Behind the Scenes of a Self-Evolving AI: The Architecture of Tian AI

try { if(localStorage) { let currentUser = localStorage.getItem('current_user'); if (currentUser) { currentUser = JSON.parse(currentUser); if (currentUser.id === 3844715) { document.getElementById('article-show-container').classList.add('current-user-is-article-author'); } } } } catch (e) { console.error(e); } Jeffrey.Feillp Posted on Apr 28 Behind the Scenes of a Self-Evolving AI: The Architecture of Tian AI #ai #architecture #python Behind the Scenes of a Self-Evolving AI: The Architecture of Tian AI Ever wondered what it takes to build an AI system that doesn't just process queries but actually grows, learns, and improves itself? This post takes you inside the architecture of Tian AI — an open-source local AI system that runs entirely on Android. No cloud. No APIs. Just code, a local language model, and a 34GB knowledge base. The Big Picture Tian AI's architecture is built around a simple insight: a small model + good architecture + local knowledge > large model alone. ┌──────────────────────────────────────────────┐ │ Web/Mobile UI │ ├──────────────────────────────────────────────┤ │ Flask API Layer │ ├────────────┬───────────┬─────────┬───────────┤ │ Thinker │ Memory │ Search │ Evolve │ │ Engine │ System │ Engine │ Engine │ ├────────────┴───────────┴─────────┴───────────┤ │ LLM Bridge (llama.cpp API) │ ├──────────────────────────────────────────────┤ │ Knowledge Base (34GB SQLite) │ └──────────────────────────────────────────────┘ Enter fullscreen mode Exit fullscreen mode 1. The Three-Layer Thinking Engine This is the heart of Tian AI. Instead of a single pass at every query, the system has three thinking modes: Fast Mode For simple queries — greetings, facts, straightforward questions. Single pass, no chaining. Response in ~0.04s. Architecture: Direct LLM call with minimal context Chain-of-Thought Mode For problems that benefit from step-by-step reasoning. The LLM is prompted to "think aloud" before answering. Architecture: User query → CoT prompt template "Let's break this down step by step..." Intermediate tokens guide reasoning Final answer extracted from reasoning chain Deep Mode For complex analysis, multi-perspective evaluation, and reflection. The system generates multiple viewpoints and synthesizes them. Architecture: Query → perspective analysis → 3 viewpoints Each viewpoint explored independently Cross-perspective synthesis Final answer with reflection 2. The 34GB Knowledge Base The knowledge base is the secret weapon. It's a pre-built SQLite database containing: 69,000+ concepts across 100+ domains 30 question patterns per concept (2.07 million queryable keys) Multi-language support (Chinese + English) Instant indexed retrieval (0.04-0.1s per query) How It Works # Simplified query flow def answer_query(user_input): # 1. Extract key concepts concepts = extract_concepts(user_input) # 2. Look up in knowledge base results = knowledge_base.search(concepts) # 3. If KB has answer, return directly if results.confidence > 0.8: return results.answer # 4. Otherwise, augment LLM with KB context context = format_kb_context(results) return llm.generate(augmented_prompt(user_input, context)) Enter fullscreen mode Exit fullscreen mode Why SQLite? No server needed — runs in-process Portable — single file Indexed — fast LIKE + FTS queries Updateable — add new knowledge without retraining 3. The Self-Modification System This is the most audacious feature: Tian AI can analyze, suggest improvements for, and apply patches to its own source…

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