163ef35dd1
Updated titles into a consistent format. Fixed links to the diagrams. Fixed typos. Note: The Templates menu in the navbar is now sorted by the file names. I'll try sorting the navbar menus by the page titles, not the page file names.
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RAG - Elasticsearch - 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.
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.
Set the OPENAI_API_KEY environment variable to access the OpenAI models.
To connect to your Elasticsearch instance, use the following environment variables:
export ELASTIC_CLOUD_ID = <ClOUD_ID>
export ELASTIC_USERNAME = <ClOUD_USERNAME>
export ELASTIC_PASSWORD = <ClOUD_PASSWORD>
For local development with Docker, use:
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:
pip install -U "langchain-cli[serve]"
To create a new LangChain project and install this as the only package, you can do:
langchain app new my-app --package rag-self-query
If you want to add this to an existing project, you can just run:
langchain app add rag-self-query
And add the following code to your server.py file:
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:
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. If you don't have access, you can skip this section
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:
langchain serve
This will start the FastAPI app with a server is running locally at http://localhost:8000
We can see all templates at http://127.0.0.1:8000/docs We can access the playground at http://127.0.0.1:8000/rag-elasticsearch/playground
We can access the template from code with:
from langserve.client import RemoteRunnable
runnable = RemoteRunnable("http://localhost:8000/rag-self-query")