RAG From Scratch: Part 14 (ColBERT)
From the video description
Our RAG From Scratch video series walks through impt RAG concepts in short / focused videos w/ code. This is the 14th video in our series and focuses on indexing with ColBERT for fine-grained similarity search. Problem: Embedding models compress text into fixed-length (vector) representations that capture the semantic content of the document. This compression is very useful for efficient search / retrieval, but puts a heavy burden on that single vector representation to capture all the semantic nuance / detail of the doc. In some cases, irrelevant / redundant content can dilute the semantic usefulness of the embedding.
Topics
Sources
Video by LangChain, played through YouTube.
- Indie Picks
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