Скачать с ютуб Reranking Simply Explained + Full Code | Langchain & Amazon Bedrock в хорошем качестве

Reranking Simply Explained + Full Code | Langchain & Amazon Bedrock

In this new AWS GenAI tutorial, you will learn how to quickly and easily use the latest and most powerful Cohere Reranker with Amazon Bedrock & Langchain. Specifically, you will learn : What is a reranker and what is a cross-encoder The difference between bi-encoders and cross-encoders How to improve your rag with reranking How to call Cohere Rerank 3.5 through AWS Bedrock Implement reranking with Langchain LCEL Build a full Langchain enhanced retriever with LM Studio local embedding and bedrock reranker. Don't forget to like and subscribe to the channel ! Music track: Marshmallow by Lukrembo Source: https://freetouse.com/music Music for Videos (Free Download) 🧠 Resources Cohere Rerank 3.5 Blog : https://cohere.com/blog/rerank-3pt5 Bi-encoders v.s. Cross-encoders Blog :   / bi-encoder-vs-cross-encoder-when-to-use-wh...   AWS Blog on Enhancing Search with Reranking : https://aws.amazon.com/blogs/big-data... Config/Credential Files for AWS login : https://docs.aws.amazon.com/cli/v1/us... 📚 Chapters 00:00 Intro 00:15 Cohere Rerank 3.5 01:16 Bi-Encoders vs. Cross-Encoders 03:22 Benchmark & Evaluation 3:55 Bedrock Support 4:19 AWS Credentials & Access 5:19 Sample Data 5:38 Method #1 : Call using Cohere 7:04 Method #2 : Call using Boto3 8:02 Langchain Integration : Vanilla Retriever 9:22 Langchain Integration : Enhanced Retriever 10:46 Discussion #rerank #langchain #rag #bedrock #cohere #python #aws #retriever #embedding #tutorial #explained #reranker