RAG is more than vector search

Introduction

This is a repository that contains the code for the article RAG is more than embeddings. Head over to the Timescale blog to read the article if you haven't already. The code is compatible for python >= 3.9.

Instructions

1. First install all the required dependencies in the requirements.txt file

pip install -r requirements.txt

2. Make sure to create a .env file that has the same environment variables as our .env.example file. You can get your DB_URL after creating a Timescale instance by following the instructions here.

3. Next, ingest in some Github Issues from the bigcode/the-stack-github-issues dataset by running the scripts/ingest.py file. This will crawl the first 100 issues that match the list of whitelisted repos in our file. We can do so by running the command below.

python3 ./scripts/ingest.py

3. We can then test the function calling ability of our model by running the scripts/eval.py file to verify that our model is choosing the right tool with respect to a user query. We can do so by running the command below.

python3 ./scripts/eval.py

4. In order to perform embedding search, we can define a new .execute function inside our tools themselves. This allows us to call a .execute() function when the tool is selected to immediately return a list of relevant results. To see this in action, run the command below and we'll fetch the top 10 relevant summaries from our database related to the kubernetes/kubernetes repository using embedding search.

python3 ./scripts/embedding_search.py

5. Lastly, we'll put it all together in the agent.py file where we'll create a one-step agent that'll be able to answer questions about specific repositories in our database. We can run this agent by executing the command below.

python3 ./scripts/agent.py