The GitBook Semantic Search Tool
Search your vectorized GitBook documents by meaning and get back the pages that actually answer the question, each with a similarity score — grounding for any agent that lives in your product docs.
Your docs have the answer — if you can find the page
GitBook spaces grow into hundreds of pages across many collections. Native search is keyword-bound, so the page a user needs stays hidden unless they guess its exact wording — and they file a ticket instead.
Keyword search misses
If the page calls it "webhook retries" and the user searches "failed callbacks," they get nothing.
Scale hides good content
The right page exists but is buried across spaces, versions, and archived collections.
No confidence signal
Native results give no sense of how relevant a hit actually is.
Hosted AI is off-limits
Internal or pre-release documentation is exactly what cannot be sent to a third-party assistant.
Meaning-aware search over your docs
Semantics
Match on intent, not exact words
Find the page however it’s phrased.
The tool embeds your query and compares it to vectorized GitBook pages, surfacing the page that answers the question even when it shares no keywords with how you asked.
- Embedding-based matching
- Synonym- and paraphrase-tolerant
- Similarity score per hit
- Tunable top_k up to 50
Beyond keywords
Grounding
Citable pages for agents
Answers point back to the source page.
Each result identifies the page it came from, so an agent can ground its answer and a human can open the exact page to verify — the difference between a guess and a trustworthy response.
Verifiable answers
Governance
Private and on-premise
Documentation stays internal.
The index and search run inside your perimeter, scoped per user with audit logging, so even pre-release or internal-only documentation is safe to make searchable.
Per-tenant, logged
Parameters
The gitbook_vector_search tool accepts these inputs when an agent calls it. Required inputs are flagged.
default: 10 Optional Maximum number of results to return (1–50).
Where GitBook search pays back
Developer docs assistant
Answer "how do I authenticate the API?" straight from the right reference page.
Support deflection
Let a support agent ground answers in your published docs before a ticket is opened.
Internal knowledge base
Find the current runbook or spec by describing the situation, not the page name.
Onboarding
Let new hires ask the docs questions and get grounded, citable answers.
Docs audits
Locate every page covering a topic to find duplicates and gaps.
Agent grounding
Give a product or support agent reliable retrieval over your documentation.
Assigned to agents, orchestrated as networks
On VDF AI, an industry’s use cases map to agents, and you assign tools like this one to those agents. Compose multiple agents into a governed, on-premise network.
What changes after you assign it
Questions about the GitBook Semantic Search tool
What is the GitBook semantic search tool?
It searches your vectorized GitBook documents by meaning and returns the most relevant pages with similarity scores. Assigned to an agent, it grounds answers in your own documentation rather than a generic model’s training data.
Do I need to keyword-match the page title?
No. Matching is semantic, so the tool finds the right page even when your query uses entirely different words than the page does — the main weakness of native keyword search.
How many results can it return?
You control depth with top_k, up to 50 results, each carrying a similarity score so an agent can drop low-confidence hits.
Is our documentation kept private?
Yes. The vector index and search run on-premise or in your sovereign cloud, scoped per user and fully audit-logged. Nothing is sent to a third party.
Can it be combined with other sources?
Yes — assign federated vector search to query GitBook, Confluence, Jira, and GitHub together, or use this tool alone when an agent only needs the docs.
Tools that work well alongside this one
Where this tool delivers value
Make your docs answer questions
See GitBook semantic search assigned to an agent that grounds answers in your own pages — on-premise.