Precision RAG

Precision RAG: Prompt Tuning For Building Enterprise Grade RAG Systems

This project focuses on Automatic Prompt Engineering (APE) for Retrieval-Augmented Generation (RAG) systems.

Business Objective:

The aims is to simplify LLM interaction through prompt engineering solutions. Through services liike:

Automatic Prompt Generation: Creates effective prompts to generate high-quality content. Automatic Evaluation Data Generation: Generates diverse test cases for prompt evaluation. Prompt Testing and Ranking: Evaluates prompts and ranks them based on effectiveness.

Setup

1. Create a virtual environment and install the required packages:

$ python3 -m venv .venv
$ source .venv/bin/activate
$ pip install -r requirements.txt

2. Create a Virtual Environment and Install Dependencies

python3.10 -m venv venv
source venv/bin/activate  # For Unix or MacOS
venvScriptsactivate     # For Windows
pip install -r requirements.txt

3. Create a free Pinecone account and get your API key from here.

4. Create a .env file and add the following variables:

OPENAI_API_KEY = [ENTER YOUR OPENAI API KEY HERE]
PINECONE_API_KEY = [ENTER YOUR PINECONE API KEY HERE]