Merge branch 'main' into main

.github/workflows/build.yml

@@ -40,7 +40,7 @@ jobs:
           python -m pip install --upgrade pip wheel
           pip install -e .
           python -c "import autogen"
-          pip install -e. pytest
+          pip install -e. pytest mock
           pip uninstall -y openai
       - name: Install unstructured if not windows
         if: matrix.os != 'windows-2019'

autogen/agentchat/conversable_agent.py

@@ -1195,7 +1195,7 @@ def generate_init_message(self, **context) -> Union[str, Dict]:
         """Generate the initial message for the agent.
 
         Override this function to customize the initial message based on user's request.
-        If not overriden, "message" needs to be provided in the context.
+        If not overridden, "message" needs to be provided in the context.
         """
         return context["message"]
 

autogen/agentchat/groupchat.py

@@ -1,5 +1,6 @@
 import logging
 import sys
+import random
 from dataclasses import dataclass
 from typing import Dict, List, Optional, Union
 import re
@@ -21,13 +22,24 @@ class GroupChat:
         When set to True and when a message is a function call suggestion,
         the next speaker will be chosen from an agent which contains the corresponding function name
         in its `function_map`.
+    - speaker_selection_method: the method for selecting the next speaker. Default is "auto".
+        Could be any of the following (case insensitive), will raise ValueError if not recognized:
+        - "auto": the next speaker is selected automatically by LLM.
+        - "manual": the next speaker is selected manually by user input.
+        - "random": the next speaker is selected randomly.
+        - "round_robin": the next speaker is selected in a round robin fashion, i.e., iterating in the same order as provided in `agents`.
+    - allow_repeat_speaker: whether to allow the same speaker to speak consecutively. Default is True.
     """
 
     agents: List[Agent]
     messages: List[Dict]
     max_round: int = 10
     admin_name: str = "Admin"
     func_call_filter: bool = True
+    speaker_selection_method: str = "auto"
+    allow_repeat_speaker: bool = True
+
+    _VALID_SPEAKER_SELECTION_METHODS = ["auto", "manual", "random", "round_robin"]
 
     @property
     def agent_names(self) -> List[str]:
@@ -55,13 +67,61 @@ def next_agent(self, agent: Agent, agents: List[Agent]) -> Agent:
     def select_speaker_msg(self, agents: List[Agent]):
         """Return the message for selecting the next speaker."""
         return f"""You are in a role play game. The following roles are available:
-{self._participant_roles()}.
+{self._participant_roles(agents)}.
 
 Read the following conversation.
 Then select the next role from {[agent.name for agent in agents]} to play. Only return the role."""
 
+    def manual_select_speaker(self, agents: List[Agent]) -> Agent:
+        """Manually select the next speaker."""
+
+        print("Please select the next speaker from the following list:")
+        _n_agents = len(agents)
+        for i in range(_n_agents):
+            print(f"{i+1}: {agents[i].name}")
+        try_count = 0
+        # Assume the user will enter a valid number within 3 tries, otherwise use auto selection to avoid blocking.
+        while try_count <= 3:
+            try_count += 1
+            if try_count >= 3:
+                print(f"You have tried {try_count} times. The next speaker will be selected automatically.")
+                break
+            try:
+                i = input("Enter the number of the next speaker (enter nothing or `q` to use auto selection): ")
+                if i == "" or i == "q":
+                    break
+                i = int(i)
+                if i > 0 and i <= _n_agents:
+                    return agents[i - 1]
+                else:
+                    raise ValueError
+            except ValueError:
+                print(f"Invalid input. Please enter a number between 1 and {_n_agents}.")
+        return None
+
     def select_speaker(self, last_speaker: Agent, selector: ConversableAgent):
         """Select the next speaker."""
+        if self.speaker_selection_method.lower() not in self._VALID_SPEAKER_SELECTION_METHODS:
+            raise ValueError(
+                f"GroupChat speaker_selection_method is set to '{self.speaker_selection_method}'. "
+                f"It should be one of {self._VALID_SPEAKER_SELECTION_METHODS} (case insensitive). "
+            )
+
+        agents = self.agents
+        n_agents = len(agents)
+        # Warn if GroupChat is underpopulated
+        if n_agents < 2:
+            raise ValueError(
+                f"GroupChat is underpopulated with {n_agents} agents. "
+                "Please add more agents to the GroupChat or use direct communication instead."
+            )
+        elif n_agents == 2 and self.speaker_selection_method.lower() != "round_robin" and self.allow_repeat_speaker:
+            logger.warning(
+                f"GroupChat is underpopulated with {n_agents} agents. "
+                "It is recommended to set speaker_selection_method to 'round_robin' or allow_repeat_speaker to False."
+                "Or, use direct communication instead."
+            )
+
         if self.func_call_filter and self.messages and "function_call" in self.messages[-1]:
             # find agents with the right function_map which contains the function name
             agents = [
@@ -80,14 +140,20 @@ def select_speaker(self, last_speaker: Agent, selector: ConversableAgent):
                         f"No agent can execute the function {self.messages[-1]['name']}. "
                         "Please check the function_map of the agents."
                     )
-        else:
-            agents = self.agents
-            # Warn if GroupChat is underpopulated
-            n_agents = len(agents)
-            if n_agents < 3:
-                logger.warning(
-                    f"GroupChat is underpopulated with {n_agents} agents. Direct communication would be more efficient."
-                )
+
+        # remove the last speaker from the list to avoid selecting the same speaker if allow_repeat_speaker is False
+        agents = agents if self.allow_repeat_speaker else [agent for agent in agents if agent != last_speaker]
+
+        if self.speaker_selection_method.lower() == "manual":
+            selected_agent = self.manual_select_speaker(agents)
+            if selected_agent:
+                return selected_agent
+        elif self.speaker_selection_method.lower() == "round_robin":
+            return self.next_agent(last_speaker, agents)
+        elif self.speaker_selection_method.lower() == "random":
+            return random.choice(agents)
+
+        # auto speaker selection
         selector.update_system_message(self.select_speaker_msg(agents))
         final, name = selector.generate_oai_reply(
             self.messages
@@ -99,26 +165,31 @@ def select_speaker(self, last_speaker: Agent, selector: ConversableAgent):
             ]
         )
         if not final:
-            # i = self._random.randint(0, len(self._agent_names) - 1)  # randomly pick an id
+            # the LLM client is None, thus no reply is generated. Use round robin instead.
             return self.next_agent(last_speaker, agents)
 
         # If exactly one agent is mentioned, use it. Otherwise, leave the OAI response unmodified
         mentions = self._mentioned_agents(name, agents)
         if len(mentions) == 1:
             name = next(iter(mentions))
+        else:
+            logger.warning(
+                f"GroupChat select_speaker failed to resolve the next speaker's name. This is because the speaker selection OAI call returned:\n{name}"
+            )
 
         # Return the result
         try:
             return self.agent_by_name(name)
         except ValueError:
-            logger.warning(
-                f"GroupChat select_speaker failed to resolve the next speaker's name. Speaker selection will default to the next speaker in the list. This is because the speaker selection OAI call returned:\n{name}"
-            )
             return self.next_agent(last_speaker, agents)
 
-    def _participant_roles(self):
+    def _participant_roles(self, agents: List[Agent] = None) -> str:
+        # Default to all agents registered
+        if agents is None:
+            agents = self.agents
+
         roles = []
-        for agent in self.agents:
+        for agent in agents:
             if agent.system_message.strip() == "":
                 logger.warning(
                     f"The agent '{agent.name}' has an empty system_message, and may not work well with GroupChat."

autogen/oai/completion.py

@@ -340,7 +340,7 @@ def _eval(cls, config: dict, prune=True, eval_only=False):
             config (dict): Hyperparameter setting for the openai api call.
             prune (bool, optional): Whether to enable pruning. Defaults to True.
             eval_only (bool, optional): Whether to evaluate only
-              (ignore the inference budget and do not rasie error when a request fails).
+              (ignore the inference budget and do not raise error when a request fails).
               Defaults to False.
 
         Returns:
@@ -949,7 +949,7 @@ def eval_func(responses, **data):
             return_responses_and_per_instance_result (bool): Whether to also return responses
                 and per instance results in addition to the aggregated results.
             logging_level (optional): logging level. Defaults to logging.WARNING.
-            **config (dict): parametes passed to the openai api call `create()`.
+            **config (dict): parameters passed to the openai api call `create()`.
 
         Returns:
             None when no valid eval_func is provided in either test or tune;

autogen/version.py

@@ -1 +1 @@
-__version__ = "0.2.0b5"
+__version__ = "0.2.0b6"