59 lines
1.5 KiB
Go
59 lines
1.5 KiB
Go
package main
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import (
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"encoding/json"
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"fmt"
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"github.com/joho/godotenv"
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uuid "github.com/satori/go.uuid"
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"knowledge/utils"
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"log"
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"os"
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)
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func main() {
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err := godotenv.Load(".env")
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// 读取环境变量
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OPENAI_API_KEY := os.Getenv("OPENAI_KEY")
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OPENAI_API_BASE := os.Getenv("OPENAI_API_BASE")
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utils.QdrantBase = os.Getenv("QDRANT_BASE")
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utils.QdrantPort = os.Getenv("QDRANT_PORT")
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sqlTool := utils.DBTool{
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Username: os.Getenv("MySQL_Username"),
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Password: os.Getenv("MySQL_Password"),
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Server: os.Getenv("MySQL_Server"),
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Port: os.Getenv("MySQL_Port"),
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Database: os.Getenv("MySQL_Database"),
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}
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gpt := utils.NewChatGptTool(OPENAI_API_BASE, OPENAI_API_KEY)
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list, err := sqlTool.QuerySql("select id, content from article where ent_id=5 limit 2,1000")
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if err != nil {
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fmt.Println("Error:", err)
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return
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}
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for _, row := range list {
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if row["content"] == "" {
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continue
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}
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// 打印文件内容
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fileData := row["content"].(string)
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response, err := gpt.GetEmbedding(fileData, "text-embedding-ada-002")
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var embeddingResponse utils.EmbeddingResponse
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json.Unmarshal([]byte(response), &embeddingResponse)
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collectionName := "data_collection"
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id := uuid.NewV4().String()
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points := []map[string]interface{}{
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{
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"id": id,
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"payload": map[string]interface{}{"text": fileData},
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"vector": embeddingResponse.Data[0].Embedding,
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},
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}
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res, err := utils.PutPoints(collectionName, points)
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log.Println(fileData, res, err)
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}
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}
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