Qdrant Vector DB
What do you like best about the product?
Qdrant is a open source
It is suitable for efficient vector search.
It allows to handle large datasets and high query loads.
It supports High Dimensional Vectors
Best thing of using Qdrant is its speed and reliability.
It is suitable for efficient vector search.
It allows to handle large datasets and high query loads.
It supports High Dimensional Vectors
Best thing of using Qdrant is its speed and reliability.
What do you dislike about the product?
I have deployed qdrant in Azure cloud using AKS, ACI,App service. The setup and integration is very complex.
I have faced timeout issues at initial creation of collection names with client. Due to less documentation it took some time for resolution.
I have faced timeout issues at initial creation of collection names with client. Due to less documentation it took some time for resolution.
What problems is the product solving and how is that benefiting you?
Qdrant allows the embeddings for matching, searching, recommending. It helps to get relevant data from the DB based on similarity search.
We are passing the matched content to LLMs. This helps in resolving model halucinations.
We are passing the matched content to LLMs. This helps in resolving model halucinations.
What is number of units? What cloud usage unit exactly refers to?