Congratulations on the launch! would be awesome to see support for MongoDB Atlas as one of the vector stores and Voyage AI as an embedding provider if you are interested. I can imagine quite a few customers that would prefer a lightweight interface for chunking- lmk how I can help make that happen from the Mongo side!

We're working on Mongo integrations!

awesome! let me know if I can be helpful in any way in connecting with Mongo resources

Could be wrong but i think here Neal is saying we are the eye mites subsisting off of AI in the long future, not the other way around.

>their embedding model is not SOTA, does not even outperform the open ones out there, and reranking is a dead end in 2025.

Are you referring to the MTEB leaderboard? It's widely believed many of those test datasets are considered during the training of most open-source text embedding models, hence why you see novel + private benchmarks discussed in many launch blogs that don't exclusively refer to MTEB. There are problems there, and it would be great to see more folks in the search benchmark dataset production space like what Marqo AI has done in recent months.

Also what makes you say reranking is dead? Mongo doesn't provide it out of the box but many other search providers like ES, Pinecone, Opensearch do so it must provide some value to their customers? Maybe you're saying it's overrated in terms of how many apps actually need it?

disclosure: I work on vector search at Mongo

>Maybe you're saying it's overrated in terms of how many apps actually need it?

Yes, my comment leans more towards that, rather than suggesting is useless.

Taking a step back, accuracy/quality of retrieval is critical as input to anything generated b/c your generated output is only as good as your input. And right now folks are struggling to adopt generative use cases due to risk and fear of how to control outputs. Therefore I think this could be bigger than you think.