docs(examples): add product fact provenance workflow - #2468
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Add a source-backed product extraction example that keeps unsupported claims visible for review. Include deterministic validation tests and cookbook documentation.
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Aug 3, 2026
## Summary - accumulate declared, nested, and unknown numeric OpenAI/Anthropic usage fields across retries while preserving non-numeric metadata - add corrective feedback when a Responses API retry receives no tool call - preserve raw iterable type hints through sync and async v2 parallel-tool wrappers - strengthen API-key-free coverage for current and future SDK usage counters ## Consolidated and superseded items - closes #2493 - consolidates contributor work from #2498, #2500, and #2501 with original commit authorship preserved - supersedes #2497 because it drops unknown `model_extra` counters - supersedes #2499 because its hand-maintained provider field lists would drift as SDKs evolve ## Validation - focused changed-surface suite: `110 passed` - broad offline v2/coverage suite: `2368 passed, 91 skipped, 73 deselected` - Ruff check and format check: passed - scoped `ty check`: passed - `uv lock --check`: passed - pre-commit hooks and `git diff --check`: passed The 73 deselected tests require live provider credentials. An unfiltered local run confirmed its 22 failures were provider network connections in the restricted environment; GitHub provider jobs remain the authoritative validation for those paths. ## Intentionally skipped - provider additions or expansions: #2436, #2435, #2423, #2409, #2384, #2322, #2306, #2298, #2283, #2168, #2086; issues #2408, #2383, #2365, #2260, #2084, #2076 - broad architecture, product, security, or streaming decisions: #2394, #2392, #2357, #2356, #2355, #2351, #2321, #2307, #2287, #2263; issues #2479, #2403, #2393, #2391, #2316, #2272, #2056 - dependency batch: #2433 - nontrivial examples and editorial/resource additions: #2468, #2405, #2401, #2354, #2346, #2311, #2305; issue #2404 These remain open because they need dedicated product, architecture, provider, security, dependency, or editorial review and are not required for the `1.15.5` patch release. <!-- CURSOR_SUMMARY --> --- > [!NOTE] > **Medium Risk** > Changes retry usage totals and reask message content on failure paths; scope is limited and heavily covered by tests, with no auth or data-store changes. > > **Overview** > Bundles three v2 retry and wrapper fixes for a patch release. > > **Retry usage accounting** replaces hand-maintained token field sums with generic `_accumulate_models` on Pydantic usage objects. Numeric fields (including nested models and `model_extra` counters) add across retries; booleans and other non-numeric metadata are not treated as billable. OpenAI and Anthropic paths share this logic. > > **OpenAI Responses reask** appends a user correction when `RESPONSES_TOOLS` validation fails but the output has no tool calls (e.g. reasoning-only), so retries include feedback instead of repeating the same request. > > **Parallel tools** in `patch_v2` skips `prepare_response_model` and does not replace `response_model` with the handler’s prepared wrapper for parallel modes, keeping raw `Iterable[...]` hints so schemas and parsed results include every member type. > > <sup>Reviewed by [Cursor Bugbot](https://cursor.com/bugbot) for commit bbddca1. Configure [here](https://www.cursor.com/dashboard/bugbot).</sup> <!-- /CURSOR_SUMMARY -->
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Describe your changes
This adds a cookbook and runnable example for source-backed product fact extraction.
source_idand exact quote for each extracted value.verified,status, and evidence coverage locally instead of trusting model-reported confidence.unverifiedso they can enter a human review queue.The example is intentionally implemented outside Instructor core. It demonstrates how to use validation context for multi-source provenance without adding a new dependency or changing the existing
CitationMixinAPI.Issue ticket number and link
No linked issue. I searched existing issues and PRs for provenance, citations, confidence, and product facts before implementing this example. The closest prior proposal (#2220/#2221) concerned a third-party trust-metadata integration rather than deterministic source validation.
Testing
pytest tests/test_product_fact_provenance_example.py tests/docs/test_examples.py -k product_fact_provenance -q— 6 passedruff check examples/product_fact_provenance tests/test_product_fact_provenance_example.pyruff format --check examples/product_fact_provenance tests/test_product_fact_provenance_example.pyty check examples/product_fact_provenance tests/test_product_fact_provenance_example.pygit diff --checkChecklist before requesting a review
This PR was prepared with OpenAI Codex and reviewed through local tests and static checks.