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// _ _
// __ _____ __ ___ ___ __ _| |_ ___
// \ \ /\ / / _ \/ _` \ \ / / |/ _` | __/ _ \
// \ V V / __/ (_| |\ V /| | (_| | || __/
// \_/\_/ \___|\__,_| \_/ |_|\__,_|\__\___|
//
// Copyright © 2016 - 2024 Weaviate B.V. All rights reserved.
//
// CONTACT: [email protected]
//
package modopenai
import (
"context"
"github.com/weaviate/weaviate/entities/models"
"github.com/weaviate/weaviate/entities/modulecapabilities"
"github.com/weaviate/weaviate/entities/moduletools"
"github.com/weaviate/weaviate/entities/schema"
"github.com/weaviate/weaviate/modules/text2vec-openai/vectorizer"
)
func (m *OpenAIModule) ClassConfigDefaults() map[string]interface{} {
return map[string]interface{}{
"vectorizeClassName": vectorizer.DefaultVectorizeClassName,
"type": vectorizer.DefaultOpenAIDocumentType,
"model": vectorizer.DefaultOpenAIModel,
"baseURL": vectorizer.DefaultBaseURL,
"modelVersion": vectorizer.PickDefaultModelVersion(vectorizer.DefaultOpenAIModel,
vectorizer.DefaultOpenAIDocumentType),
}
}
func (m *OpenAIModule) PropertyConfigDefaults(
dt *schema.DataType,
) map[string]interface{} {
return map[string]interface{}{
"skip": !vectorizer.DefaultPropertyIndexed,
"vectorizePropertyName": vectorizer.DefaultVectorizePropertyName,
}
}
func (m *OpenAIModule) ValidateClass(ctx context.Context,
class *models.Class, cfg moduletools.ClassConfig,
) error {
settings := vectorizer.NewClassSettings(cfg)
return settings.Validate(class)
}
var _ = modulecapabilities.ClassConfigurator(New())
// type ConfigValidator struct {
// logger logrus.FieldLogger
// }
// type ClassSettings interface {
// VectorizeClassName() bool
// VectorizePropertyName(propName string) bool
// PropertyIndexed(propName string) bool
// }
// func NewConfigValidator(logger logrus.FieldLogger) *ConfigValidator {
// return &ConfigValidator{logger: logger}
// }
// func (cv *ConfigValidator) Do(ctx context.Context, class *models.Class,
// cfg moduletools.ClassConfig, settings ClassSettings) error {
// // In text2vec-openai (as opposed to e.g. text2vec-contextionary) the
// // assumption is that the models will be able to deal with any words, even
// // previously unseen ones. Therefore we do not need to validate individual
// // properties, but only the overall "index state"
// if err := cv.validateIndexState(ctx, class, settings); err != nil {
// return errors.Errorf("invalid combination of properties")
// }
// cv.checkForPossibilityOfDuplicateVectors(ctx, class, settings)
// return nil
// }
// func (cv *ConfigValidator) validateIndexState(ctx context.Context,
// class *models.Class, settings ClassSettings) error {
// if settings.VectorizeClassName() {
// // if the user chooses to vectorize the classname, vector-building will
// // always be possible, no need to investigate further
// return nil
// }
// // search if there is at least one indexed, string/text prop. If found pass
// // validation
// for _, prop := range class.Properties {
// if len(prop.DataType) < 1 {
// return errors.Errorf("property %s must have at least one datatype: "+
// "got %v", prop.Name, prop.DataType)
// }
// if prop.DataType[0] != string(schema.DataTypeText) {
// // we can only vectorize text-like props
// continue
// }
// if settings.PropertyIndexed(prop.Name) {
// // found at least one, this is a valid schema
// return nil
// }
// }
// return fmt.Errorf("invalid properties: didn't find a single property which is " +
// "of type string or text and is not excluded from indexing. In addition the " +
// "class name is excluded from vectorization as well, meaning that it cannot be " +
// "used to determine the vector position. To fix this, set 'vectorizeClassName' " +
// "to true if the class name is contextionary-valid. Alternatively add at least " +
// "contextionary-valid text/string property which is not excluded from " +
// "indexing.")
// }
// func (cv *ConfigValidator) checkForPossibilityOfDuplicateVectors(
// ctx context.Context, class *models.Class, settings ClassSettings) {
// if !settings.VectorizeClassName() {
// // if the user choses not to vectorize the class name, this means they must
// // have chosen something else to vectorize, otherwise the validation would
// // have error'd before we ever got here. We can skip further checking.
// return
// }
// // search if there is at least one indexed, string/text prop. If found exit
// for _, prop := range class.Properties {
// // length check skipped, because validation has already passed
// if prop.DataType[0] != string(schema.DataTypeText) {
// // we can only vectorize text-like props
// continue
// }
// if settings.PropertyIndexed(prop.Name) {
// // found at least one
// return
// }
// }
// cv.logger.WithField("module", "text2vec-openai").
// WithField("class", class.Class).
// Warnf("text2vec-openai: Class %q does not have any properties "+
// "indexed (or only non text-properties indexed) and the vector position is "+
// "only determined by the class name. Each object will end up with the same "+
// "vector which leads to a severe performance penalty on imports. Consider "+
// "setting vectorIndexConfig.skip=true for this property", class.Class)
// }
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