mirror of https://github.com/milvus-io/milvus.git
164 lines
4.0 KiB
Go
164 lines
4.0 KiB
Go
// Licensed to the LF AI & Data foundation under one
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// or more contributor license agreements. See the NOTICE file
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// distributed with this work for additional information
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// regarding copyright ownership. The ASF licenses this file
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// to you under the Apache License, Version 2.0 (the
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// "License"); you may not use this file except in compliance
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// with the License. You may obtain a copy of the License at
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//
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// http://www.apache.org/licenses/LICENSE-2.0
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//
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// Unless required by applicable law or agreed to in writing, software
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// distributed under the License is distributed on an "AS IS" BASIS,
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// WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
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// See the License for the specific language governing permissions and
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// limitations under the License.
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package vertexai
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import (
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"bytes"
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"context"
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"encoding/json"
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"fmt"
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"net/http"
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"time"
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"golang.org/x/oauth2/google"
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"github.com/milvus-io/milvus/internal/util/function/models/utils"
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)
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type Instance struct {
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TaskType string `json:"task_type,omitempty"`
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Content string `json:"content"`
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}
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type Parameters struct {
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OutputDimensionality int64 `json:"outputDimensionality,omitempty"`
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}
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type EmbeddingRequest struct {
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Instances []Instance `json:"instances"`
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Parameters Parameters `json:"parameters,omitempty"`
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}
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type Statistics struct {
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Truncated bool `json:"truncated"`
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TokenCount int `json:"token_count"`
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}
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type Embeddings struct {
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Statistics Statistics `json:"statistics"`
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Values []float32 `json:"values"`
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}
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type Prediction struct {
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Embeddings Embeddings `json:"embeddings"`
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}
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type Metadata struct {
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BillableCharacterCount int `json:"billableCharacterCount"`
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}
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type EmbeddingResponse struct {
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Predictions []Prediction `json:"predictions"`
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Metadata Metadata `json:"metadata"`
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}
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type ErrorInfo struct {
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Code string `json:"code"`
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Message string `json:"message"`
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RequestID string `json:"request_id"`
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}
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type VertexAIEmbedding struct {
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url string
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jsonKey []byte
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scopes string
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token string
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}
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func NewVertexAIEmbedding(url string, jsonKey []byte, scopes string, token string) *VertexAIEmbedding {
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return &VertexAIEmbedding{
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url: url,
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jsonKey: jsonKey,
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scopes: scopes,
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token: token,
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}
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}
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func (c *VertexAIEmbedding) Check() error {
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if c.url == "" {
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return fmt.Errorf("VertexAI embedding url is empty")
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}
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if len(c.jsonKey) == 0 {
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return fmt.Errorf("jsonKey is empty")
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}
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if c.scopes == "" {
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return fmt.Errorf("Scopes param is empty")
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}
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return nil
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}
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func (c *VertexAIEmbedding) getAccessToken() (string, error) {
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ctx := context.Background()
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creds, err := google.CredentialsFromJSON(ctx, c.jsonKey, c.scopes)
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if err != nil {
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return "", fmt.Errorf("Failed to find credentials: %v", err)
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}
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token, err := creds.TokenSource.Token()
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if err != nil {
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return "", fmt.Errorf("Failed to get token: %v", err)
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}
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return token.AccessToken, nil
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}
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func (c *VertexAIEmbedding) Embedding(modelName string, texts []string, dim int64, taskType string, timeoutSec int64) (*EmbeddingResponse, error) {
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var r EmbeddingRequest
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for _, text := range texts {
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r.Instances = append(r.Instances, Instance{TaskType: taskType, Content: text})
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}
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if dim != 0 {
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r.Parameters.OutputDimensionality = dim
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}
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data, err := json.Marshal(r)
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if err != nil {
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return nil, err
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}
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if timeoutSec <= 0 {
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timeoutSec = utils.DefaultTimeout
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}
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ctx, cancel := context.WithTimeout(context.Background(), time.Duration(timeoutSec)*time.Second)
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defer cancel()
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req, err := http.NewRequestWithContext(ctx, http.MethodPost, c.url, bytes.NewBuffer(data))
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if err != nil {
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return nil, err
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}
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var token string
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if c.token != "" {
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token = c.token
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} else {
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token, err = c.getAccessToken()
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if err != nil {
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return nil, err
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}
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}
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req.Header.Set("Content-Type", "application/json")
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req.Header.Set("Authorization", fmt.Sprintf("Bearer %s", token))
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body, err := utils.RetrySend(req, 3)
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if err != nil {
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return nil, err
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}
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var res EmbeddingResponse
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err = json.Unmarshal(body, &res)
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if err != nil {
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return nil, err
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}
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return &res, err
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}
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