| --- |
| title: Smart Support AI |
| sdk: gradio |
| emoji: 🚀 |
| colorFrom: blue |
| colorTo: indigo |
| pinned: false |
| sdk_version: 5.34.2 |
| app_file: frontend/app.py |
| --- |
| SmartSupport AI Workspace |
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| An intelligent, enterprise-grade customer support agent designed to automate documentation lookup and streamline user assistance. The system leverages a Retrieval-Augmented Generation (RAG) architecture to securely query a localized company knowledge base, enabling precise, context-aware responses without data leakage. |
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| 🚀 Key Features |
| Contextual RAG Retrieval: Queries a persistent, pre-built vector database to fetch exact text segments matching user inquiries. |
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| Intelligent Conversation Memory: Maintains a continuous stateful chat history across sessions for fluid, natural dialogue. |
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| Deterministic Tool Routing: Orchestrates actions dynamically, allowing the agent to switch between document lookups, ticket creation, and human escalation workflows. |
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| Production-Ready Split Architecture: Decouples the lightweight client interface from the heavy server-side AI processing engine via RESTful endpoints. |
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| 🛠️ Tech Stack |
| Frontend UI: Gradio |
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| Backend API Framework: FastAPI & Uvicorn |
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| Orchestration Framework: LangChain |
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| LLM Engine: NVIDIA Nemotron Mini 4B (via langchain-nvidia-ai-endpoints) |
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| Vector Store: ChromaDB |
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| Environment Management: Python Dotenv |
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| 📁 Project Structure |
| Plaintext |
| smart_support_ai/ |
| │ |
| ├── backend/ |
| │ ├ |
| │ ├── main.py # FastAPI application hosting the /chat endpoint |
| │ ├── Agent.py # LangChain execution engine & memory layer |
| │ └── tools.py # Core tools (ChromaDB search, Ticket engine) |
| │ |
| ├── frontend/ |
| │ ├ |
| │ └── app.py # Gradio web interface client application |
| │ |
| ├── data/ |
| │ └── company_faqs.txt # Source documentation file for company data |
| │ |
| ├── chroma_db/ # Persistent directory containing pre-built vector embeddings |
| │ |
| ├── requirements.txt # Explicit project dependency listings |
| └── .env # Local environment secrets and API keys |
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| ⚡ Installation & Local Setup |
| 1. Clone and Navigate to the Repository |
| Bash |
| cd smart_support_ai |
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| 2. Configure the Environment Variables |
| Create a file named .env in the root directory of your project and populate it with your credentials: |
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| Plaintext |
| HUGGINFACE_API_KEY=your_api_key_here |
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| 3. Install System Dependencies |
| Install all required third-party packages using pip: |
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| Bash |
| pip install fastapi uvicorn gradio langchain langchain-nvidia-ai-endpoints chromadb python-dotenv |
| 🏃 How to Run the Application |
| To run the full stack, execute the backend server and frontend interface concurrently in separate terminal windows. |
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| Step 1: Initialize the FastAPI Backend |
| Launch the backend server application. It will initialize the LangChain engine and expose the necessary API routes on port 8000. |
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| Bash |
| uvicorn backend.main:app --reload |
| Verification: Navigate to http://127.0.0.1:8000/docs in your browser to inspect the interactive Swagger API documentation. |
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| Step 2: Launch the Gradio Frontend Client |
| In a new terminal window, spin up the web interface layer to connect to the active backend gateway. |
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| Bash |
| python frontend/app.py |
| Access Link: Open http://127.0.0.1:7860 in your web browser to interact with the operational support desk. |
