Evaluating RAG pipelines using LangChain and Ragas LangChain can help you build RAG pipelines, but how do you evaluate them? We have a great integration with RAGAS to do exactly that! h/t @DataScienceHarp for writing all about it Blog: https://
deci.ai/blog/evaluatin
g-rag-pipelines-using-langchain-and-ragas/
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@langchain
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Evaluating RAG Pipelines with LangChain and RAGAS
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Building RAGs with LangChain: Practical Tutorial
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Building your own RAGs with LangChain A practical tutorial from Madhav Thacker (Senior Data Scientist @Shopify
) on how to build your own RAGs with @langchain Thanks to our friends at @arizeai for organizing! YouTube: https://
youtube.com/watch?v=IUEny5
cbys8
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Multimodal RAG with LangChain and GPT-4 Vision Guide
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Multimodal RAG using Langchain Expression Language And GPT4-Vision Great guide from Plaban Nayak on multimodal RAG. We suspect this will become more and more popular – so now's a great time to become an expert! Blog: https://
medium.aiplanet.com/multimodal-rag
-using-langchain-expression-language-and-gpt4-vision-8a94c8b02d21
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Quivr: Local GenAI Second Brain with Llama2 and Docker
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Quivr: your GenAI 2nd Brain One of the most popular RAG apps out their is Quivr. Even better: it can be run completely locally Check out this awesome video by @mesudarshan on how to run it – Llama2 model with @OLLAMA – Uses @Docker & @supabase & @langchain – LangSmith for
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LangChain State of AI 2023: Year Review Trends
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As the year draws to a close, highlighting two pieces of content that really summed up 2023 for LangChain: LangChain State of AI 2023: highlighting all the trends in models, vectorstores, retrieval and evaluation that we saw over the past year https://
blog.langchain.dev/langchain-stat
e-of-ai-2023/
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Mixtral vs GPT-3.5: LangChain Integration Performance Comparison
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LangChain x Mixtral x TogetherAI This community blog post takes a deep dive into comparing Mixtral performance vs GPT-3.5 on 15 questions of reasoning, logic, and coding Great example of how to use it in LangChain via @togethercompute Blog:
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Three Levels of Building AI Agents Explained
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3 Levels of Building Agents A great article by @LucasEnkrateia goes over how to build agents in three levels of abstraction: From Scratch
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Customizing LangChain with Neo4j for Advanced RAG Systems
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Customized LangChain @neo4j Integration If you want to build advanced RAG, you probably need to know how to customize any off-the-shelf guide to your use case @tb_tomaz dives deep on how to do this with Graph DBs like Neo4j Blog: https://
github.com/langchain-ai/l
angchain/pull/15084
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ResearchPlot: AI-Powered Scenario Analysis and Flowchart Generation
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ResearchPlot This project aims to understand the user's scenario, ask relevant questions, and generate a flowchart for clear and concise representation of the findings Helpful for rubber-ducking with yourself! Code: https://
github.com/PratikSingh121
/ResearchPlot/blob/main/readme.md
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Create Customized Chat Bots with LangChain Framework
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[Community Spotlight] Create Your Customized Chat Bot with LangChain The most prevalent UX for LLM applications is chat. This blog walks through all the steps needed to create a customized Chat Bot Blog: https://
medium.com/@akturkrehsan/
create-your-customized-chat-bot-with-langchain-41be6b928428
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