feat: 实现增量渲染逻辑
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@ -22,6 +22,7 @@ luxx/
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│ ├── auth.py # 认证
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│ ├── conversations.py # 会话管理
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│ ├── messages.py # 消息处理
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│ ├── providers.py # LLM 提供商管理
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│ └── tools.py # 工具管理
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├── services/ # 服务层
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│ ├── chat.py # 聊天服务
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@ -101,15 +102,63 @@ erDiagram
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string id PK
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string conversation_id FK
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string role
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longtext content
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longtext content "JSON 格式"
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int token_count
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datetime created_at
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}
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USER ||--o{ CONVERSATION : "has"
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CONVERSATION ||--o{ MESSAGE : "has"
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USER ||--o{ LLM_PROVIDER : "configures"
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LLM_PROVIDER {
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int id PK
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int user_id FK
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string name
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string provider_type
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string base_url
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string api_key
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string default_model
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boolean is_default
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boolean enabled
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datetime created_at
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datetime updated_at
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}
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```
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### Message Content JSON 结构
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`content` 字段统一使用 JSON 格式存储：
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**User 消息：**
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```json
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{
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"text": "用户输入的文本内容",
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"attachments": [
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{"name": "utils.py", "extension": "py", "content": "..."}
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]
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}
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```
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**Assistant 消息：**
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```json
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{
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"text": "AI 回复的文本内容",
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"tool_calls": [...],
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"steps": [
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{"id": "step-0", "index": 0, "type": "thinking", "content": "..."},
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{"id": "step-1", "index": 1, "type": "text", "content": "..."},
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{"id": "step-2", "index": 2, "type": "tool_call", "id_ref": "call_xxx", "name": "...", "arguments": "..."},
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{"id": "step-3", "index": 3, "type": "tool_result", "id_ref": "call_xxx", "name": "...", "content": "..."}
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]
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}
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```
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`steps` 字段是**渲染顺序的唯一数据源**，按 `index` 顺序排列。thinking、text、tool_call、tool_result 可以在多轮迭代中穿插出现。
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### 5. 工具系统
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```mermaid
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@ -191,6 +240,9 @@ LLM API 客户端：
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| `/conversations` | GET/POST | 会话列表/创建 |
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| `/conversations/{id}` | GET/DELETE | 会话详情/删除 |
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| `/messages/stream` | POST | 流式消息发送 |
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| `/providers` | GET/POST | LLM 提供商列表/创建 |
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| `/providers/{id}` | GET/PUT/DELETE | 提供商详情/更新/删除 |
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| `/providers/{id}/test` | POST | 测试提供商连接 |
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| `/tools` | GET | 可用工具列表 |
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## 数据流
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@ -227,12 +279,29 @@ sequenceDiagram
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| 事件 | 说明 |
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|------|------|
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| `text` | 文本内容增量 |
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| `tool_call` | 工具调用请求 |
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| `tool_result` | 工具执行结果 |
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| `process_step` | 结构化步骤（thinking/text/tool_call/tool_result），携带 `id`、`index` 确保渲染顺序 |
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| `done` | 响应完成 |
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| `error` | 错误信息 |
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### process_step 事件格式
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```json
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{"type": "process_step", "step": {"id": "step-0", "index": 0, "type": "thinking", "content": "..."}}
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{"type": "process_step", "step": {"id": "step-1", "index": 1, "type": "text", "content": "回复文本..."}}
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{"type": "process_step", "step": {"id": "step-2", "index": 2, "type": "tool_call", "id_ref": "call_abc", "name": "web_search", "arguments": "{\"query\": \"...\"}"}}
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{"type": "process_step", "step": {"id": "step-3", "index": 3, "type": "tool_result", "id_ref": "call_abc", "name": "web_search", "content": "{\"success\": true, ...}"}}
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```
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| 字段 | 说明 |
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|------|------|
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| `id` | 步骤唯一标识（格式 `step-{index}`） |
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| `index` | 步骤序号，确保按正确顺序显示 |
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| `type` | 步骤类型：`thinking` / `text` / `tool_call` / `tool_result` |
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| `id_ref` | 工具调用引用 ID（仅 tool_call/tool_result） |
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| `name` | 工具名称（仅 tool_call/tool_result） |
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| `arguments` | 工具调用参数 JSON 字符串（仅 tool_call） |
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| `content` | 内容（thinking 的思考内容、text 的文本、tool_result 的返回结果） |
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## 配置示例
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### config.yaml
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@ -0,0 +1,472 @@
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<template>
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<div ref="processRef" class="process-block" :class="{ 'is-streaming': streaming }">
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<!-- Render all steps in order: thinking, text, tool_call, tool_result interleaved -->
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<template v-if="processItems.length > 0">
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<div v-for="item in processItems" :key="item.key">
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<!-- Thinking block -->
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<div v-if="item.type === 'thinking'" class="step-item thinking">
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<div class="step-header" @click="toggleItem(item.key)">
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<svg width="14" height="14" viewBox="0 0 24 24" fill="none" stroke="currentColor" stroke-width="2">
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<path d="M9.663 17h4.673M12 3v1m6.364 1.636l-.707.707M21 12h-1M4 12H3m3.343-5.657l-.707-.707m2.828 9.9a5 5 0 117.072 0l-.548.547A3.374 3.374 0 0014 18.469V19a2 2 0 11-4 0v-.531c0-.895-.356-1.754-.988-2.386l-.548-.547z"/>
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</svg>
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<span class="step-label">思考过程</span>
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<span v-if="item.summary" class="step-brief">{{ item.summary }}</span>
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<svg class="arrow" :class="{ open: expandedKeys[item.key] }" width="10" height="10" viewBox="0 0 24 24" fill="none" stroke="currentColor" stroke-width="2">
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<polyline points="6 9 12 15 18 9"></polyline>
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</svg>
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</div>
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<div v-if="expandedKeys[item.key]" class="step-content">
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<div class="thinking-text">{{ item.content }}</div>
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</div>
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</div>
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<!-- Tool call block -->
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<div v-else-if="item.type === 'tool_call'" class="step-item tool_call" :class="{ loading: item.loading }">
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<div class="step-header" @click="toggleItem(item.key)">
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<svg width="14" height="14" viewBox="0 0 24 24" fill="none" stroke="currentColor" stroke-width="2">
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<path d="M14.7 6.3a1 1 0 0 0 0 1.4l1.6 1.6a1 1 0 0 0 1.4 0l3.77-3.77a6 6 0 0 1-7.94 7.94l-6.91 6.91a2.12 2.12 0 0 1-3-3l6.91-6.91a6 6 0 0 1 7.94-7.94l-3.76 3.76z"/>
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</svg>
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<span class="step-label">{{ item.loading ? `执行工具: ${item.toolName}` : `调用工具: ${item.toolName}` }}</span>
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<span v-if="item.summary && !item.loading" class="step-brief">{{ item.summary }}</span>
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<span v-if="item.resultSummary" class="step-badge" :class="{ success: item.isSuccess, error: !item.isSuccess }">{{ item.resultSummary }}</span>
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<span v-if="item.loading" class="loading-dots">...</span>
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<svg v-if="!item.loading" class="arrow" :class="{ open: expandedKeys[item.key] }" width="10" height="10" viewBox="0 0 24 24" fill="none" stroke="currentColor" stroke-width="2">
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<polyline points="6 9 12 15 18 9"></polyline>
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</svg>
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</div>
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<div v-if="expandedKeys[item.key] && !item.loading" class="step-content">
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<div class="tool-detail" style="margin-bottom: 8px;">
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<span class="detail-label">调用参数:</span>
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<pre>{{ item.arguments }}</pre>
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</div>
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<div v-if="item.result" class="tool-detail">
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<span class="detail-label">返回结果:</span>
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<pre>{{ expandedResultKeys[item.key] ? item.result : item.resultPreview }}</pre>
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<button v-if="item.resultTruncated" class="btn-expand-result" @click.stop="toggleResultExpand(item.key)">
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{{ expandedResultKeys[item.key] ? '收起' : `展开全部 (${item.resultLength} 字符)` }}
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</button>
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</div>
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</div>
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</div>
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<!-- Text content -->
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<div v-else-if="item.type === 'text'" class="step-item text-content" v-html="item.rendered"></div>
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<!-- Tool result block -->
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<div v-else-if="item.type === 'tool_result'" class="step-item tool_result">
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<div class="step-header" @click="toggleItem(item.key)">
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<svg width="14" height="14" viewBox="0 0 24 24" fill="none" stroke="currentColor" stroke-width="2">
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<path d="M14.7 6.3a1 1 0 0 0 0 1.4l1.6 1.6a1 1 0 0 0 1.4 0l3.77-3.77a6 6 0 0 1-7.94 7.94l-6.91 6.91a2.12 2.12 0 0 1-3-3l6.91-6.91a6 6 0 0 1 7.94-7.94l-3.76 3.76z"/>
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</svg>
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<span class="step-label">工具结果: {{ item.name }}</span>
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<span v-if="item.resultSummary" class="step-badge" :class="{ success: item.isSuccess, error: !item.isSuccess }">{{ item.resultSummary }}</span>
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<svg class="arrow" :class="{ open: expandedKeys[item.key] }" width="10" height="10" viewBox="0 0 24 24" fill="none" stroke="currentColor" stroke-width="2">
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<polyline points="6 9 12 15 18 9"></polyline>
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</svg>
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</div>
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<div v-if="expandedKeys[item.key]" class="step-content">
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<div class="tool-detail">
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<span class="detail-label">返回结果:</span>
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<pre>{{ expandedResultKeys[item.key] ? item.result : item.resultPreview }}</pre>
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<button v-if="item.resultTruncated" class="btn-expand-result" @click.stop="toggleResultExpand(item.key)">
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{{ expandedResultKeys[item.key] ? '收起' : `展开全部 (${item.resultLength} 字符)` }}
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</button>
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</div>
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</div>
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</div>
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</div>
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</template>
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<!-- Active streaming indicator -->
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<div v-if="streaming" class="streaming-indicator">
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<svg class="spinner" width="14" height="14" viewBox="0 0 24 24" fill="none" stroke="currentColor" stroke-width="2">
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<path d="M21 12a9 9 0 1 1-6.219-8.56"/>
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</svg>
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<span>正在生成...</span>
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</div>
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</div>
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</template>
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<script setup>
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import { ref, computed, watch } from 'vue'
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const RESULT_PREVIEW_LIMIT = 500
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function formatJson(str) {
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try {
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const obj = typeof str === 'string' ? JSON.parse(str) : str
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return JSON.stringify(obj, null, 2)
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} catch {
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return String(str)
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}
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}
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function truncate(str, maxLen = 80) {
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const s = String(str)
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if (s.length <= maxLen) return s
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return s.substring(0, maxLen) + '...'
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}
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function buildResultFields(rawContent) {
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const formatted = formatJson(rawContent)
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const len = formatted.length
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const truncated = len > RESULT_PREVIEW_LIMIT
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return {
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result: formatted,
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resultPreview: truncated ? formatted.slice(0, RESULT_PREVIEW_LIMIT) + '\n...' : formatted,
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resultTruncated: truncated,
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resultLength: len,
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}
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}
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const props = defineProps({
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toolCalls: { type: Array, default: () => [] },
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processSteps: { type: Array, default: () => [] },
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streaming: { type: Boolean, default: false }
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})
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const expandedKeys = ref({})
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const expandedResultKeys = ref({})
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// Auto-collapse all items when a new stream starts
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watch(() => props.streaming, (v) => {
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if (v) {
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expandedKeys.value = {}
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expandedResultKeys.value = {}
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}
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})
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const processRef = ref(null)
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function toggleItem(key) {
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expandedKeys.value[key] = !expandedKeys.value[key]
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}
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function toggleResultExpand(key) {
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expandedResultKeys.value[key] = !expandedResultKeys.value[key]
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}
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function getResultSummary(result) {
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try {
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const parsed = typeof result === 'string' ? JSON.parse(result) : result
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if (parsed.success === true) return { text: '成功', success: true }
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if (parsed.success === false || parsed.error) return { text: parsed.error || '失败', success: false }
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if (parsed.results) return { text: `${parsed.results.length} 条结果`, success: true }
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return { text: '完成', success: true }
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} catch {
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return { text: '完成', success: true }
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}
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}
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function renderMarkdown(text) {
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if (!text) return ''
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// Simple markdown rendering
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return text
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.replace(/```(\w*)\n([\s\S]*?)```/g, '<pre><code>$2</code></pre>')
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.replace(/`([^`]+)`/g, '<code>$1</code>')
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.replace(/\n/g, '<br>')
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}
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// Build ordered process items from all available data (thinking, tool calls, text).
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const processItems = computed(() => {
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const items = []
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if (props.processSteps && props.processSteps.length > 0) {
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for (const step of props.processSteps) {
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if (!step) continue
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if (step.type === 'thinking') {
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items.push({
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type: 'thinking',
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content: step.content,
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summary: truncate(step.content),
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key: step.id || `thinking-${step.index}`,
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})
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} else if (step.type === 'tool_call') {
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const toolId = step.id_ref || step.id
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items.push({
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type: 'tool_call',
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toolName: step.name || '未知工具',
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arguments: formatJson(step.arguments),
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summary: truncate(step.arguments),
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id: toolId,
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key: step.id || `tool_call-${toolId || step.index}`,
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loading: false,
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result: null,
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})
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} else if (step.type === 'tool_result') {
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// 直接添加 tool_result 作为独立项
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const summary = getResultSummary(step.content)
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items.push({
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type: 'tool_result',
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id: step.id_ref || step.id,
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name: step.name || 'unknown',
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content: step.content,
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resultSummary: summary.text,
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isSuccess: summary.success,
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key: step.id || `tool_result-${step.id_ref || step.index}`,
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...buildResultFields(step.content)
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})
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} else if (step.type === 'text') {
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items.push({
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type: 'text',
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content: step.content,
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rendered: renderMarkdown(step.content) || '<span class="placeholder">...</span>',
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key: step.id || `text-${step.index}`,
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})
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}
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}
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// Mark the last tool_call as loading if it has no result yet (still executing)
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if (props.streaming && items.length > 0) {
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const last = items[items.length - 1]
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if (last.type === 'tool_call' && !last.result) {
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last.loading = true
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}
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}
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} else {
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// Fallback: legacy mode for old messages without processSteps stored in DB
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if (props.toolCalls && props.toolCalls.length > 0) {
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props.toolCalls.forEach((call, i) => {
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const toolName = call.function?.name || '未知工具'
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const resultSummary = call.result ? getResultSummary(call.result) : null
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const resultFields = call.result ? buildResultFields(call.result) : { result: null, resultPreview: null, resultTruncated: false, resultLength: 0 }
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items.push({
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type: 'tool_call',
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toolName,
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arguments: formatJson(call.function?.arguments),
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summary: truncate(call.function?.arguments),
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id: call.id,
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key: `tool_call-${call.id || i}`,
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loading: !call.result && props.streaming,
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...resultFields,
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resultSummary: resultSummary ? resultSummary.text : null,
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isSuccess: resultSummary ? resultSummary.success : undefined,
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})
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})
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}
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}
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return items
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})
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</script>
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<style scoped>
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.process-block {
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width: 100%;
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}
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/* Step items (shared) */
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.step-item {
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margin-bottom: 8px;
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}
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.step-item:last-child {
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margin-bottom: 0;
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}
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@keyframes pulse {
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0%, 100% { opacity: 0.4; }
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50% { opacity: 1; }
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}
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/* Step header (shared by thinking and tool_call) */
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.thinking .step-header,
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.tool_call .step-header {
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display: flex;
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align-items: center;
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gap: 8px;
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padding: 8px 12px;
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background: var(--code-bg);
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border: 1px solid var(--border);
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border-radius: 8px;
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cursor: pointer;
|
||||
font-size: 13px;
|
||||
transition: background 0.15s;
|
||||
}
|
||||
|
||||
.thinking .step-header:hover,
|
||||
.tool_call .step-header:hover {
|
||||
background: var(--bg-hover);
|
||||
}
|
||||
|
||||
.thinking .step-header svg:first-child {
|
||||
color: #f59e0b;
|
||||
}
|
||||
|
||||
.tool_call .step-header svg:first-child {
|
||||
color: #10b981;
|
||||
}
|
||||
|
||||
.step-label {
|
||||
font-weight: 500;
|
||||
color: var(--text);
|
||||
flex-shrink: 0;
|
||||
min-width: 130px;
|
||||
max-width: 130px;
|
||||
overflow: hidden;
|
||||
text-overflow: ellipsis;
|
||||
white-space: nowrap;
|
||||
}
|
||||
|
||||
.arrow {
|
||||
margin-left: auto;
|
||||
transition: transform 0.2s;
|
||||
color: var(--text-secondary);
|
||||
flex-shrink: 0;
|
||||
}
|
||||
|
||||
.step-badge {
|
||||
font-size: 11px;
|
||||
padding: 2px 8px;
|
||||
border-radius: 10px;
|
||||
font-weight: 500;
|
||||
}
|
||||
|
||||
.step-badge.success {
|
||||
background: rgba(16, 185, 129, 0.1);
|
||||
color: #10b981;
|
||||
}
|
||||
|
||||
.step-badge.error {
|
||||
background: rgba(239, 68, 68, 0.1);
|
||||
color: #ef4444;
|
||||
}
|
||||
|
||||
.step-brief {
|
||||
font-size: 11px;
|
||||
color: var(--text-secondary);
|
||||
overflow: hidden;
|
||||
text-overflow: ellipsis;
|
||||
white-space: nowrap;
|
||||
flex: 1;
|
||||
min-width: 0;
|
||||
}
|
||||
|
||||
.arrow.open {
|
||||
transform: rotate(180deg);
|
||||
}
|
||||
|
||||
.loading-dots {
|
||||
font-size: 16px;
|
||||
font-weight: 700;
|
||||
color: #10b981;
|
||||
animation: pulse 1s ease-in-out infinite;
|
||||
}
|
||||
|
||||
.tool_call.loading .step-header {
|
||||
background: var(--bg-hover);
|
||||
}
|
||||
|
||||
/* Tool result styling */
|
||||
.tool_result .step-header {
|
||||
display: flex;
|
||||
align-items: center;
|
||||
gap: 8px;
|
||||
padding: 8px 12px;
|
||||
background: var(--code-bg);
|
||||
border: 1px solid var(--border);
|
||||
border-radius: 8px;
|
||||
cursor: pointer;
|
||||
font-size: 13px;
|
||||
transition: background 0.15s;
|
||||
}
|
||||
|
||||
.tool_result .step-header:hover {
|
||||
background: var(--bg-hover);
|
||||
}
|
||||
|
||||
.tool_result .step-header svg:first-child {
|
||||
color: #10b981;
|
||||
}
|
||||
|
||||
/* Expandable step content panel */
|
||||
.step-content {
|
||||
padding: 12px;
|
||||
margin-top: 4px;
|
||||
background: var(--bg);
|
||||
border: 1px solid var(--border);
|
||||
border-radius: 8px;
|
||||
overflow: hidden;
|
||||
}
|
||||
|
||||
.thinking-text {
|
||||
font-size: 13px;
|
||||
color: var(--text-secondary);
|
||||
line-height: 1.6;
|
||||
white-space: pre-wrap;
|
||||
}
|
||||
|
||||
.tool-detail {
|
||||
font-size: 13px;
|
||||
}
|
||||
|
||||
.detail-label {
|
||||
color: var(--text-secondary);
|
||||
font-size: 11px;
|
||||
font-weight: 600;
|
||||
display: block;
|
||||
margin-bottom: 4px;
|
||||
}
|
||||
|
||||
.tool-detail pre {
|
||||
padding: 8px;
|
||||
background: var(--code-bg);
|
||||
border-radius: 4px;
|
||||
border: 1px solid var(--border);
|
||||
font-family: 'JetBrains Mono', 'Fira Code', monospace;
|
||||
font-size: 12px;
|
||||
line-height: 1.5;
|
||||
color: var(--text-secondary);
|
||||
overflow-x: auto;
|
||||
white-space: pre-wrap;
|
||||
word-break: break-word;
|
||||
}
|
||||
|
||||
.btn-expand-result {
|
||||
display: inline-block;
|
||||
margin-top: 6px;
|
||||
padding: 3px 10px;
|
||||
font-size: 11px;
|
||||
color: #10b981;
|
||||
background: rgba(16, 185, 129, 0.1);
|
||||
border: 1px solid rgba(16, 185, 129, 0.3);
|
||||
border-radius: 4px;
|
||||
cursor: pointer;
|
||||
transition: background 0.15s;
|
||||
}
|
||||
|
||||
.btn-expand-result:hover {
|
||||
background: rgba(16, 185, 129, 0.2);
|
||||
}
|
||||
|
||||
/* Text content */
|
||||
.text-content {
|
||||
padding: 0;
|
||||
font-size: 15px;
|
||||
line-height: 1.7;
|
||||
color: var(--text);
|
||||
word-break: break-word;
|
||||
white-space: pre-wrap;
|
||||
}
|
||||
|
||||
.text-content :deep(.placeholder) {
|
||||
color: var(--text-secondary);
|
||||
}
|
||||
|
||||
/* Streaming cursor indicator */
|
||||
.streaming-indicator {
|
||||
display: flex;
|
||||
align-items: center;
|
||||
gap: 8px;
|
||||
font-size: 12px;
|
||||
color: var(--text-secondary);
|
||||
}
|
||||
|
||||
/* Add separator only when there are step items above the indicator */
|
||||
.process-block:has(.step-item) .streaming-indicator {
|
||||
margin-top: 8px;
|
||||
padding: 8px 0 0;
|
||||
border-top: 1px solid var(--border);
|
||||
}
|
||||
</style>
|
||||
|
|
@ -36,101 +36,133 @@ api.interceptors.response.use(
|
|||
}
|
||||
)
|
||||
|
||||
/**
|
||||
* SSE 流式请求处理器
|
||||
* @param {string} url - API URL (不含 baseURL 前缀)
|
||||
* @param {object} body - 请求体
|
||||
* @param {object} callbacks - 事件回调: { onProcessStep, onDone, onError }
|
||||
* @returns {{ abort: () => void }}
|
||||
*/
|
||||
export function createSSEStream(url, body, { onProcessStep, onDone, onError }) {
|
||||
const token = localStorage.getItem('access_token')
|
||||
const controller = new AbortController()
|
||||
|
||||
const promise = (async () => {
|
||||
try {
|
||||
const res = await fetch(`/api${url}`, {
|
||||
method: 'POST',
|
||||
headers: {
|
||||
'Content-Type': 'application/json',
|
||||
'Authorization': `Bearer ${token}`
|
||||
},
|
||||
body: JSON.stringify(body),
|
||||
signal: controller.signal
|
||||
})
|
||||
|
||||
if (!res.ok) {
|
||||
const err = await res.json().catch(() => ({}))
|
||||
throw new Error(err.message || `HTTP ${res.status}`)
|
||||
}
|
||||
|
||||
const reader = res.body.getReader()
|
||||
const decoder = new TextDecoder()
|
||||
let buffer = ''
|
||||
let completed = false
|
||||
|
||||
while (true) {
|
||||
const { done, value } = await reader.read()
|
||||
if (done) break
|
||||
|
||||
buffer += decoder.decode(value, { stream: true })
|
||||
const lines = buffer.split('\n')
|
||||
buffer = lines.pop() || ''
|
||||
|
||||
let currentEvent = ''
|
||||
for (const line of lines) {
|
||||
if (line.startsWith('event: ')) {
|
||||
currentEvent = line.slice(7).trim()
|
||||
} else if (line.startsWith('data: ')) {
|
||||
const data = JSON.parse(line.slice(6))
|
||||
if (currentEvent === 'process_step' && onProcessStep) {
|
||||
onProcessStep(data)
|
||||
} else if (currentEvent === 'done' && onDone) {
|
||||
completed = true
|
||||
onDone(data)
|
||||
} else if (currentEvent === 'error' && onError) {
|
||||
onError(data.content)
|
||||
}
|
||||
}
|
||||
}
|
||||
}
|
||||
|
||||
if (!completed && onError) {
|
||||
onError('stream ended unexpectedly')
|
||||
}
|
||||
} catch (e) {
|
||||
if (e.name !== 'AbortError' && onError) {
|
||||
onError(e.message)
|
||||
}
|
||||
}
|
||||
})()
|
||||
|
||||
promise.abort = () => controller.abort()
|
||||
return promise
|
||||
}
|
||||
|
||||
// ============ 认证接口 ============
|
||||
|
||||
export const authAPI = {
|
||||
// 用户登录
|
||||
login: (data) => api.post('/auth/login', data),
|
||||
|
||||
// 用户注册
|
||||
register: (data) => api.post('/auth/register', data),
|
||||
|
||||
// 用户登出
|
||||
logout: () => api.post('/auth/logout'),
|
||||
|
||||
// 获取当前用户信息
|
||||
getMe: () => api.get('/auth/me')
|
||||
}
|
||||
|
||||
// ============ 会话接口 ============
|
||||
|
||||
export const conversationsAPI = {
|
||||
// 获取会话列表
|
||||
list: (params) => api.get('/conversations/', { params }),
|
||||
|
||||
// 创建会话
|
||||
create: (data) => api.post('/conversations/', data),
|
||||
|
||||
// 获取会话详情
|
||||
get: (id) => api.get(`/conversations/${id}`),
|
||||
|
||||
// 更新会话
|
||||
update: (id, data) => api.put(`/conversations/${id}`, data),
|
||||
|
||||
// 删除会话
|
||||
delete: (id) => api.delete(`/conversations/${id}`)
|
||||
}
|
||||
|
||||
// ============ 消息接口 ============
|
||||
|
||||
export const messagesAPI = {
|
||||
// 获取消息列表
|
||||
list: (conversationId, params) => api.get('/messages/', { params: { conversation_id: conversationId, ...params } }),
|
||||
|
||||
// 发送消息（非流式）
|
||||
send: (data) => api.post('/messages/', data),
|
||||
|
||||
// 发送消息（流式）- 使用原生 fetch 避免 axios 拦截
|
||||
sendStream: (data) => {
|
||||
const token = localStorage.getItem('access_token')
|
||||
return fetch('/api/messages/stream', {
|
||||
method: 'POST',
|
||||
headers: {
|
||||
'Content-Type': 'application/json',
|
||||
'Authorization': `Bearer ${token}`
|
||||
},
|
||||
body: JSON.stringify(data)
|
||||
})
|
||||
// 发送消息（流式）
|
||||
sendStream: (data, callbacks) => {
|
||||
return createSSEStream('/messages/stream', {
|
||||
conversation_id: data.conversation_id,
|
||||
content: data.content,
|
||||
tools_enabled: callbacks.toolsEnabled !== false
|
||||
}, callbacks)
|
||||
},
|
||||
|
||||
// 删除消息
|
||||
delete: (id) => api.delete(`/messages/${id}`)
|
||||
}
|
||||
|
||||
// ============ 工具接口 ============
|
||||
|
||||
export const toolsAPI = {
|
||||
// 获取工具列表
|
||||
list: (params) => api.get('/tools/', { params }),
|
||||
|
||||
// 获取工具详情
|
||||
get: (name) => api.get(`/tools/${name}`),
|
||||
|
||||
// 执行工具
|
||||
execute: (name, data) => api.post(`/tools/${name}/execute`, data)
|
||||
}
|
||||
|
||||
// ============ LLM Provider 接口 ============
|
||||
|
||||
export const providersAPI = {
|
||||
// 获取提供商列表
|
||||
list: () => api.get('/providers/'),
|
||||
|
||||
// 创建提供商
|
||||
create: (data) => api.post('/providers/', data),
|
||||
|
||||
// 获取提供商详情
|
||||
get: (id) => api.get(`/providers/${id}`),
|
||||
|
||||
// 更新提供商
|
||||
update: (id, data) => api.put(`/providers/${id}`, data),
|
||||
|
||||
// 删除提供商
|
||||
delete: (id) => api.delete(`/providers/${id}`),
|
||||
|
||||
// 测试连接
|
||||
test: (id) => api.post(`/providers/${id}/test`)
|
||||
}
|
||||
|
||||
// 默认导出
|
||||
export default api
|
||||
|
|
@ -9,14 +9,24 @@
|
|||
<div v-for="msg in messages" :key="msg.id" :class="['message', msg.role]">
|
||||
<div class="message-avatar">{{ msg.role === 'user' ? 'U' : 'A' }}</div>
|
||||
<div class="message-content">
|
||||
<div class="message-text">{{ msg.content }}</div>
|
||||
<!-- 显示 process_steps 或普通文本 -->
|
||||
<ProcessBlock
|
||||
v-if="msg.process_steps && msg.process_steps.length > 0"
|
||||
:process-steps="msg.process_steps"
|
||||
/>
|
||||
<div v-else class="message-text">{{ msg.content || msg.text }}</div>
|
||||
<div class="message-time">{{ formatTime(msg.created_at) }}</div>
|
||||
</div>
|
||||
</div>
|
||||
<div v-if="streaming" class="message assistant streaming">
|
||||
|
||||
<!-- 流式消息 -->
|
||||
<div v-if="streamingMessage" class="message assistant streaming">
|
||||
<div class="message-avatar">A</div>
|
||||
<div class="message-content">
|
||||
<div class="message-text">{{ streamContent }}<span class="cursor">▋</span></div>
|
||||
<ProcessBlock
|
||||
:process-steps="streamingMessage.process_steps"
|
||||
:streaming="true"
|
||||
/>
|
||||
</div>
|
||||
</div>
|
||||
</div>
|
||||
|
|
@ -40,6 +50,7 @@
|
|||
import { ref, onMounted, nextTick } from 'vue'
|
||||
import { useRoute } from 'vue-router'
|
||||
import { conversationsAPI, messagesAPI } from '../services/api.js'
|
||||
import ProcessBlock from '../components/ProcessBlock.vue'
|
||||
|
||||
const route = useRoute()
|
||||
const messages = ref([])
|
||||
|
|
@ -47,7 +58,7 @@ const inputMessage = ref('')
|
|||
const loading = ref(true)
|
||||
const sending = ref(false)
|
||||
const streaming = ref(false)
|
||||
const streamContent = ref('')
|
||||
const streamingMessage = ref(null)
|
||||
const messagesContainer = ref(null)
|
||||
const conversationId = ref(route.params.id)
|
||||
|
||||
|
|
@ -78,13 +89,21 @@ const sendMessage = async () => {
|
|||
id: Date.now(),
|
||||
role: 'user',
|
||||
content: content,
|
||||
text: content,
|
||||
attachments: [],
|
||||
process_steps: [],
|
||||
created_at: new Date().toISOString()
|
||||
})
|
||||
scrollToBottom()
|
||||
|
||||
try {
|
||||
streaming.value = true
|
||||
streamContent.value = ''
|
||||
streamingMessage.value = {
|
||||
id: Date.now() + 1,
|
||||
role: 'assistant',
|
||||
process_steps: [],
|
||||
created_at: new Date().toISOString()
|
||||
}
|
||||
|
||||
const response = await messagesAPI.sendStream({
|
||||
conversation_id: conversationId.value,
|
||||
|
|
@ -94,79 +113,77 @@ const sendMessage = async () => {
|
|||
|
||||
const reader = response.body.getReader()
|
||||
const decoder = new TextDecoder()
|
||||
let buffer = ''
|
||||
|
||||
while (true) {
|
||||
const { done, value } = await reader.read()
|
||||
if (done) break
|
||||
|
||||
const chunk = decoder.decode(value)
|
||||
const lines = chunk.split('\n')
|
||||
buffer += chunk
|
||||
|
||||
for (const line of lines) {
|
||||
if (line.startsWith('data: ')) {
|
||||
const data = line.slice(6)
|
||||
if (data === '[DONE]') continue
|
||||
// Process complete SSE events (separated by double newline)
|
||||
const events = buffer.split('\n\n')
|
||||
buffer = events.pop() || '' // Keep incomplete event in buffer
|
||||
|
||||
for (const eventStr of events) {
|
||||
if (eventStr.trim() === '') continue
|
||||
|
||||
// Parse SSE event
|
||||
// Format: "event: xxx\ndata: {...}\n" or "data: {...}\n"
|
||||
let eventType = null
|
||||
let dataStr = null
|
||||
|
||||
for (const line of eventStr.split('\n')) {
|
||||
if (line.startsWith('event: ')) {
|
||||
eventType = line.slice(7).trim()
|
||||
} else if (line.startsWith('data: ')) {
|
||||
dataStr = line.slice(6).trim()
|
||||
}
|
||||
}
|
||||
|
||||
if (dataStr === '[DONE]') continue
|
||||
|
||||
if (dataStr) {
|
||||
try {
|
||||
const parsed = JSON.parse(data)
|
||||
if (parsed.type === 'text') {
|
||||
streamContent.value += parsed.content
|
||||
} else if (parsed.type === 'tool_call') {
|
||||
// 工具调用（只在完整结果时显示一次）
|
||||
const data = parsed.data
|
||||
if (data && Array.isArray(data) && data.length > 0) {
|
||||
// 检查是否有完整的函数名
|
||||
const hasFunctionName = data.some(tc => tc.function && tc.function.name)
|
||||
if (hasFunctionName) {
|
||||
streamContent.value += '\n\n[调用工具] '
|
||||
data.forEach(tc => {
|
||||
if (tc.function && tc.function.name) {
|
||||
streamContent.value += `${tc.function.name} `
|
||||
const parsed = JSON.parse(dataStr)
|
||||
|
||||
// Handle done event
|
||||
if (eventType === 'done') {
|
||||
// Message saved to DB
|
||||
continue
|
||||
}
|
||||
})
|
||||
}
|
||||
}
|
||||
} else if (parsed.type === 'tool_result') {
|
||||
// 工具结果
|
||||
streamContent.value += '\n\n[工具结果]\n'
|
||||
if (Array.isArray(parsed.data)) {
|
||||
parsed.data.forEach(tr => {
|
||||
if (tr.content) {
|
||||
try {
|
||||
const result = JSON.parse(tr.content)
|
||||
if (result.success && result.data && result.data.results) {
|
||||
result.data.results.forEach(r => {
|
||||
streamContent.value += `• ${r.title}\n${r.snippet}\n\n`
|
||||
})
|
||||
|
||||
// Forward to ProcessBlock via streamingMessage
|
||||
if (streamingMessage.value && parsed) {
|
||||
// Normalize step format based on event type
|
||||
if (parsed.type === 'thinking' || parsed.type === 'text' || parsed.type === 'tool_call' || parsed.type === 'tool_result') {
|
||||
const step = parsed
|
||||
// Find existing step by id or create new
|
||||
const existingIndex = streamingMessage.value.process_steps.findIndex(
|
||||
s => s.id === step.id
|
||||
)
|
||||
if (existingIndex >= 0) {
|
||||
streamingMessage.value.process_steps[existingIndex] = step
|
||||
} else {
|
||||
streamContent.value += tr.content.substring(0, 500)
|
||||
streamingMessage.value.process_steps.push(step)
|
||||
}
|
||||
} catch {
|
||||
streamContent.value += tr.content.substring(0, 500)
|
||||
}
|
||||
} else {
|
||||
streamContent.value += '无结果'
|
||||
}
|
||||
})
|
||||
}
|
||||
} else if (parsed.type === 'error') {
|
||||
streamContent.value += '\n\n[错误] ' + (parsed.error || '未知错误')
|
||||
}
|
||||
} catch (e) {
|
||||
console.error('Parse error:', e, data)
|
||||
console.error('Parse error:', e)
|
||||
}
|
||||
}
|
||||
}
|
||||
}
|
||||
|
||||
// 添加助手消息
|
||||
if (streamContent.value) {
|
||||
// 将流式消息添加到消息列表
|
||||
if (streamingMessage.value) {
|
||||
messages.value.push({
|
||||
id: Date.now() + 1,
|
||||
role: 'assistant',
|
||||
content: streamContent.value,
|
||||
...streamingMessage.value,
|
||||
created_at: new Date().toISOString()
|
||||
})
|
||||
streamingMessage.value = null
|
||||
}
|
||||
} catch (e) {
|
||||
console.error('发送失败:', e)
|
||||
|
|
@ -203,12 +220,10 @@ onMounted(loadMessages)
|
|||
.message.user { flex-direction: row-reverse; }
|
||||
.message-avatar { width: 40px; height: 40px; background: var(--code-bg); border-radius: 50%; display: flex; align-items: center; justify-content: center; font-size: 1.2rem; flex-shrink: 0; }
|
||||
.message.user .message-avatar { background: var(--accent-bg); }
|
||||
.message-content { max-width: 70%; }
|
||||
.message-content { max-width: 80%; }
|
||||
.message-text { padding: 1rem; background: var(--code-bg); border-radius: 12px; line-height: 1.6; white-space: pre-wrap; }
|
||||
.message.user .message-text { background: var(--accent); color: white; }
|
||||
.message-time { font-size: 0.75rem; color: var(--text); margin-top: 0.25rem; }
|
||||
.cursor { animation: blink 1s infinite; }
|
||||
@keyframes blink { 0%, 50% { opacity: 1; } 51%, 100% { opacity: 0; } }
|
||||
.message-time { font-size: 0.75rem; color: var(--text-secondary); margin-top: 0.25rem; }
|
||||
.input-area { display: flex; gap: 0.75rem; padding: 1rem; border-top: 1px solid var(--border); }
|
||||
.input-area textarea { flex: 1; padding: 0.875rem 1rem; border: 1px solid var(--border); border-radius: 12px; resize: none; font-size: 1rem; background: var(--bg); color: var(--text); }
|
||||
.input-area textarea:focus { outline: none; border-color: var(--accent); }
|
||||
|
|
|
|||
|
|
@ -130,13 +130,36 @@ class Conversation(Base):
|
|||
|
||||
|
||||
class Message(Base):
|
||||
"""Message model"""
|
||||
"""Message model
|
||||
|
||||
content 字段统一使用 JSON 格式存储：
|
||||
|
||||
**User 消息：**
|
||||
{
|
||||
"text": "用户输入的文本内容",
|
||||
"attachments": [
|
||||
{"name": "utils.py", "extension": "py", "content": "..."}
|
||||
]
|
||||
}
|
||||
|
||||
**Assistant 消息：**
|
||||
{
|
||||
"text": "AI 回复的文本内容",
|
||||
"tool_calls": [...], // 遗留的扁平结构
|
||||
"steps": [ // 有序步骤，用于渲染（主要数据源）
|
||||
{"id": "step-0", "index": 0, "type": "thinking", "content": "..."},
|
||||
{"id": "step-1", "index": 1, "type": "text", "content": "..."},
|
||||
{"id": "step-2", "index": 2, "type": "tool_call", "id_ref": "call_xxx", "name": "...", "arguments": "..."},
|
||||
{"id": "step-3", "index": 3, "type": "tool_result", "id_ref": "call_xxx", "name": "...", "content": "..."}
|
||||
]
|
||||
}
|
||||
"""
|
||||
__tablename__ = "messages"
|
||||
|
||||
id: Mapped[str] = mapped_column(String(64), primary_key=True)
|
||||
conversation_id: Mapped[str] = mapped_column(String(64), ForeignKey("conversations.id"), nullable=False)
|
||||
role: Mapped[str] = mapped_column(String(16), nullable=False)
|
||||
content: Mapped[str] = mapped_column(Text, nullable=False)
|
||||
role: Mapped[str] = mapped_column(String(16), nullable=False) # user, assistant, system, tool
|
||||
content: Mapped[str] = mapped_column(Text, nullable=False, default="")
|
||||
token_count: Mapped[int] = mapped_column(Integer, default=0)
|
||||
created_at: Mapped[datetime] = mapped_column(DateTime, default=datetime.utcnow)
|
||||
|
||||
|
|
@ -144,11 +167,39 @@ class Message(Base):
|
|||
conversation: Mapped["Conversation"] = relationship("Conversation", back_populates="messages")
|
||||
|
||||
def to_dict(self):
|
||||
return {
|
||||
"""Convert to dictionary, extracting process_steps for frontend"""
|
||||
import json
|
||||
|
||||
result = {
|
||||
"id": self.id,
|
||||
"conversation_id": self.conversation_id,
|
||||
"role": self.role,
|
||||
"content": self.content,
|
||||
"token_count": self.token_count,
|
||||
"created_at": self.created_at.isoformat() if self.created_at else None
|
||||
}
|
||||
|
||||
# Parse content JSON
|
||||
try:
|
||||
content_obj = json.loads(self.content) if self.content else {}
|
||||
except json.JSONDecodeError:
|
||||
# Legacy plain text content
|
||||
result["content"] = self.content
|
||||
result["text"] = self.content
|
||||
result["attachments"] = []
|
||||
result["tool_calls"] = []
|
||||
result["process_steps"] = []
|
||||
return result
|
||||
|
||||
# Extract common fields
|
||||
result["text"] = content_obj.get("text", "")
|
||||
result["attachments"] = content_obj.get("attachments", [])
|
||||
result["tool_calls"] = content_obj.get("tool_calls", [])
|
||||
|
||||
# Extract steps as process_steps for frontend rendering
|
||||
result["process_steps"] = content_obj.get("steps", [])
|
||||
|
||||
# For backward compatibility
|
||||
if "content" not in result:
|
||||
result["content"] = result["text"]
|
||||
|
||||
return result
|
||||
|
|
|
|||
|
|
@ -136,44 +136,13 @@ async def stream_message(
|
|||
db.commit()
|
||||
|
||||
async def event_generator():
|
||||
full_response = ""
|
||||
|
||||
async for event in chat_service.stream_response(
|
||||
async for sse_str in chat_service.stream_response(
|
||||
conversation=conversation,
|
||||
user_message=data.content,
|
||||
tools_enabled=tools_enabled
|
||||
):
|
||||
event_type = event.get("type")
|
||||
|
||||
if event_type == "text":
|
||||
content = event.get("content", "")
|
||||
full_response += content
|
||||
yield f"data: {json.dumps({'type': 'text', 'content': content})}\n\n"
|
||||
|
||||
elif event_type == "tool_call":
|
||||
yield f"data: {json.dumps({'type': 'tool_call', 'data': event.get('data')})}\n\n"
|
||||
|
||||
elif event_type == "tool_result":
|
||||
yield f"data: {json.dumps({'type': 'tool_result', 'data': event.get('data')})}\n\n"
|
||||
|
||||
elif event_type == "done":
|
||||
try:
|
||||
ai_message = Message(
|
||||
id=generate_id("msg"),
|
||||
conversation_id=data.conversation_id,
|
||||
role="assistant",
|
||||
content=full_response,
|
||||
token_count=len(full_response) // 4
|
||||
)
|
||||
db.add(ai_message)
|
||||
db.commit()
|
||||
except Exception:
|
||||
pass
|
||||
|
||||
yield f"data: {json.dumps({'type': 'done', 'message_id': ai_message.id if 'ai_message' in dir() else None})}\n\n"
|
||||
|
||||
elif event_type == "error":
|
||||
yield f"data: {json.dumps({'type': 'error', 'error': event.get('error')})}\n\n"
|
||||
# Chat service returns raw SSE strings
|
||||
yield sse_str
|
||||
|
||||
yield "data: [DONE]\n\n"
|
||||
|
||||
|
|
|
|||
|
|
@ -1,5 +1,6 @@
|
|||
"""Chat service module"""
|
||||
import json
|
||||
import uuid
|
||||
from typing import List, Dict, Any, AsyncGenerator
|
||||
|
||||
from luxx.models import Conversation, Message
|
||||
|
|
@ -13,6 +14,11 @@ from luxx.config import config
|
|||
MAX_ITERATIONS = 10
|
||||
|
||||
|
||||
def _sse_event(event: str, data: dict) -> str:
|
||||
"""Format a Server-Sent Event string."""
|
||||
return f"event: {event}\ndata: {json.dumps(data, ensure_ascii=False)}\n\n"
|
||||
|
||||
|
||||
def get_llm_client(conversation: Conversation = None):
|
||||
"""Get LLM client, optionally using conversation's provider"""
|
||||
if conversation and conversation.provider_id:
|
||||
|
|
@ -37,7 +43,7 @@ def get_llm_client(conversation: Conversation = None):
|
|||
|
||||
|
||||
class ChatService:
|
||||
"""Chat service"""
|
||||
"""Chat service with tool support"""
|
||||
|
||||
def __init__(self):
|
||||
self.tool_executor = ToolExecutor()
|
||||
|
|
@ -66,9 +72,19 @@ class ChatService:
|
|||
).order_by(Message.created_at).all()
|
||||
|
||||
for msg in db_messages:
|
||||
# Parse JSON content if possible
|
||||
try:
|
||||
content_obj = json.loads(msg.content) if msg.content else {}
|
||||
if isinstance(content_obj, dict):
|
||||
content = content_obj.get("text", msg.content)
|
||||
else:
|
||||
content = msg.content
|
||||
except (json.JSONDecodeError, TypeError):
|
||||
content = msg.content
|
||||
|
||||
messages.append({
|
||||
"role": msg.role,
|
||||
"content": msg.content
|
||||
"content": content
|
||||
})
|
||||
finally:
|
||||
db.close()
|
||||
|
|
@ -80,163 +96,263 @@ class ChatService:
|
|||
conversation: Conversation,
|
||||
user_message: str,
|
||||
tools_enabled: bool = True
|
||||
) -> AsyncGenerator[Dict[str, Any], None]:
|
||||
) -> AsyncGenerator[Dict[str, str], None]:
|
||||
"""
|
||||
Streaming response generator
|
||||
|
||||
Event types:
|
||||
- process_step: thinking/text/tool_call/tool_result step
|
||||
- done: final response complete
|
||||
- error: on error
|
||||
Yields raw SSE event strings for direct forwarding.
|
||||
"""
|
||||
try:
|
||||
messages = self.build_messages(conversation)
|
||||
|
||||
messages.append({
|
||||
"role": "user",
|
||||
"content": user_message
|
||||
"content": json.dumps({"text": user_message, "attachments": []})
|
||||
})
|
||||
|
||||
tools = registry.list_all() if tools_enabled else None
|
||||
|
||||
iteration = 0
|
||||
|
||||
llm = get_llm_client(conversation)
|
||||
model = conversation.model or llm.default_model or "gpt-4"
|
||||
|
||||
while iteration < MAX_ITERATIONS:
|
||||
iteration += 1
|
||||
print(f"[CHAT DEBUG] ====== Starting iteration {iteration} ======")
|
||||
print(f"[CHAT DEBUG] Messages count: {len(messages)}")
|
||||
# State tracking
|
||||
all_steps = []
|
||||
all_tool_calls = []
|
||||
all_tool_results = []
|
||||
step_index = 0
|
||||
|
||||
tool_calls_this_round = None
|
||||
for iteration in range(MAX_ITERATIONS):
|
||||
print(f"[CHAT] Starting iteration {iteration + 1}, messages: {len(messages)}")
|
||||
|
||||
async for event in llm.stream_call(
|
||||
# Stream from LLM
|
||||
full_content = ""
|
||||
full_thinking = ""
|
||||
tool_calls_list = []
|
||||
thinking_step_id = None
|
||||
thinking_step_idx = None
|
||||
text_step_id = None
|
||||
text_step_idx = None
|
||||
|
||||
async for sse_line in llm.stream_call(
|
||||
model=model,
|
||||
messages=messages,
|
||||
tools=tools,
|
||||
temperature=conversation.temperature,
|
||||
max_tokens=conversation.max_tokens
|
||||
):
|
||||
event_type = event.get("type")
|
||||
# Parse SSE line
|
||||
# Format: "event: xxx\ndata: {...}\n\n"
|
||||
event_type = None
|
||||
data_str = None
|
||||
|
||||
if event_type == "content_delta":
|
||||
content = event.get("content", "")
|
||||
if content:
|
||||
print(f"[CHAT DEBUG] Iteration {iteration} content: {content[:100]}...")
|
||||
yield {"type": "text", "content": content}
|
||||
for line in sse_line.strip().split('\n'):
|
||||
if line.startswith('event: '):
|
||||
event_type = line[7:].strip()
|
||||
elif line.startswith('data: '):
|
||||
data_str = line[6:].strip()
|
||||
|
||||
elif event_type == "tool_call_delta":
|
||||
tool_call = event.get("tool_call", {})
|
||||
yield {"type": "tool_call", "data": tool_call}
|
||||
if data_str is None:
|
||||
continue
|
||||
|
||||
elif event_type == "done":
|
||||
tool_calls_this_round = event.get("tool_calls")
|
||||
print(f"[CHAT DEBUG] Done event, tool_calls: {tool_calls_this_round}")
|
||||
|
||||
if tool_calls_this_round and tools_enabled:
|
||||
print(f"[CHAT DEBUG] Executing tools: {tool_calls_this_round}")
|
||||
yield {"type": "tool_call", "data": tool_calls_this_round}
|
||||
|
||||
tool_results = self.tool_executor.process_tool_calls_parallel(
|
||||
tool_calls_this_round,
|
||||
{}
|
||||
)
|
||||
|
||||
messages.append({
|
||||
"role": "assistant",
|
||||
"content": "",
|
||||
"tool_calls": tool_calls_this_round
|
||||
})
|
||||
|
||||
for tr in tool_results:
|
||||
messages.append({
|
||||
"role": "tool",
|
||||
"tool_call_id": tr.get("tool_call_id"),
|
||||
"content": str(tr.get("result", ""))
|
||||
})
|
||||
|
||||
yield {"type": "tool_result", "data": tool_results}
|
||||
else:
|
||||
break
|
||||
|
||||
if not tool_calls_this_round or not tools_enabled:
|
||||
break
|
||||
|
||||
yield {"type": "done"}
|
||||
|
||||
except Exception as e:
|
||||
print(f"[CHAT ERROR] Exception in stream_response: {type(e).__name__}: {str(e)}")
|
||||
yield {"type": "error", "error": str(e)}
|
||||
|
||||
except Exception as e:
|
||||
yield {"type": "error", "error": str(e)}
|
||||
|
||||
def non_stream_response(
|
||||
self,
|
||||
conversation: Conversation,
|
||||
user_message: str,
|
||||
tools_enabled: bool = False
|
||||
) -> Dict[str, Any]:
|
||||
"""Non-streaming response"""
|
||||
# Handle error events from LLM
|
||||
if event_type == 'error':
|
||||
try:
|
||||
messages = self.build_messages(conversation)
|
||||
messages.append({
|
||||
"role": "user",
|
||||
"content": user_message
|
||||
error_data = json.loads(data_str)
|
||||
yield _sse_event("error", {"content": error_data.get("content", "Unknown error")})
|
||||
except json.JSONDecodeError:
|
||||
yield _sse_event("error", {"content": data_str})
|
||||
return
|
||||
|
||||
# Parse the data
|
||||
try:
|
||||
chunk = json.loads(data_str)
|
||||
except json.JSONDecodeError:
|
||||
continue
|
||||
|
||||
# Get delta
|
||||
choices = chunk.get("choices", [])
|
||||
if not choices:
|
||||
continue
|
||||
|
||||
delta = choices[0].get("delta", {})
|
||||
|
||||
# Handle reasoning (thinking)
|
||||
reasoning = delta.get("reasoning_content", "")
|
||||
if reasoning:
|
||||
full_thinking += reasoning
|
||||
if thinking_step_id is None:
|
||||
thinking_step_id = f"step-{step_index}"
|
||||
thinking_step_idx = step_index
|
||||
step_index += 1
|
||||
yield _sse_event("process_step", {
|
||||
"id": thinking_step_id,
|
||||
"index": thinking_step_idx,
|
||||
"type": "thinking",
|
||||
"content": full_thinking
|
||||
})
|
||||
|
||||
tools = registry.list_all() if tools_enabled else None
|
||||
# Handle content
|
||||
content = delta.get("content", "")
|
||||
if content:
|
||||
full_content += content
|
||||
if text_step_id is None:
|
||||
text_step_idx = step_index
|
||||
text_step_id = f"step-{text_step_idx}"
|
||||
step_index += 1
|
||||
yield _sse_event("process_step", {
|
||||
"id": text_step_id,
|
||||
"index": text_step_idx,
|
||||
"type": "text",
|
||||
"content": full_content
|
||||
})
|
||||
|
||||
iteration = 0
|
||||
# Accumulate tool calls
|
||||
tool_calls_delta = delta.get("tool_calls", [])
|
||||
for tc in tool_calls_delta:
|
||||
idx = tc.get("index", 0)
|
||||
if idx >= len(tool_calls_list):
|
||||
tool_calls_list.append({
|
||||
"id": tc.get("id", ""),
|
||||
"type": "function",
|
||||
"function": {"name": "", "arguments": ""}
|
||||
})
|
||||
func = tc.get("function", {})
|
||||
if func.get("name"):
|
||||
tool_calls_list[idx]["function"]["name"] += func["name"]
|
||||
if func.get("arguments"):
|
||||
tool_calls_list[idx]["function"]["arguments"] += func["arguments"]
|
||||
|
||||
llm_client = get_llm_client(conversation)
|
||||
model = conversation.model or llm_client.default_model or "gpt-4"
|
||||
# Save thinking step
|
||||
if thinking_step_id is not None:
|
||||
all_steps.append({
|
||||
"id": thinking_step_id,
|
||||
"index": thinking_step_idx,
|
||||
"type": "thinking",
|
||||
"content": full_thinking
|
||||
})
|
||||
|
||||
while iteration < MAX_ITERATIONS:
|
||||
iteration += 1
|
||||
# Save text step
|
||||
if text_step_id is not None:
|
||||
all_steps.append({
|
||||
"id": text_step_id,
|
||||
"index": text_step_idx,
|
||||
"type": "text",
|
||||
"content": full_content
|
||||
})
|
||||
|
||||
response = llm_client.sync_call(
|
||||
model=model,
|
||||
messages=messages,
|
||||
tools=tools,
|
||||
temperature=conversation.temperature,
|
||||
max_tokens=conversation.max_tokens
|
||||
# Handle tool calls
|
||||
if tool_calls_list:
|
||||
all_tool_calls.extend(tool_calls_list)
|
||||
|
||||
# Yield tool_call steps
|
||||
for tc in tool_calls_list:
|
||||
call_step = {
|
||||
"id": f"step-{step_index}",
|
||||
"index": step_index,
|
||||
"type": "tool_call",
|
||||
"id_ref": tc.get("id", ""),
|
||||
"name": tc["function"]["name"],
|
||||
"arguments": tc["function"]["arguments"]
|
||||
}
|
||||
all_steps.append(call_step)
|
||||
yield _sse_event("process_step", call_step)
|
||||
step_index += 1
|
||||
|
||||
# Execute tools
|
||||
tool_results = self.tool_executor.process_tool_calls_parallel(
|
||||
tool_calls_list, {}
|
||||
)
|
||||
|
||||
tool_calls = response.tool_calls
|
||||
# Yield tool_result steps
|
||||
for tr in tool_results:
|
||||
result_step = {
|
||||
"id": f"step-{step_index}",
|
||||
"index": step_index,
|
||||
"type": "tool_result",
|
||||
"id_ref": tr.get("tool_call_id", ""),
|
||||
"name": tr.get("name", ""),
|
||||
"content": tr.get("content", "")
|
||||
}
|
||||
all_steps.append(result_step)
|
||||
yield _sse_event("process_step", result_step)
|
||||
step_index += 1
|
||||
|
||||
if tool_calls and tools_enabled:
|
||||
all_tool_results.append({
|
||||
"role": "tool",
|
||||
"tool_call_id": tr.get("tool_call_id", ""),
|
||||
"content": tr.get("content", "")
|
||||
})
|
||||
|
||||
# Add assistant message with tool calls for next iteration
|
||||
messages.append({
|
||||
"role": "assistant",
|
||||
"content": response.content,
|
||||
"tool_calls": tool_calls
|
||||
"content": full_content or "",
|
||||
"tool_calls": tool_calls_list
|
||||
})
|
||||
messages.extend(all_tool_results[-len(tool_results):])
|
||||
all_tool_results = []
|
||||
continue
|
||||
|
||||
tool_results = self.tool_executor.process_tool_calls_parallel(tool_calls)
|
||||
# No tool calls - final iteration, save message
|
||||
msg_id = str(uuid.uuid4())
|
||||
self._save_message(
|
||||
conversation.id,
|
||||
msg_id,
|
||||
full_content,
|
||||
all_tool_calls,
|
||||
all_tool_results,
|
||||
all_steps
|
||||
)
|
||||
|
||||
for tr in tool_results:
|
||||
messages.append({
|
||||
"role": "tool",
|
||||
"tool_call_id": tr.get("tool_call_id"),
|
||||
"content": str(tr.get("result", ""))
|
||||
yield _sse_event("done", {
|
||||
"message_id": msg_id,
|
||||
"token_count": len(full_content) // 4
|
||||
})
|
||||
else:
|
||||
return {
|
||||
"success": True,
|
||||
"content": response.content
|
||||
}
|
||||
return
|
||||
|
||||
return {
|
||||
"success": True,
|
||||
"content": "Max iterations reached"
|
||||
}
|
||||
# Max iterations exceeded
|
||||
yield _sse_event("error", {"content": "Exceeded maximum tool call iterations"})
|
||||
|
||||
except Exception as e:
|
||||
return {
|
||||
"success": False,
|
||||
"error": str(e)
|
||||
print(f"[CHAT] Exception: {type(e).__name__}: {str(e)}")
|
||||
yield _sse_event("error", {"content": str(e)})
|
||||
|
||||
def _save_message(
|
||||
self,
|
||||
conversation_id: str,
|
||||
msg_id: str,
|
||||
full_content: str,
|
||||
all_tool_calls: list,
|
||||
all_tool_results: list,
|
||||
all_steps: list
|
||||
):
|
||||
"""Save the assistant message to database."""
|
||||
from luxx.database import SessionLocal
|
||||
from luxx.models import Message
|
||||
|
||||
content_json = {
|
||||
"text": full_content,
|
||||
"steps": all_steps
|
||||
}
|
||||
if all_tool_calls:
|
||||
content_json["tool_calls"] = all_tool_calls
|
||||
|
||||
db = SessionLocal()
|
||||
try:
|
||||
msg = Message(
|
||||
id=msg_id,
|
||||
conversation_id=conversation_id,
|
||||
role="assistant",
|
||||
content=json.dumps(content_json, ensure_ascii=False),
|
||||
token_count=len(full_content) // 4
|
||||
)
|
||||
db.add(msg)
|
||||
db.commit()
|
||||
except Exception as e:
|
||||
print(f"[CHAT] Failed to save message: {e}")
|
||||
db.rollback()
|
||||
finally:
|
||||
db.close()
|
||||
|
||||
|
||||
# Global chat service
|
||||
|
|
|
|||
|
|
@ -136,82 +136,39 @@ class LLMClient:
|
|||
messages: List[Dict],
|
||||
tools: Optional[List[Dict]] = None,
|
||||
**kwargs
|
||||
) -> AsyncGenerator[Dict[str, Any], None]:
|
||||
"""Stream call LLM API"""
|
||||
) -> AsyncGenerator[str, None]:
|
||||
"""Stream call LLM API - yields raw SSE event lines
|
||||
|
||||
Yields:
|
||||
str: Raw SSE event lines for direct forwarding
|
||||
"""
|
||||
body = self._build_body(model, messages, tools, stream=True, **kwargs)
|
||||
|
||||
# Accumulators for tool calls (need to collect from delta chunks)
|
||||
accumulated_tool_calls = {}
|
||||
print(f"[LLM] Starting stream_call for model: {model}")
|
||||
print(f"[LLM] Messages count: {len(messages)}")
|
||||
|
||||
try:
|
||||
async with httpx.AsyncClient(timeout=120.0) as client:
|
||||
print(f"[LLM] Sending request to {self.api_url}")
|
||||
async with client.stream(
|
||||
"POST",
|
||||
self.api_url,
|
||||
headers=self._build_headers(),
|
||||
json=body
|
||||
) as response:
|
||||
print(f"[LLM] Response status: {response.status_code}")
|
||||
response.raise_for_status()
|
||||
|
||||
async for line in response.aiter_lines():
|
||||
if not line.strip():
|
||||
continue
|
||||
|
||||
if line.startswith("data: "):
|
||||
data_str = line[6:]
|
||||
|
||||
if data_str == "[DONE]":
|
||||
yield {"type": "done"}
|
||||
continue
|
||||
|
||||
try:
|
||||
chunk = json.loads(data_str)
|
||||
except json.JSONDecodeError:
|
||||
continue
|
||||
|
||||
if "choices" not in chunk:
|
||||
continue
|
||||
|
||||
delta = chunk.get("choices", [{}])[0].get("delta", {})
|
||||
|
||||
# DeepSeek reasoner: use content if available, otherwise fall back to reasoning_content
|
||||
content = delta.get("content")
|
||||
reasoning = delta.get("reasoning_content", "")
|
||||
if content and isinstance(content, str) and content.strip():
|
||||
yield {"type": "content_delta", "content": content}
|
||||
elif reasoning:
|
||||
yield {"type": "content_delta", "content": reasoning}
|
||||
|
||||
# Accumulate tool calls from delta chunks (DeepSeek sends them incrementally)
|
||||
tool_calls_delta = delta.get("tool_calls", [])
|
||||
for tc in tool_calls_delta:
|
||||
idx = tc.get("index", 0)
|
||||
if idx not in accumulated_tool_calls:
|
||||
accumulated_tool_calls[idx] = {"index": idx}
|
||||
if "function" in tc:
|
||||
if "function" not in accumulated_tool_calls[idx]:
|
||||
accumulated_tool_calls[idx]["function"] = {"name": "", "arguments": ""}
|
||||
if "name" in tc["function"]:
|
||||
accumulated_tool_calls[idx]["function"]["name"] += tc["function"]["name"]
|
||||
if "arguments" in tc["function"]:
|
||||
accumulated_tool_calls[idx]["function"]["arguments"] += tc["function"]["arguments"]
|
||||
|
||||
if tool_calls_delta:
|
||||
yield {"type": "tool_call_delta", "tool_call": tool_calls_delta}
|
||||
|
||||
# Check for finish_reason to signal end of stream
|
||||
choice = chunk.get("choices", [{}])[0]
|
||||
finish_reason = choice.get("finish_reason")
|
||||
if finish_reason:
|
||||
# Build final tool_calls list from accumulated chunks
|
||||
final_tool_calls = list(accumulated_tool_calls.values()) if accumulated_tool_calls else None
|
||||
yield {"type": "done", "tool_calls": final_tool_calls}
|
||||
if line.strip():
|
||||
yield line + "\n"
|
||||
except httpx.HTTPStatusError as e:
|
||||
# Return error as an event instead of raising
|
||||
error_text = e.response.text if e.response else str(e)
|
||||
yield {"type": "error", "error": f"HTTP {e.response.status_code}: {error_text}"}
|
||||
status_code = e.response.status_code if e.response else "?"
|
||||
print(f"[LLM] HTTP error: {status_code}")
|
||||
yield f"event: error\ndata: {json.dumps({'content': f'HTTP {status_code}: Request failed'})}\n\n"
|
||||
except Exception as e:
|
||||
yield {"type": "error", "error": str(e)}
|
||||
print(f"[LLM] Exception: {type(e).__name__}: {str(e)}")
|
||||
yield f"event: error\ndata: {json.dumps({'content': str(e)})}\n\n"
|
||||
|
||||
|
||||
# Global LLM client
|
||||
|
|
|
|||
Loading…
Reference in New Issue