promptmeteo 0.1.2

Enable the use of LLMs as a conventional ML model

PromptMeteo 🔥🧔

Promptmeteo is a Python library for prompt engineering built over LangChain. It simplifies the utilization of large language models (LLMs) for various tasks through a low-code interface. To achieve this, Promptmeteo can employ different LLM models and dynamically generate prompts for specific tasks based on just a few configuration parameters.

 

🤔 What is this library for?

TL;DR: Industrialize projects powered by LLMs easily.

LLMs have the capability to address various tasks when provided with specific instructions in the form of input prompts. They can function as a "reasoning engine" for constructing applications. However, deploying and industrializing these applications poses significant challenges for two main reasons.

Firstly, prompts typically encapsulate application logic in their definition, yet they are treated merely as input arguments. This means that a poorly formulated prompt input has the potential to disrupt the application.

Secondly, crafting concrete prompts for each task is not only a laborious task but also a complex one. Minor alterations in the input prompt can result in different outcomes, rendering them highly error-prone. Additionally, when composing prompts, considerations extend beyond the task itself to include factors such as the specific LLM being used, the model's capacity, and other relevant aspects.

 

🚀 How do we solve it?

TL;DR: Treating prompts and code equally!!!

Promptmeteo aims to address the aforementioned issues by dividing the prompt definition into two distinct parts: the task logic, coded within prompt templates, and the concrete problem, included as argument variables. Promptmeteo incorporates high-level objects for various tasks, implemented through .py and .prompt files.

🏠 Prebuilt tasks

The project incorporates high-level objects designed to address various NLP tasks, including text classification, named entity recognition, and code generation. These objects only require configuration parameters for execution, eliminating the need to parse the output from the LLMs.

⚙️ Ease of Deployment

Promptmeteo modules adhere to a model interface similar to Scikit-Learn. By defining an interface with independent methods for training, predicting, saving, and loading the model, Promptmeteo enables training in a separate pipeline from prediction. This facilitates the reuse of conventional ML pipelines for LLM projects.

📦 Model Artifacts

To enhance results, LLMs can incorporate examples in their prompts. Promptmeteo supports training with examples, and for reproducibility, the training process can be stored as a binary model artifact. This allows storing and reusing training results multiple times with new data. The training process stores embeddings from the input text in a vector database, such as FAISS.

⚙️ LLMs Integration

Promptmeteo integrates different LLMs through LangChain. This includes models that can be executed locally and remote API calls from providers like OpenAI and HuggingFace.

📄 Prompt Templating

Establishing a concrete format for creating prompts in Promptmeteo (.prompt) not only facilitates programmatic use but also enables versioning of prompts. This approach aids in understanding changes when they occur and allows for the definition of code tests oriented toward prompt testing. This testing encompasses aspects such as validating language use and ensuring the prompt size is appropriate for the model.

TEMPLATE:
    "I need you to help me with a text classification task.
    {__PROMPT_DOMAIN__}
    {__PROMPT_LABELS__}

    {__CHAIN_THOUGHT__}
    {__ANSWER_FORMAT__}"

PROMPT_DOMAIN:
    "The texts you will be processing are from the {__DOMAIN__} domain."

PROMPT_LABELS:
    "I want you to classify the texts into one of the following categories:
    {__LABELS__}."

PROMPT_DETAIL:
    ""

CHAIN_THOUGHT:
    "Please provide a step-by-step argument for your answer, explain why you
    believe your final choice is justified."

ANSWER_FORMAT:
    "In your response, include only the name of the class as a single word, in
    lowercase, without punctuation, and without adding any other statements or
    words."

 

⚡ Quick start

First of all, do not forget to configure providers credentials. Refer to the configure credentials section.

✨ Create the task

You can make a prediction directly indicating the model and calling the method predict().

from promptmeteo import DocumentClassifier

clf = DocumentClassifier(
        language            = 'en',
        model_provider_name = 'hf_pipeline',
        model_name          = 'google/flan-t5-small',
        prompt_labels       = ['positive', 'neutral', 'negative']
    )

clf.predict(['so cool!!'])

[['positive']]

✨ Train the task

But you can also include examples to improve the results by calling the method train().

clf = clf.train(
    examples    = ['i am happy', 'doesnt matter', 'I hate it'],
    annotations = ['positive', 'neutral', 'negative'],
)

clf.predict(['so cool!!'])

[['positive']]

✨ Save a trained task

Once the model is trained it can be saved locally...

clf.save_model("hello_world.meteo")

✨ Load a trained task

... and loaded again to make new predictions.

from promptmeteo import DocumentClassifier

clf = DocumentClassifier(
        language            = 'en',
        model_provider_name = 'hf_pipeline',
        model_name          = 'google/flan-t5-small',
    ).load_model("hello_world.meteo")

clf.predict(['so cool!!'])

[['positive']]

Models can also be loaded without instantiating the class by using load_model() as a function instead of a method:

from promptmeteo import DocumentClassifier

clf = DocumentClassifier.load_model("hello_world.meteo")

✨ Learn more

More examples can be seen in the directory examples.

 

🚗 Usage

⚙️ Configure credentials

Create a .env with the following variables depending on the LLM provider.

Google Cloud

First you should create a Service Account with the role: Vertex AI User.

Once created, generate a key, store it locally and reference the path in the .env file:

GOOGLE_CLOUD_PROJECT_ID="MY_GOOGLE_LLM_PROJECT_ID"
GOOGLE_APPLICATION_CREDENTIALS="PATH_TO_SERVICE_ACCOUNT_KEY_FILE.json"

OpenAI

Create your Secret API key in your User settings page.

Indicate the value of the key in your .env file:

OPENAI_API_KEY="MY_OPENAI_API_KEY"

You can also pass openai_api_key as a named parameter.

Hugging Face

Create Access Token in your User settings page.

HUGGINGFACEHUB_API_TOKEN="MY_HF_API_KEY"

You can also pass huggingfacehub_api_token as a named parameter.

AWS Bedrock

Create your access keys in security credentials of your user in AWS.

Then write in the files ~/.aws/config and ~/.aws/credentials for Linux and MacOS or %USERPROFILE%\.aws\config and %USERPROFILE%\.aws\credentials for Windows:

In credentials:

[default]
aws_access_key_id = <YOUR_CREATED_AWS_KEY>
aws_secret_access_key = <YOUR_CREATED_AWS_SECRET_KEY>

In config:

[default]
region = <AWS_REGION>

⚙️ Install locally

make setup

⚙️ Run with docker

docker build -t promptmeteo:latest .
docker run --rm -i -t -v .:/home promptmeteo:latest

⚙️ Run tests

make test

📋 Current capacilities

✅ Available tasks

The current available tasks in Promptmeteo are:

task_type	description
DocumentQA	Document-level question answering
DocumentClassifier	Document-level classification
CodeGenerator	Code generation
ApiGenerator	API REST generation
ApiFormatter	API REST correction
Summarizer	Text summarization

✅ Available Model

The current available model_name and language values are:

model_provider	model_name	language
openai	gpt-3.5-turbo-16k	es
openai	gpt-3.5-turbo-16k	en
azure	gpt-3.5-turbo-16k	es
azure	gpt-3.5-turbo-16k	en
hf_hub_api	google/flan-t5-xxl	es
hf_hub_api	google/flan-t5-xxl	en
hf_pipeline	google/flan-t5-small	es
hf_pipeline	google/flan-t5-small	en
google	text-bison	es
google	text-bison	en
google	text-bison@001	es
google	text-bison@001	en
google	text-bison-32k	es
google	text-bison-32k	en
bedrock	anthropic.claude-v2	en
bedrock	anthropic.claude-v2	es