- Replacing object storage, a vector DB, a SQL database, and an external embedding API with one database for procure-to-pay docs
A document processor often ends up spread across several data stores, with PDFs in object storage, metadata in a key-value store, embeddings in a vector DB, aggregations in a SQL database, and an...
1 points0 comments17 h ago
- How do developers build a semantic search app with an AI database?
One way to build a semantic search app with an AI database is to run the embedding model inside the database itself, store each record’s vector next to its text, and let a SQL query sort records...
1 points0 comments2 d ago
- Can AI agents rely on file systems for memory as they move into production?
Sometimes. The breaking point I’ve seen is a second agent process writing to the same memory file, which makes it a shared-state problem more than a retrieval one. Files work for prototypes...
1 points0 comments5 d ago
- Can developers run ONNX embeddings and semantic search entirely inside a relational database?
Yes for both, though “entirely” comes with conditions. In Oracle AI Database 26ai, an augmented ONNX model loads with DBMS\VECTOR.LOAD\ONNX\MODEL() and gets called from SQL through...
1 points0 comments6 d ago
- What matters most when choosing a vector database?
When someone asks me for the best vector database, I don’t have one name to give. Whatever gets recommended was chosen for someone else’s workload. I’ve seen setups picked off a benchmark chart...
1 points0 comments7 d ago