init() — langchain Function Reference

Architecture documentation for the __init__() function in cache.py from the langchain codebase.

Function python LangChainCore MessageInterface

Entity Profile

LangChainCore→ MessageInterface→ __init__() — langchain Function Reference

Dependency Diagram

graph TD
  759b6234_6817_99c2_e8a7_99c6fb2ca731["__init__()"]
  b3be4e54_ae5f_c527_4e99_0843e3d30f72["CacheBackedEmbeddings"]
  759b6234_6817_99c2_e8a7_99c6fb2ca731 -->|defined in| b3be4e54_ae5f_c527_4e99_0843e3d30f72
  style 759b6234_6817_99c2_e8a7_99c6fb2ca731 fill:#6366f1,stroke:#818cf8,color:#fff

Relationship Graph

Source Code

libs/langchain/langchain_classic/embeddings/cache.py lines 142–163

    def __init__(
        self,
        underlying_embeddings: Embeddings,
        document_embedding_store: BaseStore[str, list[float]],
        *,
        batch_size: int | None = None,
        query_embedding_store: BaseStore[str, list[float]] | None = None,
    ) -> None:
        """Initialize the embedder.

        Args:
            underlying_embeddings: the embedder to use for computing embeddings.
            document_embedding_store: The store to use for caching document embeddings.
            batch_size: The number of documents to embed between store updates.
            query_embedding_store: The store to use for caching query embeddings.
                If `None`, query embeddings are not cached.
        """
        super().__init__()
        self.document_embedding_store = document_embedding_store
        self.query_embedding_store = query_embedding_store
        self.underlying_embeddings = underlying_embeddings
        self.batch_size = batch_size