JSON documents.In this notebook, we'll demo the SelfQueryRetriever with an Elasticsearch vector store.Creating an Elasticsearch vector store‚ÄãFirst, we'll want to create an Elasticsearch vector store and seed it with some data. We've created a small demo set of documents that contain summaries of movies.Note: The self-query retriever requires you to have lark installed (pip install lark). We also need the elasticsearch package.#!pip install lark elasticsearchfrom langchain.schema import Documentfrom langchain.embeddings.openai import OpenAIEmbeddingsfrom langchain.vectorstores import ElasticsearchStoreimport osimport getpassos.environ["OPENAI_API_KEY"] = getpass.getpass("OpenAI API Key:")embeddings = OpenAIEmbeddings()docs = [    Document(        page_content="A bunch of scientists bring back dinosaurs and mayhem breaks loose",        metadata={"year": 1993, "rating": 7.7, "genre": "science fiction"},    ),    Document(        page_content="Leo DiCaprio gets lost in a dream within a dream within a dream within a ...",        metadata={"year": 2010, "director": "Christopher Nolan", "rating": 8.2},    ),    Document(        page_content="A psychologist / detective gets lost in a series of dreams within dreams within dreams and Inception reused the idea",        metadata={"year": 2006, "director": "Satoshi Kon", "rating": 8.6},    ),    Document(        page_content="A bunch of normal-sized women are supremely wholesome and some men pine after them",        metadata={"year": 2019, "director": "Greta Gerwig", "rating": 8.3},    ),    Document(        page_content="Toys come alive and have a blast doing so",        metadata={"year": 1995, "genre": "animated"},    ),    Document(        page_content="Three men walk into the Zone, three men walk out of the Zone",        metadata={            "year": 1979,            "rating": 9.9,            "director": "Andrei Tarkovsky",            "genre": "science fiction",            "rating": 9.9,        },    ),]vectorstore =
Elasticsearch is a distributed, RESTful search and analytics engine.
Elasticsearch is a distributed, RESTful search and analytics engine. ->: JSON documents.In this notebook, we'll demo the SelfQueryRetriever with an Elasticsearch vector store.Creating an Elasticsearch vector store‚ÄãFirst, we'll want to create an Elasticsearch vector store and seed it with some data. We've created a small demo set of documents that contain summaries of movies.Note: The self-query retriever requires you to have lark installed (pip install lark). We also need the elasticsearch package.#!pip install lark elasticsearchfrom langchain.schema import Documentfrom langchain.embeddings.openai import OpenAIEmbeddingsfrom langchain.vectorstores import ElasticsearchStoreimport osimport getpassos.environ["OPENAI_API_KEY"] = getpass.getpass("OpenAI API Key:")embeddings = OpenAIEmbeddings()docs = [    Document(        page_content="A bunch of scientists bring back dinosaurs and mayhem breaks loose",        metadata={"year": 1993, "rating": 7.7, "genre": "science fiction"},    ),    Document(        page_content="Leo DiCaprio gets lost in a dream within a dream within a dream within a ...",        metadata={"year": 2010, "director": "Christopher Nolan", "rating": 8.2},    ),    Document(        page_content="A psychologist / detective gets lost in a series of dreams within dreams within dreams and Inception reused the idea",        metadata={"year": 2006, "director": "Satoshi Kon", "rating": 8.6},    ),    Document(        page_content="A bunch of normal-sized women are supremely wholesome and some men pine after them",        metadata={"year": 2019, "director": "Greta Gerwig", "rating": 8.3},    ),    Document(        page_content="Toys come alive and have a blast doing so",        metadata={"year": 1995, "genre": "animated"},    ),    Document(        page_content="Three men walk into the Zone, three men walk out of the Zone",        metadata={            "year": 1979,            "rating": 9.9,            "director": "Andrei Tarkovsky",            "genre": "science fiction",            "rating": 9.9,        },    ),]vectorstore =