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agromatrix: deterministic plant-id flow + confidence guard + plantnet env

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NODA1 System
2026-02-21 12:03:28 +01:00
parent 50dfcd7390
commit a87a1fe52c
3 changed files with 250 additions and 8 deletions
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232
services/router/main.py
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@@ -176,6 +176,91 @@ def _build_cautious_plant_response(base_text: str, source_count: int) -> str:
)
def _extract_image_inputs_for_plant_tools(images: Optional[List[str]], metadata: Dict[str, Any]) -> Dict[str, str]:
out: Dict[str, str] = {}
file_url = str((metadata or {}).get("file_url") or "").strip()
if file_url.startswith("http://") or file_url.startswith("https://"):
out["image_url"] = file_url
if images and isinstance(images, list):
first = images[0]
if isinstance(first, str):
s = first.strip()
if s.startswith("data:image/") and ";base64," in s:
out["image_data"] = s
elif not out.get("image_url") and (s.startswith("http://") or s.startswith("https://")):
out["image_url"] = s
return out
def _parse_tool_result_json(payload: Any) -> Dict[str, Any]:
if isinstance(payload, dict):
return payload
if isinstance(payload, str):
s = payload.strip()
if s.startswith("{") or s.startswith("["):
try:
parsed = json.loads(s)
return parsed if isinstance(parsed, dict) else {}
except Exception:
return {}
return {}
def _extract_top_candidates(tool_json: Dict[str, Any], limit: int = 3) -> List[Dict[str, Any]]:
rows = tool_json.get("top_k") if isinstance(tool_json, dict) else None
if not isinstance(rows, list):
return []
out: List[Dict[str, Any]] = []
for row in rows[:limit]:
if not isinstance(row, dict):
continue
try:
conf = float(row.get("confidence", 0.0))
except Exception:
conf = 0.0
if conf > 1.0 and conf <= 100.0:
conf = conf / 100.0
if conf < 0:
conf = 0.0
if conf > 1.0:
conf = 1.0
name = str(row.get("name") or row.get("scientific_name") or "unknown").strip()
sci = str(row.get("scientific_name") or name or "unknown").strip()
out.append({"confidence": conf, "name": name, "scientific_name": sci})
return out
def _build_agromatrix_not_sure_response(candidates: List[Dict[str, Any]], threshold: float) -> str:
if not candidates:
return (
"Не впевнений у точній ідентифікації по цьому фото. "
"Надішли, будь ласка, 2-3 чіткі фото: загальний план рослини, листок крупним планом і стебло/вузол росту."
)
lines: List[str] = []
for i, c in enumerate(candidates[:2], 1):
conf_pct = int(round(float(c.get("confidence", 0.0)) * 100))
lines.append(f"{i}) {c.get('name')} ({c.get('scientific_name')}), confidence ~{conf_pct}%")
return (
f"Не впевнений у точній ідентифікації (поріг надійності: {int(round(threshold * 100))}%).\n"
f"Найближчі варіанти:\n" + "\n".join(lines) + "\n"
"Щоб підтвердити вид, надішли чіткі фото листка (верх/низ), стебла та загального вигляду."
)
def _build_agromatrix_deterministic_fallback(candidates: List[Dict[str, Any]]) -> str:
if not candidates:
return (
"Не впевнений у точній ідентифікації по цьому фото. "
"Надішли чіткіші фото листка, стебла і загального вигляду рослини."
)
top = candidates[0]
conf_pct = int(round(float(top.get("confidence", 0.0)) * 100))
return (
f"Ймовірна ідентифікація: {top.get('name')} ({top.get('scientific_name')}), confidence ~{conf_pct}%. "
"Це результат автоматичної класифікації; для підтвердження бажано ще 1-2 фото з інших ракурсів."
)
EMPTY_ANSWER_GUARD_AGENTS = {"devtools", "monitor"}
@@ -1565,6 +1650,153 @@ async def agent_infer(agent_id: str, request: InferRequest):
# =========================================================================
if request.images and len(request.images) > 0:
logger.info(f"🖼️ Vision request: {len(request.images)} image(s)")
plant_intent = _is_plant_identification_request(request.prompt)
# Deterministic AgroMatrix policy:
# 1) run plant classifiers first (nature-id / plantnet)
# 2) apply confidence threshold
# 3) LLM only explains classifier result, no new guessing
if request_agent_id == "agromatrix" and plant_intent and TOOL_MANAGER_AVAILABLE and tool_manager:
try:
image_inputs = _extract_image_inputs_for_plant_tools(request.images, metadata)
if image_inputs:
threshold = float(
os.getenv(
"AGROMATRIX_PLANT_CONFIDENCE_MIN",
os.getenv("NATURE_ID_MIN_CONFIDENCE", "0.65"),
)
)
nature_args: Dict[str, Any] = {"top_k": 5, "min_confidence": threshold}
nature_args.update(image_inputs)
nature_res = await tool_manager.execute_tool(
"nature_id_identify",
nature_args,
agent_id=request_agent_id,
chat_id=chat_id,
user_id=user_id,
)
nature_json = _parse_tool_result_json(nature_res.result) if nature_res and nature_res.success else {}
candidates = _extract_top_candidates(nature_json, limit=3)
plantnet_key = (os.getenv("PLANTNET_API_KEY") or "").strip()
if plantnet_key:
plantnet_args: Dict[str, Any] = {"top_k": 3, "organ": "leaf"}
plantnet_args.update(image_inputs)
plantnet_res = await tool_manager.execute_tool(
"plantnet_lookup",
plantnet_args,
agent_id=request_agent_id,
chat_id=chat_id,
user_id=user_id,
)
plantnet_json = _parse_tool_result_json(plantnet_res.result) if plantnet_res and plantnet_res.success else {}
plantnet_candidates = _extract_top_candidates(plantnet_json, limit=2)
if not candidates and plantnet_candidates:
candidates = plantnet_candidates
top_conf = float(candidates[0].get("confidence", 0.0)) if candidates else 0.0
if (not candidates) or (top_conf < threshold):
response_text = _build_agromatrix_not_sure_response(candidates, threshold)
if MEMORY_RETRIEVAL_AVAILABLE and memory_retrieval and chat_id and user_id:
asyncio.create_task(
memory_retrieval.store_message(
agent_id=request_agent_id,
user_id=user_id,
username=username,
message_text=f"[Image][PlantIntent] {request.prompt}",
response_text=response_text,
chat_id=chat_id,
message_type="vision",
metadata={
"deterministic_plant_id": True,
"confidence_threshold": threshold,
"candidates": candidates,
"decision": "uncertain",
},
)
)
return InferResponse(
response=response_text,
model="plant-id-deterministic",
backend="plant-id-deterministic",
tokens_used=0,
)
# High-confidence deterministic result -> LLM explains only this result.
top = candidates[0]
classifier_payload = {
"source": nature_json.get("source") if isinstance(nature_json, dict) else "nature-id",
"threshold": threshold,
"selected": top,
"top_k": candidates,
}
explain_prompt = (
"Користувач попросив ідентифікувати рослину на фото.\n"
f"Використай ТІЛЬКИ цей deterministic результат класифікатора: {json.dumps(classifier_payload, ensure_ascii=False)}\n\n"
"Сформуй коротку відповідь українською (2-4 речення):\n"
"1) назва культури (common + scientific),\n"
"2) confidence у %, \n"
"3) 1-2 ознаки для практичної перевірки в полі.\n"
"Не вигадуй інші види. Якщо даних замало, прямо скажи: 'не впевнений'."
)
llm_model = "plant-id-deterministic"
llm_backend = "plant-id-deterministic"
llm_tokens = 0
try:
llm_resp = await internal_llm_complete(
InternalLLMRequest(
prompt=explain_prompt,
llm_profile="reasoning",
max_tokens=min(int(request.max_tokens or 220), 280),
temperature=0.1,
role_context="AgroMatrix classifier explainer",
metadata={"agent_id": "agromatrix"},
)
)
response_text = _sanitize_vision_text_for_user(llm_resp.text)
llm_model = llm_resp.model
llm_backend = f"plant-id-explainer-{llm_resp.provider}"
llm_tokens = llm_resp.tokens_used
except Exception as e:
logger.warning(f"⚠️ Deterministic plant explanation LLM failed: {e}")
response_text = ""
if not response_text:
response_text = _build_agromatrix_deterministic_fallback(candidates)
else:
low = response_text.lower()
top_name = str(top.get("name") or "").lower()
top_sci = str(top.get("scientific_name") or "").lower()
if (top_name and top_name not in low) and (top_sci and top_sci not in low):
response_text = _build_agromatrix_deterministic_fallback(candidates)
if MEMORY_RETRIEVAL_AVAILABLE and memory_retrieval and chat_id and user_id:
asyncio.create_task(
memory_retrieval.store_message(
agent_id=request_agent_id,
user_id=user_id,
username=username,
message_text=f"[Image][PlantIntent] {request.prompt}",
response_text=response_text,
chat_id=chat_id,
message_type="vision",
metadata={
"deterministic_plant_id": True,
"confidence_threshold": threshold,
"candidates": candidates,
"decision": "high_confidence",
},
)
)
return InferResponse(
response=response_text,
model=llm_model,
backend=llm_backend,
tokens_used=llm_tokens,
)
except Exception as e:
logger.warning(f"⚠️ Deterministic AgroMatrix plant flow failed, fallback to generic vision: {e}")
try:
# Use Swapper's /vision endpoint (manages model loading)
vision_payload = {

25
services/router/tool_manager.py
View File

@@ -812,6 +812,21 @@ class ToolManager:
if "web.telegram.org" in host:
return True
return False
@staticmethod
def _normalize_confidence(value: Any) -> float:
try:
v = float(value)
except Exception:
return 0.0
if v < 0:
return 0.0
# Some backends return percentages (e.g. 97.6) instead of 0..1.
if v > 1.0 and v <= 100.0:
v = v / 100.0
if v > 1.0:
v = 1.0
return v
async def execute_tool(
self,
@@ -2802,10 +2817,7 @@ class ToolManager:
if not isinstance(row, dict):
continue
conf = row.get("confidence", 0.0)
try:
conf_f = float(conf)
except Exception:
conf_f = 0.0
conf_f = self._normalize_confidence(conf)
top_k_rows.append({
"confidence": conf_f,
"name": str(row.get("name") or row.get("scientific_name") or "unknown"),
@@ -2816,10 +2828,7 @@ class ToolManager:
if not isinstance(item, dict):
continue
score = item.get("score", item.get("confidence", 0.0))
try:
score_f = float(score)
except Exception:
score_f = 0.0
score_f = self._normalize_confidence(score)
sname = item.get("scientific_name") or item.get("label") or item.get("name") or "unknown"
cname = item.get("common_name") or item.get("common") or sname
top_k_rows.append({