use cases

RAG over your local files — no vector service, no upload

Point dig at a directory and your agent can retrieve across it with hybrid search — PDFs, notes, and docs included, indexed locally, nothing uploaded to a cloud vector store.

Retrieval-augmented generation usually means shipping your files to a hosted vector database. dig does RAG over the directory as it sits on disk — locally.

How it works

  1. Index a directory. dig init ~/library && dig scan builds a content-addressed store plus a search index over paths, labels, and content — PDFs and text included.
  2. Retrieve. dig find "<query>" --mode hybrid --json returns ranked results combining full-text and semantic search. The --json surface is built for agents.
  3. Ground the answer. Your agent feeds those passages to the model. dig is the retriever; the agent and model do the generating.
dig init ~/library
dig scan
dig find "invoice acme 2024" --mode hybrid --json

Why dig for RAG

  • No external vector DB. The index is a derived view over the store — dig scan rebuilds it. Nothing to provision or keep in sync.
  • Nothing uploaded. With no model endpoint configured, dig makes zero network calls. Your documents stay on your machine.
  • Whole corpus. Content-hash dedupe and real content indexing mean every file counts, not just Markdown.
  • Any agent. Reach the same index from Claude Code, Codex, or the AI SDK over MCP.

Start

Install dig, point it at a folder, and your agent has a local retriever.

All integrations · Hybrid retrieval explained