My main use case for Vespa is implementing it as the back-end search engine for an e-commerce site, where we have about six million products, or six million SKUs, that we are selling. I implemented Vespa as an alternative for Elasticsearch. Using Vespa for the e-commerce site involved utilizing it as the backend search engine to replace Elasticsearch, which we felt was not doing us justice. The very first thing I did was convince my CIO to try out Vespa. We did a quick proof of concept, engaged with the right people through the Vespa Slack channels, and then we did the actual implementation, including A/B testing it against the previously running fully optimized Elasticsearch pipeline.
Integration Related To Ai at RedBlink Technologies
MSP
Top 20
Jun 6, 2026
My full name is Shubhank and I am serving in Redblink Technologies in Mohali where I have been doing integration work related to AI. I implemented RAG. The previous year, we were using Quadrant as a vector store. With that, we were creating many collections there. Our company discussed internally and decided to move to Vespa. This was about six or eight months ago. We are using Vespa in our RAG pipeline. We have implemented a RAG pipeline where we have document retrieval. Users can chat with their documents. We are breaking down our documents into meaningful chunks using LangChain4j and feeding that directly into Vespa as a vector store. Later, while the user chats or starts a chat with the document, we can retrieve according to the user's prompt. We have such more use cases. We have a client, CPA Pilot, where there are many text documents, so we directly chunk those documents. There are very large documents, so in Quadrant, the collections were almost full. Inside Vespa, there is no system of collections, so that also helped us. We use self-hosted Vespa for that particular client and we are chunking down the long documents using LangChain4j and hitting Vespa to store it. During retrieval, we get good results and get proper relevant scores based on the user's query.
I use Vespa as a vector database for ranking and matching. I have jobs and candidates indexed in Vespa, which is a vector database. When I have a job and need to get the first 50 candidates who match that job, a normal vector search would not retrieve the first 50 because I may need to filter or rank based on some features and fields. Vespa helps by allowing me to first select the first 200, and then within the 200, I rank the first 50 based on certain criteria.
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My main use case for Vespa is implementing it as the back-end search engine for an e-commerce site, where we have about six million products, or six million SKUs, that we are selling. I implemented Vespa as an alternative for Elasticsearch. Using Vespa for the e-commerce site involved utilizing it as the backend search engine to replace Elasticsearch, which we felt was not doing us justice. The very first thing I did was convince my CIO to try out Vespa. We did a quick proof of concept, engaged with the right people through the Vespa Slack channels, and then we did the actual implementation, including A/B testing it against the previously running fully optimized Elasticsearch pipeline.
My full name is Shubhank and I am serving in Redblink Technologies in Mohali where I have been doing integration work related to AI. I implemented RAG. The previous year, we were using Quadrant as a vector store. With that, we were creating many collections there. Our company discussed internally and decided to move to Vespa. This was about six or eight months ago. We are using Vespa in our RAG pipeline. We have implemented a RAG pipeline where we have document retrieval. Users can chat with their documents. We are breaking down our documents into meaningful chunks using LangChain4j and feeding that directly into Vespa as a vector store. Later, while the user chats or starts a chat with the document, we can retrieve according to the user's prompt. We have such more use cases. We have a client, CPA Pilot, where there are many text documents, so we directly chunk those documents. There are very large documents, so in Quadrant, the collections were almost full. Inside Vespa, there is no system of collections, so that also helped us. We use self-hosted Vespa for that particular client and we are chunking down the long documents using LangChain4j and hitting Vespa to store it. During retrieval, we get good results and get proper relevant scores based on the user's query.
I use Vespa as a vector database for ranking and matching. I have jobs and candidates indexed in Vespa, which is a vector database. When I have a job and need to get the first 50 candidates who match that job, a normal vector search would not retrieve the first 50 because I may need to filter or rank based on some features and fields. Vespa helps by allowing me to first select the first 200, and then within the 200, I rank the first 50 based on certain criteria.