Ai configuration

Collabase’s AI features run under the name CollabaseAI: the assistant panel, writing assistance in Docs, task description generation in Projects, AI nodes in Automation, and semantic search. All AI features are off by default. The settings live under Admin → Settings → AI settings, split into four pages: These are three separate models with three separate providers. A chat provider that cannot produce embeddings does not stop semantic search from working, as long as you point the embedding slot somewhere else. Each slot has its own switch, and each one degrades on its own: without an embedding model the assistant still answers, without a re-ranking model search still finds.

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Choosing a provider

The deciding question is usually whether content may leave your infrastructure. Groq and Anthropic have no embeddings API at all, so they cannot carry semantic search — but that is no longer a reason to avoid them as your chat provider: point the embedding slot at a local Ollama and keep Groq for the answers. See Semantic search below.

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Setting up a provider

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Provider configuration fields

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Self-hosted

Install Ollama on your server first, then pull the model you want (ollama pull llama3.2). Any other OpenAI-compatible endpoint works the same way.

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OpenAI

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Google Gemini

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Azure AI Foundry

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Groq

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Anthropic

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Infomaniak

Infomaniak AI Tools is OpenAI-compatible, but the product id of your account is part of the URL, so the base URL is specific to your tenant.

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Semantic search

Semantic search indexes the text of your pages so plain-language questions can be answered from your own content. Turn it on with the switch on Admin → Settings → AI settings → RAG model & pipeline. Nothing is indexed while that switch is off. Indexing needs an embedding model, which is a different kind of model from the chat model. Not every provider offers one. Groq and Anthropic are not offered as an embedding source. If one of them is your chat provider, choose Separate provider and point the embedding slot at something else — a local Ollama is the usual answer, and it keeps your page content on your own server even when the answers come from a hosted model.

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Embedding source

Changing the embedding model does not corrupt anything: search only compares entries produced by the model currently in use, so the older ones are ignored rather than mixed in. They are also useless until rebuilt, which is what the Rebuild button next to the status panel is for. It queues everything the index is currently missing and reports how many items went into the queue. Until it has run, semantic search finds progressively less while keyword search is unaffected. Rebuild after every embedding-model change. The nightly pass repairs the index on its own eventually; the button is how you get it back the same day.

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What gets indexed

Registry objects are not indexed; the assistant reaches them through its Registry skills, which check permissions per object. Unchanged content is not re-indexed, so repeatedly saving the same page costs nothing. A nightly pass also picks up anything an interruption caused the index to miss, so it converges on its own without an administrator watching it.

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How results are ranked

Two searches run over the same set of pages and their results are merged:

• Meaning, using the embedding model. Finds the right paragraph when the words differ from the question.
• Exact words, using the database’s own text search. Finds ticket keys, product names and abbreviations, which embeddings are consistently bad at.

Turning Hybrid search off leaves only the first. Two more settings control volume: Restricted pages, and everything filed underneath them, are deliberately left out of semantic search entirely, even for people who may read them. Search results are always limited to the spaces the person asking may open.

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Checking that it works

The Status panel on the RAG page calls the configured embedding endpoint for real and reports back, along with how many passages are currently indexed. An embedding model that reports as working next to an index of zero passages means nothing has been saved since you switched it on.

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Re-ranking

Re-ranking is an optional second model that reads the question and each candidate passage together and reorders them. The embedding model has to describe each passage before anyone asks a question; a re-ranking model sees both at once, which is why it orders results better. It runs last, over a short list, and it is off by default. If the re-ranking model is slow, unreachable or misconfigured, search keeps working with the normal ranking and the answer is no worse than it would have been without re-ranking. It never blocks an answer.

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What the assistant may do on its own

The assistant reads freely and writes never — not without being told so in the moment. Anything that changes something (creating or updating a work item, moving one, changing a status, writing a comment, creating or changing a page or Registry object) appears in the chat as a request to approve, showing exactly what it would do. Nothing happens until it is approved, and rejecting it changes nothing. This is not a politeness setting and cannot be switched off. The assistant reads pages, work items and comments written by other people, and text inside them can be phrased as an instruction to it. The approval is what stands between such a sentence and a change made under your name.

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Thinking and reasoning

Some models can show their reasoning before the answer. Whether that appears in the assistant panel depends entirely on the provider and the model: Reasoning is billed as output on hosted providers, so a thinking model costs more per answer than a comparable non-thinking one.

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Privacy and data handling

API keys are encrypted at rest in Collabase and are never written to logs. Assistant chats are private to the user who wrote them. There is no administrator view of a chat, and every chat is deleted 30 days after it was last written to. Support questions of the form “what did the assistant tell me” cannot be answered by looking at the transcript, only by reproducing the question.

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Next steps