AI & LLM integration patterns
Call LLM APIs, structure prompts, parse responses, and ship AI features safely.
Cosine Similarity to Retrieve Top K Chunks in Python
Compute cosine similarity between a query vector and a list of chunk vectors, then return the indices and scores of the top k most similar chunks.
import numpy as np
from numpy.linalg import norm
def cosine_similarity(vec1, vec2):
return np.dot(vec1, vec2) / (norm(vec1) * norm(vec2))
def retrieve_top_k(query_vec, chunk_vectors, k=3):
similarities = [cosine_similarity(query_vec, vec) for vec in chunk_vectors]
top_indices = sorted(range(len(similarit…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 Build an In-Memory Vector Store in Python
Build a lightweight in-memory vector store using a Python dict and cosine similarity for fast nearest-neighbor searches.
import math
from typing import Dict, List, Optional
class InMemoryVectorStore:
def __init__(self) -> None:
self.vectors: Dict[str, List[float]] = {}
self.index: Dict[str, List[str]] = {} # query -> list of ids sorted by similarity
def add(self, vector_id: str, vector: List[float]) -> None:
…How to Keep Last K Turns in a Memory Buffer in Python
A TurnBuffer class using deque with maxlen to keep only the most recent k conversation turns in memory for LLM context.
from collections import deque
class TurnBuffer:
def __init__(self, k):
self.k = k
self.turns = deque(maxlen=k)
def add(self, turn):
self.turns.append(turn)
def last_k(self):
return list(self.turns)
if __name__ == "__main__":
buffer = TurnBuffer(3)
buffer.add("tu…How to Repair Malformed JSON Braces Heuristically in Python
Heuristically fix malformed JSON by balancing braces and quotes, using a stack-based approach to add missing closing characters.
import json
import re
def repair_json(text: str) -> str:
"""Heuristically repair malformed JSON by balancing braces and quotes."""
# Trim whitespace and handle leading/trailing garbage
text = text.strip()
# Remove common non-JSON decorations
text = re.sub(r'^(Track GitHub Repository Growth in Python
A Python dashboard that fetches and displays GitHub repository statistics including stars, forks, creation date, and recent star activity using the GitHub API.
import requests
import json
from datetime import datetime, timedelta
def track_repo_growth(owner, repo):
url = f"https://api.github.com/repos/{owner}/{repo}"
headers = {"Accept": "application/vnd.github.v3+json"}
response = requests.get(url, headers=headers)
data = response.json()
name = data…Browse by section
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AI & LLM integration patterns — Python code examples
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