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# ────────────────────────────── memo/plan/execution.py ──────────────────────────────
"""
Execution Engine
Handles memory retrieval execution based on planned strategies.
"""
from typing import List, Dict, Any, Tuple, Optional
import os
from utils.logger import get_logger
from utils.rag.embeddings import EmbeddingClient
from memo.plan.intent import QueryIntent
from memo.plan.strategy import MemoryStrategy
logger = get_logger("EXECUTION_ENGINE", __name__)
class ExecutionEngine:
"""Handles memory retrieval execution based on planned strategies"""
def __init__(self, memory_system, embedder: EmbeddingClient):
self.memory_system = memory_system
self.embedder = embedder
async def execute_memory_plan(self, user_id: str, question: str, execution_plan: Dict[str, Any],
nvidia_rotator=None, project_id: Optional[str] = None) -> Tuple[str, str, Dict[str, Any]]:
"""
Execute the planned memory retrieval strategy.
Returns:
Tuple of (recent_context, semantic_context, metadata)
"""
try:
params = execution_plan["retrieval_params"]
strategy = execution_plan["strategy"]
intent = execution_plan["intent"]
# Execute based on strategy
if strategy == MemoryStrategy.FOCUSED_QA:
return await self._execute_focused_qa_retrieval(
user_id, question, params, nvidia_rotator, project_id
)
elif strategy == MemoryStrategy.RECENT_FOCUS:
return await self._execute_recent_focus_retrieval(
user_id, question, params, nvidia_rotator, project_id
)
elif strategy == MemoryStrategy.BROAD_CONTEXT:
return await self._execute_broad_context_retrieval(
user_id, question, params, nvidia_rotator, project_id
)
elif strategy == MemoryStrategy.SEMANTIC_DEEP:
return await self._execute_semantic_deep_retrieval(
user_id, question, params, nvidia_rotator, project_id
)
else: # MIXED_APPROACH
return await self._execute_mixed_approach_retrieval(
user_id, question, params, nvidia_rotator, project_id
)
except Exception as e:
logger.error(f"[EXECUTION_ENGINE] Plan execution failed: {e}")
return "", "", {"error": str(e)}
async def _execute_focused_qa_retrieval(self, user_id: str, question: str, params: Dict[str, Any],
nvidia_rotator, project_id: Optional[str]) -> Tuple[str, str, Dict[str, Any]]:
"""Execute focused Q&A retrieval for enhancement requests"""
try:
recent_context = ""
semantic_context = ""
metadata = {"strategy": "focused_qa", "qa_focus": True}
if self.memory_system.is_enhanced_available():
# Get Q&A focused memories
qa_memories = self.memory_system.enhanced_memory.get_memories(
user_id, memory_type="conversation", limit=params["recent_limit"]
)
if qa_memories:
# Use AI to select most relevant Q&A pairs for enhancement
if params["use_ai_selection"] and nvidia_rotator:
recent_context = await self._ai_select_qa_memories(
question, qa_memories, nvidia_rotator, "recent", user_id
)
else:
recent_context = await self._semantic_select_qa_memories(
question, qa_memories, params["similarity_threshold"]
)
# Get additional semantic Q&A context
all_memories = self.memory_system.enhanced_memory.get_memories(
user_id, limit=params["semantic_limit"]
)
if all_memories:
if params["use_ai_selection"] and nvidia_rotator:
semantic_context = await self._ai_select_qa_memories(
question, all_memories, nvidia_rotator, "semantic", user_id
)
else:
semantic_context = await self._semantic_select_qa_memories(
question, all_memories, params["similarity_threshold"]
)
else:
# Legacy fallback
recent_memories = self.memory_system.recent(user_id, params["recent_limit"])
rest_memories = self.memory_system.rest(user_id, params["recent_limit"])
if recent_memories:
recent_context = await self._semantic_select_qa_memories(
question, [{"content": m} for m in recent_memories], params["similarity_threshold"]
)
if rest_memories:
semantic_context = await self._semantic_select_qa_memories(
question, [{"content": m} for m in rest_memories], params["similarity_threshold"]
)
metadata["enhancement_focus"] = True
metadata["qa_memories_found"] = len(recent_context) > 0 or len(semantic_context) > 0
return recent_context, semantic_context, metadata
except Exception as e:
logger.error(f"[EXECUTION_ENGINE] Focused Q&A retrieval failed: {e}")
return "", "", {"error": str(e)}
async def _execute_recent_focus_retrieval(self, user_id: str, question: str, params: Dict[str, Any],
nvidia_rotator, project_id: Optional[str]) -> Tuple[str, str, Dict[str, Any]]:
"""Execute recent focus retrieval for clarification requests"""
try:
recent_context = ""
semantic_context = ""
metadata = {"strategy": "recent_focus"}
if self.memory_system.is_enhanced_available():
recent_memories = self.memory_system.enhanced_memory.get_memories(
user_id, memory_type="conversation", limit=params["recent_limit"]
)
if recent_memories:
recent_context = "\n\n".join([m["content"] for m in recent_memories])
# Get some semantic context
all_memories = self.memory_system.enhanced_memory.get_memories(
user_id, limit=params["semantic_limit"]
)
if all_memories:
semantic_context = await self._semantic_select_qa_memories(
question, all_memories, params["similarity_threshold"]
)
else:
# Legacy fallback
recent_memories = self.memory_system.recent(user_id, params["recent_limit"])
rest_memories = self.memory_system.rest(user_id, params["recent_limit"])
recent_context = "\n\n".join(recent_memories)
if rest_memories:
semantic_context = await self._semantic_select_qa_memories(
question, [{"content": m} for m in rest_memories], params["similarity_threshold"]
)
return recent_context, semantic_context, metadata
except Exception as e:
logger.error(f"[EXECUTION_ENGINE] Recent focus retrieval failed: {e}")
return "", "", {"error": str(e)}
async def _execute_broad_context_retrieval(self, user_id: str, question: str, params: Dict[str, Any],
nvidia_rotator, project_id: Optional[str]) -> Tuple[str, str, Dict[str, Any]]:
"""Execute broad context retrieval for comparison requests"""
try:
recent_context = ""
semantic_context = ""
metadata = {"strategy": "broad_context"}
if self.memory_system.is_enhanced_available():
# Get recent context
recent_memories = self.memory_system.enhanced_memory.get_memories(
user_id, memory_type="conversation", limit=params["recent_limit"]
)
if recent_memories:
recent_context = "\n\n".join([m["content"] for m in recent_memories])
# Get broad semantic context
all_memories = self.memory_system.enhanced_memory.get_memories(
user_id, limit=params["semantic_limit"]
)
if all_memories:
semantic_context = await self._semantic_select_qa_memories(
question, all_memories, params["similarity_threshold"]
)
else:
# Legacy fallback
recent_memories = self.memory_system.recent(user_id, params["recent_limit"])
rest_memories = self.memory_system.rest(user_id, params["recent_limit"])
recent_context = "\n\n".join(recent_memories)
semantic_context = "\n\n".join(rest_memories)
return recent_context, semantic_context, metadata
except Exception as e:
logger.error(f"[EXECUTION_ENGINE] Broad context retrieval failed: {e}")
return "", "", {"error": str(e)}
async def _execute_semantic_deep_retrieval(self, user_id: str, question: str, params: Dict[str, Any],
nvidia_rotator, project_id: Optional[str]) -> Tuple[str, str, Dict[str, Any]]:
"""Execute semantic deep retrieval for new topics"""
try:
recent_context = ""
semantic_context = ""
metadata = {"strategy": "semantic_deep"}
if self.memory_system.is_enhanced_available():
# Get all memories for deep semantic search
all_memories = self.memory_system.enhanced_memory.get_memories(
user_id, limit=params["semantic_limit"]
)
if all_memories:
if params["use_ai_selection"] and nvidia_rotator:
semantic_context = await self._ai_select_qa_memories(
question, all_memories, nvidia_rotator, "semantic", user_id
)
else:
semantic_context = await self._semantic_select_qa_memories(
question, all_memories, params["similarity_threshold"]
)
# Get some recent context
recent_memories = self.memory_system.enhanced_memory.get_memories(
user_id, memory_type="conversation", limit=params["recent_limit"]
)
if recent_memories:
recent_context = "\n\n".join([m["content"] for m in recent_memories])
else:
# Legacy fallback
all_memories = self.memory_system.all(user_id)
recent_memories = self.memory_system.recent(user_id, params["recent_limit"])
if all_memories:
semantic_context = await self._semantic_select_qa_memories(
question, [{"content": m} for m in all_memories], params["similarity_threshold"]
)
recent_context = "\n\n".join(recent_memories)
return recent_context, semantic_context, metadata
except Exception as e:
logger.error(f"[EXECUTION_ENGINE] Semantic deep retrieval failed: {e}")
return "", "", {"error": str(e)}
async def _execute_mixed_approach_retrieval(self, user_id: str, question: str, params: Dict[str, Any],
nvidia_rotator, project_id: Optional[str]) -> Tuple[str, str, Dict[str, Any]]:
"""Execute mixed approach retrieval for continuation requests"""
try:
recent_context = ""
semantic_context = ""
metadata = {"strategy": "mixed_approach"}
if self.memory_system.is_enhanced_available():
# Get recent context
recent_memories = self.memory_system.enhanced_memory.get_memories(
user_id, memory_type="conversation", limit=params["recent_limit"]
)
if recent_memories:
if params["use_ai_selection"] and nvidia_rotator:
recent_context = await self._ai_select_qa_memories(
question, recent_memories, nvidia_rotator, "recent", user_id
)
else:
recent_context = await self._semantic_select_qa_memories(
question, recent_memories, params["similarity_threshold"]
)
# Get semantic context
all_memories = self.memory_system.enhanced_memory.get_memories(
user_id, limit=params["semantic_limit"]
)
if all_memories:
if params["use_ai_selection"] and nvidia_rotator:
semantic_context = await self._ai_select_qa_memories(
question, all_memories, nvidia_rotator, "semantic", user_id
)
else:
semantic_context = await self._semantic_select_qa_memories(
question, all_memories, params["similarity_threshold"]
)
else:
# Legacy fallback
recent_memories = self.memory_system.recent(user_id, params["recent_limit"])
rest_memories = self.memory_system.rest(user_id, params["recent_limit"])
if recent_memories:
recent_context = await self._semantic_select_qa_memories(
question, [{"content": m} for m in recent_memories], params["similarity_threshold"]
)
if rest_memories:
semantic_context = await self._semantic_select_qa_memories(
question, [{"content": m} for m in rest_memories], params["similarity_threshold"]
)
return recent_context, semantic_context, metadata
except Exception as e:
logger.error(f"[EXECUTION_ENGINE] Mixed approach retrieval failed: {e}")
return "", "", {"error": str(e)}
async def _ai_select_qa_memories(self, question: str, memories: List[Dict[str, Any]],
nvidia_rotator, context_type: str, user_id: str = "") -> str:
"""Use AI to select the most relevant Q&A memories"""
try:
from utils.api.router import generate_answer_with_model
from utils.analytics import get_analytics_tracker
# Track memory agent usage
tracker = get_analytics_tracker()
if tracker:
await tracker.track_agent_usage(
user_id=user_id,
agent_name="memory",
action="select",
context="memory_selection",
metadata={"question": question[:100], "memories_count": len(memories)}
)
if not memories:
return ""
sys_prompt = f"""You are an expert at selecting the most relevant Q&A memories for {context_type} context.
Given a user's question and a list of Q&A memories, select the most relevant ones that would help provide a comprehensive and detailed answer.
Focus on:
1. Direct relevance to the question
2. Q&A pairs that provide supporting information
3. Memories that add context and depth
4. Past discussions that relate to the current question
Return ONLY the selected Q&A memories, concatenated together. If none are relevant, return nothing."""
# Format memories for AI
formatted_memories = []
for i, memory in enumerate(memories):
content = memory.get("content", "")
if content:
formatted_memories.append(f"Memory {i+1}: {content}")
user_prompt = f"""Question: {question}
Available Q&A Memories:
{chr(10).join(formatted_memories)}
Select the most relevant Q&A memories:"""
# Track memory agent usage
try:
from utils.analytics import get_analytics_tracker
tracker = get_analytics_tracker()
if tracker and user_id:
await tracker.track_agent_usage(
user_id=user_id,
agent_name="memory",
action="select",
context="memory_selection",
metadata={"context_type": context_type, "memories_count": len(memories)}
)
except Exception:
pass
# Track memory agent usage
tracker = get_analytics_tracker()
if tracker:
await tracker.track_agent_usage(
user_id=user_id,
agent_name="memory",
action="select",
context="memory_selection",
metadata={"question": question[:100], "memories_count": len(memories)}
)
# Track memo agent usage
try:
from utils.analytics import get_analytics_tracker
tracker = get_analytics_tracker()
if tracker:
await tracker.track_agent_usage(
user_id=user_id,
agent_name="memo",
action="select",
context="memory_selection",
metadata={"query": query}
)
except Exception:
pass
# Use Qwen for better memory selection reasoning
from utils.api.router import qwen_chat_completion
response = await qwen_chat_completion(sys_prompt, user_prompt, nvidia_rotator, user_id, "memory_selection")
return response.strip()
except Exception as e:
logger.warning(f"[EXECUTION_ENGINE] AI Q&A selection failed: {e}")
return ""
async def _semantic_select_qa_memories(self, question: str, memories: List[Dict[str, Any]],
threshold: float) -> str:
"""Use semantic similarity to select Q&A memories"""
try:
if not memories:
return ""
# Extract content from memories
memory_contents = [memory.get("content", "") for memory in memories if memory.get("content")]
if not memory_contents:
return ""
# Use semantic similarity
from memo.context import semantic_context
selected = await semantic_context(question, memory_contents, self.embedder, len(memory_contents))
return selected
except Exception as e:
logger.warning(f"[EXECUTION_ENGINE] Semantic Q&A selection failed: {e}")
return ""
# ────────────────────────────── Global Instance ──────────────────────────────
_execution_engine: Optional[ExecutionEngine] = None
def get_execution_engine(memory_system=None, embedder: EmbeddingClient = None) -> ExecutionEngine:
"""Get the global execution engine instance"""
global _execution_engine
if _execution_engine is None:
if not memory_system:
from memo.core import get_memory_system
memory_system = get_memory_system()
if not embedder:
from utils.rag.embeddings import EmbeddingClient
embedder = EmbeddingClient()
_execution_engine = ExecutionEngine(memory_system, embedder)
logger.info("[EXECUTION_ENGINE] Global execution engine initialized")
return _execution_engine
|