Model configuration and small prompt improvements (#24)
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- Model can be configured using ENV
- Summarization prompt improved
- Single text request prompt improved
- Chat context prompt improved

Co-authored-by: Alexey Skobkin <skobkin-ru@ya.ru>
Reviewed-on: #24
This commit is contained in:
Alexey Skobkin 2024-08-16 00:47:07 +00:00
parent 82c4d953d4
commit f28670834d
8 changed files with 103 additions and 27 deletions

View file

@ -13,4 +13,11 @@ WORKDIR /app
COPY --from=builder /build/app .
# Do not forget "/v1" in the end
ENV OPENAI_API_BASE_URL="" \
OPENAI_API_TOKEN="" \
TELEGRAM_TOKEN="" \
MODEL_TEXT_REQUEST="llama3.1:8b-instruct-q6_K" \
MODEL_SUMMARIZE_REQUEST="llama3.1:8b-instruct-q6_K"
CMD ["/app/app"]

View file

@ -8,8 +8,10 @@
```shell
docker run \
-e OLLAMA_TOKEN=123 \
-e OLLAMA_BASE_URL=http://ollama.localhost:11434/v1 \
-e OPENAI_API_TOKEN=123 \
-e OPENAI_API_BASE_URL=http://ollama.localhost:11434/v1 \
-e TELEGRAM_TOKEN=12345 \
-e MODEL_TEXT_REQUEST=llama3.1:8b-instruct-q6_K
-e MODEL_TEXT_REQUEST=mistral-nemo:12b-instruct-2407-q4_K_M
skobkin/telegram-llm-bot
```

View file

@ -23,16 +23,23 @@ type Bot struct {
llm *llm.LlmConnector
extractor *extractor.Extractor
stats *stats.Stats
models ModelSelection
markdownV1Replacer *strings.Replacer
}
func NewBot(api *telego.Bot, llm *llm.LlmConnector, extractor *extractor.Extractor) *Bot {
func NewBot(
api *telego.Bot,
llm *llm.LlmConnector,
extractor *extractor.Extractor,
models ModelSelection,
) *Bot {
return &Bot{
api: api,
llm: llm,
extractor: extractor,
stats: stats.NewStats(),
models: models,
markdownV1Replacer: strings.NewReplacer(
// https://core.telegram.org/bots/api#markdown-style
@ -122,7 +129,7 @@ func (b *Bot) inlineHandler(bot *telego.Bot, update telego.Update) {
slog.Error("Cannot retrieve an article using extractor", "error", err)
}
llmReply, err := b.llm.Summarize(article.Text, llm.ModelLlama3Uncensored )
llmReply, err := b.llm.Summarize(article.Text, b.models.TextRequestModel)
if err != nil {
slog.Error("Cannot get reply from LLM connector")
@ -148,7 +155,7 @@ func (b *Bot) inlineHandler(bot *telego.Bot, update telego.Update) {
requestContext := createLlmRequestContextFromUpdate(update)
llmReply, err := b.llm.HandleSingleRequest(iq.Query, llm.ModelLlama3Uncensored, requestContext)
llmReply, err := b.llm.HandleSingleRequest(iq.Query, b.models.TextRequestModel, requestContext)
if err != nil {
slog.Error("Cannot get reply from LLM connector")
@ -194,7 +201,7 @@ func (b *Bot) heyHandler(bot *telego.Bot, update telego.Update) {
requestContext := createLlmRequestContextFromUpdate(update)
llmReply, err := b.llm.HandleSingleRequest(userMessage, llm.ModelLlama3Uncensored, requestContext)
llmReply, err := b.llm.HandleSingleRequest(userMessage, b.models.TextRequestModel, requestContext)
if err != nil {
slog.Error("Cannot get reply from LLM connector")
@ -259,7 +266,7 @@ func (b *Bot) summarizeHandler(bot *telego.Bot, update telego.Update) {
slog.Error("Cannot retrieve an article using extractor", "error", err)
}
llmReply, err := b.llm.Summarize(article.Text, llm.ModelMistralUncensored)
llmReply, err := b.llm.Summarize(article.Text, b.models.SummarizeModel)
if err != nil {
slog.Error("Cannot get reply from LLM connector")

6
bot/models.go Normal file
View file

@ -0,0 +1,6 @@
package bot
type ModelSelection struct {
TextRequestModel string
SummarizeModel string
}

View file

@ -44,10 +44,12 @@ func createLlmRequestContextFromUpdate(update telego.Update) llm.RequestContext
}
if !rc.Inline {
// TODO: implement retrieval of chat description
chat := message.Chat
rc.Chat = llm.ChatContext{
Title: chat.Title,
Description: chat.Description,
// TODO: fill when ChatFullInfo retrieved
//Description: chat.Description,
Type: chat.Type,
}
}

View file

@ -10,9 +10,6 @@ import (
var (
ErrLlmBackendRequestFailed = errors.New("llm back-end request failed")
ErrNoChoices = errors.New("no choices in LLM response")
ModelMistralUncensored = "dolphin-mistral:7b-v2.8-q4_K_M"
ModelLlama3Uncensored = "dolphin-llama3:8b-v2.9-q4_K_M"
)
type LlmConnector struct {
@ -31,12 +28,12 @@ func NewConnector(baseUrl string, token string) *LlmConnector {
}
func (l *LlmConnector) HandleSingleRequest(text string, model string, requestContext RequestContext) (string, error) {
systemPrompt := "You're a bot in the Telegram chat. " +
"You're using a free model called \"" + model + "\". " +
"You see only messages addressed to you using commands due to privacy settings."
systemPrompt := "You're a bot in the Telegram chat.\n" +
"You're using a free model called \"" + model + "\".\n" +
"Currently you're not able to access chat history, so each message will be replied from a clean slate."
if !requestContext.Empty {
systemPrompt += " " + requestContext.Prompt()
systemPrompt += "\n" + requestContext.Prompt()
}
req := openai.ChatCompletionRequest{
@ -79,9 +76,10 @@ func (l *LlmConnector) Summarize(text string, model string) (string, error) {
{
Role: openai.ChatMessageRoleSystem,
Content: "You're a text shortener. Give a very brief summary of the main facts " +
"point by point. Format them as a list of bullet points. " +
"point by point. Format them as a list of bullet points each starting with \"-\". " +
"Avoid any commentaries and value judgement on the matter. " +
"If possible, use the same language as the original text.",
"If possible, respond in the same language as the original text." +
"Do not use any non-ASCII characters.",
},
},
}
@ -108,3 +106,37 @@ func (l *LlmConnector) Summarize(text string, model string) (string, error) {
return resp.Choices[0].Message.Content, nil
}
func (l *LlmConnector) GetModels() []string {
var result []string
models, err := l.client.ListModels(context.Background())
if err != nil {
slog.Error("llm: Model list request failed", "error", err)
return result
}
slog.Info("Model list retrieved", "models", models)
for _, model := range models.Models {
result = append(result, model.ID)
}
return result
}
func (l *LlmConnector) HasModel(id string) bool {
model, err := l.client.GetModel(context.Background(), id)
if err != nil {
slog.Error("llm: Model request failed", "error", err)
}
slog.Debug("llm: Returned model", "model", model)
if model.ID != "" {
return true
}
return false
}

View file

@ -39,15 +39,16 @@ func (c RequestContext) Prompt() string {
prompt += "You're responding to inline query, so you're not in the chat right now. "
}
prompt += "According to their profile, first name of the user who wrote you is \"" + c.User.FirstName + "\". "
prompt += "User profile:" +
"First name: \"" + c.User.FirstName + "\"\n"
if c.User.Username != "" {
prompt += "Their username is @" + c.User.Username + ". "
prompt += "Username: @" + c.User.Username + ".\n"
}
if c.User.LastName != "" {
prompt += "Their last name is \"" + c.User.LastName + "\". "
prompt += "Last name: \"" + c.User.LastName + "\"\n"
}
if c.User.IsPremium {
prompt += "They have Telegram Premium subscription. "
prompt += "Telegram Premium subscription: active."
}
return prompt

27
main.go
View file

@ -12,12 +12,31 @@ import (
)
func main() {
ollamaToken := os.Getenv("OLLAMA_TOKEN")
ollamaBaseUrl := os.Getenv("OLLAMA_BASE_URL")
apiToken := os.Getenv("OPENAI_API_TOKEN")
apiBaseUrl := os.Getenv("OPENAI_API_BASE_URL")
models := bot.ModelSelection{
TextRequestModel: os.Getenv("MODEL_TEXT_REQUEST"),
SummarizeModel: os.Getenv("MODEL_SUMMARIZE_REQUEST"),
}
slog.Info("Selected", "models", models)
telegramToken := os.Getenv("TELEGRAM_TOKEN")
llmc := llm.NewConnector(ollamaBaseUrl, ollamaToken)
llmc := llm.NewConnector(apiBaseUrl, apiToken)
slog.Info("Checking models availability")
for _, model := range []string{models.TextRequestModel, models.SummarizeModel} {
if !llmc.HasModel(model) {
slog.Error("Model not unavailable", "model", model)
os.Exit(1)
}
}
slog.Info("All needed models are available")
ext := extractor.NewExtractor()
telegramApi, err := tg.NewBot(telegramToken, tg.WithLogger(bot.NewLogger("telego: ")))
@ -26,7 +45,7 @@ func main() {
os.Exit(1)
}
botService := bot.NewBot(telegramApi, llmc, ext)
botService := bot.NewBot(telegramApi, llmc, ext, models)
err = botService.Run()
if err != nil {