sylvain
a8727654af
- agent1.py : bot XMPP connecté à Ollama avec boucle agentique - skills/web_search.py : recherche DuckDuckGo (ddgs) - skills/web_read.py : lecture et extraction de pages web - skills/memory.py : mémoire persistante SQLite (REMEMBER/RECALL) - skills/loader.py : chargement dynamique des skills
120 lines
4.4 KiB
Python
120 lines
4.4 KiB
Python
#!/usr/bin/env python3
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# -*- coding: utf-8 -*-
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import asyncio
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import sys
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import requests
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import json
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from pathlib import Path
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from slixmpp import ClientXMPP
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# Ajouter /opt/agent au path pour importer les skills
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sys.path.insert(0, "/opt/agent")
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from skills.loader import load_skills, run_skills
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# ── CONFIG ───────────────────────────────────────────────────────────────
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CONFIG_DIR = Path("/opt/agent/config")
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CONFIG_FILE = CONFIG_DIR / "config.json"
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PROMPT_FILE = CONFIG_DIR / "system_prompt.txt"
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def load_config():
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with open(CONFIG_FILE, "r", encoding="utf-8") as f:
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return json.load(f)
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def load_system_prompt():
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with open(PROMPT_FILE, "r", encoding="utf-8") as f:
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return f.read()
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cfg = load_config()
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OLLAMA_URL = cfg["ollama_url"]
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MODEL = cfg["model"]
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XMPP_JID = cfg["xmpp_jid"]
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XMPP_PASS = cfg["xmpp_pass"]
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ADMIN_JID = cfg["admin_jid"]
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SYSTEM_PROMPT = load_system_prompt()
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# Charger les skills au démarrage
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load_skills()
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conversation_history = []
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# ── LLM ──────────────────────────────────────────────────────────────────
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def call_ollama(messages: list) -> str:
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payload = {
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"model" : MODEL,
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"messages": messages,
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"stream" : False,
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"options" : {"temperature": 0.3}
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}
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response = requests.post(OLLAMA_URL, json=payload, timeout=180)
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data = response.json()
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return data["message"]["content"]
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def ask_llm(user_message: str) -> str:
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conversation_history.append({"role": "user", "content": user_message})
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messages = [{"role": "system", "content": SYSTEM_PROMPT}] + conversation_history
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try:
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# Boucle agentique : le LLM peut enchaîner plusieurs skills
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MAX_STEPS = 5
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for _ in range(MAX_STEPS):
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reply = call_ollama(messages)
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skill_triggered, result = run_skills(reply)
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if not skill_triggered:
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# Réponse finale sans commande
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conversation_history.append({"role": "assistant", "content": reply})
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return reply
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# Injecter le résultat du skill et relancer le LLM
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messages.append({"role": "assistant", "content": reply})
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messages.append({"role": "user", "content": "[Résultat skill]\n" + result})
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# Sécurité : trop d'étapes
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reply = call_ollama(messages)
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conversation_history.append({"role": "assistant", "content": reply})
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return reply
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except Exception as e:
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error_reply = "Erreur : " + str(e)
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conversation_history.append({"role": "assistant", "content": error_reply})
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return error_reply
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# ── BOT XMPP ─────────────────────────────────────────────────────────────
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class AgentBot(ClientXMPP):
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def __init__(self):
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ClientXMPP.__init__(self, XMPP_JID, XMPP_PASS)
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self.add_event_handler("session_start", self.session_start)
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self.add_event_handler("message", self.message)
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self.register_plugin('xep_0030')
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self.register_plugin('xep_0199')
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async def session_start(self, event):
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self.send_presence()
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await self.get_roster()
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self.send_message(mto=ADMIN_JID, mbody="Agent en ligne !", mtype='chat')
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async def message(self, msg):
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if msg['type'] not in ('chat', 'normal'):
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return
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if str(msg['from']).split('/')[0] != ADMIN_JID:
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return
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user_input = msg['body'].strip()
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if user_input == "!reset":
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conversation_history.clear()
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self.send_message(mto=ADMIN_JID, mbody="Conversation reinitialisee.", mtype='chat')
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return
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loop = asyncio.get_event_loop()
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reply = await loop.run_in_executor(None, ask_llm, user_input)
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self.send_message(mto=ADMIN_JID, mbody=reply, mtype='chat')
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# ── MAIN ─────────────────────────────────────────────────────────────────
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if __name__ == "__main__":
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bot = AgentBot()
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bot.connect()
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bot.loop.run_forever()
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