ColBERT
awesome-semantic-search
| ColBERT | awesome-semantic-search | |
|---|---|---|
| 4 | 3 | |
| 2,506 | 322 | |
| 6.3% | - | |
| 8.4 | 5.7 | |
| 28 days ago | 5 months ago | |
| Python | ||
| MIT License | Creative Commons Zero v1.0 Universal |
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ColBERT
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Why Vector Compression Matters
I’ll conclude by explaining how vector compression relates to ColBERT, a higher-level technique that Astra DB customers are starting to use successfully.
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How ColBERT Helps Developers Overcome the Limits of Retrieval-Augmented Generation
ColBERT is a new way of scoring passage relevance using a BERT language model that substantially solves the problems with DPR. This diagram from the first ColBERT paper shows why it’s so exciting:
- FLaNK Stack 05 Feb 2024
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New free tool that uses fine-tuned BERT model to surface answers from research papers
ColBERT and successors for retrieval.
awesome-semantic-search
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New free tool that uses fine-tuned BERT model to surface answers from research papers
Some good papers here.
- [P] Awesome Semantic Search
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Creating a collection of resources for Semanti Search.
Agrover112/awesome-semantic-search: Following repository contains resources related to Semantic Search🔎 and Semantic Similarity tasks. (github.com)
What are some alternatives?
qdrant - Qdrant - High-performance, massive-scale Vector Database for the next generation of AI. Also available in the cloud https://cloud.qdrant.io/
Milvus - A cloud-native vector database, storage for next generation AI applications
similarity - TensorFlow Similarity is a python package focused on making similarity learning quick and easy.
elasticsearch-learning-to-rank - Plugin to integrate Learning to Rank (aka machine learning for better relevance) with Elasticsearch
contract-discovery - Data and additional information regarding the paper: Contract Discovery. Dataset and a Few-Shot Semantic Retrieval Challenge with Competitive Baselines (to appear in Findings of EMNLP).
haystack - :mag: LLM orchestration framework to build customizable, production-ready LLM applications. Connect components (models, vector DBs, file converters) to pipelines or agents that can interact with your data. With advanced retrieval methods, it's best suited for building RAG, question answering, semantic search or conversational agent chatbots.
history_rag
MoE-LLaVA - Mixture-of-Experts for Large Vision-Language Models