AI on Ulabase

Your service 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. You choose the models and call them with your own keys. The vectors stay in MongoDB, next to the data they describe, and only the calls to your provider leave your service.

The pages of the AI group in the sidebar follow the order you need them: the key, the vectors, the files, the agent.

Page	What it is for
AI Keys	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.
Auto Embeddings	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 a button to the page that sets it up.
Auto Chunking	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.
MCP	Let an agent such as Claude use your collections, aggregations, change streams and GraphQL apps, within the permissions you defined.

Page

What it is for

AI Keys

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.

Auto Embeddings

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 a button to the page that sets it up.

Auto Chunking

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.

MCP

Let an agent such as Claude use your collections, aggregations, change streams and GraphQL apps, within the permissions you defined.

From a field to a semantic search

Three steps: a key, a rule, a search.

A key under AI Keys lets the service 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. A rerank block hands the best of them to the reranking model. Published on MCP, the aggregation is a search an agent can run.

When the collection grows, a vector index under Indexes, with $vectorSearch in place of $vectorScan, makes the search fast. Vector indexes and $vectorSearch come with Pro and above.

Auto Embeddings walks you through the three steps on catalog, the collection of the ecommerce starter, so MCP, the search and the shop all use the same products. Each step is a click, and the page also shows the same calls in curl, HTTPie, JavaScript and Python.

What it costs

Your 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. Searches run on your service, within your plan. $vectorScan reads every vector it compares, so an index pays off as the collection grows.

Where it applies

The AI pages of the console work on Free, Starter and Pro services. On an Enterprise service we set up semantic search with you, on the MongoDB it runs on: ask us.