llmware builds RAG applications through a four-step standardized process: first create a knowledge base (Library().create_new_library()), and then add PDF and other document files; then use industry-specific embedded models (such as industry-bert-contracts) to generate vectors; and finally combined with the retrieval results call Dragon and other models to generate answers. The whole process supports text query and semantic retrieval, the framework will automatically handle document parsing, chunking and vectorization and other complex operations, developers only need a simple API to achieve end-to-end RAG application construction.
This answer comes from the articlellmware: an open source framework for rapidly building enterprise-class RAG applicationsThe
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