Source code for langchain_elasticsearch._sync.vectorstores
importloggingfromtypingimportAny,Callable,Dict,Iterable,List,Literal,Optional,Tuple,UnionfromelasticsearchimportElasticsearchfromelasticsearch.helpers.vectorstoreimport(BM25Strategy,DenseVectorScriptScoreStrategy,DenseVectorStrategy,DistanceMetric,RetrievalStrategy,SparseVectorStrategy,)fromelasticsearch.helpers.vectorstoreimportVectorStoreasEVectorStorefromlangchain_core.documentsimportDocumentfromlangchain_core.embeddingsimportEmbeddingsfromlangchain_core.vectorstoresimportVectorStorefromlangchain_elasticsearch._utilitiesimport(ApproxRetrievalStrategy,BaseRetrievalStrategy,BM25RetrievalStrategy,DistanceStrategy,ExactRetrievalStrategy,SparseRetrievalStrategy,_hits_to_docs_scores,user_agent,)fromlangchain_elasticsearch.clientimportcreate_elasticsearch_clientfromlangchain_elasticsearch.embeddingsimportEmbeddingServiceAdapterlogger=logging.getLogger(__name__)def_convert_retrieval_strategy(langchain_strategy:BaseRetrievalStrategy,distance:Optional[DistanceStrategy]=None,)->RetrievalStrategy:ifisinstance(langchain_strategy,ApproxRetrievalStrategy):ifdistanceisNone:raiseValueError("ApproxRetrievalStrategy requires a distance strategy to be provided.")returnDenseVectorStrategy(distance=DistanceMetric[distance],model_id=langchain_strategy.query_model_id,hybrid=(Falseiflangchain_strategy.hybridisNoneelselangchain_strategy.hybrid),rrf=Falseiflangchain_strategy.rrfisNoneelselangchain_strategy.rrf,)elifisinstance(langchain_strategy,ExactRetrievalStrategy):ifdistanceisNone:raiseValueError("ExactRetrievalStrategy requires a distance strategy to be provided.")returnDenseVectorScriptScoreStrategy(distance=DistanceMetric[distance])elifisinstance(langchain_strategy,SparseRetrievalStrategy):returnSparseVectorStrategy(langchain_strategy.model_id)elifisinstance(langchain_strategy,BM25RetrievalStrategy):returnBM25Strategy(k1=langchain_strategy.k1,b=langchain_strategy.b)else:raiseTypeError(f"Strategy {langchain_strategy} not supported. To provide a "f"custom strategy, please subclass {RetrievalStrategy}.")classElasticsearchStore(VectorStore):"""`Elasticsearch` vector store. Setup: Install ``langchain_elasticsearch`` and running the Elasticsearch docker container. .. code-block:: bash pip install -qU langchain_elasticsearch docker run -p 9200:9200 \ -e "discovery.type=single-node" \ -e "xpack.security.enabled=false" \ -e "xpack.security.http.ssl.enabled=false" \ docker.elastic.co/elasticsearch/elasticsearch:8.12.1 Key init args — indexing params: index_name: str Name of the index to create. embedding: Embeddings Embedding function to use. custom_index_settings: Optional[Dict[str, Any]] A dictionary of custom settings for the index. This can include configurations like the number of shards, number of replicas, analysis settings, and other index-specific settings. If not provided, default settings will be used. Note that if the same setting is provided by both the user and the strategy, will raise an error. Key init args — client params: es_connection: Optional[Elasticsearch] Pre-existing Elasticsearch connection. es_url: Optional[str] URL of the Elasticsearch instance to connect to. es_cloud_id: Optional[str] Cloud ID of the Elasticsearch instance to connect to. es_user: Optional[str] Username to use when connecting to Elasticsearch. es_password: Optional[str] Password to use when connecting to Elasticsearch. es_api_key: Optional[str] API key to use when connecting to Elasticsearch. es_params: Optional[Dict[str, Any]] Additional parameters for the Elasticsearch client. Instantiate: .. code-block:: python from langchain_elasticsearch import ElasticsearchStore from langchain_openai import OpenAIEmbeddings vector_store = ElasticsearchStore( index_name="langchain-demo", embedding=OpenAIEmbeddings(), es_url="http://localhost:9200", ) If you want to use a cloud hosted Elasticsearch instance, you can pass in the cloud_id argument instead of the es_url argument. Instantiate from cloud: .. 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. Instantiate from existing connection: .. 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 ) Add Documents: .. code-block:: python from langchain_core.documents import Document document_1 = Document(page_content="foo", metadata={"baz": "bar"}) document_2 = Document(page_content="thud", metadata={"bar": "baz"}) document_3 = Document(page_content="i will be deleted :(") documents = [document_1, document_2, document_3] ids = ["1", "2", "3"] vector_store.add_documents(documents=documents, ids=ids) Delete Documents: .. code-block:: python vector_store.delete(ids=["3"]) Search: .. code-block:: python results = vector_store.similarity_search(query="thud",k=1) for doc in results: print(f"* {doc.page_content} [{doc.metadata}]") .. code-block:: python * thud [{'bar': 'baz'}] Search with filter: .. code-block:: python results = vector_store.similarity_search(query="thud",k=1,filter=[{"term": {"metadata.bar.keyword": "baz"}}]) for doc in results: print(f"* {doc.page_content} [{doc.metadata}]") .. code-block:: python * thud [{'bar': 'baz'}] Search with score: .. code-block:: python results = vector_store.similarity_search_with_score(query="qux",k=1) for doc, score in results: print(f"* [SIM={score:3f}] {doc.page_content} [{doc.metadata}]") .. code-block:: python * [SIM=0.916092] foo [{'baz': 'bar'}] Async: .. code-block:: python from langchain_elasticsearch import AsyncElasticsearchStore vector_store = AsyncElasticsearchStore(...) # add documents await vector_store.aadd_documents(documents=documents, ids=ids) # delete documents await vector_store.adelete(ids=["3"]) # search results = vector_store.asimilarity_search(query="thud",k=1) # search with score results = await vector_store.asimilarity_search_with_score(query="qux",k=1) for doc,score in results: print(f"* [SIM={score:3f}] {doc.page_content} [{doc.metadata}]") .. code-block:: python * [SIM=0.916092] foo [{'baz': 'bar'}] Use as Retriever: .. code-block:: bash pip install "elasticsearch[vectorstore_mmr]" .. code-block:: python retriever = vector_store.as_retriever( search_type="mmr", search_kwargs={"k": 1, "fetch_k": 2, "lambda_mult": 0.5}, ) retriever.invoke("thud") .. code-block:: python [Document(metadata={'bar': 'baz'}, page_content='thud')] **Advanced Uses:** 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. Use ExactRetrievalStrategy: .. 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. Use dot product similarity: .. 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" ) """# noqa: E501
[docs]defsimilarity_search(self,query:str,k:int=4,fetch_k:int=50,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[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. """hits=self._store.search(query=query,k=k,num_candidates=fetch_k,filter=filter,custom_query=custom_query,)docs=_hits_to_docs_scores(hits=hits,content_field=self.query_field,doc_builder=doc_builder,)return[docfordoc,_scoreindocs]
[docs]defmax_marginal_relevance_search(self,query:str,k:int=4,fetch_k:int=20,lambda_mult:float=0.5,fields:Optional[List[str]]=None,*,custom_query:Optional[Callable[[Dict[str,Any],Optional[str]],Dict[str,Any]]]=None,doc_builder:Optional[Callable[[Dict],Document]]=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. """ifself._embedding_serviceisNone:raiseValueError("maximal marginal relevance search requires an embedding service.")hits=self._store.max_marginal_relevance_search(embedding_service=self._embedding_service,query=query,vector_field=self.vector_query_field,k=k,num_candidates=fetch_k,lambda_mult=lambda_mult,fields=fields,custom_query=custom_query,)docs_scores=_hits_to_docs_scores(hits=hits,content_field=self.query_field,fields=fields,doc_builder=doc_builder,)return[docfordoc,_scoreindocs_scores]
@staticmethoddef_identity_fn(score:float)->float:returnscoredef_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-paramsreturnself._identity_fn
[docs]defsimilarity_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)andself._store.retrieval_strategy.hybrid):raiseValueError("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]defsimilarity_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)andself._store.retrieval_strategy.hybrid):raiseValueError("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]defdelete(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. """ifidsisNone:raiseValueError("please specify some IDs")returnself._store.delete(ids=ids,refresh_indices=refresh_indicesorFalse)
[docs]defadd_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. """returnself._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]defadd_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)returnself._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]@classmethoddeffrom_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")ifindex_nameisNone:raiseValueError("Please provide an index_name.")elasticsearchStore=cls(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)returnelasticsearchStore
[docs]@classmethoddeffrom_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")ifindex_nameisNone:raiseValueError("Please provide an index_name.")elasticsearchStore=cls(embedding=embedding,**kwargs)# Encode the provided texts and add them to the newly created index.elasticsearchStore.add_documents(documents,bulk_kwargs=bulk_kwargs)returnelasticsearchStore
[docs]@staticmethoddefExactRetrievalStrategy()->"ExactRetrievalStrategy":"""Used to perform brute force / exact nearest neighbor search via script_score."""returnExactRetrievalStrategy()
[docs]@staticmethoddefApproxRetrievalStrategy(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'. """returnApproxRetrievalStrategy(query_model_id=query_model_id,hybrid=hybrid,rrf=rrf)
[docs]@staticmethoddefSparseVectorRetrievalStrategy(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. """returnSparseRetrievalStrategy(model_id=model_id)
[docs]@staticmethoddefBM25RetrievalStrategy(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. """returnBM25RetrievalStrategy(k1=k1,b=b)