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.

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LINKS:
Google Notebook: https://github.com/PromtEngineer/Yout...
ColBERTv2 Paper: https://arxiv.org/pdf/2112.01488.pdf
ColBERT Github: https://github.com/stanford-futuredat...
RAGatouille: https://github.com/bclavie/RAGatouill...


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

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