[simulation] refactor talks_to to use generative agent llm (#69)

* feat: modify 'talks_to' to use generative agent LLM

* fix: disable verbose

* fix: grammar

skyagi/src/skyagi/simulation/simulation.py

@@ -2,8 +2,15 @@
 from typing import List
 
 import faiss
+from langchain import LLMChain
 from langchain.docstore import InMemoryDocstore
 from langchain.embeddings import OpenAIEmbeddings
+from langchain.prompts.chat import (
+    AIMessagePromptTemplate,
+    ChatPromptTemplate,
+    HumanMessagePromptTemplate,
+    SystemMessagePromptTemplate,
+)
 from langchain.retrievers import TimeWeightedVectorStoreRetriever
 from langchain.vectorstores import FAISS
 
@@ -77,3 +84,59 @@ def interview_agent(agent: GenerativeAgent, message: str, username: str) -> str:
     """Help the notebook user interact with the agent."""
     new_message = f"{username} says {message}"
     return agent.generate_dialogue_response(new_message)[1]
+
+
+# whether initiator wants to talk to recipient based on the observations
+def talks_to(
+    initiator: GenerativeAgent, recipient: GenerativeAgent, observations: List[str]
+) -> str:
+    instruct = "Here are the timeline of events happened for these NPC characters:\n{observation}\n"
+    instruct += "I want you to behave as {initiator_name} and talk to me as I am {recipient_name}.\n"
+    instruct += (
+        "If you do not want to or can not talk to {recipient_name}, just output NOTHING"
+    )
+    messages = [
+        SystemMessagePromptTemplate.from_template(
+            "You are the AI behind a NPC character called {initiator_name}"
+        ),
+        HumanMessagePromptTemplate.from_template(instruct),
+    ]
+    observation = "\n".join(observations)
+
+    message = (
+        LLMChain(
+            llm=initiator.llm,
+            prompt=ChatPromptTemplate.from_messages(messages),
+        )
+        .run(
+            observation=observation,
+            initiator_name=initiator.name,
+            recipient_name=recipient.name,
+        )
+        .strip()
+    )
+    if "NOTHING" in message:
+        return ""
+
+    messages.append(AIMessagePromptTemplate.from_template(message))
+    messages.append(
+        HumanMessagePromptTemplate.from_template(
+            "Did {initiator_name} talk to {recipient_name}, please answer yes or no"
+        )
+    )
+    resp = (
+        LLMChain(
+            llm=initiator.llm,
+            prompt=ChatPromptTemplate.from_messages(messages),
+        )
+        .run(
+            observation=observation,
+            initiator_name=initiator.name,
+            recipient_name=recipient.name,
+        )
+        .strip()
+    )
+    if "no" in resp:
+        return ""
+
+    return message

skyagi/src/skyagi/skyagi.py

@@ -1,7 +1,6 @@
 import os
 from typing import List
 
-import openai
 from langchain.chat_models import ChatOpenAI
 from rich.console import Console
 from rich.prompt import Prompt
@@ -12,48 +11,10 @@
     create_new_memory_retriever,
     interview_agent,
     run_conversation,
+    talks_to,
 )
 
 
-# whether amy wants to talk to bob based on the observations
-def talks_to(
-    amy: GenerativeAgent, bob: GenerativeAgent, observations: List[str]
-) -> str:
-    instruct = "Here are the timeline of events happened for these NPC characters:\n"
-    instruct += "\n".join(observations)
-    instruct += "\n"
-    instruct += (
-        f"I want to you to behave as {amy.name} and talk to me as I am {bob.name}.\n"
-    )
-    instruct += (
-        f"If you do not want to or can not talk to {bob.name}, just output NOTHING"
-    )
-
-    prompts = [
-        {
-            "role": "system",
-            "content": f"You are the AI behind a NPC character called {amy.name}",
-        },
-        {"role": "user", "content": instruct},
-    ]
-    resp = openai.ChatCompletion.create(model="gpt-3.5-turbo", messages=prompts)
-    message = resp["choices"][0]["message"]["content"]
-    if "NOTHING" in message:
-        return ""
-
-    prompts.append(resp["choices"][0]["message"])
-    prompts.append(
-        {
-            "role": "user",
-            "content": f"Did {amy.name} talk to {bob.name}, please answer yes or no",
-        }
-    )
-    resp = openai.ChatCompletion.create(model="gpt-3.5-turbo", messages=prompts)
-    if "no" in resp["choices"][0]["message"]["content"]:
-        return ""
-    return message
-
-
 def user_robot_conversation(agent_to_interview: GenerativeAgent, ctx: Context):
     ctx.console.print(
         f"Interview with {agent_to_interview.name} start, input empty line to exit",