mirror of
https://github.com/x1xhlol/system-prompts-and-models-of-ai-tools.git
synced 2026-06-18 15:29:36 +00:00
250 lines
8.6 KiB
Python
250 lines
8.6 KiB
Python
"""
|
|
LLM Provider Abstraction Layer
|
|
Supports Groq (primary) and OpenAI (fallback) with automatic failover.
|
|
"""
|
|
|
|
import time
|
|
import json
|
|
import logging
|
|
from typing import Optional, AsyncGenerator
|
|
from abc import ABC, abstractmethod
|
|
|
|
from app.config import get_settings
|
|
|
|
settings = get_settings()
|
|
logger = logging.getLogger("dealix.llm")
|
|
|
|
|
|
class LLMResponse:
|
|
"""Standardized LLM response across providers."""
|
|
def __init__(self, content: str, tokens_used: int = 0, latency_ms: int = 0,
|
|
provider: str = "", model: str = "", raw: dict = None):
|
|
self.content = content
|
|
self.tokens_used = tokens_used
|
|
self.latency_ms = latency_ms
|
|
self.provider = provider
|
|
self.model = model
|
|
self.raw = raw or {}
|
|
|
|
def to_dict(self) -> dict:
|
|
return {
|
|
"content": self.content,
|
|
"tokens_used": self.tokens_used,
|
|
"latency_ms": self.latency_ms,
|
|
"provider": self.provider,
|
|
"model": self.model,
|
|
}
|
|
|
|
def parse_json(self) -> Optional[dict]:
|
|
"""Try to parse content as JSON."""
|
|
try:
|
|
# Handle markdown code blocks
|
|
text = self.content.strip()
|
|
if text.startswith("```json"):
|
|
text = text[7:]
|
|
if text.startswith("```"):
|
|
text = text[3:]
|
|
if text.endswith("```"):
|
|
text = text[:-3]
|
|
return json.loads(text.strip())
|
|
except (json.JSONDecodeError, ValueError):
|
|
return None
|
|
|
|
|
|
class BaseLLMProvider(ABC):
|
|
"""Abstract base for LLM providers."""
|
|
|
|
@abstractmethod
|
|
async def complete(self, system_prompt: str, user_message: str,
|
|
temperature: float = None, max_tokens: int = None,
|
|
json_mode: bool = False) -> LLMResponse:
|
|
pass
|
|
|
|
@abstractmethod
|
|
async def is_available(self) -> bool:
|
|
pass
|
|
|
|
|
|
class GroqProvider(BaseLLMProvider):
|
|
"""Groq API provider — ultra-fast inference."""
|
|
|
|
def __init__(self):
|
|
from groq import AsyncGroq
|
|
self.client = AsyncGroq(api_key=settings.GROQ_API_KEY) if settings.GROQ_API_KEY else None
|
|
self.model = settings.GROQ_MODEL
|
|
self.fast_model = settings.GROQ_FAST_MODEL
|
|
|
|
async def is_available(self) -> bool:
|
|
return bool(settings.GROQ_API_KEY and self.client)
|
|
|
|
async def complete(self, system_prompt: str, user_message: str,
|
|
temperature: float = None, max_tokens: int = None,
|
|
json_mode: bool = False, fast: bool = False) -> LLMResponse:
|
|
if not self.client:
|
|
raise RuntimeError("Groq API key not configured")
|
|
|
|
model = self.fast_model if fast else self.model
|
|
start = time.time()
|
|
|
|
kwargs = {
|
|
"model": model,
|
|
"messages": [
|
|
{"role": "system", "content": system_prompt},
|
|
{"role": "user", "content": user_message},
|
|
],
|
|
"temperature": temperature or settings.LLM_TEMPERATURE,
|
|
"max_tokens": max_tokens or settings.LLM_MAX_TOKENS,
|
|
}
|
|
|
|
if json_mode:
|
|
kwargs["response_format"] = {"type": "json_object"}
|
|
|
|
response = await self.client.chat.completions.create(**kwargs)
|
|
latency = int((time.time() - start) * 1000)
|
|
|
|
return LLMResponse(
|
|
content=response.choices[0].message.content or "",
|
|
tokens_used=response.usage.total_tokens if response.usage else 0,
|
|
latency_ms=latency,
|
|
provider="groq",
|
|
model=model,
|
|
raw=response.model_dump() if hasattr(response, "model_dump") else {},
|
|
)
|
|
|
|
|
|
class OpenAIProvider(BaseLLMProvider):
|
|
"""OpenAI API provider — highest quality, fallback."""
|
|
|
|
def __init__(self):
|
|
from openai import AsyncOpenAI
|
|
self.client = AsyncOpenAI(api_key=settings.OPENAI_API_KEY) if settings.OPENAI_API_KEY else None
|
|
self.model = settings.OPENAI_MODEL
|
|
self.mini_model = settings.OPENAI_MINI_MODEL
|
|
|
|
async def is_available(self) -> bool:
|
|
return bool(settings.OPENAI_API_KEY and self.client)
|
|
|
|
async def complete(self, system_prompt: str, user_message: str,
|
|
temperature: float = None, max_tokens: int = None,
|
|
json_mode: bool = False, mini: bool = False) -> LLMResponse:
|
|
if not self.client:
|
|
raise RuntimeError("OpenAI API key not configured")
|
|
|
|
model = self.mini_model if mini else self.model
|
|
start = time.time()
|
|
|
|
kwargs = {
|
|
"model": model,
|
|
"messages": [
|
|
{"role": "system", "content": system_prompt},
|
|
{"role": "user", "content": user_message},
|
|
],
|
|
"temperature": temperature or settings.LLM_TEMPERATURE,
|
|
"max_tokens": max_tokens or settings.LLM_MAX_TOKENS,
|
|
}
|
|
|
|
if json_mode:
|
|
kwargs["response_format"] = {"type": "json_object"}
|
|
|
|
response = await self.client.chat.completions.create(**kwargs)
|
|
latency = int((time.time() - start) * 1000)
|
|
|
|
return LLMResponse(
|
|
content=response.choices[0].message.content or "",
|
|
tokens_used=response.usage.total_tokens if response.usage else 0,
|
|
latency_ms=latency,
|
|
provider="openai",
|
|
model=model,
|
|
raw=response.model_dump() if hasattr(response, "model_dump") else {},
|
|
)
|
|
|
|
|
|
class LLMRouter:
|
|
"""
|
|
Intelligent LLM routing with automatic failover.
|
|
Primary: Groq (fast, free/cheap)
|
|
Fallback: OpenAI (reliable, high quality)
|
|
"""
|
|
|
|
def __init__(self):
|
|
self.groq = GroqProvider()
|
|
self.openai = OpenAIProvider()
|
|
self._primary = settings.LLM_PRIMARY_PROVIDER
|
|
|
|
async def complete(self, system_prompt: str, user_message: str,
|
|
temperature: float = None, max_tokens: int = None,
|
|
json_mode: bool = False, provider: str = None,
|
|
fast: bool = False) -> LLMResponse:
|
|
"""
|
|
Send a completion request to the best available provider.
|
|
|
|
Args:
|
|
system_prompt: System instructions
|
|
user_message: User input
|
|
temperature: Override default temperature
|
|
max_tokens: Override default max tokens
|
|
json_mode: Request JSON output
|
|
provider: Force specific provider ("groq" or "openai")
|
|
fast: Use faster/smaller model variant
|
|
"""
|
|
# Determine provider order
|
|
if provider == "openai":
|
|
providers = [("openai", self.openai)]
|
|
elif provider == "groq":
|
|
providers = [("groq", self.groq)]
|
|
elif self._primary == "groq":
|
|
providers = [("groq", self.groq), ("openai", self.openai)]
|
|
else:
|
|
providers = [("openai", self.openai), ("groq", self.groq)]
|
|
|
|
last_error = None
|
|
for name, prov in providers:
|
|
if not await prov.is_available():
|
|
logger.warning(f"LLM provider {name} not available, trying next...")
|
|
continue
|
|
try:
|
|
kwargs = {
|
|
"system_prompt": system_prompt,
|
|
"user_message": user_message,
|
|
"temperature": temperature,
|
|
"max_tokens": max_tokens,
|
|
"json_mode": json_mode,
|
|
}
|
|
if name == "groq":
|
|
kwargs["fast"] = fast
|
|
elif name == "openai":
|
|
kwargs["mini"] = fast
|
|
|
|
result = await prov.complete(**kwargs)
|
|
logger.info(
|
|
f"LLM call: provider={name} model={result.model} "
|
|
f"tokens={result.tokens_used} latency={result.latency_ms}ms"
|
|
)
|
|
return result
|
|
except Exception as e:
|
|
last_error = e
|
|
logger.warning(f"LLM provider {name} failed: {e}, trying next...")
|
|
continue
|
|
|
|
raise RuntimeError(f"All LLM providers failed. Last error: {last_error}")
|
|
|
|
async def complete_json(self, system_prompt: str, user_message: str,
|
|
**kwargs) -> dict:
|
|
"""Shortcut: complete and parse as JSON."""
|
|
response = await self.complete(system_prompt, user_message,
|
|
json_mode=True, **kwargs)
|
|
parsed = response.parse_json()
|
|
if parsed is None:
|
|
raise ValueError(f"Failed to parse LLM response as JSON: {response.content[:200]}")
|
|
return parsed
|
|
|
|
|
|
# Singleton
|
|
_router: Optional[LLMRouter] = None
|
|
|
|
def get_llm() -> LLMRouter:
|
|
global _router
|
|
if _router is None:
|
|
_router = LLMRouter()
|
|
return _router
|