Query Analysis How-To Guides We've added a series of how-to guides for some of the trickier parts of query analysis How to add few shot examples?
How to deal with high cardinality categoricals? and more! Check out our new docs here: https://
python.langchain.com/docs/use_cases
/query_analysis/
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LangChain Query Analysis How-To Guides Released
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AI Model Selection: The New Coding Skill
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Yea. Maybe the real skill that will replace coders will be knowing which AI model is best for every potential scenario.
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Will Coders Become AI Doctors in the Future?
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Could coders be the AI Doctors of the future? If people stop learning to code, like Jensen suggests, we'll have a lot less coders in the future. I feel like this will increase the demand for coders when something's not working right with an AI. Just like I control my own
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Core Abstractions Enable Emergent System Simplicity
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you know you’ve got your new system’s core abstractions right when things you didn’t explicitly design for that used to be complex become incredibly simple
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Matryoshka Retriever: High Performance Vector Search Technique
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Matryoshka Retriever A recent blog post by @supabase described a new technique for higher performance retrieval, without compromising accuracy. So we've just released a new retriever in LangChain.js which implements this exactly! Use any vector store, and two embedding
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LangChain Matryoshka Retriever Documentation and Supabase Integration
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Read the JS docs: https://
js.langchain.com/docs/modules/d
ata_connection/retrievers/matryoshka_retriever
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Supabase article: -
VSCode Copilot Extension Built with Flowise and Ollama
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Kudos to @akirakudo911 for creating a VScode Copilot extension that is built using Flowise!
— FlowiseAI (@FlowiseAI) 2 mars 2024
Fully hosted locally using @ollamahttps://t.co/kqVjd2EMo7 pic.twitter.com/0urn9JpoOxKudos to @akirakudo911 for creating a VScode Copilot extension that is built using Flowise! Fully hosted locally using @ollama https://
marketplace.visualstudio.com/items?itemName
=AkiraKudo.kudos-gpt
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Query Analysis Techniques for RAG Systems with LLMs
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Query Analysis In OpenAI's retrieval talk on DevDay, they mention a bunch of strategies they experimented with 3 of these can be classified as **query analysis** a RAG technique that is becoming popular to do with LLMs We've added a docs deep-dive on this We cover six
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Free Learning Resources for Python, ML, and Data Science
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Learn for FREE: Python → http://
cs50.harvard.edu/python/2022/ Linear Algebra & Statistics → http://
khanacademy.org SQL → http://
sqlbolt.com Machine Learning → http://
deeplearning.ai MLOps → http://
madewithml.com Practice → http://
Kaggle.com -
Choosing Between Euclidean and Manhattan Distance Metrics
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Hi Amit, It depends on the use case, Euclidean distance is prefered if you have a continuous and normally distributed data and Manhattan if your input variables are not similar in type i.e categorical features.