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How to Build a System-User-Assistant Message List in Python
Use dataclasses to model a chat conversation and build the system/user/assistant message list expected by LLM APIs.
from dataclasses import dataclass, field
from typing import List
@dataclass
class Message:
role: str
content: str
@dataclass
class Conversation:
messages: List[Message] = field(default_factory=list)
def add_system(self, content: str) -> None:
self.messages.append(Message(role="system", con…How to Mock OpenAI Tool Call Messages in Python
Create an assistant message with a function tool call in OpenAI's chat format, useful for testing and mocking.
from openai import OpenAI
def mock_tool_call(tool_name: str, arguments: dict) -> dict:
"""Simulate a tool call message in OpenAI style."""
return {
"role": "assistant",
"content": None,
"tool_calls": [
{
"id": "call_" + "a1b2c3d4e5f6",
"type…How to Parse Chat Completion JSON in Python
Parse a mock OpenAI chat completion JSON response into a clean dictionary with content, finish reason, and model.
import json
def parse_chat_response(raw: str) -> dict:
data = json.loads(raw)
choice = data["choices"][0]
return {
"content": choice["message"]["content"],
"finish_reason": choice["finish_reason"],
"model": data["model"],
}
if __name__ == "__main__":
mock_response = '''
…How to Truncate Text to a Token Budget in Python
Truncate a string to a maximum token budget for LLM context using the tiktoken library and OpenAI's tokenizer.
import tiktoken
def truncate_to_token_budget(text, max_tokens, model="gpt-3.5-turbo"):
enc = tiktoken.encoding_for_model(model)
tokens = enc.encode(text)
if len(tokens) <= max_tokens:
return text
truncated_tokens = tokens[:max_tokens]
return enc.decode(truncated_tokens)
if __name__ == "__…How to build a function calling schema dict in Python
Build an OpenAI-compatible function calling schema dictionary with a helper function that takes name, description, parameters, and required fields.
import json
from typing import Dict, Any, List, Optional
def build_function_schema(
name: str,
description: str,
parameters: Optional[Dict[str, Any]] = None,
required: Optional[List[str]] = None
) -> Dict[str, Any]:
"""Build an OpenAI-compatible function calling schema dictionary."""
schema: …How to Mock a Whisper API Transcription Stub in Python
Simulate an OpenAI Whisper-style transcription response with a dataclass request model and a mock function that returns structured audio transcription output.
import json
from dataclasses import dataclass
from typing import Optional
@dataclass
class AudioRequest:
file_path: str
language: Optional[str] = None
def to_api_payload(self) -> dict:
return {"file": self.file_path, "language": self.language}
def mock_whisper_transcribe(payload: dict) -> dict:
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