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import json
import re
from typing import Optional, Dict, Any, Tuple
from pydantic import BaseModel, Field, validator
from huggingface_hub import InferenceClient
from huggingface_hub.errors import HfHubHTTPError
from variables import meta_prompts, prompt_refiner_model
class LLMResponse(BaseModel):
initial_prompt_evaluation: str = Field(..., description="Evaluation of the initial prompt")
refined_prompt: str = Field(..., description="The refined version of the prompt")
explanation_of_refinements: str = Field(..., description="Explanation of the refinements made")
response_content: Optional[Dict[str, Any]] = Field(None, description="Raw response content")
@validator('initial_prompt_evaluation', 'refined_prompt', 'explanation_of_refinements')
def clean_text_fields(cls, v):
if isinstance(v, str):
return v.strip().replace('\\n', '\n').replace('\\"', '"')
return v
class PromptRefiner:
def __init__(self, api_token: str, meta_prompts: dict):
self.client = InferenceClient(token=api_token, timeout=120)
self.meta_prompts = meta_prompts
def refine_prompt(self, prompt: str, meta_prompt_choice: str) -> Tuple[str, str, str, dict]:
"""Refine the given prompt using the selected meta prompt."""
try:
selected_meta_prompt = self.meta_prompts.get(
meta_prompt_choice,
self.meta_prompts["star"]
)
messages = [
{
"role": "system",
"content": 'You are an expert at refining and extending prompts. Given a basic prompt, provide a more relevant and detailed prompt.'
},
{
"role": "user",
"content": selected_meta_prompt.replace("[Insert initial prompt here]", prompt)
}
]
response = self.client.chat_completion(
model=prompt_refiner_model,
messages=messages,
max_tokens=3000,
temperature=0.8
)
response_content = response.choices[0].message.content.strip()
result = self._parse_response(response_content)
try:
llm_response = LLMResponse(**result)
return (
llm_response.initial_prompt_evaluation,
llm_response.refined_prompt,
llm_response.explanation_of_refinements,
llm_response.dict()
)
except Exception as e:
print(f"Error creating LLMResponse: {e}")
return self._create_error_response(f"Error validating response: {str(e)}")
except HfHubHTTPError as e:
return self._create_error_response("Model timeout. Please try again later.")
except Exception as e:
return self._create_error_response(f"Unexpected error: {str(e)}")
def _parse_response(self, response_content: str) -> dict:
"""Parse the LLM response content."""
try:
# Try to extract JSON from <json> tags
json_match = re.search(r'<json>\s*(.*?)\s*</json>', response_content, re.DOTALL)
if json_match:
json_str = json_match.group(1).strip()
# Clean up the JSON string
json_str = re.sub(r'\s+', ' ', json_str)
json_str = json_str.replace('•', '*') # Replace bullet points
try:
parsed_json = json.loads(json_str)
if isinstance(parsed_json, str):
parsed_json = json.loads(parsed_json)
return {
"initial_prompt_evaluation": parsed_json.get("initial_prompt_evaluation", ""),
"refined_prompt": parsed_json.get("refined_prompt", ""),
"explanation_of_refinements": parsed_json.get("explanation_of_refinements", ""),
"response_content": parsed_json
}
except json.JSONDecodeError as e:
print(f"JSON parsing error: {e}")
return self._create_error_dict(str(e))
# Fallback to regex parsing if JSON extraction fails
return self._parse_with_regex(response_content)
except Exception as e:
print(f"Error parsing response: {e}")
print(f"Raw content: {response_content}")
return self._create_error_dict(str(e))
def _parse_with_regex(self, content: str) -> dict:
"""Parse content using regex patterns when JSON parsing fails."""
output = {}
for key in ["initial_prompt_evaluation", "refined_prompt", "explanation_of_refinements"]:
pattern = rf'"{key}":\s*"(.*?)"(?:,|\}})'
match = re.search(pattern, content, re.DOTALL)
output[key] = match.group(1) if match else ""
output["response_content"] = content
return output
def _create_error_dict(self, error_message: str) -> dict:
"""Create a standardized error response dictionary."""
return {
"initial_prompt_evaluation": f"Error parsing response: {error_message}",
"refined_prompt": "",
"explanation_of_refinements": "",
"response_content": {"error": error_message}
}
def _create_error_response(self, error_message: str) -> Tuple[str, str, str, dict]:
"""Create a standardized error response tuple."""
return (
f"Error: {error_message}",
"The selected model is currently unavailable.",
"An error occurred during processing.",
{"error": error_message}
) |