embed_query() — langchain Function Reference

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

Function python AgentOrchestration ClassicChains calls 2

Entity Profile

AgentOrchestration→ ClassicChains→ embed_query() — langchain Function Reference

Dependency Diagram

graph TD
  dcdba7e0_a4e9_895f_80dd_67dcdcbaa922["embed_query()"]
  4ca44101_1e0d_43e9_f420_35c5afe4173a["HypotheticalDocumentEmbedder"]
  dcdba7e0_a4e9_895f_80dd_67dcdcbaa922 -->|defined in| 4ca44101_1e0d_43e9_f420_35c5afe4173a
  577fb638_b3d2_1be8_d2bd_5fc029666efb["embed_documents()"]
  dcdba7e0_a4e9_895f_80dd_67dcdcbaa922 -->|calls| 577fb638_b3d2_1be8_d2bd_5fc029666efb
  28b658bd_49a9_57f4_afd1_c492036ca171["combine_embeddings()"]
  dcdba7e0_a4e9_895f_80dd_67dcdcbaa922 -->|calls| 28b658bd_49a9_57f4_afd1_c492036ca171
  style dcdba7e0_a4e9_895f_80dd_67dcdcbaa922 fill:#6366f1,stroke:#818cf8,color:#fff

Relationship Graph

Source Code

libs/langchain/langchain_classic/chains/hyde/base.py lines 78–87

    def embed_query(self, text: str) -> list[float]:
        """Generate a hypothetical document and embedded it."""
        var_name = self.input_keys[0]
        result = self.llm_chain.invoke({var_name: text})
        if isinstance(self.llm_chain, LLMChain):
            documents = [result[self.output_keys[0]]]
        else:
            documents = [result]
        embeddings = self.embed_documents(documents)
        return self.combine_embeddings(embeddings)