Build a private documentation chatbot with Gemini and Supabase

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SaaS support and dev teams waste hours hunting through documentation for answers that should take seconds. This AI automation ingests any knowledge base, embeds it into a private vector store, and wires up a chatbot that retrieves accurate, source-grounded answers on demand.

What This Automation Does

Automatically scrapes and chunks an entire documentation site into small, searchable passages
Generates vector embeddings for every passage using Google Gemini and stores them in a private Supabase database
Runs a conversational AI agent that retrieves only the most relevant passages before forming any answer
Maintains short-term conversation memory so follow-up questions stay in context without re-querying the full knowledge base

Tools Used

n8n
Google Gemini
Supabase

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How To Build It

Scrape and extract documentation content

An HTTP request fetches every page of the target documentation site, raw HTML is parsed into clean readable text, and duplicate pages are removed so the knowledge base contains no redundant entries.

Chunk text into retrieval-ready passages

The cleaned text is split into small, overlapping character chunks using a recursive strategy, ensuring no single passage is too long for the embedding model and that context is never cut off mid-sentence.

Embed passages into Supabase vector store

Each text chunk is sent to Google Gemini to produce a high-dimensional numerical embedding, and the resulting vectors are written to a dedicated Supabase table so they can be queried by semantic similarity at runtime.

Configure the retrieval-augmented AI agent

A conversational agent is connected to the Supabase vector store as its exclusive knowledge source, so every response it generates is grounded in retrieved documentation passages rather than the model's general training data.

Wire in memory and test end-to-end queries

A sliding-window memory buffer is attached to the agent so it retains the last several conversation turns, then the full pipeline is tested with multi-step questions to confirm retrieval accuracy and response coherence before handoff.

Pitfalls

Supabase vector similarity searches degrade if the embedding model used at query time does not exactly match the model used during indexing, producing confidently wrong answers with no visible error.
Documentation sites that use JavaScript rendering or client-side routing will return empty or partial HTML to a plain HTTP scraper, meaning large sections of the knowledge base silently never get indexed.
Google Gemini embedding API rate limits will cause the indexing phase to fail mid-run on large documentation sites unless the workflow includes a delay or batching strategy between embedding requests.

FAQ

Can I build this without coding?

Yes. Every component is configured through visual interfaces and credential forms, with no custom code required. The trickiest part is setting up the Supabase vector extension, which involves running one SQL command in the Supabase dashboard.

How long does it take?

A first working version typically takes two to four hours, including setting up Supabase and running the initial indexing job. Adapting it to a new client's documentation site after your first build takes under an hour.

What can I charge?

Pricing is best structured as a setup fee covering ingestion, configuration, and delivery plus an optional monthly retainer for re-indexing when the client's docs are updated. The retainer angle is strong because documentation changes continuously.

Which tool is required vs optional?

Supabase and Google Gemini are both required as built: Supabase provides the vector store and Gemini produces the embeddings. The documentation source is fully swappable, so you can point the scraper at any client's help center, API reference, or internal wiki without changing anything else.

This is original DigiNo analysis. The underlying automation pattern is a community workflow template – view the original on n8n.