GPU-Bridge + LlamaIndex: Embeddings and Reranking in One Line

GPU-Bridge + LlamaIndex: Embeddings and Reranking in One Line Most RAG pipelines manage 3-4 separate billing accounts for embeddings, reranking, LLM, and document parsing. GPU-Bridge collapses all of that into one endpoint. We just shipped two LlamaIndex integrations: llama-index-embeddings-gpubridge — high-throughput text embeddings llama-index-postprocessor-gpubridge-rerank — semantic reranking Here's how to use them. Install pip install llama-index-embeddings-gpubridge pip install llama-index-postprocessor-gpubridge-rerank Get an API key at gpubridge.xyz (free to start). Embeddings from llama_index.embeddings.gpubridge import GPUBridgeEmbedding from llama_index.core import VectorStoreIndex , SimpleDirectoryReader # Set your embed model embed_model = GPUBridgeEmbedding ( api_key = " gpub_... " ) # Build an index documents = SimpleDirectoryReader ( " ./docs " ). load_data () index = VectorStoreIndex . from_documents ( documents , embed_model = embed_model , ) # Query query_engine = in