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.get_rag_config(datasource_id="<id>")

    # Handle response
    print(res)

{
  "datasource_id": "<string>",
  "description": "<string>",
  "collection_id": "<string>",
  "extractor_model": {
    "name": "<string>",
    "endpoint": "<string>"
  },
  "embedding_model": {
    "name": "<string>",
    "endpoint": "<string>",
    "dimensions": 123
  },
  "sparse_embedding_model": {
    "name": "<string>",
    "endpoint": "<string>"
  },
  "collect_metadata": false,
  "metadata_options": {
    "create_title": false,
    "extract_questions_answers": false,
    "extract_summary": false,
    "has_consumer_content": false,
    "get_bibliographical_information": false
  },
  "metadata_model": {
    "type": "<string>",
    "model_id": "<string>",
    "fields": [
      {
        "name": "<string>",
        "type": "<string>",
        "description": "<string>"
      }
    ]
  },
  "created_by": "<string>",
  "updated_by": "<string>"
}