Support function_call in autogen/agent (#1091)

* update funccall

* code format

* update to comments

* update notebook

* remove test for py3.7

* allow funccall to class functions

* add test and clean up notebook

* revise notebook and test

* update

* update mathagent

* Update flaml/autogen/agent/agent.py

Co-authored-by: Chi Wang <wang.chi@microsoft.com>

* Update flaml/autogen/agent/user_proxy_agent.py

Co-authored-by: Chi Wang <wang.chi@microsoft.com>

* revise to comments

* revise function call design, notebook and test. add doc

* code format

* ad message_to_dict function

* update mathproxyagent

* revise docstr

* update

* Update flaml/autogen/agent/math_user_proxy_agent.py

Co-authored-by: Chi Wang <wang.chi@microsoft.com>

* Update flaml/autogen/agent/math_user_proxy_agent.py

Co-authored-by: Qingyun Wu <qingyun.wu@psu.edu>

* Update flaml/autogen/agent/user_proxy_agent.py

Co-authored-by: Qingyun Wu <qingyun.wu@psu.edu>

* simply funccall in userproxyagent, rewind auto-gen.md, revise to comments

* code format

* update

* remove notebook for another pr

* revise oai_conversation part  in agent, revise function exec in user_proxy_agent

* update test_funccall

* update

* update

* fix pydantic version

* Update test/autogen/test_agent.py

Co-authored-by: Chi Wang <wang.chi@microsoft.com>

* fix bug

* fix bug

* update

* update is_termination_msg to accept dict

---------

Co-authored-by: Chi Wang <wang.chi@microsoft.com>
Co-authored-by: Qingyun Wu <qingyun.wu@psu.edu>
Co-authored-by: Li Jiang <bnujli@gmail.com>

flaml/autogen/agent/__init__.py

@@ -1,5 +1,6 @@
 from .agent import Agent
 from .assistant_agent import AssistantAgent
 from .user_proxy_agent import UserProxyAgent
+from .math_user_proxy_agent import MathUserProxyAgent
 
-__all__ = ["Agent", "AssistantAgent", "UserProxyAgent"]
+__all__ = ["Agent", "AssistantAgent", "UserProxyAgent", "MathUserProxyAgent"]

flaml/autogen/agent/agent.py

@@ -1,4 +1,5 @@
 from collections import defaultdict
+from typing import Dict, Union
 
 
 class Agent:
@@ -17,7 +18,7 @@ class Agent:
         # empty memory
         self._memory = []
         # a dictionary of conversations, default value is list
-        self._conversations = defaultdict(list)
+        self._oai_conversations = defaultdict(list)
         self._name = name
         self._system_message = system_message
 
@@ -30,22 +31,95 @@ class Agent:
         """Remember something."""
         self._memory.append(memory)
 
-    def _send(self, message, recipient):
+    @staticmethod
+    def _message_to_dict(message: Union[Dict, str]):
+        """Convert a message to a dictionary.
+
+        The message can be a string or a dictionary. The string with be put in the "content" field of the new dictionary.
+        """
+        if isinstance(message, str):
+            return {"content": message}
+        else:
+            return message
+
+    def _append_oai_message(self, message: Union[Dict, str], role, conversation_id):
+        """Append a message to the openai conversation.
+
+        If the message received is a string, it will be put in the "content" field of the new dictionary.
+        If the message received is a dictionary but does not have any of the two fields "content" or "function_call",
+            this message is not a valid openai message and will be ignored.
+
+        Args:
+            message (dict or str): message to be appended to the openai conversation.
+            role (str): role of the message, can be "assistant" or "function".
+            conversation_id (str): id of the conversation, should be the name of the recipient or sender.
+        """
+        message = self._message_to_dict(message)
+        # create openai message to be appended to the openai conversation that can be passed to oai directly.
+        oai_message = {k: message[k] for k in ("content", "function_call", "name") if k in message}
+        if "content" not in oai_message and "function_call" not in oai_message:
+            return
+
+        oai_message["role"] = "function" if message.get("role") == "function" else role
+        self._oai_conversations[conversation_id].append(oai_message)
+
+    def _send(self, message: Union[Dict, str], recipient):
         """Send a message to another agent."""
-        self._conversations[recipient.name].append({"content": message, "role": "assistant"})
+        # When the agent composes and sends the message, the role of the message is "assistant". (If 'role' exists and is 'function', it will remain unchanged.)
+        self._append_oai_message(message, "assistant", recipient.name)
         recipient.receive(message, self)
 
-    def _receive(self, message, sender):
+    def _receive(self, message: Union[Dict, str], sender):
-        """Receive a message from another agent."""
+        """Receive a message from another agent.
-        print("\n", "-" * 80, "\n", flush=True)
-        print(sender.name, "(to", f"{self.name}):", flush=True)
-        print(message, flush=True)
-        self._conversations[sender.name].append({"content": message, "role": "user"})
 
-    def receive(self, message, sender):
+        Args:
+            message (dict or str): message from the sender. If the type is dict, it may contain the following reserved fields (All fields are optional).
+                1. "content": content of the message, can be None.
+                2. "function_call": a dictionary containing the function name and arguments.
+                3. "role": role of the message, can be "assistant", "user", "function".
+                    This field is only needed to distinguish between "function" or "assistant"/"user".
+                4. "name": In most cases, this field is not needed. When the role is "function", this field is needed to indicate the function name.
+            sender: sender of an Agent instance.
+        """
+        message = self._message_to_dict(message)
+        # print the message received
+        print(sender.name, "(to", f"{self.name}):\n", flush=True)
+        if message.get("role") == "function":
+            func_print = f"***** Response from calling function \"{message['name']}\" *****"
+            print(func_print, flush=True)
+            print(message["content"], flush=True)
+            print("*" * len(func_print), flush=True)
+        else:
+            if message.get("content") is not None:
+                print(message["content"], flush=True)
+            if "function_call" in message:
+                func_print = f"***** Suggested function Call: {message['function_call'].get('name', '(No function name found)')} *****"
+                print(func_print, flush=True)
+                print(
+                    "Arguments: \n",
+                    message["function_call"].get("arguments", "(No arguments found)"),
+                    flush=True,
+                    sep="",
+                )
+                print("*" * len(func_print), flush=True)
+        print("\n", "-" * 80, flush=True, sep="")
+
+        # When the agent receives a message, the role of the message is "user". (If 'role' exists and is 'function', it will remain unchanged.)
+        self._append_oai_message(message, "user", sender.name)
+
+    def receive(self, message: Union[Dict, str], sender):
         """Receive a message from another agent.
         This method is called by the sender.
         It needs to be overriden by the subclass to perform followup actions.
+
+        Args:
+            message (dict or str): message from the sender. If the type is dict, it may contain the following reserved fields (All fields are optional).
+                1. "content": content of the message, can be None.
+                2. "function_call": a dictionary containing the function name and arguments.
+                3. "role": role of the message, can be "assistant", "user", "function".
+                    This field is only needed to distinguish between "function" or "assistant"/"user".
+                4. "name": In most cases, this field is not needed. When the role is "function", this field is needed to indicate the function name.
+            sender: sender of an Agent instance.
         """
         self._receive(message, sender)
         # perform actions based on the message

flaml/autogen/agent/assistant_agent.py

@@ -1,6 +1,7 @@
 from .agent import Agent
 from flaml.autogen.code_utils import DEFAULT_MODEL
 from flaml import oai
+from typing import Dict, Union
 
 
 class AssistantAgent(Agent):
@@ -33,16 +34,15 @@ class AssistantAgent(Agent):
         self._config.update(config)
         self._sender_dict = {}
 
-    def receive(self, message, sender):
+    def receive(self, message: Union[Dict, str], sender):
         if sender.name not in self._sender_dict:
             self._sender_dict[sender.name] = sender
-            self._conversations[sender.name] = [{"content": self._system_message, "role": "system"}]
+            self._oai_conversations[sender.name] = [{"content": self._system_message, "role": "system"}]
+
         super().receive(message, sender)
-        responses = oai.ChatCompletion.create(messages=self._conversations[sender.name], **self._config)
+        responses = oai.ChatCompletion.create(messages=self._oai_conversations[sender.name], **self._config)
-        # TODO: handle function_call
+        self._send(oai.ChatCompletion.extract_text_or_function_call(responses)[0], sender)
-        response = oai.ChatCompletion.extract_text(responses)[0]
-        self._send(response, sender)
 
     def reset(self):
         self._sender_dict.clear()
-        self._conversations.clear()
+        self._oai_conversations.clear()

flaml/autogen/agent/math_user_proxy_agent.py

@@ -84,6 +84,10 @@ Problem: """,
 
 def is_termination_msg(x):
     """Check if a message is a termination message."""
+    if isinstance(x, dict):
+        x = x.get("content")
+        if x is None:
+            return False
     cb = extract_code(x)
     contain_code = False
     for c in cb:
@@ -129,6 +133,7 @@ class MathUserProxyAgent(UserProxyAgent):
         name="MathChatAgent",  # default set to MathChatAgent
         system_message="",
         work_dir=None,
+        function_map=defaultdict(callable),
         human_input_mode="NEVER",  # Fully automated
         max_consecutive_auto_reply=None,
         is_termination_msg=is_termination_msg,
@@ -150,11 +155,12 @@ class MathUserProxyAgent(UserProxyAgent):
                     the number of auto reply reaches the max_consecutive_auto_reply.
                 (3) When "NEVER", the agent will never prompt for human input. Under this mode, the conversation stops
                     when the number of auto reply reaches the max_consecutive_auto_reply or when is_termination_msg is True.
+            function_map (dict[str, callable]): Mapping function names (passed to openai) to callable functions.
             max_consecutive_auto_reply (int): the maximum number of consecutive auto replies.
                 default to None (no limit provided, class attribute MAX_CONSECUTIVE_AUTO_REPLY will be used as the limit in this case).
                 The limit only plays a role when human_input_mode is not "ALWAYS".
-            is_termination_msg (function): a function that takes a message and returns a boolean value.
+            is_termination_msg (function): a function that takes a message in the form of a dictionary and returns a boolean value indicating if this received message is a termination message.
-                This function is used to determine if a received message is a termination message.
+                The dict can contain the following keys: "content", "role", "name", "function_call".
             use_docker (bool): whether to use docker to execute the code.
             max_invalid_q_per_step (int): (ADDED) the maximum number of invalid queries per step.
             **config (dict): other configurations.
@@ -163,6 +169,7 @@ class MathUserProxyAgent(UserProxyAgent):
             name=name,
             system_message=system_message,
             work_dir=work_dir,
+            function_map=function_map,
             human_input_mode=human_input_mode,
             max_consecutive_auto_reply=max_consecutive_auto_reply,
             is_termination_msg=is_termination_msg,
@@ -208,7 +215,7 @@ class MathUserProxyAgent(UserProxyAgent):
             return PROMPTS[prompt_type] + problem
 
     def _reset(self):
-        self._conversations.clear()
+        self._oai_conversations.clear()
         self._valid_q_count = 0
         self._total_q_count = 0
         self._accum_invalid_q_per_step = 0
@@ -288,6 +295,7 @@ class MathUserProxyAgent(UserProxyAgent):
 
     def auto_reply(self, message, sender, default_reply=""):
         """Generate an auto reply."""
+        message = message.get("content", "")
         code_blocks = extract_code(message)
 
         if len(code_blocks) == 1 and code_blocks[0][0] == UNKNOWN:
@@ -391,7 +399,7 @@ class WolframAlphaAPIWrapper(BaseModel):
 
         extra = Extra.forbid
 
-    @root_validator()
+    @root_validator(skip_on_failure=True)
     def validate_environment(cls, values: Dict) -> Dict:
         """Validate that api key and python package exists in environment."""
         wolfram_alpha_appid = get_from_dict_or_env(values, "wolfram_alpha_appid", "WOLFRAM_ALPHA_APPID")

flaml/autogen/agent/user_proxy_agent.py

@@ -1,7 +1,8 @@
-from typing import Union
 from .agent import Agent
 from flaml.autogen.code_utils import UNKNOWN, extract_code, execute_code, infer_lang
 from collections import defaultdict
+import json
+from typing import Dict, Union
 
 
 class UserProxyAgent(Agent):
@@ -15,6 +16,7 @@ class UserProxyAgent(Agent):
         system_message="",
         work_dir=None,
         human_input_mode="ALWAYS",
+        function_map={},
         max_consecutive_auto_reply=None,
         is_termination_msg=None,
         use_docker=True,
@@ -34,11 +36,12 @@ class UserProxyAgent(Agent):
                     the number of auto reply reaches the max_consecutive_auto_reply.
                 (3) When "NEVER", the agent will never prompt for human input. Under this mode, the conversation stops
                     when the number of auto reply reaches the max_consecutive_auto_reply or when is_termination_msg is True.
+            function_map (dict[str, callable]): Mapping function names (passed to openai) to callable functions.
             max_consecutive_auto_reply (int): the maximum number of consecutive auto replies.
                 default to None (no limit provided, class attribute MAX_CONSECUTIVE_AUTO_REPLY will be used as the limit in this case).
                 The limit only plays a role when human_input_mode is not "ALWAYS".
-            is_termination_msg (function): a function that takes a message and returns a boolean value.
+            is_termination_msg (function): a function that takes a message in the form of a dictionary and returns a boolean value indicating if this received message is a termination message.
-                This function is used to determine if a received message is a termination message.
+                The dict can contain the following keys: "content", "role", "name", "function_call".
             use_docker (bool or str): bool value of whether to use docker to execute the code,
                 or str value of the docker image name to use.
             **config (dict): other configurations.
@@ -47,7 +50,7 @@ class UserProxyAgent(Agent):
         self._work_dir = work_dir
         self._human_input_mode = human_input_mode
         self._is_termination_msg = (
-            is_termination_msg if is_termination_msg is not None else (lambda x: x == "TERMINATE")
+            is_termination_msg if is_termination_msg is not None else (lambda x: x.get("content") == "TERMINATE")
         )
         self._config = config
         self._max_consecutive_auto_reply = (
@@ -56,6 +59,8 @@ class UserProxyAgent(Agent):
         self._consecutive_auto_reply_counter = defaultdict(int)
         self._use_docker = use_docker
 
+        self._function_map = function_map
+
     @property
     def use_docker(self) -> Union[bool, str]:
         """bool value of whether to use docker to execute the code,
@@ -89,8 +94,8 @@ class UserProxyAgent(Agent):
                 )
                 logs = logs.decode("utf-8")
             else:
-                # TODO: could this happen?
+                # In case the language is not supported, we return an error message.
-                exitcode, logs, image = 1, f"unknown language {lang}"
+                exitcode, logs, image = 1, f"unknown language {lang}", self._use_docker
                 # raise NotImplementedError
             self._use_docker = image
             logs_all += "\n" + logs
@@ -98,11 +103,86 @@ class UserProxyAgent(Agent):
                 return exitcode, logs_all
         return exitcode, logs_all
 
-    def auto_reply(self, message, sender, default_reply=""):
+    @staticmethod
+    def _format_json_str(jstr):
+        """Remove newlines outside of quotes, and hanlde JSON escape sequences.
+
+        1. this function removes the newline in the query outside of quotes otherwise json.loads(s) will fail.
+            Ex 1:
+            "{\n"tool": "python",\n"query": "print('hello')\nprint('world')"\n}" -> "{"tool": "python","query": "print('hello')\nprint('world')"}"
+            Ex 2:
+            "{\n  \"location\": \"Boston, MA\"\n}" -> "{"location": "Boston, MA"}"
+
+        2. this function also handles JSON escape sequences inside quotes,
+            Ex 1:
+            '{"args": "a\na\na\ta"}' -> '{"args": "a\\na\\na\\ta"}'
+        """
+        result = []
+        inside_quotes = False
+        last_char = " "
+        for char in jstr:
+            if last_char != "\\" and char == '"':
+                inside_quotes = not inside_quotes
+            last_char = char
+            if not inside_quotes and char == "\n":
+                continue
+            if inside_quotes and char == "\n":
+                char = "\\n"
+            if inside_quotes and char == "\t":
+                char = "\\t"
+            result.append(char)
+        return "".join(result)
+
+    def _execute_function(self, func_call):
+        """Execute a function call and return the result.
+
+        Args:
+            func_call: a dictionary extracted from openai message at key "function_call" with keys "name" and "arguments".
+
+        Returns:
+            A tuple of (is_exec_success, result_dict).
+            is_exec_success (boolean): whether the execution is successful.
+            result_dict: a dictionary with keys "name", "role", and "content". Value of "role" is "function".
+        """
+        func_name = func_call.get("name", "")
+        func = self._function_map.get(func_name, None)
+
+        is_exec_success = False
+        if func is not None:
+            # Extract arguments from a json-like string and put it into a dict.
+            input_string = self._format_json_str(func_call.get("arguments", "{}"))
+            try:
+                arguments = json.loads(input_string)
+            except json.JSONDecodeError as e:
+                arguments = None
+                content = f"Error: {e}\n You argument should follow json format."
+
+            # Try to execute the function
+            if arguments:
+                try:
+                    content = func(**arguments)
+                    is_exec_success = True
+                except Exception as e:
+                    content = f"Error: {e}"
+        else:
+            content = f"Error: Function {func_name} not found."
+
+        return is_exec_success, {
+            "name": func_name,
+            "role": "function",
+            "content": str(content),
+        }
+
+    def auto_reply(self, message: dict, sender, default_reply=""):
         """Generate an auto reply."""
-        code_blocks = extract_code(message)
+        if "function_call" in message:
+            is_exec_success, func_return = self._execute_function(message["function_call"])
+            self._send(func_return, sender)
+            return
+
+        code_blocks = extract_code(message["content"])
         if len(code_blocks) == 1 and code_blocks[0][0] == UNKNOWN:
-            # no code block is found, lang should be `UNKNOWN``
+            # no code block is found, lang should be `UNKNOWN`
             self._send(default_reply, sender)
         else:
             # try to execute the code
@@ -110,11 +190,12 @@ class UserProxyAgent(Agent):
             exitcode2str = "execution succeeded" if exitcode == 0 else "execution failed"
             self._send(f"exitcode: {exitcode} ({exitcode2str})\nCode output: {logs}", sender)
 
-    def receive(self, message, sender):
+    def receive(self, message: Union[Dict, str], sender):
         """Receive a message from the sender agent.
         Once a message is received, this function sends a reply to the sender or simply stop.
         The reply can be generated automatically or entered manually by a human.
         """
+        message = self._message_to_dict(message)
         super().receive(message, sender)
         # default reply is empty (i.e., no reply, in this case we will try to generate auto reply)
         reply = ""

notebook/autogen_agent_auto_feedback_from_code_execution.ipynb

@@ -197,7 +197,7 @@
     "    \"user\",\n",
     "    human_input_mode=\"NEVER\",\n",
     "    max_consecutive_auto_reply=10,\n",
-    "    is_termination_msg=lambda x: x.rstrip().endswith(\"TERMINATE\") or x.rstrip().endswith('\"TERMINATE\".'),\n",
+    "    is_termination_msg=lambda x: x.get(\"content\", \"\").rstrip().endswith(\"TERMINATE\") or x.get(\"content\", \"\").rstrip().endswith('\"TERMINATE\".'),\n",
     "    work_dir=\"coding\",\n",
     "    use_docker=False,  # set to True if you are using docker\n",
     ")\n",
@@ -464,7 +464,7 @@
    "name": "python",
    "nbconvert_exporter": "python",
    "pygments_lexer": "ipython3",
-   "version": "3.9.15"
+   "version": "3.9.16"
   },
   "vscode": {
    "interpreter": {

notebook/autogen_agent_human_feedback.ipynb

@@ -148,7 +148,7 @@
     "user = UserProxyAgent(\n",
     "    name=\"user\",\n",
     "    human_input_mode=\"ALWAYS\",\n",
-    "    is_termination_msg=lambda x: x.rstrip().endswith(\"TERMINATE\"),\n",
+    "    is_termination_msg=lambda x: x.get(\"content\", \"\").rstrip().endswith(\"TERMINATE\"),\n",
     ")\n",
     "\n",
     "# the purpose of the following line is to log the conversation history\n",

notebook/autogen_agent_web_info.ipynb

@@ -108,7 +108,7 @@
     "    name=\"user\",\n",
     "    human_input_mode=\"TERMINATE\",\n",
     "    max_consecutive_auto_reply=10,\n",
-    "    is_termination_msg=lambda x: x.rstrip().endswith(\"TERMINATE\"),\n",
+    "    is_termination_msg=lambda x: x.get(\"content\", \"\").rstrip().endswith(\"TERMINATE\"),\n",
     "    work_dir='web',\n",
     ")"
    ]

test/autogen/test_agent.py

@@ -0,0 +1,40 @@
+def test_agent():
+    from flaml.autogen.agent import Agent
+
+    dummy_agent_1 = Agent(name="dummy_agent_1")
+    dummy_agent_2 = Agent(name="dummy_agent_2")
+
+    dummy_agent_1.receive("hello", dummy_agent_2)  # receive a str
+    dummy_agent_1.receive(
+        {
+            "content": "hello",
+        },
+        dummy_agent_2,
+    )  # receive a dict
+
+    # receive dict without openai fields to be printed, such as "content", 'function_call'. There should be no error raised.
+    pre_len = len(dummy_agent_1._oai_conversations["dummy_agent_2"])
+    dummy_agent_1.receive({"message": "hello"}, dummy_agent_2)
+    assert pre_len == len(
+        dummy_agent_1._oai_conversations["dummy_agent_2"]
+    ), "When the message is not an valid openai message, it should not be appended to the oai conversation."
+
+    dummy_agent_1._send("hello", dummy_agent_2)  # send a str
+    dummy_agent_1._send(
+        {
+            "content": "hello",
+        },
+        dummy_agent_2,
+    )  # send a dict
+
+    # receive dict with no openai fields
+    pre_len = len(dummy_agent_1._oai_conversations["dummy_agent_2"])
+    dummy_agent_1._send({"message": "hello"}, dummy_agent_2)  # send dict with wrong field
+
+    assert pre_len == len(
+        dummy_agent_1._oai_conversations["dummy_agent_2"]
+    ), "When the message is not a valid openai message, it should not be appended to the oai conversation."
+
+
+if __name__ == "__main__":
+    test_agent()

test/autogen/test_assistant_agent.py

@@ -23,7 +23,7 @@ def test_gpt35(human_input_mode="NEVER", max_consecutive_auto_reply=5):
         "user",
         work_dir=f"{here}/test_agent_scripts",
         human_input_mode=human_input_mode,
-        is_termination_msg=lambda x: x.rstrip().endswith("TERMINATE"),
+        is_termination_msg=lambda x: x.get("content", "").rstrip().endswith("TERMINATE"),
         max_consecutive_auto_reply=max_consecutive_auto_reply,
         use_docker="python:3",
     )
@@ -51,7 +51,7 @@ def test_create_execute_script(human_input_mode="NEVER", max_consecutive_auto_re
         "user",
         human_input_mode=human_input_mode,
         max_consecutive_auto_reply=max_consecutive_auto_reply,
-        is_termination_msg=lambda x: x.rstrip().endswith("TERMINATE"),
+        is_termination_msg=lambda x: x.get("content", "").rstrip().endswith("TERMINATE"),
     )
     assistant.receive(
         """Create and execute a script to plot a rocket without using matplotlib""",

test/autogen/test_function_call.py

@@ -60,5 +60,68 @@ def test_eval_math_responses():
     print(eval_math_responses(**arguments))
 
 
+def test_json_extraction():
+    from flaml.autogen.agent import UserProxyAgent
+
+    user = UserProxyAgent(name="test", use_docker=False)
+
+    jstr = '{\n"location": "Boston, MA"\n}'
+    assert user._format_json_str(jstr) == '{"location": "Boston, MA"}'
+
+    jstr = '{\n"code": "python",\n"query": "x=3\nprint(x)"}'
+    assert user._format_json_str(jstr) == '{"code": "python","query": "x=3\\nprint(x)"}'
+
+    jstr = '{"code": "a=\\"hello\\""}'
+    assert user._format_json_str(jstr) == '{"code": "a=\\"hello\\""}'
+
+
+def test_execute_function():
+    from flaml.autogen.agent import UserProxyAgent
+
+    # 1. test calling a simple function
+    def add_num(num_to_be_added):
+        given_num = 10
+        return num_to_be_added + given_num
+
+    user = UserProxyAgent(name="test", function_map={"add_num": add_num})
+
+    # correct execution
+    correct_args = {"name": "add_num", "arguments": '{ "num_to_be_added": 5 }'}
+    assert user._execute_function(func_call=correct_args)[1]["content"] == "15"
+
+    # function name called is wrong or doesn't exist
+    wrong_func_name = {"name": "subtract_num", "arguments": '{ "num_to_be_added": 5 }'}
+    assert "Error: Function" in user._execute_function(func_call=wrong_func_name)[1]["content"]
+
+    # arguments passed is not in correct json format
+    wrong_json_format = {
+        "name": "add_num",
+        "arguments": '{ "num_to_be_added": 5, given_num: 10 }',
+    }  # should be "given_num" with quotes
+    assert (
+        "You argument should follow json format." in user._execute_function(func_call=wrong_json_format)[1]["content"]
+    )
+
+    # function execution error with wrong arguments passed
+    wrong_args = {"name": "add_num", "arguments": '{ "num_to_be_added": 5, "given_num": 10 }'}
+    assert "Error: " in user._execute_function(func_call=wrong_args)[1]["content"]
+
+    # 2. test calling a class method
+    class AddNum:
+        def __init__(self, given_num):
+            self.given_num = given_num
+
+        def add(self, num_to_be_added):
+            self.given_num = num_to_be_added + self.given_num
+            return self.given_num
+
+    user = UserProxyAgent(name="test", function_map={"add_num": AddNum(given_num=10).add})
+    func_call = {"name": "add_num", "arguments": '{ "num_to_be_added": 5 }'}
+    assert user._execute_function(func_call=func_call)[1]["content"] == "15"
+    assert user._execute_function(func_call=func_call)[1]["content"] == "20"
+
+
 if __name__ == "__main__":
+    test_json_extraction()
+    test_execute_function()
     test_eval_math_responses()

website/docs/Use-Cases/Auto-Generation.md

@@ -38,7 +38,7 @@ user_proxy = UserProxyAgent(
     name="user_proxy",
     human_input_mode="NEVER",  # in this mode, the agent will never solicit human input but always auto reply
     max_consecutive_auto_reply=10,  # the maximum number of consecutive auto replies
-    is_termination_msg=lambda x: x.rstrip().endswith("TERMINATE") or x.rstrip().endswith('"TERMINATE".'),  # the function to determine whether a message is a termination message
+    is_termination_msg=lambda x: x.get("content", "").rstrip().endswith("TERMINATE") or x.get("content", "").rstrip().endswith('"TERMINATE".'),  # the function to determine whether a message is a termination message
     work_dir=".",
 )