langchain_community.vectorstores.momento_vector_index
.MomentoVectorIndex¶
- class langchain_community.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_community.embeddings import OpenAIEmbeddings from langchain_community.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. If you select DistanceStrategy.EUCLIDEAN_DISTANCE, Momento uses the squared Euclidean distance. Defaults to DistanceStrategy.COSINE.
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.
client (PreviewVectorIndexClient) –
kwargs (Any) –
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.
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.
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.
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.
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. If you select DistanceStrategy.EUCLIDEAN_DISTANCE, Momento uses the squared Euclidean distance. Defaults to DistanceStrategy.COSINE.
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.
client (PreviewVectorIndexClient) –
kwargs (Any) –
- 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.
documents (List[Document]) –
kwargs (Any) –
- 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.
- Parameters
texts (Iterable[str]) –
metadatas (Optional[List[dict]]) –
kwargs (Any) –
- Return type
List[str]
- 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.
documents (List[Document]) –
kwargs (Any) –
- 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 (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 ¶
Return VectorStore initialized from documents and embeddings.
- Parameters
documents (List[Document]) –
embedding (Embeddings) –
kwargs (Any) –
- Return type
VST
- async classmethod afrom_texts(texts: List[str], embedding: Embeddings, metadatas: Optional[List[dict]] = None, **kwargs: Any) VST ¶
Return VectorStore initialized from texts and embeddings.
- Parameters
texts (List[str]) –
embedding (Embeddings) –
metadatas (Optional[List[dict]]) –
kwargs (Any) –
- Return type
VST
- 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.
- Parameters
query (str) –
k (int) –
fetch_k (int) –
lambda_mult (float) –
kwargs (Any) –
- 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
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
kwargs (Any) –
- Returns
Retriever class for VectorStore.
- Return type
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.
- Parameters
query (str) –
search_type (str) –
kwargs (Any) –
- Return type
List[Document]
- async asimilarity_search(query: str, k: int = 4, **kwargs: Any) List[Document] ¶
Return docs most similar to query.
- Parameters
query (str) –
k (int) –
kwargs (Any) –
- Return type
List[Document]
- async asimilarity_search_by_vector(embedding: List[float], k: int = 4, **kwargs: Any) List[Document] ¶
Return docs most similar to embedding vector.
- Parameters
embedding (List[float]) –
k (int) –
kwargs (Any) –
- Return type
List[Document]
- 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 (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]] ¶
Run similarity search with distance asynchronously.
- Parameters
args (Any) –
kwargs (Any) –
- Return type
List[Tuple[Document, float]]
- 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.
- Parameters
documents (List[Document]) –
embedding (Embeddings) –
kwargs (Any) –
- Return type
VST
- 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
- max_marginal_relevance_search(query: str, 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
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.
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]
- 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 (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.
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 specified search type.
- Parameters
query (str) –
search_type (str) –
kwargs (Any) –
- Return type
List[Document]
- similarity_search(query: str, k: int = 4, **kwargs: Any) List[Document] [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) –
- 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.
kwargs (Any) –
- 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 (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, **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]]