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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@hwchase17
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Multi Vector Retriever: Advanced Embedding Strategies for AI
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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 -
Fine-tuning LLaMA2 Workshop: One-Day In-Person Event
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If you want to explore finetuning LLaMA2, we'll be talking at and helping out with a 1 day in-person event focused explicitly on finetuning OSS models Hopefully this guide will come in handy! RSVP here (s/o @swyx and @NaderLikeLadder for organizing): https://
partiful.com/e/T4ngRPaU2uUT
XM8pN17d
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LLMate Launches Solution for Structured Data QA
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QA over structured data is hard
— Harrison Chase (@hwchase17) 23 août 2023
Excited to so LLMate working with @manuelsoria_ and @fpingham to bring a solution to market
We've been chatting with them for a while – they've got some good ideas https://t.co/4ZLpFG4StDQA over structured data is hard Excited to so LLMate working with @manuelsoria_ and @fpingham to bring a solution to market We've been chatting with them for a while – they've got some good ideas
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Conversational Retrieval Agent Real-World Benefits
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This is great example of real-world benefits of using the conversational retrieval agent Allows you to query for multiple different terms and react to previously queried results