add_texts() — langchain Function Reference

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

Function python CoreAbstractions MessageSchema called by 1

Entity Profile

CoreAbstractions→ MessageSchema→ add_texts() — langchain Function Reference

Dependency Diagram

graph TD
  c6cde3f6_740c_9c74_569c_bdc91a7bd836["add_texts()"]
  d25f9e94_3ec0_b9ca_7d2f_eb7ef487ccab["Chroma"]
  c6cde3f6_740c_9c74_569c_bdc91a7bd836 -->|defined in| d25f9e94_3ec0_b9ca_7d2f_eb7ef487ccab
  8333d70f_d25a_107c_0310_d6f0ca8ca56e["from_texts()"]
  8333d70f_d25a_107c_0310_d6f0ca8ca56e -->|calls| c6cde3f6_740c_9c74_569c_bdc91a7bd836
  style c6cde3f6_740c_9c74_569c_bdc91a7bd836 fill:#6366f1,stroke:#818cf8,color:#fff

Relationship Graph

Source Code

libs/partners/chroma/langchain_chroma/vectorstores.py lines 597–682

    def add_texts(
        self,
        texts: Iterable[str],
        metadatas: list[dict] | None = None,
        ids: list[str] | None = None,
        **kwargs: Any,
    ) -> list[str]:
        """Run more texts through the embeddings and add to the `VectorStore`.

        Args:
            texts: Texts to add to the `VectorStore`.
            metadatas: Optional list of metadatas.
                    When querying, you can filter on this metadata.
            ids: Optional list of IDs. (Items without IDs will be assigned UUIDs)
            kwargs: Additional keyword arguments.

        Returns:
            List of IDs of the added texts.

        Raises:
            ValueError: When metadata is incorrect.
        """
        if ids is None:
            ids = [str(uuid.uuid4()) for _ in texts]
        else:
            ids = [id_ if id_ is not None else str(uuid.uuid4()) for id_ in ids]

        embeddings = None
        texts = list(texts)
        if self._embedding_function is not None:
            embeddings = self._embedding_function.embed_documents(texts)
        if metadatas:
            # fill metadatas with empty dicts if somebody
            # did not specify metadata for all texts
            length_diff = len(texts) - len(metadatas)
            if length_diff:
                metadatas = metadatas + [{}] * length_diff
            empty_ids = []
            non_empty_ids = []
            for idx, m in enumerate(metadatas):
                if m:
                    non_empty_ids.append(idx)
                else:
                    empty_ids.append(idx)
            if non_empty_ids:
                metadatas = [metadatas[idx] for idx in non_empty_ids]
                texts_with_metadatas = [texts[idx] for idx in non_empty_ids]
                embeddings_with_metadatas = (
                    [embeddings[idx] for idx in non_empty_ids]
                    if embeddings is not None and len(embeddings) > 0
                    else None
                )
                ids_with_metadata = [ids[idx] for idx in non_empty_ids]
                try:
                    self._collection.upsert(
                        metadatas=metadatas,  # type: ignore[arg-type]
                        embeddings=embeddings_with_metadatas,  # type: ignore[arg-type]
                        documents=texts_with_metadatas,
                        ids=ids_with_metadata,
                    )
                except ValueError as e:
                    if "Expected metadata value to be" in str(e):
                        msg = (
                            "Try filtering complex metadata from the document using "
                            "langchain_community.vectorstores.utils.filter_complex_metadata."
                        )
                        raise ValueError(e.args[0] + "\n\n" + msg) from e
                    raise e
            if empty_ids:
                texts_without_metadatas = [texts[j] for j in empty_ids]
                embeddings_without_metadatas = (
                    [embeddings[j] for j in empty_ids] if embeddings else None
                )
                ids_without_metadatas = [ids[j] for j in empty_ids]
                self._collection.upsert(
                    embeddings=embeddings_without_metadatas,  # type: ignore[arg-type]
                    documents=texts_without_metadatas,
                    ids=ids_without_metadatas,
                )
        else:
            self._collection.upsert(