Reference library

AI & LLM integration patterns

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

3 matches

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.

embeddings hashing numpy

Python

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…

13 0 Open

AI & LLM integration patterns easy

How to Validate LLM Output in Python

A beginner-friendly DataValidator class that checks required fields and type constraints on LLM-generated or user JSON data.

validation llm json

Python

import json
from typing import Any, Dict, List, Optional


class DataValidator:
    """Simple helper for validating LLM-generated or user data."""

    def __init__(self, required_fields: List[str], schema: Optional[Dict[str, str]] = None):
        self.required_fields = required_fields
        self.schema = schema or…

14 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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