import logging import uuid from abc import ABC, abstractmethod from typing import ( TYPE_CHECKING, Any, Callable, Dict, Iterable, List, Literal, Optional, Tuple, Union, ) import numpy as np from langchain_core._api import deprecated from langchain_core.documents import Document from langchain_core.embeddings import Embeddings from langchain_core.vectorstores import VectorStore from langchain_community.vectorstores.utils import ( DistanceStrategy, maximal_marginal_relevance, ) if TYPE_CHECKING: from elasticsearch import Elasticsearch logger = logging.getLogger(__name__) class BaseRetrievalStrategy(ABC): """Base class for `Elasticsearch` retrieval strategies.""" @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. """ @abstractmethod def index( self, dims_length: Union[int, None], vector_query_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. similarity: The similarity strategy to use, or None if not using one. Returns: Dict: The Elasticsearch settings and mappings for the strategy. """ 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. """ 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 @deprecated( "0.0.27", alternative="Use class in langchain-elasticsearch package", pending=True ) class ApproxRetrievalStrategy(BaseRetrievalStrategy): """Approximate retrieval strategy using the `HNSW` algorithm.""" 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 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 and not self.query_model_id: knn["query_vector"] = 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} def index( self, dims_length: Union[int, None], vector_query_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, }, } } } @deprecated( "0.0.27", alternative="Use class in langchain-elasticsearch package", pending=True ) class ExactRetrievalStrategy(BaseRetrievalStrategy): """Exact retrieval strategy using the `script_score` query.""" 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}, }, }, } } def index( self, dims_length: Union[int, None], vector_query_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, }, } } } @deprecated( "0.0.27", alternative="Use class in langchain-elasticsearch package", pending=True ) class SparseRetrievalStrategy(BaseRetrievalStrategy): """Sparse retrieval strategy using the `text_expansion` processor.""" def __init__(self, model_id: Optional[str] = None): self.model_id = model_id or ".elser_model_1" 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" def before_index_setup( self, client: "Elasticsearch", text_field: str, vector_query_field: str ) -> None: # If model_id is provided, create a pipeline for the model if self.model_id: 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"} }, } } ], ) def index( self, dims_length: Union[int, None], vector_query_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()}, } def require_inference(self) -> bool: return False @deprecated( "0.0.27", alternative="Use class in langchain-elasticsearch package", pending=True ) class ElasticsearchStore(VectorStore): """`Elasticsearch` vector store. Example: .. code-block:: python from langchain_community.vectorstores import ElasticsearchStore from langchain_community.embeddings.openai import OpenAIEmbeddings vectorstore = 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_community.vectorstores import ElasticsearchStore from langchain_community.embeddings.openai import OpenAIEmbeddings vectorstore = ElasticsearchStore( embedding=OpenAIEmbeddings(), index_name="langchain-demo", es_cloud_id="" es_user="elastic", es_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_community.vectorstores import ElasticsearchStore from langchain_community.embeddings.openai import OpenAIEmbeddings from elasticsearch import Elasticsearch es_connection = Elasticsearch("http://localhost:9200") vectorstore = 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_community.vectorstores import ElasticsearchStore from langchain_community.embeddings.openai import OpenAIEmbeddings vectorstore = 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_community.vectorstores import ElasticsearchStore from langchain_community.embeddings.openai import OpenAIEmbeddings from langchain_community.vectorstores.utils import DistanceStrategy vectorstore = ElasticsearchStore( "langchain-demo", embedding=OpenAIEmbeddings(), es_url="http://localhost:9200", distance_strategy="DOT_PRODUCT" ) """ 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: BaseRetrievalStrategy = ApproxRetrievalStrategy(), es_params: Optional[Dict[str, Any]] = None, ): self.embedding = embedding self.index_name = index_name self.query_field = query_field self.vector_query_field = vector_query_field self.distance_strategy = ( DistanceStrategy.COSINE if distance_strategy is None else DistanceStrategy[distance_strategy] ) self.strategy = strategy if es_connection is not None: headers = dict(es_connection._headers) headers.update({"user-agent": self.get_user_agent()}) self.client = es_connection.options(headers=headers) elif es_url is not None or es_cloud_id is not None: self.client = ElasticsearchStore.connect_to_elasticsearch( es_url=es_url, username=es_user, password=es_password, cloud_id=es_cloud_id, api_key=es_api_key, es_params=es_params, ) else: raise ValueError( """Either provide a pre-existing Elasticsearch connection, \ or valid credentials for creating a new connection.""" ) @staticmethod def get_user_agent() -> str: from langchain_community import __version__ return f"langchain-py-vs/{__version__}" @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": try: import elasticsearch except ImportError: raise ImportError( "Could not import elasticsearch python package. " "Please install it with `pip install elasticsearch`." ) if es_url and cloud_id: raise ValueError( "Both es_url and cloud_id are defined. Please provide only one." ) connection_params: Dict[str, Any] = {} if es_url: connection_params["hosts"] = [es_url] elif cloud_id: connection_params["cloud_id"] = cloud_id else: raise ValueError("Please provide either elasticsearch_url or cloud_id.") if api_key: connection_params["api_key"] = api_key elif username and password: connection_params["basic_auth"] = (username, password) if es_params is not None: connection_params.update(es_params) es_client = elasticsearch.Elasticsearch( **connection_params, headers={"user-agent": ElasticsearchStore.get_user_agent()}, ) try: es_client.info() except Exception as e: logger.error(f"Error connecting to Elasticsearch: {e}") raise e return es_client @property def embeddings(self) -> Optional[Embeddings]: return self.embedding def similarity_search( self, query: str, k: int = 4, fetch_k: int = 50, filter: Optional[List[dict]] = None, **kwargs: Any, ) -> List[Document]: """Return Elasticsearch documents most similar to query. Args: query: Text to look up documents similar to. k: Number of Documents to return. Defaults to 4. fetch_k (int): Number of Documents to fetch to pass to knn num_candidates. filter: Array of Elasticsearch filter clauses to apply to the query. Returns: List of Documents most similar to the query, in descending order of similarity. """ results = self._search( query=query, k=k, fetch_k=fetch_k, filter=filter, **kwargs ) return [doc for doc, _ in results] def max_marginal_relevance_search( self, query: str, k: int = 4, fetch_k: int = 20, lambda_mult: float = 0.5, fields: Optional[List[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. Args: 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. fields: Other fields to get from elasticsearch source. These fields will be added to the document metadata. Returns: List[Document]: A list of Documents selected by maximal marginal relevance. """ if self.embedding is None: raise ValueError("You must provide an embedding function to perform MMR") remove_vector_query_field_from_metadata = True if fields is None: fields = [self.vector_query_field] elif self.vector_query_field not in fields: fields.append(self.vector_query_field) else: remove_vector_query_field_from_metadata = False # Embed the query query_embedding = self.embedding.embed_query(query) # Fetch the initial documents got_docs = self._search( query_vector=query_embedding, k=fetch_k, fields=fields, **kwargs ) # Get the embeddings for the fetched documents got_embeddings = [doc.metadata[self.vector_query_field] for doc, _ in got_docs] # Select documents using maximal marginal relevance selected_indices = maximal_marginal_relevance( np.array(query_embedding), got_embeddings, lambda_mult=lambda_mult, k=k ) selected_docs = [got_docs[i][0] for i in selected_indices] if remove_vector_query_field_from_metadata: for doc in selected_docs: del doc.metadata[self.vector_query_field] return selected_docs @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 def similarity_search_with_score( self, query: str, k: int = 4, filter: Optional[List[dict]] = 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.strategy, ApproxRetrievalStrategy) and self.strategy.hybrid: raise ValueError("scores are currently not supported in hybrid mode") return self._search(query=query, k=k, filter=filter, **kwargs) def similarity_search_by_vector_with_relevance_scores( self, embedding: List[float], k: int = 4, filter: Optional[List[Dict]] = 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.strategy, ApproxRetrievalStrategy) and self.strategy.hybrid: raise ValueError("scores are currently not supported in hybrid mode") return self._search(query_vector=embedding, k=k, filter=filter, **kwargs) def _search( self, query: Optional[str] = None, k: int = 4, query_vector: Union[List[float], None] = None, fetch_k: int = 50, fields: Optional[List[str]] = None, filter: Optional[List[dict]] = None, custom_query: Optional[Callable[[Dict, Union[str, None]], Dict]] = 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. query_vector: Embedding to look up documents similar to. fetch_k: Number of candidates to fetch from each shard. Defaults to 50. fields: List of fields to return from Elasticsearch. Defaults to only returning the text field. filter: Array of Elasticsearch filter clauses to apply to the query. custom_query: Function to modify the Elasticsearch query body before it is sent to Elasticsearch. Returns: List of Documents most similar to the query and score for each """ if fields is None: fields = [] if "metadata" not in fields: fields.append("metadata") if self.query_field not in fields: fields.append(self.query_field) if self.embedding and query is not None and query_vector is None: query_vector = self.embedding.embed_query(query) query_body = self.strategy.query( query_vector=query_vector, query=query, k=k, fetch_k=fetch_k, vector_query_field=self.vector_query_field, text_field=self.query_field, filter=filter or [], similarity=self.distance_strategy, ) logger.debug(f"Query body: {query_body}") if custom_query is not None: query_body = custom_query(query_body, query) logger.debug(f"Calling custom_query, Query body now: {query_body}") # Perform the kNN search on the Elasticsearch index and return the results. response = self.client.search( index=self.index_name, **query_body, size=k, source=fields, ) def default_doc_builder(hit: Dict) -> Document: return Document( page_content=hit["_source"].get(self.query_field, ""), metadata=hit["_source"]["metadata"], ) doc_builder = doc_builder or default_doc_builder docs_and_scores = [] for hit in response["hits"]["hits"]: for field in fields: if field in hit["_source"] and field not in [ "metadata", self.query_field, ]: if "metadata" not in hit["_source"]: hit["_source"]["metadata"] = {} hit["_source"]["metadata"][field] = hit["_source"][field] docs_and_scores.append( ( doc_builder(hit), hit["_score"], ) ) return docs_and_scores 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. """ try: from elasticsearch.helpers import BulkIndexError, bulk except ImportError: raise ImportError( "Could not import elasticsearch python package. " "Please install it with `pip install elasticsearch`." ) body = [] if ids is None: raise ValueError("ids must be provided.") for _id in ids: body.append({"_op_type": "delete", "_index": self.index_name, "_id": _id}) if len(body) > 0: try: bulk(self.client, body, refresh=refresh_indices, ignore_status=404) logger.debug(f"Deleted {len(body)} texts from index") return True except BulkIndexError as e: logger.error(f"Error deleting texts: {e}") firstError = e.errors[0].get("index", {}).get("error", {}) logger.error(f"First error reason: {firstError.get('reason')}") raise e else: logger.debug("No texts to delete from index") return False def _create_index_if_not_exists( self, index_name: str, dims_length: Optional[int] = None ) -> None: """Create the Elasticsearch index if it doesn't already exist. Args: index_name: Name of the Elasticsearch index to create. dims_length: Length of the embedding vectors. """ if self.client.indices.exists(index=index_name): logger.debug(f"Index {index_name} already exists. Skipping creation.") else: if dims_length is None and self.strategy.require_inference(): raise ValueError( "Cannot create index without specifying dims_length " "when the index doesn't already exist. We infer " "dims_length from the first embedding. Check that " "you have provided an embedding function." ) self.strategy.before_index_setup( client=self.client, text_field=self.query_field, vector_query_field=self.vector_query_field, ) indexSettings = self.strategy.index( vector_query_field=self.vector_query_field, dims_length=dims_length, similarity=self.distance_strategy, ) logger.debug( f"Creating index {index_name} with mappings {indexSettings['mappings']}" ) self.client.indices.create(index=index_name, **indexSettings) def __add( self, texts: Iterable[str], embeddings: Optional[List[List[float]]], 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]: try: from elasticsearch.helpers import BulkIndexError, bulk except ImportError: raise ImportError( "Could not import elasticsearch python package. " "Please install it with `pip install elasticsearch`." ) bulk_kwargs = bulk_kwargs or {} ids = ids or [str(uuid.uuid4()) for _ in texts] requests = [] if create_index_if_not_exists: if embeddings: dims_length = len(embeddings[0]) else: dims_length = None self._create_index_if_not_exists( index_name=self.index_name, dims_length=dims_length ) for i, text in enumerate(texts): metadata = metadatas[i] if metadatas else {} request = { "_op_type": "index", "_index": self.index_name, self.query_field: text, "metadata": metadata, "_id": ids[i], } if embeddings: request[self.vector_query_field] = embeddings[i] requests.append(request) if len(requests) > 0: try: success, failed = bulk( self.client, requests, stats_only=True, refresh=refresh_indices, **bulk_kwargs, ) logger.debug( f"Added {success} and failed to add {failed} texts to index" ) logger.debug(f"added texts {ids} to index") return ids except BulkIndexError as e: logger.error(f"Error adding texts: {e}") firstError = e.errors[0].get("index", {}).get("error", {}) logger.error(f"First error reason: {firstError.get('reason')}") raise e else: logger.debug("No texts to add to index") return [] 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 vectorstore. Args: texts: Iterable of strings to add to the vectorstore. 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 vectorstore. """ if self.embedding is not None: # If no search_type requires inference, we use the provided # embedding function to embed the texts. embeddings = self.embedding.embed_documents(list(texts)) else: # the search_type doesn't require inference, so we don't need to # embed the texts. embeddings = None return self.__add( texts, embeddings, metadatas=metadatas, ids=ids, refresh_indices=refresh_indices, create_index_if_not_exists=create_index_if_not_exists, bulk_kwargs=bulk_kwargs, kwargs=kwargs, ) 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 vectorstore. Args: text_embeddings: Iterable pairs of string and embedding to add to the vectorstore. 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 vectorstore. """ texts, embeddings = zip(*text_embeddings) return self.__add( list(texts), list(embeddings), metadatas=metadatas, ids=ids, refresh_indices=refresh_indices, create_index_if_not_exists=create_index_if_not_exists, bulk_kwargs=bulk_kwargs, kwargs=kwargs, ) @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_community.vectorstores import ElasticsearchStore from langchain_community.embeddings.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. """ elasticsearchStore = ElasticsearchStore._create_cls_from_kwargs( embedding=embedding, **kwargs ) # Encode the provided texts and add them to the newly created index. elasticsearchStore.add_texts( texts, metadatas=metadatas, bulk_kwargs=bulk_kwargs ) return elasticsearchStore @staticmethod def _create_cls_from_kwargs( embedding: Optional[Embeddings] = None, **kwargs: Any ) -> "ElasticsearchStore": index_name = kwargs.get("index_name") if index_name is None: raise ValueError("Please provide an index_name.") es_connection = kwargs.get("es_connection") es_cloud_id = kwargs.get("es_cloud_id") es_url = kwargs.get("es_url") es_user = kwargs.get("es_user") es_password = kwargs.get("es_password") es_api_key = kwargs.get("es_api_key") vector_query_field = kwargs.get("vector_query_field") query_field = kwargs.get("query_field") distance_strategy = kwargs.get("distance_strategy") strategy = kwargs.get("strategy", ElasticsearchStore.ApproxRetrievalStrategy()) optional_args = {} if vector_query_field is not None: optional_args["vector_query_field"] = vector_query_field if query_field is not None: optional_args["query_field"] = query_field return ElasticsearchStore( index_name=index_name, embedding=embedding, es_url=es_url, es_connection=es_connection, es_cloud_id=es_cloud_id, es_user=es_user, es_password=es_password, es_api_key=es_api_key, strategy=strategy, distance_strategy=distance_strategy, **optional_args, ) @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_community.vectorstores import ElasticsearchStore from langchain_community.embeddings.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. """ elasticsearchStore = ElasticsearchStore._create_cls_from_kwargs( 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 @staticmethod def ExactRetrievalStrategy() -> "ExactRetrievalStrategy": """Used to perform brute force / exact nearest neighbor search via script_score.""" return ExactRetrievalStrategy() @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 ) @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)