Build a RAG application with Runware and LangChain

Short summary

RAG connects LLM answers to your own documents, preventing hallucination and keeping responses current. This tutorial builds a complete pipeline using LangChain for indexing and Runware for cost-effective generation, with step-by-step Python code. Runware's custom inference engine saves approximately $10K annually on tokens compared to commodity providers.

• •RAG retrieves relevant document chunks at query time to ground LLM responses in actual data
• •Tutorial covers full stack: LangChain + Runware + FastEmbed + FAISS with complete Python setup
• •Cost advantage: MiniMax on Runware is 10-20% cheaper than native API, totaling ~$10K/year savings at 10M+ tokens

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