langchain_community.vectorstores.bigquery_vector_search.BigQueryVectorSearch¶

class langchain_community.vectorstores.bigquery_vector_search.BigQueryVectorSearch(embedding: Embeddings, project_id: str, dataset_name: str, table_name: str, location: str = 'US', content_field: str = 'content', metadata_field: str = 'metadata', text_embedding_field: str = 'text_embedding', doc_id_field: str = 'doc_id', distance_strategy: DistanceStrategy = DistanceStrategy.EUCLIDEAN_DISTANCE, credentials: Optional[Any] = None)[source]¶

[Deprecated] Google Cloud BigQuery vector store.

To use, you need the following packages installed:

google-cloud-bigquery

Notes

Deprecated since version 0.0.33.

Constructor for BigQueryVectorSearch.

Parameters

  • embedding (Embeddings) – Text Embedding model to use.

  • project_id (str) – GCP project.

  • dataset_name (str) – BigQuery dataset to store documents and embeddings.

  • table_name (str) – BigQuery table name.

  • location (str, optional) – BigQuery region. Defaults to `US`(multi-region).

  • content_field (str) – Specifies the column to store the content. Defaults to content.

  • metadata_field (str) – Specifies the column to store the metadata. Defaults to metadata.

  • text_embedding_field (str) – Specifies the column to store the embeddings vector. Defaults to text_embedding.

  • doc_id_field (str) – Specifies the column to store the document id. Defaults to doc_id.

  • distance_strategy (DistanceStrategy, optional) –

    Determines the strategy employed for calculating the distance between vectors in the embedding space. Defaults to EUCLIDEAN_DISTANCE. Available options are: - COSINE: Measures the similarity between two vectors of an inner

    product space.

    • EUCLIDEAN_DISTANCE: Computes the Euclidean distance between

      two vectors. This metric considers the geometric distance in the vector space, and might be more suitable for embeddings that rely on spatial relationships. This is the default behavior

  • credentials (Credentials, optional) – Custom Google Cloud credentials to use. Defaults to None.

Attributes

embeddings

Access the query embedding object if available.

full_table_id

Methods

__init__(embedding, project_id, ...[, ...])

Constructor for BigQueryVectorSearch.

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.

add_texts_with_embeddings(texts, embs[, ...])

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.

explore_job_stats(job_id)

Return the statistics for a single job execution.

from_documents(documents, embedding, **kwargs)

Return VectorStore initialized from documents and embeddings.

from_texts(texts, embedding[, metadatas])

Return VectorStore initialized from texts and embeddings.

get_documents([ids, filter])

Search documents by their ids or metadata values.

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, ...])

Run similarity search.

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, ...])

Run similarity search with score.

similarity_search_with_score_by_vector(embedding)

Return docs most similar to embedding vector.
__init__(embedding: Embeddings, project_id: str, dataset_name: str, table_name: str, location: str = 'US', content_field: str = 'content', metadata_field: str = 'metadata', text_embedding_field: str = 'text_embedding', doc_id_field: str = 'doc_id', distance_strategy: DistanceStrategy = DistanceStrategy.EUCLIDEAN_DISTANCE, credentials: Optional[Any] = None)[source]¶

Constructor for BigQueryVectorSearch.

Parameters

  • embedding (Embeddings) – Text Embedding model to use.

  • project_id (str) – GCP project.

  • dataset_name (str) – BigQuery dataset to store documents and embeddings.

  • table_name (str) – BigQuery table name.

  • location (str, optional) – BigQuery region. Defaults to `US`(multi-region).

  • content_field (str) – Specifies the column to store the content. Defaults to content.

  • metadata_field (str) – Specifies the column to store the metadata. Defaults to metadata.

  • text_embedding_field (str) – Specifies the column to store the embeddings vector. Defaults to text_embedding.

  • doc_id_field (str) – Specifies the column to store the document id. Defaults to doc_id.

  • distance_strategy (DistanceStrategy, optional) –

    Determines the strategy employed for calculating the distance between vectors in the embedding space. Defaults to EUCLIDEAN_DISTANCE. Available options are: - COSINE: Measures the similarity between two vectors of an inner

    product space.

    • EUCLIDEAN_DISTANCE: Computes the Euclidean distance between

      two vectors. This metric considers the geometric distance in the vector space, and might be more suitable for embeddings that rely on spatial relationships. This is the default behavior

  • credentials (Credentials, optional) – Custom Google Cloud credentials to use. Defaults to None.

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: List[str], metadatas: Optional[List[dict]] = None, **kwargs: Any) → List[str][source]¶

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

Parameters

  • texts (List[str]) – List of strings to add to the vectorstore.

  • metadatas (Optional[List[dict]]) – Optional list of metadata associated with the texts.

  • kwargs (Any) –

Returns

List of ids from adding the texts into the vectorstore.

Return type

List[str]

add_texts_with_embeddings(texts: List[str], embs: List[List[float]], metadatas: Optional[List[dict]] = None, **kwargs: Any) → List[str][source]¶

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

Parameters

  • texts (List[str]) – List of strings to add to the vectorstore.

  • embs (List[List[float]]) – List of lists of floats with text embeddings for texts.

  • metadatas (Optional[List[dict]]) – Optional list of metadata associated with the texts.

  • kwargs (Any) –

Returns

List of ids from adding the texts into the vectorstore.

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

Return docs selected using the maximal marginal relevance.

Parameters

  • query (str) –

  • k (int) –

  • fetch_k (int) –

  • lambda_mult (float) –

  • filter (Optional[Dict[str, Any]]) –

  • brute_force (bool) –

  • fraction_lists_to_search (Optional[float]) –

  • kwargs (Any) –

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, filter: Optional[Dict[str, Any]] = None, brute_force: bool = False, fraction_lists_to_search: Optional[float] = None, **kwargs: Any) → List[Document][source]¶

Return docs selected using the maximal marginal relevance.

Parameters

  • embedding (List[float]) –

  • k (int) –

  • fetch_k (int) –

  • lambda_mult (float) –

  • filter (Optional[Dict[str, Any]]) –

  • brute_force (bool) –

  • fraction_lists_to_search (Optional[float]) –

  • kwargs (Any) –

Return type

List[Document]

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

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.

Parameters

  • query (str) –

  • search_type (str) –

  • kwargs (Any) –

Return type

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]]

delete(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]

explore_job_stats(job_id: str) → Dict[source]¶

Return the statistics for a single job execution.

Parameters

job_id (str) – The BigQuery Job id.

Returns

A dictionary of job statistics for a given job.

Return type

Dict

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: Embeddings, metadatas: Optional[List[dict]] = None, **kwargs: Any) → BigQueryVectorSearch[source]¶

Return VectorStore initialized from texts and embeddings.

Parameters

  • texts (List[str]) –

  • embedding (Embeddings) –

  • metadatas (Optional[List[dict]]) –

  • kwargs (Any) –

Return type

BigQueryVectorSearch

get_documents(ids: Optional[List[str]] = None, filter: Optional[Dict[str, Any]] = None) → List[Document][source]¶

Search documents by their ids or metadata values.

Parameters

  • ids (Optional[List[str]]) – List of ids of documents to retrieve from the vectorstore.

  • filter (Optional[Dict[str, Any]]) –

    Filter on metadata properties, e.g. {

    ”str_property”: “foo”, “int_property”: 123

    }

Returns

List of ids from adding the texts into the vectorstore.

Return type

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) – search query text.

  • 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.

  • filter (Optional[Dict[str, Any]]) –

    Filter on metadata properties, e.g. {

    ”str_property”: “foo”, “int_property”: 123

    }

  • brute_force (bool) – Whether to use brute force search. Defaults to False.

  • fraction_lists_to_search (Optional[float]) – Optional percentage of lists to search, must be in range 0.0 and 1.0, exclusive. If Node, uses service’s default which is 0.05.

  • 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, filter: Optional[Dict[str, Any]] = None, brute_force: bool = False, fraction_lists_to_search: Optional[float] = None, **kwargs: Any) → List[Document][source]¶

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.

  • filter (Optional[Dict[str, Any]]) –

    Filter on metadata properties, e.g. {

    ”str_property”: “foo”, “int_property”: 123

    }

  • brute_force (bool) – Whether to use brute force search. Defaults to False.

  • fraction_lists_to_search (Optional[float]) – Optional percentage of lists to search, must be in range 0.0 and 1.0, exclusive. If Node, uses service’s default which is 0.05.

  • 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]

Run similarity search.

Parameters

  • query (str) – search query text.

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

  • filter (Optional[Dict[str, Any]]) –

    Filter on metadata properties, e.g. {

    ”str_property”: “foo”, “int_property”: 123

    }

  • brute_force (bool) – Whether to use brute force search. Defaults to False.

  • fraction_lists_to_search (Optional[float]) – Optional percentage of lists to search, must be in range 0.0 and 1.0, exclusive. If Node, uses service’s default which is 0.05.

  • kwargs (Any) –

Returns

List of Documents most similar to the query vector.

Return type

List[Document]

similarity_search_by_vector(embedding: List[float], k: int = 4, filter: Optional[Dict[str, Any]] = None, brute_force: bool = False, fraction_lists_to_search: Optional[float] = None, **kwargs: Any) → List[Document][source]¶

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.

  • filter (Optional[Dict[str, Any]]) –

    Filter on metadata properties, e.g. {

    ”str_property”: “foo”, “int_property”: 123

    }

  • brute_force (bool) – Whether to use brute force search. Defaults to False.

  • fraction_lists_to_search (Optional[float]) – Optional percentage of lists to search, must be in range 0.0 and 1.0, exclusive. If Node, uses service’s default which is 0.05.

  • 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, brute_force: bool = False, fraction_lists_to_search: Optional[float] = None, **kwargs: Any) → List[Tuple[Document, float]][source]¶

Run similarity search with score.

Parameters

  • query (str) – search query text.

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

  • filter (Optional[Dict[str, Any]]) –

    Filter on metadata properties, e.g. {

    ”str_property”: “foo”, “int_property”: 123

    }

  • brute_force (bool) – Whether to use brute force search. Defaults to False.

  • fraction_lists_to_search (Optional[float]) – Optional percentage of lists to search, must be in range 0.0 and 1.0, exclusive. If Node, uses service’s default which is 0.05.

  • kwargs (Any) –

Returns

List of Documents most similar to the query vector, with similarity scores.

Return type

List[Tuple[Document, float]]

similarity_search_with_score_by_vector(embedding: List[float], k: int = 4, filter: Optional[Dict[str, Any]] = None, brute_force: bool = False, fraction_lists_to_search: Optional[float] = None, **kwargs: Any) → List[Tuple[Document, float]][source]¶

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.

  • filter (Optional[Dict[str, Any]]) –

    Filter on metadata properties, e.g. {

    ”str_property”: “foo”, “int_property”: 123

    }

  • brute_force (bool) – Whether to use brute force search. Defaults to False.

  • fraction_lists_to_search (Optional[float]) – Optional percentage of lists to search, must be in range 0.0 and 1.0, exclusive. If Node, uses service’s default which is 0.05.

  • kwargs (Any) –

Returns

List of Documents most similar to the query vector with distance.

Return type

List[Tuple[Document, float]]

Examples using BigQueryVectorSearch¶