Semantic Search & RAG Tool

The Confluence Semantic Search Tool

Search your vectorized Confluence space 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 documentation.

Explore VDF AI Agents
MeaningSemantic, not keyword, matching
ScoredEvery hit ranked by similarity
Top-50Tunable result depth
100%On-prem, pages never leave
The Documentation Problem

Your wiki has the answer — if you can find the page

Confluence spaces grow into thousands of pages. The native search is keyword-bound, so the page you need stays hidden unless you guess its exact wording. Teams ask a colleague instead of the wiki.

01

Keyword search misses

If the page calls it "incident runbook" and you search "outage steps," you get nothing.

02

Scale hides good content

The right page exists but is buried under thousands of near-duplicates and stale drafts.

03

No confidence signal

Native results give no sense of how relevant a hit actually is.

04

Hosted AI is off-limits

Internal documentation is exactly what can’t be sent to a third-party assistant.

How the Tool Works

Meaning-aware search over your space

Semantics

Match on intent, not exact words

Find the page however it’s phrased.

The tool embeds your query and compares it to vectorized Confluence 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
Intent
Meaning Match

Beyond keywords

EmbeddingsParaphraseScoredRanked

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.

Cited
Source Pages

Verifiable answers

CitationsRAGTrustVerify

Governance

Private and on-premise

Documentation stays internal.

The index and search run inside your perimeter, scoped per user with audit logging, so even sensitive internal documentation is safe to make searchable.

100%
On-Prem

Per-tenant, logged

On-premRBACAudit logSovereign
Inputs

Parameters

The confluence_vector_search tool accepts these inputs when an agent calls it. Required inputs are flagged.

Name Type Required Description
query string Required Search query for semantic matching.
user_id integer Required User ID for multi-tenant isolation.
top_k integer
default: 10
Optional Maximum number of results to return (1–50).
Where it pays back

Where Confluence search pays back

Internal helpdesk

Answer "how do I request access?" straight from the right policy page.

Engineering runbooks

Surface the correct runbook during an incident without knowing its title.

Policy lookup

Find the current HR or security policy by describing the situation, not the page name.

Onboarding

Let new hires ask the wiki questions and get grounded, citable answers.

Documentation audits

Locate every page covering a topic to find duplicates and gaps.

Agent grounding

Give a knowledge agent reliable retrieval over your documentation space.

How VDF AI connects it

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.

ROI Snapshot

What changes after you assign it

Fewer
Questions bounced to colleagues
Seconds
To the right page
Cited
Answers traceable to a page
100%
Searchable without leaving
FAQ

Questions about the Confluence Semantic Search tool

What is the Confluence semantic search tool?

It searches your vectorized Confluence pages 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 Confluence, Jira, and GitHub together, or use this tool alone when an agent only needs the wiki.

Make your wiki answer questions

See Confluence semantic search assigned to an agent that grounds answers in your own pages — on-premise.