We had a REALLY fun webinar on evaluating RAG systems yesterday For me, the highlight was Pedro from Tavrn talking about lessons learned when building Noah (their context-aware chatbot) Now on YouTube for some Friday afternoon viewing! https://
youtube.com/watch?v=fWC4Vx
olWAk
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@hwchase17
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RAG Systems Evaluation: Noah Chatbot Lessons Learned
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RAG Pipeline Technical Overview and Implementation Guide
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A great overview of what's going on under the hood in a RAG pipeline Thanks for writing this @czue
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Multiple Embeddings Strategy for Enhanced Document Retrieval
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One solution is to start creating not one but MULTIPLE embeddings per document This was the basic realization with our ParentDocumentRetriever ~2 weeks ago, but it's really much more general than that There are many ways to create multiple embeddings
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Multi Vector Retriever: Advanced Embedding Strategies for AI
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Multi Vector Retriever The basic idea: you store multiple embedding vectors per document. How do you generate these embeddings? Smaller chunks (this is ParentDocumentRetriever)
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Hypothetical questions
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Larger Context Windows Challenge Single Embeddings Per Chunk
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Language models are getting larger and larger context windows This is great, because you can pass bigger chunks in! But if you have larger chunks, then a single embedding per chunk can start to fall flat, as there can be multiple distinct topics in that longer passage
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Querying Excel Spreadsheets: Advanced Data Extraction Techniques
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I really enjoyed this post. Great in depth post on querying data from excel spreadsheets, an underexplored usecase
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RAGAS Framework for RAG Systems Evaluation with LangSmith
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RAGAS is an awesome open-source framework for evaluating RAG systems We recently integrated LangSmith RAGAS for optimal ease of evaluation We're also doing a webinar on RAG evaluation with them in 5 minutes!!! Link to webinar and blog
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Evaluating RAG Pipelines with RAGAS and LangSmith
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Blog: https://
blog.langchain.dev/evaluating-rag
-pipelines-with-ragas-langsmith/
… Webinar: https://
crowdcast.io/c/bnx91nz59cqq Hope to see you there! -
SQL QA Integration Improves Context Management for AI Applications
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It’s often hard to bring in appropriate context to do sql qa This integration should help with that!
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Evaluating RAG Systems: Insights from Application Developers
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It's always insightful to hear from actual application developers on how they approach evaluation That's why I'm excited to have @pedro_pxp on our webinar tomorrow that is focused on evaluating RAG systems! https://
crowdcast.io/c/bnx91nz59cqq