376 lines
14 KiB
Python
376 lines
14 KiB
Python
# router.py
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import os
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import logging
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from fastapi import APIRouter
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from pydantic import BaseModel
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from reasoning.reasoning import reason_check
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from reasoning.reflection import reflect_notes
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from reasoning.refine import refine_answer
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from persona.speak import speak
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from persona.identity import load_identity
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from context import collect_context, update_last_assistant_message
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from intake.intake import add_exchange_internal
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from autonomy.monologue.monologue import InnerMonologue
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from autonomy.self.state import load_self_state
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# -------------------------------------------------------------------
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# Setup
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# -------------------------------------------------------------------
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VERBOSE_DEBUG = os.getenv("VERBOSE_DEBUG", "false").lower() == "true"
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logger = logging.getLogger(__name__)
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if VERBOSE_DEBUG:
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logger.setLevel(logging.DEBUG)
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console_handler = logging.StreamHandler()
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console_handler.setFormatter(logging.Formatter(
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'%(asctime)s [ROUTER] %(levelname)s: %(message)s',
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datefmt='%H:%M:%S'
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))
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logger.addHandler(console_handler)
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try:
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os.makedirs('/app/logs', exist_ok=True)
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file_handler = logging.FileHandler('/app/logs/cortex_verbose_debug.log', mode='a')
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file_handler.setFormatter(logging.Formatter(
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'%(asctime)s [ROUTER] %(levelname)s: %(message)s',
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datefmt='%Y-%m-%d %H:%M:%S'
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))
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logger.addHandler(file_handler)
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logger.debug("VERBOSE_DEBUG enabled for router.py")
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except Exception as e:
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logger.debug(f"File logging failed: {e}")
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cortex_router = APIRouter()
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inner_monologue = InnerMonologue()
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# -------------------------------------------------------------------
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# Models
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# -------------------------------------------------------------------
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class ReasonRequest(BaseModel):
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session_id: str
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user_prompt: str
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temperature: float | None = None
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# -------------------------------------------------------------------
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# /reason endpoint
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# -------------------------------------------------------------------
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@cortex_router.post("/reason")
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async def run_reason(req: ReasonRequest):
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if VERBOSE_DEBUG:
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logger.debug(f"\n{'='*80}")
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logger.debug(f"[PIPELINE START] Session: {req.session_id}")
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logger.debug(f"[PIPELINE START] User prompt: {req.user_prompt[:200]}...")
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logger.debug(f"{'='*80}\n")
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# ----------------------------------------------------------------
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# STAGE 0 — Context
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# ----------------------------------------------------------------
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if VERBOSE_DEBUG:
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logger.debug("[STAGE 0] Collecting unified context...")
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context_state = await collect_context(req.session_id, req.user_prompt)
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if VERBOSE_DEBUG:
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logger.debug(f"[STAGE 0] Context collected - {len(context_state.get('rag', []))} RAG results")
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# ----------------------------------------------------------------
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# STAGE 0.5 — Identity
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# ----------------------------------------------------------------
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if VERBOSE_DEBUG:
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logger.debug("[STAGE 0.5] Loading identity block...")
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identity_block = load_identity(req.session_id)
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if VERBOSE_DEBUG:
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logger.debug(f"[STAGE 0.5] Identity loaded: {identity_block.get('name', 'Unknown')}")
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# ----------------------------------------------------------------
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# STAGE 0.6 — Inner Monologue (observer-only)
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# ----------------------------------------------------------------
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if VERBOSE_DEBUG:
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logger.debug("[STAGE 0.6] Running inner monologue...")
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inner_result = None
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try:
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self_state = load_self_state()
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mono_context = {
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"user_message": req.user_prompt,
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"session_id": req.session_id,
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"self_state": self_state,
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"context_summary": context_state,
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}
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inner_result = await inner_monologue.process(mono_context)
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logger.info(f"[INNER_MONOLOGUE] {inner_result}")
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# Store in context for downstream use
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context_state["monologue"] = inner_result
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except Exception as e:
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logger.warning(f"[INNER_MONOLOGUE] failed: {e}")
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# ----------------------------------------------------------------
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# STAGE 0.7 — Executive Planning (conditional)
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# ----------------------------------------------------------------
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executive_plan = None
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if inner_result and inner_result.get("consult_executive"):
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if VERBOSE_DEBUG:
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logger.debug("[STAGE 0.7] Executive consultation requested...")
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try:
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from autonomy.executive.planner import plan_execution
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executive_plan = await plan_execution(
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user_prompt=req.user_prompt,
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intent=inner_result.get("intent", "unknown"),
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context_state=context_state,
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identity_block=identity_block
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)
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logger.info(f"[EXECUTIVE] Generated plan: {executive_plan.get('summary', 'N/A')}")
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except Exception as e:
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logger.warning(f"[EXECUTIVE] Planning failed: {e}")
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executive_plan = None
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# ----------------------------------------------------------------
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# STAGE 0.8 — Autonomous Tool Invocation
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# ----------------------------------------------------------------
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tool_results = None
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autonomous_enabled = os.getenv("ENABLE_AUTONOMOUS_TOOLS", "true").lower() == "true"
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tool_confidence_threshold = float(os.getenv("AUTONOMOUS_TOOL_CONFIDENCE_THRESHOLD", "0.6"))
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if autonomous_enabled and inner_result:
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if VERBOSE_DEBUG:
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logger.debug("[STAGE 0.8] Analyzing autonomous tool needs...")
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try:
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from autonomy.tools.decision_engine import ToolDecisionEngine
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from autonomy.tools.orchestrator import ToolOrchestrator
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# Analyze which tools to invoke
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decision_engine = ToolDecisionEngine()
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tool_decision = await decision_engine.analyze_tool_needs(
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user_prompt=req.user_prompt,
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monologue=inner_result,
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context_state=context_state,
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available_tools=["RAG", "WEB", "WEATHER", "CODEBRAIN"]
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)
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# Execute tools if confidence threshold met
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if tool_decision["should_invoke_tools"] and tool_decision["confidence"] >= tool_confidence_threshold:
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orchestrator = ToolOrchestrator(tool_timeout=30)
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tool_results = await orchestrator.execute_tools(
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tools_to_invoke=tool_decision["tools_to_invoke"],
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context_state=context_state
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)
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# Format results for context injection
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tool_context = orchestrator.format_results_for_context(tool_results)
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context_state["autonomous_tool_results"] = tool_context
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if VERBOSE_DEBUG:
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summary = tool_results.get("execution_summary", {})
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logger.debug(f"[STAGE 0.8] Tools executed: {summary.get('successful', [])} succeeded")
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else:
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if VERBOSE_DEBUG:
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logger.debug(f"[STAGE 0.8] No tools invoked (confidence: {tool_decision.get('confidence', 0):.2f})")
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except Exception as e:
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logger.warning(f"[STAGE 0.8] Autonomous tool invocation failed: {e}")
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if VERBOSE_DEBUG:
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import traceback
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traceback.print_exc()
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# ----------------------------------------------------------------
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# STAGE 1 — Intake summary
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# ----------------------------------------------------------------
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intake_summary = "(no context available)"
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if context_state.get("intake"):
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l20 = context_state["intake"].get("L20")
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if isinstance(l20, dict):
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intake_summary = l20.get("summary", intake_summary)
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elif isinstance(l20, str):
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intake_summary = l20
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if VERBOSE_DEBUG:
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logger.debug(f"[STAGE 1] Intake summary extracted (L20): {intake_summary[:150]}...")
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# ----------------------------------------------------------------
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# STAGE 2 — Reflection
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# ----------------------------------------------------------------
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if VERBOSE_DEBUG:
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logger.debug("[STAGE 2] Running reflection...")
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try:
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reflection = await reflect_notes(intake_summary, identity_block=identity_block)
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reflection_notes = reflection.get("notes", [])
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except Exception as e:
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reflection_notes = []
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if VERBOSE_DEBUG:
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logger.debug(f"[STAGE 2] Reflection failed: {e}")
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# ----------------------------------------------------------------
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# STAGE 3 — Reasoning (draft)
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# ----------------------------------------------------------------
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if VERBOSE_DEBUG:
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logger.debug("[STAGE 3] Running reasoning (draft)...")
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draft = await reason_check(
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req.user_prompt,
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identity_block=identity_block,
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rag_block=context_state.get("rag", []),
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reflection_notes=reflection_notes,
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context=context_state,
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monologue=inner_result, # NEW: Pass monologue guidance
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executive_plan=executive_plan # NEW: Pass executive plan
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)
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# ----------------------------------------------------------------
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# STAGE 4 — Refinement
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# ----------------------------------------------------------------
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if VERBOSE_DEBUG:
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logger.debug("[STAGE 4] Running refinement...")
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result = await refine_answer(
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draft_output=draft,
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reflection_notes=reflection_notes,
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identity_block=identity_block,
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rag_block=context_state.get("rag", []),
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)
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final_neutral = result["final_output"]
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# ----------------------------------------------------------------
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# STAGE 5 — Persona
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# ----------------------------------------------------------------
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if VERBOSE_DEBUG:
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logger.debug("[STAGE 5] Applying persona layer...")
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# Extract tone and depth from monologue for persona guidance
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tone = inner_result.get("tone", "neutral") if inner_result else "neutral"
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depth = inner_result.get("depth", "medium") if inner_result else "medium"
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persona_answer = await speak(final_neutral, tone=tone, depth=depth)
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# ----------------------------------------------------------------
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# STAGE 6 — Session update
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# ----------------------------------------------------------------
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update_last_assistant_message(req.session_id, persona_answer)
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# ----------------------------------------------------------------
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# STAGE 6.5 — Self-state update & Pattern Learning
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# ----------------------------------------------------------------
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try:
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from autonomy.self.analyzer import analyze_and_update_state
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await analyze_and_update_state(
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monologue=inner_result or {},
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user_prompt=req.user_prompt,
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response=persona_answer,
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context=context_state
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)
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except Exception as e:
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logger.warning(f"[SELF_STATE] Update failed: {e}")
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# Pattern learning
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try:
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from autonomy.learning.pattern_learner import get_pattern_learner
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learner = get_pattern_learner()
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await learner.learn_from_interaction(
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user_prompt=req.user_prompt,
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response=persona_answer,
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monologue=inner_result or {},
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context=context_state
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)
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except Exception as e:
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logger.warning(f"[PATTERN_LEARNER] Learning failed: {e}")
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# ----------------------------------------------------------------
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# STAGE 7 — Proactive Monitoring & Suggestions
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# ----------------------------------------------------------------
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proactive_enabled = os.getenv("ENABLE_PROACTIVE_MONITORING", "true").lower() == "true"
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proactive_min_priority = float(os.getenv("PROACTIVE_SUGGESTION_MIN_PRIORITY", "0.6"))
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if proactive_enabled:
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try:
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from autonomy.proactive.monitor import get_proactive_monitor
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from autonomy.self.state import load_self_state
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monitor = get_proactive_monitor(min_priority=proactive_min_priority)
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self_state = load_self_state()
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suggestion = await monitor.analyze_session(
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session_id=req.session_id,
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context_state=context_state,
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self_state=self_state
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)
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# Append suggestion to response if exists
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if suggestion:
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suggestion_text = monitor.format_suggestion(suggestion)
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persona_answer += suggestion_text
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if VERBOSE_DEBUG:
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logger.debug(f"[STAGE 7] Proactive suggestion added: {suggestion['type']} (priority: {suggestion['priority']:.2f})")
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except Exception as e:
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logger.warning(f"[STAGE 7] Proactive monitoring failed: {e}")
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if VERBOSE_DEBUG:
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logger.debug(f"\n{'='*80}")
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logger.debug(f"[PIPELINE COMPLETE] Session: {req.session_id}")
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logger.debug(f"[PIPELINE COMPLETE] Final answer length: {len(persona_answer)} chars")
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logger.debug(f"{'='*80}\n")
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# ----------------------------------------------------------------
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# RETURN
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# ----------------------------------------------------------------
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return {
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"draft": draft,
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"neutral": final_neutral,
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"persona": persona_answer,
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"reflection": reflection_notes,
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"session_id": req.session_id,
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"context_summary": {
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"rag_results": len(context_state.get("rag", [])),
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"minutes_since_last": context_state.get("minutes_since_last_msg"),
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"message_count": context_state.get("message_count"),
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"mode": context_state.get("mode"),
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}
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}
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# -------------------------------------------------------------------
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# /ingest endpoint (internal)
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# -------------------------------------------------------------------
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class IngestPayload(BaseModel):
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session_id: str
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user_msg: str
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assistant_msg: str
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@cortex_router.post("/ingest")
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async def ingest(payload: IngestPayload):
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try:
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update_last_assistant_message(payload.session_id, payload.assistant_msg)
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except Exception as e:
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logger.warning(f"[INGEST] Session update failed: {e}")
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try:
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add_exchange_internal({
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"session_id": payload.session_id,
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"user_msg": payload.user_msg,
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"assistant_msg": payload.assistant_msg,
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})
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except Exception as e:
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logger.warning(f"[INGEST] Intake update failed: {e}")
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return {"status": "ok", "session_id": payload.session_id}
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