Add long-context document analysis use-case example for Nemotron 3 Super

[mvanhorn]

Summary

Adds a Jupyter notebook use-case example demonstrating Nemotron 3 Super's 1M native context window for practical document analysis. Closes #108.

What's included:

• Jupyter notebook (long_context_analysis_tutorial.ipynb) with 7 sections:
  1. Setup & configuration (NVIDIA API + vLLM options)
  2. Synthetic document corpus (6 interconnected technical docs for "AuroraDB" with intentional cross-document contradictions)
  3. Single-document analysis (summarization, design tradeoff extraction)
  4. Multi-document Q&A (questions requiring cross-document reasoning)
  5. Cross-document synthesis (inconsistency detection, gap analysis, risk assessment)
  6. Context length scaling (compare quality/latency with 1, 2, 3, and 6 documents)
  7. Best practices (instruction placement, citation requests, delimiter strategies)
• README with Models Used table, requirements, and quick start

Design decisions:

• Self-contained synthetic corpus with deliberate contradictions (MVCC retention 72h vs 48h) to demonstrate cross-document inconsistency detection
• No external data dependencies - everything is inline for reproducibility
• NVIDIA API as primary endpoint with vLLM self-hosted as alternative
• Focuses on the "no chunking, no RAG" value proposition of 1M context
• Cross-references to the agentic tool-calling example (Add agentic tool-calling use-case example for Nemotron 3 Super #105) and deployment cookbooks

Research & Context

Project Context

• Stars: 511 | Language: Python + Jupyter | Active maintainers: 3
• Philosophy: Complete, reproducible pipelines with open data and training recipes

Related Issues & Community Signals

• 1M context window is highlighted 4+ times in the README but zero examples demonstrate it
• r/LocalLLaMA comparison threads cite 1M context as the deciding factor vs Qwen 3.5 (128K cap)
• Reddit: someone classified 3.5M US patents on a single RTX 5090 (401 upvotes) - showing demand for large-scale document processing
• RULER benchmark scores at 1M context outperform GPT-OSS and Qwen3.5

Competitive Analysis

• Google Gemini: Has long-context examples in their cookbook repo
• Anthropic Claude: Has long-context best practices documentation
• Qwen 3.5: Caps at 128K - Nemotron's 1M is 8x larger
• Nemotron: Zero long-context examples despite it being the headline feature

What Gets Merged

• Cookbooks and use-case examples merge fast
• 18K+ lines merged in a single day for Super 3 launch
• Required: DCO sign-off, clear README, working notebook

This contribution was developed with AI assistance (Claude Code).