fix

agents.py

@@ -82,8 +82,15 @@ class DeepResearchAgent:
             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])
+                    for p in paper:
+                        prompt = get_deep_judge_relevant_prompt(p.title,p.abstract,self.topic)
+                        messages = self.wrap_messages(prompt)
+                        response = self.get_openai_response(messages)
+                        relevant = extract(response,"relevant")
+                        if relevant == "1":
+                            print(f"{p.title} is relevant to the topic {self.topic},have added to the queue")
+                            self.read_papers.add(p.title)
+                            current_papers.append(p)
                 else:
                     failed_query.append(query)
                     prompt = get_deep_rewrite_query_prompt(failed_query,topic)

app.py

@@ -15,6 +15,7 @@ import uuid
 import yaml
 import os
 
+
 lock = threading.Lock()
 app = FastAPI()
 

prompts/deep_research_agent_promts.py

@@ -256,6 +256,7 @@ Novelty:
 1. Distinguish your proposed method from existing methods (preferably by naming specific approaches).
 2. Detail the improvements your method brings compared to previous work.
 3. Clearly outline at least three contributions your idea offers to the field, including the problems it resolves and the benefits it delivers.
+4. Try to avoid stitching existing methods as much as possible, and instead continue to delve deeper to propose your own innovative points.
 
 Method: 
 1. Present a detailed description of your idea, focusing on the core method, the specific problem it solves, and enhancements over earlier research (citing relevant literature with titles).

searcher/sementic_search.py

@@ -171,8 +171,6 @@ Abstract: {paper['abstract']}
         if len(paper_list) == 0:
             return []
         paper_list = [paper for paper in paper_list if paper]
-        if len(paper_list) >= 50:
-            paper_list = random.sample(paper_list,50)
         paper_contents = []
         for paper in paper_list:
             paper_content = f"""