Flicker's assistant answers from your content, not the model's guesswork. Connect a data source, Flicker ingests and embeds it into a private knowledge base, and every answer is retrieved from what you actually gave it.
Connect a source
A source
is a set of files the assistant can read. Three kinds ship
today: a bucket
(a storage bucket your org already owns), a repo
(a connected git repository), and tickets
(a Flicker project's tickets and their documents). More
kinds plug in behind the same interface. Turn on auto_embed
and new
content is chunked and embedded as it lands — no manual re-index.
Hybrid retrieval
The assistant is agentic: it decomposes a question into sub-questions, runs a hybrid search for each — pgvector semantic similarity and Postgres full-text — and merges the results with Reciprocal Rank Fusion. Hybrid beats either alone: vectors catch meaning, full-text catches exact terms and rare tokens. A failed sub-query degrades to fewer results rather than dropping the answer.
Ask
Open Assistant under your project and ask in plain language; the answer is grounded in the passages it retrieved, and the model behind it is pluggable. A retrieval playground lets you inspect exactly what was retrieved for a query and tune the pipeline before you ship it.