`langchain_mongodb.cache`.MongoDBAtlasSemanticCache¶

class langchain_mongodb.cache.MongoDBAtlasSemanticCache(connection_string: str, embedding: Embeddings, collection_name: str = 'default', database_name: str = 'default', index_name: str = 'default', wait_until_ready: bool = False, score_threshold: Optional[float] = None, **kwargs: Dict[str, Any])[source]¶

MongoDB Atlas Semantic cache.

A Cache backed by a MongoDB Atlas server with vector-store support

Initialize Atlas VectorSearch Cache. Assumes collection exists before instantiation

Parameters

connection_string (str) – MongoDB URI to connect to MongoDB Atlas cluster.
embedding (Embeddings) – Text embedding model to use.
collection_name (str) – MongoDB Collection to add the texts to. Defaults to “default”.
database_name (str) – MongoDB Database where to store texts. Defaults to “default”.
index_name (str) – Name of the Atlas Search index. defaults to ‘default’
wait_until_ready (bool) – Block until MongoDB Atlas finishes indexing the stored text. Hard timeout of 10 seconds. Defaults to False.
score_threshold (Optional[float]) –
kwargs (Dict[str, Any]) –

Attributes

`LLM`
`RETURN_VAL`
`embeddings`	Access the query embedding object if available.

Methods

`__init__`(connection_string, embedding[, ...])	Initialize Atlas VectorSearch Cache.
`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.
`aclear`(**kwargs)	Async clear cache that can take additional keyword arguments.
`add_documents`(documents[, ids, batch_size])	Add documents to the vectorstore.
`add_texts`(texts[, metadatas, ids])	Add texts, create embeddings, and add to the Collection and index.
`adelete`([ids])	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.
`alookup`(prompt, llm_string)	Async look up based on prompt and llm_string.
`amax_marginal_relevance_search`(query[, k, ...])	Async 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)	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.
`astreaming_upsert`(items, /, batch_size, **kwargs)
`aupdate`(prompt, llm_string, return_val)	Async update cache based on prompt and llm_string.
`aupsert`(items, /, **kwargs)
`bulk_embed_and_insert_texts`(texts, metadatas)	Bulk insert single batch of texts, embeddings, and optionally ids.
`clear`(**kwargs)	Clear cache that can take additional keyword arguments.
`create_vector_search_index`(dimensions[, ...])	Creates a MongoDB Atlas vectorSearch index for the VectorStore
`delete`([ids])	Delete documents from VectorStore by ids.
`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.
`get_by_ids`(ids, /)	Get documents by their IDs.
`lookup`(prompt, llm_string)	Look up based on prompt and llm_string.
`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 a 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.
`streaming_upsert`(items, /, batch_size, **kwargs)
`update`(prompt, llm_string, return_val[, ...])	Update cache based on prompt and llm_string.
`upsert`(items, /, **kwargs)

__init__(connection_string: str, embedding: Embeddings, collection_name: str = 'default', database_name: str = 'default', index_name: str = 'default', wait_until_ready: bool = False, score_threshold: Optional[float] = None, **kwargs: Dict[str, Any])[source]¶

Initialize Atlas VectorSearch Cache. Assumes collection exists before instantiation

Parameters

connection_string (str) – MongoDB URI to connect to MongoDB Atlas cluster.
embedding (Embeddings) – Text embedding model to use.
collection_name (str) – MongoDB Collection to add the texts to. Defaults to “default”.
database_name (str) – MongoDB Database where to store texts. Defaults to “default”.
index_name (str) – Name of the Atlas Search index. defaults to ‘default’
wait_until_ready (bool) – Block until MongoDB Atlas finishes indexing the stored text. Hard timeout of 10 seconds. Defaults to False.
score_threshold (Optional[float]) –
kwargs (Dict[str, Any]) –

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]

async aclear(**kwargs: Any) → None¶

Async clear cache that can take additional keyword arguments.

Parameters: kwargs (Any) –
Return type: None

add_documents(documents: List[Document], ids: Optional[List[str]] = None, batch_size: int = 100000, **kwargs: Any) → List[str]¶

Add documents to the vectorstore.

Parameters

documents (List[Document]) – Documents to add to the vectorstore.
ids (Optional[List[str]]) – Optional list of unique ids that will be used as index in VectorStore. See note on ids in add_texts.
batch_size (int) – Number of documents to insert at a time. Tuning this may help with performance and sidestep MongoDB limits.
kwargs (Any) –

Returns

List of IDs of the added texts.

Return type

List[str]

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

Add texts, create embeddings, and add to the Collection and index.

Important notes on ids:

If _id or id is a key in the metadatas dicts, one must
pop them and provide as separate list.
They must be unique.
If they are not provided, the VectorStore will create unique ones,
stored as bson.ObjectIds internally, and strings in Langchain. These will appear in Document.metadata with key, ‘_id’.

Parameters

texts (Iterable[str]) – Iterable of strings to add to the vectorstore.
metadatas (Optional[List[Dict[str, Any]]]) – Optional list of metadatas associated with the texts.
ids (Optional[List[str]]) – Optional list of unique ids that will be used as index in VectorStore. See note on ids.
kwargs (Any) –

Returns

List of ids added to the vectorstore.

Return type

List[str]

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

Delete by vector ID or other criteria.

Parameters

ids (Optional[List[str]]) – List of ids to delete.
**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 alookup(prompt: str, llm_string: str) → Optional[Sequence[Generation]]¶

Async look up based on prompt and llm_string.

A cache implementation is expected to generate a key from the 2-tuple of prompt and llm_string (e.g., by concatenating them with a delimiter).

Parameters

prompt (str) – a string representation of the prompt. In the case of a Chat model, the prompt is a non-trivial serialization of the prompt into the language model.
llm_string (str) – A string representation of the LLM configuration. This is used to capture the invocation parameters of the LLM (e.g., model name, temperature, stop tokens, max tokens, etc.). These invocation parameters are serialized into a string representation.

Returns

On a cache miss, return None. On a cache hit, return the cached value. The cached value is a list of Generations (or subclasses).

Return type

Optional[Sequence[Generation]]

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]¶

Return docs selected using the maximal marginal relevance.

Parameters

embedding (List[float]) –
k (int) –
fetch_k (int) –
lambda_mult (float) –
kwargs (Any) –

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

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]¶

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]]

astreaming_upsert(items: AsyncIterable[Document], /, batch_size: int, **kwargs: Any) → AsyncIterator[UpsertResponse]¶

Beta

Added in 0.2.11. The API is subject to change.

Upsert documents in a streaming fashion. Async version of streaming_upsert.

Parameters

items (AsyncIterable[Document]) – Iterable of Documents to add to the vectorstore.
batch_size (int) – The size of each batch to upsert.
kwargs (Any) – Additional keyword arguments. kwargs should only include parameters that are common to all documents. (e.g., timeout for indexing, retry policy, etc.) kwargs should not include ids to avoid ambiguous semantics. Instead the ID should be provided as part of the Document object.

Yields

UpsertResponse – A response object that contains the list of IDs that were successfully added or updated in the vectorstore and the list of IDs that failed to be added or updated.

Return type

AsyncIterator[UpsertResponse]

New in version 0.2.11.

async aupdate(prompt: str, llm_string: str, return_val: Sequence[Generation]) → None¶

Async update cache based on prompt and llm_string.

The prompt and llm_string are used to generate a key for the cache. The key should match that of the look up method.

Parameters

prompt (str) – a string representation of the prompt. In the case of a Chat model, the prompt is a non-trivial serialization of the prompt into the language model.
llm_string (str) – A string representation of the LLM configuration. This is used to capture the invocation parameters of the LLM (e.g., model name, temperature, stop tokens, max tokens, etc.). These invocation parameters are serialized into a string representation.
return_val (Sequence[Generation]) – The value to be cached. The value is a list of Generations (or subclasses).

Return type

None

async aupsert(items: Sequence[Document], /, **kwargs: Any) → UpsertResponse¶

Beta

Added in 0.2.11. The API is subject to change.

Add or update documents in the vectorstore. Async version of upsert.

The upsert functionality should utilize the ID field of the Document object if it is provided. If the ID is not provided, the upsert method is free to generate an ID for the document.

When an ID is specified and the document already exists in the vectorstore, the upsert method should update the document with the new data. If the document does not exist, the upsert method should add the document to the vectorstore.

Parameters

items (Sequence[Document]) – Sequence of Documents to add to the vectorstore.
kwargs (Any) – Additional keyword arguments.

Returns

A response object that contains the list of IDs that were successfully added or updated in the vectorstore and the list of IDs that failed to be added or updated.

Return type

UpsertResponse

New in version 0.2.11.

bulk_embed_and_insert_texts(texts: Union[List[str], Iterable[str]], metadatas: Union[List[dict], Generator[dict, Any, Any]], ids: Optional[List[str]] = None) → List[str]¶

Bulk insert single batch of texts, embeddings, and optionally ids.

See add_texts for additional details.

Parameters

texts (Union[List[str], Iterable[str]]) –
metadatas (Union[List[dict], Generator[dict, Any, Any]]) –
ids (Optional[List[str]]) –

Return type

List[str]

clear(**kwargs: Any) → None[source]¶

Clear cache that can take additional keyword arguments. Any additional arguments will propagate as filtration criteria for what gets deleted. It will delete any locally cached content regardless

E.g.: # Delete only entries that have llm_string as “fake-model” self.clear(llm_string=”fake-model”)

Parameters: kwargs (Any) –
Return type: None

create_vector_search_index(dimensions: int, filters: Optional[List[Dict[str, str]]] = None, update: bool = False) → None¶

Creates a MongoDB Atlas vectorSearch index for the VectorStore

Note**: This method may fail as it requires a MongoDB Atlas with these pre-requisites:

M10 cluster or higher

https://www.mongodb.com/docs/atlas/atlas-vector-search/create-index/#prerequisites

Parameters

dimensions (int) – Number of dimensions in embedding
filters (Optional[List[Dict[str, str]]], optional) – additional filters
definition. (for index) – Defaults to None.
update (bool, optional) – Updates existing vectorSearch index. Defaults to False.

Return type

None

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

Delete documents from VectorStore by ids.

Parameters

ids (Optional[List[str]]) – List of ids to delete.
**kwargs (Any) – Other keyword arguments passed to Collection.delete_many()

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¶

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

Parameters

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

Returns

A new MongoDBAtlasVectorSearch instance.

Return type

MongoDBAtlasVectorSearch

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

VectorStore

classmethod from_texts(texts: List[str], embedding: Embeddings, metadatas: Optional[List[Dict]] = None, collection: Optional[Collection[MongoDBDocumentType]] = None, ids: Optional[List[str]] = None, **kwargs: Any) → MongoDBAtlasVectorSearch¶

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

Parameters

texts (List[str]) –
embedding (Embeddings) –
metadatas (Optional[List[Dict]]) –
collection (Optional[Collection[MongoDBDocumentType]]) –
ids (Optional[List[str]]) –
kwargs (Any) –

Return type

MongoDBAtlasVectorSearch

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.

lookup(prompt: str, llm_string: str) → Optional[Sequence[Generation]][source]¶

Look up based on prompt and llm_string.

Parameters

prompt (str) –
llm_string (str) –

Return type

Optional[Sequence[Generation]]

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]¶

Return documents 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) – (Optional) number of documents to return. Defaults to 4.
fetch_k (int) – (Optional) number of documents to fetch before passing to MMR algorithm. Defaults to 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.
pre_filter (Optional[Dict]) – (Optional) dictionary of argument(s) to prefilter on document fields.
post_filter_pipeline (Optional[List[Dict]]) – (Optional) pipeline of MongoDB aggregation stages following the vectorSearch stage.
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, pre_filter: Optional[Dict] = None, post_filter_pipeline: Optional[List[Dict]] = None, **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 (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.
pre_filter (Optional[Dict]) – (Optional) dictionary of argument(s) to prefilter on document fields.
post_filter_pipeline (Optional[List[Dict]]) – (Optional) pipeline of MongoDB aggregation stages following the vectorSearch stage.
kwargs (Any) –

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: Optional[Dict] = None, post_filter_pipeline: Optional[List[Dict]] = None, **kwargs: Any) → List[Document]¶

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 (str) – Text to look up documents similar to.
k (int) – (Optional) number of documents to return. Defaults to 4.
pre_filter (Optional[Dict]) – (Optional) dictionary of argument(s) to prefilter document fields on.
post_filter_pipeline (Optional[List[Dict]]) – (Optional) Pipeline of MongoDB aggregation stages following the vectorSearch stage.
kwargs (Any) –

Returns

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

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: Optional[Dict] = None, post_filter_pipeline: Optional[List[Dict]] = None, **kwargs: Any) → List[Tuple[Document, float]]¶

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 (str) – Text to look up documents similar to.
k (int) – (Optional) number of documents to return. Defaults to 4.
pre_filter (Optional[Dict]) – (Optional) dictionary of argument(s) to prefilter document fields on.
post_filter_pipeline (Optional[List[Dict]]) – (Optional) Pipeline of MongoDB aggregation stages following the vectorSearch stage.
kwargs (Any) –

Returns

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

Return type

List[Tuple[Document, float]]

streaming_upsert(items: Iterable[Document], /, batch_size: int, **kwargs: Any) → Iterator[UpsertResponse]¶

Beta

Added in 0.2.11. The API is subject to change.

Upsert documents in a streaming fashion.

Parameters

items (Iterable[Document]) – Iterable of Documents to add to the vectorstore.
batch_size (int) – The size of each batch to upsert.
kwargs (Any) – Additional keyword arguments. kwargs should only include parameters that are common to all documents. (e.g., timeout for indexing, retry policy, etc.) kwargs should not include ids to avoid ambiguous semantics. Instead, the ID should be provided as part of the Document object.

Yields

UpsertResponse – A response object that contains the list of IDs that were successfully added or updated in the vectorstore and the list of IDs that failed to be added or updated.

Return type

Iterator[UpsertResponse]

New in version 0.2.11.

update(prompt: str, llm_string: str, return_val: Sequence[Generation], wait_until_ready: Optional[bool] = None) → None[source]¶

Update cache based on prompt and llm_string.

Parameters

prompt (str) –
llm_string (str) –
return_val (Sequence[Generation]) –
wait_until_ready (Optional[bool]) –

Return type

None

upsert(items: Sequence[Document], /, **kwargs: Any) → UpsertResponse¶

Beta

Added in 0.2.11. The API is subject to change.

Add or update documents in the vectorstore.

The upsert functionality should utilize the ID field of the Document object if it is provided. If the ID is not provided, the upsert method is free to generate an ID for the document.

When an ID is specified and the document already exists in the vectorstore, the upsert method should update the document with the new data. If the document does not exist, the upsert method should add the document to the vectorstore.

Parameters

items (Sequence[Document]) – Sequence of Documents to add to the vectorstore.
kwargs (Any) – Additional keyword arguments.

Returns

A response object that contains the list of IDs that were successfully added or updated in the vectorstore and the list of IDs that failed to be added or updated.

Return type

UpsertResponse

New in version 0.2.11.

langchain_mongodb.cache.MongoDBAtlasSemanticCache¶

`langchain_mongodb.cache`.MongoDBAtlasSemanticCache¶