vectordbText = """
                    We will use the [Qdrant](https://qdrant.tech/documentation/) server deployment as microservice. \
                    You can either deploy it as individually or you can use it as one server to serve multiple \
                    chatbots (like in image below) by having multiple collections (multiple collection can also serve one chatbot)


                    # Qdrant Vector Database Server on Hugging Face Spaces

                    [ChaBo_QdrantServer](https://huggingface.co/spaces/GIZ/chatfed_QdrantServer/blob/main/README.md) Space hosts \
                    a Qdrant vector database instance. This is just a Infrastructural component and doesnt\
                    not serve any user application through its User Interface. However the admin task can be performed by\
                    accessing "<embedded space url>/dashboard" Ex:https://giz-chatfed-qdrantserver.hf.space/dashboard \
                    which is passsword protected.

                    **Persistence:** Data is stored persistently in the `/data/qdrant_data` directory due to enabled persistent storage.

                    **How to connect:**
                    From your client application (e.g., your retrieval microservice), use the `qdrant-client` \
                    with the host set to your Space's direct URL and the appropriate port:

                    ```python
                    from qdrant_client import QdrantClient

                    # Replace with your actual Space URL (e.g., https://your-username-qdrant-server.hf.space)
                    QDRANT_HOST = "giz-chatfed-qdrantserver.hf.space"
                    client = QdrantClient(
                        host = QDRANT_HOST,
                        port=443,  # very important that port to be used for python client
                        https=True,
                        api_key = <QDRANT_API_KEY>,)
                    ```

                    **API Documentation**: https://api.qdrant.tech/api-reference

                    """