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community[patch]: update for compatibility with Meilisearch v1.8 (#21979)
Thank you for contributing to LangChain! - [x] **PR title**: "package: description" - Where "package" is whichever of langchain, community, core, experimental, etc. is being modified. Use "docs: ..." for purely docs changes, "templates: ..." for template changes, "infra: ..." for CI changes. - Example: "community: add foobar LLM" - [x] **PR message**: ***Delete this entire checklist*** and replace with - **Description:** Updates Meilisearch vectorstore for compatibility with v1.8. Adds [”showRankingScore”: true”](https://www.meilisearch.com/docs/reference/api/search#ranking-score) in the search parameters and replaces `_semanticScore` field with ` _rankingScore` - [ ] **Add tests and docs**: If you're adding a new integration, please include 1. a test for the integration, preferably unit tests that do not rely on network access, 2. an example notebook showing its use. It lives in `docs/docs/integrations` directory. - [x] **Lint and test**: Run `make format`, `make lint` and `make test` from the root of the package(s) you've modified. See contribution guidelines for more: https://python.langchain.com/docs/contributing/ Additional guidelines: - Make sure optional dependencies are imported within a function. - Please do not add dependencies to pyproject.toml files (even optional ones) unless they are required for unit tests. - Most PRs should not touch more than one package. - Changes should be backwards compatible. - If you are adding something to community, do not re-import it in langchain. If no one reviews your PR within a few days, please @-mention one of baskaryan, efriis, eyurtsev, ccurme, vbarda, hwchase17.
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@ -243,6 +243,7 @@ class Meilisearch(VectorStore):
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"hybrid": {"semanticRatio": 1.0, "embedder": embedder_name},
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"limit": k,
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"filter": filter,
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"showRankingScore": True,
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},
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)
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@ -250,7 +251,7 @@ class Meilisearch(VectorStore):
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metadata = result[self._metadata_key]
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if self._text_key in metadata:
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text = metadata.pop(self._text_key)
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semantic_score = result["_semanticScore"]
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semantic_score = result["_rankingScore"]
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docs.append(
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(Document(page_content=text, metadata=metadata), semantic_score)
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)
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