Compare Since all these benchmarks are built using LangSmith, you can easily spot where different systems go wrong and compare them side-by-side. You can also go beyond aggregate statistics to examine the step-by-step execution of different systems on the same data point.
@langchain
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LangSmith Launches Public Q&A Benchmark Dataset
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🦜💪 Public LangSmith Benchmarks
— LangChain (@LangChain) 22 novembre 2023
Deploying LLM apps requires great evaluation, but writing evals can be painstaking.
We're launching a Q&A benchmark dataset on LangSmith so you can easily compare architectures .
Dataset: https://t.co/UEuqpyb15Q
Blog: https://t.co/Y85mNP1clJ pic.twitter.com/JkZ0MlbHROPublic LangSmith Benchmarks Deploying LLM apps requires great evaluation, but writing evals can be painstaking. We're launching a Q&A benchmark dataset on LangSmith so you can easily compare architectures . Dataset: https://
smith.langchain.com/public/452ccaf
c-18e1-4314-885b-edd735f17b9d/d
…
Blog: https://
blog.langchain.dev/public-langsmi
th-benchmarks/
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Free OSS and Google Models Access in LangSmith Playground
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OSS models and Google models in LangSmith Playground for FREE Want to experiment with new models easily? No need for an API key! We've partnered with @thefireworksai and @GoogleAI to give you access to both OSS and Google models such as Mistral-7b, LLaMA2-70b or
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LangChain Templates Hub Launches 60+ Community-Contributed LLM Templates
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LangChain Templates Hub With 60+ templates contributed by the community and our partners, LangChain templates are the easiest way to start building with LLMs But with so many, it can often be disorienting to know where to start Today were launching LangChain
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Bedrock Support Now Available in LangSmith Playground
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🛌 🪨 Bedrock support in LangSmith Playground
— LangChain (@LangChain) 22 novembre 2023
Swapping out foundational models is part of the experimentation done when developing context-aware reasoning LLM applications. With #LangSmith we're making swapping providers as easy as possible!
Now you can run the latest models… pic.twitter.com/h5Ajwzc8mqBedrock support in LangSmith Playground Swapping out foundational models is part of the experimentation done when developing context-aware reasoning LLM applications. With #LangSmith we're making swapping providers as easy as possible! Now you can run the latest models
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LangChain Wraps AI Capabilities in Opinionated Platform
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Yup, wraps a lot of preexisting capabilities up in a platform Lots of flexibility in langchain how to do streaming, configuration, persistence, etc. this is a more opinionated end to end example
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Dream: AI No-Code Tool for Building Web Apps with Natural Language
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Dream –an AI no-code tool to build fully functional web apps and components with natural language. built by Student Hacker, @calixo888
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RAG Stack Documentation: Organizing Key Strategies
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Deconstructing the RAG stack We repeatedly hear that navigating the idea maze around RAG is a challenge. We've overhauled our RAG docs and made a series of guides to organize / explain key RAG strategies. As an entry point, see our new RAG docs: https://
python.langchain.com/docs/use_cases
/question_answering/
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Tuna: No-Code LLM Fine-Tuning Dataset Generation Tool
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Tuna: a no-code tool for quickly generating LLM fine-tuning datasets from scratch built by Student Hacker, @itsandrewgao