Supercharge Your RAG with Contextualized Late Interactions

ColBERT is a fast and accurate retrieval model, enabling scalable BERT-based search over large text collections in tens of milliseconds. This can be used as a potential alternative to Dense Embeddings in Retrieval Augmented Generation. In this video we explore using ColBERTv2 with RAGatouille and compare it with OpenAI Embedding models. 🦾 Discord: https://discord.com/invite/t4eYQRUcXB ☕ Buy me a Coffee: https://ko-fi.com/promptengineering |🔴 Patreon: https://www.patreon.com/PromptEngineering 💼Consulting: https://calendly.com/engineerprompt/consulting-call 📧 Business Contact: [email protected] Become Member: http://tinyurl.com/y5h28s6h 💻 Pre-configured localGPT VM: https://bit.ly/localGPT (use Code: PromptEngineering for 50% off). Signup for Advanced RAG: https://tally.so/r/3y9bb0 LINKS: Google Notebook: https://github.com/PromtEngineer/YoutTube-Tutorial ColBERTv2 Paper: https://arxiv.org/pdf/2112.01488.pdf ColBERT Github: https://github.com/stanford-futuredata/ColBERT RAGatouille: https://github.com/bclavie/RAGatouille?tab=readme-ov-file TIMESTAMPS: [00:00] Problem with Dense Embeddings in RAG [01:52] Colbert v2 for Efficient Retrieval [04:55] RAGatouille to the rescue [05:32] Semantic Search in Action: A Practical Example with ColBERTv2 [09:33] Comparing Retrieval Performance: Colbert vs. Dense Embedding Models [12:54] Enhancing Retrieval with Increased Chunk Size All Interesting Videos: Everything LangChain: https://www.youtube.com/playlist?list=PLVEEucA9MYhOu89CX8H3MBZqayTbcCTMr Everything LLM: https://youtube.com/playlist?list=PLVEEucA9MYhNF5-zeb4Iw2Nl1OKTH-Txw Everything Midjourney: https://youtube.com/playlist?list=PLVEEucA9MYhMdrdHZtFeEebl20LPkaSmw AI Image Generation: https://youtube.com/playlist?list=PLVEEucA9MYhPVgYazU5hx6emMXtargd4z