`langchain_google_genai.google_vector_store`.GoogleVectorStore¶

class langchain_google_genai.google_vector_store.GoogleVectorStore(*, corpus_id: str, document_id: Optional[str] = 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 (Optional[str]) –
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: Optional[str] = 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 (Optional[str]) –
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: Optional[List[dict]] = 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 (Optional[List[dict]]) – 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: Optional[List[Dict[str, Any]]] = None, *, document_id: Optional[str] = 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 (Optional[List[Dict[str, Any]]]) –
document_id (Optional[str]) –
kwargs (Any) –

Return type

List[str]

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

Async delete by vector ID or other criteria.

Parameters

ids (Optional[List[str]]) – 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: Optional[List[dict]] = 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 (Optional[List[dict]]) – 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]

New 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: Optional[str] = None, display_name: Optional[str] = None) → GoogleVectorStore[source]¶

Create a Google Semantic Retriever corpus.

Parameters

corpus_id (Optional[str]) – The ID to use to create the new corpus. If not provided, Google server will provide one.
display_name (Optional[str]) – 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: Optional[str] = None, display_name: Optional[str] = None, metadata: Optional[Dict[str, Any]] = None) → GoogleVectorStore[source]¶

Create a Google Semantic Retriever document.

Parameters

corpus_id (str) – ID of an existing corpus.
document_id (Optional[str]) – The ID to use to create the new Google Semantic Retriever document. If not provided, Google server will provide one.
display_name (Optional[str]) – The title of the new document. If not provided, Google server will provide one.
metadata (Optional[Dict[str, Any]]) –

Returns

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

Return type

GoogleVectorStore

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

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]) – 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: Optional[Embeddings] = None, metadatas: Optional[List[dict[str, Any]]] = None, *, corpus_id: Optional[str] = None, document_id: Optional[str] = None, **kwargs: Any) → GoogleVectorStore[source]¶

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

Parameters

corpus_id (Optional[str]) – REQUIRED. Must be an existing corpus.
document_id (Optional[str]) – REQUIRED. Must be an existing document.
texts (List[str]) – Texts to be loaded into the vector store.
embedding (Optional[Embeddings]) –
metadatas (Optional[List[dict[str, Any]]]) –
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]

New 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: Optional[Dict[str, Any]] = None, **kwargs: Any) → List[Document][source]¶

Search the vector store for relevant texts.

Parameters

query (str) –
k (int) –
filter (Optional[Dict[str, Any]]) –
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: Optional[Dict[str, Any]] = None, **kwargs: Any) → List[Tuple[Document, float]][source]¶

Run similarity search with distance.

Parameters

query (str) –
k (int) –
filter (Optional[Dict[str, Any]]) –
kwargs (Any) –

Return type

List[Tuple[Document, float]]

langchain_google_genai.google_vector_store.GoogleVectorStore¶

`langchain_google_genai.google_vector_store`.GoogleVectorStore¶