The Amnesia Problem
The Context Window Bottleneck
Large Language Models (LLMs) suffer from a fundamental limitation known as the Context Window. While windows are growing (128k, 1M tokens), they are expensive and ephemeral. Once a session ends, the "understanding" of that data is lost. To use that data again, the model must re-read and re-process the raw text, incurring significant token costs and latency.
The Cost of Redundancy
Consider the global inefficiency:
Duplicate Processing: If 10,000 trading bots analyze the latest Federal Reserve minutes, the exact same text is tokenized and embedded 10,000 times.
Latency: Real-time applications cannot afford the seconds or minutes required to parse massive documents from scratch.
Data Silos: Valuable, structured insights are locked within the local vector databases of individual companies, inaccessible to the broader market.
ditex402 addresses this by shifting the paradigm from "Read and Process" to "Search and Recall."
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