UR-2.0 provides a user-friendly installation method, allowing users to complete the basic environment setup with just a few commands. At the same time, UR-2.0 supports a modular dependency system with on-demand loading, facilitating flexible expansion of required functional components and avoiding redundant installations.
Step 4: Install optional extension components as needed
UR-2.0 supports rich Server components, allowing developers to flexibly install required dependencies according to actual tasks:
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# If you want to use faiss for vector indexing:# You need to manually compile and install different versions of FAISS according to your hardware environment# Install faiss-cpuconda install -c pytorch faiss-cpu# Install faiss-gpuconda install -c pytorch faiss-gpu# If you want to use infinity_emb for corpus encoding and indexing:uv pip install -e ."[infinity_emb]"# If you want to use lancedb vector database:uv pip install -e ."[lancedb]"# If you want to deploy models with vLLM service:uv pip install -e ."[vllm]"# If you want to use corpus document parsing features:uv pip install -e ."[corpus]"# Install all dependencies (except faiss)uv pip install -e ."[all]"