agent.py

from typing import Optional, Dict, Any
import os
from dotenv import load_dotenv
import dspy

import prompts


class BIDSifierAgent:
	"""Wrapper around OpenAI chat API for step-wise BIDSification."""

	def __init__(
		self, *,
        provider: str,
        model: str,
        api_key: Optional[str] = None,
		api_base: Optional[str] = None,
        temperature: Optional[float | None] = 0.2,
	):
		load_dotenv()
		
		if model.startswith("gpt-5"):  # reasoning model that requires special handling
			temperature = 1.0
			max_tokens = 40000
		else:
			temperature = None
			max_tokens = 10000

		lm = dspy.LM(
			f"{provider}/{model}",
			api_key=api_key or os.getenv(f"{provider.upper()}_API_KEY"),
			temperature=temperature, max_tokens=max_tokens
		)

		dspy.configure(lm=lm)
		self.llm = lm
		self.model = model or os.getenv("BIDSIFIER_MODEL", "gpt-4o-mini")
		self.temperature = temperature

	def _build_user_prompt(self, step: str, context: Dict[str, Any]) -> str:
		dataset_xml = context.get("dataset_xml")
		readme_text = context.get("readme_text")
		publication_text = context.get("publication_text")
		output_root = context.get("output_root", "./bids_output")

		if step == "summary":
			return prompts.summarize_dataset_prompt(
				dataset_xml=dataset_xml,
				readme_text=readme_text,
				publication_text=publication_text,
			)
		if step == "create_metadata":
			return prompts.create_metadata_prompt(
				output_root=output_root,
				dataset_xml=dataset_xml,
				readme_text=readme_text,
				publication_text=publication_text,
			)
		if step == "create_structure":
			return prompts.create_structure_prompt(
				output_root=output_root,
				dataset_xml=dataset_xml,
				readme_text=readme_text,
				publication_text=publication_text,
			)
		if step == "rename_move":
			return prompts.rename_and_move_prompt(
				output_root=output_root,
				dataset_xml=dataset_xml,
				readme_text=readme_text,
				publication_text=publication_text,
			)
		raise ValueError(f"Unknown step: {step}")

	def run_step(self, step: str, context: Dict[str, Any]) -> str:
		system_msg = prompts.system_prompt()
		user_msg = self._build_user_prompt(step, context)
		resp = self.llm(
			messages=[
				{"role": "system", "content": system_msg},
				{"role": "user", "content": user_msg},
			],
			temperature=self.temperature,
		)
		return resp[0]

	def run_query(self, query: str) -> str:
		system_msg = prompts.system_prompt()
		resp = self.llm(
			messages=[
				{"role": "system", "content": system_msg},
				{"role": "user", "content": query},
			],
			temperature=self.temperature,
		)
		return resp[0]


__all__ = ["BIDSifierAgent"]