The Result Reranker Tool
Rerank a set of retrieved passages with a cross-encoder so the most relevant rise to the top — sharpening RAG accuracy by putting the best context first for the model.
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.
Result Reranker, without the risk
Capability
What it does
Reorder retrieved results by true relevance.
it reranks a set of retrieved results by their true relevance to the query.
Assignable to any agent
How it works
Predictable, inspectable behavior
Designed to be reliable.
it scores each query-passage pair with a cross-encoder rather than a coarse similarity, so the ordering reflects real relevance and the top results are the ones worth using.
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 rerank_results tool accepts these inputs when an agent calls it. Required inputs are flagged.
default: 5 Optional How many top results to keep after reranking.
How the Result Reranker tool works in practice
Result Reranker is a semantic search & rag tool you assign to a VDF AI agent. It reranks a set of retrieved results by their true relevance to the query. Its hallmarks — Rerank, Cross-encoder, Relevance — let an agent rely on it as a dependable step in a larger task rather than a brittle one-off script.
Under the hood, it scores each query-passage pair with a cross-encoder rather than a coarse similarity, so the ordering reflects real relevance and the top results are the ones worth using. It expects query and results as required inputs, 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 Result Reranker when they need to handle sharper RAG, trim context, and better answers. It rarely works alone — pair it with Hybrid Search, RAG Vector Query, and Chunk Citation to build a complete, governed workflow, then compose those steps into an on-premise VDF AI Network.
Where Result Reranker pays back
Sharper RAG
Put the most relevant chunks first for the model.
Trim context
Keep only the top few passages, dropping noise.
Better answers
Improve accuracy without re-indexing.
Fusion step
Rerank the merged output of hybrid search.
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 Result Reranker tool
What is the Result Reranker tool?
It reranks a set of retrieved results by their true relevance to the query. 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.
Why rerank after retrieval?
Initial retrieval optimizes for recall; reranking with a cross-encoder optimizes the final order for precision.
Does it need re-indexing?
No. Reranking operates on already-retrieved results, so it improves accuracy with no index changes.
What inputs does the Result Reranker tool need?
It requires query and results, and optionally accepts top_k. Each parameter is validated when an agent calls the tool, and the full call is logged for audit.
Which tools pair well with Result Reranker?
Result Reranker is commonly assigned alongside Hybrid Search, RAG Vector Query, and Chunk Citation. 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 Result Reranker 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 Result Reranker to work
See the Result Reranker tool assigned to an agent and orchestrated in a governed, on-premise network.