Tony20100/langchain_full huggingface dataset:
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 betweentwo 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
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
documents (List[Document]) – Documents to add to the vectorstore.
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]) – Iterable of strings to add to the vectorstore.
metadatas (Optional[List[dict]]) – Optional list of metadatas associated with the texts.
**kwargs (Any) – vectorstore specific parameters.
Returns
List of ids from adding the texts into the vectorstore.
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
documents (List[Document]) – Documents to add to the vectorstore.
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.
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]) – List of Documents to add to the vectorstore.
embedding (Embeddings) – Embedding function to use.
kwargs (Any) – 
Return type
VST
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]) – Texts to add to the vectorstore.
metadatas (Optional[List[dict]]) – Optional list of metadatas associated with the texts.
embedding (Embeddings) – Embedding function to use.
kwargs (Any) – 
Return type
VST
async amax_marginal_relevance_search(query: str, 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
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
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",
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) – 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.
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) – Input text.
k (int) – Number of Documents to return. Defaults to 4.
kwargs (Any) – 
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]¶
Return docs most similar to embedding vector.
Parameters
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]
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].
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.
Returns
List of Tuples of (doc, similarity_score)
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]
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]) – List of Documents to add to the vectorstore.
embedding (Embeddings) – Embedding function to use.
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 type
List[Document]
max_marginal_relevance_search(query: str, 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
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]
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.
Return docs most similar to query using 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.
Return type
List[Document]
similarity_search(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[Document][source]¶
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.
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
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.
langchain_community.vectorstores.utils.DistanceStrategy¶
class langchain_community.vectorstores.utils.DistanceStrategy(value)[source]¶
Enumerator of the Distance strategies for calculating distances
between vectors.
EUCLIDEAN_DISTANCE = 'EUCLIDEAN_DISTANCE'¶
MAX_INNER_PRODUCT = 'MAX_INNER_PRODUCT'¶
DOT_PRODUCT = 'DOT_PRODUCT'¶
JACCARD = 'JACCARD'¶
COSINE = 'COSINE'¶
Examples using DistanceStrategy¶
Google BigQuery Vector Search
Kinetica Vectorstore API
Oracle AI Vector Search: Vector Store
SAP HANA Cloud Vector Engine
SemaDB
SingleStoreDB
langchain_community.vectorstores.starrocks.get_named_result¶
langchain_community.vectorstores.starrocks.get_named_result(connection: Any, query: str) → List[dict[str, Any]][source]¶
Get a named result from a query.
:param connection: The connection to the database
:param query: The query to execute
Returns
The result of the query
Return type
List[dict[str, Any]]
Parameters
connection (Any) – 
query (str) –
langchain_community.vectorstores.aerospike.Aerospike¶
class langchain_community.vectorstores.aerospike.Aerospike(client: Client, embedding: Union[Embeddings, Callable], namespace: str, index_name: Optional[str] = None, vector_key: str = '_vector', text_key: str = '_text', id_key: str = '_id', set_name: Optional[str] = None, distance_strategy: Optional[Union[DistanceStrategy, VectorDistanceMetric]] = DistanceStrategy.EUCLIDEAN_DISTANCE)[source]¶
Aerospike vector store.
To use, you should have the aerospike_vector_search python package installed.
Initialize with Aerospike client.
Parameters
client (Client) – Aerospike client.
embedding (Union[Embeddings, Callable]) – Embeddings object or Callable (deprecated) to embed text.
namespace (str) – Namespace to use for storing vectors. This should match
index_name (Optional[str]) – Name of the index previously created in Aerospike. This
vector_key (str) – Key to use for vector in metadata. This should match the
key used during index creation.
text_key (str) – Key to use for text in metadata.
id_key (str) – Key to use for id in metadata.
set_name (Optional[str]) – Default set name to use for storing vectors.
distance_strategy (Optional[Union[DistanceStrategy, VectorDistanceMetric]]) – Distance strategy to use for similarity search
This should match the distance strategy used during index creation.
Attributes
embeddings
Access the query embedding object if available.
Methods
__init__(client, embedding, namespace[, ...])
Initialize with Aerospike client.
aadd_documents(documents, **kwargs)
Run more documents through the embeddings and add to the vectorstore.
aadd_texts(texts[, metadatas])
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, ids, set_name, ...])
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].
asimilarity_search_with_score(*args, **kwargs)
Run similarity search with distance.
convert_distance_strategy(distance_strategy)
Convert Aerospikes distance strategy to langchains DistanceStrategy enum.
delete([ids, set_name])
Delete by vector ID or other criteria.
from_documents(documents, embedding, **kwargs)
Return VectorStore initialized from documents and embeddings.