| """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 |
|
|
