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| import os | |
| import openai | |
| from utils.references import References | |
| from utils.gpt_interaction import GPTModel | |
| from utils.prompts import SYSTEM | |
| from langchain.tools import BaseTool | |
| from pydantic import BaseModel, Field | |
| from typing import Optional, Type | |
| MAX_TOKENS = 2048 | |
| openai.api_key = os.getenv("OPENAI_API_KEY") | |
| default_model = os.getenv("DEFAULT_MODEL") | |
| if default_model is None: | |
| default_model = "gpt-3.5-turbo-16k" | |
| llm = GPTModel(model=default_model, delay=1) | |
| paper_system_prompt = '''You are an assistant designed to propose choices of research direction. | |
| The user will input questions or some keywords of a fields. You need to generate some paper titles and main contributions. Ensure follow the following instructions: | |
| Instruction: | |
| - Your response should follow the JSON format. | |
| - Your response should have the following structure: | |
| { | |
| "your suggested paper title": | |
| { | |
| "summary": "an overview introducing what this paper will include", | |
| "contributions": { | |
| "contribution1": {"statement": "briefly describe this contribution", "reason": "reason why this contribution can make this paper outstanding"}, | |
| "contribution2": {"statement": "briefly describe this contribution", "reason": "reason why this contribution can make this paper outstanding"}, | |
| ... | |
| } | |
| } | |
| "your suggested paper title": | |
| { | |
| "summary": "an overview introducing what this paper will include", | |
| "contributions": { | |
| "contribution1": {"statement": "briefly describe this contribution", "reason": "reason why this contribution can make this paper outstanding"}, | |
| "contribution2": {"statement": "briefly describe this contribution", "reason": "reason why this contribution can make this paper outstanding"}, | |
| ... | |
| } | |
| } | |
| ... | |
| } | |
| - Please list three to five suggested title and at least three contributions for each paper. | |
| ''' | |
| contribution_system_prompt = '''You are an assistant designed to criticize the contributions of a paper. You will be provided Paper's Title, References and Contributions. Ensure follow the following instructions: | |
| Instruction: | |
| - Your response should follow the JSON format. | |
| - Your response should have the following structure: | |
| { | |
| "title": "the title provided by the user", | |
| "comment": "your thoughts on if this title clearly reflects the key ideas of this paper and explain why" | |
| "contributions": { | |
| "contribution1": {"statement": "briefly describe what the contribution is", | |
| "reason": "reason why the user claims it is a contribution", | |
| "judge": "your thought about if this is a novel contribution and explain why", | |
| "suggestion": "your suggestion on how to modify the research direction to enhance the novelty "}, | |
| "contribution2": {"statement": "briefly describe what the contribution is", | |
| "reason": "reason why the user claims it is a contribution", | |
| "judge": "your thought about if this is a novel contribution and explain why", | |
| "suggestion": "your suggestion on how to modify the research direction to enhance the novelty "}, | |
| ... | |
| } | |
| } | |
| - You need to carefully check if the claimed contribution has been made in the provided references, which makes the contribution not novel. | |
| - You also need to propose your concerns on if any of contributions could be incremental or just a mild modification on an existing work. | |
| ''' | |
| def find_research_directions(research_field): | |
| output, _ = llm(systems=paper_system_prompt, prompts=research_field, return_json=False) | |
| return output | |
| def find_references(title, contributions): | |
| max_tokens = MAX_TOKENS | |
| ref = References(title=title, description=f"{contributions}") | |
| keywords, _ = llm(systems=SYSTEM["keywords"], prompts=title, return_json=True) | |
| keywords = {keyword: 10 for keyword in keywords} | |
| ref.collect_papers(keywords) | |
| ref_prompt = ref.to_prompts(max_tokens=max_tokens) | |
| return ref_prompt | |
| def judge_novelty(title, contributions): | |
| max_tokens = MAX_TOKENS | |
| ref = References(title=title, description=f"{contributions}") | |
| keywords, _ = llm(systems=SYSTEM["keywords"], prompts=title, return_json=True) | |
| keywords = {keyword: 10 for keyword in keywords} | |
| ref.collect_papers(keywords) | |
| ref_prompt = ref.to_prompts(max_tokens=max_tokens) | |
| prompt = f"Title: {title}\n References: {ref_prompt}\n Contributions: {contributions}" | |
| output, _ = llm(systems=contribution_system_prompt, prompts=prompt, return_json=False) | |
| return output | |
| functions = [ | |
| { | |
| "name": "find_research_directions", | |
| "description": "when your student has already shown interests in a specific topic and provided a rough description of potential contributions, help your student to dive this direction deeper", | |
| "parameters": { | |
| "type": "object", | |
| "properties": { | |
| "research_description": { | |
| "type": "string", | |
| "description": "a paragraph with details in English describing " | |
| "(1) what is the main problem you are trying to solve " | |
| "(2) what is the main novelty of this idea (3) how to complete this research." | |
| } | |
| }, | |
| "required": ["research_description"], | |
| }, | |
| }, | |
| { | |
| "name": "find_references", | |
| "description": "find references for given details of a paper", | |
| "parameters": { | |
| "type": "object", | |
| "properties": { | |
| "title": { | |
| "type": "string", | |
| "description": "the title (in English) of the academic paper your student will write.", | |
| }, | |
| "contributions": {"type": "string", | |
| "description": "a general description on the contributions of this paper in English." | |
| "If there are multiple contributions, index them with numbers."}, | |
| }, | |
| "required": ["title", "contributions"], | |
| }, | |
| }, | |
| { | |
| "name": "judge_novelty", | |
| "description": "evaluate the novelty of a paper given its title and main contributions", | |
| "parameters": { | |
| "type": "object", | |
| "properties": { | |
| "title": { | |
| "type": "string", | |
| "description": "the title (in English) of the academic paper your student will write.", | |
| }, | |
| "contributions": {"type": "string", | |
| "description": "a general description on the contributions of this paper in English." | |
| "If there are multiple contributions, index them with numbers."}, | |
| }, | |
| "required": ["title", "contributions"], | |
| }, | |
| } | |
| ] | |
| TOOLS = {"find_research_directions": find_research_directions, "find_references": find_references, "judge_novelty": judge_novelty} | |
| class FindResearchDirectionsCheckInput(BaseModel): | |
| research_description: str = Field(..., description="a paragraph with details in English describing (1) what is the main problem you are trying to solve " | |
| "(2) what is the main novelty of this idea (3) how to complete this research.") | |
| class TitleDescriptionCheckInput(BaseModel): | |
| title: str = Field(..., description="the title of the academic paper your student will write in English.") | |
| contributions: str = Field(..., description="a general description on the contributions of this paper in English." | |
| "If there are multiple contributions, index them with numbers.") | |
| class FindResearchDirectionsTool(BaseTool): | |
| name = "find_research_directions" | |
| description = """Useful when your student has already shown interests in a specific topic and provided a rough description of | |
| potential contributions and you need to help your student to dive this direction deeper for your student. | |
| """ | |
| def _run(self, research_description: str): | |
| response = find_research_directions(research_description) | |
| return response | |
| def _arun(self, research_field: str): | |
| raise NotImplementedError("This tool does not support async") | |
| args_schema: Optional[Type[BaseModel]] = FindResearchDirectionsCheckInput | |
| class JudgeNoveltyTool(BaseTool): | |
| name = "judge_novelty" | |
| description = """Useful when you need to evaluate the novelty of your student's idea. | |
| """ | |
| def _run(self, title: str, contributions: str): | |
| response = judge_novelty(title, contributions) | |
| return response | |
| def _arun(self, title: str, contributions: str): | |
| raise NotImplementedError("This tool does not support async") | |
| args_schema: Optional[Type[BaseModel]] = TitleDescriptionCheckInput | |
| class FindReferencesTool(BaseTool): | |
| name = "find_references" | |
| description = """Useful when you need to find references for a paper. | |
| """ | |
| def _run(self, title: str, contributions: str): | |
| response = find_references(title, contributions) | |
| return response | |
| def _arun(self, title: str, contributions: str): | |
| raise NotImplementedError("This tool does not support async") | |
| args_schema: Optional[Type[BaseModel]] = TitleDescriptionCheckInput | |