mirror of
https://github.com/Tencent/WeKnora.git
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wizardchen
daa9ef500c
- Introduced a new package for managing custom agents, including CRUD operations for agent creation, retrieval, updating, and deletion. - Implemented API endpoints for listing agents and retrieving agent placeholders. - Added data structures for agent configuration and requests, enhancing the overall agent management capabilities. - Enhanced the client with methods to interact with the new agent management features, improving user experience in managing agents. These changes significantly expand the application's functionality for handling custom agents, providing users with a comprehensive toolset for agent management.
403 lines
9.2 KiB
Markdown
403 lines
9.2 KiB
Markdown
# 初始化配置 API
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[返回目录](./README.md)
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| 方法 | 路径 | 描述 |
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| ------ | ------------------------------------------------- | -------------------------- |
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| GET | `/initialization/config/:kb_id` | 获取知识库初始化配置 |
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| POST | `/initialization/initialize/:kb_id` | 初始化知识库模型配置 |
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| PUT | `/initialization/config/:kb_id` | 更新知识库模型配置 |
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| GET | `/initialization/ollama/status` | 检查 Ollama 状态 |
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| GET | `/initialization/ollama/models` | 获取本地 Ollama 模型列表 |
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| POST | `/initialization/ollama/models/check` | 检查 Ollama 模型是否可用 |
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| POST | `/initialization/ollama/models/download` | 下载 Ollama 模型 |
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| GET | `/initialization/ollama/download/progress/:task_id` | 获取下载进度 |
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| GET | `/initialization/ollama/download/tasks` | 获取所有下载任务 |
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| POST | `/initialization/remote/check` | 检查远程模型 API |
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| POST | `/initialization/embedding/test` | 测试嵌入模型 |
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| POST | `/initialization/rerank/check` | 检查重排序模型 |
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| POST | `/initialization/multimodal/test` | 测试多模态模型 |
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| POST | `/initialization/extract/text-relation` | 提取文本关系 |
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## GET `/initialization/config/:kb_id` - 获取知识库初始化配置
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**请求**:
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```curl
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curl --location 'http://localhost:8080/api/v1/initialization/config/kb-00000001' \
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--header 'X-API-Key: sk-xxxxx' \
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--header 'Content-Type: application/json'
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```
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**响应**:
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```json
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{
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"data": {
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"chat_model_id": "model-00000001",
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"embedding_model_id": "model-00000002",
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"rerank_model_id": "model-00000003",
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"multimodal_id": "model-00000004"
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},
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"success": true
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}
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```
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## POST `/initialization/initialize/:kb_id` - 初始化知识库模型配置
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**请求**:
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```curl
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curl --location 'http://localhost:8080/api/v1/initialization/initialize/kb-00000001' \
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--header 'X-API-Key: sk-xxxxx' \
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--header 'Content-Type: application/json' \
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--data '{
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"chat_model_id": "model-00000001",
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"embedding_model_id": "model-00000002",
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"rerank_model_id": "model-00000003",
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"multimodal_id": "model-00000004"
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}'
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```
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**响应**:
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```json
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{
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"success": true
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}
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```
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## PUT `/initialization/config/:kb_id` - 更新知识库模型配置
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**请求**:
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```curl
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curl --location --request PUT 'http://localhost:8080/api/v1/initialization/config/kb-00000001' \
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--header 'X-API-Key: sk-xxxxx' \
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--header 'Content-Type: application/json' \
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--data '{
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"chat_model_id": "model-00000010",
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"embedding_model_id": "model-00000002"
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}'
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```
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**响应**:
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```json
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{
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"success": true
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}
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```
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## GET `/initialization/ollama/status` - 检查 Ollama 状态
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**请求**:
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```curl
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curl --location 'http://localhost:8080/api/v1/initialization/ollama/status' \
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--header 'X-API-Key: sk-xxxxx' \
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--header 'Content-Type: application/json'
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```
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**响应**:
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```json
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{
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"data": {
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"available": true
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},
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"success": true
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}
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```
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## GET `/initialization/ollama/models` - 获取本地 Ollama 模型列表
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**请求**:
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```curl
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curl --location 'http://localhost:8080/api/v1/initialization/ollama/models' \
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--header 'X-API-Key: sk-xxxxx' \
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--header 'Content-Type: application/json'
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```
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**响应**:
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```json
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{
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"data": [
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{
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"name": "llama3:8b",
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"size": 4661211648,
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"modified_at": "2025-08-10T15:30:00+08:00"
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},
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{
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"name": "nomic-embed-text:latest",
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"size": 274302976,
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"modified_at": "2025-08-11T09:00:00+08:00"
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}
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],
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"success": true
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}
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```
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## POST `/initialization/ollama/models/check` - 检查 Ollama 模型是否可用
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**请求**:
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```curl
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curl --location 'http://localhost:8080/api/v1/initialization/ollama/models/check' \
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--header 'X-API-Key: sk-xxxxx' \
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--header 'Content-Type: application/json' \
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--data '{
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"models": ["llama3:8b", "nomic-embed-text:latest", "mistral:7b"]
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}'
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```
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**响应**:
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```json
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{
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"data": {
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"llama3:8b": true,
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"nomic-embed-text:latest": true,
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"mistral:7b": false
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},
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"success": true
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}
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```
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## POST `/initialization/ollama/models/download` - 下载 Ollama 模型
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**请求**:
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```curl
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curl --location 'http://localhost:8080/api/v1/initialization/ollama/models/download' \
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--header 'X-API-Key: sk-xxxxx' \
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--header 'Content-Type: application/json' \
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--data '{
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"model": "mistral:7b"
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}'
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```
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**响应**:
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```json
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{
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"data": {
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"id": "task-00000001",
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"modelName": "mistral:7b",
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"status": "downloading",
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"progress": 0,
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"message": "开始下载",
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"startTime": "2025-08-12T10:00:00+08:00"
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},
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"success": true
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}
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```
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## GET `/initialization/ollama/download/progress/:task_id` - 获取下载进度
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**请求**:
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```curl
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curl --location 'http://localhost:8080/api/v1/initialization/ollama/download/progress/task-00000001' \
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--header 'X-API-Key: sk-xxxxx' \
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--header 'Content-Type: application/json'
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```
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**响应**:
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```json
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{
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"data": {
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"id": "task-00000001",
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"modelName": "mistral:7b",
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"status": "downloading",
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"progress": 45.6,
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"message": "正在下载 2.1GB / 4.6GB",
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"startTime": "2025-08-12T10:00:00+08:00"
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},
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"success": true
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}
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```
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## GET `/initialization/ollama/download/tasks` - 获取所有下载任务
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**请求**:
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```curl
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curl --location 'http://localhost:8080/api/v1/initialization/ollama/download/tasks' \
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--header 'X-API-Key: sk-xxxxx' \
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--header 'Content-Type: application/json'
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```
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**响应**:
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```json
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{
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"data": [
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{
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"id": "task-00000001",
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"modelName": "mistral:7b",
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"status": "completed",
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"progress": 100,
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"message": "下载完成",
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"startTime": "2025-08-12T10:00:00+08:00",
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"endTime": "2025-08-12T10:15:00+08:00"
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},
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{
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"id": "task-00000002",
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"modelName": "llama3:70b",
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"status": "downloading",
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"progress": 30.2,
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"message": "正在下载 12.5GB / 41.4GB",
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"startTime": "2025-08-12T10:20:00+08:00"
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}
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],
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"success": true
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}
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```
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## POST `/initialization/remote/check` - 检查远程模型 API
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**请求**:
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```curl
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curl --location 'http://localhost:8080/api/v1/initialization/remote/check' \
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--header 'X-API-Key: sk-xxxxx' \
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--header 'Content-Type: application/json' \
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--data '{
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"api_url": "https://api.openai.com/v1",
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"api_key": "sk-xxxxx",
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"model": "gpt-4o"
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}'
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```
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**响应**:
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```json
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{
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"data": {
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"success": true,
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"message": "模型可用"
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},
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"success": true
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}
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```
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## POST `/initialization/embedding/test` - 测试嵌入模型
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**请求**:
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```curl
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curl --location 'http://localhost:8080/api/v1/initialization/embedding/test' \
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--header 'X-API-Key: sk-xxxxx' \
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--header 'Content-Type: application/json' \
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--data '{
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"api_url": "https://api.openai.com/v1",
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"api_key": "sk-xxxxx",
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"model": "text-embedding-3-small"
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}'
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```
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**响应**:
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```json
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{
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"data": {
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"success": true,
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"message": "嵌入模型测试通过"
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},
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"success": true
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}
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```
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## POST `/initialization/rerank/check` - 检查重排序模型
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**请求**:
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```curl
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curl --location 'http://localhost:8080/api/v1/initialization/rerank/check' \
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--header 'X-API-Key: sk-xxxxx' \
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--header 'Content-Type: application/json' \
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--data '{
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"api_url": "https://api.cohere.ai/v1",
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"api_key": "sk-xxxxx",
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"model": "rerank-english-v3.0"
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}'
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```
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**响应**:
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```json
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{
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"data": {
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"success": true,
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"message": "重排序模型可用"
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},
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"success": true
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}
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```
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## POST `/initialization/multimodal/test` - 测试多模态模型
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**请求**:
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```curl
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curl --location 'http://localhost:8080/api/v1/initialization/multimodal/test' \
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--header 'X-API-Key: sk-xxxxx' \
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--header 'Content-Type: application/json' \
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--data '{
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"api_url": "https://api.openai.com/v1",
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"api_key": "sk-xxxxx",
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"model": "gpt-4o"
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}'
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```
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**响应**:
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```json
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{
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"data": {
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"success": true,
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"message": "多模态模型测试通过"
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},
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"success": true
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}
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```
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## POST `/initialization/extract/text-relation` - 提取文本关系
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**请求**:
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```curl
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curl --location 'http://localhost:8080/api/v1/initialization/extract/text-relation' \
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--header 'X-API-Key: sk-xxxxx' \
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--header 'Content-Type: application/json' \
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--data '{
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"text": "WeKnora 是一个知识管理平台，支持多种文档格式的解析和检索。",
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"model_id": "model-00000001"
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}'
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```
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**响应**:
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```json
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{
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"data": {
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"entities": [
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{"name": "WeKnora", "type": "Product"},
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{"name": "知识管理平台", "type": "Concept"}
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],
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"relations": [
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{
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"source": "WeKnora",
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"target": "知识管理平台",
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"relation": "is_a"
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}
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]
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},
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"success": true
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}
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```
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