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>
This commit is contained in:
admin
292
lib/four_bucket_context.py
Executable file
292
lib/four_bucket_context.py
Executable file
@@ -0,0 +1,292 @@
|
||||
"""
|
||||
Four-Bucket Context Assembly - Modernized prompt construction for luzia.
|
||||
Phase 4 of Luzia modernization: Integrate hybrid retriever + semantic router into luzia CLI.
|
||||
|
||||
ENHANCED (Jan 2026): Added per-project RAG context injection.
|
||||
- Each project can have .knowledge/ directory with project-specific facts
|
||||
- Luzia injects relevant project knowledge before task dispatch
|
||||
- Falls back to global KG if no project-specific knowledge exists
|
||||
"""
|
||||
|
||||
import json
|
||||
import os
|
||||
import logging
|
||||
from typing import Dict, List, Any, Optional
|
||||
from datetime import datetime
|
||||
|
||||
logging.basicConfig(level=logging.INFO, format='%(levelname)s: %(message)s')
|
||||
logger = logging.getLogger(__name__)
|
||||
|
||||
|
||||
class FourBucketContextAssembler:
|
||||
"""Assemble 4-bucket context for luzia prompt injection."""
|
||||
|
||||
def __init__(self):
|
||||
self.hybrid_retriever = None
|
||||
self.semantic_router = None
|
||||
self.project_knowledge_loader = None
|
||||
self._initialize_components()
|
||||
|
||||
def _initialize_components(self):
|
||||
"""Lazy-load hybrid retriever, semantic router, and project knowledge loader."""
|
||||
try:
|
||||
# Import after paths are set up
|
||||
import sys
|
||||
sys.path.insert(0, os.path.dirname(__file__))
|
||||
from langchain_kg_retriever import KnowledgeGraphRetriever
|
||||
from semantic_router import SemanticRouter
|
||||
|
||||
self.hybrid_retriever = KnowledgeGraphRetriever()
|
||||
self.semantic_router = SemanticRouter()
|
||||
logger.debug("✓ Hybrid retriever and semantic router loaded")
|
||||
except Exception as e:
|
||||
logger.debug(f"Could not load new retrievers (OK for fallback): {e}")
|
||||
self.hybrid_retriever = None
|
||||
self.semantic_router = None
|
||||
|
||||
# Load project-specific knowledge loader
|
||||
try:
|
||||
from project_knowledge_loader import ProjectKnowledgeLoader
|
||||
self.project_knowledge_loader = ProjectKnowledgeLoader()
|
||||
logger.debug("✓ Project knowledge loader initialized")
|
||||
except Exception as e:
|
||||
logger.debug(f"Could not load project knowledge loader: {e}")
|
||||
self.project_knowledge_loader = None
|
||||
|
||||
def get_global_identity(self) -> str:
|
||||
"""Bucket 1: Global identity context (static)."""
|
||||
return """You are Claude, an AI assistant by Anthropic.
|
||||
You specialize in software engineering and systems administration.
|
||||
You have access to a knowledge graph of learned solutions and best practices.
|
||||
Your goal is to help users accomplish their tasks efficiently and safely."""
|
||||
|
||||
def get_project_grounding(self, project: str, user: str, cwd: str) -> str:
|
||||
"""Bucket 2: Project-specific grounding (static, highest priority)."""
|
||||
return f"""PROJECT CONTEXT (HIGHEST PRIORITY):
|
||||
- Project: {project}
|
||||
- User: {user}
|
||||
- Working Directory: {cwd}
|
||||
- Permissions: Use luzia for cross-project work
|
||||
- File Ownership: All changes must preserve ownership
|
||||
|
||||
IMPORTANT: This context is provided LAST for maximum precedence."""
|
||||
|
||||
def get_project_knowledge_context(self, project: str, query: str) -> Dict[str, Any]:
|
||||
"""Bucket 2.5: Project-specific RAG context from .knowledge/ directory."""
|
||||
|
||||
if not self.project_knowledge_loader:
|
||||
return {"source": "none", "context": "", "entities": []}
|
||||
|
||||
try:
|
||||
# Check if project has .knowledge/ directory
|
||||
if not self.project_knowledge_loader.has_knowledge(project):
|
||||
logger.debug(f"No .knowledge/ directory for {project}")
|
||||
return {"source": "none", "context": "", "entities": []}
|
||||
|
||||
# Get formatted context for prompt
|
||||
context = self.project_knowledge_loader.format_for_prompt(project, query, max_tokens=1500)
|
||||
|
||||
# Get relevant entities for metadata
|
||||
entities = self.project_knowledge_loader.search_project_knowledge(project, query, top_k=5)
|
||||
|
||||
logger.debug(f"Loaded project knowledge for {project}: {len(entities)} relevant entities")
|
||||
|
||||
return {
|
||||
"source": "project_kg",
|
||||
"context": context,
|
||||
"entities": entities,
|
||||
"timestamp": datetime.now().isoformat()
|
||||
}
|
||||
except Exception as e:
|
||||
logger.debug(f"Project knowledge retrieval failed: {e}")
|
||||
return {"source": "error", "context": "", "entities": [], "error": str(e)}
|
||||
|
||||
def get_intelligence_context(self, query: str, project: str, max_results: int = 5) -> Dict[str, Any]:
|
||||
"""Bucket 3: Dynamic intelligence from global KG retrieval."""
|
||||
|
||||
if not self.hybrid_retriever:
|
||||
return {"source": "fallback", "results": []}
|
||||
|
||||
try:
|
||||
# Build search query
|
||||
search_query = f"{project} {query}"
|
||||
|
||||
# Retrieve relevant entities
|
||||
kg_results = self.hybrid_retriever.retrieve(search_query, top_k=max_results)
|
||||
|
||||
logger.debug(f"Retrieved {len(kg_results)} KG results for '{search_query}'")
|
||||
|
||||
return {
|
||||
"source": "hybrid_retrieval",
|
||||
"timestamp": datetime.now().isoformat(),
|
||||
"results": kg_results,
|
||||
"count": len(kg_results)
|
||||
}
|
||||
except Exception as e:
|
||||
logger.debug(f"KG retrieval failed (using fallback): {e}")
|
||||
return {"source": "fallback", "results": [], "error": str(e)}
|
||||
|
||||
def get_task_context(self, query: str) -> Dict[str, Any]:
|
||||
"""Bucket 4: Dynamic task context with domain detection."""
|
||||
|
||||
task_context = {
|
||||
"original_query": query,
|
||||
"timestamp": datetime.now().isoformat()
|
||||
}
|
||||
|
||||
if self.semantic_router:
|
||||
try:
|
||||
routing = self.semantic_router.route(query)
|
||||
task_context.update({
|
||||
"detected_domain": routing["primary_domain"],
|
||||
"domain_confidence": routing["confidence"],
|
||||
"reasoning_enabled": routing["reasoning_enabled"],
|
||||
"system_instructions": routing["system_instructions"]
|
||||
})
|
||||
logger.debug(f"Detected domain: {routing['primary_domain']} ({routing['confidence']:.2f})")
|
||||
except Exception as e:
|
||||
logger.debug(f"Domain detection failed: {e}")
|
||||
|
||||
return task_context
|
||||
|
||||
def assemble_prompt_context(self, query: str, project: str, user: str, cwd: str) -> str:
|
||||
"""
|
||||
Assemble complete 5-bucket context for prompt injection.
|
||||
|
||||
Order (IMPORTANT - recency bias means LAST items have highest precedence):
|
||||
1. Bucket 1: Identity (global)
|
||||
2. Bucket 3: Global Intelligence (learned solutions from /etc/luz-knowledge/)
|
||||
3. Bucket 2.5: Project Knowledge (from .knowledge/ directory - NEW)
|
||||
4. Bucket 4: Task (domain-specific)
|
||||
5. Bucket 2: Grounding (project, placed LAST for precedence)
|
||||
"""
|
||||
|
||||
buckets = []
|
||||
|
||||
# Bucket 1: Identity
|
||||
buckets.append("## SYSTEM CONTEXT\n" + self.get_global_identity())
|
||||
|
||||
# Bucket 3: Global Intelligence
|
||||
intelligence = self.get_intelligence_context(query, project)
|
||||
if intelligence.get("results"):
|
||||
intel_text = "## LEARNED KNOWLEDGE\nRelevant solutions from global knowledge graph:\n"
|
||||
for result in intelligence["results"]:
|
||||
intel_text += f"\n- {result.get('name', 'Unknown')}"
|
||||
if result.get('content'):
|
||||
content_preview = result['content'][:100]
|
||||
intel_text += f": {content_preview}..."
|
||||
buckets.append(intel_text)
|
||||
|
||||
# Bucket 2.5: Project-Specific Knowledge (NEW - RAG from .knowledge/)
|
||||
project_knowledge = self.get_project_knowledge_context(project, query)
|
||||
if project_knowledge.get("context"):
|
||||
buckets.append("## PROJECT-SPECIFIC KNOWLEDGE\n" + project_knowledge["context"])
|
||||
logger.debug(f"Injected project knowledge ({len(project_knowledge.get('entities', []))} entities)")
|
||||
|
||||
# Bucket 4: Task
|
||||
task_info = self.get_task_context(query)
|
||||
task_text = f"## TASK CONTEXT\nDetected Domain: {task_info.get('detected_domain', 'general')}\n"
|
||||
if task_info.get("system_instructions"):
|
||||
task_text += f"\n{task_info['system_instructions']}\n"
|
||||
buckets.append(task_text)
|
||||
|
||||
# Bucket 2: Grounding (LAST for precedence)
|
||||
buckets.append("## PROJECT GROUNDING (HIGHEST PRIORITY)\n" +
|
||||
self.get_project_grounding(project, user, cwd))
|
||||
|
||||
return "\n\n".join(buckets)
|
||||
|
||||
|
||||
class ContextCache:
|
||||
"""Cache assembled context for efficiency."""
|
||||
|
||||
def __init__(self, cache_dir: str = "/tmp/luzia_context_cache"):
|
||||
self.cache_dir = cache_dir
|
||||
os.makedirs(cache_dir, exist_ok=True)
|
||||
|
||||
def get_cache_key(self, query: str, project: str) -> str:
|
||||
"""Generate cache key for context."""
|
||||
import hashlib
|
||||
key = f"{project}:{query}"
|
||||
return hashlib.md5(key.encode()).hexdigest()[:16]
|
||||
|
||||
def get(self, query: str, project: str) -> Optional[str]:
|
||||
"""Retrieve cached context if available."""
|
||||
try:
|
||||
cache_file = os.path.join(self.cache_dir, self.get_cache_key(query, project))
|
||||
if os.path.exists(cache_file):
|
||||
with open(cache_file, 'r') as f:
|
||||
data = json.load(f)
|
||||
if (datetime.now().timestamp() - data['timestamp']) < 3600: # 1 hour TTL
|
||||
logger.debug("Using cached context")
|
||||
return data['context']
|
||||
except Exception as e:
|
||||
logger.debug(f"Cache read failed: {e}")
|
||||
return None
|
||||
|
||||
def set(self, query: str, project: str, context: str):
|
||||
"""Cache assembled context."""
|
||||
try:
|
||||
cache_file = os.path.join(self.cache_dir, self.get_cache_key(query, project))
|
||||
data = {
|
||||
'timestamp': datetime.now().timestamp(),
|
||||
'context': context
|
||||
}
|
||||
with open(cache_file, 'w') as f:
|
||||
json.dump(data, f)
|
||||
except Exception as e:
|
||||
logger.debug(f"Cache write failed: {e}")
|
||||
|
||||
|
||||
# Factory function for luzia integration
|
||||
def create_context_assembler() -> FourBucketContextAssembler:
|
||||
"""Factory function to create and configure assembler."""
|
||||
return FourBucketContextAssembler()
|
||||
|
||||
|
||||
def assemble_prompt_context(query: str, project: str, user: str, cwd: str) -> str:
|
||||
"""
|
||||
High-level API for luzia to use.
|
||||
|
||||
Usage in luzia CLI:
|
||||
from four_bucket_context import assemble_prompt_context
|
||||
context = assemble_prompt_context(task_query, project_name, user, cwd)
|
||||
"""
|
||||
assembler = create_context_assembler()
|
||||
return assembler.assemble_prompt_context(query, project, user, cwd)
|
||||
|
||||
|
||||
# Testing
|
||||
if __name__ == "__main__":
|
||||
logger.info("=" * 60)
|
||||
logger.info("PHASE 4+: Five-Bucket Context Assembly (with Project RAG)")
|
||||
logger.info("=" * 60)
|
||||
|
||||
# Test context assembly
|
||||
test_query = "Create a REST API for user authentication with database"
|
||||
test_project = "musica"
|
||||
test_user = "admin"
|
||||
test_cwd = "/home/musica"
|
||||
|
||||
logger.info(f"\nAssembling context for: {test_query}")
|
||||
|
||||
assembler = create_context_assembler()
|
||||
|
||||
# Show project knowledge status
|
||||
if assembler.project_knowledge_loader:
|
||||
logger.info("\nProject Knowledge Status:")
|
||||
projects_status = assembler.project_knowledge_loader.list_projects_with_knowledge()
|
||||
for p in projects_status[:5]: # Show first 5
|
||||
status = "Has KG" if p["has_knowledge"] else "No KG"
|
||||
logger.info(f" {p['project']}: {status}")
|
||||
|
||||
context = assembler.assemble_prompt_context(test_query, test_project, test_user, test_cwd)
|
||||
|
||||
logger.info("\nGenerated 5-Bucket Context:")
|
||||
logger.info("-" * 60)
|
||||
print(context[:2000])
|
||||
if len(context) > 2000:
|
||||
print(f"\n... ({len(context) - 2000} more characters)")
|
||||
logger.info("-" * 60)
|
||||
|
||||
logger.info("\n✅ PHASE 4+ COMPLETE: Ready for luzia integration with project RAG")
|
||||
Reference in New Issue
Block a user