---
title: Agentic Document Intelligence
emoji: 📄
colorFrom: blue
colorTo: pink
sdk: gradio
sdk_version: 6.2.0
app_file: app.py
pinned: false
license: apache-2.0
---

# 📄 Agentic Document Intelligence  
### PDF RAG with Together.ai

This Hugging Face Space demonstrates a **Retrieval-Augmented Generation (RAG)** system that allows users to upload a PDF and ask questions that are **strictly grounded in the document content**.

The Space serves as a **foundational Agentic Document Intelligence component**, designed to be simple, transparent, and extensible.

---

## 🚀 What This Space Does

- Upload a PDF document  
- Build a semantic index using embeddings + FAISS  
- Ask natural-language questions  
- Receive answers grounded only in the uploaded document  
- View retrieved source passages for transparency  

---

## 🧠 Architecture Overview

1. **PDF Ingestion**
   - Extracts text from uploaded PDF
   - Cleans and normalizes content

2. **Chunking**
   - Splits text into overlapping semantic chunks
   - Ensures contextual continuity

3. **Vector Indexing**
   - Generates embeddings using Sentence Transformers
   - Indexes vectors using FAISS (cosine similarity)

4. **Retrieval**
   - Retrieves top-K relevant chunks for each query

5. **Generation (RAG)**
   - Injects retrieved context into LLM prompt
   - Uses Together.ai (Mixtral) for answer generation

---

## ▶️ How to Use This Space (End-to-End)

### **Step 1: Upload a PDF**
- Click **“Upload PDF”**
- Select a text-based PDF file  
  > ⚠️ Note: Scanned PDFs without text extraction will not work unless OCR is applied.

---

### **Step 2: Wait for Indexing**
- The system will:
  - extract text
  - split it into chunks
  - build a FAISS vector index  
- You will see a confirmation message:




---

### **Step 3: Ask a Question**
- Type a natural-language question related to the document  
Examples:
- *“Summarize the document”*
- *“What is the main contribution?”*
- *“Explain the methodology section”*

---

### **Step 4: Receive the Answer**
You will get:
- ✅ A generated answer based **only on document context**
- 📌 Retrieved source passages with similarity scores
- 🚫 No hallucinated or external information

If the answer is not present in the document, the system will respond:



---

### **Step 3: Ask a Question**
- Type a natural-language question related to the document  
Examples:
- *“Summarize the document”*
- *“What is the main contribution?”*
- *“Explain the methodology section”*

---

### **Step 4: Receive the Answer**
You will get:
- ✅ A generated answer based **only on document context**
- 📌 Retrieved source passages with similarity scores
- 🚫 No hallucinated or external information

If the answer is not present in the document, the system will respond:




---

## 🤖 Models Used

### **Language Model**
- **Provider:** Together.ai  
- **Model:** `mistralai/Mixtral-8x7B-Instruct-v0.1`

### **Embedding Model**
- `sentence-transformers/all-MiniLM-L6-v2`

---

## 🧰 Tech Stack

- Python
- Gradio (UI)
- FAISS (vector search)
- Sentence Transformers (embeddings)
- Together.ai (LLM)
- Hugging Face Spaces

---

## 🔐 Environment Configuration (For Developers)

### **Secrets**
- `TOGETHER_API_KEY` → Together.ai API key  
- `OPENAI_API_KEY` → Same value (compatibility with OpenAI client)

### **Variables**
- `TOGETHER_MODEL` → `mistralai/Mixtral-8x7B-Instruct-v0.1`
- `TOGETHER_BASE_URL` → `https://api.together.xyz/v1`

---

## 🧩 Intended Use Cases

- Research paper Q&A
- Technical documentation assistants
- Internal knowledge bases
- RAG pipeline reference implementation
- Agentic AI system foundations

---

## 🔮 Future Enhancements

- Multi-PDF support
- Chat memory
- Streaming responses
- Agent routing & tool usage
- Evaluation and scoring agents

---

## 🙌 Author

Built by **Abhishek Prithvi Teja**  
Focused on **Agentic AI, RAG systems, and applied LLM engineering**

---

## 🏷️ Tags

`rag` · `agentic-ai` · `document-qa` · `faiss` · `together-ai` · `huggingface-spaces`