Source code for langchain_elasticsearch.vectorstores

import logging
from abc import ABC, abstractmethod
from typing import (
    Any,
    Callable,
    Dict,
    Iterable,
    List,
    Literal,
    Optional,
    Tuple,
    Union,
)

from elasticsearch import Elasticsearch
from elasticsearch.helpers.vectorstore import (
    BM25Strategy,
    DenseVectorScriptScoreStrategy,
    DenseVectorStrategy,
    DistanceMetric,
    RetrievalStrategy,
    SparseVectorStrategy,
)
from elasticsearch.helpers.vectorstore import (
    VectorStore as EVectorStore,
)
from langchain_core._api.deprecation import deprecated
from langchain_core.documents import Document
from langchain_core.embeddings import Embeddings
from langchain_core.vectorstores import VectorStore

from langchain_elasticsearch._utilities import (
    DistanceStrategy,
    model_must_be_deployed,
    user_agent,
)
from langchain_elasticsearch.client import create_elasticsearch_client
from langchain_elasticsearch.embeddings import EmbeddingServiceAdapter

logger = logging.getLogger(__name__)


[docs]@deprecated("0.2.0", alternative="RetrievalStrategy", pending=True) class BaseRetrievalStrategy(ABC): """Base class for `Elasticsearch` retrieval strategies."""
[docs] @abstractmethod def query( self, query_vector: Union[List[float], None], query: Union[str, None], *, k: int, fetch_k: int, vector_query_field: str, text_field: str, filter: List[dict], similarity: Union[DistanceStrategy, None], ) -> Dict: """ Executes when a search is performed on the store. Args: query_vector: The query vector, or None if not using vector-based query. query: The text query, or None if not using text-based query. k: The total number of results to retrieve. fetch_k: The number of results to fetch initially. vector_query_field: The field containing the vector representations in the index. text_field: The field containing the text data in the index. filter: List of filter clauses to apply to the query. similarity: The similarity strategy to use, or None if not using one. Returns: Dict: The Elasticsearch query body. """
[docs] @abstractmethod def index( self, dims_length: Union[int, None], vector_query_field: str, text_field: str, similarity: Union[DistanceStrategy, None], ) -> Dict: """ Executes when the index is created. Args: dims_length: Numeric length of the embedding vectors, or None if not using vector-based query. vector_query_field: The field containing the vector representations in the index. text_field: The field containing the text data in the index. similarity: The similarity strategy to use, or None if not using one. Returns: Dict: The Elasticsearch settings and mappings for the strategy. """
[docs] def before_index_setup( self, client: "Elasticsearch", text_field: str, vector_query_field: str ) -> None: """ Executes before the index is created. Used for setting up any required Elasticsearch resources like a pipeline. Args: client: The Elasticsearch client. text_field: The field containing the text data in the index. vector_query_field: The field containing the vector representations in the index. """
[docs] def require_inference(self) -> bool: """ Returns whether or not the strategy requires inference to be performed on the text before it is added to the index. Returns: bool: Whether or not the strategy requires inference to be performed on the text before it is added to the index. """ return True
[docs]@deprecated("0.2.0", alternative="DenseVectorStrategy", pending=True) class ApproxRetrievalStrategy(BaseRetrievalStrategy): """Approximate retrieval strategy using the `HNSW` algorithm."""
[docs] def __init__( self, query_model_id: Optional[str] = None, hybrid: Optional[bool] = False, rrf: Optional[Union[dict, bool]] = True, ): self.query_model_id = query_model_id self.hybrid = hybrid # RRF has two optional parameters # 'rank_constant', 'window_size' # https://www.elastic.co/guide/en/elasticsearch/reference/current/rrf.html self.rrf = rrf
[docs] def query( self, query_vector: Union[List[float], None], query: Union[str, None], k: int, fetch_k: int, vector_query_field: str, text_field: str, filter: List[dict], similarity: Union[DistanceStrategy, None], ) -> Dict: knn = { "filter": filter, "field": vector_query_field, "k": k, "num_candidates": fetch_k, } # Embedding provided via the embedding function if query_vector is not None and not self.query_model_id: knn["query_vector"] = list(query_vector) # Case 2: Used when model has been deployed to # Elasticsearch and can infer the query vector from the query text elif query and self.query_model_id: knn["query_vector_builder"] = { "text_embedding": { "model_id": self.query_model_id, # use 'model_id' argument "model_text": query, # use 'query' argument } } else: raise ValueError( "You must provide an embedding function or a" " query_model_id to perform a similarity search." ) # If hybrid, add a query to the knn query # RRF is used to even the score from the knn query and text query # RRF has two optional parameters: {'rank_constant':int, 'window_size':int} # https://www.elastic.co/guide/en/elasticsearch/reference/current/rrf.html if self.hybrid: query_body = { "knn": knn, "query": { "bool": { "must": [ { "match": { text_field: { "query": query, } } } ], "filter": filter, } }, } if isinstance(self.rrf, dict): query_body["rank"] = {"rrf": self.rrf} elif isinstance(self.rrf, bool) and self.rrf is True: query_body["rank"] = {"rrf": {}} return query_body else: return {"knn": knn}
[docs] def before_index_setup( self, client: "Elasticsearch", text_field: str, vector_query_field: str ) -> None: if self.query_model_id: model_must_be_deployed(client, self.query_model_id)
[docs] def index( self, dims_length: Union[int, None], vector_query_field: str, text_field: str, similarity: Union[DistanceStrategy, None], ) -> Dict: """Create the mapping for the Elasticsearch index.""" if similarity is DistanceStrategy.COSINE: similarityAlgo = "cosine" elif similarity is DistanceStrategy.EUCLIDEAN_DISTANCE: similarityAlgo = "l2_norm" elif similarity is DistanceStrategy.DOT_PRODUCT: similarityAlgo = "dot_product" elif similarity is DistanceStrategy.MAX_INNER_PRODUCT: similarityAlgo = "max_inner_product" else: raise ValueError(f"Similarity {similarity} not supported.") return { "mappings": { "properties": { vector_query_field: { "type": "dense_vector", "dims": dims_length, "index": True, "similarity": similarityAlgo, }, } } }
[docs]@deprecated("0.2.0", alternative="DenseVectorScriptScoreStrategy", pending=True) class ExactRetrievalStrategy(BaseRetrievalStrategy): """Exact retrieval strategy using the `script_score` query."""
[docs] def query( self, query_vector: Union[List[float], None], query: Union[str, None], k: int, fetch_k: int, vector_query_field: str, text_field: str, filter: Union[List[dict], None], similarity: Union[DistanceStrategy, None], ) -> Dict: if similarity is DistanceStrategy.COSINE: similarityAlgo = ( f"cosineSimilarity(params.query_vector, '{vector_query_field}') + 1.0" ) elif similarity is DistanceStrategy.EUCLIDEAN_DISTANCE: similarityAlgo = ( f"1 / (1 + l2norm(params.query_vector, '{vector_query_field}'))" ) elif similarity is DistanceStrategy.DOT_PRODUCT: similarityAlgo = f""" double value = dotProduct(params.query_vector, '{vector_query_field}'); return sigmoid(1, Math.E, -value); """ else: raise ValueError(f"Similarity {similarity} not supported.") queryBool: Dict = {"match_all": {}} if filter: queryBool = {"bool": {"filter": filter}} return { "query": { "script_score": { "query": queryBool, "script": { "source": similarityAlgo, "params": {"query_vector": query_vector}, }, }, } }
[docs] def index( self, dims_length: Union[int, None], vector_query_field: str, text_field: str, similarity: Union[DistanceStrategy, None], ) -> Dict: """Create the mapping for the Elasticsearch index.""" return { "mappings": { "properties": { vector_query_field: { "type": "dense_vector", "dims": dims_length, "index": False, }, } } }
[docs]@deprecated("0.2.0", alternative="SparseVectorStrategy", pending=True) class SparseRetrievalStrategy(BaseRetrievalStrategy): """Sparse retrieval strategy using the `text_expansion` processor."""
[docs] def __init__(self, model_id: Optional[str] = None): self.model_id = model_id or ".elser_model_1"
[docs] def query( self, query_vector: Union[List[float], None], query: Union[str, None], k: int, fetch_k: int, vector_query_field: str, text_field: str, filter: List[dict], similarity: Union[DistanceStrategy, None], ) -> Dict: return { "query": { "bool": { "must": [ { "text_expansion": { f"{vector_query_field}.tokens": { "model_id": self.model_id, "model_text": query, } } } ], "filter": filter, } } }
def _get_pipeline_name(self) -> str: return f"{self.model_id}_sparse_embedding"
[docs] def before_index_setup( self, client: "Elasticsearch", text_field: str, vector_query_field: str ) -> None: if self.model_id: model_must_be_deployed(client, self.model_id) # Create a pipeline for the model client.ingest.put_pipeline( id=self._get_pipeline_name(), description="Embedding pipeline for langchain vectorstore", processors=[ { "inference": { "model_id": self.model_id, "target_field": vector_query_field, "field_map": {text_field: "text_field"}, "inference_config": { "text_expansion": {"results_field": "tokens"} }, } } ], )
[docs] def index( self, dims_length: Union[int, None], vector_query_field: str, text_field: str, similarity: Union[DistanceStrategy, None], ) -> Dict: return { "mappings": { "properties": { vector_query_field: { "properties": {"tokens": {"type": "rank_features"}} } } }, "settings": {"default_pipeline": self._get_pipeline_name()}, }
[docs] def require_inference(self) -> bool: return False
[docs]@deprecated("0.2.0", alternative="BM25Strategy", pending=True) class BM25RetrievalStrategy(BaseRetrievalStrategy): """Retrieval strategy using the native BM25 algorithm of Elasticsearch."""
[docs] def __init__(self, k1: Union[float, None] = None, b: Union[float, None] = None): self.k1 = k1 self.b = b
[docs] def query( self, query_vector: Union[List[float], None], query: Union[str, None], k: int, fetch_k: int, vector_query_field: str, text_field: str, filter: List[dict], similarity: Union[DistanceStrategy, None], ) -> Dict: return { "query": { "bool": { "must": [ { "match": { text_field: { "query": query, } }, }, ], "filter": filter, }, }, }
[docs] def index( self, dims_length: Union[int, None], vector_query_field: str, text_field: str, similarity: Union[DistanceStrategy, None], ) -> Dict: mappings: Dict = { "properties": { text_field: { "type": "text", "similarity": "custom_bm25", }, }, } settings: Dict = { "similarity": { "custom_bm25": { "type": "BM25", }, }, } if self.k1 is not None: settings["similarity"]["custom_bm25"]["k1"] = self.k1 if self.b is not None: settings["similarity"]["custom_bm25"]["b"] = self.b return {"mappings": mappings, "settings": settings}
[docs] def require_inference(self) -> bool: return False
def _convert_retrieval_strategy( langchain_strategy: BaseRetrievalStrategy, distance: Optional[DistanceStrategy] = None, ) -> RetrievalStrategy: if isinstance(langchain_strategy, ApproxRetrievalStrategy): if distance is None: raise ValueError( "ApproxRetrievalStrategy requires a distance strategy to be provided." ) return DenseVectorStrategy( distance=DistanceMetric[distance], model_id=langchain_strategy.query_model_id, hybrid=( False if langchain_strategy.hybrid is None else langchain_strategy.hybrid ), rrf=False if langchain_strategy.rrf is None else langchain_strategy.rrf, ) elif isinstance(langchain_strategy, ExactRetrievalStrategy): if distance is None: raise ValueError( "ExactRetrievalStrategy requires a distance strategy to be provided." ) return DenseVectorScriptScoreStrategy(distance=DistanceMetric[distance]) elif isinstance(langchain_strategy, SparseRetrievalStrategy): return SparseVectorStrategy(langchain_strategy.model_id) elif isinstance(langchain_strategy, BM25RetrievalStrategy): return BM25Strategy(k1=langchain_strategy.k1, b=langchain_strategy.b) else: raise TypeError( f"Strategy {langchain_strategy} not supported. To provide a " f"custom strategy, please subclass {RetrievalStrategy}." ) def _hits_to_docs_scores( hits: List[Dict[str, Any]], content_field: str, fields: Optional[List[str]] = None, doc_builder: Optional[Callable[[Dict], Document]] = None, ) -> List[Tuple[Document, float]]: if fields is None: fields = [] documents = [] def default_doc_builder(hit: Dict) -> Document: return Document( page_content=hit["_source"].get(content_field, ""), metadata=hit["_source"].get("metadata", {}), ) doc_builder = doc_builder or default_doc_builder for hit in hits: for field in fields: if "metadata" not in hit["_source"]: hit["_source"]["metadata"] = {} if field in hit["_source"] and field not in [ "metadata", content_field, ]: hit["_source"]["metadata"][field] = hit["_source"][field] doc = doc_builder(hit) documents.append((doc, hit["_score"])) return documents
[docs]class ElasticsearchStore(VectorStore): """`Elasticsearch` vector store. Example: .. code-block:: python from langchain_elasticsearch.vectorstores import ElasticsearchStore from langchain_openai import OpenAIEmbeddings store = ElasticsearchStore( embedding=OpenAIEmbeddings(), index_name="langchain-demo", es_url="http://localhost:9200" ) Args: index_name: Name of the Elasticsearch index to create. es_url: URL of the Elasticsearch instance to connect to. cloud_id: Cloud ID of the Elasticsearch instance to connect to. es_user: Username to use when connecting to Elasticsearch. es_password: Password to use when connecting to Elasticsearch. es_api_key: API key to use when connecting to Elasticsearch. es_connection: Optional pre-existing Elasticsearch connection. vector_query_field: Optional. Name of the field to store the embedding vectors in. query_field: Optional. Name of the field to store the texts in. strategy: Optional. Retrieval strategy to use when searching the index. Defaults to ApproxRetrievalStrategy. Can be one of ExactRetrievalStrategy, ApproxRetrievalStrategy, or SparseRetrievalStrategy. distance_strategy: Optional. Distance strategy to use when searching the index. Defaults to COSINE. Can be one of COSINE, EUCLIDEAN_DISTANCE, MAX_INNER_PRODUCT or DOT_PRODUCT. If you want to use a cloud hosted Elasticsearch instance, you can pass in the cloud_id argument instead of the es_url argument. Example: .. code-block:: python from langchain_elasticsearch.vectorstores import ElasticsearchStore from langchain_openai import OpenAIEmbeddings store = ElasticsearchStore( embedding=OpenAIEmbeddings(), index_name="langchain-demo", es_cloud_id="<cloud_id>" es_user="elastic", es_password="<password>" ) You can also connect to an existing Elasticsearch instance by passing in a pre-existing Elasticsearch connection via the es_connection argument. Example: .. code-block:: python from langchain_elasticsearch.vectorstores import ElasticsearchStore from langchain_openai import OpenAIEmbeddings from elasticsearch import Elasticsearch es_connection = Elasticsearch("http://localhost:9200") store = ElasticsearchStore( embedding=OpenAIEmbeddings(), index_name="langchain-demo", es_connection=es_connection ) ElasticsearchStore by default uses the ApproxRetrievalStrategy, which uses the HNSW algorithm to perform approximate nearest neighbor search. This is the fastest and most memory efficient algorithm. If you want to use the Brute force / Exact strategy for searching vectors, you can pass in the ExactRetrievalStrategy to the ElasticsearchStore constructor. Example: .. code-block:: python from langchain_elasticsearch.vectorstores import ElasticsearchStore from langchain_openai import OpenAIEmbeddings store = ElasticsearchStore( embedding=OpenAIEmbeddings(), index_name="langchain-demo", es_url="http://localhost:9200", strategy=ElasticsearchStore.ExactRetrievalStrategy() ) Both strategies require that you know the similarity metric you want to use when creating the index. The default is cosine similarity, but you can also use dot product or euclidean distance. Example: .. code-block:: python from langchain_elasticsearch.vectorstores import ElasticsearchStore from langchain_openai import OpenAIEmbeddings from langchain_community.vectorstores.utils import DistanceStrategy store = ElasticsearchStore( "langchain-demo", embedding=OpenAIEmbeddings(), es_url="http://localhost:9200", distance_strategy="DOT_PRODUCT" ) """
[docs] def __init__( self, index_name: str, *, embedding: Optional[Embeddings] = None, es_connection: Optional[Elasticsearch] = None, es_url: Optional[str] = None, es_cloud_id: Optional[str] = None, es_user: Optional[str] = None, es_api_key: Optional[str] = None, es_password: Optional[str] = None, vector_query_field: str = "vector", query_field: str = "text", distance_strategy: Optional[ Literal[ DistanceStrategy.COSINE, DistanceStrategy.DOT_PRODUCT, DistanceStrategy.EUCLIDEAN_DISTANCE, DistanceStrategy.MAX_INNER_PRODUCT, ] ] = None, strategy: Union[ BaseRetrievalStrategy, RetrievalStrategy ] = ApproxRetrievalStrategy(), es_params: Optional[Dict[str, Any]] = None, ): if isinstance(strategy, BaseRetrievalStrategy): strategy = _convert_retrieval_strategy( strategy, distance=distance_strategy or DistanceStrategy.COSINE ) embedding_service = None if embedding: embedding_service = EmbeddingServiceAdapter(embedding) if not es_connection: es_connection = create_elasticsearch_client( url=es_url, cloud_id=es_cloud_id, api_key=es_api_key, username=es_user, password=es_password, params=es_params, ) self._store = EVectorStore( client=es_connection, index=index_name, retrieval_strategy=strategy, embedding_service=embedding_service, text_field=query_field, vector_field=vector_query_field, user_agent=user_agent("langchain-py-vs"), ) self.embedding = embedding self.client = self._store.client self._embedding_service = embedding_service self.query_field = query_field self.vector_query_field = vector_query_field
[docs] def close(self) -> None: self._store.close()
@property def embeddings(self) -> Optional[Embeddings]: return self.embedding
[docs] @staticmethod def connect_to_elasticsearch( *, es_url: Optional[str] = None, cloud_id: Optional[str] = None, api_key: Optional[str] = None, username: Optional[str] = None, password: Optional[str] = None, es_params: Optional[Dict[str, Any]] = None, ) -> Elasticsearch: return create_elasticsearch_client( url=es_url, cloud_id=cloud_id, api_key=api_key, username=username, password=password, params=es_params, )
@staticmethod def _identity_fn(score: float) -> float: return score def _select_relevance_score_fn(self) -> Callable[[float], float]: """ The 'correct' relevance function may differ depending on a few things, including: - the distance / similarity metric used by the VectorStore - the scale of your embeddings (OpenAI's are unit normed. Many others are not!) - embedding dimensionality - etc. Vectorstores should define their own selection based method of relevance. """ # All scores from Elasticsearch are already normalized similarities: # https://www.elastic.co/guide/en/elasticsearch/reference/current/dense-vector.html#dense-vector-params return self._identity_fn
[docs] def similarity_search_with_score( self, query: str, k: int = 4, filter: Optional[List[dict]] = None, *, custom_query: Optional[ Callable[[Dict[str, Any], Optional[str]], Dict[str, Any]] ] = None, doc_builder: Optional[Callable[[Dict], Document]] = None, **kwargs: Any, ) -> List[Tuple[Document, float]]: """Return Elasticsearch documents most similar to query, along with scores. Args: query: Text to look up documents similar to. k: Number of Documents to return. Defaults to 4. filter: Array of Elasticsearch filter clauses to apply to the query. Returns: List of Documents most similar to the query and score for each """ if ( isinstance(self._store.retrieval_strategy, DenseVectorStrategy) and self._store.retrieval_strategy.hybrid ): raise ValueError("scores are currently not supported in hybrid mode") hits = self._store.search( query=query, k=k, filter=filter, custom_query=custom_query ) return _hits_to_docs_scores( hits=hits, content_field=self.query_field, doc_builder=doc_builder, )
[docs] def similarity_search_by_vector_with_relevance_scores( self, embedding: List[float], k: int = 4, filter: Optional[List[Dict]] = None, *, custom_query: Optional[ Callable[[Dict[str, Any], Optional[str]], Dict[str, Any]] ] = None, doc_builder: Optional[Callable[[Dict], Document]] = None, **kwargs: Any, ) -> List[Tuple[Document, float]]: """Return Elasticsearch documents most similar to query, along with scores. Args: embedding: Embedding to look up documents similar to. k: Number of Documents to return. Defaults to 4. filter: Array of Elasticsearch filter clauses to apply to the query. Returns: List of Documents most similar to the embedding and score for each """ if ( isinstance(self._store.retrieval_strategy, DenseVectorStrategy) and self._store.retrieval_strategy.hybrid ): raise ValueError("scores are currently not supported in hybrid mode") hits = self._store.search( query=None, query_vector=embedding, k=k, filter=filter, custom_query=custom_query, ) return _hits_to_docs_scores( hits=hits, content_field=self.query_field, doc_builder=doc_builder, )
[docs] def delete( self, ids: Optional[List[str]] = None, refresh_indices: Optional[bool] = True, **kwargs: Any, ) -> Optional[bool]: """Delete documents from the Elasticsearch index. Args: ids: List of ids of documents to delete. refresh_indices: Whether to refresh the index after deleting documents. Defaults to True. """ if ids is None: raise ValueError("please specify some IDs") return self._store.delete(ids=ids, refresh_indices=refresh_indices or False)
[docs] def add_texts( self, texts: Iterable[str], metadatas: Optional[List[Dict[Any, Any]]] = None, ids: Optional[List[str]] = None, refresh_indices: bool = True, create_index_if_not_exists: bool = True, bulk_kwargs: Optional[Dict] = None, **kwargs: Any, ) -> List[str]: """Run more texts through the embeddings and add to the store. Args: texts: Iterable of strings to add to the store. metadatas: Optional list of metadatas associated with the texts. ids: Optional list of ids to associate with the texts. refresh_indices: Whether to refresh the Elasticsearch indices after adding the texts. create_index_if_not_exists: Whether to create the Elasticsearch index if it doesn't already exist. *bulk_kwargs: Additional arguments to pass to Elasticsearch bulk. - chunk_size: Optional. Number of texts to add to the index at a time. Defaults to 500. Returns: List of ids from adding the texts into the store. """ return self._store.add_texts( texts=list(texts), metadatas=metadatas, ids=ids, refresh_indices=refresh_indices, create_index_if_not_exists=create_index_if_not_exists, bulk_kwargs=bulk_kwargs, )
[docs] def add_embeddings( self, text_embeddings: Iterable[Tuple[str, List[float]]], metadatas: Optional[List[dict]] = None, ids: Optional[List[str]] = None, refresh_indices: bool = True, create_index_if_not_exists: bool = True, bulk_kwargs: Optional[Dict] = None, **kwargs: Any, ) -> List[str]: """Add the given texts and embeddings to the store. Args: text_embeddings: Iterable pairs of string and embedding to add to the store. metadatas: Optional list of metadatas associated with the texts. ids: Optional list of unique IDs. refresh_indices: Whether to refresh the Elasticsearch indices after adding the texts. create_index_if_not_exists: Whether to create the Elasticsearch index if it doesn't already exist. *bulk_kwargs: Additional arguments to pass to Elasticsearch bulk. - chunk_size: Optional. Number of texts to add to the index at a time. Defaults to 500. Returns: List of ids from adding the texts into the store. """ texts, embeddings = zip(*text_embeddings) return self._store.add_texts( texts=list(texts), metadatas=metadatas, vectors=list(embeddings), ids=ids, refresh_indices=refresh_indices, create_index_if_not_exists=create_index_if_not_exists, bulk_kwargs=bulk_kwargs, )
[docs] @classmethod def from_texts( cls, texts: List[str], embedding: Optional[Embeddings] = None, metadatas: Optional[List[Dict[str, Any]]] = None, bulk_kwargs: Optional[Dict] = None, **kwargs: Any, ) -> "ElasticsearchStore": """Construct ElasticsearchStore wrapper from raw documents. Example: .. code-block:: python from langchain_elasticsearch.vectorstores import ElasticsearchStore from langchain_openai import OpenAIEmbeddings db = ElasticsearchStore.from_texts( texts, // embeddings optional if using // a strategy that doesn't require inference embeddings, index_name="langchain-demo", es_url="http://localhost:9200" ) Args: texts: List of texts to add to the Elasticsearch index. embedding: Embedding function to use to embed the texts. metadatas: Optional list of metadatas associated with the texts. index_name: Name of the Elasticsearch index to create. es_url: URL of the Elasticsearch instance to connect to. cloud_id: Cloud ID of the Elasticsearch instance to connect to. es_user: Username to use when connecting to Elasticsearch. es_password: Password to use when connecting to Elasticsearch. es_api_key: API key to use when connecting to Elasticsearch. es_connection: Optional pre-existing Elasticsearch connection. vector_query_field: Optional. Name of the field to store the embedding vectors in. query_field: Optional. Name of the field to store the texts in. distance_strategy: Optional. Name of the distance strategy to use. Defaults to "COSINE". can be one of "COSINE", "EUCLIDEAN_DISTANCE", "DOT_PRODUCT", "MAX_INNER_PRODUCT". bulk_kwargs: Optional. Additional arguments to pass to Elasticsearch bulk. """ index_name = kwargs.get("index_name") if index_name is None: raise ValueError("Please provide an index_name.") elasticsearchStore = ElasticsearchStore(embedding=embedding, **kwargs) # Encode the provided texts and add them to the newly created index. elasticsearchStore.add_texts( texts=texts, metadatas=metadatas, bulk_kwargs=bulk_kwargs ) return elasticsearchStore
[docs] @classmethod def from_documents( cls, documents: List[Document], embedding: Optional[Embeddings] = None, bulk_kwargs: Optional[Dict] = None, **kwargs: Any, ) -> "ElasticsearchStore": """Construct ElasticsearchStore wrapper from documents. Example: .. code-block:: python from langchain_elasticsearch.vectorstores import ElasticsearchStore from langchain_openai import OpenAIEmbeddings db = ElasticsearchStore.from_documents( texts, embeddings, index_name="langchain-demo", es_url="http://localhost:9200" ) Args: texts: List of texts to add to the Elasticsearch index. embedding: Embedding function to use to embed the texts. Do not provide if using a strategy that doesn't require inference. metadatas: Optional list of metadatas associated with the texts. index_name: Name of the Elasticsearch index to create. es_url: URL of the Elasticsearch instance to connect to. cloud_id: Cloud ID of the Elasticsearch instance to connect to. es_user: Username to use when connecting to Elasticsearch. es_password: Password to use when connecting to Elasticsearch. es_api_key: API key to use when connecting to Elasticsearch. es_connection: Optional pre-existing Elasticsearch connection. vector_query_field: Optional. Name of the field to store the embedding vectors in. query_field: Optional. Name of the field to store the texts in. bulk_kwargs: Optional. Additional arguments to pass to Elasticsearch bulk. """ index_name = kwargs.get("index_name") if index_name is None: raise ValueError("Please provide an index_name.") elasticsearchStore = ElasticsearchStore(embedding=embedding, **kwargs) # Encode the provided texts and add them to the newly created index. elasticsearchStore.add_documents(documents, bulk_kwargs=bulk_kwargs) return elasticsearchStore
[docs] @staticmethod def ExactRetrievalStrategy() -> "ExactRetrievalStrategy": """Used to perform brute force / exact nearest neighbor search via script_score.""" return ExactRetrievalStrategy()
[docs] @staticmethod def ApproxRetrievalStrategy( query_model_id: Optional[str] = None, hybrid: Optional[bool] = False, rrf: Optional[Union[dict, bool]] = True, ) -> "ApproxRetrievalStrategy": """Used to perform approximate nearest neighbor search using the HNSW algorithm. At build index time, this strategy will create a dense vector field in the index and store the embedding vectors in the index. At query time, the text will either be embedded using the provided embedding function or the query_model_id will be used to embed the text using the model deployed to Elasticsearch. if query_model_id is used, do not provide an embedding function. Args: query_model_id: Optional. ID of the model to use to embed the query text within the stack. Requires embedding model to be deployed to Elasticsearch. hybrid: Optional. If True, will perform a hybrid search using both the knn query and a text query. Defaults to False. rrf: Optional. rrf is Reciprocal Rank Fusion. When `hybrid` is True, and `rrf` is True, then rrf: {}. and `rrf` is False, then rrf is omitted. and isinstance(rrf, dict) is True, then pass in the dict values. rrf could be passed for adjusting 'rank_constant' and 'window_size'. """ return ApproxRetrievalStrategy( query_model_id=query_model_id, hybrid=hybrid, rrf=rrf )
[docs] @staticmethod def SparseVectorRetrievalStrategy( model_id: Optional[str] = None, ) -> "SparseRetrievalStrategy": """Used to perform sparse vector search via text_expansion. Used for when you want to use ELSER model to perform document search. At build index time, this strategy will create a pipeline that will embed the text using the ELSER model and store the resulting tokens in the index. At query time, the text will be embedded using the ELSER model and the resulting tokens will be used to perform a text_expansion query. Args: model_id: Optional. Default is ".elser_model_1". ID of the model to use to embed the query text within the stack. Requires embedding model to be deployed to Elasticsearch. """ return SparseRetrievalStrategy(model_id=model_id)
[docs] @staticmethod def BM25RetrievalStrategy( k1: Union[float, None] = None, b: Union[float, None] = None ) -> "BM25RetrievalStrategy": """Used to apply BM25 without vector search. Args: k1: Optional. This corresponds to the BM25 parameter, k1. Default is None, which uses the default setting of Elasticsearch. b: Optional. This corresponds to the BM25 parameter, b. Default is None, which uses the default setting of Elasticsearch. """ return BM25RetrievalStrategy(k1=k1, b=b)