ragkit-promptkit 0.1.0

A prompt template engine for LLM apps: validated variables, reusable few-shot blocks, and clean chat/message construction.

promptkit

A prompt template engine for LLM apps: validated variables, reusable few-shot blocks, and clean chat/message construction.

Part of the ragkit suite. Install with pip install ragkit-promptkit, then import promptkit.

promptkit replaces scattered f-strings that silently break when a variable is missing. You define templates once, get up-front validation of required variables, compose few-shot prompts, and build OpenAI-style message lists without hand-assembling dicts. Zero third-party dependencies -- standard library only, Python 3.8+.

Install

pip install ragkit-promptkit

Local development (from promptkit/):

pip install -e .

Quick Start

Define a template, inspect its variables, and render it:

from promptkit import PromptTemplate

tmpl = PromptTemplate("Summarize the following {kind} in {n} sentences:\n\n{text}")

print(tmpl.variables)   # {'kind', 'n', 'text'}

print(tmpl.render(kind="article", n=3, text="..."))

render() also accepts a single dict:

tmpl.render({"kind": "article", "n": 3, "text": "..."})

Missing variables are reported all at once

If a referenced variable has no value (and no default), you get a MissingVariableError listing every missing name -- not just the first:

from promptkit import PromptTemplate, MissingVariableError

tmpl = PromptTemplate("{greeting}, {name}! Welcome to {place}.")

try:
    tmpl.render(greeting="Hello")
except MissingVariableError as exc:
    print(exc)          # Missing required variable(s): 'name', 'place'
    print(exc.missing)  # ['name', 'place']

Extra/unknown keyword arguments are ignored, so you can safely pass a shared context dict to many templates.

Literal braces

Use {{ and }} for literal braces (same rules as str.format):

PromptTemplate('Return JSON like {{"score": {n}}}').render(n=5)
# Return JSON like {"score": 5}

Defaults and partial()

Provide defaults for optional variables, and use partial() to bake in some values while leaving the rest open:

from promptkit import PromptTemplate

tmpl = PromptTemplate(
    "You are a {tone} assistant. Answer: {question}",
    defaults={"tone": "friendly"},
)

tmpl.render(question="What is Python?")
# "You are a friendly assistant. Answer: What is Python?"

# Bake in the tone, get a new reusable template:
sarcastic = tmpl.partial(tone="sarcastic")
sarcastic.render(question="What is Python?")
# "You are a sarcastic assistant. Answer: What is Python?"

An explicit value always overrides a default. partial() returns a new template and never mutates the original.

Few-shot prompts

FewShotTemplate stitches a prefix, a list of rendered examples, and a suffix (which usually holds the final user query):

from promptkit import FewShotTemplate

few = FewShotTemplate(
    example_template="Q: {q}\nA: {a}",
    examples=[
        {"q": "2 + 2", "a": "4"},
        {"q": "3 * 3", "a": "9"},
    ],
    prefix="Solve the math problems.",
    suffix="Q: {question}\nA:",
    example_separator="\n\n",
)

# Add more examples dynamically:
few.add_example(q="10 - 4", a="6")

print(few.render(question="7 + 5"))

Output:

Solve the math problems.

Q: 2 + 2
A: 4

Q: 3 * 3
A: 9

Q: 10 - 4
A: 6

Q: 7 + 5
A:

Missing-variable validation applies across the prefix, examples, and suffix.

Chat prompts -> message dicts

ChatPromptTemplate builds OpenAI-style list[dict] message payloads. Each message content is rendered with the same PromptTemplate rules, and missing variables are aggregated across all messages:

from promptkit import ChatPromptTemplate, system, user, assistant

chat = ChatPromptTemplate.from_messages([
    system("You are a helpful {role}."),
    user("Explain {topic} to a {level} audience."),
])

messages = chat.render(role="tutor", topic="recursion", level="beginner")
# [
#   {"role": "system", "content": "You are a helpful tutor."},
#   {"role": "user", "content": "Explain recursion to a beginner audience."},
# ]

Pass messages straight to your LLM client. The system, user, and assistant helpers just return (role, template) tuples. If you prefer typed objects, chat.render_messages(...) returns Message(role, content) dataclass instances (each has .to_dict()).

Prompt registry and versioning

PromptRegistry is a tiny in-memory store for named, versioned prompts. get() returns the latest version by default:

from promptkit import PromptRegistry, PromptTemplate

registry = PromptRegistry()

registry.register(PromptTemplate("Summarize: {text}", name="summarize", version="1.0"))
registry.register(PromptTemplate("TL;DR the following:\n{text}", name="summarize", version="2.0"))

registry.list()                       # ['summarize']
registry.get("summarize")             # v2.0 (latest)
registry.get("summarize", version="1.0")  # the v1.0 template

latest = registry.get("summarize")
latest.render(text="...")

Versions are compared numerically when they look like dotted numbers ("1.0", "2.3"), falling back to string comparison otherwise.

API summary

Object	Purpose
PromptTemplate	Single-string template with {var} placeholders, defaults, partial(), and .variables.
FewShotTemplate	Prefix + examples + suffix composition with add_example().
ChatPromptTemplate	Renders (role, template) pairs to message dicts.
Message	dataclass(role, content) with .to_dict().
system / user / assistant	Helpers returning (role, template) tuples.
PromptRegistry	In-memory register / get / list with versioning.
MissingVariableError	Raised with .missing listing every unresolved variable.

License

MIT