Tony20100/langchain_full huggingface dataset:
Return documents most similar to embedding, along with scores.
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 from a query string.
__init__(table: Any, embedding: Embeddings, distance_strategy: Optional[DistanceStrategy] = DistanceStrategy.EUCLIDEAN_DISTANCE)[source]¶
Parameters
table (Any) – 
embedding (Embeddings) – 
distance_strategy (Optional[DistanceStrategy]) – 
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], batch_size: int = 32, **kwargs: Any) → List[str][source]¶
Run more documents through the embeddings and add to the vectorstore.
Parameters
(List[Document] (documents) – Documents to add to the vectorstore.
Parameters
(List[Document] (documents) – Documents to add to the vectorstore.
batch_size (Optional[int]) – Size of batch of documents to insert at once.
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]] = None, ids: Optional[List[str]] = None, batch_size: int = 32, **kwargs: Any) → List[str][source]¶
Run more texts through the embeddings and add to the vectorstore.
Parameters
texts (Iterable[str]) – Texts to add to the vectorstore.
metadatas (Optional[List[dict]]) – List of metadata corresponding to each
chunk of text.
ids (Optional[List[str]]) – List of IDs corresponding to each chunk of text.
batch_size (Optional[int]) – Size of batch of chunks of text to insert at
once.
kwargs (Any) – 
Returns
List of IDs of the added texts.
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.
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_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) → Any[source]¶
Not implemented.
Parameters
texts (List[str]) – 
embedding (Embeddings) – 
metadatas (Optional[List[dict]]) – 
kwargs (Any) – 
Return type
Any
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.
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) – 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 = 1, filter: Optional[List] = [], **kwargs: Any) → List[Document][source]¶
Run similarity search from a query string.
Parameters
Run similarity search from a query string.
Parameters
query (str) – Query string.
k (Optional[int]) – number of neighbors to retrieve.
filter (Optional[List]) – KDB.AI metadata filter clause: https://code.kx.com/kdbai/use/filter.html
kwargs (Any) – 
Returns
List of similar documents.
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_by_vector_with_score(embedding: List[float], *, k: int = 1, filter: Optional[List] = [], **kwargs: Any) → List[Tuple[Document, float]][source]¶
Return documents most similar to embedding, along with scores.
Parameters
embedding (List[float]) – query vector.
k (Optional[int]) – number of neighbors to retrieve.
filter (Optional[List]) – KDB.AI metadata filter clause: https://code.kx.com/kdbai/use/filter.html
kwargs (Any) – 
Returns
List of similar documents.
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.
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 = 1, filter: Optional[List] = [], **kwargs: Any) → List[Tuple[Document, float]][source]¶
Run similarity search with distance from a query string.
Parameters
query (str) – Query string.
k (Optional[int]) – number of neighbors to retrieve.
filter (Optional[List]) – KDB.AI metadata filter clause: https://code.kx.com/kdbai/use/filter.html
kwargs (Any) – 
Returns
List of similar documents.
Return type
List[Document]
Examples using KDBAI¶
KDB.AI
langchain_community.vectorstores.azuresearch.AzureSearch¶
class langchain_community.vectorstores.azuresearch.AzureSearch(azure_search_endpoint: str, azure_search_key: str, index_name: str, embedding_function: Union[Callable, Embeddings], search_type: str = 'hybrid', semantic_configuration_name: Optional[str] = None, fields: Optional[List[SearchField]] = None, vector_search: Optional[VectorSearch] = None, semantic_configurations: Optional[Union[SemanticConfiguration, List[SemanticConfiguration]]] = None, scoring_profiles: Optional[List[ScoringProfile]] = None, default_scoring_profile: Optional[str] = None, cors_options: Optional[CorsOptions] = None, *, vector_search_dimensions: Optional[int] = None, **kwargs: Any)[source]¶
Azure Cognitive Search vector store.
Attributes
embeddings
Access the query embedding object if available.
Methods
__init__(azure_search_endpoint, ...[, ...])
aadd_documents(documents, **kwargs)
Run more documents through the embeddings and add to the vectorstore.
aadd_embeddings(text_embeddings[, ...])
Add embeddings to an existing index.
aadd_texts(texts[, metadatas, keys])
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_embeddings(text_embeddings[, metadatas, ...])
Add embeddings to an existing index.
add_texts(texts[, metadatas, keys])
Add texts data to an existing index.
adelete([ids])
Delete by vector ID.
afrom_documents(documents, embedding, **kwargs)
Return VectorStore initialized from documents and embeddings.
afrom_embeddings(text_embeddings, embedding)
Return VectorStore initialized from documents and embeddings.
afrom_embeddings(text_embeddings, embedding)
afrom_texts(texts, embedding[, metadatas, ...])
Return VectorStore initialized from texts and embeddings.
ahybrid_max_marginal_relevance_search_with_score(query)
Return docs most similar to query with a hybrid query
ahybrid_search(query[, k])
Returns the most similar indexed documents to the query text.
ahybrid_search_with_relevance_scores(query)
ahybrid_search_with_score(query[, k, filters])
Return docs most similar to query with a hybrid query.
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.
amax_marginal_relevance_search_with_score(query)
Perform a search and return results that are reordered by MMR.
as_retriever(**kwargs)
Return AzureSearchVectorStoreRetriever initialized from this VectorStore.
asearch(query, search_type, **kwargs)
Return docs most similar to query using specified search type.
asemantic_hybrid_search(query[, k])
Returns the most similar indexed documents to the query text.
asemantic_hybrid_search_with_score(query[, ...])
Returns the most similar indexed documents to the query text.
asemantic_hybrid_search_with_score_and_rerank(query)
Return docs most similar to query with a hybrid query.
asimilarity_search(query[, k, search_type])
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].
Return docs and relevance scores in the range [0, 1].
asimilarity_search_with_score(query, *[, k])
Run similarity search with distance.
avector_search(query[, k, filters])
Returns the most similar indexed documents to the query text.
avector_search_with_score(query[, k, filters])
Return docs most similar to query.
delete([ids])
Delete by vector ID.
from_documents(documents, embedding, **kwargs)
Return VectorStore initialized from documents and embeddings.
from_embeddings(text_embeddings, embedding)
from_texts(texts, embedding[, metadatas, ...])
Return VectorStore initialized from texts and embeddings.
hybrid_max_marginal_relevance_search_with_score(query)
Return docs most similar to query with a hybrid query
hybrid_search(query[, k])
Returns the most similar indexed documents to the query text.
hybrid_search_with_relevance_scores(query[, ...])
hybrid_search_with_score(query[, k, filters])
Return docs most similar to query with a hybrid query.
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.
max_marginal_relevance_search_with_score(query)
Perform a search and return results that are reordered by MMR.
search(query, search_type, **kwargs)
Return docs most similar to query using specified search type.
semantic_hybrid_search(query[, k])
Returns the most similar indexed documents to the query text.
semantic_hybrid_search_with_score(query[, ...])
Returns the most similar indexed documents to the query text.
semantic_hybrid_search_with_score_and_rerank(query)
semantic_hybrid_search_with_score_and_rerank(query)
Return docs most similar to query with a hybrid query.
similarity_search(query[, k, search_type])
Return docs most similar to query.
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 distance.
vector_search(query[, k, filters])
Returns the most similar indexed documents to the query text.
vector_search_with_score(query[, k, filters])
Return docs most similar to query.
Parameters
azure_search_endpoint (str) – 
azure_search_key (str) – 
index_name (str) – 
embedding_function (Union[Callable, Embeddings]) – 
search_type (str) – 
semantic_configuration_name (Optional[str]) – 
fields (Optional[List[SearchField]]) – 
vector_search (Optional[VectorSearch]) – 
semantic_configurations (Optional[Union[SemanticConfiguration, List[SemanticConfiguration]]]) – 
scoring_profiles (Optional[List[ScoringProfile]]) – 
default_scoring_profile (Optional[str]) – 
cors_options (Optional[CorsOptions]) – 
vector_search_dimensions (Optional[int]) – 
kwargs (Any) –
vector_search_dimensions (Optional[int]) – 
kwargs (Any) – 
__init__(azure_search_endpoint: str, azure_search_key: str, index_name: str, embedding_function: Union[Callable, Embeddings], search_type: str = 'hybrid', semantic_configuration_name: Optional[str] = None, fields: Optional[List[SearchField]] = None, vector_search: Optional[VectorSearch] = None, semantic_configurations: Optional[Union[SemanticConfiguration, List[SemanticConfiguration]]] = None, scoring_profiles: Optional[List[ScoringProfile]] = None, default_scoring_profile: Optional[str] = None, cors_options: Optional[CorsOptions] = None, *, vector_search_dimensions: Optional[int] = None, **kwargs: Any)[source]¶
Parameters
azure_search_endpoint (str) – 
azure_search_key (str) – 
index_name (str) – 
embedding_function (Union[Callable, Embeddings]) – 
search_type (str) – 
semantic_configuration_name (Optional[str]) – 
fields (Optional[List[SearchField]]) – 
vector_search (Optional[VectorSearch]) – 
semantic_configurations (Optional[Union[SemanticConfiguration, List[SemanticConfiguration]]]) – 
scoring_profiles (Optional[List[ScoringProfile]]) – 
default_scoring_profile (Optional[str]) – 
cors_options (Optional[CorsOptions]) – 
vector_search_dimensions (Optional[int]) – 
kwargs (Any) – 
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]
Returns
List of IDs of the added texts.
Return type
List[str]
async aadd_embeddings(text_embeddings: Iterable[Tuple[str, List[float]]], metadatas: Optional[List[dict]] = None, *, keys: Optional[List[str]] = None) → List[str][source]¶
Add embeddings to an existing index.
Parameters
text_embeddings (Iterable[Tuple[str, List[float]]]) – 
metadatas (Optional[List[dict]]) – 
keys (Optional[List[str]]) – 
Return type
List[str]
async aadd_texts(texts: Iterable[str], metadatas: Optional[List[dict]] = None, *, keys: Optional[List[str]] = None, **kwargs: Any) → List[str][source]¶
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.
keys (Optional[List[str]]) – 
**kwargs – 
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_embeddings(text_embeddings: Iterable[Tuple[str, List[float]]], metadatas: Optional[List[dict]] = None, *, keys: Optional[List[str]] = None) → List[str][source]¶
Add embeddings to an existing index.
Parameters
Add embeddings to an existing index.
Parameters
text_embeddings (Iterable[Tuple[str, List[float]]]) – 
metadatas (Optional[List[dict]]) – 
keys (Optional[List[str]]) – 
Return type
List[str]
add_texts(texts: Iterable[str], metadatas: Optional[List[dict]] = None, *, keys: Optional[List[str]] = None, **kwargs: Any) → List[str][source]¶
Add texts data to an existing index.
Parameters
texts (Iterable[str]) – 
metadatas (Optional[List[dict]]) – 
keys (Optional[List[str]]) – 
kwargs (Any) – 
Return type
List[str]
async adelete(ids: Optional[List[str]] = None, **kwargs: Any) → bool[source]¶
Delete by vector ID.
Parameters
ids (Optional[List[str]]) – List of ids to delete.
kwargs (Any) – 
Returns
True if deletion is successful,
False otherwise.
Return type
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_embeddings(text_embeddings: Iterable[Tuple[str, List[float]]], embedding: Embeddings, metadatas: Optional[List[dict]] = None, *, azure_search_endpoint: str = '', azure_search_key: str = '', index_name: str = 'langchain-index', fields: Optional[List[SearchField]] = None, **kwargs: Any) → AzureSearch[source]¶
Parameters
Parameters
text_embeddings (Iterable[Tuple[str, List[float]]]) – 
embedding (Embeddings) – 
metadatas (Optional[List[dict]]) – 
azure_search_endpoint (str) – 
azure_search_key (str) – 
index_name (str) – 
fields (Optional[List[SearchField]]) – 
kwargs (Any) – 
Return type
AzureSearch
async classmethod afrom_texts(texts: List[str], embedding: Embeddings, metadatas: Optional[List[dict]] = None, azure_search_endpoint: str = '', azure_search_key: str = '', index_name: str = 'langchain-index', fields: Optional[List[SearchField]] = None, **kwargs: Any) → AzureSearch[source]¶
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.
azure_search_endpoint (str) – 
azure_search_key (str) – 
index_name (str) – 
fields (Optional[List[SearchField]]) – 
kwargs (Any) – 
Return type
AzureSearch
async ahybrid_max_marginal_relevance_search_with_score(query: str, k: int = 4, fetch_k: int = 20, lambda_mult: float = 0.5, *, filters: Optional[str] = None, **kwargs: Any) → List[Tuple[Document, float]][source]¶
Return docs most similar to query with a hybrid queryand reorder results by MMR.
Parameters
query (str) – Text to look up documents similar to.
k (int, optional) – Number of Documents to return. Defaults to 4.
k (int, optional) – Number of Documents to return. Defaults to 4.
fetch_k (int, optional) – Total results to select k from.
Defaults to 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
filters (str, optional) – Filtering expression. Defaults to None.
kwargs (Any) – 
Returns
List of Documents most similar to the query and score for each
Return type
List[Tuple[Document, float]]
async ahybrid_search(query: str, k: int = 4, **kwargs: Any) → List[Document][source]¶
Returns the most similar indexed documents to the query text.
Parameters
query (str) – The query text for which to find similar documents.
k (int) – The number of documents to return. Default is 4.
kwargs (Any) – 
Returns
A list of documents that are most similar to the query text.
Return type
List[Document]
async ahybrid_search_with_relevance_scores(query: str, k: int = 4, *, score_threshold: Optional[float] = None, **kwargs: Any) → List[Tuple[Document, float]][source]¶
Parameters
query (str) – 
k (int) – 
score_threshold (Optional[float]) – 
kwargs (Any) – 
Return type
List[Tuple[Document, float]]
async ahybrid_search_with_score(query: str, k: int = 4, filters: Optional[str] = None, **kwargs: Any) → List[Tuple[Document, float]][source]¶
Return docs most similar to query with a hybrid query.
Parameters
Return docs most similar to query with a hybrid query.
Parameters
query (str) – Text to look up documents similar to.
k (int) – Number of Documents to return. Defaults to 4.
filters (Optional[str]) – 
kwargs (Any) – 
Returns
List of Documents most similar to the query and score for each
Return type
List[Tuple[Document, float]]
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.
Maximal marginal relevance optimizes for similarity to query AND diversity
among selected documents.
Parameters
embedding (List[float]) – Embedding to look up documents similar to.