Build a production ready RAG system with Epsilla, Jina Embeddings v2, and Mistral LLM

Retrieval-augmented generation (RAG) is expected to be a key focus in 2024, enhancing large language models (LLMs) by integrating them with proprietary data to improve inference capabilities and reduce hallucinations through factual grounding. While creating a basic RAG system is relatively straightforward, developing a production-ready version necessitates significant engineering, involving tasks such as data chunking, selecting suitable embedding models, and utilizing high-performance vector databases. Epsilla, through its RAG-as-a-Service platform, simplifies this process by allowing users to connect their data, choose optimal models, and experiment with configurations. The article demonstrates building a RAG chatbot for BMW's Q3 2023 report using Epsilla's platform, Jina Embeddings, and Mistral LLM, highlighting the process of integrating data, creating an application, and collecting feedback for iterative improvement. The technology stack's transparency and trustworthiness are bolstered by the open-source nature of its core components, facilitating effective and efficient processing of extensive documents, improved semantic comprehension, and optimized storage costs.

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