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# `langchain_chroma.vectorstores`.Chroma

class langchain_chroma.vectorstores.Chroma(collection_name: str = 'langchain', embedding_function: Optional[Embeddings] = None, persist_directory: Optional[str] = None, client_settings: Optional[Settings] = None, collection_metadata: Optional[Dict] = None, client: Optional[ClientAPI] = 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.
  `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.ClientAPI]) –

    - 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[Settings] = None, collection_metadata: Optional[Dict] = None, client: Optional[ClientAPI] = 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[Settings]) –

      - collection_metadata (Optional[Dict]) –

      - client (Optional[ClientAPI]) –

      - 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[Settings] = None, client: Optional[ClientAPI] = 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[ClientAPI]) –

      - 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[Settings] = None, client: Optional[ClientAPI] = 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[ClientAPI]) –

      - 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]

  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)