File size: 3,064 Bytes
c8f6dca | 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 | // _ _
// __ _____ __ ___ ___ __ _| |_ ___
// \ \ /\ / / _ \/ _` \ \ / / |/ _` | __/ _ \
// \ V V / __/ (_| |\ V /| | (_| | || __/
// \_/\_/ \___|\__,_| \_/ |_|\__,_|\__\___|
//
// Copyright © 2016 - 2025 Weaviate B.V. All rights reserved.
//
// CONTACT: hello@weaviate.io
//
package batch
import (
"context"
"sync"
"github.com/weaviate/weaviate/usecases/modulecomponents/settings"
"github.com/weaviate/weaviate/entities/moduletools"
"github.com/weaviate/tiktoken-go"
"github.com/weaviate/weaviate/entities/models"
"github.com/weaviate/weaviate/modules/text2vec-openai/clients"
objectsvectorizer "github.com/weaviate/weaviate/usecases/modulecomponents/vectorizer"
)
type EncoderCache struct {
lock sync.RWMutex
cache map[string]*tiktoken.Tiktoken
}
func NewEncoderCache() *EncoderCache {
return &EncoderCache{cache: make(map[string]*tiktoken.Tiktoken), lock: sync.RWMutex{}}
}
func (e *EncoderCache) Get(model string) (*tiktoken.Tiktoken, bool) {
e.lock.RLock()
defer e.lock.RUnlock()
tke, ok := e.cache[model]
return tke, ok
}
func (e *EncoderCache) Set(model string, tk *tiktoken.Tiktoken) {
e.lock.Lock()
defer e.lock.Unlock()
e.cache[model] = tk
}
type TokenizerFuncType func(ctx context.Context, objects []*models.Object, skipObject []bool, cfg moduletools.ClassConfig, objectVectorizer *objectsvectorizer.ObjectVectorizer, encoderCache *EncoderCache) ([]string, []int, bool, error)
func ReturnBatchTokenizer(multiplier float32, moduleName string, lowerCaseInput bool) TokenizerFuncType {
return func(ctx context.Context, objects []*models.Object, skipObject []bool, cfg moduletools.ClassConfig, objectVectorizer *objectsvectorizer.ObjectVectorizer, encoderCache *EncoderCache) ([]string, []int, bool, error) {
texts := make([]string, len(objects))
tokenCounts := make([]int, len(objects))
var tke *tiktoken.Tiktoken
icheck := settings.NewBaseClassSettings(cfg, lowerCaseInput)
modelString := modelToModelString(icheck.Model(), moduleName)
if multiplier > 0 {
var err error
// creating the tokenizer is quite expensive => cache for each module
if tke2, ok := encoderCache.Get(modelString); ok {
tke = tke2
} else {
tke, err = tiktoken.EncodingForModel(modelString)
if err != nil {
tke, _ = tiktoken.EncodingForModel("text-embedding-ada-002")
}
encoderCache.Set(modelString, tke)
}
}
// prepare input for vectorizer, and send it to the queue. Prepare here to avoid work in the queue-worker
skipAll := true
for i := range texts {
if skipObject[i] {
continue
}
skipAll = false
text := objectVectorizer.Texts(ctx, objects[i], icheck)
texts[i] = text
if multiplier > 0 {
tokenCounts[i] = int(float32(clients.GetTokensCount(modelString, text, tke)) * multiplier)
}
}
return texts, tokenCounts, skipAll, nil
}
}
func modelToModelString(model, moduleName string) string {
if moduleName == "text2vec-openai" {
if model == "ada" {
return "text-embedding-ada-002"
}
}
return model
}
|