Reference library

AI & LLM integration patterns

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

4 matches

How to Batch Embed a List of Strings in Python

Batch embed a list of strings into deterministic pseudo-random vectors using a mock encoder class.

class MockEncoder:
    def __init__(self, dim=8, seed=42):
        self.dim = dim
        self.seed = seed

    def embed(self, text):
        # Deterministic pseudo-random embedding based on text content
        hash_val = hash(text)
        import random
        rng = random.Random(hash_val + self.seed)
        retu…

13 0 Open

AI & LLM integration patterns easy

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 Parse Chat Completion JSON in Python

Parse a mock OpenAI chat completion JSON response into a clean dictionary with content, finish reason, and model.

json openai chat-completion

Python

import json

def parse_chat_response(raw: str) -> dict:
    data = json.loads(raw)
    choice = data["choices"][0]
    return {
        "content": choice["message"]["content"],
        "finish_reason": choice["finish_reason"],
        "model": data["model"],
    }

if __name__ == "__main__":
    mock_response = '''
  …

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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