A Retrieval-Augmented Generation (RAG) application that allows users to perform semantic search over private PDF documents.

Unlike standard chatbots, this system "grounds" the AI's responses in specific, user-provided data, reducing hallucinations and enabling queries over domain-specific knowledge (contracts, manuals, research papers).

🔗 Live Demo: Deployed on Vercel

The system consists of two distinct pipelines:

• Loads raw PDF data from the documents/ folder
• Chunks text into manageable segments (1000 characters) with 200-character overlap to preserve context
• Generates vector embeddings using text-embedding-3-small
• Upserts vectors to Pinecone (Serverless)

• Converts user queries into vector embeddings
• Performs a semantic similarity search in Pinecone to retrieve the top 3 relevant chunks
• Injects these chunks as "System Context" into the LLM (GPT-4o-mini)
• Streams the response back to the user in real-time

┌─────────────────────────────────────────────────────────────────┐
│                     INGESTION PIPELINE                          │
│  PDF → Chunking → Embeddings → Pinecone Vector DB               │
└─────────────────────────────────────────────────────────────────┘

┌─────────────────────────────────────────────────────────────────┐
│                     RETRIEVAL PIPELINE                          │
│  User Query → Embedding → Pinecone Search → Context + LLM → Response │
└─────────────────────────────────────────────────────────────────┘

A common question is: "Why build this app when I can just upload a file to ChatGPT?"

This system addresses specific Enterprise constraints that consumer tools cannot:

• Node.js 18+
• Python 3.10+
• API Keys: OpenAI, Pinecone

git clone https://github.com/JithendraNara/rag-document-detective.git
cd rag-document-detective

Create a .env file in the root directory:

OPENAI_API_KEY=sk-your-openai-api-key
PINECONE_API_KEY=your-pinecone-api-key
PINECONE_INDEX_NAME=doc-chat

Before running the app, populate the vector database with your documents:

# Place your PDF files in the documents/ folder
mkdir -p documents
cp your-file.pdf documents/

# Install Python dependencies
pip install -r requirements.txt

# Run the ingestion script
python ingest.py

The script will:

• Create a Pinecone index if it doesn't exist
• Process all PDFs in the documents/ folder
• Chunk, embed, and upload to Pinecone

# Install Node.js dependencies
npm install

# Start the development server
npm run dev

Open http://localhost:3000 to start chatting with your documents!

├── app/
│   ├── api/
│   │   └── chat/
│   │       └── route.ts      # Chat API - retrieval + LLM
│   ├── admin/
│   │   └── page.tsx          # Admin page with ingestion instructions
│   ├── page.tsx              # Main chat interface
│   ├── layout.tsx            # Root layout
│   └── globals.css           # Global styles
├── documents/                 # Place PDFs here for ingestion
├── ingest.py                  # Python ingestion script
├── requirements.txt           # Python dependencies
├── package.json              # Node.js dependencies
└── README.md

chunk_size = 1000      # Characters per chunk
chunk_overlap = 200    # Overlap between chunks

topK: 3                // Number of chunks to retrieve
model: 'gpt-4o-mini'   // LLM model (cost-optimized)

This app is configured for minimal costs:

Estimated cost: < $0.01 per conversation for typical usage.

1. Push your code to GitHub
2. Import the project in Vercel
3. Add environment variables in Vercel dashboard:
   • OPENAI_API_KEY
   • PINECONE_API_KEY
   • PINECONE_INDEX_NAME
4. Deploy!

Note: Document ingestion must be done locally using the Python script. The web app handles chat/retrieval only.

MIT License - feel free to use this for your own projects!

Contributions are welcome! Please open an issue or submit a pull request.

Built with ❤️ using Next.js, Vercel AI SDK, and Pinecone