Files
wehub-resource-sync 498b235461
Build and test / Build and test AMD64 Ubuntu 22.04 (push) Failing after 0s
Publish Builder / amazonlinux2023 (push) Failing after 1s
Build and test / UT for Go (push) Has been skipped
Publish KRTE Images / KRTE (push) Failing after 1s
Build and test / Integration Test (push) Has been skipped
Build and test / Upload Code Coverage (push) Has been skipped
Publish Builder / rockylinux9 (push) Failing after 1s
Publish Builder / ubuntu22.04 (push) Failing after 0s
Publish Builder / ubuntu24.04 (push) Failing after 0s
Publish Gpu Builder / publish-gpu-builder (push) Failing after 1s
Publish Test Images / PyTest (push) Failing after 0s
Build and test / UT for Cpp (push) Has been cancelled
chore: import upstream snapshot with attribution
2026-07-13 12:31:17 +08:00

128 lines
4.0 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 (
"context"
"strings"
"github.com/milvus-io/milvus-proto/go-api/v3/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/siliconflow"
"github.com/milvus-io/milvus/pkg/v3/util/merr"
"github.com/milvus-io/milvus/pkg/v3/util/typeutil"
)
type SiliconflowEmbeddingProvider struct {
fieldDim int64
client *siliconflow.SiliconflowClient
url string
modelName string
embedDimParam int64
maxBatch int
timeoutMs int64
extraInfo *models.ModelExtraInfo
}
func NewSiliconflowEmbeddingProvider(fieldSchema *schemapb.FieldSchema, functionSchema *schemapb.FunctionSchema, params map[string]string, credentials *credentials.Credentials, extraInfo *models.ModelExtraInfo) (*SiliconflowEmbeddingProvider, error) {
fieldDim, err := typeutil.GetDim(fieldSchema)
if err != nil {
return nil, err
}
apiKey, url, err := models.ParseAKAndURL(credentials, functionSchema.Params, params, models.SiliconflowAKEnvStr, extraInfo)
if err != nil {
return nil, err
}
var modelName string
var dim int64
for _, param := range functionSchema.Params {
switch strings.ToLower(param.Key) {
case models.ModelNameParamKey:
modelName = param.Value
case models.DimParamKey:
dim, err = models.ParseAndCheckFieldDim(param.Value, fieldDim, fieldSchema.Name)
if err != nil {
return nil, err
}
default:
}
}
c, err := siliconflow.NewSiliconflowClient(apiKey)
if err != nil {
return nil, err
}
if url == "" {
url = "https://api.siliconflow.cn/v1/embeddings"
}
timeoutMs := models.ResolveTimeoutMs(functionSchema.Params)
provider := SiliconflowEmbeddingProvider{
client: c,
url: url,
fieldDim: fieldDim,
modelName: modelName,
embedDimParam: dim,
maxBatch: 32,
timeoutMs: timeoutMs,
extraInfo: extraInfo,
}
return &provider, nil
}
func (provider *SiliconflowEmbeddingProvider) MaxBatch() int {
return provider.extraInfo.BatchFactor * provider.maxBatch
}
func (provider *SiliconflowEmbeddingProvider) FieldDim() int64 {
return provider.fieldDim
}
func (provider *SiliconflowEmbeddingProvider) CallEmbedding(ctx context.Context, texts []string, _ models.TextEmbeddingMode) (any, error) {
numRows := len(texts)
data := make([][]float32, 0, numRows)
for i := 0; i < numRows; i += provider.maxBatch {
end := i + provider.maxBatch
if end > numRows {
end = numRows
}
resp, err := provider.client.Embedding(provider.url, provider.modelName, texts[i:end], "float", int(provider.embedDimParam), provider.timeoutMs)
if err != nil {
return nil, err
}
if end-i != len(resp.Data) {
return nil, merr.WrapErrFunctionFailedMsg("get embedding failed, the number of texts and embeddings does not match text:[%d], embedding:[%d]", end-i, len(resp.Data))
}
for _, item := range resp.Data {
if len(item.Embedding) != int(provider.fieldDim) {
return nil, merr.WrapErrFunctionFailedMsg("the required embedding dim is [%d], but the embedding obtained from the model is [%d]",
provider.fieldDim, len(item.Embedding))
}
data = append(data, item.Embedding)
}
}
return data, nil
}