update_documents() — langchain Function Reference

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

Function python CoreAbstractions RunnableInterface called by 1

Entity Profile

CoreAbstractions→ RunnableInterface→ update_documents() — langchain Function Reference

Dependency Diagram

graph TD
  f60e8b46_57d7_444f_8f74_649e0a5f3326["update_documents()"]
  d25f9e94_3ec0_b9ca_7d2f_eb7ef487ccab["Chroma"]
  f60e8b46_57d7_444f_8f74_649e0a5f3326 -->|defined in| d25f9e94_3ec0_b9ca_7d2f_eb7ef487ccab
  ff1e5124_d144_b0ba_680b_5e772180c3fd["update_document()"]
  ff1e5124_d144_b0ba_680b_5e772180c3fd -->|calls| f60e8b46_57d7_444f_8f74_649e0a5f3326
  style f60e8b46_57d7_444f_8f74_649e0a5f3326 fill:#6366f1,stroke:#818cf8,color:#fff

Relationship Graph

Source Code

libs/partners/chroma/langchain_chroma/vectorstores.py lines 1223–1270

    def update_documents(self, ids: list[str], documents: list[Document]) -> None:
        """Update a document in the collection.

        Args:
            ids: List of ids of the document to update.
            documents: List of documents to update.

        Raises:
            ValueError: If the embedding function is not provided.
        """
        text = [document.page_content for document in documents]
        metadata = [document.metadata for document in documents]
        if self._embedding_function is None:
            msg = "For update, you must specify an embedding function on creation."
            raise ValueError(
                msg,
            )
        embeddings = self._embedding_function.embed_documents(text)

        if hasattr(
            self._client,
            "get_max_batch_size",
        ) or hasattr(  # for Chroma 0.5.1 and above
            self._client,
            "max_batch_size",
        ):  # for Chroma 0.4.10 and above
            from chromadb.utils.batch_utils import create_batches

            for batch in create_batches(
                api=self._client,
                ids=ids,
                metadatas=metadata,  # type: ignore[arg-type]
                documents=text,
                embeddings=embeddings,  # type: ignore[arg-type]
            ):
                self._collection.update(
                    ids=batch[0],
                    embeddings=batch[1],
                    documents=batch[3],
                    metadatas=batch[2],
                )
        else:
            self._collection.update(
                ids=ids,
                embeddings=embeddings,  # type: ignore[arg-type]
                documents=text,
                metadatas=metadata,  # type: ignore[arg-type]
            )