NVIDIA/NeMo-Retriever
NeMo Retriever Library is a scalable, performance-oriented document content and metadata extraction microservice. NeMo Retriever Library uses specialized NVIDIA NIM microservices to find, contextualize, and extract text, tables, charts and images that you can use in downstream generative applications. observed · 2026-08-28
Health v2 · maintenance only
81/100
- Activity 99
- Release rhythm 74
- Longevity 52
How is this computed?
round(0.45*activity + 0.35*rhythm + 0.20*longevity); archived -> min(score, 10) — computed 2026-09-03. Adoption (stars, forks) is never an input.
- gap_med: 57
- age_days: 741
- days_rel: 95
- days_push: 7
- n_releases_24m: 10
Adoption not part of the score
2970 stars · 344 forks observed · 2026-08-28
What it is AI-extracted, prompt v1, taxonomy v1, 2026-08-30, confidence not recorded
NVIDIA's scalable document content and metadata extraction library (also known as NVIDIA Ingest) that splits documents, classifies and extracts text, tables, charts, and images via OCR, and computes embeddings for storage in LanceDB. It supports NVIDIA NIM microservices and HuggingFace models, with Helm-based Kubernetes deployment for production scale.
Use cases
- extract text and tables from pdfs for rag pipelines
- parse charts and infographics out of documents
- build a document ingestion pipeline for generative ai apps
- compute embeddings from extracted document content
- deploy a scalable document extraction microservice on kubernetes
- ocr scanned pdfs into structured json
- ingest pdfs into a vector database
When to choose
- you need high-performance, parallelized extraction of text, tables, charts, and images from documents
- you want to feed extracted content into retrieval-augmented generation pipelines
- you can use NVIDIA GPUs and NIM microservices for production-scale workloads
- you want embeddings computed and stored automatically (e.g., in LanceDB)
When to avoid
- you only need simple text extraction from plain-text or HTML files
- you have no GPU access and need a lightweight CPU-only solution
- you need a fully managed SaaS document service rather than self-hosted infrastructure
- your documents are small in number and a simple parser library would suffice
Facets
library · maturity active
ocr pdf rag etl machine-learning data-generation sdk artificial-intelligence large-language-models pdf developer-tools python self-hosted document-extraction nvidia-nim multimodal embeddings lancedb helm-chart microservice ingestion-pipeline retrieval-augmented-generation data-engineering docker kubernetes gpu linux
2 sources
- readme: https://github.com/NVIDIA/NeMo-Retriever · fetched 2026-08-28 · 9a8eae0fcd0c
- homepage: https://nvidia.github.io/NeMo-Retriever/ · fetched 2026-08-29 · 926f96d142ee
Member repositories
| Repository | Role | Health v2 |
|---|---|---|
| NVIDIA/NeMo-Retriever | main | 81 |
For agents
markdown · JSON · MCP: product_card(name="NVIDIA/NeMo-Retriever")
Data as of 2026-08-30T08:39:29.467469+00:00 · Report a problem