rag-timescale-conversation

This template is used for conversational retrieval, which is one of the most popular LLM use-cases.

It passes both a conversation history and retrieved documents into an LLM for synthesis.

Environment Setup

This template uses Timescale Vector as a vectorstore and requires that TIMESCALES_SERVICE_URL. Signup for a 90-day trial here if you don't yet have an account.

To load the sample dataset, set LOAD_SAMPLE_DATA=1. To load your own dataset see the section below.

Set the OPENAI_API_KEY environment variable to access the OpenAI models.

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-timescale-conversation

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

langchain app add rag-timescale-conversation

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

from rag_timescale_conversation import chain as rag_timescale_conversation_chain

add_routes(app, rag_timescale_conversation_chain, path="/rag-timescale_conversation")

(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-timescale-conversation/playground

We can access the template from code with:

from langserve.client import RemoteRunnable

runnable = RemoteRunnable("http://localhost:8000/rag-timescale-conversation")

See the rag_conversation.ipynb notebook for example usage.

Loading your own dataset

To load your own dataset you will have to create a load_dataset function. You can see an example, in the load_ts_git_dataset function defined in the load_sample_dataset.py file. You can then run this as a standalone function (e.g. in a bash script) or add it to chain.py (but then you should run it just once).