promptools 0.1.3.5

useful utilities for prompt engineering

promptools

useful utilities for prompt engineering

Installation

pip install promptools  # or any other dependency manager you like

Note that the validation features use pydantic>=2 as an optional dependencies. You can use pip install promptools[validation] to install it by the way.

API References

extractors

extract_json

parse JSON from raw LLM response text

Usages

Detect and parse the last JSON block from input string.

def extract_json(text: str, /, fallback: F) -> JSON | F:

It will return fallback if it fails to detect / parse JSON. Note that the default value of fallback is None.

def extract_json(text: str, /, fallback: F, expect: Type[M]) -> M | F:

You can provide a pydantic.BaseModel or a TypeAlias in the expect parameter and pydantic will validate it.

Examples

Classification example

Imagine that you are using LLM on a classification task.

from promptools.extractors import extract_json
from typing import TypedDict

class Item(TypedDict):
    index: int
    label: str

original_text = """
The result is:

```json
[
    {"index": 0, "label": "A"},
    {"index": 1, "label": "B"}
]
```
"""

print(extract_json(original_text, [], list[Item]))

The output will be:

[{'index': 0, 'label': 'A'}, {'index': 1, 'label': 'B'}]

Streaming JSON example

Imagine that you are trying to parse a malformed JSON:

from promptools.extractors import extract_json
from pydantic import BaseModel

original_text = '{"results": [{"index": 1}, {'

print(extract_json(original_text))

The output will be:

{'results': [{'index': 1}, {}]}

openai

count_token

count number of tokens in prompt

Usages

def count_token(prompt: str | list[str], enc: Encoding | None = None) -> int:

Provide your prompt / a list of prompts, get its token count. The second parameter is the tiktoken.Encoding instance, will default to get_encoding("cl100k_base") if not provided. The default tiktoken.Encoding instance is cached, and will not be re-created every time.

def count_token(prompt: dict | list[dict], enc: Encoding | None = None) -> int:

Note that it can also be a single message / a list of messages. Every message should be a dict in the schema below:

class Message(TypedDict):
    role: str
    content: str
    name: NotRequired[str]

Examples

Plain text

from tiktoken import encoding_for_model
from promptools.openai import count_token

print(count_token("hi", encoding_for_model("gpt-3.5-turbo")))

The output will be:

1

List of plain texts

from promptools.openai import count_token

print(count_token(["hi", "hello"]))

The output will be:

2

Single message

from promptools.openai import count_token

count_token({"role": "user", "content": "hi"})

The output will be:

5

List of messages

from promptools.openai import count_token

count_token([
    {"role": "user", "content": "hi"},
    {"role": "assistant", "content": "Hello! How can I assist you today?"},
])

The output will be:

21

Integrating with promplate

from promplate.prompt.chat import U, A, S
from promptools.openai import count_token

count_token([
    S @ "background" > "You are a helpful assistant.",
    U @ "example_user" > "hi",
    A @ "example_assistant" > "Hello! How can I assist you today?",
])

The output will be:

40