docs: Chroma docstrings update #22001
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| - embedding dimensionality | ||
| - etc. | ||
| Select the relevance score function based on the distance metric used by the VectorStore. | ||
| The most similar documents will have the lowest relevance score. |
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nit: newline before 567
| k: Number of Documents to return. Defaults to 4. | ||
| fetch_k: Number of Documents to fetch to pass to MMR algorithm. | ||
| lambda_mult: Number between 0 and 1 that determines the degree | ||
| embedding (List[float]): Embedding to look up documents similar to. |
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nit: don't need to add types here, they get read from function signature type hints when api ref docs are built
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Thank you for contributing to LangChain!
- [X] **PR title**: "docs: Chroma docstrings update"
- Where "package" is whichever of langchain, community, core,
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- [X] **PR message**:
- **Description:** Added and updated Chroma docstrings
- **Issue:** #21983
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Thank you for contributing to LangChain!
PR title: "docs: Chroma docstrings update"
PR message:
Add tests and docs: If you're adding a new integration, please include
docs/docs/integrationsdirectory.Lint and test: Run
make format,make lintandmake testfrom the root of the package(s) you've modified. See contribution guidelines for more: https://python.langchain.com/docs/contributing/Additional guidelines:
If no one reviews your PR within a few days, please @-mention one of baskaryan, efriis, eyurtsev, ccurme, vbarda, hwchase17.