Welcome to my comprehensive tutorial on enhancing your search and retrieval systems using Flash Rank, the ultralite and superfast Python library. This tutorial is designed to guide you through the process of adding advanced reranking capabilities to your existing pipelines using stateoftheart crossencoders.

⚡ Key Highlights:

Why Flash Rank?
1. Learn about its ultralightweight nature, requiring no Torch or Transformers, and its capability to run efficiently on CPU. Discover how it boasts the world's smallest reranking model at approximately 4MB.
2. Speed and Efficiency: Understand how Flash Rank's reranking speed depends on the number of tokens in passages, the query, and the model depth.
3. CostEffective Solutions: Dive into how FlashRank's minimal memory and time requirements make it perfect for serverless deployments like AWS Lambda.
4. Model Variety: Explore a range of models supported by FlashRank, including the default msmarcoTinyBERTL2v2, msmarcoMiniLML12v2, and more, including multilingual options.
Tutorial Coverage:

1. StepbyStep Guide: I will walk you through the process of integrating FlashRank into a Streamlit application, demonstrating each step with clear explanations and code examples.
2. Solving LLM Limitations: Learn how to tackle issues like 'lost in the middle' for long contexts in large language models (LLM), especially important for Retrieval Augmented Generation (RAG).
3. Practical Application: By the end of this tutorial, you'll have a fully functional Streamlit app that showcases the power of reranking with FlashRank.

Whether you're in academia or industry, this tutorial is geared towards making your LLM inference more efficient and costeffective.

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Deployed App Link: https://rerankingforrag.streamlit.app/
GitHub Repo: https://github.com/AIAnytime/ReRankin...
Langchain Long Context Reorder: https://python.langchain.com/docs/mod...
Weaviate ReRanking Feature: https://weaviate.io/developers/weavia...
Flash Rank GitHub: https://github.com/PrithivirajDamodar...

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