Traditional Embedding Systems
Last updated
Last updated
Static vector-based systems encode data into fixed-dimensional embeddings, capturing a snapshot of context at the time of training. However, they have key limitations:
Limited Adaptability: Once created, embeddings cannot update with new information, leading to outdated or less relevant outputs.
Weak Context Awareness: All stored data is treated equally, making it harder to prioritize the most relevant insights.
High Storage & Processing Costs: Large datasets require significant storage and computational power for retrieval.
Inefficient for Real-Time Use: Processing many embeddings at once can slow down real-time applications.
Memory Fields dynamically update and prioritize information based on real-time queries. They adjust relevance scores to ensure retrieved data is always fresh and contextually accurate. This approach reduces storage needs, improves retrieval speed, and enhances decision-making precision.