RAG & Vector Search
Retrieval-Augmented Generation (RAG) grounds a language model in your own data: you convert documents into embeddings, store them in a vector database, retrieve the passages most similar to a question, and pass those to the model as context. This hub covers embeddings, vector databases, chunking, hybrid search, and how to build "chat with your data" applications in Python.
Guides & tutorials
- Vector Databases: The Rising Star in Generative AI InfrastructureHow vector databases are transforming AI applications and LLMs by enabling efficient handling of unstructured data and fast similarity search.
- Building "Chat with your Data" Apps using Embeddings, ChatGPT, and Cosmos DB for Mongo DB vCoreUse OpenAI to enable users to chat with your business data by learning how to build a custom ChatGPT using Cosmos Mongo DB vCore and Python.
- Text-to-SQL with LLMs - Embracing the Future of Data InteractionExplore the future of database interaction with our comprehensive guide on Text-to-SQL technology using Large Language Models (LLMs).
- Building a Virtual AI Assistant (aka Copilot) for Your Software Application: Harnessing the Power of LLMs like ChatGPTExplore the new era of digital interaction with LLM-powered virtual AI assistants. This article offers rare insights into the architecture
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