langchain/templates/cassandra-entomology-rag

cassandra-entomology-rag

This template will perform RAG using Astra DB and Apache Cassandra®.

Environment Setup

For the setup, you will require:

• an Astra Vector Database. You must have a Database Administrator token, specifically the string starting with AstraCS:....
• Database ID.
• an OpenAI API Key. (More info here)

You may also use a regular Cassandra cluster. In this case, provide the USE_CASSANDRA_CLUSTER entry as shown in .env.template and the subsequent environment variables to specify how to connect to it.

The connection parameters and secrets must be provided through environment variables. Refer to .env.template for the required variables.

Usage

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 cassandra-entomology-rag

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

langchain app add cassandra-entomology-rag

And add the following code to your server.py file:

from cassandra_entomology_rag import chain as cassandra_entomology_rag_chain

add_routes(app, cassandra_entomology_rag_chain, path="/cassandra-entomology-rag")

(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. 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"

To populate the vector store, ensure that you have set all the environment variables, then from this directory, execute the following just once:

poetry run bash -c "cd [...]/cassandra_entomology_rag; python setup.py"

The output will be something like Done (29 lines inserted)..

Note: In a full application, the vector store might be populated in other ways. This step is to pre-populate the vector store with some rows for the demo RAG chains to work sensibly.

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/cassandra-entomology-rag/playground

We can access the template from code with:

from langserve.client import RemoteRunnable

runnable = RemoteRunnable("http://localhost:8000/cassandra-entomology-rag")

Reference

Stand-alone repo with LangServe chain: here.