milvus/internal/util/function/embedding/tei_embedding_provider.go
junjiejiangjjj f1ce84996d
enhance: refactor model service configuration and environment variables (#44036)
- Add enable configuration for all model service providers
- Migrate environment variables from MILVUSAI_* to MILVUS_* prefix with
backward compatibility
- Unify model service enable/disable logic using configuration
- Add tests for environment variable parsing with fallback support

#35856

Signed-off-by: junjie.jiang <junjie.jiang@zilliz.com>
2025-08-26 10:49:52 +08:00

146 lines
4.6 KiB
Go

/*
* # Licensed to the LF AI & Data foundation under one
* # or more contributor license agreements. See the NOTICE file
* # distributed with this work for additional information
* # regarding copyright ownership. The ASF licenses this file
* # to you under the Apache License, Version 2.0 (the
* # "License"); you may not use this file except in compliance
* # with the License. You may obtain a copy of the License at
* #
* # http://www.apache.org/licenses/LICENSE-2.0
* #
* # Unless required by applicable law or agreed to in writing, software
* # distributed under the License is distributed on an "AS IS" BASIS,
* # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
* # See the License for the specific language governing permissions and
* # limitations under the License.
*/
package embedding
import (
"fmt"
"strconv"
"strings"
"github.com/milvus-io/milvus-proto/go-api/v2/schemapb"
"github.com/milvus-io/milvus/internal/util/credentials"
"github.com/milvus-io/milvus/internal/util/function/models"
"github.com/milvus-io/milvus/internal/util/function/models/tei"
"github.com/milvus-io/milvus/pkg/v2/util/typeutil"
)
type TeiEmbeddingProvider struct {
fieldDim int64
client *tei.TEIClient
ingestionPrompt string
searchPrompt string
truncate bool
truncationDirection string
maxBatch int
timeoutSec int64
}
func NewTEIEmbeddingProvider(fieldSchema *schemapb.FieldSchema, functionSchema *schemapb.FunctionSchema, params map[string]string, credentials *credentials.Credentials) (*TeiEmbeddingProvider, error) {
fieldDim, err := typeutil.GetDim(fieldSchema)
if err != nil {
return nil, err
}
var endpoint, ingestionPrompt, searchPrompt string
// TEI default client batch size
maxBatch := 32
truncate := false
// TEI default is right
truncationDirection := ""
for _, param := range functionSchema.Params {
switch strings.ToLower(param.Key) {
case models.EndpointParamKey:
endpoint = param.Value
case models.IngestionPromptParamKey:
ingestionPrompt = param.Value
case models.SearchPromptParamKey:
searchPrompt = param.Value
case models.MaxClientBatchSizeParamKey:
if maxBatch, err = strconv.Atoi(param.Value); err != nil {
return nil, fmt.Errorf("[%s param's value: %s] is not a valid number", models.MaxClientBatchSizeParamKey, param.Value)
}
case models.TruncationDirectionParamKey:
if truncationDirection = param.Value; truncationDirection != "Left" && truncationDirection != "Right" {
return nil, fmt.Errorf("[%s param's value: %s] is not invalid, only supports [Left/Right]", models.TruncationDirectionParamKey, param.Value)
}
case models.TruncateParamKey:
if truncate, err = strconv.ParseBool(param.Value); err != nil {
return nil, fmt.Errorf("[%s param's value: %s] is invalid, only supports: [true/false]", models.TruncateParamKey, param.Value)
}
default:
}
}
apiKey, _, err := models.ParseAKAndURL(credentials, functionSchema.Params, params, "")
if err != nil {
return nil, err
}
c, err := tei.NewTEIClient(apiKey, endpoint)
if err != nil {
return nil, err
}
provider := TeiEmbeddingProvider{
client: c,
fieldDim: fieldDim,
ingestionPrompt: ingestionPrompt,
searchPrompt: searchPrompt,
truncationDirection: truncationDirection,
maxBatch: maxBatch,
truncate: truncate,
timeoutSec: 30,
}
return &provider, nil
}
func (provider *TeiEmbeddingProvider) MaxBatch() int {
return 5 * provider.maxBatch
}
func (provider *TeiEmbeddingProvider) FieldDim() int64 {
return provider.fieldDim
}
func (provider *TeiEmbeddingProvider) CallEmbedding(texts []string, mode models.TextEmbeddingMode) (any, error) {
numRows := len(texts)
data := make([][]float32, 0, numRows)
var prompt string
if mode == models.InsertMode {
prompt = provider.ingestionPrompt
} else {
prompt = provider.searchPrompt
}
for i := 0; i < numRows; i += provider.maxBatch {
end := i + provider.maxBatch
if end > numRows {
end = numRows
}
resp, err := provider.client.Embedding(texts[i:end], provider.truncate, provider.truncationDirection, prompt, provider.timeoutSec)
if err != nil {
return nil, err
}
if end-i != len(*resp) {
return nil, fmt.Errorf("Get embedding failed. The number of texts and embeddings does not match text:[%d], embedding:[%d]", end-i, len(*resp))
}
for _, item := range *resp {
if len(item) != int(provider.fieldDim) {
return nil, fmt.Errorf("The required embedding dim is [%d], but the embedding obtained from the model is [%d]",
provider.fieldDim, len(item))
}
data = append(data, item)
}
}
return data, nil
}