Agentic AI Reference Architecture

Knowledge & Data · Model asset

Long-Context Embedding Model

Model assetKnowledge & DataKnowledge & Dataarc:LongContextEmbeddingModel

A dense embedding model with a very large input context and bidirectional attention, able to embed long technical, legal or research documents with little or no chunking.

Responsibility. Encodes long documents into single coherent embeddings capturing long-range dependencies.

Variant of Text Embedding Model abstract

When to choose. Choose for enterprise deployments processing long technical documents, contracts or papers that routinely exceed 8,000 tokens, typically self-hosted for data sovereignty.

specializesdeployed onis target of alternativeToalternative tois target of alternativeToText Embedding Model: specializesText Embedding ModelSelf-Hosted GPU Embedding Service: deployed onSelf-Hosted GPU Embeddin…High-Accuracy General Embedding Model: is target of alternativeToHigh-Accuracy General Em…Retrieval-Optimized Embedding Model: alternative toRetrieval-Optimized Embe…Cost-Optimized Embedding Model: is target of alternativeToCost-Optimized Embedding…
Direct neighbourhood (hover for relationship types)

Relationships

deployed on structural

alternative to variability

Design guidance

Quantitative guidance

As stated by the sources; verify before use.

Classification

Technologies
NV-Embed-v2nv-embedqa-e5-v5

Sources

  1. Ch6.1: T. Nguyen, "Embeddings and RAG Fundamentals," in Mastering Agentic AI Systems: Guide for the NVIDIA NCP-AAI Exam, 1st ed. 2026, ch. 6.1. ISBN: 9798244538229.