2586 lines
112 KiB
Python
2586 lines
112 KiB
Python
from fastapi import FastAPI, HTTPException
|
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from fastapi.responses import Response
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from pydantic import BaseModel
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from typing import Literal, Optional, Dict, Any, List
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import asyncio
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import json
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import os
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import re
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import yaml
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import httpx
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import logging
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import hashlib
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import time # For latency metrics
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# CrewAI Integration
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try:
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from crewai_client import should_use_crewai, call_crewai, get_crewai_health
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CREWAI_CLIENT_AVAILABLE = True
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except ImportError:
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CREWAI_CLIENT_AVAILABLE = False
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should_use_crewai = None
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call_crewai = None
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from neo4j import AsyncGraphDatabase
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# Memory Retrieval Pipeline v3.0
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try:
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from memory_retrieval import memory_retrieval, MemoryBrief
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MEMORY_RETRIEVAL_AVAILABLE = True
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except ImportError:
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MEMORY_RETRIEVAL_AVAILABLE = False
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memory_retrieval = None
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# Tool Manager for Function Calling
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try:
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from tool_manager import ToolManager, ToolResult, format_tool_calls_for_response
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TOOL_MANAGER_AVAILABLE = True
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except ImportError:
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TOOL_MANAGER_AVAILABLE = False
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ToolManager = None
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# Runtime Guard (Envelope/Artifact validation for CLAN orchestration)
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try:
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from runtime_guard import RuntimeGuard
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RUNTIME_GUARD_AVAILABLE = True
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except ImportError:
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RUNTIME_GUARD_AVAILABLE = False
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RuntimeGuard = None
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||
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logging.basicConfig(level=logging.INFO)
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logger = logging.getLogger(__name__)
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||
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||
TRUSTED_DOMAINS_CONFIG_PATH = os.getenv("TRUSTED_DOMAINS_CONFIG_PATH", "./trusted_domains.yml")
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_trusted_domains_cache: Dict[str, Any] = {"mtime": None, "data": {}}
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||
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def _load_trusted_domains_overrides() -> Dict[str, Any]:
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||
"""Load optional trusted domains overrides editable by mentors."""
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global _trusted_domains_cache
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||
try:
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if not os.path.exists(TRUSTED_DOMAINS_CONFIG_PATH):
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return {}
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||
mtime = os.path.getmtime(TRUSTED_DOMAINS_CONFIG_PATH)
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if _trusted_domains_cache.get("mtime") == mtime:
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return _trusted_domains_cache.get("data") or {}
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||
|
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with open(TRUSTED_DOMAINS_CONFIG_PATH, "r", encoding="utf-8") as f:
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||
raw = yaml.safe_load(f) or {}
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if not isinstance(raw, dict):
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||
raw = {}
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_trusted_domains_cache = {"mtime": mtime, "data": raw}
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return raw
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||
except Exception as e:
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logger.warning(f"⚠️ Failed to load trusted domains overrides: {e}")
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||
return {}
|
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def _strip_dsml_keep_text_before(text: str) -> str:
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||
"""If response contains DSML, return only the part before the first DSML-like tag. Otherwise return empty (caller will use fallback)."""
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if not text or len(text.strip()) < 10:
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return ""
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# Find first occurrence of DSML-like patterns (tag or keyword that starts markup)
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dsml_start_patterns = [
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r"<function_calls",
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r"<invoke\s",
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r"<parameter\s",
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r"<think>",
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# DSML variants (ASCII and Unicode separators, e.g. <｜DSML｜invoke ...>)
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r"<\s*(?:\||｜)?\s*DSML",
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||
r"DSML\s*(?:\||｜)",
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r"DSML\s*>\s*",
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]
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earliest = len(text)
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for pat in dsml_start_patterns:
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m = re.search(pat, text, re.IGNORECASE | re.DOTALL)
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||
if m:
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||
earliest = min(earliest, m.start())
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if earliest == 0:
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return ""
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prefix = text[:earliest].strip()
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# Remove trailing incomplete tags
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prefix = re.sub(r"<[^>]*$", "", prefix).strip()
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return prefix if len(prefix) > 30 else ""
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def _vision_prompt_wants_web(prompt: str) -> bool:
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if not prompt:
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return False
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p = prompt.lower()
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markers = [
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"знайди", "пошукай", "пошук", "в інтернет", "в інтернеті", "у відкритих джерелах",
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"що це", "що на фото", "який це", "яка це", "identify", "find online", "search web",
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||
"назва", "бренд", "виробник", "інструкція", "дозування", "регламент", "де купити", "ціна",
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]
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||
return any(m in p for m in markers)
|
||
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||
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def _vision_answer_uncertain(answer: str) -> bool:
|
||
if not answer:
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||
return True
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||
a = answer.lower()
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||
uncertain_markers = [
|
||
"ймовірно", "можливо", "схоже", "не впевнений", "не можу визначити", "важко сказати",
|
||
"probably", "maybe", "looks like", "not sure", "cannot identify"
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]
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return any(m in a for m in uncertain_markers)
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||
|
||
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||
EMPTY_ANSWER_GUARD_AGENTS = {"devtools", "monitor"}
|
||
|
||
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||
def _normalize_text_response(text: str) -> str:
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||
return re.sub(r"\s+", " ", str(text or "")).strip()
|
||
|
||
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||
def _needs_empty_answer_recovery(text: str) -> bool:
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||
normalized = _normalize_text_response(text)
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||
if not normalized:
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return True
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low = normalized.lower()
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||
if len(normalized) < 8:
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return True
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meta_markers = (
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||
"the user", "user asked", "i need", "let me", "analysis", "thinking",
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||
"користувач", "потрібно", "спочатку", "сначала"
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)
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if any(m in low for m in meta_markers) and len(normalized) < 80:
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return True
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||
if normalized in {"...", "ok", "done"}:
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return True
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||
return False
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||
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def _image_response_needs_retry(text: str) -> bool:
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normalized = _normalize_text_response(text)
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if _needs_empty_answer_recovery(normalized):
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||
return True
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||
low = normalized.lower()
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||
blocked_markers = (
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"не можу бачити", "не можу аналізувати зображення", "опишіть фото словами",
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||
"cannot view images", "cannot analyze image", "as a text model"
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)
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if any(m in low for m in blocked_markers):
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return True
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||
return len(normalized) < 24
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||
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def _vision_response_is_blurry(text: str) -> bool:
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low = _normalize_text_response(text).lower()
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if not low:
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return False
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||
blurry_markers = (
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"розмит", "нечітк", "не дуже чітк", "blur", "blurry", "out of focus", "low quality"
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)
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return any(m in low for m in blurry_markers)
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||
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def _build_image_fallback_response(agent_id: str, prompt: str = "") -> str:
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||
if (agent_id or "").lower() == "agromatrix":
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||
return (
|
||
"Фото поки занадто нечітке, тому діагноз неточний. "
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"Надішли, будь ласка, 2-3 чіткі фото: загальний вигляд рослини, крупний план проблемної ділянки "
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||
"і (для листка) нижній бік. Якщо можеш, додай культуру та стадію росту."
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||
)
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return "Я поки не бачу достатньо деталей на фото. Надішли, будь ласка, чіткіше фото або крупний план об'єкта."
|
||
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||
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||
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def _sanitize_vision_text_for_user(text: str) -> str:
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if not text:
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return ""
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||
normalized = re.sub(r"\s+", " ", str(text)).strip()
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||
if not normalized:
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||
return ""
|
||
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||
sentences = [seg.strip() for seg in re.split(r"(?<=[.!?])\s+", normalized) if seg.strip()]
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||
meta_markers = (
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||
"okay", "the user", "user sent", "they want", "i need", "let me", "i will",
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||
"first, look at the image", "look at the image", "first, analyze",
|
||
"first, looking at the image", "looking at the image",
|
||
"хорошо", "користувач", "пользователь", "потрібно", "нужно", "спочатку", "сначала"
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)
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cleaned = [sent for sent in sentences if not any(m in sent.lower() for m in meta_markers)]
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||
if cleaned:
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||
out = " ".join(cleaned[:3]).strip()
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||
else:
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||
# If text is only meta-reasoning, prefer empty over leaking service text to user.
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||
if any(m in normalized.lower() for m in meta_markers):
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||
return ""
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||
out = " ".join(sentences[:3]).strip()
|
||
|
||
if len(out) > 700:
|
||
out = out[:700].rsplit(" ", 1)[0] + "..."
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||
return out
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||
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||
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||
def _extract_vision_search_facts(text: str, max_chars: int = 220) -> str:
|
||
fact = _sanitize_vision_text_for_user(text)
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||
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||
# If sanitizer dropped everything (meta-only), try to recover object phrase.
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||
if not fact and text:
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raw = re.sub(r"\s+", " ", str(text)).strip()
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||
raw = re.sub(r"(?i)^okay,?\s*", "", raw)
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||
raw = re.sub(r"(?i)^let\'s\s+see\.?\s*", "", raw)
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||
raw = re.sub(r"(?i)^the user sent (an image|a photo|a picture) of\s+", "", raw)
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||
raw = re.sub(r"(?i)^user sent (an image|a photo|a picture) of\s+", "", raw)
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||
raw = re.sub(r"(?i)^an image of\s+", "", raw)
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||
raw = re.sub(r"(?i)they want.*$", "", raw).strip(" .")
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||
fact = raw
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||
|
||
if not fact:
|
||
return ""
|
||
|
||
fact = re.sub(r"(?i)джерела\s*:\s*.*$", "", fact).strip()
|
||
fact = re.sub(r"[*_`#\[\]()]", "", fact)
|
||
fact = re.sub(r"\s{2,}", " ", fact).strip(" .,")
|
||
if len(fact) > max_chars:
|
||
fact = fact[:max_chars].rsplit(" ", 1)[0]
|
||
return fact
|
||
|
||
|
||
def _build_vision_web_query(prompt: str, vision_text: str) -> str:
|
||
# Keep query compact and deterministic for web_search tool.
|
||
source_intent = any(k in (prompt or "").lower() for k in ("джерел", "підтвердж", "source", "reference"))
|
||
prompt_part = (prompt or "").strip()
|
||
# Remove generic question wrappers that pollute search quality.
|
||
prompt_part = re.sub(r"(?i)що\s*це\s*на\s*фото\??", "", prompt_part).strip(" .")
|
||
prompt_part = re.sub(r"(?i)дай\s*2-?3\s*джерела", "", prompt_part).strip(" .")
|
||
prompt_part = re.sub(r"(?i)дай\s*\d+\s*джерел[а-я]*\s*для", "", prompt_part).strip(" .")
|
||
prompt_part = re.sub(r"(?i)дай\s*\d+\s*джерел[а-я]*", "", prompt_part).strip(" .")
|
||
prompt_part = re.sub(r"(?i)знайди\s*в\s*інтернеті", "", prompt_part).strip(" .")
|
||
prompt_part = re.sub(r"(?i)знайди\s*в\s*інтернеті\s*схожі\s*джерела", "", prompt_part).strip(" .")
|
||
prompt_part = re.sub(r"(?i)підтвердження", "", prompt_part).strip(" .")
|
||
prompt_part = re.sub(r"(?i)якщо\s*не\s*впевнений.*$", "", prompt_part).strip(" .")
|
||
prompt_part = re.sub(r"(?i)пошукай.*$", "", prompt_part).strip(" .")
|
||
prompt_part = re.sub(r"(?iu)\bі\b\.?$", "", prompt_part).strip(" .")
|
||
|
||
vision_part = _extract_vision_search_facts(vision_text)
|
||
if vision_part:
|
||
tokens = re.findall(r"[a-zA-Zа-яА-ЯіїєІЇЄ0-9]{3,}", vision_part.lower())
|
||
generic_tokens = {
|
||
"first", "look", "image", "photo", "picture", "context", "the", "and",
|
||
"спочатку", "подивись", "зображення", "фото", "картинка", "контекст",
|
||
}
|
||
if len(tokens) < 3 or len(vision_part) < 18 or all(t in generic_tokens for t in tokens):
|
||
# Too vague entity extraction (e.g., single word "rex") -> skip web enrichment.
|
||
return ""
|
||
if vision_part:
|
||
if prompt_part:
|
||
q = f"{vision_part}. контекст: {prompt_part}".strip(" .")
|
||
else:
|
||
q = vision_part
|
||
if source_intent:
|
||
q = f"{q} wikipedia encyclopedia"
|
||
return q.strip()
|
||
return ""
|
||
|
||
|
||
def _compact_web_search_result(raw: str, query: str = "", agent_id: str = "", max_chars: int = 900) -> str:
|
||
if not raw:
|
||
return ""
|
||
text = str(raw).strip()
|
||
if not text:
|
||
return ""
|
||
|
||
def _extract_domain(url: str) -> str:
|
||
if not url:
|
||
return ""
|
||
d = url.lower().strip()
|
||
d = d.replace("https://", "").replace("http://", "")
|
||
d = d.split("/")[0]
|
||
if d.startswith("www."):
|
||
d = d[4:]
|
||
return d
|
||
|
||
low_signal_tokens = (
|
||
"grid maker", "converter", "convert", "download", "wallpaper", "stock photo",
|
||
"instagram", "pinterest", "tiktok", "youtube", "facebook", "generator", "meme",
|
||
)
|
||
low_signal_domains = (
|
||
"pinterest.com", "instagram.com", "tiktok.com", "youtube.com",
|
||
"facebook.com", "vk.com", "yandex.", "stackexchange.com",
|
||
"zhihu.com", "baidu.com",
|
||
)
|
||
trusted_common_domains = (
|
||
"wikipedia.org", "wikidata.org", "britannica.com",
|
||
"who.int", "fao.org", "oecd.org", "worldbank.org", "un.org", "europa.eu",
|
||
"nature.com", "science.org", "sciencedirect.com", "springer.com",
|
||
)
|
||
trusted_agro_domains = (
|
||
"fao.org", "europa.eu", "ec.europa.eu", "usda.gov", "nass.usda.gov",
|
||
"ukragroconsult.com", "minagro.gov.ua", "rada.gov.ua", "kmu.gov.ua",
|
||
"agroportal.ua", "latifundist.com", "kurkul.com",
|
||
)
|
||
trusted_by_agent = {
|
||
"agromatrix": trusted_agro_domains,
|
||
"alateya": (
|
||
"europa.eu", "un.org", "worldbank.org", "oecd.org",
|
||
),
|
||
"clan": (
|
||
"europa.eu", "un.org", "wikipedia.org",
|
||
),
|
||
"daarwizz": (
|
||
"openai.com", "anthropic.com", "mistral.ai", "huggingface.co",
|
||
"python.org", "github.com",
|
||
),
|
||
"devtools": (
|
||
"github.com", "docs.python.org", "pypi.org", "docker.com",
|
||
"kubernetes.io", "fastapi.tiangolo.com", "postgresql.org",
|
||
),
|
||
"druid": (
|
||
"who.int", "nih.gov", "ncbi.nlm.nih.gov", "wikipedia.org",
|
||
),
|
||
"eonarch": (
|
||
"iea.org", "irena.org", "entsoe.eu", "europa.eu", "worldbank.org",
|
||
),
|
||
"greenfood": (
|
||
"fao.org", "who.int", "efsa.europa.eu", "usda.gov", "ec.europa.eu",
|
||
),
|
||
"senpai": (
|
||
"binance.com", "bybit.com", "coinbase.com", "kraken.com",
|
||
"coindesk.com", "cointelegraph.com", "tradingview.com",
|
||
"cftc.gov", "sec.gov", "esma.europa.eu",
|
||
),
|
||
"sofiia": (
|
||
"who.int", "nih.gov", "ncbi.nlm.nih.gov", "ema.europa.eu",
|
||
"fda.gov", "mayoclinic.org", "nhs.uk",
|
||
),
|
||
"helion": (
|
||
"iea.org", "irena.org", "entsoe.eu", "europa.eu", "worldbank.org",
|
||
),
|
||
"nutra": (
|
||
"fao.org", "who.int", "efsa.europa.eu", "fda.gov",
|
||
),
|
||
"microdao_orchestrator": (
|
||
"openai.com", "anthropic.com", "mistral.ai", "github.com",
|
||
"europa.eu", "un.org", "worldbank.org",
|
||
),
|
||
"monitor": (
|
||
"grafana.com", "prometheus.io", "elastic.co", "datadoghq.com",
|
||
"opentelemetry.io",
|
||
),
|
||
"soul": (
|
||
"who.int", "nih.gov", "ncbi.nlm.nih.gov", "wikipedia.org",
|
||
),
|
||
"yaromir": (
|
||
"europa.eu", "un.org", "worldbank.org", "wikipedia.org",
|
||
),
|
||
}
|
||
def _norm_domain_entry(value: Any) -> str:
|
||
if isinstance(value, dict):
|
||
value = value.get("url") or value.get("domain") or ""
|
||
value = str(value or "").strip().lower()
|
||
if not value:
|
||
return ""
|
||
value = value.replace("https://", "").replace("http://", "")
|
||
value = value.split("/")[0]
|
||
if value.startswith("www."):
|
||
value = value[4:]
|
||
return value
|
||
|
||
def _norm_domain_list(values: Any) -> List[str]:
|
||
out: List[str] = []
|
||
if not isinstance(values, list):
|
||
return out
|
||
for v in values:
|
||
d = _norm_domain_entry(v)
|
||
if d:
|
||
out.append(d)
|
||
return out
|
||
|
||
overrides = _load_trusted_domains_overrides()
|
||
extra_low_signal = _norm_domain_list(overrides.get("low_signal_domains"))
|
||
if extra_low_signal:
|
||
low_signal_domains = tuple(dict.fromkeys([*low_signal_domains, *extra_low_signal]))
|
||
extra_common = _norm_domain_list(overrides.get("common_domains"))
|
||
if extra_common:
|
||
trusted_common_domains = tuple(dict.fromkeys([*trusted_common_domains, *extra_common]))
|
||
agents_overrides = overrides.get("agents") if isinstance(overrides.get("agents"), dict) else {}
|
||
for a, cfg in agents_overrides.items():
|
||
if not isinstance(cfg, dict):
|
||
continue
|
||
doms = _norm_domain_list(cfg.get("domains"))
|
||
if doms:
|
||
base = trusted_by_agent.get(str(a).lower(), ())
|
||
merged = tuple(dict.fromkeys([*base, *doms]))
|
||
trusted_by_agent[str(a).lower()] = merged
|
||
agro_query_terms = {
|
||
"агро", "agro", "crop", "crops", "fertilizer", "fertilizers",
|
||
"field", "soil", "harvest", "yield", "pesticide", "herbicide",
|
||
"farm", "farming", "tractor", "зерно", "пшениц", "кукурудз",
|
||
"соняшник", "ріпак", "врожай", "ґрунт", "поле", "добрив",
|
||
"насіння", "ззр", "фермер",
|
||
}
|
||
query_terms = {t for t in re.findall(r"[a-zA-Zа-яА-ЯіїєІЇЄ0-9]{3,}", (query or "").lower())}
|
||
agro_mode = any(any(k in term for k in agro_query_terms) for term in query_terms)
|
||
agent_trusted_domains = trusted_by_agent.get((agent_id or "").lower(), ())
|
||
|
||
# Parse bullet blocks from tool output.
|
||
chunks = []
|
||
current = []
|
||
for line in text.splitlines():
|
||
ln = line.rstrip()
|
||
if ln.startswith("- ") and current:
|
||
chunks.append("\n".join(current))
|
||
current = [ln]
|
||
else:
|
||
current.append(ln)
|
||
if current:
|
||
chunks.append("\n".join(current))
|
||
|
||
scored = []
|
||
for chunk in chunks:
|
||
lines = [ln.strip() for ln in chunk.splitlines() if ln.strip()]
|
||
title = lines[0][2:].strip() if lines and lines[0].startswith("- ") else (lines[0] if lines else "")
|
||
url_line = next((ln for ln in lines if ln.lower().startswith("url:")), "")
|
||
url = url_line.split(":", 1)[1].strip() if ":" in url_line else ""
|
||
domain = _extract_domain(url)
|
||
text_blob = " ".join(lines).lower()
|
||
|
||
if any(x in domain for x in low_signal_domains):
|
||
continue
|
||
|
||
score = 0
|
||
for t in query_terms:
|
||
if t in text_blob:
|
||
score += 2
|
||
if any(tok in text_blob for tok in low_signal_tokens):
|
||
score -= 3
|
||
if domain.endswith(".gov") or domain.endswith(".gov.ua") or domain.endswith(".edu"):
|
||
score += 2
|
||
if any(domain == d or domain.endswith("." + d) for d in trusted_common_domains):
|
||
score += 2
|
||
if any(domain == d or domain.endswith("." + d) for d in agent_trusted_domains):
|
||
score += 2
|
||
if any(domain.endswith(d) for d in ("wikipedia.org", "wikidata.org", "fao.org", "europa.eu")):
|
||
score += 2
|
||
if agro_mode:
|
||
if any(domain == d or domain.endswith("." + d) for d in trusted_agro_domains):
|
||
score += 3
|
||
else:
|
||
score -= 1
|
||
if not url:
|
||
score -= 1
|
||
if len(title) < 6:
|
||
score -= 1
|
||
|
||
scored.append((score, domain, chunk))
|
||
|
||
def _is_trusted_agro(domain: str) -> bool:
|
||
if not domain:
|
||
return False
|
||
if any(domain == d or domain.endswith("." + d) for d in trusted_common_domains):
|
||
return True
|
||
return any(domain == d or domain.endswith("." + d) for d in trusted_agro_domains)
|
||
|
||
scored.sort(key=lambda x: x[0], reverse=True)
|
||
kept = []
|
||
seen_domains = set()
|
||
if agro_mode:
|
||
for s, domain, chunk in scored:
|
||
if s < 1 or not _is_trusted_agro(domain):
|
||
continue
|
||
if domain and domain in seen_domains:
|
||
continue
|
||
if domain:
|
||
seen_domains.add(domain)
|
||
kept.append(chunk)
|
||
if len(kept) >= 3:
|
||
break
|
||
|
||
if kept:
|
||
compact = "\n\n".join(kept).strip()
|
||
if len(compact) > max_chars:
|
||
compact = compact[:max_chars].rstrip() + "..."
|
||
return compact
|
||
|
||
for s, domain, chunk in scored:
|
||
if s < 2:
|
||
continue
|
||
if domain and domain in seen_domains:
|
||
continue
|
||
if domain:
|
||
seen_domains.add(domain)
|
||
kept.append(chunk)
|
||
if len(kept) >= 3:
|
||
break
|
||
if not kept:
|
||
return ""
|
||
|
||
compact = "\n\n".join(kept).strip()
|
||
if len(compact) > max_chars:
|
||
compact = compact[:max_chars].rstrip() + "..."
|
||
return compact
|
||
|
||
|
||
def _extract_sources_from_compact(compact: str, max_items: int = 3) -> List[Dict[str, str]]:
|
||
if not compact:
|
||
return []
|
||
items: List[Dict[str, str]] = []
|
||
chunks = [c for c in compact.split("\n\n") if c.strip()]
|
||
for chunk in chunks:
|
||
lines = [ln.strip() for ln in chunk.splitlines() if ln.strip()]
|
||
if not lines:
|
||
continue
|
||
title = lines[0][2:].strip() if lines[0].startswith("- ") else lines[0]
|
||
url_line = next((ln for ln in lines if ln.lower().startswith("url:")), "")
|
||
url = url_line.split(":", 1)[1].strip() if ":" in url_line else ""
|
||
if not url:
|
||
continue
|
||
items.append({"title": title[:180], "url": url[:500]})
|
||
if len(items) >= max_items:
|
||
break
|
||
return items
|
||
|
||
def _condition_matches(cond: Dict[str, Any], agent_id: str, metadata: Dict[str, Any]) -> bool:
|
||
"""Minimal matcher for router-config `when` conditions."""
|
||
if not isinstance(cond, dict):
|
||
return True
|
||
|
||
meta = metadata or {}
|
||
|
||
if "agent" in cond and cond.get("agent") != agent_id:
|
||
return False
|
||
|
||
if "mode" in cond and meta.get("mode") != cond.get("mode"):
|
||
return False
|
||
|
||
if "metadata_has" in cond:
|
||
key = cond.get("metadata_has")
|
||
if key not in meta:
|
||
return False
|
||
|
||
if "metadata_equals" in cond:
|
||
eq = cond.get("metadata_equals") or {}
|
||
for k, v in eq.items():
|
||
if meta.get(k) != v:
|
||
return False
|
||
|
||
if "task_type" in cond:
|
||
expected = cond.get("task_type")
|
||
actual = meta.get("task_type")
|
||
if isinstance(expected, list):
|
||
if actual not in expected:
|
||
return False
|
||
elif actual != expected:
|
||
return False
|
||
|
||
if "api_key_available" in cond:
|
||
env_name = cond.get("api_key_available")
|
||
if not (isinstance(env_name, str) and os.getenv(env_name)):
|
||
return False
|
||
|
||
if "and" in cond:
|
||
clauses = cond.get("and") or []
|
||
if not isinstance(clauses, list):
|
||
return False
|
||
for clause in clauses:
|
||
if not _condition_matches(clause, agent_id, meta):
|
||
return False
|
||
|
||
return True
|
||
|
||
|
||
def _select_default_llm(agent_id: str, metadata: Dict[str, Any], base_llm: str, routing_rules: List[Dict[str, Any]]) -> str:
|
||
"""Select LLM by first matching routing rule with `use_llm`."""
|
||
for rule in routing_rules:
|
||
when = rule.get("when", {})
|
||
if _condition_matches(when, agent_id, metadata):
|
||
use_llm = rule.get("use_llm")
|
||
if use_llm:
|
||
logger.info(f"🎯 Agent {agent_id} routing rule {rule.get('id', '<no-id>')} -> {use_llm}")
|
||
return use_llm
|
||
return base_llm
|
||
|
||
app = FastAPI(title="DAARION Router", version="2.0.0")
|
||
|
||
# Configuration
|
||
NATS_URL = os.getenv("NATS_URL", "nats://nats:4222")
|
||
SWAPPER_URL = os.getenv("SWAPPER_URL", "http://swapper-service:8890")
|
||
# All multimodal services now through Swapper
|
||
STT_URL = os.getenv("STT_URL", "http://swapper-service:8890") # Swapper /stt endpoint
|
||
TTS_URL = os.getenv("TTS_URL", "http://swapper-service:8890") # Swapper /tts endpoint
|
||
VISION_URL = os.getenv("VISION_URL", "http://172.18.0.1:11434") # Host Ollama
|
||
OCR_URL = os.getenv("OCR_URL", "http://swapper-service:8890") # Swapper /ocr endpoint
|
||
DOCUMENT_URL = os.getenv("DOCUMENT_URL", "http://swapper-service:8890") # Swapper /document endpoint
|
||
CITY_SERVICE_URL = os.getenv("CITY_SERVICE_URL", "http://daarion-city-service:7001")
|
||
|
||
# CrewAI Routing Configuration
|
||
CREWAI_ROUTING_ENABLED = os.getenv("CREWAI_ROUTING_ENABLED", "true").lower() == "true"
|
||
CREWAI_URL = os.getenv("CREWAI_URL", "http://dagi-staging-crewai-service:9010")
|
||
CLAN_RUNTIME_GUARD_ENABLED = os.getenv("CLAN_RUNTIME_GUARD_ENABLED", "true").lower() == "true"
|
||
CLAN_RUNTIME_GUARD_MODE = os.getenv("CLAN_RUNTIME_GUARD_MODE", "soft").lower()
|
||
CLAN_GUARD_TEST_MODE = os.getenv("CLAN_GUARD_TEST_MODE", "false").lower() == "true"
|
||
CLAN_RUNTIME_REGISTRY_PATH = os.getenv("CLAN_RUNTIME_REGISTRY_PATH", "/app/config/roles/clan/zhos/agents_registry.yaml")
|
||
CLAN_RUNTIME_ENVELOPE_SCHEMA_PATH = os.getenv("CLAN_RUNTIME_ENVELOPE_SCHEMA_PATH", "/app/docs/contracts/clan-envelope.schema.json")
|
||
CLAN_RUNTIME_ARTIFACT_SCHEMA_PATH = os.getenv("CLAN_RUNTIME_ARTIFACT_SCHEMA_PATH", "/app/docs/contracts/clan-artifact.schema.json")
|
||
CLAN_RUNTIME_CONSENT_EVENT_SCHEMA_PATH = os.getenv(
|
||
"CLAN_RUNTIME_CONSENT_EVENT_SCHEMA_PATH",
|
||
"/app/docs/contracts/clan-consent-event.schema.json",
|
||
)
|
||
|
||
# Neo4j Configuration
|
||
NEO4J_URI = os.getenv("NEO4J_BOLT_URL", "bolt://neo4j:7687")
|
||
NEO4J_USER = os.getenv("NEO4J_USER", "neo4j")
|
||
NEO4J_PASSWORD = os.getenv("NEO4J_PASSWORD", "DaarionNeo4j2026!")
|
||
|
||
# HTTP client for backend services
|
||
http_client: Optional[httpx.AsyncClient] = None
|
||
|
||
# Neo4j driver
|
||
neo4j_driver = None
|
||
neo4j_available = False
|
||
|
||
# NATS client
|
||
nc = None
|
||
nats_available = False
|
||
|
||
# Tool Manager
|
||
tool_manager = None
|
||
runtime_guard_engine = None
|
||
|
||
# Models
|
||
class FilterDecision(BaseModel):
|
||
channel_id: str
|
||
message_id: Optional[str] = None
|
||
matrix_event_id: str
|
||
microdao_id: str
|
||
decision: Literal["allow", "deny", "modify"]
|
||
target_agent_id: Optional[str] = None
|
||
rewrite_prompt: Optional[str] = None
|
||
|
||
class AgentInvocation(BaseModel):
|
||
agent_id: str
|
||
entrypoint: Literal["channel_message", "direct", "cron"] = "channel_message"
|
||
payload: Dict[str, Any]
|
||
|
||
# Load config
|
||
def load_config():
|
||
config_path = "router_config.yaml"
|
||
if os.path.exists(config_path):
|
||
with open(config_path, 'r') as f:
|
||
return yaml.safe_load(f)
|
||
return {
|
||
"messaging_inbound": {
|
||
"enabled": True,
|
||
"source_subject": "agent.filter.decision",
|
||
"target_subject": "router.invoke.agent"
|
||
}
|
||
}
|
||
|
||
def load_router_config():
|
||
"""Load main router-config.yml with agents and LLM profiles"""
|
||
# Try multiple locations
|
||
paths = [
|
||
"router-config.yml",
|
||
"/app/router-config.yml",
|
||
"../router-config.yml",
|
||
"../../router-config.yml"
|
||
]
|
||
|
||
for path in paths:
|
||
if os.path.exists(path):
|
||
with open(path, 'r') as f:
|
||
logger.info(f"✅ Loaded router config from {path}")
|
||
return yaml.safe_load(f)
|
||
|
||
logger.warning("⚠️ router-config.yml not found, using empty config")
|
||
return {"agents": {}}
|
||
|
||
config = load_config()
|
||
router_config = load_router_config()
|
||
|
||
@app.on_event("startup")
|
||
async def startup_event():
|
||
"""Initialize NATS connection and subscriptions"""
|
||
global nc, nats_available, http_client, neo4j_driver, neo4j_available, runtime_guard_engine
|
||
logger.info("🚀 DAGI Router v2.0.0 starting up...")
|
||
|
||
# Initialize HTTP client
|
||
http_client = httpx.AsyncClient(timeout=60.0)
|
||
logger.info("✅ HTTP client initialized")
|
||
|
||
# Initialize Neo4j connection
|
||
try:
|
||
neo4j_driver = AsyncGraphDatabase.driver(
|
||
NEO4J_URI,
|
||
auth=(NEO4J_USER, NEO4J_PASSWORD)
|
||
)
|
||
# Verify connection
|
||
async with neo4j_driver.session() as session:
|
||
result = await session.run("RETURN 1 as test")
|
||
await result.consume()
|
||
neo4j_available = True
|
||
logger.info(f"✅ Connected to Neo4j at {NEO4J_URI}")
|
||
except Exception as e:
|
||
logger.warning(f"⚠️ Neo4j not available: {e}")
|
||
neo4j_available = False
|
||
|
||
# Try to connect to NATS
|
||
try:
|
||
import nats
|
||
nc = await nats.connect(NATS_URL)
|
||
nats_available = True
|
||
logger.info(f"✅ Connected to NATS at {NATS_URL}")
|
||
|
||
# Subscribe to filter decisions if enabled
|
||
if config.get("messaging_inbound", {}).get("enabled", True):
|
||
asyncio.create_task(subscribe_to_filter_decisions())
|
||
else:
|
||
logger.warning("⚠️ Messaging inbound routing disabled in config")
|
||
except Exception as e:
|
||
logger.warning(f"⚠️ NATS not available: {e}")
|
||
logger.warning("⚠️ Running in test mode (HTTP only)")
|
||
nats_available = False
|
||
|
||
# Initialize Memory Retrieval Pipeline
|
||
if MEMORY_RETRIEVAL_AVAILABLE and memory_retrieval:
|
||
try:
|
||
await memory_retrieval.initialize()
|
||
logger.info("✅ Memory Retrieval Pipeline initialized")
|
||
except Exception as e:
|
||
logger.warning(f"⚠️ Memory Retrieval init failed: {e}")
|
||
|
||
# Initialize Tool Manager for function calling
|
||
global tool_manager
|
||
if TOOL_MANAGER_AVAILABLE and ToolManager:
|
||
try:
|
||
tool_manager = ToolManager(router_config)
|
||
logger.info("✅ Tool Manager initialized with function calling")
|
||
except Exception as e:
|
||
logger.warning(f"⚠️ Tool Manager init failed: {e}")
|
||
tool_manager = None
|
||
else:
|
||
tool_manager = None
|
||
|
||
# Initialize CLAN runtime guard
|
||
if RUNTIME_GUARD_AVAILABLE and RuntimeGuard and CLAN_RUNTIME_GUARD_ENABLED:
|
||
try:
|
||
runtime_guard_engine = RuntimeGuard(
|
||
registry_path=CLAN_RUNTIME_REGISTRY_PATH,
|
||
envelope_schema_path=CLAN_RUNTIME_ENVELOPE_SCHEMA_PATH,
|
||
artifact_schema_path=CLAN_RUNTIME_ARTIFACT_SCHEMA_PATH,
|
||
consent_event_schema_path=CLAN_RUNTIME_CONSENT_EVENT_SCHEMA_PATH,
|
||
mode=CLAN_RUNTIME_GUARD_MODE,
|
||
)
|
||
logger.info(
|
||
"✅ CLAN Runtime Guard initialized "
|
||
f"(mode={CLAN_RUNTIME_GUARD_MODE}, registry={CLAN_RUNTIME_REGISTRY_PATH})"
|
||
)
|
||
except Exception as e:
|
||
logger.warning(f"⚠️ Runtime Guard init failed: {e}")
|
||
runtime_guard_engine = None
|
||
else:
|
||
runtime_guard_engine = None
|
||
|
||
# Log backend URLs
|
||
logger.info(f"📡 Swapper URL: {SWAPPER_URL}")
|
||
logger.info(f"📡 STT URL: {STT_URL}")
|
||
logger.info(f"📡 Vision URL: {VISION_URL}")
|
||
logger.info(f"📡 OCR URL: {OCR_URL}")
|
||
logger.info(f"📡 Neo4j URL: {NEO4J_URI}")
|
||
|
||
async def subscribe_to_filter_decisions():
|
||
"""Subscribe to agent.filter.decision events"""
|
||
if not nc:
|
||
return
|
||
|
||
source_subject = config.get("messaging_inbound", {}).get(
|
||
"source_subject",
|
||
"agent.filter.decision"
|
||
)
|
||
|
||
try:
|
||
sub = await nc.subscribe(source_subject)
|
||
print(f"✅ Subscribed to {source_subject}")
|
||
|
||
async for msg in sub.messages:
|
||
try:
|
||
decision_data = json.loads(msg.data.decode())
|
||
await handle_filter_decision(decision_data)
|
||
except Exception as e:
|
||
print(f"❌ Error processing decision: {e}")
|
||
import traceback
|
||
traceback.print_exc()
|
||
except Exception as e:
|
||
print(f"❌ Subscription error: {e}")
|
||
|
||
async def handle_filter_decision(decision_data: dict):
|
||
"""
|
||
Process agent.filter.decision events
|
||
|
||
Only processes 'allow' decisions
|
||
Creates AgentInvocation and publishes to router.invoke.agent
|
||
"""
|
||
try:
|
||
print(f"\n🔀 Processing filter decision")
|
||
decision = FilterDecision(**decision_data)
|
||
|
||
# Only process 'allow' decisions
|
||
if decision.decision != "allow":
|
||
print(f"⏭️ Ignoring non-allow decision: {decision.decision}")
|
||
return
|
||
|
||
if not decision.target_agent_id:
|
||
print(f"⚠️ No target agent specified, skipping")
|
||
return
|
||
|
||
print(f"✅ Decision: allow")
|
||
print(f"📝 Target: {decision.target_agent_id}")
|
||
print(f"📝 Channel: {decision.channel_id}")
|
||
|
||
# Create AgentInvocation
|
||
invocation = AgentInvocation(
|
||
agent_id=decision.target_agent_id,
|
||
entrypoint="channel_message",
|
||
payload={
|
||
"channel_id": decision.channel_id,
|
||
"message_id": decision.message_id,
|
||
"matrix_event_id": decision.matrix_event_id,
|
||
"microdao_id": decision.microdao_id,
|
||
"rewrite_prompt": decision.rewrite_prompt
|
||
}
|
||
)
|
||
|
||
print(f"🚀 Created invocation for {invocation.agent_id}")
|
||
|
||
# Publish to NATS
|
||
await publish_agent_invocation(invocation)
|
||
|
||
except Exception as e:
|
||
print(f"❌ Error handling decision: {e}")
|
||
import traceback
|
||
traceback.print_exc()
|
||
|
||
async def publish_agent_invocation(invocation: AgentInvocation):
|
||
"""Publish AgentInvocation to router.invoke.agent"""
|
||
if nc and nats_available:
|
||
target_subject = config.get("messaging_inbound", {}).get(
|
||
"target_subject",
|
||
"router.invoke.agent"
|
||
)
|
||
|
||
try:
|
||
await nc.publish(target_subject, invocation.json().encode())
|
||
print(f"✅ Published invocation to {target_subject}")
|
||
except Exception as e:
|
||
print(f"❌ Error publishing to NATS: {e}")
|
||
else:
|
||
print(f"⚠️ NATS not available, invocation not published: {invocation.json()}")
|
||
|
||
|
||
|
||
# ==============================================================
|
||
# PROMETHEUS METRICS ENDPOINT
|
||
# ==============================================================
|
||
@app.get("/metrics")
|
||
async def prometheus_metrics():
|
||
"""Prometheus metrics endpoint."""
|
||
try:
|
||
from agent_metrics import get_metrics, get_content_type
|
||
return Response(content=get_metrics(), media_type=get_content_type())
|
||
except Exception as e:
|
||
logger.error(f"Metrics error: {e}")
|
||
return Response(content=b"# Error generating metrics", media_type="text/plain")
|
||
|
||
@app.get("/health")
|
||
async def health():
|
||
"""Health check endpoint"""
|
||
return {
|
||
"status": "ok",
|
||
"service": "router",
|
||
"version": "1.0.0",
|
||
"nats_connected": nats_available,
|
||
"messaging_inbound_enabled": config.get("messaging_inbound", {}).get("enabled", True)
|
||
}
|
||
|
||
@app.post("/internal/router/test-messaging", response_model=AgentInvocation)
|
||
async def test_messaging_route(decision: FilterDecision):
|
||
"""
|
||
Test endpoint for routing logic
|
||
|
||
Tests filter decision → agent invocation mapping without NATS
|
||
"""
|
||
print(f"\n🧪 Test routing request")
|
||
|
||
if decision.decision != "allow" or not decision.target_agent_id:
|
||
raise HTTPException(
|
||
status_code=400,
|
||
detail=f"Decision not routable: {decision.decision}, agent: {decision.target_agent_id}"
|
||
)
|
||
|
||
invocation = AgentInvocation(
|
||
agent_id=decision.target_agent_id,
|
||
entrypoint="channel_message",
|
||
payload={
|
||
"channel_id": decision.channel_id,
|
||
"message_id": decision.message_id,
|
||
"matrix_event_id": decision.matrix_event_id,
|
||
"microdao_id": decision.microdao_id,
|
||
"rewrite_prompt": decision.rewrite_prompt
|
||
}
|
||
)
|
||
|
||
print(f"✅ Test invocation created for {invocation.agent_id}")
|
||
return invocation
|
||
|
||
@app.on_event("shutdown")
|
||
async def shutdown_event():
|
||
"""Clean shutdown"""
|
||
global nc, http_client
|
||
if nc:
|
||
await nc.close()
|
||
logger.info("✅ NATS connection closed")
|
||
if http_client:
|
||
await http_client.aclose()
|
||
logger.info("✅ HTTP client closed")
|
||
|
||
|
||
# ============================================================================
|
||
# Backend Integration Endpoints
|
||
# ============================================================================
|
||
|
||
class InferRequest(BaseModel):
|
||
"""Request for agent inference"""
|
||
prompt: str
|
||
model: Optional[str] = None
|
||
max_tokens: Optional[int] = 2048
|
||
temperature: Optional[float] = 0.7
|
||
system_prompt: Optional[str] = None
|
||
images: Optional[List[str]] = None # List of base64 data URLs for vision
|
||
metadata: Optional[Dict[str, Any]] = None # Additional metadata (user_id, chat_id, etc.)
|
||
|
||
|
||
class InferResponse(BaseModel):
|
||
"""Response from agent inference"""
|
||
response: str
|
||
model: str
|
||
tokens_used: Optional[int] = None
|
||
backend: str
|
||
image_base64: Optional[str] = None # Generated image in base64 format
|
||
file_base64: Optional[str] = None
|
||
file_name: Optional[str] = None
|
||
file_mime: Optional[str] = None
|
||
|
||
|
||
|
||
|
||
# =========================================================================
|
||
# INTERNAL LLM API (for CrewAI and internal services)
|
||
# =========================================================================
|
||
|
||
class InternalLLMRequest(BaseModel):
|
||
prompt: str
|
||
system_prompt: Optional[str] = None
|
||
llm_profile: Optional[str] = "reasoning"
|
||
model: Optional[str] = None
|
||
max_tokens: Optional[int] = 2048
|
||
temperature: Optional[float] = 0.2
|
||
role_context: Optional[str] = None
|
||
metadata: Optional[Dict[str, Any]] = None
|
||
|
||
|
||
class InternalLLMResponse(BaseModel):
|
||
text: str
|
||
model: str
|
||
provider: str
|
||
tokens_used: int = 0
|
||
latency_ms: int = 0
|
||
|
||
|
||
class BackendStatus(BaseModel):
|
||
"""Status of a backend service"""
|
||
name: str
|
||
url: str
|
||
status: str # online, offline, error
|
||
active_model: Optional[str] = None
|
||
error: Optional[str] = None
|
||
|
||
|
||
@app.get("/backends/status", response_model=List[BackendStatus])
|
||
async def get_backends_status():
|
||
"""Get status of all backend services"""
|
||
backends = []
|
||
|
||
# Check Swapper
|
||
try:
|
||
resp = await http_client.get(f"{SWAPPER_URL}/health", timeout=5.0)
|
||
if resp.status_code == 200:
|
||
data = resp.json()
|
||
backends.append(BackendStatus(
|
||
name="swapper",
|
||
url=SWAPPER_URL,
|
||
status="online",
|
||
active_model=data.get("active_model")
|
||
))
|
||
else:
|
||
backends.append(BackendStatus(
|
||
name="swapper",
|
||
url=SWAPPER_URL,
|
||
status="error",
|
||
error=f"HTTP {resp.status_code}"
|
||
))
|
||
except Exception as e:
|
||
backends.append(BackendStatus(
|
||
name="swapper",
|
||
url=SWAPPER_URL,
|
||
status="offline",
|
||
error=str(e)
|
||
))
|
||
|
||
# Check STT
|
||
try:
|
||
resp = await http_client.get(f"{STT_URL}/health", timeout=5.0)
|
||
backends.append(BackendStatus(
|
||
name="stt",
|
||
url=STT_URL,
|
||
status="online" if resp.status_code == 200 else "error"
|
||
))
|
||
except Exception as e:
|
||
backends.append(BackendStatus(
|
||
name="stt",
|
||
url=STT_URL,
|
||
status="offline",
|
||
error=str(e)
|
||
))
|
||
|
||
# Check Vision (Ollama)
|
||
try:
|
||
resp = await http_client.get(f"{VISION_URL}/api/tags", timeout=5.0)
|
||
if resp.status_code == 200:
|
||
data = resp.json()
|
||
models = [m.get("name") for m in data.get("models", [])]
|
||
backends.append(BackendStatus(
|
||
name="vision",
|
||
url=VISION_URL,
|
||
status="online",
|
||
active_model=", ".join(models[:3]) if models else None
|
||
))
|
||
else:
|
||
backends.append(BackendStatus(
|
||
name="vision",
|
||
url=VISION_URL,
|
||
status="error"
|
||
))
|
||
except Exception as e:
|
||
backends.append(BackendStatus(
|
||
name="vision",
|
||
url=VISION_URL,
|
||
status="offline",
|
||
error=str(e)
|
||
))
|
||
|
||
# Check OCR
|
||
try:
|
||
resp = await http_client.get(f"{OCR_URL}/health", timeout=5.0)
|
||
backends.append(BackendStatus(
|
||
name="ocr",
|
||
url=OCR_URL,
|
||
status="online" if resp.status_code == 200 else "error"
|
||
))
|
||
except Exception as e:
|
||
backends.append(BackendStatus(
|
||
name="ocr",
|
||
url=OCR_URL,
|
||
status="offline",
|
||
error=str(e)
|
||
))
|
||
|
||
return backends
|
||
|
||
|
||
|
||
|
||
# =========================================================================
|
||
# INTERNAL LLM COMPLETE ENDPOINT (for CrewAI)
|
||
# =========================================================================
|
||
|
||
@app.post("/internal/llm/complete", response_model=InternalLLMResponse)
|
||
async def internal_llm_complete(request: InternalLLMRequest):
|
||
"""
|
||
Internal LLM completion endpoint.
|
||
NO routing, NO CrewAI decision, NO agent selection.
|
||
Used by CrewAI service for multi-role orchestration.
|
||
"""
|
||
import time as time_module
|
||
t0 = time_module.time()
|
||
|
||
logger.info(f"Internal LLM: profile={request.llm_profile}, role={request.role_context}")
|
||
|
||
# Get LLM profile configuration
|
||
llm_profiles = router_config.get("llm_profiles", {})
|
||
profile_name = request.llm_profile or "reasoning"
|
||
llm_profile = llm_profiles.get(profile_name, {})
|
||
|
||
provider = llm_profile.get("provider", "deepseek")
|
||
model = request.model or llm_profile.get("model", "deepseek-chat")
|
||
max_tokens = request.max_tokens or llm_profile.get("max_tokens", 2048)
|
||
temperature = request.temperature or llm_profile.get("temperature", 0.2)
|
||
|
||
# Build messages
|
||
messages = []
|
||
if request.system_prompt:
|
||
system_content = request.system_prompt
|
||
if request.role_context:
|
||
system_content = f"[Role: {request.role_context}]\n\n{system_content}"
|
||
messages.append({"role": "system", "content": system_content})
|
||
elif request.role_context:
|
||
messages.append({"role": "system", "content": f"You are acting as {request.role_context}. Respond professionally."})
|
||
|
||
messages.append({"role": "user", "content": request.prompt})
|
||
|
||
# Cloud providers
|
||
cloud_providers = [
|
||
{"name": "deepseek", "api_key_env": "DEEPSEEK_API_KEY", "base_url": "https://api.deepseek.com", "model": "deepseek-chat", "timeout": 60},
|
||
{"name": "mistral", "api_key_env": "MISTRAL_API_KEY", "base_url": "https://api.mistral.ai", "model": "mistral-large-latest", "timeout": 60},
|
||
{"name": "grok", "api_key_env": "GROK_API_KEY", "base_url": "https://api.x.ai", "model": "grok-2-1212", "timeout": 60}
|
||
]
|
||
|
||
# Respect configured provider: local profiles should stay local.
|
||
if provider in ["deepseek", "mistral", "grok"]:
|
||
cloud_providers = sorted(cloud_providers, key=lambda x: 0 if x["name"] == provider else 1)
|
||
elif provider == "ollama":
|
||
cloud_providers = []
|
||
|
||
# Try cloud providers (only when provider is cloud)
|
||
for cloud in cloud_providers:
|
||
api_key = os.getenv(cloud["api_key_env"])
|
||
if not api_key:
|
||
continue
|
||
|
||
try:
|
||
logger.debug(f"Internal LLM trying {cloud['name']}")
|
||
cloud_resp = await http_client.post(
|
||
f"{cloud['base_url']}/v1/chat/completions",
|
||
headers={"Authorization": f"Bearer {api_key}", "Content-Type": "application/json"},
|
||
json={"model": cloud["model"], "messages": messages, "max_tokens": max_tokens, "temperature": temperature, "stream": False},
|
||
timeout=cloud["timeout"]
|
||
)
|
||
|
||
if cloud_resp.status_code == 200:
|
||
data = cloud_resp.json()
|
||
response_text = data.get("choices", [{}])[0].get("message", {}).get("content", "")
|
||
tokens = data.get("usage", {}).get("total_tokens", 0)
|
||
latency = int((time_module.time() - t0) * 1000)
|
||
logger.info(f"Internal LLM success: {cloud['name']}, {tokens} tokens, {latency}ms")
|
||
return InternalLLMResponse(text=response_text, model=cloud["model"], provider=cloud["name"], tokens_used=tokens, latency_ms=latency)
|
||
except Exception as e:
|
||
logger.warning(f"Internal LLM {cloud['name']} failed: {e}")
|
||
continue
|
||
|
||
# Fallback/target local provider (Ollama)
|
||
try:
|
||
logger.info("Internal LLM to Ollama")
|
||
ollama_model = model or "qwen3:8b"
|
||
ollama_resp = await http_client.post(
|
||
"http://172.18.0.1:11434/api/generate",
|
||
json={"model": ollama_model, "prompt": request.prompt, "system": request.system_prompt or "", "stream": False, "options": {"num_predict": max_tokens, "temperature": temperature}},
|
||
timeout=120.0
|
||
)
|
||
if ollama_resp.status_code == 200:
|
||
data = ollama_resp.json()
|
||
latency = int((time_module.time() - t0) * 1000)
|
||
return InternalLLMResponse(text=data.get("response", ""), model=ollama_model, provider="ollama", tokens_used=0, latency_ms=latency)
|
||
except Exception as e:
|
||
logger.error(f"Internal LLM Ollama failed: {e}")
|
||
|
||
raise HTTPException(status_code=503, detail="All LLM providers unavailable")
|
||
|
||
|
||
@app.post("/v1/agents/{agent_id}/infer", response_model=InferResponse)
|
||
async def agent_infer(agent_id: str, request: InferRequest):
|
||
"""
|
||
Route inference request to appropriate backend.
|
||
|
||
Router decides which backend to use based on:
|
||
- Agent configuration (model, capabilities)
|
||
- Request type (text, vision, audio)
|
||
- Backend availability
|
||
|
||
System prompt is fetched from database via city-service API.
|
||
Memory context is retrieved via Memory Retrieval Pipeline v3.0.
|
||
"""
|
||
logger.info(f"🔀 Inference request for agent: {agent_id}")
|
||
logger.info(f"📝 Prompt: {request.prompt[:100]}...")
|
||
|
||
# =========================================================================
|
||
# MEMORY RETRIEVAL (v4.0 - Universal for all agents)
|
||
# =========================================================================
|
||
memory_brief_text = ""
|
||
# Extract metadata once for both retrieval and storage
|
||
metadata = request.metadata or {}
|
||
channel = "telegram" # Default
|
||
chat_id = str(metadata.get("chat_id", ""))
|
||
user_id = str(metadata.get("user_id", "")).replace("tg:", "")
|
||
username = metadata.get("username")
|
||
# Get agent_id from metadata or URL parameter
|
||
request_agent_id = metadata.get("agent_id", agent_id).lower()
|
||
|
||
if MEMORY_RETRIEVAL_AVAILABLE and memory_retrieval:
|
||
try:
|
||
if chat_id and user_id:
|
||
brief = await memory_retrieval.retrieve(
|
||
channel=channel,
|
||
chat_id=chat_id,
|
||
user_id=user_id,
|
||
agent_id=request_agent_id, # Agent-specific collections
|
||
username=username,
|
||
message=request.prompt
|
||
)
|
||
memory_brief_text = brief.to_text(max_lines=10)
|
||
if memory_brief_text:
|
||
logger.info(f"🧠 Memory brief for {request_agent_id}: {len(memory_brief_text)} chars")
|
||
except Exception as e:
|
||
logger.warning(f"⚠️ Memory retrieval failed for {request_agent_id}: {e}")
|
||
|
||
# Get system prompt from database or config
|
||
system_prompt = request.system_prompt
|
||
|
||
system_prompt_source = "request"
|
||
if system_prompt:
|
||
logger.info(f"📝 Received system_prompt from request: {len(system_prompt)} chars")
|
||
logger.debug(f"System prompt preview: {system_prompt[:200]}...")
|
||
else:
|
||
system_prompt_source = "city_service"
|
||
logger.info(f"ℹ️ No system_prompt in request for agent {agent_id}, loading from configured sources")
|
||
|
||
if not system_prompt:
|
||
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")
|
||
|
||
if not system_prompt:
|
||
system_prompt_source = "empty"
|
||
logger.warning(f"⚠️ System prompt unavailable for {agent_id}; continuing with provider defaults")
|
||
|
||
system_prompt_hash = hashlib.sha256((system_prompt or "").encode("utf-8")).hexdigest()[:12]
|
||
effective_metadata = dict(metadata)
|
||
effective_metadata["system_prompt_hash"] = system_prompt_hash
|
||
effective_metadata["system_prompt_source"] = system_prompt_source
|
||
effective_metadata["system_prompt_version"] = (
|
||
metadata.get("system_prompt_version")
|
||
or f"{agent_id}:{system_prompt_hash}"
|
||
)
|
||
logger.info(
|
||
f"🧩 Prompt meta for {agent_id}: source={system_prompt_source}, "
|
||
f"version={effective_metadata['system_prompt_version']}, hash={system_prompt_hash}"
|
||
)
|
||
|
||
# Determine which backend to use
|
||
# Use router config to get default model for agent, fallback to qwen3:8b
|
||
agent_config = router_config.get("agents", {}).get(agent_id, {})
|
||
|
||
# =========================================================================
|
||
# CREWAI DECISION: Use orchestration or direct LLM?
|
||
# =========================================================================
|
||
if CREWAI_ROUTING_ENABLED and CREWAI_CLIENT_AVAILABLE:
|
||
try:
|
||
runtime_envelope = None
|
||
if runtime_guard_engine and request_agent_id == "clan":
|
||
runtime_envelope = runtime_guard_engine.build_envelope(
|
||
agent_id=request_agent_id,
|
||
prompt=request.prompt,
|
||
metadata=effective_metadata,
|
||
)
|
||
ok_pre, pre_info = runtime_guard_engine.pre_dispatch_checks(runtime_envelope)
|
||
if not ok_pre:
|
||
stop_payload = runtime_guard_engine.stop_payload(runtime_envelope, pre_info)
|
||
logger.warning(
|
||
"🛑 Runtime guard pre-dispatch stop: "
|
||
f"code={stop_payload.get('stop_code')} request_id={stop_payload.get('request_id')} "
|
||
f"input_hash={stop_payload.get('input_hash')}"
|
||
)
|
||
return InferResponse(
|
||
response=json.dumps(stop_payload, ensure_ascii=False),
|
||
model="runtime-guard",
|
||
backend="runtime-guard",
|
||
tokens_used=0,
|
||
)
|
||
if (
|
||
CLAN_GUARD_TEST_MODE
|
||
and effective_metadata.get("guard_self_test") is True
|
||
and isinstance(effective_metadata.get("__inject_fake_agent_result"), dict)
|
||
):
|
||
fake_result = effective_metadata.get("__inject_fake_agent_result")
|
||
ok_post, post_info = runtime_guard_engine.post_return_checks(runtime_envelope, fake_result)
|
||
if not ok_post:
|
||
stop_payload = runtime_guard_engine.stop_payload(runtime_envelope, post_info)
|
||
logger.warning(
|
||
"🧪 Runtime guard self-test stop: "
|
||
f"code={stop_payload.get('stop_code')} request_id={stop_payload.get('request_id')} "
|
||
f"input_hash={stop_payload.get('input_hash')}"
|
||
)
|
||
return InferResponse(
|
||
response=json.dumps(stop_payload, ensure_ascii=False),
|
||
model="runtime-guard",
|
||
backend="runtime-guard",
|
||
tokens_used=0,
|
||
)
|
||
logger.info("🧪 Runtime guard self-test passed (fake result accepted)")
|
||
return InferResponse(
|
||
response=json.dumps({"ok": True, "self_test": True}, ensure_ascii=False),
|
||
model="runtime-guard",
|
||
backend="runtime-guard",
|
||
tokens_used=0,
|
||
)
|
||
|
||
# Get agent CrewAI config from registry (or router_config fallback)
|
||
crewai_cfg = agent_config.get("crewai", {})
|
||
|
||
use_crewai, crewai_reason = should_use_crewai(
|
||
agent_id=agent_id,
|
||
prompt=request.prompt,
|
||
agent_config=agent_config,
|
||
metadata=effective_metadata,
|
||
force_crewai=effective_metadata.get("force_crewai", False),
|
||
)
|
||
|
||
logger.info(f"🎭 CrewAI decision for {agent_id}: {use_crewai} ({crewai_reason})")
|
||
|
||
if use_crewai:
|
||
t0 = time.time()
|
||
crew_result = await call_crewai(
|
||
agent_id=agent_id,
|
||
task=request.prompt,
|
||
context={
|
||
"memory_brief": memory_brief_text,
|
||
"system_prompt": system_prompt,
|
||
"system_prompt_meta": {
|
||
"source": system_prompt_source,
|
||
"version": effective_metadata.get("system_prompt_version"),
|
||
"hash": system_prompt_hash,
|
||
},
|
||
"metadata": effective_metadata,
|
||
"runtime_envelope": runtime_envelope,
|
||
},
|
||
team=crewai_cfg.get("team"),
|
||
profile=effective_metadata.get("crewai_profile")
|
||
)
|
||
|
||
latency = time.time() - t0
|
||
|
||
if crew_result.get("success") and crew_result.get("result"):
|
||
if runtime_guard_engine and request_agent_id == "clan" and runtime_envelope:
|
||
ok_post, post_info = runtime_guard_engine.post_return_checks(runtime_envelope, crew_result)
|
||
if not ok_post:
|
||
stop_payload = runtime_guard_engine.stop_payload(runtime_envelope, post_info)
|
||
logger.warning(
|
||
"🛑 Runtime guard post-return stop: "
|
||
f"code={stop_payload.get('stop_code')} request_id={stop_payload.get('request_id')} "
|
||
f"input_hash={stop_payload.get('input_hash')}"
|
||
)
|
||
return InferResponse(
|
||
response=json.dumps(stop_payload, ensure_ascii=False),
|
||
model="runtime-guard",
|
||
backend="runtime-guard",
|
||
tokens_used=0,
|
||
)
|
||
crew_result = runtime_guard_engine.stamp_result_artifacts(runtime_envelope, crew_result)
|
||
ok_stamp, stamp_info = runtime_guard_engine.ensure_stamped_trails(crew_result)
|
||
if not ok_stamp:
|
||
stop_payload = runtime_guard_engine.stop_payload(runtime_envelope, stamp_info)
|
||
logger.warning(
|
||
"🛑 Runtime guard stamped-trail stop: "
|
||
f"code={stop_payload.get('stop_code')} request_id={stop_payload.get('request_id')} "
|
||
f"input_hash={stop_payload.get('input_hash')}"
|
||
)
|
||
return InferResponse(
|
||
response=json.dumps(stop_payload, ensure_ascii=False),
|
||
model="runtime-guard",
|
||
backend="runtime-guard",
|
||
tokens_used=0,
|
||
)
|
||
for row in runtime_guard_engine.artifact_runtime_rows(runtime_envelope, crew_result):
|
||
logger.info(json.dumps(row, ensure_ascii=False))
|
||
for row in runtime_guard_engine.consent_runtime_rows(runtime_envelope, crew_result):
|
||
logger.info(json.dumps(row, ensure_ascii=False))
|
||
for row in (crew_result.get("artifact_state_transition_rows") or []):
|
||
if isinstance(row, dict):
|
||
logger.info(json.dumps(row, ensure_ascii=False))
|
||
logger.info(f"✅ CrewAI success for {agent_id}: {latency:.2f}s")
|
||
|
||
# Store interaction in memory
|
||
if MEMORY_RETRIEVAL_AVAILABLE and memory_retrieval and chat_id and user_id:
|
||
try:
|
||
await memory_retrieval.store_interaction(
|
||
channel=channel,
|
||
chat_id=chat_id,
|
||
user_id=user_id,
|
||
agent_id=request_agent_id,
|
||
username=username,
|
||
user_message=request.prompt,
|
||
assistant_response=crew_result["result"]
|
||
)
|
||
except Exception as e:
|
||
logger.warning(f"⚠️ Memory storage failed: {e}")
|
||
|
||
return InferResponse(
|
||
response=crew_result["result"],
|
||
model="crewai-" + agent_id,
|
||
backend="crewai",
|
||
tokens_used=0
|
||
)
|
||
else:
|
||
logger.warning(f"⚠️ CrewAI failed, falling back to direct LLM")
|
||
except Exception as e:
|
||
logger.exception(f"❌ CrewAI error: {e}, falling back to direct LLM")
|
||
|
||
default_llm = agent_config.get("default_llm", "qwen3:8b")
|
||
|
||
routing_rules = router_config.get("routing", [])
|
||
default_llm = _select_default_llm(agent_id, metadata, default_llm, routing_rules)
|
||
|
||
# Get LLM profile configuration
|
||
llm_profiles = router_config.get("llm_profiles", {})
|
||
llm_profile = llm_profiles.get(default_llm, {})
|
||
provider = llm_profile.get("provider", "ollama")
|
||
|
||
# Determine model name
|
||
if provider in ["deepseek", "openai", "anthropic", "mistral"]:
|
||
model = llm_profile.get("model", "deepseek-chat")
|
||
else:
|
||
# For local ollama, use swapper model name format
|
||
model = request.model or "qwen3:8b"
|
||
|
||
# =========================================================================
|
||
# VISION PROCESSING (if images present)
|
||
# =========================================================================
|
||
if request.images and len(request.images) > 0:
|
||
logger.info(f"🖼️ Vision request: {len(request.images)} image(s)")
|
||
try:
|
||
# Use Swapper's /vision endpoint (manages model loading)
|
||
vision_payload = {
|
||
"model": "qwen3-vl-8b",
|
||
"prompt": request.prompt,
|
||
"images": request.images, # Swapper handles data URL conversion
|
||
"max_tokens": request.max_tokens or 1024,
|
||
"temperature": request.temperature or 0.7
|
||
}
|
||
|
||
# Add system prompt if available
|
||
if system_prompt:
|
||
if memory_brief_text:
|
||
vision_payload["system"] = f"{system_prompt}\n\n[INTERNAL MEMORY - do NOT repeat to user]\n{memory_brief_text}"
|
||
else:
|
||
vision_payload["system"] = system_prompt
|
||
|
||
logger.info(f"🖼️ Sending to Swapper /vision: {SWAPPER_URL}/vision")
|
||
|
||
vision_resp = await http_client.post(
|
||
f"{SWAPPER_URL}/vision",
|
||
json=vision_payload,
|
||
timeout=120.0
|
||
)
|
||
|
||
if vision_resp.status_code == 200:
|
||
vision_data = vision_resp.json()
|
||
raw_response = vision_data.get("text", "")
|
||
full_response = _sanitize_vision_text_for_user(raw_response)
|
||
vision_web_query = ""
|
||
vision_sources: List[Dict[str, str]] = []
|
||
|
||
# Debug: log full response structure
|
||
logger.info(
|
||
f"✅ Vision response: raw={len(raw_response)} chars, sanitized={len(full_response)} chars, "
|
||
f"success={vision_data.get('success')}, keys={list(vision_data.keys())}"
|
||
)
|
||
if raw_response and not full_response:
|
||
full_response = _extract_vision_search_facts(raw_response, max_chars=280)
|
||
if not full_response:
|
||
logger.warning(f"⚠️ Empty vision response! Full data: {str(vision_data)[:500]}")
|
||
|
||
# Optional vision -> web enrichment (soft policy):
|
||
# if prompt explicitly asks to search online OR vision answer is uncertain.
|
||
if (full_response or raw_response) and TOOL_MANAGER_AVAILABLE and tool_manager:
|
||
try:
|
||
wants_web = _vision_prompt_wants_web(request.prompt)
|
||
uncertain = _vision_answer_uncertain(full_response or raw_response)
|
||
if wants_web or uncertain:
|
||
query = _build_vision_web_query(request.prompt, full_response or raw_response)
|
||
if not query:
|
||
logger.info("🔎 Vision web enrich skipped: query not actionable")
|
||
else:
|
||
vision_web_query = query
|
||
search_result = await tool_manager.execute_tool(
|
||
"web_search",
|
||
{"query": query, "max_results": 3},
|
||
agent_id=request_agent_id,
|
||
chat_id=chat_id,
|
||
user_id=user_id,
|
||
)
|
||
if search_result and search_result.success and search_result.result:
|
||
|
||
compact_search = _compact_web_search_result(
|
||
search_result.result,
|
||
query=query,
|
||
agent_id=request_agent_id,
|
||
)
|
||
|
||
if compact_search and "Нічого не знайдено" not in compact_search:
|
||
vision_sources = _extract_sources_from_compact(compact_search)
|
||
|
||
base_text = full_response or "Не вдалося надійно ідентифікувати об'єкт на фото."
|
||
|
||
full_response = (
|
||
|
||
f"{base_text}\n\n"
|
||
|
||
f"Додатково знайшов у відкритих джерелах:\n{compact_search}"
|
||
|
||
)
|
||
|
||
logger.info(
|
||
"🌐 Vision web enrichment applied "
|
||
f"for agent={request_agent_id}, wants_web={wants_web}, uncertain={uncertain}, "
|
||
f"sources={len(vision_sources)}"
|
||
)
|
||
except Exception as e:
|
||
logger.warning(f"⚠️ Vision web enrichment failed: {e}")
|
||
|
||
if vision_web_query:
|
||
logger.info(
|
||
f"🗂️ Vision enrichment metadata: agent={request_agent_id}, "
|
||
f"query='{vision_web_query[:180]}', sources={len(vision_sources)}"
|
||
)
|
||
|
||
# Image quality gate: one soft retry if response looks empty/meta.
|
||
if _image_response_needs_retry(full_response):
|
||
try:
|
||
logger.warning(f"⚠️ Vision quality gate triggered for agent={request_agent_id}, retrying once")
|
||
retry_payload = dict(vision_payload)
|
||
retry_payload["prompt"] = (
|
||
"Опиши зображення по суті: що зображено, ключові деталі, можливий контекст. "
|
||
"Відповідай українською 2-4 реченнями, без службових фраз. "
|
||
f"Запит користувача: {request.prompt}"
|
||
)
|
||
retry_resp = await http_client.post(
|
||
f"{SWAPPER_URL}/vision",
|
||
json=retry_payload,
|
||
timeout=120.0
|
||
)
|
||
if retry_resp.status_code == 200:
|
||
retry_data = retry_resp.json()
|
||
retry_raw = retry_data.get("text", "")
|
||
retry_text = _sanitize_vision_text_for_user(retry_raw)
|
||
if retry_raw and not retry_text:
|
||
retry_text = _extract_vision_search_facts(retry_raw, max_chars=280)
|
||
if retry_text and not _image_response_needs_retry(retry_text):
|
||
full_response = retry_text
|
||
logger.info(f"✅ Vision quality retry improved response for agent={request_agent_id}")
|
||
except Exception as e:
|
||
logger.warning(f"⚠️ Vision quality retry failed: {e}")
|
||
|
||
if _image_response_needs_retry(full_response):
|
||
full_response = _build_image_fallback_response(request_agent_id, request.prompt)
|
||
elif request_agent_id == "agromatrix" and _vision_response_is_blurry(full_response):
|
||
full_response = _build_image_fallback_response(request_agent_id, request.prompt)
|
||
|
||
# Store vision message in agent-specific memory
|
||
if MEMORY_RETRIEVAL_AVAILABLE and memory_retrieval and chat_id and user_id and full_response:
|
||
vision_meta: Dict[str, Any] = {}
|
||
if vision_web_query:
|
||
vision_meta["vision_search_query"] = vision_web_query[:500]
|
||
if vision_sources:
|
||
vision_meta["vision_sources"] = vision_sources
|
||
asyncio.create_task(
|
||
memory_retrieval.store_message(
|
||
agent_id=request_agent_id,
|
||
user_id=user_id,
|
||
username=username,
|
||
message_text=f"[Image] {request.prompt}",
|
||
response_text=full_response,
|
||
chat_id=chat_id,
|
||
message_type="vision",
|
||
metadata=vision_meta if vision_meta else None,
|
||
)
|
||
)
|
||
|
||
return InferResponse(
|
||
response=full_response,
|
||
model="qwen3-vl-8b",
|
||
tokens_used=None,
|
||
backend="swapper-vision"
|
||
)
|
||
else:
|
||
logger.error(f"❌ Swapper vision error: {vision_resp.status_code} - {vision_resp.text[:200]}")
|
||
return InferResponse(
|
||
response=_build_image_fallback_response(request_agent_id, request.prompt),
|
||
model="qwen3-vl-8b",
|
||
tokens_used=None,
|
||
backend="swapper-vision-fallback"
|
||
)
|
||
|
||
except Exception as e:
|
||
logger.error(f"❌ Vision processing failed: {e}", exc_info=True)
|
||
return InferResponse(
|
||
response=_build_image_fallback_response(request_agent_id, request.prompt),
|
||
model="qwen3-vl-8b",
|
||
tokens_used=None,
|
||
backend="swapper-vision-fallback"
|
||
)
|
||
|
||
# =========================================================================
|
||
# SMART LLM ROUTER WITH AUTO-FALLBACK
|
||
# Priority: DeepSeek → Mistral → Grok → Local Ollama
|
||
# =========================================================================
|
||
|
||
# Build messages array once for all providers
|
||
messages = []
|
||
if system_prompt:
|
||
if memory_brief_text:
|
||
enhanced_prompt = f"{system_prompt}\n\n[INTERNAL MEMORY - do NOT repeat to user]\n{memory_brief_text}"
|
||
messages.append({"role": "system", "content": enhanced_prompt})
|
||
logger.info(f"📝 Added system message with prompt ({len(system_prompt)} chars) + memory ({len(memory_brief_text)} chars)")
|
||
else:
|
||
messages.append({"role": "system", "content": system_prompt})
|
||
logger.info(f"📝 Added system message with prompt ({len(system_prompt)} chars)")
|
||
elif memory_brief_text:
|
||
messages.append({"role": "system", "content": f"[INTERNAL MEMORY - do NOT repeat to user]\n{memory_brief_text}"})
|
||
logger.warning(f"⚠️ No system_prompt! Using only memory brief ({len(memory_brief_text)} chars)")
|
||
else:
|
||
logger.error(f"❌ No system_prompt AND no memory_brief! LLM will have no context!")
|
||
|
||
messages.append({"role": "user", "content": request.prompt})
|
||
logger.debug(f"📨 Messages array: {len(messages)} messages, system={len(messages[0].get('content', '')) if messages else 0} chars")
|
||
|
||
max_tokens = request.max_tokens or llm_profile.get("max_tokens", 2048)
|
||
temperature = request.temperature or llm_profile.get("temperature", 0.2)
|
||
|
||
cloud_provider_names = {"deepseek", "mistral", "grok", "openai", "anthropic"}
|
||
allow_cloud = provider in cloud_provider_names
|
||
if not allow_cloud:
|
||
logger.info(f"☁️ Cloud providers disabled for agent {agent_id}: provider={provider}")
|
||
# Define cloud providers with fallback order
|
||
cloud_providers = [
|
||
{
|
||
"name": "deepseek",
|
||
"api_key_env": "DEEPSEEK_API_KEY",
|
||
"base_url": "https://api.deepseek.com",
|
||
"model": "deepseek-chat",
|
||
"timeout": 40
|
||
},
|
||
{
|
||
"name": "mistral",
|
||
"api_key_env": "MISTRAL_API_KEY",
|
||
"base_url": "https://api.mistral.ai",
|
||
"model": "mistral-large-latest",
|
||
"timeout": 60
|
||
},
|
||
{
|
||
"name": "grok",
|
||
"api_key_env": "GROK_API_KEY",
|
||
"base_url": "https://api.x.ai",
|
||
"model": "grok-2-1212",
|
||
"timeout": 60
|
||
}
|
||
]
|
||
|
||
if not allow_cloud:
|
||
cloud_providers = []
|
||
|
||
# If specific provider requested, try it first
|
||
if provider in ["deepseek", "mistral", "grok"]:
|
||
# Reorder to put requested provider first
|
||
cloud_providers = sorted(cloud_providers, key=lambda x: 0 if x["name"] == provider else 1)
|
||
|
||
last_error = None
|
||
|
||
# Get tool definitions if Tool Manager is available
|
||
tools_payload = None
|
||
if TOOL_MANAGER_AVAILABLE and tool_manager:
|
||
tools_payload = tool_manager.get_tool_definitions(request_agent_id)
|
||
logger.debug(f"🔧 {len(tools_payload)} tools available for function calling")
|
||
|
||
for cloud in cloud_providers:
|
||
api_key = os.getenv(cloud["api_key_env"])
|
||
if not api_key:
|
||
logger.debug(f"⏭️ Skipping {cloud['name']}: API key not configured")
|
||
continue
|
||
|
||
try:
|
||
logger.info(f"🌐 Trying {cloud['name'].upper()} API with model: {cloud['model']}")
|
||
|
||
# Build request payload
|
||
request_payload = {
|
||
"model": cloud["model"],
|
||
"messages": messages,
|
||
"max_tokens": max_tokens,
|
||
"temperature": temperature,
|
||
"stream": False
|
||
}
|
||
|
||
# Add tools for function calling (if available and supported)
|
||
if tools_payload and cloud["name"] in ["deepseek", "mistral", "grok"]:
|
||
request_payload["tools"] = tools_payload
|
||
request_payload["tool_choice"] = "auto"
|
||
|
||
cloud_resp = await http_client.post(
|
||
f"{cloud['base_url']}/v1/chat/completions",
|
||
headers={
|
||
"Authorization": f"Bearer {api_key}",
|
||
"Content-Type": "application/json"
|
||
},
|
||
json=request_payload,
|
||
timeout=cloud["timeout"]
|
||
)
|
||
|
||
if cloud_resp.status_code == 200:
|
||
data = cloud_resp.json()
|
||
choice = data.get("choices", [{}])[0]
|
||
message = choice.get("message", {})
|
||
response_text = message.get("content", "") or ""
|
||
tokens_used = data.get("usage", {}).get("total_tokens", 0)
|
||
|
||
# Initialize tool_results to avoid UnboundLocalError
|
||
tool_results = []
|
||
|
||
# Check for tool calls (standard format)
|
||
tool_calls = message.get("tool_calls", [])
|
||
|
||
# Also check for DSML format in content (DeepSeek sometimes returns this)
|
||
# AGGRESSIVE check - any DSML-like pattern should be caught
|
||
import re
|
||
has_dsml = False
|
||
if response_text:
|
||
# Check for DSML patterns with regex (handles Unicode variations)
|
||
dsml_patterns_check = [
|
||
r'DSML', # Any mention of DSML
|
||
r'function_calls>',
|
||
r'invoke\s*name\s*=',
|
||
r'parameter\s*name\s*=',
|
||
r'<[^>]*invoke[^>]*>',
|
||
r'</[^>]*invoke[^>]*>',
|
||
]
|
||
for pattern in dsml_patterns_check:
|
||
if re.search(pattern, response_text, re.IGNORECASE):
|
||
has_dsml = True
|
||
logger.warning(f"⚠️ DSML detected via pattern: {pattern}")
|
||
break
|
||
|
||
if has_dsml:
|
||
logger.warning("⚠️ Detected DSML format in content, parsing...")
|
||
# Extract tool name and parameters from DSML
|
||
import re
|
||
# Try multiple DSML patterns
|
||
dsml_patterns = [
|
||
r'invoke name="(\w+)".*?parameter name="(\w+)"[^>]*>([^<]+)',
|
||
r'invoke\s+name="(\w+)".*?parameter\s+name="(\w+)"[^>]*>([^<]+)',
|
||
r'name="web_extract".*?url.*?>([^\s<]+)',
|
||
]
|
||
dsml_match = None
|
||
for pattern in dsml_patterns:
|
||
dsml_match = re.search(pattern, response_text, re.DOTALL | re.IGNORECASE)
|
||
if dsml_match:
|
||
break
|
||
|
||
if dsml_match and len(dsml_match.groups()) >= 3:
|
||
tool_name = dsml_match.group(1)
|
||
param_name = dsml_match.group(2)
|
||
param_value = dsml_match.group(3).strip()
|
||
# Create synthetic tool call with Mistral-compatible ID (exactly 9 chars, a-zA-Z0-9)
|
||
import string
|
||
import random
|
||
tool_call_id = ''.join(random.choices(string.ascii_letters + string.digits, k=9))
|
||
tool_calls = [{
|
||
"id": tool_call_id,
|
||
"function": {
|
||
"name": tool_name,
|
||
"arguments": json.dumps({param_name: param_value})
|
||
}
|
||
}]
|
||
logger.info(f"🔧 Parsed DSML tool call: {tool_name}({param_name}={param_value[:50]}...) id={tool_call_id}")
|
||
|
||
# ALWAYS clear DSML content - never show raw DSML to user
|
||
logger.warning(f"🧹 Clearing DSML content from response ({len(response_text)} chars)")
|
||
response_text = ""
|
||
if tool_calls and tool_manager:
|
||
max_tool_rounds = int(os.getenv("ROUTER_TOOL_MAX_ROUNDS", "10"))
|
||
logger.info(f"🔧 LLM requested tool calls; running iterative mode up to {max_tool_rounds} rounds")
|
||
|
||
all_tool_results = []
|
||
current_tool_calls = tool_calls
|
||
rounds_done = 0
|
||
oneok_ctx = {
|
||
"client_id": None,
|
||
"site_id": None,
|
||
"calc_result": None,
|
||
"quote_id": None,
|
||
}
|
||
repeated_failures = {}
|
||
|
||
while current_tool_calls and rounds_done < max_tool_rounds:
|
||
rounds_done += 1
|
||
logger.info(f"🔁 Tool round {rounds_done}/{max_tool_rounds}: {len(current_tool_calls)} call(s)")
|
||
round_results = []
|
||
abort_loop_due_repeats = False
|
||
|
||
for tc in current_tool_calls:
|
||
func = tc.get("function", {})
|
||
tool_name = func.get("name", "")
|
||
try:
|
||
tool_args = json.loads(func.get("arguments", "{}"))
|
||
except Exception:
|
||
tool_args = {}
|
||
|
||
# Light auto-repair for 1OK multi-step flows when model omits required args.
|
||
if request_agent_id == "oneok":
|
||
if tool_name == "calc_window_quote":
|
||
ip = (tool_args or {}).get("input_payload")
|
||
if isinstance(ip, dict) and isinstance(ip.get("windows"), list) and "window_units" not in ip:
|
||
ip2 = dict(ip)
|
||
ip2["window_units"] = ip.get("windows")
|
||
tool_args = dict(tool_args or {})
|
||
tool_args["input_payload"] = ip2
|
||
elif tool_name == "crm_create_quote":
|
||
quote_payload = (tool_args or {}).get("quote_payload")
|
||
if not isinstance(quote_payload, dict):
|
||
calc_res = oneok_ctx.get("calc_result") or {}
|
||
line_items = calc_res.get("line_items") if isinstance(calc_res, dict) else None
|
||
totals = calc_res.get("totals") if isinstance(calc_res, dict) else None
|
||
if isinstance(line_items, list) and isinstance(totals, dict):
|
||
tool_args = {
|
||
"quote_payload": {
|
||
"client_id": oneok_ctx.get("client_id"),
|
||
"site_id": oneok_ctx.get("site_id"),
|
||
"currency": calc_res.get("currency", "UAH"),
|
||
"line_items": line_items,
|
||
"totals": totals,
|
||
"assumptions": calc_res.get("assumptions", []),
|
||
"validity_days": 14,
|
||
"lead_time_estimate": calc_res.get("lead_time_if_known") or calc_res.get("lead_time_estimate"),
|
||
}
|
||
}
|
||
logger.info("🛠️ oneok: auto-filled crm_create_quote.quote_payload from calc context")
|
||
elif tool_name == "docs_render_quote_pdf":
|
||
quote_id = (tool_args or {}).get("quote_id")
|
||
quote_payload = (tool_args or {}).get("quote_payload")
|
||
if not quote_id and not isinstance(quote_payload, dict) and oneok_ctx.get("quote_id"):
|
||
tool_args = {"quote_id": oneok_ctx.get("quote_id")}
|
||
logger.info("🛠️ oneok: auto-filled docs_render_quote_pdf.quote_id from quote context")
|
||
elif tool_name == "schedule_propose_slots":
|
||
params = (tool_args or {}).get("params")
|
||
if not isinstance(params, dict):
|
||
tool_args = {"params": {"count": 3, "timezone": "Europe/Kyiv"}}
|
||
logger.info("🛠️ oneok: auto-filled schedule_propose_slots.params")
|
||
|
||
result = await tool_manager.execute_tool(
|
||
tool_name,
|
||
tool_args,
|
||
agent_id=request_agent_id,
|
||
chat_id=chat_id,
|
||
user_id=user_id,
|
||
)
|
||
tool_result_dict = {
|
||
"tool_call_id": tc.get("id", ""),
|
||
"name": tool_name,
|
||
"success": result.success,
|
||
"result": result.result,
|
||
"error": result.error,
|
||
"image_base64": result.image_base64,
|
||
"file_base64": result.file_base64,
|
||
"file_name": result.file_name,
|
||
"file_mime": result.file_mime,
|
||
}
|
||
if result.image_base64:
|
||
logger.info(f"🖼️ Tool {tool_name} generated image: {len(result.image_base64)} chars")
|
||
round_results.append(tool_result_dict)
|
||
all_tool_results.append(tool_result_dict)
|
||
|
||
# Track oneok context to help subsequent tool calls in the same request.
|
||
if request_agent_id == "oneok" and result.success and isinstance(result.result, dict):
|
||
if tool_name == "crm_upsert_client":
|
||
oneok_ctx["client_id"] = result.result.get("client_id") or oneok_ctx.get("client_id")
|
||
elif tool_name == "crm_upsert_site":
|
||
oneok_ctx["site_id"] = result.result.get("site_id") or oneok_ctx.get("site_id")
|
||
elif tool_name == "calc_window_quote":
|
||
oneok_ctx["calc_result"] = result.result
|
||
elif tool_name == "crm_create_quote":
|
||
oneok_ctx["quote_id"] = result.result.get("quote_id") or oneok_ctx.get("quote_id")
|
||
|
||
# Guardrail: stop if model repeats same failing tool call too many times.
|
||
sig = f"{tool_name}:{json.dumps(tool_args, ensure_ascii=False, sort_keys=True, default=str)}"
|
||
if result.success:
|
||
repeated_failures.pop(sig, None)
|
||
else:
|
||
repeated_failures[sig] = repeated_failures.get(sig, 0) + 1
|
||
if repeated_failures[sig] >= 3:
|
||
logger.warning(f"⚠️ Repeated failing tool call detected ({tool_name}) x{repeated_failures[sig]}; breaking iterative loop")
|
||
abort_loop_due_repeats = True
|
||
break
|
||
|
||
if abort_loop_due_repeats:
|
||
current_tool_calls = []
|
||
response_text = response_text or format_tool_calls_for_response(all_tool_results, fallback_mode="empty_response")
|
||
break
|
||
|
||
messages.append({"role": "assistant", "content": None, "tool_calls": current_tool_calls})
|
||
for tr in round_results:
|
||
messages.append({
|
||
"role": "tool",
|
||
"tool_call_id": tr["tool_call_id"],
|
||
"content": str(tr["result"]) if tr["success"] else f"Error: {tr['error']}"
|
||
})
|
||
|
||
logger.info("🔄 Calling LLM again after tool round")
|
||
loop_payload = {
|
||
"model": cloud["model"],
|
||
"messages": messages,
|
||
"max_tokens": max_tokens,
|
||
"temperature": temperature,
|
||
"stream": False
|
||
}
|
||
# Keep tools enabled for multi-step chains.
|
||
if tools_payload and cloud["name"] in ["deepseek", "mistral", "grok"]:
|
||
loop_payload["tools"] = tools_payload
|
||
loop_payload["tool_choice"] = "auto"
|
||
|
||
loop_resp = await http_client.post(
|
||
f"{cloud['base_url']}/v1/chat/completions",
|
||
headers={
|
||
"Authorization": f"Bearer {api_key}",
|
||
"Content-Type": "application/json"
|
||
},
|
||
json=loop_payload,
|
||
timeout=cloud["timeout"]
|
||
)
|
||
|
||
if loop_resp.status_code != 200:
|
||
logger.error(f"❌ {cloud['name'].upper()} loop call failed: {loop_resp.status_code} - {loop_resp.text[:200]}")
|
||
response_text = format_tool_calls_for_response(all_tool_results, fallback_mode="empty_response")
|
||
current_tool_calls = []
|
||
break
|
||
|
||
loop_data = loop_resp.json()
|
||
loop_message = loop_data.get("choices", [{}])[0].get("message", {})
|
||
response_text = loop_message.get("content", "") or ""
|
||
tokens_used += loop_data.get("usage", {}).get("total_tokens", 0)
|
||
current_tool_calls = loop_message.get("tool_calls", [])
|
||
|
||
# DSML fallback parsing for providers that return markup instead of tool_calls.
|
||
has_dsml_loop = False
|
||
if response_text:
|
||
dsml_patterns_check = [
|
||
r'DSML',
|
||
r'function_calls>',
|
||
r'invoke\s*name\s*=',
|
||
r'parameter\s*name\s*=',
|
||
r'<[^>]*invoke[^>]*>',
|
||
r'</[^>]*invoke[^>]*>',
|
||
]
|
||
for pattern in dsml_patterns_check:
|
||
if re.search(pattern, response_text, re.IGNORECASE):
|
||
has_dsml_loop = True
|
||
logger.warning(f"⚠️ DSML detected in loop via pattern: {pattern}")
|
||
break
|
||
|
||
if has_dsml_loop and not current_tool_calls:
|
||
dsml_patterns = [
|
||
r'invoke name="(\w+)".*?parameter name="(\w+)"[^>]*>([^<]+)',
|
||
r'invoke\s+name="(\w+)".*?parameter\s+name="(\w+)"[^>]*>([^<]+)',
|
||
]
|
||
dsml_match = None
|
||
for pattern in dsml_patterns:
|
||
dsml_match = re.search(pattern, response_text, re.DOTALL | re.IGNORECASE)
|
||
if dsml_match:
|
||
break
|
||
if dsml_match and len(dsml_match.groups()) >= 3:
|
||
import string
|
||
import random
|
||
tool_call_id = ''.join(random.choices(string.ascii_letters + string.digits, k=9))
|
||
current_tool_calls = [{
|
||
"id": tool_call_id,
|
||
"function": {
|
||
"name": dsml_match.group(1),
|
||
"arguments": json.dumps({dsml_match.group(2): dsml_match.group(3).strip()})
|
||
}
|
||
}]
|
||
response_text = ""
|
||
|
||
tool_results = all_tool_results
|
||
|
||
if current_tool_calls:
|
||
logger.warning(f"⚠️ Reached max tool rounds ({max_tool_rounds}) for {request_agent_id}, returning summary")
|
||
response_text = response_text or format_tool_calls_for_response(tool_results, fallback_mode="empty_response")
|
||
|
||
if response_text and ("DSML" in response_text or "invoke name=" in response_text or "function_calls>" in response_text):
|
||
prefix_before_dsml = _strip_dsml_keep_text_before(response_text)
|
||
if prefix_before_dsml:
|
||
logger.warning(f"🧹 DSML in loop final response: keeping text before DSML ({len(prefix_before_dsml)} chars)")
|
||
response_text = prefix_before_dsml
|
||
else:
|
||
response_text = format_tool_calls_for_response(tool_results, fallback_mode="dsml_detected")
|
||
|
||
if not response_text:
|
||
logger.warning(f"⚠️ {cloud['name'].upper()} returned empty response after iterative tool calls")
|
||
response_text = format_tool_calls_for_response(tool_results, fallback_mode="empty_response")
|
||
|
||
if response_text:
|
||
# FINAL DSML check before returning - never show DSML to user
|
||
if "DSML" in response_text or "invoke name=" in response_text or "function_calls>" in response_text:
|
||
prefix_before_dsml = _strip_dsml_keep_text_before(response_text)
|
||
if prefix_before_dsml:
|
||
logger.warning(f"🧹 DSML in final response: keeping text before DSML ({len(prefix_before_dsml)} chars)")
|
||
response_text = prefix_before_dsml
|
||
else:
|
||
logger.warning(f"🧹 DSML in final response! Replacing with fallback ({len(response_text)} chars)")
|
||
response_text = format_tool_calls_for_response(tool_results, fallback_mode="dsml_detected")
|
||
|
||
# Check if any tool generated an image
|
||
generated_image = None
|
||
generated_file_base64 = None
|
||
generated_file_name = None
|
||
generated_file_mime = None
|
||
logger.debug(f"🔍 Checking {len(tool_results)} tool results for images...")
|
||
for tr in tool_results:
|
||
img_b64 = tr.get("image_base64")
|
||
if img_b64:
|
||
generated_image = img_b64
|
||
logger.info(f"🖼️ Image generated by tool: {tr['name']} ({len(img_b64)} chars)")
|
||
file_b64 = tr.get("file_base64")
|
||
if file_b64 and not generated_file_base64:
|
||
generated_file_base64 = file_b64
|
||
generated_file_name = tr.get("file_name")
|
||
generated_file_mime = tr.get("file_mime")
|
||
logger.info(f"📄 File generated by tool: {tr['name']} ({len(file_b64)} chars)")
|
||
if not img_b64:
|
||
logger.debug(f" Tool {tr['name']}: no image_base64")
|
||
|
||
logger.info(f"✅ {cloud['name'].upper()} response received, {tokens_used} tokens")
|
||
|
||
# Store message in agent-specific memory (async, non-blocking)
|
||
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=request.prompt,
|
||
response_text=response_text,
|
||
chat_id=chat_id
|
||
)
|
||
)
|
||
|
||
return InferResponse(
|
||
response=response_text,
|
||
model=cloud["model"],
|
||
tokens_used=tokens_used,
|
||
backend=f"{cloud['name']}-cloud",
|
||
image_base64=generated_image,
|
||
file_base64=generated_file_base64,
|
||
file_name=generated_file_name,
|
||
file_mime=generated_file_mime,
|
||
)
|
||
else:
|
||
logger.warning(f"⚠️ {cloud['name'].upper()} returned empty response, trying next provider")
|
||
continue
|
||
else:
|
||
logger.warning(f"⚠️ {cloud['name'].upper()} returned {cloud_resp.status_code}, trying next...")
|
||
last_error = f"{cloud['name']}: {cloud_resp.status_code}"
|
||
continue
|
||
|
||
except Exception as e:
|
||
import traceback
|
||
error_details = traceback.format_exc()
|
||
logger.warning(f"⚠️ {cloud['name'].upper()} failed: {e}")
|
||
if not str(e).strip(): # Empty error string
|
||
logger.error(f"❌ {cloud['name'].upper()} failed with empty error! Check traceback:")
|
||
logger.error(error_details)
|
||
else:
|
||
logger.debug(f"Full error traceback: {error_details}")
|
||
last_error = f"{cloud['name']}: {str(e)}"
|
||
continue
|
||
|
||
# If all cloud providers failed, log and fall through to local
|
||
if last_error:
|
||
logger.warning(f"⚠️ All cloud providers failed ({last_error}), falling back to local Ollama")
|
||
|
||
# =========================================================================
|
||
# LOCAL PROVIDERS (Ollama via Swapper)
|
||
# =========================================================================
|
||
# Determine local model from config (not hardcoded)
|
||
# Strategy: Use agent's default_llm if it's local (ollama), otherwise find first local model
|
||
local_model = None
|
||
|
||
# Check if default_llm is local
|
||
if llm_profile.get("provider") == "ollama":
|
||
# Extract model name and convert format (qwen3:8b → qwen3:8b for Swapper)
|
||
ollama_model = llm_profile.get("model", "qwen3:8b")
|
||
local_model = ollama_model.replace(":", "-") # qwen3:8b → qwen3:8b
|
||
logger.debug(f"✅ Using agent's default local model: {local_model}")
|
||
else:
|
||
# Find first local model from config
|
||
for profile_name, profile in llm_profiles.items():
|
||
if profile.get("provider") == "ollama":
|
||
ollama_model = profile.get("model", "qwen3:8b")
|
||
local_model = ollama_model.replace(":", "-")
|
||
logger.info(f"🔄 Found fallback local model: {local_model} from profile {profile_name}")
|
||
break
|
||
|
||
# Final fallback if no local model found
|
||
if not local_model:
|
||
local_model = "qwen3:8b"
|
||
logger.warning(f"⚠️ No local model in config, using hardcoded fallback: {local_model}")
|
||
|
||
try:
|
||
# Check if Swapper is available
|
||
health_resp = await http_client.get(f"{SWAPPER_URL}/health", timeout=5.0)
|
||
if health_resp.status_code == 200:
|
||
logger.info(f"📡 Calling Swapper with local model: {local_model}")
|
||
# Generate response via Swapper (which handles model loading)
|
||
generate_resp = await http_client.post(
|
||
f"{SWAPPER_URL}/generate",
|
||
json={
|
||
"model": local_model,
|
||
"prompt": request.prompt,
|
||
"system": system_prompt,
|
||
"max_tokens": request.max_tokens,
|
||
"temperature": request.temperature,
|
||
"stream": False
|
||
},
|
||
timeout=300.0
|
||
)
|
||
|
||
if generate_resp.status_code == 200:
|
||
data = generate_resp.json()
|
||
local_response = _normalize_text_response(data.get("response", ""))
|
||
|
||
# Empty-answer gate for selected local top-level agents.
|
||
if request_agent_id in EMPTY_ANSWER_GUARD_AGENTS and _needs_empty_answer_recovery(local_response):
|
||
logger.warning(f"⚠️ Empty-answer gate triggered for {request_agent_id}, retrying local generate once")
|
||
retry_prompt = (
|
||
f"{request.prompt}\n\n"
|
||
"Відповідай коротко і конкретно (2-5 речень), без службових або мета-фраз."
|
||
)
|
||
retry_resp = await http_client.post(
|
||
f"{SWAPPER_URL}/generate",
|
||
json={
|
||
"model": local_model,
|
||
"prompt": retry_prompt,
|
||
"system": system_prompt,
|
||
"max_tokens": request.max_tokens,
|
||
"temperature": request.temperature,
|
||
"stream": False
|
||
},
|
||
timeout=300.0
|
||
)
|
||
if retry_resp.status_code == 200:
|
||
retry_data = retry_resp.json()
|
||
retry_text = _normalize_text_response(retry_data.get("response", ""))
|
||
if retry_text and not _needs_empty_answer_recovery(retry_text):
|
||
local_response = retry_text
|
||
|
||
if _needs_empty_answer_recovery(local_response):
|
||
local_response = (
|
||
"Я не отримав корисну відповідь з першої спроби. "
|
||
"Сформулюй запит коротко ще раз, і я відповім конкретно."
|
||
)
|
||
|
||
# Store in agent-specific memory
|
||
if MEMORY_RETRIEVAL_AVAILABLE and memory_retrieval and chat_id and user_id and local_response:
|
||
asyncio.create_task(
|
||
memory_retrieval.store_message(
|
||
agent_id=request_agent_id,
|
||
user_id=user_id,
|
||
username=username,
|
||
message_text=request.prompt,
|
||
response_text=local_response,
|
||
chat_id=chat_id
|
||
)
|
||
)
|
||
|
||
return InferResponse(
|
||
response=local_response,
|
||
model=local_model,
|
||
tokens_used=data.get("eval_count", 0),
|
||
backend="swapper+ollama"
|
||
)
|
||
else:
|
||
logger.error(f"❌ Swapper error: {generate_resp.status_code} - {generate_resp.text}")
|
||
except Exception as e:
|
||
logger.error(f"❌ Swapper/Ollama error: {e}")
|
||
# Fallback to direct Ollama if Swapper fails
|
||
try:
|
||
logger.info(f"🔄 Falling back to direct Ollama connection")
|
||
generate_resp = await http_client.post(
|
||
f"{VISION_URL}/api/generate",
|
||
json={
|
||
"model": "qwen3:8b", # Use actual Ollama model name
|
||
"prompt": request.prompt,
|
||
"system": system_prompt,
|
||
"stream": False,
|
||
"options": {
|
||
"num_predict": request.max_tokens,
|
||
"temperature": request.temperature
|
||
}
|
||
},
|
||
timeout=120.0
|
||
)
|
||
|
||
if generate_resp.status_code == 200:
|
||
data = generate_resp.json()
|
||
return InferResponse(
|
||
response=data.get("response", ""),
|
||
model=model,
|
||
tokens_used=data.get("eval_count", 0),
|
||
backend="ollama-direct"
|
||
)
|
||
except Exception as e2:
|
||
logger.error(f"❌ Direct Ollama fallback also failed: {e2}")
|
||
|
||
# Fallback: return error
|
||
raise HTTPException(
|
||
status_code=503,
|
||
detail=f"No backend available for model: {model}"
|
||
)
|
||
|
||
|
||
@app.get("/v1/models")
|
||
async def list_available_models():
|
||
"""List all available models across backends"""
|
||
models = []
|
||
|
||
# Get Swapper models
|
||
try:
|
||
resp = await http_client.get(f"{SWAPPER_URL}/models", timeout=5.0)
|
||
if resp.status_code == 200:
|
||
data = resp.json()
|
||
for m in data.get("models", []):
|
||
models.append({
|
||
"id": m.get("name"),
|
||
"backend": "swapper",
|
||
"size_gb": m.get("size_gb"),
|
||
"status": m.get("status", "available")
|
||
})
|
||
except Exception as e:
|
||
logger.warning(f"Cannot get Swapper models: {e}")
|
||
|
||
# Get Ollama models
|
||
try:
|
||
resp = await http_client.get(f"{VISION_URL}/api/tags", timeout=5.0)
|
||
if resp.status_code == 200:
|
||
data = resp.json()
|
||
for m in data.get("models", []):
|
||
# Avoid duplicates
|
||
model_name = m.get("name")
|
||
if not any(x.get("id") == model_name for x in models):
|
||
models.append({
|
||
"id": model_name,
|
||
"backend": "ollama",
|
||
"size_gb": round(m.get("size", 0) / 1e9, 1),
|
||
"status": "loaded"
|
||
})
|
||
except Exception as e:
|
||
logger.warning(f"Cannot get Ollama models: {e}")
|
||
|
||
return {"models": models, "total": len(models)}
|
||
|
||
|
||
# =============================================================================
|
||
# NEO4J GRAPH API ENDPOINTS
|
||
# =============================================================================
|
||
|
||
class GraphNode(BaseModel):
|
||
"""Model for creating/updating a graph node"""
|
||
label: str # Node type: User, Agent, Topic, Fact, Entity, etc.
|
||
properties: Dict[str, Any]
|
||
node_id: Optional[str] = None # If provided, update existing node
|
||
|
||
class GraphRelationship(BaseModel):
|
||
"""Model for creating a relationship between nodes"""
|
||
from_node_id: str
|
||
to_node_id: str
|
||
relationship_type: str # KNOWS, MENTIONED, RELATED_TO, CREATED_BY, etc.
|
||
properties: Optional[Dict[str, Any]] = None
|
||
|
||
class GraphQuery(BaseModel):
|
||
"""Model for querying the graph"""
|
||
cypher: Optional[str] = None # Direct Cypher query (advanced)
|
||
# Or use structured query:
|
||
node_label: Optional[str] = None
|
||
node_id: Optional[str] = None
|
||
relationship_type: Optional[str] = None
|
||
depth: int = 1 # How many hops to traverse
|
||
limit: int = 50
|
||
|
||
class GraphSearchRequest(BaseModel):
|
||
"""Natural language search in graph"""
|
||
query: str
|
||
entity_types: Optional[List[str]] = None # Filter by types
|
||
limit: int = 20
|
||
|
||
|
||
@app.post("/v1/graph/nodes")
|
||
async def create_graph_node(node: GraphNode):
|
||
"""Create or update a node in the knowledge graph"""
|
||
if not neo4j_available or not neo4j_driver:
|
||
raise HTTPException(status_code=503, detail="Neo4j not available")
|
||
|
||
try:
|
||
async with neo4j_driver.session() as session:
|
||
# Generate node_id if not provided
|
||
node_id = node.node_id or f"{node.label.lower()}_{os.urandom(8).hex()}"
|
||
|
||
# Build properties with node_id
|
||
props = {**node.properties, "node_id": node_id, "updated_at": "datetime()"}
|
||
|
||
# Create or merge node
|
||
cypher = f"""
|
||
MERGE (n:{node.label} {{node_id: $node_id}})
|
||
SET n += $properties
|
||
SET n.updated_at = datetime()
|
||
RETURN n
|
||
"""
|
||
|
||
result = await session.run(cypher, node_id=node_id, properties=node.properties)
|
||
record = await result.single()
|
||
|
||
if record:
|
||
created_node = dict(record["n"])
|
||
logger.info(f"📊 Created/updated node: {node.label} - {node_id}")
|
||
return {"status": "ok", "node_id": node_id, "node": created_node}
|
||
|
||
raise HTTPException(status_code=500, detail="Failed to create node")
|
||
|
||
except Exception as e:
|
||
logger.error(f"❌ Neo4j error creating node: {e}")
|
||
raise HTTPException(status_code=500, detail=str(e))
|
||
|
||
|
||
@app.post("/v1/graph/relationships")
|
||
async def create_graph_relationship(rel: GraphRelationship):
|
||
"""Create a relationship between two nodes"""
|
||
if not neo4j_available or not neo4j_driver:
|
||
raise HTTPException(status_code=503, detail="Neo4j not available")
|
||
|
||
try:
|
||
async with neo4j_driver.session() as session:
|
||
props_clause = ""
|
||
if rel.properties:
|
||
props_clause = " SET r += $properties"
|
||
|
||
cypher = f"""
|
||
MATCH (a {{node_id: $from_id}})
|
||
MATCH (b {{node_id: $to_id}})
|
||
MERGE (a)-[r:{rel.relationship_type}]->(b)
|
||
{props_clause}
|
||
SET r.created_at = datetime()
|
||
RETURN a.node_id as from_id, b.node_id as to_id, type(r) as rel_type
|
||
"""
|
||
|
||
result = await session.run(
|
||
cypher,
|
||
from_id=rel.from_node_id,
|
||
to_id=rel.to_node_id,
|
||
properties=rel.properties or {}
|
||
)
|
||
record = await result.single()
|
||
|
||
if record:
|
||
logger.info(f"🔗 Created relationship: {rel.from_node_id} -[{rel.relationship_type}]-> {rel.to_node_id}")
|
||
return {
|
||
"status": "ok",
|
||
"from_id": record["from_id"],
|
||
"to_id": record["to_id"],
|
||
"relationship_type": record["rel_type"]
|
||
}
|
||
|
||
raise HTTPException(status_code=404, detail="One or both nodes not found")
|
||
|
||
except Exception as e:
|
||
logger.error(f"❌ Neo4j error creating relationship: {e}")
|
||
raise HTTPException(status_code=500, detail=str(e))
|
||
|
||
|
||
@app.post("/v1/graph/query")
|
||
async def query_graph(query: GraphQuery):
|
||
"""Query the knowledge graph"""
|
||
if not neo4j_available or not neo4j_driver:
|
||
raise HTTPException(status_code=503, detail="Neo4j not available")
|
||
|
||
try:
|
||
async with neo4j_driver.session() as session:
|
||
# If direct Cypher provided, use it (with safety check)
|
||
if query.cypher:
|
||
# Basic safety: only allow read queries
|
||
if any(kw in query.cypher.upper() for kw in ["DELETE", "REMOVE", "DROP", "CREATE", "MERGE", "SET"]):
|
||
raise HTTPException(status_code=400, detail="Only read queries allowed via cypher parameter")
|
||
|
||
result = await session.run(query.cypher)
|
||
records = await result.data()
|
||
return {"status": "ok", "results": records, "count": len(records)}
|
||
|
||
# Build structured query
|
||
if query.node_id:
|
||
# Get specific node with relationships
|
||
cypher = f"""
|
||
MATCH (n {{node_id: $node_id}})
|
||
OPTIONAL MATCH (n)-[r]-(related)
|
||
RETURN n, collect({{rel: type(r), node: related}}) as connections
|
||
LIMIT 1
|
||
"""
|
||
result = await session.run(cypher, node_id=query.node_id)
|
||
|
||
elif query.node_label:
|
||
# Get nodes by label
|
||
cypher = f"""
|
||
MATCH (n:{query.node_label})
|
||
RETURN n
|
||
ORDER BY n.updated_at DESC
|
||
LIMIT $limit
|
||
"""
|
||
result = await session.run(cypher, limit=query.limit)
|
||
|
||
else:
|
||
# Get recent nodes
|
||
cypher = """
|
||
MATCH (n)
|
||
RETURN n, labels(n) as labels
|
||
ORDER BY n.updated_at DESC
|
||
LIMIT $limit
|
||
"""
|
||
result = await session.run(cypher, limit=query.limit)
|
||
|
||
records = await result.data()
|
||
return {"status": "ok", "results": records, "count": len(records)}
|
||
|
||
except Exception as e:
|
||
logger.error(f"❌ Neo4j query error: {e}")
|
||
raise HTTPException(status_code=500, detail=str(e))
|
||
|
||
|
||
@app.get("/v1/graph/search")
|
||
async def search_graph(q: str, types: Optional[str] = None, limit: int = 20):
|
||
"""Search nodes by text in properties"""
|
||
if not neo4j_available or not neo4j_driver:
|
||
raise HTTPException(status_code=503, detail="Neo4j not available")
|
||
|
||
try:
|
||
async with neo4j_driver.session() as session:
|
||
# Build label filter
|
||
label_filter = ""
|
||
if types:
|
||
labels = [t.strip() for t in types.split(",")]
|
||
label_filter = " AND (" + " OR ".join([f"n:{l}" for l in labels]) + ")"
|
||
|
||
# Search in common text properties
|
||
cypher = f"""
|
||
MATCH (n)
|
||
WHERE (
|
||
n.name CONTAINS $query OR
|
||
n.title CONTAINS $query OR
|
||
n.text CONTAINS $query OR
|
||
n.description CONTAINS $query OR
|
||
n.content CONTAINS $query
|
||
){label_filter}
|
||
RETURN n, labels(n) as labels
|
||
ORDER BY n.updated_at DESC
|
||
LIMIT $limit
|
||
"""
|
||
|
||
result = await session.run(cypher, query=q, limit=limit)
|
||
records = await result.data()
|
||
|
||
return {"status": "ok", "query": q, "results": records, "count": len(records)}
|
||
|
||
except Exception as e:
|
||
logger.error(f"❌ Neo4j search error: {e}")
|
||
raise HTTPException(status_code=500, detail=str(e))
|
||
|
||
|
||
@app.get("/v1/graph/stats")
|
||
async def get_graph_stats():
|
||
"""Get knowledge graph statistics"""
|
||
if not neo4j_available or not neo4j_driver:
|
||
raise HTTPException(status_code=503, detail="Neo4j not available")
|
||
|
||
try:
|
||
async with neo4j_driver.session() as session:
|
||
# Get node counts by label
|
||
labels_result = await session.run("""
|
||
CALL db.labels() YIELD label
|
||
CALL apoc.cypher.run('MATCH (n:`' + label + '`) RETURN count(n) as count', {}) YIELD value
|
||
RETURN label, value.count as count
|
||
""")
|
||
|
||
# If APOC not available, use simpler query
|
||
try:
|
||
labels_data = await labels_result.data()
|
||
except:
|
||
labels_result = await session.run("""
|
||
MATCH (n)
|
||
RETURN labels(n)[0] as label, count(*) as count
|
||
ORDER BY count DESC
|
||
""")
|
||
labels_data = await labels_result.data()
|
||
|
||
# Get relationship counts
|
||
rels_result = await session.run("""
|
||
MATCH ()-[r]->()
|
||
RETURN type(r) as type, count(*) as count
|
||
ORDER BY count DESC
|
||
""")
|
||
rels_data = await rels_result.data()
|
||
|
||
# Get total counts
|
||
total_result = await session.run("""
|
||
MATCH (n) RETURN count(n) as nodes
|
||
""")
|
||
total_nodes = (await total_result.single())["nodes"]
|
||
|
||
total_rels_result = await session.run("""
|
||
MATCH ()-[r]->() RETURN count(r) as relationships
|
||
""")
|
||
total_rels = (await total_rels_result.single())["relationships"]
|
||
|
||
return {
|
||
"status": "ok",
|
||
"total_nodes": total_nodes,
|
||
"total_relationships": total_rels,
|
||
"nodes_by_label": labels_data,
|
||
"relationships_by_type": rels_data
|
||
}
|
||
|
||
except Exception as e:
|
||
logger.error(f"❌ Neo4j stats error: {e}")
|
||
raise HTTPException(status_code=500, detail=str(e))
|
||
|
||
|
||
@app.on_event("shutdown")
|
||
async def shutdown_event():
|
||
"""Cleanup connections on shutdown"""
|
||
global neo4j_driver, http_client, nc
|
||
|
||
# Close Memory Retrieval
|
||
if MEMORY_RETRIEVAL_AVAILABLE and memory_retrieval:
|
||
try:
|
||
await memory_retrieval.close()
|
||
logger.info("🔌 Memory Retrieval closed")
|
||
except Exception as e:
|
||
logger.warning(f"⚠️ Memory Retrieval close error: {e}")
|
||
|
||
if neo4j_driver:
|
||
await neo4j_driver.close()
|
||
logger.info("🔌 Neo4j connection closed")
|
||
|
||
if http_client:
|
||
await http_client.aclose()
|
||
logger.info("🔌 HTTP client closed")
|
||
|
||
if nc:
|
||
await nc.close()
|
||
logger.info("🔌 NATS connection closed")
|