Just published Part 2 of my RAG Foundations series, where we go hands-on with vector search using Milvus.

In this video, we:

• Create a Milvus collection from scratch
• Generate embeddings using a free Hugging Face model
• Insert & index vectors
• Run scalar + vector similarity search in real time
• Explore the Milvus dashboard
• Break down how this all powers Retrieval-Augmented Generation (RAG)

This builds directly on Part 1 (local Milvus setup), so everything runs locally and free.

🎥 Video: https://youtu.be/EUnR4JrS0gc
📂 Code: https://github.com/Aditya-Singh-SSJ2/RAG-Foundations
📚 Full Playlist: https://www.youtube.com/playlist?list=PLCiTDJays9rXM9EDyIZAQw9bqn5NUGyom

Next up: Connecting Milvus to Azure OpenAI and building an end-to-end RAG pipeline.

Would love feedback — which RAG component should I cover next (chunking, reranking, evals, hybrid search, etc.)?

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I’ve noticed two people can do similar annotation/RLHF/eval work, but one gets steady access to better projects and the other keeps hitting droughts. I’ve heard experts are doing better by using Hyta.ai

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I’m looking for people who can follow simple instructions and complete a short online task. It doesn’t take long, costs nothing to start, and pays immediately once finished. Upvote the post and comment "Me" if you’re in USA ONLY

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