| |
|
|
| import logging |
| from typing import List, Dict, Any |
|
|
| from .base_agent import BaseAgent |
|
|
| class GenerationAgent(BaseAgent): |
| """Agent responsible for generating initial hypotheses based on the research goal.""" |
| |
| def __init__(self, model=None, temperature=None): |
| """Initialize the Generation Agent. |
| |
| Args: |
| model: Optional model override |
| temperature: Optional temperature override |
| """ |
| system_prompt = """ |
| You are a Generation Agent in an AI Co-Scientist system, responsible for proposing novel areas of interest and potential research directions based on the user's research goal. You have expertise across multiple scientific disciplines at a PhD level. |
| |
| Your role is to: |
| 1. Suggest diverse, novel, and relevant areas of interest or research directions that the user might not have considered, based on the research goal provided. |
| 2. Leverage your broad knowledge to surface surprising or emerging topics, interdisciplinary connections, and new angles for investigation. |
| 3. For each area of interest, generate multiple specific research questions that could guide further investigation into new aspects of the topic. |
| 4. Ensure each area of interest is clearly described and the research questions are actionable and thought-provoking. |
| |
| Your output should include: |
| - A list of distinct areas of interest (not just hypotheses), each with: |
| - A clear description of the area or direction |
| - 2-3 research questions that could be explored within this area |
| |
| Remember: |
| - Areas of interest should be relevant to the research goal, but can be tangential or surprising if they open up new avenues for discovery. |
| - Research questions should be specific, actionable, and designed to inspire further investigation. |
| - Avoid repeating the research goal verbatim; instead, expand on it with new perspectives. |
| """ |
| |
| super().__init__( |
| name="Generation", |
| system_prompt=system_prompt, |
| model=model, |
| temperature=temperature if temperature is not None else 0.7 |
| ) |
| |
| self.logger = logging.getLogger("agent.generation") |
| |
| def generate_hypotheses(self, research_goal: str, count: int = 5) -> List[Dict[str, Any]]: |
| """Generate initial hypotheses based on the research goal. |
| |
| Args: |
| research_goal: The research goal or question |
| count: Number of hypotheses to generate |
| |
| Returns: |
| A list of hypothesis dictionaries |
| """ |
| self.logger.info(f"Generating {count} hypotheses for research goal: {research_goal}") |
| return self.process(research_goal) |
| |
| def process(self, research_goal: str) -> list: |
| """Generate areas of interest and research questions based on the research goal.""" |
| self.logger.info(f"Generating hypotheses for research goal: {research_goal}") |
| |
| prompt = f""" |
| RESEARCH GOAL: {research_goal} |
| |
| Based on the research goal above, suggest at least 3-5 distinct AREAS OF INTEREST or potential research directions that the user might not have considered. For each area of interest: |
| - Provide a clear description of the area or direction |
| - Generate 2-3 specific research questions that could be explored within this area |
| |
| Format your response as a structured list, with each area of interest clearly separated, and each research question listed under its area. |
| Be creative, leverage your broad knowledge, and focus on novelty and relevance. |
| """ |
| |
| response = self.get_response(prompt) |
| self.logger.info(f"Raw LLM response: {response}") |
| |
| |
| return self._parse_areas_of_interest(response) |
|
|
| def _parse_areas_of_interest(self, response: str) -> list: |
| """ |
| Robustly parse LLM output for areas of interest and their research questions. |
| Handles numbered/bulleted lists, headings, and flexible formats. |
| Returns a list of dicts: { 'statement': ..., 'research_questions': [...] } |
| """ |
| import re |
| areas = [] |
| current_area = None |
| current_questions = [] |
| lines = response.splitlines() |
| area_pattern = re.compile(r"^(?:\d+\.|[-*])?\s*(Area of Interest|Area|Direction|Topic)?\s*:?\s*(.+)$", re.IGNORECASE) |
| question_pattern = re.compile(r"^(?:[-*]|\d+\.|\d+\))\s*(What|How|Why|Which|Could|Is|Are|Does|Do|Can|To what extent|In what ways|Where|When|Who|Should|Would|Might|Will|Has|Have|Did|Does)\b.+", re.IGNORECASE) |
| |
| for line in lines: |
| line = line.strip() |
| if not line: |
| continue |
| |
| if area_pattern.match(line) and not question_pattern.match(line): |
| |
| if current_area: |
| areas.append({ |
| 'statement': current_area, |
| 'research_questions': current_questions |
| }) |
| |
| match = area_pattern.match(line) |
| area_text = match.group(2).strip() |
| current_area = area_text |
| current_questions = [] |
| |
| elif question_pattern.match(line): |
| current_questions.append(line) |
| |
| elif line.endswith('?') and len(line) < 200: |
| current_questions.append(line) |
| |
| if current_area: |
| areas.append({ |
| 'statement': current_area, |
| 'research_questions': current_questions |
| }) |
| return areas |
|
|
