get_salient_docs() — langchain Function Reference

Architecture documentation for the get_salient_docs() function in time_weighted_retriever.py from the langchain codebase.

Function python LangChainCore ApiManagement called by 1

Entity Profile

LangChainCore→ ApiManagement→ get_salient_docs() — langchain Function Reference

Dependency Diagram

graph TD
  b1d1c579_6dd6_4855_0800_4ea3956c65a8["get_salient_docs()"]
  57cc5b02_6622_339b_0806_ef06db1bc8c7["TimeWeightedVectorStoreRetriever"]
  b1d1c579_6dd6_4855_0800_4ea3956c65a8 -->|defined in| 57cc5b02_6622_339b_0806_ef06db1bc8c7
  b35afa73_55af_e573_98eb_b51115b185a7["_get_relevant_documents()"]
  b35afa73_55af_e573_98eb_b51115b185a7 -->|calls| b1d1c579_6dd6_4855_0800_4ea3956c65a8
  style b1d1c579_6dd6_4855_0800_4ea3956c65a8 fill:#6366f1,stroke:#818cf8,color:#fff

Relationship Graph

Source Code

libs/langchain/langchain_classic/retrievers/time_weighted_retriever.py lines 83–96

    def get_salient_docs(self, query: str) -> dict[int, tuple[Document, float]]:
        """Return documents that are salient to the query."""
        docs_and_scores: list[tuple[Document, float]]
        docs_and_scores = self.vectorstore.similarity_search_with_relevance_scores(
            query,
            **self.search_kwargs,
        )
        results = {}
        for fetched_doc, relevance in docs_and_scores:
            if "buffer_idx" in fetched_doc.metadata:
                buffer_idx = fetched_doc.metadata["buffer_idx"]
                doc = self.memory_stream[buffer_idx]
                results[buffer_idx] = (doc, relevance)
        return results