Example/rag lotr (#98)

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FLARE chain using arvix paper q&a app

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README.md

@@ -47,6 +47,7 @@ If you're looking for in-depth tutorial-like examples, checkout the [tutorials](
 | [Improve RAG with Re-ranking](./examples/RAG_re_ranking/) | <a href="https://colab.research.google.com/github/lancedb/vectordb-recipes/blob/main/examples/RAG_re_ranking/main.ipynb"><img src="https://colab.research.google.com/assets/colab-badge.svg" alt="Open In Colab"></a> |[![Medium](https://img.shields.io/badge/Medium-12100E?style=for-the-badge&logo=medium&logoColor=white)](https://blog.lancedb.com/simplest-method-to-improve-rag-pipeline-re-ranking-cf6eaec6d544)|
 | [Improve RAG with FLARE](./examples/Advanced-RAG-with-FLARE) | <a href="https://colab.research.google.com/github/lancedb/vectordb-recipes/blob/main/examples/Advanced-RAG-with-FLARE/main.ipynb"><img src="https://colab.research.google.com/assets/colab-badge.svg" alt="Open In Colab"></a> [![Python](https://img.shields.io/badge/python-3670A0?style=for-the-badge&logo=python&logoColor=ffdd54)](.examples/instruct-multitask/main.py)  | [![Medium](https://img.shields.io/badge/Medium-12100E?style=for-the-badge&logo=medium&logoColor=white)](https://blog.lancedb.com/better-rag-with-active-retrieval-augmented-generation-flare-3b66646e2a9f)|
 | [Improve RAG with HyDE](./examples/Advance-RAG-with-HyDE/) | <a href="https://colab.research.google.com/github/lancedb/vectordb-recipes/blob/main/examples/Advance-RAG-with-HyDE/main.ipynb"><img src="https://colab.research.google.com/assets/colab-badge.svg" alt="Open In Colab"></a> |[![Medium](https://img.shields.io/badge/Medium-12100E?style=for-the-badge&logo=medium&logoColor=white)](https://blog.lancedb.com/advanced-rag-precise-zero-shot-dense-retrieval-with-hyde-0946c54dfdcb)|
+| [Improve RAG with LOTR ](./examples/Advance_RAG_LOTR/) | <a href="https://colab.research.google.com/github/lancedb/vectordb-recipes/blob/main/examples/Advance_RAG_LOTR/main.ipynb"><img src="https://colab.research.google.com/assets/colab-badge.svg" alt="Open In Colab"></a> |[![Medium](https://img.shields.io/badge/Medium-12100E?style=for-the-badge&logo=medium&logoColor=white)](https://blog.lancedb.com/better-rag-with-lotr-lord-of-retriever-23c8336b9a35)|
 | [Advanced RAG: Parent Document Retriever](./examples/parent_document_retriever/) | <a href="https://colab.research.google.com/github/lancedb/vectordb-recipes/blob/main/examples/parent_document_retriever/main.ipynb"><img src="https://colab.research.google.com/assets/colab-badge.svg" alt="Open In Colab"></a> |[![Medium](https://img.shields.io/badge/Medium-12100E?style=for-the-badge&logo=medium&logoColor=white)](https://blog.lancedb.com/modified-rag-parent-document-bigger-chunk-retriever-62b3d1e79bc6)|
 | [Hybrid search BM25 & lancedb ](./examples/Hybrid_search_bm25_lancedb/) | <a href="https://colab.research.google.com/github/lancedb/vectordb-recipes/blob/main/examples/Hybrid_search_bm25_lancedb/main.ipynb"><img src="https://colab.research.google.com/assets/colab-badge.svg" alt="Open In Colab"></a> |[![Medium](https://img.shields.io/badge/Medium-12100E?style=for-the-badge&logo=medium&logoColor=white)](https://blog.lancedb.com/hybrid-search-combining-bm25-and-semantic-search-for-better-results-with-lan-1358038fe7e6)|
 | [Evaluating Prompts with Prompttools](./examples/prompttools-eval-prompts/) | <a href="https://colab.research.google.com/github/lancedb/vectordb-recipes/blob/main/examples/prompttools-eval-prompts/main.ipynb"><img src="https://colab.research.google.com/assets/colab-badge.svg" alt="Open In Colab"></a> |  |

examples/Advance_RAG_LOTR/README.md

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+## Better RAG with LOTR - Lord of the Retriever
+
+### Overview
+This repository contains resources and code for enhancing Retrieval-Augmented Generation (RAG) systems using a novel approach termed LOTR (Lord of the Retriever). The primary focus is on addressing the 'Lost in the Middle' (LIM) challenge in RAG systems, particularly in the context of medical/healthcare data.
+
+### Features
+Advanced Retrieval Techniques: Utilizes multiple vector stores and the Merge Retriever approach to efficiently retrieve relevant documents.
+LOTR - Merger Retriever: Combines results from various retrievers to form a comprehensive, relevant document list.
+LongContextReorder (LOTR): Reorders information to ensure equal attention to all parts of the text.
+Domain-Specific Embeddings: Incorporates specialized embeddings for medical and healthcare-related data.
+
+
+## code 
+
+ Colab walkthrough for LOTR   <a href="https://colab.research.google.com/github/lancedb/vectordb-recipes/blob/main/examples/Advance_RAG_LOTR/main.ipynb"><img src="https://colab.research.google.com/assets/colab-badge.svg" alt="Open In Colab"></a>
+
+### Learn deeper in Our Blog
+For a deeper dive into the cutting-edge technologies of FLARE, and to access detailed technical knowledge, check out our Medium Blog.
+
+
+[Read the Blog Post](https://medium.com/@aksdesai1998/better-rag-enhancing-ai-with-active-retrieval-augmented-generation-flare-3b66646e2a9f)