Merge branch 'main' into add-links-to-sdk-microsoft#910

autogen/agentchat/contrib/agent_builder.py

@@ -2,12 +2,25 @@
 import time
 import subprocess as sp
 import socket
-import os
 import json
 import hashlib
 from typing import Optional, List, Dict, Tuple, Union
 
 
+def _config_check(config: Dict):
+    # check config loading
+    assert config.get("coding", None) is not None, 'Missing "coding" in your config.'
+    assert config.get("default_llm_config", None) is not None, 'Missing "default_llm_config" in your config.'
+    assert config.get("code_execution_config", None) is not None, 'Missing "code_execution_config" in your config.'
+
+    for agent_config in config["agent_configs"]:
+        assert agent_config.get("name", None) is not None, 'Missing agent "name" in your agent_configs.'
+        assert agent_config.get("model", None) is not None, 'Missing agent "model" in your agent_configs.'
+        assert (
+            agent_config.get("system_message", None) is not None
+        ), 'Missing agent "system_message" in your agent_configs.'
+
+
 class AgentBuilder:
     """
     AgentBuilder can help user build an automatic task solving process powered by multi-agent system.
@@ -37,7 +50,8 @@ class AgentBuilder:
 
     Hint:
     # Considering the effort, the position in this task should be no more then {max_agents}, less is better.
-    # Answer the name of those positions/jobs, separated by comma and use "_" instead of space. For example: Product_manager,Programmer
+    # Answer the name of those positions/jobs.
+    # Separated names by comma and use "_" instead of space. For example: Product_manager,Programmer
     # Only return the list of positions.
     """
 
@@ -69,6 +83,7 @@ def __init__(
         Args:
             config_path: path of the OpenAI api configs.
             builder_model: specify a model as the backbone of build manager.
+            agent_model: specify a model as the backbone of participant agents.
             host: endpoint host.
             endpoint_building_timeout: timeout for building up an endpoint server.
         """
@@ -89,6 +104,12 @@ def __init__(
             if self._is_port_open(host, port):
                 self.open_ports.append(str(port))
 
+    def set_builder_model(self, model: str):
+        self.builder_model = model
+
+    def set_agent_model(self, model: str):
+        self.agent_model = model
+
     @staticmethod
     def _is_port_open(host, port):
         """Check if a tcp port is open."""
@@ -128,6 +149,11 @@ def _create_agent(
             agent: a set-up agent.
         """
         config_list = autogen.config_list_from_json(self.config_path, filter_dict={"model": [model_name_or_hf_repo]})
+        if len(config_list) == 0:
+            raise RuntimeError(
+                f"Fail to initialize agent:{agent_name}: {self.builder_model} does not exist in {self.config_path}. "
+                f'If you would like to change this model, please specify the "agent_model" in the constructor.'
+            )
         if "gpt-" in model_name_or_hf_repo:
             server_id = self.openai_server_name
         else:
@@ -259,14 +285,6 @@ def build(
         """
         use_api = False
 
-        if code_execution_config is None:
-            code_execution_config = {
-                "last_n_messages": 2,
-                "work_dir": "groupchat",
-                "use_docker": False,
-                "timeout": 60,
-            }
-
         if cached_configs is None:
             use_api = True
             agent_configs = []
@@ -276,9 +294,23 @@ def build(
             default_llm_config = cached_configs["default_llm_config"]
             coding = cached_configs["coding"]
             agent_configs = cached_configs["agent_configs"]
+            code_execution_config = cached_configs["code_execution_config"]
+
+        if code_execution_config is None:
+            code_execution_config = {
+                "last_n_messages": 2,
+                "work_dir": "groupchat",
+                "use_docker": False,
+                "timeout": 60,
+            }
 
         if use_api:
             config_list = autogen.config_list_from_json(self.config_path, filter_dict={"model": [self.builder_model]})
+            if len(config_list) == 0:
+                raise RuntimeError(
+                    f"Fail to initialize build manager: {self.builder_model} does not exist in {self.config_path}. "
+                    f'If you want to change this model, please specify the "builder_model" in the constructor.'
+                )
             build_manager = autogen.OpenAIWrapper(config_list=config_list)
 
             print("Generating agents...")
@@ -294,8 +326,8 @@ def build(
                 .choices[0]
                 .message.content
             )
-            agent_name_list = resp_agent_name.split(",")
-            print(f"{resp_agent_name} are generated.")
+            agent_name_list = [agent_name.strip().replace(" ", "_") for agent_name in resp_agent_name.split(",")]
+            print(f"{agent_name_list} are generated.")
 
             agent_sys_msg_list = []
             for name in agent_name_list:
@@ -390,19 +422,31 @@ def save(self, filepath: Optional[str] = None) -> str:
 
     def load(
         self,
-        filepath: str,
+        filepath: Optional[str] = None,
+        config_json: Optional[str] = None,
         **kwargs,
     ):
         """
         Load building configs and call the build function to complete building without calling online LLMs' api.
 
         Args:
-            filepath: filepath for the save config.
+            filepath: filepath or JSON string for the save config.
+            config_json: JSON string for the save config.
         """
-        try:
-            print(f"Loding config from {filepath}")
-            cached_configs = json.load(open(filepath))
-        except FileNotFoundError:
-            raise FileNotFoundError(f"Config file {filepath} does not exist.")
-
-        return self.build(cached_configs=cached_configs, **kwargs)
+        # load json string.
+        if config_json is not None:
+            cached_configs = json.loads(config_json)
+            print("Loading config from JSON...")
+            _config_check(cached_configs)
+            return self.build(cached_configs=cached_configs, **kwargs)
+
+        # load from path.
+        if filepath is not None:
+            print(f"Loading config from {filepath}")
+            try:
+                with open(filepath) as f:
+                    cached_configs = json.load(f)
+            except FileNotFoundError as e:
+                raise FileNotFoundError(f"{filepath} does not exist.") from e
+            _config_check(cached_configs)
+            return self.build(cached_configs=cached_configs, **kwargs)

autogen/agentchat/groupchat.py

@@ -30,16 +30,16 @@ class GroupChat:
         - "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.
+    - allow_repeat_speaker: whether to allow the same speaker to speak consecutively. Default is True, in which case all speakers are allowed to speak consecutively. If allow_repeat_speaker is a list of Agents, then only those listed agents are allowed to repeat. If set to False, then no speakers are allowed to repeat.
     """
 
     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
+    max_round: Optional[int] = 10
+    admin_name: Optional[str] = "Admin"
+    func_call_filter: Optional[bool] = True
+    speaker_selection_method: Optional[str] = "auto"
+    allow_repeat_speaker: Optional[Union[bool, List[Agent]]] = True
 
     _VALID_SPEAKER_SELECTION_METHODS = ["auto", "manual", "random", "round_robin"]
 
@@ -125,6 +125,13 @@ def _prepare_and_select_agents(self, last_speaker: Agent) -> Tuple[Optional[Agen
                 f"It should be one of {self._VALID_SPEAKER_SELECTION_METHODS} (case insensitive). "
             )
 
+        # If provided a list, make sure the agent is in the list
+        allow_repeat_speaker = (
+            self.allow_repeat_speaker
+            if isinstance(self.allow_repeat_speaker, bool)
+            else last_speaker in self.allow_repeat_speaker
+        )
+
         agents = self.agents
         n_agents = len(agents)
         # Warn if GroupChat is underpopulated
@@ -133,7 +140,7 @@ def _prepare_and_select_agents(self, last_speaker: Agent) -> Tuple[Optional[Agen
                 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:
+        elif n_agents == 2 and self.speaker_selection_method.lower() != "round_robin" and 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."
@@ -159,7 +166,7 @@ def _prepare_and_select_agents(self, last_speaker: Agent) -> Tuple[Optional[Agen
                         "Please check the function_map of the agents."
                     )
         # 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]
+        agents = agents if 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)

autogen/oai/client.py

@@ -191,7 +191,7 @@ def _construct_create_params(self, create_config: Dict, extra_kwargs: Dict) -> D
     def create(self, **config):
         """Make a completion for a given config using openai's clients.
         Besides the kwargs allowed in openai's client, we allow the following additional kwargs.
-        The config in each client will be overriden by the config.
+        The config in each client will be overridden by the config.
 
         Args:
             - context (Dict | None): The context to instantiate the prompt or messages. Default to None.

autogen/token_count_utils.py

@@ -2,27 +2,33 @@
 import logging
 import json
 import tiktoken
+import re
 
 
 logger = logging.getLogger(__name__)
 
 
 def get_max_token_limit(model="gpt-3.5-turbo-0613"):
+    # Handle common azure model names/aliases
+    model = re.sub(r"^gpt\-?35", "gpt-3.5", model)
+    model = re.sub(r"^gpt4", "gpt-4", model)
+
     max_token_limit = {
         "gpt-3.5-turbo": 4096,
         "gpt-3.5-turbo-0301": 4096,
         "gpt-3.5-turbo-0613": 4096,
         "gpt-3.5-turbo-instruct": 4096,
-        "gpt-3.5-turbo-16k": 16384,
-        "gpt-35-turbo": 4096,
-        "gpt-35-turbo-16k": 16384,
-        "gpt-35-turbo-instruct": 4096,
+        "gpt-3.5-turbo-16k": 16385,
+        "gpt-3.5-turbo-16k-0613": 16385,
+        "gpt-3.5-turbo-1106": 16385,
         "gpt-4": 8192,
         "gpt-4-32k": 32768,
         "gpt-4-32k-0314": 32768,  # deprecate in Sep
         "gpt-4-0314": 8192,  # deprecate in Sep
         "gpt-4-0613": 8192,
         "gpt-4-32k-0613": 32768,
+        "gpt-4-1106-preview": 128000,
+        "gpt-4-vision-preview": 128000,
     }
     return max_token_limit[model]