| name | nemo-retriever |
| version | 2.0 |
| last_updated | 2026-08-29T00:00:00.000Z |
| tags | ["nvidia","nemo","retriever","rag","indexing","qa"] |
| description | NVIDIA NeMo Retriever deployment and usage guidance for local retrieval services, corpus ingestion, and grounded question-answering workflows. |
| license | CC-BY-4.0 AND Apache-2.0 |
| compatibility | Guidance imported from the NVIDIA NeMo Retriever skill for local retriever deployment and corpus-backed QA workflows. |
nemo-retriever
The retriever CLI indexes a folder of PDFs into LanceDB (retriever ingest) and serves vector search over it (retriever query). For any task about searching/answering questions across a folder of PDFs, use this CLI — do not write a custom RAG.
Beyond PDFs and beyond semantic search. retriever ingest also handles images, Office, HTML, TXT, audio, and video — see references/setup.md for the per-format recipe and references/install.md for the install extras ([multimedia], libreoffice, ffmpeg). For non-semantic operations — page filter, verbatim quote with citation, corpus-level aggregate, chart/image caption hits — see references/query.md. Don't fall back to native Read/Grep/Python on non-PDF inputs.
Install (if retriever is missing)
If command -v retriever returns nothing, follow references/install.md to install the NeMo Retriever Library before proceeding. It prints RETRIEVER_VENV=<path>; substitute that path for <RETRIEVER_VENV> in every example in this skill (setup, query, troubleshooting, and the CLI references).
Workflow — read the reference for the current phase, then execute
| Turn type | Read this once | Then execute |
|---|
Setup turn (first turn — ./lancedb/nv-ingest.lance doesn't exist) | references/setup.md | Build the index |
| Query turn (every subsequent turn — user asks a question) | references/query.md | One retriever query call |
| Anything errored or returned empty | references/troubleshooting.md | Apply the named recovery; do not improvise |
For the full retriever ingest / retriever query CLI specs, see references/cli/ingest.md and references/cli/query.md. You do not need these for routine turns — <RETRIEVER_VENV>/bin/retriever <subcommand> --help is faster.
Before ingesting a mixed folder, inventory extensions (find <dir> -name '*.*' | sed 's/.*\.//' | sort -u) — --input-type=auto silently drops anything outside the supported set. See references/troubleshooting.md "Unsupported file types".
Hard limits (apply to every turn)
- Setup turn: build the index in one shell command (see
references/setup.md). STOP after the index lands.
- Query turn: at most 2 Bash calls — 1
retriever query, +1 optional targeted text-extract per references/query.md. Reply and then STOP.
- No narration between tool calls. Tokens you emit between calls become input + cached input for every later turn — quadratic cost. Go straight from reading the summary to writing the JSON file.
- Banned:
TodoWrite, Glob, Grep, Read of whole PDFs, re-running setup, spawning subagents, speculative "confirmation" calls.
Long query turns (5+ tool calls, 1M+ cache-read tokens) cost ~5× a disciplined turn and almost always still produce the wrong answer. Answering partially beats timing out.
Anti-Patterns
- Indexing content before clarifying corpus boundaries, freshness, or ownership: Retrieval quality collapses when the source of truth is unstable.
- Treating embedding, chunking, and backend choices as invisible defaults: They change recall, latency, and storage cost in user-visible ways.
- Claiming grounded answers without checking the retrieved passages that supported them.
Verification Protocol
Before claiming "skill applied successfully":
- Pass/fail: The workflow names the corpus, index or backend choice, and the query path before answering deployment or QA questions.
- Pass/fail: Retrieval checks include at least one real query and inspection of the supporting passages or scores.
- Pass/fail: Ingestion or indexing advice keeps corpus freshness and reindex cost visible instead of implicit.
- Pressure-test scenario: Apply the workflow to a retriever that answers quickly but returns stale passages after a corpus update.
- Success metric: The user gets a reproducible retriever setup or debugging path with live retrieval evidence.
Cross-Client Portability
This skill is written to stay usable across GitHub Copilot, Claude Code, and Codex.
- GitHub Copilot: keep the folder in a Copilot-visible skill path or wrap the
workflow in project instructions when folder discovery is unavailable.
- Claude Code: keep the folder in a local skills directory or a compatible plugin source.
- Codex: install or sync the folder into
$CODEX_HOME/skills/nemo-retriever and restart Codex after major changes.
MCP Availability And Fallback
Preferred MCP Server: None required
- Fallback prompt: "Use the nemo-retriever skill without MCP. Rely on the local
SKILL.md, bundled references or scripts, and manual verification. Show the exact commands, evidence, and final checks you used before concluding."
- If the current host does not expose a matching server, use the bundled references, scripts, native toolchain, and manual workflow already described in this skill.
- Treat direct local verification, rendered output, logs, tests, or screenshots as the fallback evidence path before completion.
Related Skills
- notebooklm-management: Use it when retrieval-backed research needs a notebook-style grounding workflow.
- development-workflow: Use it when the retriever work also needs scoped implementation and validation checkpoints.
- cloud-design-patterns: Use it when the retriever deployment choice also needs storage, scaling, or service-boundary analysis.
