The Chunk Citation Tool
Bind every retrieved chunk to its precise source — document, page, and offset — so an agent’s answer carries verifiable citations a reader can open and check.
Your answer exists — retrieval just can’t find it
Private knowledge is only useful if an agent can retrieve exactly the right piece and ground its answer in it. Keyword search misses, hosted RAG can’t touch sensitive data, and ungrounded models make things up.
Keyword search misses
The right content is phrased differently than the query.
Ungrounded answers
Without retrieval, models invent instead of cite.
Scale hides signal
The best chunk is buried among thousands of near-matches.
Hosted RAG is off-limits
Sensitive knowledge can’t go to a third-party index.
Chunk Citation, without the risk
Capability
What it does
Attach exact source citations to each retrieved chunk.
it attaches precise source citations — document, location, and offset — to each retrieved chunk.
Assignable to any agent
How it works
Predictable, inspectable behavior
Designed to be reliable.
citations are derived from chunk metadata at retrieval time, so grounded answers point back to an exact, openable source rather than a vague reference.
Every call logged
Governance
Private, governed, on-premise
Runs inside your perimeter.
Indexing and retrieval run on-premise or in your sovereign cloud, scoped per tenant and audit-logged, so even sensitive knowledge is searchable and citable without any of it leaving your perimeter.
Per-tenant, logged
Parameters
The chunk_cite tool accepts these inputs when an agent calls it. Required inputs are flagged.
default: inline Optional Citation format to produce. inlinefootnotestructured
How the Chunk Citation tool works in practice
Chunk Citation is a semantic search & rag tool you assign to a VDF AI agent. It attaches precise source citations — document, location, and offset — to each retrieved chunk. Its hallmarks — Cite, Precise, Verifiable — let an agent rely on it as a dependable step in a larger task rather than a brittle one-off script.
Under the hood, citations are derived from chunk metadata at retrieval time, so grounded answers point back to an exact, openable source rather than a vague reference. It expects chunks as required input, so calls are explicit and easy to audit. Every call is scoped to the requesting tenant and written to an audit log, so the capability is safe to run inside a regulated, on-premise environment — the same governance model behind every VDF AI tool.
Teams reach for Chunk Citation when they need to handle trustworthy RAG, compliance, and verification. It rarely works alone — pair it with Citation Verifier, Result Reranker, and RAG Vector Query to build a complete, governed workflow, then compose those steps into an on-premise VDF AI Network.
Where Chunk Citation pays back
Trustworthy RAG
Give every answer openable source citations.
Compliance
Make regulated answers traceable to source.
Verification
Let a reader confirm each claim at its source.
Research output
Produce properly cited synthesized reports.
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 Chunk Citation tool
What is the Chunk Citation tool?
It attaches precise source citations — document, location, and offset — to each retrieved chunk. Assigned to a VDF AI agent, it runs under role-based policy with full audit logging so the capability is safe to use in production.
What does a citation include?
The source document and precise location (page or offset), so a reader can open exactly where a claim came from.
Does it verify the citation?
It produces the citation; pair it with the citation-verify tool to confirm the source supports the claim.
What inputs does the Chunk Citation tool need?
It requires chunks, and optionally accepts style. Each parameter is validated when an agent calls the tool, and the full call is logged for audit.
Which tools pair well with Chunk Citation?
Chunk Citation is commonly assigned alongside Citation Verifier, Result Reranker, and RAG Vector Query. On VDF AI you compose several tools and agents into a single governed, on-premise network.
Does it run on-premise?
Yes. Like every VDF AI tool, it can run on-premise or in your sovereign cloud, scoped per user and audit-logged, so your data never leaves your perimeter.
How do agents use it?
You assign the tool to an agent under a role-based policy; the agent calls it as one step in a task, and several agents and tools can be orchestrated together as a governed VDF AI Network.
Assign Chunk Citation to these agents
These VDF AI agents can be assigned this tool. Open an agent to see the full toolkit it can run.
Tools that work well alongside this one
Where this tool delivers value
Put Chunk Citation to work
See the Chunk Citation tool assigned to an agent and orchestrated in a governed, on-premise network.