mirror of
https://github.com/x1xhlol/system-prompts-and-models-of-ai-tools.git
synced 2026-06-17 23:09:35 +00:00
250 lines
8.6 KiB
Python
250 lines
8.6 KiB
Python
"""
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LLM Provider Abstraction Layer
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Supports Groq (primary) and OpenAI (fallback) with automatic failover.
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"""
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import time
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import json
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import logging
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from typing import Optional, AsyncGenerator
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from abc import ABC, abstractmethod
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from app.config import get_settings
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settings = get_settings()
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logger = logging.getLogger("dealix.llm")
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class LLMResponse:
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"""Standardized LLM response across providers."""
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def __init__(self, content: str, tokens_used: int = 0, latency_ms: int = 0,
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provider: str = "", model: str = "", raw: dict = None):
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self.content = content
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self.tokens_used = tokens_used
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self.latency_ms = latency_ms
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self.provider = provider
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self.model = model
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self.raw = raw or {}
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def to_dict(self) -> dict:
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return {
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"content": self.content,
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"tokens_used": self.tokens_used,
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"latency_ms": self.latency_ms,
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"provider": self.provider,
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"model": self.model,
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}
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def parse_json(self) -> Optional[dict]:
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"""Try to parse content as JSON."""
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try:
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# Handle markdown code blocks
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text = self.content.strip()
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if text.startswith("```json"):
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text = text[7:]
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if text.startswith("```"):
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text = text[3:]
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if text.endswith("```"):
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text = text[:-3]
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return json.loads(text.strip())
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except (json.JSONDecodeError, ValueError):
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return None
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class BaseLLMProvider(ABC):
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"""Abstract base for LLM providers."""
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@abstractmethod
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async def complete(self, system_prompt: str, user_message: str,
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temperature: float = None, max_tokens: int = None,
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json_mode: bool = False) -> LLMResponse:
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pass
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@abstractmethod
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async def is_available(self) -> bool:
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pass
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class GroqProvider(BaseLLMProvider):
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"""Groq API provider — ultra-fast inference."""
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def __init__(self):
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from groq import AsyncGroq
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self.client = AsyncGroq(api_key=settings.GROQ_API_KEY) if settings.GROQ_API_KEY else None
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self.model = settings.GROQ_MODEL
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self.fast_model = settings.GROQ_FAST_MODEL
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async def is_available(self) -> bool:
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return bool(settings.GROQ_API_KEY and self.client)
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async def complete(self, system_prompt: str, user_message: str,
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temperature: float = None, max_tokens: int = None,
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json_mode: bool = False, fast: bool = False) -> LLMResponse:
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if not self.client:
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raise RuntimeError("Groq API key not configured")
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model = self.fast_model if fast else self.model
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start = time.time()
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kwargs = {
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"model": model,
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"messages": [
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{"role": "system", "content": system_prompt},
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{"role": "user", "content": user_message},
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],
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"temperature": temperature or settings.LLM_TEMPERATURE,
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"max_tokens": max_tokens or settings.LLM_MAX_TOKENS,
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}
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if json_mode:
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kwargs["response_format"] = {"type": "json_object"}
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response = await self.client.chat.completions.create(**kwargs)
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latency = int((time.time() - start) * 1000)
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return LLMResponse(
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content=response.choices[0].message.content or "",
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tokens_used=response.usage.total_tokens if response.usage else 0,
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latency_ms=latency,
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provider="groq",
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model=model,
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raw=response.model_dump() if hasattr(response, "model_dump") else {},
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)
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class OpenAIProvider(BaseLLMProvider):
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"""OpenAI API provider — highest quality, fallback."""
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def __init__(self):
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from openai import AsyncOpenAI
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self.client = AsyncOpenAI(api_key=settings.OPENAI_API_KEY) if settings.OPENAI_API_KEY else None
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self.model = settings.OPENAI_MODEL
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self.mini_model = settings.OPENAI_MINI_MODEL
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async def is_available(self) -> bool:
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return bool(settings.OPENAI_API_KEY and self.client)
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async def complete(self, system_prompt: str, user_message: str,
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temperature: float = None, max_tokens: int = None,
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json_mode: bool = False, mini: bool = False) -> LLMResponse:
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if not self.client:
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raise RuntimeError("OpenAI API key not configured")
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model = self.mini_model if mini else self.model
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start = time.time()
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kwargs = {
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"model": model,
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"messages": [
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{"role": "system", "content": system_prompt},
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{"role": "user", "content": user_message},
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],
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"temperature": temperature or settings.LLM_TEMPERATURE,
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"max_tokens": max_tokens or settings.LLM_MAX_TOKENS,
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}
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if json_mode:
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kwargs["response_format"] = {"type": "json_object"}
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response = await self.client.chat.completions.create(**kwargs)
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latency = int((time.time() - start) * 1000)
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return LLMResponse(
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content=response.choices[0].message.content or "",
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tokens_used=response.usage.total_tokens if response.usage else 0,
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latency_ms=latency,
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provider="openai",
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model=model,
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raw=response.model_dump() if hasattr(response, "model_dump") else {},
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)
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class LLMRouter:
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"""
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Intelligent LLM routing with automatic failover.
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Primary: Groq (fast, free/cheap)
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Fallback: OpenAI (reliable, high quality)
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"""
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def __init__(self):
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self.groq = GroqProvider()
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self.openai = OpenAIProvider()
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self._primary = settings.LLM_PRIMARY_PROVIDER
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async def complete(self, system_prompt: str, user_message: str,
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temperature: float = None, max_tokens: int = None,
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json_mode: bool = False, provider: str = None,
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fast: bool = False) -> LLMResponse:
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"""
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Send a completion request to the best available provider.
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Args:
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system_prompt: System instructions
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user_message: User input
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temperature: Override default temperature
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max_tokens: Override default max tokens
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json_mode: Request JSON output
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provider: Force specific provider ("groq" or "openai")
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fast: Use faster/smaller model variant
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"""
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# Determine provider order
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if provider == "openai":
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providers = [("openai", self.openai)]
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elif provider == "groq":
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providers = [("groq", self.groq)]
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elif self._primary == "groq":
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providers = [("groq", self.groq), ("openai", self.openai)]
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else:
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providers = [("openai", self.openai), ("groq", self.groq)]
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last_error = None
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for name, prov in providers:
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if not await prov.is_available():
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logger.warning(f"LLM provider {name} not available, trying next...")
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continue
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try:
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kwargs = {
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"system_prompt": system_prompt,
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"user_message": user_message,
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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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if name == "groq":
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kwargs["fast"] = fast
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elif name == "openai":
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kwargs["mini"] = fast
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result = await prov.complete(**kwargs)
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logger.info(
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f"LLM call: provider={name} model={result.model} "
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f"tokens={result.tokens_used} latency={result.latency_ms}ms"
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)
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return result
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except Exception as e:
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last_error = e
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logger.warning(f"LLM provider {name} failed: {e}, trying next...")
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continue
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raise RuntimeError(f"All LLM providers failed. Last error: {last_error}")
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async def complete_json(self, system_prompt: str, user_message: str,
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**kwargs) -> dict:
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"""Shortcut: complete and parse as JSON."""
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response = await self.complete(system_prompt, user_message,
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json_mode=True, **kwargs)
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parsed = response.parse_json()
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if parsed is None:
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raise ValueError(f"Failed to parse LLM response as JSON: {response.content[:200]}")
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return parsed
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# Singleton
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_router: Optional[LLMRouter] = None
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def get_llm() -> LLMRouter:
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global _router
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if _router is None:
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_router = LLMRouter()
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return _router
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