Tony20100/langchain_full huggingface dataset:
Run similarity search with distance.
Returns
List of Tuples of (doc, similarity_score)
Parameters
args (Any) – 
kwargs (Any) – 
Return type
List[Tuple[Document, float]]
create_schema() → None[source]¶
Create a new Kinetica schema
Return type
None
create_tables_if_not_exists() → Any[source]¶
Create the table to store the texts and embeddings
Return type
Any
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]
delete_schema() → None[source]¶
Delete a Kinetica schema with cascade set to true
This method will delete a schema with all tables in it.
Return type
None
drop_tables() → None[source]¶
Delete the table
Return type
None
classmethod from_documents(documents: List[Document], embedding: Embeddings, config: KineticaSettings = KineticaSettings(host='http://127.0.0.1', port=9191, username=None, password=None, database='langchain', table='langchain_kinetica_embeddings', metric='l2'), metadatas: Optional[List[dict]] = None, collection_name: str = 'langchain_kinetica_embeddings', distance_strategy: DistanceStrategy = DistanceStrategy.EUCLIDEAN, ids: Optional[List[str]] = None, pre_delete_collection: bool = False, **kwargs: Any) → Kinetica[source]¶
Adds the list of Document passed in to the vector store and returns it
Parameters
Adds the list of Document passed in to the vector store and returns it
Parameters
cls (Type[Kinetica]) – Kinetica class
texts (List[str]) – A list of texts for which the embeddings are generated
embedding (Embeddings) – List of embeddings
config (KineticaSettings) – a KineticaSettings instance
metadatas (Optional[List[dict]], optional) – List of dicts, JSON describing
the texts/documents. Defaults to None.
collection_name (str, optional) – Kinetica schema name.
Defaults to _LANGCHAIN_DEFAULT_COLLECTION_NAME.
distance_strategy (DistanceStrategy, optional) – Distance strategy
e.g., l2, cosine etc.. Defaults to DEFAULT_DISTANCE_STRATEGY.
ids (Optional[List[str]], optional) – A list of UUIDs for each text/document.
Defaults to None.
pre_delete_collection (bool, optional) – Indicates whether the Kinetica
schema is to be deleted or not. Defaults to False.
documents (List[Document]) – 
kwargs (Any) – 
Returns
a Kinetica instance
Return type
Kinetica
classmethod from_embeddings(text_embeddings: List[Tuple[str, List[float]]], embedding: Embeddings, metadatas: Optional[List[dict]] = None, config: KineticaSettings = KineticaSettings(host='http://127.0.0.1', port=9191, username=None, password=None, database='langchain', table='langchain_kinetica_embeddings', metric='l2'), dimensions: int = Dimension.OPENAI, collection_name: str = 'langchain_kinetica_embeddings', distance_strategy: DistanceStrategy = DistanceStrategy.EUCLIDEAN, ids: Optional[List[str]] = None, pre_delete_collection: bool = False, **kwargs: Any) → Kinetica[source]¶
Adds the embeddings passed in to the vector store and returns it
Parameters
cls (Type[Kinetica]) – Kinetica class
text_embeddings (List[Tuple[str, List[float]]]) – A list of texts
and the embeddings
embedding (Embeddings) – List of embeddings
metadatas (Optional[List[dict]], optional) – List of dicts, JSON describing
the texts/documents. Defaults to None.
config (KineticaSettings) – a KineticaSettings instance
dimensions (int, optional) – Dimension for the vector data, if not passed a
default will be used. Defaults to Dimension.OPENAI.
collection_name (str, optional) – Kinetica schema name.
Defaults to _LANGCHAIN_DEFAULT_COLLECTION_NAME.
distance_strategy (DistanceStrategy, optional) – Distance strategy
e.g., l2, cosine etc.. Defaults to DEFAULT_DISTANCE_STRATEGY.
ids (Optional[List[str]], optional) – A list of UUIDs for each text/document.
Defaults to None.
pre_delete_collection (bool, optional) – Indicates whether the
Kinetica schema is to be deleted or not. Defaults to False.
kwargs (Any) – 
Returns
a Kinetica instance
Return type
Kinetica
classmethod from_texts(texts: List[str], embedding: Embeddings, metadatas: Optional[List[dict]] = None, config: KineticaSettings = KineticaSettings(host='http://127.0.0.1', port=9191, username=None, password=None, database='langchain', table='langchain_kinetica_embeddings', metric='l2'), collection_name: str = 'langchain_kinetica_embeddings', distance_strategy: DistanceStrategy = DistanceStrategy.EUCLIDEAN, ids: Optional[List[str]] = None, pre_delete_collection: bool = False, **kwargs: Any) → Kinetica[source]¶
Adds the texts passed in to the vector store and returns it
Parameters
cls (Type[Kinetica]) – Kinetica class
texts (List[str]) – A list of texts for which the embeddings are generated
embedding (Embeddings) – List of embeddings
metadatas (Optional[List[dict]], optional) – List of dicts, JSON
describing the texts/documents. Defaults to None.
config (KineticaSettings) – a KineticaSettings instance
collection_name (str, optional) – Kinetica schema name.
Defaults to _LANGCHAIN_DEFAULT_COLLECTION_NAME.
distance_strategy (DistanceStrategy, optional) – Distance strategy
e.g., l2, cosine etc.. Defaults to DEFAULT_DISTANCE_STRATEGY.
ids (Optional[List[str]], optional) – A list of UUIDs for each
text/document. Defaults to None.
pre_delete_collection (bool, optional) – Indicates whether the Kinetica
schema is to be deleted or not. Defaults to False.
kwargs (Any) – 
Returns
a Kinetica instance
Return type
Kinetica
max_marginal_relevance_search(query: str, k: int = 4, fetch_k: int = 20, lambda_mult: float = 0.5, filter: Optional[Dict[str, str]] = None, **kwargs: Any) → List[Document][source]¶
Return docs selected using the maximal marginal relevance.
Maximal marginal relevance optimizes for similarity to query AND diversityamong 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.
Defaults to 20.
lambda_mult (float) – Number between 0 and 1 that determines the degree
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, str]]) – Filter by metadata. Defaults to None.
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, str]] = None, **kwargs: Any) → List[Document][source]¶
Return docs selected using the maximal marginal relevanceto embedding vector.
Maximal marginal relevance optimizes for similarity to query AND diversityamong selected documents.
Parameters
embedding (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.
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.
filter (Optional[Dict[str, str]]) – Filter by metadata. Defaults to None.
kwargs (Any) – 
Returns
List of Documents selected by maximal marginal relevance.
Return type
List[Document]
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.
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.
filter (Optional[Dict[str, str]]) – Filter by metadata. Defaults to None.
kwargs (Any) – 
Returns
List of Documents selected by maximal marginalrelevance to the query and score for each.
Return type
List[Tuple[Document, float]]
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 = 4, filter: Optional[dict] = None, **kwargs: Any) → List[Document][source]¶
Run similarity search with Kinetica with distance.
Parameters
query (str) – Query text to search for.
k (int) – Number of results to return. Defaults to 4.
filter (Optional[Dict[str, str]]) – Filter by metadata. Defaults to None.
kwargs (Any) – 
Returns
List of Documents most similar to the query.
Return type
List[Document]
k (int) – Number of Documents to return. Defaults to 4.
filter (Optional[Dict[str, str]]) – Filter by metadata. Defaults to None.
Returns
List of Documents most similar to the query and score for each
Return type
List[Tuple[Document, float]]
similarity_search_with_score_by_vector(embedding: List[float], k: int = 4, filter: Optional[dict] = None) → List[Tuple[Document, float]][source]¶
Parameters
embedding (List[float]) – 
k (int) – 
filter (Optional[dict]) – 
Return type
List[Tuple[Document, float]]
Examples using Kinetica¶
Kinetica
Kinetica Vectorstore API
Kinetica Vectorstore based Retriever
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])
Add texts 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.
delete([ids])
Delete by vector ID or other criteria.
from_documents(documents, embedding, **kwargs)
Return VectorStore initialized from documents and embeddings.
from_texts(texts, embedding[, metadatas, ids])
Return VectorStore initialized from texts and embeddings.
max_marginal_relevance_search(query[, k, ...])
Return docs selected using the maximal marginal relevance.
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])
Return docs most similar to query.
similarity_search_by_vector(embedding[, k])
Return docs most similar to embedding vector.
similarity_search_by_vector_with_score(...)
Performs similarity search from a embeddings 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.
Parameters
app (Any) – 
embedding_function (Optional[Embeddings]) – 
page_content_field (Optional[str]) – 
embedding_field (Optional[str]) – 
input_field (Optional[str]) – 
metadata_fields (Optional[List[str]]) – 
__init__(app: Any, embedding_function: Optional[Embeddings] = None, page_content_field: Optional[str] = None, embedding_field: Optional[str] = None, input_field: Optional[str] = None, metadata_fields: Optional[List[str]] = None) → None[source]¶
Initialize with a PyVespa client.
Parameters
app (Any) – 
embedding_function (Optional[Embeddings]) – 
page_content_field (Optional[str]) – 
embedding_field (Optional[str]) – 
input_field (Optional[str]) – 
metadata_fields (Optional[List[str]]) – 
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
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, ids: Optional[List[str]] = None, **kwargs: Any) → List[str][source]¶
Add texts 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.
ids (Optional[List[str]]) – Optional list of ids associated with the texts.
kwargs (Any) – vectorstore specific parameters
Returns
List of ids from adding the texts into the vectorstore.
Return type
List[str]
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]¶
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
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.
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]
as_retriever(**kwargs: Any) → VectorStoreRetriever[source]¶
Return VectorStoreRetriever initialized from this VectorStore.
Parameters
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]
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
kwargs (Any) – 
Return type
VST
classmethod from_texts(texts: List[str], embedding: Embeddings, metadatas: Optional[List[dict]] = None, ids: Optional[List[str]] = None, **kwargs: Any) → VespaStore[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.
ids (Optional[List[str]]) – 
kwargs (Any) – 
Return type
VespaStore
max_marginal_relevance_search(query: str, k: int = 4, fetch_k: int = 20, lambda_mult: float = 0.5, **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) – 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]
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][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.
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 = 4, **kwargs: Any) → List[Document][source]¶
Return docs most similar to query.
Parameters
query (str) – Input text.
k (int) – Number of Documents to return. Defaults to 4.
kwargs (Any) –
kwargs (Any) – 
Returns
List of Documents most similar to the query.
Return type
List[Document]
similarity_search_by_vector(embedding: List[float], k: int = 4, **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.
kwargs (Any) – 
Returns
List of Documents most similar to the query vector.
Return type
List[Document]
similarity_search_by_vector_with_score(query_embedding: List[float], k: int = 4, **kwargs: Any) → List[Tuple[Document, float]][source]¶
Performs similarity search from a embeddings vector.
Parameters
query_embedding (List[float]) – Embeddings vector to search for.
k (int) – Number of results to return.
custom_query – Use this custom query instead default query (kwargs)
kwargs (Any) – other vector store specific parameters
Returns
List of ids from adding the texts into the vectorstore.
Return type
List[Tuple[Document, float]]
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
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, **kwargs: Any) → List[Tuple[Document, float]][source]¶
Run similarity search with distance.
Returns
List of Tuples of (doc, similarity_score)
Parameters
query (str) – 
k (int) – 
kwargs (Any) – 
Return type
List[Tuple[Document, float]]
Examples using VespaStore¶
Vespa