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🤝Hugging Face x LangChain partner package We're excited to announce the launch of `langchain-huggingface`, a partner package in LangChain jointly maintained with Hugging Face. LangChain users can now reliably connect to and access Hugging Face features. These include chat, text completion, and embedding models for both local & hosted instances. Read the blog post: https://lnkd.in/grpZipWw ... And stay tuned for more to come 😉

Adobe just dropped a bomb that can scale videos up to 8x with AI 💣 VideoGigaGAN is a groundbreaking AI model that takes video upscaling to the next level! Key Highlights: 8X Upscaling Power 🚀 Increase video resolution by a staggering 8 times, transforming low-quality footage into stunning visuals. Consistent Quality 🖼️ The model excels in generating incredibly detailed and high-quality output while maintaining temporal consistency across frames. Texture Restoration 🧵 VideoGigaGAN is adept at restoring and even creating intricate textures that were absent in the original low-res video. ↓ Follow Utsav Soi to learn about the latest breakthroughs in AI and tech 🚀

Overlooked in the rush to get AI tooling out the door: data quality and hygiene It's still the boring stuff that makes the engine go. Are you confident your chat interface is giving relevant, useful, and *honest* answers? Do you have metrics in place to measure data quality? How do you go about it when the data is internal business documentation and text scraped from websites? We are working up a free AI readiness course and a data maturity checklist. Follow for updates!

Interesting trend 📈 : Seeing more and more companies specifying AI revenue explicitly on the P&L to command larger multiples. The opportunity to scale with AI exceeds even traditional SaaS models and people are recognizing that with higher valuations. Anyone else see AI show up in the financials like this?

As is long-standing company policy, due to the total solar eclipse, today will be an official Budgie company holiday. But we will get back to you just as soon as we get over a cosmic sense of awe and wonderment.

⚡ RAG From Scratch: Feedback + self-reflection ⚡ Our RAG From Scratch video series walks through important RAG concepts in short / focused videos w/ code. This is the final part in our series, focusing on self-reflection + feedback to improve RAG systems. 🔧Problem: RAG systems can suffer from low quality retrieval (e.g., if a user question is out of the domain for the index) and / or hallucinations in generation. A naive retrieve-generate pipeline has no ability to detect or self-correct from these kids of errors. 💡Idea: The concept of "flow engineering" has been recently introduced by Itamar Friedman the context of code generation: iteratively build an answer to a code question w/ unit tests to check and self-correct errors. Hamel H. has a great blog post that mentions the benefit of unit-testing in inference loop. Several works have applied this RAG, such as Self-RAG (Akari Asai et al) and Corrective-RAG (Jia-Chen Gu + colleages). In both cases, checks for document relevance, hallucinations, and / or answer quality are performed in the RAG answer flow. We've implemented these ideas using LangGraph to orchestrate the checks and feedback. We've also shown that LangGraph allows both to run reliable w/ smaller OSS models. 📽️ Video: https://lnkd.in/g2XSv9gQ 💻 Code: CRAG: https://lnkd.in/ggXU2idQ Self-RAG: https://lnkd.in/gPfjKSUc Both with Mistral AI-7b + Ollama: https://lnkd.in/gQnx29_T https://lnkd.in/gtqXznND 🧠References: 1/ Self-RAG https://lnkd.in/d3B6586n 2/ C-RAG: https://lnkd.in/eNu7qQVK 3/ Flow-engineering: https://lnkd.in/gt-Wc7KU 4/ Blog on evals, covering unit tests: https://lnkd.in/etrSyFgq

This is an excellent tutorial by Sudarshan Koirala showing you how to build advanced PDF RAG with LlamaParse and purely local models for embedding, LLMs, and reranking (Groq and FastEmbed by Qdrant, flag-embedding-reranker) Having a good extraction step is super important and is inherently coupled with good indexing/retrieval techniques. This is a huge ingredient for getting RAG working even with simpler and cheaper models. Video: https://lnkd.in/g-hgEsU6 Notebook: https://lnkd.in/gzwPCusa

Does SCRUM really work for AI projects? #mlops #agile #ai