AI
AI on Ulabase
What the AI group of a service gives you: your own provider keys, vectors computed as you write, semantic search over your collections with or without an index, and agents that use your data over MCP.
A service on Ulabase can compute a vector for every document it stores, search by meaning, reorder the results with a reranking model, and let an AI agent use all of it over MCP. The models are the ones you choose, called with your own keys; the vectors stay in MongoDB, next to the data they describe. Nothing leaves your service except the calls to your provider.
The AI group of the sidebar reads in the order you meet its pages: the key, the vectors, the files, the agent, the product.
| Page | What it is for |
|---|---|
The providers the service embeds and reranks with, each with your key and its default models. Everything else in this group runs on what you set here. |
|
A rule per collection: every document written gets a vector for one of its fields, computed by your provider. Under the rule, what is still missing for a semantic search, each with the button that opens the page owning it. |
|
Rules on a file bucket: every file uploaded that matches one is split into text chunks, in a collection that embeds them and that you search like any other. |
|
Let an agent such as Claude use your collections, aggregations, change streams and GraphQL apps, within the permissions you defined. |
|
bySophia.ai |
A product of its own, built end to end on Ulabase: your documents answering questions in conversation, with the sources cited. Sold and billed apart; the page in the console says what it is, bysophia.ai is where it starts. |
From a field to a semantic search
A key set under AI Keys makes the service able to embed text. A rule under Auto Embeddings tells a collection which field to embed: every document written from then on gets its vector. An aggregation with $vectorize and $vectorScan turns a question into a vector and returns the closest documents, with no index at all; a rerank block hands the best of them to the reranking model. Published on MCP, that aggregation is a search an agent can run. When the collection grows, a vector index under Indexes and $vectorSearch in place of $vectorScan make the search fast.
Three steps, then: a key, a rule, a search. Auto Embeddings walks you through them on catalog, the collection of the ecommerce starter, so what MCP publishes, what the search finds and what the shop sells are the same products. Each step is a click, and the same calls are shown by hand in curl, HTTPie, JavaScript and Python.
What it costs
The provider bills the calls to your account: one embedding per document written and per question asked, one reranking per search that asks for it. Ulabase adds nothing on top. Searches run on your service, within your plan; $vectorScan reads the vectors it compares, so an index pays off as the collection grows.
Where it applies
The AI features run on Free and Shared services. A Dedicated service brings its own MongoDB Atlas, and vector search there is Atlas’s own: ask us and we set it up with you.