106 lines
2.3 KiB
Go
106 lines
2.3 KiB
Go
// Copyright 2023 The casbin Authors. All Rights Reserved.
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//
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// Licensed under the Apache License, Version 2.0 (the "License");
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// you may not use this file except in compliance with the License.
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// 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 ai
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import (
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"bufio"
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"context"
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"fmt"
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"io"
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"time"
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"github.com/sashabaranov/go-openai"
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)
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func splitTxt(f io.ReadCloser) []string {
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const maxLength = 210 * 3
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scanner := bufio.NewScanner(f)
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var res []string
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var temp string
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for scanner.Scan() {
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line := scanner.Text()
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if len(temp)+len(line) <= maxLength {
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temp += line
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} else {
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res = append(res, temp)
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temp = line
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}
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}
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if len(temp) > 0 {
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res = append(res, temp)
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}
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return res
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}
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func GetSplitTxt(f io.ReadCloser) []string {
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return splitTxt(f)
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}
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func getEmbedding(authToken string, text string, timeout int) ([]float32, error) {
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client := getProxyClientFromToken(authToken)
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ctx, cancel := context.WithTimeout(context.Background(), time.Duration(30+timeout*2)*time.Second)
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defer cancel()
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resp, err := client.CreateEmbeddings(ctx, openai.EmbeddingRequest{
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Input: []string{text},
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Model: openai.AdaEmbeddingV2,
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})
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if err != nil {
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return nil, err
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}
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return resp.Data[0].Embedding, nil
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}
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func GetEmbeddingSafe(authToken string, text string) ([]float32, error) {
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var embedding []float32
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var err error
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for i := 0; i < 10; i++ {
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embedding, err = getEmbedding(authToken, text, i)
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if err != nil {
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if i > 0 {
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fmt.Printf("\tFailed (%d): %s\n", i+1, err.Error())
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}
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} else {
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break
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}
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}
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if err != nil {
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return nil, err
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} else {
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return embedding, nil
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}
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}
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func GetNearestVectorIndex(target []float32, vectors [][]float32) int {
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targetNorm := norm(target)
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var res int
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max := float32(-1.0)
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for i, vector := range vectors {
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similarity := cosineSimilarity(target, vector, targetNorm)
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if similarity > max {
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max = similarity
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res = i
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}
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}
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return res
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}
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