langchain.vectorstores.elastic_vector_search.ElasticKnnSearch¶

class langchain.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')[source]¶

[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.

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)][*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.

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 0.0.265: Use ElasticsearchStore class. 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')[source]¶
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.

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.

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.

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][source]¶

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.

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

Delete by vector ID or other criteria.

Parameters

  • ids – List of ids to delete.

  • **kwargs – 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.

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

Return VectorStore initialized from texts and embeddings.

Return docs selected using the maximal marginal relevance.

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.

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

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.

Return docs most similar to query.

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

Return docs most similar to embedding vector.

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 – input text

  • k – Number of Documents to return. Defaults to 4.

  • **kwargs –

    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)

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

Run similarity search with distance asynchronously.

create_knn_index(mapping: Dict) → None[source]¶

Create a new k-NN index in Elasticsearch.

Parameters

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

Returns

None

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

Delete by vector ID or other criteria.

Parameters

  • ids – List of ids to delete.

  • **kwargs – 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.

classmethod from_texts(texts: List[str], embedding: Embeddings, metadatas: Optional[List[Dict[Any, Any]]] = None, **kwargs: Any) → ElasticKnnSearch[source]¶
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.

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.

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 docs selected using the maximal marginal relevance.

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

Parameters

  • query – Text to look up documents similar to.

  • k – Number of Documents to return. Defaults to 4.

  • fetch_k – Number of Documents to fetch to pass to MMR algorithm.

  • lambda_mult – 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.

Returns

List of Documents selected by maximal marginal relevance.

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 – Embedding to look up documents similar to.

  • k – Number of Documents to return. Defaults to 4.

  • fetch_k – Number of Documents to fetch to pass to MMR algorithm.

  • lambda_mult – 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.

Returns

List of Documents selected by maximal marginal relevance.

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

Return docs most similar to query using specified search type.

Pass through to knn_search

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

Return docs most similar to embedding vector.

Parameters

  • embedding – Embedding to look up documents similar to.

  • k – Number of Documents to return. Defaults to 4.

Returns

List of Documents most similar to the query vector.

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 – input text

  • k – Number of Documents to return. Defaults to 4.

  • **kwargs –

    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)

similarity_search_with_score(query: str, k: int = 10, **kwargs: Any) → List[Tuple[Document, float]][source]¶

Pass through to knn_search including score