Reference library

AI & LLM integration patterns

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

5 matches

Cache LLM Completions by Hashing the Prompt in Python

A simple in-memory cache that stores LLM completions keyed by a SHA-256 hash of the prompt to avoid recomputing identical requests.

llm caching hashing

Python

import hashlib
import json

class PromptCache:
    def __init__(self):
        self.cache = {}

    def _hash_prompt(self, prompt: str) -> str:
        return hashlib.sha256(prompt.encode("utf-8")).hexdigest()

    def get(self, prompt: str) -> str | None:
        key = self._hash_prompt(prompt)
        return self.ca…

15 0 Open

AI & LLM integration patterns easy

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.

streaming tokens strings

Python

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 Keep Last K Turns in a Memory Buffer in Python

A TurnBuffer class using deque with maxlen to keep only the most recent k conversation turns in memory for LLM context.

deque llm-context memory-buffer

Python

from collections import deque

class TurnBuffer:
    def __init__(self, k):
        self.k = k
        self.turns = deque(maxlen=k)

    def add(self, turn):
        self.turns.append(turn)

    def last_k(self):
        return list(self.turns)


if __name__ == "__main__":
    buffer = TurnBuffer(3)
    buffer.add("tu…

14 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 function calling schema dict in Python

Build an OpenAI-compatible function calling schema dictionary with a helper function that takes name, description, parameters, and required fields.

llm-api function-calling schema

Python

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


def build_function_schema(
    name: str,
    description: str,
    parameters: Optional[Dict[str, Any]] = None,
    required: Optional[List[str]] = None
) -> Dict[str, Any]:
    """Build an OpenAI-compatible function calling schema dictionary."""
    schema: …

15 0 Open

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