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docs/use_cases/autonomous_agents/autogpt.ipynb
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@@ -5,75 +5,9 @@
"id": "14f8b67b",
"metadata": {},
"source": [
"## AutoGPT\n",
"# AutoGPT\n",
"\n",
"* Implementation of https://github.com/Significant-Gravitas/Auto-GPT \n",
"* With LangChain primitives (LLMs, PromptTemplates, VectorStores, Embeddings, Tools)\n",
"\n",
"### Workflow\n",
"\n",
"[Temporary] Ensure you have correct Langchain version locally\n",
" \n",
"* `export SERPAPI_API_KEY`"
]
},
{
"cell_type": "code",
"execution_count": null,
"id": "a0866af1-43a8-4160-a9cf-9ffe64074493",
"metadata": {},
"outputs": [],
"source": [
"# !pip install playwright\n",
"# !playwright install "
]
},
{
"cell_type": "code",
"execution_count": null,
"id": "d27e21c5-8556-46bf-936a-f6e119ebd3a0",
"metadata": {
"tags": []
},
"outputs": [],
"source": [
"# ! playwright install "
]
},
{
"cell_type": "code",
"execution_count": null,
"id": "1cff42fd",
"metadata": {
"tags": []
},
"outputs": [],
"source": [
"# General \n",
"import os\n",
"import pandas as pd\n",
"from typing import Optional\n",
"import matplotlib.pyplot as plt\n",
"from langchain.experimental.autonomous_agents.autogpt.agent import AutoGPT\n",
"from langchain.chat_models import ChatOpenAI\n",
"from langchain.utilities import SerpAPIWrapper\n",
"from langchain.tools.human.tool import HumanInputRun\n",
"from langchain.tools.file_management.read import ReadFileTool\n",
"from langchain.tools.file_management.write import WriteFileTool\n",
"from langchain.document_loaders.url_selenium import SeleniumURLLoader\n",
"from langchain.agents.agent_toolkits.pandas.base import create_pandas_dataframe_agent"
]
},
{
"cell_type": "code",
"execution_count": null,
"id": "338dfc5d-178a-49ba-9187-bd08b648ab29",
"metadata": {
"tags": []
},
"outputs": [],
"source": [
"from langchain.document_loaders.url_playwright import PlaywrightURLLoader, Document"
"Implementation of https://github.com/Significant-Gravitas/Auto-GPT but with LangChain primitives (LLMs, PromptTemplates, VectorStores, Embeddings, Tools)"
]
},
{
@@ -81,158 +15,33 @@
"id": "192496a7",
"metadata": {},
"source": [
"### Set up tools\n",
"## Set up tools\n",
"\n",
"* We'll set up an AutoGPT with a `search` tool, and `write-file` tool, and a `read-file` tool"
]
},
{
"cell_type": "markdown",
"id": "708a426f",
"metadata": {},
"source": [
"Define any `tools` you want to use here"
"We'll set up an AutoGPT with a search tool, and write-file tool, and a read-file tool"
]
},
{
"cell_type": "code",
"execution_count": null,
"id": "cef4c150-0ef1-4a33-836b-01062fec134e",
"metadata": {
"tags": []
},
"execution_count": 1,
"id": "7c2c9b54",
"metadata": {},
"outputs": [],
"source": [
"# Tools\n",
"from typing import Optional\n",
"from langchain.agents import Tool, tool\n",
"from langchain.utilities import SerpAPIWrapper\n",
"from langchain.chains.llm_requests import LLMRequestsChain\n",
"from langchain.tools.file_management.read import ReadFileTool\n",
"from langchain.agents import Tool\n",
"from langchain.tools.file_management.write import WriteFileTool\n",
"from langchain.tools.requests.tool import RequestsGetTool, TextRequestsWrapper\n",
"from langchain.tools.file_management.read import ReadFileTool\n",
"\n",
"@tool\n",
"def process_csv(csv_file_path: str, instructions: str, output_path: Optional[str] = None) -> str:\n",
" \"\"\"Process a CSV by with pandas in a limited REPL. Only use this after writing data to disk as a csv file. Any figures must be saved to disk to be viewed by the human. Instructions should be written in natural language, not code. Assume the dataframe is already loaded.\"\"\"\n",
" try:\n",
" df = pd.read_csv(csv_file_path)\n",
" except Exception as e:\n",
" return f\"Error: {e}\"\n",
" agent = create_pandas_dataframe_agent(llm, df, max_iterations=30, verbose=True)\n",
" if output_path is not None:\n",
" instructions += f\" Save output to disk at {output_path}\"\n",
" try:\n",
" return agent.run(instructions)\n",
" except Exception as e:\n",
" return f\"Error: {e}\"\n",
" \n",
"@tool\n",
"def show_image(image_path: str) -> str:\n",
" \"\"\"Show an image from disk\"\"\"\n",
" try:\n",
" img = plt.imread(image_path)\n",
" except Exception as e:\n",
" return f\"Error: {e}\"\n",
" plt.imshow(img)\n",
" return f\"Showed image at {image_path}\"\n",
"\n",
"@tool\n",
"def current_time() -> str:\n",
" \"\"\"Show the current time\"\"\"\n",
" return f\"Current time: {datetime.datetime.now()}\" \n",
"\n",
"@tool\n",
"def scrape_links(url: str) -> str:\n",
" \"\"\"Scrape links from a webpage.\"\"\"\n",
" response, error_message = get_response(url)\n",
" if error_message:\n",
" return error_message\n",
" if not response:\n",
" return \"Error: Could not get response\"\n",
" soup = BeautifulSoup(response.text, \"html.parser\")\n",
"\n",
" for script in soup([\"script\", \"style\"]):\n",
" script.extract()\n",
"\n",
" hyperlinks = extract_hyperlinks(soup, url)\n",
"\n",
" return format_hyperlinks(hyperlinks)\n",
"\n"
]
},
{
"cell_type": "markdown",
"id": "fbd24d78-93a1-4645-bb3a-cac6fb2fc1a2",
"metadata": {},
"source": [
"### Define Web Browser Tool\n",
"\n",
"This is optional but can provide more information to the model relative to the other tools"
]
},
{
"cell_type": "code",
"execution_count": null,
"id": "e3dcd773-a462-404e-b3dd-822a5f773e0d",
"metadata": {
"tags": []
},
"outputs": [],
"source": [
"# !pip install playwright\n",
"# !playwright install >/dev/null\n",
"# !pip install bs4\n",
"# !pip install nest_asyncio"
]
},
{
"cell_type": "code",
"execution_count": null,
"id": "26b497d7-8e52-4c7f-8e7e-da0a48820a3c",
"metadata": {
"tags": []
},
"outputs": [],
"source": [
"async def async_load_playwright(url: str) -> str:\n",
" \"\"\"Load the specified URLs using Playwright and parse using BeautifulSoup.\"\"\"\n",
" from bs4 import BeautifulSoup\n",
" from playwright.async_api import async_playwright\n",
"\n",
" results = \"\"\n",
" async with async_playwright() as p:\n",
" browser = await p.chromium.launch(headless=True)\n",
" try:\n",
" page = await browser.new_page()\n",
" await page.goto(url)\n",
"\n",
" page_source = await page.content()\n",
" soup = BeautifulSoup(page_source, \"html.parser\")\n",
"\n",
" for script in soup([\"script\", \"style\"]):\n",
" script.extract()\n",
"\n",
" text = soup.get_text()\n",
" lines = (line.strip() for line in text.splitlines())\n",
" chunks = (phrase.strip() for line in lines for phrase in line.split(\" \"))\n",
" results = \"\\n\".join(chunk for chunk in chunks if chunk)\n",
" except Exception as e:\n",
" results = f\"Error: {e}\"\n",
" await browser.close()\n",
" return results\n",
"\n",
"def run_async(coro):\n",
" import asyncio\n",
" import nest_asyncio\n",
" nest_asyncio.apply()\n",
" event_loop = asyncio.get_event_loop()\n",
" return event_loop.run_until_complete(coro)\n",
"\n",
"@tool\n",
"def browse_web_page(url: str) -> str:\n",
" \"\"\"Useful for browsing websites and scraping the text information.\"\"\"\n",
" return run_async(async_load_playwright(url))"
"search = SerpAPIWrapper()\n",
"tools = [\n",
" Tool(\n",
" name = \"search\",\n",
" func=search.run,\n",
" description=\"useful for when you need to answer questions about current events. You should ask targeted questions\"\n",
" ),\n",
" WriteFileTool(),\n",
" ReadFileTool(),\n",
"]"
]
},
{
@@ -240,27 +49,35 @@
"id": "8e39ee28",
"metadata": {},
"source": [
"### Set up memory\n",
"## Set up memory\n",
"\n",
"* The memory here is used for the agents intermediate steps"
"The memory here is used for the agents intermediate steps"
]
},
{
"cell_type": "code",
"execution_count": null,
"id": "1df7b724",
"metadata": {
"tags": []
},
"execution_count": 2,
"id": "72bc204d",
"metadata": {},
"outputs": [],
"source": [
"# Memory\n",
"import faiss\n",
"from langchain.vectorstores import FAISS\n",
"from langchain.docstore import InMemoryDocstore\n",
"from langchain.embeddings import OpenAIEmbeddings\n",
"\n",
"from langchain.embeddings import OpenAIEmbeddings"
]
},
{
"cell_type": "code",
"execution_count": 3,
"id": "1df7b724",
"metadata": {},
"outputs": [],
"source": [
"# Define your embedding model\n",
"embeddings_model = OpenAIEmbeddings()\n",
"# Initialize the vectorstore as empty\n",
"import faiss\n",
"\n",
"embedding_size = 1536\n",
"index = faiss.IndexFlatL2(embedding_size)\n",
"vectorstore = FAISS(embeddings_model.embed_query, index, InMemoryDocstore({}), {})"
@@ -271,56 +88,37 @@
"id": "e40fd657",
"metadata": {},
"source": [
"### Setup model and AutoGPT\n",
"## Setup model and AutoGPT\n",
"\n",
"`Model set-up`"
"Initialize everything! We will use ChatOpenAI model"
]
},
{
"cell_type": "code",
"execution_count": null,
"id": "88c8b184-67d7-4c35-84ae-9b14bef8c4e3",
"metadata": {
"tags": []
},
"execution_count": 4,
"id": "3393bc23",
"metadata": {},
"outputs": [],
"source": [
"search = SerpAPIWrapper()\n",
"tools = [\n",
" Tool(\n",
" name = \"search\",\n",
" func=search.run,\n",
" description=\"Useful for when you need to answer questions about current events. You should ask targeted questions\"\n",
" ),\n",
" WriteFileTool(),\n",
" ReadFileTool(),\n",
" process_csv,\n",
" show_image,\n",
" RequestsGetTool(requests_wrapper=TextRequestsWrapper()),\n",
" current_time,\n",
" browse_web_page,\n",
" # huggingface_image_generation,\n",
"]"
"from langchain.experimental.autonomous_agents.autogpt.agent import AutoGPT\n",
"from langchain.chat_models import ChatOpenAI"
]
},
{
"cell_type": "code",
"execution_count": null,
"execution_count": 5,
"id": "709c08c2",
"metadata": {
"tags": []
},
"metadata": {},
"outputs": [],
"source": [
"llm = ChatOpenAI(model_name=\"gpt-4\", temperature=0.3)\n",
"agent = AutoGPT.from_llm_and_tools(\n",
" ai_name=\"Tom\",\n",
" ai_role=\"Assistant\",\n",
" tools=tools,\n",
" llm=llm,\n",
" memory=vectorstore.as_retriever(),\n",
" # human_in_the_loop=True, # Set to True if you want to add feedback at each step.\n",
" llm=ChatOpenAI(temperature=0),\n",
" memory=vectorstore.as_retriever()\n",
")\n",
"# Set verbose to be true\n",
"agent.chain.verbose = True"
]
},
@@ -329,127 +127,349 @@
"id": "fc9b51ba",
"metadata": {},
"source": [
"### AutoGPT as a research / data munger \n",
"## Run an example\n",
"\n",
"#### `inflation` and `college tuition`\n",
" \n",
"Let's use AutoGPT as researcher and data munger / cleaner.\n",
" \n",
"I spent a lot of time over the years crawling data sources and cleaning data. \n",
"\n",
"Let's see if AutoGPT can do all of this for us!\n",
"\n",
"Here is the prompt comparing `inflation` and `college tuition`."
"Here we will make it write a weather report for SF"
]
},
{
"cell_type": "code",
"execution_count": null,
"id": "3aba5e30",
"execution_count": 6,
"id": "c032b182",
"metadata": {
"tags": []
"scrolled": false
},
"outputs": [],
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"\n",
"\n",
"\u001b[1m> Entering new LLMChain chain...\u001b[0m\n",
"Prompt after formatting:\n",
"\u001b[32;1m\u001b[1;3mSystem: You are Tom, Assistant\n",
"Your decisions must always be made independently \n",
" without seeking user assistance. Play to your strengths \n",
" as an LLM and pursue simple strategies with no legal complications. \n",
" If you have completed all your tasks, \n",
" make sure to use the \"finish\" command.\n",
"\n",
"GOALS:\n",
"\n",
"1. write a weather report for SF today\n",
"\n",
"\n",
"Constraints:\n",
"1. ~4000 word limit for short term memory. Your short term memory is short, so immediately save important information to files.\n",
"2. If you are unsure how you previously did something or want to recall past events, thinking about similar events will help you remember.\n",
"3. No user assistance\n",
"4. Exclusively use the commands listed in double quotes e.g. \"command name\"\n",
"\n",
"Commands:\n",
"1. search: useful for when you need to answer questions about current events. You should ask targeted questions, args: \"tool_input\": \"\"\n",
"2. write_file: Write file to disk, args: \"file_path\": \"name of file\", \"text\": \"text to write to file\"\n",
"3. read_file: Read file from disk, args: \"file_path\": \"name of file\"\n",
"4. finish: use this to signal that you have finished all your objectives, args: \"response\": \"final response to let people know you have finished your objectives\"\n",
"\n",
"Resources:\n",
"1. Internet access for searches and information gathering.\n",
"2. Long Term memory management.\n",
"3. GPT-3.5 powered Agents for delegation of simple tasks.\n",
"4. File output.\n",
"\n",
"Performance Evaluation:\n",
"1. Continuously review and analyze your actions to ensure you are performing to the best of your abilities.\n",
"2. Constructively self-criticize your big-picture behavior constantly.\n",
"3. Reflect on past decisions and strategies to refine your approach.\n",
"4. Every command has a cost, so be smart and efficient. Aim to complete tasks in the least number of steps.\n",
"\n",
"You should only respond in JSON format as described below \n",
"Response Format: \n",
"{\n",
" \"thoughts\": {\n",
" \"text\": \"thought\",\n",
" \"reasoning\": \"reasoning\",\n",
" \"plan\": \"- short bulleted\\n- list that conveys\\n- long-term plan\",\n",
" \"criticism\": \"constructive self-criticism\",\n",
" \"speak\": \"thoughts summary to say to user\"\n",
" },\n",
" \"command\": {\n",
" \"name\": \"command name\",\n",
" \"args\": {\n",
" \"arg name\": \"value\"\n",
" }\n",
" }\n",
"} \n",
"Ensure the response can be parsed by Python json.loads\n",
"System: The current time and date is Sun Apr 16 18:38:42 2023\n",
"System: This reminds you of these events from your past:\n",
"[]\n",
"\n",
"\n",
"Human: Determine which next command to use, and respond using the format specified above:\u001b[0m\n",
"\n",
"\u001b[1m> Finished chain.\u001b[0m\n",
"{\n",
" \"thoughts\": {\n",
" \"text\": \"I think I should start by writing the weather report for SF today. I can use the 'search' command to find the current weather conditions.\",\n",
" \"reasoning\": \"The first step to completing my goals is to write the weather report for SF today. I can use the 'search' command to find the current weather conditions.\",\n",
" \"plan\": \"- Use the 'search' command to find the current weather conditions.\\n- Write the weather report for SF today.\",\n",
" \"criticism\": \"I need to make sure that I am accurate in my reporting of the weather conditions.\",\n",
" \"speak\": \"I will use the 'search' command to find the current weather conditions and then write the weather report for SF today.\"\n",
" },\n",
" \"command\": {\n",
" \"name\": \"search\",\n",
" \"args\": {\n",
" \"tool_input\": \"current weather conditions in San Francisco\"\n",
" }\n",
" }\n",
"}\n",
"\n",
"\n",
"\u001b[1m> Entering new LLMChain chain...\u001b[0m\n",
"Prompt after formatting:\n",
"\u001b[32;1m\u001b[1;3mSystem: You are Tom, Assistant\n",
"Your decisions must always be made independently \n",
" without seeking user assistance. Play to your strengths \n",
" as an LLM and pursue simple strategies with no legal complications. \n",
" If you have completed all your tasks, \n",
" make sure to use the \"finish\" command.\n",
"\n",
"GOALS:\n",
"\n",
"1. write a weather report for SF today\n",
"\n",
"\n",
"Constraints:\n",
"1. ~4000 word limit for short term memory. Your short term memory is short, so immediately save important information to files.\n",
"2. If you are unsure how you previously did something or want to recall past events, thinking about similar events will help you remember.\n",
"3. No user assistance\n",
"4. Exclusively use the commands listed in double quotes e.g. \"command name\"\n",
"\n",
"Commands:\n",
"1. search: useful for when you need to answer questions about current events. You should ask targeted questions, args: \"tool_input\": \"\"\n",
"2. write_file: Write file to disk, args: \"file_path\": \"name of file\", \"text\": \"text to write to file\"\n",
"3. read_file: Read file from disk, args: \"file_path\": \"name of file\"\n",
"4. finish: use this to signal that you have finished all your objectives, args: \"response\": \"final response to let people know you have finished your objectives\"\n",
"\n",
"Resources:\n",
"1. Internet access for searches and information gathering.\n",
"2. Long Term memory management.\n",
"3. GPT-3.5 powered Agents for delegation of simple tasks.\n",
"4. File output.\n",
"\n",
"Performance Evaluation:\n",
"1. Continuously review and analyze your actions to ensure you are performing to the best of your abilities.\n",
"2. Constructively self-criticize your big-picture behavior constantly.\n",
"3. Reflect on past decisions and strategies to refine your approach.\n",
"4. Every command has a cost, so be smart and efficient. Aim to complete tasks in the least number of steps.\n",
"\n",
"You should only respond in JSON format as described below \n",
"Response Format: \n",
"{\n",
" \"thoughts\": {\n",
" \"text\": \"thought\",\n",
" \"reasoning\": \"reasoning\",\n",
" \"plan\": \"- short bulleted\\n- list that conveys\\n- long-term plan\",\n",
" \"criticism\": \"constructive self-criticism\",\n",
" \"speak\": \"thoughts summary to say to user\"\n",
" },\n",
" \"command\": {\n",
" \"name\": \"command name\",\n",
" \"args\": {\n",
" \"arg name\": \"value\"\n",
" }\n",
" }\n",
"} \n",
"Ensure the response can be parsed by Python json.loads\n",
"System: The current time and date is Sun Apr 16 18:38:59 2023\n",
"System: This reminds you of these events from your past:\n",
"['Assistant Reply: {\\n \"thoughts\": {\\n \"text\": \"I think I should start by writing the weather report for SF today. I can use the \\'search\\' command to find the current weather conditions.\",\\n \"reasoning\": \"The first step to completing my goals is to write the weather report for SF today. I can use the \\'search\\' command to find the current weather conditions.\",\\n \"plan\": \"- Use the \\'search\\' command to find the current weather conditions.\\\\n- Write the weather report for SF today.\",\\n \"criticism\": \"I need to make sure that I am accurate in my reporting of the weather conditions.\",\\n \"speak\": \"I will use the \\'search\\' command to find the current weather conditions and then write the weather report for SF today.\"\\n },\\n \"command\": {\\n \"name\": \"search\",\\n \"args\": {\\n \"tool_input\": \"current weather conditions in San Francisco\"\\n }\\n }\\n} \\nResult: Command search returned: Cloudy skies early, then partly cloudy in the afternoon. High 56F. Winds W at 15 to 25 mph. ']\n",
"\n",
"\n",
"Human: Determine which next command to use, and respond using the format specified above:\n",
"AI: {\n",
" \"thoughts\": {\n",
" \"text\": \"I think I should start by writing the weather report for SF today. I can use the 'search' command to find the current weather conditions.\",\n",
" \"reasoning\": \"The first step to completing my goals is to write the weather report for SF today. I can use the 'search' command to find the current weather conditions.\",\n",
" \"plan\": \"- Use the 'search' command to find the current weather conditions.\\n- Write the weather report for SF today.\",\n",
" \"criticism\": \"I need to make sure that I am accurate in my reporting of the weather conditions.\",\n",
" \"speak\": \"I will use the 'search' command to find the current weather conditions and then write the weather report for SF today.\"\n",
" },\n",
" \"command\": {\n",
" \"name\": \"search\",\n",
" \"args\": {\n",
" \"tool_input\": \"current weather conditions in San Francisco\"\n",
" }\n",
" }\n",
"}\n",
"System: Command search returned: Cloudy skies early, then partly cloudy in the afternoon. High 56F. Winds W at 15 to 25 mph.\n",
"Human: Determine which next command to use, and respond using the format specified above:\u001b[0m\n"
]
},
{
"name": "stdout",
"output_type": "stream",
"text": [
"\n",
"\u001b[1m> Finished chain.\u001b[0m\n",
"{\n",
" \"thoughts\": {\n",
" \"text\": \"Now that I have the current weather conditions for SF, I can write the weather report. I will use the 'write_file' command to save the report to a file.\",\n",
" \"reasoning\": \"Now that I have the current weather conditions for SF, I can write the weather report. I will use the 'write_file' command to save the report to a file.\",\n",
" \"plan\": \"- Use the 'write_file' command to save the weather report to a file.\",\n",
" \"criticism\": \"I need to make sure that the weather report is clear and concise.\",\n",
" \"speak\": \"I will use the 'write_file' command to save the weather report to a file.\"\n",
" },\n",
" \"command\": {\n",
" \"name\": \"write_file\",\n",
" \"args\": {\n",
" \"file_path\": \"weather_report.txt\",\n",
" \"text\": \"Weather Report for San Francisco on April 16, 2023:\\n\\nCloudy skies early, then partly cloudy in the afternoon. High 56F. Winds W at 15 to 25 mph.\"\n",
" }\n",
" }\n",
"}\n",
"\n",
"\n",
"\u001b[1m> Entering new LLMChain chain...\u001b[0m\n",
"Prompt after formatting:\n",
"\u001b[32;1m\u001b[1;3mSystem: You are Tom, Assistant\n",
"Your decisions must always be made independently \n",
" without seeking user assistance. Play to your strengths \n",
" as an LLM and pursue simple strategies with no legal complications. \n",
" If you have completed all your tasks, \n",
" make sure to use the \"finish\" command.\n",
"\n",
"GOALS:\n",
"\n",
"1. write a weather report for SF today\n",
"\n",
"\n",
"Constraints:\n",
"1. ~4000 word limit for short term memory. Your short term memory is short, so immediately save important information to files.\n",
"2. If you are unsure how you previously did something or want to recall past events, thinking about similar events will help you remember.\n",
"3. No user assistance\n",
"4. Exclusively use the commands listed in double quotes e.g. \"command name\"\n",
"\n",
"Commands:\n",
"1. search: useful for when you need to answer questions about current events. You should ask targeted questions, args: \"tool_input\": \"\"\n",
"2. write_file: Write file to disk, args: \"file_path\": \"name of file\", \"text\": \"text to write to file\"\n",
"3. read_file: Read file from disk, args: \"file_path\": \"name of file\"\n",
"4. finish: use this to signal that you have finished all your objectives, args: \"response\": \"final response to let people know you have finished your objectives\"\n",
"\n",
"Resources:\n",
"1. Internet access for searches and information gathering.\n",
"2. Long Term memory management.\n",
"3. GPT-3.5 powered Agents for delegation of simple tasks.\n",
"4. File output.\n",
"\n",
"Performance Evaluation:\n",
"1. Continuously review and analyze your actions to ensure you are performing to the best of your abilities.\n",
"2. Constructively self-criticize your big-picture behavior constantly.\n",
"3. Reflect on past decisions and strategies to refine your approach.\n",
"4. Every command has a cost, so be smart and efficient. Aim to complete tasks in the least number of steps.\n",
"\n",
"You should only respond in JSON format as described below \n",
"Response Format: \n",
"{\n",
" \"thoughts\": {\n",
" \"text\": \"thought\",\n",
" \"reasoning\": \"reasoning\",\n",
" \"plan\": \"- short bulleted\\n- list that conveys\\n- long-term plan\",\n",
" \"criticism\": \"constructive self-criticism\",\n",
" \"speak\": \"thoughts summary to say to user\"\n",
" },\n",
" \"command\": {\n",
" \"name\": \"command name\",\n",
" \"args\": {\n",
" \"arg name\": \"value\"\n",
" }\n",
" }\n",
"} \n",
"Ensure the response can be parsed by Python json.loads\n",
"System: The current time and date is Sun Apr 16 18:39:10 2023\n",
"System: This reminds you of these events from your past:\n",
"['Assistant Reply: {\\n \"thoughts\": {\\n \"text\": \"I think I should start by writing the weather report for SF today. I can use the \\'search\\' command to find the current weather conditions.\",\\n \"reasoning\": \"The first step to completing my goals is to write the weather report for SF today. I can use the \\'search\\' command to find the current weather conditions.\",\\n \"plan\": \"- Use the \\'search\\' command to find the current weather conditions.\\\\n- Write the weather report for SF today.\",\\n \"criticism\": \"I need to make sure that I am accurate in my reporting of the weather conditions.\",\\n \"speak\": \"I will use the \\'search\\' command to find the current weather conditions and then write the weather report for SF today.\"\\n },\\n \"command\": {\\n \"name\": \"search\",\\n \"args\": {\\n \"tool_input\": \"current weather conditions in San Francisco\"\\n }\\n }\\n} \\nResult: Command search returned: Cloudy skies early, then partly cloudy in the afternoon. High 56F. Winds W at 15 to 25 mph. ', 'Assistant Reply: {\\n \"thoughts\": {\\n \"text\": \"Now that I have the current weather conditions for SF, I can write the weather report. I will use the \\'write_file\\' command to save the report to a file.\",\\n \"reasoning\": \"Now that I have the current weather conditions for SF, I can write the weather report. I will use the \\'write_file\\' command to save the report to a file.\",\\n \"plan\": \"- Use the \\'write_file\\' command to save the weather report to a file.\",\\n \"criticism\": \"I need to make sure that the weather report is clear and concise.\",\\n \"speak\": \"I will use the \\'write_file\\' command to save the weather report to a file.\"\\n },\\n \"command\": {\\n \"name\": \"write_file\",\\n \"args\": {\\n \"file_path\": \"weather_report.txt\",\\n \"text\": \"Weather Report for San Francisco on April 16, 2023:\\\\n\\\\nCloudy skies early, then partly cloudy in the afternoon. High 56F. Winds W at 15 to 25 mph.\"\\n }\\n }\\n} \\nResult: Command write_file returned: File written to successfully. ']\n",
"\n",
"\n",
"Human: Determine which next command to use, and respond using the format specified above:\n",
"AI: {\n",
" \"thoughts\": {\n",
" \"text\": \"I think I should start by writing the weather report for SF today. I can use the 'search' command to find the current weather conditions.\",\n",
" \"reasoning\": \"The first step to completing my goals is to write the weather report for SF today. I can use the 'search' command to find the current weather conditions.\",\n",
" \"plan\": \"- Use the 'search' command to find the current weather conditions.\\n- Write the weather report for SF today.\",\n",
" \"criticism\": \"I need to make sure that I am accurate in my reporting of the weather conditions.\",\n",
" \"speak\": \"I will use the 'search' command to find the current weather conditions and then write the weather report for SF today.\"\n",
" },\n",
" \"command\": {\n",
" \"name\": \"search\",\n",
" \"args\": {\n",
" \"tool_input\": \"current weather conditions in San Francisco\"\n",
" }\n",
" }\n",
"}\n",
"System: Command search returned: Cloudy skies early, then partly cloudy in the afternoon. High 56F. Winds W at 15 to 25 mph.\n",
"Human: Determine which next command to use, and respond using the format specified above:\n",
"AI: {\n",
" \"thoughts\": {\n",
" \"text\": \"Now that I have the current weather conditions for SF, I can write the weather report. I will use the 'write_file' command to save the report to a file.\",\n",
" \"reasoning\": \"Now that I have the current weather conditions for SF, I can write the weather report. I will use the 'write_file' command to save the report to a file.\",\n",
" \"plan\": \"- Use the 'write_file' command to save the weather report to a file.\",\n",
" \"criticism\": \"I need to make sure that the weather report is clear and concise.\",\n",
" \"speak\": \"I will use the 'write_file' command to save the weather report to a file.\"\n",
" },\n",
" \"command\": {\n",
" \"name\": \"write_file\",\n",
" \"args\": {\n",
" \"file_path\": \"weather_report.txt\",\n",
" \"text\": \"Weather Report for San Francisco on April 16, 2023:\\n\\nCloudy skies early, then partly cloudy in the afternoon. High 56F. Winds W at 15 to 25 mph.\"\n",
" }\n",
" }\n",
"}\n",
"System: Command write_file returned: File written to successfully.\n",
"Human: Determine which next command to use, and respond using the format specified above:\u001b[0m\n",
"\n",
"\u001b[1m> Finished chain.\u001b[0m\n",
"{\n",
" \"thoughts\": {\n",
" \"text\": \"I have completed all my tasks. I will use the 'finish' command to signal that I am done.\",\n",
" \"reasoning\": \"I have completed all my tasks, so I will use the 'finish' command to signal that I am done.\",\n",
" \"plan\": \"- Use the 'finish' command to signal that I am done.\",\n",
" \"criticism\": \"I need to make sure that I have completed all my tasks before using the 'finish' command.\",\n",
" \"speak\": \"I have completed all my tasks, so I will use the 'finish' command to signal that I am done.\"\n",
" },\n",
" \"command\": {\n",
" \"name\": \"finish\",\n",
" \"args\": {\n",
" \"response\": \"I have completed all my tasks. Goodbye!\"\n",
" }\n",
" }\n",
"}\n"
]
},
{
"data": {
"text/plain": [
"'I have completed all my tasks. Goodbye!'"
]
},
"execution_count": 6,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"agent.run([\"Using the 'data/' folder as scratch, get me the yearly % change in US college tuition and the yearly % change in US inflation (US CPI) every year since 1980.\"])"
]
},
{
"cell_type": "markdown",
"id": "f78012cf",
"metadata": {},
"source": [
"The command runs and writes output to `data`.\n",
" \n",
"`cleaned_college_tuition_inflation_percent_change.csv` is written.\n",
"\n",
"We write some simple code to plot this."
"agent.run([\"write a weather report for SF today\"])"
]
},
{
"cell_type": "code",
"execution_count": null,
"id": "c33c7a24",
"id": "aa264f26",
"metadata": {},
"outputs": [],
"source": [
"# Read\n",
"d = pd.read_csv(\"data/cleaned_college_tuition_inflation_percent_change.csv\")\n",
"d.set_index(\"Year\",inplace=True)\n",
"# Compute cumulative percent change\n",
"d['College Tuition % Change Cumulative'] = (1 + d['College Tuition % Change']).cumprod() * 100\n",
"d['Inflation % Change Cumulative'] = (1 + d['Inflation % Change']).cumprod() * 100\n",
"# Plot\n",
"d[['College Tuition % Change Cumulative','Inflation % Change Cumulative']].plot(color=['blue','green'])\n",
"plt.ylabel(\"Cumulative Percent Change\")"
]
},
{
"cell_type": "markdown",
"id": "f31d09ea",
"metadata": {},
"source": [
"Of course, we would want to inspect and verify the results."
]
},
{
"cell_type": "markdown",
"id": "3c59b98d",
"metadata": {},
"source": [
"### AutoGPT as a data scietntist (research, cleaning, visualization)\n",
"\n",
"#### `hey jude` \n",
"\n",
"Let's try end-to-end data retrival and plotting.\n",
"\n",
"* See my full notes here: https://rlancemartin.notion.site/Auto-GPT-Notes-9481cbd0cb364580bf97d5f41144e5cf\n",
"* Files to write \n",
"* However, reading seems to not work\n",
"* Even if I simply ask it to read from the saved file, it gets stuck and appears to loop\n",
"\n",
"`agent.run([\"Read the file hey_jude_lyrics.txt and plot the frequency of the top 5 words.\"])`"
]
},
{
"cell_type": "code",
"execution_count": null,
"id": "999f19bf",
"metadata": {},
"outputs": [],
"source": [
"llm = ChatOpenAI(model_name=\"gpt-4\", temperature=0.3)\n",
"agent = AutoGPT.from_llm_and_tools(\n",
" ai_name=\"Tom\",\n",
" ai_role=\"Look up song lyrics\",\n",
" tools=tools,\n",
" llm=llm,\n",
" memory=vectorstore.as_retriever()\n",
")\n",
"agent.chain.verbose = True\n",
"\n",
"agent.run([\"Get the lyrics to the Beatles song 'Hey Jude' and plot the frequency of the top 5 words.\"])"
]
},
{
"cell_type": "code",
"execution_count": null,
"id": "8a54c746",
"metadata": {},
"outputs": [],
"source": [
"llm = ChatOpenAI(model_name=\"gpt-4\", temperature=0.3)\n",
"agent = AutoGPT.from_llm_and_tools(\n",
" ai_name=\"Tom\",\n",
" ai_role=\"Look up song lyrics\",\n",
" tools=tools,\n",
" llm=llm,\n",
" memory=vectorstore.as_retriever()\n",
")\n",
"agent.chain.verbose = True\n",
"\n",
"agent.run([\"Read the file hey_jude_lyrics.txt and plot the frequency of the top 5 words.\"])"
]
"source": []
}
],
"metadata": {
@@ -468,7 +488,7 @@
"name": "python",
"nbconvert_exporter": "python",
"pygments_lexer": "ipython3",
"version": "3.11.2"
"version": "3.10.1"
}
},
"nbformat": 4,