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How to Truncate Text to a Token Budget in Python
Truncate a string to a maximum token budget for LLM context using the tiktoken library and OpenAI's tokenizer.
import tiktoken
def truncate_to_token_budget(text, max_tokens, model="gpt-3.5-turbo"):
enc = tiktoken.encoding_for_model(model)
tokens = enc.encode(text)
if len(tokens) <= max_tokens:
return text
truncated_tokens = tokens[:max_tokens]
return enc.decode(truncated_tokens)
if __name__ == "__…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.
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…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.
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: …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.
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…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.
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…How to randomly assign a prompt variant to each key in Python
Randomly pick one variant from a list for each prompt key, useful for A/B testing message variations.
import random
def assign_prompt_variant(prompts: dict[str, list[str]]) -> dict[str, str]:
"""Assign a random prompt variant to each prompt key."""
return {key: random.choice(variants) for key, variants in prompts.items()}
if __name__ == "__main__":
prompt_bank = {
"greeting": ["Hello!", "Hi there…JSON Mode Prompt Schema Output in Python
Extract a user object to JSON with explicit schema keys, ready for LLM JSON-mode prompts.
import json
from typing import Any, Dict
def extract_user_as_json(user: Dict[str, Any]) -> str:
"""Extract a user object and return it as JSON using explicit schema keys."""
schema_fields = ("id", "name", "email", "is_active")
user_subset = {key: user[key] for key in schema_fields if key in user}
ret…Prepare LLM prompt data with a Python helper class
A beginner-friendly Python class that collects records, converts them to JSON, and produces a quick summary for building LLM prompt context.
import json
from typing import Any, Dict, List
class DataHelper:
"""Simple helper to prepare data for LLM prompts."""
def __init__(self):
self.data = []
def add(self, item: Dict[str, Any]) -> "DataHelper":
self.data.append(item)
return self
def to_json(self) -> s…Route Tool Call Name to Python Handler Dict
Routes a tool call name to the correct Python handler function using a dictionary lookup, returning an error for unknown tools.
def get_name():
return {"name": "Alice"}
def get_age():
return {"age": 30}
def get_email():
return {"email": "alice@example.com"}
handlers = {
"get_name": get_name,
"get_age": get_age,
"get_email": get_email,
}
def route(tool_call):
handler = handlers.get(tool_call["name"])
if handl…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.
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)
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