Building Embedding Models for Large-Scale Real-World Applications Sahil Dua discusses the critical role of embedding models in powering search and RAG applications at scale. He explains the transformer-based architecture, contrastive learning techniques, and the pro...

#EmbeddingModels power search, recommendations & #RAG systems by turning data into meaningful vectors.

State-of-the-art #LLMs can create high-quality embeddings - but scaling is tricky.

🔗 This #InfoQ video walks through the end-to-end lifecycle of embedding systems: bit.ly/46eMXCX

#AI #ML

Choosing the right embedding model is vital for vector search. HN users suggest alternatives to all-MiniLM-L6-v2, stressing that model performance directly impacts search accuracy and relevance. Don't overlook this critical choice! #EmbeddingModels 2/5

LLM Embeddings Explained Like I’m 5 - Cloudthrill Curious what LLM embeddings really are and how they power tools like RAG? This guide breaks it down simply - from text-to-vector magic to fast retrieval. Bonus: explore how KV cache speeds up inference in vLLM.

✍🏻#NewBlog 𝗟𝗟𝗠 𝗘𝗺𝗯𝗲𝗱𝗱𝗶𝗻𝗴𝘀 𝗘𝘅𝗽𝗹𝗮𝗶𝗻𝗲𝗱 (Like I’m 5)
You’ve heard of RAG, vector DBs, and embeddings... 🤔
But what are 𝐞𝐦𝐛𝐞𝐝𝐝𝐢𝐧𝐠𝐬 really, and why do they matter?
learn all about it and how it works in this 5 minute post
👇🏼 cloudthrill.ca/llm_embeddin...

#LLM #Embeddingmodels #AI #RAG #VectorDB

Running Hugging Face models inside Oracle 23ai with ONNX and OML4Py About a year ago, I published a blog post on vector search in Oracle 23ai. At that time, I wasn’t aware of the ONNX format or how it could be used to bring machine learning models into the database. That realization came later, and ever since, I’ve been thinking: "I need to write a follow-up post to cover this!" Well... the time has finally come.

A year ago, I explored vector search in Oracle 23ai using external models. Now, it's all happening inside the database.

ONNX + OML4Py + Oracle 23ai = pure magic (with SQL).

New blog is out! let me know what you think!

Cheers,
FS

#Oracle23ai #AI #VectorSearch #EmbeddingModels #OracleACE

RAG Interview Questions and Answers Explore RAG interview questions with concise answers, covering retrieval mechanisms, generative models, embeddings, and evaluation metrics.

📊Why Prepare for RAG Interviews?
🔸Master #RetrievalAugmentedGeneration concepts to excel in AI-driven applications.
🔸Enhance problem-solving skills with real-world #NLP, #LLMs, and #MachineLearning scenarios.
🔸Get familiar with #VectorDatabases, #EmbeddingModels, and #PromptEngineering to stand out.