diff --git a/README-zh.md b/README-zh.md
index c90889dd..4bb8d4b5 100644
--- a/README-zh.md
+++ b/README-zh.md
@@ -53,28 +53,24 @@
 
 ## 🎉 新闻
 
-- [x] [2025.11.05]🎯📢添加**基于RAGAS的**LightRAG评估框架。
-- [x] [2025.10.22]🎯📢消除处理**大规模数据集**的瓶颈。
-- [x] [2025.09.15]🎯📢显著提升**小型LLM**（如Qwen3-30B-A3B）的知识图谱提取准确性。
-- [x] [2025.08.29]🎯📢现已支持**Reranker**，显著提升混合查询性能。
-- [x] [2025.08.04]🎯📢支持**文档删除**并重新生成知识图谱以确保查询性能。
-- [x] [2025.06.16]🎯📢我们的团队发布了[RAG-Anything](https://github.com/HKUDS/RAG-Anything)，一个用于无缝处理文本、图像、表格和方程式的全功能多模态 RAG 系统。
-- [X] [2025.06.05]🎯📢LightRAG现已集成[RAG-Anything](https://github.com/HKUDS/RAG-Anything)，支持全面的多模态文档解析与RAG能力（PDF、图片、Office文档、表格、公式等）。详见下方[多模态处理模块](https://github.com/HKUDS/LightRAG?tab=readme-ov-file#多模态文档处理rag-anything集成)。
-- [X] [2025.03.18]🎯📢LightRAG现已支持引文功能。
-- [X] [2025.02.05]🎯📢我们团队发布了[VideoRAG](https://github.com/HKUDS/VideoRAG)，用于理解超长上下文视频。
-- [X] [2025.01.13]🎯📢我们团队发布了[MiniRAG](https://github.com/HKUDS/MiniRAG)，使用小型模型简化RAG。
-- [X] [2025.01.06]🎯📢现在您可以[使用PostgreSQL进行存储](#using-postgresql-for-storage)。
-- [X] [2024.11.25]🎯📢LightRAG现在支持无缝集成[自定义知识图谱](https://github.com/HKUDS/LightRAG?tab=readme-ov-file#insert-custom-kg)，使用户能够用自己的领域专业知识增强系统。
-- [X] [2024.11.19]🎯📢LightRAG的综合指南现已在[LearnOpenCV](https://learnopencv.com/lightrag)上发布。非常感谢博客作者。
-- [X] [2024.11.11]🎯📢LightRAG现在支持[通过实体名称删除实体](https://github.com/HKUDS/LightRAG?tab=readme-ov-file#delete)。
-- [X] [2024.11.09]🎯📢推出[LightRAG Gui](https://lightrag-gui.streamlit.app)，允许您插入、查询、可视化和下载LightRAG知识。
-- [X] [2024.11.04]🎯📢现在您可以[使用Neo4J进行存储](https://github.com/HKUDS/LightRAG?tab=readme-ov-file#using-neo4j-for-storage)。
-- [X] [2024.10.29]🎯📢LightRAG现在通过`textract`支持多种文件类型，包括PDF、DOC、PPT和CSV。
-- [X] [2024.10.20]🎯📢我们为LightRAG添加了一个新功能：图形可视化。
-- [X] [2024.10.18]🎯📢我们添加了[LightRAG介绍视频](https://youtu.be/oageL-1I0GE)的链接。感谢作者！
-- [X] [2024.10.17]🎯📢我们创建了一个[Discord频道](https://discord.gg/yF2MmDJyGJ)！欢迎加入分享和讨论！🎉🎉
-- [X] [2024.10.16]🎯📢LightRAG现在支持[Ollama模型](https://github.com/HKUDS/LightRAG?tab=readme-ov-file#quick-start)！
-- [X] [2024.10.15]🎯📢LightRAG现在支持[Hugging Face模型](https://github.com/HKUDS/LightRAG?tab=readme-ov-file#quick-start)！
+- [x] [2025.11.05]🎯添加**基于RAGAS的**评估框架和**Langfuse**可观测性支持（API可随查询结果返回召回上下文）。
+- [x] [2025.10.22]🎯消除处理**大规模数据集**的性能瓶颈。
+- [x] [2025.09.15]🎯显著提升**小型LLM**（如Qwen3-30B-A3B）的知识图谱提取准确性。
+- [x] [2025.08.29]🎯现已支持**Reranker**，显著提升混合查询性能(现已设为默认查询模式)。
+- [x] [2025.08.04]🎯支持**文档删除**并重新生成知识图谱以确保查询性能。
+- [x] [2025.06.16]🎯我们的团队发布了[RAG-Anything](https://github.com/HKUDS/RAG-Anything)，一个用于无缝处理文本、图像、表格和方程式的全功能多模态 RAG 系统。
+- [x] [2025.06.05]🎯LightRAG现已集成[RAG-Anything](https://github.com/HKUDS/RAG-Anything)，支持全面的多模态文档解析与RAG能力（PDF、图片、Office文档、表格、公式等）。详见下方[多模态处理模块](https://github.com/HKUDS/LightRAG?tab=readme-ov-file#多模态文档处理rag-anything集成)。
+- [x] [2025.03.18]🎯LightRAG现已支持参考文献功能。
+- [x] [2025.02.12]🎯现在您可以使用MongoDB作为一体化存储解决方案。
+- [x] [2025.02.05]🎯我们团队发布了[VideoRAG](https://github.com/HKUDS/VideoRAG)，用于理解超长上下文视频。
+- [x] [2025.01.13]🎯我们团队发布了[MiniRAG](https://github.com/HKUDS/MiniRAG)，使用小型模型简化RAG。
+- [x] [2025.01.06]🎯现在您可以使用PostgreSQL作为一体化存储解决方案。
+- [x] [2024.11.19]🎯LightRAG的综合指南现已在[LearnOpenCV](https://learnopencv.com/lightrag)上发布。非常感谢博客作者。
+- [x] [2024.11.09]🎯推出LightRAG Webui，允许您插入、查询、可视化LightRAG知识。
+- [x] [2024.11.04]🎯现在您可以[使用Neo4J进行存储](https://github.com/HKUDS/LightRAG?tab=readme-ov-file#using-neo4j-for-storage)。
+- [x] [2024.10.18]🎯我们添加了[LightRAG介绍视频](https://youtu.be/oageL-1I0GE)的链接。感谢作者！
+- [x] [2024.10.17]🎯我们创建了一个[Discord频道](https://discord.gg/yF2MmDJyGJ)！欢迎加入分享和讨论！🎉🎉
+- [x] [2024.10.16]🎯LightRAG现在支持[Ollama模型](https://github.com/HKUDS/LightRAG?tab=readme-ov-file#quick-start)！
 
 <details>
   <summary style="font-size: 1.4em; font-weight: bold; cursor: pointer; display: list-item;">
@@ -90,6 +86,11 @@
 
 ## 安装
 
+> **💡 使用 uv 进行包管理**: 本项目使用 [uv](https://docs.astral.sh/uv/) 进行快速可靠的 Python 包管理。
+> 首先安装 uv: `curl -LsSf https://astral.sh/uv/install.sh | sh` (Unix/macOS) 或 `powershell -c "irm https://astral.sh/uv/install.ps1 | iex"` (Windows)
+>
+> **注意**: 如果您更喜欢使用 pip 也可以，但我们推荐使用 uv 以获得更好的性能和更可靠的依赖管理。
+
 ### 安装LightRAG服务器
 
 LightRAG服务器旨在提供Web UI和API支持。Web UI便于文档索引、知识图谱探索和简单的RAG查询界面。LightRAG服务器还提供兼容Ollama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。
@@ -97,8 +98,13 @@ LightRAG服务器旨在提供Web UI和API支持。Web UI便于文档索引、知
 * 从PyPI安装
 
 ```bash
-pip install "lightrag-hku[api]"
-cp env.example .env
+# 使用 uv (推荐)
+uv pip install "lightrag-hku[api]"
+# 或使用 pip
+# pip install "lightrag-hku[api]"
+
+cp env.example .env  # 使用你的LLM和Embedding模型访问参数更新.env文件
+
 lightrag-server
 ```
 
@@ -107,9 +113,17 @@ lightrag-server
 ```bash
 git clone https://github.com/HKUDS/LightRAG.git
 cd LightRAG
-# 如有必要，创建Python虚拟环境
-# 以可开发（编辑）模式安装LightRAG服务器
-pip install -e ".[api]"
+
+# 使用 uv (推荐)
+# 注意: uv sync 会自动在 .venv/ 目录创建虚拟环境
+uv sync --extra api
+source .venv/bin/activate  # 激活虚拟环境 (Linux/macOS)
+# Windows 系统: .venv\Scripts\activate
+
+# 或使用 pip 和虚拟环境
+# python -m venv .venv
+# source .venv/bin/activate  # Windows: .venv\Scripts\activate
+# pip install -e ".[api]"
 
 cp env.example .env  # 使用你的LLM和Embedding模型访问参数更新.env文件
 
@@ -140,13 +154,19 @@ docker compose up
 
 ```bash
 cd LightRAG
-pip install -e .
+# 注意: uv sync 会自动在 .venv/ 目录创建虚拟环境
+uv sync
+source .venv/bin/activate  # 激活虚拟环境 (Linux/macOS)
+# Windows 系统: .venv\Scripts\activate
+
+# 或: pip install -e .
 ```
 
 * 从PyPI安装
 
 ```bash
-pip install lightrag-hku
+uv pip install lightrag-hku
+# 或: pip install lightrag-hku
 ```
 
 ## 快速开始
@@ -198,6 +218,10 @@ python examples/lightrag_openai_demo.py
 
 > ⚠️ **如果您希望将LightRAG集成到您的项目中，建议您使用LightRAG Server提供的REST API**。LightRAG Core通常用于嵌入式应用，或供希望进行研究与评估的学者使用。
 
+### ⚠️ 重要：初始化要求
+
+LightRAG 在使用前需要显式初始化。 创建 LightRAG 实例后，您必须调用 await rag.initialize_storages()，否则将出现错误。
+
 ### 一个简单程序
 
 以下Python代码片段演示了如何初始化LightRAG、插入文本并进行查询：
@@ -207,7 +231,6 @@ import os
 import asyncio
 from lightrag import LightRAG, QueryParam
 from lightrag.llm.openai import gpt_4o_mini_complete, gpt_4o_complete, openai_embed
-from lightrag.kg.shared_storage import initialize_pipeline_status
 from lightrag.utils import setup_logger
 
 setup_logger("lightrag", level="INFO")
@@ -222,9 +245,7 @@ async def initialize_rag():
         embedding_func=openai_embed,
         llm_model_func=gpt_4o_mini_complete,
     )
-    await rag.initialize_storages()
-    await initialize_pipeline_status()
-    return rag
+    await rag.initialize_storages()    return rag
 
 async def main():
     try:
@@ -418,8 +439,6 @@ async def initialize_rag():
     )
 
     await rag.initialize_storages()
-    await initialize_pipeline_status()
-
     return rag
 ```
 
@@ -548,7 +567,6 @@ from lightrag import LightRAG
 from lightrag.llm.llama_index_impl import llama_index_complete_if_cache, llama_index_embed
 from llama_index.embeddings.openai import OpenAIEmbedding
 from llama_index.llms.openai import OpenAI
-from lightrag.kg.shared_storage import initialize_pipeline_status
 from lightrag.utils import setup_logger
 
 # 为LightRAG设置日志处理程序
@@ -565,8 +583,6 @@ async def initialize_rag():
     )
 
     await rag.initialize_storages()
-    await initialize_pipeline_status()
-
     return rag
 
 def main():
@@ -816,8 +832,6 @@ async def initialize_rag():
     # 初始化数据库连接
     await rag.initialize_storages()
     # 初始化文档处理的管道状态
-    await initialize_pipeline_status()
-
     return rag
 ```
 
@@ -867,8 +881,8 @@ rag = LightRAG(
 
 对于生产级场景，您很可能想要利用企业级解决方案。PostgreSQL可以为您提供一站式储解解决方案，作为KV存储、向量数据库（pgvector）和图数据库（apache AGE）。支持 PostgreSQL 版本为16.6或以上。
 
-* 如果您是初学者并想避免麻烦，推荐使用docker，请从这个镜像开始（请务必阅读概述）：https://hub.docker.com/r/shangor/postgres-for-rag
-* Apache AGE的性能不如Neo4j。最求高性能的图数据库请使用Noe4j。
+* 如果您是初学者并想避免麻烦，推荐使用docker，请从这个镜像开始（默认帐号密码:rag/rag）：https://hub.docker.com/r/gzdaniel/postgres-for-rag
+* Apache AGE的性能不如Neo4j。追求高性能的图数据库请使用Noe4j。
 
 </details>
 
@@ -1463,6 +1477,50 @@ LightRAG服务器提供全面的知识图谱可视化功能。它支持各种重
 
 ![iShot_2025-03-23_12.40.08](./README.assets/iShot_2025-03-23_12.40.08.png)
 
+## Langfuse 可观测性集成
+
+Langfuse 为 OpenAI 客户端提供了直接替代方案，可自动跟踪所有 LLM 交互，使开发者能够在无需修改代码的情况下监控、调试和优化其 RAG 系统。
+
+### 安装 Langfuse 可选依赖
+
+```
+pip install lightrag-hku
+pip install lightrag-hku[observability]
+
+# 或从源代码安装并启用调试模式
+pip install -e .
+pip install -e ".[observability]"
+```
+
+### 配置 Langfuse 环境变量
+
+修改 .env 文件：
+
+```
+## Langfuse 可观测性（可选）
+# LLM 可观测性和追踪平台
+# 安装命令: pip install lightrag-hku[observability]
+# 注册地址: https://cloud.langfuse.com 或自托管部署
+LANGFUSE_SECRET_KEY=""
+LANGFUSE_PUBLIC_KEY=""
+LANGFUSE_HOST="https://cloud.langfuse.com"  # 或您的自托管实例地址
+LANGFUSE_ENABLE_TRACE=true
+```
+
+### Langfuse 使用说明
+
+安装并配置完成后，Langfuse 会自动追踪所有 OpenAI LLM 调用。Langfuse 仪表板功能包括：
+
+- **追踪**：查看完整的 LLM 调用链
+- **分析**：Token 使用量、延迟、成本指标
+- **调试**：检查提示词和响应内容
+- **评估**：比较模型输出结果
+- **监控**：实时告警功能
+
+### 重要提示
+
+**注意**：LightRAG 目前仅把 OpenAI 兼容的 API 调用接入了 Langfuse。Ollama、Azure 和 AWS Bedrock 等 API 还无法使用 Langfuse 的可观测性功能。
+
 ## RAGAS评估
 
 **RAGAS**（Retrieval Augmented Generation Assessment，检索增强生成评估）是一个使用LLM对RAG系统进行无参考评估的框架。我们提供了基于RAGAS的评估脚本。详细信息请参阅[基于RAGAS的评估框架](lightrag/evaluation/README.md)。
diff --git a/README.md b/README.md
index 6a0057a4..1a946e8d 100644
--- a/README.md
+++ b/README.md
@@ -51,28 +51,24 @@
 
 ---
 ## 🎉 News
-- [x] [2025.11.05]🎯📢Add **RAGAS-based** Evaluation Framework for LightRAG.
-- [x] [2025.10.22]🎯📢Eliminate bottlenecks in processing **large-scale datasets**.
-- [x] [2025.09.15]🎯📢Significantly enhances KG extraction accuracy for **small LLMs** like Qwen3-30B-A3B.
-- [x] [2025.08.29]🎯📢**Reranker** is supported now , significantly boosting performance for mixed queries.
-- [x] [2025.08.04]🎯📢**Document deletion** with KG regeneration to ensure query performance.
-- [x] [2025.06.16]🎯📢Our team has released [RAG-Anything](https://github.com/HKUDS/RAG-Anything) an All-in-One Multimodal RAG System for seamless text, image, table, and equation processing.
-- [X] [2025.06.05]🎯📢LightRAG now supports comprehensive multimodal data handling through [RAG-Anything](https://github.com/HKUDS/RAG-Anything) integration, enabling seamless document parsing and RAG capabilities across diverse formats including PDFs, images, Office documents, tables, and formulas. Please refer to the new [multimodal section](https://github.com/HKUDS/LightRAG/?tab=readme-ov-file#multimodal-document-processing-rag-anything-integration) for details.
-- [X] [2025.03.18]🎯📢LightRAG now supports citation functionality, enabling proper source attribution.
-- [X] [2025.02.05]🎯📢Our team has released [VideoRAG](https://github.com/HKUDS/VideoRAG) understanding extremely long-context videos.
-- [X] [2025.01.13]🎯📢Our team has released [MiniRAG](https://github.com/HKUDS/MiniRAG) making RAG simpler with small models.
-- [X] [2025.01.06]🎯📢You can now [use PostgreSQL for Storage](#using-postgresql-for-storage).
-- [X] [2024.11.25]🎯📢LightRAG now supports seamless integration of [custom knowledge graphs](https://github.com/HKUDS/LightRAG?tab=readme-ov-file#insert-custom-kg), empowering users to enhance the system with their own domain expertise.
-- [X] [2024.11.19]🎯📢A comprehensive guide to LightRAG is now available on [LearnOpenCV](https://learnopencv.com/lightrag). Many thanks to the blog author.
-- [X] [2024.11.11]🎯📢LightRAG now supports [deleting entities by their names](https://github.com/HKUDS/LightRAG?tab=readme-ov-file#delete).
-- [X] [2024.11.09]🎯📢Introducing the [LightRAG Gui](https://lightrag-gui.streamlit.app), which allows you to insert, query, visualize, and download LightRAG knowledge.
-- [X] [2024.11.04]🎯📢You can now [use Neo4J for Storage](https://github.com/HKUDS/LightRAG?tab=readme-ov-file#using-neo4j-for-storage).
-- [X] [2024.10.29]🎯📢LightRAG now supports multiple file types, including PDF, DOC, PPT, and CSV via `textract`.
-- [X] [2024.10.20]🎯📢We've added a new feature to LightRAG: Graph Visualization.
-- [X] [2024.10.18]🎯📢We've added a link to a [LightRAG Introduction Video](https://youtu.be/oageL-1I0GE). Thanks to the author!
-- [X] [2024.10.17]🎯📢We have created a [Discord channel](https://discord.gg/yF2MmDJyGJ)! Welcome to join for sharing and discussions! 🎉🎉
-- [X] [2024.10.16]🎯📢LightRAG now supports [Ollama models](https://github.com/HKUDS/LightRAG?tab=readme-ov-file#quick-start)!
-- [X] [2024.10.15]🎯📢LightRAG now supports [Hugging Face models](https://github.com/HKUDS/LightRAG?tab=readme-ov-file#quick-start)!
+- [x] [2025.11.05]🎯Add **RAGAS-based** Evaluation Framework and **Langfuse** observability for LightRAG (API can return retrieved contexts with query results).
+- [x] [2025.10.22]🎯Eliminate bottlenecks in processing **large-scale datasets**.
+- [x] [2025.09.15]🎯Significantly enhances KG extraction accuracy for **small LLMs** like Qwen3-30B-A3B.
+- [x] [2025.08.29]🎯**Reranker** is supported now , significantly boosting performance for mixed queries(Set as default query mode now).
+- [x] [2025.08.04]🎯**Document deletion** with KG regeneration to ensure query performance.
+- [x] [2025.06.16]🎯Our team has released [RAG-Anything](https://github.com/HKUDS/RAG-Anything) an All-in-One Multimodal RAG System for seamless text, image, table, and equation processing.
+- [x] [2025.06.05]🎯LightRAG now supports comprehensive multimodal data handling through [RAG-Anything](https://github.com/HKUDS/RAG-Anything) integration, enabling seamless document parsing and RAG capabilities across diverse formats including PDFs, images, Office documents, tables, and formulas. Please refer to the new [multimodal section](https://github.com/HKUDS/LightRAG/?tab=readme-ov-file#multimodal-document-processing-rag-anything-integration) for details.
+- [x] [2025.03.18]🎯LightRAG now supports citation functionality, enabling proper source attribution.
+- [x] [2025.02.12]🎯You can now use MongoDB as all in-one Storage.
+- [x] [2025.02.05]🎯Our team has released [VideoRAG](https://github.com/HKUDS/VideoRAG) understanding extremely long-context videos.
+- [x] [2025.01.13]🎯Our team has released [MiniRAG](https://github.com/HKUDS/MiniRAG) making RAG simpler with small models.
+- [x] [2025.01.06]🎯You can now use PostgreSQL as all in-one Storage.
+- [x] [2024.11.19]🎯A comprehensive guide to LightRAG is now available on [LearnOpenCV](https://learnopencv.com/lightrag). Many thanks to the blog author.
+- [x] [2024.11.09]🎯Introducing the LightRAG Webui, which allows you to insert, query, visualize LightRAG knowledge.
+- [x] [2024.11.04]🎯You can now [use Neo4J for Storage](https://github.com/HKUDS/LightRAG?tab=readme-ov-file#using-neo4j-for-storage).
+- [x] [2024.10.18]🎯We've added a link to a [LightRAG Introduction Video](https://youtu.be/oageL-1I0GE). Thanks to the author!
+- [x] [2024.10.17]🎯We have created a [Discord channel](https://discord.gg/yF2MmDJyGJ)! Welcome to join for sharing and discussions! 🎉🎉
+- [x] [2024.10.16]🎯LightRAG now supports [Ollama models](https://github.com/HKUDS/LightRAG?tab=readme-ov-file#quick-start)!
 
 <details>
   <summary style="font-size: 1.4em; font-weight: bold; cursor: pointer; display: list-item;">
@@ -88,6 +84,11 @@
 
 ## Installation
 
+> **💡 Using uv for Package Management**: This project uses [uv](https://docs.astral.sh/uv/) for fast and reliable Python package management.
+> Install uv first: `curl -LsSf https://astral.sh/uv/install.sh | sh` (Unix/macOS) or `powershell -c "irm https://astral.sh/uv/install.ps1 | iex"` (Windows)
+>
+> **Note**: You can also use pip if you prefer, but uv is recommended for better performance and more reliable dependency management.
+>
 > **📦 Offline Deployment**: For offline or air-gapped environments, see the [Offline Deployment Guide](./docs/OfflineDeployment.md) for instructions on pre-installing all dependencies and cache files.
 
 ### Install LightRAG Server
@@ -97,8 +98,13 @@ The LightRAG Server is designed to provide Web UI and API support. The Web UI fa
 * Install from PyPI
 
 ```bash
-pip install "lightrag-hku[api]"
-cp env.example .env
+# Using uv (recommended)
+uv pip install "lightrag-hku[api]"
+# Or using pip
+# pip install "lightrag-hku[api]"
+
+cp env.example .env  # Update the .env with your LLM and embedding configurations
+
 lightrag-server
 ```
 
@@ -107,9 +113,17 @@ lightrag-server
 ```bash
 git clone https://github.com/HKUDS/LightRAG.git
 cd LightRAG
-# Create a Python virtual enviroment if neccesary
-# Install in editable mode with API support
-pip install -e ".[api]"
+
+# Using uv (recommended)
+# Note: uv sync automatically creates a virtual environment in .venv/
+uv sync --extra api
+source .venv/bin/activate  # Activate the virtual environment (Linux/macOS)
+# Or on Windows: .venv\Scripts\activate
+
+# Or using pip with virtual environment
+# python -m venv .venv
+# source .venv/bin/activate  # Windows: .venv\Scripts\activate
+# pip install -e ".[api]"
 
 cp env.example .env  # Update the .env with your LLM and embedding configurations
 
@@ -136,17 +150,23 @@ docker compose up
 
 ### Install  LightRAG Core
 
-* Install from source (Recommend)
+* Install from source (Recommended)
 
 ```bash
 cd LightRAG
-pip install -e .
+# Note: uv sync automatically creates a virtual environment in .venv/
+uv sync
+source .venv/bin/activate  # Activate the virtual environment (Linux/macOS)
+# Or on Windows: .venv\Scripts\activate
+
+# Or: pip install -e .
 ```
 
 * Install from PyPI
 
 ```bash
-pip install lightrag-hku
+uv pip install lightrag-hku
+# Or: pip install lightrag-hku
 ```
 
 ## Quick Start
@@ -200,10 +220,7 @@ For a streaming response implementation example, please see `examples/lightrag_o
 
 ### ⚠️ Important: Initialization Requirements
 
-**LightRAG requires explicit initialization before use.** You must call both `await rag.initialize_storages()` and `await initialize_pipeline_status()` after creating a LightRAG instance, otherwise you will encounter errors like:
-
-- `AttributeError: __aenter__` - if storages are not initialized
-- `KeyError: 'history_messages'` - if pipeline status is not initialized
+**LightRAG requires explicit initialization before use.** You must call `await rag.initialize_storages()` after creating a LightRAG instance, otherwise you will encounter errors.
 
 ### A Simple Program
 
@@ -214,7 +231,6 @@ import os
 import asyncio
 from lightrag import LightRAG, QueryParam
 from lightrag.llm.openai import gpt_4o_mini_complete, gpt_4o_complete, openai_embed
-from lightrag.kg.shared_storage import initialize_pipeline_status
 from lightrag.utils import setup_logger
 
 setup_logger("lightrag", level="INFO")
@@ -230,9 +246,7 @@ async def initialize_rag():
         llm_model_func=gpt_4o_mini_complete,
     )
     # IMPORTANT: Both initialization calls are required!
-    await rag.initialize_storages()  # Initialize storage backends
-    await initialize_pipeline_status()  # Initialize processing pipeline
-    return rag
+    await rag.initialize_storages()  # Initialize storage backends    return rag
 
 async def main():
     try:
@@ -421,8 +435,6 @@ async def initialize_rag():
     )
 
     await rag.initialize_storages()
-    await initialize_pipeline_status()
-
     return rag
 ```
 
@@ -553,7 +565,6 @@ from lightrag import LightRAG
 from lightrag.llm.llama_index_impl import llama_index_complete_if_cache, llama_index_embed
 from llama_index.embeddings.openai import OpenAIEmbedding
 from llama_index.llms.openai import OpenAI
-from lightrag.kg.shared_storage import initialize_pipeline_status
 from lightrag.utils import setup_logger
 
 # Setup log handler for LightRAG
@@ -570,8 +581,6 @@ async def initialize_rag():
     )
 
     await rag.initialize_storages()
-    await initialize_pipeline_status()
-
     return rag
 
 def main():
@@ -823,8 +832,6 @@ async def initialize_rag():
     # Initialize database connections
     await rag.initialize_storages()
     # Initialize pipeline status for document processing
-    await initialize_pipeline_status()
-
     return rag
 ```
 
@@ -838,7 +845,7 @@ see test_neo4j.py for a working example.
 For production level scenarios you will most likely want to leverage an enterprise solution. PostgreSQL can provide a one-stop solution for you as KV store, VectorDB (pgvector) and GraphDB (apache AGE). PostgreSQL version 16.6 or higher is supported.
 
 * PostgreSQL is lightweight,the whole binary distribution including all necessary plugins can be zipped to 40MB: Ref to [Windows Release](https://github.com/ShanGor/apache-age-windows/releases/tag/PG17%2Fv1.5.0-rc0) as it is easy to install for Linux/Mac.
-* If you prefer docker, please start with this image if you are a beginner to avoid hiccups (DO read the overview): https://hub.docker.com/r/shangor/postgres-for-rag
+* If you prefer docker, please start with this image if you are a beginner to avoid hiccups (Default user password:rag/rag): https://hub.docker.com/r/gzdaniel/postgres-for-rag
 * How to start? Ref to: [examples/lightrag_zhipu_postgres_demo.py](https://github.com/HKUDS/LightRAG/blob/main/examples/lightrag_zhipu_postgres_demo.py)
 * For high-performance graph database requirements, Neo4j is recommended as Apache AGE's performance is not as competitive.
 
@@ -909,8 +916,6 @@ async def initialize_rag():
     # Initialize database connections
     await rag.initialize_storages()
     # Initialize pipeline status for document processing
-    await initialize_pipeline_status()
-
     return rag
 ```
 
@@ -945,7 +950,8 @@ maxclients 500
 The `workspace` parameter ensures data isolation between different LightRAG instances. Once initialized, the `workspace` is immutable and cannot be changed.Here is how workspaces are implemented for different types of storage:
 
 - **For local file-based databases, data isolation is achieved through workspace subdirectories:** `JsonKVStorage`, `JsonDocStatusStorage`, `NetworkXStorage`, `NanoVectorDBStorage`, `FaissVectorDBStorage`.
-- **For databases that store data in collections, it's done by adding a workspace prefix to the collection name:** `RedisKVStorage`, `RedisDocStatusStorage`, `MilvusVectorDBStorage`, `QdrantVectorDBStorage`, `MongoKVStorage`, `MongoDocStatusStorage`, `MongoVectorDBStorage`, `MongoGraphStorage`, `PGGraphStorage`.
+- **For databases that store data in collections, it's done by adding a workspace prefix to the collection name:** `RedisKVStorage`, `RedisDocStatusStorage`, `MilvusVectorDBStorage`, `MongoKVStorage`, `MongoDocStatusStorage`, `MongoVectorDBStorage`, `MongoGraphStorage`, `PGGraphStorage`.
+- **For Qdrant vector database, data isolation is achieved through payload-based partitioning (Qdrant's recommended multitenancy approach):** `QdrantVectorDBStorage` uses shared collections with payload filtering for unlimited workspace scalability.
 - **For relational databases, data isolation is achieved by adding a `workspace` field to the tables for logical data separation:** `PGKVStorage`, `PGVectorStorage`, `PGDocStatusStorage`.
 - **For the Neo4j graph database, logical data isolation is achieved through labels:** `Neo4JStorage`
 
@@ -1517,16 +1523,13 @@ If you encounter these errors when using LightRAG:
 
 2. **`KeyError: 'history_messages'`**
    - **Cause**: Pipeline status not initialized
-   - **Solution**: Call `await initialize_pipeline_status()` after initializing storages
-
+   - **Solution**: Call `
 3. **Both errors in sequence**
    - **Cause**: Neither initialization method was called
    - **Solution**: Always follow this pattern:
    ```python
    rag = LightRAG(...)
-   await rag.initialize_storages()
-   await initialize_pipeline_status()
-   ```
+   await rag.initialize_storages()   ```
 
 ### Model Switching Issues
 
@@ -1542,6 +1545,50 @@ The LightRAG Server offers a comprehensive knowledge graph visualization feature
 
 ![iShot_2025-03-23_12.40.08](./README.assets/iShot_2025-03-23_12.40.08.png)
 
+## Langfuse observability integration
+
+Langfuse provides a drop-in replacement for the OpenAI client that automatically tracks all LLM interactions, enabling developers to monitor, debug, and optimize their RAG systems without code changes.
+
+### Installation with Langfuse option
+
+```
+pip install lightrag-hku
+pip install lightrag-hku[observability]
+
+# Or install from souce code with debug mode enabled
+pip install -e .
+pip install -e ".[observability]"
+```
+
+### Config Langfuse env vars
+
+modify .env file:
+
+```
+## Langfuse Observability (Optional)
+# LLM observability and tracing platform
+# Install with: pip install lightrag-hku[observability]
+# Sign up at: https://cloud.langfuse.com or self-host
+LANGFUSE_SECRET_KEY=""
+LANGFUSE_PUBLIC_KEY=""
+LANGFUSE_HOST="https://cloud.langfuse.com"  # or your self-hosted instance
+LANGFUSE_ENABLE_TRACE=true
+```
+
+### Langfuse Usage
+
+Once installed and configured, Langfuse automatically traces all OpenAI LLM calls. Langfuse dashboard features include:
+
+- **Tracing**: View complete LLM call chains
+- **Analytics**: Token usage, latency, cost metrics
+- **Debugging**: Inspect prompts and responses
+- **Evaluation**: Compare model outputs
+- **Monitoring**: Real-time alerting
+
+### Important Notice
+
+**Note**: LightRAG currently only integrates OpenAI-compatible API calls with Langfuse. APIs such as Ollama, Azure, and AWS Bedrock are not yet supported for Langfuse observability.
+
 ## RAGAS-based Evaluation
 
 **RAGAS** (Retrieval Augmented Generation Assessment) is a framework for reference-free evaluation of RAG systems using LLMs. There is an evaluation script based on RAGAS. For detailed information, please refer to [RAGAS-based Evaluation Framework](lightrag/evaluation/README.md).