aget_salient_docs() — langchain Function Reference

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

Function python LangChainCore Runnables called by 1

Entity Profile

LangChainCore→ Runnables→ aget_salient_docs() — langchain Function Reference

Dependency Diagram

graph TD
  48d5e3cc_2c4d_6412_02b8_9dbe4675bfc4["aget_salient_docs()"]
  57cc5b02_6622_339b_0806_ef06db1bc8c7["TimeWeightedVectorStoreRetriever"]
  48d5e3cc_2c4d_6412_02b8_9dbe4675bfc4 -->|defined in| 57cc5b02_6622_339b_0806_ef06db1bc8c7
  ae43f3d7_ce4c_57f0_7d32_9aaacf63415b["_aget_relevant_documents()"]
  ae43f3d7_ce4c_57f0_7d32_9aaacf63415b -->|calls| 48d5e3cc_2c4d_6412_02b8_9dbe4675bfc4
  style 48d5e3cc_2c4d_6412_02b8_9dbe4675bfc4 fill:#6366f1,stroke:#818cf8,color:#fff

Relationship Graph

Source Code

libs/langchain/langchain_classic/retrievers/time_weighted_retriever.py lines 98–113

    async def aget_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 = (
            await self.vectorstore.asimilarity_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