We stand at an inflection point in the evolution of AI systems, where the constraints that defined early context engineering are rapidly dissolving while entirely new paradigms emerge. The journey from 4,096 tokens to million-token contexts represents more than a quantitative leap—it fundamentally transforms how we architect intelligent systems, moving from careful token budgeting to orchestrating vast knowledge landscapes.
123456789101112interface MultimodalContext { text: TextSegment[]; images: ImageContext[]; audio: AudioContext[]; structured: StructuredData[]; crossReferences: CrossModalReference[]; } interface CrossModalReference { sourceModality: 'text' | 'image' | 'audio' | 'structured'; sourceId: string; targetModality: 'text' | 'image' | 'audio' | 'structured';
123456789101112import numpy as np from dataclasses import dataclass from typing import List, Optional, Dict, Any import hashlib @dataclass class ContextChunk: id: str content: str level: int # 0=document, 1=section, 2=subsection, 3=paragraph parent_id: Optional[str] children_ids: List[str]
123456789101112from dataclasses import dataclass from typing import List, Union, Optional from enum import Enum import base64 class ModalityType(Enum): TEXT = "text" IMAGE = "image" STRUCTURED = "structured" AUDIO_TRANSCRIPT = "audio_transcript" @dataclass
123456789101112from dataclasses import dataclass from typing import List, Dict, Optional from datetime import datetime import numpy as np from collections import defaultdict @dataclass class ContextInteraction: interaction_id: str timestamp: datetime query: str retrieved_doc_ids: List[str]