mirror of
https://github.com/NicolasBohn/NexQuant.git
synced 2026-08-06 19:47:44 +00:00
WinstonLiyt
0166273e15
* init a scenario for kaggle feature engineering * Added support for loading and storing RAG in Kaggle scenarios. * fix a ci bug * Add RAG after each experiment's feedback. * add a promt * fix a bug * fix a bug * add a readme * refine the code in knowledge loading
88 lines
2.8 KiB
Python
88 lines
2.8 KiB
Python
import json
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import os
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from pathlib import Path
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from jinja2 import Environment, StrictUndefined
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from rdagent.core.prompts import Prompts
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from rdagent.oai.llm_utils import APIBackend
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prompt_dict = Prompts(file_path=Path(__file__).parent / "prompts.yaml")
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def extract_knowledge_from_high_score_answers(content: str):
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sys_prompt = (
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Environment(undefined=StrictUndefined)
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.from_string(prompt_dict["extract_kaggle_knowledge_prompts"]["system"])
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.render()
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)
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user_prompt = (
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Environment(undefined=StrictUndefined)
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.from_string(prompt_dict["extract_kaggle_knowledge_prompts"]["user"])
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.render(file_content=content)
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)
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response_analysis = APIBackend().build_messages_and_create_chat_completion(
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user_prompt=user_prompt,
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system_prompt=sys_prompt,
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json_mode=True,
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)
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try:
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response_json_analysis = json.loads(response_analysis)
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except json.JSONDecodeError:
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response_json_analysis = {"error": "Failed to parse LLM's response as JSON"}
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return response_json_analysis
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def extract_knowledge_from_feedback(feedback_response: dict) -> dict:
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"""
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Extracts knowledge from LLM-generated feedback and structures it.
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"""
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sys_prompt = (
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Environment(undefined=StrictUndefined)
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.from_string(prompt_dict["extract_kaggle_knowledge_from_feedback_prompts"]["system"])
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.render()
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)
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user_prompt = (
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Environment(undefined=StrictUndefined)
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.from_string(prompt_dict["extract_kaggle_knowledge_from_feedback_prompts"]["user"])
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.render(experiment_strategy=feedback_response)
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)
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response_analysis = APIBackend().build_messages_and_create_chat_completion(
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user_prompt=user_prompt,
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system_prompt=sys_prompt,
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json_mode=True,
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)
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try:
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response_json_analysis = json.loads(response_analysis)
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except json.JSONDecodeError:
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response_json_analysis = {"error": "Failed to parse LLM's response as JSON"}
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return response_json_analysis
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def process_all_case_files(directory_path: str):
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output_file = Path(directory_path) / "kaggle_experience_results.json"
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json_output = []
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for filename in os.listdir(directory_path):
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if filename.endswith(".case"):
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file_path = os.path.join(directory_path, filename)
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with open(file_path, "r", encoding="utf-8") as file:
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content = file.read()
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knowladge = extract_knowledge_from_high_score_answers(content)
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json_output.append(knowladge)
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with open(output_file, "w", encoding="utf-8") as json_file:
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json.dump(json_output, json_file, ensure_ascii=False)
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if __name__ == "__main__":
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process_all_case_files(directory_path="git_ignore_folder/experience/tabular_cases_all")
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