Index the docs into a private knowledge base, then query it with Gemini to answer questions using only indexed content.
The AI agent indexes official docs into a private knowledge base using a RAG pipeline. It retrieves the most relevant chunks for a given question and passes them to Gemini for grounded answers. The agent responds strictly from the indexed content and logs interactions for auditing.
Core actions the agent performs to deliver grounded Q&A.
Index documentation pages into chunks
Embed chunks and store in Supabase
Query the vector store to retrieve relevant chunks
Pass chunks to Gemini with strict instruction
Return answer limited to indexed content
Log interactions and provide audit trail
Before → 5 real pain points are present: hard-to-find passages in large docs, outdated or incorrect answers, slow manual indexing, no audit trail, and fragmented knowledge. After → 5 concrete outcomes: precise, passage-backed answers; automatic indexing and updates; up-to-date knowledge; consistent responses; auditable content.
Simple three-step flow in plain terms.
Scrape the documentation, split it into chunks, generate embeddings, and store them in the Supabase vector store.
When a question is asked, fetch the top matching chunks from the vector store by similarity.
Pass retrieved chunks to Gemini with the instruction to answer only from indexed content, then present the grounded answer.
A realistic scenario of asking a docs question.
Scenario: A developer asks, "How does the IF node work?" Time: Initial indexing runs once; subsequent queries take seconds. Outcome: The agent returns a precise, cited explanation drawn only from the relevant doc passages.
Who benefits from an AI agent that sources answers from docs.
Need quick, exact references to documentation passages when implementing features.
Maintain accurate knowledge without manual curation and re-indexing.
Resolve customer questions with source-backed answers.
Manage access to the private knowledge base and ensure data governance.
Verify feature behavior against official docs for alignment.
Reuse documentation content to train teams and automate updates.
Key tools that power the AI agent and how they’re used inside it.
Stores doc chunk embeddings and performs similarity search to retrieve relevant passages.
Generates embeddings for chunks and provides grounded answers from the retrieved content.
Orchestrates indexing and chat flow from data ingestion to query processing.
Authorizes access to the vector store and configures the index and query endpoints.
Practical scenarios where the AI agent shines.
Common questions about using the AI agent with docs.
RAG stands for Retrieval-Augmented Generation. In this setup, the agent first indexes documents and creates embeddings, then uses those embeddings to retrieve the most relevant passages for a given question. The answer is then generated by an AI model based on only the retrieved passages, ensuring grounded responses. This minimizes hallucinations and ties answers to specific source content.
Indexing time depends on the size of the documentation and network speed. For a comprehensive docs set, it can take several minutes to process and store all chunks. You only need to run this once; subsequent queries reuse the stored embeddings. After the initial pass, new or updated pages are incremental, reducing re-indexing time.
The agent evidences its answers from the retrieved chunks, and Gemini is instructed to use only that content. While this significantly improves factual grounding, absolute guarantees depend on the quality of the source data. If the knowledge base isn’t fully up-to-date, the agent may reflect that state. Regular indexing and validation help maintain accuracy.
Data is stored in a private Supabase vector store. Access is controlled via credentials configured in the workflow, limiting exposure to authorized users. This setup supports governance and auditability. You can adjust permissions to fit organizational security policies.
Yes. The retrieval step aggregates the top relevant chunks from multiple documents and feeds them to Gemini. The final answer synthesizes information from those sources, preserving cross-document context. Citations reference the specific chunks that informed the reply.
Update workflows can re-run indexing for affected sections or pages. The system is designed to re-embed changed chunks and re-store them in the vector store, ensuring subsequent answers reflect the latest content. You can schedule regular re-indexing or trigger it manually as docs evolve.
Yes. You can configure which documentation sources are included in the indexing step and set prioritization rules for retrieval. You can also add or remove data sources without altering the core querying flow. Customization helps tailor the agent to organizational needs and compliance requirements.
Index the docs into a private knowledge base, then query it with Gemini to answer questions using only indexed content.
