Tony20100/langchain_full huggingface dataset:
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.
delete([ids])
Delete by vector ID or other criteria.
from_components(project_id, region, ...[, ...])
Takes the object creation out of the constructor.
from_documents(documents, embedding, **kwargs)
Return VectorStore initialized from documents and embeddings.
from_texts(texts, embedding[, metadatas])
Use from components instead.
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, ...])
similarity_search(query[, k, filter, ...])
Return docs most similar to query. :param query: The string that will be used to search for similar documents. :param k: The amount of neighbors that will be retrieved. :param filter: Optional. A list of Namespaces for filtering the matching results. For example:                [Namespace("color", ["red"], []), Namespace("shape", [], ["squared"])]                will match datapoints that satisfy "red color" but not include                datapoints with "squared shape". Please refer to                https://cloud.google.com/vertex-ai/docs/matching-engine/filtering#json                 for more detail. :param numeric_filter: Optional. A list of NumericNamespaces for filterning                        the matching results. Please refer to                        https://cloud.google.com/vertex-ai/docs/matching-engine/filtering#json                        for more detail.
similarity_search_by_vector(embedding[, k])
Return docs most similar to embedding vector.
similarity_search_by_vector_with_score(embedding)
Return docs most similar to the embedding and their cosine distance. :param embedding: Embedding to look up documents similar to. :param k: Number of Documents to return. Defaults to 4. :param filter: Optional. A list of Namespaces for filtering                the matching results. For example:                [Namespace("color", ["red"], []), Namespace("shape", [], ["squared"])]                will match datapoints that satisfy "red color" but not include                datapoints with "squared shape". Please refer to                https://cloud.google.com/vertex-ai/docs/matching-engine/filtering#json                for more detail. :param numeric_filter: Optional. A list of NumericNamespaces for filterning                        the matching results. Please refer to                        https://cloud.google.com/vertex-ai/docs/matching-engine/filtering#json                        for more detail.
similarity_search_with_relevance_scores(query)
Return docs and relevance scores in the range [0, 1].
similarity_search_with_score(query[, k, ...])
Return docs most similar to query and their cosine distance from the query. :param query: String query look up documents similar to. :param k: Number of Documents to return. Defaults to 4. :param filter: Optional. A list of Namespaces for filtering                the matching results. For example:                [Namespace("color", ["red"], []), Namespace("shape", [], ["squared"])]                will match datapoints that satisfy "red color" but not include                datapoints with "squared shape". Please refer to                https://cloud.google.com/vertex-ai/docs/matching-engine/filtering#json                for more detail. :param numeric_filter: Optional. A list of NumericNamespaces for filterning                        the matching results. Please refer to                        https://cloud.google.com/vertex-ai/docs/matching-engine/filtering#json                        for more detail.
Parameters
searcher (Searcher) – 
document_storage (DocumentStorage) – 
embbedings (Optional[Embeddings]) – 
__init__(searcher: Searcher, document_storage: DocumentStorage, embbedings: Optional[Embeddings] = None) → None¶
Constructor.
:param searcher: Object in charge of searching and storing the index.
:param document_storage: Object in charge of storing and retrieving documents.
:param embbedings: Object in charge of transforming text to embbeddings.
Parameters
searcher (Searcher) – 
document_storage (DocumentStorage) – 
embbedings (Optional[Embeddings]) – 
Return type
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: Iterable[str], metadatas: Optional[List[dict]] = None, is_complete_overwrite: bool = False, **kwargs: Any) → List[str]¶
Run more texts through the embeddings and add to the vectorstore.
:param texts: Iterable of strings to add to the vectorstore.
:param metadatas: Optional list of metadatas associated with the texts.
:param kwargs: vectorstore specific parameters.
Returns
List of ids from adding the texts into the vectorstore.
Parameters
texts (Iterable[str]) – 
metadatas (Optional[List[dict]]) – 
is_complete_overwrite (bool) –
is_complete_overwrite (bool) – 
kwargs (Any) – 
Return type
List[str]
async adelete(ids: Optional[List[str]] = None, **kwargs: Any) → Optional[bool]¶
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
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, **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
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]¶
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]
classmethod from_components(project_id: str, region: str, index_id: str, endpoint_id: str, index_staging_bucket_name: Optional[str] = None, credentials_path: Optional[str] = None, embedding: Optional[Embeddings] = None, stream_update: bool = False, datastore_client_kwargs: Optional[Dict[str, Any]] = None, datastore_kind: str = 'document_id', datastore_text_property_name: str = 'text', datastore_metadata_property_name: str = 'metadata', **kwargs: Dict[str, Any]) → VectorSearchVectorStoreDatastore[source]¶
Takes the object creation out of the constructor.
Takes the object creation out of the constructor.
# Args:project_id: The GCP project id.
region: The default location making the API calls. It must have
the same location as the GCS bucket and must be regional.
index_id: The id of the created index.
endpoint_id: The id of the created endpoint.
index_staging_bucket_name: (Optional) If the index is updated by batch,
bucket where the data will be staged before updating the index. Only
required when updating the index.
credentials_path: (Optional) The path of the Google credentials on
the local file system.
embedding: The Embeddings that will be used for
embedding the texts.
stream_update: Whether to update with streaming or batching. VectorSearch
index must be compatible with stream/batch updates.
kwargs: Additional keyword arguments to pass toVertexAIVectorSearch.__init__().
Parameters
project_id (str) – 
region (str) – 
index_id (str) – 
endpoint_id (str) – 
index_staging_bucket_name (Optional[str]) – 
credentials_path (Optional[str]) – 
embedding (Optional[Embeddings]) – 
stream_update (bool) – 
datastore_client_kwargs (Optional[Dict[str, Any]]) – 
datastore_kind (str) – 
datastore_text_property_name (str) – 
datastore_metadata_property_name (str) – 
kwargs (Dict[str, Any]) – 
Return type
VectorSearchVectorStoreDatastore
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.
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) → _BaseVertexAIVectorStore¶
Use from components instead.
Parameters
texts (List[str]) – 
embedding (Embeddings) – 
metadatas (Optional[List[dict]]) – 
kwargs (Any) – 
Return type
_BaseVertexAIVectorStore
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
Return docs most similar to query and their cosine distance from the query.
:param query: String query look up documents similar to.
:param k: Number of Documents to return. Defaults to 4.
:param filter: Optional. A list of Namespaces for filtering
the matching results.
For example:
[Namespace(“color”, [“red”], []), Namespace(“shape”, [], [“squared”])]
will match datapoints that satisfy “red color” but not include
datapoints with “squared shape”. Please refer to
https://cloud.google.com/vertex-ai/docs/matching-engine/filtering#json
for more detail.
Parameters
numeric_filter (Optional[List[NumericNamespace]]) – Optional. A list of NumericNamespaces for filterning
the matching results. Please refer to
https://cloud.google.com/vertex-ai/docs/matching-engine/filtering#json
for more detail.
query (str) – 
k (int) – 
filter (Optional[List[Namespace]]) – 
Returns
List of documents most similar to
the query text and cosine distance in float for each.
Lower score represents more similarity.
Return type
List[Tuple[Document, float]]
Examples using VectorSearchVectorStoreDatastore¶
Google Vertex AI Vector Search
langchain_community.vectorstores.pgvector.BaseModel¶
class langchain_community.vectorstores.pgvector.BaseModel(**kwargs: Any)[source]¶
Base model for the SQL stores.
A simple constructor that allows initialization from kwargs.
Sets attributes on the constructed instance using the names and
values in kwargs.
Only keys that are present as
attributes of the instance’s class are allowed. These could be,
for example, any mapped columns or relationships.
Attributes
metadata
registry
uuid
Methods
__init__(**kwargs)
A simple constructor that allows initialization from kwargs.
Parameters
kwargs (Any) – 
__init__(**kwargs: Any) → None¶
A simple constructor that allows initialization from kwargs.
Sets attributes on the constructed instance using the names and
values in kwargs.
Only keys that are present as
attributes of the instance’s class are allowed. These could be,
for example, any mapped columns or relationships.
Parameters
self (Any) – 
kwargs (Any) – 
Return type
None
langchain_elasticsearch.vectorstores.BaseRetrievalStrategy¶
class langchain_elasticsearch.vectorstores.BaseRetrievalStrategy(*args, **kwargs)[source]¶
[Deprecated] Base class for Elasticsearch retrieval strategies.
Notes
Deprecated since version 0.2.0: Use RetrievalStrategy instead.
Methods
__init__(*args, **kwargs)
before_index_setup(client, text_field, ...)
Executes before the index is created.
index(dims_length, vector_query_field, ...)
Executes when the index is created.
query(query_vector, query, *, k, fetch_k, ...)
Executes when a search is performed on the store.
require_inference()
Returns whether or not the strategy requires inference to be performed on the text before it is added to the index.
__init__(*args: Any, **kwargs: Any) → Any¶
Parameters
self (Any) – 
args (Any) – 
kwargs (Any) – 
Return type
Any
before_index_setup(client: Elasticsearch, text_field: str, vector_query_field: str) → None[source]¶
Executes before the index is created. Used for setting up
any required Elasticsearch resources like a pipeline.
Parameters
client (Elasticsearch) – The Elasticsearch client.
text_field (str) – The field containing the text data in the index.
vector_query_field (str) – The field containing the vector
representations in the index.
Return type
None
abstract index(dims_length: Optional[int], vector_query_field: str, text_field: str, similarity: Optional[DistanceStrategy]) → Dict[source]¶
Executes when the index is created.
Parameters
dims_length (Optional[int]) – Numeric length of the embedding vectors,
or None if not using vector-based query.
or None if not using vector-based query.
vector_query_field (str) – The field containing the vector
representations in the index.
text_field (str) – The field containing the text data in the index.
similarity (Optional[DistanceStrategy]) – The similarity strategy to use,
or None if not using one.
Returns
The Elasticsearch settings and mappings for the strategy.
Return type
Dict
abstract query(query_vector: Optional[List[float]], query: Optional[str], *, k: int, fetch_k: int, vector_query_field: str, text_field: str, filter: List[dict], similarity: Optional[DistanceStrategy]) → Dict[source]¶
Executes when a search is performed on the store.
Parameters
query_vector (Optional[List[float]]) – The query vector,
or None if not using vector-based query.
query (Optional[str]) – The text query, or None if not using text-based query.
k (int) – The total number of results to retrieve.
fetch_k (int) – The number of results to fetch initially.
vector_query_field (str) – The field containing the vector
representations in the index.
text_field (str) – The field containing the text data in the index.
filter (List[dict]) – List of filter clauses to apply to the query.
similarity (Optional[DistanceStrategy]) – The similarity strategy to use, or None if not using one.
Returns
The Elasticsearch query body.
Return type
Dict
require_inference() → bool[source]¶
Returns whether or not the strategy requires inference
to be performed on the text before it is added to the index.
Returns
Whether or not the strategy requires inference
to be performed on the text before it is added to the index.
Return type
bool
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.
- 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
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.
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.
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)