🧠 Personal RAG System — Full Build Plan ⚠️ Note: I can’t a...

Prompt

🧠 Personal RAG System — Full Build Plan ⚠️ Note: I can’t access or watch YouTube videos/Shorts, so I’m building this plan from my own knowledge of personal RAG systems. If the video had a specific approach, feel free to share the key points and I’ll adapt! 🎯 What This System Does You add notes → System indexes them → You ask questions → AI retrieves & answers Core Idea: Everything you learn, read, watch, or think → gets stored → later you ask anything → it remembers for you. 🏗️ High-Level Architecture User Interface: CLI / Web UI / API / Telegram Bot Ingestion Pipeline: text input, file upload, web clip, audio/voice, PDFs, YouTube Retrieval Pipeline: query input, embed query, vector search, reranking, context building Storage Layer: PostgreSQL (metadata), pgvector / vector store, JSON/MD documents, Redis (cache for sessions) LLM: GPT-4 / Claude / Ollama for generation 📦 Database Plan Option A: PostgreSQL + pgvector (Recommended — single DB for everything) Table 1: entries — raw entries (everything you add) Table 2: chunks — entries split into pieces for retrieval Table 3: conversations — Q&A history Table 4: summaries — one summary per entry Indexes: vector similarity search (HNSW), hybrid search support, filtering, auto-update of the search vector Option B: Separate Vector DB (if scaling) Primary DB: PostgreSQL (metadata, CRUD) Vector DB: Qdrant / Weaviate / ChromaDB / Milvus Cache: Redis 📝 Database Schema entries: id, title, raw_text, source_type, tags, importance (1–5), metadata, created_at, search_vector chunks (many per entry): id, entry_id, content, embedding, search_vector, chunk_index, source_type, tags, importance summaries (one per entry): entry_id, summary_text, embedding conversations: session_id, role (user/assistant), content, relevant_chunk_ids 🔧 Build Plan — Step by Step Phase 1: Foundation (Week 1) Project setup, environment, configuration, database models and connection. Modules: ingestion (chunker, embedder, ingestors, pipeline), retrieval (retriever, reranker, context builder), generation (responder), API, CLI. Phase 2: Ingestion Pipeline (Week 1–2) Steps: take input → generate summary (optional, with LLM) → chunk → embed all chunks → store in DB → save summary with embedding. Supported input types: plain text, manual note, web page, PDF, Markdown, YouTube URL, voice note, Twitter/X, generic file. Phase 3: Retrieval (Week 2) Hybrid search: Embed the query Vector search (semantic similarity), filtered by tags, source type, importance, archive status Keyword search (BM25 / ts_rank) Reciprocal Rank Fusion (RRF) to combine both results Temporal decay (recency boost) Phase 4: Generation / Q&A (Week 2–3) The assistant answers based on the retrieved context, cites its sources, says honestly when the answer isn’t in the context, is concise but thorough, and connects related ideas across notes. Flow: retrieve relevant chunks → build context → call LLM (low temperature for factual retrieval) → save conversation → update access counts → return answer with sources. 🎯 What Can Be Done — Feature Ideas 📥 Ingestion Features Feature Description Voice notes Speak → Whisper transcribes → ingest Screenshot OCR Image → OCR → ingest RSS feeds Auto-ingest articles you follow YouTube summaries Paste link → transcribe → summarize → ingest Daily journal Auto-prompt at end of day Browser extension Right-click → save any page Slack/Notion sync Periodic import Photo with captions Auto-tag images Email digest Parse important emails 🔍 Retrieval Features Feature Description Hybrid search Vector + keyword combined Time-range filter “What did I learn last week?” Tag filtering “Only my Python notes” Importance weighting Weight critical notes higher Related note discovery “Show me related to X” Flashcard mode Generate questions from notes Knowledge graph Visualize connections between notes GraphRAG/RAPTOR Hierarchical summarization for large datasets 🤖 Generation Features Feature Description Chat interface Conversational Q&A Daily briefing “What should I remember today?” Spaced repetition Scheduled review prompts Weekly digest Summary of everything added Knowledge gaps “What am I missing on topic X?” Mind map generation Visualize your knowledge Goal tracking Link notes to goals Contradiction detection Flag conflicting info 🖥️ Interface Options CLI API Telegram bot Web UI (FastAPI + React/Vue dashboard) Obsidian plugin VS Code extension 🏁 Quick-Start Minimum Viable Version Simplest version first: a single file with SQLite, OpenAI embeddings and a direct LLM call. Functions: remember (store note with embedding), recall (find most similar notes), ask (answer with sources). 📊 Scaling Roadmap Level 1 (Day 1): SQLite + OpenAI embeddings + direct call Level 2 (Week 1): PostgreSQL + pgvector + chunking + hybrid search Level 3 (Week 2): FastAPI + Web UI + multiple input types Level 4 (Month 1): Agent-based ingestion + scheduled jobs + mobile Level 5 (Month 2): Knowledge graph + spaced repetition + multi-modal Want me to go deeper on any specific part? For example: Full code for any module? Telegram bot wrapper? Obsidian/Vault integration? Multi-modal (images, audio) handling? GraphRAG knowledge graph approach? Self-hosted LLM (Ollama) version?