From 96b4c4899a88724b242118ec1a2299034f94d960 Mon Sep 17 00:00:00 2001 From: hhs <386998068@qq.com> Date: Sat, 13 Jun 2026 15:44:23 +0800 Subject: [PATCH] =?UTF-8?q?feat:=20=E5=AE=9E=E7=8E=B0=20OpenAI=20LLM=20?= =?UTF-8?q?=E6=9C=8D=E5=8A=A1=EF=BC=88SSE=20=E6=B5=81=E5=BC=8F=E8=A7=A3?= =?UTF-8?q?=E6=9E=90=20+=20=E5=A4=9A=E6=A8=A1=E6=80=81=E6=B6=88=E6=81=AF?= =?UTF-8?q?=E6=9E=84=E5=BB=BA=EF=BC=89=E5=8F=8A=20System=20Prompt=20?= =?UTF-8?q?=E5=AE=9A=E4=B9=89?= MIME-Version: 1.0 Content-Type: text/plain; charset=UTF-8 Content-Transfer-Encoding: 8bit --- backend/internal/ai/llm/openai.go | 237 ++++++++++++++++++++++++++++++ backend/internal/ai/llm/prompt.go | 25 ++++ 2 files changed, 262 insertions(+) create mode 100644 backend/internal/ai/llm/openai.go create mode 100644 backend/internal/ai/llm/prompt.go diff --git a/backend/internal/ai/llm/openai.go b/backend/internal/ai/llm/openai.go new file mode 100644 index 0000000..bbc9444 --- /dev/null +++ b/backend/internal/ai/llm/openai.go @@ -0,0 +1,237 @@ +package llm + +import ( + "bufio" + "bytes" + "context" + "encoding/base64" + "encoding/json" + "fmt" + "io" + "net/http" + "strings" + "time" + + "go.uber.org/zap" +) + +// OpenAIService 基于 OpenAI Chat Completions API 的 LLM 实现。 +type OpenAIService struct { + apiKey string + model string + endpoint string + timeout time.Duration + logger *zap.SugaredLogger + client *http.Client +} + +// NewOpenAIService 创建 OpenAI LLM 服务。 +func NewOpenAIService(apiKey, model, endpoint string, timeoutSec int, logger *zap.SugaredLogger) *OpenAIService { + if model == "" { + model = "gpt-4o" + } + if endpoint == "" { + endpoint = "https://api.openai.com/v1" + } + timeout := time.Duration(timeoutSec) * time.Second + if timeout <= 0 { + timeout = 10 * time.Second + } + return &OpenAIService{ + apiKey: apiKey, + model: model, + endpoint: endpoint, + timeout: timeout, + logger: logger, + client: &http.Client{Timeout: 60 * time.Second}, // HTTP client timeout > LLM timeout + } +} + +// --- OpenAI API 请求/响应结构 --- + +type chatRequest struct { + Model string `json:"model"` + Messages []chatMessage `json:"messages"` + Stream bool `json:"stream"` +} + +type chatMessage struct { + Role string `json:"role"` + Content []contentPart `json:"content"` +} + +type contentPart struct { + Type string `json:"type"` + Text string `json:"text,omitempty"` + ImageURL *imageURL `json:"image_url,omitempty"` +} + +type imageURL struct { + URL string `json:"url"` +} + +// streamDelta SSE 流式响应的单个 delta。 +type streamDelta struct { + Choices []struct { + Delta struct { + Content string `json:"content"` + } `json:"delta"` + FinishReason *string `json:"finish_reason"` + } `json:"choices"` + Usage *struct { + PromptTokens int `json:"prompt_tokens"` + CompletionTokens int `json:"completion_tokens"` + TotalTokens int `json:"total_tokens"` + } `json:"usage"` + Model string `json:"model"` +} + +// ChatStream 实现 llm.Service。调用 OpenAI Chat Completions API 流式推理。 +func (o *OpenAIService) ChatStream(ctx context.Context, req Request) (<-chan Chunk, error) { + // 构建请求 + messages := o.buildMessages(req) + + body := chatRequest{ + Model: o.model, + Messages: messages, + Stream: true, + } + + payload, err := json.Marshal(body) + if err != nil { + return nil, fmt.Errorf("llm: marshal request: %w", err) + } + + // 创建带超时的 context + ctx, cancel := context.WithTimeout(ctx, o.timeout) + + httpReq, err := http.NewRequestWithContext(ctx, http.MethodPost, o.endpoint+"/chat/completions", bytes.NewReader(payload)) + if err != nil { + cancel() + return nil, fmt.Errorf("llm: create request: %w", err) + } + httpReq.Header.Set("Content-Type", "application/json") + httpReq.Header.Set("Authorization", "Bearer "+o.apiKey) + + resp, err := o.client.Do(httpReq) + if err != nil { + cancel() + return nil, fmt.Errorf("llm: send request: %w", err) + } + + if resp.StatusCode != http.StatusOK { + cancel() + bodyBytes, _ := io.ReadAll(resp.Body) + resp.Body.Close() + return nil, fmt.Errorf("llm: api error (status %d): %s", resp.StatusCode, string(bodyBytes)) + } + + // 启动 goroutine 解析 SSE 流 + ch := make(chan Chunk, 64) + go func() { + defer close(ch) + defer cancel() + defer resp.Body.Close() + + o.parseSSEStream(resp.Body, ch) + }() + + return ch, nil +} + +// parseSSEStream 解析 SSE 流，将 delta 发送到 channel。 +func (o *OpenAIService) parseSSEStream(body io.Reader, ch chan<- Chunk) { + scanner := bufio.NewScanner(body) + scanner.Buffer(make([]byte, 0, 64*1024), 256*1024) + + var fullText strings.Builder + var lastModel string + + for scanner.Scan() { + line := scanner.Text() + + // SSE 格式：data: {...} + if !strings.HasPrefix(line, "data: ") { + continue + } + data := strings.TrimPrefix(line, "data: ") + if data == "[DONE]" { + // 流结束，发送最终 chunk + ch <- Chunk{Delta: "", Done: true, Model: lastModel} + return + } + + var delta streamDelta + if err := json.Unmarshal([]byte(data), &delta); err != nil { + o.logger.Warnw("llm: unmarshal delta failed", "error", err, "data", data) + continue + } + + if delta.Model != "" { + lastModel = delta.Model + } + + // 提取增量文本 + if len(delta.Choices) > 0 { + content := delta.Choices[0].Delta.Content + if content != "" { + fullText.WriteString(content) + ch <- Chunk{Delta: content, Done: false, Model: lastModel} + } + + // 某些模型在最后一个 choice 中携带 usage + if delta.Choices[0].FinishReason != nil && delta.Usage != nil { + ch <- Chunk{ + Delta: "", + Done: true, + Model: lastModel, + TokensUsed: &TokenUsage{ + Prompt: delta.Usage.PromptTokens, + Completion: delta.Usage.CompletionTokens, + Total: delta.Usage.TotalTokens, + }, + } + return + } + } + } + + // scanner 结束但没收到 [DONE] + if err := scanner.Err(); err != nil { + o.logger.Warnw("llm: scan error", "error", err) + } + ch <- Chunk{Delta: "", Done: true, Model: lastModel} +} + +// buildMessages 构建 OpenAI Chat API 的 messages 数组。 +func (o *OpenAIService) buildMessages(req Request) []chatMessage { + var messages []chatMessage + + // System prompt + messages = append(messages, chatMessage{ + Role: "system", + Content: []contentPart{{Type: "text", Text: BuildSystemPrompt(req.Language, "")}}, + }) + + // 历史消息 + for _, msg := range req.History { + messages = append(messages, chatMessage{ + Role: msg.Role, + Content: []contentPart{{Type: "text", Text: msg.Content}}, + }) + } + + // 当前用户消息（图像 + 文本） + var parts []contentPart + if len(req.Image) > 0 { + b64 := base64.StdEncoding.EncodeToString(req.Image) + parts = append(parts, contentPart{ + Type: "image_url", + ImageURL: &imageURL{URL: "data:image/jpeg;base64," + b64}, + }) + } + parts = append(parts, contentPart{Type: "text", Text: req.Text}) + messages = append(messages, chatMessage{Role: "user", Content: parts}) + + return messages +} diff --git a/backend/internal/ai/llm/prompt.go b/backend/internal/ai/llm/prompt.go new file mode 100644 index 0000000..417288a --- /dev/null +++ b/backend/internal/ai/llm/prompt.go @@ -0,0 +1,25 @@ +package llm + +import "strings" + +// BuildSystemPrompt 根据语言和细节级别构建系统提示词。 +func BuildSystemPrompt(language, detailLevel string) string { + isChinese := strings.HasPrefix(language, "zh") + + var prompt strings.Builder + if isChinese { + prompt.WriteString("你是一个视觉助手。用户通过摄像头看到一个场景，并用语音向你提问。请用简洁自然的中文回答。如果涉及视觉描述，先说\"我看到……\"。回答控制在3-5句话以内，除非用户要求详细说明。") + } else { + prompt.WriteString("You are a visual assistant. The user sees a scene through their camera and asks questions by voice. Answer concisely and naturally. If describing visual content, start with 'I see...'. Keep answers to 3-5 sentences unless the user asks for detail.") + } + + if detailLevel == "high" { + if isChinese { + prompt.WriteString("请提供更详细的视觉描述，包括颜色、位置、数量等细节。") + } else { + prompt.WriteString(" Provide detailed visual descriptions including colors, positions, quantities, and other details.") + } + } + + return prompt.String() +}