Adaptability of open architecture to future technology evolution
The framework uses dependency injection design, any LLM or embedded model that meets the interface specification can be seamlessly integrated. Developers can easily access custom models based on HuggingFace or Ollama platforms, and complete API adaptation examples are officially provided. Test data shows that when switching from GPT-3.5 to GPT-4, the quality of atlas construction automatically improves by 31%.
The forward-looking architecture is reflected in the fact that the vector storage layer is reserved for quantum computing interfaces, the graph database supports distributed expansion, and the preprocessing module is compatible with future multimodal standards. This openness ensures that the framework can continue to absorb the latest technological achievements in the field of NLP and maintain long-term competitiveness.
This answer comes from the articleLightRAG: A Lightweight Framework for Building Retrieval Augmented Generation (RAG) ApplicationsThe
