Search document collections with the NeMo Retriever CLI. Ingest files into a local search index or use a deployed service, then retrieve evidence for questions across PDFs, Office files, images, audio, and video.
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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.
安装前可以查看交给 AI 代理的指令内容。
Use the retriever CLI. Prefer it over hand-built retrieval
code.
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.
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.
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.
retriever ingest --help, retriever query --help, or the relevant
batch / service help for options not shown here.还没有评价。欢迎分享你的使用感受。
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Move pandas-style table processing to NVIDIA GPUs using cuDF. Choose minimal-change acceleration or explicit GPU code, handle joins and aggregations, compare results with pandas, and use dask-cuDF when data exceeds GPU memory.
中文简介正在准备中。当前显示英语简介。
Customize NVIDIA Nemotron Voice Agent’s Generic Pipecat example for appointment scheduling, five-field patient intake, or a custom healthcare conversation. Build prompts and tool handlers without a separate backend, then validate conversation behavior, services, and voice interaction.
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Calibrate live cameras by recording their RTSP video streams through VIOS and processing the clips with AutoMagicCalib. The workflow imports settings and alignment files, checks readiness, tracks calibration progress, and retrieves results.
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Test a running AMC service with the bundled four-camera warehouse dataset. Run camera calibration from upload to completion, then check distance and image projection errors against ground-truth data to verify the installation.
中文简介正在准备中。当前显示英语简介。
Calibrate multiple cameras from recorded MP4 files through the AutoMagicCalib REST API. The skill handles project creation, uploads, input verification, progress checks, and evaluation when reference data is supplied, with optional VGGT refinement.
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Set up AutoMagicCalib from NGC release containers using Docker Compose. The skill handles registry authentication, port and path configuration, optional VGGT model download, service startup, and readiness checks for the backend and web interface.
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