RAG ENGINE

A powerful Retrieval-Augmented Generation (RAG) system for PDF documents using LlamaParse, LangChain, and Groq. This API allows you to upload PDF files, process them into a searchable knowledge base, and query them using natural language.

Features

• 📄 Multi-PDF Processing: Upload and process multiple PDF files simultaneously
• 🔍 Intelligent Document Parsing: Uses LlamaParse for high-quality PDF text extraction
• 🧠 Smart Chunking: Automatically splits documents into optimal chunks for retrieval
• 🔎 Semantic Search: Vector-based similarity search using HuggingFace embeddings
• 💬 Natural Language Queries: Ask questions in plain English and get relevant answers
• 📚 Source Attribution: Get references to the source documents for each answer
• 💾 Persistent Storage: Vector database persists between sessions using ChromaDB
• ⚡ Fast Inference: Powered by Groq for quick response times

Architecture

┌─────────────────┐    ┌──────────────────┐    ┌─────────────────┐
│   PDF Upload    │───▶│   LlamaParse     │───▶│  Text Chunking  │
└─────────────────┘    └──────────────────┘    └─────────────────┘
                                                         │
┌─────────────────┐    ┌──────────────────┐    ┌─────────────────┐
│  User Queries   │◀───│   Groq LLM       │◀───│ Vector Database │
└─────────────────┘    └──────────────────┘    │   (ChromaDB)    │
                                                └─────────────────┘
                                                         ▲
                                                         │
                                                ┌─────────────────┐
                                                │ HuggingFace     │
                                                │ Embeddings      │
                                                └─────────────────┘

Quick Start

Prerequisites

• Python 3.8+
• LlamaCloud API Key (from LlamaIndex)
• Groq API Key (from Groq)

Installation

1. Clone the repository

   git clone https://github.com/anujgawde/rag-engine.git
   cd rag-engine

2. Install dependencies

   pip install -r requirements.txt

3. Set up environment variables Create a .env file in the root directory:

   LLAMA_CLOUD_API_KEY=your_llama_cloud_api_key
   GROQ_API_KEY=your_groq_api_key
   EMBEDDING_MODEL=sentence-transformers/all-MiniLM-L6-v2
   PERSIST_DIR=./chroma_db

4. Run the application

   python main.py

The API will be available at http://localhost:8000

API Documentation

Upload PDFs

Upload and process PDF files into the knowledge base.

Endpoint: POST /upload-pdfs

Request:

curl -X POST "http://localhost:8000/upload-pdfs" \
  -H "accept: application/json" \
  -H "Content-Type: multipart/form-data" \
  -F "[email protected]" \
  -F "[email protected]"

Response:

{
  "status": "success",
  "message": "Successfully ingested 2 PDF files",
  "files_processed": 2,
  "file_names": ["document1.pdf", "document2.pdf"]
}

Query Documents

Ask questions about your uploaded documents.

Endpoint: POST /query

Request:

curl -X POST "http://localhost:8000/query" \
  -H "accept: application/json" \
  -H "Content-Type: application/x-www-form-urlencoded" \
  -d "question=What are the main topics discussed in the documents?"

Response:

{
  "status": "success",
  "message": "Query processed successfully",
  "query": "What are the main topics discussed in the documents?",
  "answer": "Based on the documents, the main topics include...",
  "sources": [
    {
      "content": "Excerpt from the relevant document...",
      "metadata": {
        "source": "document1.pdf",
        "page": 1,
        "file_name": "document1.pdf"
      }
    }
  ]
}

Interactive API Documentation

Once the server is running, you can access the interactive API documentation at:

• Swagger UI: http://127.0.0.1:8000/docs
• ReDoc: http://localhost:8000/redoc

Configuration

Environment Variables

Variable	Description	Default
LLAMA_CLOUD_API_KEY	API key for LlamaParse service	Required
GROQ_API_KEY	API key for Groq LLM service	Required
EMBEDDING_MODEL	HuggingFace embedding model	sentence-transformers/all-MiniLM-L6-v2
PERSIST_DIR	Directory to store vector database	./chroma_db

Customization

You can customize various parameters in the PDFRAG class:

• Chunk Size: Modify chunk_size in RecursiveCharacterTextSplitter
• Chunk Overlap: Adjust chunk_overlap for better context preservation
• Retrieval Count: Change k parameter in retriever setup
• LLM Model: Switch between different Groq models
• Temperature: Adjust creativity vs consistency in responses

Examples

Example 1: Processing Research Papers

# Upload research papers
files = ["paper1.pdf", "paper2.pdf", "paper3.pdf"]
# Query: "What are the main findings across these research papers?"

Example 2: Company Documentation

# Upload company manuals, policies, and procedures
files = ["employee_handbook.pdf", "safety_procedures.pdf", "it_policies.pdf"]
# Query: "What is the company's remote work policy?"

Example 3: Legal Documents

# Upload contracts and legal documents
files = ["contract1.pdf", "terms_of_service.pdf", "privacy_policy.pdf"]
# Query: "What are the termination clauses in these contracts?"

Troubleshooting

Common Issues

1. "No documents have been ingested yet"

   • Make sure you've uploaded PDF files using the /upload-pdfs endpoint
   • Check if the vector database directory exists and contains data

2. "Error parsing PDF"

   • Ensure your PDF files are not corrupted
   • Check if your LlamaCloud API key is valid and has sufficient credits

3. Slow response times

   • Consider using a smaller embedding model for faster processing
   • Reduce chunk size or retrieval count for quicker responses

4. Memory issues

   • For large documents, consider increasing chunk size to reduce total chunks
   • Monitor system memory usage during processing

API Key Issues

• Ensure your API keys are correctly set in the .env file
• Check API key validity and quota limits
• Verify network connectivity to external services

Contributing

1. Fork the repository
2. Create a feature branch (git checkout -b feature/amazing-feature)
3. Commit your changes (git commit -m 'Add some amazing feature')
4. Push to the branch (git push origin feature/amazing-feature)
5. Open a Pull Request

Acknowledgments

• LlamaIndex for excellent PDF parsing capabilities
• LangChain for the RAG framework
• Groq for fast LLM inference
• ChromaDB for vector storage
• HuggingFace for embedding models

Support

If you encounter any issues or have questions, please:

1. Check the troubleshooting section
2. Search existing GitHub issues
3. Create a new issue with detailed information about your problem

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