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Enable defining new functions after agent creation (#285)
* Enable defining new functions after agent creation * Add notebook for function inception example * format * 1. fix bug 2. support remove function * 1. fix bug 2. support remove function * 1. add example doc 2. change test file 3. change ipynb title * Update website/docs/Examples.md --------- Co-authored-by: Li Jiang <bnujli@gmail.com> Co-authored-by: “skzhang1” <“shaokunzhang529@gmail.com”> Co-authored-by: Shaokun Zhang <shaokunzhang529@gmail.com> Co-authored-by: Qingyun Wu <qingyun.wu@psu.edu>
This commit is contained in:
Nicolas Marchildon
GitHub
parent
5547e3b919
commit
17c51fd2f0
@ -1207,6 +1207,41 @@ class ConversableAgent(Agent):
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"""
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self._function_map.update(function_map)
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def update_function_signature(self, func_sig: Union[str, Dict], is_remove: None):
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"""update a function_signature in the LLM configuration for function_call.
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Args:
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func_sig (str or dict): description/name of the function to update/remove to the model. See: https://platform.openai.com/docs/api-reference/chat/create#chat/create-functions
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is_remove: whether removing the funciton from llm_config with name 'func_sig'
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"""
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if not self.llm_config:
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error_msg = "To update a function signature, agent must have an llm_config"
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logger.error(error_msg)
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raise AssertionError(error_msg)
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if is_remove:
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if "functions" not in self.llm_config.keys():
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error_msg = "The agent config doesn't have function {name}.".format(name=func_sig)
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logger.error(error_msg)
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raise AssertionError(error_msg)
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else:
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self.llm_config["functions"] = [
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func for func in self.llm_config["functions"] if func["name"] != func_sig
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]
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else:
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if "functions" in self.llm_config.keys():
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self.llm_config["functions"] = [
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func for func in self.llm_config["functions"] if func.get("name") != func_sig["name"]
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] + [func_sig]
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else:
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self.llm_config["functions"] = [func_sig]
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if len(self.llm_config["functions"]) == 0:
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del self.llm_config["functions"]
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self.client = OpenAIWrapper(**self.llm_config)
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def can_execute_function(self, name: str) -> bool:
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"""Whether the agent can execute the function."""
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return name in self._function_map
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325
notebook/agentchat_inception_function.ipynb
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325
notebook/agentchat_inception_function.ipynb
Normal file
@ -0,0 +1,325 @@
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{
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"cells": [
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{
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"cell_type": "markdown",
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"metadata": {},
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"source": [
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"# Auto Generated Agent Chat: Function Inception\n",
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"\n",
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"AutoGen offers conversable agents powered by LLM, tool or human, which can be used to perform tasks collectively via automated chat. This framework allows tool use and human participation through multi-agent conversation. Please find documentation about this feature [here](https://microsoft.github.io/autogen/docs/Use-Cases/agent_chat).\n",
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"\n",
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"In this notebook, we demonstrate how to use `AssistantAgent` and `UserProxyAgent` to give them the ability to auto-extend the list of functions the model may call. Functions need to be registered to `UserProxyAgent`, which will be responsible for executing any function calls made by `AssistantAgent`. The assistant also needs to know the signature of functions that may be called. A special `define_function` function is registered, which registers a new function in `UserProxyAgent` and updates the configuration of the assistant.\n",
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"\n",
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"In the example scenario, the user first asks to define a function that gets a URL and prints the response body. Then the user asks to print the response body, and the assistant suggests to the user to call the new function.\n",
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"\n",
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"## Requirements\n",
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"\n",
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"AutoGen requires `Python>=3.8`."
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]
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},
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{
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"cell_type": "code",
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"execution_count": 9,
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"metadata": {},
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"outputs": [
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{
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"name": "stdout",
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"output_type": "stream",
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"text": [
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"\u001b[33muser_proxy\u001b[0m (to chatbot):\n",
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"\n",
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"Define a function that gets a URL, then prints the response body.\n",
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"Reply TERMINATE when the function is defined.\n",
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"\n",
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"--------------------------------------------------------------------------------\n"
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]
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},
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{
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"name": "stdout",
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"output_type": "stream",
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"text": [
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"\u001b[33mchatbot\u001b[0m (to user_proxy):\n",
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"\n",
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"\u001b[32m***** Suggested function Call: define_function *****\u001b[0m\n",
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"Arguments: \n",
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"{\n",
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" \"name\": \"get_url_response_body\",\n",
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" \"description\": \"Gets a URL, then prints the response body\",\n",
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" \"arguments\": \"{\\\"url\\\": {\\\"type\\\": \\\"string\\\", \\\"description\\\": \\\"The URL\\\"}}\",\n",
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" \"packages\": \"requests\",\n",
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" \"code\": \"import requests\\n\\n\\ndef get_url_response_body(url):\\n response = requests.get(url)\\n print(response.text)\\n\"\n",
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"}\n",
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"\u001b[32m****************************************************\u001b[0m\n",
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"\n",
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"--------------------------------------------------------------------------------\n",
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"\u001b[35m\n",
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">>>>>>>> EXECUTING FUNCTION define_function...\u001b[0m\n",
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"\u001b[33muser_proxy\u001b[0m (to chatbot):\n",
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"\n",
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"\u001b[32m***** Response from calling function \"define_function\" *****\u001b[0m\n",
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"A function has been added to the context of this conversation.\n",
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"Description: Gets a URL, then prints the response body\n",
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"\u001b[32m************************************************************\u001b[0m\n",
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"\n",
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"--------------------------------------------------------------------------------\n",
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"\u001b[33mchatbot\u001b[0m (to user_proxy):\n",
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"\n",
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"TERMINATE\n",
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"\n",
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"--------------------------------------------------------------------------------\n",
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"\u001b[33muser_proxy\u001b[0m (to chatbot):\n",
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"\n",
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"Print the response body of https://echo.free.beeceptor.com/\n",
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"Use the functions you know about.\n",
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"\n",
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"--------------------------------------------------------------------------------\n",
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"\u001b[33mchatbot\u001b[0m (to user_proxy):\n",
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"\n",
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"\u001b[32m***** Suggested function Call: get_url_response_body *****\u001b[0m\n",
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"Arguments: \n",
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"{\n",
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" \"url\": \"https://echo.free.beeceptor.com/\"\n",
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"}\n",
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"\u001b[32m**********************************************************\u001b[0m\n",
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"\n",
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"--------------------------------------------------------------------------------\n",
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"\u001b[35m\n",
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">>>>>>>> EXECUTING FUNCTION get_url_response_body...\u001b[0m\n",
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"execute_code:\n",
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"\n",
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"import subprocess\n",
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"print(\"Installing package: requests\")\n",
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"subprocess.run([\"pip\", \"-qq\", \"install\", \"requests\"])\n",
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"print(\"Result of get_url_response_body function execution:\")\n",
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"import requests\n",
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"\n",
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"\n",
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"def get_url_response_body(url):\n",
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" response = requests.get(url)\n",
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" print(response.text)\n",
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"\n",
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"args={'url': 'https://echo.free.beeceptor.com/'}\n",
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"result=get_url_response_body(**args)\n",
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"if result is not None: print(result)\n",
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"\n",
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"Result: Installing package: requests\n",
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"Result of get_url_response_body function execution:\n",
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"{\n",
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" \"method\": \"GET\",\n",
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" \"path\": \"/\",\n",
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" \"ip\": \"104.28.208.116\",\n",
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" \"headers\": {\n",
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" \"host\": \"echo.free.beeceptor.com\",\n",
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" \"user-agent\": \"python-requests/2.31.0\",\n",
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" \"accept\": \"*/*\",\n",
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" \"accept-encoding\": \"gzip, deflate\",\n",
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" \"x-forwarded-for\": \"104.28.208.116\",\n",
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" \"x-forwarded-host\": \"echo.free.beeceptor.com\",\n",
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" \"x-forwarded-proto\": \"https\",\n",
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" \"x-real-ip\": \"104.28.208.116\"\n",
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" },\n",
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" \"parsedQueryParams\": {}\n",
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"}\n",
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"\n",
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"\u001b[33muser_proxy\u001b[0m (to chatbot):\n",
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"\n",
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"\u001b[32m***** Response from calling function \"get_url_response_body\" *****\u001b[0m\n",
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"Installing package: requests\n",
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"Result of get_url_response_body function execution:\n",
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"{\n",
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" \"method\": \"GET\",\n",
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" \"path\": \"/\",\n",
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" \"ip\": \"104.28.208.116\",\n",
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" \"headers\": {\n",
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" \"host\": \"echo.free.beeceptor.com\",\n",
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" \"user-agent\": \"python-requests/2.31.0\",\n",
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" \"accept\": \"*/*\",\n",
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" \"accept-encoding\": \"gzip, deflate\",\n",
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" \"x-forwarded-for\": \"104.28.208.116\",\n",
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" \"x-forwarded-host\": \"echo.free.beeceptor.com\",\n",
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" \"x-forwarded-proto\": \"https\",\n",
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" \"x-real-ip\": \"104.28.208.116\"\n",
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" },\n",
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" \"parsedQueryParams\": {}\n",
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"}\n",
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"\n",
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"\u001b[32m******************************************************************\u001b[0m\n",
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"\n",
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"--------------------------------------------------------------------------------\n",
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"\u001b[33mchatbot\u001b[0m (to user_proxy):\n",
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"\n",
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"The response body of https://echo.free.beeceptor.com/ is:\n",
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"\n",
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"{\n",
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" \"method\": \"GET\",\n",
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" \"path\": \"/\",\n",
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" \"ip\": \"104.28.208.116\",\n",
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" \"headers\": {\n",
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" \"host\": \"echo.free.beeceptor.com\",\n",
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" \"user-agent\": \"python-requests/2.31.0\",\n",
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" \"accept\": \"*/*\",\n",
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" \"accept-encoding\": \"gzip, deflate\",\n",
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" \"x-forwarded-for\": \"104.28.208.116\",\n",
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" \"x-forwarded-host\": \"echo.free.beeceptor.com\",\n",
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" \"x-forwarded-proto\": \"https\",\n",
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" \"x-real-ip\": \"104.28.208.116\"\n",
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" },\n",
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" \"parsedQueryParams\": {}\n",
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"}\n",
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"\n",
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"TERMINATE\n",
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"\n",
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"--------------------------------------------------------------------------------\n"
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]
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}
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],
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"source": [
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"from autogen import AssistantAgent, UserProxyAgent, config_list_from_json\n",
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"from autogen.code_utils import execute_code\n",
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"import json\n",
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"\n",
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"config_list = config_list_from_json(\n",
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" \"OAI_CONFIG_LIST\",\n",
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" filter_dict={\n",
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" # Function calling with GPT 3.5\n",
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" \"model\": [\"gpt-3.5-turbo-16k-0613\"],\n",
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" },\n",
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")\n",
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"llm_config = {\n",
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" \"functions\": [\n",
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" {\n",
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" \"name\": \"define_function\",\n",
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" \"description\": \"Define a function to add to the context of the conversation. Necessary Python packages must be declared. Once defined, the assistant may decide to use this function, respond with a normal message.\",\n",
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" \"parameters\": {\n",
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" \"type\": \"object\",\n",
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" \"properties\": {\n",
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" \"name\": {\n",
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" \"type\": \"string\",\n",
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" \"description\": \"The name of the function to define.\",\n",
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" },\n",
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" \"description\": {\n",
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" \"type\": \"string\",\n",
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" \"description\": \"A short description of the function.\",\n",
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" },\n",
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" \"arguments\": {\n",
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" \"type\": \"string\",\n",
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" \"description\": \"JSON schema of arguments encoded as a string. For example: { \\\"url\\\": { \\\"type\\\": \\\"string\\\", \\\"description\\\": \\\"The URL\\\", }}\",\n",
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" },\n",
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" \"packages\": {\n",
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" \"type\": \"string\",\n",
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" \"description\": \"A list of package names imported by the function, and that need to be installed with pip prior to invoking the function. This solves ModuleNotFoundError.\",\n",
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" },\n",
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" \"code\": {\n",
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" \"type\": \"string\",\n",
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" \"description\": \"The implementation in Python. Do not include the function declaration.\",\n",
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" },\n",
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" },\n",
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" \"required\": [\"name\", \"description\", \"arguments\", \"packages\", \"code\"],\n",
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" },\n",
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" },\n",
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" ],\n",
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" \"config_list\": config_list,\n",
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" \"request_timeout\": 120,\n",
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"}\n",
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"\n",
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"def define_function(name, description, arguments, packages, code):\n",
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" json_args = json.loads(arguments)\n",
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" function_config = {\n",
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" \"name\": name,\n",
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" \"description\": description,\n",
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" \"parameters\": { \"type\": \"object\", \"properties\": json_args },\n",
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" # TODO Make all arguments required\n",
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" \"required\": [\"url\"],\n",
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" }\n",
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" llm_config[\"functions\"] = llm_config[\"functions\"] + [function_config]\n",
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" user_proxy.register_function(\n",
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" function_map={\n",
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" name: lambda **args: execute_func(name, packages, code, **args)\n",
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" }\n",
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" )\n",
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" assistant.update_function_signature(function_config, is_remove= False)\n",
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" return f\"A function has been added to the context of this conversation.\\nDescription: {description}\"\n",
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"\n",
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"def execute_func(name, packages, code, **args):\n",
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" pip_install = f\"\"\"print(\"Installing package: {packages}\")\\nsubprocess.run([\"pip\", \"-qq\", \"install\", \"{packages}\"])\"\"\" if packages else ''\n",
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" str = f\"\"\"\n",
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"import subprocess\n",
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"{pip_install}\n",
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"print(\"Result of {name} function execution:\")\n",
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"{code}\n",
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"args={args}\n",
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"result={name}(**args)\n",
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"if result is not None: print(result)\n",
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"\"\"\"\n",
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" print(f\"execute_code:\\n{str}\")\n",
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" result = execute_code(str)[1]\n",
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" print(f\"Result: {result}\")\n",
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" return result\n",
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"\n",
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"def _is_termination_msg(message):\n",
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" \"\"\"Check if a message is a termination message.\"\"\"\n",
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" if isinstance(message, dict):\n",
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" message = message.get(\"content\")\n",
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" if message is None:\n",
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" return False\n",
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" return message.rstrip().endswith(\"TERMINATE\")\n",
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"\n",
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"assistant = AssistantAgent(\n",
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" name=\"chatbot\",\n",
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" system_message=\"\"\"You are an assistant.\n",
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" The user will ask a question.\n",
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" You may use the provided functions before providing a final answer.\n",
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" Only use the functions you were provided.\n",
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" When the answer has been provided, reply TERMINATE.\"\"\",\n",
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" llm_config=llm_config,\n",
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")\n",
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"\n",
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"user_proxy = UserProxyAgent(\n",
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" \"user_proxy\",\n",
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" code_execution_config=False,\n",
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" is_termination_msg=_is_termination_msg,\n",
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" default_auto_reply=\"Reply TERMINATE when the initial request has been fulfilled.\",\n",
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" human_input_mode=\"NEVER\")\n",
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"\n",
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"user_proxy.register_function(\n",
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" function_map={\n",
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" \"define_function\": define_function\n",
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" }\n",
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")\n",
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"\n",
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"# user_proxy.initiate_chat(\n",
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"# assistant, message=\"What functions do you know about?\")\n",
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"\n",
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"user_proxy.initiate_chat(\n",
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" assistant, message=\"Define a function that gets a URL, then prints the response body.\\nReply TERMINATE when the function is defined.\")\n",
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"\n",
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"# user_proxy.initiate_chat(\n",
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"# assistant, message=\"List functions do you know about.\")\n",
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"\n",
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"user_proxy.initiate_chat(\n",
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" assistant, message=\"Print the response body of https://echo.free.beeceptor.com/\\nUse the functions you know about.\")\n"
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]
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}
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],
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"metadata": {
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"kernelspec": {
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"display_name": "Python 3 (ipykernel)",
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"language": "python",
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"name": "python3"
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},
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"language_info": {
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"codemirror_mode": {
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"name": "ipython",
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"version": 3
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},
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"file_extension": ".py",
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"mimetype": "text/x-python",
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"name": "python",
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"nbconvert_exporter": "python",
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"pygments_lexer": "ipython3",
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"version": "3.11.5"
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}
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},
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"nbformat": 4,
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"nbformat_minor": 4
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}
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@ -8,6 +8,7 @@ import json
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import autogen
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from autogen.math_utils import eval_math_responses
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from test_assistant_agent import KEY_LOC
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import sys
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@pytest.mark.skipif(OpenAI is None, reason="openai>=1 not installed")
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@ -188,8 +189,65 @@ async def test_a_execute_function():
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assert (await user.a_execute_function(func_call))[1]["content"] == "42"
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@pytest.mark.skipif(
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not OpenAI or not sys.version.startswith("3.10"),
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reason="do not run if openai is not installed or py!=3.10",
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)
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def test_update_function():
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config_list_gpt4 = autogen.config_list_from_json(
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"OAI_CONFIG_LIST",
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filter_dict={
|
||||
"model": ["gpt-4", "gpt-4-0314", "gpt4", "gpt-4-32k", "gpt-4-32k-0314", "gpt-4-32k-v0314"],
|
||||
},
|
||||
)
|
||||
llm_config = {
|
||||
"config_list": config_list_gpt4,
|
||||
"seed": 42,
|
||||
"functions": [],
|
||||
}
|
||||
|
||||
user_proxy = autogen.UserProxyAgent(
|
||||
name="user_proxy",
|
||||
human_input_mode="NEVER",
|
||||
is_termination_msg=lambda x: True if "TERMINATE" in x.get("content") else False,
|
||||
)
|
||||
assistant = autogen.AssistantAgent(name="test", llm_config=llm_config)
|
||||
|
||||
# Define a new function *after* the assistant has been created
|
||||
assistant.update_function_signature(
|
||||
{
|
||||
"name": "greet_user",
|
||||
"description": "Greets the user.",
|
||||
"parameters": {
|
||||
"type": "object",
|
||||
"properties": {},
|
||||
"required": [],
|
||||
},
|
||||
},
|
||||
is_remove=False,
|
||||
)
|
||||
user_proxy.initiate_chat(
|
||||
assistant,
|
||||
message="What functions do you know about in the context of this conversation? End your response with 'TERMINATE'.",
|
||||
)
|
||||
messages1 = assistant.chat_messages[user_proxy][-1]["content"]
|
||||
print(messages1)
|
||||
|
||||
assistant.update_function_signature("greet_user", is_remove=True)
|
||||
user_proxy.initiate_chat(
|
||||
assistant,
|
||||
message="What functions do you know about in the context of this conversation? End your response with 'TERMINATE'.",
|
||||
)
|
||||
messages2 = assistant.chat_messages[user_proxy][-1]["content"]
|
||||
print(messages2)
|
||||
# The model should know about the function in the context of the conversation
|
||||
assert "greet_user" in messages1
|
||||
assert "greet_user" not in messages2
|
||||
|
||||
|
||||
if __name__ == "__main__":
|
||||
# test_json_extraction()
|
||||
# test_execute_function()
|
||||
test_update_function()
|
||||
asyncio.run(test_a_execute_function())
|
||||
test_eval_math_responses()
|
||||
|
||||
@ -39,6 +39,7 @@ Links to notebook examples:
|
||||
- Task Solving with Langchain Provided Tools as Functions - [View Notebook](https://github.com/microsoft/autogen/blob/main/notebook/agentchat_langchain.ipynb)
|
||||
- **RAG**: Group Chat with Retrieval Augmented Generation (with 5 group member agents and 1 manager agent) - [View Notebook](https://github.com/microsoft/autogen/blob/main/notebook/agentchat_groupchat_RAG.ipynb)
|
||||
- In-depth Guide to OpenAI Utility Functions - [View Notebook](https://github.com/microsoft/autogen/blob/main/notebook/oai_openai_utils.ipynb)
|
||||
- Function Inception: Enable AutoGen agents to update/remove functions during conversations. - [View Notebook](https://github.com/microsoft/autogen/blob/main/notebook/agentchat_inception_function.ipynb)
|
||||
|
||||
5. **Agent Teaching and Learning**
|
||||
- Teach Agents New Skills & Reuse via Automated Chat - [View Notebook](https://github.com/microsoft/autogen/blob/main/notebook/agentchat_teaching.ipynb)
|
||||
|
||||
Loading…
x
Reference in New Issue
Block a user