aadd_documents() — langchain Function Reference

Architecture documentation for the aadd_documents() function in in_memory.py from the langchain codebase.

Function python DataProcessing TextSplitters

Entity Profile

DataProcessing→ TextSplitters→ aadd_documents() — langchain Function Reference

Dependency Diagram

graph TD
  b8dd4514_c27c_e70c_d169_5bef6e4d4617["aadd_documents()"]
  6e491709_d60f_689d_8a1a_c760b54fd120["InMemoryVectorStore"]
  b8dd4514_c27c_e70c_d169_5bef6e4d4617 -->|defined in| 6e491709_d60f_689d_8a1a_c760b54fd120
  style b8dd4514_c27c_e70c_d169_5bef6e4d4617 fill:#6366f1,stroke:#818cf8,color:#fff

Relationship Graph

Source Code

libs/core/langchain_core/vectorstores/in_memory.py lines 224–253

    async def aadd_documents(
        self, documents: list[Document], ids: list[str] | None = None, **kwargs: Any
    ) -> list[str]:
        texts = [doc.page_content for doc in documents]
        vectors = await self.embedding.aembed_documents(texts)

        if ids and len(ids) != len(texts):
            msg = (
                f"ids must be the same length as texts. "
                f"Got {len(ids)} ids and {len(texts)} texts."
            )
            raise ValueError(msg)

        id_iterator: Iterator[str | None] = (
            iter(ids) if ids else iter(doc.id for doc in documents)
        )
        ids_: list[str] = []

        for doc, vector in zip(documents, vectors, strict=False):
            doc_id = next(id_iterator)
            doc_id_ = doc_id or str(uuid.uuid4())
            ids_.append(doc_id_)
            self.store[doc_id_] = {
                "id": doc_id_,
                "vector": vector,
                "text": doc.page_content,
                "metadata": doc.metadata,
            }

        return ids_