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# `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)

  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

    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]

    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]

    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.

    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, filter: Optional[Dict[str, str]] = None, **kwargs: Any) → List[Document]

    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

    Create a new Kinetica schema

    Return type

      None

  create_tables_if_not_exists() → Any

    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

    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

    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

    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

    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

    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]

    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]
    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]]

    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]]
    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]

    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]

    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]]

    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]]
    Parameters

      - embedding (List[float]) –

      - k (int) –

      - filter (Optional[dict]) –

    Return type

      List[Tuple[Document, float]]

# Examples using Kinetica

- Kinetica

- Pip install necessary package