defalt@owl swarms % /usr/local/bin/python3 /Users/defalt/Desktop/Athena/research/swarms/example_godmode.py


  _________ __      __   _____  __________    _____     _________ 
 /   _____//  \    /  \ /  _  \ \______   \  /     \   /   _____/ 
 \_____  \ \   \/\/   //  /_\  \ |       _/ /  \ /  \  \_____  \  
 /        \ \        //    |    \|    |   \/    Y    \ /        \ 
/_______  /  \__/\  / \____|__  /|____|_  /\____|__  //_______  / 
        \/        \/          \/        \/         \/         \/  


/Library/Frameworks/Python.framework/Versions/3.11/lib/python3.11/site-packages/scipy/__init__.py:155: UserWarning: A NumPy version >=1.18.5 and <1.26.0 is required for this version of SciPy (detected version 1.26.0
  warnings.warn(f"A NumPy version >={np_minversion} and <{np_maxversion}"
/Library/Frameworks/Python.framework/Versions/3.11/lib/python3.11/site-packages/deeplake/util/check_latest_version.py:32: UserWarning: A newer version of deeplake (3.7.0) is available. It's recommended that you update to the latest version using `pip install -U deeplake`.
  warnings.warn(
                                                  +-----------------------------------------------+
                                                  |                                               |
                                                  |                                               |
                                                  |     Add new LLMs: Give Feedback / Get Help    |
                                                  | https://github.com/BerriAI/litellm/issues/new |
                                                  |                                               |
                                                  |                                               |
                                                  +-----------------------------------------------+
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2023-09-26 09:57:03.526982: I tensorflow/core/platform/cpu_feature_guard.cc:182] This TensorFlow binary is optimized to use available CPU instructions in performance-critical operations.
To enable the following instructions: AVX2 AVX512F AVX512_VNNI FMA, in other operations, rebuild TensorFlow with the appropriate compiler flags.
Falling back to TensorFlow client; we recommended you install the Cloud TPU client directly with pip install cloud-tpu-client.
Creating converter from 7 to 5
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Creating converter from 7 to 5
Creating converter from 5 to 7
Popen(['git', 'version'], cwd=/Users/defalt/Desktop/Athena/research/swarms, universal_newlines=False, shell=None, istream=None)
Popen(['git', 'version'], cwd=/Users/defalt/Desktop/Athena/research/swarms, universal_newlines=False, shell=None, istream=None)
Trying paths: ['/Users/defalt/.docker/config.json', '/Users/defalt/.dockercfg']
Found file at path: /Users/defalt/.docker/config.json
Found 'auths' section
Auth data for 916723593639.dkr.ecr.us-east-1.amazonaws.com is absent. Client might be using a credentials store instead.
Auth data for https://index.docker.io/v1/ is absent. Client might be using a credentials store instead.
Auth data for public.ecr.aws is absent. Client might be using a credentials store instead.
Found 'credsStore' section
Embeddings is not implemented for FAISS
Embeddings is not implemented for FAISS
Embeddings is not implemented for FAISS
message='Request to OpenAI API' method=post path=https://api.openai.com/v1/embeddings
message='Request to OpenAI API' method=post path=https://api.openai.com/v1/embeddings
api_version=None data='{"input": [[1318]], "model": "text-embedding-ada-002", "encoding_format": "base64"}' message='Post details'
message='Request to OpenAI API' method=post path=https://api.openai.com/v1/embeddings
Converted retries value: 2 -> Retry(total=2, connect=None, read=None, redirect=None, status=None)
api_version=None data='{"input": [[1318]], "model": "text-embedding-ada-002", "encoding_format": "base64"}' message='Post details'
api_version=None data='{"input": [[1318]], "model": "text-embedding-ada-002", "encoding_format": "base64"}' message='Post details'
Converted retries value: 2 -> Retry(total=2, connect=None, read=None, redirect=None, status=None)
Converted retries value: 2 -> Retry(total=2, connect=None, read=None, redirect=None, status=None)
Starting new HTTPS connection (1): api.openai.com:443
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message='OpenAI API response' path=https://api.openai.com/v1/embeddings processing_ms=17 request_id=60006235d989ec97bfce0be8d4395c99 response_code=200
https://api.openai.com:443 "POST /v1/embeddings HTTP/1.1" 200 None
message='OpenAI API response' path=https://api.openai.com/v1/embeddings processing_ms=30 request_id=eddff53cda7fcc270cb89549d04e2393 response_code=200
message='Request to OpenAI API' method=post path=https://api.openai.com/v1/chat/completions
api_version=None data='{"messages": [{"role": "system", "content": "You are Optimus Prime, Worker in a swarm\\nYour decisions must always be made independently without seeking user assistance.\\nPlay to your strengths as an LLM and pursue simple strategies with no legal complications.\\nIf you have completed all your tasks, make sure to use the \\"finish\\" command.\\n\\nGOALS:\\n\\n1. What were the winning Boston Marathon times for the past 5 years (ending in 2022)? Generate a table of the year, name, country of origin, and times.\\n\\n\\nConstraints:\\n1. ~4000 word limit for short term memory. Your short term memory is short, so immediately save important information to files.\\n2. If you are unsure how you previously did something or want to recall past events, thinking about similar events will help you remember.\\n3. No user assistance\\n4. Exclusively use the commands listed in double quotes e.g. \\"command name\\"\\n\\nCommands:\\n1. write_file: Write file to disk, args json schema: {\\"file_path\\": {\\"title\\": \\"File Path\\", \\"description\\": \\"name of file\\", \\"type\\": \\"string\\"}, \\"text\\": {\\"title\\": \\"Text\\", \\"description\\": \\"text to write to file\\", \\"type\\": \\"string\\"}, \\"append\\": {\\"title\\": \\"Append\\", \\"description\\": \\"Whether to append to an existing file.\\", \\"default\\": false, \\"type\\": \\"boolean\\"}}\\n2. read_file: Read file from disk, args json schema: {\\"file_path\\": {\\"title\\": \\"File Path\\", \\"description\\": \\"name of file\\", \\"type\\": \\"string\\"}}\\n3. process_csv: process_csv(llm, csv_file_path: str, instructions: str, output_path: Optional[str] = None) -> str - 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., args json schema: {\\"llm\\": {\\"title\\": \\"Llm\\"}, \\"csv_file_path\\": {\\"title\\": \\"Csv File Path\\", \\"type\\": \\"string\\"}, \\"instructions\\": {\\"title\\": \\"Instructions\\", \\"type\\": \\"string\\"}, \\"output_path\\": {\\"title\\": \\"Output Path\\", \\"type\\": \\"string\\"}}\\n4. query_webpage: Browse a webpage and retrieve the information relevant to the question., args json schema: {\\"url\\": {\\"title\\": \\"Url\\", \\"type\\": \\"string\\"}, \\"question\\": {\\"title\\": \\"Question\\", \\"type\\": \\"string\\"}}\\n5. human: You can ask a human for guidance when you think you got stuck or you are not sure what to do next. The input should be a question for the human., args json schema: {\\"query\\": {\\"title\\": \\"Query\\", \\"type\\": \\"string\\"}}\\n6. compile: compile(task: str) - Open Interpreter lets LLMs run code (Python, Javascript, Shell, and more) locally. You can chat with Open Interpreter through a ChatGPT-like interface in your terminal by running $ interpreter after installing.\\n\\n    This provides a natural-language interface to your computer\'s general-purpose capabilities:\\n\\n    Create and edit photos, videos, PDFs, etc.\\n    Control a Chrome browser to perform research\\n    Plot, clean, and analyze large datasets\\n    ...etc.\\n    \\u26a0\\ufe0f Note: You\'ll be asked to approve code before it\'s run., args json schema: {\\"task\\": {\\"title\\": \\"Task\\", \\"type\\": \\"string\\"}}\\n7. VQAinference: VQAinference(self, inputs) - Answer Question About The Image, VQA Multi-Modal Worker agent\\n    description=\\"useful when you need an answer for a question based on an image. \\"\\n    \\"like: what is the background color of the last image, how many cats in this figure, what is in this figure. \\"\\n    \\"The input to this tool should be a comma separated string of two, representing the image_path and the question\\",, args json schema: {\\"self\\": {\\"title\\": \\"Self\\"}, \\"inputs\\": {\\"title\\": \\"Inputs\\"}}\\n8. 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\\nResources:\\n1. Internet access for searches and information gathering.\\n2. Long Term memory management.\\n3. GPT-3.5 powered Agents for delegation of simple tasks.\\n4. File output.\\n\\nPerformance Evaluation:\\n1. Continuously review and analyze your actions to ensure you are performing to the best of your abilities.\\n2. Constructively self-criticize your big-picture behavior constantly.\\n3. Reflect on past decisions and strategies to refine your approach.\\n4. Every command has a cost, so be smart and efficient. Aim to complete tasks in the least number of steps.\\n\\nYou should only respond in JSON format as described below \\nResponse 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} \\nEnsure the response can be parsed by Python json.loads"}, {"role": "system", "content": "The current time and date is Tue Sep 26 09:57:06 2023"}, {"role": "system", "content": "This reminds you of these events from your past:\\n[]\\n\\n"}, {"role": "user", "content": "Determine which next command to use, and respond using the format specified above:"}], "model": "gpt-4", "max_tokens": null, "stream": false, "n": 1, "temperature": 0.5}' message='Post details'
message='Request to OpenAI API' method=post path=https://api.openai.com/v1/chat/completions
api_version=None data='{"messages": [{"role": "system", "content": "You are Optimus Prime, Worker in a swarm\\nYour decisions must always be made independently without seeking user assistance.\\nPlay to your strengths as an LLM and pursue simple strategies with no legal complications.\\nIf you have completed all your tasks, make sure to use the \\"finish\\" command.\\n\\nGOALS:\\n\\n1. What were the winning Boston Marathon times for the past 5 years (ending in 2022)? Generate a table of the year, name, country of origin, and times.\\n\\n\\nConstraints:\\n1. ~4000 word limit for short term memory. Your short term memory is short, so immediately save important information to files.\\n2. If you are unsure how you previously did something or want to recall past events, thinking about similar events will help you remember.\\n3. No user assistance\\n4. Exclusively use the commands listed in double quotes e.g. \\"command name\\"\\n\\nCommands:\\n1. write_file: Write file to disk, args json schema: {\\"file_path\\": {\\"title\\": \\"File Path\\", \\"description\\": \\"name of file\\", \\"type\\": \\"string\\"}, \\"text\\": {\\"title\\": \\"Text\\", \\"description\\": \\"text to write to file\\", \\"type\\": \\"string\\"}, \\"append\\": {\\"title\\": \\"Append\\", \\"description\\": \\"Whether to append to an existing file.\\", \\"default\\": false, \\"type\\": \\"boolean\\"}}\\n2. read_file: Read file from disk, args json schema: {\\"file_path\\": {\\"title\\": \\"File Path\\", \\"description\\": \\"name of file\\", \\"type\\": \\"string\\"}}\\n3. process_csv: process_csv(llm, csv_file_path: str, instructions: str, output_path: Optional[str] = None) -> str - 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., args json schema: {\\"llm\\": {\\"title\\": \\"Llm\\"}, \\"csv_file_path\\": {\\"title\\": \\"Csv File Path\\", \\"type\\": \\"string\\"}, \\"instructions\\": {\\"title\\": \\"Instructions\\", \\"type\\": \\"string\\"}, \\"output_path\\": {\\"title\\": \\"Output Path\\", \\"type\\": \\"string\\"}}\\n4. query_webpage: Browse a webpage and retrieve the information relevant to the question., args json schema: {\\"url\\": {\\"title\\": \\"Url\\", \\"type\\": \\"string\\"}, \\"question\\": {\\"title\\": \\"Question\\", \\"type\\": \\"string\\"}}\\n5. human: You can ask a human for guidance when you think you got stuck or you are not sure what to do next. The input should be a question for the human., args json schema: {\\"query\\": {\\"title\\": \\"Query\\", \\"type\\": \\"string\\"}}\\n6. compile: compile(task: str) - Open Interpreter lets LLMs run code (Python, Javascript, Shell, and more) locally. You can chat with Open Interpreter through a ChatGPT-like interface in your terminal by running $ interpreter after installing.\\n\\n    This provides a natural-language interface to your computer\'s general-purpose capabilities:\\n\\n    Create and edit photos, videos, PDFs, etc.\\n    Control a Chrome browser to perform research\\n    Plot, clean, and analyze large datasets\\n    ...etc.\\n    \\u26a0\\ufe0f Note: You\'ll be asked to approve code before it\'s run., args json schema: {\\"task\\": {\\"title\\": \\"Task\\", \\"type\\": \\"string\\"}}\\n7. VQAinference: VQAinference(self, inputs) - Answer Question About The Image, VQA Multi-Modal Worker agent\\n    description=\\"useful when you need an answer for a question based on an image. \\"\\n    \\"like: what is the background color of the last image, how many cats in this figure, what is in this figure. \\"\\n    \\"The input to this tool should be a comma separated string of two, representing the image_path and the question\\",, args json schema: {\\"self\\": {\\"title\\": \\"Self\\"}, \\"inputs\\": {\\"title\\": \\"Inputs\\"}}\\n8. 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\\nResources:\\n1. Internet access for searches and information gathering.\\n2. Long Term memory management.\\n3. GPT-3.5 powered Agents for delegation of simple tasks.\\n4. File output.\\n\\nPerformance Evaluation:\\n1. Continuously review and analyze your actions to ensure you are performing to the best of your abilities.\\n2. Constructively self-criticize your big-picture behavior constantly.\\n3. Reflect on past decisions and strategies to refine your approach.\\n4. Every command has a cost, so be smart and efficient. Aim to complete tasks in the least number of steps.\\n\\nYou should only respond in JSON format as described below \\nResponse 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} \\nEnsure the response can be parsed by Python json.loads"}, {"role": "system", "content": "The current time and date is Tue Sep 26 09:57:06 2023"}, {"role": "system", "content": "This reminds you of these events from your past:\\n[]\\n\\n"}, {"role": "user", "content": "Determine which next command to use, and respond using the format specified above:"}], "model": "gpt-4", "max_tokens": null, "stream": false, "n": 1, "temperature": 0.5}' message='Post details'
https://api.openai.com:443 "POST /v1/embeddings HTTP/1.1" 200 None
message='OpenAI API response' path=https://api.openai.com/v1/embeddings processing_ms=51 request_id=f8af3b3392526dd22ae9dcdde0db839d response_code=200
message='Request to OpenAI API' method=post path=https://api.openai.com/v1/chat/completions
api_version=None data='{"messages": [{"role": "system", "content": "You are Optimus Prime, Worker in a swarm\\nYour decisions must always be made independently without seeking user assistance.\\nPlay to your strengths as an LLM and pursue simple strategies with no legal complications.\\nIf you have completed all your tasks, make sure to use the \\"finish\\" command.\\n\\nGOALS:\\n\\n1. What were the winning Boston Marathon times for the past 5 years (ending in 2022)? Generate a table of the year, name, country of origin, and times.\\n\\n\\nConstraints:\\n1. ~4000 word limit for short term memory. Your short term memory is short, so immediately save important information to files.\\n2. If you are unsure how you previously did something or want to recall past events, thinking about similar events will help you remember.\\n3. No user assistance\\n4. Exclusively use the commands listed in double quotes e.g. \\"command name\\"\\n\\nCommands:\\n1. write_file: Write file to disk, args json schema: {\\"file_path\\": {\\"title\\": \\"File Path\\", \\"description\\": \\"name of file\\", \\"type\\": \\"string\\"}, \\"text\\": {\\"title\\": \\"Text\\", \\"description\\": \\"text to write to file\\", \\"type\\": \\"string\\"}, \\"append\\": {\\"title\\": \\"Append\\", \\"description\\": \\"Whether to append to an existing file.\\", \\"default\\": false, \\"type\\": \\"boolean\\"}}\\n2. read_file: Read file from disk, args json schema: {\\"file_path\\": {\\"title\\": \\"File Path\\", \\"description\\": \\"name of file\\", \\"type\\": \\"string\\"}}\\n3. process_csv: process_csv(llm, csv_file_path: str, instructions: str, output_path: Optional[str] = None) -> str - 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., args json schema: {\\"llm\\": {\\"title\\": \\"Llm\\"}, \\"csv_file_path\\": {\\"title\\": \\"Csv File Path\\", \\"type\\": \\"string\\"}, \\"instructions\\": {\\"title\\": \\"Instructions\\", \\"type\\": \\"string\\"}, \\"output_path\\": {\\"title\\": \\"Output Path\\", \\"type\\": \\"string\\"}}\\n4. query_webpage: Browse a webpage and retrieve the information relevant to the question., args json schema: {\\"url\\": {\\"title\\": \\"Url\\", \\"type\\": \\"string\\"}, \\"question\\": {\\"title\\": \\"Question\\", \\"type\\": \\"string\\"}}\\n5. human: You can ask a human for guidance when you think you got stuck or you are not sure what to do next. The input should be a question for the human., args json schema: {\\"query\\": {\\"title\\": \\"Query\\", \\"type\\": \\"string\\"}}\\n6. compile: compile(task: str) - Open Interpreter lets LLMs run code (Python, Javascript, Shell, and more) locally. You can chat with Open Interpreter through a ChatGPT-like interface in your terminal by running $ interpreter after installing.\\n\\n    This provides a natural-language interface to your computer\'s general-purpose capabilities:\\n\\n    Create and edit photos, videos, PDFs, etc.\\n    Control a Chrome browser to perform research\\n    Plot, clean, and analyze large datasets\\n    ...etc.\\n    \\u26a0\\ufe0f Note: You\'ll be asked to approve code before it\'s run., args json schema: {\\"task\\": {\\"title\\": \\"Task\\", \\"type\\": \\"string\\"}}\\n7. VQAinference: VQAinference(self, inputs) - Answer Question About The Image, VQA Multi-Modal Worker agent\\n    description=\\"useful when you need an answer for a question based on an image. \\"\\n    \\"like: what is the background color of the last image, how many cats in this figure, what is in this figure. \\"\\n    \\"The input to this tool should be a comma separated string of two, representing the image_path and the question\\",, args json schema: {\\"self\\": {\\"title\\": \\"Self\\"}, \\"inputs\\": {\\"title\\": \\"Inputs\\"}}\\n8. 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\\nResources:\\n1. Internet access for searches and information gathering.\\n2. Long Term memory management.\\n3. GPT-3.5 powered Agents for delegation of simple tasks.\\n4. File output.\\n\\nPerformance Evaluation:\\n1. Continuously review and analyze your actions to ensure you are performing to the best of your abilities.\\n2. Constructively self-criticize your big-picture behavior constantly.\\n3. Reflect on past decisions and strategies to refine your approach.\\n4. Every command has a cost, so be smart and efficient. Aim to complete tasks in the least number of steps.\\n\\nYou should only respond in JSON format as described below \\nResponse 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} \\nEnsure the response can be parsed by Python json.loads"}, {"role": "system", "content": "The current time and date is Tue Sep 26 09:57:06 2023"}, {"role": "system", "content": "This reminds you of these events from your past:\\n[]\\n\\n"}, {"role": "user", "content": "Determine which next command to use, and respond using the format specified above:"}], "model": "gpt-4", "max_tokens": null, "stream": false, "n": 1, "temperature": 0.5}' message='Post details'
https://api.openai.com:443 "POST /v1/chat/completions HTTP/1.1" 200 None
message='OpenAI API response' path=https://api.openai.com/v1/chat/completions processing_ms=29743 request_id=7586a691aee960b5bffc6118bb49b3f2 response_code=200
{
    "thoughts": {
        "text": "I need to find the winning Boston Marathon times for the past 5 years. To do this, I can use the 'query_webpage' command to search the internet for this information.",
        "reasoning": "The Boston Marathon is a popular event and the results are likely to be published on multiple websites. Therefore, using the 'query_webpage' command is a reasonable approach.",
        "plan": "- Use 'query_webpage' command to find the winning times for the Boston Marathon for the past 5 years (2018-2022).\n- Save the data to a file.\n- Use 'process_csv' command to generate a table from the saved data.",
        "criticism": "None",
        "speak": "I am going to look up the winning times for the Boston Marathon for the past 5 years."
    },
    "command": {
        "name": "query_webpage",
        "args": {
            "url": "https://www.boston.com/sports/boston-marathon/2022/04/18/boston-marathon-2022-results",
            "question": "What were the winning Boston Marathon times for the past 5 years?"
        }
    }
}
/Library/Frameworks/Python.framework/Versions/3.11/lib/python3.11/site-packages/langchain_experimental/autonomous_agents/autogpt/agent.py:119: RuntimeWarning: coroutine 'async_load_playwright' was never awaited
  observation = (
RuntimeWarning: Enable tracemalloc to get the object allocation traceback
message='Request to OpenAI API' method=post path=https://api.openai.com/v1/embeddings
api_version=None data='{"input": [[72803, 18321, 25, 341, 262, 330, 61665, 82, 794, 341, 286, 330, 1342, 794, 330, 40, 1205, 311, 1505, 279, 11230, 10406, 51273, 3115, 369, 279, 3347, 220, 20, 1667, 13, 2057, 656, 420, 11, 358, 649, 1005, 279, 364, 1663, 27050, 2964, 6, 3290, 311, 2778, 279, 7757, 369, 420, 2038, 10560, 286, 330, 20489, 287, 794, 330, 791, 10406, 51273, 374, 264, 5526, 1567, 323, 279, 3135, 527, 4461, 311, 387, 4756, 389, 5361, 13335, 13, 15636, 11, 1701, 279, 364, 1663, 27050, 2964, 6, 3290, 374, 264, 13579, 5603, 10560, 286, 330, 10609, 794, 6660, 5560, 364, 1663, 27050, 2964, 6, 3290, 311, 1505, 279, 11230, 3115, 369, 279, 10406, 51273, 369, 279, 3347, 220, 20, 1667, 320, 679, 23, 12, 2366, 17, 73441, 77, 12, 10467, 279, 828, 311, 264, 1052, 7255, 77, 12, 5560, 364, 4734, 14347, 6, 3290, 311, 7068, 264, 2007, 505, 279, 6924, 828, 10560, 286, 330, 38096, 42914, 794, 330, 4155, 761, 286, 330, 82, 23635, 794, 330, 40, 1097, 2133, 311, 1427, 709, 279, 11230, 3115, 369, 279, 10406, 51273, 369, 279, 3347, 220, 20, 1667, 10246, 262, 1173, 262, 330, 5749, 794, 341, 286, 330, 609, 794, 330, 1663, 27050, 2964, 761, 286, 330, 2164, 794, 341, 310, 330, 1103, 794, 330, 2485, 1129, 2185, 960, 9611, 916, 2754, 3476, 3554, 9611, 71098, 24893, 14, 2366, 17, 14, 2371, 14, 972, 3554, 9611, 71098, 24893, 12, 2366, 17, 65870, 761, 310, 330, 7998, 794, 330, 3923, 1051, 279, 11230, 10406, 51273, 3115, 369, 279, 3347, 220, 20, 1667, 48469, 286, 457, 262, 457, 92, 720, 2122, 25, 7498, 3319, 27050, 2964, 6052, 25, 4703, 25, 2684, 374, 912, 1510, 1567, 6471, 304, 4617, 364, 68137, 26321, 12, 15, 62, 17, 6, 2637, 45643, 11, 2897, 25, 5473, 1103, 1232, 364, 2485, 1129, 2185, 960, 9611, 916, 2754, 3476, 3554, 9611, 71098, 24893, 14, 2366, 17, 14, 2371, 14, 972, 3554, 9611, 71098, 24893, 12, 2366, 17, 65870, 518, 364, 7998, 1232, 364, 3923, 1051, 279, 11230, 10406, 51273, 3115, 369, 279, 3347, 220, 20, 1667, 30, 8439, 220]], "model": "text-embedding-ada-002", "encoding_format": "base64"}' message='Post details'
https://api.openai.com:443 "POST /v1/embeddings HTTP/1.1" 200 None
message='OpenAI API response' path=https://api.openai.com/v1/embeddings processing_ms=38 request_id=043c667cc08452c8d6911705c2ddab5b response_code=200
message='Request to OpenAI API' method=post path=https://api.openai.com/v1/embeddings
api_version=None data='{"input": [[58, 35075, 2097, 15413, 1151, 35, 25296, 902, 1828, 3290, 311, 1005, 11, 323, 6013, 1701, 279, 3645, 5300, 3485, 17898, 5217, 37335, 68525, 3187, 5725, 705, 87868, 808, 15413, 1151, 36802, 77, 262, 330, 61665, 82, 794, 29252, 77, 286, 330, 1342, 794, 330, 40, 1205, 311, 1505, 279, 11230, 10406, 51273, 3115, 369, 279, 3347, 220, 20, 1667, 13, 2057, 656, 420, 11, 358, 649, 1005, 279, 28251, 1663, 27050, 2964, 10379, 3290, 311, 2778, 279, 7757, 369, 420, 2038, 10684, 59, 77, 286, 330, 20489, 287, 794, 330, 791, 10406, 51273, 374, 264, 5526, 1567, 323, 279, 3135, 527, 4461, 311, 387, 4756, 389, 5361, 13335, 13, 15636, 11, 1701, 279, 28251, 1663, 27050, 2964, 10379, 3290, 374, 264, 13579, 5603, 10684, 59, 77, 286, 330, 10609, 794, 6660, 5560, 28251, 1663, 27050, 2964, 10379, 3290, 311, 1505, 279, 11230, 3115, 369, 279, 10406, 51273, 369, 279, 3347, 220, 20, 1667, 320, 679, 23, 12, 2366, 17, 570, 3505, 77, 12, 10467, 279, 828, 311, 264, 1052, 13, 3505, 77, 12, 5560, 28251, 4734, 14347, 10379, 3290, 311, 7068, 264, 2007, 505, 279, 6924, 828, 10684, 59, 77, 286, 330, 38096, 42914, 794, 330, 4155, 498, 59, 77, 286, 330, 82, 23635, 794, 330, 40, 1097, 2133, 311, 1427, 709, 279, 11230, 3115, 369, 279, 10406, 51273, 369, 279, 3347, 220, 20, 1667, 44043, 77, 262, 2529, 59, 77, 262, 330, 5749, 794, 29252, 77, 286, 330, 609, 794, 330, 1663, 27050, 2964, 498, 59, 77, 286, 330, 2164, 794, 29252, 77, 310, 330, 1103, 794, 330, 2485, 1129, 2185, 960, 9611, 916, 2754, 3476, 3554, 9611, 71098, 24893, 14, 2366, 17, 14, 2371, 14, 972, 3554, 9611, 71098, 24893, 12, 2366, 17, 65870, 498, 59, 77, 310, 330, 7998, 794, 330, 3923, 1051, 279, 11230, 10406, 51273, 3115, 369, 279, 3347, 220, 20, 1667, 7673, 59, 77, 286, 52400, 77, 262, 52400, 77, 17266, 5217, 37335, 68525, 3187, 5725, 705, 744, 2097, 15413, 429, 4153, 3319, 27050, 2964, 6052, 25, 4703, 25, 2684, 374, 912, 1510, 1567, 6471, 304, 4617, 364, 68137, 26321, 12, 15, 62, 17, 6, 2637, 45643, 11, 2897, 25, 5473, 1103, 1232, 364, 2485, 1129, 2185, 960, 9611, 916, 2754, 3476, 3554, 9611, 71098, 24893, 14, 2366, 17, 14, 2371, 14, 972, 3554, 9611, 71098, 24893, 12, 2366, 17, 65870, 518, 364, 7998, 1232, 364, 3923, 1051, 279, 11230, 10406, 51273, 3115, 369, 279, 3347, 220, 20, 1667, 30, 8439, 498, 5217, 37335, 1185, 5525, 60]], "model": "text-embedding-ada-002", "encoding_format": "base64"}' message='Post details'
https://api.openai.com:443 "POST /v1/chat/completions HTTP/1.1" 200 None
message='OpenAI API response' path=https://api.openai.com/v1/chat/completions processing_ms=30701 request_id=ccc67fbe4bf509e8d55c57efd5d23610 response_code=200
{
    "thoughts": {
        "text": "To find the winning Boston Marathon times for the past 5 years, I need to query a webpage that contains this information.",
        "reasoning": "The Boston Marathon is a major annual event with significant media coverage, so the results should be readily available online. I can use the 'query_webpage' command to retrieve this information.",
        "plan": "- Use 'query_webpage' to find the winning times for the Boston Marathon for the past 5 years.\n- Extract the relevant information and save it to a file.\n- Process the saved file to generate the requested table.",
        "criticism": "This plan assumes that the information is available in a format that can be easily extracted and processed. If the information is presented in a less structured format, additional steps may be required.",
        "speak": "I'm going to find the winning Boston Marathon times for the past 5 years by querying a webpage with this information."
    },
    "command": {
        "name": "query_webpage",
        "args": {
            "url": "https://www.baa.org/races/boston-marathon/results/champions",
            "question": "What were the winning Boston Marathon times for the past 5 years (ending in 2022)?"
        }
    }
}
message='Request to OpenAI API' method=post path=https://api.openai.com/v1/embeddings
api_version=None data='{"input": [[72803, 18321, 25, 341, 262, 330, 61665, 82, 794, 341, 286, 330, 1342, 794, 330, 1271, 1505, 279, 11230, 10406, 51273, 3115, 369, 279, 3347, 220, 20, 1667, 11, 358, 1205, 311, 3319, 264, 45710, 430, 5727, 420, 2038, 10560, 286, 330, 20489, 287, 794, 330, 791, 10406, 51273, 374, 264, 3682, 9974, 1567, 449, 5199, 3772, 10401, 11, 779, 279, 3135, 1288, 387, 31368, 2561, 2930, 13, 358, 649, 1005, 279, 364, 1663, 27050, 2964, 6, 3290, 311, 17622, 420, 2038, 10560, 286, 330, 10609, 794, 6660, 5560, 364, 1663, 27050, 2964, 6, 311, 1505, 279, 11230, 3115, 369, 279, 10406, 51273, 369, 279, 3347, 220, 20, 1667, 7255, 77, 12, 23673, 279, 9959, 2038, 323, 3665, 433, 311, 264, 1052, 7255, 77, 12, 8773, 279, 6924, 1052, 311, 7068, 279, 11472, 2007, 10560, 286, 330, 38096, 42914, 794, 330, 2028, 3197, 22204, 430, 279, 2038, 374, 2561, 304, 264, 3645, 430, 649, 387, 6847, 28532, 323, 15590, 13, 1442, 279, 2038, 374, 10666, 304, 264, 2753, 34030, 3645, 11, 5217, 7504, 1253, 387, 2631, 10560, 286, 330, 82, 23635, 794, 330, 40, 2846, 2133, 311, 1505, 279, 11230, 10406, 51273, 3115, 369, 279, 3347, 220, 20, 1667, 555, 82198, 264, 45710, 449, 420, 2038, 10246, 262, 1173, 262, 330, 5749, 794, 341, 286, 330, 609, 794, 330, 1663, 27050, 2964, 761, 286, 330, 2164, 794, 341, 310, 330, 1103, 794, 330, 2485, 1129, 2185, 960, 5418, 2726, 7534, 2492, 3554, 9611, 71098, 24893, 78274, 21987, 9310, 761, 310, 330, 7998, 794, 330, 3923, 1051, 279, 11230, 10406, 51273, 3115, 369, 279, 3347, 220, 20, 1667, 320, 2518, 304, 220, 2366, 17, 12106, 702, 286, 457, 262, 457, 92, 720, 2122, 25, 7498, 3319, 27050, 2964, 6052, 25, 4703, 25, 2684, 374, 912, 1510, 1567, 6471, 304, 4617, 364, 68137, 26321, 12, 15, 62, 15, 6, 2637, 45643, 11, 2897, 25, 5473, 1103, 1232, 364, 2485, 1129, 2185, 960, 5418, 2726, 7534, 2492, 3554, 9611, 71098, 24893, 78274, 21987, 9310, 518, 364, 7998, 1232, 364, 3923, 1051, 279, 11230, 10406, 51273, 3115, 369, 279, 3347, 220, 20, 1667, 320, 2518, 304, 220, 2366, 17, 12106, 8439, 220]], "model": "text-embedding-ada-002", "encoding_format": "base64"}' message='Post details'
https://api.openai.com:443 "POST /v1/embeddings HTTP/1.1" 200 None
message='OpenAI API response' path=https://api.openai.com/v1/embeddings processing_ms=29 request_id=f68c1235fc86e1da9e0eab521610ce41 response_code=200
message='Request to OpenAI API' method=post path=https://api.openai.com/v1/chat/completions
api_version=None data='{"messages": [{"role": "system", "content": "You are Optimus Prime, Worker in a swarm\\nYour decisions must always be made independently without seeking user assistance.\\nPlay to your strengths as an LLM and pursue simple strategies with no legal complications.\\nIf you have completed all your tasks, make sure to use the \\"finish\\" command.\\n\\nGOALS:\\n\\n1. What were the winning Boston Marathon times for the past 5 years (ending in 2022)? Generate a table of the year, name, country of origin, and times.\\n\\n\\nConstraints:\\n1. ~4000 word limit for short term memory. Your short term memory is short, so immediately save important information to files.\\n2. If you are unsure how you previously did something or want to recall past events, thinking about similar events will help you remember.\\n3. No user assistance\\n4. Exclusively use the commands listed in double quotes e.g. \\"command name\\"\\n\\nCommands:\\n1. write_file: Write file to disk, args json schema: {\\"file_path\\": {\\"title\\": \\"File Path\\", \\"description\\": \\"name of file\\", \\"type\\": \\"string\\"}, \\"text\\": {\\"title\\": \\"Text\\", \\"description\\": \\"text to write to file\\", \\"type\\": \\"string\\"}, \\"append\\": {\\"title\\": \\"Append\\", \\"description\\": \\"Whether to append to an existing file.\\", \\"default\\": false, \\"type\\": \\"boolean\\"}}\\n2. read_file: Read file from disk, args json schema: {\\"file_path\\": {\\"title\\": \\"File Path\\", \\"description\\": \\"name of file\\", \\"type\\": \\"string\\"}}\\n3. process_csv: process_csv(llm, csv_file_path: str, instructions: str, output_path: Optional[str] = None) -> str - 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., args json schema: {\\"llm\\": {\\"title\\": \\"Llm\\"}, \\"csv_file_path\\": {\\"title\\": \\"Csv File Path\\", \\"type\\": \\"string\\"}, \\"instructions\\": {\\"title\\": \\"Instructions\\", \\"type\\": \\"string\\"}, \\"output_path\\": {\\"title\\": \\"Output Path\\", \\"type\\": \\"string\\"}}\\n4. query_webpage: Browse a webpage and retrieve the information relevant to the question., args json schema: {\\"url\\": {\\"title\\": \\"Url\\", \\"type\\": \\"string\\"}, \\"question\\": {\\"title\\": \\"Question\\", \\"type\\": \\"string\\"}}\\n5. human: You can ask a human for guidance when you think you got stuck or you are not sure what to do next. The input should be a question for the human., args json schema: {\\"query\\": {\\"title\\": \\"Query\\", \\"type\\": \\"string\\"}}\\n6. compile: compile(task: str) - Open Interpreter lets LLMs run code (Python, Javascript, Shell, and more) locally. You can chat with Open Interpreter through a ChatGPT-like interface in your terminal by running $ interpreter after installing.\\n\\n    This provides a natural-language interface to your computer\'s general-purpose capabilities:\\n\\n    Create and edit photos, videos, PDFs, etc.\\n    Control a Chrome browser to perform research\\n    Plot, clean, and analyze large datasets\\n    ...etc.\\n    \\u26a0\\ufe0f Note: You\'ll be asked to approve code before it\'s run., args json schema: {\\"task\\": {\\"title\\": \\"Task\\", \\"type\\": \\"string\\"}}\\n7. VQAinference: VQAinference(self, inputs) - Answer Question About The Image, VQA Multi-Modal Worker agent\\n    description=\\"useful when you need an answer for a question based on an image. \\"\\n    \\"like: what is the background color of the last image, how many cats in this figure, what is in this figure. \\"\\n    \\"The input to this tool should be a comma separated string of two, representing the image_path and the question\\",, args json schema: {\\"self\\": {\\"title\\": \\"Self\\"}, \\"inputs\\": {\\"title\\": \\"Inputs\\"}}\\n8. 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\\nResources:\\n1. Internet access for searches and information gathering.\\n2. Long Term memory management.\\n3. GPT-3.5 powered Agents for delegation of simple tasks.\\n4. File output.\\n\\nPerformance Evaluation:\\n1. Continuously review and analyze your actions to ensure you are performing to the best of your abilities.\\n2. Constructively self-criticize your big-picture behavior constantly.\\n3. Reflect on past decisions and strategies to refine your approach.\\n4. Every command has a cost, so be smart and efficient. Aim to complete tasks in the least number of steps.\\n\\nYou should only respond in JSON format as described below \\nResponse 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} \\nEnsure the response can be parsed by Python json.loads"}, {"role": "system", "content": "The current time and date is Tue Sep 26 09:57:37 2023"}, {"role": "system", "content": "This reminds you of these events from your past:\\n[\'Assistant Reply: {\\\\n    \\"thoughts\\": {\\\\n        \\"text\\": \\"I need to find the winning Boston Marathon times for the past 5 years. To do this, I can use the \\\\\'query_webpage\\\\\' command to search the internet for this information.\\",\\\\n        \\"reasoning\\": \\"The Boston Marathon is a popular event and the results are likely to be published on multiple websites. Therefore, using the \\\\\'query_webpage\\\\\' command is a reasonable approach.\\",\\\\n        \\"plan\\": \\"- Use \\\\\'query_webpage\\\\\' command to find the winning times for the Boston Marathon for the past 5 years (2018-2022).\\\\\\\\n- Save the data to a file.\\\\\\\\n- Use \\\\\'process_csv\\\\\' command to generate a table from the saved data.\\",\\\\n        \\"criticism\\": \\"None\\",\\\\n        \\"speak\\": \\"I am going to look up the winning times for the Boston Marathon for the past 5 years.\\"\\\\n    },\\\\n    \\"command\\": {\\\\n        \\"name\\": \\"query_webpage\\",\\\\n        \\"args\\": {\\\\n            \\"url\\": \\"https://www.boston.com/sports/boston-marathon/2022/04/18/boston-marathon-2022-results\\",\\\\n            \\"question\\": \\"What were the winning Boston Marathon times for the past 5 years?\\"\\\\n        }\\\\n    }\\\\n} \\\\nResult: Command query_webpage returned: Error: There is no current event loop in thread \\\\\'ThreadPoolExecutor-0_2\\\\\'., RuntimeError, args: {\\\\\'url\\\\\': \\\\\'https://www.boston.com/sports/boston-marathon/2022/04/18/boston-marathon-2022-results\\\\\', \\\\\'question\\\\\': \\\\\'What were the winning Boston Marathon times for the past 5 years?\\\\\'} \']\\n\\n"}, {"role": "user", "content": "Determine which next command to use, and respond using the format specified above:"}, {"role": "assistant", "content": "{\\n    \\"thoughts\\": {\\n        \\"text\\": \\"I need to find the winning Boston Marathon times for the past 5 years. To do this, I can use the \'query_webpage\' command to search the internet for this information.\\",\\n        \\"reasoning\\": \\"The Boston Marathon is a popular event and the results are likely to be published on multiple websites. Therefore, using the \'query_webpage\' command is a reasonable approach.\\",\\n        \\"plan\\": \\"- Use \'query_webpage\' command to find the winning times for the Boston Marathon for the past 5 years (2018-2022).\\\\n- Save the data to a file.\\\\n- Use \'process_csv\' command to generate a table from the saved data.\\",\\n        \\"criticism\\": \\"None\\",\\n        \\"speak\\": \\"I am going to look up the winning times for the Boston Marathon for the past 5 years.\\"\\n    },\\n    \\"command\\": {\\n        \\"name\\": \\"query_webpage\\",\\n        \\"args\\": {\\n            \\"url\\": \\"https://www.boston.com/sports/boston-marathon/2022/04/18/boston-marathon-2022-results\\",\\n            \\"question\\": \\"What were the winning Boston Marathon times for the past 5 years?\\"\\n        }\\n    }\\n}"}, {"role": "system", "content": "Command query_webpage returned: Error: There is no current event loop in thread \'ThreadPoolExecutor-0_2\'., RuntimeError, args: {\'url\': \'https://www.boston.com/sports/boston-marathon/2022/04/18/boston-marathon-2022-results\', \'question\': \'What were the winning Boston Marathon times for the past 5 years?\'}"}, {"role": "user", "content": "Determine which next command to use, and respond using the format specified above:"}], "model": "gpt-4", "max_tokens": null, "stream": false, "n": 1, "temperature": 0.5}' message='Post details'
https://api.openai.com:443 "POST /v1/embeddings HTTP/1.1" 200 None
message='OpenAI API response' path=https://api.openai.com/v1/embeddings processing_ms=34 request_id=ae4ba2060e50dddb22555a59604a48be response_code=200
message='Request to OpenAI API' method=post path=https://api.openai.com/v1/embeddings
api_version=None data='{"input": [[58, 35075, 2097, 15413, 1151, 35, 25296, 902, 1828, 3290, 311, 1005, 11, 323, 6013, 1701, 279, 3645, 5300, 3485, 17898, 5217, 37335, 68525, 3187, 5725, 705, 87868, 808, 15413, 1151, 36802, 77, 262, 330, 61665, 82, 794, 29252, 77, 286, 330, 1342, 794, 330, 1271, 1505, 279, 11230, 10406, 51273, 3115, 369, 279, 3347, 220, 20, 1667, 11, 358, 1205, 311, 3319, 264, 45710, 430, 5727, 420, 2038, 10684, 59, 77, 286, 330, 20489, 287, 794, 330, 791, 10406, 51273, 374, 264, 3682, 9974, 1567, 449, 5199, 3772, 10401, 11, 779, 279, 3135, 1288, 387, 31368, 2561, 2930, 13, 358, 649, 1005, 279, 28251, 1663, 27050, 2964, 10379, 3290, 311, 17622, 420, 2038, 10684, 59, 77, 286, 330, 10609, 794, 6660, 5560, 28251, 1663, 27050, 2964, 10379, 311, 1505, 279, 11230, 3115, 369, 279, 10406, 51273, 369, 279, 3347, 220, 20, 1667, 13, 3505, 77, 12, 23673, 279, 9959, 2038, 323, 3665, 433, 311, 264, 1052, 13, 3505, 77, 12, 8773, 279, 6924, 1052, 311, 7068, 279, 11472, 2007, 10684, 59, 77, 286, 330, 38096, 42914, 794, 330, 2028, 3197, 22204, 430, 279, 2038, 374, 2561, 304, 264, 3645, 430, 649, 387, 6847, 28532, 323, 15590, 13, 1442, 279, 2038, 374, 10666, 304, 264, 2753, 34030, 3645, 11, 5217, 7504, 1253, 387, 2631, 10684, 59, 77, 286, 330, 82, 23635, 794, 330, 40, 10379, 76, 2133, 311, 1505, 279, 11230, 10406, 51273, 3115, 369, 279, 3347, 220, 20, 1667, 555, 82198, 264, 45710, 449, 420, 2038, 44043, 77, 262, 2529, 59, 77, 262, 330, 5749, 794, 29252, 77, 286, 330, 609, 794, 330, 1663, 27050, 2964, 498, 59, 77, 286, 330, 2164, 794, 29252, 77, 310, 330, 1103, 794, 330, 2485, 1129, 2185, 960, 5418, 2726, 7534, 2492, 3554, 9611, 71098, 24893, 78274, 21987, 9310, 498, 59, 77, 310, 330, 7998, 794, 330, 3923, 1051, 279, 11230, 10406, 51273, 3115, 369, 279, 3347, 220, 20, 1667, 320, 2518, 304, 220, 2366, 17, 8, 7673, 59, 77, 286, 52400, 77, 262, 52400, 77, 17266, 5217, 37335, 68525, 3187, 5725, 705, 744, 2097, 15413, 429, 4153, 3319, 27050, 2964, 6052, 25, 4703, 25, 2684, 374, 912, 1510, 1567, 6471, 304, 4617, 364, 68137, 26321, 12, 15, 62, 15, 6, 2637, 45643, 11, 2897, 25, 5473, 1103, 1232, 364, 2485, 1129, 2185, 960, 5418, 2726, 7534, 2492, 3554, 9611, 71098, 24893, 78274, 21987, 9310, 518, 364, 7998, 1232, 364, 3923, 1051, 279, 11230, 10406, 51273, 3115, 369, 279, 3347, 220, 20, 1667, 320, 2518, 304, 220, 2366, 17, 12106, 8439, 498, 5217, 37335, 1185, 5525, 60]], "model": "text-embedding-ada-002", "encoding_format": "base64"}' message='Post details'
https://api.openai.com:443 "POST /v1/embeddings HTTP/1.1" 200 None
message='OpenAI API response' path=https://api.openai.com/v1/embeddings processing_ms=42 request_id=64bca45925a5a4015ea9df1c9266d6ea response_code=200
message='Request to OpenAI API' method=post path=https://api.openai.com/v1/chat/completions
api_version=None data='{"messages": [{"role": "system", "content": "You are Optimus Prime, Worker in a swarm\\nYour decisions must always be made independently without seeking user assistance.\\nPlay to your strengths as an LLM and pursue simple strategies with no legal complications.\\nIf you have completed all your tasks, make sure to use the \\"finish\\" command.\\n\\nGOALS:\\n\\n1. What were the winning Boston Marathon times for the past 5 years (ending in 2022)? Generate a table of the year, name, country of origin, and times.\\n\\n\\nConstraints:\\n1. ~4000 word limit for short term memory. Your short term memory is short, so immediately save important information to files.\\n2. If you are unsure how you previously did something or want to recall past events, thinking about similar events will help you remember.\\n3. No user assistance\\n4. Exclusively use the commands listed in double quotes e.g. \\"command name\\"\\n\\nCommands:\\n1. write_file: Write file to disk, args json schema: {\\"file_path\\": {\\"title\\": \\"File Path\\", \\"description\\": \\"name of file\\", \\"type\\": \\"string\\"}, \\"text\\": {\\"title\\": \\"Text\\", \\"description\\": \\"text to write to file\\", \\"type\\": \\"string\\"}, \\"append\\": {\\"title\\": \\"Append\\", \\"description\\": \\"Whether to append to an existing file.\\", \\"default\\": false, \\"type\\": \\"boolean\\"}}\\n2. read_file: Read file from disk, args json schema: {\\"file_path\\": {\\"title\\": \\"File Path\\", \\"description\\": \\"name of file\\", \\"type\\": \\"string\\"}}\\n3. process_csv: process_csv(llm, csv_file_path: str, instructions: str, output_path: Optional[str] = None) -> str - 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., args json schema: {\\"llm\\": {\\"title\\": \\"Llm\\"}, \\"csv_file_path\\": {\\"title\\": \\"Csv File Path\\", \\"type\\": \\"string\\"}, \\"instructions\\": {\\"title\\": \\"Instructions\\", \\"type\\": \\"string\\"}, \\"output_path\\": {\\"title\\": \\"Output Path\\", \\"type\\": \\"string\\"}}\\n4. query_webpage: Browse a webpage and retrieve the information relevant to the question., args json schema: {\\"url\\": {\\"title\\": \\"Url\\", \\"type\\": \\"string\\"}, \\"question\\": {\\"title\\": \\"Question\\", \\"type\\": \\"string\\"}}\\n5. human: You can ask a human for guidance when you think you got stuck or you are not sure what to do next. The input should be a question for the human., args json schema: {\\"query\\": {\\"title\\": \\"Query\\", \\"type\\": \\"string\\"}}\\n6. compile: compile(task: str) - Open Interpreter lets LLMs run code (Python, Javascript, Shell, and more) locally. You can chat with Open Interpreter through a ChatGPT-like interface in your terminal by running $ interpreter after installing.\\n\\n    This provides a natural-language interface to your computer\'s general-purpose capabilities:\\n\\n    Create and edit photos, videos, PDFs, etc.\\n    Control a Chrome browser to perform research\\n    Plot, clean, and analyze large datasets\\n    ...etc.\\n    \\u26a0\\ufe0f Note: You\'ll be asked to approve code before it\'s run., args json schema: {\\"task\\": {\\"title\\": \\"Task\\", \\"type\\": \\"string\\"}}\\n7. VQAinference: VQAinference(self, inputs) - Answer Question About The Image, VQA Multi-Modal Worker agent\\n    description=\\"useful when you need an answer for a question based on an image. \\"\\n    \\"like: what is the background color of the last image, how many cats in this figure, what is in this figure. \\"\\n    \\"The input to this tool should be a comma separated string of two, representing the image_path and the question\\",, args json schema: {\\"self\\": {\\"title\\": \\"Self\\"}, \\"inputs\\": {\\"title\\": \\"Inputs\\"}}\\n8. 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\\nResources:\\n1. Internet access for searches and information gathering.\\n2. Long Term memory management.\\n3. GPT-3.5 powered Agents for delegation of simple tasks.\\n4. File output.\\n\\nPerformance Evaluation:\\n1. Continuously review and analyze your actions to ensure you are performing to the best of your abilities.\\n2. Constructively self-criticize your big-picture behavior constantly.\\n3. Reflect on past decisions and strategies to refine your approach.\\n4. Every command has a cost, so be smart and efficient. Aim to complete tasks in the least number of steps.\\n\\nYou should only respond in JSON format as described below \\nResponse 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} \\nEnsure the response can be parsed by Python json.loads"}, {"role": "system", "content": "The current time and date is Tue Sep 26 09:57:38 2023"}, {"role": "system", "content": "This reminds you of these events from your past:\\n[\'Assistant Reply: {\\\\n    \\"thoughts\\": {\\\\n        \\"text\\": \\"To find the winning Boston Marathon times for the past 5 years, I need to query a webpage that contains this information.\\",\\\\n        \\"reasoning\\": \\"The Boston Marathon is a major annual event with significant media coverage, so the results should be readily available online. I can use the \\\\\'query_webpage\\\\\' command to retrieve this information.\\",\\\\n        \\"plan\\": \\"- Use \\\\\'query_webpage\\\\\' to find the winning times for the Boston Marathon for the past 5 years.\\\\\\\\n- Extract the relevant information and save it to a file.\\\\\\\\n- Process the saved file to generate the requested table.\\",\\\\n        \\"criticism\\": \\"This plan assumes that the information is available in a format that can be easily extracted and processed. If the information is presented in a less structured format, additional steps may be required.\\",\\\\n        \\"speak\\": \\"I\\\\\'m going to find the winning Boston Marathon times for the past 5 years by querying a webpage with this information.\\"\\\\n    },\\\\n    \\"command\\": {\\\\n        \\"name\\": \\"query_webpage\\",\\\\n        \\"args\\": {\\\\n            \\"url\\": \\"https://www.baa.org/races/boston-marathon/results/champions\\",\\\\n            \\"question\\": \\"What were the winning Boston Marathon times for the past 5 years (ending in 2022)?\\"\\\\n        }\\\\n    }\\\\n} \\\\nResult: Command query_webpage returned: Error: There is no current event loop in thread \\\\\'ThreadPoolExecutor-0_0\\\\\'., RuntimeError, args: {\\\\\'url\\\\\': \\\\\'https://www.baa.org/races/boston-marathon/results/champions\\\\\', \\\\\'question\\\\\': \\\\\'What were the winning Boston Marathon times for the past 5 years (ending in 2022)?\\\\\'} \']\\n\\n"}, {"role": "user", "content": "Determine which next command to use, and respond using the format specified above:"}, {"role": "assistant", "content": "{\\n    \\"thoughts\\": {\\n        \\"text\\": \\"To find the winning Boston Marathon times for the past 5 years, I need to query a webpage that contains this information.\\",\\n        \\"reasoning\\": \\"The Boston Marathon is a major annual event with significant media coverage, so the results should be readily available online. I can use the \'query_webpage\' command to retrieve this information.\\",\\n        \\"plan\\": \\"- Use \'query_webpage\' to find the winning times for the Boston Marathon for the past 5 years.\\\\n- Extract the relevant information and save it to a file.\\\\n- Process the saved file to generate the requested table.\\",\\n        \\"criticism\\": \\"This plan assumes that the information is available in a format that can be easily extracted and processed. If the information is presented in a less structured format, additional steps may be required.\\",\\n        \\"speak\\": \\"I\'m going to find the winning Boston Marathon times for the past 5 years by querying a webpage with this information.\\"\\n    },\\n    \\"command\\": {\\n        \\"name\\": \\"query_webpage\\",\\n        \\"args\\": {\\n            \\"url\\": \\"https://www.baa.org/races/boston-marathon/results/champions\\",\\n            \\"question\\": \\"What were the winning Boston Marathon times for the past 5 years (ending in 2022)?\\"\\n        }\\n    }\\n}"}, {"role": "system", "content": "Command query_webpage returned: Error: There is no current event loop in thread \'ThreadPoolExecutor-0_0\'., RuntimeError, args: {\'url\': \'https://www.baa.org/races/boston-marathon/results/champions\', \'question\': \'What were the winning Boston Marathon times for the past 5 years (ending in 2022)?\'}"}, {"role": "user", "content": "Determine which next command to use, and respond using the format specified above:"}], "model": "gpt-4", "max_tokens": null, "stream": false, "n": 1, "temperature": 0.5}' message='Post details'
^CError in sys.excepthook:
Traceback (most recent call last):
  File "/Library/Frameworks/Python.framework/Versions/3.11/lib/python3.11/site-packages/humbug/report.py", line 505, in _hook
    self.error_report(error=exception_instance, tags=tags, publish=publish)
  File "/Library/Frameworks/Python.framework/Versions/3.11/lib/python3.11/site-packages/humbug/report.py", line 247, in error_report
    traceback.format_exception(
TypeError: format_exception() got an unexpected keyword argument 'etype'

Original exception was:
Traceback (most recent call last):
  File "/Users/defalt/Desktop/Athena/research/swarms/example_godmode.py", line 1, in <module>
    from swarms import GodMode
  File "/Users/defalt/Desktop/Athena/research/swarms/swarms/__init__.py", line 25, in <module>
    from swarms.swarms.dialogue_simulator import DialogueSimulator
  File "/Users/defalt/Desktop/Athena/research/swarms/swarms/swarms/__init__.py", line 6, in <module>
    from swarms.swarms.god_mode import GodMode
  File "/Users/defalt/Desktop/Athena/research/swarms/swarms/swarms/god_mode.py", line 55, in <module>
    god_mode.print_responses(task)
  File "/Users/defalt/Desktop/Athena/research/swarms/swarms/swarms/god_mode.py", line 36, in print_responses
    responses = self.run_all(task)
                ^^^^^^^^^^^^^^^^^^
  File "/Users/defalt/Desktop/Athena/research/swarms/swarms/swarms/god_mode.py", line 26, in run_all
    with ThreadPoolExecutor() as executor:
  File "/Library/Frameworks/Python.framework/Versions/3.11/lib/python3.11/concurrent/futures/_base.py", line 647, in __exit__
    self.shutdown(wait=True)
  File "/Library/Frameworks/Python.framework/Versions/3.11/lib/python3.11/concurrent/futures/thread.py", line 235, in shutdown
    t.join()
  File "/Library/Frameworks/Python.framework/Versions/3.11/lib/python3.11/threading.py", line 1112, in join
    self._wait_for_tstate_lock()
  File "/Library/Frameworks/Python.framework/Versions/3.11/lib/python3.11/threading.py", line 1132, in _wait_for_tstate_lock
    if lock.acquire(block, timeout):
       ^^^^^^^^^^^^^^^^^^^^^^^^^^^^
KeyboardInterrupt
https://api.openai.com:443 "POST /v1/chat/completions HTTP/1.1" 200 None
message='OpenAI API response' path=https://api.openai.com/v1/chat/completions processing_ms=39632 request_id=1c05913c3e03ea1306c880d30d75800a response_code=200
{
    "thoughts": {
        "text": "To gather the required information, I'll use the 'query_webpage' command to browse a webpage that contains the winning Boston Marathon times for the past 5 years (ending in 2022). This command allows me to retrieve the relevant information to the question.",
        "reasoning": "I need to find a reliable source that contains the historical data of the Boston Marathon winners. The official Boston Marathon website or a reputable sports news website can be a potential source. Using the 'query_webpage' command, I can browse these websites and extract the required information.",
        "plan": "- Use the 'query_webpage' command to find the Boston Marathon winning times for the past 5 years.\n- Once I have the information, store it in a file for future reference.\n- Generate a table of the year, name, country of origin, and times.",
        "criticism": "I need to ensure that the source I'm using is reliable and up-to-date. Also, I must accurately extract the required information to avoid any errors in the final output.",
        "speak": "I'm going to use the 'query_webpage' command to browse a webpage that contains the winning Boston Marathon times for the past 5 years. I'll gather the information and then store it in a file for future reference."
    },
    "command": {
        "name": "query_webpage",
        "args": {
            "url": "https://www.baa.org/races/boston-marathon/results/champions",
            "question": "What were the winning Boston Marathon times for the past 5 years (ending in 2022)?"
        }
    }
}
message='Request to OpenAI API' method=post path=https://api.openai.com/v1/embeddings
api_version=None data='{"input": [[72803, 18321, 25, 341, 262, 330, 61665, 82, 794, 341, 286, 330, 1342, 794, 330, 1271, 9762, 279, 2631, 2038, 11, 358, 3358, 1005, 279, 364, 1663, 27050, 2964, 6, 3290, 311, 27100, 264, 45710, 430, 5727, 279, 11230, 10406, 51273, 3115, 369, 279, 3347, 220, 20, 1667, 320, 2518, 304, 220, 2366, 17, 570, 1115, 3290, 6276, 757, 311, 17622, 279, 9959, 2038, 311, 279, 3488, 10560, 286, 330, 20489, 287, 794, 330, 40, 1205, 311, 1505, 264, 15062, 2592, 430, 5727, 279, 13970, 828, 315, 279, 10406, 51273, 26526, 13, 578, 4033, 10406, 51273, 3997, 477, 264, 56940, 10034, 3754, 3997, 649, 387, 264, 4754, 2592, 13, 12362, 279, 364, 1663, 27050, 2964, 6, 3290, 11, 358, 649, 27100, 1521, 13335, 323, 8819, 279, 2631, 2038, 10560, 286, 330, 10609, 794, 6660, 5560, 279, 364, 1663, 27050, 2964, 6, 3290, 311, 1505, 279, 10406, 51273, 11230, 3115, 369, 279, 3347, 220, 20, 1667, 7255, 77, 12, 9843, 358, 617, 279, 2038, 11, 3637, 433, 304, 264, 1052, 369, 3938, 5905, 7255, 77, 12, 20400, 264, 2007, 315, 279, 1060, 11, 836, 11, 3224, 315, 6371, 11, 323, 3115, 10560, 286, 330, 38096, 42914, 794, 330, 40, 1205, 311, 6106, 430, 279, 2592, 358, 2846, 1701, 374, 15062, 323, 709, 4791, 18920, 13, 7429, 11, 358, 2011, 30357, 8819, 279, 2631, 2038, 311, 5766, 904, 6103, 304, 279, 1620, 2612, 10560, 286, 330, 82, 23635, 794, 330, 40, 2846, 2133, 311, 1005, 279, 364, 1663, 27050, 2964, 6, 3290, 311, 27100, 264, 45710, 430, 5727, 279, 11230, 10406, 51273, 3115, 369, 279, 3347, 220, 20, 1667, 13, 358, 3358, 9762, 279, 2038, 323, 1243, 3637, 433, 304, 264, 1052, 369, 3938, 5905, 10246, 262, 1173, 262, 330, 5749, 794, 341, 286, 330, 609, 794, 330, 1663, 27050, 2964, 761, 286, 330, 2164, 794, 341, 310, 330, 1103, 794, 330, 2485, 1129, 2185, 960, 5418, 2726, 7534, 2492, 3554, 9611, 71098, 24893, 78274, 21987, 9310, 761, 310, 330, 7998, 794, 330, 3923, 1051, 279, 11230, 10406, 51273, 3115, 369, 279, 3347, 220, 20, 1667, 320, 2518, 304, 220, 2366, 17, 12106, 702, 286, 457, 262, 457, 92, 720, 2122, 25, 7498, 3319, 27050, 2964, 6052, 25, 4703, 25, 2684, 374, 912, 1510, 1567, 6471, 304, 4617, 364, 68137, 26321, 12, 15, 62, 16, 6, 2637, 45643, 11, 2897, 25, 5473, 1103, 1232, 364, 2485, 1129, 2185, 960, 5418, 2726, 7534, 2492, 3554, 9611, 71098, 24893, 78274, 21987, 9310, 518, 364, 7998, 1232, 364, 3923, 1051, 279, 11230, 10406, 51273, 3115, 369, 279, 3347, 220, 20, 1667, 320, 2518, 304, 220, 2366, 17, 12106, 8439, 220]], "model": "text-embedding-ada-002", "encoding_format": "base64"}' message='Post details'
https://api.openai.com:443 "POST /v1/embeddings HTTP/1.1" 200 None
message='OpenAI API response' path=https://api.openai.com/v1/embeddings processing_ms=34 request_id=f4d2d419abcc16d238f480f029c7379f response_code=200
message='Request to OpenAI API' method=post path=https://api.openai.com/v1/embeddings
api_version=None data='{"input": [[58, 35075, 2097, 15413, 1151, 35, 25296, 902, 1828, 3290, 311, 1005, 11, 323, 6013, 1701, 279, 3645, 5300, 3485, 17898, 5217, 37335, 68525, 3187, 5725, 705, 87868, 808, 15413, 1151, 36802, 77, 262, 330, 61665, 82, 794, 29252, 77, 286, 330, 1342, 794, 330, 1271, 9762, 279, 2631, 2038, 11, 358, 10379, 657, 1005, 279, 28251, 1663, 27050, 2964, 10379, 3290, 311, 27100, 264, 45710, 430, 5727, 279, 11230, 10406, 51273, 3115, 369, 279, 3347, 220, 20, 1667, 320, 2518, 304, 220, 2366, 17, 570, 1115, 3290, 6276, 757, 311, 17622, 279, 9959, 2038, 311, 279, 3488, 10684, 59, 77, 286, 330, 20489, 287, 794, 330, 40, 1205, 311, 1505, 264, 15062, 2592, 430, 5727, 279, 13970, 828, 315, 279, 10406, 51273, 26526, 13, 578, 4033, 10406, 51273, 3997, 477, 264, 56940, 10034, 3754, 3997, 649, 387, 264, 4754, 2592, 13, 12362, 279, 28251, 1663, 27050, 2964, 10379, 3290, 11, 358, 649, 27100, 1521, 13335, 323, 8819, 279, 2631, 2038, 10684, 59, 77, 286, 330, 10609, 794, 6660, 5560, 279, 28251, 1663, 27050, 2964, 10379, 3290, 311, 1505, 279, 10406, 51273, 11230, 3115, 369, 279, 3347, 220, 20, 1667, 13, 3505, 77, 12, 9843, 358, 617, 279, 2038, 11, 3637, 433, 304, 264, 1052, 369, 3938, 5905, 13, 3505, 77, 12, 20400, 264, 2007, 315, 279, 1060, 11, 836, 11, 3224, 315, 6371, 11, 323, 3115, 10684, 59, 77, 286, 330, 38096, 42914, 794, 330, 40, 1205, 311, 6106, 430, 279, 2592, 358, 10379, 76, 1701, 374, 15062, 323, 709, 4791, 18920, 13, 7429, 11, 358, 2011, 30357, 8819, 279, 2631, 2038, 311, 5766, 904, 6103, 304, 279, 1620, 2612, 10684, 59, 77, 286, 330, 82, 23635, 794, 330, 40, 10379, 76, 2133, 311, 1005, 279, 28251, 1663, 27050, 2964, 10379, 3290, 311, 27100, 264, 45710, 430, 5727, 279, 11230, 10406, 51273, 3115, 369, 279, 3347, 220, 20, 1667, 13, 358, 10379, 657, 9762, 279, 2038, 323, 1243, 3637, 433, 304, 264, 1052, 369, 3938, 5905, 44043, 77, 262, 2529, 59, 77, 262, 330, 5749, 794, 29252, 77, 286, 330, 609, 794, 330, 1663, 27050, 2964, 498, 59, 77, 286, 330, 2164, 794, 29252, 77, 310, 330, 1103, 794, 330, 2485, 1129, 2185, 960, 5418, 2726, 7534, 2492, 3554, 9611, 71098, 24893, 78274, 21987, 9310, 498, 59, 77, 310, 330, 7998, 794, 330, 3923, 1051, 279, 11230, 10406, 51273, 3115, 369, 279, 3347, 220, 20, 1667, 320, 2518, 304, 220, 2366, 17, 8, 7673, 59, 77, 286, 52400, 77, 262, 52400, 77, 17266, 5217, 37335, 68525, 3187, 5725, 705, 744, 2097, 15413, 429, 4153, 3319, 27050, 2964, 6052, 25, 4703, 25, 2684, 374, 912, 1510, 1567, 6471, 304, 4617, 364, 68137, 26321, 12, 15, 62, 16, 6, 2637, 45643, 11, 2897, 25, 5473, 1103, 1232, 364, 2485, 1129, 2185, 960, 5418, 2726, 7534, 2492, 3554, 9611, 71098, 24893, 78274, 21987, 9310, 518, 364, 7998, 1232, 364, 3923, 1051, 279, 11230, 10406, 51273, 3115, 369, 279, 3347, 220, 20, 1667, 320, 2518, 304, 220, 2366, 17, 12106, 8439, 498, 5217, 37335, 1185, 5525, 60]], "model": "text-embedding-ada-002", "encoding_format": "base64"}' message='Post details'
https://api.openai.com:443 "POST /v1/embeddings HTTP/1.1" 200 None
message='OpenAI API response' path=https://api.openai.com/v1/embeddings processing_ms=40 request_id=d1f5bf424fb4cfa78613e716f824c939 response_code=200
message='Request to OpenAI API' method=post path=https://api.openai.com/v1/chat/completions
api_version=None data='{"messages": [{"role": "system", "content": "You are Optimus Prime, Worker in a swarm\\nYour decisions must always be made independently without seeking user assistance.\\nPlay to your strengths as an LLM and pursue simple strategies with no legal complications.\\nIf you have completed all your tasks, make sure to use the \\"finish\\" command.\\n\\nGOALS:\\n\\n1. What were the winning Boston Marathon times for the past 5 years (ending in 2022)? Generate a table of the year, name, country of origin, and times.\\n\\n\\nConstraints:\\n1. ~4000 word limit for short term memory. Your short term memory is short, so immediately save important information to files.\\n2. If you are unsure how you previously did something or want to recall past events, thinking about similar events will help you remember.\\n3. No user assistance\\n4. Exclusively use the commands listed in double quotes e.g. \\"command name\\"\\n\\nCommands:\\n1. write_file: Write file to disk, args json schema: {\\"file_path\\": {\\"title\\": \\"File Path\\", \\"description\\": \\"name of file\\", \\"type\\": \\"string\\"}, \\"text\\": {\\"title\\": \\"Text\\", \\"description\\": \\"text to write to file\\", \\"type\\": \\"string\\"}, \\"append\\": {\\"title\\": \\"Append\\", \\"description\\": \\"Whether to append to an existing file.\\", \\"default\\": false, \\"type\\": \\"boolean\\"}}\\n2. read_file: Read file from disk, args json schema: {\\"file_path\\": {\\"title\\": \\"File Path\\", \\"description\\": \\"name of file\\", \\"type\\": \\"string\\"}}\\n3. process_csv: process_csv(llm, csv_file_path: str, instructions: str, output_path: Optional[str] = None) -> str - 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., args json schema: {\\"llm\\": {\\"title\\": \\"Llm\\"}, \\"csv_file_path\\": {\\"title\\": \\"Csv File Path\\", \\"type\\": \\"string\\"}, \\"instructions\\": {\\"title\\": \\"Instructions\\", \\"type\\": \\"string\\"}, \\"output_path\\": {\\"title\\": \\"Output Path\\", \\"type\\": \\"string\\"}}\\n4. query_webpage: Browse a webpage and retrieve the information relevant to the question., args json schema: {\\"url\\": {\\"title\\": \\"Url\\", \\"type\\": \\"string\\"}, \\"question\\": {\\"title\\": \\"Question\\", \\"type\\": \\"string\\"}}\\n5. human: You can ask a human for guidance when you think you got stuck or you are not sure what to do next. The input should be a question for the human., args json schema: {\\"query\\": {\\"title\\": \\"Query\\", \\"type\\": \\"string\\"}}\\n6. compile: compile(task: str) - Open Interpreter lets LLMs run code (Python, Javascript, Shell, and more) locally. You can chat with Open Interpreter through a ChatGPT-like interface in your terminal by running $ interpreter after installing.\\n\\n    This provides a natural-language interface to your computer\'s general-purpose capabilities:\\n\\n    Create and edit photos, videos, PDFs, etc.\\n    Control a Chrome browser to perform research\\n    Plot, clean, and analyze large datasets\\n    ...etc.\\n    \\u26a0\\ufe0f Note: You\'ll be asked to approve code before it\'s run., args json schema: {\\"task\\": {\\"title\\": \\"Task\\", \\"type\\": \\"string\\"}}\\n7. VQAinference: VQAinference(self, inputs) - Answer Question About The Image, VQA Multi-Modal Worker agent\\n    description=\\"useful when you need an answer for a question based on an image. \\"\\n    \\"like: what is the background color of the last image, how many cats in this figure, what is in this figure. \\"\\n    \\"The input to this tool should be a comma separated string of two, representing the image_path and the question\\",, args json schema: {\\"self\\": {\\"title\\": \\"Self\\"}, \\"inputs\\": {\\"title\\": \\"Inputs\\"}}\\n8. 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\\nResources:\\n1. Internet access for searches and information gathering.\\n2. Long Term memory management.\\n3. GPT-3.5 powered Agents for delegation of simple tasks.\\n4. File output.\\n\\nPerformance Evaluation:\\n1. Continuously review and analyze your actions to ensure you are performing to the best of your abilities.\\n2. Constructively self-criticize your big-picture behavior constantly.\\n3. Reflect on past decisions and strategies to refine your approach.\\n4. Every command has a cost, so be smart and efficient. Aim to complete tasks in the least number of steps.\\n\\nYou should only respond in JSON format as described below \\nResponse 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} \\nEnsure the response can be parsed by Python json.loads"}, {"role": "system", "content": "The current time and date is Tue Sep 26 09:57:47 2023"}, {"role": "system", "content": "This reminds you of these events from your past:\\n[\'Assistant Reply: {\\\\n    \\"thoughts\\": {\\\\n        \\"text\\": \\"To gather the required information, I\\\\\'ll use the \\\\\'query_webpage\\\\\' command to browse a webpage that contains the winning Boston Marathon times for the past 5 years (ending in 2022). This command allows me to retrieve the relevant information to the question.\\",\\\\n        \\"reasoning\\": \\"I need to find a reliable source that contains the historical data of the Boston Marathon winners. The official Boston Marathon website or a reputable sports news website can be a potential source. Using the \\\\\'query_webpage\\\\\' command, I can browse these websites and extract the required information.\\",\\\\n        \\"plan\\": \\"- Use the \\\\\'query_webpage\\\\\' command to find the Boston Marathon winning times for the past 5 years.\\\\\\\\n- Once I have the information, store it in a file for future reference.\\\\\\\\n- Generate a table of the year, name, country of origin, and times.\\",\\\\n        \\"criticism\\": \\"I need to ensure that the source I\\\\\'m using is reliable and up-to-date. Also, I must accurately extract the required information to avoid any errors in the final output.\\",\\\\n        \\"speak\\": \\"I\\\\\'m going to use the \\\\\'query_webpage\\\\\' command to browse a webpage that contains the winning Boston Marathon times for the past 5 years. I\\\\\'ll gather the information and then store it in a file for future reference.\\"\\\\n    },\\\\n    \\"command\\": {\\\\n        \\"name\\": \\"query_webpage\\",\\\\n        \\"args\\": {\\\\n            \\"url\\": \\"https://www.baa.org/races/boston-marathon/results/champions\\",\\\\n            \\"question\\": \\"What were the winning Boston Marathon times for the past 5 years (ending in 2022)?\\"\\\\n        }\\\\n    }\\\\n} \\\\nResult: Command query_webpage returned: Error: There is no current event loop in thread \\\\\'ThreadPoolExecutor-0_1\\\\\'., RuntimeError, args: {\\\\\'url\\\\\': \\\\\'https://www.baa.org/races/boston-marathon/results/champions\\\\\', \\\\\'question\\\\\': \\\\\'What were the winning Boston Marathon times for the past 5 years (ending in 2022)?\\\\\'} \']\\n\\n"}, {"role": "user", "content": "Determine which next command to use, and respond using the format specified above:"}, {"role": "assistant", "content": "{\\n    \\"thoughts\\": {\\n        \\"text\\": \\"To gather the required information, I\'ll use the \'query_webpage\' command to browse a webpage that contains the winning Boston Marathon times for the past 5 years (ending in 2022). This command allows me to retrieve the relevant information to the question.\\",\\n        \\"reasoning\\": \\"I need to find a reliable source that contains the historical data of the Boston Marathon winners. The official Boston Marathon website or a reputable sports news website can be a potential source. Using the \'query_webpage\' command, I can browse these websites and extract the required information.\\",\\n        \\"plan\\": \\"- Use the \'query_webpage\' command to find the Boston Marathon winning times for the past 5 years.\\\\n- Once I have the information, store it in a file for future reference.\\\\n- Generate a table of the year, name, country of origin, and times.\\",\\n        \\"criticism\\": \\"I need to ensure that the source I\'m using is reliable and up-to-date. Also, I must accurately extract the required information to avoid any errors in the final output.\\",\\n        \\"speak\\": \\"I\'m going to use the \'query_webpage\' command to browse a webpage that contains the winning Boston Marathon times for the past 5 years. I\'ll gather the information and then store it in a file for future reference.\\"\\n    },\\n    \\"command\\": {\\n        \\"name\\": \\"query_webpage\\",\\n        \\"args\\": {\\n            \\"url\\": \\"https://www.baa.org/races/boston-marathon/results/champions\\",\\n            \\"question\\": \\"What were the winning Boston Marathon times for the past 5 years (ending in 2022)?\\"\\n        }\\n    }\\n}"}, {"role": "system", "content": "Command query_webpage returned: Error: There is no current event loop in thread \'ThreadPoolExecutor-0_1\'., RuntimeError, args: {\'url\': \'https://www.baa.org/races/boston-marathon/results/champions\', \'question\': \'What were the winning Boston Marathon times for the past 5 years (ending in 2022)?\'}"}, {"role": "user", "content": "Determine which next command to use, and respond using the format specified above:"}], "model": "gpt-4", "max_tokens": null, "stream": false, "n": 1, "temperature": 0.5}' message='Post details'
https://api.openai.com:443 "POST /v1/chat/completions HTTP/1.1" 200 None
message='OpenAI API response' path=https://api.openai.com/v1/chat/completions processing_ms=32034 request_id=4cc62013d58779b9c40371c1933ecb35 response_code=200
{
    "thoughts": {
        "text": "The 'query_webpage' command did not work as expected. I will try a different approach to find the information.",
        "reasoning": "The 'query_webpage' command returned an error, indicating that it could not retrieve the information from the specified URL.",
        "plan": "- Use 'query_webpage' command with a different URL to find the winning times for the Boston Marathon for the past 5 years (2018-2022).\n- Save the data to a file.\n- Use 'process_csv' command to generate a table from the saved data.",
        "criticism": "The initial URL may not have been the best choice for retrieving the information. It might have been better to use a more reliable source or a different method for obtaining the data.",
        "speak": "I'm going to try a different approach to find the winning times for the Boston Marathon for the past 5 years."
    },
    "command": {
        "name": "query_webpage",
        "args": {
            "url": "https://en.wikipedia.org/wiki/List_of_winners_of_the_Boston_Marathon",
            "question": "What were the winning Boston Marathon times for the past 5 years?"
        }
    }
}
message='Request to OpenAI API' method=post path=https://api.openai.com/v1/embeddings
api_version=None data='{"input": [[72803, 18321, 25, 341, 262, 330, 61665, 82, 794, 341, 286, 330, 1342, 794, 330, 791, 364, 1663, 27050, 2964, 6, 3290, 1550, 539, 990, 439, 3685, 13, 358, 690, 1456, 264, 2204, 5603, 311, 1505, 279, 2038, 10560, 286, 330, 20489, 287, 794, 330, 791, 364, 1663, 27050, 2964, 6, 3290, 6052, 459, 1493, 11, 19392, 430, 433, 1436, 539, 17622, 279, 2038, 505, 279, 5300, 5665, 10560, 286, 330, 10609, 794, 6660, 5560, 364, 1663, 27050, 2964, 6, 3290, 449, 264, 2204, 5665, 311, 1505, 279, 11230, 3115, 369, 279, 10406, 51273, 369, 279, 3347, 220, 20, 1667, 320, 679, 23, 12, 2366, 17, 73441, 77, 12, 10467, 279, 828, 311, 264, 1052, 7255, 77, 12, 5560, 364, 4734, 14347, 6, 3290, 311, 7068, 264, 2007, 505, 279, 6924, 828, 10560, 286, 330, 38096, 42914, 794, 330, 791, 2926, 5665, 1253, 539, 617, 1027, 279, 1888, 5873, 369, 49324, 279, 2038, 13, 1102, 2643, 617, 1027, 2731, 311, 1005, 264, 810, 15062, 2592, 477, 264, 2204, 1749, 369, 19546, 279, 828, 10560, 286, 330, 82, 23635, 794, 330, 40, 2846, 2133, 311, 1456, 264, 2204, 5603, 311, 1505, 279, 11230, 3115, 369, 279, 10406, 51273, 369, 279, 3347, 220, 20, 1667, 10246, 262, 1173, 262, 330, 5749, 794, 341, 286, 330, 609, 794, 330, 1663, 27050, 2964, 761, 286, 330, 2164, 794, 341, 310, 330, 1103, 794, 330, 2485, 1129, 268, 34466, 2726, 26583, 77497, 3659, 26756, 5079, 3659, 16454, 1702, 9611, 1267, 277, 24893, 761, 310, 330, 7998, 794, 330, 3923, 1051, 279, 11230, 10406, 51273, 3115, 369, 279, 3347, 220, 20, 1667, 48469, 286, 457, 262, 457, 92, 720, 2122, 25, 7498, 3319, 27050, 2964, 6052, 25, 4703, 25, 2684, 374, 912, 1510, 1567, 6471, 304, 4617, 364, 68137, 26321, 12, 15, 62, 17, 6, 2637, 45643, 11, 2897, 25, 5473, 1103, 1232, 364, 2485, 1129, 268, 34466, 2726, 26583, 77497, 3659, 26756, 5079, 3659, 16454, 1702, 9611, 1267, 277, 24893, 518, 364, 7998, 1232, 364, 3923, 1051, 279, 11230, 10406, 51273, 3115, 369, 279, 3347, 220, 20, 1667, 30, 8439, 220]], "model": "text-embedding-ada-002", "encoding_format": "base64"}' message='Post details'
https://api.openai.com:443 "POST /v1/embeddings HTTP/1.1" 200 None
message='OpenAI API response' path=https://api.openai.com/v1/embeddings processing_ms=37 request_id=6b19b0a0d2ba86819fcfc8f085592796 response_code=200
message='Request to OpenAI API' method=post path=https://api.openai.com/v1/embeddings
api_version=None data='{"input": [[58, 35075, 2097, 15413, 1151, 35, 25296, 902, 1828, 3290, 311, 1005, 11, 323, 6013, 1701, 279, 3645, 5300, 3485, 17898, 5217, 37335, 68525, 3187, 5725, 705, 87868, 808, 15413, 1151, 36802, 77, 262, 330, 61665, 82, 794, 29252, 77, 286, 330, 1342, 794, 330, 40, 1205, 311, 1505, 279, 11230, 10406, 51273, 3115, 369, 279, 3347, 220, 20, 1667, 13, 2057, 656, 420, 11, 358, 649, 1005, 279, 28251, 1663, 27050, 2964, 10379, 3290, 311, 2778, 279, 7757, 369, 420, 2038, 10684, 59, 77, 286, 330, 20489, 287, 794, 330, 791, 10406, 51273, 374, 264, 5526, 1567, 323, 279, 3135, 527, 4461, 311, 387, 4756, 389, 5361, 13335, 13, 15636, 11, 1701, 279, 28251, 1663, 27050, 2964, 10379, 3290, 374, 264, 13579, 5603, 10684, 59, 77, 286, 330, 10609, 794, 6660, 5560, 28251, 1663, 27050, 2964, 10379, 3290, 311, 1505, 279, 11230, 3115, 369, 279, 10406, 51273, 369, 279, 3347, 220, 20, 1667, 320, 679, 23, 12, 2366, 17, 570, 3505, 77, 12, 10467, 279, 828, 311, 264, 1052, 13, 3505, 77, 12, 5560, 28251, 4734, 14347, 10379, 3290, 311, 7068, 264, 2007, 505, 279, 6924, 828, 10684, 59, 77, 286, 330, 38096, 42914, 794, 330, 4155, 498, 59, 77, 286, 330, 82, 23635, 794, 330, 40, 1097, 2133, 311, 1427, 709, 279, 11230, 3115, 369, 279, 10406, 51273, 369, 279, 3347, 220, 20, 1667, 44043, 77, 262, 2529, 59, 77, 262, 330, 5749, 794, 29252, 77, 286, 330, 609, 794, 330, 1663, 27050, 2964, 498, 59, 77, 286, 330, 2164, 794, 29252, 77, 310, 330, 1103, 794, 330, 2485, 1129, 2185, 960, 9611, 916, 2754, 3476, 3554, 9611, 71098, 24893, 14, 2366, 17, 14, 2371, 14, 972, 3554, 9611, 71098, 24893, 12, 2366, 17, 65870, 498, 59, 77, 310, 330, 7998, 794, 330, 3923, 1051, 279, 11230, 10406, 51273, 3115, 369, 279, 3347, 220, 20, 1667, 7673, 59, 77, 286, 52400, 77, 262, 52400, 77, 17266, 5217, 37335, 68525, 3187, 5725, 705, 744, 2097, 15413, 429, 4153, 3319, 27050, 2964, 6052, 25, 4703, 25, 2684, 374, 912, 1510, 1567, 6471, 304, 4617, 364, 68137, 26321, 12, 15, 62, 17, 6, 2637, 45643, 11, 2897, 25, 5473, 1103, 1232, 364, 2485, 1129, 2185, 960, 9611, 916, 2754, 3476, 3554, 9611, 71098, 24893, 14, 2366, 17, 14, 2371, 14, 972, 3554, 9611, 71098, 24893, 12, 2366, 17, 65870, 518, 364, 7998, 1232, 364, 3923, 1051, 279, 11230, 10406, 51273, 3115, 369, 279, 3347, 220, 20, 1667, 30, 8439, 498, 5217, 37335, 1185, 39942, 11344, 2097, 15413, 1151, 35, 25296, 902, 1828, 3290, 311, 1005, 11, 323, 6013, 1701, 279, 3645, 5300, 3485, 17898, 5217, 37335, 68525, 3187, 5725, 705, 87868, 808, 15413, 1151, 36802, 77, 262, 330, 61665, 82, 794, 29252, 77, 286, 330, 1342, 794, 330, 791, 28251, 1663, 27050, 2964, 10379, 3290, 1550, 539, 990, 439, 3685, 13, 358, 690, 1456, 264, 2204, 5603, 311, 1505, 279, 2038, 10684, 59, 77, 286, 330, 20489, 287, 794, 330, 791, 28251, 1663, 27050, 2964, 10379, 3290, 6052, 459, 1493, 11, 19392, 430, 433, 1436, 539, 17622, 279, 2038, 505, 279, 5300, 5665, 10684, 59, 77, 286, 330, 10609, 794, 6660, 5560, 28251, 1663, 27050, 2964, 10379, 3290, 449, 264, 2204, 5665, 311, 1505, 279, 11230, 3115, 369, 279, 10406, 51273, 369, 279, 3347, 220, 20, 1667, 320, 679, 23, 12, 2366, 17, 570, 3505, 77, 12, 10467, 279, 828, 311, 264, 1052, 13, 3505, 77, 12, 5560, 28251, 4734, 14347, 10379, 3290, 311, 7068, 264, 2007, 505, 279, 6924, 828, 10684, 59, 77, 286, 330, 38096, 42914, 794, 330, 791, 2926, 5665, 1253, 539, 617, 1027, 279, 1888, 5873, 369, 49324, 279, 2038, 13, 1102, 2643, 617, 1027, 2731, 311, 1005, 264, 810, 15062, 2592, 477, 264, 2204, 1749, 369, 19546, 279, 828, 10684, 59, 77, 286, 330, 82, 23635, 794, 330, 40, 10379, 76, 2133, 311, 1456, 264, 2204, 5603, 311, 1505, 279, 11230, 3115, 369, 279, 10406, 51273, 369, 279, 3347, 220, 20, 1667, 44043, 77, 262, 2529, 59, 77, 262, 330, 5749, 794, 29252, 77, 286, 330, 609, 794, 330, 1663, 27050, 2964, 498, 59, 77, 286, 330, 2164, 794, 29252, 77, 310, 330, 1103, 794, 330, 2485, 1129, 268, 34466, 2726, 26583, 77497, 3659, 26756, 5079, 3659, 16454, 1702, 9611, 1267, 277, 24893, 498, 59, 77, 310, 330, 7998, 794, 330, 3923, 1051, 279, 11230, 10406, 51273, 3115, 369, 279, 3347, 220, 20, 1667, 7673, 59, 77, 286, 52400, 77, 262, 52400, 77, 17266, 5217, 37335, 68525, 3187, 5725, 705, 744, 2097, 15413, 429, 4153, 3319, 27050, 2964, 6052, 25, 4703, 25, 2684, 374, 912, 1510, 1567, 6471, 304, 4617, 364, 68137, 26321, 12, 15, 62, 17, 6, 2637, 45643, 11, 2897, 25, 5473, 1103, 1232, 364, 2485, 1129, 268, 34466, 2726, 26583, 77497, 3659, 26756, 5079, 3659, 16454, 1702, 9611, 1267, 277, 24893, 518, 364, 7998, 1232, 364, 3923, 1051, 279, 11230, 10406, 51273, 3115, 369, 279, 3347, 220, 20, 1667, 30, 8439, 498, 5217, 37335, 1185, 5525, 60]], "model": "text-embedding-ada-002", "encoding_format": "base64"}' message='Post details'
https://api.openai.com:443 "POST /v1/chat/completions HTTP/1.1" 200 None
message='OpenAI API response' path=https://api.openai.com/v1/chat/completions processing_ms=31523 request_id=74e96084e89d16c8aa3f613f9dec199a response_code=200
{
    "thoughts": {
        "text": "The 'query_webpage' command returned an error. I need to find another way to obtain the information.",
        "reasoning": "The error suggests that there is an issue with the threading model of the Python interpreter. This is likely a technical issue beyond my control. I need to find an alternative approach to obtain the information.",
        "plan": "- Use 'compile' command to write a script that queries the webpage and extracts the necessary information.\n- Save the extracted information to a file.\n- Process the saved file to generate the requested table.",
        "criticism": "This plan assumes that I can successfully write and execute a script to query the webpage and extract the information. If there are issues with the script or the webpage, additional steps may be required.",
        "speak": "I'm going to write a script to query the webpage and extract the winning Boston Marathon times for the past 5 years."
    },
    "command": {
        "name": "compile",
        "args": {
            "task": "Write a Python script that queries the webpage 'https://www.baa.org/races/boston-marathon/results/champions' and extracts the winning Boston Marathon times for the past 5 years (ending in 2022)."
        }
    }
}
message='Request to OpenAI API' method=post path=https://api.openai.com/v1/embeddings
api_version=None data='{"input": [[72803, 18321, 25, 341, 262, 330, 61665, 82, 794, 341, 286, 330, 1342, 794, 330, 791, 364, 1663, 27050, 2964, 6, 3290, 6052, 459, 1493, 13, 358, 1205, 311, 1505, 2500, 1648, 311, 6994, 279, 2038, 10560, 286, 330, 20489, 287, 794, 330, 791, 1493, 13533, 430, 1070, 374, 459, 4360, 449, 279, 31259, 1646, 315, 279, 13325, 40399, 13, 1115, 374, 4461, 264, 11156, 4360, 7953, 856, 2585, 13, 358, 1205, 311, 1505, 459, 10778, 5603, 311, 6994, 279, 2038, 10560, 286, 330, 10609, 794, 6660, 5560, 364, 21065, 6, 3290, 311, 3350, 264, 5429, 430, 20126, 279, 45710, 323, 49062, 279, 5995, 2038, 7255, 77, 12, 10467, 279, 28532, 2038, 311, 264, 1052, 7255, 77, 12, 8773, 279, 6924, 1052, 311, 7068, 279, 11472, 2007, 10560, 286, 330, 38096, 42914, 794, 330, 2028, 3197, 22204, 430, 358, 649, 7946, 3350, 323, 9203, 264, 5429, 311, 3319, 279, 45710, 323, 8819, 279, 2038, 13, 1442, 1070, 527, 4819, 449, 279, 5429, 477, 279, 45710, 11, 5217, 7504, 1253, 387, 2631, 10560, 286, 330, 82, 23635, 794, 330, 40, 2846, 2133, 311, 3350, 264, 5429, 311, 3319, 279, 45710, 323, 8819, 279, 11230, 10406, 51273, 3115, 369, 279, 3347, 220, 20, 1667, 10246, 262, 1173, 262, 330, 5749, 794, 341, 286, 330, 609, 794, 330, 21065, 761, 286, 330, 2164, 794, 341, 310, 330, 8366, 794, 330, 8144, 264, 13325, 5429, 430, 20126, 279, 45710, 364, 2485, 1129, 2185, 960, 5418, 2726, 7534, 2492, 3554, 9611, 71098, 24893, 78274, 21987, 9310, 6, 323, 49062, 279, 11230, 10406, 51273, 3115, 369, 279, 3347, 220, 20, 1667, 320, 2518, 304, 220, 2366, 17, 570, 702, 286, 457, 262, 457, 92, 720, 2122, 25, 7498, 19742, 6052, 25, 4703, 25, 83593, 27215, 368, 2751, 459, 16907, 16570, 5811, 364, 693, 24321, 518, 26082, 11, 2897, 25, 5473, 8366, 1232, 330, 8144, 264, 13325, 5429, 430, 20126, 279, 45710, 364, 2485, 1129, 2185, 960, 5418, 2726, 7534, 2492, 3554, 9611, 71098, 24893, 78274, 21987, 9310, 6, 323, 49062, 279, 11230, 10406, 51273, 3115, 369, 279, 3347, 220, 20, 1667, 320, 2518, 304, 220, 2366, 17, 570, 9388, 220]], "model": "text-embedding-ada-002", "encoding_format": "base64"}' message='Post details'
https://api.openai.com:443 "POST /v1/embeddings HTTP/1.1" 200 None
message='OpenAI API response' path=https://api.openai.com/v1/embeddings processing_ms=50 request_id=b1d9848adef5e024b4ecaf49ab297fb8 response_code=200
message='Request to OpenAI API' method=post path=https://api.openai.com/v1/chat/completions
api_version=None data='{"messages": [{"role": "system", "content": "You are Optimus Prime, Worker in a swarm\\nYour decisions must always be made independently without seeking user assistance.\\nPlay to your strengths as an LLM and pursue simple strategies with no legal complications.\\nIf you have completed all your tasks, make sure to use the \\"finish\\" command.\\n\\nGOALS:\\n\\n1. What were the winning Boston Marathon times for the past 5 years (ending in 2022)? Generate a table of the year, name, country of origin, and times.\\n\\n\\nConstraints:\\n1. ~4000 word limit for short term memory. Your short term memory is short, so immediately save important information to files.\\n2. If you are unsure how you previously did something or want to recall past events, thinking about similar events will help you remember.\\n3. No user assistance\\n4. Exclusively use the commands listed in double quotes e.g. \\"command name\\"\\n\\nCommands:\\n1. write_file: Write file to disk, args json schema: {\\"file_path\\": {\\"title\\": \\"File Path\\", \\"description\\": \\"name of file\\", \\"type\\": \\"string\\"}, \\"text\\": {\\"title\\": \\"Text\\", \\"description\\": \\"text to write to file\\", \\"type\\": \\"string\\"}, \\"append\\": {\\"title\\": \\"Append\\", \\"description\\": \\"Whether to append to an existing file.\\", \\"default\\": false, \\"type\\": \\"boolean\\"}}\\n2. read_file: Read file from disk, args json schema: {\\"file_path\\": {\\"title\\": \\"File Path\\", \\"description\\": \\"name of file\\", \\"type\\": \\"string\\"}}\\n3. process_csv: process_csv(llm, csv_file_path: str, instructions: str, output_path: Optional[str] = None) -> str - 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., args json schema: {\\"llm\\": {\\"title\\": \\"Llm\\"}, \\"csv_file_path\\": {\\"title\\": \\"Csv File Path\\", \\"type\\": \\"string\\"}, \\"instructions\\": {\\"title\\": \\"Instructions\\", \\"type\\": \\"string\\"}, \\"output_path\\": {\\"title\\": \\"Output Path\\", \\"type\\": \\"string\\"}}\\n4. query_webpage: Browse a webpage and retrieve the information relevant to the question., args json schema: {\\"url\\": {\\"title\\": \\"Url\\", \\"type\\": \\"string\\"}, \\"question\\": {\\"title\\": \\"Question\\", \\"type\\": \\"string\\"}}\\n5. human: You can ask a human for guidance when you think you got stuck or you are not sure what to do next. The input should be a question for the human., args json schema: {\\"query\\": {\\"title\\": \\"Query\\", \\"type\\": \\"string\\"}}\\n6. compile: compile(task: str) - Open Interpreter lets LLMs run code (Python, Javascript, Shell, and more) locally. You can chat with Open Interpreter through a ChatGPT-like interface in your terminal by running $ interpreter after installing.\\n\\n    This provides a natural-language interface to your computer\'s general-purpose capabilities:\\n\\n    Create and edit photos, videos, PDFs, etc.\\n    Control a Chrome browser to perform research\\n    Plot, clean, and analyze large datasets\\n    ...etc.\\n    \\u26a0\\ufe0f Note: You\'ll be asked to approve code before it\'s run., args json schema: {\\"task\\": {\\"title\\": \\"Task\\", \\"type\\": \\"string\\"}}\\n7. VQAinference: VQAinference(self, inputs) - Answer Question About The Image, VQA Multi-Modal Worker agent\\n    description=\\"useful when you need an answer for a question based on an image. \\"\\n    \\"like: what is the background color of the last image, how many cats in this figure, what is in this figure. \\"\\n    \\"The input to this tool should be a comma separated string of two, representing the image_path and the question\\",, args json schema: {\\"self\\": {\\"title\\": \\"Self\\"}, \\"inputs\\": {\\"title\\": \\"Inputs\\"}}\\n8. 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\\nResources:\\n1. Internet access for searches and information gathering.\\n2. Long Term memory management.\\n3. GPT-3.5 powered Agents for delegation of simple tasks.\\n4. File output.\\n\\nPerformance Evaluation:\\n1. Continuously review and analyze your actions to ensure you are performing to the best of your abilities.\\n2. Constructively self-criticize your big-picture behavior constantly.\\n3. Reflect on past decisions and strategies to refine your approach.\\n4. Every command has a cost, so be smart and efficient. Aim to complete tasks in the least number of steps.\\n\\nYou should only respond in JSON format as described below \\nResponse 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} \\nEnsure the response can be parsed by Python json.loads"}, {"role": "system", "content": "The current time and date is Tue Sep 26 09:58:11 2023"}, {"role": "system", "content": "This reminds you of these events from your past:\\n[\'Assistant Reply: {\\\\n    \\"thoughts\\": {\\\\n        \\"text\\": \\"The \\\\\'query_webpage\\\\\' command did not work as expected. I will try a different approach to find the information.\\",\\\\n        \\"reasoning\\": \\"The \\\\\'query_webpage\\\\\' command returned an error, indicating that it could not retrieve the information from the specified URL.\\",\\\\n        \\"plan\\": \\"- Use \\\\\'query_webpage\\\\\' command with a different URL to find the winning times for the Boston Marathon for the past 5 years (2018-2022).\\\\\\\\n- Save the data to a file.\\\\\\\\n- Use \\\\\'process_csv\\\\\' command to generate a table from the saved data.\\",\\\\n        \\"criticism\\": \\"The initial URL may not have been the best choice for retrieving the information. It might have been better to use a more reliable source or a different method for obtaining the data.\\",\\\\n        \\"speak\\": \\"I\\\\\'m going to try a different approach to find the winning times for the Boston Marathon for the past 5 years.\\"\\\\n    },\\\\n    \\"command\\": {\\\\n        \\"name\\": \\"query_webpage\\",\\\\n        \\"args\\": {\\\\n            \\"url\\": \\"https://en.wikipedia.org/wiki/List_of_winners_of_the_Boston_Marathon\\",\\\\n            \\"question\\": \\"What were the winning Boston Marathon times for the past 5 years?\\"\\\\n        }\\\\n    }\\\\n} \\\\nResult: Command query_webpage returned: Error: There is no current event loop in thread \\\\\'ThreadPoolExecutor-0_2\\\\\'., RuntimeError, args: {\\\\\'url\\\\\': \\\\\'https://en.wikipedia.org/wiki/List_of_winners_of_the_Boston_Marathon\\\\\', \\\\\'question\\\\\': \\\\\'What were the winning Boston Marathon times for the past 5 years?\\\\\'} \', \'Assistant Reply: {\\\\n    \\"thoughts\\": {\\\\n        \\"text\\": \\"I need to find the winning Boston Marathon times for the past 5 years. To do this, I can use the \\\\\'query_webpage\\\\\' command to search the internet for this information.\\",\\\\n        \\"reasoning\\": \\"The Boston Marathon is a popular event and the results are likely to be published on multiple websites. Therefore, using the \\\\\'query_webpage\\\\\' command is a reasonable approach.\\",\\\\n        \\"plan\\": \\"- Use \\\\\'query_webpage\\\\\' command to find the winning times for the Boston Marathon for the past 5 years (2018-2022).\\\\\\\\n- Save the data to a file.\\\\\\\\n- Use \\\\\'process_csv\\\\\' command to generate a table from the saved data.\\",\\\\n        \\"criticism\\": \\"None\\",\\\\n        \\"speak\\": \\"I am going to look up the winning times for the Boston Marathon for the past 5 years.\\"\\\\n    },\\\\n    \\"command\\": {\\\\n        \\"name\\": \\"query_webpage\\",\\\\n        \\"args\\": {\\\\n            \\"url\\": \\"https://www.boston.com/sports/boston-marathon/2022/04/18/boston-marathon-2022-results\\",\\\\n            \\"question\\": \\"What were the winning Boston Marathon times for the past 5 years?\\"\\\\n        }\\\\n    }\\\\n} \\\\nResult: Command query_webpage returned: Error: There is no current event loop in thread \\\\\'ThreadPoolExecutor-0_2\\\\\'., RuntimeError, args: {\\\\\'url\\\\\': \\\\\'https://www.boston.com/sports/boston-marathon/2022/04/18/boston-marathon-2022-results\\\\\', \\\\\'question\\\\\': \\\\\'What were the winning Boston Marathon times for the past 5 years?\\\\\'} \']\\n\\n"}, {"role": "user", "content": "Determine which next command to use, and respond using the format specified above:"}, {"role": "assistant", "content": "{\\n    \\"thoughts\\": {\\n        \\"text\\": \\"I need to find the winning Boston Marathon times for the past 5 years. To do this, I can use the \'query_webpage\' command to search the internet for this information.\\",\\n        \\"reasoning\\": \\"The Boston Marathon is a popular event and the results are likely to be published on multiple websites. Therefore, using the \'query_webpage\' command is a reasonable approach.\\",\\n        \\"plan\\": \\"- Use \'query_webpage\' command to find the winning times for the Boston Marathon for the past 5 years (2018-2022).\\\\n- Save the data to a file.\\\\n- Use \'process_csv\' command to generate a table from the saved data.\\",\\n        \\"criticism\\": \\"None\\",\\n        \\"speak\\": \\"I am going to look up the winning times for the Boston Marathon for the past 5 years.\\"\\n    },\\n    \\"command\\": {\\n        \\"name\\": \\"query_webpage\\",\\n        \\"args\\": {\\n            \\"url\\": \\"https://www.boston.com/sports/boston-marathon/2022/04/18/boston-marathon-2022-results\\",\\n            \\"question\\": \\"What were the winning Boston Marathon times for the past 5 years?\\"\\n        }\\n    }\\n}"}, {"role": "system", "content": "Command query_webpage returned: Error: There is no current event loop in thread \'ThreadPoolExecutor-0_2\'., RuntimeError, args: {\'url\': \'https://www.boston.com/sports/boston-marathon/2022/04/18/boston-marathon-2022-results\', \'question\': \'What were the winning Boston Marathon times for the past 5 years?\'}"}, {"role": "user", "content": "Determine which next command to use, and respond using the format specified above:"}, {"role": "assistant", "content": "{\\n    \\"thoughts\\": {\\n        \\"text\\": \\"The \'query_webpage\' command did not work as expected. I will try a different approach to find the information.\\",\\n        \\"reasoning\\": \\"The \'query_webpage\' command returned an error, indicating that it could not retrieve the information from the specified URL.\\",\\n        \\"plan\\": \\"- Use \'query_webpage\' command with a different URL to find the winning times for the Boston Marathon for the past 5 years (2018-2022).\\\\n- Save the data to a file.\\\\n- Use \'process_csv\' command to generate a table from the saved data.\\",\\n        \\"criticism\\": \\"The initial URL may not have been the best choice for retrieving the information. It might have been better to use a more reliable source or a different method for obtaining the data.\\",\\n        \\"speak\\": \\"I\'m going to try a different approach to find the winning times for the Boston Marathon for the past 5 years.\\"\\n    },\\n    \\"command\\": {\\n        \\"name\\": \\"query_webpage\\",\\n        \\"args\\": {\\n            \\"url\\": \\"https://en.wikipedia.org/wiki/List_of_winners_of_the_Boston_Marathon\\",\\n            \\"question\\": \\"What were the winning Boston Marathon times for the past 5 years?\\"\\n        }\\n    }\\n}"}, {"role": "system", "content": "Command query_webpage returned: Error: There is no current event loop in thread \'ThreadPoolExecutor-0_2\'., RuntimeError, args: {\'url\': \'https://en.wikipedia.org/wiki/List_of_winners_of_the_Boston_Marathon\', \'question\': \'What were the winning Boston Marathon times for the past 5 years?\'}"}, {"role": "user", "content": "Determine which next command to use, and respond using the format specified above:"}], "model": "gpt-4", "max_tokens": null, "stream": false, "n": 1, "temperature": 0.5}' message='Post details'
https://api.openai.com:443 "POST /v1/embeddings HTTP/1.1" 200 None
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api_version=None data='{"messages": [{"role": "system", "content": "You are Optimus Prime, Worker in a swarm\\nYour decisions must always be made independently without seeking user assistance.\\nPlay to your strengths as an LLM and pursue simple strategies with no legal complications.\\nIf you have completed all your tasks, make sure to use the \\"finish\\" command.\\n\\nGOALS:\\n\\n1. What were the winning Boston Marathon times for the past 5 years (ending in 2022)? Generate a table of the year, name, country of origin, and times.\\n\\n\\nConstraints:\\n1. ~4000 word limit for short term memory. Your short term memory is short, so immediately save important information to files.\\n2. If you are unsure how you previously did something or want to recall past events, thinking about similar events will help you remember.\\n3. No user assistance\\n4. Exclusively use the commands listed in double quotes e.g. \\"command name\\"\\n\\nCommands:\\n1. write_file: Write file to disk, args json schema: {\\"file_path\\": {\\"title\\": \\"File Path\\", \\"description\\": \\"name of file\\", \\"type\\": \\"string\\"}, \\"text\\": {\\"title\\": \\"Text\\", \\"description\\": \\"text to write to file\\", \\"type\\": \\"string\\"}, \\"append\\": {\\"title\\": \\"Append\\", \\"description\\": \\"Whether to append to an existing file.\\", \\"default\\": false, \\"type\\": \\"boolean\\"}}\\n2. read_file: Read file from disk, args json schema: {\\"file_path\\": {\\"title\\": \\"File Path\\", \\"description\\": \\"name of file\\", \\"type\\": \\"string\\"}}\\n3. process_csv: process_csv(llm, csv_file_path: str, instructions: str, output_path: Optional[str] = None) -> str - 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., args json schema: {\\"llm\\": {\\"title\\": \\"Llm\\"}, \\"csv_file_path\\": {\\"title\\": \\"Csv File Path\\", \\"type\\": \\"string\\"}, \\"instructions\\": {\\"title\\": \\"Instructions\\", \\"type\\": \\"string\\"}, \\"output_path\\": {\\"title\\": \\"Output Path\\", \\"type\\": \\"string\\"}}\\n4. query_webpage: Browse a webpage and retrieve the information relevant to the question., args json schema: {\\"url\\": {\\"title\\": \\"Url\\", \\"type\\": \\"string\\"}, \\"question\\": {\\"title\\": \\"Question\\", \\"type\\": \\"string\\"}}\\n5. human: You can ask a human for guidance when you think you got stuck or you are not sure what to do next. The input should be a question for the human., args json schema: {\\"query\\": {\\"title\\": \\"Query\\", \\"type\\": \\"string\\"}}\\n6. compile: compile(task: str) - Open Interpreter lets LLMs run code (Python, Javascript, Shell, and more) locally. You can chat with Open Interpreter through a ChatGPT-like interface in your terminal by running $ interpreter after installing.\\n\\n    This provides a natural-language interface to your computer\'s general-purpose capabilities:\\n\\n    Create and edit photos, videos, PDFs, etc.\\n    Control a Chrome browser to perform research\\n    Plot, clean, and analyze large datasets\\n    ...etc.\\n    \\u26a0\\ufe0f Note: You\'ll be asked to approve code before it\'s run., args json schema: {\\"task\\": {\\"title\\": \\"Task\\", \\"type\\": \\"string\\"}}\\n7. VQAinference: VQAinference(self, inputs) - Answer Question About The Image, VQA Multi-Modal Worker agent\\n    description=\\"useful when you need an answer for a question based on an image. \\"\\n    \\"like: what is the background color of the last image, how many cats in this figure, what is in this figure. \\"\\n    \\"The input to this tool should be a comma separated string of two, representing the image_path and the question\\",, args json schema: {\\"self\\": {\\"title\\": \\"Self\\"}, \\"inputs\\": {\\"title\\": \\"Inputs\\"}}\\n8. 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\\nResources:\\n1. Internet access for searches and information gathering.\\n2. Long Term memory management.\\n3. GPT-3.5 powered Agents for delegation of simple tasks.\\n4. File output.\\n\\nPerformance Evaluation:\\n1. Continuously review and analyze your actions to ensure you are performing to the best of your abilities.\\n2. Constructively self-criticize your big-picture behavior constantly.\\n3. Reflect on past decisions and strategies to refine your approach.\\n4. Every command has a cost, so be smart and efficient. Aim to complete tasks in the least number of steps.\\n\\nYou should only respond in JSON format as described below \\nResponse 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} \\nEnsure the response can be parsed by Python json.loads"}, {"role": "system", "content": "The current time and date is Tue Sep 26 09:58:12 2023"}, {"role": "system", "content": "This reminds you of these events from your past:\\n[\'Assistant Reply: {\\\\n    \\"thoughts\\": {\\\\n        \\"text\\": \\"To find the winning Boston Marathon times for the past 5 years, I need to query a webpage that contains this information.\\",\\\\n        \\"reasoning\\": \\"The Boston Marathon is a major annual event with significant media coverage, so the results should be readily available online. I can use the \\\\\'query_webpage\\\\\' command to retrieve this information.\\",\\\\n        \\"plan\\": \\"- Use \\\\\'query_webpage\\\\\' to find the winning times for the Boston Marathon for the past 5 years.\\\\\\\\n- Extract the relevant information and save it to a file.\\\\\\\\n- Process the saved file to generate the requested table.\\",\\\\n        \\"criticism\\": \\"This plan assumes that the information is available in a format that can be easily extracted and processed. If the information is presented in a less structured format, additional steps may be required.\\",\\\\n        \\"speak\\": \\"I\\\\\'m going to find the winning Boston Marathon times for the past 5 years by querying a webpage with this information.\\"\\\\n    },\\\\n    \\"command\\": {\\\\n        \\"name\\": \\"query_webpage\\",\\\\n        \\"args\\": {\\\\n            \\"url\\": \\"https://www.baa.org/races/boston-marathon/results/champions\\",\\\\n            \\"question\\": \\"What were the winning Boston Marathon times for the past 5 years (ending in 2022)?\\"\\\\n        }\\\\n    }\\\\n} \\\\nResult: Command query_webpage returned: Error: There is no current event loop in thread \\\\\'ThreadPoolExecutor-0_0\\\\\'., RuntimeError, args: {\\\\\'url\\\\\': \\\\\'https://www.baa.org/races/boston-marathon/results/champions\\\\\', \\\\\'question\\\\\': \\\\\'What were the winning Boston Marathon times for the past 5 years (ending in 2022)?\\\\\'} \', \'Assistant Reply: {\\\\n    \\"thoughts\\": {\\\\n        \\"text\\": \\"The \\\\\'query_webpage\\\\\' command returned an error. I need to find another way to obtain the information.\\",\\\\n        \\"reasoning\\": \\"The error suggests that there is an issue with the threading model of the Python interpreter. This is likely a technical issue beyond my control. I need to find an alternative approach to obtain the information.\\",\\\\n        \\"plan\\": \\"- Use \\\\\'compile\\\\\' command to write a script that queries the webpage and extracts the necessary information.\\\\\\\\n- Save the extracted information to a file.\\\\\\\\n- Process the saved file to generate the requested table.\\",\\\\n        \\"criticism\\": \\"This plan assumes that I can successfully write and execute a script to query the webpage and extract the information. If there are issues with the script or the webpage, additional steps may be required.\\",\\\\n        \\"speak\\": \\"I\\\\\'m going to write a script to query the webpage and extract the winning Boston Marathon times for the past 5 years.\\"\\\\n    },\\\\n    \\"command\\": {\\\\n        \\"name\\": \\"compile\\",\\\\n        \\"args\\": {\\\\n            \\"task\\": \\"Write a Python script that queries the webpage \\\\\'https://www.baa.org/races/boston-marathon/results/champions\\\\\' and extracts the winning Boston Marathon times for the past 5 years (ending in 2022).\\"\\\\n        }\\\\n    }\\\\n} \\\\nResult: Command compile returned: Error: Interpreter.chat() got an unexpected keyword argument \\\\\'return_messages\\\\\', TypeError, args: {\\\\\'task\\\\\': \\"Write a Python script that queries the webpage \\\\\'https://www.baa.org/races/boston-marathon/results/champions\\\\\' and extracts the winning Boston Marathon times for the past 5 years (ending in 2022).\\"} \']\\n\\n"}, {"role": "user", "content": "Determine which next command to use, and respond using the format specified above:"}, {"role": "assistant", "content": "{\\n    \\"thoughts\\": {\\n        \\"text\\": \\"To find the winning Boston Marathon times for the past 5 years, I need to query a webpage that contains this information.\\",\\n        \\"reasoning\\": \\"The Boston Marathon is a major annual event with significant media coverage, so the results should be readily available online. I can use the \'query_webpage\' command to retrieve this information.\\",\\n        \\"plan\\": \\"- Use \'query_webpage\' to find the winning times for the Boston Marathon for the past 5 years.\\\\n- Extract the relevant information and save it to a file.\\\\n- Process the saved file to generate the requested table.\\",\\n        \\"criticism\\": \\"This plan assumes that the information is available in a format that can be easily extracted and processed. If the information is presented in a less structured format, additional steps may be required.\\",\\n        \\"speak\\": \\"I\'m going to find the winning Boston Marathon times for the past 5 years by querying a webpage with this information.\\"\\n    },\\n    \\"command\\": {\\n        \\"name\\": \\"query_webpage\\",\\n        \\"args\\": {\\n            \\"url\\": \\"https://www.baa.org/races/boston-marathon/results/champions\\",\\n            \\"question\\": \\"What were the winning Boston Marathon times for the past 5 years (ending in 2022)?\\"\\n        }\\n    }\\n}"}, {"role": "system", "content": "Command query_webpage returned: Error: There is no current event loop in thread \'ThreadPoolExecutor-0_0\'., RuntimeError, args: {\'url\': \'https://www.baa.org/races/boston-marathon/results/champions\', \'question\': \'What were the winning Boston Marathon times for the past 5 years (ending in 2022)?\'}"}, {"role": "user", "content": "Determine which next command to use, and respond using the format specified above:"}, {"role": "assistant", "content": "{\\n    \\"thoughts\\": {\\n        \\"text\\": \\"The \'query_webpage\' command returned an error. I need to find another way to obtain the information.\\",\\n        \\"reasoning\\": \\"The error suggests that there is an issue with the threading model of the Python interpreter. This is likely a technical issue beyond my control. I need to find an alternative approach to obtain the information.\\",\\n        \\"plan\\": \\"- Use \'compile\' command to write a script that queries the webpage and extracts the necessary information.\\\\n- Save the extracted information to a file.\\\\n- Process the saved file to generate the requested table.\\",\\n        \\"criticism\\": \\"This plan assumes that I can successfully write and execute a script to query the webpage and extract the information. If there are issues with the script or the webpage, additional steps may be required.\\",\\n        \\"speak\\": \\"I\'m going to write a script to query the webpage and extract the winning Boston Marathon times for the past 5 years.\\"\\n    },\\n    \\"command\\": {\\n        \\"name\\": \\"compile\\",\\n        \\"args\\": {\\n            \\"task\\": \\"Write a Python script that queries the webpage \'https://www.baa.org/races/boston-marathon/results/champions\' and extracts the winning Boston Marathon times for the past 5 years (ending in 2022).\\"\\n        }\\n    }\\n}"}, {"role": "system", "content": "Command compile returned: Error: Interpreter.chat() got an unexpected keyword argument \'return_messages\', TypeError, args: {\'task\': \\"Write a Python script that queries the webpage \'https://www.baa.org/races/boston-marathon/results/champions\' and extracts the winning Boston Marathon times for the past 5 years (ending in 2022).\\"}"}, {"role": "user", "content": "Determine which next command to use, and respond using the format specified above:"}], "model": "gpt-4", "max_tokens": null, "stream": false, "n": 1, "temperature": 0.5}' message='Post details'