Reference library

AI & LLM integration patterns

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

4 matches

How to Accumulate Streamed Tokens into a Final String in Python

Accumulate a stream of tokens into a single final string by concatenating each token in sequence.

def accumulate_tokens(tokens):
    """Accumulate a stream of tokens into a single final string."""
    result = ""
    for token in tokens:
        result += token
    return result


if __name__ == "__main__":
    token_stream = ["Hello", ", ", "world", "!", " This ", "is ", "accumulated."]
    final_string = accumul…

16 0 Open

AI & LLM integration patterns easy

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.

embedding batch-processing mock-encoder

Python

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…

12 0 Open

AI & LLM integration patterns easy

How to Render a Jinja-like Template from a Dict in Python

Replace {{placeholders}} in a string using values from a Python dict with a simple regex-based template renderer.

templating regex strings

Python

import re

def render_template(template, context):
    pattern = re.compile(r"\{\{\s*(\w+)\s*\}\}")
    def replace(match):
        key = match.group(1)
        return str(context.get(key, ""))
    return pattern.sub(replace, template)

if __name__ == "__main__":
    template = "Hello {{name}}, you have {{count}} new …

14 0 Open

AI & LLM integration patterns easy

Serialize and Format Data for LLM Prompts in Python

Use dataclasses and the json module to convert Python objects to JSON strings, parse them back, and format structured data into prompt-friendly text for LLM calls.

dataclasses json llm

Python

import json
from dataclasses import dataclass, asdict


@dataclass
class Recipe:
    """Simple data model to represent a recipe."""
    name: str
    cuisine: str
    prep_minutes: int


def to_json(recipe: Recipe) -> str:
    """Serialize a Recipe to a JSON string."""
    return json.dumps(asdict(recipe), indent=2)

…

14 0 Open

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