Merge branch 'main' of https://github.com/tangledup-ai/langchain-agent

.dockerignore

@@ -0,0 +1,45 @@
+# Git
+.git
+.gitignore
+
+# Python
+__pycache__/
+*.pyc
+*.pyo
+*.pyd
+.Python
+env/
+pip-log.txt
+pip-delete-this-directory.txt
+.tox/
+.coverage
+.coverage.*
+.cache
+nosetests.xml
+coverage.xml
+*.cover
+*.log
+.git
+.mypy_cache
+.pytest_cache
+.hypothesis
+
+# IDE
+.vscode/
+.idea/
+*.swp
+*.swo
+*~
+
+# OS
+.DS_Store
+.DS_Store?
+._*
+.Spotlight-V100
+.Trashes
+ehthumbs.db
+Thumbs.db
+
+# Project specific
+*.md
+!README.md

.gitignore

@@ -5,4 +5,5 @@ logs/
 
 *.pyc
 *.zip
-django.log
+django.log
+.env

Dockerfile

@@ -0,0 +1,31 @@
+# 使用Python 3.10作为基础镜像
+FROM python:3.12-slim
+
+# 设置工作目录
+WORKDIR /app
+
+# 设置环境变量
+ENV PYTHONPATH=/app
+ENV PYTHONUNBUFFERED=1
+
+# 安装系统依赖
+RUN apt-get update && apt-get install -y \
+    gcc \
+    g++ \
+    && rm -rf /var/lib/apt/lists/*
+
+# 复制项目文件
+COPY pyproject.toml ./
+COPY fastapi_server/requirements.txt ./fastapi_server/
+COPY lang_agent/ ./lang_agent/
+COPY fastapi_server/ ./fastapi_server/
+
+# 安装Python依赖
+RUN pip install --no-cache-dir -r fastapi_server/requirements.txt
+RUN pip install --no-cache-dir -e .
+
+# 暴露端口
+EXPOSE 8488
+
+# 启动命令
+CMD ["python", "fastapi_server/server.py"]

docker-compose.yml

@@ -0,0 +1,25 @@
+version: '3.8'
+
+services:
+  lang-agent-api:
+    build: .
+    container_name: lang-agent-api
+    ports:
+      - "8488:8488"
+    env_file:
+      - ./.env
+    environment:
+      - PYTHONPATH=/app
+      - PYTHONUNBUFFERED=1
+      - RAG_FOLDER_PATH=/app/assets/xiaozhan_emb
+    volumes:
+      - ./configs:/app/configs
+      - ./scripts:/app/scripts
+      - ./assets:/app/assets
+    restart: unless-stopped
+    healthcheck:
+      test: ["CMD", "python", "-c", "import urllib.request; urllib.request.urlopen('http://localhost:8488/health')"]
+      interval: 30s
+      timeout: 10s
+      retries: 3
+      start_period: 40s

fastapi_server/Dockerfile.api

@@ -0,0 +1,20 @@
+# 使用Python 3.9作为基础镜像
+FROM python:3.9-slim
+
+# 设置工作目录
+WORKDIR /app
+
+# 复制requirements文件
+COPY requirements.txt .
+
+# 安装Python依赖
+RUN pip install --no-cache-dir -r requirements.txt
+
+# 复制项目文件
+COPY . .
+
+# 暴露端口
+EXPOSE 8488
+
+# 启动命令
+CMD ["python", "server.py"]

fastapi_server/OpenAI_API_README.md

@@ -0,0 +1,220 @@
+# Lang Agent OpenAI 兼容API
+
+这是一个符合OpenAI接口规范的聊天API，允许用户使用与OpenAI API相同的方式访问您的Lang Agent服务。
+
+## 快速开始
+
+### 1. 启动服务器
+
+```bash
+cd /path/to/lang-agent/fastapi_server
+python server.py
+```
+
+服务器将在 `http://localhost:8488` 上启动。
+
+### 2. 使用API
+
+#### 使用curl命令
+
+```bash
+curl -X POST "http://localhost:8488/v1/chat/completions" \
+  -H "Authorization: Bearer 123tangledup-ai" \
+  -H "Content-Type: application/json" \
+  -d '{
+      "model": "qwen-plus",
+      "messages": [
+          {
+              "role": "system",
+              "content": "You are a helpful assistant."
+          },
+          {
+              "role": "user",
+              "content": "你是谁？"
+          }
+      ]
+  }'
+```
+
+#### 使用Python requests
+
+```python
+import requests
+
+API_BASE_URL = "http://localhost:8488"
+API_KEY = "123tangledup-ai"
+
+headers = {
+    "Authorization": f"Bearer {API_KEY}",
+    "Content-Type": "application/json"
+}
+
+data = {
+    "model": "qwen-plus",
+    "messages": [
+        {
+            "role": "system",
+            "content": "You are a helpful assistant."
+        },
+        {
+            "role": "user",
+            "content": "你是谁？"
+        }
+    ]
+}
+
+response = requests.post(f"{API_BASE_URL}/v1/chat/completions", headers=headers, json=data)
+print(response.json())
+```
+
+#### 使用OpenAI Python库
+
+```python
+from openai import OpenAI
+
+client = OpenAI(
+    api_key="123tangledup-ai",
+    base_url="http://localhost:8488/v1"
+)
+
+response = client.chat.completions.create(
+    model="qwen-plus",
+    messages=[
+        {"role": "system", "content": "You are a helpful assistant."},
+        {"role": "user", "content": "你是谁？"}
+    ]
+)
+
+print(response.choices[0].message.content)
+```
+
+## API 端点
+
+### 1. 聊天完成 `/v1/chat/completions`
+
+与OpenAI的chat completions API完全兼容。
+
+**请求参数:**
+
+| 参数 | 类型 | 必需 | 默认值 | 描述 |
+|------|------|------|--------|------|
+| model | string | 是 | - | 模型名称 |
+| messages | array | 是 | - | 消息列表 |
+| temperature | number | 否 | 0.7 | 采样温度 |
+| max_tokens | integer | 否 | 500 | 最大生成token数 |
+| stream | boolean | 否 | false | 是否流式返回 |
+| thread_id | integer | 否 | 3 | 线程ID，用于多轮对话 |
+
+**响应格式:**
+
+```json
+{
+  "id": "chatcmpl-abc123",
+  "object": "chat.completion",
+  "created": 1677652288,
+  "model": "qwen-plus",
+  "choices": [
+    {
+      "index": 0,
+      "message": {
+        "role": "assistant",
+        "content": "您好！我是一个AI助手..."
+      },
+      "finish_reason": "stop"
+    }
+  ],
+  "usage": {
+    "prompt_tokens": 56,
+    "completion_tokens": 31,
+    "total_tokens": 87
+  }
+}
+```
+
+### 2. 健康检查 `/health`
+
+检查API服务状态。
+
+**请求:**
+```bash
+GET /health
+```
+
+**响应:**
+```json
+{
+  "status": "healthy"
+}
+```
+
+### 3. API信息 `/`
+
+获取API基本信息。
+
+**请求:**
+```bash
+GET /
+```
+
+**响应:**
+```json
+{
+  "message": "Lang Agent Chat API",
+  "version": "1.0.0",
+  "description": "使用OpenAI格式调用pipeline.invoke的聊天API",
+  "authentication": "Bearer Token (API Key)",
+  "endpoints": {
+    "/v1/chat/completions": "POST - 聊天完成接口，兼容OpenAI格式，需要API密钥验证",
+    "/": "GET - API信息",
+    "/health": "GET - 健康检查接口"
+  }
+}
+```
+
+## 认证
+
+API使用Bearer Token认证。默认API密钥为 `123tangledup-ai`。
+
+在请求头中包含：
+```
+Authorization: Bearer 123tangledup-ai
+```
+
+## 测试脚本
+
+项目提供了两个测试脚本：
+
+1. **Bash脚本** (`test_openai_api.sh`) - 使用curl命令测试API
+2. **Python脚本** (`test_openai_api.py`) - 使用Python requests库测试API
+
+运行测试脚本：
+
+```bash
+# 运行Bash测试脚本
+chmod +x test_openai_api.sh
+./test_openai_api.sh
+
+# 运行Python测试脚本
+python test_openai_api.py
+```
+
+## 与OpenAI API的兼容性
+
+此API完全兼容OpenAI的chat completions API，您可以：
+
+1. 使用任何支持OpenAI API的客户端库
+2. 将base_url更改为`http://localhost:8488/v1`
+3. 使用提供的API密钥进行认证
+
+## 注意事项
+
+1. 确保服务器正在运行且可访问
+2. 流式响应(stream=true)目前可能不完全支持
+3. 模型参数(model)主要用于标识，实际使用的模型由服务器配置决定
+4. 多轮对话使用thread_id参数来维护上下文
+
+## 故障排除
+
+1. **连接错误**: 确保服务器正在运行，检查URL和端口是否正确
+2. **认证错误**: 检查API密钥是否正确设置
+3. **请求格式错误**: 确保请求体是有效的JSON格式，包含所有必需字段

fastapi_server/README.md

@@ -0,0 +1,179 @@
+# Lang Agent Chat API
+
+这是一个基于FastAPI的聊天API服务，使用OpenAI格式的请求来调用pipeline.invoke方法进行聊天。
+
+## 功能特点
+
+- 兼容OpenAI API格式的聊天接口
+- 支持多轮对话（通过thread_id）
+- 使用qwen-flash模型
+- 支持流式和非流式响应
+- 提供健康检查接口
+
+## 安装依赖
+
+```bash
+pip install -r requirements.txt
+```
+
+## 环境变量
+
+确保设置以下环境变量：
+
+```bash
+export ALI_API_KEY="your_ali_api_key"
+```
+
+## 运行服务
+
+### 方法1：使用启动脚本
+
+```bash
+./start_server.sh
+```
+
+### 方法2：直接运行Python文件
+
+```bash
+python server.py
+```
+
+服务将在 `http://localhost:8000` 启动。
+
+## API接口
+
+### 聊天完成接口
+
+**端点**: `POST /v1/chat/completions`
+
+**请求格式**:
+```json
+{
+  "model": "qwen-flash",
+  "messages": [
+    {
+      "role": "system",
+      "content": "你是一个有用的助手。"
+    },
+    {
+      "role": "user",
+      "content": "你好，请介绍一下你自己。"
+    }
+  ],
+  "temperature": 0.7,
+  "max_tokens": 1000,
+  "stream": false,
+  "thread_id": 3
+}
+```
+
+**响应格式**:
+```json
+{
+  "id": "chatcmpl-abc123",
+  "object": "chat.completion",
+  "created": 1677652288,
+  "model": "qwen-flash",
+  "choices": [
+    {
+      "index": 0,
+      "message": {
+        "role": "assistant",
+        "content": "你好！我是小盏，是半盏青年茶馆的智能助手..."
+      },
+      "finish_reason": "stop"
+    }
+  ]
+}
+```
+
+### API信息接口
+
+**端点**: `GET /`
+
+返回API的基本信息。
+
+### 健康检查接口
+
+**端点**: `GET /health`
+
+返回服务的健康状态。
+
+## 使用示例
+
+### 使用OpenAI Python客户端库
+
+首先安装OpenAI库：
+
+```bash
+pip install openai
+```
+
+然后使用以下代码：
+
+```python
+from openai import OpenAI
+
+# 设置API基础URL和API密钥（这里使用一个虚拟的密钥，因为我们没有实现认证）
+client = OpenAI(
+    api_key="your-api-key",  # 这里可以使用任意值，因为我们的API没有实现认证
+    base_url="http://localhost:8000/v1"
+)
+
+# 发送聊天请求
+response = client.chat.completions.create(
+    model="qwen-flash",
+    messages=[
+        {"role": "system", "content": "你是一个有用的助手。"},
+        {"role": "user", "content": "你好，请介绍一下你自己。"}
+    ],
+    temperature=0.7,
+    thread_id=1  # 用于多轮对话
+)
+
+print(response.choices[0].message.content)
+```
+
+### 使用curl
+
+```bash
+curl -X POST "http://localhost:8000/v1/chat/completions" \
+-H "Content-Type: application/json" \
+-d '{
+  "model": "qwen-flash",
+  "messages": [
+    {
+      "role": "user",
+      "content": "你好，请介绍一下你自己。"
+    }
+  ]
+}'
+```
+
+### 使用Python requests
+
+```python
+import requests
+
+url = "http://localhost:8000/v1/chat/completions"
+headers = {"Content-Type": "application/json"}
+data = {
+    "model": "qwen-flash",
+    "messages": [
+        {
+            "role": "user",
+            "content": "你好，请介绍一下你自己。"
+        }
+    ]
+}
+
+response = requests.post(url, headers=headers, json=data)
+print(response.json())
+```
+
+## 注意事项
+
+1. 确保已设置正确的API密钥环境变量
+2. 默认使用qwen-flash模型，可以通过修改代码中的配置来更改模型
+3. thread_id用于多轮对话，相同的thread_id会保持对话上下文
+4. 目前stream参数设置为true时，仍会返回非流式响应（可根据需要进一步实现）

fastapi_server/docker-compose.api.yml

@@ -0,0 +1,18 @@
+version: '3.8'
+
+services:
+  lang-agent-api:
+    build:
+      context: .
+      dockerfile: Dockerfile.api
+    ports:
+      - "8488:8488"
+    environment:
+      - PYTHONUNBUFFERED=1
+    restart: unless-stopped
+    healthcheck:
+      test: ["CMD", "curl", "-f", "http://localhost:8488/health"]
+      interval: 30s
+      timeout: 10s
+      retries: 3
+      start_period: 40s

fastapi_server/openai_client_example.py

@@ -0,0 +1,129 @@
+#!/usr/bin/env python3
+"""
+使用OpenAI Python客户端库调用我们的FastAPI聊天API的示例
+"""
+
+from openai import OpenAI
+import os
+
+# 设置API基础URL和API密钥（这里使用一个虚拟的密钥，因为我们没有实现认证）
+client = OpenAI(
+    api_key="your-api-key",  # 这里可以使用任意值，因为我们的API没有实现认证
+    base_url="http://localhost:8000/v1"
+)
+
+def simple_chat():
+    """简单的聊天示例"""
+    print("=" * 50)
+    print("简单聊天示例")
+    print("=" * 50)
+    
+    response = client.chat.completions.create(
+        model="qwen-flash",
+        messages=[
+            {"role": "user", "content": "你好，请介绍一下你自己。"}
+        ],
+        temperature=0.7,
+        thread_id=1
+    )
+    
+    print(f"助手回复: {response.choices[0].message.content}")
+    print("\n")
+
+def multi_turn_chat():
+    """多轮对话示例"""
+    print("=" * 50)
+    print("多轮对话示例")
+    print("=" * 50)
+    
+    # 第一轮对话
+    print("第一轮对话:")
+    response1 = client.chat.completions.create(
+        model="qwen-flash",
+        messages=[
+            {"role": "user", "content": "你推荐什么茶？"}
+        ],
+        temperature=0.7,
+        thread_id=2
+    )
+    
+    print(f"用户: 你推荐什么茶？")
+    print(f"助手: {response1.choices[0].message.content}")
+    
+    # 第二轮对话，使用相同的thread_id
+    print("\n第二轮对话:")
+    response2 = client.chat.completions.create(
+        model="qwen-flash",
+        messages=[
+            {"role": "user", "content": "为什么推荐这个茶？"}
+        ],
+        temperature=0.7,
+        thread_id=2  # 使用相同的thread_id
+    )
+    
+    print(f"用户: 为什么推荐这个茶？")
+    print(f"助手: {response2.choices[0].message.content}")
+    print("\n")
+
+def system_prompt_example():
+    """使用系统提示的示例"""
+    print("=" * 50)
+    print("系统提示示例")
+    print("=" * 50)
+    
+    response = client.chat.completions.create(
+        model="qwen-flash",
+        messages=[
+            {"role": "system", "content": "你是一个专业的茶艺师，用简洁的语言回答问题，不超过50字。"},
+            {"role": "user", "content": "请介绍一下普洱茶。"}
+        ],
+        temperature=0.3,
+        thread_id=3
+    )
+    
+    print(f"用户: 请介绍一下普洱茶。")
+    print(f"助手: {response.choices[0].message.content}")
+    print("\n")
+
+def interactive_chat():
+    """交互式聊天示例"""
+    print("=" * 50)
+    print("交互式聊天 (输入'quit'退出)")
+    print("=" * 50)
+    
+    thread_id = 4  # 为这个会话分配一个固定的thread_id
+    
+    while True:
+        user_input = input("你: ")
+        if user_input.lower() == 'quit':
+            break
+        
+        try:
+            response = client.chat.completions.create(
+                model="qwen-flash",
+                messages=[
+                    {"role": "user", "content": user_input}
+                ],
+                temperature=0.7,
+                thread_id=thread_id
+            )
+            
+            print(f"助手: {response.choices[0].message.content}")
+        except Exception as e:
+            print(f"错误: {str(e)}")
+
+if __name__ == "__main__":
+    print("使用OpenAI客户端库调用FastAPI聊天API示例")
+    print("注意: 确保服务器在 http://localhost:8000 上运行\n")
+    
+    # 简单聊天示例
+    simple_chat()
+    
+    # 多轮对话示例
+    multi_turn_chat()
+    
+    # 系统提示示例
+    system_prompt_example()
+    
+    # 交互式聊天示例
+    interactive_chat()

fastapi_server/requirements.txt

@@ -0,0 +1,24 @@
+fastapi>=0.104.0
+uvicorn>=0.24.0
+pydantic>=2.0.0,<2.12
+loguru>=0.7.0
+python-dotenv>=1.0.0
+langchain==1.0
+langchain-core>=0.1.0
+langchain-community
+langchain-openai
+langchain-mcp-adapters
+langgraph>=0.0.40
+tyro>=0.7.0
+commentjson>=0.9.0
+matplotlib>=3.7.0
+Pillow>=10.0.0
+jax>=0.4.0
+httpx[socks]
+dashscope
+websockets>=11.0.3
+mcp>=1.8.1
+mcp-proxy>=0.8.2
+faiss-cpu
+fastmcp
+pandas

fastapi_server/server.py

@@ -0,0 +1,315 @@
+from fastapi import FastAPI, HTTPException, Depends, Security
+from fastapi.middleware.cors import CORSMiddleware
+from fastapi.security import HTTPBearer, HTTPAuthorizationCredentials
+from fastapi.responses import StreamingResponse
+from pydantic import BaseModel, Field
+from typing import List, Optional, Dict, Any, Union
+import os
+import sys
+import time
+import uvicorn
+import httpx
+import openai
+import json
+from loguru import logger
+
+# 添加父目录到系统路径，以便导入lang_agent模块
+sys.path.append(os.path.dirname(os.path.dirname(os.path.abspath(__file__))))
+
+from lang_agent.pipeline import Pipeline, PipelineConfig
+
+# 定义OpenAI格式的请求模型
+class ChatMessage(BaseModel):
+    role: str = Field(..., description="消息角色，可以是 'system', 'user', 'assistant'")
+    content: str = Field(..., description="消息内容")
+
+class ChatCompletionRequest(BaseModel):
+    model: str = Field(default="qwen-flash", description="模型名称")
+    messages: List[ChatMessage] = Field(..., description="对话消息列表")
+    temperature: Optional[float] = Field(default=0.7, description="采样温度")
+    max_tokens: Optional[int] = Field(default=500, description="最大生成token数")
+    stream: Optional[bool] = Field(default=False, description="是否流式返回")
+    thread_id: Optional[int] = Field(default=3, description="线程ID，用于多轮对话")
+    llm_provider: Optional[str] = Field(default="openai", description="LLM提供商")
+    base_url: Optional[str] = Field(default="https://dashscope.aliyuncs.com/compatible-mode/v1", description="LLM API基础URL")
+
+class ChatCompletionResponseChoice(BaseModel):
+    index: int
+    message: ChatMessage
+    finish_reason: str
+
+class ChatCompletionResponseUsage(BaseModel):
+    prompt_tokens: int
+    completion_tokens: int
+    total_tokens: int
+
+class ChatCompletionResponse(BaseModel):
+    id: str
+    object: str = "chat.completion"
+    created: int
+    model: str
+    choices: List[ChatCompletionResponseChoice]
+    usage: Optional[ChatCompletionResponseUsage] = None
+
+# OpenAI客户端包装类
+class OpenAIClientWrapper:
+    def __init__(
+        self,
+        api_key: Optional[str] = None,
+        base_url: Optional[str] = None,
+        timeout: float = 60.0,
+        model_name: str = "qwen-flash",
+        max_tokens: int = 500,
+        temperature: float = 0.7,
+        top_p: float = 1.0,
+        frequency_penalty: float = 0.0,
+    ):
+        """
+        初始化OpenAI客户端包装器
+        
+        Args:
+            api_key: API密钥，如果为None则从环境变量OPENAI_API_KEY获取
+            base_url: API基础URL，如果为None则从环境变量OPENAI_BASE_URL获取
+            timeout: 请求超时时间（秒）
+            model_name: 默认模型名称
+            max_tokens: 默认最大token数
+            temperature: 默认采样温度
+            top_p: 默认top_p参数
+            frequency_penalty: 默认频率惩罚
+        """
+        self.api_key = api_key or os.getenv("OPENAI_API_KEY", "")
+        self.base_url = base_url or os.getenv("OPENAI_BASE_URL", None)
+        self.timeout = timeout
+        self.model_name = model_name
+        self.max_tokens = max_tokens
+        self.temperature = temperature
+        self.top_p = top_p
+        self.frequency_penalty = frequency_penalty
+        
+        self.client = openai.OpenAI(
+            api_key=self.api_key,
+            base_url=self.base_url,
+            timeout=httpx.Timeout(self.timeout)
+        )
+    
+    def response(self, session_id: str, dialogue: List[Dict[str, str]], **kwargs):
+        """
+        生成聊天响应（流式）
+        
+        Args:
+            session_id: 会话ID
+            dialogue: 对话消息列表，格式为 [{"role": "user", "content": "..."}, ...]
+            **kwargs: 额外的参数，可以覆盖默认的max_tokens, temperature, top_p, frequency_penalty
+        
+        Returns:
+            OpenAI流式响应对象
+        """
+        try:
+            responses = self.client.chat.completions.create(
+                model=self.model_name,
+                messages=dialogue,
+                stream=True,
+                max_tokens=kwargs.get("max_tokens", self.max_tokens),
+                temperature=kwargs.get("temperature", self.temperature),
+                top_p=kwargs.get("top_p", self.top_p),
+                frequency_penalty=kwargs.get("frequency_penalty", self.frequency_penalty),
+            )
+            return responses
+        except Exception as e:
+            logger.error(f"OpenAI客户端响应错误: {str(e)}")
+            raise
+
+# 初始化FastAPI应用
+app = FastAPI(title="Lang Agent Chat API", description="使用OpenAI格式调用pipeline.invoke的聊天API")
+
+# 设置API密钥
+API_KEY = "123tangledup-ai"
+
+# 创建安全方案
+security = HTTPBearer()
+
+# 验证API密钥的依赖项
+# async def verify_api_key(credentials: HTTPAuthorizationCredentials = Security(security)):
+#     if credentials.credentials != API_KEY:
+#         raise HTTPException(
+#             status_code=401,
+#             detail="无效的API密钥",
+#             headers={"WWW-Authenticate": "Bearer"},
+#         )
+#     return credentials
+
+# 添加CORS中间件
+app.add_middleware(
+    CORSMiddleware,
+    allow_origins=["*"],
+    allow_credentials=True,
+    allow_methods=["*"],
+    allow_headers=["*"],
+)
+
+# 初始化Pipeline
+pipeline_config = PipelineConfig()
+pipeline_config.llm_name = "qwen-flash"
+pipeline_config.llm_provider = "openai"
+pipeline_config.base_url = "https://dashscope.aliyuncs.com/compatible-mode/v1"
+
+pipeline = Pipeline(pipeline_config)
+
+# 初始化OpenAI客户端包装器（可选，用于直接调用OpenAI API）
+openai_client = OpenAIClientWrapper(
+    api_key=os.getenv("OPENAI_API_KEY"),
+    base_url="https://dashscope.aliyuncs.com/compatible-mode/v1",
+    timeout=60.0,
+    model_name="qwen-flash",
+    max_tokens=500,
+    temperature=0.7,
+    top_p=1.0,
+    frequency_penalty=0.0,
+)
+
+def generate_streaming_chunks(full_text: str, response_id: str, model: str, chunk_size: int = 10):
+    """
+    Generate streaming chunks from non-streaming result
+    """
+    created_time = int(time.time())
+    
+    # Stream content chunks
+    for i in range(0, len(full_text), chunk_size):
+        chunk = full_text[i:i + chunk_size]
+        if chunk:
+            chunk_data = {
+                "id": response_id,
+                "object": "chat.completion.chunk",
+                "created": created_time,
+                "model": model,
+                "choices": [
+                    {
+                        "index": 0,
+                        "delta": {"content": chunk},
+                        "finish_reason": None
+                    }
+                ]
+            }
+            yield f"data: {json.dumps(chunk_data)}\n\n"
+    
+    # Send final chunk with finish_reason
+    final_chunk = {
+        "id": response_id,
+        "object": "chat.completion.chunk",
+        "created": created_time,
+        "model": model,
+        "choices": [
+            {
+                "index": 0,
+                "delta": {},
+                "finish_reason": "stop"
+            }
+        ]
+    }
+    yield f"data: {json.dumps(final_chunk)}\n\n"
+    yield "data: [DONE]\n\n"
+
+@app.post("/v1/chat/completions")
+async def chat_completions(
+    request: ChatCompletionRequest#, 
+    # credentials: HTTPAuthorizationCredentials = Depends(verify_api_key)
+):
+    """
+    使用OpenAI格式的聊天完成API
+    """
+    try:
+        # 提取用户消息
+        user_message = None
+        system_message = None
+        
+        # TODO: wrap this sht as human and system message
+        for message in request.messages:
+            if message.role == "user":
+                user_message = message.content
+            elif message.role == "system" or message.role == "assistant":
+                system_message = message.content
+        
+        if not user_message:
+            raise HTTPException(status_code=400, detail="缺少用户消息")
+        
+        # 调用pipeline的chat方法 (always get non-streaming result)
+        response_content = pipeline.chat(
+            inp=user_message,
+            as_stream=False,  # Always get full result, then chunk it if streaming
+            thread_id=request.thread_id
+        )
+        
+        # Ensure response_content is a string
+        if not isinstance(response_content, str):
+            response_content = str(response_content)
+        
+        logger.info(f"Pipeline response - Length: {len(response_content)}, Content: {repr(response_content[:200])}")
+        
+        if len(response_content) == 0:
+            logger.warning("Pipeline returned empty response!")
+        
+        response_id = f"chatcmpl-{os.urandom(12).hex()}"
+        
+        # If streaming requested, return streaming response
+        if request.stream:
+            return StreamingResponse(
+                generate_streaming_chunks(
+                    full_text=response_content,
+                    response_id=response_id,
+                    model=request.model,
+                    chunk_size=10
+                ),
+                media_type="text/event-stream"
+            )
+        
+        # Otherwise return normal response
+        response = ChatCompletionResponse(
+            id=response_id,
+            created=int(time.time()),
+            model=request.model,
+            choices=[
+                ChatCompletionResponseChoice(
+                    index=0,
+                    message=ChatMessage(role="assistant", content=response_content),
+                    finish_reason="stop"
+                )
+            ]
+        )
+        
+        return response
+    
+    except Exception as e:
+        logger.error(f"处理聊天请求时出错: {str(e)}")
+        raise HTTPException(status_code=500, detail=f"内部服务器错误: {str(e)}")
+
+@app.get("/")
+async def root():
+    """
+    根路径，返回API信息
+    """
+    return {
+        "message": "Lang Agent Chat API",
+        "version": "1.0.0",
+        "description": "使用OpenAI格式调用pipeline.invoke的聊天API",
+        "authentication": "Bearer Token (API Key)",
+        "endpoints": {
+            "/v1/chat/completions": "POST - 聊天完成接口，兼容OpenAI格式，需要API密钥验证",
+            "/": "GET - API信息",
+            "/health": "GET - 健康检查接口"
+        }
+    }
+
+@app.get("/health")
+async def health_check():
+    """
+    健康检查接口
+    """
+    return {"status": "healthy"}
+
+if __name__ == "__main__":
+    uvicorn.run(
+        "server:app",
+        host="0.0.0.0",
+        port=8488,
+        reload=True
+    )

fastapi_server/start_server.sh

@@ -0,0 +1,19 @@
+#!/bin/bash
+
+echo "启动Lang Agent Chat API服务器..."
+
+# 检查Python环境
+if ! command -v python &> /dev/null; then
+    echo "错误: 未找到Python。请确保Python已安装并添加到PATH中。"
+    exit 1
+fi
+
+# 检查环境变量
+if [ -z "$ALI_API_KEY" ]; then
+    echo "警告: 未设置ALI_API_KEY环境变量。请确保已设置此变量。"
+    echo "例如: export ALI_API_KEY='your_api_key'"
+fi
+
+# 启动服务器
+cd "$(dirname "$0")"
+python server.py

fastapi_server/test_openai_client.py

@@ -0,0 +1,79 @@
+#!/usr/bin/env python3
+"""
+Simple test for OpenAI client chat.completions.create
+"""
+import os
+import httpx
+import openai
+from dotenv import load_dotenv
+
+load_dotenv()
+
+print("Initializing OpenAI client...")
+print(f"Base URL: http://localhost:8488/v1")
+print(f"API Key set: {'Yes' if os.getenv('ALI_API_KEY') else 'No'}")
+
+# Initialize client (pointing to FastAPI server from server.py)
+client = openai.OpenAI(
+    api_key=os.getenv("ALI_API_KEY"),
+    base_url="http://localhost:8488/v1",
+    timeout=httpx.Timeout(60.0)
+)
+
+print("\nTesting chat completion (non-streaming)...")
+# try:
+#     # Test chat completion (non-streaming first)
+#     response = client.chat.completions.create(
+#         model="qwen-flash",
+#         messages=[
+#             {'role':'system', 'content': 'your name is steve'}
+#             ,{"role": "user", "content": "Say hello!"}],
+#         stream=False,
+#         max_tokens=100,
+#         temperature=0.7
+#     )
+    
+#     print(f"Response ID: {response.id}")
+#     print(f"Model: {response.model}")
+#     print(f"Content: {response.choices[0].message.content}")
+#     print("\n✓ Non-streaming test successful!")
+    
+# except Exception as e:
+#     print(f"\n✗ Error: {str(e)}")
+#     import traceback
+#     traceback.print_exc()
+
+print("\nTesting chat completion (streaming)...")
+try:
+    # Test streaming with same message as non-streaming test
+    response = client.chat.completions.create(
+        model="qwen-flash",
+        messages=[
+            {'role':'system', 'content': 'your name is steve'},
+            {"role": "user", "content": "Say hello!"}
+        ],
+        stream=True,
+        max_tokens=100,
+        temperature=0.7
+    )
+    
+    print("Streaming response:")
+    full_content = ""
+    chunk_count = 0
+    for chunk in response:
+        chunk_count += 1
+        if hasattr(chunk, 'choices') and len(chunk.choices) > 0:
+            if hasattr(chunk.choices[0], 'delta') and chunk.choices[0].delta.content:
+                content = chunk.choices[0].delta.content
+                print(content, end="", flush=True)
+                full_content += content
+    
+    print(f"\n\nTotal chunks received: {chunk_count}")
+    print(f"Full content: {repr(full_content)}")
+    print(f"Content length: {len(full_content)}")
+    print("\n✓ Streaming test successful!")
+    
+except Exception as e:
+    print(f"\n✗ Error: {str(e)}")
+    import traceback
+    traceback.print_exc()

lang_agent/config.py

@@ -6,6 +6,9 @@ from typing import Dict
 import os
 
 from loguru import logger
+from dotenv import load_dotenv
+
+load_dotenv()
 
 ## NOTE: base classes taken from nerfstudio
 class PrintableConfig:

lang_agent/rag/simple.py

@@ -3,7 +3,7 @@ from typing import Type, List
 import tyro
 from mcp.server.fastmcp import FastMCP
 from loguru import logger
-import os
+import os.path as osp
 
 from langchain_community.vectorstores import FAISS
 from langchain_core.documents.base import Document
@@ -21,8 +21,16 @@ class SimpleRagConfig(ToolConfig, KeyConfig):
     model_name:str = "text-embedding-v4"
     """embedding model name"""
 
-    folder_path:str = "/home/smith/projects/work/langchain-agent/assets/xiaozhan_emb"
+    folder_path:str = None
-    """path to local database"""
+    """path to docker database"""
+    
+    def __post_init__(self):
+        super().__post_init__()
+        if self.folder_path is None:
+            self.folder_path = osp.join(osp.dirname(osp.dirname(osp.dirname(__file__))), "assets", "xiaozhan_emb")
+            logger.info(f"no rag database provided, using default {self.folder_path}")
+
+    
 
 
 
@@ -31,6 +39,8 @@ class SimpleRag(LangToolBase):
         self.config = config
         self.emb = QwenEmbeddings(self.config.api_key,
                                   self.config.model_name)
+        
+        
         self.vec_store = FAISS.load_local(
             folder_path=self.config.folder_path,
             embeddings=self.emb,

lang_agent/tool_manager.py

@@ -6,7 +6,7 @@ import inspect
 import asyncio
 import os.path as osp
 from loguru import logger
-from fastmcp.tools.tool import FunctionTool
+from fastmcp.tools.tool import Tool
 
 from lang_agent.config import InstantiateConfig, ToolConfig
 from lang_agent.base import LangToolBase
@@ -80,7 +80,7 @@ class ToolManager:
     def _get_tool_fnc(self, tool_obj:LangToolBase)->List:
         fnc_list = []
         for fnc in tool_obj.get_tool_fnc():
-            if isinstance(fnc, FunctionTool):
+            if isinstance(fnc, Tool):
                 fnc = fnc.fn
             fnc_list.append(fnc)
         

scripts/make_eval_dataset.py

@@ -24,7 +24,7 @@ examples = [
         "inputs": {"text": ["我要购买一杯野星星",
                             "我要再加一杯"]},
         "outputs": {"answer": "你的野星星已经下单成功",
-                    "tool_use": ["retrieve|get_resources",
+                    "tool_use": ["retrieve|get_resource",
                                  "start_shopping_session",
                                  "add_to_cart",
                                  "create_wechat_pay",