afrom_documents() — langchain Function Reference

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

Function python DataProcessing TextSplitters calls 1

Entity Profile

DataProcessing→ TextSplitters→ afrom_documents() — langchain Function Reference

Dependency Diagram

graph TD
  4ed18cca_a7fc_92c8_c198_a06d26cd571b["afrom_documents()"]
  6c336ac6_f55c_1ad7_6db3_73dbd71fb625["VectorStore"]
  4ed18cca_a7fc_92c8_c198_a06d26cd571b -->|defined in| 6c336ac6_f55c_1ad7_6db3_73dbd71fb625
  755352fa_b98e_ac4a_7fcc_641f75863436["afrom_texts()"]
  4ed18cca_a7fc_92c8_c198_a06d26cd571b -->|calls| 755352fa_b98e_ac4a_7fcc_641f75863436
  style 4ed18cca_a7fc_92c8_c198_a06d26cd571b fill:#6366f1,stroke:#818cf8,color:#fff

Relationship Graph

Source Code

libs/core/langchain_core/vectorstores/base.py lines 817–844

    async def afrom_documents(
        cls,
        documents: list[Document],
        embedding: Embeddings,
        **kwargs: Any,
    ) -> Self:
        """Async return `VectorStore` initialized from documents and embeddings.

        Args:
            documents: List of `Document` objects to add to the `VectorStore`.
            embedding: Embedding function to use.
            **kwargs: Additional keyword arguments.

        Returns:
            `VectorStore` initialized from documents and embeddings.
        """
        texts = [d.page_content for d in documents]
        metadatas = [d.metadata for d in documents]

        if "ids" not in kwargs:
            ids = [doc.id for doc in documents]

            # If there's at least one valid ID, we'll assume that IDs
            # should be used.
            if any(ids):
                kwargs["ids"] = ids

        return await cls.afrom_texts(texts, embedding, metadatas=metadatas, **kwargs)