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Update notebook contrib guidance, update a few notebooks for site (#1651
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* update some notebooks

* Update contributing.md

* remove os

---------

Co-authored-by: Eric Zhu <[email protected]>
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@jackgerrits @ekzhu
jackgerrits and ekzhu authored Feb 14, 2024
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37 changes: 10 additions & 27 deletions notebook/agentchat_RetrieveChat.ipynb
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"- [Example 5: Solve comprehensive QA problems with RetrieveChat's unique feature `Update Context`](#example-5)\n",
"- [Example 6: Solve comprehensive QA problems with customized prompt and few-shot learning](#example-6)\n",
"\n",
"\\:\\:\\:info Requirements\n",
"\n",
"````{=mdx}\n",
":::info Requirements\n",
"Some extra dependencies are needed for this notebook, which can be installed via pip:\n",
"\n",
"```bash\n",
"pip install pyautogen[retrievechat] flaml[automl]\n",
"```\n",
"\n",
"For more information, please refer to the [installation guide](/docs/installation/).\n",
"\n",
"\\:\\:\\:\n"
":::\n",
"````"
]
},
{
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"# a vector database instance\n",
"from autogen.retrieve_utils import TEXT_FORMATS\n",
"\n",
"config_list = autogen.config_list_from_json(\n",
" env_or_file=\"OAI_CONFIG_LIST\",\n",
" filter_dict={\n",
" \"model\": {\n",
" \"gpt-4\",\n",
" \"gpt4\",\n",
" \"gpt-4-32k\",\n",
" \"gpt-4-32k-0314\",\n",
" \"gpt-35-turbo\",\n",
" \"gpt-3.5-turbo\",\n",
" }\n",
" },\n",
")\n",
"config_list = autogen.config_list_from_json(env_or_file=\"OAI_CONFIG_LIST\")\n",
"\n",
"assert len(config_list) > 0\n",
"print(\"models to use: \", [config_list[i][\"model\"] for i in range(len(config_list))])"
Expand All @@ -101,18 +90,12 @@
"cell_type": "markdown",
"metadata": {},
"source": [
"\\:\\:\\:tip\n",
"````{=mdx}\n",
":::tip\n",
"Learn more about configuring LLMs for agents [here](/docs/llm_configuration).\n",
":::\n",
"````\n",
"\n",
"Learn more about the various ways to configure LLM endpoints [here](/docs/llm_configuration).\n",
"\n",
"\\:\\:\\:"
]
},
{
"attachments": {},
"cell_type": "markdown",
"metadata": {},
"source": [
"## Construct agents for RetrieveChat\n",
"\n",
"We start by initializing the `RetrieveAssistantAgent` and `RetrieveUserProxyAgent`. The system message needs to be set to \"You are a helpful assistant.\" for RetrieveAssistantAgent. The detailed instructions are given in the user message. Later we will use the `RetrieveUserProxyAgent.generate_init_prompt` to combine the instructions and a retrieval augmented generation task for an initial prompt to be sent to the LLM assistant."
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18 changes: 9 additions & 9 deletions notebook/agentchat_auto_feedback_from_code_execution.ipynb
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"\n",
"In this notebook, we demonstrate how to use `AssistantAgent` and `UserProxyAgent` to write code and execute the code. Here `AssistantAgent` is an LLM-based agent that can write Python code (in a Python coding block) for a user to execute for a given task. `UserProxyAgent` is an agent which serves as a proxy for the human user to execute the code written by `AssistantAgent`, or automatically execute the code. Depending on the setting of `human_input_mode` and `max_consecutive_auto_reply`, the `UserProxyAgent` either solicits feedback from the human user or returns auto-feedback based on the result of code execution (success or failure and corresponding outputs) to `AssistantAgent`. `AssistantAgent` will debug the code and suggest new code if the result contains error. The two agents keep communicating to each other until the task is done.\n",
"\n",
"\\:\\:\\:info Requirements\n",
"\n",
"````{=mdx}\n",
":::info Requirements\n",
"Install `pyautogen`:\n",
"```bash\n",
"pip install pyautogen\n",
"```\n",
"\n",
"For more information, please refer to the [installation guide](/docs/installation/).\n",
"\n",
"\\:\\:\\:"
":::\n",
"````"
]
},
{
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"cell_type": "markdown",
"metadata": {},
"source": [
"\\:\\:\\:tip\n",
"\n",
"Learn more about the various ways to configure LLM endpoints [here](/docs/llm_configuration).\n",
"\n",
"\\:\\:\\:"
"````{=mdx}\n",
":::tip\n",
"Learn more about configuring LLMs for agents [here](/docs/llm_configuration).\n",
":::\n",
"````"
]
},
{
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27 changes: 10 additions & 17 deletions notebook/agentchat_function_call_async.ipynb
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"\n",
"In this notebook, we demonstrate how to use `AssistantAgent` and `UserProxyAgent` to make function calls with the new feature of OpenAI models (in model version 0613). A specified prompt and function configs must be passed to `AssistantAgent` to initialize the agent. The corresponding functions must be passed to `UserProxyAgent`, which will execute any function calls made by `AssistantAgent`. Besides this requirement of matching descriptions with functions, we recommend checking the system message in the `AssistantAgent` to ensure the instructions align with the function call descriptions.\n",
"\n",
"\\:\\:\\:info Requirements\n",
"\n",
"````{=mdx}\n",
":::info Requirements\n",
"Install `pyautogen`:\n",
"```bash\n",
"pip install pyautogen\n",
"```\n",
"\n",
"For more information, please refer to the [installation guide](/docs/installation/).\n",
"\n",
"\\:\\:\\:\n"
":::\n",
"````\n"
]
},
{
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"config_list = autogen.config_list_from_json(env_or_file=\"OAI_CONFIG_LIST\")"
]
},
{
"attachments": {},
"cell_type": "markdown",
"id": "92fde41f",
"metadata": {},
"source": [
"\\:\\:\\:tip\n",
"\n",
"Learn more about the various ways to configure LLM endpoints [here](/docs/llm_configuration).\n",
"\n",
"\\:\\:\\:"
]
},
{
"attachments": {},
"cell_type": "markdown",
"id": "2b9526e7",
"metadata": {},
"source": [
"````{=mdx}\n",
":::tip\n",
"Learn more about configuring LLMs for agents [here](/docs/llm_configuration).\n",
":::\n",
"````\n",
"\n",
"## Making Async and Sync Function Calls\n",
"\n",
"In this example, we demonstrate function call execution with `AssistantAgent` and `UserProxyAgent`. With the default system prompt of `AssistantAgent`, we allow the LLM assistant to perform tasks with code, and the `UserProxyAgent` would extract code blocks from the LLM response and execute them. With the new \"function_call\" feature, we define functions and specify the description of the function in the OpenAI config for the `AssistantAgent`. Then we register the functions in `UserProxyAgent`."
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94 changes: 23 additions & 71 deletions notebook/agentchat_groupchat.ipynb
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"cell_type": "markdown",
"metadata": {},
"source": [
"<a href=\"https://colab.research.google.com/github/microsoft/autogen/blob/main/notebook/agentchat_groupchat.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
]
},
{
"attachments": {},
"cell_type": "markdown",
"metadata": {},
"source": [
"# Auto Generated Agent Chat: Group Chat\n",
"<!--\n",
"tags: [\"orchestration\", \"group chat\"]\n",
"description: |\n",
" Explore the utilization of large language models in automated group chat scenarios, where agents perform tasks collectively, demonstrating how they can be configured, interact with each other, and retrieve specific information from external resources.\n",
"-->\n",
"\n",
"# Group Chat\n",
"\n",
"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.\n",
"Please find documentation about this feature [here](https://microsoft.github.io/autogen/docs/Use-Cases/agent_chat).\n",
"\n",
"This notebook is modified based on https://github.com/microsoft/FLAML/blob/4ea686af5c3e8ff24d9076a7a626c8b28ab5b1d7/notebook/autogen_multiagent_roleplay_chat.ipynb\n",
"\n",
"## Requirements\n",
"\n",
"AutoGen requires `Python>=3.8`. To run this notebook example, please install:\n",
"````{=mdx}\n",
":::info Requirements\n",
"Install `pyautogen`:\n",
"```bash\n",
"pip install pyautogen\n",
"```"
]
},
{
"cell_type": "code",
"execution_count": 105,
"metadata": {},
"outputs": [],
"source": [
"%%capture --no-stderr\n",
"# %pip install \"pyautogen>=0.2.3\""
"```\n",
"\n",
"For more information, please refer to the [installation guide](/docs/installation/).\n",
":::\n",
"````"
]
},
{
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"source": [
"import autogen\n",
"\n",
"config_list_gpt4 = autogen.config_list_from_json(\n",
"config_list = autogen.config_list_from_json(\n",
" \"OAI_CONFIG_LIST\",\n",
" filter_dict={\n",
" \"model\": [\"gpt-4\", \"gpt-4-0314\", \"gpt4\", \"gpt-4-32k\", \"gpt-4-32k-0314\", \"gpt-4-32k-v0314\"],\n",
" },\n",
")\n",
"# config_list_gpt35 = autogen.config_list_from_json(\n",
"# \"OAI_CONFIG_LIST\",\n",
"# filter_dict={\n",
"# \"model\": {\n",
"# \"gpt-3.5-turbo\",\n",
"# \"gpt-3.5-turbo-16k\",\n",
"# \"gpt-3.5-turbo-0301\",\n",
"# \"chatgpt-35-turbo-0301\",\n",
"# \"gpt-35-turbo-v0301\",\n",
"# },\n",
"# },\n",
"# )"
")"
]
},
{
"attachments": {},
"cell_type": "markdown",
"metadata": {},
"source": [
"It first looks for environment variable \"OAI_CONFIG_LIST\" which needs to be a valid json string. If that variable is not found, it then looks for a json file named \"OAI_CONFIG_LIST\". It filters the configs by models (you can filter by other keys as well). Only the gpt-4 models are kept in the list based on the filter condition.\n",
"\n",
"The config list looks like the following:\n",
"```python\n",
"config_list = [\n",
" {\n",
" 'model': 'gpt-4',\n",
" 'api_key': '<your OpenAI API key here>',\n",
" },\n",
" {\n",
" 'model': 'gpt-4',\n",
" 'api_key': '<your Azure OpenAI API key here>',\n",
" 'base_url': '<your Azure OpenAI API base here>',\n",
" 'api_type': 'azure',\n",
" 'api_version': '2023-06-01-preview',\n",
" },\n",
" {\n",
" 'model': 'gpt-4-32k',\n",
" 'api_key': '<your Azure OpenAI API key here>',\n",
" 'base_url': '<your Azure OpenAI API base here>',\n",
" 'api_type': 'azure',\n",
" 'api_version': '2023-06-01-preview',\n",
" },\n",
"]\n",
"```\n",
"````{=mdx}\n",
":::tip\n",
"Learn more about configuring LLMs for agents [here](/docs/llm_configuration).\n",
":::\n",
"````\n",
"\n",
"You can set the value of config_list in any way you prefer. Please refer to this [notebook](https://github.com/microsoft/autogen/blob/main/website/docs/llm_configuration.ipynb) for full code examples of the different methods."
]
},
{
"attachments": {},
"cell_type": "markdown",
"metadata": {},
"source": [
"## Construct Agents"
]
},
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"metadata": {},
"outputs": [],
"source": [
"llm_config = {\"config_list\": config_list_gpt4, \"cache_seed\": 42}\n",
"llm_config = {\"config_list\": config_list, \"cache_seed\": 42}\n",
"user_proxy = autogen.UserProxyAgent(\n",
" name=\"User_proxy\",\n",
" system_message=\"A human admin.\",\n",
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