The Vector Delete by Repository Tool
Remove all vectors belonging to a repository from the store in one call so retired, private, or stale content stops surfacing in search — clean index hygiene and a real deletion path.
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.
Vector Delete by Repository, without the risk
Capability
What it does
Purge a repository’s vectors from the index.
it deletes every vector associated with a given repository from the store.
Assignable to any agent
How it works
Predictable, inspectable behavior
Designed to be reliable.
deletion is scoped and logged, so removing content from retrieval is a single auditable action — important for retiring data and honoring deletion requirements.
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 rag.vector_delete_by_repo tool accepts these inputs when an agent calls it. Required inputs are flagged.
How the Vector Delete by Repository tool works in practice
Vector Delete by Repository is a semantic search & rag tool you assign to a VDF AI agent. It deletes every vector associated with a given repository from the store. Its hallmarks — Delete, Purge, Scoped — let an agent rely on it as a dependable step in a larger task rather than a brittle one-off script.
Under the hood, deletion is scoped and logged, so removing content from retrieval is a single auditable action — important for retiring data and honoring deletion requirements. It expects collection and repo 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 Vector Delete by Repository when they need to handle retire content, index hygiene, and compliance. It rarely works alone — pair it with Vector Upsert, Batch Embed & Upsert, and Audit Trail Query to build a complete, governed workflow, then compose those steps into an on-premise VDF AI Network.
Where Vector Delete by Repository pays back
Retire content
Stop a decommissioned repo from surfacing in search.
Index hygiene
Remove stale vectors before a full re-index.
Compliance
Delete content to honor data-removal requirements.
Access changes
Purge vectors when a source loses permission.
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 Vector Delete by Repository tool
What is the Vector Delete by Repository tool?
It deletes every vector associated with a given repository from the store. 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.
Is deletion complete?
Yes. All vectors for the repository are removed from the collection, and the action is logged.
Why is this important for compliance?
It gives you a concrete, auditable path to remove content from retrieval when data must be forgotten.
What inputs does the Vector Delete by Repository tool need?
It requires collection and repo. Each parameter is validated when an agent calls the tool, and the full call is logged for audit.
Which tools pair well with Vector Delete by Repository?
Vector Delete by Repository is commonly assigned alongside Vector Upsert, Batch Embed & Upsert, and Audit Trail 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.
Tools that work well alongside this one
Where this tool delivers value
Put Vector Delete by Repository to work
See the Vector Delete by Repository tool assigned to an agent and orchestrated in a governed, on-premise network.