AzureCosmosDBNoSqlVectorSearch#
- class langchain_community.vectorstores.azure_cosmos_db_no_sql.AzureCosmosDBNoSqlVectorSearch(*, cosmos_client: CosmosClient, embedding: Embeddings, vector_embedding_policy: Dict[str, Any], indexing_policy: Dict[str, Any], cosmos_container_properties: Dict[str, Any], cosmos_database_properties: Dict[str, Any], database_name: str = 'vectorSearchDB', container_name: str = 'vectorSearchContainer', create_container: bool = True)[source]#
Azure Cosmos DB for NoSQL vector store.
- To use, you should have both:
the
azure-cosmos
python package installed
You can read more about vector search using AzureCosmosDBNoSQL here: https://learn.microsoft.com/en-us/azure/cosmos-db/nosql/vector-search
Constructor for AzureCosmosDBNoSqlVectorSearch
- Parameters:
cosmos_client (CosmosClient) – Client used to connect to azure cosmosdb no sql account.
database_name (str) – Name of the database to be created.
container_name (str) – Name of the container to be created.
embedding (Embeddings) – Text embedding model to use.
vector_embedding_policy (Dict[str, Any]) – Vector Embedding Policy for the container.
indexing_policy (Dict[str, Any]) – Indexing Policy for the container.
cosmos_container_properties (Dict[str, Any]) – Container Properties for the container.
cosmos_database_properties (Dict[str, Any]) – Database Properties for the container.
create_container (bool)
Attributes
embeddings
Access the query embedding object if available.
Methods
__init__
(*, cosmos_client, embedding, ...[, ...])Constructor for AzureCosmosDBNoSqlVectorSearch
aadd_documents
(documents, **kwargs)Async run more documents through the embeddings and add to the vectorstore.
aadd_texts
(texts[, metadatas, ids])Async run more texts through the embeddings and add to the vectorstore.
add_documents
(documents, **kwargs)Add or update documents in the vectorstore.
add_texts
(texts[, metadatas])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, ids])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.
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.
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 ID or other criteria.
delete_document_by_id
([document_id])Removes a Specific Document by id
from_documents
(documents, embedding, **kwargs)Return VectorStore initialized from documents and embeddings.
from_texts
(texts, embedding[, metadatas])Create an AzureCosmosDBNoSqlVectorSearch vectorstore from raw texts.
get_by_ids
(ids, /)Get documents by their IDs.
max_marginal_relevance_search
(query[, k, ...])Return docs selected using the maximal marginal relevance.
Return docs selected using the maximal marginal relevance.
search
(query, search_type, **kwargs)Return docs most similar to query using a specified search type.
similarity_search
(query[, k, pre_filter, ...])Return docs most similar to query.
similarity_search_by_vector
(embedding[, k])Return docs most similar to embedding vector.
Return docs and relevance scores in the range [0, 1].
similarity_search_with_score
(query[, k, ...])Run similarity search with distance.
- __init__(*, cosmos_client: CosmosClient, embedding: Embeddings, vector_embedding_policy: Dict[str, Any], indexing_policy: Dict[str, Any], cosmos_container_properties: Dict[str, Any], cosmos_database_properties: Dict[str, Any], database_name: str = 'vectorSearchDB', container_name: str = 'vectorSearchContainer', create_container: bool = True)[source]#
Constructor for AzureCosmosDBNoSqlVectorSearch
- Parameters:
cosmos_client (CosmosClient) – Client used to connect to azure cosmosdb no sql account.
database_name (str) – Name of the database to be created.
container_name (str) – Name of the container to be created.
embedding (Embeddings) – Text embedding model to use.
vector_embedding_policy (Dict[str, Any]) – Vector Embedding Policy for the container.
indexing_policy (Dict[str, Any]) – Indexing Policy for the container.
cosmos_container_properties (Dict[str, Any]) – Container Properties for the container.
cosmos_database_properties (Dict[str, Any]) – Database Properties for the container.
create_container (bool)
- 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: 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:
texts (Iterable[str]) – Iterable of strings to add to the vectorstore.
metadatas (list[dict] | None) – Optional list of metadatas associated with the texts. Default is None.
ids (list[str] | None) – Optional list
**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_texts(texts: Iterable[str], metadatas: List[dict] | None = None, **kwargs: Any) List[str] [source]#
Run more texts through the embeddings and add to the vectorstore.
- Parameters:
texts (Iterable[str]) – Iterable of strings to add to the vectorstore.
metadatas (List[dict] | None) – Optional list of metadatas associated with the texts.
kwargs (Any)
- Returns:
List of ids from adding the texts into the vectorstore.
- Return type:
List[str]
- async adelete(ids: list[str] | None = None, **kwargs: Any) bool | None #
Async delete by vector ID or other criteria.
- Parameters:
ids (list[str] | None) – 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:
- async classmethod afrom_texts(texts: list[str], embedding: Embeddings, metadatas: list[dict] | None = None, *, ids: list[str] | None = 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 (list[dict] | None) – Optional list of metadatas associated with the texts. Default is None.
ids (list[str] | None) – Optional list of IDs associated with the texts.
kwargs (Any) – Additional keyword arguments.
- Returns:
VectorStore initialized from texts and embeddings.
- Return type:
- 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]
Added 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
- Returns:
Retriever class for VectorStore.
- Return type:
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
- 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]] #
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: List[str] | None = None, **kwargs: Any) bool | None [source]#
Delete by vector ID or other criteria.
- Parameters:
ids (List[str] | None) – 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]
- delete_document_by_id(document_id: str | None = None) None [source]#
Removes a Specific Document by id
- Parameters:
document_id (str | None) – The document identifier
- Return type:
None
- classmethod from_documents(documents: list[Document], embedding: Embeddings, **kwargs: Any) VST #
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:
- classmethod from_texts(texts: List[str], embedding: Embeddings, metadatas: List[dict] | None = None, **kwargs: Any) AzureCosmosDBNoSqlVectorSearch [source]#
Create an AzureCosmosDBNoSqlVectorSearch vectorstore from raw texts.
- Parameters:
texts (List[str]) – the texts to insert.
embedding (Embeddings) – the embedding function to use in the store.
metadatas (List[dict] | None) – metadata dicts for the texts.
**kwargs (Any) – you can pass any argument that you would to
add_texts()
and/or to the ‘AstraDB’ constructor (see these methods for details). These arguments will be routed to the respective methods as they are.
- Returns:
an AzureCosmosDBNoSqlVectorSearch vectorstore.
- Return type:
- 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]
Added in version 0.2.11.
- max_marginal_relevance_search(query: str, k: int = 4, fetch_k: int = 20, lambda_mult: float = 0.5, **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. 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]
- max_marginal_relevance_search_by_vector(embedding: List[float], k: int = 4, fetch_k: int = 20, lambda_mult: float = 0.5, **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. 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]
- 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, pre_filter: Dict | None = None, with_embedding: bool = False, **kwargs: Any) List[Document] [source]#
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.
pre_filter (Dict | None)
with_embedding (bool)
**kwargs
- Returns:
List of Documents most similar to the query.
- Return type:
List[Document]
- similarity_search_by_vector(embedding: list[float], k: int = 4, **kwargs: Any) list[Document] #
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]
- 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, pre_filter: Dict | None = None, with_embedding: bool = False) List[Tuple[Document, float]] [source]#
Run similarity search with distance.
- Parameters:
*args – Arguments to pass to the search method.
**kwargs – Arguments to pass to the search method.
query (str)
k (int)
pre_filter (Dict | None)
with_embedding (bool)
- Returns:
List of Tuples of (doc, similarity_score).
- Return type:
List[Tuple[Document, float]]
Examples using AzureCosmosDBNoSqlVectorSearch