Nemo Retriever

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). The query turn is two retrieval passes — see §Query turn below (inline, no reference read needed); references/cli/query.md holds only the fallback detail (exact-term, chart text-extract, compose-reply). 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

Query turn — query, then answer

Run two complementary passes — these are your FIRST calls; don't ls/find/sed/Read to orient first. Semantic hybrid finds topically-relevant pages; a lexical (sparse/BM25) pass on the exact term finds the precise page a number/code/proper-noun lives on, which dense retrieval often misses:

• Semantic pass — the full question, hybrid (dense + lexical fusion): <RETRIEVER_VENV>/bin/retriever query "<question>" --format evidence --retrieval-mode hybrid --top-k 10
• Lexical pass — the EXACT term/figure/code/proper-noun the question targets (just the term, not the whole question — that's what makes BM25 precise): <RETRIEVER_VENV>/bin/retriever query "<exact term, e.g. Management VaR / Level 3 / a code>" --format evidence --retrieval-mode sparse --top-k 10

Each returns { evidence: [ { text, source, locator, modality, fidelity, score, citation } ], coverage: {...} }. Then:

• Query until sure. One lexical pass per named term; re-query freely to disambiguate. These filings repeat near-identical tables (e.g. many "Level 3" tables for different segments) — when several candidates come back, query for the consolidated / total figure (e.g. "consolidated total Level 3 assets liabilities", or the exact row/section name) and read the competing pages before deciding. Under-querying is the main cause of wrong answers.
• Ground every figure in a source line. Quote the exact evidence line that states each number/name and copy the value from it. Never state a figure you can't point to in the evidence — say "not provided"; don't infer, round, or compute it.
• Prefer a prose statement over a table cell when both give the value (prose is unambiguous, e.g. "Level 3 assets and liabilities were $9,194 million and $28,755 million, respectively"). Read a table cell by its row label × column header, not by position.
• Copy figures verbatim in the document's own units and scale ($27,132 million, not $27.1 billion/27,132); cover every entity / period / category the question names. Lead with the values (or a bare Yes/No).
• Trust by fidelity (verbatim > ocr > transcribed > vlm_caption): a number resting only on a vlm_caption is unconfirmed — quote it tagged "(chart-derived, unconfirmed)" unless a higher-fidelity item agrees. Never fabricate from adjacent text.
• Open references/cli/query.md ONLY for the fallback path (chart text-extract, compose-reply detail).

For the full retriever ingest CLI spec, see references/cli/ingest.md. For retriever query flags, <RETRIEVER_VENV>/bin/retriever query --help is authoritative (and faster) — you do not need it for routine turns.

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: query until the answer is fully supported — a semantic pass plus a lexical (sparse) pass per named term, re-querying as needed to disambiguate similar tables (commonly 4–8 retriever calls). Don't stop early to save calls; stop only when each figure is pinned to a source line.
• No narration between tool calls. Tokens you emit between calls become input + cached input for every later turn — quadratic cost. Go straight from the evidence to your answer.
• Banned: TodoWrite, Glob, Grep, Read of whole PDFs, re-running setup, spawning subagents, speculative "confirmation" calls.

Spend the calls you need to get the figures right — accuracy matters more than minimizing calls here. Only avoid genuinely wasteful loops (re-running identical queries, reading whole PDFs, 15+ calls). A fully-supported answer beats a cheap partial one.