updated:-

README
requirements

README.md

@@ -11,3 +11,38 @@ 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

requirements.txt

@@ -7,10 +7,10 @@ sentence-transformers
 numpy
 
 # CPU Related -- helped for quantisation
-#llama-cpp-python
-#huggingface-hub
+llama-cpp-python
+huggingface-hub
 
-# GPU Related
-transformers
-accelerate
-bitsandbytes
+# # GPU Related
+# transformers
+# accelerate
+# bitsandbytes