Table of contents

Vector database

%3 cluster_d20742e9_6cb0_4cc7_90a6_83048290f417 Vector database _90c94794_d0ae_4827_bf0e_07d76db88ac1 Weaviate _4d9d8087_66bc_46c5_989c_6e38318b42fd Qdrant _b8c09743_8f30_4276_8835_63993b733090 LLMs _4d9d8087_66bc_46c5_989c_6e38318b42fd->_b8c09743_8f30_4276_8835_63993b733090 _9f749a2d_475b_47b4_99c9_5df96496057d Databases _f0daa053_2787_4eb3_98f0_2951ee7e6b8a Text Indexes _f0daa053_2787_4eb3_98f0_2951ee7e6b8a->_9f749a2d_475b_47b4_99c9_5df96496057d _20f73b78_fd65_404b_8b54_5ff3da65d442 Word embedding __0:cluster_d20742e9_6cb0_4cc7_90a6_83048290f417->_9f749a2d_475b_47b4_99c9_5df96496057d __1:cluster_d20742e9_6cb0_4cc7_90a6_83048290f417->_f0daa053_2787_4eb3_98f0_2951ee7e6b8a __2:cluster_d20742e9_6cb0_4cc7_90a6_83048290f417->_20f73b78_fd65_404b_8b54_5ff3da65d442

Databases centered around use cases related to word embeddings, usually for near-text search (see Text Indexes for more classic techniques) or similar use-cases.

Weaviate

I've used this in the past. It might be tricky getting used to some of their quicks, but I don't have any specific reference points to compare it with.

Qdrant

Seen it mentioned on some papers, usually related to LLM [ RAG ] .