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# `langchain_community.vectorstores.chroma`.Chroma

class langchain_community.vectorstores.chroma.Chroma(collection_name: str = 'langchain', embedding_function: Optional[Embeddings] = None, persist_directory: Optional[str] = None, client_settings: Optional[chromadb.config.Settings] = None, collection_metadata: Optional[Dict] = None, client: Optional[chromadb.Client] = None, relevance_score_fn: Optional[Callable[[float], float]] = None)

  ChromaDB vector store.

  To use, you should have the `chromadb` python package installed.

  Example

      from langchain_community.vectorstores import Chroma
      from langchain_community.embeddings.openai import OpenAIEmbeddings

      embeddings = OpenAIEmbeddings()
      vectorstore = Chroma("langchain_store", embeddings)

  Initialize with a Chroma client.

  Attributes

  `embeddings` | Access the query embedding object if available.

  Methods

  `__init__`([collection_name, ...]) | Initialize with a Chroma client.
  `aadd_documents`(documents, **kwargs) | Run more documents through the embeddings and add to the vectorstore.
  `aadd_texts`(texts[, metadatas]) | Run more texts through the embeddings and add to the vectorstore.
  `add_documents`(documents, **kwargs) | Run more documents through the embeddings and add to the vectorstore.
  `add_images`(uris[, metadatas, ids]) | Run more images through the embeddings and add to the vectorstore.
  `add_texts`(texts[, metadatas, ids]) | Run more texts through the embeddings and add to the vectorstore.
  `adelete`([ids]) | Delete by vector ID or other criteria.
  `afrom_documents`(documents, embedding, **kwargs) | Return VectorStore initialized from documents and embeddings.
  `afrom_texts`(texts, embedding[, metadatas]) | Return VectorStore initialized from texts and embeddings.
  `amax_marginal_relevance_search`(query[, k, ...]) | Return docs selected using the maximal marginal relevance.
  `amax_marginal_relevance_search_by_vector`(...) | Return docs selected using the maximal marginal relevance.
  `as_retriever`(**kwargs) | Return VectorStoreRetriever initialized from this VectorStore.
  `asearch`(query, search_type, **kwargs) | Return docs most similar to query using specified search type.
  `asimilarity_search`(query[, k]) | Return docs most similar to query.
  `asimilarity_search_by_vector`(embedding[, k]) | Return docs most similar to embedding vector.
  `asimilarity_search_with_relevance_scores`(query) | Return docs and relevance scores in the range [0, 1], asynchronously.
  `asimilarity_search_with_score`(*args, **kwargs) | Run similarity search with distance asynchronously.
  `delete`([ids]) | Delete by vector IDs.
  `delete_collection`() | Delete the collection.
  `encode_image`(uri) | Get base64 string from image URI.
  `from_documents`(documents[, embedding, ids, ...]) | Create a Chroma vectorstore from a list of documents.
  `from_texts`(texts[, embedding, metadatas, ...]) | Create a Chroma vectorstore from a raw documents.
  `get`([ids, where, limit, offset, ...]) | Gets the collection.
  `max_marginal_relevance_search`(query[, k, ...]) | Return docs selected using the maximal marginal relevance.
  `max_marginal_relevance_search_by_vector`(...) | Return docs selected using the maximal marginal relevance.
  `persist`() | [Deprecated] Persist the collection.
  `search`(query, search_type, **kwargs) | Return docs most similar to query using specified search type.
  `similarity_search`(query[, k, filter]) | Run similarity search with Chroma.
  `similarity_search_by_vector`(embedding[, k, ...]) | Return docs most similar to embedding vector.
  `similarity_search_by_vector_with_relevance_scores`(...) | Return docs most similar to embedding vector and similarity score.
  `similarity_search_with_relevance_scores`(query) | Return docs and relevance scores in the range [0, 1].
  `similarity_search_with_score`(query[, k, ...]) | Run similarity search with Chroma with distance.
  `update_document`(document_id, document) | Update a document in the collection.
  `update_documents`(ids, documents) | Update a document in the collection.

  Parameters

    - collection_name (str) –

    - embedding_function (Optional[Embeddings]) –

    - persist_directory (Optional[str]) –

    - client_settings (Optional[chromadb.config.Settings]) –

    - collection_metadata (Optional[Dict]) –

    - client (Optional[chromadb.Client]) –

    - relevance_score_fn (Optional[Callable[[float], float]]) –

  __init__(collection_name: str = 'langchain', embedding_function: Optional[Embeddings] = None, persist_directory: Optional[str] = None, client_settings: Optional[chromadb.config.Settings] = None, collection_metadata: Optional[Dict] = None, client: Optional[chromadb.Client] = None, relevance_score_fn: Optional[Callable[[float], float]] = None) → None

    Initialize with a Chroma client.

    Parameters

      - collection_name (str) –

      - embedding_function (Optional[Embeddings]) –

      - persist_directory (Optional[str]) –

      - client_settings (Optional[chromadb.config.Settings]) –

      - collection_metadata (Optional[Dict]) –

      - client (Optional[chromadb.Client]) –

      - relevance_score_fn (Optional[Callable[[float], float]]) –

    Return type

      None

  async aadd_documents(documents: List[Document], **kwargs: Any) → List[str]

    Run more documents through the embeddings and add to the vectorstore.

    Parameters

      - (List[Document] (documents) – Documents to add to the vectorstore.

      - documents (List[Document]) –

      - kwargs (Any) –

    Returns

      List of IDs of the added texts.

    Return type

      List[str]

  async aadd_texts(texts: Iterable[str], metadatas: Optional[List[dict]] = None, **kwargs: Any) → List[str]

    Run more texts through the embeddings and add to the vectorstore.

    Parameters

      - texts (Iterable[str]) –

      - metadatas (Optional[List[dict]]) –

      - kwargs (Any) –

    Return type

      List[str]

  add_documents(documents: List[Document], **kwargs: Any) → List[str]

    Run more documents through the embeddings and add to the vectorstore.

    Parameters

      - (List[Document] (documents) – Documents to add to the vectorstore.

      - documents (List[Document]) –

      - kwargs (Any) –

    Returns

      List of IDs of the added texts.

    Return type

      List[str]

  add_images(uris: List[str], metadatas: Optional[List[dict]] = None, ids: Optional[List[str]] = None, **kwargs: Any) → List[str]

    Run more images through the embeddings and add to the vectorstore.

    Parameters

      - List[str] (uris) – File path to the image.

      - metadatas (Optional[List[dict]], optional) – Optional list of metadatas.

      - ids (Optional[List[str]], optional) – Optional list of IDs.

      - uris (List[str]) –

      - kwargs (Any) –

    Returns

      List of IDs of the added images.

    Return type

      List[str]

  add_texts(texts: Iterable[str], metadatas: Optional[List[dict]] = None, ids: Optional[List[str]] = None, **kwargs: Any) → List[str]

    Run more texts through the embeddings and add to the vectorstore.

    Parameters

      - texts (Iterable[str]) – Texts to add to the vectorstore.

      - metadatas (Optional[List[dict]], optional) – Optional list of metadatas.

      - ids (Optional[List[str]], optional) – Optional list of IDs.

      - kwargs (Any) –

    Returns

      List of IDs of the added texts.

    Return type

      List[str]

  async adelete(ids: Optional[List[str]] = None, **kwargs: Any) → Optional[bool]

    Delete by vector ID or other criteria.

    Parameters

      - ids (Optional[List[str]]) – List of ids to delete.

      - **kwargs (Any) – Other keyword arguments that subclasses might use.

    Returns

      True if deletion is successful, False otherwise, None if not implemented.

    Return type

      Optional[bool]

  async classmethod afrom_documents(documents: List[Document], embedding: Embeddings, **kwargs: Any) → VST

    Return VectorStore initialized from documents and embeddings.

    Parameters

      - documents (List[Document]) –

      - embedding (Embeddings) –

      - kwargs (Any) –

    Return type

      VST

  async classmethod afrom_texts(texts: List[str], embedding: Embeddings, metadatas: Optional[List[dict]] = None, **kwargs: Any) → VST

    Return VectorStore initialized from texts and embeddings.

    Parameters

      - texts (List[str]) –

      - embedding (Embeddings) –

      - metadatas (Optional[List[dict]]) –

      - kwargs (Any) –

    Return type

      VST

  async amax_marginal_relevance_search(query: str, k: int = 4, fetch_k: int = 20, lambda_mult: float = 0.5, **kwargs: Any) → List[Document]

    Return docs selected using the maximal marginal relevance.

    Maximal marginal relevance optimizes for similarity to query AND diversity among selected documents.

    Parameters

      - query (str) – Text to look up documents similar to.

      - k (int) – Number of Documents to return. Defaults to 4.

      - fetch_k (int) – Number of Documents to fetch to pass to MMR algorithm.

      - lambda_mult (float) – Number between 0 and 1 that determines the degree of diversity among the results with 0 corresponding to maximum diversity and 1 to minimum diversity. Defaults to 0.5.

      - kwargs (Any) –

    Returns

      List of Documents selected by maximal marginal relevance.

    Return type

      List[Document]

  async amax_marginal_relevance_search_by_vector(embedding: List[float], k: int = 4, fetch_k: int = 20, lambda_mult: float = 0.5, **kwargs: Any) → List[Document]

    Return docs selected using the maximal marginal relevance.

    Parameters

      - embedding (List[float]) –

      - k (int) –

      - fetch_k (int) –

      - lambda_mult (float) –

      - kwargs (Any) –

    Return type

      List[Document]

  as_retriever(**kwargs: Any) → VectorStoreRetriever

    Return VectorStoreRetriever initialized from this VectorStore.

    Parameters

      - search_type (Optional[str]) – Defines the type of search that the Retriever should perform. Can be “similarity” (default), “mmr”, or “similarity_score_threshold”.

      - search_kwargs (Optional[Dict]) –

        Keyword arguments to pass to the search function. Can include things like:

        > k: Amount of documents to return (Default: 4) score_threshold: Minimum relevance threshold

        > > for similarity_score_threshold

        > fetch_k: Amount of documents to pass to MMR algorithm (Default: 20) lambda_mult: Diversity of results returned by MMR;

        > > 1 for minimum diversity and 0 for maximum. (Default: 0.5)

        > filter: Filter by document metadata

      - kwargs (Any) –

    Returns

      Retriever class for VectorStore.

    Return type

      VectorStoreRetriever

    Examples:

        # Retrieve more documents with higher diversity
        # Useful if your dataset has many similar documents
        docsearch.as_retriever(
            search_type="mmr",
            search_kwargs={'k': 6, 'lambda_mult': 0.25}
        )

        # Fetch more documents for the MMR algorithm to consider
        # But only return the top 5
        docsearch.as_retriever(
            search_type="mmr",
            search_kwargs={'k': 5, 'fetch_k': 50}
        )

        # Only retrieve documents that have a relevance score
        # Above a certain threshold
        docsearch.as_retriever(
            search_type="similarity_score_threshold",
            search_kwargs={'score_threshold': 0.8}
        )

        # Only get the single most similar document from the dataset
        docsearch.as_retriever(search_kwargs={'k': 1})

        # Use a filter to only retrieve documents from a specific paper
        docsearch.as_retriever(
            search_kwargs={'filter': {'paper_title':'GPT-4 Technical Report'}}
        )

  async asearch(query: str, search_type: str, **kwargs: Any) → List[Document]

    Return docs most similar to query using specified search type.

    Parameters

      - query (str) –

      - search_type (str) –

      - kwargs (Any) –

    Return type

      List[Document]

  async asimilarity_search(query: str, k: int = 4, **kwargs: Any) → List[Document]

    Return docs most similar to query.

    Parameters

      - query (str) –

      - k (int) –

      - kwargs (Any) –

    Return type

      List[Document]

  async asimilarity_search_by_vector(embedding: List[float], k: int = 4, **kwargs: Any) → List[Document]

    Return docs most similar to embedding vector.

    Parameters

      - embedding (List[float]) –

      - k (int) –

      - kwargs (Any) –

    Return type

      List[Document]

  async asimilarity_search_with_relevance_scores(query: str, k: int = 4, **kwargs: Any) → List[Tuple[Document, float]]

    Return docs and relevance scores in the range [0, 1], asynchronously.

    0 is dissimilar, 1 is most similar.

    Parameters

      - query (str) – input text

      - k (int) – Number of Documents to return. Defaults to 4.

      - **kwargs (Any) –

        kwargs to be passed to similarity search. Should include: score_threshold: Optional, a floating point value between 0 to 1 to

        > filter the resulting set of retrieved docs

    Returns

      List of Tuples of (doc, similarity_score)

    Return type

      List[Tuple[Document, float]]

  async asimilarity_search_with_score(*args: Any, **kwargs: Any) → List[Tuple[Document, float]]

    Run similarity search with distance asynchronously.

    Parameters

      - args (Any) –

      - kwargs (Any) –

    Return type

      List[Tuple[Document, float]]

  delete(ids: Optional[List[str]] = None, **kwargs: Any) → None

    Delete by vector IDs.

    Parameters

      - ids (Optional[List[str]]) – List of ids to delete.

      - kwargs (Any) –

    Return type

      None

  delete_collection() → None

    Delete the collection.

    Return type

      None

  encode_image(uri: str) → str

    Get base64 string from image URI.

    Parameters

      uri (str) –

    Return type

      str

  classmethod from_documents(documents: List[Document], embedding: Optional[Embeddings] = None, ids: Optional[List[str]] = None, collection_name: str = 'langchain', persist_directory: Optional[str] = None, client_settings: Optional[chromadb.config.Settings] = None, client: Optional[chromadb.Client] = None, collection_metadata: Optional[Dict] = None, **kwargs: Any) → Chroma

    Create a Chroma vectorstore from a list of documents.

    If a persist_directory is specified, the collection will be persisted there. Otherwise, the data will be ephemeral in-memory.

    Parameters

      - collection_name (str) – Name of the collection to create.

      - persist_directory (Optional[str]) – Directory to persist the collection.

      - ids (Optional[List[str]]) – List of document IDs. Defaults to None.

      - documents (List[Document]) – List of documents to add to the vectorstore.

      - embedding (Optional[Embeddings]) – Embedding function. Defaults to None.

      - client_settings (Optional[chromadb.config.Settings]) – Chroma client settings

      - collection_metadata (Optional[Dict]) – Collection configurations. Defaults to None.

      - client (Optional[chromadb.Client]) –

      - kwargs (Any) –

    Returns

      Chroma vectorstore.

    Return type

      Chroma

  classmethod from_texts(texts: List[str], embedding: Optional[Embeddings] = None, metadatas: Optional[List[dict]] = None, ids: Optional[List[str]] = None, collection_name: str = 'langchain', persist_directory: Optional[str] = None, client_settings: Optional[chromadb.config.Settings] = None, client: Optional[chromadb.Client] = None, collection_metadata: Optional[Dict] = None, **kwargs: Any) → Chroma

    Create a Chroma vectorstore from a raw documents.

    If a persist_directory is specified, the collection will be persisted there. Otherwise, the data will be ephemeral in-memory.

    Parameters

      - texts (List[str]) – List of texts to add to the collection.

      - collection_name (str) – Name of the collection to create.

      - persist_directory (Optional[str]) – Directory to persist the collection.

      - embedding (Optional[Embeddings]) – Embedding function. Defaults to None.

      - metadatas (Optional[List[dict]]) – List of metadatas. Defaults to None.

      - ids (Optional[List[str]]) – List of document IDs. Defaults to None.

      - client_settings (Optional[chromadb.config.Settings]) – Chroma client settings

      - collection_metadata (Optional[Dict]) – Collection configurations. Defaults to None.

      - client (Optional[chromadb.Client]) –

      - kwargs (Any) –

    Returns

      Chroma vectorstore.

    Return type

      Chroma

  get(ids: Optional[OneOrMany[ID]] = None, where: Optional[Where] = None, limit: Optional[int] = None, offset: Optional[int] = None, where_document: Optional[WhereDocument] = None, include: Optional[List[str]] = None) → Dict[str, Any]

    Gets the collection.

    Parameters

      - ids (Optional[OneOrMany[ID]]) – The ids of the embeddings to get. Optional.

      - where (Optional[Where]) – A Where type dict used to filter results by. E.g. {“color” : “red”, “price”: 4.20}. Optional.

      - limit (Optional[int]) – The number of documents to return. Optional.

      - offset (Optional[int]) – The offset to start returning results from. Useful for paging results with limit. Optional.

      - where_document (Optional[WhereDocument]) – A WhereDocument type dict used to filter by the documents. E.g. {$contains: “hello”}. Optional.

      - include (Optional[List[str]]) – A list of what to include in the results. Can contain “embeddings”, “metadatas”, “documents”. Ids are always included. Defaults to [“metadatas”, “documents”]. Optional.

    Return type

      Dict[str, Any]

  max_marginal_relevance_search(query: str, k: int = 4, fetch_k: int = 20, lambda_mult: float = 0.5, filter: Optional[Dict[str, str]] = None, where_document: Optional[Dict[str, str]] = None, **kwargs: Any) → List[Document]

    Return docs selected using the maximal marginal relevance. Maximal marginal relevance optimizes for similarity to query AND diversity among selected documents.

    Parameters

      - query (str) – Text to look up documents similar to.

      - k (int) – Number of Documents to return. Defaults to 4.

      - fetch_k (int) – Number of Documents to fetch to pass to MMR algorithm.

      - lambda_mult (float) – Number between 0 and 1 that determines the degree of diversity among the results with 0 corresponding to maximum diversity and 1 to minimum diversity. Defaults to 0.5.

      - filter (Optional[Dict[str, str]]) – Filter by metadata. Defaults to None.

      - where_document (Optional[Dict[str, str]]) –

      - kwargs (Any) –

    Returns

      List of Documents selected by maximal marginal relevance.

    Return type

      List[Document]

  max_marginal_relevance_search_by_vector(embedding: List[float], k: int = 4, fetch_k: int = 20, lambda_mult: float = 0.5, filter: Optional[Dict[str, str]] = None, where_document: Optional[Dict[str, str]] = None, **kwargs: Any) → List[Document]

    Return docs selected using the maximal marginal relevance. Maximal marginal relevance optimizes for similarity to query AND diversity among selected documents.

    Parameters

      - embedding (List[float]) – Embedding to look up documents similar to.

      - k (int) – Number of Documents to return. Defaults to 4.

      - fetch_k (int) – Number of Documents to fetch to pass to MMR algorithm.

      - lambda_mult (float) – Number between 0 and 1 that determines the degree of diversity among the results with 0 corresponding to maximum diversity and 1 to minimum diversity. Defaults to 0.5.

      - filter (Optional[Dict[str, str]]) – Filter by metadata. Defaults to None.

      - where_document (Optional[Dict[str, str]]) –

      - kwargs (Any) –

    Returns

      List of Documents selected by maximal marginal relevance.

    Return type

      List[Document]

  persist() → None

    [Deprecated] Persist the collection.

    This can be used to explicitly persist the data to disk. It will also be called automatically when the object is destroyed.

    Since Chroma 0.4.x the manual persistence method is no longer supported as docs are automatically persisted.

    Notes

    Deprecated since version langchain-community==0.1.17: Since Chroma 0.4.x the manual persistence method is no longer supported as docs are automatically persisted.

    Return type

      None

  search(query: str, search_type: str, **kwargs: Any) → List[Document]

    Return docs most similar to query using specified search type.

    Parameters

      - query (str) –

      - search_type (str) –

      - kwargs (Any) –

    Return type

      List[Document]

  similarity_search(query: str, k: int = 4, filter: Optional[Dict[str, str]] = None, **kwargs: Any) → List[Document]

    Run similarity search with Chroma.

    Parameters

      - query (str) – Query text to search for.

      - k (int) – Number of results to return. Defaults to 4.

      - filter (Optional[Dict[str, str]]) – Filter by metadata. Defaults to None.

      - kwargs (Any) –

    Returns

      List of documents most similar to the query text.

    Return type

      List[Document]

  similarity_search_by_vector(embedding: List[float], k: int = 4, filter: Optional[Dict[str, str]] = None, where_document: Optional[Dict[str, str]] = None, **kwargs: Any) → List[Document]

    Return docs most similar to embedding vector. :param embedding: Embedding to look up documents similar to. :type embedding: List[float] :param k: Number of Documents to return. Defaults to 4. :type k: int :param filter: Filter by metadata. Defaults to None. :type filter: Optional[Dict[str, str]]

    Returns

      List of Documents most similar to the query vector.

    Parameters

      - embedding (List[float]) –

      - k (int) –

      - filter (Optional[Dict[str, str]]) –

      - where_document (Optional[Dict[str, str]]) –

      - kwargs (Any) –

    Return type

      List[Document]

  similarity_search_by_vector_with_relevance_scores(embedding: List[float], k: int = 4, filter: Optional[Dict[str, str]] = None, where_document: Optional[Dict[str, str]] = None, **kwargs: Any) → List[Tuple[Document, float]]

    Return docs most similar to embedding vector and similarity score.

    Parameters

      - embedding (List[float]) – Embedding to look up documents similar to.

      - k (int) – Number of Documents to return. Defaults to 4.

      - filter (Optional[Dict[str, str]]) – Filter by metadata. Defaults to None.

      - where_document (Optional[Dict[str, str]]) –

      - kwargs (Any) –

    Returns

      List of documents most similar to the query text and cosine distance in float for each. Lower score represents more similarity.

    Return type

      List[Tuple[Document, float]]

  similarity_search_with_relevance_scores(query: str, k: int = 4, **kwargs: Any) → List[Tuple[Document, float]]

    Return docs and relevance scores in the range [0, 1].

    0 is dissimilar, 1 is most similar.

    Parameters

      - query (str) – input text

      - k (int) – Number of Documents to return. Defaults to 4.

      - **kwargs (Any) –

        kwargs to be passed to similarity search. Should include: score_threshold: Optional, a floating point value between 0 to 1 to

        > filter the resulting set of retrieved docs

    Returns

      List of Tuples of (doc, similarity_score)

    Return type

      List[Tuple[Document, float]]

  similarity_search_with_score(query: str, k: int = 4, filter: Optional[Dict[str, str]] = None, where_document: Optional[Dict[str, str]] = None, **kwargs: Any) → List[Tuple[Document, float]]

    Run similarity search with Chroma with distance.

    Parameters

      - query (str) – Query text to search for.

      - k (int) – Number of results to return. Defaults to 4.

      - filter (Optional[Dict[str, str]]) – Filter by metadata. Defaults to None.

      - where_document (Optional[Dict[str, str]]) –

      - kwargs (Any) –

    Returns

      List of documents most similar to the query text and cosine distance in float for each. Lower score represents more similarity.

    Return type

      List[Tuple[Document, float]]

  update_document(document_id: str, document: Document) → None

    Update a document in the collection.

    Parameters

      - document_id (str) – ID of the document to update.

      - document (Document) – Document to update.

    Return type

      None

  update_documents(ids: List[str], documents: List[Document]) → None

    Update a document in the collection.

    Parameters

      - ids (List[str]) – List of ids of the document to update.

      - documents (List[Document]) – List of documents to update.

    Return type

      None

# Examples using Chroma

- Chroma

- Retrievers

- You need the dgml-utils package to use the DocugamiLoader (run pip install directly without “poetry run” if you are not using poetry)