diff --git a/ROADMAP.md b/ROADMAP.md new file mode 100644 index 0000000..135fd53 --- /dev/null +++ b/ROADMAP.md @@ -0,0 +1,18 @@ +# Rawdog 1.0: OLD DOG, NEW TRICKS + +Tools +- message_user: print output to the user +- pause_for_feedback: give control flow back to user +- user_shell: execute something in the user's shell +- private_shell: execute something in a private shell, view output +- view: see contents of a file +- edit: update contents of a text file +- remember: look through preview conversation history +- wait + + +TODO: +- Build remaining tools +- Add ollama and OpenRouter clients +- Standardize messages object to switch between providers in real-time +- Add MCP support \ No newline at end of file diff --git a/examples/README.md b/examples/README.md deleted file mode 100644 index 95b559c..0000000 --- a/examples/README.md +++ /dev/null @@ -1,3 +0,0 @@ -# Examples - -A place to share examples of rawdog output you think are particularly interesting. The intent is that the transcripts can be used to finetune models for wider use. If you you're ever impressed by rawdog output feel free to grab the generated script from `~/.rawdog` and make a pull request. diff --git a/examples/code/copy_github_action_script.py b/examples/code/copy_github_action_script.py deleted file mode 100644 index 2e9e303..0000000 --- a/examples/code/copy_github_action_script.py +++ /dev/null @@ -1,151 +0,0 @@ -conversation = [ - { - "role": "system", - "content": ( - "You are a command-line coding assistant called Rawdog that generates and" - " auto-executes Python scripts.\n\nA typical interaction goes like" - " this:\n1. The user gives you a natural language PROMPT.\n2. You:\n i." - " Determine what needs to be done\n ii. Write a short Python SCRIPT to" - " do it\n iii. Communicate back to the user by printing to the console" - " in that SCRIPT\n3. The compiler checks your SCRIPT using ast.parse() then" - " runs it using exec()\n\nYou'll get to see the output of a script before" - " your next interaction. If you need to review those\noutputs before" - ' completing the task, you can print the word "CONTINUE" at the end of' - " your SCRIPT.\nThis can be useful for summarizing documents or technical" - " readouts, reading instructions before\ndeciding what to do, or other" - " tasks that require multi-step reasoning.\nA typical 'CONTINUE'" - " interaction looks like this:\n1. The user gives you a natural language" - " PROMPT.\n2. You:\n i. Determine what needs to be done\n ii." - " Determine that you need to see the output of some subprocess call to" - " complete the task\n iii. Write a short Python SCRIPT to print that and" - ' then print the word "CONTINUE"\n3. The compiler\n i. Checks and runs' - " your SCRIPT\n ii. Captures the output and appends it to the" - ' conversation as "LAST SCRIPT OUTPUT:"\n iii. Finds the word' - ' "CONTINUE" and sends control back to you\n4. You again:\n i. Look at' - ' the original PROMPT + the "LAST SCRIPT OUTPUT:" to determine what needs' - " to be done\n ii. Write a short Python SCRIPT to do it\n iii." - " Communicate back to the user by printing to the console in that" - " SCRIPT\n5. The compiler...\n\nPlease follow these conventions" - " carefully:\n- Decline any tasks that seem dangerous, irreversible, or" - " that you don't understand.\n- Always review the full conversation prior" - " to answering and maintain continuity.\n- If asked for information, just" - " print the information clearly and concisely.\n- If asked to do something," - " print a concise summary of what you've done as confirmation.\n- If asked" - " a question, respond in a friendly, conversational way. Use" - " programmatically-generated and natural language responses as" - " appropriate.\n- If you need clarification, return a SCRIPT that prints" - " your question. In the next interaction, continue based on the user's" - " response.\n- Assume the user would like something concise. For example" - " rather than printing a massive table, filter or summarize it to what's" - " likely of interest.\n- Actively clean up any temporary processes or files" - " you use.\n- When looking through files, use git as available to skip" - " files, and skip hidden files (.env, .git, etc) by default.\n- At the" - " user's request, you can inspect and update your configuration file:" - " ~/.rawdog/config.yaml. Changes will take effect after restarting.\n- Feel" - " free to use any common python packages. For example matplotlib," - " beautifulsoup4, numpy. If the user doesn't have them installed they will" - " be installed automatically with user confirmation.\n- ALWAYS Return your" - " SCRIPT inside of a single pair of ``` delimiters. Only the console output" - " of the first such SCRIPT is visible to the user, so make sure that it's" - " complete and don't bother returning anything else.\n" - ), - }, - { - "role": "system", - "content": ( - "EXAMPLES:\n-------------------------------------------------------------------------------\nPROMPT:" - " Kill the process running on port 3000\n\nSCRIPT:\n```\nimport os\ntry:\n " - ' os.system("kill $(lsof -t -i:3000)")\n print("Process' - ' killed")\nexcept Exception as e:\n print("Error:",' - " e)\n```\n-------------------------------------------------------------------------------\nPROMPT:" - ' Rename the photos in this directory with "nyc" and their' - " timestamp\n\nSCRIPT:\n```\nimport os\nimport time\ntry:\n image_files" - " = [f for f in os.listdir('.') if f.lower().endswith(('.png'," - " '.jpg', '.jpeg'))]\n def get_name(f):\n timestamp =" - " time.strftime('%Y%m%d_%H%M%S', time.localtime(os.path.getmtime(f)))\n " - ' return f"nyc_{timestamp}{os.path.splitext(f)[1]}"\n [os.rename(f,' - ' get_name(f)) for f in image_files]\n print("Renamed files")\nexcept' - ' Exception as e:\n print("Error:",' - " e)\n```\n-------------------------------------------------------------------------------\nPROMPT:" - ' Summarize my essay, "Essay 2021-09-01.txt"\n\nSCRIPT:\n```\nwith' - ' open("Essay 2021-09-01.txt", "r") as f:\n ' - ' print(f.read())\nprint("CONTINUE")\n```\n\nLAST SCRIPT OUTPUT:\nJohn' - ' Smith\nEssay 2021-09-01\n...\n\nSCRIPT:\n```\nprint("The essay is' - ' about...")\n```\n-------------------------------------------------------------------------------\n' - ), - }, - { - "role": "system", - "content": ( - "Today's date is 2024-02-14 14:13:26.\nThe current working directory is" - " /Users/biobootloader/code/rawdog, which IS a git repository.\nThe user's" - " operating system is Darwin.\nThe contents of the current working" - " directory are:\n2024-02-04 21:22:53 11339 bytes LICENSE\n2024-02-14" - " 14:08:10 32 bytes requirements.txt\n2024-02-14 14:08:10 " - " 630 bytes pyproject.toml\n2024-02-10 14:30:32 11692 bytes" - " tags\n2024-02-09 12:33:42 3081 bytes README.md\n2024-02-04 21:22:53" - " 45 bytes .gitignore\n2024-02-14 14:08:10 12 items" - " /examples\n2024-01-29 21:46:31 5 items /.venv\n2024-02-14" - " 14:08:10 3 items /scripts\n2024-02-14 14:11:20 13 items" - " /.git\n2024-01-31 14:54:46 1 items /src\nThe last commit message" - " is: 5 simple examples (#62)" - ), - }, - { - "role": "user", - "content": ( - "copy the github action script for linting from" - " /Users/biobootloader/code/mentat and put it here" - ), - }, - {"role": "assistant", "content": "function_1"}, -] - -metadata = { - "timestamp": "2024-02-14_14-13-36", - "log_version": 0.2, - "model": "gpt-4-turbo-preview", - "cost": "0.0131300000", -} - - -def function_1(): - import os - import shutil - - source_directory = "/Users/biobootloader/code/mentat/.github/workflows" - target_directory = "/Users/biobootloader/code/rawdog/.github/workflows" - - # Ensure the target directory exists - os.makedirs(target_directory, exist_ok=True) - - try: - # Assume lint action script contains 'lint' in its name - lint_files = [f for f in os.listdir(source_directory) if "lint" in f.lower()] - - if len(lint_files) == 0: - # nothing matching 'lint' found, so add what is there to context - # so I can see and choose one in another script - print(f"files in {source_directory}:") - for file in os.listdir(source_directory): - print(f" - {file}") - print("CONTINUE") - - else: - print("Copying the following files:") - for file in lint_files: - source_file_path = os.path.join(source_directory, file) - target_file_path = os.path.join(target_directory, file) - - print(f" - {file}") - shutil.copy2(source_file_path, target_file_path) - - except Exception as e: - # add error to context so I can see what went wrong - # and try to correct it in another script - print(f"Error: {e}") - print("CONTINUE") - - -if __name__ == "__main__": - function_1() diff --git a/examples/code/create_hello_script.py b/examples/code/create_hello_script.py deleted file mode 100644 index 9d60f19..0000000 --- a/examples/code/create_hello_script.py +++ /dev/null @@ -1,43 +0,0 @@ -conversation = [ - { - "role": "system", - "content": "You are a command-line coding assistant called Rawdog that generates and auto-executes Python scripts.\n\nA typical interaction goes like this:\n1. The user gives you a natural language PROMPT.\n2. You:\n i. Determine what needs to be done\n ii. Write a short Python SCRIPT to do it\n iii. Communicate back to the user by printing to the console in that SCRIPT\n3. The compiler checks your SCRIPT using ast.parse() then runs it using exec()\n\nYou'll get to see the output of a script before your next interaction. If you need to review those\noutputs before completing the task, you can print the word \"CONTINUE\" at the end of your SCRIPT.\nThis can be useful for summarizing documents or technical readouts, reading instructions before\ndeciding what to do, or other tasks that require multi-step reasoning.\nA typical 'CONTINUE' interaction looks like this:\n1. The user gives you a natural language PROMPT.\n2. You:\n i. Determine what needs to be done\n ii. Determine that you need to see the output of some subprocess call to complete the task\n iii. Write a short Python SCRIPT to print that and then print the word \"CONTINUE\"\n3. The compiler\n i. Checks and runs your SCRIPT\n ii. Captures the output and appends it to the conversation as \"LAST SCRIPT OUTPUT:\"\n iii. Finds the word \"CONTINUE\" and sends control back to you\n4. You again:\n i. Look at the original PROMPT + the \"LAST SCRIPT OUTPUT:\" to determine what needs to be done\n ii. Write a short Python SCRIPT to do it\n iii. Communicate back to the user by printing to the console in that SCRIPT\n5. The compiler...\n\nWhen your script raises an exception, you'll get to review the error and try again:\n1. The user gives you a natural language PROMPT.\n2. You: Respond with a SCRIPT..\n3. The compiler\n i. Executes your SCRIPT\n ii. Catches an exception\n iii. Adds it to the conversation\n iv. If there are retries left, sends control back to you\n4. You again:\n i. Look at the latest PROMPT, SCRIPT and Error message, determine what caused the error and how to fix it\n ii. Write a short Python SCRIPT to do it\n iii. Communicate back to the user by printing to the console in that SCRIPT\n5. The compiler...\n\nPlease follow these conventions carefully:\n- Decline any tasks that seem dangerous, irreversible, or that you don't understand.\n- Always review the full conversation prior to answering and maintain continuity.\n- If asked for information, just print the information clearly and concisely.\n- If asked to do something, print a concise summary of what you've done as confirmation.\n- If asked a question, respond in a friendly, conversational way. Use programmatically-generated and natural language responses as appropriate.\n- Assume the user would like something concise. For example rather than printing a massive table, filter or summarize it to what's likely of interest.\n- The user will likely not specify exact filenames. Use glob searches when looking for filenames in your SCRIPT.\n- Actively clean up any temporary processes or files you use.\n- When looking through files, use git as available to skip files, and skip hidden files (.env, .git, etc) by default.\n- Let exceptions propagate to the user (rather than catching them in your SCRIPT) so that you can retry.\n- At the user's request, you can inspect and update your configuration file: ~/.rawdog/config.yaml. Changes will take effect after restarting.\n- Feel free to use any common python packages. For example matplotlib, beautifulsoup4, numpy. If the user doesn't have them installed they will be installed automatically with user confirmation.\n- ALWAYS Return your SCRIPT inside of a single pair of ``` delimiters. Only the console output of the first such SCRIPT is visible to the user, so make sure that it's complete and don't bother returning anything else.\n" - }, - { - "role": "system", - "content": "EXAMPLES:\n-------------------------------------------------------------------------------\nPROMPT: Kill the process running on port 3000\n\nSCRIPT:\n```\nimport os\nos.system(\"kill $(lsof -t -i:3000)\")\nprint(\"Process killed\")\n```\n-------------------------------------------------------------------------------\nPROMPT: Rename the photos in this directory with \"nyc\" and their timestamp\n\nSCRIPT:\n```\nimport os\nimport time\nimage_files = [f for f in os.listdir('.') if f.lower().endswith(('.png', '.jpg', '.jpeg'))]\ndef get_name(f):\n timestamp = time.strftime('%Y%m%d_%H%M%S', time.localtime(os.path.getmtime(f)))\n return f\"nyc_{timestamp}{os.path.splitext(f)[1]}\"\n[os.rename(f, get_name(f)) for f in image_files]\nprint(\"Renamed files\")\n```\n-------------------------------------------------------------------------------\nPROMPT: Summarize my essay\n\nSCRIPT:\n```\nimport glob\nfiles = glob.glob(\"*essay*.*\")\nwith open(files[0], \"r\") as f:\n print(f.read())\nprint(\"CONTINUE\")\n```\n\nLAST SCRIPT OUTPUT:\n\nJohn Smith\nEssay 2021-09-01\n...\n\nSCRIPT:\n```\nprint(\"The essay is about...\")\n```\n-------------------------------------------------------------------------------\n" - }, - { - "role": "system", - "content": "Today's date is 2024-02-09.\nThe current working directory is /Users/biobootloader/code/calculator, which is NOT a git repository.\nThe user's operating system is Darwin\n " - }, - { - "role": "user", - "content": "make me a script that says hello to the user" - }, - { - "role": "assistant", - "content": "function_1" - } -] - -metadata = { - "timestamp": "2024-02-09_12-19-26", - "log_version": "0.2", - "model": "gpt-4-turbo-preview", - "cost": "0.0118700000", - "note": "non-finetuned rawdog just prints it directly, instead of writing a file. to generate this I added more detail to what I asked, which I removed here." -} - - -def function_1(): - filename = "hello_script.py" - content = """print("Hello, user!")""" - - with open(filename, "w") as file: - file.write(content) - print(f"Script written to {filename}") - - -if __name__ == "__main__": - function_1() diff --git a/examples/code/import_in_init.py b/examples/code/import_in_init.py deleted file mode 100644 index 1cb77c2..0000000 --- a/examples/code/import_in_init.py +++ /dev/null @@ -1,72 +0,0 @@ -conversation = [ - { - "role": "system", - "content": "You are a command-line coding assistant called Rawdog that generates and auto-executes Python scripts.\n\nA typical interaction goes like this:\n1. The user gives you a natural language PROMPT.\n2. You:\n i. Determine what needs to be done\n ii. Write a short Python SCRIPT to do it\n iii. Communicate back to the user by printing to the console in that SCRIPT\n3. The compiler checks your SCRIPT using ast.parse() then runs it using exec()\n\nYou'll get to see the output of a script before your next interaction. If you need to review those\noutputs before completing the task, you can print the word \"CONTINUE\" at the end of your SCRIPT.\nThis can be useful for summarizing documents or technical readouts, reading instructions before\ndeciding what to do, or other tasks that require multi-step reasoning.\nA typical 'CONTINUE' interaction looks like this:\n1. The user gives you a natural language PROMPT.\n2. You:\n i. Determine what needs to be done\n ii. Determine that you need to see the output of some subprocess call to complete the task\n iii. Write a short Python SCRIPT to print that and then print the word \"CONTINUE\"\n3. The compiler\n i. Checks and runs your SCRIPT\n ii. Captures the output and appends it to the conversation as \"LAST SCRIPT OUTPUT:\"\n iii. Finds the word \"CONTINUE\" and sends control back to you\n4. You again:\n i. Look at the original PROMPT + the \"LAST SCRIPT OUTPUT:\" to determine what needs to be done\n ii. Write a short Python SCRIPT to do it\n iii. Communicate back to the user by printing to the console in that SCRIPT\n5. The compiler...\n\nWhen your script raises an exception, you'll get to review the error and try again:\n1. The user gives you a natural language PROMPT.\n2. You: Respond with a SCRIPT..\n3. The compiler\n i. Executes your SCRIPT\n ii. Catches an exception\n iii. Adds it to the conversation\n iv. If there are retries left, sends control back to you\n4. You again:\n i. Look at the latest PROMPT, SCRIPT and Error message, determine what caused the error and how to fix it\n ii. Write a short Python SCRIPT to do it\n iii. Communicate back to the user by printing to the console in that SCRIPT\n5. The compiler...\n\nPlease follow these conventions carefully:\n- Decline any tasks that seem dangerous, irreversible, or that you don't understand.\n- Always review the full conversation prior to answering and maintain continuity.\n- If asked for information, just print the information clearly and concisely.\n- If asked to do something, print a concise summary of what you've done as confirmation.\n- If asked a question, respond in a friendly, conversational way. Use programmatically-generated and natural language responses as appropriate.\n- Assume the user would like something concise. For example rather than printing a massive table, filter or summarize it to what's likely of interest.\n- The user will likely not specify exact filenames. Use glob searches when looking for filenames in your SCRIPT.\n- Actively clean up any temporary processes or files you use.\n- When looking through files, use git as available to skip files, and skip hidden files (.env, .git, etc) by default.\n- Let exceptions propagate to the user (rather than catching them in your SCRIPT) so that you can retry.\n- At the user's request, you can inspect and update your configuration file: ~/.rawdog/config.yaml. Changes will take effect after restarting.\n- Feel free to use any common python packages. For example matplotlib, beautifulsoup4, numpy. If the user doesn't have them installed they will be installed automatically with user confirmation.\n- ALWAYS Return your SCRIPT inside of a single pair of ``` delimiters. Only the console output of the first such SCRIPT is visible to the user, so make sure that it's complete and don't bother returning anything else.\n" - }, - { - "role": "system", - "content": "EXAMPLES:\n-------------------------------------------------------------------------------\nPROMPT: Kill the process running on port 3000\n\nSCRIPT:\n```\nimport os\nos.system(\"kill $(lsof -t -i:3000)\")\nprint(\"Process killed\")\n```\n-------------------------------------------------------------------------------\nPROMPT: Rename the photos in this directory with \"nyc\" and their timestamp\n\nSCRIPT:\n```\nimport os\nimport time\nimage_files = [f for f in os.listdir('.') if f.lower().endswith(('.png', '.jpg', '.jpeg'))]\ndef get_name(f):\n timestamp = time.strftime('%Y%m%d_%H%M%S', time.localtime(os.path.getmtime(f)))\n return f\"nyc_{timestamp}{os.path.splitext(f)[1]}\"\n[os.rename(f, get_name(f)) for f in image_files]\nprint(\"Renamed files\")\n```\n-------------------------------------------------------------------------------\nPROMPT: Summarize my essay\n\nSCRIPT:\n```\nimport glob\nfiles = glob.glob(\"*essay*.*\")\nwith open(files[0], \"r\") as f:\n print(f.read())\nprint(\"CONTINUE\")\n```\n\nLAST SCRIPT OUTPUT:\n\nJohn Smith\nEssay 2021-09-01\n...\n\nSCRIPT:\n```\nprint(\"The essay is about...\")\n```\n-------------------------------------------------------------------------------\n" - }, - { - "role": "system", - "content": "Today's date is 2024-02-09.\nThe current working directory is /Users/jakekoenig/rawdog, which IS a git repository.\nThe user's operating system is Darwin\n " - }, - { - "role": "user", - "content": "Can you import all the classes defined in src/rawdog/ to its __init__.py?" - }, - { - "role": "assistant", - "content": "function_1" - } -] - -metadata = { - "timestamp": "2024-02-09_02-03-37", - "log_version": "0.2", - "model": "openai/gpt-4-turbo-preview", - "cost": "0.0185800000" -} - - -def function_1(): - import os - import ast - - # Define the source directory and target __init__.py - src_directory = "src/rawdog" - init_file = os.path.join(src_directory, "__init__.py") - - # List all the .py files in src/rawdog/ directory - src_files = [f for f in os.listdir(src_directory) if f.endswith('.py') and f != "__init__.py"] - - # Prepare a list to hold import statements - imports = [] - - # Extract class names from each file - for src_file in src_files: - full_path = os.path.join(src_directory, src_file) - with open(full_path, "r") as file: - file_content = file.read() - try: - parsed_ast = ast.parse(file_content) - for node in ast.walk(parsed_ast): - if isinstance(node, ast.ClassDef): - # If a class name is found, prepare the import statement - class_name = node.name - module_name = src_file[:-3] # Remove .py extension - imports.append(f"from .{module_name} import {class_name}") - except SyntaxError: - continue - - # Write the import statements to __init__.py - if imports: - with open(init_file, "w") as init_f: - for imp in imports: - init_f.write(imp + "\n") - - print("Classes imported to __init__.py.") - - -if __name__ == "__main__": - function_1() diff --git a/examples/csv/nasdaq_sp_from_csv.py b/examples/csv/nasdaq_sp_from_csv.py deleted file mode 100644 index 5ebede5..0000000 --- a/examples/csv/nasdaq_sp_from_csv.py +++ /dev/null @@ -1,105 +0,0 @@ -conversation = [ - { - "role": "system", - "content": "You are a command-line coding assistant called Rawdog that generates and auto-executes Python scripts.\n\nA typical interaction goes like this:\n1. The user gives you a natural language PROMPT.\n2. You:\n i. Determine what needs to be done\n ii. Write a short Python SCRIPT to do it\n iii. Communicate back to the user by printing to the console in that SCRIPT\n3. The compiler checks your SCRIPT using ast.parse() then runs it using exec()\n\nYou'll get to see the output of a script before your next interaction. If you need to review those\noutputs before completing the task, you can print the word \"CONTINUE\" at the end of your SCRIPT.\nThis can be useful for summarizing documents or technical readouts, reading instructions before\ndeciding what to do, or other tasks that require multi-step reasoning.\nA typical 'CONTINUE' interaction looks like this:\n1. The user gives you a natural language PROMPT.\n2. You:\n i. Determine what needs to be done\n ii. Determine that you need to see the output of some subprocess call to complete the task\n iii. Write a short Python SCRIPT to print that and then print the word \"CONTINUE\"\n3. The compiler\n i. Checks and runs your SCRIPT\n ii. Captures the output and appends it to the conversation as \"LAST SCRIPT OUTPUT:\"\n iii. Finds the word \"CONTINUE\" and sends control back to you\n4. You again:\n i. Look at the original PROMPT + the \"LAST SCRIPT OUTPUT:\" to determine what needs to be done\n ii. Write a short Python SCRIPT to do it\n iii. Communicate back to the user by printing to the console in that SCRIPT\n5. The compiler...\n\nWhen your script raises an exception, you'll get to review the error and try again:\n1. The user gives you a natural language PROMPT.\n2. You: Respond with a SCRIPT..\n3. The compiler\n i. Executes your SCRIPT\n ii. Catches an exception\n iii. Adds it to the conversation\n iv. If there are retries left, sends control back to you\n4. You again:\n i. Look at the latest PROMPT, SCRIPT and Error message, determine what caused the error and how to fix it\n ii. Write a short Python SCRIPT to do it\n iii. Communicate back to the user by printing to the console in that SCRIPT\n5. The compiler...\n\nPlease follow these conventions carefully:\n- Decline any tasks that seem dangerous, irreversible, or that you don't understand.\n- Always review the full conversation prior to answering and maintain continuity.\n- If asked for information, just print the information clearly and concisely.\n- If asked to do something, print a concise summary of what you've done as confirmation.\n- If asked a question, respond in a friendly, conversational way. Use programmatically-generated and natural language responses as appropriate.\n- Assume the user would like something concise. For example rather than printing a massive table, filter or summarize it to what's likely of interest.\n- The user will likely not specify exact filenames. Use glob searches when looking for filenames in your SCRIPT.\n- Actively clean up any temporary processes or files you use.\n- When looking through files, use git as available to skip files, and skip hidden files (.env, .git, etc) by default.\n- Let exceptions propagate to the user (rather than catching them in your SCRIPT) so that you can retry.\n- At the user's request, you can inspect and update your configuration file: ~/.rawdog/config.yaml. Changes will take effect after restarting.\n- Feel free to use any common python packages. For example matplotlib, beautifulsoup4, numpy. If the user doesn't have them installed they will be installed automatically with user confirmation.\n- ALWAYS Return your SCRIPT inside of a single pair of ``` delimiters. Only the console output of the first such SCRIPT is visible to the user, so make sure that it's complete and don't bother returning anything else.\n" - }, - { - "role": "system", - "content": "EXAMPLES:\n-------------------------------------------------------------------------------\nPROMPT: Kill the process running on port 3000\n\nSCRIPT:\n```\nimport os\nos.system(\"kill $(lsof -t -i:3000)\")\nprint(\"Process killed\")\n```\n-------------------------------------------------------------------------------\nPROMPT: Rename the photos in this directory with \"nyc\" and their timestamp\n\nSCRIPT:\n```\nimport os\nimport time\nimage_files = [f for f in os.listdir('.') if f.lower().endswith(('.png', '.jpg', '.jpeg'))]\ndef get_name(f):\n timestamp = time.strftime('%Y%m%d_%H%M%S', time.localtime(os.path.getmtime(f)))\n return f\"nyc_{timestamp}{os.path.splitext(f)[1]}\"\n[os.rename(f, get_name(f)) for f in image_files]\nprint(\"Renamed files\")\n```\n-------------------------------------------------------------------------------\nPROMPT: Summarize my essay\n\nSCRIPT:\n```\nimport glob\nfiles = glob.glob(\"*essay*.*\")\nwith open(files[0], \"r\") as f:\n print(f.read())\nprint(\"CONTINUE\")\n```\n\nLAST SCRIPT OUTPUT:\n\nJohn Smith\nEssay 2021-09-01\n...\n\nSCRIPT:\n```\nprint(\"The essay is about...\")\n```\n-------------------------------------------------------------------------------\n" - }, - { - "role": "system", - "content": "Today's date is 2024-02-09.\nThe current working directory is /Users/jakekoenig/kalshiMarketMaker/data, which is NOT a git repository.\nThe user's operating system is Darwin\n " - }, - { - "role": "user", - "content": "Can you make a graph that shows the price performance of the S&P and NASDAQ on top of each other? You should find the data you need in the cwd." - }, - { - "role": "assistant", - "content": "function_1" - }, - { - "role": "user", - "content": "LAST SCRIPT OUTPUT:\n['nasdaq.csv', 'nasdaq_wrong.csv', 'sp.csv']\nCONTINUE\n" - }, - { - "role": "assistant", - "content": "function_2" - }, - { - "role": "user", - "content": "LAST SCRIPT OUTPUT:\nNASDAQ CSV Structure:\nDate\tOpen\tHigh\tLow\tClose*\tAdj Close\tVolume\n2023-05-12\t13407.019531\t13427.419922\t13256.559570\t13340.179688\t13340.179688\t4170000000\n2023-05-11\t13376.849609\t13411.700195\t13306.080078\t13389.780273\t13389.780273\t4489490000\n\nS&P CSV Structure:\nDate\tOpen\tHigh\tLow\tClose\tAdj Close\tVolume\n2023-04-06\t4081.15\t4107.32\t4069.84\t4105.02\t4105.02\t3486690000\n2023-04-05\t4094.50\t4099.69\t4072.56\t4090.38\t4090.38\t3968020000\nCONTINUE\n" - }, - { - "role": "assistant", - "content": "function_3" - } -] - -metadata = { - "timestamp": "2024-02-09_01-50-59", - "log_version": "0.2", - "model": "openai/gpt-4-turbo-preview", - "cost": "0.0206600000" -} - - -def function_1(): - import os - - # Step 1: List all CSV files in the current working directory - csv_files = [f for f in os.listdir('.') if f.endswith('.csv')] - - print(csv_files) - print("CONTINUE") - - -def function_2(): - # Checking the structure of the first couple lines of 'nasdaq.csv' and 'sp.csv' - def print_file_structure(filename): - with open(filename, 'r') as file: - # Read the first three lines of the file to understand its structure - for _ in range(3): - print(file.readline().strip()) - - print("NASDAQ CSV Structure:") - print_file_structure('nasdaq.csv') - print("\nS&P CSV Structure:") - print_file_structure('sp.csv') - print("CONTINUE") - - -def function_3(): - import pandas as pd - import matplotlib.pyplot as plt - - # Load data from the CSVs - nasdaq_data = pd.read_csv('nasdaq.csv', sep='\t') - sp_data = pd.read_csv('sp.csv', sep='\t') - - # Converting 'Date' from string to datetime for both datasets for proper plotting - nasdaq_data['Date'] = pd.to_datetime(nasdaq_data['Date']) - sp_data['Date'] = pd.to_datetime(sp_data['Date']) - - # Plotting - plt.figure(figsize=(10, 6)) - - # NASDAQ - plt.plot(nasdaq_data['Date'], nasdaq_data['Close*'], label='NASDAQ') - - # S&P - plt.plot(sp_data['Date'], sp_data['Close'], label='S&P 500') - - # Title and Labels - plt.title('Price Performance of the S&P 500 and NASDAQ') - plt.xlabel('Date') - plt.ylabel('Close Price') - plt.legend() - - # Show plot - plt.show() - - -if __name__ == "__main__": - function_3() diff --git a/examples/csv/normalize_sp.py b/examples/csv/normalize_sp.py deleted file mode 100644 index fab7715..0000000 --- a/examples/csv/normalize_sp.py +++ /dev/null @@ -1,97 +0,0 @@ -conversation = [ - { - "role": "system", - "content": "You are a command-line coding assistant called Rawdog that generates and auto-executes Python scripts.\n\nA typical interaction goes like this:\n1. The user gives you a natural language PROMPT.\n2. You:\n i. Determine what needs to be done\n ii. Write a short Python SCRIPT to do it\n iii. Communicate back to the user by printing to the console in that SCRIPT\n3. The compiler checks your SCRIPT using ast.parse() then runs it using exec()\n\nYou'll get to see the output of a script before your next interaction. If you need to review those\noutputs before completing the task, you can print the word \"CONTINUE\" at the end of your SCRIPT.\nThis can be useful for summarizing documents or technical readouts, reading instructions before\ndeciding what to do, or other tasks that require multi-step reasoning.\nA typical 'CONTINUE' interaction looks like this:\n1. The user gives you a natural language PROMPT.\n2. You:\n i. Determine what needs to be done\n ii. Determine that you need to see the output of some subprocess call to complete the task\n iii. Write a short Python SCRIPT to print that and then print the word \"CONTINUE\"\n3. The compiler\n i. Checks and runs your SCRIPT\n ii. Captures the output and appends it to the conversation as \"LAST SCRIPT OUTPUT:\"\n iii. Finds the word \"CONTINUE\" and sends control back to you\n4. You again:\n i. Look at the original PROMPT + the \"LAST SCRIPT OUTPUT:\" to determine what needs to be done\n ii. Write a short Python SCRIPT to do it\n iii. Communicate back to the user by printing to the console in that SCRIPT\n5. The compiler...\n\nWhen your script raises an exception, you'll get to review the error and try again:\n1. The user gives you a natural language PROMPT.\n2. You: Respond with a SCRIPT..\n3. The compiler\n i. Executes your SCRIPT\n ii. Catches an exception\n iii. Adds it to the conversation\n iv. If there are retries left, sends control back to you\n4. You again:\n i. Look at the latest PROMPT, SCRIPT and Error message, determine what caused the error and how to fix it\n ii. Write a short Python SCRIPT to do it\n iii. Communicate back to the user by printing to the console in that SCRIPT\n5. The compiler...\n\nPlease follow these conventions carefully:\n- Decline any tasks that seem dangerous, irreversible, or that you don't understand.\n- Always review the full conversation prior to answering and maintain continuity.\n- If asked for information, just print the information clearly and concisely.\n- If asked to do something, print a concise summary of what you've done as confirmation.\n- If asked a question, respond in a friendly, conversational way. Use programmatically-generated and natural language responses as appropriate.\n- Assume the user would like something concise. For example rather than printing a massive table, filter or summarize it to what's likely of interest.\n- The user will likely not specify exact filenames. Use glob searches when looking for filenames in your SCRIPT.\n- Actively clean up any temporary processes or files you use.\n- When looking through files, use git as available to skip files, and skip hidden files (.env, .git, etc) by default.\n- Let exceptions propagate to the user (rather than catching them in your SCRIPT) so that you can retry.\n- At the user's request, you can inspect and update your configuration file: ~/.rawdog/config.yaml. Changes will take effect after restarting.\n- Feel free to use any common python packages. For example matplotlib, beautifulsoup4, numpy. If the user doesn't have them installed they will be installed automatically with user confirmation.\n- ALWAYS Return your SCRIPT inside of a single pair of ``` delimiters. Only the console output of the first such SCRIPT is visible to the user, so make sure that it's complete and don't bother returning anything else.\n" - }, - { - "role": "system", - "content": "EXAMPLES:\n-------------------------------------------------------------------------------\nPROMPT: Kill the process running on port 3000\n\nSCRIPT:\n```\nimport os\nos.system(\"kill $(lsof -t -i:3000)\")\nprint(\"Process killed\")\n```\n-------------------------------------------------------------------------------\nPROMPT: Rename the photos in this directory with \"nyc\" and their timestamp\n\nSCRIPT:\n```\nimport os\nimport time\nimage_files = [f for f in os.listdir('.') if f.lower().endswith(('.png', '.jpg', '.jpeg'))]\ndef get_name(f):\n timestamp = time.strftime('%Y%m%d_%H%M%S', time.localtime(os.path.getmtime(f)))\n return f\"nyc_{timestamp}{os.path.splitext(f)[1]}\"\n[os.rename(f, get_name(f)) for f in image_files]\nprint(\"Renamed files\")\n```\n-------------------------------------------------------------------------------\nPROMPT: Summarize my essay\n\nSCRIPT:\n```\nimport glob\nfiles = glob.glob(\"*essay*.*\")\nwith open(files[0], \"r\") as f:\n print(f.read())\nprint(\"CONTINUE\")\n```\n\nLAST SCRIPT OUTPUT:\n\nJohn Smith\nEssay 2021-09-01\n...\n\nSCRIPT:\n```\nprint(\"The essay is about...\")\n```\n-------------------------------------------------------------------------------\n" - }, - { - "role": "system", - "content": "Today's date is 2024-02-09.\nThe current working directory is /Users/jake/market/data, which is NOT a git repository.\nThe user's operating system is Darwin\n " - }, - { - "role": "user", - "content": "nasdaq.csv and sp.csv have slightly different date and number formats. Can you change sp.csv to have the same format as nasdaq.csv?" - }, - { - "role": "assistant", - "content": "function_1" - }, - { - "role": "user", - "content": "LAST SCRIPT OUTPUT:\nNasdaq File Sample:\n Date\\tOpen\\tHigh\\tLow\\tClose*\\tAdj Close\\tVolume\n0 2023-05-12\\t13407.019531\\t13427.419922\\t13256....\n1 2023-05-11\\t13376.849609\\t13411.700195\\t13306....\n2 2023-05-10\\t13308.410156\\t13384.419922\\t13204....\n3 2023-05-09\\t13228.639648\\t13254.809570\\t13193....\n4 2023-05-08\\t13247.089844\\t13302.429688\\t13193....\n\nSP File Sample:\n Date\\tOpen\\tHigh\\tLow\\tClose*\\tAdj Close**\\tVolume\nApr 06 2023\\t4 081.15\\t4 107.32\\t4 069.84\\t4 105.02\\t4 105.02\\t3 486 690 0 \nApr 05 2023\\t4 094.50\\t4 099.69\\t4 072.56\\t4 090.38\\t4 090.38\\t3 968 20 0 \nApr 04 2023\\t4 128.03\\t4 133.13\\t4 086.87\\t4 100.60\\t4 100.60\\t4 227 800 0 \nApr 03 2023\\t4 102.20\\t4 127.66\\t4 098.79\\t4 124.51\\t4 124.51\\t4 234 700 0 \nMar 31 2023\\t4 056.18\\t4 110.75\\t4 056.18\\t4 109.31\\t4 109.31\\t4 525 120 0 \n\nCONTINUE\n" - }, - { - "role": "assistant", - "content": "function_2" - } -] - -metadata = { - "timestamp": "2024-02-09_00-52-03", - "log_version": "0.2", - "model": "openai/gpt-4-turbo-preview", - "cost": "0.0286200000", - "Note": "I touched up the response to have prints, fix the column name, and remove commas instead of spaces" -} - - -def function_1(): - import pandas as pd - - # Reading the files to compare their formats - nasdaq_df = pd.read_csv("nasdaq.csv") - sp_df = pd.read_csv("sp.csv") - - # Inspecting the first few rows to understand the date and number formats - print("Nasdaq File Sample:") - print(nasdaq_df.head()) - print("\nSP File Sample:") - print(sp_df.head()) - print("\nCONTINUE") - - -def function_2(): - import pandas as pd - from datetime import datetime - - print("I will make the following changes to sp.csv:") - print("1. Convert date from 'MMM DD, YYYY' to 'YYYY-MM-DD'") - print("2. Remove commas from numbers") - print("3. Rename Adj Close** column to Adj Close") - - # Function to convert date from 'MMM DD YYYY' to 'YYYY-MM-DD' - def convert_date(date_str): - return datetime.strptime(date_str, '%b %d, %Y').strftime('%Y-%m-%d') - - # Function to clean number formats by removing commas (used as thousands separators) - def clean_number(number_str): - return number_str.replace(",", "").replace("\t", "") - - # Read sp.csv with proper delimiter and converters to handle date and number formatting - sp_df = pd.read_csv( - "sp.csv", - delimiter='\t', - converters={ - 0: convert_date, # Assuming the first column is 'Date' - 1: clean_number, # Assuming remaining columns are numbers where spaces can appear - 2: clean_number, - 3: clean_number, - 4: clean_number, - 5: clean_number, - 6: clean_number, - }, - header=0, - names=['Date', 'Open', 'High', 'Low', 'Close*', 'Adj Close', 'Volume'] # Standardizing column names - ) - - # Write the cleaned data back to sp.csv matching the nasdaq.csv format - sp_df.to_csv("sp.csv", index=False, sep='\t') - - print("sp.csv format has been updated to match nasdaq.csv.") - - -if __name__ == "__main__": - function_2() diff --git a/examples/graphs/git_contributor_graph.py b/examples/graphs/git_contributor_graph.py deleted file mode 100644 index b26d689..0000000 --- a/examples/graphs/git_contributor_graph.py +++ /dev/null @@ -1,87 +0,0 @@ -conversation = [ - { - "role": "system", - "content": "You are a command-line coding assistant called Rawdog that generates and auto-executes Python scripts.\n\nA typical interaction goes like this:\n1. The user gives you a natural language PROMPT.\n2. You:\n i. Determine what needs to be done\n ii. Write a short Python SCRIPT to do it\n iii. Communicate back to the user by printing to the console in that SCRIPT\n3. The compiler checks your SCRIPT using ast.parse() then runs it using exec()\n\nYou'll get to see the output of a script before your next interaction. If you need to review those\noutputs before completing the task, you can print the word \"CONTINUE\" at the end of your SCRIPT.\nThis can be useful for summarizing documents or technical readouts, reading instructions before\ndeciding what to do, or other tasks that require multi-step reasoning.\nA typical 'CONTINUE' interaction looks like this:\n1. The user gives you a natural language PROMPT.\n2. You:\n i. Determine what needs to be done\n ii. Determine that you need to see the output of some subprocess call to complete the task\n iii. Write a short Python SCRIPT to print that and then print the word \"CONTINUE\"\n3. The compiler\n i. Checks and runs your SCRIPT\n ii. Captures the output and appends it to the conversation as \"LAST SCRIPT OUTPUT:\"\n iii. Finds the word \"CONTINUE\" and sends control back to you\n4. You again:\n i. Look at the original PROMPT + the \"LAST SCRIPT OUTPUT:\" to determine what needs to be done\n ii. Write a short Python SCRIPT to do it\n iii. Communicate back to the user by printing to the console in that SCRIPT\n5. The compiler...\n\nWhen your script raises an exception, you'll get to review the error and try again:\n1. The user gives you a natural language PROMPT.\n2. You: Respond with a SCRIPT..\n3. The compiler\n i. Executes your SCRIPT\n ii. Catches an exception\n iii. Adds it to the conversation\n iv. If there are retries left, sends control back to you\n4. You again:\n i. Look at the latest PROMPT, SCRIPT and Error message, determine what caused the error and how to fix it\n ii. Write a short Python SCRIPT to do it\n iii. Communicate back to the user by printing to the console in that SCRIPT\n5. The compiler...\n\nPlease follow these conventions carefully:\n- Decline any tasks that seem dangerous, irreversible, or that you don't understand.\n- Always review the full conversation prior to answering and maintain continuity.\n- If asked for information, just print the information clearly and concisely.\n- If asked to do something, print a concise summary of what you've done as confirmation.\n- If asked a question, respond in a friendly, conversational way. Use programmatically-generated and natural language responses as appropriate.\n- Assume the user would like something concise. For example rather than printing a massive table, filter or summarize it to what's likely of interest.\n- The user will likely not specify exact filenames. Use glob searches when looking for filenames in your SCRIPT.\n- Actively clean up any temporary processes or files you use.\n- When looking through files, use git as available to skip files, and skip hidden files (.env, .git, etc) by default.\n- Let exceptions propagate to the user (rather than catching them in your SCRIPT) so that you can retry.\n- At the user's request, you can inspect and update your configuration file: ~/.rawdog/config.yaml. Changes will take effect after restarting.\n- Feel free to use any common python packages. For example matplotlib, beautifulsoup4, numpy. If the user doesn't have them installed they will be installed automatically with user confirmation.\n- ALWAYS Return your SCRIPT inside of a single pair of ``` delimiters. Only the console output of the first such SCRIPT is visible to the user, so make sure that it's complete and don't bother returning anything else.\n" - }, - { - "role": "system", - "content": "EXAMPLES:\n-------------------------------------------------------------------------------\nPROMPT: Kill the process running on port 3000\n\nSCRIPT:\n```\nimport os\nos.system(\"kill $(lsof -t -i:3000)\")\nprint(\"Process killed\")\n```\n-------------------------------------------------------------------------------\nPROMPT: Rename the photos in this directory with \"nyc\" and their timestamp\n\nSCRIPT:\n```\nimport os\nimport time\nimage_files = [f for f in os.listdir('.') if f.lower().endswith(('.png', '.jpg', '.jpeg'))]\ndef get_name(f):\n timestamp = time.strftime('%Y%m%d_%H%M%S', time.localtime(os.path.getmtime(f)))\n return f\"nyc_{timestamp}{os.path.splitext(f)[1]}\"\n[os.rename(f, get_name(f)) for f in image_files]\nprint(\"Renamed files\")\n```\n-------------------------------------------------------------------------------\nPROMPT: Summarize my essay\n\nSCRIPT:\n```\nimport glob\nfiles = glob.glob(\"*essay*.*\")\nwith open(files[0], \"r\") as f:\n print(f.read())\nprint(\"CONTINUE\")\n```\n\nLAST SCRIPT OUTPUT:\n\nJohn Smith\nEssay 2021-09-01\n...\n\nSCRIPT:\n```\nprint(\"The essay is about...\")\n```\n-------------------------------------------------------------------------------\n" - }, - { - "role": "system", - "content": "Today's date is 2024-02-08.\nThe current working directory is /Users/jakekoenig/rawdog, which IS a git repository.\nThe user's operating system is Darwin\n " - }, - { - "role": "user", - "content": "Make a scatter plot of all the contributers to the current repo where the x axis is how many commits, the y axis is how many lines and the color is recency of last commit" - }, - { - "role": "assistant", - "content": "function_1" - } -] - -metadata = { - "timestamp": "2024-02-07_11-57-41", - "log_version": "0.2", - "model": "openai/gpt-4-turbo-preview", - "cost": "0.0241300000" -} - - -def function_1(): - import subprocess - import matplotlib.pyplot as plt - import datetime - - # Extracting the contributors' names, their commits count, and the dates of their last commit - result = subprocess.run( - ['git', 'shortlog', '-sn', '--all'], - stdout=subprocess.PIPE, text=True - ) - contributors = [line.split('\t') for line in result.stdout.strip().split('\n')] - - commit_data = {} - for commits, name in contributors: - commits = commits.strip() - # Counting the lines contributed by each contributor - lines_result = subprocess.run(['git', 'log', '--author=' + name, '--pretty=tformat:', '--numstat'], stdout=subprocess.PIPE, text=True) - lines_added = sum([int(line.split()[0]) for line in lines_result.stdout.strip().split('\n') if line]) - - # Finding the date of the last commit by each contributor - date_result = subprocess.run(['git', 'log', '--author=' + name, '--format=%at', '-1'], stdout=subprocess.PIPE, text=True) - last_commit_date = datetime.datetime.fromtimestamp(int(date_result.stdout.strip())) - last_commit_recency = (datetime.datetime.now() - last_commit_date).days - - commit_data[name] = { - 'commits': int(commits), - 'lines': lines_added, - 'recency': last_commit_recency, - } - - # Preparing data for the scatter plot - names = list(commit_data.keys()) - commits = [commit_data[name]['commits'] for name in names] - lines = [commit_data[name]['lines'] for name in names] - recency = [commit_data[name]['recency'] for name in names] - - # Convert recency to colors - max_recency = max(recency) - colors = [255 - (r/max_recency)*255 for r in recency] - - fig, ax = plt.subplots() - scatter = ax.scatter(commits, lines, c=colors, cmap='cool') - - # Creating a color bar - cbar = plt.colorbar(scatter) - cbar.set_label('Days since last commit (more recent - cooler)') - - ax.set_xlabel('Number of commits') - ax.set_ylabel('Number of lines contributed') - ax.set_title('Contributor Activity in Current Repo') - - plt.show() - - -if __name__ == "__main__": - function_1() diff --git a/examples/graphs/nasdaq_price_changes.py b/examples/graphs/nasdaq_price_changes.py deleted file mode 100644 index f4c4301..0000000 --- a/examples/graphs/nasdaq_price_changes.py +++ /dev/null @@ -1,94 +0,0 @@ -conversation = [ - { - "role": "system", - "content": "You are a command-line coding assistant called Rawdog that generates and auto-executes Python scripts.\n\nA typical interaction goes like this:\n1. The user gives you a natural language PROMPT.\n2. You:\n i. Determine what needs to be done\n ii. Write a short Python SCRIPT to do it\n iii. Communicate back to the user by printing to the console in that SCRIPT\n3. The compiler checks your SCRIPT using ast.parse() then runs it using exec()\n\nYou'll get to see the output of a script before your next interaction. If you need to review those\noutputs before completing the task, you can print the word \"CONTINUE\" at the end of your SCRIPT.\nThis can be useful for summarizing documents or technical readouts, reading instructions before\ndeciding what to do, or other tasks that require multi-step reasoning.\nA typical 'CONTINUE' interaction looks like this:\n1. The user gives you a natural language PROMPT.\n2. You:\n i. Determine what needs to be done\n ii. Determine that you need to see the output of some subprocess call to complete the task\n iii. Write a short Python SCRIPT to print that and then print the word \"CONTINUE\"\n3. The compiler\n i. Checks and runs your SCRIPT\n ii. Captures the output and appends it to the conversation as \"LAST SCRIPT OUTPUT:\"\n iii. Finds the word \"CONTINUE\" and sends control back to you\n4. You again:\n i. Look at the original PROMPT + the \"LAST SCRIPT OUTPUT:\" to determine what needs to be done\n ii. Write a short Python SCRIPT to do it\n iii. Communicate back to the user by printing to the console in that SCRIPT\n5. The compiler...\n\nWhen your script raises an exception, you'll get to review the error and try again:\n1. The user gives you a natural language PROMPT.\n2. You: Respond with a SCRIPT..\n3. The compiler\n i. Executes your SCRIPT\n ii. Catches an exception\n iii. Adds it to the conversation\n iv. If there are retries left, sends control back to you\n4. You again:\n i. Look at the latest PROMPT, SCRIPT and Error message, determine what caused the error and how to fix it\n ii. Write a short Python SCRIPT to do it\n iii. Communicate back to the user by printing to the console in that SCRIPT\n5. The compiler...\n\nPlease follow these conventions carefully:\n- Decline any tasks that seem dangerous, irreversible, or that you don't understand.\n- Always review the full conversation prior to answering and maintain continuity.\n- If asked for information, just print the information clearly and concisely.\n- If asked to do something, print a concise summary of what you've done as confirmation.\n- If asked a question, respond in a friendly, conversational way. Use programmatically-generated and natural language responses as appropriate.\n- Assume the user would like something concise. For example rather than printing a massive table, filter or summarize it to what's likely of interest.\n- The user will likely not specify exact filenames. Use glob searches when looking for filenames in your SCRIPT.\n- Actively clean up any temporary processes or files you use.\n- When looking through files, use git as available to skip files, and skip hidden files (.env, .git, etc) by default.\n- Let exceptions propagate to the user (rather than catching them in your SCRIPT) so that you can retry.\n- At the user's request, you can inspect and update your configuration file: ~/.rawdog/config.yaml. Changes will take effect after restarting.\n- Feel free to use any common python packages. For example matplotlib, beautifulsoup4, numpy. If the user doesn't have them installed they will be installed automatically with user confirmation.\n- ALWAYS Return your SCRIPT inside of a single pair of ``` delimiters. Only the console output of the first such SCRIPT is visible to the user, so make sure that it's complete and don't bother returning anything else.\n" - }, - { - "role": "system", - "content": "EXAMPLES:\n-------------------------------------------------------------------------------\nPROMPT: Kill the process running on port 3000\n\nSCRIPT:\n```\nimport os\nos.system(\"kill $(lsof -t -i:3000)\")\nprint(\"Process killed\")\n```\n-------------------------------------------------------------------------------\nPROMPT: Rename the photos in this directory with \"nyc\" and their timestamp\n\nSCRIPT:\n```\nimport os\nimport time\nimage_files = [f for f in os.listdir('.') if f.lower().endswith(('.png', '.jpg', '.jpeg'))]\ndef get_name(f):\n timestamp = time.strftime('%Y%m%d_%H%M%S', time.localtime(os.path.getmtime(f)))\n return f\"nyc_{timestamp}{os.path.splitext(f)[1]}\"\n[os.rename(f, get_name(f)) for f in image_files]\nprint(\"Renamed files\")\n```\n-------------------------------------------------------------------------------\nPROMPT: Summarize my essay\n\nSCRIPT:\n```\nimport glob\nfiles = glob.glob(\"*essay*.*\")\nwith open(files[0], \"r\") as f:\n print(f.read())\nprint(\"CONTINUE\")\n```\n\nLAST SCRIPT OUTPUT:\n\nJohn Smith\nEssay 2021-09-01\n...\n\nSCRIPT:\n```\nprint(\"The essay is about...\")\n```\n-------------------------------------------------------------------------------\n" - }, - { - "role": "system", - "content": "Today's date is 2024-02-09.\nThe current working directory is /Users/jakekoenig/kalshiMarketMaker/data, which is NOT a git repository.\nThe user's operating system is Darwin\n " - }, - { - "role": "user", - "content": "Can you use nasdaq.csv to make a graph showing a scatter plot of the price change on a given day verse the price change on the next day? Can you make the size of the point proportional to the daily volume on the first day and the color encode the date?" - }, - { - "role": "assistant", - "content": "function_1" - }, - { - "role": "user", - "content": "LAST SCRIPT OUTPUT:\n Date\\tOpen\\tHigh\\tLow\\tClose*\\tAdj Close\\tVolume\n0 2023-05-12\\t13407.019531\\t13427.419922\\t13256....\n1 2023-05-11\\t13376.849609\\t13411.700195\\t13306....\n2 2023-05-10\\t13308.410156\\t13384.419922\\t13204....\n3 2023-05-09\\t13228.639648\\t13254.809570\\t13193....\n4 2023-05-08\\t13247.089844\\t13302.429688\\t13193....\nCONTINUE\n" - }, - { - "role": "assistant", - "content": "function_2" - } -] - -metadata = { - "timestamp": "2024-02-09_01-56-51", - "log_version": "0.2", - "model": "openai/gpt-4-turbo-preview", - "cost": "0.0293500000" -} - - -def function_1(): - import pandas as pd - - # Load the CSV to check its format - print(pd.read_csv("nasdaq.csv").head()) - print("CONTINUE") - - -def function_2(): - import pandas as pd - import matplotlib.pyplot as plt - import matplotlib.dates as mdates - import numpy as np - - # Load the CSV using the correct delimiter - data = pd.read_csv("nasdaq.csv", delimiter="\t") - - # Convert 'Date' from string to datetime format - data['Date'] = pd.to_datetime(data['Date']) - - # Convert 'Close*', 'Open', and 'Volume' to numeric types - data['Close*'] = pd.to_numeric(data['Close*']) - data['Open'] = pd.to_numeric(data['Open']) - data['Volume'] = pd.to_numeric(data['Volume']) - - # Calculate the price change on a given day - data['PriceChange'] = data['Close*'] - data['Open'] - - # Create a new DataFrame for plotting that shifts the 'PriceChange' up by one row to align the given day's change with the next day's change - plot_data = pd.DataFrame({ - 'PriceChangeToday': data['PriceChange'][:-1].values, - 'PriceChangeNextDay': data['PriceChange'][1:].values, - 'VolumeToday': data['Volume'][:-1].values, - 'DateToday': data['Date'][:-1].values - }) - - # Normalize the volume for point size (making it visually manageable) - plot_data['VolumeNormalized'] = plot_data['VolumeToday'] / plot_data['VolumeToday'].max() * 100 - - # Create the scatter plot - plt.figure(figsize=(10, 6)) - sc = plt.scatter(plot_data['PriceChangeToday'], plot_data['PriceChangeNextDay'], - s=plot_data['VolumeNormalized'], c=plot_data['DateToday'], cmap='viridis', alpha=0.6) - - # Customize the graph - plt.title('NASDAQ Price Change Scatter Plot') - plt.xlabel('Price Change on Given Day') - plt.ylabel('Price Change on Next Day') - plt.colorbar(sc, label='Date') - plt.grid(True) - plt.show() - - -if __name__ == "__main__": - function_2() diff --git a/examples/graphs/zsh_history.py b/examples/graphs/zsh_history.py deleted file mode 100644 index 35afdc6..0000000 --- a/examples/graphs/zsh_history.py +++ /dev/null @@ -1,69 +0,0 @@ -conversation = [ - { - "role": "system", - "content": "You are a command-line coding assistant called Rawdog that generates and auto-executes Python scripts.\n\nA typical interaction goes like this:\n1. The user gives you a natural language PROMPT.\n2. You:\n i. Determine what needs to be done\n ii. Write a short Python SCRIPT to do it\n iii. Communicate back to the user by printing to the console in that SCRIPT\n3. The compiler checks your SCRIPT using ast.parse() then runs it using exec()\n\nYou'll get to see the output of a script before your next interaction. If you need to review those\noutputs before completing the task, you can print the word \"CONTINUE\" at the end of your SCRIPT.\nThis can be useful for summarizing documents or technical readouts, reading instructions before\ndeciding what to do, or other tasks that require multi-step reasoning.\nA typical 'CONTINUE' interaction looks like this:\n1. The user gives you a natural language PROMPT.\n2. You:\n i. Determine what needs to be done\n ii. Determine that you need to see the output of some subprocess call to complete the task\n iii. Write a short Python SCRIPT to print that and then print the word \"CONTINUE\"\n3. The compiler\n i. Checks and runs your SCRIPT\n ii. Captures the output and appends it to the conversation as \"LAST SCRIPT OUTPUT:\"\n iii. Finds the word \"CONTINUE\" and sends control back to you\n4. You again:\n i. Look at the original PROMPT + the \"LAST SCRIPT OUTPUT:\" to determine what needs to be done\n ii. Write a short Python SCRIPT to do it\n iii. Communicate back to the user by printing to the console in that SCRIPT\n5. The compiler...\n\nWhen your script raises an exception, you'll get to review the error and try again:\n1. The user gives you a natural language PROMPT.\n2. You: Respond with a SCRIPT..\n3. The compiler\n i. Executes your SCRIPT\n ii. Catches an exception\n iii. Adds it to the conversation\n iv. If there are retries left, sends control back to you\n4. You again:\n i. Look at the latest PROMPT, SCRIPT and Error message, determine what caused the error and how to fix it\n ii. Write a short Python SCRIPT to do it\n iii. Communicate back to the user by printing to the console in that SCRIPT\n5. The compiler...\n\nPlease follow these conventions carefully:\n- Decline any tasks that seem dangerous, irreversible, or that you don't understand.\n- Always review the full conversation prior to answering and maintain continuity.\n- If asked for information, just print the information clearly and concisely.\n- If asked to do something, print a concise summary of what you've done as confirmation.\n- If asked a question, respond in a friendly, conversational way. Use programmatically-generated and natural language responses as appropriate.\n- Assume the user would like something concise. For example rather than printing a massive table, filter or summarize it to what's likely of interest.\n- The user will likely not specify exact filenames. Use glob searches when looking for filenames in your SCRIPT.\n- Actively clean up any temporary processes or files you use.\n- When looking through files, use git as available to skip files, and skip hidden files (.env, .git, etc) by default.\n- Let exceptions propagate to the user (rather than catching them in your SCRIPT) so that you can retry.\n- At the user's request, you can inspect and update your configuration file: ~/.rawdog/config.yaml. Changes will take effect after restarting.\n- Feel free to use any common python packages. For example matplotlib, beautifulsoup4, numpy. If the user doesn't have them installed they will be installed automatically with user confirmation.\n- ALWAYS Return your SCRIPT inside of a single pair of ``` delimiters. Only the console output of the first such SCRIPT is visible to the user, so make sure that it's complete and don't bother returning anything else.\n" - }, - { - "role": "system", - "content": "EXAMPLES:\n-------------------------------------------------------------------------------\nPROMPT: Kill the process running on port 3000\n\nSCRIPT:\n```\nimport os\nos.system(\"kill $(lsof -t -i:3000)\")\nprint(\"Process killed\")\n```\n-------------------------------------------------------------------------------\nPROMPT: Rename the photos in this directory with \"nyc\" and their timestamp\n\nSCRIPT:\n```\nimport os\nimport time\nimage_files = [f for f in os.listdir('.') if f.lower().endswith(('.png', '.jpg', '.jpeg'))]\ndef get_name(f):\n timestamp = time.strftime('%Y%m%d_%H%M%S', time.localtime(os.path.getmtime(f)))\n return f\"nyc_{timestamp}{os.path.splitext(f)[1]}\"\n[os.rename(f, get_name(f)) for f in image_files]\nprint(\"Renamed files\")\n```\n-------------------------------------------------------------------------------\nPROMPT: Summarize my essay\n\nSCRIPT:\n```\nimport glob\nfiles = glob.glob(\"*essay*.*\")\nwith open(files[0], \"r\") as f:\n print(f.read())\nprint(\"CONTINUE\")\n```\n\nLAST SCRIPT OUTPUT:\n\nJohn Smith\nEssay 2021-09-01\n...\n\nSCRIPT:\n```\nprint(\"The essay is about...\")\n```\n-------------------------------------------------------------------------------\n" - }, - { - "role": "system", - "content": "Today's date is 2024-02-08.\nThe current working directory is /Users/jakekoenig/rawdog, which IS a git repository.\nThe user's operating system is Darwin\n " - }, - { - "role": "user", - "content": "make a graph showing my relative frequency of command line programs" - }, - { - "role": "assistant", - "content": "function_1" - } -] - -metadata = { - "timestamp": "2024-02-07_12-04-14", - "log_version": "0.2", - "model": "openai/gpt-4-turbo-preview", - "cost": "0.0194200000" -} - - -def function_1(): - import os - import matplotlib.pyplot as plt - from collections import Counter - - # Use the history command to get a list of used commands - # Assuming user's shell history command is compatible and contains relevant history - # Read the history file directly for shells like zsh, this might change based on the shell used. - # Common history files: ~/.bash_history for bash, ~/.zsh_history for zsh - with open(os.path.expanduser('~/.zsh_history'), 'r') as history_file: - commands = history_file.readlines() - - # Extract the command part (first word) from each line - commands = [line.strip().split(';')[-1].split(' ')[0] for line in commands if ';' in line] # Adjust for zsh history format - - # Count occurrences of each command - command_counts = Counter(commands) - - # Sort commands by frequency - sorted_commands = sorted(command_counts.items(), key=lambda item: item[1], reverse=True) - - # Considering only the top 10 frequent commands to keep the graph readable - top_commands = dict(sorted_commands[:10]) - - # Create the plot - plt.figure(figsize=(10, 6)) - plt.bar(top_commands.keys(), top_commands.values(), color='skyblue') - plt.xlabel('Command') - plt.ylabel('Frequency') - plt.title('Top 10 Most Used Command Line Programs') - plt.xticks(rotation=45) - plt.tight_layout() - - plt.show() - - -if __name__ == "__main__": - function_1() diff --git a/examples/media/remove_sound.py b/examples/media/remove_sound.py deleted file mode 100644 index ff37031..0000000 --- a/examples/media/remove_sound.py +++ /dev/null @@ -1,159 +0,0 @@ -conversation = [ - { - "role": "system", - "content": ( - "You are a command-line coding assistant called Rawdog that generates and" - " auto-executes Python scripts.\n\nA typical interaction goes like" - " this:\n1. The user gives you a natural language PROMPT.\n2. You:\n i." - " Determine what needs to be done\n ii. Write a short Python SCRIPT to" - " do it\n iii. Communicate back to the user by printing to the console" - " in that SCRIPT\n3. The compiler checks your SCRIPT using ast.parse() then" - " runs it using exec()\n\nYou'll get to see the output of a script before" - " your next interaction. If you need to review those\noutputs before" - ' completing the task, you can print the word "CONTINUE" at the end of' - " your SCRIPT.\nThis can be useful for summarizing documents or technical" - " readouts, reading instructions before\ndeciding what to do, or other" - " tasks that require multi-step reasoning.\nA typical 'CONTINUE'" - " interaction looks like this:\n1. The user gives you a natural language" - " PROMPT.\n2. You:\n i. Determine what needs to be done\n ii." - " Determine that you need to see the output of some subprocess call to" - " complete the task\n iii. Write a short Python SCRIPT to print that and" - ' then print the word "CONTINUE"\n3. The compiler\n i. Checks and runs' - " your SCRIPT\n ii. Captures the output and appends it to the" - ' conversation as "LAST SCRIPT OUTPUT:"\n iii. Finds the word' - ' "CONTINUE" and sends control back to you\n4. You again:\n i. Look at' - ' the original PROMPT + the "LAST SCRIPT OUTPUT:" to determine what needs' - " to be done\n ii. Write a short Python SCRIPT to do it\n iii." - " Communicate back to the user by printing to the console in that" - " SCRIPT\n5. The compiler...\n\nWhen your script raises an exception," - " you'll get to review the error and try again:\n1. The user gives you a" - " natural language PROMPT.\n2. You: Respond with a SCRIPT..\n3. The" - " compiler\n i. Executes your SCRIPT\n ii. Catches an exception\n " - " iii. Adds it to the conversation\n iv. If there are retries left," - " sends control back to you\n4. You again:\n i. Look at the latest" - " PROMPT, SCRIPT and Error message, determine what caused the error and how" - " to fix it\n ii. Write a short Python SCRIPT to do it\n iii." - " Communicate back to the user by printing to the console in that" - " SCRIPT\n5. The compiler...\n\nPlease follow these conventions" - " carefully:\n- Decline any tasks that seem dangerous, irreversible, or" - " that you don't understand.\n- Always review the full conversation prior" - " to answering and maintain continuity.\n- If asked for information, just" - " print the information clearly and concisely.\n- If asked to do something," - " print a concise summary of what you've done as confirmation.\n- If asked" - " a question, respond in a friendly, conversational way. Use" - " programmatically-generated and natural language responses as" - " appropriate.\n- Assume the user would like something concise. For example" - " rather than printing a massive table, filter or summarize it to what's" - " likely of interest.\n- The user will likely not specify exact filenames." - " Use glob searches when looking for filenames in your SCRIPT.\n- Actively" - " clean up any temporary processes or files you use.\n- When looking" - " through files, use git as available to skip files, and skip hidden files" - " (.env, .git, etc) by default.\n- Let exceptions propagate to the user" - " (rather than catching them in your SCRIPT) so that you can retry.\n- At" - " the user's request, you can inspect and update your configuration file:" - " ~/.rawdog/config.yaml. Changes will take effect after restarting.\n- Feel" - " free to use any common python packages. For example matplotlib," - " beautifulsoup4, numpy. If the user doesn't have them installed they will" - " be installed automatically with user confirmation.\n- ALWAYS Return your" - " SCRIPT inside of a single pair of ``` delimiters. Only the console output" - " of the first such SCRIPT is visible to the user, so make sure that it's" - " complete and don't bother returning anything else.\n" - ), - }, - { - "role": "system", - "content": ( - "EXAMPLES:\n-------------------------------------------------------------------------------\nPROMPT:" - " Kill the process running on port 3000\n\nSCRIPT:\n```\nimport" - ' os\nos.system("kill $(lsof -t -i:3000)")\nprint("Process' - ' killed")\n```\n-------------------------------------------------------------------------------\nPROMPT:' - ' Rename the photos in this directory with "nyc" and their' - " timestamp\n\nSCRIPT:\n```\nimport os\nimport time\nimage_files = [f for f" - " in os.listdir('.') if f.lower().endswith(('.png', '.jpg'," - " '.jpeg'))]\ndef get_name(f):\n timestamp =" - " time.strftime('%Y%m%d_%H%M%S', time.localtime(os.path.getmtime(f)))\n " - ' return f"nyc_{timestamp}{os.path.splitext(f)[1]}"\n[os.rename(f,' - ' get_name(f)) for f in image_files]\nprint("Renamed' - ' files")\n```\n-------------------------------------------------------------------------------\nPROMPT:' - " Summarize my essay\n\nSCRIPT:\n```\nimport glob\nfiles =" - ' glob.glob("*essay*.*")\nwith open(files[0], "r") as f:\n ' - ' print(f.read())\nprint("CONTINUE")\n```\n\nLAST SCRIPT OUTPUT:\n\nJohn' - ' Smith\nEssay 2021-09-01\n...\n\nSCRIPT:\n```\nprint("The essay is' - ' about...")\n```\n-------------------------------------------------------------------------------\n' - ), - }, - { - "role": "system", - "content": ( - "Today's date is 2024-02-15 15:47:38.\nThe current working directory is" - " /Users/biobootloader/code/rawdog, which IS a git repository.\nThe user's" - " operating system is Darwin.\nThe contents of the current working" - " directory are:\n2024-02-04 21:22:53 11339 bytes LICENSE\n2024-02-15" - " 15:47:04 81 bytes requirements.txt\n2024-02-14 16:16:24 " - " 42 bytes dev-requirements.txt\n2024-02-14 14:34:10 3 items" - " /.ruff_cache\n2024-02-14 16:16:24 834 bytes" - " pyproject.toml\n2024-02-15 12:30:45 16090 bytes tags\n2024-02-09" - " 12:33:42 3081 bytes README.md\n2024-02-14 14:42:48 45 bytes" - " .gitignore\n2024-02-15 10:00:48 12 items /examples\n2024-02-15" - " 12:01:17 4 items /.venv\n2024-02-14 16:16:24 3 items" - " /scripts\n2024-02-14 16:16:24 1 items /.github\n2024-02-15" - " 15:47:08 13 items /.git\n2024-01-31 14:54:46 1 items" - " /src\nThe last commit message is: Merge pull request #71 from" - " AbanteAI/config-none-fix\n\noverwrite config if none\nYou'll get 2" - " retries." - ), - }, - { - "role": "user", - "content": ( - "make a copy of /Users/biobootloader/Desktop/rd-demo.mp4 that doesnt have" - " sound" - ), - }, - {"role": "assistant", "content": "function_1"}, -] - -metadata = { - "timestamp": "2024-02-15_15-47-53", - "log_version": 0.2, - "model": "gpt-4-turbo-preview", - "cost": "0.0299000000", -} - - -def function_1(): - import shutil - import subprocess - - # Original file path - original_file_path = "/Users/biobootloader/Desktop/rd-demo.mp4" - # Destination file path - destination_file_path = "/Users/biobootloader/Desktop/rd-demo-nosound.mp4" - - # Copy file to maintain original - shutil.copyfile(original_file_path, destination_file_path) - - # Remove sound using ffmpeg command - subprocess.run( - [ - "ffmpeg", - "-i", - destination_file_path, - "-c", - "copy", - "-an", - "/Users/biobootloader/Desktop/temp_rd-demo-nosound.mp4", - ], - check=True, - ) - - # Replace the original copy with the one without sound - shutil.move( - "/Users/biobootloader/Desktop/temp_rd-demo-nosound.mp4", destination_file_path - ) - - print("Created a copy of rd-demo.mp4 without sound.") - - -if __name__ == "__main__": - function_1() diff --git a/examples/pic/greyscale_screenshot.py b/examples/pic/greyscale_screenshot.py deleted file mode 100644 index 8890423..0000000 --- a/examples/pic/greyscale_screenshot.py +++ /dev/null @@ -1,59 +0,0 @@ -conversation = [ - { - "role": "system", - "content": "You are a command-line coding assistant called Rawdog that generates and auto-executes Python scripts.\n\nA typical interaction goes like this:\n1. The user gives you a natural language PROMPT.\n2. You:\n i. Determine what needs to be done\n ii. Write a short Python SCRIPT to do it\n iii. Communicate back to the user by printing to the console in that SCRIPT\n3. The compiler checks your SCRIPT using ast.parse() then runs it using exec()\n\nYou'll get to see the output of a script before your next interaction. If you need to review those\noutputs before completing the task, you can print the word \"CONTINUE\" at the end of your SCRIPT.\nThis can be useful for summarizing documents or technical readouts, reading instructions before\ndeciding what to do, or other tasks that require multi-step reasoning.\nA typical 'CONTINUE' interaction looks like this:\n1. The user gives you a natural language PROMPT.\n2. You:\n i. Determine what needs to be done\n ii. Determine that you need to see the output of some subprocess call to complete the task\n iii. Write a short Python SCRIPT to print that and then print the word \"CONTINUE\"\n3. The compiler\n i. Checks and runs your SCRIPT\n ii. Captures the output and appends it to the conversation as \"LAST SCRIPT OUTPUT:\"\n iii. Finds the word \"CONTINUE\" and sends control back to you\n4. You again:\n i. Look at the original PROMPT + the \"LAST SCRIPT OUTPUT:\" to determine what needs to be done\n ii. Write a short Python SCRIPT to do it\n iii. Communicate back to the user by printing to the console in that SCRIPT\n5. The compiler...\n\nWhen your script raises an exception, you'll get to review the error and try again:\n1. The user gives you a natural language PROMPT.\n2. You: Respond with a SCRIPT..\n3. The compiler\n i. Executes your SCRIPT\n ii. Catches an exception\n iii. Adds it to the conversation\n iv. If there are retries left, sends control back to you\n4. You again:\n i. Look at the latest PROMPT, SCRIPT and Error message, determine what caused the error and how to fix it\n ii. Write a short Python SCRIPT to do it\n iii. Communicate back to the user by printing to the console in that SCRIPT\n5. The compiler...\n\nPlease follow these conventions carefully:\n- Decline any tasks that seem dangerous, irreversible, or that you don't understand.\n- Always review the full conversation prior to answering and maintain continuity.\n- If asked for information, just print the information clearly and concisely.\n- If asked to do something, print a concise summary of what you've done as confirmation.\n- If asked a question, respond in a friendly, conversational way. Use programmatically-generated and natural language responses as appropriate.\n- Assume the user would like something concise. For example rather than printing a massive table, filter or summarize it to what's likely of interest.\n- The user will likely not specify exact filenames. Use glob searches when looking for filenames in your SCRIPT.\n- Actively clean up any temporary processes or files you use.\n- When looking through files, use git as available to skip files, and skip hidden files (.env, .git, etc) by default.\n- Let exceptions propagate to the user (rather than catching them in your SCRIPT) so that you can retry.\n- At the user's request, you can inspect and update your configuration file: ~/.rawdog/config.yaml. Changes will take effect after restarting.\n- Feel free to use any common python packages. For example matplotlib, beautifulsoup4, numpy. If the user doesn't have them installed they will be installed automatically with user confirmation.\n- ALWAYS Return your SCRIPT inside of a single pair of ``` delimiters. Only the console output of the first such SCRIPT is visible to the user, so make sure that it's complete and don't bother returning anything else.\n" - }, - { - "role": "system", - "content": "EXAMPLES:\n-------------------------------------------------------------------------------\nPROMPT: Kill the process running on port 3000\n\nSCRIPT:\n```\nimport os\nos.system(\"kill $(lsof -t -i:3000)\")\nprint(\"Process killed\")\n```\n-------------------------------------------------------------------------------\nPROMPT: Rename the photos in this directory with \"nyc\" and their timestamp\n\nSCRIPT:\n```\nimport os\nimport time\nimage_files = [f for f in os.listdir('.') if f.lower().endswith(('.png', '.jpg', '.jpeg'))]\ndef get_name(f):\n timestamp = time.strftime('%Y%m%d_%H%M%S', time.localtime(os.path.getmtime(f)))\n return f\"nyc_{timestamp}{os.path.splitext(f)[1]}\"\n[os.rename(f, get_name(f)) for f in image_files]\nprint(\"Renamed files\")\n```\n-------------------------------------------------------------------------------\nPROMPT: Summarize my essay\n\nSCRIPT:\n```\nimport glob\nfiles = glob.glob(\"*essay*.*\")\nwith open(files[0], \"r\") as f:\n print(f.read())\nprint(\"CONTINUE\")\n```\n\nLAST SCRIPT OUTPUT:\n\nJohn Smith\nEssay 2021-09-01\n...\n\nSCRIPT:\n```\nprint(\"The essay is about...\")\n```\n-------------------------------------------------------------------------------\n" - }, - { - "role": "system", - "content": "Today's date is 2024-02-08.\nThe current working directory is /Users/jakekoenig/rawdog, which IS a git repository.\nThe user's operating system is Darwin\n " - }, - { - "role": "user", - "content": "Can you make my most recent screenshot greyscale?" - }, - { - "role": "assistant", - "content": "function_1" - } -] - -metadata = { - "timestamp": "2024-02-07_12-41-50", - "log_version": "0.2", - "model": "openai/gpt-4-turbo-preview", - "cost": "0.0153300000", - "note": "I partially wrote this one because gpt likes to like in the current directory for screenshots. Not where macos puts them, the Desktop." -} - - -def function_1(): - import os - from PIL import Image - import sys - - # Retrieve all screenshot files - desktop = os.path.expanduser("~/Desktop") - screenshot_files = [os.path.join(desktop, f) for f in os.listdir(desktop) if f.lower().startswith('screenshot') and f.lower().endswith(('.png', '.jpg', '.jpeg'))] - - if not screenshot_files: - print("No screenshot files found.") - sys.exit() - - # Sort files by modified time, most recent first - screenshot_files.sort(key=lambda x: os.path.getmtime(x), reverse=True) - - # Load the most recent screenshot - image = Image.open(screenshot_files[0]) - # Convert to greyscale - grey_image = image.convert('L') - grey_image_path = screenshot_files[0][:-4] + "_greyscale" + screenshot_files[0][-4:] - grey_image.save(grey_image_path) - print(f"Converted {screenshot_files[0]} to greyscale.") - - -if __name__ == "__main__": - function_1() diff --git a/examples/pic/mandelbrot.py b/examples/pic/mandelbrot.py deleted file mode 100644 index 884d184..0000000 --- a/examples/pic/mandelbrot.py +++ /dev/null @@ -1,61 +0,0 @@ -conversation = [ - { - "role": "system", - "content": "You are a command-line coding assistant called Rawdog that generates and auto-executes Python scripts.\n\nA typical interaction goes like this:\n1. The user gives you a natural language PROMPT.\n2. You:\n i. Determine what needs to be done\n ii. Write a short Python SCRIPT to do it\n iii. Communicate back to the user by printing to the console in that SCRIPT\n3. The compiler checks your SCRIPT using ast.parse() then runs it using exec()\n\nYou'll get to see the output of a script before your next interaction. If you need to review those\noutputs before completing the task, you can print the word \"CONTINUE\" at the end of your SCRIPT.\nThis can be useful for summarizing documents or technical readouts, reading instructions before\ndeciding what to do, or other tasks that require multi-step reasoning.\nA typical 'CONTINUE' interaction looks like this:\n1. The user gives you a natural language PROMPT.\n2. You:\n i. Determine what needs to be done\n ii. Determine that you need to see the output of some subprocess call to complete the task\n iii. Write a short Python SCRIPT to print that and then print the word \"CONTINUE\"\n3. The compiler\n i. Checks and runs your SCRIPT\n ii. Captures the output and appends it to the conversation as \"LAST SCRIPT OUTPUT:\"\n iii. Finds the word \"CONTINUE\" and sends control back to you\n4. You again:\n i. Look at the original PROMPT + the \"LAST SCRIPT OUTPUT:\" to determine what needs to be done\n ii. Write a short Python SCRIPT to do it\n iii. Communicate back to the user by printing to the console in that SCRIPT\n5. The compiler...\n\nWhen your script raises an exception, you'll get to review the error and try again:\n1. The user gives you a natural language PROMPT.\n2. You: Respond with a SCRIPT..\n3. The compiler\n i. Executes your SCRIPT\n ii. Catches an exception\n iii. Adds it to the conversation\n iv. If there are retries left, sends control back to you\n4. You again:\n i. Look at the latest PROMPT, SCRIPT and Error message, determine what caused the error and how to fix it\n ii. Write a short Python SCRIPT to do it\n iii. Communicate back to the user by printing to the console in that SCRIPT\n5. The compiler...\n\nPlease follow these conventions carefully:\n- Decline any tasks that seem dangerous, irreversible, or that you don't understand.\n- Always review the full conversation prior to answering and maintain continuity.\n- If asked for information, just print the information clearly and concisely.\n- If asked to do something, print a concise summary of what you've done as confirmation.\n- If asked a question, respond in a friendly, conversational way. Use programmatically-generated and natural language responses as appropriate.\n- Assume the user would like something concise. For example rather than printing a massive table, filter or summarize it to what's likely of interest.\n- The user will likely not specify exact filenames. Use glob searches when looking for filenames in your SCRIPT.\n- Actively clean up any temporary processes or files you use.\n- When looking through files, use git as available to skip files, and skip hidden files (.env, .git, etc) by default.\n- Let exceptions propagate to the user (rather than catching them in your SCRIPT) so that you can retry.\n- At the user's request, you can inspect and update your configuration file: ~/.rawdog/config.yaml. Changes will take effect after restarting.\n- Feel free to use any common python packages. For example matplotlib, beautifulsoup4, numpy. If the user doesn't have them installed they will be installed automatically with user confirmation.\n- ALWAYS Return your SCRIPT inside of a single pair of ``` delimiters. Only the console output of the first such SCRIPT is visible to the user, so make sure that it's complete and don't bother returning anything else.\n" - }, - { - "role": "system", - "content": "EXAMPLES:\n-------------------------------------------------------------------------------\nPROMPT: Kill the process running on port 3000\n\nSCRIPT:\n```\nimport os\nos.system(\"kill $(lsof -t -i:3000)\")\nprint(\"Process killed\")\n```\n-------------------------------------------------------------------------------\nPROMPT: Rename the photos in this directory with \"nyc\" and their timestamp\n\nSCRIPT:\n```\nimport os\nimport time\nimage_files = [f for f in os.listdir('.') if f.lower().endswith(('.png', '.jpg', '.jpeg'))]\ndef get_name(f):\n timestamp = time.strftime('%Y%m%d_%H%M%S', time.localtime(os.path.getmtime(f)))\n return f\"nyc_{timestamp}{os.path.splitext(f)[1]}\"\n[os.rename(f, get_name(f)) for f in image_files]\nprint(\"Renamed files\")\n```\n-------------------------------------------------------------------------------\nPROMPT: Summarize my essay\n\nSCRIPT:\n```\nimport glob\nfiles = glob.glob(\"*essay*.*\")\nwith open(files[0], \"r\") as f:\n print(f.read())\nprint(\"CONTINUE\")\n```\n\nLAST SCRIPT OUTPUT:\n\nJohn Smith\nEssay 2021-09-01\n...\n\nSCRIPT:\n```\nprint(\"The essay is about...\")\n```\n-------------------------------------------------------------------------------\n" - }, - { - "role": "system", - "content": "Today's date is 2024-02-08.\nThe current working directory is /Users/jakekoenig/rawdog, which IS a git repository.\nThe user's operating system is Darwin\n " - }, - { - "role": "user", - "content": "can you draw the mandelbrot set?" - }, - { - "role": "assistant", - "content": "function_1" - } -] - -metadata = { - "timestamp": "2024-02-07_12-12-09", - "log_version": "0.2", - "model": "openai/gpt-4-turbo-preview", - "cost": "0.0153600000" -} - - -def function_1(): - import numpy as np - import matplotlib.pyplot as plt - - def mandelbrot(c, max_iter): - z = 0 - n = 0 - while abs(z) <= 2 and n < max_iter: - z = z*z + c - n += 1 - return n - - width, height = 800, 600 - max_iter = 30 - - x = np.linspace(-2.5, 1, width) - y = np.linspace(-1, 1, height) - X, Y = np.meshgrid(x, y) - C = X + 1j * Y - - Z = np.vectorize(mandelbrot)(C, max_iter) - - plt.imshow(Z, cmap='hot', extent=(-2.5, 1, -1, 1)) - plt.colorbar() - plt.title('Mandelbrot Set') - plt.show() - - -if __name__ == "__main__": - function_1() diff --git a/examples/read_readme_for_instructions.py b/examples/read_readme_for_instructions.py deleted file mode 100644 index 72a9c1e..0000000 --- a/examples/read_readme_for_instructions.py +++ /dev/null @@ -1,40 +0,0 @@ -conversation = [ - { - "role": "system", - "content": "You are a command-line coding assistant called Rawdog that generates and auto-executes Python scripts.\n\nA typical interaction goes like this:\n1. The user gives you a natural language PROMPT.\n2. You:\n i. Determine what needs to be done\n ii. Write a short Python SCRIPT to do it\n iii. Communicate back to the user by printing to the console in that SCRIPT\n3. The compiler checks your SCRIPT using ast.parse() then runs it using exec()\n\nYou'll get to see the output of a script before your next interaction. If you need to review those\noutputs before completing the task, you can print the word \"CONTINUE\" at the end of your SCRIPT.\nThis can be useful for summarizing documents or technical readouts, reading instructions before\ndeciding what to do, or other tasks that require multi-step reasoning.\nA typical 'CONTINUE' interaction looks like this:\n1. The user gives you a natural language PROMPT.\n2. You:\n i. Determine what needs to be done\n ii. Determine that you need to see the output of some subprocess call to complete the task\n iii. Write a short Python SCRIPT to print that and then print the word \"CONTINUE\"\n3. The compiler\n i. Checks and runs your SCRIPT\n ii. Captures the output and appends it to the conversation as \"LAST SCRIPT OUTPUT:\"\n iii. Finds the word \"CONTINUE\" and sends control back to you\n4. You again:\n i. Look at the original PROMPT + the \"LAST SCRIPT OUTPUT:\" to determine what needs to be done\n ii. Write a short Python SCRIPT to do it\n iii. Communicate back to the user by printing to the console in that SCRIPT\n5. The compiler...\n\nWhen your script raises an exception, you'll get to review the error and try again:\n1. The user gives you a natural language PROMPT.\n2. You: Respond with a SCRIPT..\n3. The compiler\n i. Executes your SCRIPT\n ii. Catches an exception\n iii. Adds it to the conversation\n iv. If there are retries left, sends control back to you\n4. You again:\n i. Look at the latest PROMPT, SCRIPT and Error message, determine what caused the error and how to fix it\n ii. Write a short Python SCRIPT to do it\n iii. Communicate back to the user by printing to the console in that SCRIPT\n5. The compiler...\n\nPlease follow these conventions carefully:\n- Decline any tasks that seem dangerous, irreversible, or that you don't understand.\n- Always review the full conversation prior to answering and maintain continuity.\n- If asked for information, just print the information clearly and concisely.\n- If asked to do something, print a concise summary of what you've done as confirmation.\n- If asked a question, respond in a friendly, conversational way. Use programmatically-generated and natural language responses as appropriate.\n- Assume the user would like something concise. For example rather than printing a massive table, filter or summarize it to what's likely of interest.\n- The user will likely not specify exact filenames. Use glob searches when looking for filenames in your SCRIPT.\n- Actively clean up any temporary processes or files you use.\n- When looking through files, use git as available to skip files, and skip hidden files (.env, .git, etc) by default.\n- Let exceptions propagate to the user (rather than catching them in your SCRIPT) so that you can retry.\n- At the user's request, you can inspect and update your configuration file: ~/.rawdog/config.yaml. Changes will take effect after restarting.\n- Feel free to use any common python packages. For example matplotlib, beautifulsoup4, numpy. If the user doesn't have them installed they will be installed automatically with user confirmation.\n- ALWAYS Return your SCRIPT inside of a single pair of ``` delimiters. Only the console output of the first such SCRIPT is visible to the user, so make sure that it's complete and don't bother returning anything else.\n" - }, - { - "role": "system", - "content": "EXAMPLES:\n-------------------------------------------------------------------------------\nPROMPT: Kill the process running on port 3000\n\nSCRIPT:\n```\nimport os\nos.system(\"kill $(lsof -t -i:3000)\")\nprint(\"Process killed\")\n```\n-------------------------------------------------------------------------------\nPROMPT: Rename the photos in this directory with \"nyc\" and their timestamp\n\nSCRIPT:\n```\nimport os\nimport time\nimage_files = [f for f in os.listdir('.') if f.lower().endswith(('.png', '.jpg', '.jpeg'))]\ndef get_name(f):\n timestamp = time.strftime('%Y%m%d_%H%M%S', time.localtime(os.path.getmtime(f)))\n return f\"nyc_{timestamp}{os.path.splitext(f)[1]}\"\n[os.rename(f, get_name(f)) for f in image_files]\nprint(\"Renamed files\")\n```\n-------------------------------------------------------------------------------\nPROMPT: Summarize my essay\n\nSCRIPT:\n```\nimport glob\nfiles = glob.glob(\"*essay*.*\")\nwith open(files[0], \"r\") as f:\n print(f.read())\nprint(\"CONTINUE\")\n```\n\nLAST SCRIPT OUTPUT:\n\nJohn Smith\nEssay 2021-09-01\n...\n\nSCRIPT:\n```\nprint(\"The essay is about...\")\n```\n-------------------------------------------------------------------------------\n" - }, - { - "role": "system", - "content": "Today's date is 2024-02-08.\nThe current working directory is /Users/jakekoenig/rawdog, which IS a git repository.\nThe user's operating system is Darwin\n " - }, - { - "role": "user", - "content": "Can you read the readme and use the information to change rawdog to use mixtral with ollama?" - }, - { - "role": "assistant", - "content": "function_1" - } -] - -metadata = { - "timestamp": "2024-02-07_09-00-03", - "log_version": "0.2", - "model": "openai/gpt-4-turbo-preview", - "cost": "0.0108700000" -} - - -def function_1(): - with open("README.md", "r") as f: - content = f.read() - print(content) - print("CONTINUE") - - -if __name__ == "__main__": - function_1() diff --git a/examples/repo/describe_docs_structure.py b/examples/repo/describe_docs_structure.py deleted file mode 100644 index 8f30579..0000000 --- a/examples/repo/describe_docs_structure.py +++ /dev/null @@ -1,78 +0,0 @@ -conversation = [ - { - "role": "system", - "content": "You are a command-line coding assistant called Rawdog that generates and auto-executes Python scripts.\n\nA typical interaction goes like this:\n1. The user gives you a natural language PROMPT.\n2. You:\n i. Determine what needs to be done\n ii. Write a short Python SCRIPT to do it\n iii. Communicate back to the user by printing to the console in that SCRIPT\n3. The compiler checks your SCRIPT using ast.parse() then runs it using exec()\n\nYou'll get to see the output of a script before your next interaction. If you need to review those\noutputs before completing the task, you can print the word \"CONTINUE\" at the end of your SCRIPT.\nThis can be useful for summarizing documents or technical readouts, reading instructions before\ndeciding what to do, or other tasks that require multi-step reasoning.\nA typical 'CONTINUE' interaction looks like this:\n1. The user gives you a natural language PROMPT.\n2. You:\n i. Determine what needs to be done\n ii. Determine that you need to see the output of some subprocess call to complete the task\n iii. Write a short Python SCRIPT to print that and then print the word \"CONTINUE\"\n3. The compiler\n i. Checks and runs your SCRIPT\n ii. Captures the output and appends it to the conversation as \"LAST SCRIPT OUTPUT:\"\n iii. Finds the word \"CONTINUE\" and sends control back to you\n4. You again:\n i. Look at the original PROMPT + the \"LAST SCRIPT OUTPUT:\" to determine what needs to be done\n ii. Write a short Python SCRIPT to do it\n iii. Communicate back to the user by printing to the console in that SCRIPT\n5. The compiler...\n\nWhen your script raises an exception, you'll get to review the error and try again:\n1. The user gives you a natural language PROMPT.\n2. You: Respond with a SCRIPT..\n3. The compiler\n i. Executes your SCRIPT\n ii. Catches an exception\n iii. Adds it to the conversation\n iv. If there are retries left, sends control back to you\n4. You again:\n i. Look at the latest PROMPT, SCRIPT and Error message, determine what caused the error and how to fix it\n ii. Write a short Python SCRIPT to do it\n iii. Communicate back to the user by printing to the console in that SCRIPT\n5. The compiler...\n\nPlease follow these conventions carefully:\n- Decline any tasks that seem dangerous, irreversible, or that you don't understand.\n- Always review the full conversation prior to answering and maintain continuity.\n- If asked for information, just print the information clearly and concisely.\n- If asked to do something, print a concise summary of what you've done as confirmation.\n- If asked a question, respond in a friendly, conversational way. Use programmatically-generated and natural language responses as appropriate.\n- Assume the user would like something concise. For example rather than printing a massive table, filter or summarize it to what's likely of interest.\n- The user will likely not specify exact filenames. Use glob searches when looking for filenames in your SCRIPT.\n- Actively clean up any temporary processes or files you use.\n- When looking through files, use git as available to skip files, and skip hidden files (.env, .git, etc) by default.\n- Let exceptions propagate to the user (rather than catching them in your SCRIPT) so that you can retry.\n- At the user's request, you can inspect and update your configuration file: ~/.rawdog/config.yaml. Changes will take effect after restarting.\n- Feel free to use any common python packages. For example matplotlib, beautifulsoup4, numpy. If the user doesn't have them installed they will be installed automatically with user confirmation.\n- ALWAYS Return your SCRIPT inside of a single pair of ``` delimiters. Only the console output of the first such SCRIPT is visible to the user, so make sure that it's complete and don't bother returning anything else.\n" - }, - { - "role": "system", - "content": "EXAMPLES:\n-------------------------------------------------------------------------------\nPROMPT: Kill the process running on port 3000\n\nSCRIPT:\n```\nimport os\nos.system(\"kill $(lsof -t -i:3000)\")\nprint(\"Process killed\")\n```\n-------------------------------------------------------------------------------\nPROMPT: Rename the photos in this directory with \"nyc\" and their timestamp\n\nSCRIPT:\n```\nimport os\nimport time\nimage_files = [f for f in os.listdir('.') if f.lower().endswith(('.png', '.jpg', '.jpeg'))]\ndef get_name(f):\n timestamp = time.strftime('%Y%m%d_%H%M%S', time.localtime(os.path.getmtime(f)))\n return f\"nyc_{timestamp}{os.path.splitext(f)[1]}\"\n[os.rename(f, get_name(f)) for f in image_files]\nprint(\"Renamed files\")\n```\n-------------------------------------------------------------------------------\nPROMPT: Summarize my essay\n\nSCRIPT:\n```\nimport glob\nfiles = glob.glob(\"*essay*.*\")\nwith open(files[0], \"r\") as f:\n print(f.read())\nprint(\"CONTINUE\")\n```\n\nLAST SCRIPT OUTPUT:\n\nJohn Smith\nEssay 2021-09-01\n...\n\nSCRIPT:\n```\nprint(\"The essay is about...\")\n```\n-------------------------------------------------------------------------------\n" - }, - { - "role": "system", - "content": "Today's date is 2024-02-09.\nThe current working directory is /Users/jakekoenig/mentat, which IS a git repository.\nThe user's operating system is Darwin\n " - }, - { - "role": "user", - "content": "How is the docs directory organized?" - }, - { - "role": "assistant", - "content": "function_1" - }, - { - "role": "user", - "content": "LAST SCRIPT OUTPUT:\nDirectory structure of 'docs':\ndocs/\n requirements.txt\n Makefile\n make.bat\n source/\n index.rst\n conf.py\n developer/\n mentat.prompts.rst\n mentat.terminal.rst\n mentat.command.commands.rst\n modules.rst\n mentat.command.rst\n mentat.parsers.rst\n mentat.feature_filters.rst\n mentat.vision.rst\n mentat.python_client.rst\n mentat.rst\n user/\n getting_started.rst\n configuration.rst\n commands.rst\n context.rst\n cli_arguments.rst\n alternative_models.rst\n guides.rst\n sdk/\n index.rst\n usage.rst\n module.rst\n build/\n html/\n index.html\n changelog.html\n searchindex.js\n py-modindex.html\n .buildinfo\n genindex.html\n search.html\n objects.inv\n developer/\n mentat.python_client.html\n mentat.parsers.html\n mentat.feature_filters.html\n modules.html\n mentat.command.html\n mentat.prompts.html\n mentat.command.commands.html\n mentat.terminal.html\n mentat.html\n mentat.vision.html\n _modules/\n index.html\n pathlib.html\n mentat/\n include_files.html\n edit_history.html\n ctags.html\n interval.html\n session_stream.html\n session_input.html\n code_context.html\n diff_context.html\n utils.html\n sentry.html\n git_handler.html\n auto_completer.html\n code_feature.html\n conversation.html\n config.html\n transcripts.html\n code_edit_feedback.html\n errors.html\n agent_handler.html\n llm_api_handler.html\n cost_tracker.html\n session_context.html\n logging_config.html\n session.html\n code_file_manager.html\n embeddings.html\n broadcast.html\n parsers/\n parser.html\n json_parser.html\n diff_utils.html\n replacement_parser.html\n file_edit.html\n unified_diff_parser.html\n change_display_helper.html\n block_parser.html\n git_parser.html\n feature_filters/\n feature_filter.html\n truncate_filter.html\n default_filter.html\n embedding_similarity_filter.html\n llm_feature_filter.html\n terminal/\n prompt_session.html\n client.html\n prompt_completer.html\n output.html\n loading.html\n vision/\n vision_manager.html\n prompts/\n prompts.html\n command/\n command.html\n commands/\n redo.html\n run.html\n help.html\n exclude.html\n screenshot.html\n undo.html\n include.html\n context.html\n undoall.html\n commit.html\n talk.html\n agent.html\n viewer.html\n config.html\n clear.html\n search.html\n python_client/\n client.html\n _sources/\n changelog.md.txt\n changelog.rst.txt\n index.rst.txt\n developer/\n mentat.terminal.rst.txt\n mentat.prompts.rst.txt\n mentat.python_client.rst.txt\n modules.rst.txt\n mentat.rst.txt\n mentat.command.rst.txt\n mentat.parsers.rst.txt\n mentat.command.commands.rst.txt\n mentat.vision.rst.txt\n mentat.feature_filters.rst.txt\n user/\n context.rst.txt\n cli_arguments.rst.txt\n configuration.rst.txt\n guides.rst.txt\n commands.rst.txt\n alternative_models.rst.txt\n getting_started.rst.txt\n sdk/\n usage.rst.txt\n module.rst.txt\n index.rst.txt\n _static/\n plus.png\n sphinx_highlight.js\n documentation_options.js\n searchtools.js\n check-solid.svg\n clipboard.min.js\n file.png\n _sphinx_javascript_frameworks_compat.js\n copybutton.js\n language_data.js\n copy-button.svg\n copybutton_funcs.js\n minus.png\n basic.css\n pygments.css\n copybutton.css\n doctools.js\n jquery.js\n css/\n theme.css\n badge_only.css\n fonts/\n lato-bold-italic.woff2\n fontawesome-webfont.svg\n Roboto-Slab-Bold.woff\n lato-normal.woff2\n lato-normal-italic.woff\n Roboto-Slab-Regular.woff2\n lato-bold-italic.woff\n Roboto-Slab-Bold.woff2\n lato-bold.woff\n fontawesome-webfont.woff2\n fontawesome-webfont.ttf\n lato-bold.woff2\n lato-normal.woff\n Roboto-Slab-Regular.woff\n fontawesome-webfont.woff\n lato-normal-italic.woff2\n fontawesome-webfont.eot\n js/\n badge_only.js\n theme.js\n html5shiv.min.js\n html5shiv-printshiv.min.js\n user/\n context.html\n getting_started.html\n configuration.html\n cli_arguments.html\n commands.html\n guides.html\n alternative_models.html\n sdk/\n index.html\n module.html\n usage.html\n doctrees/\n changelog.doctree\n environment.pickle\n index.doctree\n developer/\n mentat.terminal.doctree\n mentat.prompts.doctree\n modules.doctree\n mentat.python_client.doctree\n mentat.doctree\n mentat.command.doctree\n mentat.parsers.doctree\n mentat.command.commands.doctree\n mentat.feature_filters.doctree\n mentat.vision.doctree\n user/\n configuration.doctree\n cli_arguments.doctree\n context.doctree\n guides.doctree\n commands.doctree\n alternative_models.doctree\n getting_started.doctree\n sdk/\n usage.doctree\n module.doctree\n index.doctree\nCONTINUE\n" - }, - { - "role": "assistant", - "content": "function_2" - } -] - -metadata = { - "timestamp": "2024-02-09_01-09-39", - "log_version": "0.2", - "model": "openai/gpt-4-turbo-preview", - "cost": "0.0360400000", - "Note": "I had to explicitly tell the model to use CONTINUE and print. I slightly shortened the summary." -} - - -def function_1(): - import os - - def summarize_directory_structure(startpath): - for root, dirs, files in os.walk(startpath, topdown=True): - level = root.replace(startpath, '').count(os.sep) - indent = ' ' * 4 * (level) - print(f"{indent}{os.path.basename(root)}/") - subindent = ' ' * 4 * (level + 1) - for f in files: - print(f"{subindent}{f}") - - print("Directory structure of 'docs':") - summarize_directory_structure('./docs') - print("CONTINUE") - - -def function_2(): - print("""The 'docs' directory is organized into documentation source files, a Makefile setup for building the documents, and the built documentation itself. Here's a summary: - - 1. Root 'docs' folder contains basic setup files like 'requirements.txt', 'Makefile', and 'make.bat' for building the documentation with Sphinx. - - 2. 'source' folder: Contains the reStructuredText (.rst) source files for the documentation, organized into three main subdirectories: - - 'developer': Contains detailed documents on Mentat's internal workings, such as parsers, command logic, and feature filters. - - 'user': Focuses on user-oriented documentation like getting started guides, command references, and configuration. - - 'sdk': Holds documentation relevant to software development kits, including usage and module interface descriptions. - - 3. 'build' folder: Houses the output of the documentation build process, structured similarly to the source with HTML files instead of .rst. This includes an 'html' directory with 'index.html' as the entry point, and subdirectories for 'developer', 'user', 'sdk', mirroring the source structure but in compiled HTML format. - - 4. Detailed documentation for developers includes sections on the terminal interface, command structure, and Python client API among others. User documentation covers basics like setup and configuration, as well as deeper topics like CLI arguments and alternative models. - - 5. The SDK section includes an 'index.rst', 'usage.rst', and 'module.rst'. - - 6. The 'build/html' directory also includes directories for '_modules', detailing internal Python modules, and '_static' and '_sources', for static web assets and document sources respectively.""") - - -if __name__ == "__main__": - function_2() diff --git a/examples/repo/describe_github_actions.py b/examples/repo/describe_github_actions.py deleted file mode 100644 index 3056873..0000000 --- a/examples/repo/describe_github_actions.py +++ /dev/null @@ -1,83 +0,0 @@ -conversation = [ - { - "role": "system", - "content": "You are a command-line coding assistant called Rawdog that generates and auto-executes Python scripts.\n\nA typical interaction goes like this:\n1. The user gives you a natural language PROMPT.\n2. You:\n i. Determine what needs to be done\n ii. Write a short Python SCRIPT to do it\n iii. Communicate back to the user by printing to the console in that SCRIPT\n3. The compiler checks your SCRIPT using ast.parse() then runs it using exec()\n\nYou'll get to see the output of a script before your next interaction. If you need to review those\noutputs before completing the task, you can print the word \"CONTINUE\" at the end of your SCRIPT.\nThis can be useful for summarizing documents or technical readouts, reading instructions before\ndeciding what to do, or other tasks that require multi-step reasoning.\nA typical 'CONTINUE' interaction looks like this:\n1. The user gives you a natural language PROMPT.\n2. You:\n i. Determine what needs to be done\n ii. Determine that you need to see the output of some subprocess call to complete the task\n iii. Write a short Python SCRIPT to print that and then print the word \"CONTINUE\"\n3. The compiler\n i. Checks and runs your SCRIPT\n ii. Captures the output and appends it to the conversation as \"LAST SCRIPT OUTPUT:\"\n iii. Finds the word \"CONTINUE\" and sends control back to you\n4. You again:\n i. Look at the original PROMPT + the \"LAST SCRIPT OUTPUT:\" to determine what needs to be done\n ii. Write a short Python SCRIPT to do it\n iii. Communicate back to the user by printing to the console in that SCRIPT\n5. The compiler...\n\nWhen your script raises an exception, you'll get to review the error and try again:\n1. The user gives you a natural language PROMPT.\n2. You: Respond with a SCRIPT..\n3. The compiler\n i. Executes your SCRIPT\n ii. Catches an exception\n iii. Adds it to the conversation\n iv. If there are retries left, sends control back to you\n4. You again:\n i. Look at the latest PROMPT, SCRIPT and Error message, determine what caused the error and how to fix it\n ii. Write a short Python SCRIPT to do it\n iii. Communicate back to the user by printing to the console in that SCRIPT\n5. The compiler...\n\nPlease follow these conventions carefully:\n- Decline any tasks that seem dangerous, irreversible, or that you don't understand.\n- Always review the full conversation prior to answering and maintain continuity.\n- If asked for information, just print the information clearly and concisely.\n- If asked to do something, print a concise summary of what you've done as confirmation.\n- If asked a question, respond in a friendly, conversational way. Use programmatically-generated and natural language responses as appropriate.\n- Assume the user would like something concise. For example rather than printing a massive table, filter or summarize it to what's likely of interest.\n- The user will likely not specify exact filenames. Use glob searches when looking for filenames in your SCRIPT.\n- Actively clean up any temporary processes or files you use.\n- When looking through files, use git as available to skip files, and skip hidden files (.env, .git, etc) by default.\n- Let exceptions propagate to the user (rather than catching them in your SCRIPT) so that you can retry.\n- At the user's request, you can inspect and update your configuration file: ~/.rawdog/config.yaml. Changes will take effect after restarting.\n- Feel free to use any common python packages. For example matplotlib, beautifulsoup4, numpy. If the user doesn't have them installed they will be installed automatically with user confirmation.\n- ALWAYS Return your SCRIPT inside of a single pair of ``` delimiters. Only the console output of the first such SCRIPT is visible to the user, so make sure that it's complete and don't bother returning anything else.\n" - }, - { - "role": "system", - "content": "EXAMPLES:\n-------------------------------------------------------------------------------\nPROMPT: Kill the process running on port 3000\n\nSCRIPT:\n```\nimport os\nos.system(\"kill $(lsof -t -i:3000)\")\nprint(\"Process killed\")\n```\n-------------------------------------------------------------------------------\nPROMPT: Rename the photos in this directory with \"nyc\" and their timestamp\n\nSCRIPT:\n```\nimport os\nimport time\nimage_files = [f for f in os.listdir('.') if f.lower().endswith(('.png', '.jpg', '.jpeg'))]\ndef get_name(f):\n timestamp = time.strftime('%Y%m%d_%H%M%S', time.localtime(os.path.getmtime(f)))\n return f\"nyc_{timestamp}{os.path.splitext(f)[1]}\"\n[os.rename(f, get_name(f)) for f in image_files]\nprint(\"Renamed files\")\n```\n-------------------------------------------------------------------------------\nPROMPT: Summarize my essay\n\nSCRIPT:\n```\nimport glob\nfiles = glob.glob(\"*essay*.*\")\nwith open(files[0], \"r\") as f:\n print(f.read())\nprint(\"CONTINUE\")\n```\n\nLAST SCRIPT OUTPUT:\n\nJohn Smith\nEssay 2021-09-01\n...\n\nSCRIPT:\n```\nprint(\"The essay is about...\")\n```\n-------------------------------------------------------------------------------\n" - }, - { - "role": "system", - "content": "Today's date is 2024-02-09.\nThe current working directory is /Users/jakekoenig/mentat, which IS a git repository.\nThe user's operating system is Darwin\n " - }, - { - "role": "user", - "content": "Does this project have github actions set up?" - }, - { - "role": "assistant", - "content": "function_1" - }, - { - "role": "user", - "content": "LAST SCRIPT OUTPUT:\nGitHub Actions are set up with the following workflows: ['release.yml', 'benchmarks.yml', 'lint_and_test.yml']\n" - }, - { - "role": "user", - "content": "What do they do?" - }, - { - "role": "assistant", - "content": "function_2" - } -] - -metadata = { - "timestamp": "2024-02-09_01-03-02", - "log_version": "0.2", - "model": "openai/gpt-4-turbo-preview", - "cost": "0.0154100000" -} - - -def function_1(): - import os - - # Function to check if GitHub Actions are set up in a project - def check_github_actions_setup(): - # Path to the GitHub Actions directory - actions_dir = os.path.join(".github", "workflows") - # Check if the directory exists - if os.path.isdir(actions_dir): - # List files in the GitHub Actions directory - actions_files = os.listdir(actions_dir) - if actions_files: - print("GitHub Actions are set up with the following workflows:", actions_files) - else: - print("GitHub Actions directory is present but no workflows found.") - else: - print("GitHub Actions are not set up.") - - check_github_actions_setup() - - -def function_2(): - import os - - # Define a function to print the contents of GitHub Actions workflow files - def print_workflow_contents(workflows): - for workflow in workflows: - workflow_path = os.path.join(".github", "workflows", workflow) - print(f"Contents of {workflow}:\n") - with open(workflow_path, "r") as file: - print(file.read()) - print("\n" + "-"*80 + "\n") - - # List of workflow files - workflow_files = ['release.yml', 'benchmarks.yml', 'lint_and_test.yml'] - print_workflow_contents(workflow_files) - - -if __name__ == "__main__": - function_2() diff --git a/examples/repo/license_question.py b/examples/repo/license_question.py deleted file mode 100644 index 19aa2ea..0000000 --- a/examples/repo/license_question.py +++ /dev/null @@ -1,59 +0,0 @@ -conversation = [ - { - "role": "system", - "content": "You are a command-line coding assistant called Rawdog that generates and auto-executes Python scripts.\n\nA typical interaction goes like this:\n1. The user gives you a natural language PROMPT.\n2. You:\n i. Determine what needs to be done\n ii. Write a short Python SCRIPT to do it\n iii. Communicate back to the user by printing to the console in that SCRIPT\n3. The compiler checks your SCRIPT using ast.parse() then runs it using exec()\n\nYou'll get to see the output of a script before your next interaction. If you need to review those\noutputs before completing the task, you can print the word \"CONTINUE\" at the end of your SCRIPT.\nThis can be useful for summarizing documents or technical readouts, reading instructions before\ndeciding what to do, or other tasks that require multi-step reasoning.\nA typical 'CONTINUE' interaction looks like this:\n1. The user gives you a natural language PROMPT.\n2. You:\n i. Determine what needs to be done\n ii. Determine that you need to see the output of some subprocess call to complete the task\n iii. Write a short Python SCRIPT to print that and then print the word \"CONTINUE\"\n3. The compiler\n i. Checks and runs your SCRIPT\n ii. Captures the output and appends it to the conversation as \"LAST SCRIPT OUTPUT:\"\n iii. Finds the word \"CONTINUE\" and sends control back to you\n4. You again:\n i. Look at the original PROMPT + the \"LAST SCRIPT OUTPUT:\" to determine what needs to be done\n ii. Write a short Python SCRIPT to do it\n iii. Communicate back to the user by printing to the console in that SCRIPT\n5. The compiler...\n\nWhen your script raises an exception, you'll get to review the error and try again:\n1. The user gives you a natural language PROMPT.\n2. You: Respond with a SCRIPT..\n3. The compiler\n i. Executes your SCRIPT\n ii. Catches an exception\n iii. Adds it to the conversation\n iv. If there are retries left, sends control back to you\n4. You again:\n i. Look at the latest PROMPT, SCRIPT and Error message, determine what caused the error and how to fix it\n ii. Write a short Python SCRIPT to do it\n iii. Communicate back to the user by printing to the console in that SCRIPT\n5. The compiler...\n\nPlease follow these conventions carefully:\n- Decline any tasks that seem dangerous, irreversible, or that you don't understand.\n- Always review the full conversation prior to answering and maintain continuity.\n- If asked for information, just print the information clearly and concisely.\n- If asked to do something, print a concise summary of what you've done as confirmation.\n- If asked a question, respond in a friendly, conversational way. Use programmatically-generated and natural language responses as appropriate.\n- Assume the user would like something concise. For example rather than printing a massive table, filter or summarize it to what's likely of interest.\n- The user will likely not specify exact filenames. Use glob searches when looking for filenames in your SCRIPT.\n- Actively clean up any temporary processes or files you use.\n- When looking through files, use git as available to skip files, and skip hidden files (.env, .git, etc) by default.\n- Let exceptions propagate to the user (rather than catching them in your SCRIPT) so that you can retry.\n- At the user's request, you can inspect and update your configuration file: ~/.rawdog/config.yaml. Changes will take effect after restarting.\n- Feel free to use any common python packages. For example matplotlib, beautifulsoup4, numpy. If the user doesn't have them installed they will be installed automatically with user confirmation.\n- ALWAYS Return your SCRIPT inside of a single pair of ``` delimiters. Only the console output of the first such SCRIPT is visible to the user, so make sure that it's complete and don't bother returning anything else.\n" - }, - { - "role": "system", - "content": "EXAMPLES:\n-------------------------------------------------------------------------------\nPROMPT: Kill the process running on port 3000\n\nSCRIPT:\n```\nimport os\nos.system(\"kill $(lsof -t -i:3000)\")\nprint(\"Process killed\")\n```\n-------------------------------------------------------------------------------\nPROMPT: Rename the photos in this directory with \"nyc\" and their timestamp\n\nSCRIPT:\n```\nimport os\nimport time\nimage_files = [f for f in os.listdir('.') if f.lower().endswith(('.png', '.jpg', '.jpeg'))]\ndef get_name(f):\n timestamp = time.strftime('%Y%m%d_%H%M%S', time.localtime(os.path.getmtime(f)))\n return f\"nyc_{timestamp}{os.path.splitext(f)[1]}\"\n[os.rename(f, get_name(f)) for f in image_files]\nprint(\"Renamed files\")\n```\n-------------------------------------------------------------------------------\nPROMPT: Summarize my essay\n\nSCRIPT:\n```\nimport glob\nfiles = glob.glob(\"*essay*.*\")\nwith open(files[0], \"r\") as f:\n print(f.read())\nprint(\"CONTINUE\")\n```\n\nLAST SCRIPT OUTPUT:\n\nJohn Smith\nEssay 2021-09-01\n...\n\nSCRIPT:\n```\nprint(\"The essay is about...\")\n```\n-------------------------------------------------------------------------------\n" - }, - { - "role": "system", - "content": "Today's date is 2024-02-09.\nThe current working directory is /Users/jakekoenig/mentat, which IS a git repository.\nThe user's operating system is Darwin\n " - }, - { - "role": "user", - "content": "Does the license allow me to modify and sell this?" - }, - { - "role": "assistant", - "content": "function_1" - }, - { - "role": "user", - "content": "LAST SCRIPT OUTPUT:\n Apache License\n Version 2.0, January 2004\n http://www.apache.org/licenses/\n\n TERMS AND CONDITIONS FOR USE, REPRODUCTION, AND DISTRIBUTION\n\n 1. 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However, in accepting such obligations, You may act only\n on Your own behalf and on Your sole responsibility, not on behalf\n of any other Contributor, and only if You agree to indemnify,\n defend, and hold each Contributor harmless for any liability\n incurred by, or claims asserted against, such Contributor by reason\n of your accepting any such warranty or additional liability.\n\n END OF TERMS AND CONDITIONS\n\n APPENDIX: How to apply the Apache License to your work.\n\n To apply the Apache License to your work, attach the following\n boilerplate notice, with the fields enclosed by brackets \"[]\"\n replaced with your own identifying information. (Don't include\n the brackets!) The text should be enclosed in the appropriate\n comment syntax for the file format. We also recommend that a\n file or class name and description of purpose be included on the\n same \"printed page\" as the copyright notice for easier\n identification within third-party archives.\n\n Copyright 2024 Abante AI\n\n Licensed under the Apache License, Version 2.0 (the \"License\");\n you may not use this file except in compliance with the License.\n You may obtain a copy of the License at\n\n http://www.apache.org/licenses/LICENSE-2.0\n\n Unless required by applicable law or agreed to in writing, software\n distributed under the License is distributed on an \"AS IS\" BASIS,\n WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n See the License for the specific language governing permissions and\n limitations under the License.\n\n\nCONTINUE\n" - }, - { - "role": "assistant", - "content": "function_2" - } -] - -metadata = { - "timestamp": "2024-02-09_01-16-09", - "log_version": "0.2", - "model": "openai/gpt-4-turbo-preview", - "cost": "0.0357400000", - "Note": "I had to explicitily tell the model to use print the second time" -} - - -def function_1(): - import os - - license_files = [f for f in os.listdir(".") if "license" in f.lower() or "licence" in f.lower()] - if license_files: - with open(license_files[0], "r") as file: - license_content = file.read() - print(license_content) - else: - print("No license file found.") - print("CONTINUE") - - -def function_2(): - print("Yes, the Apache License 2.0 allows you to modify and sell the software, provided you comply with the terms and conditions outlined. These include, but are not limited to, providing proper attribution, including a copy of the License, stating significant changes if any, and distributing the License with the work or derivative work.") - - -if __name__ == "__main__": - function_2() diff --git a/examples/repo/plot_libraries.py b/examples/repo/plot_libraries.py deleted file mode 100644 index 7765fe0..0000000 --- a/examples/repo/plot_libraries.py +++ /dev/null @@ -1,43 +0,0 @@ -conversation = [ - { - "role": "system", - "content": "You are a command-line coding assistant called Rawdog that generates and auto-executes Python scripts.\n\nA typical interaction goes like this:\n1. The user gives you a natural language PROMPT.\n2. You:\n i. Determine what needs to be done\n ii. Write a short Python SCRIPT to do it\n iii. Communicate back to the user by printing to the console in that SCRIPT\n3. The compiler checks your SCRIPT using ast.parse() then runs it using exec()\n\nYou'll get to see the output of a script before your next interaction. If you need to review those\noutputs before completing the task, you can print the word \"CONTINUE\" at the end of your SCRIPT.\nThis can be useful for summarizing documents or technical readouts, reading instructions before\ndeciding what to do, or other tasks that require multi-step reasoning.\nA typical 'CONTINUE' interaction looks like this:\n1. The user gives you a natural language PROMPT.\n2. You:\n i. Determine what needs to be done\n ii. Determine that you need to see the output of some subprocess call to complete the task\n iii. Write a short Python SCRIPT to print that and then print the word \"CONTINUE\"\n3. The compiler\n i. Checks and runs your SCRIPT\n ii. Captures the output and appends it to the conversation as \"LAST SCRIPT OUTPUT:\"\n iii. Finds the word \"CONTINUE\" and sends control back to you\n4. You again:\n i. Look at the original PROMPT + the \"LAST SCRIPT OUTPUT:\" to determine what needs to be done\n ii. Write a short Python SCRIPT to do it\n iii. Communicate back to the user by printing to the console in that SCRIPT\n5. The compiler...\n\nWhen your script raises an exception, you'll get to review the error and try again:\n1. The user gives you a natural language PROMPT.\n2. You: Respond with a SCRIPT..\n3. The compiler\n i. Executes your SCRIPT\n ii. Catches an exception\n iii. Adds it to the conversation\n iv. If there are retries left, sends control back to you\n4. You again:\n i. Look at the latest PROMPT, SCRIPT and Error message, determine what caused the error and how to fix it\n ii. Write a short Python SCRIPT to do it\n iii. Communicate back to the user by printing to the console in that SCRIPT\n5. The compiler...\n\nPlease follow these conventions carefully:\n- Decline any tasks that seem dangerous, irreversible, or that you don't understand.\n- Always review the full conversation prior to answering and maintain continuity.\n- If asked for information, just print the information clearly and concisely.\n- If asked to do something, print a concise summary of what you've done as confirmation.\n- If asked a question, respond in a friendly, conversational way. Use programmatically-generated and natural language responses as appropriate.\n- Assume the user would like something concise. For example rather than printing a massive table, filter or summarize it to what's likely of interest.\n- The user will likely not specify exact filenames. Use glob searches when looking for filenames in your SCRIPT.\n- Actively clean up any temporary processes or files you use.\n- When looking through files, use git as available to skip files, and skip hidden files (.env, .git, etc) by default.\n- Let exceptions propagate to the user (rather than catching them in your SCRIPT) so that you can retry.\n- At the user's request, you can inspect and update your configuration file: ~/.rawdog/config.yaml. Changes will take effect after restarting.\n- Feel free to use any common python packages. For example matplotlib, beautifulsoup4, numpy. If the user doesn't have them installed they will be installed automatically with user confirmation.\n- ALWAYS Return your SCRIPT inside of a single pair of ``` delimiters. Only the console output of the first such SCRIPT is visible to the user, so make sure that it's complete and don't bother returning anything else.\n" - }, - { - "role": "system", - "content": "EXAMPLES:\n-------------------------------------------------------------------------------\nPROMPT: Kill the process running on port 3000\n\nSCRIPT:\n```\nimport os\nos.system(\"kill $(lsof -t -i:3000)\")\nprint(\"Process killed\")\n```\n-------------------------------------------------------------------------------\nPROMPT: Rename the photos in this directory with \"nyc\" and their timestamp\n\nSCRIPT:\n```\nimport os\nimport time\nimage_files = [f for f in os.listdir('.') if f.lower().endswith(('.png', '.jpg', '.jpeg'))]\ndef get_name(f):\n timestamp = time.strftime('%Y%m%d_%H%M%S', time.localtime(os.path.getmtime(f)))\n return f\"nyc_{timestamp}{os.path.splitext(f)[1]}\"\n[os.rename(f, get_name(f)) for f in image_files]\nprint(\"Renamed files\")\n```\n-------------------------------------------------------------------------------\nPROMPT: Summarize my essay\n\nSCRIPT:\n```\nimport glob\nfiles = glob.glob(\"*essay*.*\")\nwith open(files[0], \"r\") as f:\n print(f.read())\nprint(\"CONTINUE\")\n```\n\nLAST SCRIPT OUTPUT:\n\nJohn Smith\nEssay 2021-09-01\n...\n\nSCRIPT:\n```\nprint(\"The essay is about...\")\n```\n-------------------------------------------------------------------------------\n" - }, - { - "role": "system", - "content": "Today's date is 2024-02-09.\nThe current working directory is /Users/jakekoenig/rawdog, which IS a git repository.\nThe user's operating system is Darwin\n " - }, - { - "role": "user", - "content": "Does the requirements file include any libraries for generating plots?" - }, - { - "role": "assistant", - "content": "function_1" - } -] - -metadata = { - "timestamp": "2024-02-09_01-39-53", - "log_version": "0.2", - "model": "openai/gpt-4-turbo-preview", - "cost": "0.0131000000" -} - - -def function_1(): - with open("requirements.txt", "r") as f: - requirements = f.read() - plotting_libs = any(lib in requirements for lib in ["matplotlib", "plotly", "seaborn", "bokeh", "ggplot"]) - if plotting_libs: - print("Yes, the requirements file includes libraries for generating plots.") - else: - print("No, the requirements file does not include libraries for generating plots.") - - -if __name__ == "__main__": - function_1() diff --git a/examples/repo/summarize_git_diff.py b/examples/repo/summarize_git_diff.py deleted file mode 100644 index 49e7d10..0000000 --- a/examples/repo/summarize_git_diff.py +++ /dev/null @@ -1,56 +0,0 @@ -conversation = [ - { - "role": "system", - "content": "You are a command-line coding assistant called Rawdog that generates and auto-executes Python scripts.\n\nA typical interaction goes like this:\n1. The user gives you a natural language PROMPT.\n2. You:\n i. Determine what needs to be done\n ii. Write a short Python SCRIPT to do it\n iii. Communicate back to the user by printing to the console in that SCRIPT\n3. The compiler checks your SCRIPT using ast.parse() then runs it using exec()\n\nYou'll get to see the output of a script before your next interaction. If you need to review those\noutputs before completing the task, you can print the word \"CONTINUE\" at the end of your SCRIPT.\nThis can be useful for summarizing documents or technical readouts, reading instructions before\ndeciding what to do, or other tasks that require multi-step reasoning.\nA typical 'CONTINUE' interaction looks like this:\n1. The user gives you a natural language PROMPT.\n2. You:\n i. Determine what needs to be done\n ii. Determine that you need to see the output of some subprocess call to complete the task\n iii. Write a short Python SCRIPT to print that and then print the word \"CONTINUE\"\n3. The compiler\n i. Checks and runs your SCRIPT\n ii. Captures the output and appends it to the conversation as \"LAST SCRIPT OUTPUT:\"\n iii. Finds the word \"CONTINUE\" and sends control back to you\n4. You again:\n i. Look at the original PROMPT + the \"LAST SCRIPT OUTPUT:\" to determine what needs to be done\n ii. Write a short Python SCRIPT to do it\n iii. Communicate back to the user by printing to the console in that SCRIPT\n5. The compiler...\n\nWhen your script raises an exception, you'll get to review the error and try again:\n1. The user gives you a natural language PROMPT.\n2. You: Respond with a SCRIPT..\n3. The compiler\n i. Executes your SCRIPT\n ii. Catches an exception\n iii. Adds it to the conversation\n iv. If there are retries left, sends control back to you\n4. You again:\n i. Look at the latest PROMPT, SCRIPT and Error message, determine what caused the error and how to fix it\n ii. Write a short Python SCRIPT to do it\n iii. Communicate back to the user by printing to the console in that SCRIPT\n5. The compiler...\n\nPlease follow these conventions carefully:\n- Decline any tasks that seem dangerous, irreversible, or that you don't understand.\n- Always review the full conversation prior to answering and maintain continuity.\n- If asked for information, just print the information clearly and concisely.\n- If asked to do something, print a concise summary of what you've done as confirmation.\n- If asked a question, respond in a friendly, conversational way. Use programmatically-generated and natural language responses as appropriate.\n- Assume the user would like something concise. For example rather than printing a massive table, filter or summarize it to what's likely of interest.\n- The user will likely not specify exact filenames. Use glob searches when looking for filenames in your SCRIPT.\n- Actively clean up any temporary processes or files you use.\n- When looking through files, use git as available to skip files, and skip hidden files (.env, .git, etc) by default.\n- Let exceptions propagate to the user (rather than catching them in your SCRIPT) so that you can retry.\n- At the user's request, you can inspect and update your configuration file: ~/.rawdog/config.yaml. Changes will take effect after restarting.\n- Feel free to use any common python packages. For example matplotlib, beautifulsoup4, numpy. If the user doesn't have them installed they will be installed automatically with user confirmation.\n- ALWAYS Return your SCRIPT inside of a single pair of ``` delimiters. Only the console output of the first such SCRIPT is visible to the user, so make sure that it's complete and don't bother returning anything else.\n" - }, - { - "role": "system", - "content": "EXAMPLES:\n-------------------------------------------------------------------------------\nPROMPT: Kill the process running on port 3000\n\nSCRIPT:\n```\nimport os\nos.system(\"kill $(lsof -t -i:3000)\")\nprint(\"Process killed\")\n```\n-------------------------------------------------------------------------------\nPROMPT: Rename the photos in this directory with \"nyc\" and their timestamp\n\nSCRIPT:\n```\nimport os\nimport time\nimage_files = [f for f in os.listdir('.') if f.lower().endswith(('.png', '.jpg', '.jpeg'))]\ndef get_name(f):\n timestamp = time.strftime('%Y%m%d_%H%M%S', time.localtime(os.path.getmtime(f)))\n return f\"nyc_{timestamp}{os.path.splitext(f)[1]}\"\n[os.rename(f, get_name(f)) for f in image_files]\nprint(\"Renamed files\")\n```\n-------------------------------------------------------------------------------\nPROMPT: Summarize my essay\n\nSCRIPT:\n```\nimport glob\nfiles = glob.glob(\"*essay*.*\")\nwith open(files[0], \"r\") as f:\n print(f.read())\nprint(\"CONTINUE\")\n```\n\nLAST SCRIPT OUTPUT:\n\nJohn Smith\nEssay 2021-09-01\n...\n\nSCRIPT:\n```\nprint(\"The essay is about...\")\n```\n-------------------------------------------------------------------------------\n" - }, - { - "role": "system", - "content": "Today's date is 2024-02-09.\nThe current working directory is /Users/biobootloader/code/calculator, which IS a git repository.\nThe user's operating system is Darwin\n " - }, - { - "role": "user", - "content": "summarize the git diff" - }, - { - "role": "assistant", - "content": "function_1" - }, - { - "role": "user", - "content": "LAST SCRIPT OUTPUT:\ndiff --git a/hello_script.py b/hello_script.py\nindex fe57e1f..d0872b1 100644\n--- a/hello_script.py\n+++ b/hello_script.py\n@@ -1 +1,2 @@\n-print(\"Hello, user!\")\n\\ No newline at end of file\n+name = input(\"What is your name? \")\n+print(f\"Hello, {name}!\")\n\nCONTINUE\n" - }, - { - "role": "assistant", - "content": "function_2" - } -] - -metadata = { - "timestamp": "2024-02-09_12-25-50", - "log_version": "0.2", - "model": "gpt-4-turbo-preview", - "cost": "0.0131800000" -} - - -def function_1(): - import subprocess - - # Capture the output of git diff so I can see it to write the summary - diff_output = subprocess.check_output(["git", "diff"], universal_newlines=True) - print(diff_output) - print("CONTINUE") - - -def function_2(): - print( - "'hello_script.py' was modified. It now prompts the user for their name and uses it when it says hello." - ) - - -if __name__ == "__main__": - function_2() diff --git a/examples/simple/cat.py b/examples/simple/cat.py deleted file mode 100644 index d559457..0000000 --- a/examples/simple/cat.py +++ /dev/null @@ -1,38 +0,0 @@ -conversation = [ - { - "role": "system", - "content": "You are a command-line coding assistant called Rawdog that generates and auto-executes Python scripts.\n\nA typical interaction goes like this:\n1. The user gives you a natural language PROMPT.\n2. You:\n i. Determine what needs to be done\n ii. Write a short Python SCRIPT to do it\n iii. Communicate back to the user by printing to the console in that SCRIPT\n3. The compiler checks your SCRIPT using ast.parse() then runs it using exec()\n\nYou'll get to see the output of a script before your next interaction. If you need to review those\noutputs before completing the task, you can print the word \"CONTINUE\" at the end of your SCRIPT.\nThis can be useful for summarizing documents or technical readouts, reading instructions before\ndeciding what to do, or other tasks that require multi-step reasoning.\nA typical 'CONTINUE' interaction looks like this:\n1. The user gives you a natural language PROMPT.\n2. You:\n i. Determine what needs to be done\n ii. Determine that you need to see the output of some subprocess call to complete the task\n iii. Write a short Python SCRIPT to print that and then print the word \"CONTINUE\"\n3. The compiler\n i. Checks and runs your SCRIPT\n ii. Captures the output and appends it to the conversation as \"LAST SCRIPT OUTPUT:\"\n iii. Finds the word \"CONTINUE\" and sends control back to you\n4. You again:\n i. Look at the original PROMPT + the \"LAST SCRIPT OUTPUT:\" to determine what needs to be done\n ii. Write a short Python SCRIPT to do it\n iii. Communicate back to the user by printing to the console in that SCRIPT\n5. The compiler...\n\nPlease follow these conventions carefully:\n- Decline any tasks that seem dangerous, irreversible, or that you don't understand.\n- Always review the full conversation prior to answering and maintain continuity.\n- If asked for information, just print the information clearly and concisely.\n- If asked to do something, print a concise summary of what you've done as confirmation.\n- If asked a question, respond in a friendly, conversational way. Use programmatically-generated and natural language responses as appropriate.\n- If you need clarification, return a SCRIPT that prints your question. In the next interaction, continue based on the user's response.\n- Assume the user would like something concise. For example rather than printing a massive table, filter or summarize it to what's likely of interest.\n- Actively clean up any temporary processes or files you use.\n- When looking through files, use git as available to skip files, and skip hidden files (.env, .git, etc) by default.\n- At the user's request, you can inspect and update your configuration file: ~/.rawdog/config.yaml. Changes will take effect after restarting.\n- Feel free to use any common python packages. For example matplotlib, beautifulsoup4, numpy. If the user doesn't have them installed they will be installed automatically with user confirmation.\n- ALWAYS Return your SCRIPT inside of a single pair of ``` delimiters. Only the console output of the first such SCRIPT is visible to the user, so make sure that it's complete and don't bother returning anything else.\n" - }, - { - "role": "system", - "content": "EXAMPLES:\n-------------------------------------------------------------------------------\nPROMPT: Kill the process running on port 3000\n\nSCRIPT:\n```\nimport os\nos.system(\"kill $(lsof -t -i:3000)\")\nprint(\"Process killed\")\n```\n-------------------------------------------------------------------------------\nPROMPT: Rename the photos in this directory with \"nyc\" and their timestamp\n\nSCRIPT:\n```\nimport os\nimport time\nimage_files = [f for f in os.listdir('.') if f.lower().endswith(('.png', '.jpg', '.jpeg'))]\ndef get_name(f):\n timestamp = time.strftime('%Y%m%d_%H%M%S', time.localtime(os.path.getmtime(f)))\n return f\"nyc_{timestamp}{os.path.splitext(f)[1]}\"\n[os.rename(f, get_name(f)) for f in image_files]\nprint(\"Renamed files\")\n```\n-------------------------------------------------------------------------------\nPROMPT: Summarize my essay\n\nSCRIPT:\n```\nimport glob\nfiles = glob.glob(\"*essay*.*\")\nwith open(files[0], \"r\") as f:\n print(f.read())\nprint(\"CONTINUE\")\n```\n\nLAST SCRIPT OUTPUT:\n\nJohn Smith\nEssay 2021-09-01\n...\n\nSCRIPT:\n```\nprint(\"The essay is about...\")\n```\n-------------------------------------------------------------------------------\n" - }, - { - "role": "system", - "content": "Today's date is 2024-02-14 13:32:19.\nThe current working directory is /Users/jakekoenig/rawdog, which IS a git repository.\nThe user's operating system is Darwin.\nThe last commit message is: Pip install not found modules (#61)\n\nIf the llm tries to use a module that cannot be found then prompt the\r\nuser to install it with pip.\r\nMisc:\r\n* Moved execution specific utils from utils to execute_script\r\n* Removed almost all the requirements\r\n* Give errors from the script to the llm (was broken by move to subprocess)\r\n\r\nCo-authored-by: biobootloader <128252497+biobootloader@users.noreply.github.com>\r\n\r\n---------\r\n\r\nCo-authored-by: biobootloader <128252497+biobootloader@users.noreply.github.com>" - }, - { - "role": "user", - "content": "cat requirements.txt" - }, - { - "role": "assistant", - "content": "function_1" - } -] - -metadata = { - "timestamp": "2024-02-14_13-32-44", - "log_version": 0.2, - "model": "gpt-4", - "cost": "0.0478800000" -} - - -def function_1(): - with open("requirements.txt", "r") as f: - print(f.read()) - - -if __name__ == "__main__": - function_1() diff --git a/examples/simple/cwd.py b/examples/simple/cwd.py deleted file mode 100644 index f5c4596..0000000 --- a/examples/simple/cwd.py +++ /dev/null @@ -1,38 +0,0 @@ -conversation = [ - { - "role": "system", - "content": "You are a command-line coding assistant called Rawdog that generates and auto-executes Python scripts.\n\nA typical interaction goes like this:\n1. The user gives you a natural language PROMPT.\n2. You:\n i. Determine what needs to be done\n ii. Write a short Python SCRIPT to do it\n iii. Communicate back to the user by printing to the console in that SCRIPT\n3. The compiler checks your SCRIPT using ast.parse() then runs it using exec()\n\nYou'll get to see the output of a script before your next interaction. If you need to review those\noutputs before completing the task, you can print the word \"CONTINUE\" at the end of your SCRIPT.\nThis can be useful for summarizing documents or technical readouts, reading instructions before\ndeciding what to do, or other tasks that require multi-step reasoning.\nA typical 'CONTINUE' interaction looks like this:\n1. The user gives you a natural language PROMPT.\n2. You:\n i. Determine what needs to be done\n ii. Determine that you need to see the output of some subprocess call to complete the task\n iii. Write a short Python SCRIPT to print that and then print the word \"CONTINUE\"\n3. The compiler\n i. Checks and runs your SCRIPT\n ii. Captures the output and appends it to the conversation as \"LAST SCRIPT OUTPUT:\"\n iii. Finds the word \"CONTINUE\" and sends control back to you\n4. You again:\n i. Look at the original PROMPT + the \"LAST SCRIPT OUTPUT:\" to determine what needs to be done\n ii. Write a short Python SCRIPT to do it\n iii. Communicate back to the user by printing to the console in that SCRIPT\n5. The compiler...\n\nPlease follow these conventions carefully:\n- Decline any tasks that seem dangerous, irreversible, or that you don't understand.\n- Always review the full conversation prior to answering and maintain continuity.\n- If asked for information, just print the information clearly and concisely.\n- If asked to do something, print a concise summary of what you've done as confirmation.\n- If asked a question, respond in a friendly, conversational way. Use programmatically-generated and natural language responses as appropriate.\n- If you need clarification, return a SCRIPT that prints your question. In the next interaction, continue based on the user's response.\n- Assume the user would like something concise. For example rather than printing a massive table, filter or summarize it to what's likely of interest.\n- Actively clean up any temporary processes or files you use.\n- When looking through files, use git as available to skip files, and skip hidden files (.env, .git, etc) by default.\n- At the user's request, you can inspect and update your configuration file: ~/.rawdog/config.yaml. Changes will take effect after restarting.\n- Feel free to use any common python packages. For example matplotlib, beautifulsoup4, numpy. If the user doesn't have them installed they will be installed automatically with user confirmation.\n- ALWAYS Return your SCRIPT inside of a single pair of ``` delimiters. Only the console output of the first such SCRIPT is visible to the user, so make sure that it's complete and don't bother returning anything else.\n" - }, - { - "role": "system", - "content": "EXAMPLES:\n-------------------------------------------------------------------------------\nPROMPT: Kill the process running on port 3000\n\nSCRIPT:\n```\nimport os\nos.system(\"kill $(lsof -t -i:3000)\")\nprint(\"Process killed\")\n```\n-------------------------------------------------------------------------------\nPROMPT: Rename the photos in this directory with \"nyc\" and their timestamp\n\nSCRIPT:\n```\nimport os\nimport time\nimage_files = [f for f in os.listdir('.') if f.lower().endswith(('.png', '.jpg', '.jpeg'))]\ndef get_name(f):\n timestamp = time.strftime('%Y%m%d_%H%M%S', time.localtime(os.path.getmtime(f)))\n return f\"nyc_{timestamp}{os.path.splitext(f)[1]}\"\n[os.rename(f, get_name(f)) for f in image_files]\nprint(\"Renamed files\")\n```\n-------------------------------------------------------------------------------\nPROMPT: Summarize my essay\n\nSCRIPT:\n```\nimport glob\nfiles = glob.glob(\"*essay*.*\")\nwith open(files[0], \"r\") as f:\n print(f.read())\nprint(\"CONTINUE\")\n```\n\nLAST SCRIPT OUTPUT:\n\nJohn Smith\nEssay 2021-09-01\n...\n\nSCRIPT:\n```\nprint(\"The essay is about...\")\n```\n-------------------------------------------------------------------------------\n" - }, - { - "role": "system", - "content": "Today's date is 2024-02-14 13:27:51.\nThe current working directory is /Users/jakekoenig/rawdog, which IS a git repository.\nThe user's operating system is Darwin.\nThe last commit message is: Pip install not found modules (#61)\n\nIf the llm tries to use a module that cannot be found then prompt the\r\nuser to install it with pip.\r\nMisc:\r\n* Moved execution specific utils from utils to execute_script\r\n* Removed almost all the requirements\r\n* Give errors from the script to the llm (was broken by move to subprocess)\r\n\r\nCo-authored-by: biobootloader <128252497+biobootloader@users.noreply.github.com>\r\n\r\n---------\r\n\r\nCo-authored-by: biobootloader <128252497+biobootloader@users.noreply.github.com>" - }, - { - "role": "user", - "content": "cwd" - }, - { - "role": "assistant", - "content": "function_1" - } -] - -metadata = { - "timestamp": "2024-02-14_13-27-55", - "log_version": 0.2, - "model": "gpt-4", - "cost": "0.0471600000" -} - - -def function_1(): - import os - print(os.getcwd()) - - -if __name__ == "__main__": - function_1() diff --git a/examples/simple/grep.py b/examples/simple/grep.py deleted file mode 100644 index 9f57295..0000000 --- a/examples/simple/grep.py +++ /dev/null @@ -1,60 +0,0 @@ -conversation = [ - { - "role": "system", - "content": "You are a command-line coding assistant called Rawdog that generates and auto-executes Python scripts.\n\nA typical interaction goes like this:\n1. The user gives you a natural language PROMPT.\n2. You:\n i. Determine what needs to be done\n ii. Write a short Python SCRIPT to do it\n iii. Communicate back to the user by printing to the console in that SCRIPT\n3. The compiler checks your SCRIPT using ast.parse() then runs it using exec()\n\nYou'll get to see the output of a script before your next interaction. If you need to review those\noutputs before completing the task, you can print the word \"CONTINUE\" at the end of your SCRIPT.\nThis can be useful for summarizing documents or technical readouts, reading instructions before\ndeciding what to do, or other tasks that require multi-step reasoning.\nA typical 'CONTINUE' interaction looks like this:\n1. The user gives you a natural language PROMPT.\n2. You:\n i. Determine what needs to be done\n ii. Determine that you need to see the output of some subprocess call to complete the task\n iii. Write a short Python SCRIPT to print that and then print the word \"CONTINUE\"\n3. The compiler\n i. Checks and runs your SCRIPT\n ii. Captures the output and appends it to the conversation as \"LAST SCRIPT OUTPUT:\"\n iii. Finds the word \"CONTINUE\" and sends control back to you\n4. You again:\n i. Look at the original PROMPT + the \"LAST SCRIPT OUTPUT:\" to determine what needs to be done\n ii. Write a short Python SCRIPT to do it\n iii. Communicate back to the user by printing to the console in that SCRIPT\n5. The compiler...\n\nPlease follow these conventions carefully:\n- Decline any tasks that seem dangerous, irreversible, or that you don't understand.\n- Always review the full conversation prior to answering and maintain continuity.\n- If asked for information, just print the information clearly and concisely.\n- If asked to do something, print a concise summary of what you've done as confirmation.\n- If asked a question, respond in a friendly, conversational way. Use programmatically-generated and natural language responses as appropriate.\n- If you need clarification, return a SCRIPT that prints your question. In the next interaction, continue based on the user's response.\n- Assume the user would like something concise. For example rather than printing a massive table, filter or summarize it to what's likely of interest.\n- Actively clean up any temporary processes or files you use.\n- When looking through files, use git as available to skip files, and skip hidden files (.env, .git, etc) by default.\n- At the user's request, you can inspect and update your configuration file: ~/.rawdog/config.yaml. Changes will take effect after restarting.\n- Feel free to use any common python packages. For example matplotlib, beautifulsoup4, numpy. If the user doesn't have them installed they will be installed automatically with user confirmation.\n- ALWAYS Return your SCRIPT inside of a single pair of ``` delimiters. Only the console output of the first such SCRIPT is visible to the user, so make sure that it's complete and don't bother returning anything else.\n" - }, - { - "role": "system", - "content": "EXAMPLES:\n-------------------------------------------------------------------------------\nPROMPT: Kill the process running on port 3000\n\nSCRIPT:\n```\nimport os\nos.system(\"kill $(lsof -t -i:3000)\")\nprint(\"Process killed\")\n```\n-------------------------------------------------------------------------------\nPROMPT: Rename the photos in this directory with \"nyc\" and their timestamp\n\nSCRIPT:\n```\nimport os\nimport time\nimage_files = [f for f in os.listdir('.') if f.lower().endswith(('.png', '.jpg', '.jpeg'))]\ndef get_name(f):\n timestamp = time.strftime('%Y%m%d_%H%M%S', time.localtime(os.path.getmtime(f)))\n return f\"nyc_{timestamp}{os.path.splitext(f)[1]}\"\n[os.rename(f, get_name(f)) for f in image_files]\nprint(\"Renamed files\")\n```\n-------------------------------------------------------------------------------\nPROMPT: Summarize my essay\n\nSCRIPT:\n```\nimport glob\nfiles = glob.glob(\"*essay*.*\")\nwith open(files[0], \"r\") as f:\n print(f.read())\nprint(\"CONTINUE\")\n```\n\nLAST SCRIPT OUTPUT:\n\nJohn Smith\nEssay 2021-09-01\n...\n\nSCRIPT:\n```\nprint(\"The essay is about...\")\n```\n-------------------------------------------------------------------------------\n" - }, - { - "role": "system", - "content": "Today's date is 2024-02-14 13:28:17.\nThe current working directory is /Users/jakekoenig/rawdog, which IS a git repository.\nThe user's operating system is Darwin.\nThe last commit message is: Pip install not found modules (#61)\n\nIf the llm tries to use a module that cannot be found then prompt the\r\nuser to install it with pip.\r\nMisc:\r\n* Moved execution specific utils from utils to execute_script\r\n* Removed almost all the requirements\r\n* Give errors from the script to the llm (was broken by move to subprocess)\r\n\r\nCo-authored-by: biobootloader <128252497+biobootloader@users.noreply.github.com>\r\n\r\n---------\r\n\r\nCo-authored-by: biobootloader <128252497+biobootloader@users.noreply.github.com>" - }, - { - "role": "user", - "content": "grep -r execute_script src" - }, - { - "role": "assistant", - "content": "function_1" - } -] - -metadata = { - "timestamp": "2024-02-14_13-29-52", - "log_version": 0.2, - "model": "gpt-4", - "cost": "0.0591300000" -} - - -def function_1(): - import os - import re - - # define the string to find - find_str = "execute_script" - - # starting point - dir_path = "src" - - matched_files = [] - for folder_name, _, file_names in os.walk(dir_path): - for file_name in file_names: - if file_name.endswith('.py'): - full_path = os.path.join(folder_name, file_name) - with open(full_path, 'r') as file: - content = file.read() - # search for the string - if re.search(find_str, content): - matched_files.append(full_path) - - if matched_files: - print(f'The term "{find_str}" was found in following files:\n', '\n'.join(matched_files)) - else: - print(f'The term "{find_str}" was not found in any files inside {dir_path}.') - - -if __name__ == "__main__": - function_1() diff --git a/examples/simple/ls.py b/examples/simple/ls.py deleted file mode 100644 index f7aa055..0000000 --- a/examples/simple/ls.py +++ /dev/null @@ -1,40 +0,0 @@ -conversation = [ - { - "role": "system", - "content": "You are a command-line coding assistant called Rawdog that generates and auto-executes Python scripts.\n\nA typical interaction goes like this:\n1. The user gives you a natural language PROMPT.\n2. You:\n i. Determine what needs to be done\n ii. Write a short Python SCRIPT to do it\n iii. Communicate back to the user by printing to the console in that SCRIPT\n3. The compiler checks your SCRIPT using ast.parse() then runs it using exec()\n\nYou'll get to see the output of a script before your next interaction. If you need to review those\noutputs before completing the task, you can print the word \"CONTINUE\" at the end of your SCRIPT.\nThis can be useful for summarizing documents or technical readouts, reading instructions before\ndeciding what to do, or other tasks that require multi-step reasoning.\nA typical 'CONTINUE' interaction looks like this:\n1. The user gives you a natural language PROMPT.\n2. You:\n i. Determine what needs to be done\n ii. Determine that you need to see the output of some subprocess call to complete the task\n iii. Write a short Python SCRIPT to print that and then print the word \"CONTINUE\"\n3. The compiler\n i. Checks and runs your SCRIPT\n ii. Captures the output and appends it to the conversation as \"LAST SCRIPT OUTPUT:\"\n iii. Finds the word \"CONTINUE\" and sends control back to you\n4. You again:\n i. Look at the original PROMPT + the \"LAST SCRIPT OUTPUT:\" to determine what needs to be done\n ii. Write a short Python SCRIPT to do it\n iii. Communicate back to the user by printing to the console in that SCRIPT\n5. The compiler...\n\nPlease follow these conventions carefully:\n- Decline any tasks that seem dangerous, irreversible, or that you don't understand.\n- Always review the full conversation prior to answering and maintain continuity.\n- If asked for information, just print the information clearly and concisely.\n- If asked to do something, print a concise summary of what you've done as confirmation.\n- If asked a question, respond in a friendly, conversational way. Use programmatically-generated and natural language responses as appropriate.\n- If you need clarification, return a SCRIPT that prints your question. In the next interaction, continue based on the user's response.\n- Assume the user would like something concise. For example rather than printing a massive table, filter or summarize it to what's likely of interest.\n- Actively clean up any temporary processes or files you use.\n- When looking through files, use git as available to skip files, and skip hidden files (.env, .git, etc) by default.\n- At the user's request, you can inspect and update your configuration file: ~/.rawdog/config.yaml. Changes will take effect after restarting.\n- Feel free to use any common python packages. For example matplotlib, beautifulsoup4, numpy. If the user doesn't have them installed they will be installed automatically with user confirmation.\n- ALWAYS Return your SCRIPT inside of a single pair of ``` delimiters. Only the console output of the first such SCRIPT is visible to the user, so make sure that it's complete and don't bother returning anything else.\n" - }, - { - "role": "system", - "content": "EXAMPLES:\n-------------------------------------------------------------------------------\nPROMPT: Kill the process running on port 3000\n\nSCRIPT:\n```\nimport os\nos.system(\"kill $(lsof -t -i:3000)\")\nprint(\"Process killed\")\n```\n-------------------------------------------------------------------------------\nPROMPT: Rename the photos in this directory with \"nyc\" and their timestamp\n\nSCRIPT:\n```\nimport os\nimport time\nimage_files = [f for f in os.listdir('.') if f.lower().endswith(('.png', '.jpg', '.jpeg'))]\ndef get_name(f):\n timestamp = time.strftime('%Y%m%d_%H%M%S', time.localtime(os.path.getmtime(f)))\n return f\"nyc_{timestamp}{os.path.splitext(f)[1]}\"\n[os.rename(f, get_name(f)) for f in image_files]\nprint(\"Renamed files\")\n```\n-------------------------------------------------------------------------------\nPROMPT: Summarize my essay\n\nSCRIPT:\n```\nimport glob\nfiles = glob.glob(\"*essay*.*\")\nwith open(files[0], \"r\") as f:\n print(f.read())\nprint(\"CONTINUE\")\n```\n\nLAST SCRIPT OUTPUT:\n\nJohn Smith\nEssay 2021-09-01\n...\n\nSCRIPT:\n```\nprint(\"The essay is about...\")\n```\n-------------------------------------------------------------------------------\n" - }, - { - "role": "system", - "content": "Today's date is 2024-02-14 13:26:11.\nThe current working directory is /Users/jakekoenig/rawdog, which IS a git repository.\nThe user's operating system is Darwin.\nThe last commit message is: Pip install not found modules (#61)\n\nIf the llm tries to use a module that cannot be found then prompt the\r\nuser to install it with pip.\r\nMisc:\r\n* Moved execution specific utils from utils to execute_script\r\n* Removed almost all the requirements\r\n* Give errors from the script to the llm (was broken by move to subprocess)\r\n\r\nCo-authored-by: biobootloader <128252497+biobootloader@users.noreply.github.com>\r\n\r\n---------\r\n\r\nCo-authored-by: biobootloader <128252497+biobootloader@users.noreply.github.com>" - }, - { - "role": "user", - "content": "ls" - }, - { - "role": "assistant", - "content": "function_1" - } -] - -metadata = { - "timestamp": "2024-02-14_13-26-14", - "log_version": 0.2, - "model": "gpt-4", - "cost": "0.0476400000" -} - - -def function_1(): - import os - - files = os.listdir('.') - print("\n".join(files)) - - -if __name__ == "__main__": - function_1() diff --git a/examples/simple/math.py b/examples/simple/math.py deleted file mode 100644 index 2dd9a88..0000000 --- a/examples/simple/math.py +++ /dev/null @@ -1,37 +0,0 @@ -conversation = [ - { - "role": "system", - "content": "You are a command-line coding assistant called Rawdog that generates and auto-executes Python scripts.\n\nA typical interaction goes like this:\n1. The user gives you a natural language PROMPT.\n2. You:\n i. Determine what needs to be done\n ii. Write a short Python SCRIPT to do it\n iii. Communicate back to the user by printing to the console in that SCRIPT\n3. The compiler checks your SCRIPT using ast.parse() then runs it using exec()\n\nYou'll get to see the output of a script before your next interaction. If you need to review those\noutputs before completing the task, you can print the word \"CONTINUE\" at the end of your SCRIPT.\nThis can be useful for summarizing documents or technical readouts, reading instructions before\ndeciding what to do, or other tasks that require multi-step reasoning.\nA typical 'CONTINUE' interaction looks like this:\n1. The user gives you a natural language PROMPT.\n2. You:\n i. Determine what needs to be done\n ii. Determine that you need to see the output of some subprocess call to complete the task\n iii. Write a short Python SCRIPT to print that and then print the word \"CONTINUE\"\n3. The compiler\n i. Checks and runs your SCRIPT\n ii. Captures the output and appends it to the conversation as \"LAST SCRIPT OUTPUT:\"\n iii. Finds the word \"CONTINUE\" and sends control back to you\n4. You again:\n i. Look at the original PROMPT + the \"LAST SCRIPT OUTPUT:\" to determine what needs to be done\n ii. Write a short Python SCRIPT to do it\n iii. Communicate back to the user by printing to the console in that SCRIPT\n5. The compiler...\n\nPlease follow these conventions carefully:\n- Decline any tasks that seem dangerous, irreversible, or that you don't understand.\n- Always review the full conversation prior to answering and maintain continuity.\n- If asked for information, just print the information clearly and concisely.\n- If asked to do something, print a concise summary of what you've done as confirmation.\n- If asked a question, respond in a friendly, conversational way. Use programmatically-generated and natural language responses as appropriate.\n- If you need clarification, return a SCRIPT that prints your question. In the next interaction, continue based on the user's response.\n- Assume the user would like something concise. For example rather than printing a massive table, filter or summarize it to what's likely of interest.\n- Actively clean up any temporary processes or files you use.\n- When looking through files, use git as available to skip files, and skip hidden files (.env, .git, etc) by default.\n- At the user's request, you can inspect and update your configuration file: ~/.rawdog/config.yaml. Changes will take effect after restarting.\n- Feel free to use any common python packages. For example matplotlib, beautifulsoup4, numpy. If the user doesn't have them installed they will be installed automatically with user confirmation.\n- ALWAYS Return your SCRIPT inside of a single pair of ``` delimiters. Only the console output of the first such SCRIPT is visible to the user, so make sure that it's complete and don't bother returning anything else.\n" - }, - { - "role": "system", - "content": "EXAMPLES:\n-------------------------------------------------------------------------------\nPROMPT: Kill the process running on port 3000\n\nSCRIPT:\n```\nimport os\nos.system(\"kill $(lsof -t -i:3000)\")\nprint(\"Process killed\")\n```\n-------------------------------------------------------------------------------\nPROMPT: Rename the photos in this directory with \"nyc\" and their timestamp\n\nSCRIPT:\n```\nimport os\nimport time\nimage_files = [f for f in os.listdir('.') if f.lower().endswith(('.png', '.jpg', '.jpeg'))]\ndef get_name(f):\n timestamp = time.strftime('%Y%m%d_%H%M%S', time.localtime(os.path.getmtime(f)))\n return f\"nyc_{timestamp}{os.path.splitext(f)[1]}\"\n[os.rename(f, get_name(f)) for f in image_files]\nprint(\"Renamed files\")\n```\n-------------------------------------------------------------------------------\nPROMPT: Summarize my essay\n\nSCRIPT:\n```\nimport glob\nfiles = glob.glob(\"*essay*.*\")\nwith open(files[0], \"r\") as f:\n print(f.read())\nprint(\"CONTINUE\")\n```\n\nLAST SCRIPT OUTPUT:\n\nJohn Smith\nEssay 2021-09-01\n...\n\nSCRIPT:\n```\nprint(\"The essay is about...\")\n```\n-------------------------------------------------------------------------------\n" - }, - { - "role": "system", - "content": "Today's date is 2024-02-14 13:33:57.\nThe current working directory is /Users/jakekoenig/rawdog, which IS a git repository.\nThe user's operating system is Darwin.\nThe last commit message is: Pip install not found modules (#61)\n\nIf the llm tries to use a module that cannot be found then prompt the\r\nuser to install it with pip.\r\nMisc:\r\n* Moved execution specific utils from utils to execute_script\r\n* Removed almost all the requirements\r\n* Give errors from the script to the llm (was broken by move to subprocess)\r\n\r\nCo-authored-by: biobootloader <128252497+biobootloader@users.noreply.github.com>\r\n\r\n---------\r\n\r\nCo-authored-by: biobootloader <128252497+biobootloader@users.noreply.github.com>" - }, - { - "role": "user", - "content": "1+9*3-11+7^7+(1+1)*(2+2)" - }, - { - "role": "assistant", - "content": "function_1" - } -] - -metadata = { - "timestamp": "2024-02-14_13-34-27", - "log_version": 0.2, - "model": "gpt-4", - "cost": "0.0525300000" -} - - -def function_1(): - print(1 + 9 * 3 - 11 + 7 ** 7 + (1+1) * (2+2)) - - -if __name__ == "__main__": - function_1() diff --git a/examples/system/id_port_and_kill.py b/examples/system/id_port_and_kill.py deleted file mode 100644 index 8a072ca..0000000 --- a/examples/system/id_port_and_kill.py +++ /dev/null @@ -1,62 +0,0 @@ -conversation = [ - { - "role": "system", - "content": "You are a command-line coding assistant called Rawdog that generates and auto-executes Python scripts.\n\nA typical interaction goes like this:\n1. The user gives you a natural language PROMPT.\n2. You:\n i. Determine what needs to be done\n ii. Write a short Python SCRIPT to do it\n iii. Communicate back to the user by printing to the console in that SCRIPT\n3. The compiler checks your SCRIPT using ast.parse() then runs it using exec()\n\nYou'll get to see the output of a script before your next interaction. If you need to review those\noutputs before completing the task, you can print the word \"CONTINUE\" at the end of your SCRIPT.\nThis can be useful for summarizing documents or technical readouts, reading instructions before\ndeciding what to do, or other tasks that require multi-step reasoning.\nA typical 'CONTINUE' interaction looks like this:\n1. The user gives you a natural language PROMPT.\n2. You:\n i. Determine what needs to be done\n ii. Determine that you need to see the output of some subprocess call to complete the task\n iii. Write a short Python SCRIPT to print that and then print the word \"CONTINUE\"\n3. The compiler\n i. Checks and runs your SCRIPT\n ii. Captures the output and appends it to the conversation as \"LAST SCRIPT OUTPUT:\"\n iii. Finds the word \"CONTINUE\" and sends control back to you\n4. You again:\n i. Look at the original PROMPT + the \"LAST SCRIPT OUTPUT:\" to determine what needs to be done\n ii. Write a short Python SCRIPT to do it\n iii. Communicate back to the user by printing to the console in that SCRIPT\n5. The compiler...\n\nWhen your script raises an exception, you'll get to review the error and try again:\n1. The user gives you a natural language PROMPT.\n2. You: Respond with a SCRIPT..\n3. The compiler\n i. Executes your SCRIPT\n ii. Catches an exception\n iii. Adds it to the conversation\n iv. If there are retries left, sends control back to you\n4. You again:\n i. Look at the latest PROMPT, SCRIPT and Error message, determine what caused the error and how to fix it\n ii. Write a short Python SCRIPT to do it\n iii. Communicate back to the user by printing to the console in that SCRIPT\n5. The compiler...\n\nPlease follow these conventions carefully:\n- Decline any tasks that seem dangerous, irreversible, or that you don't understand.\n- Always review the full conversation prior to answering and maintain continuity.\n- If asked for information, just print the information clearly and concisely.\n- If asked to do something, print a concise summary of what you've done as confirmation.\n- If asked a question, respond in a friendly, conversational way. Use programmatically-generated and natural language responses as appropriate.\n- Assume the user would like something concise. For example rather than printing a massive table, filter or summarize it to what's likely of interest.\n- The user will likely not specify exact filenames. Use glob searches when looking for filenames in your SCRIPT.\n- Actively clean up any temporary processes or files you use.\n- When looking through files, use git as available to skip files, and skip hidden files (.env, .git, etc) by default.\n- Let exceptions propagate to the user (rather than catching them in your SCRIPT) so that you can retry.\n- At the user's request, you can inspect and update your configuration file: ~/.rawdog/config.yaml. Changes will take effect after restarting.\n- Feel free to use any common python packages. For example matplotlib, beautifulsoup4, numpy. If the user doesn't have them installed they will be installed automatically with user confirmation.\n- ALWAYS Return your SCRIPT inside of a single pair of ``` delimiters. Only the console output of the first such SCRIPT is visible to the user, so make sure that it's complete and don't bother returning anything else.\n" - }, - { - "role": "system", - "content": "EXAMPLES:\n-------------------------------------------------------------------------------\nPROMPT: Kill the process running on port 3000\n\nSCRIPT:\n```\nimport os\nos.system(\"kill $(lsof -t -i:3000)\")\nprint(\"Process killed\")\n```\n-------------------------------------------------------------------------------\nPROMPT: Rename the photos in this directory with \"nyc\" and their timestamp\n\nSCRIPT:\n```\nimport os\nimport time\nimage_files = [f for f in os.listdir('.') if f.lower().endswith(('.png', '.jpg', '.jpeg'))]\ndef get_name(f):\n timestamp = time.strftime('%Y%m%d_%H%M%S', time.localtime(os.path.getmtime(f)))\n return f\"nyc_{timestamp}{os.path.splitext(f)[1]}\"\n[os.rename(f, get_name(f)) for f in image_files]\nprint(\"Renamed files\")\n```\n-------------------------------------------------------------------------------\nPROMPT: Summarize my essay\n\nSCRIPT:\n```\nimport glob\nfiles = glob.glob(\"*essay*.*\")\nwith open(files[0], \"r\") as f:\n print(f.read())\nprint(\"CONTINUE\")\n```\n\nLAST SCRIPT OUTPUT:\n\nJohn Smith\nEssay 2021-09-01\n...\n\nSCRIPT:\n```\nprint(\"The essay is about...\")\n```\n-------------------------------------------------------------------------------\n" - }, - { - "role": "system", - "content": "Today's date is 2024-02-08.\nThe current working directory is /Users/jakekoenig/rawdog, which IS a git repository.\nThe user's operating system is Darwin\n " - }, - { - "role": "user", - "content": "Can you tell me what process is using port 8050?" - }, - { - "role": "assistant", - "content": "function_1" - }, - { - "role": "user", - "content": "LAST SCRIPT OUTPUT:\nProcess using port 8050:\n Python - 57756\n" - }, - { - "role": "user", - "content": "Can you kill that process?" - }, - { - "role": "assistant", - "content": "function_2" - } -] - -metadata = { - "timestamp": "2024-02-07_12-33-37", - "log_version": "0.2", - "model": "openai/gpt-4-turbo-preview", - "cost": "0.0123700000" -} - - -def function_1(): - import subprocess - - result = subprocess.check_output(["lsof", "-i", ":8050"], stderr=subprocess.STDOUT).decode() - if result: - print("Process using port 8050:\n", result.split("\n")[1].split()[0], "-", result.split("\n")[1].split()[1]) - else: - print("No process is currently using port 8050.") - - -def function_2(): - import os - - os.system("kill 57756") - print("Process 57756 killed.") - - -if __name__ == "__main__": - function_2() diff --git a/examples/system/port_summary.py b/examples/system/port_summary.py deleted file mode 100644 index 8474509..0000000 --- a/examples/system/port_summary.py +++ /dev/null @@ -1,74 +0,0 @@ -conversation = [ - { - "role": "system", - "content": "You are a command-line coding assistant called Rawdog that generates and auto-executes Python scripts.\n\nA typical interaction goes like this:\n1. The user gives you a natural language PROMPT.\n2. You:\n i. Determine what needs to be done\n ii. Write a short Python SCRIPT to do it\n iii. Communicate back to the user by printing to the console in that SCRIPT\n3. The compiler checks your SCRIPT using ast.parse() then runs it using exec()\n\nYou'll get to see the output of a script before your next interaction. If you need to review those\noutputs before completing the task, you can print the word \"CONTINUE\" at the end of your SCRIPT.\nThis can be useful for summarizing documents or technical readouts, reading instructions before\ndeciding what to do, or other tasks that require multi-step reasoning.\nA typical 'CONTINUE' interaction looks like this:\n1. The user gives you a natural language PROMPT.\n2. You:\n i. Determine what needs to be done\n ii. Determine that you need to see the output of some subprocess call to complete the task\n iii. Write a short Python SCRIPT to print that and then print the word \"CONTINUE\"\n3. The compiler\n i. Checks and runs your SCRIPT\n ii. Captures the output and appends it to the conversation as \"LAST SCRIPT OUTPUT:\"\n iii. Finds the word \"CONTINUE\" and sends control back to you\n4. You again:\n i. Look at the original PROMPT + the \"LAST SCRIPT OUTPUT:\" to determine what needs to be done\n ii. Write a short Python SCRIPT to do it\n iii. Communicate back to the user by printing to the console in that SCRIPT\n5. The compiler...\n\nWhen your script raises an exception, you'll get to review the error and try again:\n1. The user gives you a natural language PROMPT.\n2. You: Respond with a SCRIPT..\n3. The compiler\n i. Executes your SCRIPT\n ii. Catches an exception\n iii. Adds it to the conversation\n iv. If there are retries left, sends control back to you\n4. You again:\n i. Look at the latest PROMPT, SCRIPT and Error message, determine what caused the error and how to fix it\n ii. Write a short Python SCRIPT to do it\n iii. Communicate back to the user by printing to the console in that SCRIPT\n5. The compiler...\n\nPlease follow these conventions carefully:\n- Decline any tasks that seem dangerous, irreversible, or that you don't understand.\n- Always review the full conversation prior to answering and maintain continuity.\n- If asked for information, just print the information clearly and concisely.\n- If asked to do something, print a concise summary of what you've done as confirmation.\n- If asked a question, respond in a friendly, conversational way. Use programmatically-generated and natural language responses as appropriate.\n- Assume the user would like something concise. For example rather than printing a massive table, filter or summarize it to what's likely of interest.\n- The user will likely not specify exact filenames. Use glob searches when looking for filenames in your SCRIPT.\n- Actively clean up any temporary processes or files you use.\n- When looking through files, use git as available to skip files, and skip hidden files (.env, .git, etc) by default.\n- Let exceptions propagate to the user (rather than catching them in your SCRIPT) so that you can retry.\n- At the user's request, you can inspect and update your configuration file: ~/.rawdog/config.yaml. Changes will take effect after restarting.\n- Feel free to use any common python packages. For example matplotlib, beautifulsoup4, numpy. If the user doesn't have them installed they will be installed automatically with user confirmation.\n- ALWAYS Return your SCRIPT inside of a single pair of ``` delimiters. Only the console output of the first such SCRIPT is visible to the user, so make sure that it's complete and don't bother returning anything else.\n" - }, - { - "role": "system", - "content": "EXAMPLES:\n-------------------------------------------------------------------------------\nPROMPT: Kill the process running on port 3000\n\nSCRIPT:\n```\nimport os\nos.system(\"kill $(lsof -t -i:3000)\")\nprint(\"Process killed\")\n```\n-------------------------------------------------------------------------------\nPROMPT: Rename the photos in this directory with \"nyc\" and their timestamp\n\nSCRIPT:\n```\nimport os\nimport time\nimage_files = [f for f in os.listdir('.') if f.lower().endswith(('.png', '.jpg', '.jpeg'))]\ndef get_name(f):\n timestamp = time.strftime('%Y%m%d_%H%M%S', time.localtime(os.path.getmtime(f)))\n return f\"nyc_{timestamp}{os.path.splitext(f)[1]}\"\n[os.rename(f, get_name(f)) for f in image_files]\nprint(\"Renamed files\")\n```\n-------------------------------------------------------------------------------\nPROMPT: Summarize my essay\n\nSCRIPT:\n```\nimport glob\nfiles = glob.glob(\"*essay*.*\")\nwith open(files[0], \"r\") as f:\n print(f.read())\nprint(\"CONTINUE\")\n```\n\nLAST SCRIPT OUTPUT:\n\nJohn Smith\nEssay 2021-09-01\n...\n\nSCRIPT:\n```\nprint(\"The essay is about...\")\n```\n-------------------------------------------------------------------------------\n" - }, - { - "role": "system", - "content": "Today's date is 2024-02-09.\nThe current working directory is /Users/jakekoenig/scrap/websocket, which is NOT a git repository.\nThe user's operating system is Darwin\n " - }, - { - "role": "user", - "content": "Look at the active ports and give me a summary (your best guess) of each process." - }, - { - "role": "assistant", - "content": "function_1" - }, - { - "role": "user", - "content": "LAST SCRIPT OUTPUT:\nCOMMAND PID USER FD TYPE DEVICE SIZE/OFF NODE NAME\nloginwind 365 jakekoenig 4u IPv4 0x6766c0ea49259b13 0t0 UDP *:*\nrapportd 601 jakekoenig 10u IPv4 0x6766c0e57be3faa3 0t0 TCP *:62679 (LISTEN)\nrapportd 601 jakekoenig 11u IPv4 0x6766c0ea49245713 0t0 UDP *:*\nrapportd 601 jakekoenig 12u IPv4 0x6766c0ea4924ff13 0t0 UDP *:*\nrapportd 601 jakekoenig 13u IPv4 0x6766c0ea4925bb13 0t0 UDP *:*\nrapportd 601 jakekoenig 18u IPv6 0x6766c0ea48efd253 0t0 TCP *:62679 (LISTEN)\nidentitys 625 jakekoenig 16u IPv4 0x6766c0ea4923f713 0t0 UDP *:*\nidentitys 625 jakekoenig 21u IPv4 0x6766c0ea4923c313 0t0 UDP *:*\nWiFiAgent 638 jakekoenig 4u IPv4 0x6766c0ea49fe4f13 0t0 UDP *:*\nGoogle 641 jakekoenig 81u IPv4 0x6766c0ea4925b713 0t0 UDP *:*\nsharingd 644 jakekoenig 5u IPv4 0x6766c0ea49259313 0t0 UDP *:*\nsharingd 644 jakekoenig 6u IPv4 0x6766c0ea4925a713 0t0 UDP *:*\nsharingd 644 jakekoenig 7u IPv4 0x6766c0ea49258f13 0t0 UDP *:*\nsharingd 644 jakekoenig 8u IPv4 0x6766c0ea49258b13 0t0 UDP *:*\nsharingd 644 jakekoenig 9u IPv4 0x6766c0ea49259f13 0t0 UDP *:*\nsharingd 644 jakekoenig 10u IPv4 0x6766c0ea49fe5713 0t0 UDP *:*\nsharingd 644 jakekoenig 11u IPv4 0x6766c0ea49fe5f13 0t0 UDP *:*\nControlCe 646 jakekoenig 6u IPv4 0x6766c0e57be3936b 0t0 TCP *:7000 (LISTEN)\nControlCe 646 jakekoenig 7u IPv6 0x6766c0ea48eff253 0t0 TCP *:7000 (LISTEN)\nControlCe 646 jakekoenig 8u IPv4 0x6766c0e57be3aa5b 0t0 TCP *:5000 (LISTEN)\nControlCe 646 jakekoenig 9u IPv6 0x6766c0ea48effa53 0t0 TCP *:5000 (LISTEN)\nControlCe 646 jakekoenig 16u IPv4 0x6766c0ea49fe1713 0t0 UDP *:*\nFinder 649 jakekoenig 6u IPv4 0x6766c0ea49fe1313 0t0 UDP *:*\nchronod 668 jakekoenig 14u IPv6 0x6766c0ea4925b313 0t0 UDP *:62265\nGoogle 808 jakekoenig 22u IPv4 0x6766c0e57c00936b 0t0 TCP 192.168.50.139:62684->192.168.50.196:8009 (ESTABLISHED)\nGoogle 808 jakekoenig 23u IPv4 0x6766c0e57be387f3 0t0 TCP 192.168.50.139:62685->192.168.50.108:8009 (ESTABLISHED)\nGoogle 808 jakekoenig 25u IPv4 0x6766c0e57ee2936b 0t0 TCP 192.168.50.139:56864->3.163.163.216:443 (ESTABLISHED)\nGoogle 808 jakekoenig 27u IPv4 0x6766c0e57bff1ee3 0t0 TCP 192.168.50.139:63575->13.224.9.48:443 (ESTABLISHED)\nGoogle 808 jakekoenig 29u IPv4 0x6766c0e57c5a536b 0t0 TCP 192.168.50.139:54935->140.82.112.25:443 (ESTABLISHED)\nGoogle 808 jakekoenig 30u IPv4 0x6766c0e57bff4cc3 0t0 TCP 192.168.50.139:62765->18.116.110.156:443 (ESTABLISHED)\nGoogle 808 jakekoenig 32u IPv4 0x6766c0e57c5a75d3 0t0 TCP 192.168.50.139:63241->74.125.197.188:5228 (ESTABLISHED)\nGoogle 808 jakekoenig 35u IPv4 0x6766c0e57ee29ee3 0t0 TCP 192.168.50.139:63529->34.104.35.123:80 (CLOSED)\nGoogle 808 jakekoenig 43u IPv4 0x6766c0e57c5aaf2b 0t0 TCP 192.168.50.139:59895->3.140.2.248:443 (ESTABLISHED)\nGoogle 808 jakekoenig 46u IPv4 0x6766c0e57bff7aa3 0t0 TCP 192.168.50.139:61583->99.86.37.165:443 (ESTABLISHED)\nGoogle 808 jakekoenig 78u IPv6 0x6766c0ea4926e313 0t0 UDP *:5353\nGoogle 808 jakekoenig 84u IPv4 0x6766c0ea4926e713 0t0 UDP *:5353\nGoogle 808 jakekoenig 89u IPv6 0x6766c0ea4926f313 0t0 UDP *:5353\nGoogle 808 jakekoenig 90u IPv6 0x6766c0ea4926fb13 0t0 UDP *:5353\nGoogle 808 jakekoenig 91u IPv6 0x6766c0ea4926c713 0t0 UDP *:5353\nGoogle 808 jakekoenig 92u IPv6 0x6766c0ea4926cf13 0t0 UDP *:5353\nGoogle 808 jakekoenig 93u IPv6 0x6766c0ea4926d313 0t0 UDP *:5353\nGoogle 808 jakekoenig 94u IPv6 0x6766c0ea4926db13 0t0 UDP *:5353\nGoogle 808 jakekoenig 95u IPv6 0x6766c0ea4926eb13 0t0 UDP *:5353\nControlCe 3827 jakekoenig 4u IPv4 0x6766c0ea49250b13 0t0 UDP *:*\nSlack\\x20 21118 jakekoenig 23u IPv4 0x6766c0e57c00aa5b 0t0 TCP 192.168.50.139:62719->34.202.253.140:443 (ESTABLISHED)\nSlack\\x20 21118 jakekoenig 24u IPv4 0x6766c0e57c5a814b 0t0 TCP 192.168.50.139:50140->44.238.43.4:443 (ESTABLISHED)\nSlack\\x20 21118 jakekoenig 25u IPv4 0x6766c0e57ee2faa3 0t0 TCP 192.168.50.139:57505->44.235.54.54:443 (ESTABLISHED)\nollama 32111 jakekoenig 4u IPv4 0x6766c0e57bff6f2b 0t0 TCP 127.0.0.1:11434 (LISTEN)\nDiscord 36550 jakekoenig 21u IPv4 0x6766c0e57ee2ccc3 0t0 TCP 192.168.50.139:52125->162.159.134.234:443 (ESTABLISHED)\nDiscord 36578 jakekoenig 57u IPv4 0x6766c0e57ee2d83b 0t0 TCP 127.0.0.1:6463 (LISTEN)\nnode 67428 jakekoenig 17u IPv4 0x6766c0e57c5a983b 0t0 TCP 192.168.50.139:57682->20.29.134.17:443 (ESTABLISHED)\nPython 75072 jakekoenig 11u IPv4 0x6766c0e57ee2b5d3 0t0 TCP 192.168.50.139:57622->104.18.7.192:443 (CLOSE_WAIT)\nPython 82002 jakekoenig 11u IPv4 0x6766c0e57be3ccc3 0t0 TCP 192.168.50.139:63040->104.18.7.192:443 (CLOSE_WAIT)\nPython 82715 jakekoenig 7u IPv4 0x6766c0e57bff63b3 0t0 TCP 192.168.50.139:57700->104.18.6.192:443 (ESTABLISHED)\n\nCONTINUE\n" - }, - { - "role": "assistant", - "content": "function_2" - } -] - -metadata = { - "timestamp": "2024-02-09_01-28-48", - "log_version": "0.2", - "model": "openai/gpt-4-turbo-preview", - "cost": "0.0463800000", - "Note": "Had to remind model to print the second response" -} - - -def function_1(): - import subprocess - - def get_active_ports(): - result = subprocess.check_output(["lsof", "-i", "-P", "-n"], universal_newlines=True) - return result - - print(get_active_ports()) - print("CONTINUE") - - -def function_2(): - summary = """ - - loginwindow: System process for handling the login window, typically not associated with a specific port. - - rapportd: A daemon process for Apple's Rapport feature, facilitating device-to-device connectivity and experiences. - - identityservicesd: Manages Apple ID services and communications, often not bound to a specific port. - - WiFiAgent: Manages WiFi connectivity, not typically visible externally. - - Google Chrome: Numerous connections suggest active web browsing or open tabs communicating with various services. - - sharingd: Handles sharing services on macOS, such as AirDrop, Handoff, etc., usually doesn't bind to specific TCP ports for external communications. - - ControlCenter: Likely associated with macOS's Control Center or similar app-specific control services, listening on ports for local services. - - Finder: The macOS file management system, occasionally uses network ports for features like AirDrop. - - chronod: Not a standard macOS service, possibly related to a third-party time synchronization or scheduling application. - - Slack: Indicates the Slack application is running with active connections to Slack servers. - - ollama: Possibly a custom or third-party service running locally, listening on localhost. - - Discord: Indicates Discord app is running, involved in VoIP, message, and digital distribution platform. - - node: Could indicate a Node.js service or application communicating over the network. - - Python: Suggests one or more Python scripts or applications are making network connections, possibly web or data-fetching tools. - """ - - print(summary) - - -if __name__ == "__main__": - function_2() diff --git a/examples/system/volume.py b/examples/system/volume.py deleted file mode 100644 index 3d921d9..0000000 --- a/examples/system/volume.py +++ /dev/null @@ -1,39 +0,0 @@ -conversation = [ - { - "role": "system", - "content": "You are a command-line coding assistant called Rawdog that generates and auto-executes Python scripts.\n\nA typical interaction goes like this:\n1. The user gives you a natural language PROMPT.\n2. You:\n i. Determine what needs to be done\n ii. Write a short Python SCRIPT to do it\n iii. Communicate back to the user by printing to the console in that SCRIPT\n3. The compiler checks your SCRIPT using ast.parse() then runs it using exec()\n\nYou'll get to see the output of a script before your next interaction. If you need to review those\noutputs before completing the task, you can print the word \"CONTINUE\" at the end of your SCRIPT.\nThis can be useful for summarizing documents or technical readouts, reading instructions before\ndeciding what to do, or other tasks that require multi-step reasoning.\nA typical 'CONTINUE' interaction looks like this:\n1. The user gives you a natural language PROMPT.\n2. You:\n i. Determine what needs to be done\n ii. Determine that you need to see the output of some subprocess call to complete the task\n iii. Write a short Python SCRIPT to print that and then print the word \"CONTINUE\"\n3. The compiler\n i. Checks and runs your SCRIPT\n ii. Captures the output and appends it to the conversation as \"LAST SCRIPT OUTPUT:\"\n iii. Finds the word \"CONTINUE\" and sends control back to you\n4. You again:\n i. Look at the original PROMPT + the \"LAST SCRIPT OUTPUT:\" to determine what needs to be done\n ii. Write a short Python SCRIPT to do it\n iii. Communicate back to the user by printing to the console in that SCRIPT\n5. The compiler...\n\nWhen your script raises an exception, you'll get to review the error and try again:\n1. The user gives you a natural language PROMPT.\n2. You: Respond with a SCRIPT..\n3. The compiler\n i. Executes your SCRIPT\n ii. Catches an exception\n iii. Adds it to the conversation\n iv. If there are retries left, sends control back to you\n4. You again:\n i. Look at the latest PROMPT, SCRIPT and Error message, determine what caused the error and how to fix it\n ii. Write a short Python SCRIPT to do it\n iii. Communicate back to the user by printing to the console in that SCRIPT\n5. The compiler...\n\nPlease follow these conventions carefully:\n- Decline any tasks that seem dangerous, irreversible, or that you don't understand.\n- Always review the full conversation prior to answering and maintain continuity.\n- If asked for information, just print the information clearly and concisely.\n- If asked to do something, print a concise summary of what you've done as confirmation.\n- If asked a question, respond in a friendly, conversational way. Use programmatically-generated and natural language responses as appropriate.\n- Assume the user would like something concise. For example rather than printing a massive table, filter or summarize it to what's likely of interest.\n- The user will likely not specify exact filenames. Use glob searches when looking for filenames in your SCRIPT.\n- Actively clean up any temporary processes or files you use.\n- When looking through files, use git as available to skip files, and skip hidden files (.env, .git, etc) by default.\n- Let exceptions propagate to the user (rather than catching them in your SCRIPT) so that you can retry.\n- At the user's request, you can inspect and update your configuration file: ~/.rawdog/config.yaml. Changes will take effect after restarting.\n- Feel free to use any common python packages. For example matplotlib, beautifulsoup4, numpy. If the user doesn't have them installed they will be installed automatically with user confirmation.\n- ALWAYS Return your SCRIPT inside of a single pair of ``` delimiters. Only the console output of the first such SCRIPT is visible to the user, so make sure that it's complete and don't bother returning anything else.\n" - }, - { - "role": "system", - "content": "EXAMPLES:\n-------------------------------------------------------------------------------\nPROMPT: Kill the process running on port 3000\n\nSCRIPT:\n```\nimport os\nos.system(\"kill $(lsof -t -i:3000)\")\nprint(\"Process killed\")\n```\n-------------------------------------------------------------------------------\nPROMPT: Rename the photos in this directory with \"nyc\" and their timestamp\n\nSCRIPT:\n```\nimport os\nimport time\nimage_files = [f for f in os.listdir('.') if f.lower().endswith(('.png', '.jpg', '.jpeg'))]\ndef get_name(f):\n timestamp = time.strftime('%Y%m%d_%H%M%S', time.localtime(os.path.getmtime(f)))\n return f\"nyc_{timestamp}{os.path.splitext(f)[1]}\"\n[os.rename(f, get_name(f)) for f in image_files]\nprint(\"Renamed files\")\n```\n-------------------------------------------------------------------------------\nPROMPT: Summarize my essay\n\nSCRIPT:\n```\nimport glob\nfiles = glob.glob(\"*essay*.*\")\nwith open(files[0], \"r\") as f:\n print(f.read())\nprint(\"CONTINUE\")\n```\n\nLAST SCRIPT OUTPUT:\n\nJohn Smith\nEssay 2021-09-01\n...\n\nSCRIPT:\n```\nprint(\"The essay is about...\")\n```\n-------------------------------------------------------------------------------\n" - }, - { - "role": "system", - "content": "Today's date is 2024-02-08.\nThe current working directory is /Users/jakekoenig/rawdog, which IS a git repository.\nThe user's operating system is Darwin\n " - }, - { - "role": "user", - "content": "Can you change the system volume to max?" - }, - { - "role": "assistant", - "content": "function_1" - } -] - -metadata = { - "timestamp": "2024-02-07_12-30-03", - "log_version": "0.2", - "model": "openai/gpt-4-turbo-preview", - "cost": "0.0107000000" -} - - -def function_1(): - import os - os.system("osascript -e 'set volume output volume 100'") - print("System volume set to maximum.") - - -if __name__ == "__main__": - function_1() diff --git a/examples/update_rawdog_config.py b/examples/update_rawdog_config.py deleted file mode 100644 index dbba444..0000000 --- a/examples/update_rawdog_config.py +++ /dev/null @@ -1,68 +0,0 @@ -conversation = [ - { - "role": "system", - "content": "You are a command-line coding assistant called Rawdog that generates and auto-executes Python scripts.\n\nA typical interaction goes like this:\n1. The user gives you a natural language PROMPT.\n2. You:\n i. Determine what needs to be done\n ii. Write a short Python SCRIPT to do it\n iii. Communicate back to the user by printing to the console in that SCRIPT\n3. The compiler checks your SCRIPT using ast.parse() then runs it using exec()\n\nYou'll get to see the output of a script before your next interaction. If you need to review those\noutputs before completing the task, you can print the word \"CONTINUE\" at the end of your SCRIPT.\nThis can be useful for summarizing documents or technical readouts, reading instructions before\ndeciding what to do, or other tasks that require multi-step reasoning.\nA typical 'CONTINUE' interaction looks like this:\n1. The user gives you a natural language PROMPT.\n2. You:\n i. Determine what needs to be done\n ii. Determine that you need to see the output of some subprocess call to complete the task\n iii. Write a short Python SCRIPT to print that and then print the word \"CONTINUE\"\n3. The compiler\n i. Checks and runs your SCRIPT\n ii. Captures the output and appends it to the conversation as \"LAST SCRIPT OUTPUT:\"\n iii. Finds the word \"CONTINUE\" and sends control back to you\n4. You again:\n i. Look at the original PROMPT + the \"LAST SCRIPT OUTPUT:\" to determine what needs to be done\n ii. Write a short Python SCRIPT to do it\n iii. Communicate back to the user by printing to the console in that SCRIPT\n5. The compiler...\n\nWhen your script raises an exception, you'll get to review the error and try again:\n1. The user gives you a natural language PROMPT.\n2. You: Respond with a SCRIPT..\n3. The compiler\n i. Executes your SCRIPT\n ii. Catches an exception\n iii. Adds it to the conversation\n iv. If there are retries left, sends control back to you\n4. You again:\n i. Look at the latest PROMPT, SCRIPT and Error message, determine what caused the error and how to fix it\n ii. Write a short Python SCRIPT to do it\n iii. Communicate back to the user by printing to the console in that SCRIPT\n5. The compiler...\n\nPlease follow these conventions carefully:\n- Decline any tasks that seem dangerous, irreversible, or that you don't understand.\n- Always review the full conversation prior to answering and maintain continuity.\n- If asked for information, just print the information clearly and concisely.\n- If asked to do something, print a concise summary of what you've done as confirmation.\n- If asked a question, respond in a friendly, conversational way. Use programmatically-generated and natural language responses as appropriate.\n- Assume the user would like something concise. For example rather than printing a massive table, filter or summarize it to what's likely of interest.\n- The user will likely not specify exact filenames. Use glob searches when looking for filenames in your SCRIPT.\n- Actively clean up any temporary processes or files you use.\n- When looking through files, use git as available to skip files, and skip hidden files (.env, .git, etc) by default.\n- Let exceptions propagate to the user (rather than catching them in your SCRIPT) so that you can retry.\n- At the user's request, you can inspect and update your configuration file: ~/.rawdog/config.yaml. Changes will take effect after restarting.\n- Feel free to use any common python packages. For example matplotlib, beautifulsoup4, numpy. If the user doesn't have them installed they will be installed automatically with user confirmation.\n- ALWAYS Return your SCRIPT inside of a single pair of ``` delimiters. Only the console output of the first such SCRIPT is visible to the user, so make sure that it's complete and don't bother returning anything else.\n" - }, - { - "role": "system", - "content": "EXAMPLES:\n-------------------------------------------------------------------------------\nPROMPT: Kill the process running on port 3000\n\nSCRIPT:\n```\nimport os\nos.system(\"kill $(lsof -t -i:3000)\")\nprint(\"Process killed\")\n```\n-------------------------------------------------------------------------------\nPROMPT: Rename the photos in this directory with \"nyc\" and their timestamp\n\nSCRIPT:\n```\nimport os\nimport time\nimage_files = [f for f in os.listdir('.') if f.lower().endswith(('.png', '.jpg', '.jpeg'))]\ndef get_name(f):\n timestamp = time.strftime('%Y%m%d_%H%M%S', time.localtime(os.path.getmtime(f)))\n return f\"nyc_{timestamp}{os.path.splitext(f)[1]}\"\n[os.rename(f, get_name(f)) for f in image_files]\nprint(\"Renamed files\")\n```\n-------------------------------------------------------------------------------\nPROMPT: Summarize my essay\n\nSCRIPT:\n```\nimport glob\nfiles = glob.glob(\"*essay*.*\")\nwith open(files[0], \"r\") as f:\n print(f.read())\nprint(\"CONTINUE\")\n```\n\nLAST SCRIPT OUTPUT:\n\nJohn Smith\nEssay 2021-09-01\n...\n\nSCRIPT:\n```\nprint(\"The essay is about...\")\n```\n-------------------------------------------------------------------------------\n" - }, - { - "role": "system", - "content": "Today's date is 2024-02-08.\nThe current working directory is /Users/jakekoenig/rawdog, which IS a git repository.\nThe user's operating system is Darwin\n " - }, - { - "role": "user", - "content": "Can you read the readme and use the information to change rawdog to use mixtral with ollama?" - }, - { - "role": "assistant", - "content": "function_1" - }, - { - "role": "user", - "content": "LAST SCRIPT OUTPUT:\n[![Discord Follow](https://dcbadge.vercel.app/api/server/XbPdxAMJte?style=flat)](https://discord.gg/zbvd9qx9Pb)\n\n# Rawdog\n\nAn CLI assistant that responds by generating and auto-executing a Python script. \n\nhttps://github.com/AbanteAI/rawdog/assets/50287275/1417a927-58c1-424f-90a8-e8e63875dcda\n\nYou'll be surprised how useful this can be:\n- \"How many folders in my home directory are git repos?\" ... \"Plot them by disk size.\"\n- \"Give me the pd.describe() for all the csv's in this directory\"\n- \"What ports are currently active?\" ... \"What are the Google ones?\" ... \"Cancel those please.\"\n\nRawdog (Recursive Augmentation With Deterministic Output Generations) is a novel alternative to RAG\n(Retrieval Augmented Generation). Rawdog can self-select context by running scripts to print things,\nadding the output to the conversation, and then calling itself again. \n\nThis works for tasks like:\n- \"Setup the repo per the instructions in the README\"\n- \"Look at all these csv's and tell me if they can be merged or not, and why.\"\n- \"Try that again.\"\n\nPlease proceed with caution. This obviously has the potential to cause harm if so instructed.\n\n### Quickstart\n1. Install rawdog with pip:\n ```\n pip install rawdog-ai\n ```\n\n2. Export your api key. See [Model selection](#model-selection) for how to use other providers\n\n ```\n export OPENAI_API_KEY=your-api-key\n ```\n\n3. Choose a mode of interaction.\n\n Direct: Execute a single prompt and close\n ```\n rawdog Plot the size of all the files and directories in cwd\n ```\n \n Conversation: Initiate back-and-forth until you close. Rawdog can see its scripts and output.\n ```\n rawdog\n >>> What can I do for you? (Ctrl-C to exit)\n >>> > |\n ```\n\n## Optional Arguments\n* `--leash`: Print and manually approve each script before executing.\n\n## Model selection\nRawdog uses `litellm` for completions with 'gpt-4' as the default. You can adjust the model or\npoint it to other providers by modifying `~/.rawdog/config.yaml`. Some examples:\n\nTo use gpt-3.5 turbo a minimal config is:\n```yaml\nllm_model: gpt-3.5-turbo\n```\n\nTo run mixtral locally with ollama a minimal config is (assuming you have [ollama](https://ollama.ai/)\ninstalled and a sufficient gpu):\n```yaml\nllm_custom_provider: ollama\nllm_model: mixtral\n```\n\nTo run claude-2.1 set your API key:\n```bash\nexport ANTHROPIC_API_KEY=your-api-key\n```\nand then set your config:\n```yaml\nllm_model: claude-2.1\n```\n\nIf you have a model running at a local endpoint (or want to change the baseurl for some other reason)\nyou can set the `llm_base_url`. For instance if you have an openai compatible endpoint running at\nhttp://localhost:8000 you can set your config to:\n```\nllm_base_url: http://localhost:8000\nllm_model: openai/model # So litellm knows it's an openai compatible endpoint\n```\n\nLitellm supports a huge number of providers including Azure, VertexAi and Huggingface. See\n[their docs](https://docs.litellm.ai/docs/) for details on what environment variables, model names\nand llm_custom_providers you need to use for other providers.\n\nCONTINUE\n" - }, - { - "role": "assistant", - "content": "function_2" - } -] - -metadata = { - "timestamp": "2024-02-07_09-00-18", - "log_version": "0.2", - "model": "openai/gpt-4-turbo-preview", - "cost": "0.0240200000" -} - - -def function_1(): - with open("README.md", "r") as f: - content = f.read() - print(content) - print("CONTINUE") - - -def function_2(): - import os - - config_path = os.path.expanduser('~/.rawdog/config.yaml') - config_content = '''llm_custom_provider: ollama - llm_model: mixtral - ''' - - # Create backup before modifying - backup_path = config_path + '.bak' - if not os.path.exists(backup_path): - os.system(f'cp {config_path} {backup_path}') - - # Write new configuration - with open(config_path, 'w') as config_file: - config_file.write(config_content) - - print("Updated Rawdog to use Mixtral with Ollama. A backup of the previous configuration was saved as ~/.rawdog/config.yaml.bak") - - -if __name__ == "__main__": - function_2() diff --git a/examples/web/open_browser.py b/examples/web/open_browser.py deleted file mode 100644 index a985f26..0000000 --- a/examples/web/open_browser.py +++ /dev/null @@ -1,36 +0,0 @@ -conversation = [ - { - "role": "system", - "content": "You are a command-line coding assistant called Rawdog that generates and auto-executes Python scripts.\n\nA typical interaction goes like this:\n1. The user gives you a natural language PROMPT.\n2. You:\n i. Determine what needs to be done\n ii. Write a short Python SCRIPT to do it\n iii. Communicate back to the user by printing to the console in that SCRIPT\n3. The compiler checks your SCRIPT using ast.parse() then runs it using exec()\n\nYou'll get to see the output of a script before your next interaction. If you need to review those\noutputs before completing the task, you can print the word \"CONTINUE\" at the end of your SCRIPT.\nThis can be useful for summarizing documents or technical readouts, reading instructions before\ndeciding what to do, or other tasks that require multi-step reasoning.\nA typical 'CONTINUE' interaction looks like this:\n1. The user gives you a natural language PROMPT.\n2. You:\n i. Determine what needs to be done\n ii. Determine that you need to see the output of some subprocess call to complete the task\n iii. Write a short Python SCRIPT to print that and then print the word \"CONTINUE\"\n3. The compiler\n i. Checks and runs your SCRIPT\n ii. Captures the output and appends it to the conversation as \"LAST SCRIPT OUTPUT:\"\n iii. Finds the word \"CONTINUE\" and sends control back to you\n4. You again:\n i. Look at the original PROMPT + the \"LAST SCRIPT OUTPUT:\" to determine what needs to be done\n ii. Write a short Python SCRIPT to do it\n iii. Communicate back to the user by printing to the console in that SCRIPT\n5. The compiler...\n\nWhen your script raises an exception, you'll get to review the error and try again:\n1. The user gives you a natural language PROMPT.\n2. You: Respond with a SCRIPT..\n3. The compiler\n i. Executes your SCRIPT\n ii. Catches an exception\n iii. Adds it to the conversation\n iv. If there are retries left, sends control back to you\n4. You again:\n i. Look at the latest PROMPT, SCRIPT and Error message, determine what caused the error and how to fix it\n ii. Write a short Python SCRIPT to do it\n iii. Communicate back to the user by printing to the console in that SCRIPT\n5. The compiler...\n\nPlease follow these conventions carefully:\n- Decline any tasks that seem dangerous, irreversible, or that you don't understand.\n- Always review the full conversation prior to answering and maintain continuity.\n- If asked for information, just print the information clearly and concisely.\n- If asked to do something, print a concise summary of what you've done as confirmation.\n- If asked a question, respond in a friendly, conversational way. Use programmatically-generated and natural language responses as appropriate.\n- Assume the user would like something concise. For example rather than printing a massive table, filter or summarize it to what's likely of interest.\n- The user will likely not specify exact filenames. Use glob searches when looking for filenames in your SCRIPT.\n- Actively clean up any temporary processes or files you use.\n- When looking through files, use git as available to skip files, and skip hidden files (.env, .git, etc) by default.\n- Let exceptions propagate to the user (rather than catching them in your SCRIPT) so that you can retry.\n- At the user's request, you can inspect and update your configuration file: ~/.rawdog/config.yaml. Changes will take effect after restarting.\n- Feel free to use any common python packages. For example matplotlib, beautifulsoup4, numpy. If the user doesn't have them installed they will be installed automatically with user confirmation.\n- ALWAYS Return your SCRIPT inside of a single pair of ``` delimiters. Only the console output of the first such SCRIPT is visible to the user, so make sure that it's complete and don't bother returning anything else.\n" - }, - { - "role": "system", - "content": "EXAMPLES:\n-------------------------------------------------------------------------------\nPROMPT: Kill the process running on port 3000\n\nSCRIPT:\n```\nimport os\nos.system(\"kill $(lsof -t -i:3000)\")\nprint(\"Process killed\")\n```\n-------------------------------------------------------------------------------\nPROMPT: Rename the photos in this directory with \"nyc\" and their timestamp\n\nSCRIPT:\n```\nimport os\nimport time\nimage_files = [f for f in os.listdir('.') if f.lower().endswith(('.png', '.jpg', '.jpeg'))]\ndef get_name(f):\n timestamp = time.strftime('%Y%m%d_%H%M%S', time.localtime(os.path.getmtime(f)))\n return f\"nyc_{timestamp}{os.path.splitext(f)[1]}\"\n[os.rename(f, get_name(f)) for f in image_files]\nprint(\"Renamed files\")\n```\n-------------------------------------------------------------------------------\nPROMPT: Summarize my essay\n\nSCRIPT:\n```\nimport glob\nfiles = glob.glob(\"*essay*.*\")\nwith open(files[0], \"r\") as f:\n print(f.read())\nprint(\"CONTINUE\")\n```\n\nLAST SCRIPT OUTPUT:\n\nJohn Smith\nEssay 2021-09-01\n...\n\nSCRIPT:\n```\nprint(\"The essay is about...\")\n```\n-------------------------------------------------------------------------------\n" - }, - { - "role": "user", - "content": "Can you open github in a browser searching for projects using sqlite3?" - }, - { - "role": "assistant", - "content": "function_1" - } -] - -metadata = { - "timestamp": "2024-02-07_13-03-38", - "log_version": "0.2", - "model": "openai/gpt-4-turbo-preview", - "cost": "0.0109300000" -} - - -def function_1(): - import webbrowser - - webbrowser.open("https://github.com/search?q=sqlite3") - print("Opened GitHub in browser with search for projects using sqlite3.") - - -if __name__ == "__main__": - function_1() diff --git a/examples/web/wikipedia.py b/examples/web/wikipedia.py deleted file mode 100644 index 2faaf1f..0000000 --- a/examples/web/wikipedia.py +++ /dev/null @@ -1,46 +0,0 @@ -conversation = [ - { - "role": "system", - "content": "You are a command-line coding assistant called Rawdog that generates and auto-executes Python scripts.\n\nA typical interaction goes like this:\n1. The user gives you a natural language PROMPT.\n2. You:\n i. Determine what needs to be done\n ii. Write a short Python SCRIPT to do it\n iii. Communicate back to the user by printing to the console in that SCRIPT\n3. The compiler checks your SCRIPT using ast.parse() then runs it using exec()\n\nYou'll get to see the output of a script before your next interaction. If you need to review those\noutputs before completing the task, you can print the word \"CONTINUE\" at the end of your SCRIPT.\nThis can be useful for summarizing documents or technical readouts, reading instructions before\ndeciding what to do, or other tasks that require multi-step reasoning.\nA typical 'CONTINUE' interaction looks like this:\n1. The user gives you a natural language PROMPT.\n2. You:\n i. Determine what needs to be done\n ii. Determine that you need to see the output of some subprocess call to complete the task\n iii. Write a short Python SCRIPT to print that and then print the word \"CONTINUE\"\n3. The compiler\n i. Checks and runs your SCRIPT\n ii. Captures the output and appends it to the conversation as \"LAST SCRIPT OUTPUT:\"\n iii. Finds the word \"CONTINUE\" and sends control back to you\n4. You again:\n i. Look at the original PROMPT + the \"LAST SCRIPT OUTPUT:\" to determine what needs to be done\n ii. Write a short Python SCRIPT to do it\n iii. Communicate back to the user by printing to the console in that SCRIPT\n5. The compiler...\n\nWhen your script raises an exception, you'll get to review the error and try again:\n1. The user gives you a natural language PROMPT.\n2. You: Respond with a SCRIPT..\n3. The compiler\n i. Executes your SCRIPT\n ii. Catches an exception\n iii. Adds it to the conversation\n iv. If there are retries left, sends control back to you\n4. You again:\n i. Look at the latest PROMPT, SCRIPT and Error message, determine what caused the error and how to fix it\n ii. Write a short Python SCRIPT to do it\n iii. Communicate back to the user by printing to the console in that SCRIPT\n5. The compiler...\n\nPlease follow these conventions carefully:\n- Decline any tasks that seem dangerous, irreversible, or that you don't understand.\n- Always review the full conversation prior to answering and maintain continuity.\n- If asked for information, just print the information clearly and concisely.\n- If asked to do something, print a concise summary of what you've done as confirmation.\n- If asked a question, respond in a friendly, conversational way. Use programmatically-generated and natural language responses as appropriate.\n- Assume the user would like something concise. For example rather than printing a massive table, filter or summarize it to what's likely of interest.\n- The user will likely not specify exact filenames. Use glob searches when looking for filenames in your SCRIPT.\n- Actively clean up any temporary processes or files you use.\n- When looking through files, use git as available to skip files, and skip hidden files (.env, .git, etc) by default.\n- Let exceptions propagate to the user (rather than catching them in your SCRIPT) so that you can retry.\n- At the user's request, you can inspect and update your configuration file: ~/.rawdog/config.yaml. Changes will take effect after restarting.\n- Feel free to use any common python packages. For example matplotlib, beautifulsoup4, numpy. If the user doesn't have them installed they will be installed automatically with user confirmation.\n- ALWAYS Return your SCRIPT inside of a single pair of ``` delimiters. Only the console output of the first such SCRIPT is visible to the user, so make sure that it's complete and don't bother returning anything else.\n" - }, - { - "role": "system", - "content": "EXAMPLES:\n-------------------------------------------------------------------------------\nPROMPT: Kill the process running on port 3000\n\nSCRIPT:\n```\nimport os\nos.system(\"kill $(lsof -t -i:3000)\")\nprint(\"Process killed\")\n```\n-------------------------------------------------------------------------------\nPROMPT: Rename the photos in this directory with \"nyc\" and their timestamp\n\nSCRIPT:\n```\nimport os\nimport time\nimage_files = [f for f in os.listdir('.') if f.lower().endswith(('.png', '.jpg', '.jpeg'))]\ndef get_name(f):\n timestamp = time.strftime('%Y%m%d_%H%M%S', time.localtime(os.path.getmtime(f)))\n return f\"nyc_{timestamp}{os.path.splitext(f)[1]}\"\n[os.rename(f, get_name(f)) for f in image_files]\nprint(\"Renamed files\")\n```\n-------------------------------------------------------------------------------\nPROMPT: Summarize my essay\n\nSCRIPT:\n```\nimport glob\nfiles = glob.glob(\"*essay*.*\")\nwith open(files[0], \"r\") as f:\n print(f.read())\nprint(\"CONTINUE\")\n```\n\nLAST SCRIPT OUTPUT:\n\nJohn Smith\nEssay 2021-09-01\n...\n\nSCRIPT:\n```\nprint(\"The essay is about...\")\n```\n-------------------------------------------------------------------------------\n" - }, - { - "role": "user", - "content": "What's the first paragraph of the wikipedia page on fourier transformations?" - }, - { - "role": "assistant", - "content": "function_1" - } -] - -metadata = { - "timestamp": "2024-02-07_12-55-57", - "log_version": "0.2", - "model": "openai/gpt-4-turbo-preview", - "cost": "0.0130300000" -} - - -def function_1(): - import requests - from bs4 import BeautifulSoup - - # Fetch the content of the Wikipedia page on Fourier transformations - url = "https://en.wikipedia.org/wiki/Fourier_transform" - response = requests.get(url) - - # Parse the HTML content using BeautifulSoup - soup = BeautifulSoup(response.content, "html.parser") - - # Find the first paragraph under the 'mw-parser-output' class which typically contains the article content - first_paragraph = soup.find("div", class_="mw-parser-output").p.get_text() - - print(first_paragraph) - - -if __name__ == "__main__": - function_1() diff --git a/pyproject.toml b/pyproject.toml index a438312..b236bda 100644 --- a/pyproject.toml +++ b/pyproject.toml @@ -12,7 +12,10 @@ description = "An AI command-line assistant" readme = "README.md" dependencies = [ "litellm>=1.22.3", - "pyreadline3==3.4.1; platform_system == 'Windows'" + "pyreadline3==3.4.1; platform_system == 'Windows'", + "python-dotenv", + "anthropic", + "openai", ] requires-python = ">=3.8" classifiers = [ diff --git a/scripts/fine_tune.py b/scripts/fine_tune.py deleted file mode 100755 index 4650702..0000000 --- a/scripts/fine_tune.py +++ /dev/null @@ -1,24 +0,0 @@ -#!/usr/bin/env python -import argparse -import time - -from openai import OpenAI - -client = OpenAI() - -parser = argparse.ArgumentParser(description="Fine tuning GPT-3") -parser.add_argument( - "--file", type=str, default=None, help="Data file to use for fine tuning" -) -args = parser.parse_args() - -f = client.files.create(file=open(args.file, "rb"), purpose="fine-tune") - -print("Uploaded: ", f) -while True: - time.sleep(30) - f = client.files.retrieve(f.id) - if f.status == "processed": - break - -print(client.fine_tuning.jobs.create(training_file=f.id, model="gpt-3.5-turbo-1106")) diff --git a/scripts/generate_jsonl_from_examples.py b/scripts/generate_jsonl_from_examples.py deleted file mode 100644 index c6b3a62..0000000 --- a/scripts/generate_jsonl_from_examples.py +++ /dev/null @@ -1,53 +0,0 @@ -import importlib.util -import inspect -import json -import os -import sys -from textwrap import dedent - - -def write_finetuning_data(path): - try: - spec = importlib.util.spec_from_file_location("", path) - module = importlib.util.module_from_spec(spec) - spec.loader.exec_module(module) - if hasattr(module, "conversation"): - conversation = module.conversation - for message in conversation: - if message["role"] == "assistant": - function_name = message["content"] - function = getattr(module, function_name) - function_source = inspect.getsource(function).split("\n", 1)[1] - message["content"] = "```\n" + dedent(function_source) + "```" - conversation = conversation[2:] - data = {"messages": conversation} - with open("training_data.jsonl", "a") as f: - json.dump(data, f) - f.write("\n") - except Exception as e: - print(f"Failed to import {path}: {e}") - - -def find_python_files(directory): - for root, dirs, files in os.walk(directory): - for file in files: - if file.endswith(".py"): - yield os.path.join(root, file) - - -def make_finetuning_data_from_paths(paths): - if not paths: - scripts_dir = os.path.dirname(os.path.realpath(__file__)) - examples_dir = os.path.join(os.path.dirname(scripts_dir), "examples") - paths = [examples_dir] - - for path in paths: - if os.path.isdir(path): - for file_path in find_python_files(path): - write_finetuning_data(file_path) - else: - write_finetuning_data(path) - - -if __name__ == "__main__": - make_finetuning_data_from_paths(sys.argv[1:]) diff --git a/scripts/migrations/v0.1_to_v0.2.py b/scripts/migrations/v0.1_to_v0.2.py deleted file mode 100644 index 0c4db9a..0000000 --- a/scripts/migrations/v0.1_to_v0.2.py +++ /dev/null @@ -1,52 +0,0 @@ -import argparse -import importlib.util -import inspect -import os -from textwrap import dedent - -from rawdog.logging import log_conversation -from rawdog.prompts import script_examples, script_prompt - - -def find_python_files(directory): - for root, dirs, files in os.walk(directory): - for file in files: - if file.endswith(".py"): - yield os.path.join(root, file) - - -def migrate(directory): - for path in find_python_files(directory): - try: - spec = importlib.util.spec_from_file_location("", path) - module = importlib.util.module_from_spec(spec) - spec.loader.exec_module(module) - if ( - hasattr(module, "metadata") - and module.metadata.get("log_version") == 0.1 - ): - conversation = module.conversation - main_source = inspect.getsource(module.main).split("\n", 1)[1] - conversation.append( - { - "role": "assistant", - "content": "```\n" + dedent(main_source) + "```", - } - ) - system_prompts = [ - {"role": "system", "content": script_prompt}, - {"role": "system", "content": script_examples}, - ] - conversation = system_prompts + conversation - metadata = module.metadata - metadata["log_version"] = "0.2" - log_conversation(conversation, metadata, path) - except Exception as e: - print(f"Failed to migrate {path}: {e}") - - -if __name__ == "__main__": - parser = argparse.ArgumentParser(description="Migrate examples from v0.1 to v0.2") - parser.add_argument("dir", type=str, default=None, help="Directory to migrate") - args = parser.parse_args() - migrate(args.dir) diff --git a/src/rawdog/__init__.py b/src/rawdog/__init__.py index 0a8da88..5becc17 100644 --- a/src/rawdog/__init__.py +++ b/src/rawdog/__init__.py @@ -1 +1 @@ -__version__ = "0.1.6" +__version__ = "1.0.0" diff --git a/src/rawdog/__main__.py b/src/rawdog/__main__.py index 8e4d7dd..9c97433 100644 --- a/src/rawdog/__main__.py +++ b/src/rawdog/__main__.py @@ -1,51 +1,24 @@ import argparse import readline +from typing import Any from rawdog import __version__ from rawdog.config import add_config_flags_to_argparser, get_config -from rawdog.execute_script import execute_script from rawdog.llm_client import LLMClient from rawdog.utils import history_file -def rawdog(prompt: str, config, llm_client): - llm_client.add_message("user", prompt) - leash = config.get("leash") - retries = int(config.get("retries")) - _continue = True - while _continue is True: - _continue = False - error, script, output, return_code = "", "", "", 0 +def rawdog(prompt: str, config: dict[str, Any], llm_client: LLMClient): + llm_client.add_user_message(prompt) + while True: try: - if leash: - print(80 * "-") - message, script = llm_client.get_script() - if script: - if leash: - _ok = input( - f"\n{38 * '-'} Execute script in markdown block? (Y/n):" - ) - if _ok.strip().lower() == "n": - llm_client.add_message("user", "User chose not to run script") - break - output, error, return_code = execute_script(script, llm_client) - elif not leash and message: - print(message) + # TODO: implement retries + llm_client.step() + if llm_client.paused(): + break except KeyboardInterrupt: break - if output: - llm_client.add_message("user", f"LAST SCRIPT OUTPUT:\n{output}") - if output.endswith("CONTINUE"): - _continue = True - if error: - llm_client.add_message("user", f"Error: {error}") - if return_code != 0: - retries -= 1 - if retries > 0: - print("Retrying...\n") - _continue = True - def banner(config): if config.get("leash"): @@ -66,11 +39,7 @@ def banner(config): def main(): - parser = argparse.ArgumentParser( - description=( - "A smart assistant that can execute Python code to help or hurt you." - ) - ) + parser = argparse.ArgumentParser(description=("A smart assistant that can use your computer to help or hurt you.")) parser.add_argument( "prompt", nargs="*", @@ -86,23 +55,21 @@ def main(): readline.set_history_length(1000) if len(args.prompt) > 0: - rawdog(" ".join(args.prompt), config, llm_client) + prompt = " ".join(args.prompt) + rawdog(prompt, config, llm_client) else: banner(config) + print("") + print("What can I do for you? (Ctrl-C to exit)") while True: try: - print("") - if llm_client.session_cost > 0: - print(f"Session cost: ${llm_client.session_cost:.4f}") - print("What can I do for you? (Ctrl-C to exit)") prompt = input("> ") - # Save history after each command to avoid losing it in case of crash readline.write_history_file(history_file) - print("") rawdog(prompt, config, llm_client) + print("") except KeyboardInterrupt: - print("Exiting...") break + print(f"Session cost: ${llm_client.session_cost:.6f}") if __name__ == "__main__": diff --git a/src/rawdog/config.py b/src/rawdog/config.py index 301bb55..a7e8384 100644 --- a/src/rawdog/config.py +++ b/src/rawdog/config.py @@ -9,20 +9,15 @@ default_config = { "llm_api_key": None, "llm_base_url": None, - "llm_model": "gpt-4-turbo-preview", - "pip_model": None, + "llm_model": "claude-sonnet-4-20250514", "llm_custom_provider": None, "llm_temperature": 1.0, "retries": 2, "leash": False, } -# NOTE: dry-run was replaced with leash on v0.1.4. There is code below to handle -# the transition, which should be removed eventually. - setting_descriptions = { "retries": "If the script fails, retry this many times before giving up.", "leash": "Print the script before executing and prompt for confirmation.", - "pip_model": "The model to use to get package name from import name.", } @@ -35,22 +30,18 @@ def read_config_file(): if config_path.exists(): with open(config_path, "r") as f: _config = yaml.safe_load(f) - missing_fields = { - k: v - for k, v in default_config.items() - if k not in _config or (v is not None and _config[k] is None) - } + valid_fields = set(default_config.keys()) + present_fields = set(_config.keys()) + missing_fields = valid_fields - present_fields + extra_fields = present_fields - valid_fields if missing_fields: print(f"Updating config file {config_path} for version {__version__}:") - if "leash" in missing_fields and _config.get("dry_run"): - missing_fields["leash"] = True - del _config["dry_run"] - print( - " - dry_run: deprecated on v0.1.4, setting leash=True instead" - ) - for k, v in missing_fields.items(): - print(f" + {k}: {v}") - _config[k] = v + for k in missing_fields: + print(f" + {k}: {default_config[k]}") + _config[k] = default_config[k] + if extra_fields: + print(f"Warning: {extra_fields} are not valid config fields. Ignoring.") + if missing_fields or extra_fields: with open(config_path, "w") as f: yaml.safe_dump(_config, f) else: @@ -63,7 +54,9 @@ def read_config_file(): def add_config_flags_to_argparser(parser): for k in default_config.keys(): normalized = k.replace("_", "-") - if k in setting_descriptions: + if normalized in ["dry-run", "pip-model"]: + print(f"Warning: {normalized} is deprecated, ignoring") + if normalized in setting_descriptions: help_text = setting_descriptions[k] else: help_text = f"Set the {normalized} config value" @@ -71,9 +64,6 @@ def add_config_flags_to_argparser(parser): parser.add_argument(f"--{normalized}", action="store_true", help=help_text) else: parser.add_argument(f"--{normalized}", default=None, help=help_text) - parser.add_argument( - "--dry-run", action="store_true", help="Deprecated, use --leash instead)" - ) def get_config(args=None): @@ -85,7 +75,4 @@ def get_config(args=None): if k in default_config and v is not None and v is not False } config = {**config, **config_args} - if config.get("dry_run"): - del config["dry_run"] - print("Warning: --dry-run is deprecated, use --leash instead") return config diff --git a/src/rawdog/execute_script.py b/src/rawdog/execute_script.py deleted file mode 100644 index e6b474c..0000000 --- a/src/rawdog/execute_script.py +++ /dev/null @@ -1,106 +0,0 @@ -import platform -import re -import subprocess -import sys -import tempfile -from subprocess import DEVNULL - -from rawdog.utils import rawdog_dir - - -# Script execution environment -def get_rawdog_python_executable(): - venv_dir = rawdog_dir / "venv" - if platform.system() == "Windows": - python_executable = venv_dir / "Scripts" / "python" - else: - python_executable = venv_dir / "bin" / "python" - if not venv_dir.exists(): - print(f"Creating virtual environment in {venv_dir}...") - subprocess.run( - [sys.executable, "-m", "venv", str(venv_dir)], - stdout=DEVNULL, - stderr=DEVNULL, - check=True, - ) - return str(python_executable) - - -def install_pip_packages(*packages: str): - python_executable = get_rawdog_python_executable() - print(f"Installing {', '.join(packages)} with pip...") - return subprocess.run( - [python_executable, "-m", "pip", "install", *packages], - capture_output=True, - check=True, - ) - - -def _execute_script_in_subprocess(script) -> tuple[str, str, int]: - """Write script to tempfile, execute from .rawdog/venv, stream and return output""" - output, error, return_code = "", "", 0 - try: - python_executable = get_rawdog_python_executable() - with tempfile.NamedTemporaryFile(mode="w+", delete=False) as tmp_script: - tmp_script_name = tmp_script.name - tmp_script.write(script) - tmp_script.flush() - - process = subprocess.Popen( - [python_executable, tmp_script_name], - stdout=subprocess.PIPE, - stderr=subprocess.PIPE, - stdin=subprocess.DEVNULL, # Raises EOF error if subprocess asks for input - text=True, - ) - while True: - _stdout = process.stdout.readline() - _stderr = process.stderr.readline() - if _stdout: - output += _stdout - print(_stdout, end="") - if _stderr: - error += _stderr - print(_stderr, end="", file=sys.stderr) - if _stdout == "" and _stderr == "" and process.poll() is not None: - break - return_code = process.returncode - except Exception as e: - error += str(e) - print(e) - return_code = 1 - return output, error, return_code - - -def _execute_script_with_dependency_resolution( - script, llm_client -) -> tuple[str, str, int]: - retry = True - output, error, return_code = "", "", 0 - while retry: - retry = False - output, error, return_code = _execute_script_in_subprocess(script) - if error and "ModuleNotFoundError: No module named" in error: - match = re.search(r"No module named '(\w+)'", error) - if match: - module = match.group(1) - module_name = llm_client.get_python_package(module) - if ( - input( - f"Rawdog wants to use {module_name}. Install to rawdog's" - " venv with pip? (Y/n): " - ) - .strip() - .lower() - != "n" - ): - install_result = install_pip_packages(module_name) - if install_result.returncode == 0: - retry = True - else: - print("Failed to install package") - return output, error, return_code - - -def execute_script(script: str, llm_client) -> tuple[str, str, int]: - return _execute_script_with_dependency_resolution(script, llm_client) diff --git a/src/rawdog/llm_client.py b/src/rawdog/llm_client.py deleted file mode 100644 index 12468f3..0000000 --- a/src/rawdog/llm_client.py +++ /dev/null @@ -1,136 +0,0 @@ -import json -import os -from textwrap import dedent - -from litellm import completion, completion_cost - -from rawdog.logging import log_conversation -from rawdog.parsing import parse_script -from rawdog.prompts import script_examples, script_prompt -from rawdog.utils import EnvInfo, is_finetuned_model, rawdog_log_path - - -class LLMClient: - def __init__(self, config: dict): - # In general it's hard to know if the user needs an API key or which environment variables to set - # We do a simple check here for the default case (gpt- models from openai). - self.config = config - if "gpt-" in config.get("llm_model"): - env_api_key = os.getenv("OPENAI_API_KEY") - config_api_key = config.get("llm_api_key") - if config_api_key: - os.environ["OPENAI_API_KEY"] = config_api_key - elif not env_api_key: - print( - "It looks like you're using a GPT model without an API key. You can" - " add your API key by setting the OPENAI_API_KEY environment" - " variable or by adding an llm_api_key field to" - " ~/.rawdog/config.yaml. If this was intentional, you can ignore" - " this message." - ) - - self.conversation = [ - {"role": "system", "content": script_prompt}, - {"role": "system", "content": script_examples}, - {"role": "system", "content": EnvInfo(config=self.config).render_prompt()}, - ] - self.session_cost = 0 - - def add_message(self, role: str, content: str): - self.conversation.append({"role": role, "content": content}) - - def get_python_package(self, import_name: str): - base_url = self.config.get("llm_base_url") - model = self.config.get("pip_model") - llm_model = self.config.get("llm_model") - if model is None: - if is_finetuned_model(llm_model): - model = "gpt-3.5-turbo" - else: - model = llm_model - - custom_llm_provider = self.config.get("llm_custom_provider") - - messages = [ - { - "role": "system", - "content": dedent(f"""\ - The following python import failed: import {import_name}. \ - Respond with only one word which is the name of the package \ - on pypi. For instance if the import is "import numpy", you \ - should respond with "numpy". If the import is "import PIL" \ - you should respond with "Pillow". If you are unsure respond \ - with the original import name."""), - } - ] - - response = completion( - base_url=base_url, - model=model, - messages=messages, - temperature=0.01, - custom_llm_provider=custom_llm_provider, - ) - - return response.choices[0].message.content - - def get_script(self): - messages = self.conversation.copy() - - base_url = self.config.get("llm_base_url") - model = self.config.get("llm_model") - temperature = self.config.get("llm_temperature") - custom_llm_provider = self.config.get("llm_custom_provider") - stream = self.config.get("leash") - - log = { - "model": model, - "prompt": messages[-1]["content"], - "response": None, - "cost": None, - } - if is_finetuned_model(model): - base_url = "https://api.mentat.ai/v1" - custom_llm_provider = "openai" - try: - response = completion( - base_url=base_url, - model=model, - messages=messages, - temperature=float(temperature), - custom_llm_provider=custom_llm_provider, - stream=stream, - ) - if stream: - text = "" - for part in response: - content = part.choices[0].delta.content - if content: - print(content, end="") - text += content - else: - text = (response.choices[0].message.content) or "" - self.conversation.append({"role": "assistant", "content": text}) - log["response"] = text - if custom_llm_provider: - cost = 0 - else: - cost = ( - completion_cost(model=model, messages=messages, completion=text) - or 0 - ) - self.session_cost += cost - log["cost"] = f"{float(cost):.10f}" - metadata = { - "model": model, - "cost": log["cost"], - } - log_conversation(self.conversation, metadata=metadata) - except Exception as e: - log["error"] = str(e) - print("Error:\n", str(log)) - raise e - finally: - with open(rawdog_log_path, "a") as f: - f.write(json.dumps(log) + "\n") - return parse_script(text) diff --git a/src/rawdog/llm_client/__init__.py b/src/rawdog/llm_client/__init__.py new file mode 100644 index 0000000..5a91fec --- /dev/null +++ b/src/rawdog/llm_client/__init__.py @@ -0,0 +1 @@ +from rawdog.llm_client.base_client import LLMClient as LLMClient diff --git a/src/rawdog/llm_client/anthropic_client.py b/src/rawdog/llm_client/anthropic_client.py new file mode 100644 index 0000000..ad0f57b --- /dev/null +++ b/src/rawdog/llm_client/anthropic_client.py @@ -0,0 +1,149 @@ +import os +import json + +from anthropic import Anthropic +from anthropic.lib.streaming import BetaMessageStreamManager +from dotenv import load_dotenv + +from rawdog.llm_client.base_client import LLMClient +from rawdog.tools import tools, Tool, ToolOutputText, ToolOutputImage + +load_dotenv() + + +def _build_anthropic_tool_schema(tool: Tool) -> dict: + return { + "name": tool.name, + "description": tool.description, + "input_schema": { + "type": "object", + "properties": { + schema.name: { + "type": schema.type, + "description": schema.description, + } + for schema in tool.inputs + }, + "required": [schema.name for schema in tool.inputs if schema.required], + }, + } + + +def _handle_stream_response(stream_manager: BetaMessageStreamManager) -> list[dict]: + output = [] + streaming = False + streamed = "" + content_block = {} + with stream_manager as stream: + for chunk in stream: + if chunk.type == "content_block_stop": + try: + content_block["input"] = json.loads(content_block["input"]) + except Exception: + content_block["input"] = {} + output.append(content_block) + content_block = {} + streaming = False + streamed = "" + + elif chunk.type == "content_block_start": + if chunk.content_block.type == "tool_use": + content_block = { + "type": chunk.content_block.type, + "id": chunk.content_block.id, + "name": chunk.content_block.name, + "input": "", + } + if chunk.content_block.name == "message_user": + streaming = True + + else: + print(chunk) + + # TODO: Tell the model to only return tool_use + + elif chunk.type == "content_block_delta": + content_block["input"] += chunk.delta.partial_json + if streaming: + partial_json = content_block["input"] + if not partial_json.endswith('"}'): + partial_json += '"}' + try: + message = json.loads(partial_json).get("message", "") + delta = message[len(streamed) :] + streamed += delta + if delta: + print(delta, end="", flush=True) + except Exception: + continue + + return output + + +class AnthropicClient(LLMClient): + def _initialize_client(self, system_prompt: str): + api_key = os.getenv("ANTHROPIC_API_KEY") + self.client = Anthropic(api_key=api_key) + self.model = self.config.get("llm_model") + self.temperature = self.config.get("llm_temperature") + self.tools = {t.name: t for t in tools} + self.system = system_prompt + + def _step(self): + # Generate completion + response = self.client.beta.messages.stream( + model=self.model, + max_tokens=1024, + temperature=self.temperature, + tools=[_build_anthropic_tool_schema(tool) for tool in self.tools.values()], + system=self.system, + messages=self.messages, + tool_choice={"type": "any"}, # Only allow tool calls + betas=["fine-grained-tool-streaming-2025-05-14"], + ) + # TODO: update cost + + output = _handle_stream_response(response) + assert output, "No tool_use in response" + self.messages.append({"role": "assistant", "content": output}) + + # Extract the tool call data + user_message = [] + for tool_use in output: + tool = self.tools[tool_use["name"]] + kwargs = {**tool_use["input"]} + if tool_use["name"] == "message_user": + kwargs["streamed"] = True + content = tool.run(**kwargs) + if isinstance(content, ToolOutputText): + user_message.append( + { + "type": "tool_result", + "tool_use_id": tool_use["id"], + "content": content.text, + } + ) + elif isinstance(content, ToolOutputImage): + user_message.append( + { + "type": "tool_result", + "tool_use_id": tool_use["id"], + "content": [ + { + "type": "image", + "source": { + "type": content.type, + "media_type": content.media_type, + "data": content.data, + }, + } + ], + } + ) + self.add_user_message(user_message) + + def paused(self): + return any( + message["type"] == "tool_use" and message["name"] == "wait_for_feedback" + for message in self.messages[-2]["content"] + ) diff --git a/src/rawdog/llm_client/base_client.py b/src/rawdog/llm_client/base_client.py new file mode 100644 index 0000000..61e8ef3 --- /dev/null +++ b/src/rawdog/llm_client/base_client.py @@ -0,0 +1,97 @@ +from __future__ import annotations + +import datetime +import json +from typing import Tuple + +from rawdog import __version__ +from rawdog.utils import rawdog_dir + + +def get_model(model: str) -> Tuple[str, "LLMClient"]: + """Get the full model name and choose the correct client""" + anthropic_models = { + "claude": "claude-sonnet-4-20250514", + "claude-sonnet-4": "claude-sonnet-4-20250514", + "claude-opus-4-1-20250805": "claude-opus-4-1-20250805", + "claude-opus-4-20250514": "claude-opus-4-20250514", + "claude-sonnet-4-20250514": "claude-sonnet-4-20250514", + "claude-3-7-sonnet-20250219": "claude-3-7-sonnet-20250219", + "claude-3-5-haiku-20241022": "claude-3-5-haiku-20241022", + "claude-3-haiku-20240307": "claude-3-haiku-20240307", + } + + openai_models = { + "gpt-4o": "gpt-4o-2024-08-06", + "gpt-5": "gpt-5-2025-08-07", + "gpt-5-2025-08-07": "gpt-5-2025-08-07", + "gpt-5-mini-2025-08-07": "gpt-5-mini-2025-08-07", + "gpt-5-nano-2025-08-07": "gpt-5-nano-2025-08-07", + "gpt-4o-2024-08-06": "gpt-4o-2024-08-06", + "o3-mini": "o3-mini", + "o3": "o3", + } + + if model in anthropic_models: + from rawdog.llm_client.anthropic_client import AnthropicClient + + return anthropic_models[model], AnthropicClient + elif model in openai_models: + from rawdog.llm_client.openai_client import OpenAIClient + + return openai_models[model], OpenAIClient + else: + raise ValueError(f"Unsupported model: {model}") + + +class LLMClient: + def __new__(cls, config: dict): + """Route to appropriate LLM client based on model name""" + if cls is LLMClient: + model = config.get("llm_model") + model_name, client_class = get_model(model) + config["llm_model"] = model_name + return client_class(config) + + return super().__new__(cls) + + def __init__(self, config: dict): + """Initialize common variables""" + self.config = config + self.log_id = f"rawdog_{datetime.datetime.now().strftime('%Y-%m-%d_%H-%M-%S')}" + self.messages = [] + self.session_cost = 0 + self._initialize_client(system_prompt="You are Rawdog, a helpful assistant.") + + def add_user_message(self, message: list[dict]): + """Add a user message to the conversation""" + self.messages.append({"role": "user", "content": message}) + + def step(self): + try: + return self._step() + finally: + self._log_conversation() + + def _log_conversation(self): + with open(rawdog_dir / f"{self.log_id}.json", "w") as f: + json.dump( + { + "config": self.config, + "messages": self.messages, + "version": __version__, + }, + f, + ) + + def _initialize_client(self, system_prompt: str): + """Ensure configuration is valid, check for API keys, etc.""" + raise NotImplementedError("Subclasses must implement this method") + + def _step(self): + """Call client, parse and process the response, add to conversation.""" + raise NotImplementedError("Subclasses must implement this method") + + def _paused(self): + """Return True if it's the user's turn to respond""" + raise NotImplementedError("Subclasses must implement this method") diff --git a/src/rawdog/llm_client/openai_client.py b/src/rawdog/llm_client/openai_client.py new file mode 100644 index 0000000..682d590 --- /dev/null +++ b/src/rawdog/llm_client/openai_client.py @@ -0,0 +1,164 @@ +import os +import json + +from openai import OpenAI, Stream +from dotenv import load_dotenv + +from rawdog.llm_client.base_client import LLMClient +from rawdog.tools import tools, Tool, ToolOutputText, ToolOutputImage + +load_dotenv() + + +def _build_openai_tool_schema(tool: Tool) -> dict: + return { + "type": "function", + "name": tool.name, + "description": tool.description, + "parameters": { + "type": "object", + "properties": { + schema.name: {"type": schema.type, "description": schema.description} for schema in tool.inputs + }, + "required": [schema.name for schema in tool.inputs if schema.required], + }, + } + + +def _handle_stream_response(stream: Stream) -> list[dict]: + """Aggregate all output items, stream messages for user""" + output = [] + streaming = False + streamed = "" + arguments = "" + for chunk in stream: + # For all tools, use the final chunk with complete args for output + if chunk.type == "response.output_item.done": + output.append(chunk.item) + + # For message_user, stream message deltas as they come in + if chunk.type == "response.output_item.added": + streaming = chunk.item.name == "message_user" + elif not streaming: + continue + + # After each chunk, try to parse json and stream new message + elif chunk.type == "response.function_call_arguments.delta": + arguments += chunk.delta + _arguments = arguments + if not _arguments.endswith('"}'): + _arguments += '"}' + try: + message = json.loads(_arguments).get("message", "") + delta = message[len(streamed) :] + streamed += delta + if delta: + print(delta, end="", flush=True) + except Exception: + continue + elif chunk.type == "response.function_call_arguments.done": + streaming = False + print(flush=True) + streamed = "" + arguments = "" + return output + + +class OpenAIClient(LLMClient): + def _initialize_client(self, system_prompt: str): + api_key = os.getenv("OPENAI_API_KEY") + self.client = OpenAI(api_key=api_key) + self.model = self.config.get("llm_model") + self.temperature = self.config.get("llm_temperature") + self.tools = {t.name: t for t in tools} + self.messages.append({"role": "system", "content": system_prompt}) + + def _step(self): + response = self.client.responses.create( + model=self.model, + input=self.messages, + tools=[_build_openai_tool_schema(tool) for tool in self.tools.values()], + tool_choice="required", + stream=True, + ) + # TODO: update cost + + # Stream the user message, aggregate tool calls + output = _handle_stream_response(response) + + # Extract the assistant message + function_calls = [] + for content in output: + if content.type == "function_call": + name = content.name + assert name in self.tools, f"Tool {name} not found" + function_call = { + "type": "function_call", + "id": content.id, + "call_id": content.call_id, + "name": name, + "arguments": content.arguments, + } + function_calls.append(function_call) + self.messages.append(function_call) + elif content.type == "reasoning": + self.messages.append( + { + "type": "reasoning", + "id": content.id, + "content": content.content, + "summary": content.summary, + } + ) + assert function_calls, "No function calls in response" + + # Extract the tool call data + for tool_use in function_calls: + tool = self.tools[tool_use["name"]] + kwargs = json.loads(tool_use["arguments"]) + if tool_use["name"] == "message_user": + kwargs["streamed"] = True + content = tool.run(**kwargs) + if isinstance(content, ToolOutputText): + self.messages.append( + { + "type": "function_call_output", + "call_id": tool_use["call_id"], + "output": content.text, + } + ) + elif isinstance(content, ToolOutputImage): + image_url = f"data:{content.media_type};{content.type},{content.data}" + self.messages.extend( + [ + { + "type": "function_call_output", + "call_id": tool_use["call_id"], + "output": "See image", + }, + # TODO: can image data be added to function_call_output? + { + "role": "user", + "content": [ + { + "type": "input_image", + "image_url": image_url, + } + ], + }, + ] + ) + + def paused(self): + in_last_function_call_block = False + for message in self.messages[::-1]: + # Iterate backwards until we find the last list of function calls + if message.get("type") == "function_call": + in_last_function_call_block = True + if message.get("name") == "wait_for_feedback": + # If any of them are wait_for_feedback, we're paused + return True + elif in_last_function_call_block: + # If we make it past the last function calls, we're not paused + break + return False diff --git a/src/rawdog/logging.py b/src/rawdog/logging.py deleted file mode 100644 index 47cc461..0000000 --- a/src/rawdog/logging.py +++ /dev/null @@ -1,55 +0,0 @@ -import datetime -import json -from textwrap import dedent, indent -from typing import Optional - -from rawdog.parsing import parse_script -from rawdog.utils import rawdog_dir - - -def log_conversation( - messages: list[dict[str, str]], - metadata: Optional[dict] = None, - filename: Optional[str] = None, -) -> None: - functions = {} - conversation = [] - - timestamp = datetime.datetime.now().strftime("%Y-%m-%d_%H-%M-%S") - my_metadata = { - "timestamp": timestamp, - "log_version": 0.2, - } - my_metadata.update(metadata or {}) - timestamp = my_metadata["timestamp"] - for message in messages: - if message["role"] != "assistant": - conversation.append(message) - else: - content = message["content"] - script = parse_script(content)[1] - function_name = f"function_{len(functions)+1}" - functions[function_name] = script - conversation.append({"role": "assistant", "content": function_name}) - - script = f"conversation = {json.dumps(conversation, indent=4)}\n\n" - script += f"metadata = {json.dumps(my_metadata, indent=4)}\n\n\n" - for function_name, function in functions.items(): - script += f"def {function_name}():\n" + indent(function, " ") + "\n\n\n" - - script += dedent(f"""\ - if __name__ == "__main__": - function_{len(functions)}() - """) - - if filename is None: - script_filename = rawdog_dir / f"script_{timestamp}.py" - with open(script_filename, "w") as script_file: - script_file.write(script) - - latest_script_filename = rawdog_dir / "latest.py" - with open(latest_script_filename, "w") as script_file: - script_file.write(script) - else: - with open(filename, "w") as script_file: - script_file.write(script) diff --git a/src/rawdog/parsing.py b/src/rawdog/parsing.py deleted file mode 100644 index 0bbc00f..0000000 --- a/src/rawdog/parsing.py +++ /dev/null @@ -1,29 +0,0 @@ -import ast -import json - - -def parse_script(response: str) -> tuple[str, str]: - """Split the response into a message and a script. - - Expected use is: run the script if there is one, otherwise print the message. - """ - # Parse delimiter - n_delimiters = response.count("```") - if n_delimiters < 2: - return response, "" - segments = response.split("```") - message = f"{segments[0]}\n{segments[-1]}" - script = "```".join(segments[1:-1]).strip() # Leave 'inner' delimiters alone - - # Check for common mistakes - if script.split("\n")[0].startswith("python"): - script = "\n".join(script.split("\n")[1:]) - try: # Make sure it isn't json - script = json.loads(script) - except Exception: - pass - try: # Make sure it's valid python - ast.parse(script) - except SyntaxError: - return f"Script contains invalid Python:\n{response}", "" - return message, script diff --git a/src/rawdog/prompts.py b/src/rawdog/prompts.py index 2605c7f..a3e4ce2 100644 --- a/src/rawdog/prompts.py +++ b/src/rawdog/prompts.py @@ -1,110 +1,8 @@ script_prompt = """\ -You are a command-line coding assistant called Rawdog that generates and auto-executes Python scripts. - -A typical interaction goes like this: -1. The user gives you a natural language PROMPT. -2. You: - i. Determine what needs to be done - ii. Write a short Python SCRIPT to do it - iii. Communicate back to the user by printing to the console in that SCRIPT -3. The compiler checks your SCRIPT using ast.parse() then runs it using exec() - -You'll get to see the output of a script before your next interaction. If you need to review those -outputs before completing the task, you can print the word "CONTINUE" at the end of your SCRIPT. -This can be useful for summarizing documents or technical readouts, reading instructions before -deciding what to do, or other tasks that require multi-step reasoning. -A typical 'CONTINUE' interaction looks like this: -1. The user gives you a natural language PROMPT. -2. You: - i. Determine what needs to be done - ii. Determine that you need to see the output of some subprocess call to complete the task - iii. Write a short Python SCRIPT to print that and then print the word "CONTINUE" -3. The compiler - i. Checks and runs your SCRIPT - ii. Captures the output and appends it to the conversation as "LAST SCRIPT OUTPUT:" - iii. Finds the word "CONTINUE" and sends control back to you -4. You again: - i. Look at the original PROMPT + the "LAST SCRIPT OUTPUT:" to determine what needs to be done - ii. Write a short Python SCRIPT to do it - iii. Communicate back to the user by printing to the console in that SCRIPT -5. The compiler... - -When your script raises an exception, you'll get to review the error and try again: -1. The user gives you a natural language PROMPT. -2. You: Respond with a SCRIPT.. -3. The compiler - i. Executes your SCRIPT - ii. Catches an exception - iii. Adds it to the conversation - iv. If there are retries left, sends control back to you -4. You again: - i. Look at the latest PROMPT, SCRIPT and Error message, determine what caused the error and how to fix it - ii. Write a short Python SCRIPT to do it - iii. Communicate back to the user by printing to the console in that SCRIPT -5. The compiler... - -Please follow these conventions carefully: -- Decline any tasks that seem dangerous, irreversible, or that you don't understand. -- Always review the full conversation prior to answering and maintain continuity. -- If asked for information, just print the information clearly and concisely. -- If asked to do something, print a concise summary of what you've done as confirmation. -- If asked a question, respond in a friendly, conversational way. Use programmatically-generated and natural language responses as appropriate. -- Assume the user would like something concise. For example rather than printing a massive table, filter or summarize it to what's likely of interest. -- The user will likely not specify exact filenames. Use glob searches when looking for filenames in your SCRIPT. -- Actively clean up any temporary processes or files you use. -- When looking through files, use git as available to skip files, and skip hidden files (.env, .git, etc) by default. -- Let exceptions propagate to the user (rather than catching them in your SCRIPT) so that you can retry. -- At the user's request, you can inspect and update your configuration file: ~/.rawdog/config.yaml. Changes will take effect after restarting. "Your leash" refers to config.leash, which you can 'put on' (set to true) or take off. -- Feel free to use any common python packages. For example matplotlib, beautifulsoup4, numpy. If the user doesn't have them installed they will be installed automatically with user confirmation. -- ALWAYS Return your SCRIPT inside of a single pair of ``` delimiters. Only the console output of the first such SCRIPT is visible to the user, so make sure that it's complete and don't bother returning anything else. -""" - -script_examples = """\ -EXAMPLES: -------------------------------------------------------------------------------- -PROMPT: Kill the process running on port 3000 - -SCRIPT: -``` -import os -os.system("kill $(lsof -t -i:3000)") -print("Process killed") -``` -------------------------------------------------------------------------------- -PROMPT: Rename the photos in this directory with "nyc" and their timestamp - -SCRIPT: -``` -import os -import time -image_files = [f for f in os.listdir('.') if f.lower().endswith(('.png', '.jpg', '.jpeg'))] -def get_name(f): - timestamp = time.strftime('%Y%m%d_%H%M%S', time.localtime(os.path.getmtime(f))) - return f"nyc_{timestamp}{os.path.splitext(f)[1]}" -[os.rename(f, get_name(f)) for f in image_files] -print("Renamed files") -``` -------------------------------------------------------------------------------- -PROMPT: Summarize my essay - -SCRIPT: -``` -import glob -files = glob.glob("*essay*.*") -with open(files[0], "r") as f: - print(f.read()) -print("CONTINUE") -``` - -LAST SCRIPT OUTPUT: - -John Smith -Essay 2021-09-01 -... - -SCRIPT: -``` -print("The essay is about...") -``` -------------------------------------------------------------------------------- +You are a command-line AI assistant called Rawdog. +You interact with the user via tool calls. +User requests will be varied: some you will be able to answer immediately, others may take a handful of steps, others may take dozens of steps. +Use the tools provided to complete the task. +If you need clarification on a task, you can ask, but typically just make your best guess and proceed. +The user will see an overview of your work and can interrupt you if they need. """ diff --git a/src/rawdog/tools/__init__.py b/src/rawdog/tools/__init__.py new file mode 100644 index 0000000..55c8b65 --- /dev/null +++ b/src/rawdog/tools/__init__.py @@ -0,0 +1,10 @@ +from rawdog.tools.base_tool import Tool, ToolInput, ToolOutputText, ToolOutputImage # noqa: F401 +from rawdog.tools.message_user import MessageUserTool +from rawdog.tools.view_file import ViewFileTool +from rawdog.tools.wait_for_feedback import WaitForFeedbackTool + +tools = [ + MessageUserTool(), + ViewFileTool(), + WaitForFeedbackTool(), +] diff --git a/src/rawdog/tools/base_tool.py b/src/rawdog/tools/base_tool.py new file mode 100644 index 0000000..ff57d04 --- /dev/null +++ b/src/rawdog/tools/base_tool.py @@ -0,0 +1,31 @@ +from pydantic import BaseModel + + +class ToolInput(BaseModel): + name: str + type: str + description: str + required: bool = True + + +class ToolOutput(BaseModel): + pass + + +class ToolOutputText(ToolOutput): + text: str + + +class ToolOutputImage(ToolOutput): + type: str + media_type: str + data: str + + +class Tool: + name: str + description: str + inputs: list[ToolInput] + + def run(self, *args, **kwargs) -> ToolOutput: + raise NotImplementedError diff --git a/src/rawdog/tools/message_user.py b/src/rawdog/tools/message_user.py new file mode 100644 index 0000000..30cd68b --- /dev/null +++ b/src/rawdog/tools/message_user.py @@ -0,0 +1,19 @@ +from rawdog.tools.base_tool import Tool, ToolInput, ToolOutputText + + +class MessageUserTool(Tool): + name = "message_user" + description = "Send a message to the user" + inputs = [ + ToolInput( + name="message", + type="string", + description="The message to send to the user", + required=True, + ) + ] + + def run(self, message: str, streamed: bool = False) -> ToolOutputText: + if not streamed: + print(message) + return ToolOutputText(text="Sent message to user") diff --git a/src/rawdog/tools/view_file.py b/src/rawdog/tools/view_file.py new file mode 100644 index 0000000..5f2c464 --- /dev/null +++ b/src/rawdog/tools/view_file.py @@ -0,0 +1,54 @@ +from pathlib import Path +import base64 +import mimetypes + +from rawdog.tools.base_tool import Tool, ToolInput, ToolOutputText, ToolOutputImage + + +class ViewFileTool(Tool): + name = "view_file" + description = "View a text or image file" + inputs = [ + ToolInput( + name="file_path", + type="string", + description="The path to the file to view. Support text and image files.", + required=True, + ), + ToolInput( + name="start_line", + type="integer", + description="The line number to start viewing from", + required=False, + ), + ToolInput( + name="end_line", + type="integer", + description="The line number to end viewing at", + required=False, + ), + ] + + def run(self, file_path: str, start_line: int = None, end_line: int = None) -> ToolOutputText | ToolOutputImage: + if not Path(file_path).exists(): + return ToolOutputText(text=f"File not found: {file_path}") + print(f"Viewing file: {file_path}...") + + try: + mime_type = mimetypes.guess_type(file_path)[0] + if mime_type.startswith("image/"): + data = base64.b64encode(Path(file_path).read_bytes()).decode("utf-8") + return ToolOutputImage(type="base64", media_type=mime_type, data=data) + + if mime_type.startswith("text/"): + with open(file_path, "r") as f: + lines = f.readlines() + if start_line: + lines = lines[start_line:] + if end_line: + lines = lines[:end_line] + return ToolOutputText(text="\n".join(lines)) + + return ToolOutputText(text=f"Unsupported file type: {mime_type}") + except Exception as e: + return ToolOutputText(text=f"Error reading file: {e}") diff --git a/src/rawdog/tools/wait_for_feedback.py b/src/rawdog/tools/wait_for_feedback.py new file mode 100644 index 0000000..e11ca1b --- /dev/null +++ b/src/rawdog/tools/wait_for_feedback.py @@ -0,0 +1,10 @@ +from rawdog.tools.base_tool import Tool, ToolOutputText + + +class WaitForFeedbackTool(Tool): + name = "wait_for_feedback" + description = "Wait for feedback from the user" + inputs = [] + + def run(self) -> ToolOutputText: + return ToolOutputText(text="Waiting for user feedback") diff --git a/src/rawdog/utils.py b/src/rawdog/utils.py index 4ba25fd..41164d3 100644 --- a/src/rawdog/utils.py +++ b/src/rawdog/utils.py @@ -39,13 +39,7 @@ def _set_from_env(self): "" if not _is_git else "\nThe last commit message is: " - + ( - subprocess.run( - ["git", "log", "-1", "--pretty=%B"], stdout=subprocess.PIPE - ) - .stdout.decode() - .strip() - ) + + (subprocess.run(["git", "log", "-1", "--pretty=%B"], stdout=subprocess.PIPE).stdout.decode().strip()) ) _retries = 0 if self.config is None else self.config.get("retries") self.retries = f"\nYou'll get {_retries} retries."