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| title: AssistantBot | |
| emoji: 😻 | |
| colorFrom: red | |
| colorTo: pink | |
| sdk: gradio | |
| sdk_version: 6.14.0 | |
| python_version: '3.13' | |
| app_file: app.py | |
| pinned: false | |
| Check out the configuration reference at https://huggingface.co/docs/hub/spaces-config-reference | |
| # Ask Julia: AI Assistant | |
| A lightweight, CPU-optimized Retrieval-Augmented Generation (RAG) assistant designed specifically for the `diffusion.fyi` knowledge base. | |
| This project allows users to ask complex questions about flow matching, diffusion models, and custom research notes (like the "Julia Constant"), receiving accurate, mathematically formatted answers in real-time. | |
| ## Key Features | |
| * **CPU-Optimized Inference:** Powered by the `Qwen-2.5-1.5B-Instruct-GGUF` model via `llama-cpp-python`. It is heavily optimized to run on standard CPUs (like a free Hugging Face Space) without needing an expensive GPU. | |
| * **Custom Knowledge Retrieval (RAG):** Uses a local FAISS vector database to read and retrieve specific context from the `knowledge_base` directory before answering. | |
| * **Native Math Rendering:** The frontend fully supports LaTeX. Inline math (`$x$`) and block equations (`$$y=x$$`) are parsed and beautifully rendered using MathJax. | |
| * **Responsive UI:** Features a sleek, mobile-friendly slide-out sidebar that doesn't obstruct the reading material. | |
| * **Strict Hallucination Control:** The system prompt is engineered with strict low-temperature (`0.1`) constraints to ensure the bot *only* answers based on the provided notes and refuses to guess unknown concepts. | |
| ## Tech Stack | |
| * **Backend:** FastAPI, Python 3.11 | |
| * **AI/LLM:** `llama-cpp-python`, Hugging Face Hub (`Qwen/Qwen2.5-1.5B-Instruct-GGUF`) | |
| * **RAG Engine:** FAISS (CPU), `sentence-transformers` (`BAAI/bge-small-en-v1.5`) | |
| * **Frontend:** Vanilla HTML, CSS (Flexbox), JavaScript, MathJax, Marked.js | |
| * **Deployment:** Docker (Hugging Face Spaces compatible) | |
| ## Repository Structure | |
| ```text | |
| ├── app.py # Main FastAPI application and API routes | |
| ├── llm_cpu.py # LLM initialization and strict prompt formatting logic | |
| ├── rag.py # Vector DB initialization and context retrieval | |
| ├── index.html # The frontend user interface (UI) | |
| ├── Dockerfile # Custom Dockerfile for non-root CPU cloud deployment | |
| ├── requirements.txt # Python dependencies | |
| └── knowledge_base/ # Markdown files containing the core research notes | |
| ├── faiss_index.bin # Pre-computed vector embeddings | |
| └── faiss_metadata... # Metadata linking vectors to text chunks |