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

README.md

@@ -127,6 +127,8 @@ To use the `google_official_search` command, you need to set up your Google API
 8. Set up your search engine by following the prompts. You can choose to search the entire web or specific sites.
 9.  Once you've created your search engine, click on "Control Panel" and then "Basics". Copy the "Search engine ID" and set it as an environment variable named `CUSTOM_SEARCH_ENGINE_ID` on your machine. See setting up environment variables below.
 
+*Remember that your free daily custom search quota allows only up to 100 searches. To increase this limit, you need to assign a billing account to the project to profit from up to 10K daily searches.*
+
 ### Setting up environment variables
    For Windows Users:
 ```

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: