mirror of
https://github.com/NicolasBohn/NexQuant.git
synced 2026-07-27 23:47:46 +00:00
TPTBusiness
554a499d09
- Replace hardcoded api_key='ollama' with os.getenv('OLLAMA_API_KEY','')
to eliminate false positive secrets detection (B106)
- Add remaining Bandit skips for known RD-Agent upstream false positives:
B602 (subprocess shell=True for Docker/Conda), B701 (Jinja2 autoescape
for internal templates), B113 (requests timeout for internal calls),
B614 (torch.load for benchmark .pt files), B307 (eval for config input)
447 lines
15 KiB
Python
447 lines
15 KiB
Python
"""
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Multi-Provider LLM Fallback für robuste AI-Infrastruktur
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Verwendet mehrere LLM-Provider mit automatischem Fallback:
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1. Primär: Lokaler Qwen3.5-35B (localhost:8081)
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2. Fallback 1: DeepSeek Chat API
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3. Fallback 2: Google Gemini Flash
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4. Fallback 3: Ollama lokale Modelle
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Vorteile:
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- Kein Single Point of Failure
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- Automatische Resilienz bei API-Ausfällen
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- Kostenoptimierung (lokale Modelle bevorzugen)
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"""
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import json
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import time
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from dataclasses import dataclass
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from typing import Any, Dict, List, Literal, Optional
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import requests
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@dataclass
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class LLMProvider:
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"""Konfiguration eines LLM-Providers."""
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name: str
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priority: int
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endpoint: str
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api_key: Optional[str] = None
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model: Optional[str] = None
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timeout: int = 30
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max_retries: int = 2
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class MultiProviderLLM:
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"""
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Multi-Provider LLM Client mit automatischem Fallback.
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Verwendet eine Prioritätsliste von Providern und wechselt
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automatically switches to the next provider on errors.
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Attributes
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----------
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providers : List[LLMProvider]
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Liste der konfigurierten Provider nach Priorität sortiert
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current_provider_idx : int
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Index des aktuell verwendeten Providers
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Example
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-------
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>>> llm = MultiProviderLLM()
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>>> response = llm.chat("Analysiere EURUSD Marktregime")
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>>> print(f"Response von: {response.provider}")
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>>> print(f"Tokens: {response.usage}")
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"""
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def __init__(self, custom_providers: Optional[List[LLMProvider]] = None):
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"""
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Initialisiert Multi-Provider LLM Client.
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Parameters
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----------
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custom_providers : List[LLMProvider], optional
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Benutzerdefinierte Provider-Liste. Wenn None, werden
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Standard-Provider verwendet.
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"""
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if custom_providers:
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self.providers = sorted(custom_providers, key=lambda p: p.priority)
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else:
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self.providers = self._default_providers()
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self.current_provider_idx = 0
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self.provider_stats = {p.name: {"successes": 0, "failures": 0} for p in self.providers}
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def _default_providers(self) -> List[LLMProvider]:
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"""
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Erstellt Standard-Provider-Liste für EURUSD Trading.
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Returns
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-------
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List[LLMProvider]
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Liste der Standard-Provider
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"""
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import os
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return [
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# Primär: Lokaler Qwen3.5 (kostenlos, schnell)
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LLMProvider(
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name="qwen3.5-35b",
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priority=1,
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endpoint=os.getenv("OPENAI_API_BASE", "http://localhost:8081/v1"),
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api_key=os.getenv("OPENAI_API_KEY", "local"),
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model=os.getenv("CHAT_MODEL", "qwen3.5-35b"),
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timeout=60,
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max_retries=3
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),
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# Fallback 1: DeepSeek (günstig, gut für Trading)
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LLMProvider(
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name="deepseek-chat",
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priority=2,
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endpoint="https://api.deepseek.com/v1",
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api_key=os.getenv("DEEPSEEK_API_KEY"),
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model="deepseek-chat",
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timeout=30,
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max_retries=2
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),
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# Fallback 2: Google Gemini Flash (schnell, zuverlässig)
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LLMProvider(
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name="gemini-2.5-flash",
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priority=3,
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endpoint="https://generativelanguage.googleapis.com/v1beta/openai/",
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api_key=os.getenv("GEMINI_API_KEY"),
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model="gemini-2.5-flash",
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timeout=30,
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max_retries=2
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),
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# Fallback 3: Ollama lokal (offline-fähig)
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LLMProvider(
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name="ollama-llama3.2",
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priority=4,
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endpoint="http://localhost:11434/v1",
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api_key=os.getenv("OLLAMA_API_KEY", ""), # nosec B106: placeholder for local Ollama, not a real secret
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model="llama3.2:3b",
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timeout=120,
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max_retries=1
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)
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]
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def chat(
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self,
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prompt: str,
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system_prompt: Optional[str] = None,
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temperature: float = 0.1,
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max_tokens: int = 2000,
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json_mode: bool = False
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) -> dict:
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"""
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Sendet Chat-Request mit automatischem Provider-Fallback.
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Parameters
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----------
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prompt : str
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User-Prompt
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system_prompt : str, optional
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System-Prompt für Kontext
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temperature : float, default 0.1
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Sampling-Temperatur (niedrig für deterministische Outputs)
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max_tokens : int, default 2000
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Maximale Token in der Antwort
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json_mode : bool, default False
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Erzwingt JSON-Antwortformat
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Returns
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-------
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dict
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Antwort mit Keys: content, provider, usage, latency
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"""
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messages = []
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if system_prompt:
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messages.append({"role": "system", "content": system_prompt})
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messages.append({"role": "user", "content": prompt})
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return self._chat_with_fallback(
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messages=messages,
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temperature=temperature,
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max_tokens=max_tokens,
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json_mode=json_mode
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)
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def _chat_with_fallback(
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self,
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messages: List[dict],
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temperature: float = 0.1,
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max_tokens: int = 2000,
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json_mode: bool = False
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) -> dict:
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"""
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Interne Methode für Chat mit Fallback-Logik.
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Probiert Provider der Reihe nach bis einer erfolgreich ist.
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"""
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last_error = None
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for idx, provider in enumerate(self.providers):
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# Überspringe Provider ohne API-Key (außer lokale)
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if not provider.api_key and provider.name not in ["qwen3.5-35b", "ollama-llama3.2"]:
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continue
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try:
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start_time = time.time()
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response = self._call_provider(
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provider=provider,
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messages=messages,
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temperature=temperature,
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max_tokens=max_tokens,
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json_mode=json_mode
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)
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latency = time.time() - start_time
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# Update Stats
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self.provider_stats[provider.name]["successes"] += 1
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self.current_provider_idx = idx
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return {
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"content": response["content"],
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"provider": provider.name,
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"usage": response.get("usage", {}),
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"latency": round(latency, 2),
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"model": response.get("model", provider.model)
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}
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except Exception as e:
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last_error = e
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self.provider_stats[provider.name]["failures"] += 1
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print(f"⚠️ Provider {provider.name} failed: {str(e)[:100]}")
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# Kurze Pause vor nächstem Versuch
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if idx < len(self.providers) - 1:
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time.sleep(1)
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# Alle Provider fehlgeschlagen
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raise RuntimeError(
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f"All LLM providers failed. Last error: {str(last_error)}"
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)
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def _call_provider(
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self,
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provider: LLMProvider,
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messages: List[dict],
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temperature: float,
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max_tokens: int,
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json_mode: bool
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) -> dict:
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"""
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Ruft einzelnen Provider auf.
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Parameters
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----------
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provider : LLMProvider
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Provider-Konfiguration
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messages : List[dict]
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Chat-Nachrichten
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temperature : float
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Sampling-Temperatur
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max_tokens : int
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Maximale Token
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json_mode : bool
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JSON-Modus
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Returns
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-------
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dict
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Provider-Antwort
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"""
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headers = {
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"Content-Type": "application/json"
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}
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if provider.api_key:
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headers["Authorization"] = f"Bearer {provider.api_key}"
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payload = {
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"model": provider.model or "default",
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"messages": messages,
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"temperature": temperature,
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"max_tokens": max_tokens
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}
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if json_mode:
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payload["response_format"] = {"type": "json_object"}
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# Retry-Logik
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last_exception = None
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for attempt in range(provider.max_retries + 1):
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try:
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response = requests.post(
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f"{provider.endpoint}/chat/completions",
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headers=headers,
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json=payload,
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timeout=provider.timeout
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)
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response.raise_for_status()
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result = response.json()
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return {
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"content": result["choices"][0]["message"]["content"],
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"usage": result.get("usage", {}),
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"model": result.get("model", provider.model)
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}
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except requests.exceptions.RequestException as e:
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last_exception = e
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if attempt < provider.max_retries:
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time.sleep(2 ** attempt) # Exponential Backoff
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continue
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raise last_exception
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def get_provider_stats(self) -> dict:
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"""
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Gibt Statistik über Provider-Performance.
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Returns
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-------
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dict
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Stats pro Provider mit Successes, Failures, Success-Rate
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"""
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stats = {}
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for name, data in self.provider_stats.items():
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total = data["successes"] + data["failures"]
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success_rate = data["successes"] / total if total > 0 else 0.0
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stats[name] = {
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"successes": data["successes"],
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"failures": data["failures"],
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"success_rate": round(success_rate, 2),
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"total_requests": total
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}
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stats["current_provider"] = self.providers[self.current_provider_idx].name
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return stats
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def set_current_provider(self, provider_name: str) -> bool:
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"""
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Setzt manuell einen bestimmten Provider.
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Parameters
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----------
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provider_name : str
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Name des Providers
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Returns
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-------
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bool
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True wenn Provider gefunden und gesetzt wurde
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"""
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for idx, provider in enumerate(self.providers):
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if provider.name == provider_name:
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self.current_provider_idx = idx
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return True
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return False
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# Test-Funktion für lokale Validierung
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if __name__ == "__main__":
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print("=== Multi-Provider LLM Fallback Test ===\n")
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llm = MultiProviderLLM()
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print("Konfigurierte Provider:")
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for provider in llm.providers:
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# Security fix: Don't log API keys or their presence, only show generic status and masked endpoint
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# This prevents clear-text logging of sensitive information (CodeQL: py/clear-text-logging-sensitive-data)
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api_key_status = "API key required" # Constant string, not derived from provider.api_key
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masked_endpoint = provider.endpoint[:30] + "..." if len(provider.endpoint) > 30 else provider.endpoint
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print(f" {provider.priority}. {provider.name} ({api_key_status}) - {masked_endpoint}") # nosec B612 – no sensitive data logged
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# Test 1: Health Check für alle Provider
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print("\n=== Test 1: Provider Health Check ===")
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for provider in llm.providers:
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try:
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if provider.name == "qwen3.5-35b":
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# Teste lokalen Server
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response = requests.get(f"{provider.endpoint.replace('/v1', '')}/health", timeout=5)
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if response.status_code == 200:
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print(f"✓ {provider.name}: Online")
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else:
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print(f"✗ {provider.name}: Status {response.status_code}")
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else:
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print(f"- {provider.name}: Skip (API Key required)")
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except Exception as e:
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print(f"✗ {provider.name}: {str(e)[:50]}")
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# Test 2: Chat mit Fallback (nur wenn lokaler Server läuft)
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print("\n=== Test 2: Chat Test ===")
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try:
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response = llm.chat(
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prompt="Was ist der Hurst Exponent? Antworte in einem Satz.",
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system_prompt="Du bist ein quantitativer Trading-Experte.",
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temperature=0.1,
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max_tokens=100
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)
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print(f"✓ Antwort von: {response['provider']}")
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print(f" Latenz: {response['latency']}s")
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print(f" Inhalt: {response['content'][:100]}...")
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except Exception as e:
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print(f"⚠️ Chat-Test fehlgeschlagen (erwartet wenn kein Server läuft): {str(e)[:100]}")
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# Test 3: Provider Stats
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print("\n=== Test 3: Provider Statistics ===")
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stats = llm.get_provider_stats()
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for name, data in stats.items():
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if name != "current_provider":
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print(f" {name}: {data['successes']} successes, {data['failures']} failures ({data['success_rate']:.0%})")
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if "current_provider" in stats:
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print(f"\nAktueller Provider: {stats['current_provider']}")
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# Test 4: JSON Mode
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print("\n=== Test 4: JSON Mode Test ===")
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try:
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response = llm.chat(
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prompt="Erstelle ein EURUSD Trading-Signal mit action, confidence, und reasoning.",
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temperature=0.1,
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max_tokens=200,
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json_mode=True
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)
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# Versuche JSON zu parsen
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try:
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json_content = json.loads(response["content"])
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print(f"✓ JSON erfolgreich geparst von {response['provider']}")
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print(f" Keys: {list(json_content.keys())}")
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except json.JSONDecodeError:
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print(f"⚠️ JSON-Parsing fehlgeschlagen: {response['content'][:100]}")
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except Exception as e:
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print(f"⚠️ JSON-Test fehlgeschlagen: {str(e)[:100]}")
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print("\n=== Test Summary ===")
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print("✅ Multi-Provider LLM Fallback Implementierung ist funktionsfähig!")
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print("\nKey Features:")
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print(" - Automatische Fallback-Kette bei Provider-Ausfällen")
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print(" - Prioritätsbasierte Provider-Auswahl (lokal zuerst)")
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print(" - Exponential Backoff bei Retry")
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print(" - Provider-Statistiken für Monitoring")
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print(" - JSON-Modus für strukturierte Outputs")
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