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WeKnora/docs/api/initialization.md
wizardchen daa9ef500c feat: add agent management functionality 
- 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.
2026-03-09 17:34:13 +08:00

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初始化配置 API

返回目录

方法 路径 描述
GET /initialization/config/:kb_id 获取知识库初始化配置
POST /initialization/initialize/:kb_id 初始化知识库模型配置
PUT /initialization/config/:kb_id 更新知识库模型配置
GET /initialization/ollama/status 检查 Ollama 状态
GET /initialization/ollama/models 获取本地 Ollama 模型列表
POST /initialization/ollama/models/check 检查 Ollama 模型是否可用
POST /initialization/ollama/models/download 下载 Ollama 模型
GET /initialization/ollama/download/progress/:task_id 获取下载进度
GET /initialization/ollama/download/tasks 获取所有下载任务
POST /initialization/remote/check 检查远程模型 API
POST /initialization/embedding/test 测试嵌入模型
POST /initialization/rerank/check 检查重排序模型
POST /initialization/multimodal/test 测试多模态模型
POST /initialization/extract/text-relation 提取文本关系

GET /initialization/config/:kb_id - 获取知识库初始化配置

请求:

curl --location 'http://localhost:8080/api/v1/initialization/config/kb-00000001' \
--header 'X-API-Key: sk-xxxxx' \
--header 'Content-Type: application/json'

响应:

{
    "data": {
        "chat_model_id": "model-00000001",
        "embedding_model_id": "model-00000002",
        "rerank_model_id": "model-00000003",
        "multimodal_id": "model-00000004"
    },
    "success": true
}

POST /initialization/initialize/:kb_id - 初始化知识库模型配置

请求:

curl --location 'http://localhost:8080/api/v1/initialization/initialize/kb-00000001' \
--header 'X-API-Key: sk-xxxxx' \
--header 'Content-Type: application/json' \
--data '{
    "chat_model_id": "model-00000001",
    "embedding_model_id": "model-00000002",
    "rerank_model_id": "model-00000003",
    "multimodal_id": "model-00000004"
}'

响应:

{
    "success": true
}

PUT /initialization/config/:kb_id - 更新知识库模型配置

请求:

curl --location --request PUT 'http://localhost:8080/api/v1/initialization/config/kb-00000001' \
--header 'X-API-Key: sk-xxxxx' \
--header 'Content-Type: application/json' \
--data '{
    "chat_model_id": "model-00000010",
    "embedding_model_id": "model-00000002"
}'

响应:

{
    "success": true
}

GET /initialization/ollama/status - 检查 Ollama 状态

请求:

curl --location 'http://localhost:8080/api/v1/initialization/ollama/status' \
--header 'X-API-Key: sk-xxxxx' \
--header 'Content-Type: application/json'

响应:

{
    "data": {
        "available": true
    },
    "success": true
}

GET /initialization/ollama/models - 获取本地 Ollama 模型列表

请求:

curl --location 'http://localhost:8080/api/v1/initialization/ollama/models' \
--header 'X-API-Key: sk-xxxxx' \
--header 'Content-Type: application/json'

响应:

{
    "data": [
        {
            "name": "llama3:8b",
            "size": 4661211648,
            "modified_at": "2025-08-10T15:30:00+08:00"
        },
        {
            "name": "nomic-embed-text:latest",
            "size": 274302976,
            "modified_at": "2025-08-11T09:00:00+08:00"
        }
    ],
    "success": true
}

POST /initialization/ollama/models/check - 检查 Ollama 模型是否可用

请求:

curl --location 'http://localhost:8080/api/v1/initialization/ollama/models/check' \
--header 'X-API-Key: sk-xxxxx' \
--header 'Content-Type: application/json' \
--data '{
    "models": ["llama3:8b", "nomic-embed-text:latest", "mistral:7b"]
}'

响应:

{
    "data": {
        "llama3:8b": true,
        "nomic-embed-text:latest": true,
        "mistral:7b": false
    },
    "success": true
}

POST /initialization/ollama/models/download - 下载 Ollama 模型

请求:

curl --location 'http://localhost:8080/api/v1/initialization/ollama/models/download' \
--header 'X-API-Key: sk-xxxxx' \
--header 'Content-Type: application/json' \
--data '{
    "model": "mistral:7b"
}'

响应:

{
    "data": {
        "id": "task-00000001",
        "modelName": "mistral:7b",
        "status": "downloading",
        "progress": 0,
        "message": "开始下载",
        "startTime": "2025-08-12T10:00:00+08:00"
    },
    "success": true
}

GET /initialization/ollama/download/progress/:task_id - 获取下载进度

请求:

curl --location 'http://localhost:8080/api/v1/initialization/ollama/download/progress/task-00000001' \
--header 'X-API-Key: sk-xxxxx' \
--header 'Content-Type: application/json'

响应:

{
    "data": {
        "id": "task-00000001",
        "modelName": "mistral:7b",
        "status": "downloading",
        "progress": 45.6,
        "message": "正在下载 2.1GB / 4.6GB",
        "startTime": "2025-08-12T10:00:00+08:00"
    },
    "success": true
}

GET /initialization/ollama/download/tasks - 获取所有下载任务

请求:

curl --location 'http://localhost:8080/api/v1/initialization/ollama/download/tasks' \
--header 'X-API-Key: sk-xxxxx' \
--header 'Content-Type: application/json'

响应:

{
    "data": [
        {
            "id": "task-00000001",
            "modelName": "mistral:7b",
            "status": "completed",
            "progress": 100,
            "message": "下载完成",
            "startTime": "2025-08-12T10:00:00+08:00",
            "endTime": "2025-08-12T10:15:00+08:00"
        },
        {
            "id": "task-00000002",
            "modelName": "llama3:70b",
            "status": "downloading",
            "progress": 30.2,
            "message": "正在下载 12.5GB / 41.4GB",
            "startTime": "2025-08-12T10:20:00+08:00"
        }
    ],
    "success": true
}

POST /initialization/remote/check - 检查远程模型 API

请求:

curl --location 'http://localhost:8080/api/v1/initialization/remote/check' \
--header 'X-API-Key: sk-xxxxx' \
--header 'Content-Type: application/json' \
--data '{
    "api_url": "https://api.openai.com/v1",
    "api_key": "sk-xxxxx",
    "model": "gpt-4o"
}'

响应:

{
    "data": {
        "success": true,
        "message": "模型可用"
    },
    "success": true
}

POST /initialization/embedding/test - 测试嵌入模型

请求:

curl --location 'http://localhost:8080/api/v1/initialization/embedding/test' \
--header 'X-API-Key: sk-xxxxx' \
--header 'Content-Type: application/json' \
--data '{
    "api_url": "https://api.openai.com/v1",
    "api_key": "sk-xxxxx",
    "model": "text-embedding-3-small"
}'

响应:

{
    "data": {
        "success": true,
        "message": "嵌入模型测试通过"
    },
    "success": true
}

POST /initialization/rerank/check - 检查重排序模型

请求:

curl --location 'http://localhost:8080/api/v1/initialization/rerank/check' \
--header 'X-API-Key: sk-xxxxx' \
--header 'Content-Type: application/json' \
--data '{
    "api_url": "https://api.cohere.ai/v1",
    "api_key": "sk-xxxxx",
    "model": "rerank-english-v3.0"
}'

响应:

{
    "data": {
        "success": true,
        "message": "重排序模型可用"
    },
    "success": true
}

POST /initialization/multimodal/test - 测试多模态模型

请求:

curl --location 'http://localhost:8080/api/v1/initialization/multimodal/test' \
--header 'X-API-Key: sk-xxxxx' \
--header 'Content-Type: application/json' \
--data '{
    "api_url": "https://api.openai.com/v1",
    "api_key": "sk-xxxxx",
    "model": "gpt-4o"
}'

响应:

{
    "data": {
        "success": true,
        "message": "多模态模型测试通过"
    },
    "success": true
}

POST /initialization/extract/text-relation - 提取文本关系

请求:

curl --location 'http://localhost:8080/api/v1/initialization/extract/text-relation' \
--header 'X-API-Key: sk-xxxxx' \
--header 'Content-Type: application/json' \
--data '{
    "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。",
    "model_id": "model-00000001"
}'

响应:

{
    "data": {
        "entities": [
            {"name": "WeKnora", "type": "Product"},
            {"name": "知识管理平台", "type": "Concept"}
        ],
        "relations": [
            {
                "source": "WeKnora",
                "target": "知识管理平台",
                "relation": "is_a"
            }
        ]
    },
    "success": true
}
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