There is a big UI part here, because for multimodal data analytics, we think it's crucial for people to see and hear data.
For the RAG search, many DBs have built-in vector search, but chunking, indexing, and maintaining the index are kind of on your own. This may not be a problem for technical people, but it's a hassle for data people who own hundreds of data products within a company. Therefore, we have a semantic search index builder that allows one to build an auto-refreshing semantic search index with no code, and completely keep hands free from coming up with their own vectors.
In addition, data analysis often needs to interrogate the search results further. For example, let's say we have used pgvector to find all the photos related to the Golden Gate Bridge. But then we want to interrogate questions like which of these images has someone wearing a blue shirt. We have to apply another model, and that is outside of a normal DB's responsibility.
There is a big UI part here, because for multimodal data analytics, we think it's crucial for people to see and hear data.
For the RAG search, many DBs have built-in vector search, but chunking, indexing, and maintaining the index are kind of on your own. This may not be a problem for technical people, but it's a hassle for data people who own hundreds of data products within a company. Therefore, we have a semantic search index builder that allows one to build an auto-refreshing semantic search index with no code, and completely keep hands free from coming up with their own vectors.
In addition, data analysis often needs to interrogate the search results further. For example, let's say we have used pgvector to find all the photos related to the Golden Gate Bridge. But then we want to interrogate questions like which of these images has someone wearing a blue shirt. We have to apply another model, and that is outside of a normal DB's responsibility.