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This template allows interacting with Elasticsearch analytics databases in natural language using LLMs.

It builds search queries via the Elasticsearch DSL API (filters and aggregations).

Environment Setup​

Set the OPENAI_API_KEY environment variable to access the OpenAI models.

Installing Elasticsearch​

There are a number of ways to run Elasticsearch. However, one recommended way is through Elastic Cloud.

Create a free trial account on Elastic Cloud.

With a deployment, update the connection string.

Password and connection (elasticsearch url) can be found on the deployment console.

Note that the Elasticsearch client must have permissions for index listing, mapping description, and search queries.

Populating with data​

If you want to populate the DB with some example info, you can run python

This will create a customers index. In this package, we specify indexes to generate queries against, and we specify ["customers"]. This is specific to setting up your Elastic index.


To use this package, you should first have the LangChain CLI installed:

pip install -U langchain-cli

To create a new LangChain project and install this as the only package, you can do:

langchain app new my-app --package elastic-query-generator

If you want to add this to an existing project, you can just run:

langchain app add elastic-query-generator

And add the following code to your file:

from elastic_query_generator.chain import chain as elastic_query_generator_chain

add_routes(app, elastic_query_generator_chain, path="/elastic-query-generator")

(Optional) Let's now configure LangSmith. LangSmith will help us trace, monitor and debug LangChain applications. You can sign up for LangSmith here. If you don't have access, you can skip this section

export LANGCHAIN_API_KEY=<your-api-key>
export LANGCHAIN_PROJECT=<your-project> # if not specified, defaults to "default"

If you are inside this directory, then you can spin up a LangServe instance directly by:

langchain serve

This will start the FastAPI app with a server is running locally at http://localhost:8000

We can see all templates at We can access the playground at

We can access the template from code with:

from langserve.client import RemoteRunnable

runnable = RemoteRunnable("http://localhost:8000/elastic-query-generator")

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