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--- |
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title: Agentic Document Intelligence |
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emoji: 📄 |
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colorFrom: blue |
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colorTo: pink |
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sdk: gradio |
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sdk_version: 6.2.0 |
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app_file: app.py |
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pinned: false |
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license: apache-2.0 |
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--- |
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# 📄 Agentic Document Intelligence |
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### PDF RAG with Together.ai |
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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**. |
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The Space serves as a **foundational Agentic Document Intelligence component**, designed to be simple, transparent, and extensible. |
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--- |
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## 🚀 What This Space Does |
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- Upload a PDF document |
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- Build a semantic index using embeddings + FAISS |
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- Ask natural-language questions |
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- Receive answers grounded only in the uploaded document |
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- View retrieved source passages for transparency |
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--- |
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## 🧠 Architecture Overview |
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1. **PDF Ingestion** |
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- Extracts text from uploaded PDF |
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- Cleans and normalizes content |
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2. **Chunking** |
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- Splits text into overlapping semantic chunks |
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- Ensures contextual continuity |
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3. **Vector Indexing** |
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- Generates embeddings using Sentence Transformers |
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- Indexes vectors using FAISS (cosine similarity) |
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4. **Retrieval** |
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- Retrieves top-K relevant chunks for each query |
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5. **Generation (RAG)** |
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- Injects retrieved context into LLM prompt |
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- Uses Together.ai (Mixtral) for answer generation |
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--- |
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## ▶️ How to Use This Space (End-to-End) |
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### **Step 1: Upload a PDF** |
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- Click **“Upload PDF”** |
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- Select a text-based PDF file |
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> ⚠️ Note: Scanned PDFs without text extraction will not work unless OCR is applied. |
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### **Step 2: Wait for Indexing** |
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- The system will: |
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- extract text |
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- split it into chunks |
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- build a FAISS vector index |
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- You will see a confirmation message: |
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--- |
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### **Step 3: Ask a Question** |
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- Type a natural-language question related to the document |
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Examples: |
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- *“Summarize the document”* |
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- *“What is the main contribution?”* |
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- *“Explain the methodology section”* |
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### **Step 4: Receive the Answer** |
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You will get: |
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- ✅ A generated answer based **only on document context** |
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- 📌 Retrieved source passages with similarity scores |
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- 🚫 No hallucinated or external information |
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If the answer is not present in the document, the system will respond: |
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--- |
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### **Step 3: Ask a Question** |
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- Type a natural-language question related to the document |
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Examples: |
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- *“Summarize the document”* |
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- *“What is the main contribution?”* |
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- *“Explain the methodology section”* |
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--- |
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### **Step 4: Receive the Answer** |
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You will get: |
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- ✅ A generated answer based **only on document context** |
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- 📌 Retrieved source passages with similarity scores |
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- 🚫 No hallucinated or external information |
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If the answer is not present in the document, the system will respond: |
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--- |
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## 🤖 Models Used |
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### **Language Model** |
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- **Provider:** Together.ai |
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- **Model:** `mistralai/Mixtral-8x7B-Instruct-v0.1` |
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### **Embedding Model** |
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- `sentence-transformers/all-MiniLM-L6-v2` |
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--- |
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## 🧰 Tech Stack |
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- Python |
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- Gradio (UI) |
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- FAISS (vector search) |
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- Sentence Transformers (embeddings) |
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- Together.ai (LLM) |
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- Hugging Face Spaces |
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--- |
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## 🔐 Environment Configuration (For Developers) |
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### **Secrets** |
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- `TOGETHER_API_KEY` → Together.ai API key |
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- `OPENAI_API_KEY` → Same value (compatibility with OpenAI client) |
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### **Variables** |
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- `TOGETHER_MODEL` → `mistralai/Mixtral-8x7B-Instruct-v0.1` |
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- `TOGETHER_BASE_URL` → `https://api.together.xyz/v1` |
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--- |
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## 🧩 Intended Use Cases |
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- Research paper Q&A |
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- Technical documentation assistants |
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- Internal knowledge bases |
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- RAG pipeline reference implementation |
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- Agentic AI system foundations |
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--- |
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## 🔮 Future Enhancements |
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- Multi-PDF support |
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- Chat memory |
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- Streaming responses |
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- Agent routing & tool usage |
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- Evaluation and scoring agents |
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--- |
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## 🙌 Author |
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Built by **Abhishek Prithvi Teja** |
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Focused on **Agentic AI, RAG systems, and applied LLM engineering** |
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--- |
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## 🏷️ Tags |
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`rag` · `agentic-ai` · `document-qa` · `faiss` · `together-ai` · `huggingface-spaces` |
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