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How to make a RAG application with LangChain4j
Retrieval-augmented generation, or RAG, introduces some serious capabilities to your large language models (LLMs). These applications can answer questions about your specific corpus of knowledge, while leveraging all the nuance and sophistication of a traditional LLM. This tutorial will take you through the ins and outs of creating a Q&A chatbot using RAG. The application will: Retrieve data from a MongoDB Atlas database. Embed and store documents as vector embeddings.
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As a journalist, you can create a free Muck Rack account to customize your profile, list your contact preferences, and upload a portfolio of your best work.Get in touch with Import Com.mongodb.client.model.create
Contact Import Com.mongodb.client.model.create, search articles and posts on X, monitor coverage, and track replies from one place.
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