`langchain-chroma`¶

Modules:

Name	Description
`vectorstores`	This is the langchain_chroma.vectorstores module.

Classes:

Name	Description
`Chroma`	Chroma vector store integration.

Chroma ¶

Bases: VectorStore

Chroma vector store integration.

Setup

Install chromadb, langchain-chroma packages:

.. code-block:: bash

pip install -qU chromadb langchain-chroma

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

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

Instantiate

.. code-block:: python

from langchain_chroma import Chroma
from langchain_openai import OpenAIEmbeddings

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

Add Documents

.. code-block:: 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

.. code-block:: python

updated_document = Document(
    page_content="qux",
    metadata={"bar": "baz"},
)

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

Delete Documents

.. code-block:: python

vector_store.delete(ids=["3"])

Search

.. code-block:: python

results = vector_store.similarity_search(query="thud", k=1)
for doc in results:
    print(f"* {doc.page_content} [{doc.metadata}]")

.. code-block:: python

*thud[{"baz": "bar"}]

Search with filter

.. code-block:: python

results = vector_store.similarity_search(
    query="thud", k=1, filter={"baz": "bar"}
)
for doc in results:
    print(f"* {doc.page_content} [{doc.metadata}]")

.. code-block:: python

*foo[{"baz": "bar"}]

Search with score

.. code-block:: python

results = vector_store.similarity_search_with_score(query="qux", k=1)
for doc, score in results:
    print(f"* [SIM={score:3f}] {doc.page_content} [{doc.metadata}]")

.. code-block:: python

* [SIM=0.000000] qux [{'bar': 'baz', 'baz': 'bar'}]

Async

.. code-block:: python

# add documents
# await vector_store.aadd_documents(documents=documents, ids=ids)

# delete documents
# await vector_store.adelete(ids=["3"])

# search
# results = vector_store.asimilarity_search(query="thud",k=1)

# search with score
results = await vector_store.asimilarity_search_with_score(query="qux", k=1)
for doc, score in results:
    print(f"* [SIM={score:3f}] {doc.page_content} [{doc.metadata}]")

.. code-block:: python

* [SIM=0.335463] foo [{'baz': 'bar'}]

Use as Retriever

.. code-block:: python

retriever = vector_store.as_retriever(
    search_type="mmr",
    search_kwargs={"k": 1, "fetch_k": 2, "lambda_mult": 0.5},
)
retriever.invoke("thud")

.. code-block:: python

[Document(metadata={"baz": "bar"}, page_content="thud")]

Methods:

Name	Description
`aget_by_ids`	Async get documents by their IDs.
`adelete`	Async delete by vector ID or other criteria.
`aadd_texts`	Async run more texts through the embeddings and add to the vectorstore.
`add_documents`	Add or update documents in the vectorstore.
`aadd_documents`	Async run more documents through the embeddings and add to the vectorstore.
`search`	Return docs most similar to query using a specified search type.
`asearch`	Async return docs most similar to query using a specified search type.
`asimilarity_search_with_score`	Async run similarity search with distance.
`similarity_search_with_relevance_scores`	Return docs and relevance scores in the range [0, 1].
`asimilarity_search_with_relevance_scores`	Async return docs and relevance scores in the range [0, 1].
`asimilarity_search`	Async return docs most similar to query.
`asimilarity_search_by_vector`	Async return docs most similar to embedding vector.
`amax_marginal_relevance_search`	Async return docs selected using the maximal marginal relevance.
`amax_marginal_relevance_search_by_vector`	Async return docs selected using the maximal marginal relevance.
`afrom_documents`	Async return VectorStore initialized from documents and embeddings.
`afrom_texts`	Async return VectorStore initialized from texts and embeddings.
`as_retriever`	Return VectorStoreRetriever initialized from this VectorStore.
`__init__`	Initialize with a Chroma client.
`encode_image`	Get base64 string from image URI.
`fork`	Fork this vector store.
`add_images`	Run more images through the embeddings and add to the vectorstore.
`add_texts`	Run more texts through the embeddings and add to the vectorstore.
`similarity_search`	Run similarity search with Chroma.
`similarity_search_by_vector`	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_score`	Run similarity search with Chroma with distance.
`similarity_search_with_vectors`	Run similarity search with Chroma with vectors.
`similarity_search_by_image`	Search for similar images based on the given image URI.
`similarity_search_by_image_with_relevance_score`	Search for similar images based on the given image URI.
`max_marginal_relevance_search_by_vector`	Return docs selected using the maximal marginal relevance.
`max_marginal_relevance_search`	Return docs selected using the maximal marginal relevance.
`delete_collection`	Delete the collection.
`reset_collection`	Resets the collection.
`get`	Gets the collection.
`get_by_ids`	Get documents by their IDs.
`update_document`	Update a document in the collection.
`update_documents`	Update a document in the collection.
`from_texts`	Create a Chroma vectorstore from a raw documents.
`from_documents`	Create a Chroma vectorstore from a list of documents.
`delete`	Delete by vector IDs.

Attributes:

Name	Type	Description
`embeddings`	`Optional[Embeddings]`	Access the query embedding object.

embeddings `property` ¶

embeddings: Optional[Embeddings]

Access the query embedding object.

aget_by_ids `async` ¶

aget_by_ids(ids: Sequence[str]) -> list[Document]

Async get documents by their IDs.

The returned documents are expected to have the ID field set to the ID of the document in the vector store.

Fewer documents may be returned than requested if some IDs are not found or if there are duplicated IDs.

Users should not assume that the order of the returned documents matches the order of the input IDs. Instead, users should rely on the ID field of the returned documents.

This method should NOT raise exceptions if no documents are found for some IDs.

Parameters:

Name	Type	Description	Default
`ids`	`Sequence[str]`	List of ids to retrieve.	required

Returns:

Type	Description
`list[Document]`	List of Documents.

Added in version 0.2.11

adelete `async` ¶

adelete(
    ids: list[str] | None = None, **kwargs: Any
) -> bool | None

Async delete by vector ID or other criteria.

Parameters:

Name	Type	Description	Default
`ids`	`list[str] \| None`	List of ids to delete. If None, delete all. Default is None.	`None`
`**kwargs`	`Any`	Other keyword arguments that subclasses might use.	`{}`

Returns:

Type	Description
`bool \| None`	Optional[bool]: True if deletion is successful,
`bool \| None`	False otherwise, None if not implemented.

aadd_texts `async` ¶

aadd_texts(
    texts: Iterable[str],
    metadatas: list[dict] | None = None,
    *,
    ids: list[str] | None = None,
    **kwargs: Any
) -> list[str]

Async run more texts through the embeddings and add to the vectorstore.

Parameters:

Name	Type	Description	Default
`texts`	`Iterable[str]`	Iterable of strings to add to the vectorstore.	required
`metadatas`	`list[dict] \| None`	Optional list of metadatas associated with the texts. Default is None.	`None`
`ids`	`list[str] \| None`	Optional list	`None`
`**kwargs`	`Any`	vectorstore specific parameters.	`{}`

Returns:

Type	Description
`list[str]`	List of ids from adding the texts into the vectorstore.

Raises:

Type	Description
`ValueError`	If the number of metadatas does not match the number of texts.
`ValueError`	If the number of ids does not match the number of texts.

add_documents ¶

add_documents(
    documents: list[Document], **kwargs: Any
) -> list[str]

Add or update documents in the vectorstore.

Parameters:

Name	Type	Description	Default
`documents`	`list[Document]`	Documents to add to the vectorstore.	required
`kwargs`	`Any`	Additional keyword arguments. if kwargs contains ids and documents contain ids, the ids in the kwargs will receive precedence.	`{}`

Returns:

Type	Description
`list[str]`	List of IDs of the added texts.

aadd_documents `async` ¶

aadd_documents(
    documents: list[Document], **kwargs: Any
) -> list[str]

Async run more documents through the embeddings and add to the vectorstore.

Parameters:

Name	Type	Description	Default
`documents`	`list[Document]`	Documents to add to the vectorstore.	required
`kwargs`	`Any`	Additional keyword arguments.	`{}`

Returns:

Type	Description
`list[str]`	List of IDs of the added texts.

search ¶

search(
    query: str, search_type: str, **kwargs: Any
) -> list[Document]

Return docs most similar to query using a specified search type.

Parameters:

Name	Type	Description	Default
`query`	`str`	Input text	required
`search_type`	`str`	Type of search to perform. Can be "similarity", "mmr", or "similarity_score_threshold".	required
`**kwargs`	`Any`	Arguments to pass to the search method.	`{}`

Returns:

Type	Description
`list[Document]`	List of Documents most similar to the query.

Raises:

Type	Description
`ValueError`	If search_type is not one of "similarity", "mmr", or "similarity_score_threshold".

asearch `async` ¶

asearch(
    query: str, search_type: str, **kwargs: Any
) -> list[Document]

Async return docs most similar to query using a specified search type.

Parameters:

Name	Type	Description	Default
`query`	`str`	Input text.	required
`search_type`	`str`	Type of search to perform. Can be "similarity", "mmr", or "similarity_score_threshold".	required
`**kwargs`	`Any`	Arguments to pass to the search method.	`{}`

Returns:

Type	Description
`list[Document]`	List of Documents most similar to the query.

Raises:

Type	Description
`ValueError`	If search_type is not one of "similarity", "mmr", or "similarity_score_threshold".

asimilarity_search_with_score `async` ¶

asimilarity_search_with_score(
    *args: Any, **kwargs: Any
) -> list[tuple[Document, float]]

Async run similarity search with distance.

Parameters:

Name	Type	Description	Default
`*args`	`Any`	Arguments to pass to the search method.	`()`
`**kwargs`	`Any`	Arguments to pass to the search method.	`{}`

Returns:

Type	Description
`list[tuple[Document, float]]`	List of Tuples of (doc, similarity_score).

similarity_search_with_relevance_scores ¶

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:

Name	Type	Description	Default
`query`	`str`	Input text.	required
`k`	`int`	Number of Documents to return. Defaults to 4.	`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:

Type	Description
`list[tuple[Document, float]]`	List of Tuples of (doc, similarity_score).

asimilarity_search_with_relevance_scores `async` ¶

asimilarity_search_with_relevance_scores(
    query: str, k: int = 4, **kwargs: Any
) -> list[tuple[Document, float]]

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

0 is dissimilar, 1 is most similar.

Parameters:

Name	Type	Description	Default
`query`	`str`	Input text.	required
`k`	`int`	Number of Documents to return. Defaults to 4.	`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:

Type	Description
`list[tuple[Document, float]]`	List of Tuples of (doc, similarity_score)

asimilarity_search `async` ¶

asimilarity_search(
    query: str, k: int = 4, **kwargs: Any
) -> list[Document]

Async return docs most similar to query.

Parameters:

Name	Type	Description	Default
`query`	`str`	Input text.	required
`k`	`int`	Number of Documents to return. Defaults to 4.	`4`
`**kwargs`	`Any`	Arguments to pass to the search method.	`{}`

Returns:

Type	Description
`list[Document]`	List of Documents most similar to the query.

asimilarity_search_by_vector `async` ¶

asimilarity_search_by_vector(
    embedding: list[float], k: int = 4, **kwargs: Any
) -> list[Document]

Async return docs most similar to embedding vector.

Parameters:

Name	Type	Description	Default
`embedding`	`list[float]`	Embedding to look up documents similar to.	required
`k`	`int`	Number of Documents to return. Defaults to 4.	`4`
`**kwargs`	`Any`	Arguments to pass to the search method.	`{}`

Returns:

Type	Description
`list[Document]`	List of Documents most similar to the query vector.

amax_marginal_relevance_search `async` ¶

amax_marginal_relevance_search(
    query: str,
    k: int = 4,
    fetch_k: int = 20,
    lambda_mult: float = 0.5,
    **kwargs: Any
) -> list[Document]

Async return docs selected using the maximal marginal relevance.

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

Parameters:

Name	Type	Description	Default
`query`	`str`	Text to look up documents similar to.	required
`k`	`int`	Number of Documents to return. Defaults to 4.	`4`
`fetch_k`	`int`	Number of Documents to fetch to pass to MMR algorithm. Default is 20.	`20`
`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.	`0.5`
`**kwargs`	`Any`	Arguments to pass to the search method.	`{}`

Returns:

Type	Description
`list[Document]`	List of Documents selected by maximal marginal relevance.

amax_marginal_relevance_search_by_vector `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]

Async return docs selected using the maximal marginal relevance.

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

Parameters:

Name	Type	Description	Default
`embedding`	`list[float]`	Embedding to look up documents similar to.	required
`k`	`int`	Number of Documents to return. Defaults to 4.	`4`
`fetch_k`	`int`	Number of Documents to fetch to pass to MMR algorithm. Default is 20.	`20`
`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.	`0.5`
`**kwargs`	`Any`	Arguments to pass to the search method.	`{}`

Returns:

Type	Description
`list[Document]`	List of Documents selected by maximal marginal relevance.

afrom_documents `async` `classmethod` ¶

afrom_documents(
    documents: list[Document],
    embedding: Embeddings,
    **kwargs: Any
) -> Self

Async return VectorStore initialized from documents and embeddings.

Parameters:

Name	Type	Description	Default
`documents`	`list[Document]`	List of Documents to add to the vectorstore.	required
`embedding`	`Embeddings`	Embedding function to use.	required
`kwargs`	`Any`	Additional keyword arguments.	`{}`

Returns:

Name	Type	Description
`VectorStore`	`Self`	VectorStore initialized from documents and embeddings.

afrom_texts `async` `classmethod` ¶

afrom_texts(
    texts: list[str],
    embedding: Embeddings,
    metadatas: list[dict] | None = None,
    *,
    ids: list[str] | None = None,
    **kwargs: Any
) -> Self

Async return VectorStore initialized from texts and embeddings.

Parameters:

Name	Type	Description	Default
`texts`	`list[str]`	Texts to add to the vectorstore.	required
`embedding`	`Embeddings`	Embedding function to use.	required
`metadatas`	`list[dict] \| None`	Optional list of metadatas associated with the texts. Default is None.	`None`
`ids`	`list[str] \| None`	Optional list of IDs associated with the texts.	`None`
`kwargs`	`Any`	Additional keyword arguments.	`{}`

Returns:

Name	Type	Description
`VectorStore`	`Self`	VectorStore initialized from texts and embeddings.

as_retriever ¶

as_retriever(**kwargs: Any) -> VectorStoreRetriever

Return VectorStoreRetriever initialized from this VectorStore.

Parameters:

Name	Type	Description	Default
`**kwargs`	`Any`	Keyword arguments to pass to the search function. Can include: 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:

Name	Type	Description
`VectorStoreRetriever`	`VectorStoreRetriever`	Retriever class for VectorStore.

Examples:

.. code-block:: python

# 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"}}
)

init ¶

__init__(
    collection_name: str = _LANGCHAIN_DEFAULT_COLLECTION_NAME,
    embedding_function: Optional[Embeddings] = None,
    persist_directory: Optional[str] = None,
    host: Optional[str] = None,
    port: Optional[int] = None,
    headers: Optional[dict[str, str]] = None,
    chroma_cloud_api_key: Optional[str] = None,
    tenant: Optional[str] = None,
    database: Optional[str] = None,
    client_settings: Optional[Settings] = None,
    collection_metadata: Optional[dict] = None,
    collection_configuration: Optional[
        CreateCollectionConfiguration
    ] = None,
    client: Optional[ClientAPI] = None,
    relevance_score_fn: Optional[
        Callable[[float], float]
    ] = None,
    create_collection_if_not_exists: Optional[bool] = True,
    *,
    ssl: bool = False
) -> None

Initialize with a Chroma client.

Parameters:

Name	Type	Description	Default
`collection_name`	`str`	Name of the collection to create.	`_LANGCHAIN_DEFAULT_COLLECTION_NAME`
`embedding_function`	`Optional[Embeddings]`	Embedding class object. Used to embed texts.	`None`
`persist_directory`	`Optional[str]`	Directory to persist the collection.	`None`
`host`	`Optional[str]`	Hostname of a deployed Chroma server.	`None`
`port`	`Optional[int]`	Connection port for a deployed Chroma server. Default is 8000.	`None`
`ssl`	`bool`	Whether to establish an SSL connection with a deployed Chroma server. Default is False.	`False`
`headers`	`Optional[dict[str, str]]`	HTTP headers to send to a deployed Chroma server.	`None`
`chroma_cloud_api_key`	`Optional[str]`	Chroma Cloud API key.	`None`
`tenant`	`Optional[str]`	Tenant ID. Required for Chroma Cloud connections. Default is 'default_tenant' for local Chroma servers.	`None`
`database`	`Optional[str]`	Database name. Required for Chroma Cloud connections. Default is 'default_database'.	`None`
`client_settings`	`Optional[Settings]`	Chroma client settings	`None`
`collection_metadata`	`Optional[dict]`	Collection configurations.	`None`
`collection_configuration`	`Optional[CreateCollectionConfiguration]`	Index configuration for the collection. Defaults to None.	`None`
`client`	`Optional[ClientAPI]`	Chroma client. Documentation: https://docs.trychroma.com/reference/python/client	`None`
`relevance_score_fn`	`Optional[Callable[[float], float]]`	Function to calculate relevance score from distance. Used only in `similarity_search_with_relevance_scores`	`None`
`create_collection_if_not_exists`	`Optional[bool]`	Whether to create collection if it doesn't exist. Defaults to True.	`True`

__ensure_collection ¶

__ensure_collection() -> None

Ensure that the collection exists or create it.

__query_collection ¶

__query_collection(
    query_texts: Optional[list[str]] = None,
    query_embeddings: Optional[list[list[float]]] = None,
    n_results: int = 4,
    where: Optional[dict[str, str]] = None,
    where_document: Optional[dict[str, str]] = None,
    **kwargs: Any
) -> Union[list[Document], QueryResult]

Query the chroma collection.

Parameters:

Name	Type	Description	Default
`query_texts`	`Optional[list[str]]`	List of query texts.	`None`
`query_embeddings`	`Optional[list[list[float]]]`	List of query embeddings.	`None`
`n_results`	`int`	Number of results to return. Defaults to 4.	`4`
`where`	`Optional[dict[str, str]]`	dict used to filter results by metadata. E.g. {"color" : "red"}.	`None`
`where_document`	`Optional[dict[str, str]]`	dict used to filter by the document contents. E.g. {"$contains": "hello"}.	`None`
`kwargs`	`Any`	Additional keyword arguments to pass to Chroma collection query.	`{}`

Returns:

Type	Description
`Union[list[Document], QueryResult]`	List of `n_results` nearest neighbor embeddings for provided
`Union[list[Document], QueryResult]`	query_embeddings or query_texts.

See more: https://docs.trychroma.com/reference/py-collection#query

encode_image `staticmethod` ¶

encode_image(uri: str) -> str

Get base64 string from image URI.

fork ¶

fork(new_name: str) -> Chroma

Fork this vector store.

Parameters:

Name	Type	Description	Default
`new_name`	`str`	New name for the forked store.	required

Returns:

Type	Description
`Chroma`	A new Chroma store forked from this vector store.

add_images ¶

add_images(
    uris: list[str],
    metadatas: Optional[list[dict]] = None,
    ids: Optional[list[str]] = None,
) -> list[str]

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

Parameters:

Name	Type	Description	Default
`uris`	`list[str]`	File path to the image.	required
`metadatas`	`Optional[list[dict]]`	Optional list of metadatas. When querying, you can filter on this metadata.	`None`
`ids`	`Optional[list[str]]`	Optional list of IDs. (Items without IDs will be assigned UUIDs)	`None`

Returns:

Type	Description
`list[str]`	List of IDs of the added images.

Raises:

Type	Description
`ValueError`	When metadata is incorrect.

add_texts ¶

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:

Name	Type	Description	Default
`texts`	`Iterable[str]`	Texts to add to the vectorstore.	required
`metadatas`	`Optional[list[dict]]`	Optional list of metadatas. When querying, you can filter on this metadata.	`None`
`ids`	`Optional[list[str]]`	Optional list of IDs. (Items without IDs will be assigned UUIDs)	`None`
`kwargs`	`Any`	Additional keyword arguments.	`{}`

Returns:

Type	Description
`list[str]`	List of IDs of the added texts.

Raises:

Type	Description
`ValueError`	When metadata is incorrect.

similarity_search ¶

similarity_search(
    query: str,
    k: int = DEFAULT_K,
    filter: Optional[dict[str, str]] = None,
    **kwargs: Any
) -> list[Document]

Run similarity search with Chroma.

Parameters:

Name	Type	Description	Default
`query`	`str`	Query text to search for.	required
`k`	`int`	Number of results to return. Defaults to 4.	`DEFAULT_K`
`filter`	`Optional[dict[str, str]]`	Filter by metadata. Defaults to None.	`None`
`kwargs`	`Any`	Additional keyword arguments to pass to Chroma collection query.	`{}`

Returns:

Type	Description
`list[Document]`	List of documents most similar to the query text.

similarity_search_by_vector ¶

similarity_search_by_vector(
    embedding: list[float],
    k: int = DEFAULT_K,
    filter: Optional[dict[str, str]] = None,
    where_document: Optional[dict[str, str]] = None,
    **kwargs: Any
) -> list[Document]

Return docs most similar to embedding vector.

Parameters:

Name	Type	Description	Default
`embedding`	`list[float]`	Embedding to look up documents similar to.	required
`k`	`int`	Number of Documents to return. Defaults to 4.	`DEFAULT_K`
`filter`	`Optional[dict[str, str]]`	Filter by metadata. Defaults to None.	`None`
`where_document`	`Optional[dict[str, str]]`	dict used to filter by the document contents. E.g. {"$contains": "hello"}.	`None`
`kwargs`	`Any`	Additional keyword arguments to pass to Chroma collection query.	`{}`

Returns:

Type	Description
`list[Document]`	List of Documents most similar to the query vector.

similarity_search_by_vector_with_relevance_scores ¶

similarity_search_by_vector_with_relevance_scores(
    embedding: list[float],
    k: int = DEFAULT_K,
    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:

Name	Type	Description	Default
`embedding`	`List[float]`	Embedding to look up documents similar to.	required
`k`	`int`	Number of Documents to return. Defaults to 4.	`DEFAULT_K`
`filter`	`Optional[dict[str, str]]`	Filter by metadata. Defaults to None.	`None`
`where_document`	`Optional[dict[str, str]]`	dict used to filter by the documents. E.g. {"$contains": "hello"}.	`None`
`kwargs`	`Any`	Additional keyword arguments to pass to Chroma collection query.	`{}`

Returns:

Type	Description
`list[tuple[Document, float]]`	List of documents most similar to the query text and relevance score
`list[tuple[Document, float]]`	in float for each. Lower score represents more similarity.

similarity_search_with_score ¶

similarity_search_with_score(
    query: str,
    k: int = DEFAULT_K,
    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:

Name	Type	Description	Default
`query`	`str`	Query text to search for.	required
`k`	`int`	Number of results to return. Defaults to 4.	`DEFAULT_K`
`filter`	`Optional[dict[str, str]]`	Filter by metadata. Defaults to None.	`None`
`where_document`	`Optional[dict[str, str]]`	dict used to filter by document contents. E.g. {"$contains": "hello"}.	`None`
`kwargs`	`Any`	Additional keyword arguments to pass to Chroma collection query.	`{}`

Returns:

Type	Description
`list[tuple[Document, float]]`	List of documents most similar to the query text and
`list[tuple[Document, float]]`	distance in float for each. Lower score represents more similarity.

similarity_search_with_vectors ¶

similarity_search_with_vectors(
    query: str,
    k: int = DEFAULT_K,
    filter: Optional[dict[str, str]] = None,
    where_document: Optional[dict[str, str]] = None,
    **kwargs: Any
) -> list[tuple[Document, ndarray]]

Run similarity search with Chroma with vectors.

Parameters:

Name	Type	Description	Default
`query`	`str`	Query text to search for.	required
`k`	`int`	Number of results to return. Defaults to 4.	`DEFAULT_K`
`filter`	`Optional[dict[str, str]]`	Filter by metadata. Defaults to None.	`None`
`where_document`	`Optional[dict[str, str]]`	dict used to filter by the document contents. E.g. {"$contains": "hello"}.	`None`
`kwargs`	`Any`	Additional keyword arguments to pass to Chroma collection query.	`{}`

Returns:

Type	Description
`list[tuple[Document, ndarray]]`	List of documents most similar to the query text and
`list[tuple[Document, ndarray]]`	embedding vectors for each.

similarity_search_by_image ¶

similarity_search_by_image(
    uri: str,
    k: int = DEFAULT_K,
    filter: Optional[dict[str, str]] = None,
    **kwargs: Any
) -> list[Document]

Search for similar images based on the given image URI.

Parameters:

Name	Type	Description	Default
`uri`	`str`	URI of the image to search for.	required
`k`	`int`	Number of results to return. Defaults to `DEFAULT_K`.	`DEFAULT_K`
`filter`	`Optional[Dict[str, str]]`	Filter by metadata.	`None`
`**kwargs`	`Any`	Additional arguments to pass to function.	`{}`

Returns:

Type	Description
`list[Document]`	List of Images most similar to the provided image.
`list[Document]`	Each element in list is a LangChain Document Object.
`list[Document]`	The page content is b64 encoded image, metadata is default or
`list[Document]`	as defined by user.

Raises:

Type	Description
`ValueError`	If the embedding function does not support image embeddings.

similarity_search_by_image_with_relevance_score ¶

similarity_search_by_image_with_relevance_score(
    uri: str,
    k: int = DEFAULT_K,
    filter: Optional[dict[str, str]] = None,
    **kwargs: Any
) -> list[tuple[Document, float]]

Search for similar images based on the given image URI.

Parameters:

Name	Type	Description	Default
`uri`	`str`	URI of the image to search for.	required
`k`	`int`	Number of results to return.	`DEFAULT_K`
`filter`	`Optional[Dict[str, str]]`	Filter by metadata.	`None`
`**kwargs`	`Any`	Additional arguments to pass to function.	`{}`

Returns:

Type	Description
`list[tuple[Document, float]]`	List[Tuple[Document, float]]: List of tuples containing documents similar
`list[tuple[Document, float]]`	to the query image and their similarity scores.
`list[tuple[Document, float]]`	0^th element in each tuple is a LangChain Document Object.
`list[tuple[Document, float]]`	The page content is b64 encoded img, metadata is default or defined by user.

Raises:

Type	Description
`ValueError`	If the embedding function does not support image embeddings.

max_marginal_relevance_search_by_vector ¶

max_marginal_relevance_search_by_vector(
    embedding: list[float],
    k: int = DEFAULT_K,
    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:

Name	Type	Description	Default
`embedding`	`list[float]`	Embedding to look up documents similar to.	required
`k`	`int`	Number of Documents to return. Defaults to 4.	`DEFAULT_K`
`fetch_k`	`int`	Number of Documents to fetch to pass to MMR algorithm. Defaults to 20.	`20`
`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.	`0.5`
`filter`	`Optional[dict[str, str]]`	Filter by metadata. Defaults to None.	`None`
`where_document`	`Optional[dict[str, str]]`	dict used to filter by the document contents. E.g. {"$contains": "hello"}.	`None`
`kwargs`	`Any`	Additional keyword arguments to pass to Chroma collection query.	`{}`

Returns:

Type	Description
`list[Document]`	List of Documents selected by maximal marginal relevance.

max_marginal_relevance_search ¶

max_marginal_relevance_search(
    query: str,
    k: int = DEFAULT_K,
    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:

Name	Type	Description	Default
`query`	`str`	Text to look up documents similar to.	required
`k`	`int`	Number of Documents to return. Defaults to 4.	`DEFAULT_K`
`fetch_k`	`int`	Number of Documents to fetch to pass to MMR algorithm.	`20`
`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.	`0.5`
`filter`	`Optional[dict[str, str]]`	Filter by metadata. Defaults to None.	`None`
`where_document`	`Optional[dict[str, str]]`	dict used to filter by the document contents. E.g. {"$contains": "hello"}.	`None`
`kwargs`	`Any`	Additional keyword arguments to pass to Chroma collection query.	`{}`

Returns:

Type	Description
`list[Document]`	List of Documents selected by maximal marginal relevance.

Raises:

Type	Description
`ValueError`	If the embedding function is not provided.

delete_collection ¶

delete_collection() -> None

Delete the collection.

reset_collection ¶

reset_collection() -> None

Resets the collection.

Resets the collection by deleting the collection and recreating an empty one.

get ¶

get(
    ids: Optional[Union[str, list[str]]] = 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:

Name	Type	Description	Default
`ids`	`Optional[Union[str, list[str]]]`	The ids of the embeddings to get. Optional.	`None`
`where`	`Optional[Where]`	A Where type dict used to filter results by. E.g. `{"$and": [{"color": "red"}, {"price": 4.20}]}` Optional.	`None`
`limit`	`Optional[int]`	The number of documents to return. Optional.	`None`
`offset`	`Optional[int]`	The offset to start returning results from. Useful for paging results with limit. Optional.	`None`
`where_document`	`Optional[WhereDocument]`	A WhereDocument type dict used to filter by the documents. E.g. `{"$contains": "hello"}`. Optional.	`None`
`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.	`None`

Returns:

Type	Description
`dict[str, Any]`	A dict with the keys `"ids"`, `"embeddings"`, `"metadatas"`, `"documents"`.

get_by_ids ¶

get_by_ids(ids: Sequence[str]) -> list[Document]

Get documents by their IDs.

The returned documents are expected to have the ID field set to the ID of the document in the vector store.

Fewer documents may be returned than requested if some IDs are not found or if there are duplicated IDs.

Users should not assume that the order of the returned documents matches the order of the input IDs. Instead, users should rely on the ID field of the returned documents.

This method should NOT raise exceptions if no documents are found for some IDs.

Parameters:

Name	Type	Description	Default
`ids`	`Sequence[str]`	List of ids to retrieve.	required

Returns:

Type	Description
`list[Document]`	List of Documents.

Added in 0.2.1

update_document ¶

update_document(
    document_id: str, document: Document
) -> None

Update a document in the collection.

Parameters:

Name	Type	Description	Default
`document_id`	`str`	ID of the document to update.	required
`document`	`Document`	Document to update.	required

update_documents ¶

update_documents(
    ids: list[str], documents: list[Document]
) -> None

Update a document in the collection.

Parameters:

Name	Type	Description	Default
`ids`	`list[str]`	List of ids of the document to update.	required
`documents`	`list[Document]`	List of documents to update.	required

Raises:

Type	Description
`ValueError`	If the embedding function is not provided.

from_texts `classmethod` ¶

from_texts(
    texts: list[str],
    embedding: Optional[Embeddings] = None,
    metadatas: Optional[list[dict]] = None,
    ids: Optional[list[str]] = None,
    collection_name: str = _LANGCHAIN_DEFAULT_COLLECTION_NAME,
    persist_directory: Optional[str] = None,
    host: Optional[str] = None,
    port: Optional[int] = None,
    headers: Optional[dict[str, str]] = None,
    chroma_cloud_api_key: Optional[str] = None,
    tenant: Optional[str] = None,
    database: Optional[str] = None,
    client_settings: Optional[Settings] = None,
    client: Optional[ClientAPI] = None,
    collection_metadata: Optional[dict] = None,
    collection_configuration: Optional[
        CreateCollectionConfiguration
    ] = None,
    *,
    ssl: bool = False,
    **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:

Name	Type	Description	Default
`texts`	`list[str]`	List of texts to add to the collection.	required
`collection_name`	`str`	Name of the collection to create.	`_LANGCHAIN_DEFAULT_COLLECTION_NAME`
`persist_directory`	`Optional[str]`	Directory to persist the collection.	`None`
`host`	`Optional[str]`	Hostname of a deployed Chroma server.	`None`
`port`	`Optional[int]`	Connection port for a deployed Chroma server. Default is 8000.	`None`
`ssl`	`bool`	Whether to establish an SSL connection with a deployed Chroma server. Default is False.	`False`
`headers`	`Optional[dict[str, str]]`	HTTP headers to send to a deployed Chroma server.	`None`
`chroma_cloud_api_key`	`Optional[str]`	Chroma Cloud API key.	`None`
`tenant`	`Optional[str]`	Tenant ID. Required for Chroma Cloud connections. Default is 'default_tenant' for local Chroma servers.	`None`
`database`	`Optional[str]`	Database name. Required for Chroma Cloud connections. Default is 'default_database'.	`None`
`embedding`	`Optional[Embeddings]`	Embedding function. Defaults to None.	`None`
`metadatas`	`Optional[list[dict]]`	List of metadatas. Defaults to None.	`None`
`ids`	`Optional[list[str]]`	List of document IDs. Defaults to None.	`None`
`client_settings`	`Optional[Settings]`	Chroma client settings.	`None`
`client`	`Optional[ClientAPI]`	Chroma client. Documentation: https://docs.trychroma.com/reference/python/client	`None`
`collection_metadata`	`Optional[dict]`	Collection configurations. Defaults to None.	`None`
`collection_configuration`	`Optional[CreateCollectionConfiguration]`	Index configuration for the collection. Defaults to None.	`None`
`kwargs`	`Any`	Additional keyword arguments to initialize a Chroma client.	`{}`

Returns:

Name	Type	Description
`Chroma`	`Chroma`	Chroma vectorstore.

from_documents `classmethod` ¶

from_documents(
    documents: list[Document],
    embedding: Optional[Embeddings] = None,
    ids: Optional[list[str]] = None,
    collection_name: str = _LANGCHAIN_DEFAULT_COLLECTION_NAME,
    persist_directory: Optional[str] = None,
    host: Optional[str] = None,
    port: Optional[int] = None,
    headers: Optional[dict[str, str]] = None,
    chroma_cloud_api_key: Optional[str] = None,
    tenant: Optional[str] = None,
    database: Optional[str] = None,
    client_settings: Optional[Settings] = None,
    client: Optional[ClientAPI] = None,
    collection_metadata: Optional[dict] = None,
    collection_configuration: Optional[
        CreateCollectionConfiguration
    ] = None,
    *,
    ssl: bool = False,
    **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:

Name	Type	Description	Default
`collection_name`	`str`	Name of the collection to create.	`_LANGCHAIN_DEFAULT_COLLECTION_NAME`
`persist_directory`	`Optional[str]`	Directory to persist the collection.	`None`
`host`	`Optional[str]`	Hostname of a deployed Chroma server.	`None`
`port`	`Optional[int]`	Connection port for a deployed Chroma server. Default is 8000.	`None`
`ssl`	`bool`	Whether to establish an SSL connection with a deployed Chroma server. Default is False.	`False`
`headers`	`Optional[dict[str, str]]`	HTTP headers to send to a deployed Chroma server.	`None`
`chroma_cloud_api_key`	`Optional[str]`	Chroma Cloud API key.	`None`
`tenant`	`Optional[str]`	Tenant ID. Required for Chroma Cloud connections. Default is 'default_tenant' for local Chroma servers.	`None`
`database`	`Optional[str]`	Database name. Required for Chroma Cloud connections. Default is 'default_database'.	`None`
`ids`		List of document IDs. Defaults to None.	required
`documents`	`list[Document]`	List of documents to add to the vectorstore.	required
`embedding`	`Optional[Embeddings]`	Embedding function. Defaults to None.	`None`
`client_settings`	`Optional[Settings]`	Chroma client settings.	`None`
`client`	`Optional[ClientAPI]`	Chroma client. Documentation: https://docs.trychroma.com/reference/python/client	`None`
`collection_metadata`	`Optional[dict]`	Collection configurations. Defaults to None.	`None`
`collection_configuration`	`Optional[CreateCollectionConfiguration]`	Index configuration for the collection. Defaults to None.	`None`
`kwargs`	`Any`	Additional keyword arguments to initialize a Chroma client.	`{}`

Returns:

Name	Type	Description
`Chroma`	`Chroma`	Chroma vectorstore.

delete ¶

delete(
    ids: Optional[list[str]] = None, **kwargs: Any
) -> None

Delete by vector IDs.

Parameters:

Name	Type	Description	Default
`ids`	`Optional[list[str]]`	List of ids to delete.	`None`
`kwargs`	`Any`	Additional keyword arguments.	`{}`

langchain-chroma¶

Chroma ¶

embeddings property ¶

aget_by_ids async ¶

adelete async ¶

aadd_texts async ¶

add_documents ¶

aadd_documents async ¶

search ¶

asearch async ¶

asimilarity_search_with_score async ¶

similarity_search_with_relevance_scores ¶

asimilarity_search_with_relevance_scores async ¶

asimilarity_search async ¶

asimilarity_search_by_vector async ¶

amax_marginal_relevance_search async ¶

amax_marginal_relevance_search_by_vector async ¶

afrom_documents async classmethod ¶

afrom_texts async classmethod ¶

as_retriever ¶

__init__ ¶

__ensure_collection ¶

__query_collection ¶

encode_image staticmethod ¶

fork ¶

add_images ¶

add_texts ¶

similarity_search ¶

similarity_search_by_vector ¶

similarity_search_by_vector_with_relevance_scores ¶

similarity_search_with_score ¶

similarity_search_with_vectors ¶

similarity_search_by_image ¶

similarity_search_by_image_with_relevance_score ¶

max_marginal_relevance_search_by_vector ¶

max_marginal_relevance_search ¶

delete_collection ¶

reset_collection ¶

get ¶

get_by_ids ¶

update_document ¶

update_documents ¶

from_texts classmethod ¶

from_documents classmethod ¶

delete ¶

`langchain-chroma`¶

embeddings `property` ¶

aget_by_ids `async` ¶

adelete `async` ¶

aadd_texts `async` ¶

aadd_documents `async` ¶

asearch `async` ¶

asimilarity_search_with_score `async` ¶

asimilarity_search_with_relevance_scores `async` ¶

asimilarity_search `async` ¶

asimilarity_search_by_vector `async` ¶

amax_marginal_relevance_search `async` ¶

amax_marginal_relevance_search_by_vector `async` ¶

afrom_documents `async` `classmethod` ¶

afrom_texts `async` `classmethod` ¶

init ¶

encode_image `staticmethod` ¶

from_texts `classmethod` ¶

from_documents `classmethod` ¶