Reference library

AI & LLM integration patterns

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

4 matches

How to Create a Mock Text Embedding with Hash in Python

Generate deterministic mock text embeddings using SHA-256 hashing and numpy, producing normalized vectors for similarity testing without an LLM.

embeddings hashing numpy

Python

import hashlib
import numpy as np

def mock_embed(text: str, dim: int = 10, seed: int = 42) -> np.ndarray:
    """Generate a deterministic mock embedding using a hash function.
    
    Args:
        text: Input text to embed
        dim: Dimension of the output vector
        seed: Seed for reproducibility
    
    R…

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 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 randomly assign a prompt variant to each key in Python

Randomly pick one variant from a list for each prompt key, useful for A/B testing message variations.

random dictionary a/b-testing

Python

import random

def assign_prompt_variant(prompts: dict[str, list[str]]) -> dict[str, str]:
    """Assign a random prompt variant to each prompt key."""
    return {key: random.choice(variants) for key, variants in prompts.items()}

if __name__ == "__main__":
    prompt_bank = {
        "greeting": ["Hello!", "Hi there…

14 0 Open

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