agents.py

import json
import time
from searcher import Result,SementicSearcher
from LLM import openai_llm
from prompts import *
from utils import extract


def get_llm(model = "gpt4o-0513"):
    return openai_llm(model)

def get_llms():

    main_llm = get_llm("gpt4o-0513")
    cheap_llm = get_llm("gpt-4o-mini")
    return main_llm,cheap_llm

def judge_idea(i,j,idea0,idea1,topic,llm):
    prompt = get_judge_idea_all_prompt(idea0,idea1,topic)
    messages = [{"role":"user","content":prompt}]
    response = llm.response(messages)
    novelty = extract(response,"novelty")
    relevance = extract(response,"relevance")
    significance = extract(response,"significance")
    clarity = extract(response,"clarity")
    feasibility = extract(response,"feasibility")
    effectiveness = extract(response,"effectiveness")
    return i,j,novelty,relevance,significance,clarity,feasibility,effectiveness

class DeepResearchAgent:
    def __init__(self,llm = None,cheap_llm=None,publicationData = None,ban_paper = [],**kwargs) -> None:
        self.reader = SementicSearcher(ban_paper = ban_paper)
        self.begin_time = time.time()
        self.llm = llm
        self.cheap_llm = cheap_llm
        self.read_papers = set()
        self.paper_storage = []
        self.paper_info_for_refine_experiment = []
        self.search_qeuries = []
        self.deep_research_chains = []
        self.deep_ideas = []
        self.check_novel_results = []
        self.score_results = []
        self.topic =None


        self.publicationData = publicationData
        self.improve_cnt = kwargs.get("improve_cnt",1)
        self.max_chain_length = kwargs.get("max_chain_length",5)
        self.min_chain_length = kwargs.get("min_chain_length",3)
        self.max_chain_numbers = kwargs.get("max_chain_numbers",10)

    def wrap_messages(self,prompt):
        return [{"role":"user","content":prompt}]

    def get_openai_response(self,messages):
        return self.llm.response(messages)
    
    def get_cheap_openai_response(self,messages):
        return self.cheap_llm.response(messages,max_tokens = 16000)

    def get_search_query(self,topic = None,query=None):
        prompt = get_deep_search_query_prompt(topic,query)
        messages = self.wrap_messages(prompt)
        response = self.get_openai_response(messages)
        search_query = extract(response,"queries")
        try:
            search_query = json.loads(search_query)
            self.search_qeuries.append({"query":query,"search_query":search_query})
        except:
            search_query = [query]
        return search_query

    def generate_idea_with_chain(self,topic):
        self.topic = topic
        print(f"begin to generate search query for {topic}")
        search_query = self.get_search_query(topic=topic)
        papers = []
        for query in search_query:
            failed_query = []
            current_papers = []
            cnt = 0
            while len(current_papers) == 0 and cnt < 10:
                paper = self.reader.search(query,1,paper_list=self.read_papers,llm=self.llm,rerank_query=f"{topic}",publicationDate=self.publicationData)
                if paper and len(paper) > 0 and paper[0]:
                    self.read_papers.add(paper[0].title)
                    current_papers.append(paper[0])
                else:
                    failed_query.append(query)
                    prompt = get_deep_rewrite_query_prompt(failed_query,topic)
                    messages = self.wrap_messages(prompt)
                    new_query = self.get_openai_response(messages)
                    new_query = extract(new_query,"query")
                    print(f"Failed to search papers for {query}, regenerating query {new_query} to search papers.")
                    query = new_query
                cnt += 1
            papers.extend(current_papers)
            if len(papers) >= self.max_chain_numbers:
                break

        if len(papers) == 0:
            print(f"failed to generate idea {topic}")
            return None,None,None,None,None,None,None,None,None

        idea,idea_chain,experiment,entities,trend,future,human,year = self.deep_research_paper_with_chain(papers[0])
        
        print(f"successfully generated idea")
        return idea,experiment,entities,idea_chain,idea,trend,future,human,year
    
    def get_paper_idea_experiment_references_info(self,paper):
        article = paper.article
        if not article:
            return None
        paper_content = self.reader.read_paper_content(article)
        prompt = get_deep_reference_prompt(paper_content,self.topic)
        messages = self.wrap_messages(prompt)
        response = self.get_cheap_openai_response(messages)
        entities = extract(response,"entities")
        idea = extract(response,"idea")
        experiment = extract(response,"experiment")
        references = extract(response,"references")
        return idea,experiment,entities,references,paper.title

    def get_article_idea_experiment_references_info(self,article):
        paper_content = self.reader.read_paper_content_with_ref(article)
        prompt = get_deep_reference_prompt(paper_content,self.topic)
        messages = self.wrap_messages(prompt)
        response = self.get_cheap_openai_response(messages)
        entities = extract(response,"entities")
        idea = extract(response,"idea")
        experiment = extract(response,"experiment")
        references = extract(response,"references")
        return idea,experiment,entities,references

    
    def deep_research_paper_with_chain(self,paper:Result): 
        print(f"begin to deep research paper {paper.title}")
        article = paper.article
        if not article:
            print(f"failed to deep research paper {paper.title}")
            return None
        idea_chain = []
        idea_papers = []
        experiments = []
        total_entities = []
        years = []
        idea,experiment,entities,references = self.get_article_idea_experiment_references_info(article)
        try:
            references = json.loads(references)
        except:
            references = []
        total_entities.append(entities)
        idea_chain.append(idea)
        idea_papers.append(paper.title)
        experiments.append(experiment)
        years.append(paper.year)

        current_title = paper.title
        current_abstract = paper.abstract

        # search before
        while len(idea_chain)<self.max_chain_length:
            rerank_query = f"{self.topic} {current_title} {current_abstract}"
            citation_paper = self.reader.search_related_paper(current_title,need_reference=False,rerank_query=rerank_query,llm=self.llm,paper_list=idea_papers)
            if not citation_paper:
                print(f"failed to find citation paper for {current_title}")
                break
            title = citation_paper.title
            abstract = citation_paper.abstract
            prompt = get_deep_judge_relevant_prompt(current_title,current_abstract,self.topic)
            messages = self.wrap_messages(prompt)
            response = self.get_openai_response(messages)
            relevant = extract(response,"relevant")
            if relevant != "0":
                result = self.get_paper_idea_experiment_references_info(citation_paper)
                if not result:
                    break
                idea,experiment,entities,_,_ = result
                idea_chain.append(idea)
                experiments.append(experiment)
                total_entities.append(entities)
                idea_papers.append(citation_paper.title)
                years.append(citation_paper.year)
                current_title = citation_paper.title
                current_abstract = citation_paper.abstract
            else:
                print(f"the paper {title} is not relevant")
                break

        current_title = paper.title
        current_abstract = paper.abstract
        # search after
        while len(idea_chain) < self.max_chain_length and len(references) > 0:
            search_paper = []
            article = None
            print(f"The references find:{references}")
            while len(references) > 0 and len(search_paper) == 0:
                reference = references[0]
                references.pop(0)
                if reference in self.read_papers:
                    continue
                search_paper = self.reader.search(reference,3,llm=self.llm,publicationDate=self.publicationData,paper_list= idea_papers)
                if len(search_paper) > 0:
                    s_p = search_paper[0]
                    if s_p and  s_p.title not in self.read_papers:
                        prompt = get_deep_judge_relevant_prompt(current_title,current_abstract,self.topic)
                        messages = self.wrap_messages(prompt)
                        response = self.get_openai_response(messages)
                        relevant = extract(response,"relevant")
                        if relevant != "0" or len(idea_chain) < self.min_chain_length:
                            article = s_p.article
                            if article:
                                cite_paper = s_p
                                break
                        else:
                            print(f"the paper {s_p.title} is not relevant")
                search_paper = []
            
            if not article:
                rerank_query = f"topic: {self.topic} Title: {current_title} Abstract: {current_abstract}"
                search_paper = self.reader.search_related_paper(current_title,need_citation=False,rerank_query = rerank_query,llm=self.llm,paper_list=idea_papers)
                if not search_paper:
                    print(f"failed to find citation paper for {current_title}")
                    continue
                s_p = search_paper
                if len(idea_chain) < self.min_chain_length:
                    article = s_p.article
                    if not article:
                        continue
                    else:
                        cite_paper = s_p
                        break
                else:
                    if s_p and s_p.title not in self.read_papers:
                        prompt = get_deep_judge_relevant_prompt(current_title,current_abstract,self.topic)
                        messages = self.wrap_messages(prompt)
                        response = self.get_openai_response(messages)
                        relevant = extract(response,"relevant")
                        if relevant == "1" or len(idea_chain) < self.min_chain_length:
                            article = s_p.article
                            if not article:
                                continue
                            else:
                                cite_paper = s_p
                                break
            if not article:
                print(f"failed to find citation paper for {current_title}")
                continue
            
            print("find the citation paper, begin to deep research")
            paper_content = self.reader.read_paper_content_with_ref(article)
            prompt = get_deep_reference_prompt(paper_content,self.topic)
            messages = self.wrap_messages(prompt)
            response = self.get_cheap_openai_response(messages)
            idea = extract(response,"idea")
            references = extract(response,"references")
            experiment = extract(response,"experiment")
            entities = extract(response,"entities")
            try:
                references = json.loads(references)
            except:
                references = []
            current_title = cite_paper.title
            current_abstract = cite_paper.abstract
            years = [cite_paper.year] + years
            idea_chain = [idea] + idea_chain
            idea_papers = [cite_paper.title] + idea_papers
            experiments = [experiment] + experiments
            total_entities = [entities] + total_entities
            if len(idea_chain) >= self.min_chain_length:
                if cite_paper.citations_conut > 1000:
                    break
        
        print("successfully generate idea chain")
        idea_chains = ""
        for i,idea,title in zip(range(len(idea_chain)),idea_chain,idea_papers):
            idea_chains += f"{i}.Paper:{title} idea:{idea}\n \n"
        
        prompt = get_deep_trend_idea_chains_prompt(idea_chains,entities,self.topic)
        messages = self.wrap_messages(prompt)
        response = self.get_openai_response(messages)
        trend = extract(response,"trend")

        self.deep_research_chains.append({"idea_chains":idea_chains,"trend":trend,"topic":self.topic,"ideas":idea_chain,"experiments":experiments,"entities":total_entities,"years":years})
        prompt = f"""The current research topic is: {self.topic}. Please help me summarize and refine the following entities by merging, simplifying, or deleting them : {total_entities}
    Please output strictly in the following format: 
    <entities> {{cleaned entities}}</entities>
"""
        messages = self.wrap_messages(prompt)
        response = self.get_openai_response(messages)
        total_entities = extract(response,"entities")
        bad_case = []
        prompt = get_deep_generate_future_direciton_prompt(idea_chain,trend,self.topic,total_entities)
        messages = self.wrap_messages(prompt)
        response = self.get_openai_response(messages)
        future = extract(response,"future")
        human = extract(response,"human")
        

        prompt = get_deep_generate_idea_prompt(idea_chains,trend,self.topic,total_entities,future,bad_case)
        messages = self.wrap_messages(prompt)
        response = self.get_openai_response(messages)
        method = extract(response,"method")
        novelty = extract(response,"novelty")
        motivation = extract(response,"motivation")
        idea = {"motivation":motivation,"novelty":novelty,"method":method}
        prompt = get_deep_final_idea_prompt(idea_chains,trend,idea,self.topic)
        messages = self.wrap_messages(prompt)
        response = self.get_openai_response(messages)
        final_idea = extract(response,"final_idea")

        idea = final_idea
        self.deep_ideas.append(idea)
        print(f"successfully deep research paper {paper.title}")
        return idea,idea_chains,trend,experiments,total_entities,future,human,years
    

if __name__ == "__main__":
    reader = SementicSearcher()