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
aadd_documents(documents, **kwargs)
Async run more documents through the embeddings and add to the vectorstore.
aadd_texts(texts[, metadatas])
Async run more texts through the embeddings and add to the vectorstore.
add_documents(documents, **kwargs)
Add or update documents in 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])
Async delete by vector ID or other criteria.
afrom_documents(documents, embedding, **kwargs)
Async return VectorStore initialized from documents and embeddings.
afrom_texts(texts, embedding[, metadatas])
Async return VectorStore initialized from texts and embeddings.
aget_by_ids(ids, /)
Async get documents by their IDs.




    

amax_marginal_relevance_search(query[, k, ...])
Async return docs selected using the maximal marginal relevance.
amax_marginal_relevance_search_by_vector(...)
Async return docs selected using the maximal marginal relevance.
as_retriever(**kwargs)
Return VectorStoreRetriever initialized from this VectorStore.
asearch(query, search_type, **kwargs)
Async return docs most similar to query using a specified search type.
asimilarity_search(query[, k])
Async return docs most similar to query.
asimilarity_search_by_vector(embedding[, k])
Async return docs most similar to embedding vector.
asimilarity_search_with_relevance_scores(query)
Async return docs and relevance scores in the range [0, 1].
asimilarity_search_with_score(*args, **kwargs)
Async run similarity search with distance.
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.
get_by_ids(ids, /)
Get documents by their IDs.
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 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.
search(query, search_type, **kwargs)
Return docs most similar to query using a specified search type.
similarity_search(query[, k, filter])
Run similarity search with Chroma.
similarity_search_by_image(uri[, k, filter])
Search for similar images based on the given image URI.
similarity_search_by_image_with_relevance_score(uri)
Search for similar images based on the given image URI.
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.
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[source]¶
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
async aadd_documents(documents: List[Document], **kwargs: Any) → List[str]¶
Async run more documents through the embeddings and add to
the vectorstore.




    

Parameters
documents (List[Document]) – Documents to add to the vectorstore.
kwargs (Any) – Additional keyword arguments.
Returns
List of IDs of the added texts.
Raises
ValueError – If the number of IDs does not match the number of documents.
Return type
List[str]
async aadd_texts(texts: Iterable[str], metadatas: Optional[List[dict]] = None, **kwargs: Any) → List[str]¶
Async run more texts through the embeddings and add to the vectorstore.
Parameters
texts (Iterable[str]) – Iterable of strings to add to the vectorstore.
metadatas (Optional[List[dict]]) – Optional list of metadatas associated with the texts.
Default is None.
**kwargs (Any) – vectorstore specific parameters.
Returns
List of ids from adding the texts into the vectorstore.
Raises
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.
Return type
List[str]
add_documents(documents: List[Document], **kwargs: Any) → List[str]¶
Add or update documents in the vectorstore.
Parameters
documents (List[Document]) – Documents to add to the vectorstore.
kwargs (Any) – Additional keyword arguments.
if kwargs contains ids and documents contain ids,
the ids in the kwargs will receive precedence.
Returns
List of IDs of the added texts.
Raises
ValueError – If the number of ids does not match the number of documents.
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
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][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.
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]¶
Async delete by vector ID or other criteria.
Parameters
ids (Optional[List[str]]) – List of ids to delete. If None, delete all. Default is None.
**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¶
Async return VectorStore initialized from documents and embeddings.
Parameters
documents (List[Document]) – List of Documents to add to the vectorstore.
embedding (Embeddings) – Embedding function to use.
kwargs (Any) – Additional keyword arguments.
Returns
VectorStore initialized from documents and embeddings.
Return type
VectorStore
async classmethod afrom_texts(texts: List[str], embedding: Embeddings, metadatas: Optional[List[dict]] = None, **kwargs: Any) → VST¶
Async return VectorStore initialized from texts and embeddings.
Parameters
texts (List[str]) – Texts to add to the vectorstore.
embedding (Embeddings) – Embedding function to use.
metadatas (Optional[List[dict]]) – Optional list of metadatas associated with the texts.
Default is None.
kwargs (Any) – Additional keyword arguments.
Returns
VectorStore initialized from texts and embeddings.
Return type
VectorStore
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
ids (Sequence[str]) – List of ids to retrieve.
Returns
List of Documents.
Return type
List[Document]
New in version 0.2.11.
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
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.
Default is 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.
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]¶
Async 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.
Default is 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.
**kwargs (Any) – Arguments to pass to the search method.
Returns
List of Documents selected by maximal marginal relevance.
Return type
List[Document]
as_retriever(**kwargs: Any) → VectorStoreRetriever¶
Return VectorStoreRetriever initialized from this VectorStore.
Parameters
**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
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]¶
Async return docs most similar to query using a specified search type.
Parameters
query (str) – Input text.
search_type (str) – Type of search to perform. Can be “similarity”,
“mmr”, or “similarity_score_threshold”.
**kwargs (Any) – Arguments to pass to the search method.
Returns
List of Documents most similar to the query.
Raises
ValueError – If search_type is not one of “similarity”,
    “mmr”, or “similarity_score_threshold”.
Return type
List[Document]
async asimilarity_search(query: str, k: int = 4, **kwargs: Any) → List[Document]¶
Async return docs most similar to query.
Parameters
query (str) – Input text.
k (int) – Number of Documents to return. Defaults to 4.
**kwargs (Any) – Arguments to pass to the search method.
Returns
List of Documents most similar to the query.
Return type
List[Document]
async asimilarity_search_by_vector(embedding: List[float], k: int = 4, **kwargs: Any) → List[Document]¶
Async return docs most similar to embedding vector.
Parameters
embedding (List[float]) – Embedding to look up documents similar to.
k (int) – Number of Documents to return. Defaults to 4.
**kwargs (Any) – Arguments to pass to the search method.
Returns
List of Documents most similar to the query vector.
Return type
List[Document]
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
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
async asimilarity_search_with_score(*args: Any, **kwargs: Any) → List[Tuple[Document, float]]¶
Async run similarity search with distance.
Parameters





    
*args (Any) – Arguments to pass to the search method.
**kwargs (Any) – Arguments to pass to the search method.
Returns
List of Tuples of (doc, similarity_score).
Return type
List[Tuple[Document, float]]
delete(ids: Optional[List[str]] = None, **kwargs: Any) → None[source]¶
Delete by vector IDs.
Parameters
ids (Optional[List[str]]) – List of ids to delete.
kwargs (Any) – 
Return type
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.
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[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.
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][source]¶
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]
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
ids (Sequence[str]) – List of ids to retrieve.
Returns
List of Documents.
Return type
List[Document]
New in version 0.2.11.
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][source]¶
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][source]¶
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[source]¶
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.
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.
Return type
search(query: str, search_type: str, **kwargs: Any) → List[Document]¶
Return docs most similar to query using a specified search type.
Parameters
query (str) – Input text
search_type (str) – Type of search to perform. Can be “similarity”,
“mmr”, or “similarity_score_threshold”.
**kwargs (Any) – Arguments to pass to the search method.
Returns
List of Documents most similar to the query.
Raises
ValueError – If search_type is not one of “similarity”,
    “mmr”, or “similarity_score_threshold”.
Return type
List[Document]
similarity_search(query: str, k: int = 4, filter: Optional[Dict[str, str]] = None, **kwargs: Any) → List[Document][source]¶
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_image(uri: str, k: int = 4, filter: Optional[Dict[str, str]] = None, **kwargs: Any) → List[Document][source]¶
Search for similar images based on the given image URI.




    

Parameters
uri (str) – URI of the image to search for.
k (int, optional) – Number of results to return. Defaults to DEFAULT_K.
filter (Optional[Dict[str, str]], optional) – Filter by metadata.
**kwargs (Any) – Additional arguments to pass to function.
Returns
List of Images most similar to the provided image.
Each element in list is a Langchain Document Object.
The page content is b64 encoded image, metadata is default or
as defined by user.
Raises
ValueError – If the embedding function does not support image embeddings.
Return type
List[Document]
similarity_search_by_image_with_relevance_score(uri: str, k: int = 4, filter: Optional[Dict[str, str]] = None, **kwargs: Any) → List[Tuple[Document, float]][source]¶
Search for similar images based on the given image URI.
Parameters
uri (str) – URI of the image to search for.
k (int, optional) – Number of results to return.
DEFAULT_K. (Defaults to) – 
filter (Optional[Dict[str, str]], optional) – Filter by metadata.
**kwargs (Any) – Additional arguments to pass to function.
Returns
List of tuples containing documents similar
to the query image and their similarity scores.
0th element in each tuple is a Langchain Document Object.
The page content is b64 encoded img, metadata is default or defined by user.
Return type
List[Tuple[Document, float]]
Raises
ValueError – If the embedding function does not support image embeddings.
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.
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]][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.
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.
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.
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[source]¶
Update a document in the collection.
Parameters
document_id (str) – ID of the document to update.
document (Document) – Document to update.
Return type
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.
Return type
Build a Local RAG Application
Build a Query Analysis System
Build a Retrieval Augmented Generation (RAG) App
Chroma
Confident
Conversational RAG
Docugami
Google Cloud Vertex AI Reranker
How deal with high cardinality categoricals when doing query analysis
How to add chat history
How to add retrieval to chatbots
How to create and query vector stores
How to do “self-querying” retrieval
How to get your RAG application to return sources
How to handle cases where no queries are generated
How to handle multiple queries when doing query analysis
How to handle multiple retrievers when doing query analysis
How to reorder retrieved results to mitigate the “lost in the middle” effect
How to retrieve using multiple vectors per document
How to select examples by similarity
How to stream results from your RAG application
How to use few shot examples
How to use few shot examples in chat models
How to use the MultiQueryRetriever
How to use the Parent Document Retriever
LOTR (Merger Retriever)
Psychic
RePhraseQuery
Vector stores and retrievers