promptools 0.0.2

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}, {}]}