support communicate method for agent (#24)

Co-authored-by: Haofei Yu <[email protected]>

research_town/agents/agent_base.py

@@ -7,6 +7,7 @@
 from ..utils.author_relation import bfs
 from ..utils.paper_collection import get_bert_embedding
 from .agent_prompting import (
+    communicate_with_multiple_researchers,
     generate_ideas,
     summarize_research_direction,
     summarize_research_field,
@@ -128,7 +129,7 @@ def get_profile(self, author_name: str) -> Dict[str, Any]:
             return {"info": "fail!"}
 
     def communicate(self, message: Dict[str, str]) -> str:
-        return "hello"
+        return communicate_with_multiple_researchers(message)[0]
 
     def read_paper(
         self, external_data: Dict[str, Dict[str, List[str]]], domain: str

research_town/agents/agent_prompting.py

@@ -131,3 +131,36 @@ def summarize_research_direction(personal_info: str) -> List[str]:
         )
     content = completion.choices[0].message["content"]
     return [content]
+
+def communicate_with_multiple_researchers(input: Dict[str, str]):
+    """
+    This is a single-round chat method. One that contains a chat history can better enable
+    """
+    single_round_chat_serialize = [f"Message from researcher named {name}: {message}" for name, message in input.items()]
+    single_round_chat_serialize_all = "\n".join(single_round_chat_serialize)
+    prompt_qa = (
+        "Please continue in a conversation with other fellow researchers for me, where you will address their concerns in a scholarly way. "
+        "Here are the messages from other researchers: {single_round_chat_serialize_all}"
+    )
+    openai.api_key = KEY
+    input = {"single_round_chat_serialize_all": single_round_chat_serialize_all}
+    prompt = prompt_qa.format_map(input)
+    try:
+        completion = openai.ChatCompletion.create(
+            model=llm_model,
+            messages=[{"role": "user", "content": prompt}],
+            temperature=0,
+            seed=42,
+            top_p=0,
+        )
+    except Exception:
+        time.sleep(20)
+        completion = openai.ChatCompletion.create(
+            model=llm_model,
+            messages=[{"role": "user", "content": prompt}],
+            temperature=0,
+            seed=42,
+            top_p=0,
+        )
+    content = completion.choices[0].message["content"]
+    return [content]

tests/test_agent_base.py

@@ -9,7 +9,10 @@ def test_get_profile():
 
 
 def test_communicate():
-    pass
+    research_agent = BaseResearchAgent("Jiaxuan You")
+    response = research_agent.communicate({"Alice": "I believe in the potential of using automous agents to simulate the current research pipeline."})
+    assert isinstance(response, str)
+    assert response != ""
 
 '''
 def test_read_paper():