langchain.vectorstores.momento_vector_index.MomentoVectorIndex

class langchain.vectorstores.momento_vector_index.MomentoVectorIndex(embedding: Embeddings, client: PreviewVectorIndexClient, index_name: str = 'default', distance_strategy: DistanceStrategy = DistanceStrategy.COSINE, text_field: str = 'text', ensure_index_exists: bool = True, **kwargs: Any)[source]

Momento Vector Index (MVI) vector store.

Momento Vector Index is a serverless vector index that can be used to store and search vectors. To use you should have the momento python package installed.

Example

from langchain.embeddings import OpenAIEmbeddings
from langchain.vectorstores import MomentoVectorIndex
from momento import (
    CredentialProvider,
    PreviewVectorIndexClient,
    VectorIndexConfigurations,
)

vectorstore = MomentoVectorIndex(
    embedding=OpenAIEmbeddings(),
    client=PreviewVectorIndexClient(
        VectorIndexConfigurations.Default.latest(),
        credential_provider=CredentialProvider.from_environment_variable(
            "MOMENTO_API_KEY"
        ),
    ),
    index_name="my-index",
)

Initialize a Vector Store backed by Momento Vector Index.

Parameters
  • embedding (Embeddings) – The embedding function to use.

  • configuration (VectorIndexConfiguration) – The configuration to initialize the Vector Index with.

  • credential_provider (CredentialProvider) – The credential provider to authenticate the Vector Index with.

  • index_name (str, optional) – The name of the index to store the documents in. Defaults to “default”.

  • distance_strategy (DistanceStrategy, optional) – The distance strategy to use. Defaults to DistanceStrategy.COSINE. If you select DistanceStrategy.EUCLIDEAN_DISTANCE, Momento uses the squared Euclidean distance.

  • text_field (str, optional) – The name of the metadata field to store the original text in. Defaults to “text”.

  • ensure_index_exists (bool, optional) – Whether to ensure that the index exists before adding documents to it. Defaults to True.

Attributes

embeddings

Access the query embedding object if available.

Methods

__init__(embedding, client[, index_name, ...])

Initialize a Vector Store backed by Momento Vector Index.

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.

from_documents(documents, embedding, **kwargs)

Return VectorStore initialized from documents and embeddings.

from_texts(texts, embedding[, metadatas])

Return the Vector Store initialized from texts and embeddings.

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])

Search for similar documents to the query string.

similarity_search_by_vector(embedding[, k])

Search for similar documents to the query vector.

similarity_search_with_relevance_scores(query)

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

similarity_search_with_score(query[, k])

Search for similar documents to the query string.

similarity_search_with_score_by_vector(embedding)

Search for similar documents to the query vector.

__init__(embedding: Embeddings, client: PreviewVectorIndexClient, index_name: str = 'default', distance_strategy: DistanceStrategy = DistanceStrategy.COSINE, text_field: str = 'text', ensure_index_exists: bool = True, **kwargs: Any)[source]

Initialize a Vector Store backed by Momento Vector Index.

Parameters
  • embedding (Embeddings) – The embedding function to use.

  • configuration (VectorIndexConfiguration) – The configuration to initialize the Vector Index with.

  • credential_provider (CredentialProvider) – The credential provider to authenticate the Vector Index with.

  • index_name (str, optional) – The name of the index to store the documents in. Defaults to “default”.

  • distance_strategy (DistanceStrategy, optional) – The distance strategy to use. Defaults to DistanceStrategy.COSINE. If you select DistanceStrategy.EUCLIDEAN_DISTANCE, Momento uses the squared Euclidean distance.

  • text_field (str, optional) – The name of the metadata field to store the original text in. Defaults to “text”.

  • ensure_index_exists (bool, optional) – Whether to ensure that the index exists before adding documents to it. Defaults to True.

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]] = 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 (Optional[List[dict]]) – Optional list of metadatas associated with the texts.

  • kwargs (Any) – Other optional parameters. Specifically:

  • ids (-) – List of ids to use for the texts. Defaults to None, in which case uuids are generated.

Returns

List of ids from adding the texts into 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 – 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.

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

Delete by vector ID.

Parameters
  • ids (List[str]) – List of ids to delete.

  • kwargs (Any) – Other optional parameters (unused)

Returns

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

Return type

Optional[bool]

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, **kwargs: Any) VST[source]

Return the Vector Store initialized from texts and embeddings.

Parameters
  • cls (Type[VST]) – The Vector Store class to use to initialize the Vector Store.

  • texts (List[str]) – The texts to initialize the Vector Store with.

  • embedding (Embeddings) – The embedding function to use.

  • metadatas (Optional[List[dict]], optional) – The metadata associated with the texts. Defaults to None.

  • kwargs (Any) – Vector Store specific parameters. The following are forwarded to the Vector Store constructor and required:

  • index_name (-) – The name of the index to store the documents in. Defaults to “default”.

  • text_field (-) – The name of the metadata field to store the original text in. Defaults to “text”.

  • distance_strategy (-) – The distance strategy to use. Defaults to DistanceStrategy.COSINE. If you select DistanceStrategy.EUCLIDEAN_DISTANCE, Momento uses the squared Euclidean distance.

  • ensure_index_exists (-) – Whether to ensure that the index exists before adding documents to it. Defaults to True.

  • key (Additionally you can either pass in a client or an API) –

  • client (-) – The Momento Vector Index client to use.

  • api_key (-) – The configuration to use to initialize the Vector Index with. Defaults to None. If None, the configuration is initialized from the environment variable MOMENTO_API_KEY.

Returns

Momento Vector Index vector store initialized from texts and

embeddings.

Return type

VST

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]

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.

Search for similar documents to the query string.

Parameters
  • query (str) – The query string to search for.

  • k (int, optional) – The number of results to return. Defaults to 4.

Returns

A list of documents that are similar to the query.

Return type

List[Document]

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

Search for similar documents to the query vector.

Parameters
  • embedding (List[float]) – The query vector to search for.

  • k (int, optional) – The number of results to return. Defaults to 4.

Returns

A list of documents that are similar to the query.

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 – 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, **kwargs: Any) List[Tuple[Document, float]][source]

Search for similar documents to the query string.

Parameters
  • query (str) – The query string to search for.

  • k (int, optional) – The number of results to return. Defaults to 4.

  • kwargs (Any) – Vector Store specific search parameters. The following are forwarded to the Momento Vector Index:

  • top_k (-) – The number of results to return.

Returns

A list of tuples of the form

(Document, score).

Return type

List[Tuple[Document, float]]

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

Search for similar documents to the query vector.

Parameters
  • embedding (List[float]) – The query vector to search for.

  • k (int, optional) – The number of results to return. Defaults to 4.

  • kwargs (Any) – Vector Store specific search parameters. The following are forwarded to the Momento Vector Index:

  • top_k (-) – The number of results to return.

Returns

A list of tuples of the form

(Document, score).

Return type

List[Tuple[Document, float]]