langchain.vectorstores.azure_cosmos_db.AzureCosmosDBVectorSearch

class langchain.vectorstores.azure_cosmos_db.AzureCosmosDBVectorSearch(collection: Collection[CosmosDBDocumentType], embedding: Embeddings, *, index_name: str = 'vectorSearchIndex', text_key: str = 'textContent', embedding_key: str = 'vectorContent')[source]

Azure Cosmos DB for MongoDB vCore vector store.

To use, you should have both: - the pymongo python package installed - a connection string associated with a MongoDB VCore Cluster

Example

. code-block:: python

from langchain.vectorstores import AzureCosmosDBVectorSearch from langchain.embeddings.openai import OpenAIEmbeddings from pymongo import MongoClient

mongo_client = MongoClient(“<YOUR-CONNECTION-STRING>”) collection = mongo_client[“<db_name>”][“<collection_name>”] embeddings = OpenAIEmbeddings() vectorstore = AzureCosmosDBVectorSearch(collection, embeddings)

Constructor for AzureCosmosDBVectorSearch

Parameters
  • collection – MongoDB collection to add the texts to.

  • embedding – Text embedding model to use.

  • index_name – Name of the Atlas Search index.

  • text_key – MongoDB field that will contain the text for each document.

  • embedding_key – MongoDB field that will contain the embedding for each document.

Attributes

embeddings

Access the query embedding object if available.

Methods

__init__(collection, embedding, *[, ...])

Constructor for AzureCosmosDBVectorSearch

aadd_documents(documents, **kwargs)

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

aadd_texts(texts[, metadatas])

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

add_documents(documents, **kwargs)

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

add_texts(texts[, metadatas])

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

adelete([ids])

Delete by vector ID or other criteria.

afrom_documents(documents, embedding, **kwargs)

Return VectorStore initialized from documents and embeddings.

afrom_texts(texts, embedding[, metadatas])

Return VectorStore initialized from texts and embeddings.

amax_marginal_relevance_search(query[, k, ...])

Return docs selected using the maximal marginal relevance.

amax_marginal_relevance_search_by_vector(...)

Return docs selected using the maximal marginal relevance.

as_retriever(**kwargs)

Return VectorStoreRetriever initialized from this VectorStore.

asearch(query, search_type, **kwargs)

Return docs most similar to query using specified search type.

asimilarity_search(query[, k])

Return docs most similar to query.

asimilarity_search_by_vector(embedding[, k])

Return docs most similar to embedding vector.

asimilarity_search_with_relevance_scores(query)

Return docs and relevance scores in the range [0, 1], asynchronously.

asimilarity_search_with_score(*args, **kwargs)

Run similarity search with distance asynchronously.

create_index([num_lists, dimensions, similarity])

Creates an index using the index name specified at

delete([ids])

Delete by vector ID or other criteria.

delete_document_by_id([document_id])

Removes a Specific Document by Id

delete_index()

Deletes the index specified during instance construction if it exists

from_connection_string(connection_string, ...)

Creates an Instance of AzureCosmosDBVectorSearch from a Connection String

from_documents(documents, embedding, **kwargs)

Return VectorStore initialized from documents and embeddings.

from_texts(texts, embedding[, metadatas, ...])

Return VectorStore initialized from texts and embeddings.

get_index_name()

Returns the index name

index_exists()

Verifies if the specified index name during instance

max_marginal_relevance_search(query[, k, ...])

Return docs selected using the maximal marginal relevance.

max_marginal_relevance_search_by_vector(...)

Return docs selected using the maximal marginal relevance.

search(query, search_type, **kwargs)

Return docs most similar to query using specified search type.

similarity_search(query[, k])

Return docs most similar to query.

similarity_search_by_vector(embedding[, k])

Return docs most similar to embedding vector.

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 distance.

__init__(collection: Collection[CosmosDBDocumentType], embedding: Embeddings, *, index_name: str = 'vectorSearchIndex', text_key: str = 'textContent', embedding_key: str = 'vectorContent')[source]

Constructor for AzureCosmosDBVectorSearch

Parameters
  • collection – MongoDB collection to add the texts to.

  • embedding – Text embedding model to use.

  • index_name – Name of the Atlas Search index.

  • text_key – MongoDB field that will contain the text for each document.

  • embedding_key – MongoDB field that will contain the embedding for each document.

async aadd_documents(documents: List[Document], **kwargs: Any) List[str]

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

Parameters

(List[Document] (documents) – Documents to add to the vectorstore.

Returns

List of IDs of the added texts.

Return type

List[str]

async aadd_texts(texts: Iterable[str], metadatas: Optional[List[dict]] = None, **kwargs: Any) List[str]

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

add_documents(documents: List[Document], **kwargs: Any) List[str]

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

Parameters

(List[Document] (documents) – Documents to add to the vectorstore.

Returns

List of IDs of the added texts.

Return type

List[str]

add_texts(texts: Iterable[str], metadatas: Optional[List[Dict[str, Any]]] = None, **kwargs: Any) List[source]

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

Parameters
  • texts – Iterable of strings to add to the vectorstore.

  • metadatas – Optional list of metadatas associated with the texts.

  • kwargs – vectorstore specific parameters

Returns

List of ids from adding the texts into the vectorstore.

async adelete(ids: Optional[List[str]] = None, **kwargs: Any) Optional[bool]

Delete by vector ID or other criteria.

Parameters
  • ids – List of ids to delete.

  • **kwargs – Other keyword arguments that subclasses might use.

Returns

True if deletion is successful, False otherwise, None if not implemented.

Return type

Optional[bool]

async classmethod afrom_documents(documents: List[Document], embedding: Embeddings, **kwargs: Any) VST

Return VectorStore initialized from documents and embeddings.

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

Return VectorStore initialized from texts and embeddings.

Return docs selected using the maximal marginal relevance.

async amax_marginal_relevance_search_by_vector(embedding: List[float], k: int = 4, fetch_k: int = 20, lambda_mult: float = 0.5, **kwargs: Any) List[Document]

Return docs selected using the maximal marginal relevance.

as_retriever(**kwargs: Any) VectorStoreRetriever

Return VectorStoreRetriever initialized from this VectorStore.

Parameters
  • search_type (Optional[str]) – Defines the type of search that the Retriever should perform. Can be “similarity” (default), “mmr”, or “similarity_score_threshold”.

  • search_kwargs (Optional[Dict]) –

    Keyword arguments to pass to the search function. Can include things like:

    k: Amount of documents to return (Default: 4) score_threshold: Minimum relevance threshold

    for similarity_score_threshold

    fetch_k: Amount of documents to pass to MMR algorithm (Default: 20) lambda_mult: Diversity of results returned by MMR;

    1 for minimum diversity and 0 for maximum. (Default: 0.5)

    filter: Filter by document metadata

Returns

Retriever class for VectorStore.

Return type

VectorStoreRetriever

Examples:

# Retrieve more documents with higher diversity
# Useful if your dataset has many similar documents
docsearch.as_retriever(
    search_type="mmr",
    search_kwargs={'k': 6, 'lambda_mult': 0.25}
)

# Fetch more documents for the MMR algorithm to consider
# But only return the top 5
docsearch.as_retriever(
    search_type="mmr",
    search_kwargs={'k': 5, 'fetch_k': 50}
)

# Only retrieve documents that have a relevance score
# Above a certain threshold
docsearch.as_retriever(
    search_type="similarity_score_threshold",
    search_kwargs={'score_threshold': 0.8}
)

# Only get the single most similar document from the dataset
docsearch.as_retriever(search_kwargs={'k': 1})

# Use a filter to only retrieve documents from a specific paper
docsearch.as_retriever(
    search_kwargs={'filter': {'paper_title':'GPT-4 Technical Report'}}
)
async asearch(query: str, search_type: str, **kwargs: Any) List[Document]

Return docs most similar to query using specified search type.

Return docs most similar to query.

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

Return docs most similar to embedding vector.

async asimilarity_search_with_relevance_scores(query: str, k: int = 4, **kwargs: Any) List[Tuple[Document, float]]

Return docs and relevance scores in the range [0, 1], asynchronously.

0 is dissimilar, 1 is most similar.

Parameters
  • query – input text

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

  • **kwargs

    kwargs to be passed to similarity search. Should include: score_threshold: Optional, a floating point value between 0 to 1 to

    filter the resulting set of retrieved docs

Returns

List of Tuples of (doc, similarity_score)

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

Run similarity search with distance asynchronously.

create_index(num_lists: int = 100, dimensions: int = 1536, similarity: CosmosDBSimilarityType = CosmosDBSimilarityType.COS) dict[str, Any][source]
Creates an index using the index name specified at

instance construction

Setting the numLists parameter correctly is important for achieving

good accuracy and performance. Since the vector store uses IVF as the indexing strategy, you should create the index only after you have loaded a large enough sample documents to ensure that the centroids for the respective buckets are faily distributed.

We recommend that numLists is set to documentCount/1000 for up

to 1 million documents and to sqrt(documentCount) for more than 1 million documents. As the number of items in your database grows, you should tune numLists to be larger in order to achieve good latency performance for vector search.

If you’re experimenting with a new scenario or creating a small demo, you can start with numLists set to 1 to perform a brute-force search across all vectors. This should provide you with the most accurate results from the vector search, however be aware that the search speed and latency will be slow. After your initial setup, you should go ahead and tune the numLists parameter using the above guidance.

Parameters
  • num_lists – This integer is the number of clusters that the inverted file (IVF) index uses to group the vector data. We recommend that numLists is set to documentCount/1000 for up to 1 million documents and to sqrt(documentCount) for more than 1 million documents. Using a numLists value of 1 is akin to performing brute-force search, which has limited performance

  • dimensions – Number of dimensions for vector similarity. The maximum number of supported dimensions is 2000

  • similarity

    Similarity metric to use with the IVF index.

    Possible options are:
    • CosmosDBSimilarityType.COS (cosine distance),

    • CosmosDBSimilarityType.L2 (Euclidean distance), and

    • CosmosDBSimilarityType.IP (inner product).

Returns

An object describing the created index

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

Delete by vector ID or other criteria.

Parameters
  • ids – List of ids to delete.

  • **kwargs – Other keyword arguments that subclasses might use.

Returns

True if deletion is successful, False otherwise, None if not implemented.

Return type

Optional[bool]

delete_document_by_id(document_id: Optional[str] = None) None[source]

Removes a Specific Document by Id

Parameters

document_id – The document identifier

delete_index() None[source]

Deletes the index specified during instance construction if it exists

classmethod from_connection_string(connection_string: str, namespace: str, embedding: Embeddings, **kwargs: Any) AzureCosmosDBVectorSearch[source]

Creates an Instance of AzureCosmosDBVectorSearch from a Connection String

Parameters
  • connection_string – The MongoDB vCore instance connection string

  • namespace – The namespace (database.collection)

  • embedding – The embedding utility

  • **kwargs – Dynamic keyword arguments

Returns

an instance of the vector store

classmethod from_documents(documents: List[Document], embedding: Embeddings, **kwargs: Any) VST

Return VectorStore initialized from documents and embeddings.

classmethod from_texts(texts: List[str], embedding: Embeddings, metadatas: Optional[List[dict]] = None, collection: Optional[Collection[CosmosDBDocumentType]] = None, **kwargs: Any) AzureCosmosDBVectorSearch[source]

Return VectorStore initialized from texts and embeddings.

get_index_name() str[source]

Returns the index name

Returns

Returns the index name

index_exists() bool[source]
Verifies if the specified index name during instance

construction exists on the collection

Returns

Returns True on success and False if no such index exists

on the collection

Return docs selected using the maximal marginal relevance.

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

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

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

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

  • lambda_mult – Number between 0 and 1 that determines the degree of diversity among the results with 0 corresponding to maximum diversity and 1 to minimum diversity. Defaults to 0.5.

Returns

List of Documents selected by maximal marginal relevance.

max_marginal_relevance_search_by_vector(embedding: List[float], k: int = 4, fetch_k: int = 20, lambda_mult: float = 0.5, **kwargs: Any) List[Document][source]

Return docs selected using the maximal marginal relevance.

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

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

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

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

  • lambda_mult – Number between 0 and 1 that determines the degree of diversity among the results with 0 corresponding to maximum diversity and 1 to minimum diversity. Defaults to 0.5.

Returns

List of Documents selected by maximal marginal relevance.

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

Return docs most similar to query using specified search type.

Return docs most similar to query.

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

Return docs most similar to embedding vector.

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

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

Returns

List of Documents most similar to the query vector.

similarity_search_with_relevance_scores(query: str, k: int = 4, **kwargs: Any) List[Tuple[Document, float]]

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

0 is dissimilar, 1 is most similar.

Parameters
  • query – input text

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

  • **kwargs

    kwargs to be passed to similarity search. Should include: score_threshold: Optional, a floating point value between 0 to 1 to

    filter the resulting set of retrieved docs

Returns

List of Tuples of (doc, similarity_score)

similarity_search_with_score(query: str, k: int = 4) List[Tuple[Document, float]][source]

Run similarity search with distance.