RAG is a pragmatic and effective approach to using large language models in the enterprise. Learn how it works, why we need it, and how to implement it with OpenAI and LangChain. Typically, the use of ...
RAG is an approach that combines Gen AI LLMs with information retrieval techniques. Essentially, RAG allows LLMs to access external knowledge stored in databases, documents, and other information ...
The OpenAI Responses API is a robust and versatile tool designed to streamline the development of Retrieval-Augmented Generation (RAG) systems. By automating intricate processes such as document ...
What if the way we retrieve information from massive datasets could mirror the precision and adaptability of human reading—without relying on pre-built indexes or embeddings? OpenAI’s latest ...
Generative AI depends on data to build responses to user queries. Training large language models (LLMs) uses huge volumes of data—for example, OpenAI’s GPT-3 used the CommonCrawl data set, which stood ...
Microsoft released Azure Cosmos DB Python SDK version 4.14.0, a stable update designed to support advanced AI workloads and enhance performance for data-driven applications. The release includes new ...
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