vision:: Extend my capabilities by an order of magnitude through AI inference that understands my complete context, goals, and patterns goal:: Build a transparent, learning system that vectorizes all my interactions (text, audio, visual) into a searchable second brain, uses this context to enhance LLM interactions, and continuously improves through real-time feedback ## System Architecture ### Core Components - **Input Pipeline**: Raw vectorization gateway - minimal preprocessing to preserve information density - **Second Brain**: Multi-modal vector storage with contextual, hierarchical, and temporal awareness - **AI OS**: Orchestration layer that searches second brain, assembles context, and engineers prompts for maximum LLM effectiveness - **LLM Layer**: External/internal inference models (GPT-4, Claude, local models) - **Feedback Loop**: Ground-up pipeline with metadata storage for continuous improvement ### Data Flow ``` Any Input → Multimodal Embedding Pipeline → Second Brain → Control Logic → Proactive Actions ↑ ↓ └────── Training Feedback Loop ←─────┘ ``` ``` User Query → Small LLM → "I need contexts A, B, C" → RAG searches → Assembled context → Big LLM → Response ↑ ↑ (routing step) (expensive inference) ``` ### System Architecture Diagram ```mermaid graph TB subgraph "Input Sources" A1[Text/Documents] A2[Audio Conversations] A3[Internet Content] A4[Images/Visual] A5[LLM Interactions] end subgraph "Input Pipeline" B1[Raw Vectorization<br/>Minimal preprocessing] B2[Git Commit Versioning] B3[Metadata Tagging] end subgraph "Second Brain" C1[Hierarchical Index<br/>Doc→Section→Para→Sent] C2[Temporal Index<br/>Time-weighted vectors] C3[Contextual Index<br/>Query-aware chunks] C4[Vector Store<br/>768D shared space] end subgraph "AI OS - Orchestration Layer" D1[Query Router] D2[RAG Search<br/>Multi-index retrieval] D3[Context Assembler<br/>Goals/Projects/History] D4[Prompt Engineer<br/>Dynamic templates] end subgraph "LLM Layer" F1[GPT-4/Claude/Local] F2[Inference] F3[Response] end subgraph "Feedback System" E1[Implicit Signals<br/>Clicks/Ignores/Rephrase] E2[Explicit Ratings] E3[Benchmark Suite] E4[Preference Learning] end A1 & A2 & A3 & A4 & A5 --> B1 B1 --> B2 B2 --> B3 B3 --> C1 & C2 & C3 C1 & C2 & C3 --> C4 C4 --> D2 D1 --> D2 D2 --> D3 D3 --> D4 D4 --> F1 F1 --> F2 F2 --> F3 F3 --> E1 & E2 E1 & E2 --> E3 E3 --> E4 E4 -.-> D3 E4 -.-> D4 style B2 fill:#f9f,stroke:#333,stroke-width:4px style E3 fill:#9f9,stroke:#333,stroke-width:4px style D3 fill:#bbf,stroke:#333,stroke-width:4px ``` ## Technical Architecture Deep Dive ### Input Pipeline - Raw vs Clean Data **Minimal preprocessing is key** - With embeddings and LLMs, raw data often outperforms heavily cleaned data because: - Context is preserved (typos, speech patterns, informal language) - Personal communication style remains intact - LLMs handle messiness better than traditional NLP - Only clean: encoding issues, extreme formatting problems ### Token Limits for Context Injection - **GPT-4 Turbo**: 128k tokens (~300 pages) - **Claude 3**: 200k tokens (~500 pages) - **Gemini 1.5 Pro**: 1M tokens (~2500 pages) - **Practical limit**: 20-50k tokens for cost/latency balance ### LlamaIndex: Framework or Components? **LlamaIndex = Opinionated framework** with escape hatches: - **What it provides**: Query engines, index structures, chains, loaders - **What it enforces**: Document/node abstractions, callback system - **Limitations you'll hit**: - Rigid document model (hard to add custom metadata flows) - Limited control over embedding batching - Opaque prompt templates - Hard to implement custom ranking algorithms **Component-based alternative approach**: - Use `langchain` for just chains/prompts - `chromadb` or `qdrant` for pure vector ops - Custom orchestration layer - More work but full control ### Does Context Assembly Exist? **Mostly you'll build it** - Current tools: - LlamaIndex has basic `ComposableGraph` for combining indexes - Langchain has `MultiVectorRetriever` for hierarchical search - But dynamic goal/project/context assembly? That's custom - GPT-4's function calling can help with routing logic ### Feedback & Learning (Built from Ground Up) - **Metadata Storage**: Every interaction tagged for improvement tracking - **Implicit Signals**: Click patterns, query refinements, result usage - **Preference Learning**: Model/prompt selection based on success patterns - **Automated Testing**: Benchmark suite for any system changes ## Implementation Roadmap (V1 Focus) ### Phase 1: Embedding Foundation & Benchmarking (Week 1) - [ ] Set up git commit-based embedding versioning system - [ ] Create benchmark suite from 5GB corpus (50-100 test queries) - [ ] Test embedding models (MiniLM vs BGE vs Nomic) - [ ] Build feedback tracking infrastructure ### Phase 2: Advanced RAG (Week 2) - [ ] Implement hierarchical chunking with LlamaIndex - [ ] Add contextual embeddings (query-aware dynamic chunks) - [ ] Configure temporal weighting system - [ ] Build custom reranker for your domain ### Phase 3: AI OS - Orchestration Layer (Week 3) - [ ] Build context assembler for goals/projects/history - [ ] Create routing logic (which indexes to search) - [ ] Implement dynamic prompt engineering - [ ] Add transparency layer (see why results were chosen) ### Phase 4: Integration & Feedback Loop (Week 4) - [ ] Connect to LLMs via litellm - [ ] Implement preference learning from implicit signals - [ ] Create daily check-in conversation interface - [ ] Set up A/B testing for prompt/model improvements ### V2 Features (Future) - Audio input pipeline with emotion/urgency detection - AI OS with proactive triggers - Goal-oriented automation - Multi-agent coordination ## Benchmarking Strategy ### Metrics - **Retrieval**: Recall@K, Precision@K, MRR (Mean Reciprocal Rank) - **End-to-End**: "Did user get needed information?" success rate - **Latency**: Query response time across modalities - **Drift**: Alignment with current user preferences over time ### Test Suite - Representative queries from existing 5MB corpus - Cross-modal retrieval tests (audio → related text) - Temporal accuracy (recent vs outdated information) - Intent classification accuracy ### Versioning Approach - `main`: Current embeddings for live queries - `feature/*`: Test new embedding approaches - Tagged releases: Rollback points for performance regression - Diff-based updates: Only re-embed changed documents ## Technical Stack (2025 Open Source) ### Recommended Approach: Hybrid **Start with LlamaIndex for speed, plan to extract components** ### Core Components - **Embeddings**: - `sentence-transformers/all-MiniLM-L6-v2` (fast, good quality) - `BAAI/bge-large-en-v1.5` (best open source quality) - `nomic-ai/nomic-embed-text-v1` (long context: 8k tokens) - **Vector Store Options**: - `Qdrant` - Best performance, rust-based, excellent metadata filtering - `ChromaDB` - Simplest setup, good DX, pure Python - `Weaviate` - Best hybrid search, but heavier - **Orchestration Layer**: - Start: `LlamaIndex 0.10.x` for rapid prototyping - Migrate: Custom Python with `fastapi` + `pydantic` - Why: You'll need custom context assembly logic - **LLM Integration**: - `litellm` - Universal interface for all LLMs - `guidance` - Better prompt control than Langchain - Local: `ollama` + `llama-cpp-python` - **Benchmarking**: - `ragas` - RAG-specific metrics - `pytest-benchmark` - Performance tracking - Custom eval harness for your specific use cases ### Build vs Framework Decision Tree ``` If exploring/prototyping → LlamaIndex If need custom control → Components If production system → Start framework, extract components ``` ### What LlamaIndex Actually Is **Scaffolding that provides**: - Document loaders (100+ sources) - Index abstractions (vector, list, tree, graph) - Query engines with routing - Evaluation tools - Observability/callbacks **Not**: - A vector database (uses others) - An embedding model (uses others) - An LLM (uses others) - A web framework Think of it as "RAG Rails" - opinionated patterns you can follow or break. ## Key Innovations 1. **Git-based Knowledge Versioning**: Track embedding evolution with rollback capability 2. **Multi-Embedding Per Source**: Combine semantic, emotional, and contextual vectors 3. **Transparent Processing**: Every decision visible for debugging and improvement 4. **Daily Conversation Integration**: Morning/evening check-ins for temporal triggers ## Open Architecture Questions ### AI OS Design - **Query Decomposition**: How much happens in AI OS vs LLM? Where's the boundary? - **Context Assembly**: Custom build needed - how to dynamically pull goals/projects/history? - **Prompt Engineering**: Static templates vs learned/evolved prompts? - **Router Logic**: Semantic similarity vs explicit rules vs learned behavior? ### RAG Optimization - Should embeddings be query-dependent or query-independent? - How to balance temporal decay vs topical relevance? - Multi-vector retrieval: When to search different indexes? - Reranking: Lightweight model or LLM-based? ### Feedback Integration - Where to inject improvements: embedding layer, retrieval, or prompts? - How to detect distribution shift in your second brain? - A/B testing: How to run experiments without disrupting daily use? - Preference learning: Implicit only or require explicit labels? ### Scale Considerations - At what point does 5GB → 50GB → 500GB break the architecture? - How to handle embedding versioning with growing corpus? - Incremental vs full reindexing strategies? ## Current Status - **Existing Assets**: 5GB textual data in Obsidian second brain - **Current Setup**: Obsidian vector search plugin with Ollama - **Immediate Priority**: Advanced embeddings + LLM integration + feedback loop ## Success Criteria (V1) - Contextual search dramatically outperforms current static chunking - LLM responses include relevant project/goal context automatically - Visible improvement through benchmark metrics - Daily conversation habit established with tangible productivity gains - Clear feedback → improvement pipeline operational