RAG (Retrieval-Augmented Generation) with LanceDB 🔓🧐
Build RAG (Retrieval-Augmented Generation) with LanceDB, a powerful solution for efficient vector-based information retrieval 📊.
Experience the Future of Search 🔄
🤖 RAG enables AI toretrieve relevant information from external sources and use it togenerate more accurate and context-specific responses. 💻 LanceDB provides a robust framework for integrating LLMs with external knowledge sources 📝.
| RAG | Description | Links |
|---|---|---|
| RAG with Matryoshka Embeddings and LlamaIndex 🪆🔗 | UtilizeMatryoshka embeddings andLlamaIndex to improve the efficiency and accuracy of your RAG models. 📈✨ |   |
| Improve RAG with Re-ranking 📈🔄 | Enhance your RAG applications by implementingre-ranking strategies for more relevant document retrieval. 📚🔍 |  |
| Instruct-Multitask 🧠🎯 | Integrate theInstruct Embedding Model with LanceDB to streamline your embedding API, reducing redundant code and overhead. 🌐📊 |  |
| Improve RAG with HyDE 🌌🔍 | UseHypothetical Document Embeddings for efficient, accurate, and unsupervised dense retrieval. 📄🔍 | |
| Improve RAG with LOTR 🧙♂️📜 | Enhance RAG withLord of the Retriever (LOTR) to address 'Lost in the Middle' challenges, especially in medical data. 🌟📜 | |
| Advanced RAG: Parent Document Retriever 📑🔗 | UseParent Document & Bigger Chunk Retriever to maintain context and relevance when generating related content. 🎵📄 | |
| Corrective RAG with Langgraph 🔧📊 | Enhance RAG reliability withCorrective RAG (CRAG) by self-reflecting and fact-checking for accurate and trustworthy results. ✅🔍 | |
| Contextual Compression with RAG 🗜️🧠 | Applycontextual compression techniques to condense large documents while retaining essential information. 📄🗜️ | |
| Improve RAG with FLARE 🔥 | Enable users to ask questions directly toacademic papers, focusing onArXiv papers, withForward-LookingActiveREtrieval augmented generation.🚀🌟 | |
| Query Expansion and Reranker 🔍🔄 | Enhance RAG with query expansion using Large Language Models and advancedreranking methods likeCross Encoders,ColBERT v2, andFlashRank for improved document retrieval precision and recall 🔍📈 | |
| RAG Fusion ⚡🌐 | Build RAG Fusion, utilize theRRF algorithm to rerank documents based on user queries ! UseLanceDB as vector database to store and retrieve documents related to queries viaOPENAI Embeddings⚡🌐 | |
| Agentic RAG 🤖📚 | Build autonomous information retrieval withAgentic RAG, a framework ofintelligent agents that collaborate to synthesize, summarize, and compare data across sources, that enables proactive and informed decision-making 🤖📚 | |