ask-llm

ask-llm: the easiest way to supercharge your apps with LLM!

ask-llm is a very simple yet powerful package that can turn anything into a LLM interaction. You just need to decorate a function with @ask(), write a prompt into the docstring, give it a return type, and there you go, you got your LLM interaction. This takes inspiration from the awesome langchain-decorators package. It works out of the box with OpenAI (by setting a OPENAI_API_KEY env var), but is compatible with all BaseChatModel from Langchain.

Features

• Write your prompts in docstrings using Jinja templating language.
• Access your function args in your prompt.
• (Almost) full access in your prompt to every attributes / properties of objects as soon as it's not a callable.
• Conversation support
• Fully compatible with Langchain / LCEL. Returns either the LLM response, the formatted ChatPromptTemplate or a chain prompt | llm.
• Access the decorated function return value inside your prompt. Use {{ __result__ }} anywhere in your prompt.
• Format the LLM answer using Pydantic objects as return type, or regular python return types.
• If passed a Pydantic object as return type, LLM will retry if it fails to answer on the first shot.
• If using GPT Vision, supports an image parameter to send alongside your prompt. You can input your image either as an url, a path, or a base64 string.
  and many more to come...

Installation

Either using pip or by cloning this repo. pip install ask-llm

Then, import the decorator: from ask_llm import ask

To make it work out of the box with OpenAI, define an env var: OPENAI_API_KEY=<your key>

Now, you are able to use the decorator:

import requests
from functools import cached_property
from ask_llm import ask
from pydantic import BaseModel


class BlogArticle(BaseModel):
    title: str
    content: str


class WikipediaAPI:
    def __init__(self, title: str):
        self.title = title

    @cached_property
    def wikipedia_article(self) -> str | None:
        try:
            response = requests.get(
                f"https://en.wikipedia.org/api/rest_v1/page/summary/{self.title}"
            )
            return response.json()["extract"]
        except:
            return


class BlogArticleWriter:
    def __init__(
        self,
        title: str,
        keywords: list,
        nb_headings: str,
        nb_paragraphs: str,
        input_bulletpoints: bool,
    ):
        self.title = title
        self.keywords = keywords
        self.nb_headings = nb_headings
        self.nb_paragraphs = nb_paragraphs
        self.input_bulletpoints = input_bulletpoints

        self.wikipedia_api = WikipediaAPI(title=self.title)

    @ask()
    def write_blog_article(self, author: str) -> BlogArticle:
        """
        As an expert copywriter specialized in SEO and content writing, your task is to write a very informative blog article
        about the topic {{ self.title }}.
        You should create {{ self.nb_headings }} highly engaging headings made of {{ self.nb_paragraphs }} paragraphs each.
        Use subheadings and line breaks when appropriate.

        {% if self.input_bulletpoints %}
          You should also include bulletpoints in the article.
        {% endif %}

        {% if self.wikipedia_api.wikipedia_article %}
          Here is a brief summary of the topic:
          {{ self.wikipedia_api.wikipedia_article }}
        {% endif %}

        {% if self.keywords %}
          The following keywords should be included in the article:
          {% for keyword in self.keywords %}
            - {{ keyword }}
          {% endfor %}
        {% endif %}

        The article should be written by {{ author }}.
        """

writer = BlogArticleWriter(
    title="Large language models", 
    keywords=["llm", "open source", "python", "nlp"], 
    nb_headings=3, 
    nb_paragraphs=3, 
    input_bulletpoints=True
)

blog_article = writer.write_blog_article("Florian")

Langsmith trace: https://smith.langchain.com/public/0abe7b97-d43e-4c10-9bba-be9f6c2892d6/r

An other example, with an image:

from ask_llm import ask


@ask()
def describe_image(image: str) -> str:
    """
    Describe this image precisely.
    """


description = describe_image(image="https://upload.wikimedia.org/wikipedia/commons/thumb/8/85/Tour_Eiffel_Wikimedia_Commons_%28cropped%29.jpg/800px-Tour_Eiffel_Wikimedia_Commons_%28cropped%29.jpg")

Langsmith trace: https://smith.langchain.com/public/18f39957-93f5-47f2-a264-051c11cca2e8/r

Conversation example:

from ask_llm import ask


@ask()
def conversation(instruction: str) -> str:
    """
    {% chat %}
        {% message system %}
            You are an helpful assistant that can answer all the user questions.
        {% endmessage %}
        {% message ai %}
            Hello, Arnaud! How can I help you today?
        {% endmessage %}
        {% message human %}
            {{ instruction }}
        {% endmessage %}
    {% endchat %}
    Answer in CAPS LOCK
    """

Settings

You can pass numerous arguments to the decorator:

• call: bool = True To call the LLM or to return the prepared chain prompt | llm
• verbose: bool = False To enable verbose mode
• return_prompt_only: bool = False To return only the formatted ChatPromptTemplate
• model_name: str = "gpt-4-vision-preview" The model name
• max_tokens: int = 4096 max tokens for the answer
• image_quality: str = "high" If passed an image, the quality parameter for Vision API
• chat_model_class: BaseChatModel = ChatOpenAI The Langchain subclass of BaseChatModel to use.
• **llm_kwargs Other kwargs to pass to the BaseChatModel if any.

Conclusion

Possibilities with this are endless. Hope you're gonna like it! More features and examples are coming. Special integration with Django is also coming. Do not hesitate to iterate and contribute to the project! Please submit PRs and issues 🙏