaN \n", + "2 100.0 3.0 1.007491 False NaN \n", + "3 400.0 0.0 1.019867 True NaN \n", + "4 NaN NaN 0.363362 NaN 120.0 \n", + "5 NaN NaN 0.716646 NaN 120.0 \n", + "\n", + " simulated_llm_calls duplicate_calls_avoided \n", + "0 NaN NaN \n", + "1 NaN NaN \n", + "2 NaN NaN \n", + "3 NaN NaN \n", + "4 120.0 0.0 \n", + "5 30.0 90.0 " + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "summary_rows = []\n", + "summary_rows.extend(incremental_runs)\n", + "summary_rows.extend(parallel_rows)\n", + "summary_rows.extend(coalescing_rows)\n", + "show(pd.DataFrame(summary_rows))" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "cleanup", + "metadata": {}, + "outputs": [], + "source": [ + "# Run this after exporting any results you want to keep.\n", + "shutil.rmtree(workspace)\n", + "print(f\"Removed temporary workspace: {workspace}\")" + ] + } + ], + "metadata": { + "kernelspec": { + "display_name": "benchmark-qed (3.13.2)", + "language": "python", + "name": "python3" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.13.2" + } + }, + "nbformat": 4, + "nbformat_minor": 5 +} diff --git a/docs/notebooks/retrieval_metrics.ipynb b/docs/notebooks/retrieval_metrics.ipynb index e0fd10b..72e0208 100644 --- a/docs/notebooks/retrieval_metrics.ipynb +++ b/docs/notebooks/retrieval_metrics.ipynb @@ -55,6 +55,7 @@ "import tiktoken\n", "from pydantic import SecretStr\n", "\n", + "from benchmark_qed.cache import create_default_cache_config\n", "from benchmark_qed.config.llm_config import LLMConfig, LLMProvider\n", "\n", "logging.basicConfig(level=logging.INFO)\n", @@ -76,8 +77,10 @@ "INPUT_DATA_PATH = Path(\"./example_answers\")\n", "REFERENCE_DATA_PATH = Path(\"./example_answers/retrieval_reference\")\n", "OUTPUT_DATA_PATH = Path(\"./output/retrieval_scores\")\n", - "CACHE_DIR = Path(\"./example_answers/retrieval_reference/cache\")\n", - "CACHE_ENABLED = True\n", + "CACHE_CONFIG = create_default_cache_config(\n", + " \"./example_answers/retrieval_reference/cache\",\n", + " database_name=\"relevance_cache.sqlite3\",\n", + ")\n", "\n", "# MODEL CONFIGS\n", "API_KEY = SecretStr(os.getenv(\"OPENAI_API_KEY\", \"\"))\n", @@ -217,14 +220,13 @@ " llm_client=llm,\n", " llm_config=COMPLETION_LLM_CONFIG,\n", " concurrent_requests=32,\n", - " cache_dir=CACHE_DIR,\n", - " cache_enabled=CACHE_ENABLED,\n", + " cache_config=CACHE_CONFIG,\n", ")\n", "\n", "print(\"✅ Part 1 setup complete\")\n", "print(f\" BingRelevanceRater caching: {relevance_rater.cache_enabled}\")\n", "if relevance_rater.cache_enabled:\n", - " print(f\" Cache directory: {relevance_rater.cache_dir}\")\n", + " print(f\" Cache configuration: {relevance_rater.cache_config}\")\n", " stats = relevance_rater.get_cache_stats()\n", " print(f\" Cache files: {stats['cache_files']}, Size: {stats['cache_size_mb']} MB\")" ] @@ -368,14 +370,13 @@ " llm_client=llm,\n", " llm_config=COMPLETION_LLM_CONFIG,\n", " concurrent_requests=32,\n", - " cache_dir=CACHE_DIR,\n", - " cache_enabled=CACHE_ENABLED,\n", + " cache_config=CACHE_CONFIG,\n", ")\n", "\n", "print(\"✅ Part 2 setup complete\")\n", "print(f\" BingRelevanceRater caching: {relevance_rater.cache_enabled}\")\n", "if relevance_rater.cache_enabled:\n", - " print(f\" Cache directory: {relevance_rater.cache_dir}\")\n", + " print(f\" Cache configuration: {relevance_rater.cache_config}\")\n", " stats = relevance_rater.get_cache_stats()\n", " print(f\" Cache files: {stats['cache_files']}, Size: {stats['cache_size_mb']} MB\")" ] diff --git a/mkdocs.yaml b/mkdocs.yaml index 0112ac5..a173909 100644 --- a/mkdocs.yaml +++ b/mkdocs.yaml @@ -38,6 +38,7 @@ nav: - CLI: - AutoQ: cli/autoq.md - AutoE: cli/autoe.md + - Evaluation Caches: cli/cache.md - Retrieval Metrics: cli/retrieval_scores.md - LLM Configuration: cli/llm_config.md - Config Init: cli/config_init.md @@ -48,6 +49,7 @@ nav: - AutoD: notebooks/autod.ipynb - Assertion Generation: notebooks/assertion_gen.ipynb - Retrieval Metrics: notebooks/retrieval_metrics.ipynb + - Cache and Parallelism Benchmark: notebooks/cache_parallelism_benchmark.ipynb - Datasets: datasets.md repo_url: https://github.com/microsoft/benchmark-qed diff --git a/pyproject.toml b/pyproject.toml index 4a37cda..66845d7 100644 --- a/pyproject.toml +++ b/pyproject.toml @@ -32,10 +32,12 @@ dependencies = [ "typer>=0.26.8", "matplotlib>=3.11.0", "seaborn>=0.13.2", - "graphrag-common>=3.1.0", - "graphrag-input>=3.1.0", - "graphrag-storage>=3.1.0", - "graphrag-llm>=3.1.0" + "graphrag-common==3.3.0", + "graphrag-input==3.3.0", + "graphrag-storage==3.3.0", + "graphrag-llm==3.3.0", + "graphrag-cache==3.3.0", + "filelock>=3.20.3", ] [dependency-groups] @@ -77,6 +79,11 @@ source = "uv-dynamic-versioning" # the upstream pin is relaxed. override-dependencies = ["litellm>=1.84.0"] +[[tool.uv.index]] +name = "cfs-feed" +url = "https://packagefeedproxy.microsoft.io/pypi/simple/" +default = true + [tool.uv-dynamic-versioning] vcs = "git" style = "pep440" diff --git a/ruff.toml b/ruff.toml index cb3ad55..449924a 100644 --- a/ruff.toml +++ b/ruff.toml @@ -72,31 +72,31 @@ select = [ ] ignore = [ - "E501", # Line too long, handled by formatter - "ANN401", # Disable typing.Any; this is used a fair amount - "ARG002", # Conflicts with interface argument checking - "RUF031", # Avoid parentheses with tuples; this causes issues with some networkx edge lookups - "TC006", # Use quotes in casted types; this is kind of weird and seems like it would affect refactoring - "TD003", # We're not tracking TODOs as issues , - "PGH003", # Use specific rule when ignoring issue - need a way to by bypass Pylint checks; this raises on type: ignore - "FBT001", "FBT002", # Disable default positional boolean. + "line-too-long", # Line too long, handled by formatter + "any-type", # Disable typing.Any; this is used a fair amount + "unused-method-argument", # Conflicts with interface argument checking + "incorrectly-parenthesized-tuple-in-subscript", # Avoid parentheses with tuples; this causes issues with some networkx edge lookups + "runtime-cast-value", # Use quotes in casted types; this is kind of weird and seems like it would affect refactoring + "missing-todo-link", # We're not tracking TODOs as issues , + "blanket-type-ignore", # Use specific rule when ignoring issue - need a way to by bypass Pylint checks; this raises on type: ignore + "boolean-type-hint-positional-argument", "boolean-default-value-positional-argument", # Disable default positional boolean. # The following preview rules were newly surfaced by the ruff 0.15 upgrade on # pre-existing code. They are ignored here to keep the dependency-update sweep # focused; address them in a dedicated lint-cleanup pass. - "RUF069", # float-equality-comparison - "ASYNC240", # blocking-path-method-in-async-function - "D420", # incorrect-section-order - "D421", # property-docstring-starts-with-verb - "RUF050", # unnecessary-if - "RUF067", # non-empty-init-module + "float-equality-comparison", # float-equality-comparison + "blocking-path-method-in-async-function", # blocking-path-method-in-async-function + "incorrect-section-order", # incorrect-section-order + "property-docstring-starts-with-verb", # property-docstring-starts-with-verb + "unnecessary-if", # unnecessary-if + "non-empty-init-module", # non-empty-init-module ] [lint.per-file-ignores] -"tests/*" = ["S", "D", "ANN", "T201", "ASYNC", "ARG", "PTH", "TRY", "SLF001", "DOC"] -"*.ipynb" = ["T201"] +"tests/*" = ["S", "D", "ANN", "print", "ASYNC", "ARG", "PTH", "TRY", "private-member-access", "DOC"] +"*.ipynb" = ["print"] # Stats/reporting modules that intentionally use print for CLI output -"benchmark_qed/autoq/question_gen/data_questions/assertion_gen/stats.py" = ["T201"] -"benchmark_qed/autoe/retrieval_metrics/relevance_assessment/compare_raters.py" = ["T201"] +"benchmark_qed/autoq/question_gen/data_questions/assertion_gen/stats.py" = ["print"] +"benchmark_qed/autoe/retrieval_metrics/relevance_assessment/compare_raters.py" = ["print"] [lint.mccabe] # The default here is 10, and we should try to bring this down. 27 is pretty high. diff --git a/tests/autod/io/document_test.py b/tests/autod/io/document_test.py index b45e258..626771b 100644 --- a/tests/autod/io/document_test.py +++ b/tests/autod/io/document_test.py @@ -331,7 +331,7 @@ async def test_create_documents_unsupported_input_type( ): input_file = tmp_path / "text_doc_1.txt" input_file.write_text("doc 1", encoding="utf-8") - with pytest.raises(ValueError): # noqa: PT011, PT012 + with pytest.raises(ValueError): # ruff: ignore[pytest-raises-too-broad, pytest-raises-with-multiple-statements] if file_or_dir == "file": await create_documents(str(input_file), input_type="goblin") else: diff --git a/tests/autoe/assertion/cache_test.py b/tests/autoe/assertion/cache_test.py new file mode 100644 index 0000000..7be8763 --- /dev/null +++ b/tests/autoe/assertion/cache_test.py @@ -0,0 +1,149 @@ +# Copyright (c) Microsoft Corporation. +# Licensed under the MIT License. +"""Tests for standard assertion score caching.""" + +import asyncio +from pathlib import Path +from typing import Any + +import pandas as pd +import pytest + +from benchmark_qed.autoe.assertion import standard +from benchmark_qed.autoe.data_model import AssertionLLMResponse +from benchmark_qed.cache import create_default_cache_config +from benchmark_qed.config.llm_config import AuthType, LLMConfig + + +def _answers(answer: str = "answer") -> pd.DataFrame: + return pd.DataFrame([ + { + "question_id": "q1", + "question_text": "question", + "answer": answer, + } + ]) + + +def _assertions(assertion: str = "assertion") -> pd.DataFrame: + return pd.DataFrame([ + { + "question_id": "q1", + "question_text": "question", + "assertion": assertion, + } + ]) + + +def _config(tmp_path: Path) -> dict[str, Any]: + return { + "llm_client": object(), + "llm_config": LLMConfig( + auth_type=AuthType.AzureManagedIdentity, + call_args={"temperature": 0}, + ), + "answers": _answers(), + "assertions": _assertions(), + "trials": 2, + "include_score_id_in_prompt": False, + "cache_config": create_default_cache_config( + tmp_path, + database_name="assertion.sqlite3", + ), + } + + +def test_second_assertion_run_reuses_all_trials( + tmp_path: Path, monkeypatch: Any +) -> None: + call_count = 0 + + async def fake_chat(*args: Any, **kwargs: Any) -> Any: + nonlocal call_count + await asyncio.sleep(0) + call_count += 1 + return type( + "Response", + (), + { + "formatted_response": AssertionLLMResponse( + score=1, + reasoning="reason", + ) + }, + )() + + monkeypatch.setattr(standard, "chat", fake_chat) + kwargs = _config(tmp_path) + + first = standard.get_assertion_scores(**kwargs) + second = standard.get_assertion_scores(**kwargs) + + assert call_count == 2 + pd.testing.assert_frame_equal(first, second) + + +def test_answer_and_assertion_changes_invalidate_cache( + tmp_path: Path, monkeypatch: Any +) -> None: + call_count = 0 + + async def fake_chat(*args: Any, **kwargs: Any) -> Any: + nonlocal call_count + await asyncio.sleep(0) + call_count += 1 + return type( + "Response", + (), + { + "formatted_response": AssertionLLMResponse( + score=1, + reasoning="reason", + ) + }, + )() + + monkeypatch.setattr(standard, "chat", fake_chat) + kwargs = _config(tmp_path) + kwargs["trials"] = 1 + + standard.get_assertion_scores(**kwargs) + kwargs["answers"] = _answers("changed answer") + standard.get_assertion_scores(**kwargs) + kwargs["assertions"] = _assertions("changed assertion") + standard.get_assertion_scores(**kwargs) + + assert call_count == 3 + + +def test_failed_assertion_call_is_not_cached(tmp_path: Path, monkeypatch: Any) -> None: + call_count = 0 + + async def fake_chat(*args: Any, **kwargs: Any) -> Any: + nonlocal call_count + await asyncio.sleep(0) + call_count += 1 + if call_count == 1: + message = "transient failure" + raise RuntimeError(message) + return type( + "Response", + (), + { + "formatted_response": AssertionLLMResponse( + score=1, + reasoning="reason", + ) + }, + )() + + monkeypatch.setattr(standard, "chat", fake_chat) + kwargs = _config(tmp_path) + kwargs["trials"] = 1 + + with pytest.raises(RuntimeError, match="transient failure"): + standard.get_assertion_scores(**kwargs) + result = standard.get_assertion_scores(**kwargs) + + assert call_count == 2 + assert len(result) == 1 diff --git a/tests/autoe/chunk_assertion/cache_test.py b/tests/autoe/chunk_assertion/cache_test.py index 9ce9756..726cb4e 100644 --- a/tests/autoe/chunk_assertion/cache_test.py +++ b/tests/autoe/chunk_assertion/cache_test.py @@ -2,12 +2,185 @@ # Licensed under the MIT License. """Tests for the content-addressed (assertion, chunk) cache.""" +import asyncio +import json +import sqlite3 +import time +from concurrent.futures import ProcessPoolExecutor, ThreadPoolExecutor from pathlib import Path +from threading import Event +from typing import Any +import pytest +from graphrag_cache import CacheConfig, CacheType +from graphrag_storage import StorageConfig, StorageType + +import benchmark_qed.cache as cache_module from benchmark_qed.autoe.chunk_assertion.cache import ( ContentAddressedCache, + build_cache_metadata, compute_cache_key, + compute_config_fingerprint, + compute_logical_key, +) +from benchmark_qed.cache import CacheStore, create_default_cache_config, inspect_cache + + +def _cache_config(cache_path: Path | str) -> CacheConfig: + path = Path(cache_path) + return create_default_cache_config(path.parent, database_name=path.name) + + +def test_retries_locked_sqlite_initialization( + tmp_path: Path, monkeypatch: pytest.MonkeyPatch +) -> None: + real_create_cache = cache_module.create_cache + call_count = 0 + + def flaky_create_cache(config: CacheConfig) -> Any: + nonlocal call_count + call_count += 1 + if call_count == 1: + message = "database is locked" + raise sqlite3.OperationalError(message) + return real_create_cache(config) + + monkeypatch.setattr(cache_module, "create_cache", flaky_create_cache) + + cache_module.create_configured_cache( + _cache_config(tmp_path / "cache.sqlite3"), "test" + ) + + assert call_count == 2 + + +@pytest.mark.parametrize( + "cache_type", [CacheType.Sqlite, CacheType.Json, CacheType.Memory] ) +async def test_supported_cache_backends_roundtrip( + tmp_path: Path, cache_type: CacheType +) -> None: + if cache_type == CacheType.Sqlite: + config = _cache_config(tmp_path / "cache.sqlite3") + elif cache_type == CacheType.Json: + config = CacheConfig( + type=cache_type, + storage=StorageConfig( + type=StorageType.File, base_dir=str(tmp_path / "json") + ), + ) + else: + config = CacheConfig(type=cache_type, storage=None) + + store = CacheStore(config, "test") + assert ( + await store.put_many([ + ("key", "value", {"backend": cache_type}), + ("other", "other-value", {"backend": cache_type}), + ]) + == 2 + ) + assert await store.get("key") == ("value", {"backend": cache_type}) + assert await store.get_many(["key", "missing", "other", "key"]) == { + "key": ("value", {"backend": cache_type}), + "other": ("other-value", {"backend": cache_type}), + } + + +@pytest.mark.parametrize("cache_type", [CacheType.Sqlite, CacheType.Json]) +async def test_cleared_persistent_cache_can_be_reused( + tmp_path: Path, cache_type: CacheType +) -> None: + if cache_type == CacheType.Sqlite: + config = _cache_config(tmp_path / "cache.sqlite3") + else: + config = CacheConfig( + type=cache_type, + storage=StorageConfig( + type=StorageType.File, base_dir=str(tmp_path / "json") + ), + ) + store = CacheStore(config, "test") + assert ( + await store.put_many( + [("old", "old-value", {"version": "old"})], + identities={"old": ("old-logical", "old-config")}, + ) + == 1 + ) + await store.set_property_if_absent("schema", "old") + + await store.clear() + + assert await store.get("old") is None + assert await store.get_property("schema") is None + assert await store.claim("new", "owner", ttl_seconds=30) + publication = await store.publish( + "new", + "new-value", + {"version": "new"}, + owner_id="owner", + logical_key="new-logical", + config_fingerprint="new-config", + ) + await store.set_property_if_absent("schema", "new") + + assert publication.accepted + assert publication.inserted + assert await store.get("new") == ("new-value", {"version": "new"}) + assert await store.get_property("schema") == "new" + assert await store.find_alternate_configurations([ + ("new-logical", "different-config") + ]) == [{"version": "new"}] + + +async def test_noop_cache_does_not_persist(tmp_path: Path) -> None: + store = CacheStore(CacheConfig(type=CacheType.Noop, storage=None), "test") + + await store.put_many([("key", "value", {})]) + + assert await store.get("key") is None + + +def _write_cache_entry(cache_path: str, cache_key: str, grade: str) -> int: + async def write() -> int: + cache = ContentAddressedCache(_cache_config(cache_path)) + await cache.put(cache_key, grade, {"writer": cache_key}) + return await cache.flush() + + return asyncio.run(write()) + + +def _try_claim(cache_path: str, owner_id: str) -> bool: + store = CacheStore(_cache_config(cache_path), "lease-test") + return store.try_acquire("shared", owner_id, ttl_seconds=30) + + +def _compute_once(cache_path: str, owner_id: str) -> bool: + async def compute() -> bool: + store = CacheStore(_cache_config(cache_path), "coalescing-test") + while True: + if await store.get("shared") is not None: + return False + if await store.claim("shared", owner_id, ttl_seconds=5): + await asyncio.sleep(0.1) + await store.publish( + "shared", + "result", + {"owner": owner_id}, + owner_id=owner_id, + logical_key="logical", + config_fingerprint="config", + ) + return True + await asyncio.sleep(0.02) + + return asyncio.run(compute()) + + +def _acquire_then_exit(cache_path: str) -> bool: + store = CacheStore(_cache_config(cache_path), "interruption-test") + return store.try_acquire("abandoned", "crashed", ttl_seconds=0.2) class TestComputeCacheKey: @@ -43,59 +216,450 @@ def test_distinguishes_prompts(self) -> None: "a", "b", user_prompt="u2" ) + def test_credentials_do_not_affect_key(self) -> None: + """Credentials are neither persisted nor included in cache identity.""" + first = compute_cache_key("a", "b", call_args={"api_key": "first"}) + second = compute_cache_key("a", "b", call_args={"api_key": "second"}) + assert first == second + + +def test_build_cache_metadata_redacts_credentials() -> None: + metadata = build_cache_metadata( + model="test-model", + call_args={ + "temperature": 0, + "max_tokens": 100, + "api_key": "secret", + "headers": {"Authorization": "Bearer secret"}, + }, + system_prompt="system", + user_prompt="user", + ) + + assert metadata["model"] == "test-model" + assert metadata["call_args"] == { + "temperature": 0, + "max_tokens": 100, + "api_key": "", + "headers": {"Authorization": ""}, + } + class TestContentAddressedCache: """Tests for ContentAddressedCache persistence semantics.""" - def test_put_get_roundtrip(self, tmp_path: Path) -> None: + async def test_put_get_roundtrip(self, tmp_path: Path) -> None: """Stored grades are retrievable and missing keys return None.""" - cache = ContentAddressedCache(tmp_path / "cache.jsonl") - cache.put("k1", "full_support") - assert cache.get("k1") == "full_support" - assert cache.get("missing") is None + cache = ContentAddressedCache(_cache_config(tmp_path / "cache.sqlite3")) + await cache.put("k1", "full_support") + assert await cache.get("k1") == "full_support" + assert await cache.get("missing") is None + + async def test_get_many_includes_pending_and_persisted_grades( + self, tmp_path: Path + ) -> None: + cache_path = tmp_path / "cache.sqlite3" + cache = ContentAddressedCache(_cache_config(cache_path)) + await cache.put("persisted", "full_support") + await cache.flush() + + reloaded = ContentAddressedCache(_cache_config(cache_path)) + await reloaded.put("pending", "partial_support") - def test_put_is_idempotent_for_new_count(self, tmp_path: Path) -> None: + assert await reloaded.get_many([ + "persisted", + "missing", + "pending", + "persisted", + ]) == { + "persisted": "full_support", + "pending": "partial_support", + } + + async def test_put_is_idempotent_for_new_count(self, tmp_path: Path) -> None: """Re-putting an existing key does not increment the new-entry count.""" - cache = ContentAddressedCache(tmp_path / "cache.jsonl") - cache.put("k1", "full_support") - cache.put("k1", "no_support") + cache = ContentAddressedCache(_cache_config(tmp_path / "cache.sqlite3")) + await cache.put("k1", "full_support") + await cache.put("k1", "no_support") assert cache.new_count == 1 - assert cache.get("k1") == "full_support" + assert await cache.get("k1") == "full_support" - def test_flush_persists_and_reloads(self, tmp_path: Path) -> None: + async def test_flush_persists_and_reloads(self, tmp_path: Path) -> None: """Flushed entries survive a reload from disk.""" - cache_path = tmp_path / "cache.jsonl" - cache = ContentAddressedCache(cache_path) - cache.put("k1", "full_support") - cache.put("k2", "partial_support") - cache.flush() - - reloaded = ContentAddressedCache(cache_path) - assert reloaded.get("k1") == "full_support" - assert reloaded.get("k2") == "partial_support" - - def test_repeated_flush_does_not_duplicate(self, tmp_path: Path) -> None: - """Incremental flushes rewrite the file without duplicating entries.""" - cache_path = tmp_path / "cache.jsonl" - cache = ContentAddressedCache(cache_path) - - cache.put("k1", "full_support") - cache.flush() - cache.put("k2", "no_support") - cache.flush() - - line_count = sum( - 1 for line in cache_path.read_text(encoding="utf-8").splitlines() if line - ) - assert line_count == 2 - - reloaded = ContentAddressedCache(cache_path) - assert reloaded.get("k1") == "full_support" - assert reloaded.get("k2") == "no_support" - - def test_flush_noop_when_no_new_entries(self, tmp_path: Path) -> None: - """Flushing with no new entries leaves the file untouched.""" - cache_path = tmp_path / "cache.jsonl" - cache = ContentAddressedCache(cache_path) - cache.flush() - assert not cache_path.exists() + cache_path = tmp_path / "cache.sqlite3" + cache = ContentAddressedCache(_cache_config(cache_path)) + await cache.put("k1", "full_support") + await cache.put("k2", "partial_support") + await cache.flush() + + reloaded = ContentAddressedCache(_cache_config(cache_path)) + assert await reloaded.get("k1") == "full_support" + assert await reloaded.get("k2") == "partial_support" + + async def test_repeated_flush_does_not_duplicate(self, tmp_path: Path) -> None: + """Incremental flushes preserve one row per cache key.""" + cache_path = tmp_path / "cache.sqlite3" + cache = ContentAddressedCache(_cache_config(cache_path)) + + await cache.put("k1", "full_support") + await cache.flush() + await cache.put("k2", "no_support") + await cache.flush() + + with sqlite3.connect(cache_path) as connection: + row_count = connection.execute( + "SELECT COUNT(*) FROM cache_entries" + ).fetchone() + assert row_count == (2,) + + reloaded = ContentAddressedCache(_cache_config(cache_path)) + assert await reloaded.get("k1") == "full_support" + assert await reloaded.get("k2") == "no_support" + + async def test_flush_noop_when_no_new_entries(self, tmp_path: Path) -> None: + """Flushing with no new entries leaves the database empty.""" + cache_path = tmp_path / "cache.sqlite3" + cache = ContentAddressedCache(_cache_config(cache_path)) + assert await cache.flush() == 0 + with sqlite3.connect(cache_path) as connection: + row_count = connection.execute( + "SELECT COUNT(*) FROM cache_entries" + ).fetchone() + assert row_count == (0,) + + async def test_metadata_persists(self, tmp_path: Path) -> None: + cache_path = tmp_path / "cache.sqlite3" + metadata = build_cache_metadata(model="gpt-test", call_args={"temperature": 0}) + cache = ContentAddressedCache(_cache_config(cache_path)) + await cache.put("k1", "full_support", metadata) + await cache.flush() + + assert ( + await ContentAddressedCache(_cache_config(cache_path)).get_metadata("k1") + == metadata + ) + + async def test_imports_legacy_jsonl_cache(self, tmp_path: Path) -> None: + legacy_path = tmp_path / "cache.jsonl" + legacy_path.write_text( + json.dumps({"key": "legacy", "grade": "partial_support"}) + "\n", + encoding="utf-8", + ) + + cache = ContentAddressedCache( + create_default_cache_config( + tmp_path, + database_name="cache.sqlite3", + ) + ) + + assert cache.cache_path == tmp_path / "cache.sqlite3" + assert await cache.get("legacy") == "partial_support" + assert await cache.get_metadata("legacy") == { + "schema_version": 1, + "source": "legacy_jsonl", + } + + async def test_concurrent_processes_preserve_all_entries( + self, tmp_path: Path + ) -> None: + cache_path = tmp_path / "cache.sqlite3" + entries = [(f"k{index}", f"grade-{index}") for index in range(12)] + + with ProcessPoolExecutor(max_workers=4) as executor: + futures = [ + executor.submit(_write_cache_entry, str(cache_path), key, grade) + for key, grade in entries + ] + for future in futures: + future.result(timeout=30) + + cache = ContentAddressedCache(_cache_config(cache_path)) + assert {key: await cache.get(key) for key, _grade in entries} == dict(entries) + + async def test_concurrent_same_key_uses_first_writer(self, tmp_path: Path) -> None: + cache_path = tmp_path / "cache.sqlite3" + grades = [f"grade-{index}" for index in range(8)] + + with ProcessPoolExecutor(max_workers=4) as executor: + futures = [ + executor.submit(_write_cache_entry, str(cache_path), "shared", grade) + for grade in grades + ] + inserted = [future.result(timeout=30) for future in futures] + + assert inserted.count(1) == 1 + assert ( + await ContentAddressedCache(_cache_config(cache_path)).get("shared") + in grades + ) + with sqlite3.connect(cache_path) as connection: + row_count = connection.execute( + "SELECT COUNT(*) FROM cache_entries WHERE key = 'shared'" + ).fetchone() + assert row_count == (1,) + + def test_only_one_process_acquires_same_work(self, tmp_path: Path) -> None: + cache_path = tmp_path / "cache.sqlite3" + + with ProcessPoolExecutor(max_workers=4) as executor: + acquired = list( + executor.map( + _try_claim, + [str(cache_path)] * 8, + [f"owner-{index}" for index in range(8)], + ) + ) + + assert acquired.count(True) == 1 + assert inspect_cache(cache_path)["active_leases"] == 1 + + async def test_concurrent_processes_compute_same_work_once( + self, tmp_path: Path + ) -> None: + cache_path = tmp_path / "cache.sqlite3" + + with ProcessPoolExecutor(max_workers=4) as executor: + computed = list( + executor.map( + _compute_once, + [str(cache_path)] * 4, + [f"owner-{index}" for index in range(4)], + ) + ) + + assert computed.count(True) == 1 + store = CacheStore(_cache_config(cache_path), "coalescing-test") + assert await store.get("shared") is not None + assert inspect_cache(cache_path)["active_leases"] == 0 + + def test_expired_lease_is_recovered_after_writer_interruption( + self, tmp_path: Path + ) -> None: + store = CacheStore(_cache_config(tmp_path / "cache.sqlite3"), "lease-test") + + assert store.try_acquire("k1", "crashed", ttl_seconds=1, now=100) + assert not store.try_acquire("k1", "waiting", ttl_seconds=1, now=100.5) + assert store.try_acquire("k1", "recovered", ttl_seconds=1, now=101) + + def test_lease_ownership_transitions_are_atomic( + self, tmp_path: Path, monkeypatch: pytest.MonkeyPatch + ) -> None: + store = CacheStore(_cache_config(tmp_path / "cache.sqlite3"), "lease-test") + assert store.try_acquire("k1", "expired", ttl_seconds=1, now=100) + + write_started = Event() + allow_write = Event() + real_write_lease = CacheStore._write_lease + + def delayed_write_lease(lease_path: Path, lease: dict[str, Any]) -> None: + if lease["owner_id"] == "recovered": + write_started.set() + assert allow_write.wait(timeout=5) + real_write_lease(lease_path, lease) + + monkeypatch.setattr( + CacheStore, "_write_lease", staticmethod(delayed_write_lease) + ) + + with ThreadPoolExecutor(max_workers=3) as executor: + acquire = executor.submit( + store.try_acquire, + "k1", + "recovered", + ttl_seconds=30, + now=101, + ) + assert write_started.wait(timeout=5) + renew = executor.submit( + store.renew_leases, + ["k1"], + "expired", + ttl_seconds=30, + ) + release = executor.submit(store.release_leases, ["k1"], "expired") + + assert not renew.done() + assert not release.done() + allow_write.set() + + assert acquire.result(timeout=5) + assert renew.result(timeout=5) == 0 + release.result(timeout=5) + + lease = json.loads(store._lease_path("k1").read_text(encoding="utf-8")) + assert lease["owner_id"] == "recovered" + + def test_incomplete_lease_is_replaced_under_contention( + self, tmp_path: Path + ) -> None: + store = CacheStore(_cache_config(tmp_path / "cache.sqlite3"), "lease-test") + lease_path = store._lease_path("k1") + lease_path.parent.mkdir(parents=True) + lease_path.write_text("{", encoding="utf-8") + owners = [f"owner-{index}" for index in range(8)] + + with ThreadPoolExecutor(max_workers=len(owners)) as executor: + acquired = list( + executor.map( + lambda owner: store.try_acquire( + "k1", owner, ttl_seconds=30, now=100 + ), + owners, + ) + ) + + assert acquired.count(True) == 1 + lease = json.loads(lease_path.read_text(encoding="utf-8")) + assert lease["owner_id"] == owners[acquired.index(True)] + + def test_recovers_lease_abandoned_by_exited_process(self, tmp_path: Path) -> None: + cache_path = tmp_path / "cache.sqlite3" + with ProcessPoolExecutor(max_workers=1) as executor: + assert executor.submit(_acquire_then_exit, str(cache_path)).result( + timeout=30 + ) + + time.sleep(0.25) + store = CacheStore(_cache_config(cache_path), "interruption-test") + assert store.try_acquire("abandoned", "recovered", ttl_seconds=1) + + async def test_publish_releases_lease_atomically(self, tmp_path: Path) -> None: + cache_path = tmp_path / "cache.sqlite3" + store = CacheStore(_cache_config(cache_path), "lease-test") + assert store.try_acquire("k1", "owner", ttl_seconds=30) + + publication = await store.publish( + "k1", + "value", + {"model": "test"}, + owner_id="owner", + logical_key="logical", + config_fingerprint="config", + ) + + assert publication.accepted + assert publication.inserted + assert publication.value == "value" + assert publication.metadata == {"model": "test"} + assert await store.get("k1") == ("value", {"model": "test"}) + assert inspect_cache(cache_path)["active_leases"] == 0 + + async def test_publish_returns_canonical_first_writer_result( + self, tmp_path: Path + ) -> None: + cache_path = tmp_path / "cache.sqlite3" + owner = CacheStore(_cache_config(cache_path), "lease-test") + writer = CacheStore(_cache_config(cache_path), "lease-test") + assert owner.try_acquire("k1", "owner", ttl_seconds=30) + assert await writer.put_many([("k1", "canonical", {"writer": "first"})]) == 1 + + publication = await owner.publish( + "k1", + "later", + {"writer": "later"}, + owner_id="owner", + logical_key="logical", + config_fingerprint="config", + ) + + assert publication.accepted + assert not publication.inserted + assert publication.value == "canonical" + assert publication.metadata == {"writer": "first"} + assert await owner.get("k1") == ("canonical", {"writer": "first"}) + + async def test_expired_owner_cannot_publish_after_reacquisition( + self, tmp_path: Path + ) -> None: + cache_path = tmp_path / "cache.sqlite3" + expired_owner = CacheStore(_cache_config(cache_path), "lease-test") + current_owner = CacheStore(_cache_config(cache_path), "lease-test") + assert expired_owner.try_acquire("k1", "expired", ttl_seconds=0.01) + time.sleep(0.02) + assert current_owner.try_acquire("k1", "current", ttl_seconds=30) + + rejected = await expired_owner.publish( + "k1", + "stale", + {"writer": "expired"}, + owner_id="expired", + logical_key="logical", + config_fingerprint="config", + ) + accepted = await current_owner.publish( + "k1", + "current", + {"writer": "current"}, + owner_id="current", + logical_key="logical", + config_fingerprint="config", + ) + + assert not rejected.accepted + assert not rejected.inserted + assert accepted.accepted + assert accepted.inserted + assert await current_owner.get("k1") == ( + "current", + {"writer": "current"}, + ) + + async def test_migrates_local_schema_to_graphrag_cache( + self, tmp_path: Path + ) -> None: + cache_path = tmp_path / "cache.sqlite3" + with sqlite3.connect(cache_path) as connection: + connection.execute( + """ + CREATE TABLE cache_entries ( + namespace TEXT NOT NULL, + key TEXT NOT NULL, + value_json TEXT NOT NULL, + metadata_json TEXT NOT NULL, + created_at TEXT NOT NULL DEFAULT CURRENT_TIMESTAMP, + PRIMARY KEY (namespace, key) + ) + """ + ) + connection.execute( + """ + INSERT INTO cache_entries(namespace, key, value_json, metadata_json) + VALUES ('chunk_assertion', 'old', '"full_support"', '{}') + """ + ) + + store = CacheStore(_cache_config(cache_path), "chunk_assertion") + + assert await store.get("old") == ("full_support", {}) + with sqlite3.connect(cache_path) as connection: + columns = { + row[1] for row in connection.execute("PRAGMA table_info(cache_entries)") + } + assert columns == {"namespace", "key", "value_json"} + + async def test_reports_alternate_configuration(self, tmp_path: Path) -> None: + cache = ContentAddressedCache(_cache_config(tmp_path / "cache.sqlite3")) + assertion = "assertion" + chunk = "chunk" + first_metadata = build_cache_metadata(model="first") + first_key = compute_cache_key(assertion, chunk, model="first") + logical_key = compute_logical_key(assertion, chunk) + first_fingerprint = compute_config_fingerprint(model="first") + assert await cache.claim(first_key, "owner") + await cache.publish( + first_key, + "full_support", + first_metadata, + owner_id="owner", + logical_key=logical_key, + config_fingerprint=first_fingerprint, + ) + + mismatch_count, fields = await cache.find_configuration_mismatches( + [(logical_key, compute_config_fingerprint(model="second"))], + build_cache_metadata(model="second"), + ) + + assert mismatch_count == 1 + assert fields == ["model"] diff --git a/tests/autoe/chunk_assertion/scoring_test.py b/tests/autoe/chunk_assertion/scoring_test.py index f2ed9f4..318c16f 100644 --- a/tests/autoe/chunk_assertion/scoring_test.py +++ b/tests/autoe/chunk_assertion/scoring_test.py @@ -6,6 +6,7 @@ across runs, with the LLM call mocked out. """ +import asyncio from pathlib import Path from types import SimpleNamespace from typing import TYPE_CHECKING, Any, cast @@ -13,10 +14,12 @@ import pytest from graphrag_storage.file_storage import FileStorage +from benchmark_qed.autoe.chunk_assertion.cache import ContentAddressedCache from benchmark_qed.autoe.chunk_assertion.scoring import run_assertion_eval_chunk_mode from benchmark_qed.autoe.data_model.retrieval_result import ( load_retrieval_results_from_dicts, ) +from benchmark_qed.cache import create_default_cache_config if TYPE_CHECKING: from graphrag_llm.completion import LLMCompletion @@ -44,7 +47,8 @@ def patched_chat( ) -> None: """Patch benchmark_qed.llm.chat to return grades from grade_by_chunk.""" - async def _fake_chat(_llm: Any, messages: list[dict[str, str]], **_: Any) -> Any: # noqa: RUF029 + async def _fake_chat(_llm: Any, messages: list[dict[str, str]], **_: Any) -> Any: + await asyncio.sleep(0) call_counter[0] += 1 chunk_text = messages[-1]["content"] return SimpleNamespace(content=grade_by_chunk.get(chunk_text, "no_support")) @@ -59,9 +63,15 @@ async def _run( cache_path: Path, *, k_list: list[int] | None = None, + model: str = "test-model", ) -> dict[str, Any]: """Invoke run_assertion_eval_chunk_mode with test-friendly defaults.""" output_dir.mkdir(parents=True, exist_ok=True) + database_path = ( + cache_path.with_suffix(".sqlite3") + if cache_path.suffix == ".jsonl" + else cache_path + ) return await run_assertion_eval_chunk_mode( load_retrieval_results_from_dicts( eval_results, @@ -73,11 +83,14 @@ async def _run( llm_client=cast("LLMCompletion", object()), llm_config=cast( "LLMConfig", - SimpleNamespace(concurrent_requests=2, call_args={}, model="test-model"), + SimpleNamespace(concurrent_requests=2, call_args={}, model=model), ), output_storage=FileStorage(base_dir=str(output_dir)), pass_threshold=0.5, - cache_path=cache_path, + cache_config=create_default_cache_config( + database_path.parent, + database_name=database_path.name, + ), k_list=k_list or [1], system_prompt="judge", user_prompt="{chunk}", @@ -177,9 +190,12 @@ async def test_alignment_by_question_id( @pytest.mark.usefixtures("patched_chat") async def test_second_run_uses_cache( - tmp_path: Path, grade_by_chunk: dict[str, str], call_counter: list[int] + tmp_path: Path, + grade_by_chunk: dict[str, str], + call_counter: list[int], + monkeypatch: pytest.MonkeyPatch, ) -> None: - """A second run with the same cache makes no new LLM calls.""" + """A second run bulk-loads the cache and makes no new LLM calls.""" grade_by_chunk.update({"a": "full_support", "b": "no_support"}) eval_results = [ { @@ -206,5 +222,105 @@ async def test_second_run_uses_cache( calls_after_first = call_counter[0] assert calls_after_first == 2 # one call per (assertion, chunk) pair + get_many_calls = 0 + real_get_many = ContentAddressedCache.get_many + + async def tracked_get_many( + cache: ContentAddressedCache, cache_keys: list[str] + ) -> dict[str, str]: + nonlocal get_many_calls + get_many_calls += 1 + return await real_get_many(cache, cache_keys) + + async def fail_individual_get( + _cache: ContentAddressedCache, _cache_key: str + ) -> str | None: + await asyncio.sleep(0) + pytest.fail("warm-cache scan used individual get() instead of get_many()") + + monkeypatch.setattr(ContentAddressedCache, "get_many", tracked_get_many) + monkeypatch.setattr(ContentAddressedCache, "get", fail_individual_get) + await _run(eval_results, question_set, tmp_path / "out2", cache_path) assert call_counter[0] == calls_after_first # fully served from cache + assert get_many_calls == 1 + + +@pytest.mark.usefixtures("patched_chat") +async def test_duplicate_pairs_share_one_llm_call( + tmp_path: Path, grade_by_chunk: dict[str, str], call_counter: list[int] +) -> None: + """Identical uncached pairs across questions share one in-process request.""" + grade_by_chunk["same chunk"] = "full_support" + eval_results = [ + { + "question_id": question_id, + "text": "Q", + "context": [{"text": "same chunk", "chunk_id": "c1", "rank": 1}], + } + for question_id in ("q1", "q2") + ] + question_set = { + "assertions": [ + { + "question_id": question_id, + "question_text": "Q", + "assertions": [{"statement": "same assertion"}], + } + for question_id in ("q1", "q2") + ] + } + + summaries = await _run( + eval_results, + question_set, + tmp_path / "out", + tmp_path / "cache.sqlite3", + ) + + assert call_counter[0] == 1 + assert summaries["all"].successful_calls == 2 + + +@pytest.mark.usefixtures("patched_chat") +async def test_warns_when_cached_configuration_differs( + tmp_path: Path, + grade_by_chunk: dict[str, str], + capsys: pytest.CaptureFixture[str], +) -> None: + grade_by_chunk["chunk"] = "full_support" + eval_results = [ + { + "question_id": "q1", + "text": "Q", + "context": [{"text": "chunk", "chunk_id": "c1", "rank": 1}], + } + ] + question_set = { + "assertions": [ + { + "question_id": "q1", + "question_text": "Q", + "assertions": [{"statement": "assertion"}], + } + ] + } + cache_path = tmp_path / "cache.sqlite3" + await _run( + eval_results, + question_set, + tmp_path / "out1", + cache_path, + model="first-model", + ) + capsys.readouterr() + + await _run( + eval_results, + question_set, + tmp_path / "out2", + cache_path, + model="second-model", + ) + + assert "same inputs with different model" in capsys.readouterr().out diff --git a/tests/autoe/pairwise/__init__.py b/tests/autoe/pairwise/__init__.py new file mode 100644 index 0000000..45fbe80 --- /dev/null +++ b/tests/autoe/pairwise/__init__.py @@ -0,0 +1,3 @@ +# Copyright (c) Microsoft Corporation. +# Licensed under the MIT License. +"""Tests for pairwise evaluation.""" diff --git a/tests/autoe/pairwise/cache_test.py b/tests/autoe/pairwise/cache_test.py new file mode 100644 index 0000000..0a7c213 --- /dev/null +++ b/tests/autoe/pairwise/cache_test.py @@ -0,0 +1,244 @@ +# Copyright (c) Microsoft Corporation. +# Licensed under the MIT License. +"""Tests for pairwise score caching.""" + +import asyncio +from pathlib import Path +from typing import TYPE_CHECKING, Any, cast + +import pandas as pd +import pytest +from graphrag_cache import CacheConfig, CacheType + +from benchmark_qed.autoe.config import Criteria +from benchmark_qed.autoe.data_model import PairwiseLLMResponse +from benchmark_qed.autoe.pairwise import scores +from benchmark_qed.autoe.pairwise.cache import ( + PairwiseScoreCache, + build_cache_metadata, + compute_cache_key, + compute_logical_key, +) +from benchmark_qed.cache import create_default_cache_config +from benchmark_qed.config.llm_config import AuthType, LLMConfig + +if TYPE_CHECKING: + from graphrag_llm.completion import LLMCompletion + + +def _metadata(*, model: str = "gpt-4.1") -> dict[str, Any]: + return build_cache_metadata( + model=model, + llm_provider="openai.chat", + init_args={}, + call_args={"temperature": 0}, + custom_providers=[], + system_prompt="system", + user_prompt="user", + include_score_id_in_prompt=True, + ) + + +def _logical_key(*, trial: int = 0) -> str: + return compute_logical_key( + question="question", + answer_1_name="base", + answer_1="base answer", + answer_2_name="other", + answer_2="other answer", + criteria_name="relevance", + criteria_description="description", + trial=trial, + ) + + +def test_cache_key_distinguishes_trials_and_model() -> None: + assert compute_cache_key(_logical_key(trial=0), _metadata()) != compute_cache_key( + _logical_key(trial=1), _metadata() + ) + assert compute_cache_key(_logical_key(), _metadata()) != compute_cache_key( + _logical_key(), _metadata(model="gpt-4o") + ) + + +def test_cache_metadata_redacts_credentials() -> None: + metadata = build_cache_metadata( + model="model", + llm_provider="custom.chat", + init_args={"api_key": "secret"}, + call_args={"headers": {"Authorization": "secret"}}, + custom_providers=[], + system_prompt="system", + user_prompt="user", + include_score_id_in_prompt=False, + ) + + assert metadata["init_args"]["api_key"] == "" + assert metadata["call_args"]["headers"]["Authorization"] == "" + + +async def test_noop_cache_does_not_reuse_scores(monkeypatch: Any) -> None: + call_count = 0 + + async def fake_chat(*args: Any, **kwargs: Any) -> Any: + nonlocal call_count + await asyncio.sleep(0) + call_count += 1 + return type( + "Response", + (), + {"formatted_response": PairwiseLLMResponse(winner=1, reasoning="reason")}, + )() + + monkeypatch.setattr(scores, "chat", fake_chat) + cache_config = CacheConfig(type=CacheType.Noop, storage=None) + + await scores._get_pairwise_score_with_cache( + cast("LLMCompletion", object()), + cache=scores.PairwiseScoreCache(cache_config), + cache_metadata=_metadata(), + question="question", + answer_1_name="base", + answer_1="base answer", + answer_2_name="other", + answer_2="other answer", + criteria_name="relevance", + criteria_description="description", + assessment_system_prompt=scores.Template( + "$criteria_name $criteria_description" + ), + assessment_user_prompt=scores.Template( + "$score_id $question $answer1 $answer2 $criteria_name $criteria_description" + ), + complete_callback=None, + trial=0, + include_score_id_in_prompt=True, + additional_call_args={}, + ) + + assert call_count == 1 + + +async def test_publish_in_claim_window_returns_cached_winner( + monkeypatch: pytest.MonkeyPatch, +) -> None: + cache = PairwiseScoreCache(CacheConfig(type=CacheType.Memory, storage=None)) + canonical = {"reasoning": "cached winner"} + get_calls = 0 + judge_called = False + completed = 0 + + async def racing_get( + _cache_key: str, + ) -> tuple[Any, dict[str, Any]] | None: + nonlocal get_calls + await asyncio.sleep(0) + get_calls += 1 + return None if get_calls == 1 else (canonical, {}) + + async def fail_if_judged(*args: Any, **kwargs: Any) -> dict[str, Any]: + nonlocal judge_called + await asyncio.sleep(0) + judge_called = True + return {"reasoning": "wrong"} + + def on_complete() -> None: + nonlocal completed + completed += 1 + + monkeypatch.setattr(cache._store, "get", racing_get) + monkeypatch.setattr(scores, "get_pairwise_score", fail_if_judged) + + result = await scores._get_pairwise_score_with_cache( + cast("LLMCompletion", object()), + cache=cache, + cache_metadata=_metadata(), + question="question", + answer_1_name="base", + answer_1="base answer", + answer_2_name="other", + answer_2="other answer", + criteria_name="relevance", + criteria_description="description", + assessment_system_prompt=scores.Template( + "$criteria_name $criteria_description" + ), + assessment_user_prompt=scores.Template( + "$score_id $question $answer1 $answer2 $criteria_name $criteria_description" + ), + complete_callback=on_complete, + trial=0, + include_score_id_in_prompt=True, + additional_call_args={}, + ) + + cache_key = compute_cache_key(_logical_key(), _metadata()) + assert result == canonical + assert not judge_called + assert completed == 1 + assert cache._store.try_acquire(cache_key, "next-owner", ttl_seconds=30) + cache._store.release_leases([cache_key], "next-owner") + + +@pytest.mark.parametrize("include_score_id_in_prompt", [True, False]) +def test_four_trials_are_independent_and_rerun_uses_cache( + tmp_path: Path, + monkeypatch: Any, + *, + include_score_id_in_prompt: bool, +) -> None: + call_count = 0 + + async def fake_chat(*args: Any, **kwargs: Any) -> Any: + nonlocal call_count + await asyncio.sleep(0) + call_count += 1 + return type( + "Response", + (), + {"formatted_response": PairwiseLLMResponse(winner=1, reasoning="reason")}, + )() + + monkeypatch.setattr(scores, "chat", fake_chat) + llm_config = LLMConfig( + auth_type=AuthType.AzureManagedIdentity, + call_args={"temperature": 0}, + ) + cache_config = create_default_cache_config( + tmp_path, + database_name="pairwise.sqlite3", + ) + base_answers = pd.DataFrame([ + { + "question_id": "q1", + "question_text": "question", + "answer": "base answer", + } + ]) + other_answers = pd.DataFrame([ + { + "question_id": "q1", + "question_text": "question", + "answer": "other answer", + } + ]) + kwargs = { + "llm_client": object(), + "llm_config": llm_config, + "base_name": "base", + "other_name": "other", + "base_answers": base_answers, + "other_answers": other_answers, + "criteria": [Criteria(name="relevance", description="description")], + "trials": 4, + "include_score_id_in_prompt": include_score_id_in_prompt, + "cache_config": cache_config, + } + + first = scores.get_pairwise_scores(**kwargs) + second = scores.get_pairwise_scores(**kwargs) + + assert call_count == 4 + assert first["trial"].tolist() == [0, 1, 2, 3] + assert first["answer_1_name"].tolist() == ["base", "other", "base", "other"] + pd.testing.assert_frame_equal(first, second) diff --git a/tests/autoe/pairwise/differential_cache_test.py b/tests/autoe/pairwise/differential_cache_test.py new file mode 100644 index 0000000..fe5219f --- /dev/null +++ b/tests/autoe/pairwise/differential_cache_test.py @@ -0,0 +1,254 @@ +# Copyright (c) Microsoft Corporation. +# Licensed under the MIT License. +"""Tests for differential pairwise stage caching.""" + +import asyncio +from pathlib import Path +from typing import Any + +import pandas as pd +import pytest + +from benchmark_qed.autoe.config import Criteria +from benchmark_qed.autoe.data_model import ( + DifferentialCriterionVerdict, + DifferentialPairwiseLLMResponse, + PairwiseExtractionLLMResponse, +) +from benchmark_qed.autoe.pairwise import differential +from benchmark_qed.cache import create_default_cache_config +from benchmark_qed.config.llm_config import AuthType, LLMConfig + + +def _answers(answer: str) -> pd.DataFrame: + return pd.DataFrame([ + { + "question_id": "q1", + "question_text": "question", + "answer": answer, + } + ]) + + +def _config(tmp_path: Path) -> dict[str, Any]: + return { + "llm_client": object(), + "llm_config": LLMConfig( + auth_type=AuthType.AzureManagedIdentity, + call_args={"temperature": 0}, + ), + "base_name": "base", + "other_name": "other", + "base_answers": _answers("base answer"), + "other_answers": _answers("other answer"), + "criteria": [Criteria(name="relevance", description="description")], + "trials": 2, + "include_score_id_in_prompt": False, + "cache_config": create_default_cache_config( + tmp_path, + database_name="differential_pairwise.sqlite3", + ), + } + + +def _response(value: Any) -> Any: + return type("Response", (), {"formatted_response": value})() + + +def test_second_differential_run_reuses_both_stages( + tmp_path: Path, monkeypatch: Any +) -> None: + calls = {"extraction": 0, "verdict": 0} + + async def fake_chat(*args: Any, **kwargs: Any) -> Any: + await asyncio.sleep(0) + if kwargs["response_format"] is PairwiseExtractionLLMResponse: + calls["extraction"] += 1 + return _response( + PairwiseExtractionLLMResponse( + common="common", + unique_answer_1="first", + unique_answer_2="second", + ) + ) + calls["verdict"] += 1 + return _response( + DifferentialPairwiseLLMResponse( + verdicts=[ + DifferentialCriterionVerdict( + criteria="relevance", + winner=1, + reasoning="reason", + ) + ] + ) + ) + + monkeypatch.setattr(differential, "chat", fake_chat) + kwargs = _config(tmp_path) + + first = differential.get_differential_pairwise_scores(**kwargs) + second = differential.get_differential_pairwise_scores(**kwargs) + + assert calls == {"extraction": 2, "verdict": 2} + pd.testing.assert_frame_equal(first, second) + + +def test_judge_failure_resumes_from_cached_extraction( + tmp_path: Path, monkeypatch: Any +) -> None: + calls = {"extraction": 0, "verdict": 0} + fail_judge = True + + async def fake_chat(*args: Any, **kwargs: Any) -> Any: + nonlocal fail_judge + await asyncio.sleep(0) + if kwargs["response_format"] is PairwiseExtractionLLMResponse: + calls["extraction"] += 1 + return _response( + PairwiseExtractionLLMResponse( + common="common", + unique_answer_1="first", + unique_answer_2="second", + ) + ) + calls["verdict"] += 1 + if fail_judge: + fail_judge = False + msg = "transient" + raise ConnectionError(msg) + return _response( + DifferentialPairwiseLLMResponse( + verdicts=[ + DifferentialCriterionVerdict( + criteria="relevance", + winner=1, + reasoning="reason", + ) + ] + ) + ) + + monkeypatch.setattr(differential, "chat", fake_chat) + kwargs = _config(tmp_path) + kwargs["trials"] = 1 + + try: + differential.get_differential_pairwise_scores(**kwargs) + except ConnectionError: + pass + else: + msg = "Expected the first judge call to fail" + raise AssertionError(msg) + + result = differential.get_differential_pairwise_scores(**kwargs) + + assert calls == {"extraction": 1, "verdict": 2} + assert len(result) == 1 + + +@pytest.mark.parametrize( + ("invalid_verdict", "error"), + [ + (DifferentialPairwiseLLMResponse(verdicts=[]), "criterion 'relevance'"), + ( + DifferentialPairwiseLLMResponse( + verdicts=[ + DifferentialCriterionVerdict( + criteria="relevance", + winner=3, + reasoning="invalid", + ) + ] + ), + "invalid winner 3", + ), + ], +) +def test_invalid_verdict_is_not_cached( + tmp_path: Path, + monkeypatch: Any, + invalid_verdict: DifferentialPairwiseLLMResponse, + error: str, +) -> None: + calls = {"extraction": 0, "verdict": 0} + + async def fake_chat(*args: Any, **kwargs: Any) -> Any: + await asyncio.sleep(0) + if kwargs["response_format"] is PairwiseExtractionLLMResponse: + calls["extraction"] += 1 + return _response( + PairwiseExtractionLLMResponse( + common="common", + unique_answer_1="first", + unique_answer_2="second", + ) + ) + calls["verdict"] += 1 + if calls["verdict"] == 1: + return _response(invalid_verdict) + return _response( + DifferentialPairwiseLLMResponse( + verdicts=[ + DifferentialCriterionVerdict( + criteria="relevance", + winner=1, + reasoning="valid", + ) + ] + ) + ) + + monkeypatch.setattr(differential, "chat", fake_chat) + kwargs = _config(tmp_path) + kwargs["trials"] = 1 + + with pytest.raises(RuntimeError, match=error): + differential.get_differential_pairwise_scores(**kwargs) + + result = differential.get_differential_pairwise_scores(**kwargs) + + assert calls == {"extraction": 1, "verdict": 2} + assert len(result) == 1 + + +def test_criteria_change_reuses_extraction_only( + tmp_path: Path, monkeypatch: Any +) -> None: + calls = {"extraction": 0, "verdict": 0} + + async def fake_chat(*args: Any, **kwargs: Any) -> Any: + await asyncio.sleep(0) + if kwargs["response_format"] is PairwiseExtractionLLMResponse: + calls["extraction"] += 1 + return _response( + PairwiseExtractionLLMResponse( + common="common", + unique_answer_1="first", + unique_answer_2="second", + ) + ) + calls["verdict"] += 1 + user_prompt = kwargs["messages"][1]["content"] + criterion = "accuracy" if "accuracy" in user_prompt else "relevance" + return _response( + DifferentialPairwiseLLMResponse( + verdicts=[ + DifferentialCriterionVerdict( + criteria=criterion, + winner=1, + reasoning="reason", + ) + ] + ) + ) + + monkeypatch.setattr(differential, "chat", fake_chat) + kwargs = _config(tmp_path) + kwargs["trials"] = 1 + + differential.get_differential_pairwise_scores(**kwargs) + kwargs["criteria"] = [Criteria(name="accuracy", description="description")] + differential.get_differential_pairwise_scores(**kwargs) + + assert calls == {"extraction": 1, "verdict": 2} diff --git a/tests/autoe/reference/__init__.py b/tests/autoe/reference/__init__.py new file mode 100644 index 0000000..d1b5054 --- /dev/null +++ b/tests/autoe/reference/__init__.py @@ -0,0 +1,3 @@ +# Copyright (c) Microsoft Corporation. +# Licensed under the MIT License. +"""Tests for reference-based evaluation.""" diff --git a/tests/autoe/reference/cache_test.py b/tests/autoe/reference/cache_test.py new file mode 100644 index 0000000..448743c --- /dev/null +++ b/tests/autoe/reference/cache_test.py @@ -0,0 +1,105 @@ +# Copyright (c) Microsoft Corporation. +# Licensed under the MIT License. +"""Tests for reference score caching.""" + +import asyncio +from pathlib import Path +from typing import Any + +import pandas as pd + +from benchmark_qed.autoe.config import Criteria +from benchmark_qed.autoe.data_model import ReferenceLLMResponse +from benchmark_qed.autoe.reference import scores +from benchmark_qed.cache import create_default_cache_config +from benchmark_qed.config.llm_config import AuthType, LLMConfig + + +def _answers(answer: str) -> pd.DataFrame: + return pd.DataFrame([ + { + "question_id": "q1", + "question_text": "question", + "answer": answer, + } + ]) + + +def _config(tmp_path: Path) -> dict[str, Any]: + return { + "llm_client": object(), + "llm_config": LLMConfig( + auth_type=AuthType.AzureManagedIdentity, + call_args={"temperature": 0}, + ), + "generated_answers": _answers("generated answer"), + "reference_answers": _answers("reference answer"), + "criteria": [Criteria(name="correctness", description="description")], + "trials": 2, + "include_score_id_in_prompt": False, + "cache_config": create_default_cache_config( + tmp_path, + database_name="reference.sqlite3", + ), + } + + +def test_second_reference_run_reuses_all_trials( + tmp_path: Path, monkeypatch: Any +) -> None: + call_count = 0 + + async def fake_chat(*args: Any, **kwargs: Any) -> Any: + nonlocal call_count + await asyncio.sleep(0) + call_count += 1 + return type( + "Response", + (), + { + "formatted_response": ReferenceLLMResponse( + score=8, + reasoning="reason", + ) + }, + )() + + monkeypatch.setattr(scores, "chat", fake_chat) + kwargs = _config(tmp_path) + + first = scores.get_reference_scores(**kwargs) + second = scores.get_reference_scores(**kwargs) + + assert call_count == 2 + pd.testing.assert_frame_equal(first, second) + + +def test_score_range_change_invalidates_reference_cache( + tmp_path: Path, monkeypatch: Any +) -> None: + call_count = 0 + + async def fake_chat(*args: Any, **kwargs: Any) -> Any: + nonlocal call_count + await asyncio.sleep(0) + call_count += 1 + return type( + "Response", + (), + { + "formatted_response": ReferenceLLMResponse( + score=4, + reasoning="reason", + ) + }, + )() + + monkeypatch.setattr(scores, "chat", fake_chat) + kwargs = _config(tmp_path) + kwargs["trials"] = 1 + + scores.get_reference_scores(**kwargs) + kwargs["score_max"] = 5 + scores.get_reference_scores(**kwargs) + + assert call_count == 2 diff --git a/tests/autoe/relevance_cache_test.py b/tests/autoe/relevance_cache_test.py new file mode 100644 index 0000000..fc345a1 --- /dev/null +++ b/tests/autoe/relevance_cache_test.py @@ -0,0 +1,195 @@ +# Copyright (c) Microsoft Corporation. +# Licensed under the MIT License. +"""Tests for retrieval relevance caching.""" + +import json +from pathlib import Path +from typing import Any + +import pytest +from graphrag_cache import CacheConfig + +from benchmark_qed.autod.data_model.text_unit import TextUnit +from benchmark_qed.autoe.data_model.relevance import ( + RelevanceAssessmentItem, + RelevanceAssessmentResponse, +) +from benchmark_qed.autoe.retrieval_metrics.relevance_assessment.base import ( + RelevanceRater, +) +from benchmark_qed.cache import ( + CacheStore, + create_default_cache_config, +) + + +class StubRelevanceRater(RelevanceRater): + def __init__( + self, + cache_config: CacheConfig | None, + *, + rater_params: dict[str, Any] | None = None, + ) -> None: + self.calls: list[list[str]] = [] + self.rater_params = rater_params or {"model": "test-model"} + super().__init__(cache_config=cache_config) + + def _get_cache_relevant_params(self) -> dict[str, Any]: + return self.rater_params + + async def _rate_relevance_impl( + self, query: str, text_units: list[TextUnit] + ) -> RelevanceAssessmentResponse: + self.calls.append([unit.id for unit in text_units]) + return RelevanceAssessmentResponse( + assessment=[ + RelevanceAssessmentItem( + text_unit=unit, + reasoning=f"{query}:{unit.text}", + score=len(unit.text) % 4, + ) + for unit in text_units + ] + ) + + +def _text_unit(unit_id: str, text: str) -> TextUnit: + return TextUnit(id=unit_id, short_id=None, text=text) + + +def _cache_config(cache_dir: Path) -> CacheConfig: + return create_default_cache_config( + cache_dir, + database_name="relevance_cache.sqlite3", + ) + + +async def test_second_rater_uses_sqlite_cache(tmp_path: Path) -> None: + units = [_text_unit("one", "first"), _text_unit("two", "second")] + first = StubRelevanceRater(_cache_config(tmp_path)) + expected = await first.rate_relevance("query", units) + + second = StubRelevanceRater(_cache_config(tmp_path)) + actual = await second.rate_relevance("query", units) + + assert len(first.calls) == 1 + assert second.calls == [] + assert actual == expected + assert second.cache_hits == 2 + assert second.get_cache_stats()["cache_files"] == 2 + + +async def test_duplicate_pairs_share_one_assessment(tmp_path: Path) -> None: + rater = StubRelevanceRater(_cache_config(tmp_path)) + units = [_text_unit("semantic", "same"), _text_unit("centroid", " SAME ")] + + result = await rater.rate_relevance("query", units) + + assert rater.calls == [["semantic"]] + assert len(result.assessment) == 2 + assert [item.text_unit.id for item in result.assessment if item.text_unit] == [ + "semantic", + "centroid", + ] + assert result.assessment[0].reasoning == result.assessment[1].reasoning + assert result.assessment[0].score == result.assessment[1].score + assert rater.cache_misses == 2 + assert rater.get_cache_stats()["cache_files"] == 1 + + cached_rater = StubRelevanceRater(_cache_config(tmp_path)) + cached_result = await cached_rater.rate_relevance("query", units) + + assert cached_rater.calls == [] + assert [ + item.text_unit.id for item in cached_result.assessment if item.text_unit + ] == ["semantic", "centroid"] + + +async def test_configuration_metadata_is_redacted(tmp_path: Path) -> None: + params = { + "model": "test-model", + "call_args": {"temperature": 0, "api_key": "secret"}, + } + rater = StubRelevanceRater(_cache_config(tmp_path), rater_params=params) + unit = _text_unit("one", "text") + await rater.rate_relevance("query", [unit]) + + key = rater._generate_cache_key("query", unit, params) + entry = await CacheStore(_cache_config(tmp_path), "StubRelevanceRater").get(key) + + assert entry is not None + assert entry[1]["rater_params"]["call_args"] == { + "api_key": "", + "temperature": 0, + } + assert "secret" not in json.dumps(entry) + + +async def test_imports_legacy_per_key_json(tmp_path: Path) -> None: + unit = _text_unit("one", "legacy") + key_builder = StubRelevanceRater(None) + key = key_builder._generate_cache_key( + "query", unit, key_builder._get_cache_relevant_params() + ) + assessment = RelevanceAssessmentItem( + text_unit=unit, + reasoning="from legacy cache", + score=3, + ) + (tmp_path / f"{key}.json").write_text( + json.dumps({ + "timestamp": "2026-09-11T00:00:00+00:00", + "query": "query", + "assessment_item": assessment.model_dump(), + }), + encoding="utf-8", + ) + + rater = StubRelevanceRater(_cache_config(tmp_path)) + result = await rater.rate_relevance("query", [unit]) + + assert rater.calls == [] + assert result.assessment == [assessment] + + +async def test_clear_cache_removes_sqlite_and_legacy_entries(tmp_path: Path) -> None: + rater = StubRelevanceRater(_cache_config(tmp_path)) + await rater.rate_relevance("query", [_text_unit("one", "text")]) + legacy_file = tmp_path / "legacy.json" + legacy_file.write_text("{}", encoding="utf-8") + + await rater.clear_cache() + + assert rater.get_cache_stats()["cache_files"] == 0 + assert not legacy_file.exists() + assert rater.cache_hits == 0 + assert rater.cache_misses == 0 + + +async def test_invalid_result_count_is_reported(tmp_path: Path) -> None: + class InvalidRater(StubRelevanceRater): + async def _rate_relevance_impl( + self, query: str, text_units: list[TextUnit] + ) -> RelevanceAssessmentResponse: + return RelevanceAssessmentResponse(assessment=[]) + + rater = InvalidRater(_cache_config(tmp_path)) + + with pytest.raises(RuntimeError, match="returned 0 results for 1"): + await rater.rate_relevance("query", [_text_unit("one", "text")]) + + +async def test_warns_when_relevance_configuration_differs( + tmp_path: Path, caplog: pytest.LogCaptureFixture +) -> None: + unit = _text_unit("one", "text") + first = StubRelevanceRater(_cache_config(tmp_path), rater_params={"model": "first"}) + await first.rate_relevance("query", [unit]) + + second = StubRelevanceRater( + _cache_config(tmp_path), rater_params={"model": "second"} + ) + with caplog.at_level("WARNING"): + await second.rate_relevance("query", [unit]) + + assert "same inputs with different rater_params" in caplog.text diff --git a/tests/autoq/__init__.py b/tests/autoq/__init__.py new file mode 100644 index 0000000..59e481e --- /dev/null +++ b/tests/autoq/__init__.py @@ -0,0 +1,2 @@ +# Copyright (c) Microsoft Corporation. +# Licensed under the MIT License. diff --git a/tests/autoq/test_global_question_gen.py b/tests/autoq/test_global_question_gen.py new file mode 100644 index 0000000..cf371ac --- /dev/null +++ b/tests/autoq/test_global_question_gen.py @@ -0,0 +1,28 @@ +# Copyright (c) 2025 Microsoft Corporation. + +from unittest.mock import MagicMock + +from graphrag_llm.completion import LLMCompletion + +from benchmark_qed.autod.data_processor.embedding import TextEmbedder +from benchmark_qed.autoq.config import AssertionConfig +from benchmark_qed.autoq.question_gen.data_questions.global_question_gen import ( + DataGlobalQuestionGen, +) + + +def test_claim_extractor_params_are_forwarded_to_local_extractor() -> None: + llm_params = {"temperature": 0.0} + assertion_config = AssertionConfig() + assertion_config.global_.max_assertions = 0 + + generator = DataGlobalQuestionGen( + llm=MagicMock(spec=LLMCompletion), + text_embedder=MagicMock(spec=TextEmbedder), + local_questions=[], + claim_extractor_params={"llm_params": llm_params}, + assertion_config=assertion_config, + enable_question_validation=False, + ) + + assert generator.claim_extractor.local_claim_extractor.llm_params is llm_params diff --git a/tests/test_cache_cli.py b/tests/test_cache_cli.py new file mode 100644 index 0000000..a0a1c87 --- /dev/null +++ b/tests/test_cache_cli.py @@ -0,0 +1,34 @@ +# Copyright (c) Microsoft Corporation. +# Licensed under the MIT License. +"""Tests for cache inspection commands.""" + +import json +from pathlib import Path + +from typer.testing import CliRunner + +from benchmark_qed.__main__ import app +from benchmark_qed.cache import CacheStore, create_default_cache_config + + +async def test_cache_inspect_json(tmp_path: Path) -> None: + cache_path = tmp_path / "cache.sqlite3" + store = CacheStore( + create_default_cache_config(tmp_path, database_name=cache_path.name), + "test", + ) + await store.put_many( + [("key", {"score": 3}, {"model": "test-model"})], + identities={"key": ("logical", "config")}, + ) + + result = CliRunner().invoke(app, ["cache", "inspect", str(cache_path), "--json"]) + + assert result.exit_code == 0 + details = json.loads(result.stdout) + assert details["backend"] == "graphrag-cache" + assert details["active_leases"] == 0 + assert details["namespaces"] == [ + {"namespace": "test", "entries": 1}, + {"namespace": "test/configurations", "entries": 1}, + ] diff --git a/tests/test_init_config_cli.py b/tests/test_init_config_cli.py index 0adf47a..dbfe690 100644 --- a/tests/test_init_config_cli.py +++ b/tests/test_init_config_cli.py @@ -37,6 +37,66 @@ def test_init_autoe_blob_scaffolds_active_storage_sections(tmp_path: Path) -> No assert "output_storage:\n type: blob\n container_name: my-output" in settings +def test_init_chunk_assertion_scaffolds_cache_config(tmp_path: Path) -> None: + init(ConfigType.autoe_chunk_assertion, tmp_path) + + settings = (tmp_path / "settings.yaml").read_text(encoding="utf-8") + + assert ( + "retrieval_path: " + "input/vector_rag_short_context/data_local_retrieval_results.json" in settings + ) + assert "assertions_path: input/data_local_assertions.json" in settings + assert "cache_config:\n type: sqlite" in settings + assert "base_dir: .benchmark_qed_cache/chunk_assertions" in settings + assert "database_name: chunk_assertions.sqlite3" in settings + assert "cache_dir:" not in settings + + +def test_init_pairwise_scaffolds_cache_config(tmp_path: Path) -> None: + init(ConfigType.autoe_pairwise, tmp_path) + + settings = (tmp_path / "settings.yaml").read_text(encoding="utf-8") + + assert "cache_config:\n type: sqlite" in settings + assert "base_dir: .benchmark_qed_cache/pairwise" in settings + assert "database_name: pairwise.sqlite3" in settings + assert "Set type: none and storage: null" in settings + + +def test_init_differential_pairwise_scaffolds_stage_cache(tmp_path: Path) -> None: + init(ConfigType.autoe_differential_pairwise, tmp_path) + + settings = (tmp_path / "settings.yaml").read_text(encoding="utf-8") + + assert "cache_config:\n type: sqlite" in settings + assert "base_dir: .benchmark_qed_cache/differential_pairwise" in settings + assert "database_name: differential_pairwise.sqlite3" in settings + assert "Caches extraction and verdict stages independently" in settings + + +def test_init_reference_scaffolds_judgment_cache(tmp_path: Path) -> None: + init(ConfigType.autoe_reference, tmp_path) + + settings = (tmp_path / "settings.yaml").read_text(encoding="utf-8") + + assert "cache_config:\n type: sqlite" in settings + assert "base_dir: .benchmark_qed_cache/reference" in settings + assert "database_name: reference.sqlite3" in settings + assert "Reuses completed question/criterion/trial judgments" in settings + + +def test_init_assertion_scaffolds_judgment_cache(tmp_path: Path) -> None: + init(ConfigType.autoe_assertion, tmp_path) + + settings = (tmp_path / "settings.yaml").read_text(encoding="utf-8") + + assert "cache_config:\n type: sqlite" in settings + assert "base_dir: .benchmark_qed_cache/assertion" in settings + assert "database_name: assertion.sqlite3" in settings + assert "Reuses completed question/assertion/trial judgments" in settings + + def test_init_autoq_blob_with_custom_values(tmp_path: Path) -> None: """Blob mode with custom values uploads settings with pre-filled values to blob storage.""" with patch("benchmark_qed.cli.init_config._write_to_blob") as mock_write_blob: diff --git a/tests/test_interactive_init.py b/tests/test_interactive_init.py index dc37f10..03c8355 100644 --- a/tests/test_interactive_init.py +++ b/tests/test_interactive_init.py @@ -18,6 +18,7 @@ from benchmark_qed.cli.yaml_renderer import ( _render_llm_section, render_autoe_assertion_yaml, + render_autoe_chunk_assertion_yaml, render_autoe_pairwise_yaml, render_autoe_reference_yaml, render_autoq_yaml, @@ -152,6 +153,16 @@ def _default_assertion_config() -> dict[str, Any]: } +def _default_chunk_assertion_config() -> dict[str, Any]: + return { + "chat_provider": _openai_chat_provider(), + "generated": {"name": "vector_rag"}, + "assertions": {"assertions_path": "input/data_local_assertions.json"}, + "k_list": [5, 10, 20, 50], + "pass_threshold": 0.5, + } + + # ═══════════════════════════════════════════════════════════════════════════ # 1. YAML Renderer Tests # ═══════════════════════════════════════════════════════════════════════════ @@ -231,6 +242,7 @@ def test_render_autoe_pairwise_yaml(self): assert "others" in parsed assert "question_sets" in parsed assert parsed["trials"] == 4 + assert parsed["cache_config"]["type"] == "sqlite" assert "llm_config" in parsed assert "prompt_config" in parsed assert isinstance(parsed["prompt_config"], dict) @@ -265,6 +277,7 @@ def test_render_autoe_reference_yaml(self): assert parsed["score_max"] == 10 assert "llm_config" in parsed assert "prompt_config" in parsed + assert parsed["cache_config"]["type"] == "sqlite" assert isinstance(parsed["prompt_config"], dict) def test_render_autoe_reference_yaml_multiple_generated(self): @@ -288,10 +301,32 @@ def test_render_autoe_assertion_yaml(self): assert "assertions" in parsed assert parsed["pass_threshold"] == pytest.approx(0.5) assert parsed["trials"] == 4 + assert parsed["cache_config"]["type"] == "sqlite" + assert ( + parsed["cache_config"]["storage"]["base_dir"] + == ".benchmark_qed_cache/assertion" + ) + assert parsed["cache_config"]["database_name"] == "assertion.sqlite3" assert "llm_config" in parsed assert "prompt_config" in parsed assert isinstance(parsed["prompt_config"], dict) + def test_render_autoe_chunk_assertion_yaml_uses_documented_inputs(self): + """Chunk assertion YAML defaults match the bundled data walkthrough.""" + yaml_content = render_autoe_chunk_assertion_yaml( + _default_chunk_assertion_config() + ) + parsed = yaml.safe_load(yaml_content) + + assert ( + parsed["generated"]["retrieval_path"] + == "input/vector_rag_short_context/data_local_retrieval_results.json" + ) + assert ( + parsed["assertions"]["assertions_path"] + == "input/data_local_assertions.json" + ) + # ═══════════════════════════════════════════════════════════════════════════ # 2. LLM Section Rendering Tests diff --git a/tests/test_load_config.py b/tests/test_load_config.py index 2ff3d98..695f1c8 100644 --- a/tests/test_load_config.py +++ b/tests/test_load_config.py @@ -10,7 +10,15 @@ from graphrag_common.config.load_config import ConfigParsingError from pydantic import ValidationError -from benchmark_qed.autoe.config import AssertionConfig, PairwiseConfig, ReferenceConfig +from benchmark_qed.autoe.config import ( + AssertionConfig, + DifferentialPairwiseConfig, + PairwiseConfig, + ReferenceConfig, + RetrievalReferenceConfig, + RetrievalScoresConfig, +) +from benchmark_qed.autoe.data_model.chunk_assertion import ChunkAssertionConfig from benchmark_qed.autoq.config import QuestionGenerationConfig from benchmark_qed.config.llm_config import AuthType, LLMConfig @@ -18,6 +26,161 @@ class TestLoadConfigBasicFunctionality: """Test basic functionality of load_config with different config types.""" + def test_loads_graphrag_cache_config(self) -> None: + config = ChunkAssertionConfig.model_validate({ + "generated": {"name": "test"}, + "assertions": {"assertions_path": "assertions.json"}, + "cache_config": {"type": "memory", "storage": None}, + "llm_config": {"auth_type": "azure_managed_identity"}, + }) + + assert config.cache_config.type == "memory" + + def test_rejects_removed_cache_dir(self) -> None: + with pytest.raises(ValidationError, match="replaced by cache_config"): + ChunkAssertionConfig.model_validate({ + "generated": {"name": "test"}, + "assertions": {"assertions_path": "assertions.json"}, + "cache_dir": ".cache", + "llm_config": {"auth_type": "azure_managed_identity"}, + }) + + def test_retrieval_reference_loads_json_cache_config(self) -> None: + config = RetrievalReferenceConfig.model_validate({ + "questions_path": "questions.json", + "text_units_path": "text_units.parquet", + "output_dir": "output", + "cache_config": { + "type": "json", + "storage": {"type": "file", "base_dir": "cache"}, + }, + "llm_config": {"auth_type": "azure_managed_identity"}, + "embedding_config": {"auth_type": "azure_managed_identity"}, + }) + + assert config.cache_config.type == "json" + assert config.cache_config.storage is not None + assert config.cache_config.storage.base_dir == "cache" + + def test_retrieval_scores_loads_memory_cache_config(self) -> None: + config = RetrievalScoresConfig.model_validate({ + "reference_dir": "reference", + "clusters_path": "clusters.json", + "text_units_path": "text_units.parquet", + "output_dir": "output", + "cache_config": {"type": "memory", "storage": None}, + "llm_config": {"auth_type": "azure_managed_identity"}, + }) + + assert config.cache_config.type == "memory" + + def test_differential_pairwise_uses_stage_cache_by_default(self) -> None: + config = DifferentialPairwiseConfig.model_validate({ + "llm_config": {"auth_type": "azure_managed_identity"}, + }) + + assert config.cache_config.type == "sqlite" + assert config.cache_config.storage is not None + assert ( + config.cache_config.storage.base_dir + == ".benchmark_qed_cache/differential_pairwise" + ) + assert config.cache_config.database_name == "differential_pairwise.sqlite3" + + def test_differential_pairwise_can_disable_cache(self) -> None: + config = DifferentialPairwiseConfig.model_validate({ + "cache_config": {"type": "none", "storage": None}, + "llm_config": {"auth_type": "azure_managed_identity"}, + }) + + assert config.cache_config.type == "none" + + def test_reference_uses_judgment_cache_by_default(self) -> None: + config = ReferenceConfig.model_validate({ + "reference": {"name": "reference", "answer_base_path": "reference.json"}, + "llm_config": {"auth_type": "azure_managed_identity"}, + }) + + assert config.cache_config.type == "sqlite" + assert config.cache_config.storage is not None + assert config.cache_config.storage.base_dir == ".benchmark_qed_cache/reference" + assert config.cache_config.database_name == "reference.sqlite3" + + def test_reference_can_disable_cache(self) -> None: + config = ReferenceConfig.model_validate({ + "reference": {"name": "reference", "answer_base_path": "reference.json"}, + "cache_config": {"type": "none", "storage": None}, + "llm_config": {"auth_type": "azure_managed_identity"}, + }) + + assert config.cache_config.type == "none" + + def test_assertion_uses_judgment_cache_by_default(self) -> None: + config = AssertionConfig.model_validate({ + "generated": {"name": "test", "answer_base_path": "answers.json"}, + "assertions": {"assertions_path": "assertions.json"}, + "llm_config": {"auth_type": "azure_managed_identity"}, + }) + + assert config.cache_config.type == "sqlite" + assert config.cache_config.storage is not None + assert config.cache_config.storage.base_dir == ".benchmark_qed_cache/assertion" + assert config.cache_config.database_name == "assertion.sqlite3" + + def test_assertion_can_disable_cache(self) -> None: + config = AssertionConfig.model_validate({ + "generated": {"name": "test", "answer_base_path": "answers.json"}, + "assertions": {"assertions_path": "assertions.json"}, + "cache_config": {"type": "none", "storage": None}, + "llm_config": {"auth_type": "azure_managed_identity"}, + }) + + assert config.cache_config.type == "none" + + def test_sqlite_cache_remains_local_with_blob_output(self) -> None: + config = ChunkAssertionConfig.model_validate({ + "generated": {"name": "test"}, + "assertions": {"assertions_path": "assertions.json"}, + "cache_config": { + "type": "sqlite", + "storage": { + "type": "file", + "base_dir": ".benchmark_qed_cache", + }, + "database_name": "chunk_assertions.sqlite3", + }, + "output_storage": { + "type": "blob", + "container_name": "results", + "account_url": "https://example.blob.core.windows.net", + }, + "llm_config": {"auth_type": "azure_managed_identity"}, + }) + + assert config.cache_config.storage is not None + assert config.cache_config.storage.type == "file" + assert config.output_storage is not None + assert config.output_storage.type == "blob" + + def test_sqlite_cache_rejects_remote_storage(self) -> None: + with pytest.raises( + ValidationError, + match="Cache type 'sqlite' requires storage type 'file'", + ): + ChunkAssertionConfig.model_validate({ + "generated": {"name": "test"}, + "assertions": {"assertions_path": "assertions.json"}, + "cache_config": { + "type": "sqlite", + "storage": { + "type": "blob", + "container_name": "cache", + "account_url": "https://example.blob.core.windows.net", + }, + }, + "llm_config": {"auth_type": "azure_managed_identity"}, + }) + def test_load_pairwise_config_yaml(self, tmp_path: Path): """Test loading a PairwiseConfig from a YAML file.""" config_data = { @@ -60,6 +223,17 @@ def test_load_pairwise_config_yaml(self, tmp_path: Path): assert config.base.name == "base_condition" assert len(config.others) == 1 assert config.others[0].name == "other_condition" + assert config.cache_config.type == "sqlite" + assert config.cache_config.storage is not None + assert config.cache_config.storage.base_dir == 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