`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')[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.

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')[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.

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 knnBeta vector search.

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 knnBeta Operator available in MongoDB Atlas Search. This feature is in early access and available only for evaluation purposes, to validate functionality, and to gather feedback from a small closed group of early access users. It is not recommended for production deployments as we may introduce breaking changes. For more: https://www.mongodb.com/docs/atlas/atlas-search/knn-beta

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 knnBeta vector search.

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.

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 knnBeta vector search.

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¶