在使用 Golang 和 LLM 进行“假期”测试（之前的帖子...）之后，我一直在寻找一种在 Go 中实现 LangChain 调用的简单方法，最好使用 watsonx.ai。
幸运的是，我找到了以下 Github 存储库：https://github.com/tmc/langchaingo（向 Travis Cline 行屈膝礼 https://github.com/tmc）。
在他的存储库中，有一个特定的文件夹：https://github.com/tmc/langchaingo/blob/main/examples/watsonx-llm-example/watsonx_example.go 引起了我的注意！
所以像往常一样，我建立了一个项目并尝试实现它，并提出了我自己的想法（à ma酱？）。
像往常一样需要环境变量，我设置了一个 .env 文件，稍后在应用程序中使用。
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"
在上一篇文章中，我提到尝试计算法学硕士发送和接收的代币数量。这项工作仍在进行中，因此我直接在应用程序中使用了“tiktoken-go”库，并打算对其进行一些更改（在不久的将来？）。无论如何，就我目前的进度而言，它并没有真正起作用，但它就在那里。
对于应用程序本身，我几乎按原样使用了 Travis 存储库中的代码，并添加和包装了以下功能；
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")
}
效果很好，输出如下所示。
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
瞧！
我将为版本 0.2 实现以下功能；
这是我从 Go 应用程序调用 LangChain 的工作的一个非常简单的反映。
敬请期待更多精彩内容？
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