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AI & LLM integration patterns

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

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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.

cosine-similarity retrieval embeddings

Python

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…

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AI & LLM integration patterns easy

How to Chunk a Long Document for RAG Retrieval in Python

Split text into overlapping chunks at sentence boundaries using a custom Python function suitable for RAG retrieval pipelines.

rag text-chunking nlp

Python

import re
from pathlib import Path

def chunk_document(text, chunk_size=500, overlap=100):
    """Split text into overlapping chunks suitable for RAG retrieval."""
    # Normalize whitespace
    text = re.sub(r'\s+', ' ', text).strip()
    
    chunks = []
    start = 0
    while start < len(text):
        end = min(s…

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AI & LLM integration patterns easy

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.

rag llm retrieval

Python

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())
        question_wo…

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