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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
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