Reference library

AI & LLM integration patterns

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

4 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…

12 0 Open

AI & LLM integration patterns easy

How to Mock an LLM Client in Python

Create a simple mock LLM client that returns a canned completion for testing or development without a real API.

llm mock testing

Python

from dataclasses import dataclass


@dataclass
class MockLLMClient:
    canned_response: str = "This is a canned completion."

    def complete(self, prompt: str) -> str:
        return f"{self.canned_response} [to: {prompt[:20]}]"


if __name__ == "__main__":
    client = MockLLMClient()
    result = client.complete(…

16 0 Open

AI & LLM integration patterns easy

How to Parse an LLM Response in Python

This code parses a JSON string from an LLM response, stripping code fences and handling common issues like whitespace, returning a Python dictionary.

llm json parsing

Python

import json
from typing import Any, Dict, List


def parse_llm_response(response: str) -> Dict[str, Any]:
    """Parse a JSON string from an LLM response, handling common edge cases."""
    # Remove code fences if present
    cleaned = response.strip()
    if cleaned.startswith("

13 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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