Susulan ujian "cuti" saya (siaran sebelumnya…) tentang menggunakan Golang dan LLM, saya sedang mencari cara mudah untuk melaksanakan panggilan LangChain dalam Go, dan sebaik-baiknya menggunakan watsonx.ai.
Nasib baik saya menjumpai repositori Github berikut: https://github.com/tmc/langchaingo (curtsy to Travis Cline https://github.com/tmc).
Dalam repositorinya, terdapat folder khusus ini: https://github.com/tmc/langchaingo/blob/main/examples/watsonx-llm-example/watsonx_example.go yang menarik perhatian saya!
Jadi seperti biasa saya membina projek dan cuba melaksanakannya dan juga meletakkan idea saya sendiri (à ma sos ?).
Seperti biasa kerana terdapat keperluan pada pembolehubah persekitaran, saya menyediakan fail .env yang kemudiannya digunakan dalam apl.
export WATSONX_API_KEY="your-watsonx-api-key" export WATSONX_PROJECT_ID="your-watsonx-projectid" # I used the US-SOUTH, could be any other region of IBM Cloud export SERVICE_URL="https://us-south.ml.cloud.ibm.com"
Dalam catatan sebelum ini saya menyebut cuba mengira bilangan token yang dihantar dan diterima daripada LLM. Kerja itu masih WIP, jadi saya menggunakan terus pustaka "tiktoken-go" dalam apl saya dengan idea untuk membuat beberapa perubahan padanya (dalam masa terdekat?). Bagaimanapun, dalam kes keadaan kemajuan saya sekarang, ia tidak begitu berkesan, tetapi ia ada.
Untuk apl itu sendiri, saya menggunakan kod Travis daripada repositorinya hampir seperti sedia ada, dan menambah serta membungkusnya dengan ciri berikut;
package main
import (
"context"
"fmt"
"log"
"os"
"os/exec"
"runtime"
"fyne.io/fyne/v2"
"fyne.io/fyne/v2/app"
"fyne.io/fyne/v2/container"
"fyne.io/fyne/v2/dialog"
"fyne.io/fyne/v2/widget"
"github.com/joho/godotenv"
"github.com/pkoukk/tiktoken-go"
"github.com/tmc/langchaingo/llms"
"github.com/tmc/langchaingo/llms/watsonx"
)
const (
_tokenApproximation = 4
)
const (
_gpt35TurboContextSize = 4096
_gpt432KContextSize = 32768
_gpt4ContextSize = 8192
_textDavinci3ContextSize = 4097
_textBabbage1ContextSize = 2048
_textAda1ContextSize = 2048
_textCurie1ContextSize = 2048
_codeDavinci2ContextSize = 8000
_codeCushman1ContextSize = 2048
_textBisonContextSize = 2048
_chatBisonContextSize = 2048
_defaultContextSize = 2048
)
// nolint:gochecknoglobals
var modelToContextSize = map[string]int{
"gpt-3.5-turbo": _gpt35TurboContextSize,
"gpt-4-32k": _gpt432KContextSize,
"gpt-4": _gpt4ContextSize,
"text-davinci-003": _textDavinci3ContextSize,
"text-curie-001": _textCurie1ContextSize,
"text-babbage-001": _textBabbage1ContextSize,
"text-ada-001": _textAda1ContextSize,
"code-davinci-002": _codeDavinci2ContextSize,
"code-cushman-001": _codeCushman1ContextSize,
}
var tokens int
func runCmd(name string, arg ...string) {
cmd := exec.Command(name, arg...)
cmd.Stdout = os.Stdout
cmd.Run()
}
func ClearTerminal() {
switch runtime.GOOS {
case "darwin":
runCmd("clear")
case "linux":
runCmd("clear")
case "windows":
runCmd("cmd", "/c", "cls")
default:
runCmd("clear")
}
}
func promptEntryDialog() string {
var promptEntry string
// Create a new Fyne application
myApp := app.New()
myWindow := myApp.NewWindow("Prompt Entry Dialog")
// Variable to store user input
var userInput string
// Button to show the dialog
button := widget.NewButton("Click to Enter your prompt's text", func() {
entry := widget.NewEntry()
dialog.ShowCustomConfirm("Input Dialog", "OK", "Cancel", entry, func(confirm bool) {
if confirm {
userInput = entry.Text
promptEntry = userInput
fmt.Println("User Input:", userInput) // Print to the console
myWindow.Close()
}
}, myWindow)
})
// Add the button to the window
myWindow.SetContent(container.NewVBox(
widget.NewLabel("Click the button below to enter text:"),
button,
))
// Set the window size and run the application
myWindow.Resize(fyne.NewSize(400, 200))
myWindow.ShowAndRun()
return promptEntry
}
func CountTokens(model, text string, inorout string) int {
var txtLen int
e, err := tiktoken.EncodingForModel(model)
if err != nil {
e, err = tiktoken.GetEncoding("gpt2")
if err != nil {
log.Printf("[WARN] Failed to calculate number of tokens for model, falling back to approximate count")
txtLen = len([]rune(text))
fmt.Println("Guessed tokens for the "+inorout+" text:", txtLen/_tokenApproximation)
return txtLen
}
}
return len(e.Encode(text, nil, nil))
}
func GetModelContextSize(model string) int {
contextSize, ok := modelToContextSize[model]
if !ok {
return _defaultContextSize
}
return contextSize
}
func CalculateMaxTokens(model, text string) int {
return GetModelContextSize(model) - CountTokens(model, text, text)
}
func main() {
var prompt, model string
// read the '.env' file
err := godotenv.Load()
if err != nil {
log.Fatal("Error loading .env file")
}
ApiKey := os.Getenv("WATSONX_API_KEY")
if ApiKey == "" {
log.Fatal("WATSONX_API_KEY environment variable is not set")
}
ServiceURL := os.Getenv("SERVICE_URL")
if ServiceURL == "" {
log.Fatal("SERVICE_URL environment variable is not set")
}
ProjectID := os.Getenv("WATSONX_PROJECT_ID")
if ProjectID == "" {
log.Fatal("WATSONX_PROJECT_ID environment variable is not set")
}
// LLM from watsonx.ai
model = "ibm/granite-13b-instruct-v2"
// model = "meta-llama/llama-3-70b-instruct"
llm, err := watsonx.New(
model,
//// Optional parameters: to be implemented if needed - Not used at this stage but all ready
// wx.WithWatsonxAPIKey(ApiKey),
// wx.WithWatsonxProjectID("YOUR WATSONX PROJECT ID"),
)
if err != nil {
log.Fatal(err)
}
ctx := context.Background()
prompt = promptEntryDialog()
// for the output visibility on the consol - getting rid of system messages
ClearTerminal()
// Use the entry variable here
fmt.Println("Calling the llm with the user's prompt:", prompt)
tokens = CountTokens(model, prompt, "input")
completion, err := llms.GenerateFromSinglePrompt(
ctx,
llm,
prompt,
llms.WithTopK(10),
llms.WithTopP(0.95),
llms.WithSeed(25),
)
// Check for errors
if err != nil {
log.Fatal(err)
}
fmt.Println(completion)
tokens = CountTokens(model, completion, "output")
}
Yang berfungsi dengan baik kerana output ditunjukkan di bawah.
Calling the llm with the user's prompt: What is the distance in Kilmometers from Earth to Moon? 2024/12/31 11:08:04 [WARN] Failed to calculate number of tokens for model, falling back to approximate count Guessed tokens for the input text: 13 The distance from Earth to the Moon is about 384,400 kilometers. 2024/12/31 11:08:04 [WARN] Failed to calculate number of tokens for model, falling back to approximate count Guessed tokens for the output text: 16 ##### Calling the llm with the user's prompt: What is the name of the capital city of France? 2024/12/31 11:39:28 [WARN] Failed to calculate number of tokens for model, falling back to approximate count Guessed tokens for the input text: 11 Paris 2024/12/31 11:39:28 [WARN] Failed to calculate number of tokens for model, falling back to approximate count Guessed tokens for the output text: 1
Voilà!
Saya akan melaksanakan ciri berikut untuk versi 0.2;
Ini adalah gambaran yang sangat mudah tentang kerja saya semasa memanggil LangChain daripada aplikasi Go.
Nantikan lebih banyak lagi yang akan datang ?
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