kelindar/search
Go library for embedded vector search and semantic embeddings using llama.cpp
This helps developers integrate semantic search into their Go applications. You provide text, which is converted into numerical embeddings using a BERT model, and the library finds other relevant text based on these embeddings. This tool is ideal for Go developers building applications that need to understand and search content based on meaning, rather than just keywords.
528 stars.
Use this if you are a Go developer building a small-to-medium scale application (under 100,000 entries) and need to add semantic search capabilities to understand text content based on its meaning.
Not ideal if you are working with very large datasets exceeding 100,000 entries, require complex query operations like multi-field filtering, or are using highly complex, high-dimensional embeddings from large language models without sufficient GPU resources.
Stars
528
Forks
23
Language
Go
License
MIT
Category
Last pushed
Mar 06, 2026
Commits (30d)
0
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