# 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 = export ELASTIC_USERNAME = export ELASTIC_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. You can sign up for LangSmith [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= export LANGCHAIN_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") ```