We are using the default query language for Qdrant, and we have not used anything else. Whatever Qdrant provides by default, we are using it, and we are satisfied with that. The metrics I use to evaluate the performance in indexing and retrieving vectors with Qdrant focus on response time. Response time is the primary metric. Qdrant has reduced our response time to less than one second for our 128 KB token sizes, and we are satisfied with that performance. Qdrant is open source, which means the software is free if you handle it yourself, but you need one or two engineers working on it. Since it is free, it is very good compared to other databases. I rate this review an overall 8.
Currently, we are using a vector database called Qdrant, but most of our tasks are agentic, and we don't have it anymore. I can answer a few questions about Qdrant. I have used Qdrant's hybrid search capability. The use of multiple query languages has impacted my data query processes mostly as Q&A. We use the Ragas metrics to evaluate Qdrant's performance in indexing and retrieving vectors. All the metrics I consider in Ragas are useful. In my company, we have around eight or nine people using Qdrant. I think Qdrant is popular enough in my region, but they can probably promote it more. I rate this review a 9 out of 10.
Open Source Databases are essential for businesses seeking customizable database solutions. They offer flexibility, security, and active community support, making them a popular choice for a wide range of applications and industries.Known for their adaptability, Open Source Databases enable organizations to tailor database management systems to their specific requirements. With the freedom to modify code, users can optimize performance and security in ways that proprietary databases might not...
We are using the default query language for Qdrant, and we have not used anything else. Whatever Qdrant provides by default, we are using it, and we are satisfied with that. The metrics I use to evaluate the performance in indexing and retrieving vectors with Qdrant focus on response time. Response time is the primary metric. Qdrant has reduced our response time to less than one second for our 128 KB token sizes, and we are satisfied with that performance. Qdrant is open source, which means the software is free if you handle it yourself, but you need one or two engineers working on it. Since it is free, it is very good compared to other databases. I rate this review an overall 8.
Currently, we are using a vector database called Qdrant, but most of our tasks are agentic, and we don't have it anymore. I can answer a few questions about Qdrant. I have used Qdrant's hybrid search capability. The use of multiple query languages has impacted my data query processes mostly as Q&A. We use the Ragas metrics to evaluate Qdrant's performance in indexing and retrieving vectors. All the metrics I consider in Ragas are useful. In my company, we have around eight or nine people using Qdrant. I think Qdrant is popular enough in my region, but they can probably promote it more. I rate this review a 9 out of 10.