147 lines
3.7 KiB
Go
147 lines
3.7 KiB
Go
package llm
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import (
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"context"
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"errors"
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"github.com/sashabaranov/go-openai"
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"log/slog"
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"slices"
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"strconv"
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)
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var (
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ErrLlmBackendRequestFailed = errors.New("llm back-end request failed")
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ErrNoChoices = errors.New("no choices in LLM response")
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)
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type LlmConnector struct {
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client *openai.Client
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}
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func NewConnector(baseUrl string, token string) *LlmConnector {
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config := openai.DefaultConfig(token)
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config.BaseURL = baseUrl
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client := openai.NewClientWithConfig(config)
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return &LlmConnector{
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client: client,
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}
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}
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func (l *LlmConnector) HandleChatMessage(userMessage ChatMessage, model string, requestContext RequestContext) (string, error) {
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systemPrompt := "You're a bot in the Telegram chat.\n" +
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"You're using a free model called \"" + model + "\".\n\n" +
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requestContext.Prompt()
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historyLength := len(requestContext.Chat.History)
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if historyLength > 0 {
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systemPrompt += "\nYou have access to last " + strconv.Itoa(historyLength) + " messages in this chat."
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}
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req := openai.ChatCompletionRequest{
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Model: model,
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Messages: []openai.ChatCompletionMessage{
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{
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Role: openai.ChatMessageRoleSystem,
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Content: systemPrompt,
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},
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},
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}
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if historyLength > 0 {
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for _, msg := range requestContext.Chat.History {
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req.Messages = append(req.Messages, chatMessageToOpenAiChatCompletionMessage(msg))
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}
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}
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req.Messages = append(req.Messages, chatMessageToOpenAiChatCompletionMessage(userMessage))
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resp, err := l.client.CreateChatCompletion(context.Background(), req)
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if err != nil {
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slog.Error("llm: LLM back-end request failed", "error", err)
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return "", ErrLlmBackendRequestFailed
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}
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slog.Debug("llm: Received LLM back-end response", "response", resp)
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if len(resp.Choices) < 1 {
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slog.Error("llm: LLM back-end reply has no choices")
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return "", ErrNoChoices
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}
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return resp.Choices[0].Message.Content, nil
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}
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func (l *LlmConnector) Summarize(text string, model string) (string, error) {
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req := openai.ChatCompletionRequest{
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Model: model,
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Messages: []openai.ChatCompletionMessage{
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{
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Role: openai.ChatMessageRoleSystem,
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Content: "You're a text shortener. Give a very brief summary of the main facts " +
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"point by point. Format them as a list of bullet points each starting with \"-\". " +
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"Avoid any commentaries and value judgement on the matter. " +
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"If possible, respond in the same language as the original text." +
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"Do not use any non-ASCII characters.",
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},
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},
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}
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req.Messages = append(req.Messages, openai.ChatCompletionMessage{
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Role: openai.ChatMessageRoleUser,
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Content: text,
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})
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resp, err := l.client.CreateChatCompletion(context.Background(), req)
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if err != nil {
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slog.Error("llm: LLM back-end request failed", "error", err)
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return "", ErrLlmBackendRequestFailed
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}
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slog.Debug("llm: Received LLM back-end response", resp)
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if len(resp.Choices) < 1 {
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slog.Error("llm: LLM back-end reply has no choices")
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return "", ErrNoChoices
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}
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return resp.Choices[0].Message.Content, nil
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}
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func (l *LlmConnector) HasAllModels(modelIds []string) (bool, map[string]bool) {
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modelList, err := l.client.ListModels(context.Background())
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if err != nil {
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slog.Error("llm: Model list request failed", "error", err)
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}
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slog.Info("llm: Returned model list", "models", modelList)
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slog.Info("llm: Checking for requested models", "requested", modelIds)
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requestedModelsCount := len(modelIds)
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searchResult := make(map[string]bool, requestedModelsCount)
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for _, modelId := range modelIds {
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searchResult[modelId] = false
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}
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for _, model := range modelList.Models {
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if slices.Contains(modelIds, model.ID) {
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searchResult[model.ID] = true
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}
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}
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for _, v := range searchResult {
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if !v {
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return false, searchResult
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}
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}
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return true, searchResult
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}
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