⚙️ Installation and configuration

Install

To install the stable version of the library use pip.

(.venv) $ pip install promptmeteo

Configure credentials

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

Google Cloud

First you should create a [Service Account](https://cloud.google.com/vertex-ai/docs/general/custom-service-account#configure) 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](https://platform.openai.com/account/api-keys).

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](https://huggingface.co/settings/tokens).

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>