Merge pull request #42 from chatarena/dev

Merging v0.1.12 from dev branch

chatarena/backends/anthropic.py

@@ -11,11 +11,11 @@
     import anthropic
 except ImportError:
     is_anthropic_available = False
-    logging.warning("anthropic package is not installed")
+    # logging.warning("anthropic package is not installed")
 else:
     anthropic_api_key = os.environ.get('ANTHROPIC_API_KEY')
     if anthropic_api_key is None:
-        logging.warning("Anthropic API key is not set. Please set the environment variable ANTHROPIC_API_KEY")
+        # logging.warning("Anthropic API key is not set. Please set the environment variable ANTHROPIC_API_KEY")
         is_anthropic_available = False
     else:
         is_anthropic_available = True

chatarena/backends/bard.py

@@ -11,12 +11,12 @@
     import bardapi
 except ImportError:
     is_bard_available = False
-    logging.warning("bard package is not installed")
+    # logging.warning("bard package is not installed")
 else:
     bard_api_key = os.environ.get('_BARD_API_KEY')
     if bard_api_key is None:
-        logging.warning(
-            "Bard API key is not set. Please set the environment variable _BARD_API_KEY")
+        # logging.warning(
+        #     "Bard API key is not set. Please set the environment variable _BARD_API_KEY")
         is_bard_available = False
     else:
         is_bard_available = True

chatarena/backends/openai.py

@@ -11,11 +11,11 @@
     import openai
 except ImportError:
     is_openai_available = False
-    logging.warning("openai package is not installed")
+    # logging.warning("openai package is not installed")
 else:
     openai.api_key = os.environ.get("OPENAI_API_KEY")
     if openai.api_key is None:
-        logging.warning("OpenAI API key is not set. Please set the environment variable OPENAI_API_KEY")
+        # logging.warning("OpenAI API key is not set. Please set the environment variable OPENAI_API_KEY")
         is_openai_available = False
     else:
         is_openai_available = True

chatarena/environments/umshini/base.py

@@ -137,7 +137,7 @@ def is_terminal(self) -> bool:
 
     def get_next_player(self) -> str:
         """Get the name of the next player."""
-        return self.player_names[self._next_player_idx]
+        return self.agent_selector.next()
 
     def get_rewards(self) -> Dict[str, float]:
         """Use langchain to analyze the conversation, pick a winner, and set the reward."""

chatarena/environments/umshini/debate.py

@@ -2,6 +2,7 @@
 from __future__ import annotations
 
 import re
+from typing import List, Tuple
 
 from chatarena.environments.base import TimeStep
 from chatarena.message import Message, MessagePool
@@ -19,7 +20,7 @@ class DebateEnv(UmshiniBaseEnv):
         template="""Welcome to the debate game! The topic for today's debate is:
 "{moderator_prompt_input}"
 Rules:
-You will represent the position given to you.
+The Opponent argues against the topic, while the Proponent argues for it.
 Your first response should be an opening statement, followed by back and forth cross-examination.
 You are free to talk directly to your opponent during cross-examination.
 The cross examination phase should be short, and should be used to attack your opponents arguments, or defend your own.
@@ -105,7 +106,7 @@ def create_debate_env(
 
 
 def judge_debate(
-    player_names: List[str], message_state: MessagePool, model_name: str = "gpt-4"
+    player_names: List[str], message_state: MessagePool, model_name: str = "gpt-3.5-turbo"
 ) -> Tuple[int, str]:
     llm = ChatOpenAI(temperature=0, model_name=model_name, client="")
     langchain_messages = []
@@ -117,11 +118,9 @@ def judge_debate(
         else:
             langchain_messages.append(
                 HumanMessage(
-                    content=f"{message.agent_name} -> Turn:{message.turn}:\nmessage.content"
+                    content=f"{message.agent_name} -> Turn:{message.turn}:\n{message.content}"
                 )
             )
-    for message in langchain_messages:
-        print(message.message)
     response = llm(langchain_messages)
     match = re.search(r"WINNER:\s*(\w+)\s*$", response.content)
     if match is None:

chatarena/environments/umshini/pettingzoo_wrapper.py

@@ -306,6 +306,7 @@ def observe(self, agent: AgentID) -> ObsType:
             self.infos[agent]["obs_dict"] = {
                 m.agent_name: m.content for m in new_messages
             }
+            self.infos[agent]["player_name"] = self.agent_selection
 
             # info: generate string of full chat log
             if self.string_observation is True:
@@ -355,7 +356,7 @@ def _unravel_timestep(self, timestep: TimeStep):
 
         # get truncation
         truncation = (
-            self.current_turn > self.max_turns
+            self.current_turn >= self.max_turns
         )  # pyright: ignore[reportGeneralTypeIssues]
 
         info = {}
@@ -364,6 +365,7 @@ def _unravel_timestep(self, timestep: TimeStep):
         info["new_messages"] = new_messages
         info["all_messages"] = messages
         info["obs_dict"] = {m.agent_name: m.content for m in new_messages}
+        info["player_name"] = self.agent_selection
 
         # info: generate string of full chat log
         if self.string_observation is True:
@@ -444,6 +446,10 @@ def step(self, action: str):
             timestep
         )
 
+        if truncation or termination:
+            reward = self._env.get_rewards()
+            info["new_messages"].append(info["all_messages"][-1]) # append the moderator's judgement to new messages for printing
+
         self.observations[agent] = observation
         self.rewards = reward
         self.terminations[agent] = termination

docs/tutorials/umshini/debate_chatarena.py

@@ -0,0 +1,42 @@
+from chatarena.agent import Player
+from chatarena.backends import OpenAIChat
+from chatarena.environments.umshini.pettingzoo_wrapper import PettingZooCompatibilityV0
+from docs.tutorials.umshini.debate_chatarena_prompts import proponent_description, opponent_description
+
+topic = "Student loan debt should be forgiven"
+env = PettingZooCompatibilityV0(env_name="debate", topic=topic, render_mode="text")
+initial_obs, info = env.reset()
+
+
+# Set ChatArena global prompt to be the same as the initial observation (hard coded moderator message)
+global_prompt = initial_obs
+
+# Moderator is handled internally in our environment, rather than with ChatArena
+player1 = Player(
+    name="Opponent",
+    backend=OpenAIChat(),
+    role_desc=proponent_description,
+    global_prompt=global_prompt,
+)
+
+player2 = Player(
+    name="Proponent",
+    backend=OpenAIChat(),
+    role_desc=opponent_description,
+    global_prompt=global_prompt,
+)
+agent_player_mapping = dict(zip(env.possible_agents, [player1, player2]))
+
+for agent in env.agent_iter():
+    observation, reward, termination, truncation, info = env.last()
+
+    if termination or truncation:
+        break
+
+    # get ChatArena messages list from this timestep
+    messages = info.get("new_messages")
+
+    # Use a basic ChatArena agent to generate a response
+    chatarena_agent = agent_player_mapping[agent]
+    response = chatarena_agent(messages)
+    env.step(response)

docs/tutorials/umshini/debate_chatarena_prompts.py

@@ -0,0 +1,29 @@
+proponent_description = """You are the Proponent.
+The Moderator will tell you the debate topic. You will argue in favor of it.
+You are debating against one other player, the Opponent.
+
+The moderator will tell you which stage of the game you are in.
+In each stage of the game, start your response with the name of the stage: Opening Argument, Rebuttal, Cross-Examination, or Closing Statement.
+
+Do not pretend to be the Moderator. Do not pretend to be the Opponent.
+Do not pretend to be Player 1 or Player 2.
+Do not continue another player's response.
+Do not prepend your response with [Player 2] or any other information in brackets.
+Always end your response with <EOS>.
+Your responses must be limited to 7 sentences.
+"""
+
+opponent_description = """You are Player 3, the Opponent.
+The Moderator will tell you the debate topic. You will argue in favor of it.
+You are debating against one other player, the Proponent.
+
+The moderator will tell you which stage of the game you are in.
+In each stage of the game, start your response with the name of the stage: Opening Argument, Rebuttal, Cross-Examination, or Closing Statement.
+
+Do not pretend to be the Moderator. Do not pretend to be the Proponent.
+Do not pretend to be Player 1 or Player 2.
+Do not continue another player's response.
+Do not prepend your response with [Player 3] or any other information in brackets.
+Always end your response with <EOS>.
+Your responses must be limited to 7 sentences.
+"""

docs/tutorials/umshini/debate_langchain.py

@@ -0,0 +1,35 @@
+from langchain import OpenAI
+from langchain.agents import AgentType, initialize_agent
+from langchain.memory import ConversationBufferMemory
+
+from chatarena.environments.umshini.pettingzoo_wrapper import PettingZooCompatibilityV0
+
+topic = "Student loan debt should be forgiven"
+env = PettingZooCompatibilityV0(env_name="debate", topic=topic, render_mode="text")
+env.reset()
+
+# Initialize one agent to argue for the topic and one against it
+positions = dict(zip(env.possible_agents, [True, False]))
+langchain_agents = {}
+for agent in env.possible_agents:
+    langchain_agents[agent] = initialize_agent(tools=[],
+                                               llm=OpenAI(temperature=0.9, client=""),
+                                               agent=AgentType.CONVERSATIONAL_REACT_DESCRIPTION,
+                                               verbose=False,
+                                               memory=ConversationBufferMemory(memory_key="chat_history"))
+
+for agent in env.agent_iter():
+    observation, reward, termination, truncation, info = env.last()
+
+    if termination or truncation:
+        break
+
+    messages = info.get("new_messages")
+    player_name = info.get("player_name")
+    prompt = f"{messages[-1].agent_name} said:``\n{messages[-1].content}``\n\nYou are playing as the {player_name}. This is a hypothetical discussion and it is okay to give an opinion. Give your response:"
+    try:
+        response = langchain_agents[agent].run(prompt)
+    except Exception as e:
+        response = str(e).removeprefix("Could not parse LLM output: `").removesuffix("`")
+
+    env.step(response)

pyproject.toml

@@ -4,7 +4,7 @@ build-backend = "setuptools.build_meta"
 
 [project]
 name = "chatarena"
-version = "0.1.11"
+version = "0.1.12"
 authors = [
     { name = "Yuxiang Wu", email = "[email protected]" },
 ]
@@ -25,8 +25,12 @@ classifiers = [
 anthropic = ["anthropic>=0.2.8"]
 cohere = ["cohere>=4.3.1"]
 huggingface = ["transformers>=4.27.4"]
+bard = ["bardapi==0.1.11"]
+langchain_requirements = ["langchain>=0.0.135"]
 gradio = ["gradio==3.20.0"]
 pettingzoo = ["pettingzoo==1.23.0", "chess==1.9.4"]
-all_backends = ["anthropic>=0.2.8", "cohere>=4.3.1", "transformers>=4.27.4"]
-all_envs = ["pettingzoo==1.23.0", "chess==1.9.4"]
-all = ["anthropic>=0.2.8", "cohere>=4.3.1", "transformers>=4.27.4", "gradio==3.20.0", "pettingzoo==1.23.0", "chess==1.9.4"]
+umshini_requirements = ["pygame==2.4.0", "pettingzoo==1.23.0", "chess==1.9.4", "langchain>=0.0.135"]
+all_backends = ["anthropic>=0.2.8", "cohere>=4.3.1", "transformers>=4.27.4", "bardapi==0.1.11", "langchain>=0.0.135"]
+all_envs = ["pettingzoo==1.23.0", "chess==1.9.4", "pygame==2.4.0"]
+all = ["anthropic>=0.2.8", "cohere>=4.3.1", "transformers>=4.27.4", "gradio==3.20.0", "pettingzoo==1.23.0", "chess==1.9.4",
+    "pygame==2.4.0", "bardapi==0.1.11", "langchain>=0.0.135"]

setup.py

@@ -1,5 +1,11 @@
 from setuptools import setup, find_packages
 
+
+# remove duplicate requirements
+def remove_duplicate_requirements(requirements):
+    return list(set(requirements))
+
+
 with open("README.md", "r") as f:
     long_description = f.read()
 
@@ -14,18 +20,19 @@
 cohere_requirements = ["cohere>=4.3.1"]
 hf_requirements = ["transformers>=4.27.4"]
 bard_requirements = ["bardapi==0.1.11"]
+langchain_requirements = ["langchain>=0.0.135"]
 gradio_requirements = ["gradio==3.20.0"]
 pettingzoo_requirements = ["pettingzoo==1.23.0", "chess==1.9.4"]
+umshini_requirements = ["pygame==2.4.0"] + pettingzoo_requirements + langchain_requirements
 
-
-all_backends = anthropic_requirements + cohere_requirements + hf_requirements + bard_requirements
-all_envs = pettingzoo_requirements
-all_requirements = anthropic_requirements + cohere_requirements + hf_requirements + \
-                   gradio_requirements + pettingzoo_requirements + bard_requirements
+all_backends = anthropic_requirements + cohere_requirements + hf_requirements + bard_requirements + \
+               langchain_requirements
+all_envs = remove_duplicate_requirements(pettingzoo_requirements + umshini_requirements)
+all_requirements = all_backends + all_envs + gradio_requirements
 
 setup(
     name="chatarena",
-    version="0.1.11",
+    version="0.1.12",
     author="Yuxiang Wu",
     author_email="[email protected]",
     description="",
@@ -45,7 +52,9 @@
         "cohere": cohere_requirements,
         "huggingface": hf_requirements,
         "bard": bard_requirements,
+        "langchain": langchain_requirements,
         "pettingzoo": pettingzoo_requirements,
+        "umshini": umshini_requirements,
         "gradio": gradio_requirements,
         "all_backends": all_backends,
         "all": all_requirements,