The Repository Chunker Tool
Break a repository into coherent, size-bounded chunks with metadata so it can be embedded and retrieved accurately — the first step in making a codebase searchable by meaning.
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.
Repository Chunker, without the risk
Capability
What it does
Split a repository into retrieval-ready chunks.
it splits a repository into coherent, size-bounded chunks with metadata, ready for embedding.
Assignable to any agent
How it works
Predictable, inspectable behavior
Designed to be reliable.
chunking respects code structure and attaches path and symbol metadata, so downstream retrieval returns meaningful units rather than arbitrary text slices.
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.chunk_repository tool accepts these inputs when an agent calls it. Required inputs are flagged.
default: 512 Optional Target maximum size per chunk.
default: 64 Optional Token overlap between adjacent chunks.
How the Repository Chunker tool works in practice
Repository Chunker is a semantic search & rag tool you assign to a VDF AI agent. It splits a repository into coherent, size-bounded chunks with metadata, ready for embedding. Its hallmarks — Chunk, Structured, Metadata — let an agent rely on it as a dependable step in a larger task rather than a brittle one-off script.
Under the hood, chunking respects code structure and attaches path and symbol metadata, so downstream retrieval returns meaningful units rather than arbitrary text slices. It expects repo 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 Repository Chunker when they need to handle index prep, better recall, and pipelines. It rarely works alone — pair it with Embedding Generator, Vector Upsert, and Batch Embed & Upsert to build a complete, governed workflow, then compose those steps into an on-premise VDF AI Network.
Where Repository Chunker pays back
Index prep
Prepare a codebase for semantic search.
Better recall
Chunk on structure so retrieval returns whole units.
Pipelines
Feed chunks into embedding and upsert steps.
Custom RAG
Build bespoke retrieval over private code.
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 Repository Chunker tool
What is the Repository Chunker tool?
It splits a repository into coherent, size-bounded chunks with metadata, ready for embedding. 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 chunk on structure?
Structure-aware chunks keep functions and sections intact, so retrieved context is coherent rather than cut mid-thought.
What comes next?
Embed the chunks and upsert them with the embed and vector-upsert tools to make the repo searchable.
What inputs does the Repository Chunker tool need?
It requires repo, and optionally accepts max_tokens and overlap. Each parameter is validated when an agent calls the tool, and the full call is logged for audit.
Which tools pair well with Repository Chunker?
Repository Chunker is commonly assigned alongside Embedding Generator, Vector Upsert, 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 Repository Chunker to work
See the Repository Chunker tool assigned to an agent and orchestrated in a governed, on-premise network.