Reduced app code

Author: martin-martin

prompt-engineering/app.py

@@ -3,39 +3,15 @@
 import tomllib
 from pathlib import Path
 
-import openai
+from openai import OpenAI
 
 # Authenticate
-openai.api_key = os.getenv("OPENAI_API_KEY")
+client = OpenAI(api_key=os.getenv("OPENAI_API_KEY"))
 
-
-class Settings(dict):
-    """Handle loading and accessing application settings from file."""
-
-    @classmethod
-    def load(cls, path) -> "Settings":
-        """Load TOML settings file and pass it to class constuctor."""
-        with path.open("rb") as file:
-            return cls(tomllib.load(file))
-
-    def __init__(self, *args, **kwargs) -> None:
-        """Add general settings and prompts as instance attributes."""
-        super().__init__(*args, **kwargs)
-        # Settings
-        self.chat_models = self["general"]["chat_models"]
-        self.model = self["general"]["model"]
-        self.max_tokens = self["general"]["max_tokens"]
-        self.temperature = self["general"]["temperature"]
-        self.model_supports_chat_completions = self.model in self.chat_models
-        # Prompts
-        self.instruction_prompt = self["prompts"]["instruction_prompt"]
-        self.role_prompt = self["prompts"]["role_prompt"]
-        self.positive_example = self["prompts"]["positive_example"]
-        self.positive_reasoning = self["prompts"]["positive_reasoning"]
-        self.positive_output = self["prompts"]["positive_output"]
-        self.negative_example = self["prompts"]["negative_example"]
-        self.negative_reasoning = self["prompts"]["negative_reasoning"]
-        self.negative_output = self["prompts"]["negative_output"]
+# Load settings file
+settings_path = Path("settings.toml")
+with settings_path.open("rb") as settings_file:
+    SETTINGS = tomllib.load(settings_file)
 
 
 def parse_args() -> argparse.Namespace:
@@ -47,52 +23,34 @@ def parse_args() -> argparse.Namespace:
 
 def main(args: argparse.Namespace) -> None:
     file_content = args.file_path.read_text("utf-8")
-    settings = Settings.load(Path("settings.toml"))
-    if settings.model_supports_chat_completions:
-        print(get_chat_completion(file_content, settings))
-    else:
-        print(get_completion(file_content, settings))
-
-
-def get_completion(content: str, settings: Settings) -> str:
-    """Send a request to the /completions endpoint."""
-    response = openai.Completion.create(
-        model=settings.model,
-        prompt=assemble_prompt(content, settings),
-        max_tokens=settings.max_tokens,
-        temperature=settings.temperature,
-    )
-    return response["choices"][0]["text"]
+    print(get_chat_completion(file_content))
 
 
-def get_chat_completion(content: str, settings: Settings) -> str:
+def get_chat_completion(content: str) -> str:
     """Send a request to the /chat/completions endpoint."""
-    response = openai.ChatCompletion.create(
-        model=settings.model,
-        messages=assemble_chat_messages(content, settings),
-        temperature=settings.temperature,
+    response = client.chat.completions.create(
+        model=SETTINGS["general"]["model"],
+        messages=assemble_chat_messages(content),
+        temperature=SETTINGS["general"]["temperature"],
+        seed=12345,  # Doesn't do anything for older models
     )
-    return response["choices"][0]["message"]["content"]
-
-
-def assemble_prompt(content: str, settings: Settings) -> str:
-    """Combine all text input into a single prompt."""
-    return f">>>>>\n{content}\n<<<<<\n\n" + settings.instruction_prompt
-
-
-def assemble_chat_messages(content: str, settings: Settings) -> list[dict]:
-    """Combine all messages into a well-formatted dictionary."""
-    return [
-        {"role": "system", "content": settings.role_prompt},
-        {"role": "user", "content": settings.negative_example},
-        {"role": "system", "content": settings.negative_reasoning},
-        {"role": "assistant", "content": settings.negative_output},
-        {"role": "user", "content": settings.positive_example},
-        {"role": "system", "content": settings.positive_reasoning},
-        {"role": "assistant", "content": settings.positive_output},
+    return response.choices[0].message.content
+
+
+def assemble_chat_messages(content: str) -> list[dict]:
+    """Combine all messages into a well-formatted list of dicts."""
+    messages = [
+        {"role": "system", "content": SETTINGS["prompts"]["role_prompt"]},
+        {"role": "user", "content": SETTINGS["prompts"]["negative_example"]},
+        {"role": "system", "content": SETTINGS["prompts"]["negative_reasoning"]},
+        {"role": "assistant", "content": SETTINGS["prompts"]["negative_output"]},
+        {"role": "user", "content": SETTINGS["prompts"]["positive_example"]},
+        {"role": "system", "content": SETTINGS["prompts"]["positive_reasoning"]},
+        {"role": "assistant", "content": SETTINGS["prompts"]["positive_output"]},
         {"role": "user", "content": f">>>>>\n{content}\n<<<<<"},
-        {"role": "user", "content": settings.instruction_prompt},
+        {"role": "user", "content": SETTINGS["prompts"]["instruction_prompt"]},
     ]
+    return messages
 
 
 if __name__ == "__main__":