langchain.vectorstores.chroma.Chroma

class langchain.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)[source]

ChromaDB vector store.

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

Example

from langchain.vectorstores import Chroma
from langchain.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()

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.

__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[source]

Initialize with a Chroma client.

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.

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.

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.

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][source]

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

Parameters
  • images (List[List[float]]) – Images to add to the vectorstore.

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

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

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][source]

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.

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 – List of ids to delete.

  • **kwargs – 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.

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

Return VectorStore initialized from texts and embeddings.

Return docs selected using the maximal marginal relevance.

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.

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

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.

Return docs most similar to query.

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

Return docs most similar to embedding vector.

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 – input text

  • k – Number of Documents to return. Defaults to 4.

  • **kwargs

    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)

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

Run similarity search with distance asynchronously.

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

Delete by vector IDs.

Parameters

ids – List of ids to delete.

delete_collection() None[source]

Delete the collection.

encode_image(uri: str) str[source]

Get base64 string from image URI.

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[source]

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.

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[source]

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.

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][source]

Gets the collection.

Parameters
  • ids – The ids of the embeddings to get. Optional.

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

  • limit – The number of documents to return. Optional.

  • offset – The offset to start returning results from. Useful for paging results with limit. Optional.

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

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

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

Parameters
  • query – Text to look up documents similar to.

  • k – Number of Documents to return. Defaults to 4.

  • fetch_k – Number of Documents to fetch to pass to MMR algorithm.

  • lambda_mult – 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.

Returns

List of Documents selected by maximal marginal relevance.

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][source]

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

Parameters
  • embedding – Embedding to look up documents similar to.

  • k – Number of Documents to return. Defaults to 4.

  • fetch_k – Number of Documents to fetch to pass to MMR algorithm.

  • lambda_mult – 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.

Returns

List of Documents selected by maximal marginal relevance.

persist() None[source]

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.

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

Return docs most similar to query using specified search type.

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.

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][source]

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.

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]][source]

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.

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 – input text

  • k – Number of Documents to return. Defaults to 4.

  • **kwargs

    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)

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]][source]

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.

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[source]

Update a document in the collection.

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

  • document (Document) – Document to update.

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

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.

Examples using Chroma