NVIDIA/NeMo-Retriever
NeMo Retriever
NeMo-Retriever is a framework designed for building end-to-end document ingestion and retrieval-augmented generation pipelines. It provides a suite of tools for processing, classifying, and structuring diverse file formats, transforming raw enterprise data into searchable information assets for generative artificial intelligence applications.
The system distinguishes itself through its specialized capabilities for parsing complex document layouts, including tables, charts, and infographics, using integrated optical character recognition and multi-modal extraction. It utilizes a microservice-based architecture to orchestrate parallelized data ingestion, ensuring that large-scale document collections are efficiently split, chunked, and enriched for downstream use.
The platform covers the full lifecycle of data preparation, from initial file ingestion and element classification to the generation of high-dimensional numerical vector representations. These embeddings are managed within a vector-database-backed environment to enable rapid, context-aware semantic search and retrieval for large language models.
Features
- Retrieval Augmented Generation Pipelines - Builds data processing workflows that prepare enterprise documents to provide accurate, context-aware information for large language models.
- Retrieval-Augmented Generation Frameworks - Provides a toolkit for building pipelines that extract, chunk, and embed enterprise document data for generative AI applications.
- Vector-Database-Backed Retrievals - Maps content into high-dimensional numerical spaces to enable rapid similarity searching for context-aware generative AI workflows.
- Optical Character Recognition - Applies automated visual analysis to digitize text within complex document layouts like tables and infographics.
- Vector Embeddings - Ships a scalable service that transforms raw text and media into numerical vector representations for semantic search.
- Document Data Extraction - Parses complex files like tables and charts into structured formats to make internal business information searchable.
- Multi-Format Content Extractors - Parses diverse media formats including office documents and audiovisual files to isolate structured text and metadata.
- Automated Document Ingestion - Manages end-to-end automated workflows for splitting, chunking, and enriching raw data files for retrieval systems.
- Document Ingestion Pipelines - Processes directories of files through configurable pipelines that split, chunk, and enrich metadata for retrieval systems.
- Document Extraction Tools - Offers specialized utilities for parsing complex documents and media to isolate structured content for language model workflows.
- Parallelized Ingestion - Executes concurrent document splitting and chunking workflows to maximize throughput for large-scale enterprise datasets.
- Multi-Modal Content Normalizers - Normalizes heterogeneous inputs including images and audiovisual media into uniform text representations for downstream processing.
- Context Search Retrievers - Retrieves relevant document snippets based on search criteria to provide accurate context for language models.
- Semantic Embedding Generators - Transforms raw text and media content into numerical vector representations for fast semantic search.
- Microservices Orchestration - Orchestrates distributed processing tasks across specialized containers to transform raw unstructured data into structured information assets.
Star history
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