Vercel AI SDK: RAG Guide
Retrieval-augmented generation (RAG) chatbots enhance Large Language Models (LLMs) by accessing external information for accurate responses. The process involves embedding queries, retrieving relevant material, and setting up projects with various tools.
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This guide introduces the concept of retrieval-augmented generation (RAG) chatbots, explaining how they enhance Large Language Models (LLMs) by providing specific information relevant to the prompt. RAG addresses the limitation of LLMs being restricted to their training data, enabling them to access external information for more accurate responses. The process involves embedding a user's query, retrieving relevant source material, and passing it as context to the model. Embeddings represent words or phrases as vectors in a high-dimensional space, facilitating semantic search. Chunking, breaking down source material into smaller pieces, is crucial for effective embedding. The guide details setting up a project using Next.js, Vercel AI SDK, OpenAI, Drizzle ORM, Postgres with pgvector, and other tools. It covers creating embeddings, storing them in a database, and integrating them into a chatbot application. The tutorial includes steps for database setup, migration, and API key generation, emphasizing the importance of chunking, embedding logic, and server actions for efficient RAG implementation.
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2 commentsRelated
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