ABOUT

Building production-ready RAG applications requires integrating multiple components — document retrievers, embedding models, LLMs, and generation pipelines — with careful optimization for latency, relevance, and scalability. Existing RAG frameworks are either too complex to configure or lack optimization for specific hardware platforms. FastRAG solves this by providing a modular RAG framework with built-in support for dense retrievers (DPR, ColBERT, BGE), sparse retrievers (SPLADE, BM25), and hybrid retrieval strategies, multi-step reasoning patterns (query rewriting, HyDE, self-RAG), optimized inference pipelines for Intel CPUs and GPUs with ONNX Runtime and OpenVINO integration, and a clean Python API for composing retrieval and generation workflows — enabling teams to deploy efficient RAG pipelines without deep expertise in each component.

INTEGRATION GUIDE

1. Build production RAG applications with hybrid retrieval combining dense embeddings and sparse keyword search for higher relevance 2. Deploy low-latency document question answering pipelines on CPU-based infrastructure using FastRAG's OpenVINO-optimized model inference 3. Implement multi-step RAG strategies like query rewriting and hypothetical document embeddings (HyDE) to improve first-retrieval quality 4. Create custom RAG pipelines with modular retriever, reranker, and generator components using FastRAG's composable API and pipeline abstractions 5. Evaluate RAG pipeline quality with FastRAG's built-in benchmarking tools for retrieval precision, generation faithfulness, and end-to-end latency

FASTRAG

ABOUT

INTEGRATION GUIDE

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