`langchain_community.vectorstores.mongodb_atlas`.MongoDBAtlasVectorSearch¶

class langchain_community.vectorstores.mongodb_atlas.MongoDBAtlasVectorSearch(collection: Collection[MongoDBDocumentType], embedding: Embeddings, *, index_name: str = 'default', text_key: str = 'text', embedding_key: str = 'embedding', relevance_score_fn: str = 'cosine')[source]¶

MongoDB Atlas Vector Search vector store.

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

Atlas Search index

Example

from langchain_community.vectorstores import MongoDBAtlasVectorSearch
from langchain_community.embeddings.openai import OpenAIEmbeddings
from pymongo import MongoClient

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

Parameters

collection – MongoDB collection to add the texts to.
embedding – Text embedding model to use.
text_key – MongoDB field that will contain the text for each document.
embedding_key – MongoDB field that will contain the embedding for each document.
index_name – Name of the Atlas Search index.
relevance_score_fn – The similarity score used for the index.
supported (Currently) – Euclidean, cosine, and dot product.

Attributes

embeddings

Access the query embedding object if available.

Methods

`__init__`(collection, embedding, *[, ...])	param collection MongoDB collection to add the texts to.
`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.
`delete`([ids])	Delete by vector ID or other criteria.
`from_connection_string`(connection_string, ...)	Construct a MongoDB Atlas Vector Search vector store from a MongoDB connection URI.
`from_documents`(documents, embedding, **kwargs)	Return VectorStore initialized from documents and embeddings.
`from_texts`(texts, embedding[, metadatas, ...])	Construct a MongoDB Atlas Vector Search vector store from raw documents.
`max_marginal_relevance_search`(query[, k, ...])	Return documents 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, pre_filter, ...])	Return MongoDB documents most similar to the given 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, ...])	Return MongoDB documents most similar to the given query and their scores.

__init__(collection: Collection[MongoDBDocumentType], embedding: Embeddings, *, index_name: str = 'default', text_key: str = 'text', embedding_key: str = 'embedding', relevance_score_fn: str = 'cosine')[source]¶

Parameters

collection – MongoDB collection to add the texts to.
embedding – Text embedding model to use.
text_key – MongoDB field that will contain the text for each document.
embedding_key – MongoDB field that will contain the embedding for each document.
index_name – Name of the Atlas Search index.
relevance_score_fn – The similarity score used for the index.
supported (Currently) – Euclidean, cosine, and dot product.

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.

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.

async amax_marginal_relevance_search(query: str, k: int = 4, fetch_k: int = 20, lambda_mult: float = 0.5, **kwargs: Any) → List[Document]¶: 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.

async asimilarity_search(query: str, k: int = 4, **kwargs: Any) → List[Document]¶: Return docs most similar to query.

async asimilarity_search_by_vector(embedding: List[float], k: int = 4, **kwargs: Any) → List[Document]¶: Return docs most similar to embedding vector.

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

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

0 is dissimilar, 1 is most similar.

Parameters

query – input text
k – Number of Documents to return. Defaults to 4.
**kwargs –
kwargs to be passed to similarity search. Should include: score_threshold: Optional, a floating point value between 0 to 1 to

filter the resulting set of retrieved docs

Returns

List of Tuples of (doc, similarity_score)

async asimilarity_search_with_score(*args: Any, **kwargs: Any) → List[Tuple[Document, float]]¶: Run similarity search with distance asynchronously.

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

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

Construct a MongoDB Atlas Vector Search vector store from a MongoDB connection URI.

Parameters

connection_string – A valid MongoDB connection URI.
namespace – A valid MongoDB namespace (database and collection).
embedding – The text embedding model to use for the vector store.

Returns

A new MongoDBAtlasVectorSearch instance.

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[MongoDBDocumentType]] = None, **kwargs: Any) → MongoDBAtlasVectorSearch[source]¶

Construct a MongoDB Atlas Vector Search vector store from raw documents.

This is a user-friendly interface that:

Embeds documents.
Adds the documents to a provided MongoDB Atlas Vector Search index
(Lucene)

This is intended to be a quick way to get started.

Example

max_marginal_relevance_search(query: str, k: int = 4, fetch_k: int = 20, lambda_mult: float = 0.5, pre_filter: Optional[Dict] = None, post_filter_pipeline: Optional[List[Dict]] = None, **kwargs: Any) → List[Document][source]¶

Return documents 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 – (Optional) number of documents to return. Defaults to 4.
fetch_k – (Optional) number of documents to fetch before passing to MMR algorithm. Defaults to 20.
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.
pre_filter – (Optional) dictionary of argument(s) to prefilter on document fields.
post_filter_pipeline – (Optional) pipeline of MongoDB aggregation stages following the vectorSearch stage.

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]¶

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.

similarity_search(query: str, k: int = 4, pre_filter: Optional[Dict] = None, post_filter_pipeline: Optional[List[Dict]] = None, **kwargs: Any) → List[Document][source]¶

Return MongoDB documents most similar to the given query.

Uses the vectorSearch operator available in MongoDB Atlas Search. For more: https://www.mongodb.com/docs/atlas/atlas-vector-search/vector-search-stage/

Parameters

query – Text to look up documents similar to.
k – (Optional) number of documents to return. Defaults to 4.
pre_filter – (Optional) dictionary of argument(s) to prefilter document fields on.
post_filter_pipeline – (Optional) Pipeline of MongoDB aggregation stages following the vectorSearch stage.

Returns

List of documents most similar to the query and their scores.

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, pre_filter: Optional[Dict] = None, post_filter_pipeline: Optional[List[Dict]] = None) → List[Tuple[Document, float]][source]¶

Return MongoDB documents most similar to the given query and their scores.

Uses the vectorSearch operator available in MongoDB Atlas Search. For more: https://www.mongodb.com/docs/atlas/atlas-vector-search/vector-search-stage/

Parameters

query – Text to look up documents similar to.
k – (Optional) number of documents to return. Defaults to 4.
pre_filter – (Optional) dictionary of argument(s) to prefilter document fields on.
post_filter_pipeline – (Optional) Pipeline of MongoDB aggregation stages following the vectorSearch stage.

Returns

List of documents most similar to the query and their scores.

Examples using MongoDBAtlasVectorSearch¶

MongoDB Atlas

langchain_community.vectorstores.mongodb_atlas.MongoDBAtlasVectorSearch¶

Examples using MongoDBAtlasVectorSearch¶

`langchain_community.vectorstores.mongodb_atlas`.MongoDBAtlasVectorSearch¶