jmontag21/rag

RAG System for pdf documents

Internal Document RAG System

Overview

This project is an implementation of a Retrieval-Augmented Generation (RAG) system designed to process and retrieve relevant information from internal company documents, specifically earnings reports. The system leverages OpenAI's GPT-4o Vision model for semantic chunking and embedding generation, and ZillizDB (Milvus) for vector storage and retrieval.

Features

• Embedder: text-embedding-3-large
• Model: GPT-4o Vision
• Chunking Method: Semantic Chunking by paragraph/section/table
• Distance Metric: Euclidean Distance (L2)
• Index Type: IVF_FLAT
• Metadata Filters: Company name, Document type, Date (TODO)

Setup Instructions

Prerequisites

• Python 3.8 or later
• pip (Python package installer)
• Git

Installation

1. Clone the repository:

   git clone https://github.com/your-username/internal-document-rag-system.git
   cd internal-document-rag-system

2. Set up virtual environment (optional but recommended):

   python -m venv venv
   source venv/bin/activate  # On Windows use `venv\Scripts\activate`

3. Install the required Python packages:

   pip install -r requirements.txt

Configuration

1. API Keys and ZillizDB Credentials:

   Update the api_key and zilliz_credentials variables in the main script with your OpenAI and ZillizDB credentials.

2. Folder Path:

   Set the folder_path variable to the directory containing your PDF documents.

Example Usage

1. Querying:

   query = "give me a breakdown of revenues from each quarter from 2021 that is available and please put it into tables for me"
   response = run_query_pipeline(query)
   print("LLM Response:")
   print(response)

Requirements

requirements.txt

openai
numpy
pymupdf
pymilvus