The Vector Upsert Tool
Add or update embedding vectors with their metadata in the vector store so new and changed content becomes immediately searchable — keeping your index fresh and governed.
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 Upsert, without the risk
Capability
What it does
Insert or update vectors in the store.
it inserts or updates vectors and their metadata in the vector store.
Assignable to any agent
How it works
Predictable, inspectable behavior
Designed to be reliable.
upserts are keyed and scoped per tenant with logging, so the index stays current and isolated, and re-indexing content updates in place rather than duplicating.
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_upsert tool accepts these inputs when an agent calls it. Required inputs are flagged.
How the Vector Upsert tool works in practice
Vector Upsert is a semantic search & rag tool you assign to a VDF AI agent. It inserts or updates vectors and their metadata in the vector store. Its hallmarks — Upsert, Index, Keyed — let an agent rely on it as a dependable step in a larger task rather than a brittle one-off script.
Under the hood, upserts are keyed and scoped per tenant with logging, so the index stays current and isolated, and re-indexing content updates in place rather than duplicating. It expects collection and vectors 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 Upsert when they need to handle fresh index, in-place updates, and pipelines. It rarely works alone — pair it with Embedding Generator, Vector Delete by Repository, and Batch Embed & Upsert to build a complete, governed workflow, then compose those steps into an on-premise VDF AI Network.
Where Vector Upsert pays back
Fresh index
Add newly embedded content to the store.
In-place updates
Re-index changed documents without duplicates.
Pipelines
Complete a chunk → embed → upsert flow.
Multi-tenant
Keep each tenant’s vectors isolated.
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 Upsert tool
What is the Vector Upsert tool?
It inserts or updates vectors and their metadata in the vector 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.
Does upsert avoid duplicates?
Yes. Upserting by id updates existing vectors in place instead of creating duplicates.
Is it tenant-scoped?
Yes. Writes are scoped per tenant and logged.
What inputs does the Vector Upsert tool need?
It requires collection and vectors. Each parameter is validated when an agent calls the tool, and the full call is logged for audit.
Which tools pair well with Vector Upsert?
Vector Upsert is commonly assigned alongside Embedding Generator, Vector Delete by Repository, and Batch Embed & Upsert. 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 Upsert to work
See the Vector Upsert tool assigned to an agent and orchestrated in a governed, on-premise network.