Dynamic section retrieval, demo notebook: https://
github.com/run-llama/llam
a_parse/blob/main/examples/advanced_rag/dynamic_section_retrieval.ipynb
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@akshay_pachaar
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Dynamic Section Retrieval Demo Notebook LlamaParse
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Dynamic Section Retrieval Solves RAG Context Fragmentation Problem
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Dynamic section retrieval solves a major pain point in RAG! We've all struggled with naive RAG techniques that return fragmented context without awareness of the surrounding document. It leaves us with disjointed chunks and incomplete information. But not anymore! @Llama_Index
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Wrap-up: Python and ML/AI Engineering Resources
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That's a wrap! If you interested in: – Python – ML/AI Engineering 1. Find me → @akshay_pachaar 2. Sign up for our newsletter for in-depth lessons and get a FREE eBook with 150+ core DS/ML lessons → @DailyDoseOfDS_
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NEO: World’s First AI Machine Learning Engineer Tool
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World’s first-ever AI machine learning engineer is here!
— Akshay 🚀 (@akshay_pachaar) 15 novembre 2024
Introducing NEO, an AI machine learning engineer that will save you 1000s of hours.
NEO works on a proprietary algorithm aimed at helping ML teams achieve their best.
Here’s how @withneo (NEO) will change how AI/ML… pic.twitter.com/aBiDcNq4jbWorld’s first-ever AI machine learning engineer is here! Introducing NEO, an AI machine learning engineer that will save you 1000s of hours. NEO works on a proprietary algorithm aimed at helping ML teams achieve their best. Here’s how @withneo (NEO) will change how AI/ML
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Python and ML/AI Engineering Learning Resources Newsletter
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If you're interested in: – Python – ML/AI Engineering 1. Find me → @akshay_pachaar 2. Sign up for our newsletter for in-depth lessons and get a FREE eBook with 150+ core DS/ML lessons → @DailyDoseOfDS_ Cheers!
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10 Most Common Loss Functions in Machine Learning
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10 most common loss functions in machine learning:
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Local OpenAI Swarm Agents with Ollama Integration
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100% Local OpenAI Swarm Agents! OpenAI Swarm is an educational framework that explores ergonomic, lightweight multi-agent orchestration. It's fairly easy to integrate with locally running LLMs through Ollama.
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Find me → @akshay_pachaar And stay tuned for more on -
AI and Machine Learning Tutorial with Bi/Cross-Encoders Deep Dive
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If you enjoyed this tutorial: Find me →
@akshay_pachaar
Everyday, I write about AI and Machine Learning! Also, check our deep-dive on Bi/Cross-Encoders: -
Combining Bi-Encoders and Cross-Encoders in RAG Systems
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Combining Both in RAG Systems To get the best of both worlds: Retrieve: Use Bi-Encoders as embedding-model to efficiently retrieve top candidate documents. Rerank: Use Cross-Encoders to rerank these candidates for better accuracy. Check this out 7/n
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CrossEncoders vs BiEncoders: Scalability Trade-offs in Similarity Scoring
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Let's say you have 10,000 sentences, CrossEncoders would require computing similarity scores for about 50 Million sentence combinations (¹⁰⁰⁰⁰C₂), which can take hours while bi-encoder would do it in seconds. But we have a great use for cross encoders… 6/n