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AI & LLM integration patterns

Call LLM APIs, structure prompts, parse responses, and ship AI features safely.

5 matches

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…

13 0 Open

AI & LLM integration patterns easy

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.

openai tool-calls mock

Python

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…

14 0 Open

AI & LLM integration patterns easy

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.

json openai chat-completion

Python

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 = '''
  …

14 0 Open

AI & LLM integration patterns easy

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.

tiktoken llm tokens

Python

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__ == "__…

16 0 Open

AI & LLM integration patterns easy

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.

llm-api function-calling schema

Python

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: …

14 0 Open

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