lancedb/docs/src/examples/python_examples/rag.md

RAG: Revolutionize Information Retrieval with LanceDB 🔓🧐

Unlock the full potential of Retrieval-Augmented Generation (RAG) with LanceDB, a solution for efficient vector-based information retrieval 📊.

Experience the Future of Search 🔄

RAG integrates large language models (LLMs) with scalable knowledge bases, enabling efficient information retrieval and answer generation 🤖. By applying RAG to industry-specific use cases, developers can optimize query processing 📊, reduce response latency ⏱️, and improve resource utilization 💻. LanceDB provides a robust framework for integrating LLMs with external knowledge sources, facilitating accurate and informative responses 📝.

RAG	Description	Links
RAG with Matryoshka Embeddings and LlamaIndex 🪆🔗	Utilize Matryoshka embeddings and LlamaIndex to improve the efficiency and accuracy of your RAG models. 📈✨
Improve RAG with Re-ranking 📈🔄	Enhance your RAG applications by implementing re-ranking strategies for more relevant document retrieval. 📚🔍
Instruct-Multitask 🧠🎯	Integrate the Instruct Embedding Model with LanceDB to streamline your embedding API, reducing redundant code and overhead. 🌐📊
Improve RAG with HyDE 🌌🔍	Use Hypothetical Document Embeddings for efficient, accurate, and unsupervised dense retrieval. 📄🔍
Improve RAG with LOTR 🧙‍♂️📜	Enhance RAG with Lord of the Retriever (LOTR) to address 'Lost in the Middle' challenges, especially in medical data. 🌟📜
Advanced RAG: Parent Document Retriever 📑🔗	Use Parent Document & Bigger Chunk Retriever to maintain context and relevance when generating related content. 🎵📄
Corrective RAG with Langgraph 🔧📊	Enhance RAG reliability with Corrective RAG (CRAG) by self-reflecting and fact-checking for accurate and trustworthy results. ✅🔍
Contextual Compression with RAG 🗜️🧠	Apply contextual compression techniques to condense large documents while retaining essential information. 📄🗜️
Improve RAG with FLARE 🔥	Enable users to ask questions directly to academic papers, focusing on ArXiv papers, with Forward-Looking Active REtrieval augmented generation.🚀🌟
Query Expansion and Reranker 🔍🔄	Enhance RAG with query expansion using Large Language Models and advanced reranking methods like Cross Encoders, ColBERT v2, and FlashRank for improved document retrieval precision and recall 🔍📈
RAG Fusion ⚡🌐	Revolutionize search with RAG Fusion, utilizing the RRF algorithm to rerank documents based on user queries, and leveraging LanceDB and OPENAI Embeddings for efficient information retrieval ⚡🌐
Agentic RAG 🤖📚	Unlock autonomous information retrieval with Agentic RAG, a framework of intelligent agents that collaborate to synthesize, summarize, and compare data across sources, enabling proactive and informed decision-making 🤖📚