Remove import time loading of config from llm_utils (#3245)

autogpt/llm_utils.py

@@ -13,8 +13,6 @@ from autogpt.config import Config
 from autogpt.logs import logger
 from autogpt.types.openai import Message
 
-CFG = Config()
-
 
 def retry_openai_api(
     num_retries: int = 10,
@@ -86,8 +84,9 @@ def call_ai_function(
     Returns:
         str: The response from the function
     """
+    cfg = Config()
     if model is None:
-        model = CFG.smart_llm_model
+        model = cfg.smart_llm_model
     # For each arg, if any are None, convert to "None":
     args = [str(arg) if arg is not None else "None" for arg in args]
     # parse args to comma separated string
@@ -109,7 +108,7 @@ def call_ai_function(
 def create_chat_completion(
     messages: List[Message],  # type: ignore
     model: Optional[str] = None,
-    temperature: float = CFG.temperature,
+    temperature: float = None,
     max_tokens: Optional[int] = None,
 ) -> str:
     """Create a chat completion using the OpenAI API
@@ -123,13 +122,17 @@ def create_chat_completion(
     Returns:
         str: The response from the chat completion
     """
+    cfg = Config()
+    if temperature is None:
+        temperature = cfg.temperature
+
     num_retries = 10
     warned_user = False
-    if CFG.debug_mode:
+    if cfg.debug_mode:
         print(
             f"{Fore.GREEN}Creating chat completion with model {model}, temperature {temperature}, max_tokens {max_tokens}{Fore.RESET}"
         )
-    for plugin in CFG.plugins:
+    for plugin in cfg.plugins:
         if plugin.can_handle_chat_completion(
             messages=messages,
             model=model,
@@ -148,9 +151,9 @@ def create_chat_completion(
     for attempt in range(num_retries):
         backoff = 2 ** (attempt + 2)
         try:
-            if CFG.use_azure:
+            if cfg.use_azure:
                 response = api_manager.create_chat_completion(
-                    deployment_id=CFG.get_azure_deployment_id_for_model(model),
+                    deployment_id=cfg.get_azure_deployment_id_for_model(model),
                     model=model,
                     messages=messages,
                     temperature=temperature,
@@ -165,7 +168,7 @@ def create_chat_completion(
                 )
             break
         except RateLimitError:
-            if CFG.debug_mode:
+            if cfg.debug_mode:
                 print(
                     f"{Fore.RED}Error: ", f"Reached rate limit, passing...{Fore.RESET}"
                 )
@@ -180,7 +183,7 @@ def create_chat_completion(
                 raise
             if attempt == num_retries - 1:
                 raise
-        if CFG.debug_mode:
+        if cfg.debug_mode:
             print(
                 f"{Fore.RED}Error: ",
                 f"API Bad gateway. Waiting {backoff} seconds...{Fore.RESET}",
@@ -194,12 +197,12 @@ def create_chat_completion(
             + f"Try running Auto-GPT again, and if the problem the persists try running it with `{Fore.CYAN}--debug{Fore.RESET}`.",
         )
         logger.double_check()
-        if CFG.debug_mode:
+        if cfg.debug_mode:
             raise RuntimeError(f"Failed to get response after {num_retries} retries")
         else:
             quit(1)
     resp = response.choices[0].message["content"]
-    for plugin in CFG.plugins:
+    for plugin in cfg.plugins:
         if not plugin.can_handle_on_response():
             continue
         resp = plugin.on_response(resp)
@@ -215,11 +218,12 @@ def get_ada_embedding(text: str) -> List[int]:
     Returns:
         List[int]: The embedding.
     """
+    cfg = Config()
     model = "text-embedding-ada-002"
     text = text.replace("\n", " ")
 
-    if CFG.use_azure:
-        kwargs = {"engine": CFG.get_azure_deployment_id_for_model(model)}
+    if cfg.use_azure:
+        kwargs = {"engine": cfg.get_azure_deployment_id_for_model(model)}
     else:
         kwargs = {"model": model}
 
@@ -247,8 +251,9 @@ def create_embedding(
     Returns:
         openai.Embedding: The embedding object.
     """
+    cfg = Config()
     return openai.Embedding.create(
         input=[text],
-        api_key=CFG.openai_api_key,
+        api_key=cfg.openai_api_key,
         **kwargs,
     )