An enterprise-focused framework from deepset for building production RAG and search systems. Its modular pipeline architecture and evaluation tools make it a solid choice for serious, maintainable deployments, and it runs with local models.
The most widely used framework for building LLM applications, with a huge set of integrations for models, vector stores, and tools. It is a common backbone for RAG systems, and works with local models through Ollama and llama.cpp.
A data framework focused on connecting LLMs to your own data, with strong document loading, indexing, and retrieval. It is a natural choice when the heart of your application is RAG over documents, and works with local models.
An open-source RAG engine built around deep document understanding, template-based chunking, and grounded, citation-backed answers.
Hugging Face's fast serving solution for text-embedding, reranking, and sequence-classification models, the retrieval-side counterpart to a text-generation server.