## Elasticsearch

> [Elasticsearch](https://www.elastic.co/elasticsearch/) is a distributed, RESTful search and analytics engine. It provides a distributed, multi-tenant-capable full-text search engine with an HTTP web interface and schema-free JSON documents.

In this notebook, we’ll demo the `SelfQueryRetriever` with an `Elasticsearch` vector store.

## Creating an Elasticsearch vector store[​](#creating-an-elasticsearch-vector-store "Direct link to Creating an Elasticsearch vector store")

First, 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 --upgrade --quiet  U lark langchain langchain-elasticsearch
```

```
WARNING: You are using pip version 22.0.4; however, version 23.3 is available.You should consider upgrading via the '/Users/joe/projects/elastic/langchain/libs/langchain/.venv/bin/python3 -m pip install --upgrade pip' command.
```

```
import getpassimport osfrom langchain_core.documents import Documentfrom langchain_elasticsearch import ElasticsearchStorefrom langchain_openai import OpenAIEmbeddingsos.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,            "director": "Andrei Tarkovsky",            "genre": "science fiction",            "rating": 9.9,        },    ),]vectorstore = ElasticsearchStore.from_documents(    docs,    embeddings,    index_name="elasticsearch-self-query-demo",    es_url="http://localhost:9200",)
```

## Creating our self-querying retriever[​](#creating-our-self-querying-retriever "Direct link to Creating our self-querying retriever")

Now we can instantiate our retriever. To do this we’ll need to provide some information upfront about the metadata fields that our documents support and a short description of the document contents.

```
from langchain.chains.query_constructor.base import AttributeInfofrom langchain.retrievers.self_query.base import SelfQueryRetrieverfrom langchain_openai import OpenAImetadata_field_info = [    AttributeInfo(        name="genre",        description="The genre of the movie",        type="string or list[string]",    ),    AttributeInfo(        name="year",        description="The year the movie was released",        type="integer",    ),    AttributeInfo(        name="director",        description="The name of the movie director",        type="string",    ),    AttributeInfo(        name="rating", description="A 1-10 rating for the movie", type="float"    ),]document_content_description = "Brief summary of a movie"llm = OpenAI(temperature=0)retriever = SelfQueryRetriever.from_llm(    llm, vectorstore, document_content_description, metadata_field_info, verbose=True)
```

## Testing it out[​](#testing-it-out "Direct link to Testing it out")

And now we can try actually using our retriever!

```
# This example only specifies a relevant queryretriever.get_relevant_documents("What are some movies about dinosaurs")
```

```
[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='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'}), 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})]
```

```
# This example specifies a query and a filterretriever.get_relevant_documents("Has Greta Gerwig directed any movies about women")
```

```
[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})]
```

## Filter k[​](#filter-k "Direct link to Filter k")

We can also use the self query retriever to specify `k`: the number of documents to fetch.

We can do this by passing `enable_limit=True` to the constructor.

```
retriever = SelfQueryRetriever.from_llm(    llm,    vectorstore,    document_content_description,    metadata_field_info,    enable_limit=True,    verbose=True,)
```

```
# This example only specifies a relevant queryretriever.get_relevant_documents("what are two movies about dinosaurs")
```

```
[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='Toys come alive and have a blast doing so', metadata={'year': 1995, 'genre': 'animated'})]
```

## Complex queries in Action![​](#complex-queries-in-action "Direct link to Complex queries in Action!")

We’ve tried out some simple queries, but what about more complex ones? Let’s try out a few more complex queries that utilize the full power of Elasticsearch.

```
retriever.get_relevant_documents(    "what animated or comedy movies have been released in the last 30 years about animated toys?")
```

```
[Document(page_content='Toys come alive and have a blast doing so', metadata={'year': 1995, 'genre': 'animated'})]
```

```
vectorstore.client.indices.delete(index="elasticsearch-self-query-demo")
```