as_retriever() — langchain Function Reference

Architecture documentation for the as_retriever() function in base.py from the langchain codebase.

Function python DataProcessing DocumentLoaders calls 1

Entity Profile

DataProcessing→ DocumentLoaders→ as_retriever() — langchain Function Reference

Dependency Diagram

graph TD
  f04f6652_019e_eef8_c59b_9b31d2db2ea8["as_retriever()"]
  6c336ac6_f55c_1ad7_6db3_73dbd71fb625["VectorStore"]
  f04f6652_019e_eef8_c59b_9b31d2db2ea8 -->|defined in| 6c336ac6_f55c_1ad7_6db3_73dbd71fb625
  612a5115_56cc_8662_cf37_eedd6f8624e6["_get_retriever_tags()"]
  f04f6652_019e_eef8_c59b_9b31d2db2ea8 -->|calls| 612a5115_56cc_8662_cf37_eedd6f8624e6
  style f04f6652_019e_eef8_c59b_9b31d2db2ea8 fill:#6366f1,stroke:#818cf8,color:#fff

Relationship Graph

Source Code

libs/core/langchain_core/vectorstores/base.py lines 905–961

    def as_retriever(self, **kwargs: Any) -> VectorStoreRetriever:
        """Return `VectorStoreRetriever` initialized from this `VectorStore`.

        Args:
            **kwargs: Keyword arguments to pass to the search function.

                Can include:

                * `search_type`: Defines the type of search that the Retriever should
                    perform. Can be `'similarity'` (default), `'mmr'`, or
                    `'similarity_score_threshold'`.
                * `search_kwargs`: Keyword arguments to pass to the search function.

                    Can include things like:

                    * `k`: Amount of documents to return (Default: `4`)
                    * `score_threshold`: Minimum relevance threshold
                        for `similarity_score_threshold`
                    * `fetch_k`: Amount of documents to pass to MMR algorithm
                        (Default: `20`)
                    * `lambda_mult`: Diversity of results returned by MMR;
                        `1` for minimum diversity and 0 for maximum. (Default: `0.5`)
                    * `filter`: Filter by document metadata

        Returns:
            Retriever class for `VectorStore`.

        Examples:
        ```python
        # Retrieve more documents with higher diversity
        # Useful if your dataset has many similar documents
        docsearch.as_retriever(
            search_type="mmr", search_kwargs={"k": 6, "lambda_mult": 0.25}
        )

        # Fetch more documents for the MMR algorithm to consider
        # But only return the top 5
        docsearch.as_retriever(search_type="mmr", search_kwargs={"k": 5, "fetch_k": 50})

        # Only retrieve documents that have a relevance score
        # Above a certain threshold
        docsearch.as_retriever(
            search_type="similarity_score_threshold",
            search_kwargs={"score_threshold": 0.8},
        )

        # Only get the single most similar document from the dataset
        docsearch.as_retriever(search_kwargs={"k": 1})

        # Use a filter to only retrieve documents from a specific paper
        docsearch.as_retriever(
            search_kwargs={"filter": {"paper_title": "GPT-4 Technical Report"}}
        )
        ```
        """
        tags = kwargs.pop("tags", None) or [*self._get_retriever_tags()]
        return VectorStoreRetriever(vectorstore=self, tags=tags, **kwargs)