Chroma Class — langchain Architecture

Architecture documentation for the Chroma class in vectorstores.py from the langchain codebase.

Class python

Entity Profile

Dependency Diagram

graph TD
  d25f9e94_3ec0_b9ca_7d2f_eb7ef487ccab["Chroma"]
  9d2a2799_754f_4de7_e4e6_081d8ea620e0["VectorStore"]
  d25f9e94_3ec0_b9ca_7d2f_eb7ef487ccab -->|extends| 9d2a2799_754f_4de7_e4e6_081d8ea620e0
  d4a05de9_1f0f_3b21_8171_181bb47227ef["vectorstores.py"]
  d25f9e94_3ec0_b9ca_7d2f_eb7ef487ccab -->|defined in| d4a05de9_1f0f_3b21_8171_181bb47227ef
  5850e811_c55d_a2f8_15f1_17047150a50a["__init__()"]
  d25f9e94_3ec0_b9ca_7d2f_eb7ef487ccab -->|method| 5850e811_c55d_a2f8_15f1_17047150a50a
  5b1a46f4_cf99_423b_b281_e9ded4030f18["__ensure_collection()"]
  d25f9e94_3ec0_b9ca_7d2f_eb7ef487ccab -->|method| 5b1a46f4_cf99_423b_b281_e9ded4030f18
  13e731c1_dd84_1521_13d7_e6e72dedc9f8["_collection()"]
  d25f9e94_3ec0_b9ca_7d2f_eb7ef487ccab -->|method| 13e731c1_dd84_1521_13d7_e6e72dedc9f8
  2c07cafb_bafd_b1ba_ea9d_b567233e8c5a["embeddings()"]
  d25f9e94_3ec0_b9ca_7d2f_eb7ef487ccab -->|method| 2c07cafb_bafd_b1ba_ea9d_b567233e8c5a
  180f6c1b_bb48_bf57_69cd_8e866f8993df["__query_collection()"]
  d25f9e94_3ec0_b9ca_7d2f_eb7ef487ccab -->|method| 180f6c1b_bb48_bf57_69cd_8e866f8993df
  1fa15e9e_3ad0_16db_c7ff_a4a326370de0["encode_image()"]
  d25f9e94_3ec0_b9ca_7d2f_eb7ef487ccab -->|method| 1fa15e9e_3ad0_16db_c7ff_a4a326370de0
  7d6b252e_48b9_704e_b6e9_207055bfbff1["fork()"]
  d25f9e94_3ec0_b9ca_7d2f_eb7ef487ccab -->|method| 7d6b252e_48b9_704e_b6e9_207055bfbff1
  37a23069_1b1e_eff0_d830_afb8dfa98dd5["add_images()"]
  d25f9e94_3ec0_b9ca_7d2f_eb7ef487ccab -->|method| 37a23069_1b1e_eff0_d830_afb8dfa98dd5
  c6cde3f6_740c_9c74_569c_bdc91a7bd836["add_texts()"]
  d25f9e94_3ec0_b9ca_7d2f_eb7ef487ccab -->|method| c6cde3f6_740c_9c74_569c_bdc91a7bd836
  1807ab11_64ca_e35a_9cf9_7abe78780268["hybrid_search()"]
  d25f9e94_3ec0_b9ca_7d2f_eb7ef487ccab -->|method| 1807ab11_64ca_e35a_9cf9_7abe78780268
  c2e030db_b7f7_376a_9ad2_1a06dee8dcd8["similarity_search()"]
  d25f9e94_3ec0_b9ca_7d2f_eb7ef487ccab -->|method| c2e030db_b7f7_376a_9ad2_1a06dee8dcd8
  76615968_7cd6_d5f7_a619_c032edfc26cb["similarity_search_by_vector()"]
  d25f9e94_3ec0_b9ca_7d2f_eb7ef487ccab -->|method| 76615968_7cd6_d5f7_a619_c032edfc26cb

Relationship Graph

Source Code

libs/partners/chroma/langchain_chroma/vectorstores.py lines 155–1459

class Chroma(VectorStore):
    """Chroma vector store integration.

    Setup:
        Install `chromadb`, `langchain-chroma` packages:

        ```bash
        pip install -qU chromadb langchain-chroma
        ```

    Key init args — indexing params:
        collection_name:
            Name of the collection.
        embedding_function:
            Embedding function to use.

    Key init args — client params:
        client:
            Chroma client to use.
        client_settings:
            Chroma client settings.
        persist_directory:
            Directory to persist the collection.
        host:
            Hostname of a deployed Chroma server.
        port:
            Connection port for a deployed Chroma server. Default is 8000.
        ssl:
            Whether to establish an SSL connection with a deployed Chroma server. Default is False.
        headers:
            HTTP headers to send to a deployed Chroma server.
        chroma_cloud_api_key:
            Chroma Cloud API key.
        tenant:
            Tenant ID. Required for Chroma Cloud connections. Default is 'default_tenant' for local Chroma servers.
        database:
            Database name. Required for Chroma Cloud connections. Default is 'default_database'.

    Instantiate:
        ```python
        from langchain_chroma import Chroma
        from langchain_openai import OpenAIEmbeddings

        vector_store = Chroma(
            collection_name="foo",
            embedding_function=OpenAIEmbeddings(),
            # other params...
        )
        ```

    Add Documents:
        ```python
        from langchain_core.documents import Document

        document_1 = Document(page_content="foo", metadata={"baz": "bar"})
        document_2 = Document(page_content="thud", metadata={"bar": "baz"})
        document_3 = Document(page_content="i will be deleted :(")

        documents = [document_1, document_2, document_3]
        ids = ["1", "2", "3"]
        vector_store.add_documents(documents=documents, ids=ids)
        ```

    Update Documents:
        ```python
        updated_document = Document(
            page_content="qux",
            metadata={"bar": "baz"},
        )

        vector_store.update_documents(ids=["1"], documents=[updated_document])
        ```

    Delete Documents:
        ```python
        vector_store.delete(ids=["3"])
        ```

    Search:
        ```python
        results = vector_store.similarity_search(query="thud", k=1)