Merge branch 'main'

lightrag/__init__.py

@@ -1,5 +1,5 @@
 from .lightrag import LightRAG as LightRAG, QueryParam as QueryParam
 
-__version__ = "1.2.8"
+__version__ = "1.2.9"
 __author__ = "Zirui Guo"
 __url__ = "https://github.com/HKUDS/LightRAG"

lightrag/api/README-zh.md

@@ -0,0 +1,559 @@
+# LightRAG 服务器和 Web 界面
+
+LightRAG 服务器旨在提供 Web 界面和 API 支持。Web 界面便于文档索引、知识图谱探索和简单的 RAG 查询界面。LightRAG 服务器还提供了与 Ollama 兼容的接口，旨在将 LightRAG 模拟为 Ollama 聊天模型。这使得 AI 聊天机器人（如 Open WebUI）可以轻松访问 LightRAG。
+
+![image-20250323122538997](./README.assets/image-20250323122538997.png)
+
+![image-20250323122754387](./README.assets/image-20250323122754387.png)
+
+![image-20250323123011220](./README.assets/image-20250323123011220.png)
+
+## 入门指南
+
+### 安装
+
+* 从 PyPI 安装
+
+```bash
+pip install "lightrag-hku[api]"
+```
+
+* 从源代码安装
+
+```bash
+# 克隆仓库
+git clone https://github.com/HKUDS/lightrag.git
+
+# 切换到仓库目录
+cd lightrag
+
+# 如有必要，创建 Python 虚拟环境
+# 以可编辑模式安装并支持 API
+pip install -e ".[api]"
+```
+
+### 启动 LightRAG 服务器前的准备
+
+LightRAG 需要同时集成 LLM（大型语言模型）和嵌入模型以有效执行文档索引和查询操作。在首次部署 LightRAG 服务器之前，必须配置 LLM 和嵌入模型的设置。LightRAG 支持绑定到各种 LLM/嵌入后端：
+
+* ollama
+* lollms
+* openai 或 openai 兼容
+* azure_openai
+
+建议使用环境变量来配置 LightRAG 服务器。项目根目录中有一个名为 `env.example` 的示例环境变量文件。请将此文件复制到启动目录并重命名为 `.env`。之后，您可以在 `.env` 文件中修改与 LLM 和嵌入模型相关的参数。需要注意的是，LightRAG 服务器每次启动时都会将 `.env` 中的环境变量加载到系统环境变量中。由于 LightRAG 服务器会优先使用系统环境变量中的设置，如果您在通过命令行启动 LightRAG 服务器后修改了 `.env` 文件，则需要执行 `source .env` 使新设置生效。
+
+以下是 LLM 和嵌入模型的一些常见设置示例：
+
+* OpenAI LLM + Ollama 嵌入
+
+```
+LLM_BINDING=openai
+LLM_MODEL=gpt-4o
+LLM_BINDING_HOST=https://api.openai.com/v1
+LLM_BINDING_API_KEY=your_api_key
+MAX_TOKENS=32768                # 发送给 LLM 的最大 token 数（小于模型上下文大小）
+
+EMBEDDING_BINDING=ollama
+EMBEDDING_BINDING_HOST=http://localhost:11434
+EMBEDDING_MODEL=bge-m3:latest
+EMBEDDING_DIM=1024
+# EMBEDDING_BINDING_API_KEY=your_api_key
+```
+
+* Ollama LLM + Ollama 嵌入
+
+```
+LLM_BINDING=ollama
+LLM_MODEL=mistral-nemo:latest
+LLM_BINDING_HOST=http://localhost:11434
+# LLM_BINDING_API_KEY=your_api_key
+MAX_TOKENS=8192                  # 发送给 LLM 的最大 token 数（基于您的 Ollama 服务器容量）
+
+EMBEDDING_BINDING=ollama
+EMBEDDING_BINDING_HOST=http://localhost:11434
+EMBEDDING_MODEL=bge-m3:latest
+EMBEDDING_DIM=1024
+# EMBEDDING_BINDING_API_KEY=your_api_key
+```
+
+### 启动 LightRAG 服务器
+
+LightRAG 服务器支持两种运行模式：
+* 简单高效的 Uvicorn 模式
+
+```
+lightrag-server
+```
+* 多进程 Gunicorn + Uvicorn 模式（生产模式，不支持 Windows 环境）
+
+```
+lightrag-gunicorn --workers 4
+```
+`.env` 文件必须放在启动目录中。启动时，LightRAG 服务器将创建一个文档目录（默认为 `./inputs`）和一个数据目录（默认为 `./rag_storage`）。这允许您从不同目录启动多个 LightRAG 服务器实例，每个实例配置为监听不同的网络端口。
+
+以下是一些常用的启动参数：
+
+- `--host`：服务器监听地址（默认：0.0.0.0）
+- `--port`：服务器监听端口（默认：9621）
+- `--timeout`：LLM 请求超时时间（默认：150 秒）
+- `--log-level`：日志级别（默认：INFO）
+- --input-dir：指定要扫描文档的目录（默认：./input）
+
+### 启动时自动扫描
+
+当使用 `--auto-scan-at-startup` 参数启动任何服务器时，系统将自动：
+
+1. 扫描输入目录中的新文件
+2. 为尚未在数据库中的新文档建立索引
+3. 使所有内容立即可用于 RAG 查询
+
+> `--input-dir` 参数指定要扫描的输入目录。您可以从 webui 触发输入目录扫描。
+
+### Gunicorn + Uvicorn 的多工作进程
+
+LightRAG 服务器可以在 `Gunicorn + Uvicorn` 预加载模式下运行。Gunicorn 的多工作进程（多进程）功能可以防止文档索引任务阻塞 RAG 查询。使用 CPU 密集型文档提取工具（如 docling）在纯 Uvicorn 模式下可能会导致整个系统被阻塞。
+
+虽然 LightRAG 服务器使用一个工作进程来处理文档索引流程，但通过 Uvicorn 的异步任务支持，可以并行处理多个文件。文档索引速度的瓶颈主要在于 LLM。如果您的 LLM 支持高并发，您可以通过增加 LLM 的并发级别来加速文档索引。以下是几个与并发处理相关的环境变量及其默认值：
+
+```
+WORKERS=2                    # 工作进程数，不大于 (2 x 核心数) + 1
+MAX_PARALLEL_INSERT=2        # 一批中并行处理的文件数
+MAX_ASYNC=4                  # LLM 的最大并发请求数
+```
+
+### 将 Lightrag 安装为 Linux 服务
+
+从示例文件 `lightrag.sevice.example` 创建您的服务文件 `lightrag.sevice`。修改服务文件中的 WorkingDirectory 和 ExecStart：
+
+```text
+Description=LightRAG Ollama Service
+WorkingDirectory=<lightrag 安装目录>
+ExecStart=<lightrag 安装目录>/lightrag/api/lightrag-api
+```
+
+修改您的服务启动脚本：`lightrag-api`。根据需要更改 python 虚拟环境激活命令：
+
+```shell
+#!/bin/bash
+
+# 您的 python 虚拟环境激活命令
+source /home/netman/lightrag-xyj/venv/bin/activate
+# 启动 lightrag api 服务器
+lightrag-server
+```
+
+安装 LightRAG 服务。如果您的系统是 Ubuntu，以下命令将生效：
+
+```shell
+sudo cp lightrag.service /etc/systemd/system/
+sudo systemctl daemon-reload
+sudo systemctl start lightrag.service
+sudo systemctl status lightrag.service
+sudo systemctl enable lightrag.service
+```
+
+## Ollama 模拟
+
+我们为 LightRAG 提供了 Ollama 兼容接口，旨在将 LightRAG 模拟为 Ollama 聊天模型。这使得支持 Ollama 的 AI 聊天前端（如 Open WebUI）可以轻松访问 LightRAG。
+
+### 将 Open WebUI 连接到 LightRAG
+
+启动 lightrag-server 后，您可以在 Open WebUI 管理面板中添加 Ollama 类型的连接。然后，一个名为 lightrag:latest 的模型将出现在 Open WebUI 的模型管理界面中。用户随后可以通过聊天界面向 LightRAG 发送查询。对于这种用例，最好将 LightRAG 安装为服务。
+
+Open WebUI 使用 LLM 来执行会话标题和会话关键词生成任务。因此，Ollama 聊天补全 API 会检测并将 OpenWebUI 会话相关请求直接转发给底层 LLM。Open WebUI 的截图：
+
+![image-20250323194750379](./README.assets/image-20250323194750379.png)
+
+### 在聊天中选择查询模式
+
+查询字符串中的查询前缀可以决定使用哪种 LightRAG 查询模式来生成响应。支持的前缀包括：
+
+```
+/local
+/global
+/hybrid
+/naive
+/mix
+/bypass
+```
+
+例如，聊天消息 "/mix 唐僧有几个徒弟" 将触发 LightRAG 的混合模式查询。没有查询前缀的聊天消息默认会触发混合模式查询。
+
+"/bypass" 不是 LightRAG 查询模式，它会告诉 API 服务器将查询连同聊天历史直接传递给底层 LLM。因此用户可以使用 LLM 基于聊天历史回答问题。如果您使用 Open WebUI 作为前端，您可以直接切换到普通 LLM 模型，而不是使用 /bypass 前缀。
+
+## API 密钥和认证
+
+默认情况下，LightRAG 服务器可以在没有任何认证的情况下访问。我们可以使用 API 密钥或账户凭证配置服务器以确保其安全。
+
+* API 密钥
+
+```
+LIGHTRAG_API_KEY=your-secure-api-key-here
+WHITELIST_PATHS=/health,/api/*
+```
+
+> 健康检查和 Ollama 模拟端点默认不进行 API 密钥检查。
+
+* 账户凭证（Web 界面需要登录后才能访问）
+
+LightRAG API 服务器使用基于 HS256 算法的 JWT 认证。要启用安全访问控制，需要以下环境变量：
+
+```bash
+# JWT 认证
+AUTH_USERNAME=admin      # 登录名
+AUTH_PASSWORD=admin123   # 密码
+TOKEN_SECRET=your-key    # JWT 密钥
+TOKEN_EXPIRE_HOURS=4     # 过期时间
+```
+
+> 目前仅支持配置一个管理员账户和密码。尚未开发和实现完整的账户系统。
+
+如果未配置账户凭证，Web 界面将以访客身份访问系统。因此，即使仅配置了 API 密钥，所有 API 仍然可以通过访客账户访问，这仍然不安全。因此，要保护 API，需要同时配置这两种认证方法。
+
+## Azure OpenAI 后端配置
+
+可以使用以下 Azure CLI 命令创建 Azure OpenAI API（您需要先从 [https://docs.microsoft.com/en-us/cli/azure/install-azure-cli](https://docs.microsoft.com/en-us/cli/azure/install-azure-cli) 安装 Azure CLI）：
+
+```bash
+# 根据需要更改资源组名称、位置和 OpenAI 资源名称
+RESOURCE_GROUP_NAME=LightRAG
+LOCATION=swedencentral
+RESOURCE_NAME=LightRAG-OpenAI
+
+az login
+az group create --name $RESOURCE_GROUP_NAME --location $LOCATION
+az cognitiveservices account create --name $RESOURCE_NAME --resource-group $RESOURCE_GROUP_NAME  --kind OpenAI --sku S0 --location swedencentral
+az cognitiveservices account deployment create --resource-group $RESOURCE_GROUP_NAME  --model-format OpenAI --name $RESOURCE_NAME --deployment-name gpt-4o --model-name gpt-4o --model-version "2024-08-06"  --sku-capacity 100 --sku-name "Standard"
+az cognitiveservices account deployment create --resource-group $RESOURCE_GROUP_NAME  --model-format OpenAI --name $RESOURCE_NAME --deployment-name text-embedding-3-large --model-name text-embedding-3-large --model-version "1"  --sku-capacity 80 --sku-name "Standard"
+az cognitiveservices account show --name $RESOURCE_NAME --resource-group $RESOURCE_GROUP_NAME --query "properties.endpoint"
+az cognitiveservices account keys list --name $RESOURCE_NAME -g $RESOURCE_GROUP_NAME
+```
+
+最后一个命令的输出将提供 OpenAI API 的端点和密钥。您可以使用这些值在 `.env` 文件中设置环境变量。
+
+```
+# .env 中的 Azure OpenAI 配置
+LLM_BINDING=azure_openai
+LLM_BINDING_HOST=your-azure-endpoint
+LLM_MODEL=your-model-deployment-name
+LLM_BINDING_API_KEY=your-azure-api-key
+AZURE_OPENAI_API_VERSION=2024-08-01-preview  # 可选，默认为最新版本
+EMBEDDING_BINDING=azure_openai  # 如果使用 Azure OpenAI 进行嵌入
+EMBEDDING_MODEL=your-embedding-deployment-name
+```
+
+## LightRAG 服务器详细配置
+
+API 服务器可以通过三种方式配置（优先级从高到低）：
+
+* 命令行参数
+* 环境变量或 .env 文件
+* Config.ini（仅用于存储配置）
+
+大多数配置都有默认设置，详细信息请查看示例文件：`.env.example`。数据存储配置也可以通过 config.ini 设置。为方便起见，提供了示例文件 `config.ini.example`。
+
+### 支持的 LLM 和嵌入后端
+
+LightRAG 支持绑定到各种 LLM/嵌入后端：
+
+* ollama
+* lollms
+* openai 和 openai 兼容
+* azure_openai
+
+使用环境变量 `LLM_BINDING` 或 CLI 参数 `--llm-binding` 选择 LLM 后端类型。使用环境变量 `EMBEDDING_BINDING` 或 CLI 参数 `--embedding-binding` 选择嵌入后端类型。
+
+### 实体提取配置
+* ENABLE_LLM_CACHE_FOR_EXTRACT：为实体提取启用 LLM 缓存（默认：true）
+
+在测试环境中将 `ENABLE_LLM_CACHE_FOR_EXTRACT` 设置为 true 以减少 LLM 调用成本是很常见的做法。
+
+### 支持的存储类型
+
+LightRAG 使用 4 种类型的存储用于不同目的：
+
+* KV_STORAGE：llm 响应缓存、文本块、文档信息
+* VECTOR_STORAGE：实体向量、关系向量、块向量
+* GRAPH_STORAGE：实体关系图
+* DOC_STATUS_STORAGE：文档索引状态
+
+每种存储类型都有几种实现：
+
+* KV_STORAGE 支持的实现名称
+
+```
+JsonKVStorage    JsonFile(默认)
+MongoKVStorage   MogonDB
+RedisKVStorage   Redis
+TiDBKVStorage    TiDB
+PGKVStorage      Postgres
+OracleKVStorage  Oracle
+```
+
+* GRAPH_STORAGE 支持的实现名称
+
+```
+NetworkXStorage      NetworkX(默认)
+Neo4JStorage         Neo4J
+MongoGraphStorage    MongoDB
+TiDBGraphStorage     TiDB
+AGEStorage           AGE
+GremlinStorage       Gremlin
+PGGraphStorage       Postgres
+OracleGraphStorage   Postgres
+```
+
+* VECTOR_STORAGE 支持的实现名称
+
+```
+NanoVectorDBStorage         NanoVector(默认)
+MilvusVectorDBStorge        Milvus
+ChromaVectorDBStorage       Chroma
+TiDBVectorDBStorage         TiDB
+PGVectorStorage             Postgres
+FaissVectorDBStorage        Faiss
+QdrantVectorDBStorage       Qdrant
+OracleVectorDBStorage       Oracle
+MongoVectorDBStorage        MongoDB
+```
+
+* DOC_STATUS_STORAGE 支持的实现名称
+
+```
+JsonDocStatusStorage        JsonFile(默认)
+PGDocStatusStorage          Postgres
+MongoDocStatusStorage       MongoDB
+```
+
+### 如何选择存储实现
+
+您可以通过环境变量选择存储实现。在首次启动 API 服务器之前，您可以将以下环境变量设置为特定的存储实现名称：
+
+```
+LIGHTRAG_KV_STORAGE=PGKVStorage
+LIGHTRAG_VECTOR_STORAGE=PGVectorStorage
+LIGHTRAG_GRAPH_STORAGE=PGGraphStorage
+LIGHTRAG_DOC_STATUS_STORAGE=PGDocStatusStorage
+```
+
+在向 LightRAG 添加文档后，您不能更改存储实现选择。目前尚不支持从一个存储实现迁移到另一个存储实现。更多信息请阅读示例 env 文件或 config.ini 文件。
+
+### LightRag API 服务器命令行选项
+
+| 参数 | 默认值 | 描述 |
+|-----------|---------|-------------|
+| --host | 0.0.0.0 | 服务器主机 |
+| --port | 9621 | 服务器端口 |
+| --working-dir | ./rag_storage | RAG 存储的工作目录 |
+| --input-dir | ./inputs | 包含输入文档的目录 |
+| --max-async | 4 | 最大异步操作数 |
+| --max-tokens | 32768 | 最大 token 大小 |
+| --timeout | 150 | 超时时间（秒）。None 表示无限超时（不推荐） |
+| --log-level | INFO | 日志级别（DEBUG、INFO、WARNING、ERROR、CRITICAL） |
+| --verbose | - | 详细调试输出（True、False） |
+| --key | None | 用于认证的 API 密钥。保护 lightrag 服务器免受未授权访问 |
+| --ssl | False | 启用 HTTPS |
+| --ssl-certfile | None | SSL 证书文件路径（如果启用 --ssl 则必需） |
+| --ssl-keyfile | None | SSL 私钥文件路径（如果启用 --ssl 则必需） |
+| --top-k | 50 | 要检索的 top-k 项目数；在"local"模式下对应实体，在"global"模式下对应关系。 |
+| --cosine-threshold | 0.4 | 节点和关系检索的余弦阈值，与 top-k 一起控制节点和关系的检索。 |
+| --llm-binding | ollama | LLM 绑定类型（lollms、ollama、openai、openai-ollama、azure_openai） |
+| --embedding-binding | ollama | 嵌入绑定类型（lollms、ollama、openai、azure_openai） |
+| auto-scan-at-startup | - | 扫描输入目录中的新文件并开始索引 |
+
+### 使用示例
+
+#### 使用 ollama 默认本地服务器作为 llm 和嵌入后端运行 Lightrag 服务器
+
+Ollama 是 llm 和嵌入的默认后端，因此默认情况下您可以不带参数运行 lightrag-server，将使用默认值。确保已安装 ollama 并且正在运行，且默认模型已安装在 ollama 上。
+
+```bash
+# 使用 ollama 运行 lightrag，llm 使用 mistral-nemo:latest，嵌入使用 bge-m3:latest
+lightrag-server
+
+# 使用认证密钥
+lightrag-server --key my-key
+```
+
+#### 使用 lollms 默认本地服务器作为 llm 和嵌入后端运行 Lightrag 服务器
+
+```bash
+# 使用 lollms 运行 lightrag，llm 使用 mistral-nemo:latest，嵌入使用 bge-m3:latest
+# 在 .env 或 config.ini 中配置 LLM_BINDING=lollms 和 EMBEDDING_BINDING=lollms
+lightrag-server
+
+# 使用认证密钥
+lightrag-server --key my-key
+```
+
+#### 使用 openai 服务器作为 llm 和嵌入后端运行 Lightrag 服务器
+
+```bash
+# 使用 openai 运行 lightrag，llm 使用 GPT-4o-mini，嵌入使用 text-embedding-3-small
+# 在 .env 或 config.ini 中配置：
+# LLM_BINDING=openai
+# LLM_MODEL=GPT-4o-mini
+# EMBEDDING_BINDING=openai
+# EMBEDDING_MODEL=text-embedding-3-small
+lightrag-server
+
+# 使用认证密钥
+lightrag-server --key my-key
+```
+
+#### 使用 azure openai 服务器作为 llm 和嵌入后端运行 Lightrag 服务器
+
+```bash
+# 使用 azure_openai 运行 lightrag
+# 在 .env 或 config.ini 中配置：
+# LLM_BINDING=azure_openai
+# LLM_MODEL=your-model
+# EMBEDDING_BINDING=azure_openai
+# EMBEDDING_MODEL=your-embedding-model
+lightrag-server
+
+# 使用认证密钥
+lightrag-server --key my-key
+```
+
+**重要说明：**
+- 对于 LoLLMs：确保指定的模型已安装在您的 LoLLMs 实例中
+- 对于 Ollama：确保指定的模型已安装在您的 Ollama 实例中
+- 对于 OpenAI：确保您已设置 OPENAI_API_KEY 环境变量
+- 对于 Azure OpenAI：按照先决条件部分所述构建和配置您的服务器
+
+要获取任何服务器的帮助，使用 --help 标志：
+```bash
+lightrag-server --help
+```
+
+注意：如果您不需要 API 功能，可以使用以下命令安装不带 API 支持的基本包：
+```bash
+pip install lightrag-hku
+```
+
+## API 端点
+
+所有服务器（LoLLMs、Ollama、OpenAI 和 Azure OpenAI）都为 RAG 功能提供相同的 REST API 端点。当 API 服务器运行时，访问：
+
+- Swagger UI：http://localhost:9621/docs
+- ReDoc：http://localhost:9621/redoc
+
+您可以使用提供的 curl 命令或通过 Swagger UI 界面测试 API 端点。确保：
+
+1. 启动适当的后端服务（LoLLMs、Ollama 或 OpenAI）
+2. 启动 RAG 服务器
+3. 使用文档管理端点上传一些文档
+4. 使用查询端点查询系统
+5. 如果在输入目录中放入新文件，触发文档扫描
+
+### 查询端点
+
+#### POST /query
+使用不同搜索模式查询 RAG 系统。
+
+```bash
+curl -X POST "http://localhost:9621/query" \
+    -H "Content-Type: application/json" \
+    -d '{"query": "您的问题", "mode": "hybrid", ""}'
+```
+
+#### POST /query/stream
+从 RAG 系统流式获取响应。
+
+```bash
+curl -X POST "http://localhost:9621/query/stream" \
+    -H "Content-Type: application/json" \
+    -d '{"query": "您的问题", "mode": "hybrid"}'
+```
+
+### 文档管理端点
+
+#### POST /documents/text
+直接将文本插入 RAG 系统。
+
+```bash
+curl -X POST "http://localhost:9621/documents/text" \
+    -H "Content-Type: application/json" \
+    -d '{"text": "您的文本内容", "description": "可选描述"}'
+```
+
+#### POST /documents/file
+向 RAG 系统上传单个文件。
+
+```bash
+curl -X POST "http://localhost:9621/documents/file" \
+    -F "file=@/path/to/your/document.txt" \
+    -F "description=可选描述"
+```
+
+#### POST /documents/batch
+一次上传多个文件。
+
+```bash
+curl -X POST "http://localhost:9621/documents/batch" \
+    -F "files=@/path/to/doc1.txt" \
+    -F "files=@/path/to/doc2.txt"
+```
+
+#### POST /documents/scan
+
+触发输入目录中新文件的文档扫描。
+
+```bash
+curl -X POST "http://localhost:9621/documents/scan" --max-time 1800
+```
+
+> 根据所有新文件的预计索引时间调整 max-time。
+
+#### DELETE /documents
+
+从 RAG 系统中清除所有文档。
+
+```bash
+curl -X DELETE "http://localhost:9621/documents"
+```
+
+### Ollama 模拟端点
+
+#### GET /api/version
+
+获取 Ollama 版本信息。
+
+```bash
+curl http://localhost:9621/api/version
+```
+
+#### GET /api/tags
+
+获取 Ollama 可用模型。
+
+```bash
+curl http://localhost:9621/api/tags
+```
+
+#### POST /api/chat
+
+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。
+
+```shell
+curl -N -X POST http://localhost:9621/api/chat -H "Content-Type: application/json" -d \
+  '{"model":"lightrag:latest","messages":[{"role":"user","content":"猪八戒是谁"}],"stream":true}'
+```
+
+> 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)
+
+#### POST /api/generate
+
+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。
+
+### 实用工具端点
+
+#### GET /health
+检查服务器健康状况和配置。
+
+```bash
+curl "http://localhost:9621/health"
+
+```

lightrag/api/README.md

@@ -153,10 +153,6 @@ sudo systemctl status lightrag.service
 sudo systemctl enable lightrag.service
 ```
 
-
-
-
-
 ## Ollama Emulation
 
 We provide an Ollama-compatible interfaces for LightRAG, aiming to emulate LightRAG as an Ollama chat model. This allows AI chat frontends supporting Ollama, such as Open WebUI, to access LightRAG easily.
@@ -196,8 +192,11 @@ By default, the LightRAG Server can be accessed without any authentication. We c
 
 ```
 LIGHTRAG_API_KEY=your-secure-api-key-here
+WHITELIST_PATHS=/health,/api/*
 ```
 
+> Health check and Ollama emuluation endpoins is exclude from API-KEY check by default.
+
 * Account credentials (the web UI requires login before access)
 
 LightRAG API Server implements JWT-based authentication using HS256 algorithm. To enable secure access control, the following environment variables are required:
@@ -317,7 +316,7 @@ OracleGraphStorage   Postgres
 
 ```
 NanoVectorDBStorage         NanoVector(default)
-MilvusVectorDBStorge        Milvus
+MilvusVectorDBStorage       Milvus
 ChromaVectorDBStorage       Chroma
 TiDBVectorDBStorage         TiDB
 PGVectorStorage             Postgres

lightrag/api/__init__.py

@@ -1 +1 @@
-__api_version__ = "1.2.2"
+__api_version__ = "1.2.5"

lightrag/api/lightrag_server.py

@@ -18,7 +18,7 @@ from fastapi.middleware.cors import CORSMiddleware
 from contextlib import asynccontextmanager
 from dotenv import load_dotenv
 from lightrag.api.utils_api import (
-    get_api_key_dependency,
+    get_combined_auth_dependency,
     parse_args,
     get_default_host,
     display_splash_screen,
@@ -41,7 +41,6 @@ from lightrag.kg.shared_storage import (
     get_namespace_data,
     get_pipeline_status_lock,
     initialize_pipeline_status,
-    get_all_update_flags_status,
 )
 from fastapi.security import OAuth2PasswordRequestForm
 from .auth import auth_handler
@@ -136,19 +135,28 @@ def create_app(args):
             await rag.finalize_storages()
 
     # Initialize FastAPI
-    app = FastAPI(
-        title="LightRAG API",
-        description="API for querying text using LightRAG with separate storage and input directories"
+    app_kwargs = {
+        "title": "LightRAG Server API",
+        "description": "Providing API for LightRAG core, Web UI and Ollama Model Emulation"
         + "(With authentication)"
         if api_key
         else "",
-        version=__api_version__,
-        openapi_url="/openapi.json",  # Explicitly set OpenAPI schema URL
-        docs_url="/docs",  # Explicitly set docs URL
-        redoc_url="/redoc",  # Explicitly set redoc URL
-        openapi_tags=[{"name": "api"}],
-        lifespan=lifespan,
-    )
+        "version": __api_version__,
+        "openapi_url": "/openapi.json",  # Explicitly set OpenAPI schema URL
+        "docs_url": "/docs",  # Explicitly set docs URL
+        "redoc_url": "/redoc",  # Explicitly set redoc URL
+        "openapi_tags": [{"name": "api"}],
+        "lifespan": lifespan,
+    }
+
+    # Configure Swagger UI parameters
+    # Enable persistAuthorization and tryItOutEnabled for better user experience
+    app_kwargs["swagger_ui_parameters"] = {
+        "persistAuthorization": True,
+        "tryItOutEnabled": True,
+    }
+
+    app = FastAPI(**app_kwargs)
 
     def get_cors_origins():
         """Get allowed origins from environment variable
@@ -168,8 +176,8 @@ def create_app(args):
         allow_headers=["*"],
     )
 
-    # Create the optional API key dependency
-    optional_api_key = get_api_key_dependency(api_key)
+    # Create combined auth dependency for all endpoints
+    combined_auth = get_combined_auth_dependency(api_key)
 
     # Create working directory if it doesn't exist
     Path(args.working_dir).mkdir(parents=True, exist_ok=True)
@@ -200,6 +208,7 @@ def create_app(args):
             kwargs["response_format"] = GPTKeywordExtractionFormat
         if history_messages is None:
             history_messages = []
+        kwargs["temperature"] = args.temperature
         return await openai_complete_if_cache(
             args.llm_model,
             prompt,
@@ -222,6 +231,7 @@ def create_app(args):
             kwargs["response_format"] = GPTKeywordExtractionFormat
         if history_messages is None:
             history_messages = []
+        kwargs["temperature"] = args.temperature
         return await azure_openai_complete_if_cache(
             args.llm_model,
             prompt,
@@ -302,6 +312,7 @@ def create_app(args):
             },
             namespace_prefix=args.namespace_prefix,
             auto_manage_storages_states=False,
+            max_parallel_insert=args.max_parallel_insert,
         )
     else:  # azure_openai
         rag = LightRAG(
@@ -331,6 +342,7 @@ def create_app(args):
             },
             namespace_prefix=args.namespace_prefix,
             auto_manage_storages_states=False,
+            max_parallel_insert=args.max_parallel_insert,
         )
 
     # Add routes
@@ -339,7 +351,7 @@ def create_app(args):
     app.include_router(create_graph_routes(rag, api_key))
 
     # Add Ollama API routes
-    ollama_api = OllamaAPI(rag, top_k=args.top_k)
+    ollama_api = OllamaAPI(rag, top_k=args.top_k, api_key=api_key)
     app.include_router(ollama_api.router, prefix="/api")
 
     @app.get("/")
@@ -347,7 +359,7 @@ def create_app(args):
         """Redirect root path to /webui"""
         return RedirectResponse(url="/webui")
 
-    @app.get("/auth-status", dependencies=[Depends(optional_api_key)])
+    @app.get("/auth-status")
     async def get_auth_status():
         """Get authentication status and guest token if auth is not configured"""
 
@@ -373,7 +385,7 @@ def create_app(args):
             "api_version": __api_version__,
         }
 
-    @app.post("/login", dependencies=[Depends(optional_api_key)])
+    @app.post("/login")
     async def login(form_data: OAuth2PasswordRequestForm = Depends()):
         if not auth_handler.accounts:
             # Authentication not configured, return guest token
@@ -406,12 +418,9 @@ def create_app(args):
             "api_version": __api_version__,
         }
 
-    @app.get("/health", dependencies=[Depends(optional_api_key)])
+    @app.get("/health", dependencies=[Depends(combined_auth)])
     async def get_status():
         """Get current system status"""
-        # Get update flags status for all namespaces
-        update_status = await get_all_update_flags_status()
-
         username = os.getenv("AUTH_USERNAME")
         password = os.getenv("AUTH_PASSWORD")
         if not (username and password):
@@ -439,7 +448,6 @@ def create_app(args):
                 "vector_storage": args.vector_storage,
                 "enable_llm_cache_for_extract": args.enable_llm_cache_for_extract,
             },
-            "update_status": update_status,
             "core_version": core_version,
             "api_version": __api_version__,
             "auth_mode": auth_mode,

lightrag/api/routers/document_routes.py

@@ -17,15 +17,13 @@ from pydantic import BaseModel, Field, field_validator
 from lightrag import LightRAG
 from lightrag.base import DocProcessingStatus, DocStatus
 from lightrag.api.utils_api import (
-    get_api_key_dependency,
+    get_combined_auth_dependency,
     global_args,
-    get_auth_dependency,
 )
 
 router = APIRouter(
     prefix="/documents",
     tags=["documents"],
-    dependencies=[Depends(get_auth_dependency())],
 )
 
 # Temporary file prefix
@@ -113,6 +111,7 @@ class PipelineStatusResponse(BaseModel):
         request_pending: Flag for pending request for processing
         latest_message: Latest message from pipeline processing
         history_messages: List of history messages
+        update_status: Status of update flags for all namespaces
     """
 
     autoscanned: bool = False
@@ -125,6 +124,7 @@ class PipelineStatusResponse(BaseModel):
     request_pending: bool = False
     latest_message: str = ""
     history_messages: Optional[List[str]] = None
+    update_status: Optional[dict] = None
 
     class Config:
         extra = "allow"  # Allow additional fields from the pipeline status
@@ -475,8 +475,8 @@ async def run_scanning_process(rag: LightRAG, doc_manager: DocumentManager):
         if not new_files:
             return
 
-        # Get MAX_PARALLEL_INSERT from global_args
-        max_parallel = global_args["max_parallel_insert"]
+        # Get MAX_PARALLEL_INSERT from global_args["main_args"]
+        max_parallel = global_args["main_args"].max_parallel_insert
         # Calculate batch size as 2 * MAX_PARALLEL_INSERT
         batch_size = 2 * max_parallel
 
@@ -505,9 +505,10 @@ async def run_scanning_process(rag: LightRAG, doc_manager: DocumentManager):
 def create_document_routes(
     rag: LightRAG, doc_manager: DocumentManager, api_key: Optional[str] = None
 ):
-    optional_api_key = get_api_key_dependency(api_key)
+    # Create combined auth dependency for document routes
+    combined_auth = get_combined_auth_dependency(api_key)
 
-    @router.post("/scan", dependencies=[Depends(optional_api_key)])
+    @router.post("/scan", dependencies=[Depends(combined_auth)])
     async def scan_for_new_documents(background_tasks: BackgroundTasks):
         """
         Trigger the scanning process for new documents.
@@ -523,7 +524,7 @@ def create_document_routes(
         background_tasks.add_task(run_scanning_process, rag, doc_manager)
         return {"status": "scanning_started"}
 
-    @router.post("/upload", dependencies=[Depends(optional_api_key)])
+    @router.post("/upload", dependencies=[Depends(combined_auth)])
     async def upload_to_input_dir(
         background_tasks: BackgroundTasks, file: UploadFile = File(...)
     ):
@@ -568,7 +569,7 @@ def create_document_routes(
             raise HTTPException(status_code=500, detail=str(e))
 
     @router.post(
-        "/text", response_model=InsertResponse, dependencies=[Depends(optional_api_key)]
+        "/text", response_model=InsertResponse, dependencies=[Depends(combined_auth)]
     )
     async def insert_text(
         request: InsertTextRequest, background_tasks: BackgroundTasks
@@ -603,7 +604,7 @@ def create_document_routes(
     @router.post(
         "/texts",
         response_model=InsertResponse,
-        dependencies=[Depends(optional_api_key)],
+        dependencies=[Depends(combined_auth)],
     )
     async def insert_texts(
         request: InsertTextsRequest, background_tasks: BackgroundTasks
@@ -636,7 +637,7 @@ def create_document_routes(
             raise HTTPException(status_code=500, detail=str(e))
 
     @router.post(
-        "/file", response_model=InsertResponse, dependencies=[Depends(optional_api_key)]
+        "/file", response_model=InsertResponse, dependencies=[Depends(combined_auth)]
     )
     async def insert_file(
         background_tasks: BackgroundTasks, file: UploadFile = File(...)
@@ -681,7 +682,7 @@ def create_document_routes(
     @router.post(
         "/file_batch",
         response_model=InsertResponse,
-        dependencies=[Depends(optional_api_key)],
+        dependencies=[Depends(combined_auth)],
     )
     async def insert_batch(
         background_tasks: BackgroundTasks, files: List[UploadFile] = File(...)
@@ -742,7 +743,7 @@ def create_document_routes(
             raise HTTPException(status_code=500, detail=str(e))
 
     @router.delete(
-        "", response_model=InsertResponse, dependencies=[Depends(optional_api_key)]
+        "", response_model=InsertResponse, dependencies=[Depends(combined_auth)]
     )
     async def clear_documents():
         """
@@ -771,7 +772,7 @@ def create_document_routes(
 
     @router.get(
         "/pipeline_status",
-        dependencies=[Depends(optional_api_key)],
+        dependencies=[Depends(combined_auth)],
         response_model=PipelineStatusResponse,
     )
     async def get_pipeline_status() -> PipelineStatusResponse:
@@ -798,13 +799,34 @@ def create_document_routes(
             HTTPException: If an error occurs while retrieving pipeline status (500)
         """
         try:
-            from lightrag.kg.shared_storage import get_namespace_data
+            from lightrag.kg.shared_storage import (
+                get_namespace_data,
+                get_all_update_flags_status,
+            )
 
             pipeline_status = await get_namespace_data("pipeline_status")
 
+            # Get update flags status for all namespaces
+            update_status = await get_all_update_flags_status()
+
+            # Convert MutableBoolean objects to regular boolean values
+            processed_update_status = {}
+            for namespace, flags in update_status.items():
+                processed_flags = []
+                for flag in flags:
+                    # Handle both multiprocess and single process cases
+                    if hasattr(flag, "value"):
+                        processed_flags.append(bool(flag.value))
+                    else:
+                        processed_flags.append(bool(flag))
+                processed_update_status[namespace] = processed_flags
+
             # Convert to regular dict if it's a Manager.dict
             status_dict = dict(pipeline_status)
 
+            # Add processed update_status to the status dictionary
+            status_dict["update_status"] = processed_update_status
+
             # Convert history_messages to a regular list if it's a Manager.list
             if "history_messages" in status_dict:
                 status_dict["history_messages"] = list(status_dict["history_messages"])
@@ -819,7 +841,7 @@ def create_document_routes(
             logger.error(traceback.format_exc())
             raise HTTPException(status_code=500, detail=str(e))
 
-    @router.get("", dependencies=[Depends(optional_api_key)])
+    @router.get("", dependencies=[Depends(combined_auth)])
     async def documents() -> DocsStatusesResponse:
         """
         Get the status of all documents in the system.

lightrag/api/routers/graph_routes.py

@@ -5,15 +5,15 @@ This module contains all graph-related routes for the LightRAG API.
 from typing import Optional
 from fastapi import APIRouter, Depends
 
-from ..utils_api import get_api_key_dependency, get_auth_dependency
+from ..utils_api import get_combined_auth_dependency
 
-router = APIRouter(tags=["graph"], dependencies=[Depends(get_auth_dependency())])
+router = APIRouter(tags=["graph"])
 
 
 def create_graph_routes(rag, api_key: Optional[str] = None):
-    optional_api_key = get_api_key_dependency(api_key)
+    combined_auth = get_combined_auth_dependency(api_key)
 
-    @router.get("/graph/label/list", dependencies=[Depends(optional_api_key)])
+    @router.get("/graph/label/list", dependencies=[Depends(combined_auth)])
     async def get_graph_labels():
         """
         Get all graph labels
@@ -23,7 +23,7 @@ def create_graph_routes(rag, api_key: Optional[str] = None):
         """
         return await rag.get_graph_labels()
 
-    @router.get("/graphs", dependencies=[Depends(optional_api_key)])
+    @router.get("/graphs", dependencies=[Depends(combined_auth)])
     async def get_knowledge_graph(
         label: str, max_depth: int = 3, min_degree: int = 0, inclusive: bool = False
     ):

lightrag/api/routers/ollama_api.py

@@ -11,7 +11,8 @@ import asyncio
 from ascii_colors import trace_exception
 from lightrag import LightRAG, QueryParam
 from lightrag.utils import encode_string_by_tiktoken
-from lightrag.api.utils_api import ollama_server_infos
+from lightrag.api.utils_api import ollama_server_infos, get_combined_auth_dependency
+from fastapi import Depends
 
 
 # query mode according to query prefix (bypass is not LightRAG quer mode)
@@ -122,20 +123,24 @@ def parse_query_mode(query: str) -> tuple[str, SearchMode]:
 
 
 class OllamaAPI:
-    def __init__(self, rag: LightRAG, top_k: int = 60):
+    def __init__(self, rag: LightRAG, top_k: int = 60, api_key: Optional[str] = None):
         self.rag = rag
         self.ollama_server_infos = ollama_server_infos
         self.top_k = top_k
+        self.api_key = api_key
         self.router = APIRouter(tags=["ollama"])
         self.setup_routes()
 
     def setup_routes(self):
-        @self.router.get("/version")
+        # Create combined auth dependency for Ollama API routes
+        combined_auth = get_combined_auth_dependency(self.api_key)
+
+        @self.router.get("/version", dependencies=[Depends(combined_auth)])
         async def get_version():
             """Get Ollama version information"""
             return OllamaVersionResponse(version="0.5.4")
 
-        @self.router.get("/tags")
+        @self.router.get("/tags", dependencies=[Depends(combined_auth)])
         async def get_tags():
             """Return available models acting as an Ollama server"""
             return OllamaTagResponse(
@@ -158,7 +163,7 @@ class OllamaAPI:
                 ]
             )
 
-        @self.router.post("/generate")
+        @self.router.post("/generate", dependencies=[Depends(combined_auth)])
         async def generate(raw_request: Request, request: OllamaGenerateRequest):
             """Handle generate completion requests acting as an Ollama model
             For compatibility purpose, the request is not processed by LightRAG,
@@ -324,7 +329,7 @@ class OllamaAPI:
                 trace_exception(e)
                 raise HTTPException(status_code=500, detail=str(e))
 
-        @self.router.post("/chat")
+        @self.router.post("/chat", dependencies=[Depends(combined_auth)])
         async def chat(raw_request: Request, request: OllamaChatRequest):
             """Process chat completion requests acting as an Ollama model
             Routes user queries through LightRAG by selecting query mode based on prefix indicators.

lightrag/api/routers/query_routes.py

@@ -8,12 +8,12 @@ from typing import Any, Dict, List, Literal, Optional
 
 from fastapi import APIRouter, Depends, HTTPException
 from lightrag.base import QueryParam
-from ..utils_api import get_api_key_dependency, get_auth_dependency
+from ..utils_api import get_combined_auth_dependency
 from pydantic import BaseModel, Field, field_validator
 
 from ascii_colors import trace_exception
 
-router = APIRouter(tags=["query"], dependencies=[Depends(get_auth_dependency())])
+router = APIRouter(tags=["query"])
 
 
 class QueryRequest(BaseModel):
@@ -139,10 +139,10 @@ class QueryResponse(BaseModel):
 
 
 def create_query_routes(rag, api_key: Optional[str] = None, top_k: int = 60):
-    optional_api_key = get_api_key_dependency(api_key)
+    combined_auth = get_combined_auth_dependency(api_key)
 
     @router.post(
-        "/query", response_model=QueryResponse, dependencies=[Depends(optional_api_key)]
+        "/query", response_model=QueryResponse, dependencies=[Depends(combined_auth)]
     )
     async def query_text(request: QueryRequest):
         """
@@ -176,7 +176,7 @@ def create_query_routes(rag, api_key: Optional[str] = None, top_k: int = 60):
             trace_exception(e)
             raise HTTPException(status_code=500, detail=str(e))
 
-    @router.post("/query/stream", dependencies=[Depends(optional_api_key)])
+    @router.post("/query/stream", dependencies=[Depends(combined_auth)])
     async def query_text_stream(request: QueryRequest):
         """
         This endpoint performs a retrieval-augmented generation (RAG) query and streams the response.

lightrag/api/utils_api.py

@@ -4,22 +4,42 @@ Utility functions for the LightRAG API.
 
 import os
 import argparse
-from typing import Optional
+from typing import Optional, List, Tuple
 import sys
 import logging
 from ascii_colors import ASCIIColors
 from lightrag.api import __api_version__
-from fastapi import HTTPException, Security, Depends, Request, status
+from fastapi import HTTPException, Security, Request, status
 from dotenv import load_dotenv
 from fastapi.security import APIKeyHeader, OAuth2PasswordBearer
 from starlette.status import HTTP_403_FORBIDDEN
 from .auth import auth_handler
+from ..prompt import PROMPTS
 
 # Load environment variables
 load_dotenv()
 
 global_args = {"main_args": None}
 
+# Get whitelist paths from environment variable, only once during initialization
+default_whitelist = "/health,/api/*"
+whitelist_paths = os.getenv("WHITELIST_PATHS", default_whitelist).split(",")
+
+# Pre-compile path matching patterns
+whitelist_patterns: List[Tuple[str, bool]] = []
+for path in whitelist_paths:
+    path = path.strip()
+    if path:
+        # If path ends with /*, match all paths with that prefix
+        if path.endswith("/*"):
+            prefix = path[:-2]
+            whitelist_patterns.append((prefix, True))  # (prefix, is_prefix_match)
+        else:
+            whitelist_patterns.append((path, False))  # (exact_path, is_prefix_match)
+
+# Global authentication configuration
+auth_configured = bool(auth_handler.accounts)
+
 
 class OllamaServerInfos:
     # Constants for emulated Ollama model information
@@ -34,47 +54,114 @@ class OllamaServerInfos:
 ollama_server_infos = OllamaServerInfos()
 
 
-def get_auth_dependency():
-    # Set default whitelist paths
-    whitelist = os.getenv("WHITELIST_PATHS", "/login,/health").split(",")
+def get_combined_auth_dependency(api_key: Optional[str] = None):
+    """
+    Create a combined authentication dependency that implements authentication logic
+    based on API key, OAuth2 token, and whitelist paths.
 
-    async def dependency(
+    Args:
+        api_key (Optional[str]): API key for validation
+
+    Returns:
+        Callable: A dependency function that implements the authentication logic
+    """
+    # Use global whitelist_patterns and auth_configured variables
+    # whitelist_patterns and auth_configured are already initialized at module level
+
+    # Only calculate api_key_configured as it depends on the function parameter
+    api_key_configured = bool(api_key)
+
+    # Create security dependencies with proper descriptions for Swagger UI
+    oauth2_scheme = OAuth2PasswordBearer(
+        tokenUrl="login", auto_error=False, description="OAuth2 Password Authentication"
+    )
+
+    # If API key is configured, create an API key header security
+    api_key_header = None
+    if api_key_configured:
+        api_key_header = APIKeyHeader(
+            name="X-API-Key", auto_error=False, description="API Key Authentication"
+        )
+
+    async def combined_dependency(
         request: Request,
-        token: str = Depends(OAuth2PasswordBearer(tokenUrl="login", auto_error=False)),
+        token: str = Security(oauth2_scheme),
+        api_key_header_value: Optional[str] = None
+        if api_key_header is None
+        else Security(api_key_header),
     ):
-        # Check if authentication is configured
-        auth_configured = bool(auth_handler.accounts)
+        # 1. Check if path is in whitelist
+        path = request.url.path
+        for pattern, is_prefix in whitelist_patterns:
+            if (is_prefix and path.startswith(pattern)) or (
+                not is_prefix and path == pattern
+            ):
+                return  # Whitelist path, allow access
 
-        # If authentication is not configured, skip all validation
-        if not auth_configured:
-            return
+        # 2. Validate token first if provided in the request (Ensure 401 error if token is invalid)
+        if token:
+            try:
+                token_info = auth_handler.validate_token(token)
+                # Accept guest token if no auth is configured
+                if not auth_configured and token_info.get("role") == "guest":
+                    return
+                # Accept non-guest token if auth is configured
+                if auth_configured and token_info.get("role") != "guest":
+                    return
 
-        # For configured auth, allow whitelist paths without token
-        if request.url.path in whitelist:
-            return
-
-        # Require token for all other paths when auth is configured
-        if not token:
-            raise HTTPException(
-                status_code=status.HTTP_401_UNAUTHORIZED, detail="Token required"
-            )
-
-        try:
-            token_info = auth_handler.validate_token(token)
-            # Reject guest tokens when authentication is configured
-            if token_info.get("role") == "guest":
+                # Token validation failed, immediately return 401 error
                 raise HTTPException(
                     status_code=status.HTTP_401_UNAUTHORIZED,
-                    detail="Authentication required. Guest access not allowed when authentication is configured.",
+                    detail="Invalid token. Please login again.",
                 )
-        except Exception:
+            except HTTPException as e:
+                # If already a 401 error, re-raise it
+                if e.status_code == status.HTTP_401_UNAUTHORIZED:
+                    raise
+                # For other exceptions, continue processing
+
+        # 3. Acept all request if no API protection needed
+        if not auth_configured and not api_key_configured:
+            return
+
+        # 4. Validate API key if provided and API-Key authentication is configured
+        if (
+            api_key_configured
+            and api_key_header_value
+            and api_key_header_value == api_key
+        ):
+            return  # API key validation successful
+
+        ### Authentication failed ####
+
+        # if password authentication is configured but not provided, ensure 401 error if auth_configured
+        if auth_configured and not token:
             raise HTTPException(
-                status_code=status.HTTP_401_UNAUTHORIZED, detail="Invalid token"
+                status_code=status.HTTP_401_UNAUTHORIZED,
+                detail="No credentials provided. Please login.",
             )
 
-        return
+        # if api key is provided but validation failed
+        if api_key_header_value:
+            raise HTTPException(
+                status_code=HTTP_403_FORBIDDEN,
+                detail="Invalid API Key",
+            )
 
-    return dependency
+        # if api_key_configured but not provided
+        if api_key_configured and not api_key_header_value:
+            raise HTTPException(
+                status_code=HTTP_403_FORBIDDEN,
+                detail="API Key required",
+            )
+
+        # Otherwise: refuse access and return 403 error
+        raise HTTPException(
+            status_code=HTTP_403_FORBIDDEN,
+            detail="API Key required or login authentication required.",
+        )
+
+    return combined_dependency
 
 
 def get_api_key_dependency(api_key: Optional[str]):
@@ -88,19 +175,37 @@ def get_api_key_dependency(api_key: Optional[str]):
     Returns:
         Callable: A dependency function that validates the API key.
     """
-    if not api_key:
+    # Use global whitelist_patterns and auth_configured variables
+    # whitelist_patterns and auth_configured are already initialized at module level
+
+    # Only calculate api_key_configured as it depends on the function parameter
+    api_key_configured = bool(api_key)
+
+    if not api_key_configured:
         # If no API key is configured, return a dummy dependency that always succeeds
-        async def no_auth():
+        async def no_auth(request: Request = None, **kwargs):
             return None
 
         return no_auth
 
-    # If API key is configured, use proper authentication
+    # If API key is configured, use proper authentication with Security for Swagger UI
     api_key_header = APIKeyHeader(name="X-API-Key", auto_error=False)
 
     async def api_key_auth(
-        api_key_header_value: Optional[str] = Security(api_key_header),
+        request: Request,
+        api_key_header_value: Optional[str] = Security(
+            api_key_header, description="API Key for authentication"
+        ),
     ):
+        # Check if request path is in whitelist
+        path = request.url.path
+        for pattern, is_prefix in whitelist_patterns:
+            if (is_prefix and path.startswith(pattern)) or (
+                not is_prefix and path == pattern
+            ):
+                return  # Whitelist path, allow access
+
+        # Non-whitelist path, validate API key
         if not api_key_header_value:
             raise HTTPException(
                 status_code=HTTP_403_FORBIDDEN, detail="API Key required"
@@ -364,7 +469,7 @@ def parse_args(is_uvicorn_mode: bool = False) -> argparse.Namespace:
     )
 
     # Get MAX_PARALLEL_INSERT from environment
-    global_args["max_parallel_insert"] = get_env_value("MAX_PARALLEL_INSERT", 2, int)
+    args.max_parallel_insert = get_env_value("MAX_PARALLEL_INSERT", 2, int)
 
     # Handle openai-ollama special case
     if args.llm_binding == "openai-ollama":
@@ -395,6 +500,9 @@ def parse_args(is_uvicorn_mode: bool = False) -> argparse.Namespace:
         "ENABLE_LLM_CACHE_FOR_EXTRACT", True, bool
     )
 
+    # Inject LLM temperature configuration
+    args.temperature = get_env_value("TEMPERATURE", 0.5, float)
+
     # Select Document loading tool (DOCLING, DEFAULT)
     args.document_loading_engine = get_env_value("DOCUMENT_LOADING_ENGINE", "DEFAULT")
 
@@ -462,6 +570,12 @@ def display_splash_screen(args: argparse.Namespace) -> None:
     ASCIIColors.yellow(f"{args.llm_binding_host}")
     ASCIIColors.white("    ├─ Model: ", end="")
     ASCIIColors.yellow(f"{args.llm_model}")
+    ASCIIColors.white("    ├─ Temperature: ", end="")
+    ASCIIColors.yellow(f"{args.temperature}")
+    ASCIIColors.white("    ├─ Max Async for LLM: ", end="")
+    ASCIIColors.yellow(f"{args.max_async}")
+    ASCIIColors.white("    ├─ Max Tokens: ", end="")
+    ASCIIColors.yellow(f"{args.max_tokens}")
     ASCIIColors.white("    └─ Timeout: ", end="")
     ASCIIColors.yellow(f"{args.timeout if args.timeout else 'None (infinite)'}")
 
@@ -477,13 +591,12 @@ def display_splash_screen(args: argparse.Namespace) -> None:
     ASCIIColors.yellow(f"{args.embedding_dim}")
 
     # RAG Configuration
+    summary_language = os.getenv("SUMMARY_LANGUAGE", PROMPTS["DEFAULT_LANGUAGE"])
     ASCIIColors.magenta("\n⚙️ RAG Configuration:")
-    ASCIIColors.white("    ├─ Max Async for LLM: ", end="")
-    ASCIIColors.yellow(f"{args.max_async}")
+    ASCIIColors.white("    ├─ Summary Language: ", end="")
+    ASCIIColors.yellow(f"{summary_language}")
     ASCIIColors.white("    ├─ Max Parallel Insert: ", end="")
-    ASCIIColors.yellow(f"{global_args['max_parallel_insert']}")
-    ASCIIColors.white("    ├─ Max Tokens: ", end="")
-    ASCIIColors.yellow(f"{args.max_tokens}")
+    ASCIIColors.yellow(f"{args.max_parallel_insert}")
     ASCIIColors.white("    ├─ Max Embed Tokens: ", end="")
     ASCIIColors.yellow(f"{args.max_embed_tokens}")
     ASCIIColors.white("    ├─ Chunk Size: ", end="")

lightrag/api/webui/index.html

@@ -8,8 +8,8 @@
     <link rel="icon" type="image/svg+xml" href="logo.png" />
     <meta name="viewport" content="width=device-width, initial-scale=1.0" />
     <title>Lightrag</title>
-    <script type="module" crossorigin src="/webui/assets/index-qXLILB5u.js"></script>
-    <link rel="stylesheet" crossorigin href="/webui/assets/index-BcBS1RaQ.css">
+    <script type="module" crossorigin src="/webui/assets/index-DJ53id6i.js"></script>
+    <link rel="stylesheet" crossorigin href="/webui/assets/index-BwFyYQzx.css">
   </head>
   <body>
     <div id="root"></div>

lightrag/kg/faiss_impl.py

@@ -19,7 +19,6 @@ from .shared_storage import (
     get_storage_lock,
     get_update_flag,
     set_all_update_flags,
-    is_multiprocess,
 )
 
 
@@ -73,9 +72,7 @@ class FaissVectorDBStorage(BaseVectorStorage):
         # Acquire lock to prevent concurrent read and write
         async with self._storage_lock:
             # Check if storage was updated by another process
-            if (is_multiprocess and self.storage_updated.value) or (
-                not is_multiprocess and self.storage_updated
-            ):
+            if self.storage_updated.value:
                 logger.info(
                     f"Process {os.getpid()} FAISS reloading {self.namespace} due to update by another process"
                 )
@@ -83,10 +80,7 @@ class FaissVectorDBStorage(BaseVectorStorage):
                 self._index = faiss.IndexFlatIP(self._dim)
                 self._id_to_meta = {}
                 self._load_faiss_index()
-                if is_multiprocess:
-                    self.storage_updated.value = False
-                else:
-                    self.storage_updated = False
+                self.storage_updated.value = False
         return self._index
 
     async def upsert(self, data: dict[str, dict[str, Any]]) -> None:
@@ -343,18 +337,19 @@ class FaissVectorDBStorage(BaseVectorStorage):
             self._id_to_meta = {}
 
     async def index_done_callback(self) -> None:
-        # Check if storage was updated by another process
-        if is_multiprocess and self.storage_updated.value:
-            # Storage was updated by another process, reload data instead of saving
-            logger.warning(
-                f"Storage for FAISS {self.namespace} was updated by another process, reloading..."
-            )
-            async with self._storage_lock:
-                self._index = faiss.IndexFlatIP(self._dim)
-                self._id_to_meta = {}
-                self._load_faiss_index()
-                self.storage_updated.value = False
-            return False  # Return error
+        async with self._storage_lock:
+            # Check if storage was updated by another process
+            if self.storage_updated.value:
+                # Storage was updated by another process, reload data instead of saving
+                logger.warning(
+                    f"Storage for FAISS {self.namespace} was updated by another process, reloading..."
+                )
+                async with self._storage_lock:
+                    self._index = faiss.IndexFlatIP(self._dim)
+                    self._id_to_meta = {}
+                    self._load_faiss_index()
+                    self.storage_updated.value = False
+                return False  # Return error
 
         # Acquire lock and perform persistence
         async with self._storage_lock:
@@ -364,10 +359,7 @@ class FaissVectorDBStorage(BaseVectorStorage):
                 # Notify other processes that data has been updated
                 await set_all_update_flags(self.namespace)
                 # Reset own update flag to avoid self-reloading
-                if is_multiprocess:
-                    self.storage_updated.value = False
-                else:
-                    self.storage_updated = False
+                self.storage_updated.value = False
             except Exception as e:
                 logger.error(f"Error saving FAISS index for {self.namespace}: {e}")
                 return False  # Return error

lightrag/kg/mongo_impl.py

@@ -221,6 +221,7 @@ class MongoDocStatusStorage(DocStatusStorage):
                 created_at=doc.get("created_at"),
                 updated_at=doc.get("updated_at"),
                 chunks_count=doc.get("chunks_count", -1),
+                file_path=doc.get("file_path", doc["_id"]),
             )
             for doc in result
         }

lightrag/kg/nano_vector_db_impl.py

@@ -20,7 +20,6 @@ from .shared_storage import (
     get_storage_lock,
     get_update_flag,
     set_all_update_flags,
-    is_multiprocess,
 )
 
 
@@ -57,16 +56,14 @@ class NanoVectorDBStorage(BaseVectorStorage):
         # Get the update flag for cross-process update notification
         self.storage_updated = await get_update_flag(self.namespace)
         # Get the storage lock for use in other methods
-        self._storage_lock = get_storage_lock()
+        self._storage_lock = get_storage_lock(enable_logging=False)
 
     async def _get_client(self):
         """Check if the storage should be reloaded"""
         # Acquire lock to prevent concurrent read and write
         async with self._storage_lock:
             # Check if data needs to be reloaded
-            if (is_multiprocess and self.storage_updated.value) or (
-                not is_multiprocess and self.storage_updated
-            ):
+            if self.storage_updated.value:
                 logger.info(
                     f"Process {os.getpid()} reloading {self.namespace} due to update by another process"
                 )
@@ -76,10 +73,7 @@ class NanoVectorDBStorage(BaseVectorStorage):
                     storage_file=self._client_file_name,
                 )
                 # Reset update flag
-                if is_multiprocess:
-                    self.storage_updated.value = False
-                else:
-                    self.storage_updated = False
+                self.storage_updated.value = False
 
             return self._client
 
@@ -206,19 +200,20 @@ class NanoVectorDBStorage(BaseVectorStorage):
 
     async def index_done_callback(self) -> bool:
         """Save data to disk"""
-        # Check if storage was updated by another process
-        if is_multiprocess and self.storage_updated.value:
-            # Storage was updated by another process, reload data instead of saving
-            logger.warning(
-                f"Storage for {self.namespace} was updated by another process, reloading..."
-            )
-            self._client = NanoVectorDB(
-                self.embedding_func.embedding_dim,
-                storage_file=self._client_file_name,
-            )
-            # Reset update flag
-            self.storage_updated.value = False
-            return False  # Return error
+        async with self._storage_lock:
+            # Check if storage was updated by another process
+            if self.storage_updated.value:
+                # Storage was updated by another process, reload data instead of saving
+                logger.warning(
+                    f"Storage for {self.namespace} was updated by another process, reloading..."
+                )
+                self._client = NanoVectorDB(
+                    self.embedding_func.embedding_dim,
+                    storage_file=self._client_file_name,
+                )
+                # Reset update flag
+                self.storage_updated.value = False
+                return False  # Return error
 
         # Acquire lock and perform persistence
         async with self._storage_lock:
@@ -228,10 +223,7 @@ class NanoVectorDBStorage(BaseVectorStorage):
                 # Notify other processes that data has been updated
                 await set_all_update_flags(self.namespace)
                 # Reset own update flag to avoid self-reloading
-                if is_multiprocess:
-                    self.storage_updated.value = False
-                else:
-                    self.storage_updated = False
+                self.storage_updated.value = False
                 return True  # Return success
             except Exception as e:
                 logger.error(f"Error saving data for {self.namespace}: {e}")

lightrag/kg/networkx_impl.py

@@ -21,7 +21,6 @@ from .shared_storage import (
     get_storage_lock,
     get_update_flag,
     set_all_update_flags,
-    is_multiprocess,
 )
 
 MAX_GRAPH_NODES = int(os.getenv("MAX_GRAPH_NODES", 1000))
@@ -110,9 +109,7 @@ class NetworkXStorage(BaseGraphStorage):
         # Acquire lock to prevent concurrent read and write
         async with self._storage_lock:
             # Check if data needs to be reloaded
-            if (is_multiprocess and self.storage_updated.value) or (
-                not is_multiprocess and self.storage_updated
-            ):
+            if self.storage_updated.value:
                 logger.info(
                     f"Process {os.getpid()} reloading graph {self.namespace} due to update by another process"
                 )
@@ -121,10 +118,7 @@ class NetworkXStorage(BaseGraphStorage):
                     NetworkXStorage.load_nx_graph(self._graphml_xml_file) or nx.Graph()
                 )
                 # Reset update flag
-                if is_multiprocess:
-                    self.storage_updated.value = False
-                else:
-                    self.storage_updated = False
+                self.storage_updated.value = False
 
             return self._graph
 
@@ -401,18 +395,19 @@ class NetworkXStorage(BaseGraphStorage):
 
     async def index_done_callback(self) -> bool:
         """Save data to disk"""
-        # Check if storage was updated by another process
-        if is_multiprocess and self.storage_updated.value:
-            # Storage was updated by another process, reload data instead of saving
-            logger.warning(
-                f"Graph for {self.namespace} was updated by another process, reloading..."
-            )
-            self._graph = (
-                NetworkXStorage.load_nx_graph(self._graphml_xml_file) or nx.Graph()
-            )
-            # Reset update flag
-            self.storage_updated.value = False
-            return False  # Return error
+        async with self._storage_lock:
+            # Check if storage was updated by another process
+            if self.storage_updated.value:
+                # Storage was updated by another process, reload data instead of saving
+                logger.warning(
+                    f"Graph for {self.namespace} was updated by another process, reloading..."
+                )
+                self._graph = (
+                    NetworkXStorage.load_nx_graph(self._graphml_xml_file) or nx.Graph()
+                )
+                # Reset update flag
+                self.storage_updated.value = False
+                return False  # Return error
 
         # Acquire lock and perform persistence
         async with self._storage_lock:
@@ -422,10 +417,7 @@ class NetworkXStorage(BaseGraphStorage):
                 # Notify other processes that data has been updated
                 await set_all_update_flags(self.namespace)
                 # Reset own update flag to avoid self-reloading
-                if is_multiprocess:
-                    self.storage_updated.value = False
-                else:
-                    self.storage_updated = False
+                self.storage_updated.value = False
                 return True  # Return success
             except Exception as e:
                 logger.error(f"Error saving graph for {self.namespace}: {e}")

lightrag/kg/shared_storage.py

@@ -24,7 +24,7 @@ def direct_log(message, level="INFO", enable_output: bool = True):
 T = TypeVar("T")
 LockType = Union[ProcessLock, asyncio.Lock]
 
-is_multiprocess = None
+_is_multiprocess = None
 _workers = None
 _manager = None
 _initialized = None
@@ -218,10 +218,10 @@ class UnifiedLock(Generic[T]):
 
 def get_internal_lock(enable_logging: bool = False) -> UnifiedLock:
     """return unified storage lock for data consistency"""
-    async_lock = _async_locks.get("internal_lock") if is_multiprocess else None
+    async_lock = _async_locks.get("internal_lock") if _is_multiprocess else None
     return UnifiedLock(
         lock=_internal_lock,
-        is_async=not is_multiprocess,
+        is_async=not _is_multiprocess,
         name="internal_lock",
         enable_logging=enable_logging,
         async_lock=async_lock,
@@ -230,10 +230,10 @@ def get_internal_lock(enable_logging: bool = False) -> UnifiedLock:
 
 def get_storage_lock(enable_logging: bool = False) -> UnifiedLock:
     """return unified storage lock for data consistency"""
-    async_lock = _async_locks.get("storage_lock") if is_multiprocess else None
+    async_lock = _async_locks.get("storage_lock") if _is_multiprocess else None
     return UnifiedLock(
         lock=_storage_lock,
-        is_async=not is_multiprocess,
+        is_async=not _is_multiprocess,
         name="storage_lock",
         enable_logging=enable_logging,
         async_lock=async_lock,
@@ -242,10 +242,10 @@ def get_storage_lock(enable_logging: bool = False) -> UnifiedLock:
 
 def get_pipeline_status_lock(enable_logging: bool = False) -> UnifiedLock:
     """return unified storage lock for data consistency"""
-    async_lock = _async_locks.get("pipeline_status_lock") if is_multiprocess else None
+    async_lock = _async_locks.get("pipeline_status_lock") if _is_multiprocess else None
     return UnifiedLock(
         lock=_pipeline_status_lock,
-        is_async=not is_multiprocess,
+        is_async=not _is_multiprocess,
         name="pipeline_status_lock",
         enable_logging=enable_logging,
         async_lock=async_lock,
@@ -254,10 +254,10 @@ def get_pipeline_status_lock(enable_logging: bool = False) -> UnifiedLock:
 
 def get_graph_db_lock(enable_logging: bool = False) -> UnifiedLock:
     """return unified graph database lock for ensuring atomic operations"""
-    async_lock = _async_locks.get("graph_db_lock") if is_multiprocess else None
+    async_lock = _async_locks.get("graph_db_lock") if _is_multiprocess else None
     return UnifiedLock(
         lock=_graph_db_lock,
-        is_async=not is_multiprocess,
+        is_async=not _is_multiprocess,
         name="graph_db_lock",
         enable_logging=enable_logging,
         async_lock=async_lock,
@@ -266,10 +266,10 @@ def get_graph_db_lock(enable_logging: bool = False) -> UnifiedLock:
 
 def get_data_init_lock(enable_logging: bool = False) -> UnifiedLock:
     """return unified data initialization lock for ensuring atomic data initialization"""
-    async_lock = _async_locks.get("data_init_lock") if is_multiprocess else None
+    async_lock = _async_locks.get("data_init_lock") if _is_multiprocess else None
     return UnifiedLock(
         lock=_data_init_lock,
-        is_async=not is_multiprocess,
+        is_async=not _is_multiprocess,
         name="data_init_lock",
         enable_logging=enable_logging,
         async_lock=async_lock,
@@ -297,7 +297,7 @@ def initialize_share_data(workers: int = 1):
     global \
         _manager, \
         _workers, \
-        is_multiprocess, \
+        _is_multiprocess, \
         _storage_lock, \
         _internal_lock, \
         _pipeline_status_lock, \
@@ -312,14 +312,14 @@ def initialize_share_data(workers: int = 1):
     # Check if already initialized
     if _initialized:
         direct_log(
-            f"Process {os.getpid()} Shared-Data already initialized (multiprocess={is_multiprocess})"
+            f"Process {os.getpid()} Shared-Data already initialized (multiprocess={_is_multiprocess})"
         )
         return
 
     _workers = workers
 
     if workers > 1:
-        is_multiprocess = True
+        _is_multiprocess = True
         _manager = Manager()
         _internal_lock = _manager.Lock()
         _storage_lock = _manager.Lock()
@@ -343,7 +343,7 @@ def initialize_share_data(workers: int = 1):
             f"Process {os.getpid()} Shared-Data created for Multiple Process (workers={workers})"
         )
     else:
-        is_multiprocess = False
+        _is_multiprocess = False
         _internal_lock = asyncio.Lock()
         _storage_lock = asyncio.Lock()
         _pipeline_status_lock = asyncio.Lock()
@@ -372,7 +372,7 @@ async def initialize_pipeline_status():
             return
 
         # Create a shared list object for history_messages
-        history_messages = _manager.list() if is_multiprocess else []
+        history_messages = _manager.list() if _is_multiprocess else []
         pipeline_namespace.update(
             {
                 "autoscanned": False,  # Auto-scan started
@@ -401,7 +401,7 @@ async def get_update_flag(namespace: str):
 
     async with get_internal_lock():
         if namespace not in _update_flags:
-            if is_multiprocess and _manager is not None:
+            if _is_multiprocess and _manager is not None:
                 _update_flags[namespace] = _manager.list()
             else:
                 _update_flags[namespace] = []
@@ -409,7 +409,7 @@ async def get_update_flag(namespace: str):
                 f"Process {os.getpid()} initialized updated flags for namespace: [{namespace}]"
             )
 
-        if is_multiprocess and _manager is not None:
+        if _is_multiprocess and _manager is not None:
             new_update_flag = _manager.Value("b", False)
         else:
             # Create a simple mutable object to store boolean value for compatibility with mutiprocess
@@ -434,11 +434,7 @@ async def set_all_update_flags(namespace: str):
             raise ValueError(f"Namespace {namespace} not found in update flags")
         # Update flags for both modes
         for i in range(len(_update_flags[namespace])):
-            if is_multiprocess:
-                _update_flags[namespace][i].value = True
-            else:
-                # Use .value attribute instead of direct assignment
-                _update_flags[namespace][i].value = True
+            _update_flags[namespace][i].value = True
 
 
 async def clear_all_update_flags(namespace: str):
@@ -452,11 +448,7 @@ async def clear_all_update_flags(namespace: str):
             raise ValueError(f"Namespace {namespace} not found in update flags")
         # Update flags for both modes
         for i in range(len(_update_flags[namespace])):
-            if is_multiprocess:
-                _update_flags[namespace][i].value = False
-            else:
-                # Use .value attribute instead of direct assignment
-                _update_flags[namespace][i].value = False
+            _update_flags[namespace][i].value = False
 
 
 async def get_all_update_flags_status() -> Dict[str, list]:
@@ -474,7 +466,7 @@ async def get_all_update_flags_status() -> Dict[str, list]:
         for namespace, flags in _update_flags.items():
             worker_statuses = []
             for flag in flags:
-                if is_multiprocess:
+                if _is_multiprocess:
                     worker_statuses.append(flag.value)
                 else:
                     worker_statuses.append(flag)
@@ -518,7 +510,7 @@ async def get_namespace_data(namespace: str) -> Dict[str, Any]:
 
     async with get_internal_lock():
         if namespace not in _shared_dicts:
-            if is_multiprocess and _manager is not None:
+            if _is_multiprocess and _manager is not None:
                 _shared_dicts[namespace] = _manager.dict()
             else:
                 _shared_dicts[namespace] = {}
@@ -538,7 +530,7 @@ def finalize_share_data():
     """
     global \
         _manager, \
-        is_multiprocess, \
+        _is_multiprocess, \
         _storage_lock, \
         _internal_lock, \
         _pipeline_status_lock, \
@@ -558,11 +550,11 @@ def finalize_share_data():
         return
 
     direct_log(
-        f"Process {os.getpid()} finalizing storage data (multiprocess={is_multiprocess})"
+        f"Process {os.getpid()} finalizing storage data (multiprocess={_is_multiprocess})"
     )
 
     # In multi-process mode, shut down the Manager
-    if is_multiprocess and _manager is not None:
+    if _is_multiprocess and _manager is not None:
         try:
             # Clear shared resources before shutting down Manager
             if _shared_dicts is not None:
@@ -604,7 +596,7 @@ def finalize_share_data():
     # Reset global variables
     _manager = None
     _initialized = None
-    is_multiprocess = None
+    _is_multiprocess = None
     _shared_dicts = None
     _init_flags = None
     _storage_lock = None