AI & LLM integration patterns
Call LLM APIs, structure prompts, parse responses, and ship AI features safely.
How to Batch Embed a List of Strings in Python
Batch embed a list of strings into deterministic pseudo-random vectors using a mock encoder class.
class MockEncoder:
def __init__(self, dim=8, seed=42):
self.dim = dim
self.seed = seed
def embed(self, text):
# Deterministic pseudo-random embedding based on text content
hash_val = hash(text)
import random
rng = random.Random(hash_val + self.seed)
retu…How to Build a Simple Semantic Cache for Similar Prompts in Python
Mock a semantic cache that finds the closest matching prompt using word-overlap similarity and returns cached results above a threshold.
prompt_cache = [
"What is the capital of France?",
"How does recursion work?",
"Best practices for Python logging?",
"Explain binary search in one line.",
"How to reverse a string in Python?"
]
def normalize(text):
return " ".join(text.lower().split())
def similarity(a, b):
a_words = set(…How to Compute a Mock BLEU Score with n-gram Overlap in Python
Evaluate text similarity with a simplified BLEU score using word-level n-gram precision and a brevity penalty.
from collections import Counter
def bleu_score(reference, candidate, n=2):
"""
Compute a simplified BLEU score with n-gram precision and brevity penalty.
Mock demo using word-level n-grams.
"""
ref_tokens = reference.lower().split()
cand_tokens = candidate.lower().split()
# Compute n-…How to Create a Mock LLM Judge Rubric Score in Python
Scores a response against a rubric by counting keyword matches, returning total, percentage, and per-criterion feedback.
def judge_score(response, rubric):
"""Mock LLM judge that scores a response against a rubric."""
total = 0
max_total = 0
feedback = []
for criterion, rubric_item in rubric.items():
max_points = rubric_item["max"]
description = rubric_item["description"]
# Simple mock scori…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.
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…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 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.
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(…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 Stream Tokens from a Mock LLM in Python
Simulate real-time LLM streaming by yielding tokens one at a time with a delay, making it easy to test streaming UIs.
import time
from typing import Generator
def stream_tokens(text: str, delay: float = 0.05) -> Generator[str, None, None]:
"""Simulate an LLM streaming tokens word by word."""
for word in text.split():
yield word
time.sleep(delay)
if __name__ == "__main__":
sample = "Hello world! This is…How to build a mock RAG pipeline in Python
Build a minimal Retrieval-Augmented Generation pipeline that retrieves the best-matching document by keyword overlap and generates a template-based answer.
def simple_rag_pipeline(question, documents):
"""
A minimal mock RAG pipeline: retrieve relevant context, then generate an answer.
"""
# Step 1: Retrieve — mock retrieval by simple keyword scoring
scores = []
for doc in documents:
doc_words = set(doc.lower().split())
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AI & LLM integration patterns — Python code examples
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