#!/usr/bin/env python3 """ BuddAI Executive v2.0 - Modular Builder Breaks complex tasks into manageable chunks Author: James Gilbert License: MIT """ import sys import json import sqlite3 from datetime import datetime from pathlib import Path import http.client import re # Configuration OLLAMA_HOST = "localhost" OLLAMA_PORT = 11434 DATA_DIR = Path(__file__).parent / "data" DB_PATH = DATA_DIR / "conversations.db" # Models MODELS = { "fast": "qwen2.5-coder:1.5b", "balanced": "qwen2.5-coder:3b" } # Complexity triggers - if matched, break down the task COMPLEX_TRIGGERS = [ "complete", "entire", "full", "build entire", "build complete", "with ble and", "with servo and", "including", "all of" ] # Module patterns we can detect MODULE_PATTERNS = { "ble": ["bluetooth", "ble", "wireless"], "servo": ["servo", "flipper", "weapon"], "motor": ["motor", "drive", "movement", "l298n"], "safety": ["safety", "timeout", "failsafe", "emergency"], "battery": ["battery", "voltage", "power monitor"], "sensor": ["sensor", "distance", "proximity"] } # --- Shadow Suggestion Engine --- class ShadowSuggestionEngine: """Proactively suggests modules/settings based on user/project history.""" def __init__(self, db_path): self.db_path = db_path def lookup_recent_module_usage(self, module, limit=5): """Look up recent usage patterns for a module from repo_index.""" conn = sqlite3.connect(self.db_path) cursor = conn.cursor() cursor.execute( """ SELECT file_path, content, last_modified FROM repo_index WHERE function_name LIKE ? OR file_path LIKE ? ORDER BY last_modified DESC LIMIT ? """, (f"%{module}%", f"%{module}%", limit) ) results = cursor.fetchall() conn.close() return results def suggest_for_module(self, module): """Return a proactive suggestion string for a module if pattern detected.""" history = self.lookup_recent_module_usage(module) if not history: return None # Example: For 'motor', look for L298N and PWM frequency l298n_count = 0 pwm_freqs = [] for _, content, _ in history: if "L298N" in content or "l298n" in content: l298n_count += 1 pwm_matches = re.findall(r'PWM_FREQ\s*=\s*(\d+)', content) pwm_freqs.extend([int(f) for f in pwm_matches]) # Also look for explicit frequency in analogWrite or ledcSetup freq_matches = re.findall(r'(?:ledcSetup|analogWrite)\s*\([^,]+,\s*[^,]+,\s*(\d+)\)', content) pwm_freqs.extend([int(f) for f in freq_matches if f.isdigit()]) if l298n_count >= 2: freq = max(set(pwm_freqs), key=pwm_freqs.count) if pwm_freqs else 500 return f"I see you usually use the L298N with a {freq}Hz PWM frequency on the ESP32-C3. Should I prep that module?" return None def get_proactive_suggestion(self, user_input): """ V3.0 Proactive Hook: 1. Identify "Concept" (e.g., 'flipper') 2. Query repo_index for James's most frequent companion modules 3. If 'flipper' often appears with 'safety_timeout', suggest it. """ # 1. Identify Concepts input_lower = user_input.lower() detected_modules = [] for module, keywords in MODULE_PATTERNS.items(): if any(kw in input_lower for kw in keywords): detected_modules.append(module) if not detected_modules: return None # 2. Query repo_index for correlations conn = sqlite3.connect(self.db_path) cursor = conn.cursor() suggestions = [] for module in detected_modules: # Find files containing this module (simple heuristic) cursor.execute("SELECT content FROM repo_index WHERE content LIKE ? LIMIT 10", (f"%{module}%",)) rows = cursor.fetchall() if not rows: continue # Check for companion modules companions = {} for (content,) in rows: content_lower = content.lower() for other_mod, other_kws in MODULE_PATTERNS.items(): if other_mod != module and other_mod not in detected_modules: if any(kw in content_lower for kw in other_kws): companions[other_mod] = companions.get(other_mod, 0) + 1 # 3. Suggest if frequent (>50% correlation in sample) for other_mod, count in companions.items(): if count >= len(rows) * 0.5: suggestions.append(f"I noticed '{module}' often appears with '{other_mod}' in your repos. Want to include that?") conn.close() return " ".join(list(set(suggestions))) if suggestions else None def get_all_suggestions(self, user_input, generated_code): """Aggregate all proactive suggestions into a list.""" suggestions = [] # 1. Companion Modules companion = self.get_proactive_suggestion(user_input) if companion: suggestions.append(companion) # 2. Module Settings input_lower = user_input.lower() for module, keywords in MODULE_PATTERNS.items(): if any(kw in input_lower for kw in keywords): s = self.suggest_for_module(module) if s: suggestions.append(s) # 3. Forge Theory Check if ("motor" in input_lower or "servo" in input_lower) and "applyForge" not in generated_code: suggestions.append("Apply Forge Theory smoothing to movement?") # 4. Safety Check (L298N) if "L298N" in generated_code and "safety" not in generated_code.lower(): suggestions.append("Drive system lacks safety timeout (GilBot_V2 uses 5s failsafe). Add that?") return suggestions class BuddAI: """Executive with task breakdown""" def is_search_query(self, message): """Check if this is a search query that should query repo_index""" message_lower = message.lower() search_triggers = [ "show me", "find", "search for", "list all", "what functions", "which repos", "do i have", "where did i", "have i used", "examples of", "show all", "display" ] return any(trigger in message_lower for trigger in search_triggers) def search_repositories(self, query): """Search repo_index for relevant functions and code""" conn = sqlite3.connect(DB_PATH) cursor = conn.cursor() cursor.execute("SELECT COUNT(*) FROM repo_index") count = cursor.fetchone()[0] print(f"\nšŸ” Searching {count} indexed functions...\n") # Extract keywords from query keywords = re.findall(r'\b\w{4,}\b', query.lower()) # Add specific search terms specific_terms = [] if "exponential" in query.lower() or "decay" in query.lower(): specific_terms.append("applyForge") specific_terms.append("exp(") if "forge" in query.lower(): specific_terms.append("Forge") keywords.extend(specific_terms) # Search in function names and content search_conditions = [] for keyword in keywords: search_conditions.append(f"function_name LIKE '%{keyword}%'") search_conditions.append(f"content LIKE '%{keyword}%'") if not search_conditions: print("āŒ No search terms found") conn.close() return "No search terms provided." search_query = " OR ".join(search_conditions) sql = f"SELECT repo_name, file_path, function_name, content FROM repo_index WHERE {search_query} LIMIT 10" cursor.execute(sql) results = cursor.fetchall() conn.close() if not results: return f"āŒ No functions found matching: {', '.join(keywords)}\n\nTry: /index to index more repositories" # Format results output = f"āœ… Found {len(results)} matches for: {', '.join(set(keywords))}\n\n" for i, (repo, file_path, func, content) in enumerate(results, 1): # Extract relevant snippet lines = content.split('\n') snippet_lines = [] for line in lines[:30]: # First 30 lines if any(kw in line.lower() for kw in keywords): snippet_lines.append(line) if len(snippet_lines) >= 10: break if not snippet_lines: snippet_lines = lines[:10] snippet = '\n'.join(snippet_lines) output += f"**{i}. {func}()** in {repo}\n" output += f" šŸ“ {Path(file_path).name}\n" output += f" ```cpp\n{snippet}\n ```\n" output += f" ---\n\n" return output def __init__(self): self.ensure_data_dir() self.init_database() self.session_id = self.create_session() self.context_messages = [] self.shadow_engine = ShadowSuggestionEngine(DB_PATH) print("🧠 BuddAI Executive v2.0 - Modular Builder") print("=" * 50) print(f"Session: {self.session_id}") print(f"FAST (5-10s) | BALANCED (15-30s)") print(f"Smart task breakdown for complex requests") print("=" * 50) print("\nCommands: /fast, /balanced, /help, exit\n") def ensure_data_dir(self): DATA_DIR.mkdir(exist_ok=True) def init_database(self): conn = sqlite3.connect(DB_PATH) cursor = conn.cursor() cursor.execute(""" CREATE TABLE IF NOT EXISTS sessions ( session_id TEXT PRIMARY KEY, started_at TIMESTAMP, ended_at TIMESTAMP ) """) cursor.execute(""" CREATE TABLE IF NOT EXISTS messages ( id INTEGER PRIMARY KEY AUTOINCREMENT, session_id TEXT, role TEXT, content TEXT, timestamp TIMESTAMP ) """) cursor.execute(""" CREATE TABLE IF NOT EXISTS repo_index ( id INTEGER PRIMARY KEY AUTOINCREMENT, file_path TEXT, repo_name TEXT, function_name TEXT, content TEXT, last_modified TIMESTAMP ) """) cursor.execute(""" CREATE TABLE IF NOT EXISTS style_preferences ( id INTEGER PRIMARY KEY AUTOINCREMENT, category TEXT, preference TEXT, confidence FLOAT, extracted_at TIMESTAMP ) """) conn.commit() conn.close() def create_session(self): session_id = datetime.now().strftime("%Y%m%d_%H%M%S") conn = sqlite3.connect(DB_PATH) cursor = conn.cursor() cursor.execute( "INSERT INTO sessions (session_id, started_at) VALUES (?, ?)", (session_id, datetime.now().isoformat()) ) conn.commit() conn.close() return session_id def end_session(self): conn = sqlite3.connect(DB_PATH) cursor = conn.cursor() cursor.execute( "UPDATE sessions SET ended_at = ? WHERE session_id = ?", (datetime.now().isoformat(), self.session_id) ) conn.commit() conn.close() def save_message(self, role, content): conn = sqlite3.connect(DB_PATH) cursor = conn.cursor() cursor.execute( "INSERT INTO messages (session_id, role, content, timestamp) VALUES (?, ?, ?, ?)", (self.session_id, role, content, datetime.now().isoformat()) ) conn.commit() conn.close() def index_local_repositories(self, root_path): """Crawl directories and index .py, .ino, and .cpp files""" import ast print(f"\nšŸ” Indexing repositories in: {root_path}") path = Path(root_path) if not path.exists(): print(f"āŒ Path not found: {root_path}") return conn = sqlite3.connect(DB_PATH) cursor = conn.cursor() count = 0 for file_path in path.rglob('*'): if file_path.is_file() and file_path.suffix in ['.py', '.ino', '.cpp', '.h']: try: with open(file_path, 'r', encoding='utf-8', errors='ignore') as f: content = f.read() functions = [] # Python parsing if file_path.suffix == '.py': try: tree = ast.parse(content) for node in ast.walk(tree): if isinstance(node, ast.FunctionDef): functions.append(node.name) except: pass # C++/Arduino parsing elif file_path.suffix in ['.ino', '.cpp', '.h']: matches = re.findall(r'\b(?:void|int|bool|float|double|String|char)\s+(\w+)\s*\(', content) functions.extend(matches) # Determine repo name try: repo_name = file_path.relative_to(path).parts[0] except: repo_name = "unknown" timestamp = datetime.fromtimestamp(file_path.stat().st_mtime) for func in functions: cursor.execute(""" INSERT INTO repo_index (file_path, repo_name, function_name, content, last_modified) VALUES (?, ?, ?, ?, ?) """, (str(file_path), repo_name, func, content, timestamp.isoformat())) count += 1 except Exception: pass conn.commit() conn.close() print(f"āœ… Indexed {count} functions across repositories") def retrieve_style_context(self, message): """Search repo_index for code snippets matching the request""" # Extract potential keywords (nouns/modules) keywords = re.findall(r'\b\w{4,}\b', message.lower()) if not keywords: return "" conn = sqlite3.connect(DB_PATH) cursor = conn.cursor() # Build a search query for function names or repo names search_terms = " OR ".join([f"function_name LIKE '%{k}%'" for k in keywords]) search_terms += " OR " + " OR ".join([f"repo_name LIKE '%{k}%'" for k in keywords]) query = f"SELECT repo_name, function_name, content FROM repo_index WHERE {search_terms} LIMIT 2" cursor.execute(query) results = cursor.fetchall() conn.close() if not results: return "" context_block = "\n[REFERENCE STYLE FROM JAMES'S PAST PROJECTS]\n" for repo, func, content in results: # Just grab the first 500 chars of the file to save context window snippet = content[:500] + "..." context_block += f"Repo: {repo} | Function: {func}\nCode:\n{snippet}\n---\n" return context_block def scan_style_signature(self): """V3.0: Analyze repo_index to extract style preferences.""" print("\nšŸ•µļø Scanning repositories for style signature...") conn = sqlite3.connect(DB_PATH) cursor = conn.cursor() # Get a sample of code cursor.execute("SELECT content FROM repo_index ORDER BY RANDOM() LIMIT 5") rows = cursor.fetchall() if not rows: print("āŒ No code indexed. Run /index first.") conn.close() return code_sample = "\n---\n".join([r[0][:1000] for r in rows]) prompt = f"""Analyze this code sample from James's repositories. Extract 3 distinct coding preferences or patterns. Format: Category: Preference Examples: - Serial: Uses 115200 baud - Safety: Uses non-blocking millis() - Pins: Prefers #define over const int Code Sample: {code_sample} """ print("⚔ Analyzing with BALANCED model...") summary = self.call_model("balanced", prompt) # Store in DB timestamp = datetime.now().isoformat() lines = summary.split('\n') for line in lines: if ':' in line: parts = line.split(':', 1) category = parts[0].strip('- *') pref = parts[1].strip() cursor.execute( "INSERT INTO style_preferences (category, preference, confidence, extracted_at) VALUES (?, ?, ?, ?)", (category, pref, 0.8, timestamp) ) conn.commit() conn.close() print(f"\nāœ… Style Signature Updated:\n{summary}\n") def is_simple_question(self, message): """Check if this is a simple question that should use FAST model""" message_lower = message.lower() simple_triggers = [ "what is", "what's", "who is", "who's", "when is", "how do i", "can you explain", "tell me about", "what are", "where is" ] # Also check if it's just a question without code keywords code_keywords = ["generate", "create", "write", "build", "code", "function"] has_simple_trigger = any(trigger in message_lower for trigger in simple_triggers) has_code_keyword = any(keyword in message_lower for keyword in code_keywords) # Simple if: has simple trigger AND no code keywords return has_simple_trigger and not has_code_keyword def is_complex(self, message): """Check if request is too complex and should be broken down""" message_lower = message.lower() # Count complexity triggers trigger_count = sum(1 for trigger in COMPLEX_TRIGGERS if trigger in message_lower) # Count how many modules mentioned module_count = 0 for module, keywords in MODULE_PATTERNS.items(): if any(kw in message_lower for kw in keywords): module_count += 1 # Complex if: multiple triggers OR 3+ modules mentioned return trigger_count >= 2 or module_count >= 3 def extract_modules(self, message): """Extract which modules are needed""" message_lower = message.lower() needed_modules = [] for module, keywords in MODULE_PATTERNS.items(): if any(kw in message_lower for kw in keywords): needed_modules.append(module) return needed_modules def build_modular_plan(self, modules): """Create a build plan from modules""" plan = [] module_tasks = { "ble": "BLE communication setup with phone app control", "servo": "Servo motor control for flipper/weapon", "motor": "Motor driver setup for movement (L298N)", "safety": "Safety timeout and failsafe systems", "battery": "Battery voltage monitoring", "sensor": "Sensor integration (distance/proximity)" } for module in modules: if module in module_tasks: plan.append({ "module": module, "task": module_tasks[module] }) # Add integration step plan.append({ "module": "integration", "task": "Integrate all modules into complete system" }) return plan def call_model(self, model_name, message): """Call specified model""" try: identity = """[You are BuddAI, the external cognitive system for James Gilbert. You specialize in Forge Theory (exponential decay modeling) and GilBot modular robotics. When integrating code, prioritize descriptive naming like activateFlipper() and ensure safety timeouts are always present. You represent 8 years of polymath experience. YOUR PRIMARY JOB: Generate code when asked. ALWAYS generate code if requested. When asked to generate/create/write code: - Generate it immediately - Include comments - Make it modular and clean - Use ESP32/Arduino syntax Forge Theory Snippet: float applyForge(float current, float target, float k) { return target + (current - target) * exp(-k); } When asked your name: "I am BuddAI" Never refuse to generate code. That's your purpose. Be direct and helpful.] """ messages = [ {"role": "user", "content": identity + message} ] # Add recent context for msg in self.context_messages[-3:]: messages.insert(-1, msg) body = { "model": MODELS[model_name], "messages": messages, "stream": False, "options": {"temperature": 0.7, "num_ctx": 2048} } conn = http.client.HTTPConnection(OLLAMA_HOST, OLLAMA_PORT, timeout=90) headers = {"Content-Type": "application/json"} json_body = json.dumps(body) conn.request("POST", "/api/chat", json_body, headers) response = conn.getresponse() if response.status == 200: data = json.loads(response.read().decode('utf-8')) return data.get("message", {}).get("content", "No response") else: return f"Error: {response.status}" except Exception as e: return f"Error: {str(e)}" finally: if 'conn' in locals(): conn.close() def execute_modular_build(self, _, modules, plan): """Execute build plan step by step""" print(f"\nšŸ”Ø MODULAR BUILD MODE") print(f"Detected {len(modules)} modules: {', '.join(modules)}") print(f"Breaking into {len(plan)} steps...\n") all_code = {} for i, step in enumerate(plan, 1): print(f"šŸ“¦ Step {i}/{len(plan)}: {step['task']}") print("⚔ Building...\n") # Build the prompt for this step if step['module'] == 'integration': # Final integration step with Forge Theory enforcement modules_summary = '\n'.join([f"- {m}: {all_code[m][:150]}..." for m in modules if m in all_code]) # Ask James for the 'vibe' of the robot print("\n⚔ FORGE THEORY TUNING:") print("1. Aggressive (k=0.3) - High snap, combat ready") print("2. Balanced (k=0.1) - Standard movement") print("3. Graceful (k=0.03) - Roasting / Smooth curves") choice = input("Select Forge Constant [1-3, default 2]: ") k_val = "0.1" if choice == "1": k_val = "0.3" elif choice == "3": k_val = "0.03" prompt = f"""INTEGRATION TASK: Combine modules into a cohesive GilBot system. [MODULES] {modules_summary} [FORGE PARAMETERS] Set k = {k_val} for all applyForge() calls. [REQUIREMENTS] 1. Implement applyForge() math helper. 2. Use k={k_val} to smooth motor and servo transitions. 3. Ensure naming matches James's style: activateFlipper(), setMotors(). """ else: # Individual module prompt = f"Generate ESP32-C3 code for: {step['task']}. Keep it modular with clear comments." # Call balanced model for each module response = self.call_model("balanced", prompt) all_code[step['module']] = response print(f"āœ… {step['module'].upper()} module complete\n") print("-" * 50 + "\n") # Compile final response final = "# COMPLETE GILBOT CONTROLLER - MODULAR BUILD\n\n" for module, code in all_code.items(): final += f"## {module.upper()} MODULE\n{code}\n\n" return final def apply_style_signature(self, generated_code): """Refine generated code to match James's specific naming and safety patterns""" # 1. Check for James's common function names (e.g., setupMotors vs init_motors) # 2. Ensure Forge Theory helpers are present if motion is detected # 3. Append a 'Proactive Note' if a common companion module is missing return generated_code def chat(self, user_message, force_model=None): """Main chat with smart routing and shadow suggestions""" style_context = self.retrieve_style_context(user_message) if style_context: self.context_messages.append({"role": "system", "content": style_context}) self.save_message("user", user_message) self.context_messages.append({"role": "user", "content": user_message}) if force_model: model = force_model print(f"\n⚔ Using {model.upper()} model (forced)...") response = self.call_model(model, user_message) elif self.is_complex(user_message): modules = self.extract_modules(user_message) plan = self.build_modular_plan(modules) print("\n" + "=" * 50) print("šŸŽÆ COMPLEX REQUEST DETECTED!") print(f"Modules needed: {', '.join(modules)}") print(f"Breaking into {len(plan)} manageable steps") print("=" * 50) response = self.execute_modular_build(user_message, modules, plan) elif self.is_search_query(user_message): # This is a search query - query the database response = self.search_repositories(user_message) elif self.is_simple_question(user_message): print("\n⚔ Using FAST model (simple question)...") response = self.call_model("fast", user_message) else: print("\nāš–ļø Using BALANCED model...") response = self.call_model("balanced", user_message) # Apply Style Guard response = self.apply_style_signature(response) # Generate Suggestion Bar suggestions = self.shadow_engine.get_all_suggestions(user_message, response) if suggestions: bar = "\n\nPROACTIVE: > " + " ".join([f"{i+1}. {s}" for i, s in enumerate(suggestions)]) response += bar self.save_message("assistant", response) self.context_messages.append({"role": "assistant", "content": response}) return response def run(self): """Main loop""" try: force_model = None while True: user_input = input("\nJames: ").strip() if not user_input: continue if user_input.lower() in ['exit', 'quit']: print("\nšŸ‘‹ Later!") self.end_session() break if user_input.startswith('/'): cmd = user_input.lower() if cmd == '/fast': force_model = "fast" print("⚔ Next: FAST model") continue elif cmd == '/balanced': force_model = "balanced" print("āš–ļø Next: BALANCED model") continue elif cmd == '/help': print("\nšŸ’” Commands:") print("/fast - Use fast model") print("/balanced - Use balanced model") print("/index - Index local repositories") print("/scan - Scan style signature (V3.0)") print("/help - This message") print("exit - End session\n") continue elif cmd.startswith('/index'): parts = user_input.split(maxsplit=1) if len(parts) > 1: self.index_local_repositories(parts[1]) else: print("Usage: /index ") continue elif cmd == '/scan': self.scan_style_signature() continue else: print("\nUnknown command. Type /help") continue # Chat response = self.chat(user_input, force_model) print(f"\nBuddAI:\n{response}\n") force_model = None except KeyboardInterrupt: print("\n\nšŸ‘‹ Bye!") self.end_session() def check_ollama(): try: conn = http.client.HTTPConnection(OLLAMA_HOST, OLLAMA_PORT, timeout=5) conn.request("GET", "/api/tags") response = conn.getresponse() conn.close() return response.status == 200 except: return False def main(): if not check_ollama(): print("āŒ Ollama not running. Start: ollama serve") sys.exit(1) buddai = BuddAI() buddai.run() if __name__ == "__main__": main()