chris
d053e7c16c
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139 lines
4.3 KiB
Python
139 lines
4.3 KiB
Python
"""
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emma_server.py — HTTP-wrapper rundt Emma-agenten.
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Start: cd /home/chris_christiansen/OSVauco && python -m emma.emma_server
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Port: 8765
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Endepunkter:
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POST /ask {"prompt": "...", "history": [...]} → {"response": "..."}
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GET /health → {"status": "ok", "model": "...", "memory_patterns": N}
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GET /stats → Flynn-statistikk
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Modell-prioritet (iht. llm-stack.md):
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1. qwen2.5-coder:7b (kode/repo-arbeid, tools)
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2. qwen2.5:7b (generell, tools)
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3. gemma3:4b (fallback, ingen tools)
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Regler (iht. emma_identity.py + HANDOFF.md):
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- Emma rapporterer kun til Chris Christiansen
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- Guardrails kan ikke deaktiveres
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- Aldri deploy uten eksplisitt GO fra Chris
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- Gitea er primær Git
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"""
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import sys
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import json
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import logging
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from pathlib import Path
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from http.server import BaseHTTPRequestHandler, HTTPServer
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sys.path.insert(0, str(Path(__file__).parent.parent))
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from emma.emma_backend_ollama import OllamaBackend
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from emma.emma_resonance_persistent import PersistentMorphicMemory
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from emma.emma_mcot import EmmaMCoTAgent
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from emma.emma_guardrails import EmmaGuardrails
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from emma.emma_flynn import FlynnTracker
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logging.basicConfig(
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level=logging.INFO,
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format="%(asctime)s [emma-server] %(levelname)s %(message)s",
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)
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log = logging.getLogger("emma-server")
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# Modell-prioritet iht. llm-stack.md
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DEFAULT_MODEL = "qwen2.5-coder:7b"
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DEFAULT_EMBED = "qwen2.5:3b" # brukes til morfisk resonans-embedding
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PORT = 8765
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def _build_agent(model: str = DEFAULT_MODEL) -> tuple:
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llm = OllamaBackend(model=model, embed_model=DEFAULT_EMBED)
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memory = PersistentMorphicMemory()
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guardrails = EmmaGuardrails()
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agent = EmmaMCoTAgent(llm=llm, memory=memory)
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return agent, guardrails, memory
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# Bygg agent ved oppstart
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log.info(f"Laster Emma med modell={DEFAULT_MODEL} ...")
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_agent, _guardrails, _memory = _build_agent()
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log.info(f"Emma klar | {len(_memory)} morfiske m\u00f8nstre lastet")
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class EmmaHandler(BaseHTTPRequestHandler):
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def log_message(self, format, *args): # noqa: A002
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log.info(f"{self.address_string()} {format % args}")
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def _send_json(self, code: int, data: dict):
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body = json.dumps(data, ensure_ascii=False).encode()
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self.send_response(code)
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self.send_header("Content-Type", "application/json; charset=utf-8")
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self.send_header("Content-Length", str(len(body)))
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self.end_headers()
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self.wfile.write(body)
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def _read_body(self) -> dict:
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length = int(self.headers.get("Content-Length", 0))
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raw = self.rfile.read(length) if length else b"{}"
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try:
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return json.loads(raw)
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except json.JSONDecodeError:
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return {}
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def do_GET(self):
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if self.path == "/health":
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self._send_json(200, {
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"status": "ok",
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"model": DEFAULT_MODEL,
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"memory_patterns": len(_memory),
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"agent": "EmmaMCoTAgent",
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})
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elif self.path == "/stats":
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stats = FlynnTracker().summary() or {}
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self._send_json(200, stats)
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else:
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self._send_json(404, {"error": "not found"})
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def do_POST(self):
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if self.path != "/ask":
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self._send_json(404, {"error": "not found"})
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return
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body = self._read_body()
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prompt = (body.get("prompt") or "").strip()
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history = body.get("history") or []
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if not prompt:
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self._send_json(400, {"error": "prompt er p\u00e5krevd"})
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return
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# Guardrail-sjekk
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ok, reason = _guardrails.can_execute(prompt)
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if not ok:
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log.warning(f"Guardrail blokkerte: {reason}")
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self._send_json(403, {"error": reason, "blocked": True})
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return
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log.info(f"Sp\u00f8rsm\u00e5l: {prompt[:80]}")
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try:
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response = _agent.run(prompt, history)
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self._send_json(200, {"response": response, "model": DEFAULT_MODEL})
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except Exception as e:
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log.error(f"Agent-feil: {e}")
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self._send_json(500, {"error": str(e)})
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def main():
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server = HTTPServer(("0.0.0.0", PORT), EmmaHandler)
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log.info(f"Emma HTTP-server lytter p\u00e5 port {PORT}")
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try:
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server.serve_forever()
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except KeyboardInterrupt:
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log.info("Emma server stoppet")
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server.server_close()
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if __name__ == "__main__":
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main()
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