File size: 3,366 Bytes
0fdd5ce | 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 | """AUBIN çerçeve bağlantıları (ağır bağımlılıklar yalnız çağrılınca yüklenir).
from aubin.integrations import langchain_tool, llamaindex_selector, crewai_router
tool = langchain_tool(engine) # LangChain / LangGraph aracı: girdi {"state", "questions"} → karar JSON
sel = llamaindex_selector(engine, choices) # LlamaIndex: sorguya göre seçeneklerden birini seçer
route = crewai_router(engine, {"billing": "...", "tech": "..."}) # CrewAI: görevi doğru ajana yönlendirir
`engine`: AubinEngine, Aubin, AubinEnsemble ya da AubinLearning (hepsi .decide sunar); uzak sunucu için RemoteAubin.
"""
from __future__ import annotations
import json
import urllib.request
class RemoteAubin:
"""Çalışan `aubin serve` sunucusuna bağlanır (Python'da model yüklemeden): .decide / .predict / .learn."""
def __init__(self, base_url="http://127.0.0.1:8009", api_key=None, timeout=60):
self.base, self.key, self.timeout = base_url.rstrip("/"), api_key, timeout
def _post(self, path, body):
h = {"Content-Type": "application/json"}
if self.key:
h["Authorization"] = f"Bearer {self.key}"
req = urllib.request.Request(self.base + path, data=json.dumps(body).encode(), headers=h)
return json.loads(urllib.request.urlopen(req, timeout=self.timeout).read())
def decide(self, state, questions):
return self._post("/decide", {"state": state, "questions": questions})
def predict(self, state, questions, min_confidence=None):
return self._post("/v1/systemone", {"state": state, "questions": questions, "min_confidence": min_confidence})
def learn(self, state, questions, answers):
return self._post("/learn", {"state": state, "questions": questions, "answers": answers})
def _predict(engine, state, questions):
return engine.predict(state, questions) if hasattr(engine, "predict") else {"answers": engine.decide(state, questions)}
def langchain_tool(engine, name="aubin_decide", description=None):
from langchain_core.tools import StructuredTool
def run(state: str, questions: dict) -> str:
return json.dumps(_predict(engine, state, questions), ensure_ascii=False)
return StructuredTool.from_function(run, name=name, description=description or
"Typed, calibrated decision over a text state: choice / score / yes-no questions.")
def llamaindex_selector(engine, choices):
"""choices: [açıklama, ...] → sorgu için seçilen indeks + güven (LlamaIndex RouterQueryEngine'e uyarlanabilir)."""
crit = {str(i): c for i, c in enumerate(choices)}
def select(query):
r = _predict(engine, query, {"route": {"type": "choice", "instructions": "Which option best handles this query?", "criteria": crit}})
a = r["answers"]["route"]
return int(a.get("choice") or 0), a.get("confidence")
return select
def crewai_router(engine, agents):
"""agents: {ad: görev tanımı} → görev metnini en uygun ajana yönlendiren fonksiyon."""
def route(task_text):
r = _predict(engine, task_text, {"agent": {"type": "choice", "instructions": "Which agent should handle this task?", "criteria": agents}})
return r["answers"]["agent"].get("choice"), r["answers"]["agent"].get("confidence")
return route
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