Saltar al contenido
Marketplace

nemo-retriever

de nvidia

Use when searching, extracting, ingesting, or querying a document collection with the NeMo Retriever 26.8.1 CLI, including local LanceDB indexes and deployed Retriever services. Use for PDFs, images, Office files, HTML, text, audio, and video; not for editing documents or web search.

Resumen

SKILL.mdLeída en 0e0d506

NeMo Retriever

Use the retriever CLI. Prefer it over hand-built retrieval code.

Install only when missing

Create a project-local Python environment:

uv venv .venv --python 3.12
export PATH="$PWD/.venv/bin:$PATH"

Install the package variant required by the workflow:

# Remote NIM or service client
uv pip install --python .venv/bin/python "nemo-retriever==26.8.1"

# Local GPU ingestion
uv pip install --python .venv/bin/python "nemo-retriever[local]==26.8.1"

# Local service using Hugging Face models
uv pip install --python .venv/bin/python \
  "nemo-retriever[service,local]==26.8.1"

# Local audio or video ingestion
uv pip install --python .venv/bin/python \
  "nemo-retriever[local,multimedia]==26.8.1"

Do not clone NeMo Retriever or install from a Git URL. If retriever is already on PATH, use that installation.

Local workflow

Build a local index:

retriever ingest <file-or-directory> \
  --lancedb-uri lancedb --table-name nemo-retriever

Query it:

retriever query "<question>" \
  --lancedb-uri lancedb --table-name nemo-retriever \
  --top-k 5 --format evidence

Use retriever ingest batch only for an explicitly requested Ray batch run.

Service workflow

Use these forms for an already deployed Retriever service:

retriever ingest service <file-or-directory> \
  --service-url "$RETRIEVER_SERVICE_URL"

retriever query service "<question>" \
  --service-url "$RETRIEVER_SERVICE_URL" \
  --top-k 5 --format evidence

Set NEMO_RETRIEVER_API_TOKEN when the service requires Bearer authentication. Do not pass local LanceDB flags to the service commands.

Rules

  • Use the existing index or service when one is provided; do not rebuild it.
  • Use retriever ingest --help, retriever query --help, or the relevant batch / service help for options not shown here.
  • Answer only from retrieved evidence; preserve source and page metadata when the task requests citations.

Archivos 5 archivos

Instálala a mano

Deska ejecuta esto por ti, para cada agente que elijas. Para ejecutarlo tú en una terminal:

npx skills add nvidia/skills --skill nemo-retriever

Más de nvidia

  • rag-blueprintnvidiaNVIDIA RAG Blueprint — deploy, configure, troubleshoot, and manage. Handles any RAG action: deploy, install, start, enable, disable, toggle, change, configure, troubleshoot, debug, fix, shutdown, stop, or tear down any RAG feature or service (Agentic RAG, VLM, guardrails, query rewriting, models, search, ingestion, observability, summarization, reasoning, and more).3,100 instalaciones en skills.sh0
  • accelerated-computing-cudfnvidiaOfficial NVIDIA-authored guidance for NVIDIA cuDF GPU DataFrames, pandas acceleration, dask-cuDF, ETL, joins, groupby, CSV/Parquet I/O, nullable semantics, and multi-GPU DataFrame workloads.3,000 instalaciones en skills.sh0
  • cuopt-developernvidiaModify, build, test, debug, and contribute to NVIDIA cuOpt (C++/CUDA, Python, server, CI). Use for solver internals, PRs, DCO, and code conventions.2,600 instalaciones en skills.sh0
  • cudaq-guidenvidiaUse for CUDA-Q setup, simulation targets, QPU access, and @cudaq.kernel authoring guidance.2,500 instalaciones en skills.sh0
  • cuopt-installnvidiaInstall cuOpt for Python, C, or server via pip, conda, or Docker; verify the install. For building cuOpt from source, see cuopt-developer.2,500 instalaciones en skills.sh0
Todas las skills de nvidia