"I am facing an issue with RAG while using Qdrant vector database and ChatGPT limits."

“I created a project using RAG, and I used Qdrant as a vector database. However, it did not work efficiently when I tried to insert a large point. How can I solve this problem? Also, when I retrieved my points from Qdrant, it was expensive to send all the data to ChatGPT, and I struggled to deal with ChatGPT’s limits. I tried to summarize the data, but it was not effective. What should I do to send my data with the same meaning to LLM?”

What’s your “limit”/“top” parameter when you search?

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I apologize I can’t understand your question. but, In regards to the search, I needed all the retrieved data and I wanted to make it as concise as possible without losing its meaning. This was important so that LLM would understand what I was looking for. At the same time, I wanted to avoid exceeding the limits of ChatGPT and incurring additional costs.
@Diet

you can use tiktoken to calculate the max number of documents you can add

well that, that is an engineeing problem.

you can:

  • improve the quality of your corpus
  • improve the quality of your query
  • improve the quality of your prompt
  • use a cheaper model

but without further information, it’s hard to say :thinking:

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Thank you for your concern. Your information has been very helpful. Can you recommend the best vector database to work with?
@Diet

np :slight_smile:

Really depends on what you want to do. FAISS is an option, Milvus is often a good choice, Qdrant probably isn’t too bad either. Sometimes it’s enough to just use a for loop if you have < 1000 docs.

I think I can achieve good results with faiss if I try again. Thank you once again.
@Diet

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