
# elastic-query-generator

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](https://cloud.elastic.co/registration?utm_source=langchain&utm_content=langserve).

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 ingest.py`.

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.

## Usage

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

```shell
pip install -U langchain-cli
```

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

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

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

```shell
langchain app add elastic-query-generator
```

And add the following code to your `server.py` file:
```python
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. 
LangSmith is currently in private beta, you can sign up [here](https://smith.langchain.com/). 
If you don't have access, you can skip this section

```shell
export LANGCHAIN_TRACING_V2=true
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:

```shell
langchain serve
```

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

We can see all templates at [http://127.0.0.1:8000/docs](http://127.0.0.1:8000/docs)
We can access the playground at [http://127.0.0.1:8000/elastic-query-generator/playground](http://127.0.0.1:8000/elastic-query-generator/playground)  

We can access the template from code with:

```python
from langserve.client import RemoteRunnable

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