from meibelai import Meibelai
import os


with Meibelai(
    api_key_header=os.getenv("MEIBELAI_API_KEY_HEADER", ""),
) as m_client:

    res = m_client.datasources.rag.add_rag_config(datasource_id="<id>", collection_id="<id>", description="which gloomy regarding rosemary provider vivaciously fat willow", extractor_model={
        "name": "<value>",
        "endpoint": "<value>",
    }, embedding_model={
        "name": "<value>",
        "endpoint": "<value>",
        "dimensions": 66789,
    }, sparse_embedding_model={
        "name": "<value>",
        "endpoint": "<value>",
    }, collect_metadata=False, metadata_options={
        "create_title": None,
        "extract_questions_answers": False,
        "extract_summary": True,
        "has_consumer_content": True,
        "get_bibliographical_information": False,
    })

    # Handle response
    print(res)

{
  "message": "<string>"
}