Update README
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@ -593,112 +593,20 @@ LightRAG 中的文档处理流程有些复杂,分为两个主要阶段:提
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4. 使用查询端点查询系统
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5. 如果在输入目录中放入新文件,触发文档扫描
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### 查询端点
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## 异步文档索引与进度跟踪
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#### POST /query
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使用不同搜索模式查询 RAG 系统。
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LightRAG采用异步文档索引机制,便于前端监控和查询文档处理进度。用户通过指定端点上传文件或插入文本时,系统将返回唯一的跟踪ID,以便实时监控处理进度。
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```bash
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curl -X POST "http://localhost:9621/query" \
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-H "Content-Type: application/json" \
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-d '{"query": "您的问题", "mode": "hybrid", ""}'
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```
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**支持生成跟踪ID的API端点:**
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* `/documents/upload`
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* `/documents/text`
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* `/documents/texts`
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#### POST /query/stream
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从 RAG 系统流式获取响应。
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**文档处理状态查询端点:**
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* `/track_status/{track_id}`
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```bash
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curl -X POST "http://localhost:9621/query/stream" \
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-H "Content-Type: application/json" \
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-d '{"query": "您的问题", "mode": "hybrid"}'
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```
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### 文档管理端点
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#### POST /documents/text
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直接将文本插入 RAG 系统。
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```bash
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curl -X POST "http://localhost:9621/documents/text" \
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-H "Content-Type: application/json" \
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-d '{"text": "您的文本内容", "description": "可选描述"}'
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```
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#### POST /documents/file
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向 RAG 系统上传单个文件。
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```bash
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curl -X POST "http://localhost:9621/documents/file" \
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-F "file=@/path/to/your/document.txt" \
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-F "description=可选描述"
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```
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#### POST /documents/batch
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一次上传多个文件。
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```bash
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curl -X POST "http://localhost:9621/documents/batch" \
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-F "files=@/path/to/doc1.txt" \
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-F "files=@/path/to/doc2.txt"
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```
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#### POST /documents/scan
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触发输入目录中新文件的文档扫描。
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```bash
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curl -X POST "http://localhost:9621/documents/scan" --max-time 1800
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```
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> 根据所有新文件的预计索引时间调整 max-time。
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#### DELETE /documents
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从 RAG 系统中清除所有文档。
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```bash
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curl -X DELETE "http://localhost:9621/documents"
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```
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### Ollama 模拟端点
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#### GET /api/version
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获取 Ollama 版本信息。
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```bash
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curl http://localhost:9621/api/version
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```
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#### GET /api/tags
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获取 Ollama 可用模型。
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```bash
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curl http://localhost:9621/api/tags
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```
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#### POST /api/chat
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处理聊天补全请求。通过根据查询前缀选择查询模式将用户查询路由到 LightRAG。检测并将 OpenWebUI 会话相关请求(用于元数据生成任务)直接转发给底层 LLM。
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```shell
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curl -N -X POST http://localhost:9621/api/chat -H "Content-Type: application/json" -d \
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'{"model":"lightrag:latest","messages":[{"role":"user","content":"猪八戒是谁"}],"stream":true}'
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```
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> 有关 Ollama API 的更多信息,请访问:[Ollama API 文档](https://github.com/ollama/ollama/blob/main/docs/api.md)
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#### POST /api/generate
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处理生成补全请求。为了兼容性目的,该请求不由 LightRAG 处理,而是由底层 LLM 模型处理。
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### 实用工具端点
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#### GET /health
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检查服务器健康状况和配置。
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```bash
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curl "http://localhost:9621/health"
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```
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该端点提供全面的状态信息,包括:
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* 文档处理状态(待处理/处理中/已处理/失败)
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* 内容摘要和元数据
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* 处理失败时的错误信息
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* 创建和更新时间戳
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@ -534,111 +534,26 @@ You can test the API endpoints using the provided curl commands or through the S
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4. Query the system using the query endpoints
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5. Trigger document scan if new files are put into the inputs directory
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### Query Endpoints:
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## Asynchronous Document Indexing with Progress Tracking
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#### POST /query
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Query the RAG system with options for different search modes.
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LightRAG implements asynchronous document indexing to enable frontend monitoring and querying of document processing progress. Upon uploading files or inserting text through designated endpoints, a unique Track ID is returned to facilitate real-time progress monitoring.
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```bash
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curl -X POST "http://localhost:9621/query" \
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-H "Content-Type: application/json" \
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-d '{"query": "Your question here", "mode": "hybrid"}'
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```
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**API Endpoints Supporting Track ID Generation:**
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#### POST /query/stream
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Stream responses from the RAG system.
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* `/documents/upload`
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* `/documents/text`
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* `/documents/texts`
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```bash
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curl -X POST "http://localhost:9621/query/stream" \
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-H "Content-Type: application/json" \
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-d '{"query": "Your question here", "mode": "hybrid"}'
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```
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**Document Processing Status Query Endpoint:**
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* `/track_status/{track_id}`
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### Document Management Endpoints:
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This endpoint provides comprehensive status information including:
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* Document processing status (pending/processing/processed/failed)
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* Content summary and metadata
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* Error messages if processing failed
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* Timestamps for creation and updates
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#### POST /documents/text
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Insert text directly into the RAG system.
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```bash
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curl -X POST "http://localhost:9621/documents/text" \
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-H "Content-Type: application/json" \
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-d '{"text": "Your text content here", "description": "Optional description"}'
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```
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#### POST /documents/file
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Upload a single file to the RAG system.
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```bash
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curl -X POST "http://localhost:9621/documents/file" \
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-F "file=@/path/to/your/document.txt" \
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-F "description=Optional description"
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```
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#### POST /documents/batch
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Upload multiple files at once.
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```bash
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curl -X POST "http://localhost:9621/documents/batch" \
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-F "files=@/path/to/doc1.txt" \
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-F "files=@/path/to/doc2.txt"
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```
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#### POST /documents/scan
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Trigger document scan for new files in the input directory.
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```bash
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curl -X POST "http://localhost:9621/documents/scan" --max-time 1800
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```
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> Adjust max-time according to the estimated indexing time for all new files.
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#### DELETE /documents
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Clear all documents from the RAG system.
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```bash
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curl -X DELETE "http://localhost:9621/documents"
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```
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### Ollama Emulation Endpoints:
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#### GET /api/version
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Get Ollama version information.
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```bash
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curl http://localhost:9621/api/version
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```
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#### GET /api/tags
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Get available Ollama models.
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```bash
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curl http://localhost:9621/api/tags
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```
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#### POST /api/chat
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Handle chat completion requests. Routes user queries through LightRAG by selecting query mode based on query prefix. Detects and forwards OpenWebUI session-related requests (for metadata generation task) directly to the underlying LLM.
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```shell
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curl -N -X POST http://localhost:9621/api/chat -H "Content-Type: application/json" -d \
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'{"model":"lightrag:latest","messages":[{"role":"user","content":"猪八戒是谁"}],"stream":true}'
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```
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> For more information about Ollama API, please visit: [Ollama API documentation](https://github.com/ollama/ollama/blob/main/docs/api.md)
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#### POST /api/generate
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Handle generate completion requests. For compatibility purposes, the request is not processed by LightRAG, and will be handled by the underlying LLM model.
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### Utility Endpoints:
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#### GET /health
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Check server health and configuration.
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```bash
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curl "http://localhost:9621/health"
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```
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