Generative AI with LangChain! @benji1a #BigData #Analytics #DataScience #AI #MachineLearning #IoT #IIoT #Python #RStats #TensorFlow #Java #JavaScript #GoLang #CloudComputing #Serverless #DataScientist #Linux #Books #Programming #Coding #100DaysofCode https://
geni.us/Gen-AI-LangCha
in
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AI
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Generative AI with LangChain: Tools and Techniques
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AI with Python Cookbook: Essential Guide for Data Scientists
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AI with Python Cookbook! @benji1a #BigData #Analytics #DataScience #AI #MachineLearning #IoT #IIoT #Python #RStats #TensorFlow #Java #JavaScript #ReactJS #GoLang #CloudComputing #Serverless #DataScientist #Linux #Books #Programming #Coding #100DaysofCode https://
geni.us/PyCookbook -

Harness Launches Generative AI Assistant for Developer Efficiency
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Harness releases generative AI assistant to help increase developer efficiency
#AI #AIio #BigData #ML #NLU #Futureofwork @ahier @guzmand
@DavidBrin @denisegarth @dez_blanchfield @diioannid @DioFavatas @gerald_bader http://
ow.ly/qx3k30swABJ -
Mission Impossible AI Portrayal Wildly Implausible Entertainment
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Ditto in the new Mission Impossible movie. Rogue AI and so wildly implausible that it was only enjoyable once I recognized that *everything* about the movie is implausible, and gave in. 🙂 https://
washingtonpost.com/technology/202
3/07/28/mission-impossible-ai-not-realistic/
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Google DeepMind’s Gemini to surpass GPT-4 with AlphaGo-LLM combination
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Google DeepMind’s CEO @demishassabis says their new AI model "Gemini" will far surpass the capabilities GPT-4 Gemini (still several months from release) is a multi-modal AI system that combines their Go-winning program, AlphaGo, with LLM capabilities. Google acquired
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LangChain RunnableMap Parallel Operations Guide
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In this case, we have a `RunnableMap` (the dictionary) For these operations, they are run in parallel See our guide on that here: https://
python.langchain.com/docs/guides/ex
pression_language/interface#parallelism
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LangSmith Parallel Execution Optimization in Runnable Maps
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If we look at the LangSmith trace, we can see that the overall RunnableMap step is less than the sum of its components (because those components are run in parallel
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LangChain Expression Language enables parallel operations optimization
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If you do these operations sequentially, they can start to add up That's why it's important to do them in parallel One nice attribute of the LangChain Expression Language is it tries to do things in parallel where ever possible
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Multi-source retrieval API calls optimization challenges
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Each retrieval step requires it own API calls: – For SQL, you need to call an LLM to generate a query
– For vectorstore, you need to create an embedding And this is just for two retrieval sources – but it's easy to imagine extending this to more. -
Three Essential Tools for Startups: CSV Import, Analytics, Search
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Thank you to: @oneschema_co
—Import CSV data 10x faster: https://
oneschema.co/lenny @mixpanel
—Event analytics that everyone can trust, use, and afford: https://
mixpanel.com/startups @brave
—An independent, global search index you can use to power your app: https://
brave.com/lenny