Method addDocumentsHandler

ragserver/ragserver/main.go:70–122

(w http.ResponseWriter, req *http.Request)

}

func (rs *ragServer) addDocumentsHandler(w http.ResponseWriter, req *http.Request) {
	// Parse HTTP request from JSON.
	type document struct {
		Text string
	}
	type addRequest struct {
		Documents []document
	}
	ar := &addRequest{}

	err := readRequestJSON(req, ar)
	if err != nil {
		http.Error(w, err.Error(), http.StatusBadRequest)
		return
	}

	// Use the batch embedding API to embed all documents at once.
	batch := rs.embModel.NewBatch()
	for _, doc := range ar.Documents {
		batch.AddContent(genai.Text(doc.Text))
	}
	log.Printf("invoking embedding model with %v documents", len(ar.Documents))
	rsp, err := rs.embModel.BatchEmbedContents(rs.ctx, batch)
	if err != nil {
		http.Error(w, err.Error(), http.StatusInternalServerError)
		return
	}
	if len(rsp.Embeddings) != len(ar.Documents) {
		http.Error(w, "embedded batch size mismatch", http.StatusInternalServerError)
		return
	}

	// Convert our documents - along with their embedding vectors - into types
	// used by the Weaviate client library.
	objects := make([]*models.Object, len(ar.Documents))
	for i, doc := range ar.Documents {
		objects[i] = &models.Object{
			Class: "Document",
			Properties: map[string]any{
				"text": doc.Text,
			},
			Vector: rsp.Embeddings[i].Values,
		}
	}

	// Store documents with embeddings in the Weaviate DB.
	log.Printf("storing %v objects in weaviate", len(objects))
	_, err = rs.wvClient.Batch().ObjectsBatcher().WithObjects(objects...).Do(rs.ctx)
	if err != nil {
		http.Error(w, err.Error(), http.StatusInternalServerError)
		return
	}
}

func (rs *ragServer) queryHandler(w http.ResponseWriter, req *http.Request) {
	// Parse HTTP request from JSON.