Knowa – Open-Source LLM Context Optimizer
Knowa is an open-source tool designed to optimize the context used in large language models (LLMs) by efficiently indexing documents. It significantly reduces the number of tokens sent to LLMs, resulting in substantial cost savings while maintaining answer quality. This tool is particularly beneficial as AI applications scale from prototypes to full production environments.
- ▪Knowa indexes documents as vector chunks and full-text pages, extracting only relevant information for queries.
- ▪The tool can reduce the context sent to LLMs by 90-99%, which helps manage costs effectively.
- ▪Knowa supports various document sources and enhances retrieval accuracy through a named-entity knowledge graph.
Opening excerpt (first ~120 words) tap to expand
Knowa The problem: LLM token costs compound fast The naive approach to building AI-powered apps is to load your documents into the prompt and let the LLM figure it out. It works in demos. It breaks in production. A 1,000-page knowledge base is roughly 2–4 million tokens. At current API pricing, sending that on every request costs dollars per query. At 10,000 queries a day that is tens of thousands of dollars a month — for context that is 95% irrelevant to the question being asked. As AI usage scales across teams and products, this becomes the dominant cost line. Knowa's core job is to solve this.
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Excerpt limited to ~120 words for fair-use compliance. The full article is at GitHub.