philiprehberger-prompt-builder 0.1.0

Type-safe prompt template builder for LLM APIs

philiprehberger-prompt-builder

Type-safe prompt template builder for LLM APIs.

Install

pip install philiprehberger-prompt-builder

Usage

Fluent Builder

from philiprehberger_prompt_builder import Prompt

messages = (
    Prompt()
    .system("You are a helpful assistant.")
    .user("Summarize this article: {article}")
    .render(article="Long article text here...")
)
# [{"role": "system", "content": "You are a helpful assistant."},
#  {"role": "user", "content": "Summarize this article: Long article text here..."}]

Few-Shot Examples

messages = (
    Prompt()
    .system("Classify the sentiment.")
    .example(user="I love this!", assistant="positive")
    .example(user="Terrible product.", assistant="negative")
    .user("{text}")
    .render(text="Pretty good actually")
)

Reusable Templates

from philiprehberger_prompt_builder import PromptTemplate

summarizer = PromptTemplate(
    system="You are a {tone} summarizer. Output in {format}.",
    user="Summarize: {content}",
    defaults={"tone": "concise", "format": "bullet points"},
)

# Use with defaults
messages = summarizer.render(content="Article text...")

# Override defaults
messages = summarizer.render(content="...", tone="detailed", format="paragraphs")

# Create variant
verbose = summarizer.extend(tone="thorough", format="essay")

Token Estimation

prompt = Prompt().system("...").user("{text}")
estimated = prompt.estimate_tokens(text="Hello world")

API

Method	Description
.system(content)	Add a system message
.user(content)	Add a user message
.assistant(content)	Add an assistant message
.example(user, assistant)	Add a few-shot example pair
.render(**kwargs)	Render with variable substitution → list of dicts
.estimate_tokens(**kwargs)	Rough token count (~4 chars/token)

License

MIT