Merge branch 'Torantulino:master' into kinance-resolve-debug-config-conflict

scripts/ai_config.py

@@ -3,9 +3,27 @@ import data
 import os
 
 class AIConfig:
-    """Class to store the AI's name, role, and goals."""
-    def __init__(self, ai_name="", ai_role="", ai_goals=[]):
-        """Initialize the AIConfig class"""
+    """
+    A class object that contains the configuration information for the AI
+
+    Attributes:
+        ai_name (str): The name of the AI.
+        ai_role (str): The description of the AI's role.
+        ai_goals (list): The list of objectives the AI is supposed to complete.
+    """
+
+    def __init__(self, ai_name: str="", ai_role: str="", ai_goals: list=[]) -> None:
+        """
+        Initialize a class instance
+
+        Parameters:
+            ai_name (str): The name of the AI.
+            ai_role (str): The description of the AI's role.
+            ai_goals (list): The list of objectives the AI is supposed to complete.
+        Returns:
+            None
+        """
+
         self.ai_name = ai_name
         self.ai_role = ai_role
         self.ai_goals = ai_goals
@@ -14,8 +32,19 @@ class AIConfig:
     SAVE_FILE = os.path.join(os.path.dirname(__file__), '..', 'ai_settings.yaml')
 
     @classmethod
-    def load(cls, config_file=SAVE_FILE):
-        """Load variables from yaml file if it exists, otherwise use defaults."""
+    def load(cls: object, config_file: str=SAVE_FILE) -> object:
+        """
+        Returns class object with parameters (ai_name, ai_role, ai_goals) loaded from yaml file if yaml file exists, 
+        else returns class with no parameters.
+
+        Parameters:
+           cls (class object): An AIConfig Class object.
+           config_file (int): The path to the config yaml file. DEFAULT: "../ai_settings.yaml"
+
+        Returns:
+            cls (object): A instance of given cls object  
+        """
+
         try:
             with open(config_file) as file:
                 config_params = yaml.load(file, Loader=yaml.FullLoader)
@@ -28,15 +57,32 @@ class AIConfig:
 
         return cls(ai_name, ai_role, ai_goals)
 
-    def save(self, config_file=SAVE_FILE):
-        """Save variables to yaml file."""
+    def save(self, config_file: str=SAVE_FILE) -> None:
+        """
+        Saves the class parameters to the specified file yaml file path as a yaml file.
+
+        Parameters: 
+            config_file(str): The path to the config yaml file. DEFAULT: "../ai_settings.yaml"
+
+        Returns:
+            None 
+        """
+
         config = {"ai_name": self.ai_name, "ai_role": self.ai_role, "ai_goals": self.ai_goals}
         with open(config_file, "w") as file:
             yaml.dump(config, file)
 
+    def construct_full_prompt(self) -> str:
+        """
+        Returns a prompt to the user with the class information in an organized fashion.
+
+        Parameters: 
+            None
+
+        Returns:
+            full_prompt (str): A string containing the intitial prompt for the user including the ai_name, ai_role and ai_goals.
+        """
 
-    def construct_full_prompt(self):
-        """Construct the full prompt for the AI to use."""
         prompt_start = """Your decisions must always be made independently without seeking user assistance. Play to your strengths as an LLM and pursue simple strategies with no legal complications."""
 
         # Construct full prompt
@@ -46,3 +92,4 @@ class AIConfig:
 
         full_prompt += f"\n\n{data.load_prompt()}"
         return full_prompt
+

scripts/ai_functions.py

@@ -5,9 +5,17 @@ from call_ai_function import call_ai_function
 from json_parser import fix_and_parse_json
 cfg = Config()
 
-# Evaluating code
+
 def evaluate_code(code: str) -> List[str]:
-    """Evaluates the given code and returns a list of suggestions for improvements."""
+    """
+    A function that takes in a string and returns a response from create chat completion api call.
+
+    Parameters:
+        code (str): Code to be evaluated.
+    Returns:
+        A result string from create chat completion. A list of suggestions to improve the code.
+    """
+
     function_string = "def analyze_code(code: str) -> List[str]:"
     args = [code]
     description_string = """Analyzes the given code and returns a list of suggestions for improvements."""
@@ -17,9 +25,17 @@ def evaluate_code(code: str) -> List[str]:
     return result_string
 
 
-# Improving code
 def improve_code(suggestions: List[str], code: str) -> str:
-    """Improves the provided code based on the suggestions provided, making no other changes."""
+    """
+    A function that takes in code and suggestions and returns a response from create chat completion api call. 
+
+    Parameters:
+        suggestions (List): A list of suggestions around what needs to be improved.
+        code (str): Code to be improved.
+    Returns:
+        A result string from create chat completion. Improved code in response.
+    """
+
     function_string = (
         "def generate_improved_code(suggestions: List[str], code: str) -> str:"
     )
@@ -30,9 +46,18 @@ def improve_code(suggestions: List[str], code: str) -> str:
     return result_string
 
 
-# Writing tests
+
 def write_tests(code: str, focus: List[str]) -> str:
-    """Generates test cases for the existing code, focusing on specific areas if required."""
+    """
+    A function that takes in code and focus topics and returns a response from create chat completion api call.
+
+    Parameters:
+        focus (List): A list of suggestions around what needs to be improved.
+        code (str): Code for test cases to be generated against.
+    Returns:
+        A result string from create chat completion. Test cases for the submitted code in response.
+    """
+
     function_string = (
         "def create_test_cases(code: str, focus: Optional[str] = None) -> str:"
     )
@@ -41,5 +66,3 @@ def write_tests(code: str, focus: List[str]) -> str:
 
     result_string = call_ai_function(function_string, args, description_string)
     return result_string
-
-

scripts/chat.py

@@ -64,14 +64,14 @@ def chat_with_ai(
             model = cfg.fast_llm_model # TODO: Change model from hardcode to argument
             # Reserve 1000 tokens for the response
             
-            if cfg.debug_mode:
+            if cfg.debug:
                 print(f"Token limit: {token_limit}")
                 
             send_token_limit = token_limit - 1000
 
             relevant_memory = permanent_memory.get_relevant(str(full_message_history[-5:]), 10)
 
-            if cfg.debug_mode:
+            if cfg.debug:
                 print('Memory Stats: ', permanent_memory.get_stats())
 
             next_message_to_add_index, current_tokens_used, insertion_index, current_context = generate_context(
@@ -110,7 +110,7 @@ def chat_with_ai(
             # assert tokens_remaining >= 0, "Tokens remaining is negative. This should never happen, please submit a bug report at https://www.github.com/Torantulino/Auto-GPT"
 
             # Debug print the current context
-            if cfg.debug_mode:
+            if cfg.debug:
                 print(f"Token limit: {token_limit}")
                 print(f"Send Token Count: {current_tokens_used}")
                 print(f"Tokens remaining for response: {tokens_remaining}")

scripts/json_parser.py

@@ -91,7 +91,7 @@ def fix_json(json_str: str, schema: str) -> str:
     result_string = call_ai_function(
         function_string, args, description_string, model=cfg.fast_llm_model
     )
-    if cfg.debug_mode:
+    if cfg.debug:
         print("------------ JSON FIX ATTEMPT ---------------")
         print(f"Original JSON: {json_str}")
         print("-----------")

scripts/main.py

@@ -17,6 +17,8 @@ import traceback
 import yaml
 import argparse
 
+cfg = Config()
+
 def check_openai_api_key():
     """Check if the OpenAI API key is set in config.py or as an environment variable."""
     if not cfg.openai_api_key: