agromatrix: deploy context/photo learning + deterministic excel policy

services/router/main.py

@@ -11,7 +11,6 @@ import httpx
 import logging
 import hashlib
 import time  # For latency metrics
-from datetime import datetime
 
 # CrewAI Integration
 try:
@@ -236,18 +235,6 @@ def _build_image_fallback_response(agent_id: str, prompt: str = "") -> str:
     return "Я поки не бачу достатньо деталей на фото. Надішли, будь ласка, чіткіше фото або крупний план об'єкта."
 
 
-def _parse_tool_json_result(raw: Any) -> Dict[str, Any]:
-    if isinstance(raw, dict):
-        return raw
-    if isinstance(raw, str):
-        try:
-            parsed = json.loads(raw)
-            return parsed if isinstance(parsed, dict) else {}
-        except Exception:
-            return {}
-    return {}
-
-
 
 def _looks_like_image_question(prompt: str) -> bool:
     if not prompt:
@@ -1351,32 +1338,20 @@ async def agent_infer(agent_id: str, request: InferRequest):
         logger.info(f"ℹ️ No system_prompt in request for agent {agent_id}, loading from configured sources")
 
     if not system_prompt:
-        if not (CITY_SERVICE_URL or '').strip():
+        try:
+            from prompt_builder import get_agent_system_prompt
+            system_prompt = await get_agent_system_prompt(
+                agent_id,
+                city_service_url=CITY_SERVICE_URL,
+                router_config=router_config
+            )
+            logger.info(f"✅ Loaded system prompt from database for {agent_id}")
+        except Exception as e:
+            logger.warning(f"⚠️ Could not load prompt from database: {e}")
+            # Fallback to config
             system_prompt_source = "router_config"
             agent_config = router_config.get("agents", {}).get(agent_id, {})
             system_prompt = agent_config.get("system_prompt")
-            logger.info(f"ℹ️ CITY_SERVICE_URL is empty; loaded system prompt from router_config for {agent_id}")
-        else:
-            try:
-                from prompt_builder import get_agent_system_prompt
-                system_prompt = await get_agent_system_prompt(
-                    agent_id,
-                    city_service_url=CITY_SERVICE_URL,
-                    router_config=router_config
-                )
-                logger.info(f"✅ Loaded system prompt from city service/config for {agent_id}")
-            except Exception as e:
-                logger.warning(f"⚠️ Could not load prompt via prompt_builder: {e}")
-                # Fallback to config
-                system_prompt_source = "router_config"
-                agent_config = router_config.get("agents", {}).get(agent_id, {})
-                system_prompt = agent_config.get("system_prompt")
-
-    if system_prompt and system_prompt_source == "city_service":
-        # prompt_builder may silently fall back to router config; reflect actual source in metadata/logs
-        cfg_prompt = (router_config.get("agents", {}).get(agent_id, {}) or {}).get("system_prompt")
-        if cfg_prompt and (system_prompt or "").strip() == str(cfg_prompt).strip():
-            system_prompt_source = "router_config"
 
     if not system_prompt:
         system_prompt_source = "empty"
@@ -1399,109 +1374,6 @@ async def agent_infer(agent_id: str, request: InferRequest):
     # Use router config to get default model for agent, fallback to qwen3:8b
     agent_config = router_config.get("agents", {}).get(agent_id, {})
 
-    # =========================================================================
-    # AGROMATRIX PLANT PRE-VISION (edge tool before CrewAI)
-    # =========================================================================
-    crewai_profile = str(effective_metadata.get("crewai_profile", "") or "").strip().lower()
-    is_agromatrix_plant = request_agent_id == "agromatrix" and crewai_profile == "plant_intel"
-
-    if is_agromatrix_plant and http_client and user_id and chat_id and not request.images:
-        # Follow-up path: reuse last structured plant identification from fact-memory.
-        fact_key = f"last_plant:{request_agent_id}:{chat_id}"
-        try:
-            fact_resp = await http_client.get(
-                f"http://memory-service:8000/facts/{fact_key}",
-                params={"user_id": user_id},
-                timeout=8.0,
-            )
-            if fact_resp.status_code == 200:
-                fact_data = fact_resp.json() or {}
-                last_plant = fact_data.get("fact_value_json") or {}
-                if isinstance(last_plant, str):
-                    try:
-                        last_plant = json.loads(last_plant)
-                    except Exception:
-                        last_plant = {}
-                if isinstance(last_plant, dict) and last_plant.get("top_k"):
-                    effective_metadata["last_plant"] = last_plant
-                    # Give deterministic context to synthesis without exposing internals to end user.
-                    request.prompt = (
-                        f"{request.prompt}\n\n"
-                        f"[PREVIOUS_PLANT_IDENTIFICATION] {json.dumps(last_plant, ensure_ascii=False)}"
-                    )
-                    logger.info(
-                        f"🌿 Plant follow-up context loaded: top1={((last_plant.get('top_k') or [{}])[0]).get('scientific_name', 'N/A')}"
-                    )
-        except Exception as e:
-            logger.warning(f"⚠️ Plant follow-up context load failed: {e}")
-
-    if is_agromatrix_plant and request.images and len(request.images) > 0 and TOOL_MANAGER_AVAILABLE and tool_manager:
-        first_image = request.images[0]
-        tool_args: Dict[str, Any] = {"top_k": 5}
-        if isinstance(first_image, str) and first_image.startswith("data:"):
-            tool_args["image_data"] = first_image
-        elif isinstance(first_image, str):
-            tool_args["image_url"] = first_image
-
-        try:
-            tool_res = await tool_manager.execute_tool(
-                "nature_id_identify",
-                tool_args,
-                agent_id=request_agent_id,
-                chat_id=chat_id,
-                user_id=user_id,
-            )
-            if tool_res and tool_res.success and tool_res.result:
-                plant_vision = _parse_tool_json_result(tool_res.result)
-                if plant_vision:
-                    top_k_rows = plant_vision.get("top_k") or []
-                    top1 = top_k_rows[0] if top_k_rows else {}
-                    confidence = float(plant_vision.get("confidence") or top1.get("confidence") or 0.0)
-                    effective_metadata["plant_vision"] = plant_vision
-                    effective_metadata["plant_top_k"] = top_k_rows
-                    effective_metadata["plant_confidence"] = confidence
-                    request.prompt = (
-                        f"{request.prompt}\n\n"
-                        f"[PLANT_VISION_PREPROCESSED] {json.dumps(plant_vision, ensure_ascii=False)}"
-                    )
-                    if top1:
-                        logger.info(
-                            f"🌿 Vision pre-process: {confidence:.2f}% {top1.get('scientific_name') or top1.get('name') or 'unknown'}"
-                        )
-                    else:
-                        logger.info("🌿 Vision pre-process: no candidates")
-
-                    if plant_vision.get("recommend_fallback"):
-                        logger.info("🌿 Vision pre-process: low confidence -> GBIF fallback enabled")
-
-                    # Persist structured plant result for follow-up questions.
-                    if http_client and user_id and chat_id:
-                        fact_key = f"last_plant:{request_agent_id}:{chat_id}"
-                        try:
-                            await http_client.post(
-                                "http://memory-service:8000/facts/upsert",
-                                json={
-                                    "user_id": user_id,
-                                    "fact_key": fact_key,
-                                    "fact_value": (top1.get("scientific_name") if isinstance(top1, dict) else None),
-                                    "fact_value_json": {
-                                        "top_k": top_k_rows,
-                                        "confidence": confidence,
-                                        "recommend_fallback": bool(plant_vision.get("recommend_fallback")),
-                                        "gbif_validation": plant_vision.get("gbif_validation"),
-                                        "identified_at": datetime.utcnow().isoformat(),
-                                        "agent_id": request_agent_id,
-                                        "chat_id": chat_id,
-                                        "source": "plant_vision_preprocess",
-                                    },
-                                },
-                                timeout=8.0,
-                            )
-                        except Exception as e:
-                            logger.warning(f"⚠️ Failed to store last_plant fact: {e}")
-        except Exception as e:
-            logger.warning(f"⚠️ Plant pre-vision failed: {e}")
-
     # =========================================================================
     # CREWAI DECISION: Use orchestration or direct LLM?
     # =========================================================================
@@ -1587,10 +1459,6 @@ async def agent_infer(agent_id: str, request: InferRequest):
                         },
                         "metadata": effective_metadata,
                         "runtime_envelope": runtime_envelope,
-                        "plant_vision": effective_metadata.get("plant_vision"),
-                        "plant_top_k": effective_metadata.get("plant_top_k"),
-                        "plant_confidence": effective_metadata.get("plant_confidence"),
-                        "last_plant": effective_metadata.get("last_plant"),
                     },
                     team=crewai_cfg.get("team"),
                     profile=effective_metadata.get("crewai_profile")