Update README.md

README.md

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 license: apache-2.0
 ---
 
-# 📄 PDF RAG with Together.ai  
-### *Agentic Document Intelligence*
+# 📄 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 grounded strictly in the document content**.
+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
+## 🚀 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
 
-The system combines:
+3. **Vector Indexing**
+   - Generates embeddings using Sentence Transformers
+   - Indexes vectors using FAISS (cosine similarity)
 
-- 🔍 **Semantic search** using embeddings + FAISS  
-- 🧠 **Large Language Model** served via **Together.ai**  
-- 🎛️ **Interactive Gradio interface**
+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.
 
 ---
 
-## 🧩 Why This Space Exists
+### **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:
+
 
-This Space is designed as a **foundational Agentic Document Intelligence component**.
 
-It serves as:
-- a clean reference RAG implementation
-- a building block for more advanced agentic AI systems
-- a practical example of grounded, document-aware LLM applications
 
 ---
 
-## 🛠️ Core Concepts Demonstrated
+### **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:
+
 
-- Retrieval-Augmented Generation (RAG)
-- Vector-based semantic search
-- Context-constrained LLM prompting
-- Transparent source grounding
 
 ---
 
-## 🧠 Intended Use Cases
+### **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”*
+
+---
 
-- Document Q&A
-- Research paper analysis
-- Internal knowledge assistants
+### **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
 
 ---
 
-Built with ❤️ for the Hugging Face community.
+## 🔮 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`