Add Langfuse observability integration documentation

README-zh.md

@@ -53,7 +53,7 @@
 
 ## 🎉 新闻
 
-- [x] [2025.11.05]🎯📢添加**基于RAGAS的**LightRAG评估框架。
+- [x] [2025.11.05]🎯📢添加**基于RAGAS的**评估框架和**Langfuse**可观测性支持。
 - [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。
@@ -1463,6 +1463,50 @@ 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)。

README.md

@@ -51,7 +51,7 @@
 
 ---
 ## 🎉 News
-- [x] [2025.11.05]🎯📢Add **RAGAS-based** Evaluation Framework for LightRAG.
+- [x] [2025.11.05]🎯📢Add **RAGAS-based** Evaluation Framework and **Langfuse** observability 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.
@@ -1543,6 +1543,50 @@ 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).