mirror of
https://github.com/NicolasBohn/NexQuant.git
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Linlang
805f337fc4
* change_log_object * lint code * delete comments * change_log_object * change_log_object * fix import test error * update code * update code * fix bugs * skip mypy error * skip mypy error * skip mypy error * Start the flask server before running the demo. * achieve front and back interaction * fix github-advanced-security comments * fix github-advanced-security comments * tmp ignore * fix CI * move some logic * change format * adjust logic * log2json changes * tmp * fix * fix bug * refine log2json between 5 scenarios * fix * refine codes * fix logic * use localhost * add loop & all_duration param for old scenario startup * merge control logic * add README for server ui api * update README * reuse code in logger * add loop_n and all_duration param * fix upload * ui server now use port in setting * fix port setting * fix port setting * fix mypy check * refine logger and log storage * fix ruff error * fix CI * refine logger, loop, storage * bind one FileStorage with one logger * not truncate log storage * refine LoopBase.load(), use `checkout` instead of `output_path` and `do_truncate` * clear session folder when loading loop to run * move component info init step to ExpGen Class * Update rdagent/utils/workflow.py * move truncate_session function to LoopBase class * add checkout param for other scenarios * fix bug * move WebStorage to UI * change web_storage name * add randomname to requirements * add typer * fix requirements --------- Co-authored-by: WinstonLiyte <1957922024@qq.com> Co-authored-by: Bowen Xian <xianbowen@outlook.com> Co-authored-by: you-n-g <you-n-g@users.noreply.github.com>
50 lines
1.2 KiB
Python
50 lines
1.2 KiB
Python
"""
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This is the preliminary version of the APE (Automated Prompt Engineering)
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"""
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import pickle
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from pathlib import Path
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from rdagent.log.conf import LOG_SETTINGS
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def get_llm_qa(file_path):
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data_flt = []
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with open(file_path, "rb") as f:
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data = pickle.load(f)
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print(len(data))
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for item in data:
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if "debug_llm" in item["tag"]:
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data_flt.append(item)
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return data_flt
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# Example usage
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# use
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file_path = Path(LOG_SETTINGS.trace_path) / "debug_llm.pkl"
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llm_qa = get_llm_qa(file_path)
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print(len(llm_qa))
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print(llm_qa[0])
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# Initialize APE backend
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from rdagent.oai.llm_utils import APIBackend
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from rdagent.utils.agent.tpl import T
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api = APIBackend()
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# Analyze test data and generate improved prompts
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for qa in llm_qa:
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# Generate system prompt for APE
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system_prompt = T(".prompts:ape.system").r()
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# Generate user prompt with context from LLM QA
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user_prompt = T(".prompts:ape.user").r(
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system=qa["obj"].get("system", ""), user=qa["obj"]["user"], answer=qa["obj"]["resp"]
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)
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analysis_result = api.build_messages_and_create_chat_completion(
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system_prompt=system_prompt, user_prompt=user_prompt
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)
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print(f"█" * 60)
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yes = input("Do you want to continue? (y/n)")
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