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Token Limit
Technical constraint specifying the maximum number of tokens that can be included in a model's request or response. This limitation directly impacts the amount of contextual information that can be used in RAG systems.
Context Compression
Semantic reduction technique of context to preserve essential information while respecting token limits. It uses methods like summarization or extraction to optimize available space.
Hierarchical Context
Organizational structure of context into multiple hierarchical levels to optimize space and improve information relevance. It enables efficient management of relationships between different parts of the context.
Context Chunking
Intelligent segmentation of context into coherent blocks to optimize processing and integration of retrieved information. This technique improves relevance and reduces redundancy in RAG systems.
Dynamic Context Window
Adaptive context window whose size adjusts dynamically based on the complexity and relevance of information. This approach optimizes the use of computational resources.
Context Relevance Scoring
Quantitative evaluation mechanism of each context segment's relevance to the user query. This scoring guides the selection and prioritization of information to include in the window.
Context Pruning
Selective elimination process of less relevant contextual information to optimize available space. This technique maintains the most useful data while respecting token constraints.
Context Overflow Handling
Management strategy when context exceeds the window's maximum capacity, including truncation, compression, or reorganization techniques. These mechanisms ensure processing continuity.
Multi-turn Context Management
Sophisticated management of context across multiple conversational exchanges to maintain coherence and relevance. It requires continuous optimization of the window to integrate new elements.
Context Embedding
Vector representation of context enabling effective semantic evaluation and optimized integration in RAG systems. This technique facilitates search and contextual similarity.
Semantic Context Retrieval
Context retrieval process based on semantic understanding rather than exact keyword matching. This approach improves the relevance of information integrated into the window.
Context Window Padding
Technique of strategically adding contextual information to optimize the use of available space without exceeding limits. Padding ensures better coherence and contextual continuity.
Context Window Attention
Weighted attention mechanism applied specifically to the context window to identify the most relevant segments. This technique optimizes information selection in RAG systems.
Adaptive Context Sizing
Dynamic adjustment of context window size based on the specific requirements of each query. This customized approach maximizes the efficiency of contextual processing.
Context Caching Strategy
Context caching method to optimize performance and reduce computational load in RAG systems. It allows for quick retrieval of relevant contextual information.
Long Context Transformer
Model architecture optimized for efficient processing of very long contexts exceeding traditional limits. It uses efficient attention mechanisms to handle contextual expansion.
Context Fusion
Process of intelligently integrating multiple contextual sources to create a unified and coherent representation. This technique optimizes the use of space in the context window.
Context Window Optimization
Set of techniques and algorithms aimed at maximizing the efficiency and relevance of information contained in the context window. It combines compression, selection and strategic organization of data.