The Batch Embed & Upsert Tool
Embed a batch of chunks and upsert them into the vector store in a single governed operation — the efficient path to (re)indexing an entire repository or document set at once.
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.
Batch Embed & Upsert, without the risk
Capability
What it does
Embed and index a whole corpus in one pass.
it embeds a batch of texts and upserts the resulting vectors into the store in one operation.
Assignable to any agent
How it works
Predictable, inspectable behavior
Designed to be reliable.
it fuses embedding and indexing into a single, resumable batch that runs on-premise, so large corpora index efficiently without content leaving your perimeter.
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.batch_embed_upsert tool accepts these inputs when an agent calls it. Required inputs are flagged.
How the Batch Embed & Upsert tool works in practice
Batch Embed & Upsert is a semantic search & rag tool you assign to a VDF AI agent. It embeds a batch of texts and upserts the resulting vectors into the store in one operation. Its hallmarks — Batch, Embed+Index, Efficient — 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 fuses embedding and indexing into a single, resumable batch that runs on-premise, so large corpora index efficiently without content leaving your perimeter. It expects collection and items 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 Batch Embed & Upsert when they need to handle full re-index, efficiency, and onboarding data. It rarely works alone — pair it with Embedding Generator, Vector Upsert, and Repository Chunker to build a complete, governed workflow, then compose those steps into an on-premise VDF AI Network.
Where Batch Embed & Upsert pays back
Full re-index
Vectorize an entire repo or corpus at once.
Efficiency
Avoid round-tripping embed and upsert separately.
Onboarding data
Stand up retrieval over a new dataset quickly.
Scheduled refresh
Re-embed and re-index on a schedule.
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 Batch Embed & Upsert tool
What is the Batch Embed & Upsert tool?
It embeds a batch of texts and upserts the resulting vectors into the store in one operation. 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.
When should I use this over embed + upsert?
Use batch embed & upsert for bulk (re)indexing; use the separate tools for incremental, one-off updates.
Is it resumable?
Yes, when paired with checkpoints, a large batch can resume after interruption.
What inputs does the Batch Embed & Upsert tool need?
It requires collection and items, and optionally accepts model. Each parameter is validated when an agent calls the tool, and the full call is logged for audit.
Which tools pair well with Batch Embed & Upsert?
Batch Embed & Upsert is commonly assigned alongside Embedding Generator, Vector Upsert, and Repository Chunker. 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 Batch Embed & Upsert to work
See the Batch Embed & Upsert tool assigned to an agent and orchestrated in a governed, on-premise network.