mirror of https://github.com/microsoft/autogen.git
909 lines
63 KiB
Plaintext
909 lines
63 KiB
Plaintext
{
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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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"<a href=\"https://colab.research.google.com/github/microsoft/autogen/blob/main/notebook/agentchat_compression.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
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]
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},
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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: Conversations with Chat History Compression Enabled\n",
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"\n",
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"**CompressibleAgent will be deprecated.** \n",
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"\n",
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"Refer to https://github.com/microsoft/autogen/blob/main/notebook/agentchat_capability_long_context_handling.ipynb for long context handling capability.\n",
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"\n",
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"AutoGen offers conversable agents powered by LLM, tools, or humans, which can be used to perform tasks collectively via automated chat. This framework allows tool use and human participance 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 enable compression of history messages using the `CompressibleAgent`. While this agent retains all the default functionalities of the `AssistantAgent`, it also provides the added feature of compression when activated through the `compress_config` setting.\n",
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"\n",
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"Different compression modes are supported:\n",
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"1. `compress_config=False` (Default): `CompressibleAgent` is equivalent to `AssistantAgent`.\n",
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"2. `compress_config=True` or `compress_config={\"mode\": \"TERMINATE\"}`: no compression will be performed. However, we will count token usage before sending requests to the OpenAI model. The conversation will be terminated directly if the total token usage exceeds the maximum token usage allowed by the model (to avoid the token limit error from OpenAI API).\n",
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"3. `compress_config={\"mode\": \"COMPRESS\", \"trigger_count\": <your pre-set number>}, \"leave_last_n\": <your pre-set number>`: compression is enabled.\n",
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" ```python\n",
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" # default compress_config\n",
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" compress_config = {\n",
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" \"mode\": \"COMPRESS\",\n",
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" \"compress_function\": None,\n",
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" \"trigger_count\": 0.7, # default to 0.7, or your pre-set number\n",
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" \"broadcast\": True, # the compressed with be broadcast to sender. This will not be used in groupchat.\n",
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"\n",
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" # the following settings are for this mode only\n",
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" \"leave_last_n\": 2, # leave the last n messages in the history to avoid compression\n",
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" \"verbose\": False, # if True, print out the content to be compressed and the compressed content\n",
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" }\n",
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" ```\n",
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" Currently, our compression logic is as follows:\n",
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" 1. We will always leave the first user message (as well as system prompts) and compress the rest of the history messages.\n",
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" 2. You can choose to not compress the last n messages in the history with \"leave_last_n\".\n",
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" 2. The summary is performed on a per-message basis, with the role of the messages (See compressed content in the example below).\n",
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"\n",
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"4. `compress_config={\"mode\": \"CUSTOMIZED\", \"compress_function\": <A customized function for compression>}`: the `compress_function` function will be called on trigger count. The function should accept a list of messages as input and return a tuple of (is_success: bool, compressed_messages: List[Dict]). The whole message history (except system prompt) will be passed.\n",
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"\n",
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"\n",
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"By adjusting `trigger_count`, you can decide when to compress the history messages based on existing tokens. If this is a float number between 0 and 1, it is interpreted as a ratio of max tokens allowed by the model. For example, the AssistantAgent uses gpt-4 with max tokens 8192, the trigger_count = 0.7 * 8192 = 5734.4 -> 5734. Do not set `trigger_count` to the max tokens allowed by the model, since the same LLM is employed for compression and it needs tokens to generate the compressed content. \n",
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"\n",
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"\n",
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"\n",
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"## Limitations\n",
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"- For now, the compression feature **is not well-supported for groupchat**. If you initialize a `CompressibleAgent` in a groupchat with compression, the compressed cannot be broadcast to all other agents in the groupchat. If you use this feature in groupchat, extra cost will be incurred since compression will be performed on at per-agent basis.\n",
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"- We do not support async compression for now.\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`. To run this notebook example, please install:\n",
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"```bash\n",
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"pip install pyautogen\n",
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"```"
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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": 1,
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"metadata": {},
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"outputs": [],
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"source": [
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"# %pip install pyautogen~=0.1.0"
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]
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},
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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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"## Set your API Endpoint\n",
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"\n",
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"The [`config_list_from_json`](https://microsoft.github.io/autogen/docs/reference/oai/openai_utils#config_list_from_json) function loads a list of configurations from an environment variable or a json file.\n"
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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": 2,
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"metadata": {},
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"outputs": [],
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"source": [
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"# define functions according to the function description\n",
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"from IPython import get_ipython\n",
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"\n",
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"import autogen\n",
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"from autogen.agentchat.contrib.compressible_agent import CompressibleAgent\n",
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"from autogen.agentchat.contrib.math_user_proxy_agent import MathUserProxyAgent\n",
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"\n",
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"config_list = autogen.config_list_from_json(\n",
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" \"OAI_CONFIG_LIST\",\n",
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" filter_dict={\n",
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" \"model\": [\"gpt-4-1106-preview\"],\n",
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" },\n",
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")"
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]
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},
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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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"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).\n",
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"\n",
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"The config list looks like the following:\n",
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"```python\n",
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"config_list = [\n",
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" {\n",
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" 'model': 'gpt-4',\n",
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" 'api_key': '<your OpenAI API key here>',\n",
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" },\n",
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" {\n",
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" 'model': 'gpt-4',\n",
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" 'api_key': '<your Azure OpenAI API key here>',\n",
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" 'base_url': '<your Azure OpenAI API base here>',\n",
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" 'api_type': 'azure',\n",
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" 'api_version': '2024-02-15-preview',\n",
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" },\n",
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" {\n",
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" 'model': 'gpt-4-32k',\n",
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" 'api_key': '<your Azure OpenAI API key here>',\n",
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" 'base_url': '<your Azure OpenAI API base here>',\n",
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" 'api_type': 'azure',\n",
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" 'api_version': '2024-02-15-preview',\n",
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" },\n",
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"]\n",
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"```\n",
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"\n",
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"If you open this notebook in colab, you can upload your files by clicking the file icon on the left panel and then choose \"upload file\" icon.\n",
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"\n",
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"You can set the value of config_list in other ways you prefer, e.g., loading from a YAML file."
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]
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},
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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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"## Example 1\n",
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"This example is from [agentchat_MathChat.ipynb](https://github.com/microsoft/autogen/blob/main/notebook/agentchat_MathChat.ipynb). Compression with code execution.\n",
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"\n",
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"You must set the `model` field in `llm_config`, as it will be used to calculate the token usage.\n",
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"\n",
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"Note: we set `trigger_count=600`, and `leave_last_n=2`. In this example, we set a low trigger_count to demonstrate the compression feature. \n",
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"The token count after compression is still bigger than trigger count, mainly because the trigger count is low an the first and last 2 messages are not compressed. Thus, the compression is performed at each turn. In practice, you want to adjust the trigger_count to a bigger number and properly set the `leave_last_n` to avoid compression at each turn. \n"
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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": 3,
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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[33mmathproxyagent\u001b[0m (to assistant):\n",
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"\n",
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"Let's use Python to solve a math problem.\n",
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"\n",
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"Query requirements:\n",
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"You should always use the 'print' function for the output and use fractions/radical forms instead of decimals.\n",
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"You can use packages like sympy to help you.\n",
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"You must follow the formats below to write your code:\n",
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"```python\n",
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"# your code\n",
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"```\n",
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"\n",
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"First state the key idea to solve the problem. You may choose from three ways to solve the problem:\n",
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"Case 1: If the problem can be solved with Python code directly, please write a program to solve it. You can enumerate all possible arrangements if needed.\n",
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"Case 2: If the problem is mostly reasoning, you can solve it by yourself directly.\n",
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"Case 3: If the problem cannot be handled in the above two ways, please follow this process:\n",
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"1. Solve the problem step by step (do not over-divide the steps).\n",
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"2. Take out any queries that can be asked through Python (for example, any calculations or equations that can be calculated).\n",
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"3. Wait for me to give the results.\n",
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"4. Continue if you think the result is correct. If the result is invalid or unexpected, please correct your query or reasoning.\n",
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"\n",
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"After all the queries are run and you get the answer, put the answer in \\boxed{}.\n",
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"\n",
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"Problem:\n",
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"Find all $x$ that satisfy the inequality $(2x+10)(x+3)<(3x+9)(x+8)$. Express your answer in interval notation.\n",
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"\n",
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"--------------------------------------------------------------------------------\n",
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"\u001b[33massistant\u001b[0m (to mathproxyagent):\n",
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"\n",
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"Key Idea:\n",
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"To solve this inequality $(2x + 10)(x + 3) < (3x + 9)(x + 8)$, we'll first expand both sides of the inequality, then collect all terms on one side to form a quadratic inequality. After simplifying, we will factor the quadratic expression if possible. Then, we'll determine the critical points of the inequality by setting the factors to zero. Finally, we'll use a sign chart or test values within intervals determined by the critical points to find the intervals where the original inequality is satisfied.\n",
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"\n",
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"Case 1 applies here, so I will write a Python program using sympy that simplifies the inequality, factors the resulting expression, solves for the critical points, and then determines the intervals where the inequality holds true.\n",
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"\n",
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"```python\n",
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"from sympy import symbols, solve, simplify\n",
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"\n",
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"# Define the variable\n",
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"x = symbols('x')\n",
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"\n",
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"# Given inequality\n",
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"lhs = (2*x + 10)*(x + 3)\n",
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"rhs = (3*x + 9)*(x + 8)\n",
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"\n",
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"# Move all terms to one side of the inequality\n",
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"inequality = simplify(lhs - rhs) < 0\n",
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"\n",
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"# Solve the inequality\n",
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"solutions = solve(inequality, x)\n",
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"\n",
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"# Print the solution using interval notation\n",
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"print(solutions)\n",
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"```\n",
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"\n",
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"Running this code will provide us with the solution in interval notation. We'll express the final answer in the requested format afterward.\n",
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"\n",
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"--------------------------------------------------------------------------------\n",
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"\u001b[33mmathproxyagent\u001b[0m (to assistant):\n",
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"\n",
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"((-oo < x) & (x < -14)) | ((-3 < x) & (x < oo))\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": "stderr",
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"output_type": "stream",
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"text": [
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"Warning: Compression skipped at trigger count threshold. The first msg and last 2 msgs will not be compressed. current msg count: 3. Consider raising trigger_count.\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[33massistant\u001b[0m (to mathproxyagent):\n",
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"\n",
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"The solution obtained from running the Python code suggests that the values of $x$ that satisfy the inequality $(2x + 10)(x + 3) < (3x + 9)(x + 8)$ are in the intervals $(-\\infty, -14)$ and $(-3, \\infty)$.\n",
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"\n",
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"Therefore, the answer in interval notation is:\n",
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"\n",
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"$$\n",
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"\\boxed{(-\\infty, -14) \\cup (-3, \\infty)}\n",
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"$$\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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"data": {
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"text/plain": [
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"ChatResult(chat_id=None, chat_history=[{'content': \"Let's use Python to solve a math problem.\\n\\nQuery requirements:\\nYou should always use the 'print' function for the output and use fractions/radical forms instead of decimals.\\nYou can use packages like sympy to help you.\\nYou must follow the formats below to write your code:\\n```python\\n# your code\\n```\\n\\nFirst state the key idea to solve the problem. You may choose from three ways to solve the problem:\\nCase 1: If the problem can be solved with Python code directly, please write a program to solve it. You can enumerate all possible arrangements if needed.\\nCase 2: If the problem is mostly reasoning, you can solve it by yourself directly.\\nCase 3: If the problem cannot be handled in the above two ways, please follow this process:\\n1. Solve the problem step by step (do not over-divide the steps).\\n2. Take out any queries that can be asked through Python (for example, any calculations or equations that can be calculated).\\n3. Wait for me to give the results.\\n4. Continue if you think the result is correct. If the result is invalid or unexpected, please correct your query or reasoning.\\n\\nAfter all the queries are run and you get the answer, put the answer in \\\\boxed{}.\\n\\nProblem:\\nFind all $x$ that satisfy the inequality $(2x+10)(x+3)<(3x+9)(x+8)$. Express your answer in interval notation.\", 'role': 'assistant'}, {'content': \"Key Idea:\\nTo solve this inequality $(2x + 10)(x + 3) < (3x + 9)(x + 8)$, we'll first expand both sides of the inequality, then collect all terms on one side to form a quadratic inequality. After simplifying, we will factor the quadratic expression if possible. Then, we'll determine the critical points of the inequality by setting the factors to zero. Finally, we'll use a sign chart or test values within intervals determined by the critical points to find the intervals where the original inequality is satisfied.\\n\\nCase 1 applies here, so I will write a Python program using sympy that simplifies the inequality, factors the resulting expression, solves for the critical points, and then determines the intervals where the inequality holds true.\\n\\n```python\\nfrom sympy import symbols, solve, simplify\\n\\n# Define the variable\\nx = symbols('x')\\n\\n# Given inequality\\nlhs = (2*x + 10)*(x + 3)\\nrhs = (3*x + 9)*(x + 8)\\n\\n# Move all terms to one side of the inequality\\ninequality = simplify(lhs - rhs) < 0\\n\\n# Solve the inequality\\nsolutions = solve(inequality, x)\\n\\n# Print the solution using interval notation\\nprint(solutions)\\n```\\n\\nRunning this code will provide us with the solution in interval notation. We'll express the final answer in the requested format afterward.\", 'role': 'user'}, {'content': '((-oo < x) & (x < -14)) | ((-3 < x) & (x < oo))', 'role': 'assistant'}, {'content': 'The solution obtained from running the Python code suggests that the values of $x$ that satisfy the inequality $(2x + 10)(x + 3) < (3x + 9)(x + 8)$ are in the intervals $(-\\\\infty, -14)$ and $(-3, \\\\infty)$.\\n\\nTherefore, the answer in interval notation is:\\n\\n$$\\n\\\\boxed{(-\\\\infty, -14) \\\\cup (-3, \\\\infty)}\\n$$', 'role': 'user'}], summary='The solution obtained from running the Python code suggests that the values of $x$ that satisfy the inequality $(2x + 10)(x + 3) < (3x + 9)(x + 8)$ are in the intervals $(-\\\\infty, -14)$ and $(-3, \\\\infty)$.\\n\\nTherefore, the answer in interval notation is:\\n\\n$$\\n\\\\boxed{(-\\\\infty, -14) \\\\cup (-3, \\\\infty)}\\n$$', cost=({'total_cost': 0.052199999999999996, 'gpt-4': {'cost': 0.052199999999999996, 'prompt_tokens': 954, 'completion_tokens': 393, 'total_tokens': 1347}}, {'total_cost': 0.052199999999999996, 'gpt-4': {'cost': 0.052199999999999996, 'prompt_tokens': 954, 'completion_tokens': 393, 'total_tokens': 1347}}), human_input=[])"
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]
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},
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"execution_count": 3,
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"metadata": {},
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"output_type": "execute_result"
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}
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],
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"source": [
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"# 1. replace AssistantAgent with CompressibleAgent\n",
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"assistant = CompressibleAgent(\n",
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" name=\"assistant\",\n",
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" system_message=\"You are a helpful assistant.\",\n",
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" llm_config={\n",
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" \"timeout\": 600,\n",
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" \"cache_seed\": 42,\n",
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" \"config_list\": config_list,\n",
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" \"model\": \"gpt-4-1106-preview\", # you must set the model field in llm_config, as it will be used to calculate the token usage.\n",
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" },\n",
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" compress_config={\n",
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" \"mode\": \"COMPRESS\",\n",
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" \"trigger_count\": 600, # set this to a large number for less frequent compression\n",
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" \"verbose\": True, # to allow printing of compression information: context before and after compression\n",
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" \"leave_last_n\": 2,\n",
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" },\n",
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")\n",
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"\n",
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"# 2. create the MathUserProxyAgent instance named \"mathproxyagent\"\n",
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"mathproxyagent = MathUserProxyAgent(\n",
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" name=\"mathproxyagent\",\n",
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" human_input_mode=\"NEVER\",\n",
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" code_execution_config={\n",
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" \"use_docker\": False\n",
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" }, # Please set use_docker=True if docker is available to run the generated code. Using docker is safer than running the generated code directly.\n",
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" max_consecutive_auto_reply=5,\n",
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")\n",
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"math_problem = (\n",
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" \"Find all $x$ that satisfy the inequality $(2x+10)(x+3)<(3x+9)(x+8)$. Express your answer in interval notation.\"\n",
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")\n",
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"mathproxyagent.initiate_chat(assistant, message=mathproxyagent.message_generator, problem=math_problem)"
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]
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},
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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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"## Example 2\n",
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"This example is from [agentchat_function_call.ipynb](https://github.com/microsoft/autogen/blob/main/notebook/agentchat_function_call.ipynb). Compression with function calls. "
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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": 4,
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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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"Draw two agents chatting with each other with an example dialog. Don't add plt.show().\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: python *****\u001b[0m\n",
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"Arguments: \n",
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"{\n",
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" \"cell\": \"import matplotlib.pyplot as plt\\nimport numpy as np\\n\\nfig, ax = plt.subplots()\\n\\n# Define the agents as circles\\nagent1 = plt.Circle((0.4, 0.5), 0.1, color='blue')\\nagent2 = plt.Circle((0.6, 0.5), 0.1, color='red')\\n\\n# Draw the agents\\nax.add_artist(agent1)\\nax.add_artist(agent2)\\n\\n# Example dialog boxes\\nplt.text(0.28, 0.6, \\\"Hello!\\\", fontsize=12, bbox=dict(facecolor='white', alpha=0.5))\\nplt.text(0.58, 0.6, \\\"Hi there!\\\", fontsize=12, bbox=dict(facecolor='white', alpha=0.5))\\n\\n# Set the limits and remove axes\\nax.set_xlim(0, 1)\\nax.set_ylim(0, 1)\\nax.axis('off')\\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 python...\u001b[0m\n"
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]
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},
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{
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"data": {
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"text/plain": [
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"(0.0, 1.0, 0.0, 1.0)"
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]
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},
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"execution_count": 4,
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"metadata": {},
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"output_type": "execute_result"
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|
},
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{
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"data": {
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"image/png": 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",
|
|
"text/plain": [
|
|
"<Figure size 640x480 with 1 Axes>"
|
|
]
|
|
},
|
|
"metadata": {},
|
|
"output_type": "display_data"
|
|
},
|
|
{
|
|
"name": "stdout",
|
|
"output_type": "stream",
|
|
"text": [
|
|
"\u001b[33muser_proxy\u001b[0m (to chatbot):\n",
|
|
"\n",
|
|
"\u001b[32m***** Response from calling function \"python\" *****\u001b[0m\n",
|
|
"(0.0, 1.0, 0.0, 1.0)\n",
|
|
"\u001b[32m***************************************************\u001b[0m\n",
|
|
"\n",
|
|
"--------------------------------------------------------------------------------\n",
|
|
"\u001b[33mchatbot\u001b[0m (to user_proxy):\n",
|
|
"\n",
|
|
"The two agents have been drawn, each represented as a circle, and an example of their dialogue is displayed above them. Since `plt.show()` was not to be included, the plot is not displayed here, but the agents along with their dialogue would appear within the figure's coordinate system, which extends from 0 to 1 on both the x and y axes.\n",
|
|
"\n",
|
|
"--------------------------------------------------------------------------------\n",
|
|
"\u001b[33muser_proxy\u001b[0m (to chatbot):\n",
|
|
"\n",
|
|
"\n",
|
|
"\n",
|
|
"--------------------------------------------------------------------------------\n",
|
|
"\u001b[33mchatbot\u001b[0m (to user_proxy):\n",
|
|
"\n",
|
|
"TERMINATE\n",
|
|
"\n",
|
|
"--------------------------------------------------------------------------------\n"
|
|
]
|
|
},
|
|
{
|
|
"data": {
|
|
"text/plain": [
|
|
"ChatResult(chat_id=None, chat_history=[{'content': \"Draw two agents chatting with each other with an example dialog. Don't add plt.show().\", 'role': 'assistant'}, {'function_call': {'arguments': '{\\n \"cell\": \"import matplotlib.pyplot as plt\\\\nimport numpy as np\\\\n\\\\nfig, ax = plt.subplots()\\\\n\\\\n# Define the agents as circles\\\\nagent1 = plt.Circle((0.4, 0.5), 0.1, color=\\'blue\\')\\\\nagent2 = plt.Circle((0.6, 0.5), 0.1, color=\\'red\\')\\\\n\\\\n# Draw the agents\\\\nax.add_artist(agent1)\\\\nax.add_artist(agent2)\\\\n\\\\n# Example dialog boxes\\\\nplt.text(0.28, 0.6, \\\\\"Hello!\\\\\", fontsize=12, bbox=dict(facecolor=\\'white\\', alpha=0.5))\\\\nplt.text(0.58, 0.6, \\\\\"Hi there!\\\\\", fontsize=12, bbox=dict(facecolor=\\'white\\', alpha=0.5))\\\\n\\\\n# Set the limits and remove axes\\\\nax.set_xlim(0, 1)\\\\nax.set_ylim(0, 1)\\\\nax.axis(\\'off\\')\\\\n\"\\n}', 'name': 'python'}, 'content': None, 'role': 'assistant'}, {'content': '(0.0, 1.0, 0.0, 1.0)', 'name': 'python', 'role': 'function'}, {'content': \"The two agents have been drawn, each represented as a circle, and an example of their dialogue is displayed above them. Since `plt.show()` was not to be included, the plot is not displayed here, but the agents along with their dialogue would appear within the figure's coordinate system, which extends from 0 to 1 on both the x and y axes.\", 'role': 'user'}, {'content': '', 'role': 'assistant'}, {'content': 'TERMINATE', 'role': 'user'}], summary='', cost=({'total_cost': 0.04767, 'gpt-4': {'cost': 0.04767, 'prompt_tokens': 973, 'completion_tokens': 308, 'total_tokens': 1281}}, {'total_cost': 0.04767, 'gpt-4': {'cost': 0.04767, 'prompt_tokens': 973, 'completion_tokens': 308, 'total_tokens': 1281}}), human_input=[])"
|
|
]
|
|
},
|
|
"execution_count": 4,
|
|
"metadata": {},
|
|
"output_type": "execute_result"
|
|
}
|
|
],
|
|
"source": [
|
|
"llm_config = {\n",
|
|
" \"model\": \"gpt-4-1106-preview\",\n",
|
|
" \"functions\": [\n",
|
|
" {\n",
|
|
" \"name\": \"python\",\n",
|
|
" \"description\": \"run cell in ipython and return the execution result.\",\n",
|
|
" \"parameters\": {\n",
|
|
" \"type\": \"object\",\n",
|
|
" \"properties\": {\n",
|
|
" \"cell\": {\n",
|
|
" \"type\": \"string\",\n",
|
|
" \"description\": \"Valid Python cell to execute.\",\n",
|
|
" }\n",
|
|
" },\n",
|
|
" \"required\": [\"cell\"],\n",
|
|
" },\n",
|
|
" },\n",
|
|
" {\n",
|
|
" \"name\": \"sh\",\n",
|
|
" \"description\": \"run a shell script and return the execution result.\",\n",
|
|
" \"parameters\": {\n",
|
|
" \"type\": \"object\",\n",
|
|
" \"properties\": {\n",
|
|
" \"script\": {\n",
|
|
" \"type\": \"string\",\n",
|
|
" \"description\": \"Valid shell script to execute.\",\n",
|
|
" }\n",
|
|
" },\n",
|
|
" \"required\": [\"script\"],\n",
|
|
" },\n",
|
|
" },\n",
|
|
" ],\n",
|
|
" \"config_list\": config_list,\n",
|
|
" \"timeout\": 120,\n",
|
|
"}\n",
|
|
"\n",
|
|
"chatbot = CompressibleAgent(\n",
|
|
" name=\"chatbot\",\n",
|
|
" system_message=\"For coding tasks, only use the functions you have been provided with. Reply TERMINATE when the task is done.\",\n",
|
|
" llm_config=llm_config,\n",
|
|
" compress_config={\n",
|
|
" \"mode\": \"COMPRESS\",\n",
|
|
" \"trigger_count\": 600, # set this to a large number for less frequent compression\n",
|
|
" \"verbose\": True, # set this to False to suppress the compression log\n",
|
|
" \"leave_last_n\": 2,\n",
|
|
" },\n",
|
|
")\n",
|
|
"\n",
|
|
"# create a UserProxyAgent instance named \"user_proxy\"\n",
|
|
"user_proxy = autogen.UserProxyAgent(\n",
|
|
" name=\"user_proxy\",\n",
|
|
" is_termination_msg=lambda x: x.get(\"content\", \"\") and x.get(\"content\", \"\").rstrip().endswith(\"TERMINATE\"),\n",
|
|
" human_input_mode=\"NEVER\",\n",
|
|
" max_consecutive_auto_reply=10,\n",
|
|
" code_execution_config={\n",
|
|
" \"work_dir\": \"coding\",\n",
|
|
" \"use_docker\": False,\n",
|
|
" }, # Please set use_docker=True if docker is available to run the generated code. Using docker is safer than running the generated code directly.\n",
|
|
")\n",
|
|
"\n",
|
|
"\n",
|
|
"def exec_python(cell):\n",
|
|
" ipython = get_ipython()\n",
|
|
" result = ipython.run_cell(cell)\n",
|
|
" log = str(result.result)\n",
|
|
" if result.error_before_exec is not None:\n",
|
|
" log += f\"\\n{result.error_before_exec}\"\n",
|
|
" if result.error_in_exec is not None:\n",
|
|
" log += f\"\\n{result.error_in_exec}\"\n",
|
|
" return log\n",
|
|
"\n",
|
|
"\n",
|
|
"def exec_sh(script):\n",
|
|
" return user_proxy.execute_code_blocks([(\"sh\", script)])\n",
|
|
"\n",
|
|
"\n",
|
|
"# register the functions\n",
|
|
"user_proxy.register_function(\n",
|
|
" function_map={\n",
|
|
" \"python\": exec_python,\n",
|
|
" \"sh\": exec_sh,\n",
|
|
" }\n",
|
|
")\n",
|
|
"\n",
|
|
"# start the conversation\n",
|
|
"user_proxy.initiate_chat(\n",
|
|
" chatbot,\n",
|
|
" message=\"Draw two agents chatting with each other with an example dialog. Don't add plt.show().\",\n",
|
|
")"
|
|
]
|
|
},
|
|
{
|
|
"cell_type": "markdown",
|
|
"metadata": {},
|
|
"source": [
|
|
"## Example 3\n",
|
|
"This example is from [agent_chat_web_info.ipynb](https://github.com/microsoft/autogen/blob/main/notebook/agentchat_web_info.ipynb). \n",
|
|
"We use this example to demonstrate how to pass in a customized compression function. We pass in an compression function `constrain_num_messages`, which constrains the number of messages to be 3 or less. \n",
|
|
"The customized function should accept a list of messages as input and return a tuple of `(is_success: bool, compressed_messages: List[Dict])`."
|
|
]
|
|
},
|
|
{
|
|
"cell_type": "code",
|
|
"execution_count": 5,
|
|
"metadata": {},
|
|
"outputs": [
|
|
{
|
|
"name": "stdout",
|
|
"output_type": "stream",
|
|
"text": [
|
|
"\u001b[33muser_proxy\u001b[0m (to assistant):\n",
|
|
"\n",
|
|
"Show me the YTD gain of 10 largest technology companies as of today.\n",
|
|
"\n",
|
|
"--------------------------------------------------------------------------------\n",
|
|
"\u001b[33massistant\u001b[0m (to user_proxy):\n",
|
|
"\n",
|
|
"To compute the Year-To-Date (YTD) gains of the 10 largest technology companies, I can fetch the latest stock price and the closing price from the last trading day of the previous year. Then calculate the percentage increase for each company.\n",
|
|
"\n",
|
|
"First, we should fetch the current stock prices and the closing prices as of the last trading day of the previous year for these companies. For this, we can use a financial data API like Alpha Vantage, Yahoo Finance, or similar, which would require an API key and internet access, but I can't perform actions that require internet access.\n",
|
|
"\n",
|
|
"Instead, I will provide you with Python code that you'd need to run on your local machine. This code utilizes the `yfinance` Python library, which is widely used for retrieving historical market data from Yahoo Finance. If you don't have `yfinance` installed, you'll need to install it by running `pip install yfinance` in your command line.\n",
|
|
"\n",
|
|
"Here is the code you'll need to execute:\n",
|
|
"\n",
|
|
"```python\n",
|
|
"# filename: ytd_gains.py\n",
|
|
"\n",
|
|
"import yfinance as yf\n",
|
|
"from datetime import datetime, timedelta\n",
|
|
"\n",
|
|
"# Define the ticker symbols for the 10 largest tech companies.\n",
|
|
"# This is a sample list and may not represent the current top 10 companies.\n",
|
|
"# You would need to replace this with the actual tickers of the top 10 tech companies.\n",
|
|
"tech_companies = [\"AAPL\", \"MSFT\", \"GOOGL\", \"AMZN\", \"FB\", \"TSLA\", \"NVDA\", \"V\", \"ADBE\", \"INTC\"]\n",
|
|
"\n",
|
|
"# Compute the last day of the last year\n",
|
|
"end_of_last_year = datetime(datetime.now().year - 1, 12, 31)\n",
|
|
"\n",
|
|
"# Retrieve the data and calculate YTD gain for each company\n",
|
|
"ytd_gains = {}\n",
|
|
"for symbol in tech_companies:\n",
|
|
" try:\n",
|
|
" # Fetch historical data\n",
|
|
" stock = yf.Ticker(symbol)\n",
|
|
" last_price = stock.history(period=\"1d\")['Close'][-1]\n",
|
|
" prev_close = stock.history(start=end_of_last_year, end=end_of_last_year + timedelta(days=1))['Close'][0]\n",
|
|
"\n",
|
|
" # Calculate YTD gain\n",
|
|
" ytd_gain = ((last_price - prev_close) / prev_close) * 100\n",
|
|
" ytd_gains[symbol] = ytd_gain\n",
|
|
" except Exception as e:\n",
|
|
" # Handle errors by skipping the company and printing an error message\n",
|
|
" print(f\"Error retrieving data for {symbol}: {e}\")\n",
|
|
"\n",
|
|
"# Print the YTD gains\n",
|
|
"for symbol, gain in ytd_gains.items():\n",
|
|
" print(f\"{symbol}: {gain:.2f}% YTD Gain\")\n",
|
|
"\n",
|
|
"```\n",
|
|
"\n",
|
|
"Make sure that `yfinance` is installed and then run this Python script (`ytd_gains.py`). The script will print out the YTD gains for the listed technology companies as a percentage.\n",
|
|
"\n",
|
|
"Note that the list of the 10 largest technology companies must be updated to reflect the current market situation. If you do not have the updated list, let me know, and I can attempt to retrieve this information for you using different methods.\n",
|
|
"\n",
|
|
"--------------------------------------------------------------------------------\n",
|
|
"\u001b[31m\n",
|
|
">>>>>>>> USING AUTO REPLY...\u001b[0m\n",
|
|
"\u001b[31m\n",
|
|
">>>>>>>> EXECUTING CODE BLOCK 0 (inferred language is python)...\u001b[0m\n",
|
|
"\u001b[33muser_proxy\u001b[0m (to assistant):\n",
|
|
"\n",
|
|
"exitcode: 0 (execution succeeded)\n",
|
|
"Code output: \n",
|
|
"Error retrieving data for AAPL: index 0 is out of bounds for axis 0 with size 0\n",
|
|
"Error retrieving data for MSFT: index 0 is out of bounds for axis 0 with size 0\n",
|
|
"Error retrieving data for GOOGL: index 0 is out of bounds for axis 0 with size 0\n",
|
|
"Error retrieving data for AMZN: index 0 is out of bounds for axis 0 with size 0\n",
|
|
"Error retrieving data for FB: index -1 is out of bounds for axis 0 with size 0\n",
|
|
"Error retrieving data for TSLA: index 0 is out of bounds for axis 0 with size 0\n",
|
|
"Error retrieving data for NVDA: index 0 is out of bounds for axis 0 with size 0\n",
|
|
"Error retrieving data for V: index 0 is out of bounds for axis 0 with size 0\n",
|
|
"Error retrieving data for ADBE: index 0 is out of bounds for axis 0 with size 0\n",
|
|
"Error retrieving data for INTC: index 0 is out of bounds for axis 0 with size 0\n",
|
|
"\n",
|
|
"\n",
|
|
"--------------------------------------------------------------------------------\n",
|
|
"\u001b[33massistant\u001b[0m (to user_proxy):\n",
|
|
"\n",
|
|
"It seems that the script encountered an error when trying to fetch data for the provided ticker symbols. This might be because either the market is closed and the latest data is not yet available, or because the dates specified do not have available data (e.g., the end of last year might have been a weekend or holiday).\n",
|
|
"\n",
|
|
"Let's adjust the code to be more robust by checking for valid trading days around the end of last year and to handle cases where the stock data might not be available. Here's the updated code:\n",
|
|
"\n",
|
|
"```python\n",
|
|
"# filename: ytd_gains.py\n",
|
|
"\n",
|
|
"import yfinance as yf\n",
|
|
"from datetime import datetime\n",
|
|
"\n",
|
|
"def get_last_valid_trading_day(year):\n",
|
|
" last_day = datetime(year, 12, 31)\n",
|
|
" # Start moving backwards from December 31st to find the last valid trading day of the year\n",
|
|
" while True:\n",
|
|
" stock_data = yf.download('SPY', start=last_day.strftime('%Y-%m-%d'), end=(last_day + timedelta(days=1)).strftime('%Y-%m-%d'))\n",
|
|
" if not stock_data.empty:\n",
|
|
" return last_day\n",
|
|
" last_day -= timedelta(days=1)\n",
|
|
"\n",
|
|
"# Define the ticker symbols for the 10 largest tech companies.\n",
|
|
"tech_companies = [\"AAPL\", \"MSFT\", \"GOOGL\", \"AMZN\", \"FB\", \"TSLA\", \"NVDA\", \"V\", \"ADBE\", \"INTC\"]\n",
|
|
"\n",
|
|
"# Compute the last valid trading day of the last year\n",
|
|
"end_of_last_year = get_last_valid_trading_day(datetime.now().year - 1)\n",
|
|
"\n",
|
|
"# Retrieve the data and calculate YTD gain for each company\n",
|
|
"ytd_gains = {}\n",
|
|
"for symbol in tech_companies:\n",
|
|
" try:\n",
|
|
" # Fetch historical data\n",
|
|
" stock = yf.Ticker(symbol)\n",
|
|
" last_price = stock.history(period='1d')['Close'].iloc[-1]\n",
|
|
" prev_close = stock.history(start=end_of_last_year, end=end_of_last_year + timedelta(days=1))['Close'].iloc[0]\n",
|
|
"\n",
|
|
" # Calculate YTD gain\n",
|
|
" ytd_gain = ((last_price - prev_close) / prev_close) * 100\n",
|
|
" ytd_gains[symbol] = ytd_gain\n",
|
|
" except Exception as e:\n",
|
|
" # Handle errors by skipping the company and printing an error message\n",
|
|
" print(f\"Error retrieving data for {symbol}: {e}\")\n",
|
|
"\n",
|
|
"# Print the YTD gains\n",
|
|
"for symbol, gain in ytd_gains.items():\n",
|
|
" print(f\"{symbol}: {gain:.2f}% YTD Gain\")\n",
|
|
"```\n",
|
|
"\n",
|
|
"Please execute the updated `ytd_gains.py` script. The function `get_last_valid_trading_day` will find the last valid trading day of the previous year to handle non-trading days correctly, and the fetch for current price will get the latest available data point even if the market has not yet closed for today.\n",
|
|
"\n",
|
|
"--------------------------------------------------------------------------------\n",
|
|
"\u001b[31m\n",
|
|
">>>>>>>> USING AUTO REPLY...\u001b[0m\n",
|
|
"\u001b[31m\n",
|
|
">>>>>>>> EXECUTING CODE BLOCK 0 (inferred language is python)...\u001b[0m\n",
|
|
"\u001b[33muser_proxy\u001b[0m (to assistant):\n",
|
|
"\n",
|
|
"exitcode: 1 (execution failed)\n",
|
|
"Code output: \n",
|
|
"Traceback (most recent call last):\n",
|
|
" File \"ytd_gains.py\", line 19, in <module>\n",
|
|
" end_of_last_year = get_last_valid_trading_day(datetime.now().year - 1)\n",
|
|
" File \"ytd_gains.py\", line 10, in get_last_valid_trading_day\n",
|
|
" stock_data = yf.download('SPY', start=last_day.strftime('%Y-%m-%d'), end=(last_day + timedelta(days=1)).strftime('%Y-%m-%d'))\n",
|
|
"NameError: name 'timedelta' is not defined\n",
|
|
"\n",
|
|
"\n",
|
|
"--------------------------------------------------------------------------------\n",
|
|
"\u001b[35mToken Count (including 468 tokens from system msg and function descriptions). Before compression : 2115 | After: 1223\u001b[0m\n",
|
|
"--------------------------------------------------------------------------------\n",
|
|
"\u001b[33massistant\u001b[0m (to user_proxy):\n",
|
|
"\n",
|
|
"It seems I forgot to import the `timedelta` class from the `datetime` module. I will correct this oversight and provide the updated code. Here is the corrected code including the missed import:\n",
|
|
"\n",
|
|
"```python\n",
|
|
"# filename: ytd_gains.py\n",
|
|
"\n",
|
|
"import yfinance as yf\n",
|
|
"from datetime import datetime, timedelta\n",
|
|
"\n",
|
|
"def get_last_valid_trading_day(year):\n",
|
|
" last_day = datetime(year, 12, 31)\n",
|
|
" # Start moving backwards from December 31st to find the last valid trading day of the year\n",
|
|
" while True:\n",
|
|
" stock_data = yf.download('SPY', start=last_day.strftime('%Y-%m-%d'), end=(last_day + timedelta(days=1)).strftime('%Y-%m-%d'))\n",
|
|
" if not stock_data.empty:\n",
|
|
" return last_day\n",
|
|
" last_day -= timedelta(days=1)\n",
|
|
"\n",
|
|
"# Define the ticker symbols for the 10 largest tech companies.\n",
|
|
"tech_companies = [\"AAPL\", \"MSFT\", \"GOOGL\", \"AMZN\", \"FB\", \"TSLA\", \"NVDA\", \"V\", \"ADBE\", \"INTC\"]\n",
|
|
"\n",
|
|
"# Compute the last valid trading day of the last year\n",
|
|
"end_of_last_year = get_last_valid_trading_day(datetime.now().year - 1)\n",
|
|
"\n",
|
|
"# Retrieve the data and calculate YTD gain for each company\n",
|
|
"ytd_gains = {}\n",
|
|
"for symbol in tech_companies:\n",
|
|
" try:\n",
|
|
" # Fetch historical data\n",
|
|
" stock = yf.Ticker(symbol)\n",
|
|
" last_price = stock.history(period='1d')['Close'].iloc[-1]\n",
|
|
" prev_close = stock.history(start=end_of_last_year, end=end_of_last_year + timedelta(days=1))['Close'].iloc[0]\n",
|
|
"\n",
|
|
" # Calculate YTD gain\n",
|
|
" ytd_gain = ((last_price - prev_close) / prev_close) * 100\n",
|
|
" ytd_gains[symbol] = ytd_gain\n",
|
|
" except Exception as e:\n",
|
|
" # Handle errors by skipping the company and printing an error message\n",
|
|
" print(f\"Error retrieving data for {symbol}: {e}\")\n",
|
|
"\n",
|
|
"# Print the YTD gains\n",
|
|
"for symbol, gain in ytd_gains.items():\n",
|
|
" print(f\"{symbol}: {gain:.2f}% YTD Gain\")\n",
|
|
"```\n",
|
|
"\n",
|
|
"Please execute the corrected `ytd_gains.py` script. This should resolve the `NameError` and provide you with the YTD gain for each company.\n",
|
|
"\n",
|
|
"--------------------------------------------------------------------------------\n",
|
|
"\u001b[31m\n",
|
|
">>>>>>>> USING AUTO REPLY...\u001b[0m\n",
|
|
"\u001b[31m\n",
|
|
">>>>>>>> EXECUTING CODE BLOCK 0 (inferred language is python)...\u001b[0m\n",
|
|
"\u001b[33muser_proxy\u001b[0m (to assistant):\n",
|
|
"\n",
|
|
"exitcode: 0 (execution succeeded)\n",
|
|
"Code output: \n",
|
|
"Error retrieving data for FB: single positional indexer is out-of-bounds\n",
|
|
"AAPL: -6.57% YTD Gain\n",
|
|
"MSFT: 10.70% YTD Gain\n",
|
|
"GOOGL: -1.83% YTD Gain\n",
|
|
"AMZN: 17.30% YTD Gain\n",
|
|
"TSLA: -18.45% YTD Gain\n",
|
|
"NVDA: 66.15% YTD Gain\n",
|
|
"V: 8.96% YTD Gain\n",
|
|
"ADBE: -4.30% YTD Gain\n",
|
|
"INTC: -12.54% YTD Gain\n",
|
|
"\n",
|
|
"\n",
|
|
"--------------------------------------------------------------------------------\n",
|
|
"\u001b[35mToken Count (including 468 tokens from system msg and function descriptions). Before compression : 1865 | After: 1133\u001b[0m\n",
|
|
"--------------------------------------------------------------------------------\n",
|
|
"\u001b[33massistant\u001b[0m (to user_proxy):\n",
|
|
"\n",
|
|
"It looks like an error occurred while retrieving data for FB, which stands for Facebook. This can happen if the ticker symbol has changed or if there was an issue with the data for the date range we were trying to fetch. Facebook, Inc. has been renamed to Meta Platforms, Inc., and its stock symbol is now \"META.\" \n",
|
|
"\n",
|
|
"I will update the list of technology companies to include \"META\" instead of \"FB\" and run the code again:\n",
|
|
"\n",
|
|
"```python\n",
|
|
"# filename: ytd_gains_updated.py\n",
|
|
"\n",
|
|
"import yfinance as yf\n",
|
|
"from datetime import datetime, timedelta\n",
|
|
"\n",
|
|
"def get_last_valid_trading_day(year):\n",
|
|
" last_day = datetime(year, 12, 31)\n",
|
|
" # Start moving backwards from December 31st to find the last valid trading day of the year\n",
|
|
" while True:\n",
|
|
" stock_data = yf.download('SPY', start=last_day.strftime('%Y-%m-%d'), end=(last_day + timedelta(days=1)).strftime('%Y-%m-%d'))\n",
|
|
" if not stock_data.empty:\n",
|
|
" return last_day\n",
|
|
" last_day -= timedelta(days=1)\n",
|
|
"\n",
|
|
"# Define the ticker symbols for the 10 largest tech companies with FB replaced by META.\n",
|
|
"tech_companies = [\"AAPL\", \"MSFT\", \"GOOGL\", \"AMZN\", \"META\", \"TSLA\", \"NVDA\", \"V\", \"ADBE\", \"INTC\"]\n",
|
|
"\n",
|
|
"# Compute the last valid trading day of the last year\n",
|
|
"end_of_last_year = get_last_valid_trading_day(datetime.now().year - 1)\n",
|
|
"\n",
|
|
"# Retrieve the data and calculate YTD gain for each company\n",
|
|
"ytd_gains = {}\n",
|
|
"for symbol in tech_companies:\n",
|
|
" try:\n",
|
|
" # Fetch historical data\n",
|
|
" stock = yf.Ticker(symbol)\n",
|
|
" last_price = stock.history(period='1d')['Close'].iloc[-1]\n",
|
|
" prev_close = stock.history(start=end_of_last_year, end=end_of_last_year + timedelta(days=1))['Close'].iloc[0]\n",
|
|
"\n",
|
|
" # Calculate YTD gain\n",
|
|
" ytd_gain = ((last_price - prev_close) / prev_close) * 100\n",
|
|
" ytd_gains[symbol] = ytd_gain\n",
|
|
" except Exception as e:\n",
|
|
" # Handle errors by skipping the company and printing an error message\n",
|
|
" print(f\"Error retrieving data for {symbol}: {e}\")\n",
|
|
"\n",
|
|
"# Print the YTD gains\n",
|
|
"for symbol, gain in ytd_gains.items():\n",
|
|
" print(f\"{symbol}: {gain:.2f}% YTD Gain\")\n",
|
|
"```\n",
|
|
"\n",
|
|
"Please execute the updated `ytd_gains_updated.py` script to get the YTD gain of Meta Platforms, Inc. along with the other technology companies.\n",
|
|
"\n",
|
|
"--------------------------------------------------------------------------------\n",
|
|
"\u001b[31m\n",
|
|
">>>>>>>> USING AUTO REPLY...\u001b[0m\n",
|
|
"\u001b[31m\n",
|
|
">>>>>>>> EXECUTING CODE BLOCK 0 (inferred language is python)...\u001b[0m\n",
|
|
"\u001b[33muser_proxy\u001b[0m (to assistant):\n",
|
|
"\n",
|
|
"exitcode: 0 (execution succeeded)\n",
|
|
"Code output: \n",
|
|
"AAPL: -6.57% YTD Gain\n",
|
|
"MSFT: 10.70% YTD Gain\n",
|
|
"GOOGL: -1.83% YTD Gain\n",
|
|
"AMZN: 17.30% YTD Gain\n",
|
|
"META: 42.06% YTD Gain\n",
|
|
"TSLA: -18.45% YTD Gain\n",
|
|
"NVDA: 66.15% YTD Gain\n",
|
|
"V: 8.96% YTD Gain\n",
|
|
"ADBE: -4.30% YTD Gain\n",
|
|
"INTC: -12.54% YTD Gain\n",
|
|
"\n",
|
|
"\n",
|
|
"--------------------------------------------------------------------------------\n",
|
|
"\u001b[35mToken Count (including 468 tokens from system msg and function descriptions). Before compression : 1828 | After: 1186\u001b[0m\n",
|
|
"--------------------------------------------------------------------------------\n",
|
|
"\u001b[33massistant\u001b[0m (to user_proxy):\n",
|
|
"\n",
|
|
"The YTD (Year-To-Date) gain for the 10 largest technology companies as of today are as follows:\n",
|
|
"\n",
|
|
"1. Apple Inc. (AAPL): -6.57% YTD Gain\n",
|
|
"2. Microsoft Corporation (MSFT): 10.70% YTD Gain\n",
|
|
"3. Alphabet Inc. (GOOGL): -1.83% YTD Gain\n",
|
|
"4. Amazon.com, Inc. (AMZN): 17.30% YTD Gain\n",
|
|
"5. Meta Platforms, Inc. (META, formerly FB): 42.06% YTD Gain\n",
|
|
"6. Tesla, Inc. (TSLA): -18.45% YTD Gain\n",
|
|
"7. NVIDIA Corporation (NVDA): 66.15% YTD Gain\n",
|
|
"8. Visa Inc. (V): 8.96% YTD Gain\n",
|
|
"9. Adobe Inc. (ADBE): -4.30% YTD Gain\n",
|
|
"10. Intel Corporation (INTC): -12.54% YTD Gain\n",
|
|
"\n",
|
|
"These YTD gains reflect the percentage change in the stock price of each company from the last trading day of the previous year to the most recent available trading data.\n",
|
|
"\n",
|
|
"If you need any further assistance, please let me know.\n",
|
|
"\n",
|
|
"TERMINATE\n",
|
|
"\n",
|
|
"--------------------------------------------------------------------------------\n",
|
|
"\u001b[31m\n",
|
|
">>>>>>>> NO HUMAN INPUT RECEIVED.\u001b[0m\n"
|
|
]
|
|
},
|
|
{
|
|
"data": {
|
|
"text/plain": [
|
|
"ChatResult(chat_id=None, chat_history=[{'content': 'Show me the YTD gain of 10 largest technology companies as of today.', 'role': 'assistant'}, {'content': 'It looks like an error occurred while retrieving data for FB, which stands for Facebook. This can happen if the ticker symbol has changed or if there was an issue with the data for the date range we were trying to fetch. Facebook, Inc. has been renamed to Meta Platforms, Inc., and its stock symbol is now \"META.\" \\n\\nI will update the list of technology companies to include \"META\" instead of \"FB\" and run the code again:\\n\\n```python\\n# filename: ytd_gains_updated.py\\n\\nimport yfinance as yf\\nfrom datetime import datetime, timedelta\\n\\ndef get_last_valid_trading_day(year):\\n last_day = datetime(year, 12, 31)\\n # Start moving backwards from December 31st to find the last valid trading day of the year\\n while True:\\n stock_data = yf.download(\\'SPY\\', start=last_day.strftime(\\'%Y-%m-%d\\'), end=(last_day + timedelta(days=1)).strftime(\\'%Y-%m-%d\\'))\\n if not stock_data.empty:\\n return last_day\\n last_day -= timedelta(days=1)\\n\\n# Define the ticker symbols for the 10 largest tech companies with FB replaced by META.\\ntech_companies = [\"AAPL\", \"MSFT\", \"GOOGL\", \"AMZN\", \"META\", \"TSLA\", \"NVDA\", \"V\", \"ADBE\", \"INTC\"]\\n\\n# Compute the last valid trading day of the last year\\nend_of_last_year = get_last_valid_trading_day(datetime.now().year - 1)\\n\\n# Retrieve the data and calculate YTD gain for each company\\nytd_gains = {}\\nfor symbol in tech_companies:\\n try:\\n # Fetch historical data\\n stock = yf.Ticker(symbol)\\n last_price = stock.history(period=\\'1d\\')[\\'Close\\'].iloc[-1]\\n prev_close = stock.history(start=end_of_last_year, end=end_of_last_year + timedelta(days=1))[\\'Close\\'].iloc[0]\\n\\n # Calculate YTD gain\\n ytd_gain = ((last_price - prev_close) / prev_close) * 100\\n ytd_gains[symbol] = ytd_gain\\n except Exception as e:\\n # Handle errors by skipping the company and printing an error message\\n print(f\"Error retrieving data for {symbol}: {e}\")\\n\\n# Print the YTD gains\\nfor symbol, gain in ytd_gains.items():\\n print(f\"{symbol}: {gain:.2f}% YTD Gain\")\\n```\\n\\nPlease execute the updated `ytd_gains_updated.py` script to get the YTD gain of Meta Platforms, Inc. along with the other technology companies.', 'role': 'user'}, {'content': 'exitcode: 0 (execution succeeded)\\nCode output: \\nAAPL: -6.57% YTD Gain\\nMSFT: 10.70% YTD Gain\\nGOOGL: -1.83% YTD Gain\\nAMZN: 17.30% YTD Gain\\nMETA: 42.06% YTD Gain\\nTSLA: -18.45% YTD Gain\\nNVDA: 66.15% YTD Gain\\nV: 8.96% YTD Gain\\nADBE: -4.30% YTD Gain\\nINTC: -12.54% YTD Gain\\n', 'role': 'assistant'}, {'content': 'The YTD (Year-To-Date) gain for the 10 largest technology companies as of today are as follows:\\n\\n1. Apple Inc. (AAPL): -6.57% YTD Gain\\n2. Microsoft Corporation (MSFT): 10.70% YTD Gain\\n3. Alphabet Inc. (GOOGL): -1.83% YTD Gain\\n4. Amazon.com, Inc. (AMZN): 17.30% YTD Gain\\n5. Meta Platforms, Inc. (META, formerly FB): 42.06% YTD Gain\\n6. Tesla, Inc. (TSLA): -18.45% YTD Gain\\n7. NVIDIA Corporation (NVDA): 66.15% YTD Gain\\n8. Visa Inc. (V): 8.96% YTD Gain\\n9. Adobe Inc. (ADBE): -4.30% YTD Gain\\n10. Intel Corporation (INTC): -12.54% YTD Gain\\n\\nThese YTD gains reflect the percentage change in the stock price of each company from the last trading day of the previous year to the most recent available trading data.\\n\\nIf you need any further assistance, please let me know.\\n\\nTERMINATE', 'role': 'user'}], summary='The YTD (Year-To-Date) gain for the 10 largest technology companies as of today are as follows:\\n\\n1. Apple Inc. (AAPL): -6.57% YTD Gain\\n2. Microsoft Corporation (MSFT): 10.70% YTD Gain\\n3. Alphabet Inc. (GOOGL): -1.83% YTD Gain\\n4. Amazon.com, Inc. (AMZN): 17.30% YTD Gain\\n5. Meta Platforms, Inc. (META, formerly FB): 42.06% YTD Gain\\n6. Tesla, Inc. (TSLA): -18.45% YTD Gain\\n7. NVIDIA Corporation (NVDA): 66.15% YTD Gain\\n8. Visa Inc. (V): 8.96% YTD Gain\\n9. Adobe Inc. (ADBE): -4.30% YTD Gain\\n10. Intel Corporation (INTC): -12.54% YTD Gain\\n\\nThese YTD gains reflect the percentage change in the stock price of each company from the last trading day of the previous year to the most recent available trading data.\\n\\nIf you need any further assistance, please let me know.\\n\\n', cost=({'total_cost': 0.31437, 'gpt-4': {'cost': 0.31437, 'prompt_tokens': 5401, 'completion_tokens': 2539, 'total_tokens': 7940}}, {'total_cost': 0.31437, 'gpt-4': {'cost': 0.31437, 'prompt_tokens': 5401, 'completion_tokens': 2539, 'total_tokens': 7940}}), human_input=[''])"
|
|
]
|
|
},
|
|
"execution_count": 5,
|
|
"metadata": {},
|
|
"output_type": "execute_result"
|
|
}
|
|
],
|
|
"source": [
|
|
"def constrain_num_messages(messages):\n",
|
|
" \"\"\"Constrain the number of messages to 3.\n",
|
|
"\n",
|
|
" This is an example of a customized compression function.\n",
|
|
"\n",
|
|
" Returns:\n",
|
|
" bool: whether the compression is successful.\n",
|
|
" list: the compressed messages.\n",
|
|
" \"\"\"\n",
|
|
" if len(messages) <= 3:\n",
|
|
" # do nothing\n",
|
|
" return False, None\n",
|
|
"\n",
|
|
" # save the first and last two messages\n",
|
|
" return True, messages[:1] + messages[-2:]\n",
|
|
"\n",
|
|
"\n",
|
|
"# create a CompressibleAgent instance named \"assistant\"\n",
|
|
"assistant = CompressibleAgent(\n",
|
|
" name=\"assistant\",\n",
|
|
" llm_config={\n",
|
|
" \"timeout\": 600,\n",
|
|
" \"cache_seed\": 43,\n",
|
|
" \"config_list\": config_list,\n",
|
|
" \"model\": \"gpt-4-1106-preview\",\n",
|
|
" },\n",
|
|
" compress_config={\n",
|
|
" \"mode\": \"CUSTOMIZED\",\n",
|
|
" \"compress_function\": constrain_num_messages, # this is required for customized compression\n",
|
|
" \"trigger_count\": 1600,\n",
|
|
" },\n",
|
|
")\n",
|
|
"\n",
|
|
"# create a UserProxyAgent instance named \"user_proxy\"\n",
|
|
"user_proxy = autogen.UserProxyAgent(\n",
|
|
" name=\"user_proxy\",\n",
|
|
" human_input_mode=\"TERMINATE\",\n",
|
|
" max_consecutive_auto_reply=10,\n",
|
|
" is_termination_msg=lambda x: x.get(\"content\", \"\").rstrip().endswith(\"TERMINATE\")\n",
|
|
" or x.get(\"content\", \"\").rstrip().endswith(\"TERMINATE.\"),\n",
|
|
" code_execution_config={\n",
|
|
" \"work_dir\": \"web\",\n",
|
|
" \"use_docker\": False,\n",
|
|
" }, # Please set use_docker=True if docker is available to run the generated code. Using docker is safer than running the generated code directly.\n",
|
|
" system_message=\"\"\"Reply TERMINATE if the task has been solved at full satisfaction.\n",
|
|
"Otherwise, reply CONTINUE, or the reason why the task is not solved yet.\"\"\",\n",
|
|
")\n",
|
|
"\n",
|
|
"user_proxy.initiate_chat(\n",
|
|
" assistant,\n",
|
|
" message=\"\"\"Show me the YTD gain of 10 largest technology companies as of today.\"\"\",\n",
|
|
")"
|
|
]
|
|
}
|
|
],
|
|
"metadata": {
|
|
"kernelspec": {
|
|
"display_name": "msft",
|
|
"language": "python",
|
|
"name": "python3"
|
|
},
|
|
"language_info": {
|
|
"codemirror_mode": {
|
|
"name": "ipython",
|
|
"version": 3
|
|
},
|
|
"file_extension": ".py",
|
|
"mimetype": "text/x-python",
|
|
"name": "python",
|
|
"nbconvert_exporter": "python",
|
|
"pygments_lexer": "ipython3",
|
|
"version": "3.10.13"
|
|
},
|
|
"orig_nbformat": 4
|
|
},
|
|
"nbformat": 4,
|
|
"nbformat_minor": 2
|
|
}
|