I have been using Qdrant for almost one and a half years. This was actually one of the first vector databases that we picked up in our organization. We started using the RAG modules to create a personalized company-based AI or a company-based LLM which would help answer questions from the employees, and we used Qdrant to store all the documentation, resources, and all other help links and help documents parsed into vector databases. After that, we moved into using Supabase vectors. The main use case for Qdrant was to upload help resources and articles, as we have a repository of helpful resources or help documentation which the team can refer to in order to do a particular thing. For example, there is a workflow for how to onboard a new team member. The entire workflow has been broken down into multiple different steps and multiple different checklists, which is maintained in a documentation. This documentation can be given to a team member for a better understanding to follow the process through and onboard a team member. What we did is we used Qdrant to create a vector database where we can store all of these documents, creating embeddings out of them and storing them in the vector database in Qdrant. This can then be referred to by an LLM agent that retrieves these documents or answers based on inquiries. Instead of giving someone this document, we can give them access to the agent, and they can ask how to onboard a team member. The agent would refer to the document from Qdrant based on the vector database, fetch the results, and show them the exact contents of the document in a proper LLM format with checklists, allowing users to ask further questions for context.
My main use case was utilizing Qdrant as a vector database to store documents in vector format, which made searching easier whenever a query was passed. I implemented it in a chatbot, specifically a RAG chatbot, which stands for Retrieval-Augmented Generation. I stored the business documents of companies into Qdrant's knowledge base or vector database. Using Qdrant as my vector database for the RAG chatbot definitely helped with document search and chatbot accuracy. Initially, it was very easy to implement vector search in Qdrant and to embed the documents that needed to be stored. The RAG chatbot was a simple PDF stored in the knowledge base in properly defined chunks and could be queried anytime when a question was passed. Accuracy-wise, the chatbot achieved approximately seventy percent accuracy, although it needed more fine-tuning and guardrails to make it more accurate, which was insightful for me in using Qdrant. Regarding my main use case and experience using Qdrant for my RAG chatbot and document search, I started by coding and implementing the RAG chatbot alongside Qdrant as a vector database. Recently, I discovered an innovative way of using Qdrant, which is Qdrant Cloud. This allows users to utilize Qdrant not just in coding projects but also in no-code projects. As an AI automation engineer, I have created no-code automations for HR and recruiting agencies, such as an ATS screener and resume screener, where I built a proper vector database for the recruiting agency to store all the resumes they have. They can query the top N results matching their job descriptions without me needing to code the entire solution, thanks to Qdrant Cloud. It made implementation much easier and took me less than a week, whereas a coded project would normally take at least a month. One of the best features Qdrant offers is definitely Qdrant Cloud, as it can be easily deployed and implemented in no-code platforms without limitations tied to a coding approach. Additionally, the HNSW method for searching through the vector database is very fast and accurate compared to just normal similarity search that most vector databases provide. Moreover, Qdrant allows users to view how the vectors are stored, including checking the three-dimensional diagrams of the stored vectors. The specific feature that helped me solve problems or save time is Qdrant Cloud, especially when I built a resume screener for the recruiting agency in under a week. This significant time-saving benefit comes from using Qdrant Cloud in no-code automation workflows. Another use case I had was creating a complete vector database for the company, where I stored contracts in PDF format. This allows the company's founder to query the bot and swiftly get accurate answers about these documents, enhancing the whole process significantly in terms of speed and simplicity due to the easier access provided by Qdrant Cloud.
Qdrant is a powerful tool for efficiently organizing and searching large volumes of data. It is particularly useful for tasks such as data indexing, similarity search, and recommendation systems.
With fast and accurate results, it is suitable for various applications including e-commerce, content management, and data analysis. Users appreciate Qdrant's efficient search capabilities, high performance, and ease of use.
Its quick and accurate retrieval of relevant information allows...
I have been using Qdrant for almost one and a half years. This was actually one of the first vector databases that we picked up in our organization. We started using the RAG modules to create a personalized company-based AI or a company-based LLM which would help answer questions from the employees, and we used Qdrant to store all the documentation, resources, and all other help links and help documents parsed into vector databases. After that, we moved into using Supabase vectors. The main use case for Qdrant was to upload help resources and articles, as we have a repository of helpful resources or help documentation which the team can refer to in order to do a particular thing. For example, there is a workflow for how to onboard a new team member. The entire workflow has been broken down into multiple different steps and multiple different checklists, which is maintained in a documentation. This documentation can be given to a team member for a better understanding to follow the process through and onboard a team member. What we did is we used Qdrant to create a vector database where we can store all of these documents, creating embeddings out of them and storing them in the vector database in Qdrant. This can then be referred to by an LLM agent that retrieves these documents or answers based on inquiries. Instead of giving someone this document, we can give them access to the agent, and they can ask how to onboard a team member. The agent would refer to the document from Qdrant based on the vector database, fetch the results, and show them the exact contents of the document in a proper LLM format with checklists, allowing users to ask further questions for context.
My main use case was utilizing Qdrant as a vector database to store documents in vector format, which made searching easier whenever a query was passed. I implemented it in a chatbot, specifically a RAG chatbot, which stands for Retrieval-Augmented Generation. I stored the business documents of companies into Qdrant's knowledge base or vector database. Using Qdrant as my vector database for the RAG chatbot definitely helped with document search and chatbot accuracy. Initially, it was very easy to implement vector search in Qdrant and to embed the documents that needed to be stored. The RAG chatbot was a simple PDF stored in the knowledge base in properly defined chunks and could be queried anytime when a question was passed. Accuracy-wise, the chatbot achieved approximately seventy percent accuracy, although it needed more fine-tuning and guardrails to make it more accurate, which was insightful for me in using Qdrant. Regarding my main use case and experience using Qdrant for my RAG chatbot and document search, I started by coding and implementing the RAG chatbot alongside Qdrant as a vector database. Recently, I discovered an innovative way of using Qdrant, which is Qdrant Cloud. This allows users to utilize Qdrant not just in coding projects but also in no-code projects. As an AI automation engineer, I have created no-code automations for HR and recruiting agencies, such as an ATS screener and resume screener, where I built a proper vector database for the recruiting agency to store all the resumes they have. They can query the top N results matching their job descriptions without me needing to code the entire solution, thanks to Qdrant Cloud. It made implementation much easier and took me less than a week, whereas a coded project would normally take at least a month. One of the best features Qdrant offers is definitely Qdrant Cloud, as it can be easily deployed and implemented in no-code platforms without limitations tied to a coding approach. Additionally, the HNSW method for searching through the vector database is very fast and accurate compared to just normal similarity search that most vector databases provide. Moreover, Qdrant allows users to view how the vectors are stored, including checking the three-dimensional diagrams of the stored vectors. The specific feature that helped me solve problems or save time is Qdrant Cloud, especially when I built a resume screener for the recruiting agency in under a week. This significant time-saving benefit comes from using Qdrant Cloud in no-code automation workflows. Another use case I had was creating a complete vector database for the company, where I stored contracts in PDF format. This allows the company's founder to query the bot and swiftly get accurate answers about these documents, enhancing the whole process significantly in terms of speed and simplicity due to the easier access provided by Qdrant Cloud.
Our use case for Qdrant is AI data analysis.
My primary use cases for Qdrant are legal and educational.