langchain_community.vectorstores.matching_engine.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: Optional[Credentials] = None, *, document_id_key: Optional[str] = None)

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.

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)	Run more documents through the embeddings and add to the vectorstore.
aadd_texts(texts[, metadatas])	Run more texts through the embeddings and add to the vectorstore.
add_documents(documents, **kwargs)	Run more documents through the embeddings and add to the vectorstore.
add_texts(texts[, metadatas])	Run more texts through the embeddings and add to the vectorstore.
adelete([ids])	Delete by vector ID or other criteria.
afrom_documents(documents, embedding, **kwargs)	Return VectorStore initialized from documents and embeddings.
afrom_texts(texts, embedding[, metadatas])	Return VectorStore initialized from texts and embeddings.
amax_marginal_relevance_search(query[, k, ...])	Return docs selected using the maximal marginal relevance.
amax_marginal_relevance_search_by_vector(...)	Return docs selected using the maximal marginal relevance.
as_retriever(**kwargs)	Return VectorStoreRetriever initialized from this VectorStore.
asearch(query, search_type, **kwargs)	Return docs most similar to query using specified search type.
asimilarity_search(query[, k])	Return docs most similar to query.
asimilarity_search_by_vector(embedding[, k])	Return docs most similar to embedding vector.
asimilarity_search_with_relevance_scores(query)	Return docs and relevance scores in the range [0, 1], asynchronously.
asimilarity_search_with_score(*args, **kwargs)	Run similarity search with distance asynchronously.
delete([ids])	Delete by vector ID or other criteria.
from_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.
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, 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: Optional[Credentials] = None, *, document_id_key: Optional[str] = None)

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.

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]

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]

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

Parameters

• texts – Iterable of strings to add to the vectorstore.

• metadatas – Optional list of metadatas associated with the texts.

• kwargs – vectorstore specific parameters.

Returns

List of ids from adding the texts into the vectorstore.

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

Delete by vector ID or other criteria.

Parameters

• ids – List of ids to delete.

• **kwargs – Other keyword arguments that subclasses might use.

Returns

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

Return type

Optional[bool]

async classmethod afrom_documents(documents: List[Document], embedding: Embeddings, **kwargs: Any) → VST

Return VectorStore initialized from documents and embeddings.

async classmethod afrom_texts(texts: List[str], embedding: Embeddings, metadatas: Optional[List[dict]] = None, **kwargs: Any) → VST

Return VectorStore initialized from texts and embeddings.

async amax_marginal_relevance_search(query: str, k: int = 4, fetch_k: int = 20, lambda_mult: float = 0.5, **kwargs: Any) → List[Document]

Return docs selected using the maximal marginal relevance.

async amax_marginal_relevance_search_by_vector(embedding: List[float], k: int = 4, fetch_k: int = 20, lambda_mult: float = 0.5, **kwargs: Any) → List[Document]

Return docs selected using the maximal marginal relevance.

as_retriever(**kwargs: Any) → VectorStoreRetriever

Return VectorStoreRetriever initialized from this VectorStore.

Parameters

• search_type (Optional[str]) – Defines the type of search that the Retriever should perform. Can be “similarity” (default), “mmr”, or “similarity_score_threshold”.

• search_kwargs (Optional[Dict]) –

  Keyword arguments to pass to the search function. Can include things like:

  k: Amount of documents to return (Default: 4) score_threshold: Minimum relevance threshold

  for similarity_score_threshold

  fetch_k: Amount of documents to pass to MMR algorithm (Default: 20) lambda_mult: Diversity of results returned by MMR;

  1 for minimum diversity and 0 for maximum. (Default: 0.5)

  filter: Filter by document metadata

Returns

Retriever class for VectorStore.

Return type

VectorStoreRetriever

Examples:

# Retrieve more documents with higher diversity
# Useful if your dataset has many similar documents
docsearch.as_retriever(
    search_type="mmr",
    search_kwargs={'k': 6, 'lambda_mult': 0.25}
)

# Fetch more documents for the MMR algorithm to consider
# But only return the top 5
docsearch.as_retriever(
    search_type="mmr",
    search_kwargs={'k': 5, 'fetch_k': 50}
)

# Only retrieve documents that have a relevance score
# Above a certain threshold
docsearch.as_retriever(
    search_type="similarity_score_threshold",
    search_kwargs={'score_threshold': 0.8}
)

# Only get the single most similar document from the dataset
docsearch.as_retriever(search_kwargs={'k': 1})

# Use a filter to only retrieve documents from a specific paper
docsearch.as_retriever(
    search_kwargs={'filter': {'paper_title':'GPT-4 Technical Report'}}
)

async asearch(query: str, search_type: str, **kwargs: Any) → List[Document]

Return docs most similar to query using specified search type.

async asimilarity_search(query: str, k: int = 4, **kwargs: Any) → List[Document]

Return docs most similar to query.

async asimilarity_search_by_vector(embedding: List[float], k: int = 4, **kwargs: Any) → List[Document]

Return docs most similar to embedding vector.

async asimilarity_search_with_relevance_scores(query: str, k: int = 4, **kwargs: Any) → List[Tuple[Document, float]]

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

0 is dissimilar, 1 is most similar.

Parameters

• query – input text

• k – Number of Documents to return. Defaults to 4.

• **kwargs –

  kwargs to be passed to similarity search. Should include: score_threshold: Optional, a floating point value between 0 to 1 to

  filter the resulting set of retrieved docs

Returns

List of Tuples of (doc, similarity_score)

async asimilarity_search_with_score(*args: Any, **kwargs: Any) → List[Tuple[Document, float]]

Run similarity search with distance asynchronously.

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

Delete by vector ID or other criteria.

Parameters

• ids – List of ids to delete.

• **kwargs – Other keyword arguments that subclasses might use.

Returns

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

Return type

Optional[bool]

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

Takes the object creation out of the constructor.

Parameters

• project_id – The GCP project id.

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

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

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

• created. (order for the index to be) –

• index_id – The id of the created index.

• endpoint_id – The id of the created endpoint.

• credentials_path – (Optional) The path of the Google credentials on

• system. (the local file) –

• embedding – The Embeddings that will be used for

• texts. (embedding the) –

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

Returns

A configured MatchingEngine with the texts added to the index.

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) → MatchingEngine

Use from components instead.

max_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.

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.

similarity_search(query: str, k: int = 4, filter: Optional[List[Namespace]] = None, **kwargs: Any) → List[Document]

Return docs most similar to query.

Parameters

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

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

• filter –

  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

A list of k matching documents.

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

Return docs most similar to the embedding.

Parameters

• embedding – Embedding to look up documents similar to.

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

• filter –

  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

A list of k matching documents.

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

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

Parameters

• embedding – Embedding to look up documents similar to.

• k – Number of Documents to return. Defaults to 4.

• filter – 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 – 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, filter: Optional[List[Namespace]] = None) → List[Tuple[Document, float]]

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

Parameters

• query – String query look up documents similar to.

• k – Number of Documents to return. Defaults to 4.

• filter – 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]]

Examples using MatchingEngine

• Google Vertex AI MatchingEngine