ColBERTv2: Effective and Efficient Retrieval via Lightweight Late Interaction · HackerPapers discussions

ColBERTv2: Effective and Efficient Retrieval via Lightweight Late Interaction — mentioned by popidge

Wed, 03 Dec 2025 14:55:13 GMT

Chunking strategy is really difficult and, like you say, so important to RAG. I'm currently battling with it in a "Podcast archive -> active social trend" clip-finder app I'm working on. You have to really understand your source material and how it's formatted, consider preprocessing, consider when and where semantic breaks happen and how you can deterministically handle that in the specific domain. Adjacency similarity is a must, otherwise you leave perfectly cromulent results on the table because they didn't have the right cosine score in a vacuum. There is some early stuff from Apple's…

Paper and discussion history. A mention is not an endorsement.

ColBERTv2: Effective and Efficient Retrieval via Lightweight Late Interaction — mentioned by mtbarta3

Fri, 05 Apr 2024 18:24:52 GMT

We're building LintDB to productionize late interaction retrieval. If you're not familiar with late interaction, here's the colBERTv2 paper: https://arxiv.org/pdf/2112.01488.pdf We built LintDB because we had a hard time getting the retrieval performance we wanted in our RAG application. colBERT calculates a document's score based on the maximum similarity across multiple vectors per document, and this was more successful for our task. The only problem was that traditional vector databases didn't support it. Vespa supports this type of indexing, actually. But the learning curve was high and I…

Paper and discussion history. A mention is not an endorsement.

ColBERTv2: Effective and Efficient Retrieval via Lightweight Late Interaction — HN submission

Thu, 04 Jan 2024 16:48:34 GMT

Show HN: RAGatouille, a simple lib to use&train top retrieval models in RAG apps

Hey HN! If you’re at all interested in LLMs/LLM-apps, you’ve probably heard of RAG: Retrieval-Assisted Generation, i.e. retrieving relevant documents to give to your LLM as context to answer user queries. Today, I’m releasing RAGatouille v0.0.1, whose aim is to make it as easy as can be to improve your RAG pipelines by leveraging state-of-the-art Information Retrieval research. As of right now, there’s quite a big gap between common everyday practice and the IR literature, and a lot of the gap is because there just aren’t good ways to quickly try out and leverage SotA IR techniques.…

15 points; 5 discussion replies on HN. Paper and source history. A submission is not an endorsement.

ColBERTv2: Effective and Efficient Retrieval via Lightweight Late Interaction — mentioned by svcrunch

Wed, 09 Nov 2022 20:43:57 GMT

This is an area that's dear to me, I'm the cofounder of Vectara and have been working with embedding-based semantic search, aka neural search or neural IR, since 2017. To whether Google uses semantic search, the answer is yes, very heavily [1][2]. Not only that, but they have led, and continue to lead, much of the pioneering research in NLP and neural IR for the past decade [3][4][5]. Technical challenges lie along a few primary dimensions. The first has been search quality, because, while early neural systems like Google Talk to Books [5][6] demonstrated the potential of these techniques…

Paper and discussion history. A mention is not an endorsement.

ColBERTv2: Effective and Efficient Retrieval via Lightweight Late Interaction — HN submission

Sun, 05 Dec 2021 17:36:32 GMT

ColBERTv2: Effective and Efﬁcient Retrieval via Lightweight Late Interaction

1 point; 0 discussion replies on HN. Paper and source history. A submission is not an endorsement.