Hi @shikuz, @HahaBill, , and the Joplin community!
My name is Vividh Yadav, and I am an Artificial Intelligence and Machine Learning student at BIT Mesra. I am applying for the "Chat with your note collection using AI" project.
I was drawn to this idea because it perfectly aligns with my recent work. I built a RAG-based document chatbot architecture utilizing Python, LangChain, and Llama 3.3. Navigating the challenges of document parsing, chunking strategies, and local LLM integration gave me a strong foundation for this project. Furthermore, my background in building full-stack web applications (using React and Node.js) gives me the experience to weave this AI pipeline into Joplin's existing React UI.
Since the deadline is approaching, here is my technical approach and proposed timeline. I would love any quick feedback, specifically regarding the optimal local vector storage solutions for Joplin's ecosystem!
1. Technical Approach & Architecture
Overview: I propose building a Joplin plugin utilizing a Retrieval-Augmented Generation (RAG) architecture. To maintain Joplin’s core philosophy of privacy, the solution will run entirely locally. The plugin will extract note data via the Joplin Data API, generate embeddings, and store them in a local vector database.
Data Ingestion & Vectorization: Using the Joplin API, the plugin will fetch raw Markdown notes. I will utilize LangChain.js to chunk the text efficiently. These chunks will be passed through a local embedding model (e.g., via Ollama or Transformers.js) and stored in a lightweight local vector store like ChromaDB.
LLM Integration (RAG Pipeline): When a user asks a question, the query will be embedded and compared against the local vector database using cosine similarity. The top 'K' most relevant note chunks will be retrieved and injected into the system prompt as context. This enriched prompt will be sent to a local LLM (like Llama 3 via Ollama) to generate an accurate response without data leaving the user's machine.
User Interface: The frontend will be developed as a custom React panel within the Joplin desktop app, featuring a ChatGPT-style interface with real-time typing indicators for streaming LLM responses and clickable citations linking back to the original source notes.
2. Proposed Timeline (350 Hours / ~12 Weeks)
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Community Bonding (May): Finalize the tech stack with mentors (specifically choosing the best local vector DB). Set up the local development environment and Joplin Plugin API.
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Weeks 1-3 (Data Ingestion): Develop the script to fetch and parse Markdown notes. Implement LangChain.js text splitters. Integrate a local embedding model and write vectors to the local database.
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Weeks 4-6 (RAG Pipeline): Build the semantic search function. Set up Ollama integration. Write dynamic prompt templates.
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Weeks 7-9 (React UI): Build the interactive chat panel plugin using React and TypeScript. Implement state management for chat history and handle streaming text responses.
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Weeks 10-11 (Optimization): Optimize the ingestion script for massive note collections to prevent UI freezing. Refine the chunking strategy.
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Week 12 (Documentation): Extensive bug testing. Write user documentation and a developer setup guide. Submit the final Pull Request.
GitHub: https://github.com/VividhDesign LeetCode:https://leetcode.com/u/DoNotFear/ (To demonstrate C++/DSA problem-solving speed)
Looking forward to hearing your thoughts!