GoogleVectorStore#

class langchain_google_genai.google_vector_store.GoogleVectorStore(*, corpus_id: str, document_id: str | None = None, **kwargs: Any)[source]#

Google GenerativeAI Vector Store.

Currently, it computes the embedding vectors on the server side.

Example: Add texts to an existing corpus.

store = GoogleVectorStore(corpus_id=”123”) store.add_documents(documents, document_id=”456”)

Example: Create a new corpus.

store = GoogleVectorStore.create_corpus(
corpus_id=”123”, display_name=”My Google corpus”)

Example: Query the corpus for relevant passages.

store.as_retriever() .get_relevant_documents(“Who caught the gingerbread man?”)

Example: Ask the corpus for grounded responses!

aqa = store.as_aqa() response = aqa.invoke(“Who caught the gingerbread man?”) print(response.answer) print(response.attributed_passages) print(response.answerability_probability)

You can also operate at Google’s Document level.

Example: Add texts to an existing Google Vector Store Document.

doc_store = GoogleVectorStore(corpus_id=”123”, document_id=”456”) doc_store.add_documents(documents)

Example: Create a new Google Vector Store Document.

doc_store = GoogleVectorStore.create_document(
corpus_id=”123”, document_id=”456”, display_name=”My Google document”)

Example: Query the Google document.

doc_store.as_retriever() .get_relevant_documents(“Who caught the gingerbread man?”)

For more details, see the class’s methods.

Returns an existing Google Semantic Retriever corpus or document.

If just the corpus ID is provided, the vector store operates over all documents within that corpus.

If the document ID is provided, the vector store operates over just that document.

Raises:

DoesNotExistsException if the IDs do not match to anything on Google – server. In this case, consider using create_corpus or create_document to create one.

Parameters:

corpus_id (str)
document_id (str | None)
kwargs (Any)

Attributes

`corpus_id`	Returns the corpus ID managed by this vector store.
`document_id`	Returns the document ID managed by this vector store.
`embeddings`	Access the query embedding object if available.
`name`	Returns the name of the Google entity.

Methods

`__init__`(*, corpus_id[, document_id])	Returns an existing Google Semantic Retriever corpus or document.
`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.
`add_documents`(documents, **kwargs)	Add or update documents in the vectorstore.
`add_texts`(texts[, metadatas, document_id])	Add texts to the vector store.
`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])	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_aqa`(**kwargs)	Construct a Google Generative AI AQA engine.
`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.
`create_corpus`([corpus_id, display_name])	Create a Google Semantic Retriever corpus.
`create_document`(corpus_id[, document_id, ...])	Create a Google Semantic Retriever document.
`delete`([ids])	Delete chunnks.
`from_documents`(documents, embedding, **kwargs)	Return VectorStore initialized from documents and embeddings.
`from_texts`(texts[, embedding, metadatas, ...])	Returns a vector store of an existing document with the specified text.
`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])	Search the vector store for relevant texts.
`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, filter])	Run similarity search with distance.

__init__(*, corpus_id: str, document_id: str | None = None, **kwargs: Any)[source]#

Returns an existing Google Semantic Retriever corpus or document.

If just the corpus ID is provided, the vector store operates over all documents within that corpus.

If the document ID is provided, the vector store operates over just that document.

Raises:

DoesNotExistsException if the IDs do not match to anything on Google – server. In this case, consider using create_corpus or create_document to create one.

Parameters:

corpus_id (str)
document_id (str | None)
kwargs (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: list[dict] | 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.
**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[str, Any]] | None = None, *, document_id: str | None = None, **kwargs: Any) → List[str][source]#

Add texts to the vector store.

If the vector store points to a corpus (instead of a document), you must also provide a document_id.

Returns:

Chunk’s names created on Google servers.

Parameters:

texts (Iterable[str])
metadatas (List[Dict[str, Any]] | None)
document_id (str | None)
kwargs (Any)

Return type:

List[str]

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

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, **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.
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 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]#

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_aqa(**kwargs: Any) → Runnable[str, AqaOutput][source]#

Construct a Google Generative AI AQA engine.

All arguments are optional.

Parameters:

answer_style – See google.ai.generativelanguage.GenerateAnswerRequest.AnswerStyle.
safety_settings – See google.ai.generativelanguage.SafetySetting.
temperature – 0.0 to 1.0.
kwargs (Any)

Return type:

Runnable[str, AqaOutput]

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]]

classmethod create_corpus(corpus_id: str | None = None, display_name: str | None = None) → GoogleVectorStore[source]#

Create a Google Semantic Retriever corpus.

Parameters:

corpus_id (str | None) – The ID to use to create the new corpus. If not provided, Google server will provide one.
display_name (str | None) – The title of the new corpus. If not provided, Google server will provide one.

Returns:

An instance of vector store that points to the newly created corpus.

Return type:

GoogleVectorStore

classmethod create_document(corpus_id: str, document_id: str | None = None, display_name: str | None = None, metadata: Dict[str, Any] | None = None) → GoogleVectorStore[source]#

Create a Google Semantic Retriever document.

Parameters:

corpus_id (str) – ID of an existing corpus.
document_id (str | None) – The ID to use to create the new Google Semantic Retriever document. If not provided, Google server will provide one.
display_name (str | None) – The title of the new document. If not provided, Google server will provide one.
metadata (Dict[str, Any] | None)

Returns:

An instance of vector store that points to the newly created document.

Return type:

GoogleVectorStore

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

Delete chunnks.

Note that the “ids” are not corpus ID or document ID. Rather, these are the entity names returned by add_texts.

Returns:

True if successful. Otherwise, you should get an exception anyway.

Parameters:

ids (List[str] | None)
kwargs (Any)

Return type:

bool | None

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 | None = None, metadatas: List[dict[str, Any]] | None = None, *, corpus_id: str | None = None, document_id: str | None = None, **kwargs: Any) → GoogleVectorStore[source]#

Returns a vector store of an existing document with the specified text.

Parameters:

corpus_id (str | None) – REQUIRED. Must be an existing corpus.
document_id (str | None) – REQUIRED. Must be an existing document.
texts (List[str]) – Texts to be loaded into the vector store.
embedding (Embeddings | None)
metadatas (List[dict[str, Any]] | None)
kwargs (Any)

Returns:

A vector store pointing to the specified Google Semantic Retriever Document.

Raises:

DoesNotExistsException if the IDs do not match to anything at – Google server.

Return type:

GoogleVectorStore

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.

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 (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]

similarity_search(query: str, k: int = 4, filter: Dict[str, Any] | None = None, **kwargs: Any) → List[Document][source]#

Search the vector store for relevant texts.

Parameters:

query (str)
k (int)
filter (Dict[str, Any] | None)
kwargs (Any)

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, filter: Dict[str, Any] | None = None, **kwargs: Any) → List[Tuple[Document, float]][source]#

Run similarity search with distance.

Parameters:

query (str)
k (int)
filter (Dict[str, Any] | None)
kwargs (Any)

Return type:

List[Tuple[Document, float]]