Rag

The OpenAI Cookbook is an official, open-source repository of examples and guides for building real-world applications with the OpenAI API.[1][2] It provides production-ready code snippets, advanced techniques, and step-by-step walkthroughs covering everything from basic API calls to complex agent workflows, making it the ultimate resource for developers transitioning from LLM theory to practical deployment.[4] Whether you’re new to OpenAI or scaling AI features in production, this tutorial takes you from setup to mastery with the Cookbook’s most valuable examples. ...

Retrieval-Augmented Generation (RAG) revolutionized AI by grounding LLMs in external knowledge, but traditional flat-chunk retrieval struggles with long, complex documents requiring multi-hop reasoning. RAPTOR (Recursive Abstractive Processing for Tree-Organized Retrieval) solves this by building hierarchical trees of clustered summaries, enabling retrieval across abstraction levels for superior context and accuracy.[1][2] In this zero-to-hero tutorial, you’ll learn RAPTOR’s mechanics, why it outperforms standard RAG, and how to implement it step-by-step with code. We’ll cover pitfalls, tuning, and best practices, empowering developers to deploy production-ready pipelines. ...

HyDE (Hypothetical Document Embeddings) transforms retrieval-augmented generation (RAG) by generating fake, relevance-capturing documents from user queries, enabling zero-shot retrieval that outperforms traditional methods.[1][2] This concise tutorial takes developers from basics to production-ready implementation, with Python code, pitfalls, and scaling tips. What is HyDE and Why Does It Matter? Traditional RAG embeds user queries directly and matches them against document embeddings in a vector store, but this fails when queries are short, vague, or mismatch document styles—like informal questions versus formal passages.[4][5] HyDE solves this by using a language model (LLM) to hallucinate a hypothetical document that mimics the target corpus, then embeds that for retrieval.[1][2] ...

As an expert LLM infrastructure engineer, I’ve deployed countless inference systems where time-to-first-token (TTFT) and GPU efficiency make or break production performance. Enter LMCache—a game-changing KV cache layer that delivers 3-10x delay reductions by enabling “prefill-once, reuse-everywhere” semantics across serving engines like vLLM.[1][2] This zero-to-hero tutorial takes you from conceptual understanding to production deployment, covering architecture, integration, pitfalls, and real-world wins. Whether you’re building multi-turn chatbots or RAG pipelines, LMCache will transform your LLM serving stack. ...

Introduction Retrieval-augmented generation (RAG), semantic search, and intelligent question-answering are now core building blocks of modern AI applications. But wiring together vector databases, file converters, retrievers, LLMs, and evaluation in a robust way is non‑trivial. Haystack, an open‑source Python framework by deepset, is designed to make this tractable: it gives you a full toolkit to ingest data, search it efficiently, query it with LLMs, run evaluation, and deploy to production. ...