Embeddings

Table of Contents Introduction What Are Vector Databases? Why Vector Databases Matter for LLMs Core Concepts: Embeddings, Similarity Search, and RAG Top Vector Databases Compared Getting Started: Installation and Setup Practical Python Examples Indexing Strategies Querying and Retrieval Performance and Scaling Considerations Best Practices for LLM Integration Conclusion Top 10 Learning Resources Introduction The explosion of large language models (LLMs) has fundamentally changed how we build intelligent applications. However, LLMs have a critical limitation: they operate on fixed training data and lack real-time access to external information. This is where vector databases enter the picture. ...

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] ...

Introduction Retrieval-Augmented Generation (RAG) has become a foundational pattern for building accurate, scalable, and fact-grounded applications with large language models (LLMs). At its core, RAG combines a retrieval component (to fetch relevant pieces of knowledge) with a generation component (the LLM) that produces answers conditioned on that retrieved context. This article breaks RAG down from first principles: the indexing and retrieval stages, the augmentation of prompts, the generation step, common challenges, practical mitigations, and code examples to get you started. ...