✨Feat: 实现思维链问答

main.go

@@ -13,16 +13,32 @@ import (
 	"github.com/tmc/langchaingo/memory"
 )
 
+const (
+	model     = "Qwen/Qwen3-Coder-30B-A3B-Instruct"
+	base_url  = "https://api.siliconflow.cn/v1"
+	token     = "sk-udqgogvqgfriqvsehkpxgaejfclvoikbntjeaeijhqnhnviw"
+	cotPrompt = `请按照以下步骤分析这道算法题：
+	1. 算法判断：判断题目类型和所需算法
+	2. 基本思路：说明解题思路和关键点
+	3. 框架代码：给出代码框架
+	4. 易错解析：指出常见错误和注意事项
+
+	题目：%s
+
+	请按上述格式进行分析：`
+)
+
 func main() {
 	llm, err := openai.New(
-		openai.WithModel("Qwen/Qwen3-Coder-30B-A3B-Instruct"),
+		openai.WithModel(model),
-		openai.WithBaseURL("https://api.siliconflow.cn/v1"),
+		openai.WithBaseURL(base_url),
-		openai.WithToken("sk-udqgogvqgfriqvsehkpxgaejfclvoikbntjeaeijhqnhnviw"),
+		openai.WithToken(token),
 	)
 	if err != nil {
 		log.Fatal(err)
 	}
 
+	// [Memory] Init
 	chatMemory := memory.NewConversationBuffer()
 	ctx := context.Background()
 	reader := bufio.NewReader(os.Stdin)
@@ -39,16 +55,19 @@ func main() {
 			break
 		}
 
-		messages, _ := chatMemory.ChatHistory.Messages(ctx)
+		var fullPrompt string
-
+		if isAlgorithmQuestion(userInput) {
-		// Conversation
+			fullPrompt = fmt.Sprintf(cotPrompt, userInput)
-		var conversation string
+		} else {
-		for _, msg := range messages {
+			messages, _ := chatMemory.ChatHistory.Messages(ctx)
-			conversation += msg.GetContent() + "\n"
+			// Conversation
+			var conversation string
+			for _, msg := range messages {
+				conversation += msg.GetContent() + "\n"
+			}
+			fullPrompt = conversation + "Human: " + userInput + "\nAssistant:"
 		}
 
-		fullPrompt := conversation + "Human: " + userInput + "\nAssistant:"
-
 		response, err := llms.GenerateFromSinglePrompt(
 			ctx,
 			llm,
@@ -66,3 +85,13 @@ func main() {
 	}
 
 }
+
+func isAlgorithmQuestion(input string) bool {
+	keywords := []string{"算法", "编程", "题目", "解题", "代码实现"}
+	for _, keyword := range keywords {
+		if strings.Contains(input, keyword) {
+			return true
+		}
+	}
+	return false
+}