embedding-search

README

Embedding Search

Semantic search over a document corpus using model embeddings and cosine similarity.

This example embeds a set of documents and a user query, then ranks documents by similarity to find the most relevant matches. This is the retrieval component of a RAG (retrieval-augmented generation) pipeline.

How it works

1. Loads a GGUF model using the zerfoo.Load one-line API
2. Embeds all corpus documents with model.Embed
3. Embeds the user query
4. Ranks documents by cosine similarity using Embedding.CosineSimilarity
5. Prints the top-N most relevant results

Usage

go build -o embedding-search ./examples/embedding-search/
./embedding-search --model path/to/model.gguf
./embedding-search --model path/to/model.gguf --query "memory management" --top 5

Flags

Flag	Default	Description
--model	(required)	Path to GGUF model file or HuggingFace model ID
--query	"How does Go handle memory?"	Search query
--top	3	Number of results to display

Documentation

Overview

Command embedding-search demonstrates semantic search using model embeddings.

It embeds a corpus of documents and a query, then ranks documents by cosine similarity to find the most relevant matches. This is the retrieval half of a RAG (retrieval-augmented generation) system.

Usage:

go build -o embedding-search ./examples/embedding-search/
./embedding-search --model path/to/model.gguf
./embedding-search --model path/to/model.gguf --query "memory management"