mirror of https://github.com/mudler/LocalAI.git
73 lines
1.5 KiB
Go
73 lines
1.5 KiB
Go
package embedded
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import (
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"embed"
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"fmt"
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"slices"
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"strings"
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"github.com/go-skynet/LocalAI/pkg/downloader"
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"github.com/rs/zerolog/log"
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"github.com/go-skynet/LocalAI/pkg/assets"
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"gopkg.in/yaml.v3"
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)
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var modelShorteners map[string]string
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//go:embed model_library.yaml
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var modelLibrary []byte
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//go:embed models/*
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var embeddedModels embed.FS
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func ModelShortURL(s string) string {
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if _, ok := modelShorteners[s]; ok {
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s = modelShorteners[s]
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}
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return s
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}
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func init() {
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err := yaml.Unmarshal(modelLibrary, &modelShorteners)
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if err != nil {
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log.Error().Err(err).Msg("error while unmarshalling embedded modelLibrary")
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}
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}
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func GetRemoteLibraryShorteners(url string) (map[string]string, error) {
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remoteLibrary := map[string]string{}
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err := downloader.GetURI(url, func(_ string, i []byte) error {
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return yaml.Unmarshal(i, &remoteLibrary)
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})
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if err != nil {
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return nil, fmt.Errorf("error downloading remote library: %s", err.Error())
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}
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return remoteLibrary, err
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}
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// ExistsInModelsLibrary checks if a model exists in the embedded models library
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func ExistsInModelsLibrary(s string) bool {
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f := fmt.Sprintf("%s.yaml", s)
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a := []string{}
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for _, j := range assets.ListFiles(embeddedModels) {
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a = append(a, strings.TrimPrefix(j, "models/"))
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}
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return slices.Contains(a, f)
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}
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// ResolveContent returns the content in the embedded model library
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func ResolveContent(s string) ([]byte, error) {
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if ExistsInModelsLibrary(s) {
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return embeddedModels.ReadFile(fmt.Sprintf("models/%s.yaml", s))
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}
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return nil, fmt.Errorf("cannot find model %s", s)
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}
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