WebRAG Edge extension security report

# WebRAG - 网页知识库助手 A browser extension that builds a local RAG (Retrieval-Augmented Generation) knowledge base from web pages, powered by Ollama for complete privacy and local processing. ## Features - 📚 Build personal knowledge base from web pages - 🌐 Batch import links with search and filtering - 🔍 Semantic search powered by deep learning embeddings - 💬 AI-powered Q&A with Markdown support - 🔒 100% local processing - your data stays private - 📦 Multiple knowledge bases management - 🇨🇳 Excellent Chinese language support ## Quick Start ### Installation 1. Open Edge and navigate to `edge://extensions` 2. Enable "Developer mode" (toggle in top right) 3. Click "Load unpacked" and select the `rag-extension` folder 4. Pin the extension to your toolbar ### Ollama Setup **Important:** Ollama must be configured for CORS to work with browser extensions. Start Ollama with CORS enabled: ```bash # macOS/Linux OLLAMA_ORIGINS="*" ollama serve ``` Download required models: ```bash # Chat model (required) ollama pull qwen2.5:7b # Embedding model (required for Chinese) ollama pull lrs33/bce-embedding-base_v1 ``` ## Usage ### 1. Add Current Page Click "Add Current Page" to save the current webpage to your knowledge base. ### 2. Batch Add Links Click "Add Page Links" to batch import multiple pages: - Extracts all links from current page - Search and filter links - Select multiple pages to add ### 3. Search Knowledge Base Enter your question and click "Search" to find relevant content using semantic search. ### 4. AI Q&A Click "AI Answer" to get intelligent responses based on your knowledge base. Responses support full Markdown formatting including code blocks, tables, and lists. ## Configuration Open the extension and scroll to "LLM Config" section: - **Chat Model**: Select your installed model (e.g., `qwen2.5:7b`) - **Embedding Model**: Select Chinese-optimized model (e.g., `lrs33/bce-embedding-base_v1`) The extension automatically loads available models from Ollama. ## Technical Stack | Component | Technology | |-----------|------------| | **Frontend** | Vanilla JavaScript + Extension API | | **Embedding** | Ollama Embeddings API (supports Chinese) | | **Vector Store** | IndexedDB (browser local storage) | | **LLM** | Ollama (local inference) | | **Markdown** | marked.js | | **Similarity** | Cosine similarity | ## Recommended Models ### For Chinese Content - **Chat**: `qwen2.5:7b`, `qwen:14b`, `yi:34b` - **Embedding**: `lrs33/bce-embedding-base_v1`, `bge-large-zh-v1.5` ### For English Content - **Chat**: `llama3.2`, `mistral`, `phi3` - **Embedding**: `nomic-embed-text`, `mxbai-embed-large` ## Data Privacy ✅ **100% Local Processing** - All data stored in browser IndexedDB - AI inference runs on your local Ollama - No data sent to external servers - Complete privacy and security