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# rag-self-query
This template performs RAG using the self-query retrieval technique. The main idea is to let an LLM convert unstructured queries into structured queries. See the [docs for more on how this works ](https://python.langchain.com/docs/modules/data_connection/retrievers/self_query ).
## Environment Setup
In this template we'll use OpenAI models and an Elasticsearch vector store, but the approach generalizes to all LLMs/ChatModels and [a number of vector stores ](https://python.langchain.com/docs/integrations/retrievers/self_query/ ).
Set the `OPENAI_API_KEY` environment variable to access the OpenAI models.
To connect to your Elasticsearch instance, use the following environment variables:
```bash
export ELASTIC_CLOUD_ID = < ClOUD_ID >
export ELASTIC_USERNAME = < ClOUD_USERNAME >
export ELASTIC_PASSWORD = < ClOUD_PASSWORD >
```
For local development with Docker, use:
```bash
export ES_URL = "http://localhost:9200"
docker run -p 9200:9200 -e "discovery.type=single-node" -e "xpack.security.enabled=false" -e "xpack.security.http.ssl.enabled=false" docker.elastic.co/elasticsearch/elasticsearch:8.9.0
```
## Usage
To use this package, you should first have the LangChain CLI installed:
```shell
pip install -U "langchain-cli[serve]"
```
To create a new LangChain project and install this as the only package, you can do:
```shell
langchain app new my-app --package rag-self-query
```
If you want to add this to an existing project, you can just run:
```shell
langchain app add rag-self-query
```
And add the following code to your `server.py` file:
```python
from rag_self_query import chain
add_routes(app, chain, path="/rag-elasticsearch")
```
To populate the vector store with the sample data, from the root of the directory run:
```bash
python ingest.py
```
(Optional) Let's now configure LangSmith.
LangSmith will help us trace, monitor and debug LangChain applications.
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You can sign up for LangSmith [here ](https://smith.langchain.com/ ).
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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/rag-elasticsearch/playground ](http://127.0.0.1:8000/rag-elasticsearch/playground )
We can access the template from code with:
```python
from langserve.client import RemoteRunnable
runnable = RemoteRunnable("http://localhost:8000/rag-self-query")
```
