RAG-LLM

This repository contains code for implementing retrieval-augmented generation (RAG) with LLM agents with tools . We perform RAG using 2 kinds of databases: a tabular RDBMS (Postgres) and a graph database (Neo4j). We use OpenAI's GPT-3.5 & GPT-4 LLMs, and Langchain & OpenAI tools for interacting with the LLM agents.

Setup

To create the environment, run the following command:

conda create -n rag-llm python=3.11

To activate the environment, run the following command:

conda activate rag-llm

Install the required packages using the following command:

poetry install

Save the configuration of your postgres and neo4j databases in the .env file in the root directory of the project. The OPENAI_API_KEY should also be saved here to interact with OpenAI's LLMs.

Save other configurations in thesrc/config.py file.

Usage

The main.ipynb contains example code for loading & processing the postgres & neo4j databases, and for performing RAG with LLM agents.

Save the sample queries test set in the path defined in the src/config.py file as TEST_QUERIES_PATH. It should a .csv file with the following columns: id, query, difficulty, answer.

To evaluate the SQL RAG framework on the sample test set, run the following command:

python src/eval_sql_rag.py

To evaluate the Graph RAG framework on the sample test set, run the following command:

python src/eval_graph_rag.py