# Chat with Your Notes: A Guide to Local RAG in NoteRich In an era where information overload is the norm, finding the right piece of knowledge within your own notes can be harder than searching the entire web. Traditional search tools look for exact keyword matches, but they often miss the *context* or the *connection* between ideas. **NoteRich** changes this paradigm with its built-in **Local Knowledge Base RAG (Retrieval-Augmented Generation)**. This feature allows you to "chat" with your personal library, letting AI synthesize answers based strictly on what *you* have written, stored, and curated—all while keeping your data 100% private and local. ## 🧠 What is Local RAG? RAG stands for **Retrieval-Augmented Generation**. In simple terms, it’s a two-step process: 1. **Retrieve:** The system searches your local notes for relevant information related to your question. 2. **Generate:** It sends only those specific snippets to the AI, which then crafts a coherent answer based on that context. Unlike cloud-based AI assistants that might hallucinate facts or use outdated public data, NoteRich’s RAG is grounded in **your truth**. It doesn’t just guess; it references your own work. ### Why Local First? * **Privacy:** Your notes never leave your device during the retrieval phase. Only the specific, anonymized context needed for the answer is sent to the AI service. * **Speed:** By indexing locally, search results are instant. * **Ownership:** You maintain full control over your intellectual property. --- ## 🚀 How It Works Under the Hood NoteRich employs a sophisticated **Fusion Retrieval Strategy** to ensure you get the most accurate results, even from thousands of notes. ```mermaid graph TD A[User Question] --> B(Local Pre-processing) B --> C{Keyword Extraction} C -->|TextRank Algorithm| D[Identify Key Concepts] D --> E[Candidate Selection] E -->|Weighted Scoring| F[Top Relevant Chunks] F --> G[AI Context Assembly] G --> H[LLM Generation] H --> I[Final Answer with Citations] style A fill:#f9f,stroke:#333,stroke-width:2px style I fill:#bbf,stroke:#333,stroke-width:2px style B fill:#e1f5fe,stroke:#01579b,stroke-width:2px ``` ### 1. Intelligent Indexing When you create or edit a note, NoteRich automatically segments the content into meaningful chunks. It uses `Intl.Segmenter` for multi-language support, ensuring that Chinese, English, and other languages are tokenized correctly for maximum search precision. ### 2. Fusion Search Strategy Instead of relying on a single method, NoteRich combines multiple algorithms: * **Weighted Keyword Matching:** Uses IDF (Inverse Document Frequency) to prioritize rare, significant words over common ones. * **TextRank Graph Analysis:** Maps the relationship between sentences to identify the most "central" and important paragraphs in your notes. * **Cascade Filtering:** For large libraries, it first performs a fast global scan, then re-ranks the top candidates using deeper semantic analysis. ### 3. Smart Context Assembly Before sending data to the AI, NoteRich optimizes the prompt: * **Token Budgeting:** It calculates the exact token count to stay within the AI's context window. * **Relevance Thresholding:** Low-scored chunks are discarded to prevent noise. * **Special Note Handling:** Files marked as `.prompt` or `.skill` are treated as high-priority instructions, ensuring the AI follows your specific guidelines. --- ## 🛠️ Using Chat with Notes Using this feature is as simple as having a conversation. ### Step 1: Enable RAG Mode Open the **AI Assistant** dialog (via the toolbar or `/ai` command). Look for the **"Chat with Notes"** toggle. When enabled, the AI will automatically scan your current workspace. ### Step 2: Ask Natural Questions You don’t need to use complex search operators. Just ask questions like: * *"What were my key takeaways from the last product meeting?"* * *"Summarize my research on React performance optimization."* * *"Find all notes related to 'Project Alpha' and list the pending tasks."* ### Step 3: Review & Verify NoteRich provides **transparent citations**. Every answer generated by the AI includes references to the original notes. You can click these citations to jump directly to the source paragraph, ensuring you can always verify the information. --- ## 🔒 Privacy & Security Architecture We believe that privacy is not a feature—it’s a foundation. * **Local Processing:** All indexing, keyword extraction, and initial filtering happen entirely in your browser using Web Workers. Your raw note data is never uploaded to our servers for indexing. * **Signed Requests:** When context is sent to the AI backend, it is protected by HMAC-SHA256 signatures, preventing tampering. * **No Persistent Storage:** The AI service does not store your conversation history or your note contents. It processes the request and forgets it immediately. --- ## 💡 Pro Tips for Better Results 1. **Use Clear Titles:** Descriptive titles help the retrieval engine categorize your notes more effectively. 2. **Tag Strategically:** While RAG is semantic, using consistent tags (e.g., `#project-x`, `#idea`) helps the system group related concepts. 3. **Keep Notes Updated:** The RAG index updates in real-time. If you change a fact in your notes, the AI will reflect that change in its next answer. 4. **Use Special Files:** Create notes ending in `.prompt` to give the AI persistent instructions (e.g., "Always answer in concise bullet points"). These are prioritized in every query. --- ## Conclusion NoteRich’s Local RAG transforms your static notes into a dynamic, interactive knowledge base. It’s not just about storing information; it’s about **connecting** it. By combining the power of local-first architecture with advanced AI retrieval, NoteRich ensures that your second brain is always ready to help you think better. Ready to unlock the full potential of your notes? Try **Chat with Notes** today. --- <div class="flex flex-wrap gap-2 mt-8 mb-12"> <span class="px-3 py-1 bg-[#f4f4f5] border border-[#eaeaea] rounded-full text-xs font-medium text-[#666]">Local-First</span> <span class="px-3 py-1 bg-[#f4f4f5] border border-[#eaeaea] rounded-full text-xs font-medium text-[#666]">RAG</span> <span class="px-3 py-1 bg-[#f4f4f5] border border-[#eaeaea] rounded-full text-xs font-medium text-[#666]">Privacy</span> <span class="px-3 py-1 bg-[#f4f4f5] border border-[#eaeaea] rounded-full text-xs font-medium text-[#666]">PKM</span> <span class="px-3 py-1 bg-[#f4f4f5] border border-[#eaeaea] rounded-full text-xs font-medium text-[#666]">AI Assistant</span> <span class="px-3 py-1 bg-[#f4f4f5] border border-[#eaeaea] rounded-full text-xs font-medium text-[#666]">Knowledge Base</span> </div>