"Reset password"Follow the prompts to reset the passwordHow to obtain an API key?‚ÄãTo obtain an API key:Log in to the Elastic Cloud console at https://cloud.elastic.coOpen Kibana and go to Stack Management > API KeysClick "Create API key"Enter a name for the API key and click "Create"Copy the API key and paste it into the api_key parameterElastic Cloud‚ÄãTo connect to an Elasticsearch instance on Elastic Cloud, you can use either the es_cloud_id parameter or es_url.Example:        from langchain.vectorstores.elasticsearch import ElasticsearchStore        from langchain.embeddings import OpenAIEmbeddings        embedding = OpenAIEmbeddings()        elastic_vector_search = ElasticsearchStore(            es_cloud_id="<cloud_id>",            index_name="test_index",            embedding=embedding,            es_user="elastic",            es_password="changeme"        )We want to use OpenAIEmbeddings so we have to get the OpenAI API Key.import osimport getpassos.environ["OPENAI_API_KEY"] = getpass.getpass("OpenAI API Key:")Basic Example‚ÄãThis example we are going to load "state_of_the_union.txt" via the TextLoader, chunk the text into 500 word chunks, and then index each chunk into Elasticsearch.Once the data is indexed, we perform a simple query to find the top 4 chunks that similar to the query "What did the president say about Ketanji Brown Jackson".Elasticsearch is running locally on localhost:9200 with docker. For more details on how to connect to Elasticsearch from Elastic Cloud, see connecting with authentication above.from langchain.embeddings.openai import OpenAIEmbeddingsfrom langchain.vectorstores import ElasticsearchStorefrom langchain.document_loaders import TextLoaderfrom langchain.text_splitter import CharacterTextSplitterloader = TextLoader("../../modules/state_of_the_union.txt")documents = loader.load()text_splitter = CharacterTextSplitter(chunk_size=500, chunk_overlap=0)docs = text_splitter.split_documents(documents)embeddings = OpenAIEmbeddings()db
Elasticsearch is a distributed, RESTful search and analytics engine, capable of performing both vector and lexical search. It is built on top of the Apache Lucene library.
Elasticsearch is a distributed, RESTful search and analytics engine, capable of performing both vector and lexical search. It is built on top of the Apache Lucene library. ->: "Reset password"Follow the prompts to reset the passwordHow to obtain an API key?‚ÄãTo obtain an API key:Log in to the Elastic Cloud console at https://cloud.elastic.coOpen Kibana and go to Stack Management > API KeysClick "Create API key"Enter a name for the API key and click "Create"Copy the API key and paste it into the api_key parameterElastic Cloud‚ÄãTo connect to an Elasticsearch instance on Elastic Cloud, you can use either the es_cloud_id parameter or es_url.Example:        from langchain.vectorstores.elasticsearch import ElasticsearchStore        from langchain.embeddings import OpenAIEmbeddings        embedding = OpenAIEmbeddings()        elastic_vector_search = ElasticsearchStore(            es_cloud_id="<cloud_id>",            index_name="test_index",            embedding=embedding,            es_user="elastic",            es_password="changeme"        )We want to use OpenAIEmbeddings so we have to get the OpenAI API Key.import osimport getpassos.environ["OPENAI_API_KEY"] = getpass.getpass("OpenAI API Key:")Basic Example‚ÄãThis example we are going to load "state_of_the_union.txt" via the TextLoader, chunk the text into 500 word chunks, and then index each chunk into Elasticsearch.Once the data is indexed, we perform a simple query to find the top 4 chunks that similar to the query "What did the president say about Ketanji Brown Jackson".Elasticsearch is running locally on localhost:9200 with docker. For more details on how to connect to Elasticsearch from Elastic Cloud, see connecting with authentication above.from langchain.embeddings.openai import OpenAIEmbeddingsfrom langchain.vectorstores import ElasticsearchStorefrom langchain.document_loaders import TextLoaderfrom langchain.text_splitter import CharacterTextSplitterloader = TextLoader("../../modules/state_of_the_union.txt")documents = loader.load()text_splitter = CharacterTextSplitter(chunk_size=500, chunk_overlap=0)docs = text_splitter.split_documents(documents)embeddings = OpenAIEmbeddings()db