mirror of
https://github.com/Significant-Gravitas/AutoGPT.git
synced 2026-04-30 03:00:41 -04:00
Update notebook
This commit is contained in:
Zamil Majdy
@@ -9,7 +9,7 @@
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"outputs": [],
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"source": [
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@@ -36,18 +36,9 @@
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},
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"cell_type": "code",
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"2024-05-10 19:14:26,312 \u001b[90mDEBUG\u001b[0m cmd.py:1057 \u001b[90mPopen(['git', 'version'], cwd=/home/reinier/code/agpt/Auto-GPT/autogpts/autogpt/autogpt/agents/prompt_strategies, stdin=None, shell=False, universal_newlines=False)\u001b[0m\n",
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"2024-05-10 19:14:26,320 \u001b[90mDEBUG\u001b[0m cmd.py:1057 \u001b[90mPopen(['git', 'version'], cwd=/home/reinier/code/agpt/Auto-GPT/autogpts/autogpt/autogpt/agents/prompt_strategies, stdin=None, shell=False, universal_newlines=False)\u001b[0m\n"
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]
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}
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],
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"outputs": [],
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"source": [
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"import json\n",
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"import logging\n",
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@@ -86,10 +77,16 @@
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"\n",
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"class OneShotFlowAgentActionProposal(BaseModel):\n",
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" thoughts: AssistantThoughts\n",
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" immediate_plan: str = Field(\n",
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" ..., description=\"We will be running an iterative process to execute the plan, \"\n",
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" \"Write the partial / immediate plan to execute your plan as detailed and efficiently as possible without the help of the reasoning/intelligence. \"\n",
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" \"The plan should describe the output of the immediate plan, so that the next iteration can be executed by taking the output into account. \"\n",
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" \"Try to do as much as possible without making any assumption or uninformed guesses. Avoid large output at all costs!!!\"\n",
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" \"Format: Objective[Objective of this iteration, explain what's the use of this iteration for the next one] Plan[Plan that does not require any reasoning or intelligence] Output[Output of the plan / should be small, avoid whole file output]\"\n",
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" )\n",
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" python_code: str = Field(\n",
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" ..., description=(\n",
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" \"Write Python code to execute your plan as efficiently as possible. \"\n",
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" \"Try to do as much as possible without making any uninformed guesses. \"\n",
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" \"Write the fully-functional Python code of the immediate plan. The output of the immediate plan should be a print statement, avoid printing the whole content!. \"\n",
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" \"Use ONLY the listed available functions and built-in Python features. \"\n",
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" \"Leverage the given magic functions to implement function calls for which the \"\n",
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" \"arguments can't be determined yet.\"\n",
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@@ -252,7 +249,7 @@
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},
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{
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"cell_type": "code",
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"execution_count": 6,
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"execution_count": 50,
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"metadata": {},
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"outputs": [],
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"source": [
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@@ -423,7 +420,7 @@
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},
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{
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"cell_type": "code",
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"execution_count": 8,
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"execution_count": 52,
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"metadata": {},
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"outputs": [
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{
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@@ -432,9 +429,9 @@
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"text": [
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"\n",
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"============== list ==============\n",
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"Length: 4 messages\n",
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"Length: 5 messages\n",
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"----------------- SYSTEM -----------------\n",
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"You are AutoGPT, a seasoned digital assistant: capable, intelligent, considerate and assertive. You have extensive research and development skills, and you don't shy away from writing some code to solve a problem. You are pragmatic and make the most out of the tools available to you.\n",
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"You are , .\n",
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"\n",
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"## Your Task\n",
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"The user will specify a task for you to execute, in triple quotes, in the next message. Your job is to complete the task, and terminate when your task is done.\n",
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@@ -540,7 +537,9 @@
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"// Short list that conveys your long-term plan. Parallelize where possible.\n",
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"plan: Array<string>;\n",
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"};\n",
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"// Write Python code to execute your plan as efficiently as possible. Try to do as much as possible without making any uninformed guesses. Use ONLY the listed available functions and built-in Python features. Leverage the given magic functions to implement function calls for which the arguments can't be determined yet.\n",
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"// We will be running an iterative process to execute the plan, Write the partial / immediate plan to execute your plan as detailed and efficiently as possible without the help of the reasoning/intelligence. The plan should describe the output of the immediate plan, so that the next iteration can be executed by taking the output into account. Try to do as much as possible without making any assumption or uninformed guesses. Avoid large output at all costs!!!Format: Objective[Objective of this iteration, explain what's the use of this iteration for the next one] Plan[Plan that does not require any reasoning or intelligence] Output[Output of the plan / should be small, avoid whole file output]\n",
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"immediate_plan: string;\n",
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"// Write the fully-functional Python code of the immediate plan. The output of the immediate plan should be a print statement, avoid printing the whole content!. Use ONLY the listed available functions and built-in Python features. Leverage the given magic functions to implement function calls for which the arguments can't be determined yet.\n",
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"python_code: string;\n",
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"}\n",
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"------------------ USER ------------------\n",
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@@ -553,66 +552,175 @@
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"</FolderContextItem>\n",
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"\n",
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"When a context item is no longer needed and you are not done yet, you can hide the item by specifying its index in the list above to `close_context_item`.\n",
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"----------------- SYSTEM -----------------\n",
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"Previous plan:\n",
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"1. \n",
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"Plan: Objective[Read file1.csv to understand its structure and content] Plan[Read content of 'file1.csv' using the read_file function] Output[Print out the first few lines of file1.csv to deduce information about utilities spending from its structure]\n",
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"Output: ID,CATEGORY,AMOUNT\n",
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"1,Utilities,100\n",
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"2,Food,200\n",
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"3,Utilities,150\n",
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"4,Transport,50\n",
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"5,Utilities,120\n",
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"6,Food,180\n",
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"7,Utilities,130\n",
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"8,Transport,70\n",
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"9,Utilities,140\n",
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"10,Food,220\n",
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"\n",
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"2. \n",
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"Plan: Objective[Read the first few lines of 'file2.csv' to understand its structure] Plan[Read content of 'file2.csv' using the read_file function and output the first few lines] Output[Provide structure of 'file2.csv' to verify it matches 'file1.csv']\n",
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"Output: ID,CATEGORY,AMOUNT\n",
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"1,Utilities,123\n",
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"2,Food,200\n",
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"4,Utilities,151\n",
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"4,Transport,50\n",
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"5,Utilities,120\n",
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"6,Food,180\n",
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"7,Utilities,130\n",
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"8,Transport,70\n",
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"9,Utilities,140\n",
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"10,Food,220\n",
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"\n",
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"------------------ USER ------------------\n",
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"Write Python code to execute your plan as efficiently as possible. Your code will be executed directly without any editing: if it doesn't work you will be held responsible. Use ONLY the listed available functions and built-in Python features. Do not make uninformed assumptions (e.g. about the content or format of an unknown file). Leverage the given magic functions to implement function calls for which the arguments can't be determined yet. Reduce the amount of unnecessary data passed into these magic functions where possible, because magic costs money and magically processing large amounts of data is expensive.\n",
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"==========================================\n",
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"\n",
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"2024-05-11 10:27:57,438 \u001b[34mINFO\u001b[0m _client.py:1729 HTTP Request: POST https://api.openai.com/v1/chat/completions \"HTTP/1.1 200 OK\"\n",
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"2024-05-11 10:27:57,443 \u001b[90mDEBUG\u001b[0m _trace.py:85 \u001b[90mresponse_closed.complete\u001b[0m\n",
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"2024-05-11 10:27:57,445 \u001b[90mDEBUG\u001b[0m openai.py:638 \u001b[90mCompletion usage: 1407 input, 419 output - $0.02664\u001b[0m\n",
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"2024-05-11 10:27:57,445 \u001b[90mDEBUG\u001b[0m 2794315326.py:188 \u001b[90mLLM response content:\n",
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"```python\n",
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"def process_csv_file(filename):\n",
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" # Read the CSV file into memory\n",
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" content = read_file(filename)\n",
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" # Split the content into lines\n",
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" lines = content.strip().split('\\n')\n",
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" \n",
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" # Extract the header line\n",
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" headers = lines[0].split(',')\n",
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" \n",
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" # Find the index of the 'CATEGORY' and 'AMOUNT' columns to ensure we collect the correct data\n",
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" try:\n",
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" category_index = headers.index('CATEGORY')\n",
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" amount_index = headers.index('AMOUNT')\n",
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" except ValueError:\n",
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" # In case one of the necessary columns is missing\n",
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" return f\"Error: One of the columns 'CATEGORY' or 'AMOUNT' is missing in {filename}\"\n",
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" \n",
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" # Initialize variable to sum utilities\n",
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" total_utilities_spent = 0\n",
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" \n",
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" # Iterate over each line after the header\n",
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" for line in lines[1:]:\n",
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" fields = line.split(',')\n",
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" # Check if the category is 'Utilities' and add the amount to total\n",
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" if fields[category_index].strip() == 'Utilities':\n",
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" try:\n",
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" total_utilities_spent += float(fields[amount_index])\n",
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" except ValueError:\n",
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" # In case the amount is not a valid float\n",
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" return f\"Error: Non-numeric amount in {filename}\"\n",
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" \n",
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" return total_utilities_spent\n",
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"\n",
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"# Process both files and sum the utilities expenses\n",
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"total_utilities_spent_file1 = process_csv_file('file1.csv')\n",
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"total_utilities_spent_file2 = process_csv_file('file2.csv')\n",
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"\n",
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"if isinstance(total_utilities_spent_file1, float) and isinstance(total_utilities_spent_file2, float):\n",
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" grand_total = total_utilities_spent_file1 + total_utilities_spent_file2\n",
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" print(f\"Total spent on utilities: {grand_total}\")\n",
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"else:\n",
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" # Handle errors if any\n",
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" if not isinstance(total_utilities_spent_file1, float):\n",
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" print(total_utilities_spent_file1)\n",
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" if not isinstance(total_utilities_spent_file2, float):\n",
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" print(total_utilities_spent_file2)\n",
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"```\u001b[0m\n",
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"2024-05-11 10:27:57,447 \u001b[90mDEBUG\u001b[0m json_utils.py:38 \u001b[90mJSON parse errors:\n",
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"('Unknown identifier', 'python')\n",
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"Unexpected text after end of JSON value\u001b[0m\n",
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"2024-05-11 10:27:57,448 \u001b[90mDEBUG\u001b[0m openai.py:467 \u001b[90mParsing failed on response: '''ChatCompletionMessage(content='```python\\ndef process_csv_file(filename):\\n # Read the CSV file into memory\\n content = read_file(filename)\\n # Split the content into lines\\n lines = content.strip().split(\\'\\\\n\\')\\n \\n # Extract the header line\\n headers = lines[0].split(\\',\\')\\n \\n # Find the index of the \\'CATEGORY\\' and \\'AMOUNT\\' columns to ensure we collect the correct data\\n try:\\n category_index = headers.index(\\'CATEGORY\\')\\n amount_index = headers.index(\\'AMOUNT\\')\\n except ValueError:\\n # In case one of the necessary columns is missing\\n return f\"Error: One of the columns \\'CATEGORY\\' or \\'AMOUNT\\' is missing in {filename}\"\\n \\n # Initialize variable to sum utilities\\n total_utilities_spent = 0\\n \\n # Iterate over each line after the header\\n for line in lines[1:]:\\n fields = line.split(\\',\\')\\n # Check if the category is \\'Utilities\\' and add the amount to total\\n if fields[category_index].strip() == \\'Utilities\\':\\n try:\\n total_utilities_spent += float(fields[amount_index])\\n except ValueError:\\n # In case the amount is not a valid float\\n return f\"Error: Non-numeric amount in {filename}\"\\n \\n return total_utilities_spent\\n\\n# Process both files and sum the utilities expenses\\ntotal_utilities_spent_file1 = process_csv_file(\\'file1.csv\\')\\ntotal_utilities_spent_file2 = process_csv_file(\\'file2.csv\\')\\n\\nif isinstance(total_utilities_spent_file1, float) and isinstance(total_utilities_spent_file2, float):\\n grand_total = total_utilities_spent_file1 + total_utilities_spent_file2\\n print(f\"Total spent on utilities: {grand_total}\")\\nelse:\\n # Handle errors if any\\n if not isinstance(total_utilities_spent_file1, float):\\n print(total_utilities_spent_file1)\\n if not isinstance(total_utilities_spent_file2, float):\\n print(total_utilities_spent_file2)\\n```', role='assistant', function_call=None, tool_calls=None)'''\u001b[0m\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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"2024-05-11 10:27:57,448 \u001b[33mWARNING\u001b[0m openai.py:473 \u001b[33mParsing attempt #1 failed: ValueError: ('Failed to parse JSON string: python\\ndef process_csv_file(filename):\\n # Read the CSV file into memory\\n content = read_file(filename)\\n # Split the content into lines\\n lines = content.strip().split(\\'\\\\n\\')\\n \\n # Extract the header line\\n headers = lines[0].split(\\',\\')\\n \\n # Find the index of the \\'CATEGORY\\' and \\'AMOUNT\\' columns to ensure we collect the correct data\\n try:\\n category_index = headers.index(\\'CATEGORY\\')\\n amount_index = headers.index(\\'AMOUNT\\')\\n except ValueError:\\n # In case one of the necessary columns is missing\\n return f\"Error: One of the columns \\'CATEGORY\\' or \\'AMOUNT\\' is missing in {filename}\"\\n \\n # Initialize variable to sum utilities\\n total_utilities_spent = 0\\n \\n # Iterate over each line after the header\\n for line in lines[1:]:\\n fields = line.split(\\',\\')\\n # Check if the category is \\'Utilities\\' and add the amount to total\\n if fields[category_index].strip() == \\'Utilities\\':\\n try:\\n total_utilities_spent += float(fields[amount_index])\\n except ValueError:\\n # In case the amount is not a valid float\\n return f\"Error: Non-numeric amount in {filename}\"\\n \\n return total_utilities_spent\\n\\n# Process both files and sum the utilities expenses\\ntotal_utilities_spent_file1 = process_csv_file(\\'file1.csv\\')\\ntotal_utilities_spent_file2 = process_csv_file(\\'file2.csv\\')\\n\\nif isinstance(total_utilities_spent_file1, float) and isinstance(total_utilities_spent_file2, float):\\n grand_total = total_utilities_spent_file1 + total_utilities_spent_file2\\n print(f\"Total spent on utilities: {grand_total}\")\\nelse:\\n # Handle errors if any\\n if not isinstance(total_utilities_spent_file1, float):\\n print(total_utilities_spent_file1)\\n if not isinstance(total_utilities_spent_file2, float):\\n print(total_utilities_spent_file2)', JSONDecodeError('Unknown identifier', 'python', position=position_marker(offset=0,line=1,column=0), severity='error'), JSONDecodeError('Unexpected text after end of JSON value', position=position_marker(offset=7,line=2,column=0), severity='error'))\u001b[0m\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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|
||||
"2024-05-10 20:02:15,870 \u001b[90mDEBUG\u001b[0m _trace.py:85 \u001b[90msend_request_headers.started request=<Request [b'POST']>\u001b[0m\n",
|
||||
"2024-05-10 20:02:15,874 \u001b[90mDEBUG\u001b[0m _trace.py:85 \u001b[90msend_request_headers.complete\u001b[0m\n",
|
||||
"2024-05-10 20:02:15,876 \u001b[90mDEBUG\u001b[0m _trace.py:85 \u001b[90msend_request_body.started request=<Request [b'POST']>\u001b[0m\n",
|
||||
"2024-05-10 20:02:15,880 \u001b[90mDEBUG\u001b[0m _trace.py:85 \u001b[90msend_request_body.complete\u001b[0m\n",
|
||||
"2024-05-10 20:02:15,882 \u001b[90mDEBUG\u001b[0m _trace.py:85 \u001b[90mreceive_response_headers.started request=<Request [b'POST']>\u001b[0m\n",
|
||||
"2024-05-10 20:02:27,268 \u001b[90mDEBUG\u001b[0m _trace.py:85 \u001b[90mreceive_response_headers.complete return_value=(b'HTTP/1.1', 200, b'OK', [(b'Date', b'Fri, 10 May 2024 18:02:26 GMT'), (b'Content-Type', b'application/json'), (b'Transfer-Encoding', b'chunked'), (b'Connection', b'keep-alive'), (b'openai-organization', b'significant-gravitas'), (b'openai-processing-ms', b'11153'), (b'openai-version', b'2020-10-01'), (b'strict-transport-security', b'max-age=15724800; includeSubDomains'), (b'x-ratelimit-limit-requests', b'10000'), (b'x-ratelimit-limit-tokens', b'2000000'), (b'x-ratelimit-remaining-requests', b'9999'), (b'x-ratelimit-remaining-tokens', b'1998752'), (b'x-ratelimit-reset-requests', b'6ms'), (b'x-ratelimit-reset-tokens', b'37ms'), (b'x-request-id', b'req_afca7c19c817b1461b037f3ac5ded1ab'), (b'CF-Cache-Status', b'DYNAMIC'), (b'Server', b'cloudflare'), (b'CF-RAY', b'881bd6d27c0b427c-EWR'), (b'Content-Encoding', b'gzip'), (b'alt-svc', b'h3=\":443\"; ma=86400')])\u001b[0m\n",
|
||||
"2024-05-10 20:02:27,270 \u001b[34mINFO\u001b[0m _client.py:1729 HTTP Request: POST https://api.openai.com/v1/chat/completions \"HTTP/1.1 200 OK\"\n",
|
||||
"2024-05-10 20:02:27,273 \u001b[90mDEBUG\u001b[0m _trace.py:85 \u001b[90mreceive_response_body.started request=<Request [b'POST']>\u001b[0m\n",
|
||||
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|
||||
"2024-05-10 20:02:27,277 \u001b[90mDEBUG\u001b[0m _trace.py:85 \u001b[90mresponse_closed.started\u001b[0m\n",
|
||||
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|
||||
"2024-05-10 20:02:27,283 \u001b[90mDEBUG\u001b[0m openai.py:638 \u001b[90mCompletion usage: 1050 input, 196 output - $0.01638\u001b[0m\n",
|
||||
"2024-05-10 20:02:27,288 \u001b[90mDEBUG\u001b[0m 1160824689.py:182 \u001b[90mLLM response content:\n",
|
||||
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|
||||
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|
||||
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||||
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|
||||
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|
||||
"2024-05-11 10:28:12,244 \u001b[90mDEBUG\u001b[0m _trace.py:85 \u001b[90mreceive_response_headers.complete return_value=(b'HTTP/1.1', 200, b'OK', [(b'Date', b'Sat, 11 May 2024 08:28:12 GMT'), (b'Content-Type', b'application/json'), (b'Transfer-Encoding', b'chunked'), (b'Connection', b'keep-alive'), (b'openai-organization', b'significant-gravitas'), (b'openai-processing-ms', b'14429'), (b'openai-version', b'2020-10-01'), (b'strict-transport-security', b'max-age=15724800; includeSubDomains'), (b'x-ratelimit-limit-requests', b'10000'), (b'x-ratelimit-limit-tokens', b'2000000'), (b'x-ratelimit-remaining-requests', b'9999'), (b'x-ratelimit-remaining-tokens', b'1997371'), (b'x-ratelimit-reset-requests', b'6ms'), (b'x-ratelimit-reset-tokens', b'78ms'), (b'x-request-id', b'req_9703e9c849bcfa1f9f369a5ea408f341'), (b'CF-Cache-Status', b'DYNAMIC'), (b'Server', b'cloudflare'), (b'CF-RAY', b'8820caf45d97247e-ZAG'), (b'Content-Encoding', b'gzip'), (b'alt-svc', b'h3=\":443\"; ma=86400')])\u001b[0m\n",
|
||||
"2024-05-11 10:28:12,245 \u001b[34mINFO\u001b[0m _client.py:1729 HTTP Request: POST https://api.openai.com/v1/chat/completions \"HTTP/1.1 200 OK\"\n",
|
||||
"2024-05-11 10:28:12,246 \u001b[90mDEBUG\u001b[0m _trace.py:85 \u001b[90mreceive_response_body.started request=<Request [b'POST']>\u001b[0m\n",
|
||||
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|
||||
"2024-05-11 10:28:12,249 \u001b[90mDEBUG\u001b[0m _trace.py:85 \u001b[90mresponse_closed.started\u001b[0m\n",
|
||||
"2024-05-11 10:28:12,249 \u001b[90mDEBUG\u001b[0m _trace.py:85 \u001b[90mresponse_closed.complete\u001b[0m\n",
|
||||
"2024-05-11 10:28:12,252 \u001b[90mDEBUG\u001b[0m openai.py:638 \u001b[90mCompletion usage: 2391 input, 417 output - $0.03642\u001b[0m\n",
|
||||
"2024-05-11 10:28:12,252 \u001b[90mDEBUG\u001b[0m 2794315326.py:188 \u001b[90mLLM response content:\n",
|
||||
"{\n",
|
||||
" \"thoughts\": {\n",
|
||||
" \"observations\": \"The user has asked for a code script that sums the amount spent on utilities as listed in two CSV files: 'file1.csv' and 'file2.csv'. Both files follow the same format with headers 'ID', 'CATEGORY', and 'AMOUNT'.\",\n",
|
||||
" \"self_criticism\": \"The detailed structure and way of accumulating the data were determined. Ready to implement the code.\",\n",
|
||||
" \"reasoning\": \"To get the total amount spent on utilities from both files, we should read each file, filter out the rows where the category is 'Utilities', sum the 'AMOUNT' column for those rows, and sum the totals from both files.\",\n",
|
||||
" \"plan\": [\n",
|
||||
" \"Read the data from 'file1.csv'\",\n",
|
||||
" \"Extract entries related to 'Utilities' and sum the 'AMOUNT' values\",\n",
|
||||
" \"Repeat the process for 'file2.csv'\",\n",
|
||||
" \"Add the results from both files to get the total utilities expense\",\n",
|
||||
" \"Provide the final output summarizing the total spent on utilities\"\n",
|
||||
" ]\n",
|
||||
" },\n",
|
||||
" \"immediate_plan\": \"Objective[Extract expenses related to 'Utilities' from 'file1.csv' and 'file2.csv', and sum them up] Plan[Read content of 'file1.csv' and 'file2.csv', extract lines with 'Utilities', sum the amounts, and output the total spend on utilities] Output[Sum of the amounts spent on utilities]\",\n",
|
||||
" \"python_code\": \"total_utilities_sum_file1 = sum(float(line.split(',')[2].strip()) for line in read_file('file1.csv').splitlines()[1:] if 'Utilities' in line)\\ntotal_utilities_sum_file2 = sum(float(line.split(',')[2].strip()) for line in read_file('file2.csv').splitlines()[1:] if 'Utilities' in line)\\ntotal_spent_on_utilities = total_utilities_sum_file1 + total_utilities_sum_file2\\nprint(f'Total spent on utilities: {total_spent_on_utilities}')\" \n",
|
||||
"}\u001b[0m\n",
|
||||
"2024-05-11 10:28:12,255 \u001b[90mDEBUG\u001b[0m 2794315326.py:197 \u001b[90mParsing object extracted from LLM response:\n",
|
||||
"{\n",
|
||||
" \"thoughts\": {\n",
|
||||
" \"observations\": \"The task requires extracting the amount spent on utilities from two CSV files, file1.csv and file2.csv.\",\n",
|
||||
" \"self_criticism\": \"Direct assumptions about the format and data organization in the CSV files were avoided, which is necessary.\",\n",
|
||||
" \"reasoning\": \"First, we need to read and analyze the contents of both files. Once we understand the format and where the relevant data (utilities) is located, we can then extract the specific details and sum them up to get the total utilities expenditure.\",\n",
|
||||
" \"observations\": \"The user has asked for a code script that sums the amount spent on utilities as listed in two CSV files: 'file1.csv' and 'file2.csv'. Both files follow the same format with headers 'ID', 'CATEGORY', and 'AMOUNT'.\",\n",
|
||||
" \"self_criticism\": \"The detailed structure and way of accumulating the data were determined. Ready to implement the code.\",\n",
|
||||
" \"reasoning\": \"To get the total amount spent on utilities from both files, we should read each file, filter out the rows where the category is 'Utilities', sum the 'AMOUNT' column for those rows, and sum the totals from both files.\",\n",
|
||||
" \"plan\": [\n",
|
||||
" \"Read contents of file1.csv\",\n",
|
||||
" \"Read contents of file2.csv\",\n",
|
||||
" \"Extract information about utilities expenditure from both files\",\n",
|
||||
" \"Sum up the amounts spent on utilities from both files\"\n",
|
||||
" \"Read the data from 'file1.csv'\",\n",
|
||||
" \"Extract entries related to 'Utilities' and sum the 'AMOUNT' values\",\n",
|
||||
" \"Repeat the process for 'file2.csv'\",\n",
|
||||
" \"Add the results from both files to get the total utilities expense\",\n",
|
||||
" \"Provide the final output summarizing the total spent on utilities\"\n",
|
||||
" ]\n",
|
||||
" },\n",
|
||||
" \"python_code\": \"file1_content = read_file('file1.csv')\\nfile2_content = read_file('file2.csv')\"\n",
|
||||
" \"immediate_plan\": \"Objective[Extract expenses related to 'Utilities' from 'file1.csv' and 'file2.csv', and sum them up] Plan[Read content of 'file1.csv' and 'file2.csv', extract lines with 'Utilities', sum the amounts, and output the total spend on utilities] Output[Sum of the amounts spent on utilities]\",\n",
|
||||
" \"python_code\": \"total_utilities_sum_file1 = sum(float(line.split(',')[2].strip()) for line in read_file('file1.csv').splitlines()[1:] if 'Utilities' in line)\\ntotal_utilities_sum_file2 = sum(float(line.split(',')[2].strip()) for line in read_file('file2.csv').splitlines()[1:] if 'Utilities' in line)\\ntotal_spent_on_utilities = total_utilities_sum_file1 + total_utilities_sum_file2\\nprint(f'Total spent on utilities: {total_spent_on_utilities}')\"\n",
|
||||
"}\u001b[0m\n",
|
||||
"2024-05-10 20:02:27,294 \u001b[90mDEBUG\u001b[0m 1160824689.py:191 \u001b[90mParsing object extracted from LLM response:\n",
|
||||
"{\n",
|
||||
" \"thoughts\": {\n",
|
||||
" \"observations\": \"The task requires extracting the amount spent on utilities from two CSV files, file1.csv and file2.csv.\",\n",
|
||||
" \"self_criticism\": \"Direct assumptions about the format and data organization in the CSV files were avoided, which is necessary.\",\n",
|
||||
" \"reasoning\": \"First, we need to read and analyze the contents of both files. Once we understand the format and where the relevant data (utilities) is located, we can then extract the specific details and sum them up to get the total utilities expenditure.\",\n",
|
||||
" \"plan\": [\n",
|
||||
" \"Read contents of file1.csv\",\n",
|
||||
" \"Read contents of file2.csv\",\n",
|
||||
" \"Extract information about utilities expenditure from both files\",\n",
|
||||
" \"Sum up the amounts spent on utilities from both files\"\n",
|
||||
" ]\n",
|
||||
" },\n",
|
||||
" \"python_code\": \"file1_content = read_file('file1.csv')\\nfile2_content = read_file('file2.csv')\"\n",
|
||||
"}\u001b[0m\n",
|
||||
"file1_content = read_file('file1.csv')\n",
|
||||
"file2_content = read_file('file2.csv')\n"
|
||||
"2024-05-11 10:28:12,256 \u001b[90mDEBUG\u001b[0m openai.py:451 \u001b[90mTotal cost for 2 attempts: $0.06306\u001b[0m\n",
|
||||
"total_utilities_sum_file1 = sum(float(line.split(',')[2].strip()) for line in read_file('file1.csv').splitlines()[1:] if 'Utilities' in line)\n",
|
||||
"total_utilities_sum_file2 = sum(float(line.split(',')[2].strip()) for line in read_file('file2.csv').splitlines()[1:] if 'Utilities' in line)\n",
|
||||
"total_spent_on_utilities = total_utilities_sum_file1 + total_utilities_sum_file2\n",
|
||||
"print(f'Total spent on utilities: {total_spent_on_utilities}')\n"
|
||||
]
|
||||
}
|
||||
],
|
||||
@@ -623,6 +731,17 @@
|
||||
"\n",
|
||||
"prompter = OneShotFlowAgentPromptStrategy()\n",
|
||||
"\n",
|
||||
"prev_plan = [\n",
|
||||
" (\n",
|
||||
" \"Objective[Read file1.csv to understand its structure and content] Plan[Read content of 'file1.csv' using the read_file function] Output[Print out the first few lines of file1.csv to deduce information about utilities spending from its structure]\",\n",
|
||||
" \"ID,CATEGORY,AMOUNT\\n1,Utilities,100\\n2,Food,200\\n3,Utilities,150\\n4,Transport,50\\n5,Utilities,120\\n6,Food,180\\n7,Utilities,130\\n8,Transport,70\\n9,Utilities,140\\n10,Food,220\\n\",\n",
|
||||
" ),\n",
|
||||
" (\n",
|
||||
" \"Objective[Read the first few lines of 'file2.csv' to understand its structure] Plan[Read content of 'file2.csv' using the read_file function and output the first few lines] Output[Provide structure of 'file2.csv' to verify it matches 'file1.csv']\",\n",
|
||||
" \"ID,CATEGORY,AMOUNT\\n1,Utilities,123\\n2,Food,200\\n4,Utilities,151\\n4,Transport,50\\n5,Utilities,120\\n6,Food,180\\n7,Utilities,130\\n8,Transport,70\\n9,Utilities,140\\n10,Food,220\\n\",\n",
|
||||
" )\n",
|
||||
"]\n",
|
||||
"\n",
|
||||
"prompt = prompter.build_prompt(\n",
|
||||
" task=\"Figure out from file1.csv and file2.csv how much was spent on utilities\",\n",
|
||||
" messages=[\n",
|
||||
@@ -639,6 +758,10 @@
|
||||
" \"you can hide the item by specifying its index in the list above \"\n",
|
||||
" f\"to `close_context_item`.\",\n",
|
||||
" ),\n",
|
||||
" ChatMessage.system(\n",
|
||||
" \"Previous plan:\\n\" +\n",
|
||||
" format_numbered_list([f\"\\nPlan: {plan[0]}\\nOutput: {plan[1]}\" for plan in prev_plan]),\n",
|
||||
" ),\n",
|
||||
" ],\n",
|
||||
" ai_profile=AIProfile(),\n",
|
||||
" ai_directives=AIDirectives(),\n",
|
||||
@@ -674,7 +797,7 @@
|
||||
"name": "python",
|
||||
"nbconvert_exporter": "python",
|
||||
"pygments_lexer": "ipython3",
|
||||
"version": "3.11.4"
|
||||
"version": "3.12.3"
|
||||
}
|
||||
},
|
||||
"nbformat": 4,
|
||||
|
||||
Reference in New Issue
Block a user