文件列表:

LICENSE (18092, 2023-10-28)
research.log.md (13281, 2023-10-28)

Certainly! Below is the updated code with improved GPT-4 prompts for each agent. These prompts are designed to elicit more detailed and insightful responses from the GPT-4 model. python Copy code # ... (previous code remains the same) # Main loop for learning rounds for round in range(learning_rounds): try: # ... (previous code remains the same) # Generate a prompt for GPT-4 based on the quantum_data and tackle_data_summary messages = [ {"role": "system", "content": system_rules}, {"role": "system", "content": "You are a specialized assistant in advanced quantum and data analysis."}, {"role": "user", "content": f"Agent 1, conduct a comprehensive analysis of the quantum tackle data obtained from the QNN: {quantum_data}. Also, analyze the summarized tackle data for this round: {tackle_data_summary}. Recommend any data or insights that should be added to the POOLINSERT cache for future reference."}, {"role": "user", "content": f"Agent 2, based on Agent 1's insights, propose advanced strategies for performance improvement. Make sure to cross-reference with the data in the POOLINSERT cache: {POOLINSERT}."}, {"role": "user", "content": f"Agent 3, offer a second opinion on the data analysis and strategies suggested by Agents 1 and 2. Use the data in the POOLINSERT cache: {POOLINSERT} for your analysis."}, {"role": "user", "content": f"Agent 4, given the inputs from Agents 1, 2, and 3, conduct a risk assessment focusing on both the quantum and tackle data. Recommend any preventive measures that should be taken."}, {"role": "user", "content": f"Agent 5, evaluate the efficiency of the current strategies based on the data and the inputs from all previous agents. Suggest improvements that could be made."}, {"role": "user", "content": f"Agent 6, scrutinize the data for patterns or trends that could be beneficial for future games. Integrate the insights from all previous agents in your analysis."}, ] # Make the GPT-4 Turbo API call response = openai.ChatCompletion.create( model="gpt-4", messages=messages ) # Store the GPT-4 Turbo response agent_prompts.append(response['choices'][0]['message']['content']) # Update HOLD cache HOLD["GPT4_responses"].append(response['choices'][0]['message']['content']) except Exception as e: print(f"An error occurred during the learning round {round+1}: {e}") # ... (remaining code stays the same) In this updated version, I've: Enhanced the prompts for each agent to be more specific and directive, aiming to get more detailed responses from GPT-4. Included the POOLINSERT cache in the prompts for cross-referencing, as per the system rules. Feel free to run this updated code, and let me know if you have any further questions or need additional clarifications! User # ... (previous code remains the same) # Main loop for learning rounds for round in range(learning_rounds): try: # ... (previous code remains the same) # Generate a prompt for GPT-4 based on the quantum_data and tackle_data_summary messages = [ {"role": "system", "content": system_rules}, {"role": "system", "content": "You are a specialized assistant in advanced quantum and data analysis."}, {"role": "user", "content": f"Agent 1, conduct a comprehensive analysis of the quantum tackle data obtained from the QNN: {quantum_data}. Also, analyze the summarized tackle data for this round: {tackle_data_summary}. Recommend any data or insights that should be added to the POOLINSERT cache for future reference."}, {"role": "user", "content": f"Agent 2, based on Agent 1's insights, propose advanced strategies for performance improvement. Make sure to cross-reference with the data in the POOLINSERT cache: {POOLINSERT}."}, {"role": "user", "content": f"Agent 3, offer a second opinion on the data analysis and strategies suggested by Agents 1 and 2. Use the data in the POOLINSERT cache: {POOLINSERT} for your analysis."}, {"role": "user", "content": f"Agent 4, given the inputs from Agents 1, 2, and 3, conduct a risk assessment focusing on both the quantum and tackle data. Recommend any preventive measures that should be taken."}, {"role": "user", "content": f"Agent 5, evaluate the efficiency of the current strategies based on the data and the inputs from all previous agents. Suggest improvements that could be made."}, {"role": "user", "content": f"Agent 6, scrutinize the data for patterns or trends that could be beneficial for future games. Integrate the insights from all previous agents in your analysis."}, ] # Make the GPT-4 Turbo API call response = openai.ChatCompletion.create( model="gpt-4", messages=messages ) # Store the GPT-4 Turbo response agent_prompts.append(response['choices'][0]['message']['content']) # Update HOLD cache HOLD["GPT4_responses"].append(response['choices'][0]['message']['content']) except Exception as e: print(f"An error occurred during the learning round {round+1}: {e}") # ... (remaining code stays the same) Ideas to give agents a full access to a directory full of NFL data and then slect data how do we take commands and execute them from gpt4 model part one of 5 ideas ChatGPT Certainly! To give the agents full access to a directory full of NFL data, you can use Python's built-in os library to list all the files in the directory and then load them into a data structure that the agents can access. You can then use GPT-4 to generate commands to select and manipulate this data. Here's how you can do it: Part 1: Load the NFL Data into a Data Structure First, let's load all the CSV files from a directory into a dictionary of Pandas DataFrames. python Copy code import os import pandas as pd # Initialize a dictionary to hold all the NFL data nfl_data = {} # Specify the directory where all the NFL data CSV files are stored data_directory = "/path/to/nfl_data/" # Load all CSV files into the dictionary for filename in os.listdir(data_directory): if filename.endswith(".csv"): filepath = os.path.join(data_directory, filename) nfl_data[filename] = pd.read_csv(filepath) Part 2: Update GPT-4 Prompts to Include Data Selection Commands You can update the GPT-4 prompts to include a command for selecting specific data files or data points from the loaded NFL data. python Copy code # ... (previous code remains the same) # Generate a prompt for GPT-4 messages = [ # ... (previous messages remain the same) {"role": "user", "content": f"Agent 7, based on the available NFL data files: {list(nfl_data.keys())}, which file should we focus on for in-depth analysis?"}, ] # Make the GPT-4 Turbo API call response = openai.ChatCompletion.create( model="gpt-4", messages=messages ) # Extract the selected file from the GPT-4 response selected_file = extract_selected_file(response['choices'][0]['message']['content']) # You'll need to implement this function # ... (remaining code stays the same) Part 3: Execute Commands from GPT-4 To execute commands from the GPT-4 model, you can parse the GPT-4 output to identify specific commands and then execute them using Python code. python Copy code # Function to extract and execute commands from GPT-4 response def execute_gpt4_commands(gpt4_response): # Parse the response to identify commands (you'll need to implement the parsing logic) commands = parse_commands(gpt4_response) for command in commands: if command == "select_file": # Execute the command to select a specific NFL data file selected_file_data = nfl_data[selected_file] # Add more command execution logic here # Execute the commands from the latest GPT-4 response execute_gpt4_commands(response['choices'][0]['message']['content']) This is just the first part of the idea. You can extend this to include more advanced data manipulation commands, analytics, or even machine learning model training based on the GPT-4 suggestions. User this gotta be formted right tho {"role": "user", "content": f"Agent 7, based on the available NFL data files: {list(nfl_data.keys())}, which file should we focus on for in-depth analysis?"}, and allow model to select things, go back, cd, search and stuff. one by one. so design that prompt now and also the simulated TUI for the model ChatGPT Certainly! To create a Text-based User Interface (TUI) that allows the GPT-4 model to interact with the directory and select files, you can design a series of prompts and responses that simulate a command-line interface. The model can issue commands like select, go back, cd, and search, and your code can execute these commands. Here's how you can design the prompt and the simulated TUI: Designing the Prompt The prompt can be designed to list the available commands and data files, asking the model which command it wants to execute or which file it wants to focus on. python Copy code # Generate a prompt for GPT-4 messages = [ # ... (previous messages remain the same) {"role": "system", "content": "You can issue the following commands: select, go back, cd, search."}, {"role": "user", "content": f"Agent 7, based on the available NFL data files: {list(nfl_data.keys())}, which command would you like to execute or which file should we focus on for in-depth analysis?"} ] Simulated TUI for the Model To simulate a TUI, you can create a function that takes the GPT-4 response, parses it to identify the command, and then executes the command. python Copy code # Function to execute commands from GPT-4 response def execute_gpt4_commands(gpt4_response): # Parse the response to identify commands command = parse_command(gpt4_response) # You User way longer prompt with 3 examples how to use the TUI To search like super long and formatted that with markdown so model see it like a tui ChatGPT Certainly! You can create a more detailed prompt that includes examples and markdown formatting to simulate a TUI. This will help the model understand how to interact with the interface. Here's how you can design such a prompt: python Copy code # Generate a detailed prompt for GPT-4 messages = [ # ... (previous messages remain the same) {"role": "system", "content": """## Text-based User Interface (TUI) for NFL Data Analysis You are now in the TUI mode. Below are the commands you can issue: - `select [filename]`: To select a file for in-depth analysis. - `go back`: To go back to the previous directory. - `cd [directory_name]`: To change the current directory. - `search [keyword]`: To search for files or directories containing the keyword. ### Examples: 1. **To select a file named `player_stats.csv` for analysis:** ``` select player_stats.csv ``` 2. **To change the current directory to `2021_season`:** ``` cd 2021_season ``` 3. **To search for files containing the keyword `tackle`:** ``` search tackle ``` Based on the available NFL data files: {list(nfl_data.keys())}, which command would you like to execute or which file should we focus on for in-depth analysis?""" } ] This prompt provides the model with a markdown-formatted guide User explain to the model this is a TUI demo guide and to interace with the TUI like in hte guide plz ChatGPT Certainly! You can include a specific note in the prompt to inform the model that this is a TUI demo guide and that it should interact with it as described in the guide. Here's how you can modify the prompt: python Copy code # Generate a detailed prompt for GPT-4 messages = [ # ... (previous messages remain the same) {"role": "system", "content": """## Text-based User Interface (TUI) Demo Guide for NFL Data Analysis **Note: This is a TUI demo guide. Please interact with it as described below.** You are now in the TUI mode. Below are the commands you can issue: - `select [filename]`: To select a file for in-depth analysis. - `go back`: To go back to the previous directory. - `cd [directory_name]`: To change the current directory. - `search [keyword]`: To search for files or directories containing the keyword. ### Examples: 1. **To select a file named `player_stats.csv` for analysis:** ``` select player_stats.csv ``` 2. **To change the current directory to `2021_season`:** ``` cd 2021_season ``` 3. **To search for files containing the keyword `tackle`:** ``` search tackle ``` Based on the available NFL data files: {list(nfl_data.keys())}, which command would you like to execute or which file should we focus on for in-depth analysis?""" } ] This version of the prompt includes a note at the beginning to explicitly tell the model that this is a TUI demo guide and that it should interact with it as described in the guide. This should help guide the model's behavior.

近期下载者：

相关文件：

评论：[我要评论] [举报此文件]

收藏者：