🌲 Vectorless RAG — Document Intelligence

No embeddings. No vector DB. Pure LLM reasoning.

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🧠 How It Works

Traditional Vector RAG
  PDF → chunks → embeddings → cosine similarity → hope it's relevant → answer

Vectorless RAG (this project)
  PDF → document TREE → LLM reasons over tree → picks exact sections → answer

The 3-step pipeline

Step 1 — PARSE
  PyMuPDF extracts text page by page.
  Font-size analysis detects headings → builds a hierarchical Document Tree.

Step 2 — TREE SEARCH  (the key insight)
  LLM receives the compact tree (like a Table of Contents with section IDs).
  LLM reasons: "Which sections most likely contain the answer?"
  Returns a JSON array of section_ids.
  → No similarity math. No embeddings. Just reasoning.

Step 3 — ANSWER
  Full text of the chosen sections is retrieved (with page numbers).
  LLM synthesises a cited, markdown-formatted answer.
  Response is streamed token-by-token to the UI.

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🚀 Quick Start

Prerequisites

• Python 3.10+
• Ollama installed and running
• At least one model pulled:

ollama pull llama3.2        # recommended (fast, good reasoning)
# or
ollama pull mistral
ollama pull phi3
ollama pull gemma2

1 — Clone / download

git clone https://github.com/Sdinzsh/RAG.git
cd RAG

2 — Install dependencies

pip install -r requirements.txt

3 — Start Ollama

ollama serve          # if not already running as a service

4 — Run the app

python app.py

Open http://localhost:5000 in your browser.

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🖥️ Web UI Features

Feature	Detail
PDF Upload	Drag-and-drop or click to browse (up to 50 MB)
Document Tree	Sidebar shows all sections/pages with page numbers
Section Highlight	Sections used for each answer are highlighted in the tree
Streaming Chat	Answers stream token-by-token like ChatGPT
Source Badges	Each answer shows which sections/pages were used
Multi-turn Chat	Conversation history maintained per session
Model Selector	Switch between any Ollama model at the top
Clear History	Reset conversation context without re-uploading

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📁 Project Structure

RAG/
├── app.py              ← Flask backend + REST API
├── rag_engine.py       ← Core RAG logic
│   ├── build_document_tree()   ← PDF → Section tree
│   ├── find_relevant_sections() ← LLM tree search
│   └── answer_query_stream()   ← Full RAG pipeline
├── templates/
│   └── index.html      ← Web UI (dark industrial theme)
├── uploads/            ← Uploaded PDFs (auto-created)
└── requirements.txt

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⚙️ Configuration

Edit the top of rag_engine.py:

OLLAMA_BASE       = "http://localhost:11434"   # Ollama server URL
DEFAULT_MODEL     = "llama3.2"                 # Default model
TOP_K_SECTIONS    = 4                          # Max sections per query
MAX_SECTION_CHARS = 3000                       # Chars per section in context

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🔌 API Endpoints

Method	Endpoint	Description
GET	/	Web UI
GET	/api/models	List available Ollama models
POST	/api/upload	Upload PDF + build document tree
POST	/api/chat	Chat query → SSE stream
GET	/api/session/<id>	Session info
POST	/api/session/<id>/clear	Clear chat history

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💡 Tips

• Best models for reasoning: llama3.2, mistral, gemma2, phi3
• Large PDFs: The tree search is O(sections) not O(pages) — works well on big docs
• Structured PDFs (reports, textbooks): heading detection works best
• Scanned PDFs: Won't work — needs OCR pre-processing