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)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, document_id])Add texts to the vector store.
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_aqa
(**kwargs)Construct a Google Generative AI AQA engine.
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.
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.
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, filter])Search the vector store for relevant texts.
similarity_search_by_vector
(embedding[, k])Return docs most similar to embedding vector.
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] ¶
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[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]¶
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.
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]
- 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_aqa(**kwargs: Any) Runnable[str, AqaOutput] [source]¶
Construct a Google Generative AI AQA engine.
All arguments are optional.
- 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]]
- 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
- 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
- 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]) –
embedding (Embeddings) –
kwargs (Any) –
- Return type
VST
- 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
- 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.
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] ¶
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, 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) –
- 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]]