New initiate_chats Interface for Managing Dependent Chats in Conver…

…sableAgent (#1402) * add initiate_chats implementation and example * update notebook * improve takeaway method * improve print * improve print * improve print * improve print * add tests * minor changes * format * correct typo * make prompt a parameter * add takeaway method * groupchat messages * add SoM example * fix typo * fix SoM typo * simplify chat function * add carryover * update notebook * doc * remove async for now * remove condition on reply * correct argument name * add notebook in website * format * make get_chat_takeaway private * rename takeaway method and add example * removing SoM example for now * carryover test * add test * takeaway_method * update tests * update notebook * chats_queue * add get_chat_takeaway * delete * add test * Update autogen/agentchat/conversable_agent.py Co-authored-by: Eric Zhu <[email protected]> * docstr * wording etc * add chat res * revise title * update agent_utils * unify the async method * add todo about overriding * attribute check * ChatResult type * revise test * takeaway to summary * cache and documentation * Use cache in summarize chat; polish tests --------- Co-authored-by: Chi Wang <[email protected]> Co-authored-by: Eric Zhu <[email protected]>
microsoft · Feb 6, 2024 · 3e33a2c · 3e33a2c
1 parent 7811c15
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diff --git a/autogen/agent_utils.py b/autogen/agent_utils.py
@@ -1,8 +1,7 @@
 from typing import List, Dict, Tuple
-from autogen import Agent
 
 
-def gather_usage_summary(agents: List[Agent]) -> Tuple[Dict[str, any], Dict[str, any]]:
+def gather_usage_summary(agents: List) -> Tuple[Dict[str, any], Dict[str, any]]:
     """Gather usage summary from all agents.
 
     Args:
@@ -44,7 +43,7 @@ def aggregate_summary(usage_summary: Dict[str, any], agent_summary: Dict[str, an
     actual_usage_summary = {"total_cost": 0}
 
     for agent in agents:
-        if agent.client:
+        if getattr(agent, "client", None):
             aggregate_summary(total_usage_summary, agent.client.total_usage_summary)
             aggregate_summary(actual_usage_summary, agent.client.actual_usage_summary)
 

diff --git a/autogen/agentchat/chat.py b/autogen/agentchat/chat.py
@@ -0,0 +1,19 @@
+import logging
+from typing import Dict, List
+from dataclasses import dataclass
+
+logger = logging.getLogger(__name__)
+
+
+@dataclass
+class ChatResult:
+    """(Experimental) The result of a chat. Almost certain to be changed."""
+
+    chat_history: List[Dict[str, any]] = None
+    """The chat history."""
+    summary: str = None
+    """A summary obtained from the chat."""
+    cost: tuple = None  # (dict, dict) - (total_cost, actual_cost_with_cache)
+    """The cost of the chat. a tuple of (total_cost, total_actual_cost), where total_cost is a dictionary of cost information, and total_actual_cost is a dictionary of information on the actual incurred cost with cache."""
+    human_input: List[str] = None
+    """A list of human input solicited during the chat."""
diff --git a/autogen/agentchat/conversable_agent.py b/autogen/agentchat/conversable_agent.py
diff --git a/notebook/agentchat_auto_feedback_from_code_execution.ipynb b/notebook/agentchat_auto_feedback_from_code_execution.ipynb
diff --git a/notebook/agentchat_multi_task_chats.ipynb b/notebook/agentchat_multi_task_chats.ipynb
diff --git a/test/agentchat/test_chats.py b/test/agentchat/test_chats.py
@@ -0,0 +1,202 @@
+from autogen import AssistantAgent, UserProxyAgent
+from autogen import GroupChat, GroupChatManager
+from test_assistant_agent import KEY_LOC, OAI_CONFIG_LIST
+import pytest
+from conftest import skip_openai
+import autogen
+
+
+@pytest.mark.skipif(skip_openai, reason="requested to skip openai tests")
+def test_chats_group():
+    config_list = autogen.config_list_from_json(
+        OAI_CONFIG_LIST,
+        file_location=KEY_LOC,
+    )
+    financial_tasks = [
+        """What are the full names of NVDA and TESLA.""",
+        """Pros and cons of the companies I'm interested in. Keep it short.""",
+    ]
+
+    writing_tasks = ["""Develop a short but engaging blog post using any information provided."""]
+
+    user_proxy = UserProxyAgent(
+        name="User_proxy",
+        system_message="A human admin.",
+        human_input_mode="NEVER",
+        code_execution_config={
+            "last_n_messages": 1,
+            "work_dir": "groupchat",
+            "use_docker": False,
+        },
+        is_termination_msg=lambda x: x.get("content", "") and x.get("content", "").rstrip().endswith("TERMINATE"),
+    )
+
+    financial_assistant = AssistantAgent(
+        name="Financial_assistant",
+        llm_config={"config_list": config_list},
+    )
+
+    writer = AssistantAgent(
+        name="Writer",
+        llm_config={"config_list": config_list},
+        system_message="""
+        You are a professional writer, known for
+        your insightful and engaging articles.
+        You transform complex concepts into compelling narratives.
+        Reply "TERMINATE" in the end when everything is done.
+        """,
+    )
+
+    critic = AssistantAgent(
+        name="Critic",
+        system_message="""Critic. Double check plan, claims, code from other agents and provide feedback. Check whether the plan includes adding verifiable info such as source URL.
+        Reply "TERMINATE" in the end when everything is done.
+        """,
+        llm_config={"config_list": config_list},
+    )
+
+    groupchat_1 = GroupChat(agents=[user_proxy, financial_assistant, critic], messages=[], max_round=50)
+
+    groupchat_2 = GroupChat(agents=[user_proxy, writer, critic], messages=[], max_round=50)
+
+    manager_1 = GroupChatManager(
+        groupchat=groupchat_1,
+        name="Research_manager",
+        llm_config={"config_list": config_list},
+        code_execution_config={
+            "last_n_messages": 1,
+            "work_dir": "groupchat",
+            "use_docker": False,
+        },
+        is_termination_msg=lambda x: x.get("content", "").find("TERMINATE") >= 0,
+    )
+    manager_2 = GroupChatManager(
+        groupchat=groupchat_2,
+        name="Writing_manager",
+        llm_config={"config_list": config_list},
+        code_execution_config={
+            "last_n_messages": 1,
+            "work_dir": "groupchat",
+            "use_docker": False,
+        },
+        is_termination_msg=lambda x: x.get("content", "").find("TERMINATE") >= 0,
+    )
+
+    user = UserProxyAgent(
+        name="User",
+        human_input_mode="NEVER",
+        is_termination_msg=lambda x: x.get("content", "") and x.get("content", "").rstrip().endswith("TERMINATE"),
+        code_execution_config={
+            "last_n_messages": 1,
+            "work_dir": "tasks",
+            "use_docker": False,
+        },  # Please set use_docker=True if docker is available to run the generated code. Using docker is safer than running the generated code directly.
+    )
+    chat_res = user.initiate_chats(
+        [
+            {
+                "recipient": financial_assistant,
+                "message": financial_tasks[0],
+                "summary_method": "last_msg",
+            },
+            {
+                "recipient": manager_1,
+                "message": financial_tasks[1],
+                "summary_method": "reflection_with_llm",
+            },
+            {"recipient": manager_2, "message": writing_tasks[0]},
+        ]
+    )
+
+    chat_w_manager = chat_res[manager_2]
+    print(chat_w_manager.chat_history, chat_w_manager.summary, chat_w_manager.cost)
+
+    manager_2_res = user.get_chat_results(manager_2)
+    all_res = user.get_chat_results()
+    print(manager_2_res.summary, manager_2_res.cost)
+    print(all_res[financial_assistant].human_input)
+    print(all_res[manager_1].summary)
+
+
+@pytest.mark.skipif(skip_openai, reason="requested to skip openai tests")
+def test_chats():
+    config_list = autogen.config_list_from_json(
+        OAI_CONFIG_LIST,
+        file_location=KEY_LOC,
+    )
+
+    financial_tasks = [
+        """What are the full names of NVDA and TESLA.""",
+        """Pros and cons of the companies I'm interested in. Keep it short.""",
+    ]
+
+    writing_tasks = ["""Develop a short but engaging blog post using any information provided."""]
+
+    financial_assistant_1 = AssistantAgent(
+        name="Financial_assistant_1",
+        llm_config={"config_list": config_list},
+    )
+    financial_assistant_2 = AssistantAgent(
+        name="Financial_assistant_2",
+        llm_config={"config_list": config_list},
+    )
+    writer = AssistantAgent(
+        name="Writer",
+        llm_config={"config_list": config_list},
+        is_termination_msg=lambda x: x.get("content", "").find("TERMINATE") >= 0,
+        system_message="""
+            You are a professional writer, known for
+            your insightful and engaging articles.
+            You transform complex concepts into compelling narratives.
+            Reply "TERMINATE" in the end when everything is done.
+            """,
+    )
+
+    user = UserProxyAgent(
+        name="User",
+        human_input_mode="NEVER",
+        is_termination_msg=lambda x: x.get("content", "").find("TERMINATE") >= 0,
+        code_execution_config={
+            "last_n_messages": 1,
+            "work_dir": "tasks",
+            "use_docker": False,
+        },  # Please set use_docker=True if docker is available to run the generated code. Using docker is safer than running the generated code directly.
+    )
+
+    chat_res = user.initiate_chats(
+        [
+            {
+                "recipient": financial_assistant_1,
+                "message": financial_tasks[0],
+                "clear_history": True,
+                "silent": False,
+                "summary_method": "last_msg",
+            },
+            {
+                "recipient": financial_assistant_2,
+                "message": financial_tasks[1],
+                "summary_method": "reflection_with_llm",
+            },
+            {
+                "recipient": writer,
+                "message": writing_tasks[0],
+                "carryover": "I want to include a figure or a table of data in the blogpost.",
+                "summary_method": "last_msg",
+            },
+        ]
+    )
+
+    chat_w_writer = chat_res[writer]
+    print(chat_w_writer.chat_history, chat_w_writer.summary, chat_w_writer.cost)
+
+    writer_res = user.get_chat_results(writer)
+    all_res = user.get_chat_results()
+    print(writer_res.summary, writer_res.cost)
+    print(all_res[financial_assistant_1].human_input)
+    print(all_res[financial_assistant_1].summary)
+    # print(blogpost.summary, insights_and_blogpost)
+
+
+if __name__ == "__main__":
+    # test_chats()
+    test_chats_group()
diff --git a/website/docs/Examples.md b/website/docs/Examples.md
@@ -22,7 +22,8 @@ Links to notebook examples:
    - Automated Task Solving with Coding & Planning Agents - [View Notebook](https://github.com/microsoft/autogen/blob/main/notebook/agentchat_planning.ipynb)
    - Automated Task Solving with transition paths specified in a graph - [View Notebook](https://github.com/microsoft/autogen/blob/main/notebook/agentchat_graph_modelling_language_using_select_speaker.ipynb)
    - Running a group chat as an inner-monolgue via the SocietyOfMindAgent - [View Notebook](https://github.com/microsoft/autogen/blob/main/notebook/agentchat_society_of_mind.ipynb)
-
+1. **Sequential Multi-Agent Chats**
+   - Automated Sequential Multi-Agent Chats - [View Notebook](https://github.com/microsoft/autogen/blob/main/notebook/agentchat_multi_task_chats.ipynb)
 1. **Applications**
 
    - Automated Chess Game Playing & Chitchatting by GPT-4 Agents - [View Notebook](https://github.com/microsoft/autogen/blob/main/notebook/agentchat_chess.ipynb)