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: Logger | None = None, relevance_score_fn: Callable[[float], float] | None = 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)	Async run more documents through the embeddings and add to the vectorstore.
`aadd_texts`(texts[, metadatas])	Async run more texts through the embeddings and add to the vectorstore.
`add_documents`(documents, **kwargs)	Add or update documents in 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])	Async delete by vector ID or other criteria.
`afrom_documents`(documents, embedding, **kwargs)	Async return VectorStore initialized from documents and embeddings.
`afrom_texts`(texts, embedding[, metadatas])	Async return VectorStore initialized from texts and embeddings.
`aget_by_ids`(ids, /)	Async get documents by their IDs.
`amax_marginal_relevance_search`(query[, k, ...])	Async 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)	Async return docs most similar to query using a specified search type.
`asimilarity_search`(query[, k])	Async return docs most similar to query.
`asimilarity_search_by_vector`(embedding[, k])	Async return docs most similar to embedding vector.
`asimilarity_search_with_relevance_scores`(query)	Async return docs and relevance scores in the range [0, 1].
`asimilarity_search_with_score`(args, *kwargs)	Async run similarity search with distance.
`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
`get_by_ids`(ids, /)	Get documents by their IDs.
`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 a 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: Logger | None = None, relevance_score_fn: Callable[[float], float] | None = 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 (Callable[[float], float] | None)

Return type:

None

async aadd_documents(documents: list[Document], **kwargs: Any) → list[str]#

Async run more documents through the embeddings and add to the vectorstore.

Parameters:

documents (list[Document]) – Documents to add to the vectorstore.
kwargs (Any) – Additional keyword arguments.

Returns:

List of IDs of the added texts.

Raises:

ValueError – If the number of IDs does not match the number of documents.

Return type:

list[str]

async aadd_texts(texts: Iterable[str], metadatas: list[dict] | None = None, **kwargs: Any) → list[str]#

Async run more texts through the embeddings and add to the vectorstore.

Parameters:

texts (Iterable[str]) – Iterable of strings to add to the vectorstore.
metadatas (list[dict] | None) – Optional list of metadatas associated with the texts. Default is None.
**kwargs (Any) – vectorstore specific parameters.

Returns:

List of ids from adding the texts into the vectorstore.

Raises:

ValueError – If the number of metadatas does not match the number of texts.
ValueError – If the number of ids does not match the number of texts.

Return type:

list[str]

add_documents(documents: list[Document], **kwargs: Any) → list[str]#

Add or update documents in the vectorstore.

Parameters:

documents (list[Document]) – Documents to add to the vectorstore.
kwargs (Any) – Additional keyword arguments. if kwargs contains ids and documents contain ids, the ids in the kwargs will receive precedence.

Returns:

List of IDs of the added texts.

Raises:

ValueError – If the number of ids does not match the number of documents.

Return type:

list[str]

add_embeddings(texts: Iterable[str], embeddings: List[List[float]], metadatas: List[dict] | None = None, ids: List[str] | None = 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 (List[dict] | None) – List of metadatas associated with the texts.
ids (List[str] | None) – List of ids for the text embedding pairs
kwargs (Any) – vectorstore specific parameters

Return type:

List[str]

add_texts(texts: Iterable[str], metadatas: List[dict] | None = None, ids: List[str] | None = 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 (List[dict] | None) – Optional list of metadatas (JSON data) associated with the texts.
ids (List[str] | None) – 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: list[str] | None = None, **kwargs: Any) → bool | None#

Async delete by vector ID or other criteria.

Parameters:

ids (list[str] | None) – List of ids to delete. If None, delete all. Default is None.
**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#

Async 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) – Additional keyword arguments.

Returns:

VectorStore initialized from documents and embeddings.

Return type:

VectorStore

async classmethod afrom_texts(texts: list[str], embedding: Embeddings, metadatas: list[dict] | None = None, **kwargs: Any) → VST#

Async return VectorStore initialized from texts and embeddings.

Parameters:

texts (list[str]) – Texts to add to the vectorstore.
embedding (Embeddings) – Embedding function to use.
metadatas (list[dict] | None) – Optional list of metadatas associated with the texts. Default is None.
kwargs (Any) – Additional keyword arguments.

Returns:

VectorStore initialized from texts and embeddings.

Return type:

VectorStore

async aget_by_ids(ids: Sequence[str], /) → list[Document]#

Async get documents by their IDs.

The returned documents are expected to have the ID field set to the ID of the document in the vector store.

Fewer documents may be returned than requested if some IDs are not found or if there are duplicated IDs.

Users should not assume that the order of the returned documents matches the order of the input IDs. Instead, users should rely on the ID field of the returned documents.

This method should NOT raise exceptions if no documents are found for some IDs.

Parameters:: ids (Sequence[str]) – List of ids to retrieve.
Returns:: List of Documents.
Return type:: list[Document]

Added in version 0.2.11.

async amax_marginal_relevance_search(query: str, k: int = 4, fetch_k: int = 20, lambda_mult: float = 0.5, **kwargs: Any) → list[Document]#

Async 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. Default is 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.
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, filter: Dict[str, str] | None = 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 (Dict[str, str] | None)
kwargs (Any)

Return type:

List[Document]

as_retriever(**kwargs: Any) → VectorStoreRetriever#

Return VectorStoreRetriever initialized from this VectorStore.

Parameters:

**kwargs (Any) –

Keyword arguments to pass to the search function. Can include: 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

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]#

Async return docs most similar to query using a 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.

Returns:

List of Documents most similar to the query.

Raises:

ValueError – If search_type is not one of “similarity”, “mmr”, or “similarity_score_threshold”.

Return type:

list[Document]

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

Async return docs most similar to query.

Parameters:

query (str) – Input text.
k (int) – Number of Documents to return. Defaults to 4.
**kwargs (Any) – Arguments to pass to the search method.

Returns:

List of Documents most similar to the query.

Return type:

list[Document]

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

Async 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) – Arguments to pass to the search method.

Returns:

List of Documents most similar to the query vector.

Return type:

list[Document]

async asimilarity_search_with_relevance_scores(query: str, k: int = 4, **kwargs: Any) → list[tuple[Document, float]]#

Async return docs and relevance scores in the range [0, 1].

0 is dissimilar, 1 is most similar.

Parameters:

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

filter the resulting set of retrieved docs

Returns:

List of Tuples of (doc, similarity_score)

Return type:

list[tuple[Document, float]]

async asimilarity_search_with_score(*args: Any, **kwargs: Any) → list[tuple[Document, float]]#

Async run similarity search with distance.

Parameters:

*args (Any) – Arguments to pass to the search method.
**kwargs (Any) – Arguments to pass to the search method.

Returns:

List of Tuples of (doc, similarity_score).

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: list[str] | None = None, **kwargs: Any) → bool | None#

Delete by vector ID or other criteria.

Parameters:

ids (list[str] | None) – List of ids to delete. If None, delete all. Default is None.
**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: List[dict] | None = None, collection_name: str = 'langchain_kinetica_embeddings', distance_strategy: DistanceStrategy = DistanceStrategy.EUCLIDEAN, ids: List[str] | None = None, pre_delete_collection: bool = False, *, schema_name: str = 'langchain', **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.
schema_name (str, optional) – Kinetica schema name. Defaults to _LANGCHAIN_DEFAULT_SCHEMA_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: List[dict] | None = 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: List[str] | None = None, pre_delete_collection: bool = False, *, schema_name: str = 'langchain', **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.
schema_name (str, optional) – Kinetica schema name. Defaults to _LANGCHAIN_DEFAULT_SCHEMA_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: List[dict] | None = 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: List[str] | None = None, pre_delete_collection: bool = False, *, schema_name: str = 'langchain', **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.
schema_name (str, optional) – Kinetica schema name. Defaults to _LANGCHAIN_DEFAULT_SCHEMA_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

get_by_ids(ids: Sequence[str], /) → list[Document]#

Get documents by their IDs.

The returned documents are expected to have the ID field set to the ID of the document in the vector store.

Fewer documents may be returned than requested if some IDs are not found or if there are duplicated IDs.

Users should not assume that the order of the returned documents matches the order of the input IDs. Instead, users should rely on the ID field of the returned documents.

This method should NOT raise exceptions if no documents are found for some IDs.

Parameters:: ids (Sequence[str]) – List of ids to retrieve.
Returns:: List of Documents.
Return type:: list[Document]

Added in version 0.2.11.

max_marginal_relevance_search(query: str, k: int = 4, fetch_k: int = 20, lambda_mult: float = 0.5, filter: Dict[str, str] | None = 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: Dict[str, str] | None = 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: dict | None = 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: Dict[str, str] | None = 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 a 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.

Returns:

List of Documents most similar to the query.

Raises:

ValueError – If search_type is not one of “similarity”, “mmr”, or “similarity_score_threshold”.

Return type:

list[Document]

similarity_search(query: str, k: int = 4, filter: dict | None = 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: dict | None = 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: dict | None = 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: dict | None = None) → List[Tuple[Document, float]][source]#

Parameters:

embedding (List[float])
k (int)
filter (dict | None)

Return type:

List[Tuple[Document, float]]

Examples using Kinetica