milvus/internal/util/function/models/vertexai/vertexai_text_embedding.go

164 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 vertexai
import (
"bytes"
"context"
"encoding/json"
"fmt"
"net/http"
"time"
"golang.org/x/oauth2/google"
"github.com/milvus-io/milvus/internal/util/function/models/utils"
)
type Instance struct {
TaskType string `json:"task_type,omitempty"`
Content string `json:"content"`
}
type Parameters struct {
OutputDimensionality int64 `json:"outputDimensionality,omitempty"`
}
type EmbeddingRequest struct {
Instances []Instance `json:"instances"`
Parameters Parameters `json:"parameters,omitempty"`
}
type Statistics struct {
Truncated bool `json:"truncated"`
TokenCount int `json:"token_count"`
}
type Embeddings struct {
Statistics Statistics `json:"statistics"`
Values []float32 `json:"values"`
}
type Prediction struct {
Embeddings Embeddings `json:"embeddings"`
}
type Metadata struct {
BillableCharacterCount int `json:"billableCharacterCount"`
}
type EmbeddingResponse struct {
Predictions []Prediction `json:"predictions"`
Metadata Metadata `json:"metadata"`
}
type ErrorInfo struct {
Code string `json:"code"`
Message string `json:"message"`
RequestID string `json:"request_id"`
}
type VertexAIEmbedding struct {
url string
jsonKey []byte
scopes string
token string
}
func NewVertexAIEmbedding(url string, jsonKey []byte, scopes string, token string) *VertexAIEmbedding {
return &VertexAIEmbedding{
url: url,
jsonKey: jsonKey,
scopes: scopes,
token: token,
}
}
func (c *VertexAIEmbedding) Check() error {
if c.url == "" {
return fmt.Errorf("VertexAI embedding url is empty")
}
if len(c.jsonKey) == 0 {
return fmt.Errorf("jsonKey is empty")
}
if c.scopes == "" {
return fmt.Errorf("Scopes param is empty")
}
return nil
}
func (c *VertexAIEmbedding) getAccessToken() (string, error) {
ctx := context.Background()
creds, err := google.CredentialsFromJSON(ctx, c.jsonKey, c.scopes)
if err != nil {
return "", fmt.Errorf("Failed to find credentials: %v", err)
}
token, err := creds.TokenSource.Token()
if err != nil {
return "", fmt.Errorf("Failed to get token: %v", err)
}
return token.AccessToken, nil
}
func (c *VertexAIEmbedding) Embedding(modelName string, texts []string, dim int64, taskType string, timeoutSec int64) (*EmbeddingResponse, error) {
var r EmbeddingRequest
for _, text := range texts {
r.Instances = append(r.Instances, Instance{TaskType: taskType, Content: text})
}
if dim != 0 {
r.Parameters.OutputDimensionality = dim
}
data, err := json.Marshal(r)
if err != nil {
return nil, err
}
if timeoutSec <= 0 {
timeoutSec = utils.DefaultTimeout
}
ctx, cancel := context.WithTimeout(context.Background(), time.Duration(timeoutSec)*time.Second)
defer cancel()
req, err := http.NewRequestWithContext(ctx, http.MethodPost, c.url, bytes.NewBuffer(data))
if err != nil {
return nil, err
}
var token string
if c.token != "" {
token = c.token
} else {
token, err = c.getAccessToken()
if err != nil {
return nil, err
}
}
req.Header.Set("Content-Type", "application/json")
req.Header.Set("Authorization", fmt.Sprintf("Bearer %s", token))
body, err := utils.RetrySend(req, 3)
if err != nil {
return nil, err
}
var res EmbeddingResponse
err = json.Unmarshal(body, &res)
if err != nil {
return nil, err
}
return &res, err
}