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AI & LLM integration patterns

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

5 matches

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…

16 0 Open

AI & LLM integration patterns easy

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.

vector-store cosine-similarity embeddings

Python

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:
…

12 0 Open

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…

15 0 Open

AI & LLM integration patterns easy

How to Log Prompts and Completions as JSONL Audit Files in Python

Read a JSONL file of LLM prompt–completion pairs, compute totals and averages, then write an audit summary with timestamps.

jsonl audit llm

Python

import json
from pathlib import Path
from datetime import datetime


def audit_jsonl(filepath):
    logs = []
    with open(filepath, encoding="utf-8") as f:
        for line in f:
            line = line.strip()
            if not line:
                continue
            entry = json.loads(line)
            logs.ap…

15 0 Open

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…

14 0 Open

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