MatchingEngine#

class langchain_community.vectorstores.matching_engine.MatchingEngine(project_id: str, index: MatchingEngineIndex, endpoint: MatchingEngineIndexEndpoint, embedding: Embeddings, gcs_client: storage.Client, gcs_bucket_name: str, credentials: Credentials | None = None, *, document_id_key: str | None = None)[source]#

Deprecated since version 0.0.12: Use :class:`~langchain_google_vertexai.VectorSearchVectorStore` instead.

Google Vertex AI Vector Search (previously Matching Engine) vector store.

While the embeddings are stored in the Matching Engine, the embedded documents will be stored in GCS.

An existing Index and corresponding Endpoint are preconditions for using this module.

See usage in docs/integrations/vectorstores/google_vertex_ai_vector_search.ipynb

Note that this implementation is mostly meant for reading if you are planning to do a real time implementation. While reading is a real time operation, updating the index takes close to one hour.

Google Vertex AI Vector Search (previously Matching Engine)

implementation of the vector store.

While the embeddings are stored in the Matching Engine, the embedded documents will be stored in GCS.

An existing Index and corresponding Endpoint are preconditions for using this module.

See usage in docs/integrations/vectorstores/google_vertex_ai_vector_search.ipynb.

Note that this implementation is mostly meant for reading if you are planning to do a real time implementation. While reading is a real time operation, updating the index takes close to one hour.

Parameters:

  • project_id (str)

  • index (MatchingEngineIndex)

  • endpoint (MatchingEngineIndexEndpoint)

  • embedding (Embeddings)

  • gcs_client (storage.Client)

  • gcs_bucket_name (str)

  • credentials (Optional[Credentials])

  • document_id_key (Optional[str])

project_id#

The GCS project id.

index#

The created index class. See ~:func:MatchingEngine.from_components.

endpoint#

The created endpoint class. See ~:func:MatchingEngine.from_components.

embedding#

A Embeddings that will be used for embedding the text sent. If none is sent, then the multilingual Tensorflow Universal Sentence Encoder will be used.

gcs_client#

The GCS client.

gcs_bucket_name#

The GCS bucket name.

credentials#

Created GCP credentials.

Type:

Optional

document_id_key#

Key for storing document ID in document metadata. If None, document ID will not be returned in document metadata.

Type:

Optional

Attributes

embeddings

Access the query embedding object if available.

Methods

__init__(project_id, index, endpoint, ...[, ...])

Google Vertex AI Vector Search (previously Matching Engine)

aadd_documents(documents, **kwargs)

Async run more documents through the embeddings and add to the vectorstore.

aadd_texts(texts[, metadatas, ids])

Async run more texts through the embeddings and add to the vectorstore.

add_documents(documents, **kwargs)

Add or update documents in the vectorstore.

add_texts(texts[, metadatas])

Run more texts through the embeddings and add to the vectorstore.

adelete([ids])

Async 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, ids])

Async return VectorStore initialized from texts and embeddings.

aget_by_ids(ids, /)

Async get documents by their IDs.

amax_marginal_relevance_search(query[, k, ...])

Async return docs selected using the maximal marginal relevance.

amax_marginal_relevance_search_by_vector(...)

Async 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.

delete([ids])

Delete by vector ID or other criteria.

from_components(project_id, region, ...[, ...])

Takes the object creation out of the constructor.

from_documents(documents, embedding, **kwargs)

Return VectorStore initialized from documents and embeddings.

from_texts(texts, embedding[, metadatas])

Use from components instead.

get_by_ids(ids, /)

Get documents by their IDs.

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 a specified search type.

similarity_search(query[, k, filter])

Return docs most similar to query.

similarity_search_by_vector(embedding[, k, ...])

Return docs most similar to the embedding.

similarity_search_by_vector_with_score(embedding)

Return docs most similar to the embedding and their cosine distance.

similarity_search_with_relevance_scores(query)

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

similarity_search_with_score(query[, k, filter])

Return docs most similar to query and their cosine distance from the query.
__init__(project_id: str, index: MatchingEngineIndex, endpoint: MatchingEngineIndexEndpoint, embedding: Embeddings, gcs_client: storage.Client, gcs_bucket_name: str, credentials: Credentials | None = None, *, document_id_key: str | None = None)[source]#
Google Vertex AI Vector Search (previously Matching Engine)

implementation of the vector store.

While the embeddings are stored in the Matching Engine, the embedded documents will be stored in GCS.

An existing Index and corresponding Endpoint are preconditions for using this module.

See usage in docs/integrations/vectorstores/google_vertex_ai_vector_search.ipynb.

Note that this implementation is mostly meant for reading if you are planning to do a real time implementation. While reading is a real time operation, updating the index takes close to one hour.

Parameters:

  • project_id (str)

  • index (MatchingEngineIndex)

  • endpoint (MatchingEngineIndexEndpoint)

  • embedding (Embeddings)

  • gcs_client (storage.Client)

  • gcs_bucket_name (str)

  • credentials (Optional[Credentials])

  • document_id_key (Optional[str])

project_id#

The GCS project id.

index#

The created index class. See ~:func:MatchingEngine.from_components.

endpoint#

The created endpoint class. See ~:func:MatchingEngine.from_components.

embedding#

A Embeddings that will be used for embedding the text sent. If none is sent, then the multilingual Tensorflow Universal Sentence Encoder will be used.

gcs_client#

The GCS client.

gcs_bucket_name#

The GCS bucket name.

credentials#

Created GCP credentials.

Type:

Optional

document_id_key#

Key for storing document ID in document metadata. If None, document ID will not be returned in document metadata.

Type:

Optional

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: list[dict] | None = None, *, ids: list[str] | None = 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 (list[dict] | None) – Optional list of metadatas associated with the texts. Default is None.

  • ids (list[str] | None) – Optional list

  • **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]

add_documents(documents: list[Document], **kwargs: Any) → list[str]#

Add or update documents in the vectorstore.

Parameters:

  • documents (list[Document]) – Documents to add to the vectorstore.

  • kwargs (Any) – Additional keyword arguments. if kwargs contains ids and documents contain ids, the ids in the kwargs will receive precedence.

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]

add_texts(texts: Iterable[str], metadatas: List[dict] | None = 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 (List[dict] | None) – Optional list of metadatas associated with the texts.

  • kwargs (Any) – vectorstore specific parameters.

Returns:

List of ids from adding the texts into the vectorstore.

Return type:

List[str]

async adelete(ids: list[str] | None = None, **kwargs: Any) → bool | None#

Async delete by vector ID or other criteria.

Parameters:

  • ids (list[str] | None) – List of ids to delete. If None, delete all. Default is None.

  • **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: list[dict] | None = None, *, ids: list[str] | None = 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 (list[dict] | None) – Optional list of metadatas associated with the texts. Default is None.

  • ids (list[str] | None) – Optional list of IDs associated with the texts.

  • 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]

Added in version 0.2.11.

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]#

Async 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. 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) – Arguments to pass to the search method.

Returns:

List of Documents selected by maximal marginal relevance.

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 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]]

delete(ids: list[str] | None = None, **kwargs: Any) → bool | None#

Delete by vector ID or other criteria.

Parameters:

  • ids (list[str] | None) – List of ids to delete. If None, delete all. Default is None.

  • **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]

classmethod from_components(project_id: str, region: str, gcs_bucket_name: str, index_id: str, endpoint_id: str, credentials_path: str | None = None, embedding: Embeddings | None = None, **kwargs: Any) → MatchingEngine[source]#

Takes the object creation out of the constructor.

Parameters:

  • project_id (str) – The GCP project id.

  • region (str) – The default location making the API calls. It must have

  • regional. (the same location as the GCS bucket and must be)

  • gcs_bucket_name (str) – The location where the vectors will be stored in

  • created. (order for the index to be)

  • index_id (str) – The id of the created index.

  • endpoint_id (str) – The id of the created endpoint.

  • credentials_path (str | None) – (Optional) The path of the Google credentials on

  • system. (the local file)

  • embedding (Embeddings | None) – The Embeddings that will be used for

  • texts. (embedding the)

  • kwargs (Any) – Additional keyword arguments to pass to MatchingEngine.__init__().

Returns:

A configured MatchingEngine with the texts added to the index.

Return type:

MatchingEngine

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: List[dict] | None = None, **kwargs: Any) → MatchingEngine[source]#

Use from components instead.

Parameters:

  • texts (List[str])

  • embedding (Embeddings)

  • metadatas (List[dict] | None)

  • kwargs (Any)

Return type:

MatchingEngine

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]

Added in version 0.2.11.

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) – Arguments to pass to the search method.

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]#

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. 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) – Arguments to pass to the search method.

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]

Return docs most similar to query.

Parameters:

  • query (str) – The string that will be used to search for similar documents.

  • k (int) – The amount of neighbors that will be retrieved.

  • filter (Optional[List[Namespace]]) –

    Optional. A list of Namespaces for filtering the matching results. For example: [Namespace(“color”, [“red”], []), Namespace(“shape”, [], [“squared”])] will match datapoints that satisfy “red color” but not include datapoints with “squared shape”. Please refer to https://cloud.google.com/vertex-ai/docs/matching-engine/filtering#json

    for more detail.

  • kwargs (Any)

Returns:

A list of k matching documents.

Return type:

List[Document]

similarity_search_by_vector(embedding: List[float], k: int = 4, filter: List[Namespace] | None = None, **kwargs: Any) → List[Document][source]#

Return docs most similar to the embedding.

Parameters:

  • embedding (List[float]) – Embedding to look up documents similar to.

  • k (int) – The amount of neighbors that will be retrieved.

  • filter (Optional[List[Namespace]]) –

    Optional. A list of Namespaces for filtering the matching results. For example: [Namespace(“color”, [“red”], []), Namespace(“shape”, [], [“squared”])] will match datapoints that satisfy “red color” but not include datapoints with “squared shape”. Please refer to https://cloud.google.com/vertex-ai/docs/matching-engine/filtering#json

    for more detail.

  • kwargs (Any)

Returns:

A list of k matching documents.

Return type:

List[Document]

similarity_search_by_vector_with_score(embedding: List[float], k: int = 4, filter: List[Namespace] | None = None) → List[Tuple[Document, float]][source]#

Return docs most similar to the embedding and their cosine distance.

Parameters:

  • embedding (List[float]) – Embedding to look up documents similar to.

  • k (int) – Number of Documents to return. Defaults to 4.

  • filter (Optional[List[Namespace]]) – Optional. A list of Namespaces for filtering the matching results. For example: [Namespace(“color”, [“red”], []), Namespace(“shape”, [], [“squared”])] will match datapoints that satisfy “red color” but not include datapoints with “squared shape”. Please refer to https://cloud.google.com/vertex-ai/docs/matching-engine/filtering#json for more detail.

Returns:

List of documents most similar to the query text and cosine distance in float for each. Lower score represents more similarity.

Return type:

List[Tuple[Document, float]]

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, filter: List[Namespace] | None = None) → List[Tuple[Document, float]][source]#

Return docs most similar to query and their cosine distance from the query.

Parameters:

  • query (str) – String query look up documents similar to.

  • k (int) – Number of Documents to return. Defaults to 4.

  • filter (Optional[List[Namespace]]) – Optional. A list of Namespaces for filtering the matching results. For example: [Namespace(“color”, [“red”], []), Namespace(“shape”, [], [“squared”])] will match datapoints that satisfy “red color” but not include datapoints with “squared shape”. Please refer to https://cloud.google.com/vertex-ai/docs/matching-engine/filtering#json for more detail.

Returns:

List of documents most similar to the query text and cosine distance in float for each. Lower score represents more similarity.

Return type:

List[Tuple[Document, float]]