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.
Great question! The answer is two fold:
1. Not like a vector database, in addition to searching, VolansDB also store the files (pointers) directly in the table. So you are able to manage files (RBAC etc.) as table cells, apply batch data processing jobs easily with SQL, and even unstructured data lineage & pipeline.
2. VolansDB is columnar so it's optimized for analytical use cases rather than for product DB access patterns.
I don't think OP's question is the same as the one asked about Dropbox. The Dropbox question was about why the service was needed at all when you could use Unix tools like rsync to achieve the same thing. The answer to that was simple: not everyone is a tech-savvy user who wants to mess with command lines. On the other hand, OP's question was about whether there's already a mainstream database and extension that does what Roe AI does. They got a response, and it was helpful. The "You can do this with rsync!" argument became a meme, but now it's almost overshadowed by the knee-jerk "That's what they said about Dropbox!" response.
Indeed, it is a big factor of survivorship bias. The refrain against Dropbox is notable because Dropbox is the exception, being successful, not the norm. People don't realize how many startups have failed where people echoed the same old HN comment regarding these startups as having been said about Dropbox too.
it's the same as the quote we keep hearing "every big new thing started out looking like a toy". Somehow a bunch of people who should know better (intentionally?) misinterpreted that as every new toy is the next big thing.
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.