ChaBo_README / vectorDB.py
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adding other components
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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
"""