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# `langchain_community.vectorstores.elastic_vector_search`.ElasticKnnSearch

class langchain_community.vectorstores.elastic_vector_search.ElasticKnnSearch(index_name: str, embedding: Embeddings, es_connection: Optional['Elasticsearch'] = None, es_cloud_id: Optional[str] = None, es_user: Optional[str] = None, es_password: Optional[str] = None, vector_query_field: Optional[str] = 'vector', query_field: Optional[str] = 'text')

  [Deprecated] [DEPRECATED] Elasticsearch with k-nearest neighbor search (k-NN) vector store.

  Recommended to use ElasticsearchStore instead, which supports metadata filtering, customising the query retriever and much more!

  You can read more on ElasticsearchStore: https://python.langchain.com/docs/integrations/vectorstores/elasticsearch

  It creates an Elasticsearch index of text data that can be searched using k-NN search. The text data is transformed into vector embeddings using a provided embedding model, and these embeddings are stored in the Elasticsearch index.

  Parameters

    - index_name (str) –

    - embedding (Embeddings) –

    - es_connection (Optional['Elasticsearch']) –

    - es_cloud_id (Optional[str]) –

    - es_user (Optional[str]) –

    - es_password (Optional[str]) –

    - vector_query_field (Optional[str]) –

    - query_field (Optional[str]) –

  index_name

    The name of the Elasticsearch index.

    Type

      str

  embedding

    The embedding model to use for transforming text data into vector embeddings.

    Type

      Embeddings

  es_connection

    An existing Elasticsearch connection.

    Type

      Elasticsearch, optional

  es_cloud_id

    The Cloud ID of your Elasticsearch Service deployment.

    Type

      str, optional

  es_user

    The username for your Elasticsearch Service deployment.

    Type

      str, optional

  es_password

    The password for your Elasticsearch Service deployment.

    Type

      str, optional

  vector_query_field

    The name of the field in the Elasticsearch index that contains the vector embeddings.

    Type

      str, optional

  query_field

    The name of the field in the Elasticsearch index that contains the original text data.

    Type

      str, optional

  Usage:

        >>> from embeddings import Embeddings
        >>> embedding = Embeddings.load('glove')
        >>> es_search = ElasticKnnSearch('my_index', embedding)
        >>> es_search.add_texts(['Hello world!', 'Another text'])
        >>> results = es_search.knn_search('Hello')
        [(Document(page_content='Hello world!', metadata={}), 0.9)]

  Notes

  Deprecated since version 0.0.1: Use Use ElasticsearchStore class in langchain-elasticsearch package instead.

  Attributes

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

  Methods

  `__init__`(index_name, embedding[, ...]) |
  `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_texts`(texts[, metadatas, model_id, ...]) | Add a list of texts to the Elasticsearch index.
  `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_knn_index`(mapping) | Create a new k-NN index in Elasticsearch.
  `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]) | Create a new ElasticKnnSearch instance and add a list of texts to the
  `knn_hybrid_search`([query, k, query_vector, ...]) | Perform a hybrid k-NN and text search on the Elasticsearch index.
  `knn_search`([query, k, query_vector, ...]) | Perform a k-NN search on the Elasticsearch index.
  `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.
  `search`(query, search_type, **kwargs) | Return docs most similar to query using specified search type.
  `similarity_search`(query[, k, filter]) | Pass through to knn_search
  `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]) | Pass through to knn_search including score

  __init__(index_name: str, embedding: Embeddings, es_connection: Optional['Elasticsearch'] = None, es_cloud_id: Optional[str] = None, es_user: Optional[str] = None, es_password: Optional[str] = None, vector_query_field: Optional[str] = 'vector', query_field: Optional[str] = 'text')
    Parameters

      - index_name (str) –

      - embedding (Embeddings) –

      - es_connection (Optional['Elasticsearch']) –

      - es_cloud_id (Optional[str]) –

      - es_user (Optional[str]) –

      - es_password (Optional[str]) –

      - vector_query_field (Optional[str]) –

      - query_field (Optional[str]) –

  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_texts(texts: Iterable[str], metadatas: Optional[List[Dict[Any, Any]]] = None, model_id: Optional[str] = None, refresh_indices: bool = False, **kwargs: Any) → List[str]

    Add a list of texts to the Elasticsearch index.

    Parameters

      - texts (Iterable[str]) – The texts to add to the index.

      - metadatas (List[Dict[Any, Any]], optional) – A list of metadata dictionaries to associate with the texts.

      - model_id (str, optional) – The ID of the model to use for transforming the texts into vectors.

      - refresh_indices (bool, optional) – Whether to refresh the Elasticsearch indices after adding the texts.

      - **kwargs – Arbitrary keyword arguments.

    Returns

      A list of IDs for the added texts.

    Return type

      List[str]

  async adelete(ids: Optional[List[str]] = None, **kwargs: Any) → Optional[bool]

    Delete by vector ID or other criteria.

    Parameters

      - ids (Optional[List[str]]) – List of ids to delete.

      - **kwargs (Any) – Other keyword arguments that subclasses might use.

    Returns

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

    Return type

      Optional[bool]

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

    Return VectorStore initialized from documents and embeddings.

    Parameters

      - documents (List[Document]) –

      - 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, **kwargs: Any) → List[Document]

    Return docs selected using the maximal marginal relevance.

    Parameters

      - embedding (List[float]) –

      - k (int) –

      - fetch_k (int) –

      - lambda_mult (float) –

      - 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_knn_index(mapping: Dict) → None

    Create a new k-NN index in Elasticsearch.

    Parameters

      mapping (Dict) – The mapping to use for the new index.

    Returns

      None

    Return type

      None

  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]

  classmethod from_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

  classmethod from_texts(texts: List[str], embedding: Embeddings, metadatas: Optional[List[Dict[Any, Any]]] = None, **kwargs: Any) → ElasticKnnSearch
    Create a new ElasticKnnSearch instance and add a list of texts to the

      Elasticsearch index.

    Parameters

      - texts (List[str]) – The texts to add to the index.

      - embedding (Embeddings) – The embedding model to use for transforming the texts into vectors.

      - metadatas (List[Dict[Any, Any]], optional) – A list of metadata dictionaries to associate with the texts.

      - **kwargs – Arbitrary keyword arguments.

    Returns

      A new ElasticKnnSearch instance.

    Return type

      ElasticKnnSearch

  knn_hybrid_search(query: Optional[str] = None, k: Optional[int] = 10, query_vector: Optional[List[float]] = None, model_id: Optional[str] = None, size: Optional[int] = 10, source: Optional[bool] = True, knn_boost: Optional[float] = 0.9, query_boost: Optional[float] = 0.1, fields: Optional[Union[List[Mapping[str, Any]], Tuple[Mapping[str, Any], ...]]] = None, page_content: Optional[str] = 'text') → List[Tuple[Document, float]]

    Perform a hybrid k-NN and text search on the Elasticsearch index.

    Parameters

      - query (str, optional) – The query text to search for.

      - k (int, optional) – The number of nearest neighbors to return.

      - query_vector (List[float], optional) – The query vector to search for.

      - model_id (str, optional) – The ID of the model to use for transforming the query text into a vector.

      - size (int, optional) – The number of search results to return.

      - source (bool, optional) – Whether to return the source of the search results.

      - knn_boost (float, optional) – The boost value to apply to the k-NN search results.

      - query_boost (float, optional) – The boost value to apply to the text search results.

      - fields (List[Mapping[str, Any]], optional) – The fields to return in the search results.

      - page_content (str, optional) – The name of the field that contains the page content.

    Returns

      A list of tuples, where each tuple contains a Document object and a score.

    Return type

      List[Tuple[Document, float]]

  knn_search(query: Optional[str] = None, k: Optional[int] = 10, query_vector: Optional[List[float]] = None, model_id: Optional[str] = None, size: Optional[int] = 10, source: Optional[bool] = True, fields: Optional[Union[List[Mapping[str, Any]], Tuple[Mapping[str, Any], ...]]] = None, page_content: Optional[str] = 'text') → List[Tuple[Document, float]]

    Perform a k-NN search on the Elasticsearch index.

    Parameters

      - query (str, optional) – The query text to search for.

      - k (int, optional) – The number of nearest neighbors to return.

      - query_vector (List[float], optional) – The query vector to search for.

      - model_id (str, optional) – The ID of the model to use for transforming the query text into a vector.

      - size (int, optional) – The number of search results to return.

      - source (bool, optional) – Whether to return the source of the search results.

      - fields (List[Mapping[str, Any]], optional) – The fields to return in the search results.

      - page_content (str, optional) – The name of the field that contains the page content.

    Returns

      A list of tuples, where each tuple contains a Document object and a score.

    Return type

      List[Tuple[Document, float]]

  max_marginal_relevance_search(query: str, k: int = 4, fetch_k: int = 20, lambda_mult: float = 0.5, **kwargs: Any) → List[Document]

    Return docs selected using the maximal marginal relevance.

    Maximal marginal relevance optimizes for similarity to query AND diversity among selected documents.

    Parameters

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

      - k (int) – Number of Documents to return. Defaults to 4.

      - fetch_k (int) – Number of Documents to fetch to pass to MMR algorithm.

      - lambda_mult (float) – Number between 0 and 1 that determines the degree of diversity among the results with 0 corresponding to maximum diversity and 1 to minimum diversity. Defaults to 0.5.

      - kwargs (Any) –

    Returns

      List of Documents selected by maximal marginal relevance.

    Return type

      List[Document]

  max_marginal_relevance_search_by_vector(embedding: List[float], k: int = 4, fetch_k: int = 20, lambda_mult: float = 0.5, **kwargs: Any) → List[Document]

    Return docs selected using the maximal marginal relevance.

    Maximal marginal relevance optimizes for similarity to query AND diversity among selected documents.

    Parameters

      - embedding (List[float]) – Embedding to look up documents similar to.

      - k (int) – Number of Documents to return. Defaults to 4.

      - fetch_k (int) – Number of Documents to fetch to pass to MMR algorithm.

      - lambda_mult (float) – Number between 0 and 1 that determines the degree of diversity among the results with 0 corresponding to maximum diversity and 1 to minimum diversity. Defaults to 0.5.

      - kwargs (Any) –

    Returns

      List of Documents selected by maximal marginal relevance.

    Return type

      List[Document]

  search(query: str, search_type: str, **kwargs: Any) → List[Document]

    Return docs most similar to query using specified search type.

    Parameters

      - query (str) –

      - search_type (str) –

      - kwargs (Any) –

    Return type

      List[Document]

  similarity_search(query: str, k: int = 4, filter: Optional[dict] = None, **kwargs: Any) → List[Document]

    Pass through to knn_search

    Parameters

      - query (str) –

      - k (int) –

      - filter (Optional[dict]) –

      - kwargs (Any) –

    Return type

      List[Document]

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

    Return docs most similar to embedding vector.

    Parameters

      - embedding (List[float]) – Embedding to look up documents similar to.

      - k (int) – Number of Documents to return. Defaults to 4.

      - kwargs (Any) –

    Returns

      List of Documents most similar to the query vector.

    Return type

      List[Document]

  similarity_search_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 = 10, **kwargs: Any) → List[Tuple[Document, float]]

    Pass through to knn_search including score

    Parameters

      - query (str) –

      - k (int) –

      - kwargs (Any) –

    Return type

      List[Tuple[Document, float]]