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"""
LLM Provider Interface for Flare
"""
import os
from abc import ABC, abstractmethod
from typing import Dict, List, Optional
import httpx
from openai import AsyncOpenAI
from utils import log
class LLMInterface(ABC):
"""Abstract base class for LLM providers"""
@abstractmethod
async def generate(self, system_prompt: str, user_input: str, context: List[Dict]) -> str:
"""Generate response from LLM"""
pass
@abstractmethod
async def startup(self, project_config: Dict) -> bool:
"""Initialize LLM with project config"""
pass
class SparkLLM(LLMInterface):
"""Existing Spark integration"""
def __init__(self, spark_endpoint: str, spark_token: str):
self.spark_endpoint = spark_endpoint.rstrip("/")
self.spark_token = spark_token
async def generate(self, system_prompt: str, user_input: str, context: List[Dict]) -> str:
headers = {
"Authorization": f"Bearer {self.spark_token}",
"Content-Type": "application/json"
}
payload = {
"system_prompt": system_prompt,
"user_input": user_input,
"context": context
}
try:
async with httpx.AsyncClient(timeout=60) as client:
response = await client.post(
f"{self.spark_endpoint}/generate",
json=payload,
headers=headers
)
response.raise_for_status()
data = response.json()
# Try different response fields
raw = data.get("model_answer", "").strip()
if not raw:
raw = (data.get("assistant") or data.get("text", "")).strip()
return raw
except Exception as e:
log(f"❌ Spark error: {e}")
raise
async def startup(self, project_config: Dict) -> bool:
"""Send startup request to Spark"""
# Existing Spark startup logic
return True
class GPT4oLLM(LLMInterface):
"""OpenAI GPT integration"""
def __init__(self, api_key: str, model: str = "gpt-4o-mini"):
self.api_key = api_key
self.model = model
self.client = AsyncOpenAI(api_key=api_key)
log(f"✅ Initialized GPT LLM with model: {model}")
async def generate(self, system_prompt: str, user_input: str, context: List[Dict]) -> str:
try:
# Convert context to OpenAI format
messages = []
# Add system prompt
messages.append({"role": "system", "content": system_prompt})
# Add conversation history
for msg in context[-10:]: # Last 10 messages
role = "user" if msg["role"] == "user" else "assistant"
messages.append({"role": role, "content": msg["content"]})
# Add current user input
messages.append({"role": "user", "content": user_input})
# Call OpenAI API
response = await self.client.chat.completions.create(
model=self.model,
messages=messages,
temperature=0.3, # Low temperature for consistency
max_tokens=512
)
content = response.choices[0].message.content.strip()
log(f"🪄 GPT response (first 120 chars): {content[:120]}")
# Log token usage for cost tracking
if response.usage:
log(f"📊 Tokens used - Input: {response.usage.prompt_tokens}, Output: {response.usage.completion_tokens}")
return content
except Exception as e:
log(f"❌ GPT error: {e}")
raise
async def startup(self, project_config: Dict) -> bool:
"""Validate API key"""
try:
# Test API key with a simple request
test_response = await self.client.models.list()
log(f"✅ OpenAI API key validated, available models: {len(test_response.data)}")
return True
except Exception as e:
log(f"❌ Invalid OpenAI API key: {e}")
return False |