ai_old/llm/openai/chat.go

package openai

import (
	"apigo.cc/ai/ai/interface/llm"
	"bytes"
	"context"
	"encoding/binary"
	"fmt"
	"github.com/sashabaranov/go-openai"
	"github.com/ssgo/log"
	"strings"
	"time"
)

func (lm *LLM) FastAsk(messages []llm.ChatMessage, callback func(answer string)) (string, llm.Usage, error) {
	return lm.Ask(messages, llm.ChatConfig{
		Model: ModelGPT_4o_mini_2024_07_18,
	}, callback)
}

func (lm *LLM) LongAsk(messages []llm.ChatMessage, callback func(answer string)) (string, llm.Usage, error) {
	return lm.Ask(messages, llm.ChatConfig{
		Model: ModelGPT_4_32k_0613,
	}, callback)
}

func (lm *LLM) BatterAsk(messages []llm.ChatMessage, callback func(answer string)) (string, llm.Usage, error) {
	return lm.Ask(messages, llm.ChatConfig{
		Model: ModelGPT_4_turbo,
	}, callback)
}

func (lm *LLM) BestAsk(messages []llm.ChatMessage, callback func(answer string)) (string, llm.Usage, error) {
	return lm.Ask(messages, llm.ChatConfig{
		Model: ModelGPT_4o_2024_08_06,
	}, callback)
}

func (lm *LLM) MultiAsk(messages []llm.ChatMessage, callback func(answer string)) (string, llm.Usage, error) {
	return lm.Ask(messages, llm.ChatConfig{
		Model: ModelGPT_4o_mini_2024_07_18,
	}, callback)
}

func (lm *LLM) BestMultiAsk(messages []llm.ChatMessage, callback func(answer string)) (string, llm.Usage, error) {
	return lm.Ask(messages, llm.ChatConfig{
		Model: ModelGPT_4o_2024_08_06,
	}, callback)
}

func (lm *LLM) CodeInterpreterAsk(messages []llm.ChatMessage, callback func(answer string)) (string, llm.Usage, error) {
	return lm.Ask(messages, llm.ChatConfig{
		Model: ModelGPT_4o,
		Tools: map[string]any{llm.ToolCodeInterpreter: nil},
	}, callback)
}

func (lm *LLM) WebSearchAsk(messages []llm.ChatMessage, callback func(answer string)) (string, llm.Usage, error) {
	return lm.Ask(messages, llm.ChatConfig{
		Model: ModelGPT_4o_mini_2024_07_18,
		Tools: map[string]any{llm.ToolWebSearch: nil},
	}, callback)
}

func (lm *LLM) Ask(messages []llm.ChatMessage, config llm.ChatConfig, callback func(answer string)) (string, llm.Usage, error) {
	openaiConf := openai.DefaultConfig(lm.config.ApiKey)
	if lm.config.Endpoint != "" {
		openaiConf.BaseURL = lm.config.Endpoint
	}

	config.SetDefault(&lm.config.ChatConfig)

	agentMessages := make([]openai.ChatCompletionMessage, len(messages))
	for i, msg := range messages {
		var contents []openai.ChatMessagePart
		if msg.Contents != nil {
			contents = make([]openai.ChatMessagePart, len(msg.Contents))
			for j, inPart := range msg.Contents {
				part := openai.ChatMessagePart{}
				part.Type = TypeMap[inPart.Type]
				switch inPart.Type {
				case llm.TypeText:
					part.Text = inPart.Content
				case llm.TypeImage:
					part.ImageURL = &openai.ChatMessageImageURL{
						URL:    inPart.Content,
						Detail: openai.ImageURLDetailAuto,
					}
				}
				contents[j] = part
			}
		}
		if len(contents) == 1 && contents[0].Type == llm.TypeText {
			agentMessages[i] = openai.ChatCompletionMessage{
				Role:    RoleMap[msg.Role],
				Content: contents[0].Text,
			}
		} else {
			agentMessages[i] = openai.ChatCompletionMessage{
				Role:         RoleMap[msg.Role],
				MultiContent: contents,
			}
		}
	}

	opt := openai.ChatCompletionRequest{
		Model:       config.GetModel(),
		Messages:    agentMessages,
		MaxTokens:   config.GetMaxTokens(),
		Temperature: float32(config.GetTemperature()),
		TopP:        float32(config.GetTopP()),
		StreamOptions: &openai.StreamOptions{
			IncludeUsage: true,
		},
	}

	for name := range config.GetTools() {
		switch name {
		case llm.ToolCodeInterpreter:
			opt.Tools = append(opt.Tools, openai.Tool{Type: "code_interpreter"})
		case llm.ToolWebSearch:
		}
	}

	c := openai.NewClientWithConfig(openaiConf)
	if callback != nil {
		opt.Stream = true
		r, err := c.CreateChatCompletionStream(context.Background(), opt)
		if err == nil {
			results := make([]string, 0)
			usage := llm.Usage{}
			for {
				if r2, err := r.Recv(); err == nil {
					if r2.Choices != nil {
						for _, ch := range r2.Choices {
							text := ch.Delta.Content
							callback(text)
							results = append(results, text)
						}
					}
					if r2.Usage != nil {
						usage.AskTokens += int64(r2.Usage.PromptTokens)
						usage.AnswerTokens += int64(r2.Usage.CompletionTokens)
						usage.TotalTokens += int64(r2.Usage.TotalTokens)
					}
				} else {
					break
				}
			}
			_ = r.Close()
			return strings.Join(results, ""), usage, nil
		} else {
			log.DefaultLogger.Error(err.Error())
			return "", llm.Usage{}, err
		}
	} else {
		t1 := time.Now().UnixMilli()
		if r, err := c.CreateChatCompletion(context.Background(), opt); err == nil {
			t2 := time.Now().UnixMilli() - t1
			results := make([]string, 0)
			if r.Choices != nil {
				for _, ch := range r.Choices {
					results = append(results, ch.Message.Content)
				}
			}
			return strings.Join(results, ""), llm.Usage{
				AskTokens:    int64(r.Usage.PromptTokens),
				AnswerTokens: int64(r.Usage.CompletionTokens),
				TotalTokens:  int64(r.Usage.TotalTokens),
				UsedTime:     t2,
			}, nil
		} else {
			//fmt.Println(u.BMagenta(err.Error()), u.BMagenta(u.JsonP(r)))
			return "", llm.Usage{}, err
		}
	}
}

func (lm *LLM) FastEmbedding(text string) ([]byte, llm.Usage, error) {
	return lm.Embedding(text, string(openai.AdaEmbeddingV2))
}

func (lm *LLM) BestEmbedding(text string) ([]byte, llm.Usage, error) {
	return lm.Embedding(text, string(openai.LargeEmbedding3))
}

func (lm *LLM) Embedding(text, model string) ([]byte, llm.Usage, error) {
	fmt.Println(111, model, text)
	openaiConf := openai.DefaultConfig(lm.config.ApiKey)
	if lm.config.Endpoint != "" {
		openaiConf.BaseURL = lm.config.Endpoint
	}

	c := openai.NewClientWithConfig(openaiConf)
	req := openai.EmbeddingRequest{
		Input:          text,
		Model:          openai.EmbeddingModel(model),
		User:           "",
		EncodingFormat: "",
		Dimensions:     0,
	}

	t1 := time.Now().UnixMilli()
	if r, err := c.CreateEmbeddings(context.Background(), req); err == nil {
		t2 := time.Now().UnixMilli() - t1
		buf := new(bytes.Buffer)
		if r.Data != nil {
			for _, ch := range r.Data {
				for _, v := range ch.Embedding {
					_ = binary.Write(buf, binary.LittleEndian, v)
				}
			}
		}
		fmt.Println(len(buf.Bytes()))
		return buf.Bytes(), llm.Usage{
			AskTokens:    int64(r.Usage.PromptTokens),
			AnswerTokens: int64(r.Usage.CompletionTokens),
			TotalTokens:  int64(r.Usage.TotalTokens),
			UsedTime:     t2,
		}, nil
	} else {
		fmt.Println(err.Error())
		return nil, llm.Usage{}, err
	}
}