ragkit is a Go package designed to simplify the implementation of Retrieval-Augmented Generation (RAG) systems. It includes the definition and implementation of a VectorStore interface that performs document indexing and retrieval, providing tools for vectorization and semantic search capabilities.
go get github.com/suapapa/go_ragkitimport (
// ...
ragkit "github.com/suapapa/go_ragkit"
vstore_helper "github.com/suapapa/go_ragkit/vector_store/weaviate/helper"
)
func main() {
// Initialize vector store (Weaviate + Ollama)
vstore, err := vstore_helper.NewWeaviateOllamaVectorStore(
"DoolyFamily", // vector DB class name
vstore_helper.DefaultOllamaEmbedModel,
)
if err != nil {
panic(err)
}
// Create documents from text
docs := ragkit.MakeDocsFromTexts(
[]string{
"고길동의 집에는 둘리, 도우너, 또치, 희동이, 철수, 영희가 살고 있다.",
"희동이는 고길동의 조카이다.",
// ...
},
nil,
)
// Index documents
_, err = vstore.Index(context.Background(), docs...)
if err != nil {
panic(err)
}
// Perform semantic search
query := "고길동과 희동이의 관계?"
results, err := vstore.RetrieveText(context.Background(), query, 1)
if err != nil {
panic(err)
}
// Print result
fmt.Println(results[0].Text) // <- 희동이는 고길동의 조카이다.
}Pre-requirement - launch Weaviate for local vector DB:
docker run -it --rm \
-p 8080:8080 -p 50051:50051 \
cr.weaviate.io/semitechnologies/weaviate:1.30.2Contributions are welcome! Please feel free to submit a Pull Request.
This project is licensed under the MIT License - see the LICENSE file for details.