from_documents() — langchain Function Reference

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

Function python DataProcessing DocumentLoaders calls 1

Entity Profile

DataProcessing→ DocumentLoaders→ from_documents() — langchain Function Reference

Dependency Diagram

graph TD
  d53e755c_f22a_3702_9ca0_af9f8caaa28d["from_documents()"]
  6c336ac6_f55c_1ad7_6db3_73dbd71fb625["VectorStore"]
  d53e755c_f22a_3702_9ca0_af9f8caaa28d -->|defined in| 6c336ac6_f55c_1ad7_6db3_73dbd71fb625
  6bba087d_29e7_c047_38e4_a8acc2e8b69d["from_texts()"]
  d53e755c_f22a_3702_9ca0_af9f8caaa28d -->|calls| 6bba087d_29e7_c047_38e4_a8acc2e8b69d
  style d53e755c_f22a_3702_9ca0_af9f8caaa28d fill:#6366f1,stroke:#818cf8,color:#fff

Relationship Graph

Source Code

libs/core/langchain_core/vectorstores/base.py lines 787–814

    def from_documents(
        cls,
        documents: list[Document],
        embedding: Embeddings,
        **kwargs: Any,
    ) -> Self:
        """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 cls.from_texts(texts, embedding, metadatas=metadatas, **kwargs)