NexQuant/rdagent/components/coder/model_coder/task_loader.py

from __future__ import annotations

import json
import re
from pathlib import Path

from rdagent.components.coder.model_coder.model import ModelExperiment, ModelTask
from rdagent.components.document_reader.document_reader import (
    load_and_process_pdfs_by_langchain,
)
from rdagent.components.loader.task_loader import ModelTaskLoader
from rdagent.core.log import RDAgentLog
from rdagent.core.prompts import Prompts
from rdagent.oai.llm_utils import APIBackend

document_process_prompts = Prompts(file_path=Path(__file__).parent / "prompts.yaml")


def extract_model_from_doc(doc_content: str) -> dict:
    """
    Extract model information from document content.

    Parameters
    ----------
    doc_content : str
        Document content.

    Returns
    -------
    dict
        {factor_name: dict{description, formulation, variables}}
    """
    session = APIBackend().build_chat_session(
        session_system_prompt=document_process_prompts["extract_model_formulation_system"],
    )
    current_user_prompt = doc_content

    # Extract model information from document content.
    model_dict = {}

    for _ in range(10):
        # try to extract model information from the document content, retry at most 10 times.
        extract_result_resp = session.build_chat_completion(
            user_prompt=current_user_prompt,
            json_mode=False,
        )
        re_search_res = re.search(r"```json(.*)```", extract_result_resp, re.S)
        ret_json_str = re_search_res.group(1) if re_search_res is not None else ""
        try:
            ret_dict = json.loads(ret_json_str)
            parse_success = bool(isinstance(ret_dict, dict))
        except json.JSONDecodeError:
            parse_success = False
        if ret_json_str is None or not parse_success:
            current_user_prompt = "Your response didn't follow the instruction might be wrong json format. Try again."
        else:
            for name, formulation_and_description in ret_dict.items():
                if name not in model_dict:
                    model_dict[name] = formulation_and_description
            if len(model_dict) == 0:
                current_user_prompt = "No model extracted. Please try again."
            else:
                break

    RDAgentLog().info(f"已经完成{len(model_dict)}个模型的提取")

    return model_dict


def merge_file_to_model_dict_to_model_dict(
    file_to_model_dict: dict[str, dict],
) -> dict:
    model_dict = {}
    for file_name in file_to_model_dict:
        for model_name in file_to_model_dict[file_name]:
            model_dict.setdefault(model_name, [])
            model_dict[model_name].append(file_to_model_dict[file_name][model_name])

    model_dict_simple_deduplication = {}
    for model_name in model_dict:
        if len(model_dict[model_name]) > 1:
            model_dict_simple_deduplication[model_name] = max(
                model_dict[model_name],
                key=lambda x: len(x["formulation"]),
            )
        else:
            model_dict_simple_deduplication[model_name] = model_dict[model_name][0]
    return model_dict_simple_deduplication


def extract_model_from_docs(docs_dict):
    model_dict = {}
    for doc_name, doc_content in docs_dict.items():
        model_dict[doc_name] = extract_model_from_doc(doc_content)
    return model_dict


class ModelExperimentLoaderFromDict(ModelTaskLoader):
    def load(self, model_dict: dict) -> list:
        """Load data from a dict."""
        task_l = []
        for model_name, model_data in model_dict.items():
            task = ModelTask(
                name=model_name,
                description=model_data["description"],
                formulation=model_data["formulation"],
                variables=model_data["variables"],
                key=model_name,
            )
            task_l.append(task)
        return ModelExperiment(sub_tasks=task_l)


class ModelExperimentLoaderFromPDFfiles(ModelTaskLoader):
    def load(self, file_or_folder_path: Path) -> dict:
        docs_dict = load_and_process_pdfs_by_langchain(Path(file_or_folder_path))  # dict{file_path:content}
        model_dict = extract_model_from_docs(
            docs_dict
        )  # dict{file_name: dict{model_name: dict{description, formulation, variables}}}
        model_dict = merge_file_to_model_dict_to_model_dict(
            model_dict
        )  # dict {model_name: dict{description, formulation, variables}}
        return ModelExperimentLoaderFromDict().load(model_dict)


def main(path="../test_doc"):
    doc_dict = load_and_process_pdfs_by_langchain(Path(path))
    print(doc_dict.keys())  # if you run code like "python -u", the print content will be truncated


import fire

if __name__ == "__main__":
    fire.Fire(main)