Reference library

AI & LLM integration patterns

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

4 matches

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-…

12 0 Open

AI & LLM integration patterns easy

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.

llm evaluation rubric

Python

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…

15 0 Open

AI & LLM integration patterns easy

How to compute ROUGE recall in Python

Compute ROUGE recall by counting token overlap between a reference and candidate summary with pure Python.

rouge nlp evaluation

Python

def rouge_recall(reference, candidate):
    ref_tokens = reference.lower().split()
    cand_tokens = candidate.lower().split()

    ref_counts = {}
    for token in ref_tokens:
        ref_counts[token] = ref_counts.get(token, 0) + 1

    cand_counts = {}
    for token in cand_tokens:
        cand_counts[token] = cand…

12 0 Open

AI & LLM integration patterns easy

How to compute exact match metric in Python

Computes the exact match (EM) metric for LLM outputs by normalizing text and comparing predictions against references.

exact-match metric evaluation

Python

def compute_exact_match(predictions, references):
    def normalize(text):
        import re
        text = text.lower().strip()
        text = re.sub(r'\b(a|an|the)\b', ' ', text)
        text = re.sub(r'[^a-z0-9\s]', '', text)
        text = ' '.join(text.split())
        return text

    matches = sum(1 for pred, r…

12 0 Open

Browse by section

Each section groups closely related Python snippets.