Building your own RAG chatbot with Upstash

This post details the creation of an open-source Custom Content Retrieval-Augmented Generation (RAG) Chatbot utilizing Upstash Vector, Upstash Redis, Hugging Face Inference API, Replicate LLAMA-2-70B Chat model, and Vercel. The process involves setting up a serverless database for storing chatbot conversations and embedding vectors to facilitate context retrieval for user messages. It explains the technical stack, including Node.js, Next.js, LangChain, and TailwindCSS, and provides a step-by-step guide to configuring Upstash services, creating and storing embeddings, querying vectors for relevant context, and utilizing the LLAMA-2-70B Chat model for predictions. The project culminates with the deployment of the chatbot on Vercel, emphasizing the scalability and efficiency of using Upstash's serverless architecture for dynamic context management in AI-driven conversations.

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