Refactor cockpit to use DockerTmuxController pattern
Based on claude-code-tools TmuxCLIController, this refactor: - Added DockerTmuxController class for robust tmux session management - Implements send_keys() with configurable delay_enter - Implements capture_pane() for output retrieval - Implements wait_for_prompt() for pattern-based completion detection - Implements wait_for_idle() for content-hash-based idle detection - Implements wait_for_shell_prompt() for shell prompt detection Also includes workflow improvements: - Pre-task git snapshot before agent execution - Post-task commit protocol in agent guidelines Co-Authored-By: Claude Opus 4.5 <noreply@anthropic.com>
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admin
205
lib/chat_intent_parser.py
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205
lib/chat_intent_parser.py
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#!/usr/bin/env python3
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"""
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Chat Intent Parser - Determine what type of query the user is making
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"""
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import re
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from typing import Dict, Tuple
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class ChatIntentParser:
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"""Parse user queries to determine intent and scope"""
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# Patterns for different intents
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PATTERNS = {
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'kg_search': {
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'patterns': [
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r'(search|find|look for|show me).*in.*knowledge|what.*entity|find.*entity',
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r'(entity|concept|topic).*named?',
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],
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'keywords': ['entity', 'concept', 'topic', 'knowledge', 'search']
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},
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'project_info': {
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'patterns': [
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r'(project|projects).*info|tell.*project',
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r'what.*project|list.*project|show.*project',
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],
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'keywords': ['project', 'projects']
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},
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'system_status': {
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'patterns': [
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r'(system|status|health|running|services)',
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r'(disk|memory|cpu|load|uptime)',
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r'(docker|container|process)',
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],
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'keywords': ['system', 'status', 'health', 'disk', 'memory', 'running']
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},
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'architecture': {
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'patterns': [
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r'(architecture|structure|how.*work|design)',
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r'(component|module|service).*architecture',
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],
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'keywords': ['architecture', 'structure', 'design', 'component']
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},
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'help': {
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'patterns': [
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r'(help|what can|commands|available)',
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r'(how.*use|guide|tutorial)',
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],
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'keywords': ['help', 'commands', 'guide']
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}
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}
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def __init__(self):
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"""Initialize parser"""
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pass
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def parse(self, query: str) -> Dict:
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"""Parse query and determine intent"""
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query_lower = query.lower().strip()
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result = {
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'original_query': query,
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'query_lower': query_lower,
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'intent': 'general',
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'confidence': 0.0,
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'scope': 'all',
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'keywords': self._extract_keywords(query_lower),
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'suggestions': []
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}
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# Check for explicit scope flags
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if query_lower.startswith('--kg ') or ' --kg ' in query_lower:
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result['scope'] = 'kg'
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query_lower = query_lower.replace('--kg ', '').replace(' --kg ', '')
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elif query_lower.startswith('--local ') or ' --local ' in query_lower:
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result['scope'] = 'local_memory'
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query_lower = query_lower.replace('--local ', '').replace(' --local ', '')
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elif query_lower.startswith('--bash ') or ' --bash ' in query_lower:
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result['scope'] = 'bash'
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query_lower = query_lower.replace('--bash ', '').replace(' --bash ', '')
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elif query_lower.startswith('--think ') or ' --think ' in query_lower:
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result['scope'] = 'reasoning'
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query_lower = query_lower.replace('--think ', '').replace(' --think ', '')
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# Detect intent from patterns
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best_intent = 'general'
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best_score = 0.0
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for intent, config in self.PATTERNS.items():
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score = self._calculate_score(query_lower, config)
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if score > best_score:
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best_score = score
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best_intent = intent
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result['intent'] = best_intent
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result['confidence'] = min(1.0, best_score)
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# Generate suggestions
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result['suggestions'] = self._suggest_queries(best_intent, query_lower)
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return result
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def _extract_keywords(self, query: str) -> list:
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"""Extract important keywords from query"""
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# Simple keyword extraction - words longer than 4 characters
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words = re.findall(r'\b[a-z_]{4,}\b', query)
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# Remove common stop words
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stop_words = {'what', 'that', 'this', 'with', 'from', 'show', 'tell', 'give', 'find'}
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keywords = [w for w in words if w not in stop_words]
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return list(set(keywords))[:5] # Return top 5 unique keywords
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def _calculate_score(self, query: str, config: Dict) -> float:
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"""Calculate how well query matches intent"""
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score = 0.0
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# Check patterns
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for pattern in config['patterns']:
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if re.search(pattern, query, re.IGNORECASE):
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score += 0.4
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# Check keywords
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query_words = set(query.lower().split())
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matching_keywords = sum(1 for kw in config['keywords'] if kw in query_words)
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score += min(0.6, matching_keywords * 0.2)
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return score
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def _suggest_queries(self, intent: str, query: str) -> list:
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"""Suggest related queries based on intent"""
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suggestions = {
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'kg_search': [
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'List all research entities',
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'Show me recent findings',
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'What is stored in the sysadmin domain'
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],
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'project_info': [
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'List all projects',
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'Show project structure',
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'What projects are active'
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],
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'system_status': [
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'Show disk usage',
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'List running services',
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'What is the system load',
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'Show memory usage'
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],
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'architecture': [
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'Tell me about the system architecture',
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'Show me the component structure',
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'How do services communicate'
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],
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'help': [
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'What commands are available',
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'Show me examples',
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'How do I search the knowledge graph'
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]
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}
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return suggestions.get(intent, [])
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def extract_search_term(self, query: str) -> str:
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"""Extract main search term from query"""
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# Remove common prefixes/suffixes
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query = re.sub(r'^(show|find|search|list|tell|what|how)\s+', '', query, flags=re.IGNORECASE)
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query = re.sub(r'\s+(please|thanks|help|info|details)$', '', query, flags=re.IGNORECASE)
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# Extract quoted terms first
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quoted = re.findall(r'"([^"]+)"', query)
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if quoted:
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return quoted[0]
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# Otherwise return first significant phrase
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words = [w for w in query.split() if len(w) > 3]
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return words[0] if words else query.strip()
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def is_multi_turn(self, query: str) -> bool:
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"""Check if query suggests multi-turn conversation"""
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multi_turn_indicators = [
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'more', 'also', 'next', 'then', 'tell me more',
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'what else', 'continue', 'go on', 'further'
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]
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query_lower = query.lower()
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return any(indicator in query_lower for indicator in multi_turn_indicators)
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if __name__ == '__main__':
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import json
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parser = ChatIntentParser()
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test_queries = [
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'what is the system status',
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'find me entities in the KG',
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'list all projects',
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'tell me about the architecture',
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'--bash show disk usage',
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'--think analyze performance patterns'
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]
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for query in test_queries:
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result = parser.parse(query)
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print(f"Query: {query}")
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print(f"Intent: {result['intent']} (confidence: {result['confidence']:.2f})")
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print(f"Scope: {result['scope']}")
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print(f"Keywords: {result['keywords']}")
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print()
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