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Llama Stack v0.1.0 introduces a stable API release enabling developers to build RAG applications and agents, integrate with various tools, and use telemetry for monitoring and evaluation. This release provides a comprehensive interface, rich provider ecosystem, and multiple developer interfaces, along with sample applications for Python, iOS, and Android.
This page provides documentation for the rerank API, including endpoints, request parameters, and response formats.
This article discusses how to overcome limitations of retrieval-augmented generation (RAG) models by creating an AI assistant using advanced SQL vector queries. The author uses tools such as MyScaleDB, OpenAI, LangChain, Hugging Face and the HackerNews API to develop an application that enhances the accuracy and efficiency of data retrieval process.
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