ai-tools-suite/backend/routers/compare.py
2025-12-27 15:33:06 +00:00

85 lines
1.9 KiB
Python

"""Model Comparator Router"""
from fastapi import APIRouter
from pydantic import BaseModel
from typing import Optional
router = APIRouter()
class CompareRequest(BaseModel):
prompt: str
models: list[str]
temperature: float = 0.7
max_tokens: int = 500
class ModelResponse(BaseModel):
model: str
response: str
latency_ms: float
tokens_used: int
estimated_cost: float
class CompareResult(BaseModel):
prompt: str
responses: list[ModelResponse]
fastest: str
cheapest: str
quality_scores: Optional[dict] = None
class EvalRequest(BaseModel):
prompt: str
responses: dict # model -> response
criteria: list[str] = ["coherence", "accuracy", "relevance", "helpfulness"]
@router.post("/run", response_model=CompareResult)
async def compare_models(request: CompareRequest):
"""Run a prompt against multiple models and compare"""
# TODO: Implement model comparison
return CompareResult(
prompt=request.prompt,
responses=[],
fastest="",
cheapest=""
)
@router.post("/evaluate")
async def evaluate_responses(request: EvalRequest):
"""Evaluate and score model responses"""
# TODO: Implement response evaluation
return {
"scores": {},
"winner": None,
"analysis": ""
}
@router.get("/benchmarks")
async def list_benchmarks():
"""List available benchmark prompts"""
return {
"benchmarks": [
{"name": "general_qa", "prompts": 10},
{"name": "coding", "prompts": 15},
{"name": "creative_writing", "prompts": 8},
{"name": "reasoning", "prompts": 12},
]
}
@router.post("/benchmark/{benchmark_name}")
async def run_benchmark(
benchmark_name: str,
models: list[str]
):
"""Run a full benchmark suite against models"""
# TODO: Implement benchmark running
return {
"benchmark": benchmark_name,
"results": {},
"summary": ""
}