Retrieval Augmented Generation in Production with Haystack (Early Release)
Retrieval Augmented Generation in Production with Haystack (Early Release)
English | 2024 | ISBN: 9781098165161 | 125 pages | MOBI | 1.25 Mb
English | 2024 | ISBN: 9781098165161 | 125 pages | MOBI | 1.25 Mb
In today's rapidly changing AI technology environment, software engineers often struggle to build real-world applications with large language models (LLM). The benefits of incorporating open source LLMs into existing workflows is often offset by the need to create custom components. That's where Haystack comes in. This open source framework is a collection of the most useful tools, integrations, and infrastructure building blocks to help you design and build scalable, API-driven LLM backends.
With Haystack, it's easy to build extractive or generative QA, Google-like semantic search to query large-scale textual data, or a reliable and secure ChatGPT-like experience on top of technical documentation. This guide serves as a collection of useful retrieval augmented generation (RAG) mental models and offers ML engineers, AI engineers, and backend engineers a practical blueprint for the LLM software development lifecycle.
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