`langchain_community.vectorstores.kinetica`.Kinetica¶

class langchain_community.vectorstores.kinetica.Kinetica(config: KineticaSettings, embedding_function: Embeddings, collection_name: str = 'langchain_kinetica_embeddings', schema_name: str = 'langchain', distance_strategy: DistanceStrategy = DistanceStrategy.EUCLIDEAN, pre_delete_collection: bool = False, logger: Optional[Logger] = None, relevance_score_fn: Optional[Callable[[float], float]] = None)[source]¶

Kinetica vector store.

To use, you should have the gpudb python package installed.

Parameters

kinetica_settings – Kinetica connection settings class.
embedding_function (Embeddings) – Any embedding function implementing langchain.embeddings.base.Embeddings interface.
collection_name (str) – The name of the collection to use. (default: langchain) NOTE: This is not the name of the table, but the name of the collection. The tables will be created when initializing the store (if not exists) So, make sure the user has the right permissions to create tables.
distance_strategy (DistanceStrategy) – The distance strategy to use. (default: COSINE)
pre_delete_collection (bool) – If True, will delete the collection if it exists. (default: False). Useful for testing.
engine_args – SQLAlchemy’s create engine arguments.
config (KineticaSettings) –
schema_name (str) –
logger (Optional[logging.Logger]) –
relevance_score_fn (Optional[Callable[[float], float]]) –

Example

from langchain_community.vectorstores import Kinetica, KineticaSettings
from langchain_community.embeddings.openai import OpenAIEmbeddings

kinetica_settings = KineticaSettings(
    host="http://127.0.0.1", username="", password=""
    )
COLLECTION_NAME = "kinetica_store"
embeddings = OpenAIEmbeddings()
vectorstore = Kinetica.from_documents(
    documents=docs,
    embedding=embeddings,
    collection_name=COLLECTION_NAME,
    config=kinetica_settings,
)

Constructor for the Kinetica class

Parameters

config (KineticaSettings) – a KineticaSettings instance
embedding_function (Embeddings) – embedding function to use
collection_name (str, optional) – the Kinetica table name. Defaults to _LANGCHAIN_DEFAULT_COLLECTION_NAME.
schema_name (str, optional) – the Kinetica table name. Defaults to _LANGCHAIN_DEFAULT_SCHEMA_NAME.
distance_strategy (DistanceStrategy, optional) – _description_. Defaults to DEFAULT_DISTANCE_STRATEGY.
pre_delete_collection (bool, optional) – _description_. Defaults to False.
logger (Optional[logging.Logger], optional) – _description_. Defaults to None.
relevance_score_fn (Optional[Callable[[float], float]]) –

Attributes

`distance_strategy`
`embeddings`	Access the query embedding object if available.

Methods

`__init__`(config, embedding_function[, ...])	Constructor for the Kinetica class
`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_embeddings`(texts, embeddings[, ...])	Add embeddings to the vectorstore.
`add_texts`(texts[, metadatas, ids])	Run more texts through the embeddings and add to the vectorstore.
`adelete`([ids])	Delete by vector ID or other criteria.
`afrom_documents`(documents, embedding, **kwargs)	Return VectorStore initialized from documents and embeddings.
`afrom_texts`(texts, embedding[, metadatas])	Return VectorStore initialized from texts and embeddings.
`amax_marginal_relevance_search`(query[, k, ...])	Return docs selected using the maximal marginal relevance.
`amax_marginal_relevance_search_by_vector`(...)	Return docs selected using the maximal marginal relevance.
`as_retriever`(**kwargs)	Return VectorStoreRetriever initialized from this VectorStore.
`asearch`(query, search_type, **kwargs)	Return docs most similar to query using specified search type.
`asimilarity_search`(query[, k])	Return docs most similar to query.
`asimilarity_search_by_vector`(embedding[, k])	Return docs most similar to embedding vector.
`asimilarity_search_with_relevance_scores`(query)	Return docs and relevance scores in the range [0, 1], asynchronously.
`asimilarity_search_with_score`(args, *kwargs)	Run similarity search with distance asynchronously.
`create_schema`()	Create a new Kinetica schema
`create_tables_if_not_exists`()	Create the table to store the texts and embeddings
`delete`([ids])	Delete by vector ID or other criteria.
`delete_schema`()	Delete a Kinetica schema with cascade set to true This method will delete a schema with all tables in it.
`drop_tables`()	Delete the table
`from_documents`(documents, embedding[, ...])	Adds the list of Document passed in to the vector store and returns it
`from_embeddings`(text_embeddings, embedding)	Adds the embeddings passed in to the vector store and returns it
`from_texts`(texts, embedding[, metadatas, ...])	Adds the texts passed in to the vector store and returns it
`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)	Return docs selected using the maximal marginal relevance with score.
`max_marginal_relevance_search_with_score_by_vector`(...)	Return docs selected using the maximal marginal relevance with score
`search`(query, search_type, **kwargs)	Return docs most similar to query using specified search type.
`similarity_search`(query[, k, filter])	Run similarity search with Kinetica with distance.
`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, filter])	Return docs most similar to query.
`similarity_search_with_score_by_vector`(embedding)

__init__(config: KineticaSettings, embedding_function: Embeddings, collection_name: str = 'langchain_kinetica_embeddings', schema_name: str = 'langchain', distance_strategy: DistanceStrategy = DistanceStrategy.EUCLIDEAN, pre_delete_collection: bool = False, logger: Optional[Logger] = None, relevance_score_fn: Optional[Callable[[float], float]] = None) → None[source]¶

Constructor for the Kinetica class

Parameters

config (KineticaSettings) – a KineticaSettings instance
embedding_function (Embeddings) – embedding function to use
collection_name (str, optional) – the Kinetica table name. Defaults to _LANGCHAIN_DEFAULT_COLLECTION_NAME.
schema_name (str, optional) – the Kinetica table name. Defaults to _LANGCHAIN_DEFAULT_SCHEMA_NAME.
distance_strategy (DistanceStrategy, optional) – _description_. Defaults to DEFAULT_DISTANCE_STRATEGY.
pre_delete_collection (bool, optional) – _description_. Defaults to False.
logger (Optional[logging.Logger], optional) – _description_. Defaults to None.
relevance_score_fn (Optional[Callable[[float], float]]) –

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

(List[Document] (documents) – Documents to add to the vectorstore.
documents (List[Document]) –
kwargs (Any) –

Returns

List of IDs of the added texts.

Return type

List[str]

async aadd_texts(texts: Iterable[str], metadatas: Optional[List[dict]] = None, **kwargs: Any) → List[str]¶

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

Parameters

texts (Iterable[str]) –
metadatas (Optional[List[dict]]) –
kwargs (Any) –

Return type

List[str]

add_documents(documents: List[Document], **kwargs: Any) → List[str]¶

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

Parameters

(List[Document] (documents) – Documents to add to the vectorstore.
documents (List[Document]) –
kwargs (Any) –

Returns

List of IDs of the added texts.

Return type

List[str]

add_embeddings(texts: Iterable[str], embeddings: List[List[float]], metadatas: Optional[List[dict]] = None, ids: Optional[List[str]] = None, **kwargs: Any) → List[str][source]¶

Add embeddings to the vectorstore.

Parameters

texts (Iterable[str]) – Iterable of strings to add to the vectorstore.
embeddings (List[List[float]]) – List of list of embedding vectors.
metadatas (Optional[List[dict]]) – List of metadatas associated with the texts.
ids (Optional[List[str]]) – List of ids for the text embedding pairs
kwargs (Any) – vectorstore specific parameters

Return type

List[str]

add_texts(texts: Iterable[str], metadatas: Optional[List[dict]] = None, ids: 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 (JSON data) associated with the texts.
ids (Optional[List[str]]) – List of IDs (UUID) for the texts supplied; will be generated if None
kwargs (Any) – vectorstore specific parameters

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]¶

Delete by vector ID or other criteria.

Parameters

ids (Optional[List[str]]) – List of ids to delete.
**kwargs (Any) – Other keyword arguments that subclasses might use.

Returns

True if deletion is successful, False otherwise, None if not implemented.

Return type

Optional[bool]

async classmethod afrom_documents(documents: List[Document], embedding: Embeddings, **kwargs: Any) → VST¶

Return VectorStore initialized from documents and embeddings.

Parameters

documents (List[Document]) –
embedding (Embeddings) –
kwargs (Any) –

Return type

VST

async classmethod afrom_texts(texts: List[str], embedding: Embeddings, metadatas: Optional[List[dict]] = None, **kwargs: Any) → VST¶

Return VectorStore initialized from texts and embeddings.

Parameters

texts (List[str]) –
embedding (Embeddings) –
metadatas (Optional[List[dict]]) –
kwargs (Any) –

Return type

VST

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.

Parameters

query (str) –
k (int) –
fetch_k (int) –
lambda_mult (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, str]] = None, **kwargs: Any) → List[Document][source]¶

Return docs selected using the maximal marginal relevance.

Parameters

embedding (List[float]) –
k (int) –
fetch_k (int) –
lambda_mult (float) –
filter (Optional[Dict[str, str]]) –
kwargs (Any) –

Return type

List[Document]

as_retriever(**kwargs: Any) → VectorStoreRetriever¶

Return VectorStoreRetriever initialized from this VectorStore.

Parameters

search_type (Optional[str]) – Defines the type of search that the Retriever should perform. Can be “similarity” (default), “mmr”, or “similarity_score_threshold”.
search_kwargs (Optional[Dict]) –
Keyword arguments to pass to the search function. Can include things like:

k: Amount of documents to return (Default: 4) score_threshold: Minimum relevance threshold

for similarity_score_threshold

fetch_k: Amount of documents to pass to MMR algorithm (Default: 20) lambda_mult: Diversity of results returned by MMR;

1 for minimum diversity and 0 for maximum. (Default: 0.5)

filter: Filter by document metadata
kwargs (Any) –

Returns

Retriever class for VectorStore.

Return type

VectorStoreRetriever

Examples:

# Retrieve more documents with higher diversity
# Useful if your dataset has many similar documents
docsearch.as_retriever(
    search_type="mmr",
    search_kwargs={'k': 6, 'lambda_mult': 0.25}
)

# Fetch more documents for the MMR algorithm to consider
# But only return the top 5
docsearch.as_retriever(
    search_type="mmr",
    search_kwargs={'k': 5, 'fetch_k': 50}
)

# Only retrieve documents that have a relevance score
# Above a certain threshold
docsearch.as_retriever(
    search_type="similarity_score_threshold",
    search_kwargs={'score_threshold': 0.8}
)

# Only get the single most similar document from the dataset
docsearch.as_retriever(search_kwargs={'k': 1})

# Use a filter to only retrieve documents from a specific paper
docsearch.as_retriever(
    search_kwargs={'filter': {'paper_title':'GPT-4 Technical Report'}}
)

async asearch(query: str, search_type: str, **kwargs: Any) → List[Document]¶

Return docs most similar to query using specified search type.

Parameters

query (str) –
search_type (str) –
kwargs (Any) –

Return type

List[Document]

async asimilarity_search(query: str, k: int = 4, **kwargs: Any) → List[Document]¶

Return docs most similar to query.

Parameters

query (str) –
k (int) –
kwargs (Any) –

Return type

List[Document]

async asimilarity_search_by_vector(embedding: List[float], k: int = 4, **kwargs: Any) → List[Document]¶

Return docs most similar to embedding vector.

Parameters

embedding (List[float]) –
k (int) –
kwargs (Any) –

Return type

List[Document]

async asimilarity_search_with_relevance_scores(query: str, k: int = 4, **kwargs: Any) → List[Tuple[Document, float]]¶

Return docs and relevance scores in the range [0, 1], asynchronously.

0 is dissimilar, 1 is most similar.

Parameters

query (str) – input text
k (int) – Number of Documents to return. Defaults to 4.
**kwargs (Any) –
kwargs to be passed to similarity search. Should include: score_threshold: Optional, a floating point value between 0 to 1 to

filter the resulting set of retrieved docs

Returns

List of Tuples of (doc, similarity_score)

Return type

List[Tuple[Document, float]]

async asimilarity_search_with_score(*args: Any, **kwargs: Any) → List[Tuple[Document, float]]¶

Run similarity search with distance asynchronously.

Parameters

args (Any) –
kwargs (Any) –

Return type

List[Tuple[Document, float]]

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

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 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. 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]

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 relevance: to embedding vector.
Maximal marginal relevance optimizes for similarity to query AND diversity: among 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]

max_marginal_relevance_search_with_score(query: str, k: int = 4, fetch_k: int = 20, lambda_mult: float = 0.5, filter: Optional[dict] = None, **kwargs: Any) → List[Tuple[Document, float]][source]¶

Return docs selected using the maximal marginal relevance with score.

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. 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 to the query and score for each.

Return type

List[Tuple[Document, float]]

max_marginal_relevance_search_with_score_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[Tuple[Document, float]][source]¶

Return docs selected using the maximal marginal relevance with score: to embedding vector.
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. 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 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) –
search_type (str) –
kwargs (Any) –

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]

similarity_search_by_vector(embedding: List[float], k: int = 4, filter: Optional[dict] = None, **kwargs: Any) → List[Document][source]¶

Return docs most similar to embedding vector.

Parameters

embedding (List[float]) – Embedding to look up documents similar to.
k (int) – Number of Documents to return. Defaults to 4.
filter (Optional[Dict[str, str]]) – Filter by metadata. Defaults to None.
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] = None) → List[Tuple[Document, float]][source]¶

Return docs most similar to query.

Parameters

query (str) – Text to look up documents similar to.
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]]

langchain_community.vectorstores.kinetica.Kinetica¶

`langchain_community.vectorstores.kinetica`.Kinetica¶