combine_docs() — langchain Function Reference

Architecture documentation for the combine_docs() function in map_rerank.py from the langchain codebase.

Function python AgentOrchestration ClassicChains calls 1

Entity Profile

AgentOrchestration→ ClassicChains→ combine_docs() — langchain Function Reference

Dependency Diagram

graph TD
  3ee542d4_6c5f_5591_2901_27a70f133c10["combine_docs()"]
  ee80b7d6_d087_8cc2_13e6_51659dee89b4["MapRerankDocumentsChain"]
  3ee542d4_6c5f_5591_2901_27a70f133c10 -->|defined in| ee80b7d6_d087_8cc2_13e6_51659dee89b4
  04e51b41_5072_aa90_26eb_d0a9395e5846["_process_results()"]
  3ee542d4_6c5f_5591_2901_27a70f133c10 -->|calls| 04e51b41_5072_aa90_26eb_d0a9395e5846
  style 3ee542d4_6c5f_5591_2901_27a70f133c10 fill:#6366f1,stroke:#818cf8,color:#fff

Relationship Graph

Source Code

libs/langchain/langchain_classic/chains/combine_documents/map_rerank.py lines 170–195

    def combine_docs(
        self,
        docs: list[Document],
        callbacks: Callbacks = None,
        **kwargs: Any,
    ) -> tuple[str, dict]:
        """Combine documents in a map rerank manner.

        Combine by mapping first chain over all documents, then reranking the results.

        Args:
            docs: List of documents to combine
            callbacks: Callbacks to be passed through
            **kwargs: additional parameters to be passed to LLM calls (like other
                input variables besides the documents)

        Returns:
            The first element returned is the single string output. The second
            element returned is a dictionary of other keys to return.
        """
        results = self.llm_chain.apply_and_parse(
            # FYI - this is parallelized and so it is fast.
            [{self.document_variable_name: d.page_content, **kwargs} for d in docs],
            callbacks=callbacks,
        )
        return self._process_results(docs, results)