| --- |
| title: Enterprise AI Document Intelligence Platform |
| emoji: 🤖 |
| colorFrom: blue |
| colorTo: indigo |
| sdk: gradio |
| sdk_version: 6.20.0 |
| app_file: app.py |
| pinned: false |
| license: mit |
| --- |
| |
| # 🤖 Enterprise AI Document Intelligence Platform |
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| An AI-powered Retrieval-Augmented Generation (RAG) application that enables users to upload PDF documents, perform semantic search, and interact with them using natural language. The application retrieves the most relevant document context using vector embeddings and generates grounded responses using Groq's Llama 3.3 model. |
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| > 🚀 **Live Demo:** *(Add your Hugging Face Space URL after deployment)* |
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| --- |
|
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| ## ✨ Features |
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| - 📄 **Upload & Analyze PDFs** |
| - Upload enterprise documents, resumes, reports, manuals, or research papers. |
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| - 🔍 **Semantic Search** |
| - Retrieves the most relevant document sections using Sentence Transformers and FAISS vector search. |
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| - 🤖 **AI-Powered Question Answering** |
| - Generates context-aware answers using Groq Llama 3.3 with Retrieval-Augmented Generation (RAG). |
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| - 💬 **Interactive Chat Interface** |
| - Ask follow-up questions naturally through a clean Gradio interface. |
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| - 🛡 **Grounded Responses** |
| - Answers are generated only from the uploaded document context to reduce hallucinations. |
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| --- |
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| ## 🛠 Tech Stack |
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| | Category | Technologies | |
| |----------|--------------| |
| | Frontend | Gradio | |
| | LLM | Groq Llama 3.3-70B Versatile | |
| | Embeddings | Sentence Transformers (all-MiniLM-L6-v2) | |
| | Vector Database | FAISS | |
| | PDF Processing | PyMuPDF | |
| | Language | Python | |
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| --- |
|
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| ## 🏗 Architecture |
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| ```text |
| PDF Upload |
| │ |
| ▼ |
| PyMuPDF |
| │ |
| ▼ |
| Text Chunking |
| │ |
| ▼ |
| Sentence Transformer Embeddings |
| │ |
| ▼ |
| FAISS Vector Index |
| │ |
| ▼ |
| Semantic Retrieval |
| │ |
| ▼ |
| Groq Llama 3.3 |
| │ |
| ▼ |
| Grounded AI Response |
| ``` |
|
|
| --- |
|
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| ## 🚀 Getting Started |
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| ### 1. Clone the repository |
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| ```bash |
| git clone <repository-url> |
| cd Enterprise-AI-HF-Space |
| ``` |
|
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| ### 2. Create a virtual environment |
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| ```bash |
| python -m venv .venv |
| ``` |
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| Activate it: |
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| **Windows** |
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| ```bash |
| .venv\Scripts\activate |
| ``` |
|
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| **Linux / macOS** |
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| ```bash |
| source .venv/bin/activate |
| ``` |
|
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| ### 3. Install dependencies |
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| ```bash |
| pip install -r requirements.txt |
| ``` |
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| ### 4. Configure environment variables |
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| Create a `.env` file: |
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| ```env |
| GROQ_API_KEY=your_groq_api_key |
| ``` |
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| ### 5. Run the application |
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| ```bash |
| python app.py |
| ``` |
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| --- |
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| ## 📌 Example Questions |
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| - Summarize this document. |
| - What are the key technical skills? |
| - List the projects mentioned. |
| - What experience does this resume highlight? |
| - What are the major findings in this report? |
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| --- |
|
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| ## 🎯 Use Cases |
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| - Resume Analysis |
| - Enterprise Knowledge Search |
| - Research Paper Exploration |
| - Policy & Compliance Documents |
| - Technical Documentation |
| - Business Reports |
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| --- |
|
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| ## 📄 License |
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| This project is released under the MIT License. |
