_aembed_texts() — langchain Function Reference

Architecture documentation for the _aembed_texts() function in vectorstores.py from the langchain codebase.

Function python CoreAbstractions MessageSchema called by 1

Entity Profile

CoreAbstractions→ MessageSchema→ _aembed_texts() — langchain Function Reference

Dependency Diagram

graph TD
  b81b33c0_c789_80eb_7445_6fda6aa905e0["_aembed_texts()"]
  bf62db79_4217_463c_798f_6f8528ed0d6e["Qdrant"]
  b81b33c0_c789_80eb_7445_6fda6aa905e0 -->|defined in| bf62db79_4217_463c_798f_6f8528ed0d6e
  7268a9dd_664a_8cff_f4f9_a345b28dafd3["_agenerate_rest_batches()"]
  7268a9dd_664a_8cff_f4f9_a345b28dafd3 -->|calls| b81b33c0_c789_80eb_7445_6fda6aa905e0
  style b81b33c0_c789_80eb_7445_6fda6aa905e0 fill:#6366f1,stroke:#818cf8,color:#fff

Relationship Graph

Source Code

libs/partners/qdrant/langchain_qdrant/vectorstores.py lines 2168–2195

    async def _aembed_texts(self, texts: Iterable[str]) -> list[list[float]]:
        """Embed search texts.

        Used to provide backward compatibility with `embedding_function` argument.

        Args:
            texts: Iterable of texts to embed.

        Returns:
            List of floats representing the texts embedding.

        """
        if self.embeddings is not None:
            embeddings = await self.embeddings.aembed_documents(list(texts))
            if hasattr(embeddings, "tolist"):
                embeddings = embeddings.tolist()
        elif self._embeddings_function is not None:
            embeddings = []
            for text in texts:
                embedding = self._embeddings_function(text)
                if hasattr(embeddings, "tolist"):
                    embedding = embedding.tolist()
                embeddings.append(embedding)
        else:
            msg = "Neither of embeddings or embedding_function is set"
            raise ValueError(msg)

        return embeddings