koffiwind/Qdrant-RAG
Deploying RAG powered by Qdrant as vector db and fastembed for embedding and retrieval
❓ QUESTION #1:
Why do we want to support streaming? What about streaming is important, or useful?
ANSWER #1:
The goal of streaming in this context is to render the generated answers in chunks. Thus reducing latency specifically for answers containing a lot of tokens
❓ QUESTION #2:
Why are we using User Session here? What about Python makes us need to use this? Why not just store everything in a global variable?
ANSWER #2:
Users sessions are used to keep track of users activity. It can be used to retrieve contxt from previous conversations or separate conversions
❓ Discussion Question #1:
Upload a PDF file of the recent DeepSeek-R1 paper and ask the following questions:
- What is RL and how does it help reasoning?
- What is the difference between DeepSeek-R1 and DeepSeek-R1-Zero?
- What is this paper about?
Does this application pass your vibe check? Are there any immediate pitfalls you're noticing?
❓ Discussion
Not really. He doesnt know what is RL but he can respond to the other questions...
🚧 CHALLENGE MODE 🚧
Added Qdrant as vector db
Hugging Face Space link :