golang cobra使用chatgpt qdrant实现ai知识库

❯ kabi -h
a local knowledge base, based on chatgpt and qdrant
usage:
  kbai [flags]
  kbai [command]
available commands:
  completion  generate the autocompletion script for the specified shell
  help        help about any command
  import      import data to vector database
  search      ask the knowledge base example: kbai ask --msg 'first, the chicken or the egg'
flags:
      --apikey string       openai apikey:default from env apikey
      --collection string   qdrant collection name default: kubernetes (default "kubernetes")
  -h, --help                help for kbai
      --proxy string        http client proxy default:socks5://127.0.0.1:1080  (default "socks5://127.0.0.1:1080")
      --qdrant string       qdrant address default: 127.0.0.1:6334 (default "127.0.0.1:6334")
      --vectorsize uint     qdrant vector size default: 1536 (default 1536)
use "kbai [command] --help" for more information about a command.

docker run --rm -p 6334:6334 qdrant/qdrant

git clone https://github.com/webws/embedding-knowledge-base.git
cd ./embedding-knowledge-base

export apikey=xxx

go run ./ import --datafile ./example/data.json

[
    {
        "questions": "这是问题",
        "answers": "这是答案"
    },
]

go run ./ search --msg "网关是什么"

go run ./ search --msg "苹果不洗能吃吗"
rearch term violation or exceeding category

qdrantclient := qdrant.newqdrantclient(configflags.qdrant, configflags.collection, configflags.vectorsize)
            defer qdrantclient.close()
            aiclient, err := ai.newaiclient(configflags.proxy, configflags.apikey)
            if err != nil {
                return err
            }
            if err = qdrantclient.createcollection(configflags.collection, configflags.vectorsize); err != nil {
                return err
            }
            qas, err := converttoqas(datafile)
            if err != nil {
                return err
            }
            points := []*pb.pointstruct{}
            logger.infow("import", "data", qas)
            qpslenth := len(qas)
            for i, qa := range qas {
                embedding, err := aiclient.simplegetvec(qa.questions)
                if err != nil {
                    logger.errorw("simplegetvec", "err", err, "question", qa.questions, "index", i, "total", qpslenth)
                    return err
                }
                point := buildpoint(qa.questions, qa.answers, embedding)
                points = append(points, point)
            }

qdrantclient := qdrant.newqdrantclient(configflags.qdrant, configflags.collection, configflags.vectorsize)
            defer qdrantclient.close()
            aiclient, err := ai.newaiclient(configflags.proxy, configflags.apikey)
            if err != nil {
                return err
            }
            vector, err := aiclient.simplegetvec(msg)
            if err != nil {
                return err
            }
            points, err := qdrantclient.search(vector)
            if err != nil {
                logger.errorw("qdrant search fail", "err", err)
                return err
            }
            if len(points) == 0 {
                fmt.println("rearch term violation or exceeding category")
                return nil
                // return errors.new("rearch term violation or exceeding category")
            }
            // score less than 0.8, rearch term violation or exceeding category
            if points[0].score < 0.8 {
                fmt.println("rearch term violation or exceeding category")
                return nil
                // return errors.new("rearch term violation or exceeding category")
            }