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import abc
import asyncio
import json
import os
import time
from dataclasses import dataclass, field
from typing import Any, Dict, List, Optional, Tuple, Union
from .protocol import Request, Response, ServerConfig
from .utils import JudgePromptBuilder, ResponseParser
class ServerInterface(abc.ABC):
"""Abstract base class for judge implementations"""
def __init__(self, config: Optional[ServerConfig] = None):
self.config = config or ServerConfig(model_name="gpt-4")
@abc.abstractmethod
def evaluate(self, request: Request) -> Response:
"""
Evaluate the given request and return a response
Args:
request: JudgeRequest containing the evaluation context
Returns:
JudgeResponse with the evaluation result
"""
pass
@abc.abstractmethod
def is_available(self) -> bool:
"""Check if the judge service is available"""
pass
def prepare_messages(self, request: Request) -> List[Dict[str, Any]]:
"""Prepare messages in the format expected by the API"""
messages = request.messages.copy()
# Add system prompt if configured
if self.config.system_prompt and not any(m.get("role") == "system" for m in messages):
messages.insert(0, {"role": "system", "content": self.config.system_prompt})
return messages
def evaluate_binary(self, question: str, answer: str, prediction: str, output_format: str = "0/1", custom_prompt: Optional[str] = None, **kwargs) -> Dict[str, Any]:
"""Evaluate binary correctness"""
# Build prompt
prompt = JudgePromptBuilder.build_binary_prompt(question=question, answer=answer, prediction=prediction, output_format=output_format, custom_prompt=custom_prompt, **kwargs)
# Create request
request = Request(messages=[{"role": "user", "content": prompt}], question=question, answer=answer, prediction=prediction, config=self.config)
# Evaluate
response = self.evaluate(request)
# Parse result
parsed_result = ResponseParser.parse_binary_response(response.content, output_format)
return {"result": parsed_result, "raw_response": response.content, "model": response.model_used, "prompt": prompt, "success": response.success}
def evaluate_comparative(
self, question: str, response1: str, response2: str, context: Optional[str] = None, score_range: Tuple[int, int] = (1, 10), custom_prompt: Optional[str] = None, images: Optional[List[Union[str, bytes]]] = None, **kwargs
) -> Dict[str, Any]:
"""Evaluate comparative responses"""
# Build prompt
prompt = JudgePromptBuilder.build_comparative_prompt(question=question, response1=response1, response2=response2, context=context, score_range=score_range, custom_prompt=custom_prompt, **kwargs)
# Create request
request = Request(messages=[{"role": "user", "content": prompt}], question=question, response1=response1, response2=response2, context=context, images=images, config=self.config)
# Evaluate
response = self.evaluate(request)
# Parse result
scores = ResponseParser.parse_comparative_response(response.content)
return {"scores": scores, "raw_response": response.content, "model": response.model_used, "prompt": prompt, "success": response.success}
def evaluate_with_rubric(self, question: str, prediction: str, rubric: Dict[str, Any], **kwargs) -> Dict[str, Any]:
"""Evaluate with a custom rubric"""
# Build rubric prompt
rubric_text = "\n".join([f"- {k}: {v}" for k, v in rubric.items()])
prompt = f"""Evaluate the following response according to the given rubric.
Question: {question}
Response: {prediction}
Rubric:
{rubric_text}
Provide a JSON response with scores for each rubric item."""
request = Request(messages=[{"role": "user", "content": prompt}], config=self.config)
# Evaluate
response = self.evaluate(request)
# Parse JSON result
parsed_result = ResponseParser.parse_json_response(response.content)
return {"scores": parsed_result, "raw_response": response.content, "model": response.model_used, "prompt": prompt, "success": response.success}
class AsyncServerInterface(ServerInterface):
"""Abstract base class for async judge implementations"""
def __init__(self, config: Optional[ServerConfig] = None):
super().__init__(config)
self.semaphore = asyncio.Semaphore(config.max_concurrent)
@abc.abstractmethod
async def evaluate_async(self, request: Request) -> Response:
"""
Asynchronously evaluate the given request and return a response
Args:
request: JudgeRequest containing the evaluation context
Returns:
JudgeResponse with the evaluation result
"""
pass
async def evaluate_batch(self, requests: List[Request]) -> List[Response]:
"""
Evaluate multiple requests concurrently
Args:
requests: List of JudgeRequests to evaluate
Returns:
List of JudgeResponses in the same order as requests
"""
tasks = [self.evaluate_async(request) for request in requests]
return await asyncio.gather(*tasks)
def evaluate(self, request: Request) -> Response:
"""Synchronous wrapper for async evaluation"""
loop = asyncio.get_event_loop()
return loop.run_until_complete(self.evaluate_async(request))
async def evaluate_binary_async(self, question: str, answer: str, prediction: str, output_format: str = "0/1", custom_prompt: Optional[str] = None, **kwargs) -> Dict[str, Any]:
"""Asynchronously evaluate binary correctness"""
# Build prompt
prompt = JudgePromptBuilder.build_binary_prompt(question=question, answer=answer, prediction=prediction, output_format=output_format, custom_prompt=custom_prompt, **kwargs)
# Create request
request = Request(messages=[{"role": "user", "content": prompt}], question=question, config=self.config)
# Evaluate
response = await self.evaluate_async(request)
# Parse result
parsed_result = ResponseParser.parse_binary_response(response.content, output_format)
return {"result": parsed_result, "raw_response": response.content, "model": response.model_used, "prompt": prompt, "success": response.success}
async def evaluate_binary_batch_async(self, questions: List[str], answers: List[str], predictions: List[str], output_format: str = "0/1", custom_prompt: Optional[str] = None, **kwargs) -> List[Dict[str, Any]]:
"""Asynchronously evaluate multiple binary correctness tasks"""
if not (len(questions) == len(answers) == len(predictions)):
raise ValueError("All input lists must have the same length")
tasks = []
for q, a, p in zip(questions, answers, predictions):
task = self.evaluate_binary_async(q, a, p, output_format, custom_prompt, **kwargs)
tasks.append(task)
return await asyncio.gather(*tasks)
async def evaluate_comparative_async(
self, question: str, response1: str, response2: str, context: Optional[str] = None, score_range: Tuple[int, int] = (1, 10), custom_prompt: Optional[str] = None, images: Optional[List[Union[str, bytes]]] = None, **kwargs
) -> Dict[str, Any]:
"""Asynchronously evaluate comparative responses"""
# Build prompt
prompt = JudgePromptBuilder.build_comparative_prompt(question=question, response1=response1, response2=response2, context=context, score_range=score_range, custom_prompt=custom_prompt, **kwargs)
# Create request
request = Request(messages=[{"role": "user", "content": prompt}], question=question, response1=response1, response2=response2, context=context, images=images, config=self.config)
# Evaluate
response = await self.evaluate_async(request)
# Parse result
scores = ResponseParser.parse_comparative_response(response.content)
return {"scores": scores, "raw_response": response.content, "model": response.model_used, "prompt": prompt, "success": response.success}
async def evaluate_comparative_batch_async(
self,
questions: List[str],
responses1: List[str],
responses2: List[str],
contexts: Optional[List[Optional[str]]] = None,
score_range: Tuple[int, int] = (1, 10),
custom_prompt: Optional[str] = None,
images_list: Optional[List[Optional[List[Union[str, bytes]]]]] = None,
**kwargs,
) -> List[Dict[str, Any]]:
"""Asynchronously evaluate multiple comparative response tasks"""
if not (len(questions) == len(responses1) == len(responses2)):
raise ValueError("Questions and responses lists must have the same length")
if contexts is None:
contexts = [None] * len(questions)
if images_list is None:
images_list = [None] * len(questions)
tasks = []
for q, r1, r2, ctx, imgs in zip(questions, responses1, responses2, contexts, images_list):
task = self.evaluate_comparative_async(q, r1, r2, ctx, score_range, custom_prompt, imgs, **kwargs)
tasks.append(task)
return await asyncio.gather(*tasks)
async def evaluate_with_rubric_async(self, question: str, prediction: str, rubric: Dict[str, Any], **kwargs) -> Dict[str, Any]:
"""Asynchronously evaluate with a custom rubric"""
# Build rubric prompt
rubric_text = "\n".join([f"- {k}: {v}" for k, v in rubric.items()])
prompt = f"""Evaluate the following response according to the given rubric.
Question: {question}
Response: {prediction}
Rubric:
{rubric_text}
Provide a JSON response with scores for each rubric item."""
# Create request with JSON response format
request = Request(messages=[{"role": "user", "content": prompt}], question=question, prediction=prediction, config=self.config)
# Evaluate
response = await self.evaluate_async(request)
# Parse JSON result
parsed_result = ResponseParser.parse_json_response(response.content)
return {"scores": parsed_result, "raw_response": response.content, "model": response.model_used, "prompt": prompt, "success": response.success}
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