Building a Multilingual RAG with Milvus, LangChain, and OpenAI LLM

Retrieval Augmented Generation (RAG) is a popular technique used to build GenAI applications powered by large language models (LLMs). It enhances an LLM's output by providing contextual information on which the model wasn’t pre-trained. Multilingual RAG is an extended RAG that handles text data in multiple languages. Building a multilingual RAG involves using embedding models, vector databases, and LLMs as core components. The choice of embedding model is crucial for supporting multiple languages.

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