diff --git a/.DS_Store b/.DS_Store new file mode 100644 index 0000000..022c56e Binary files /dev/null and b/.DS_Store differ diff --git a/.dockerignore b/.dockerignore new file mode 100644 index 0000000..e0ca18d --- /dev/null +++ b/.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 \ No newline at end of file diff --git a/Dockerfile b/Dockerfile new file mode 100644 index 0000000..906aeaa --- /dev/null +++ b/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"] \ No newline at end of file diff --git a/docker-compose.yml b/docker-compose.yml new file mode 100644 index 0000000..e4af20d --- /dev/null +++ b/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 \ No newline at end of file diff --git a/fastapi_server/Dockerfile.api b/fastapi_server/Dockerfile.api new file mode 100644 index 0000000..691bd1e --- /dev/null +++ b/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"] \ No newline at end of file diff --git a/fastapi_server/OpenAI_API_README.md b/fastapi_server/OpenAI_API_README.md new file mode 100644 index 0000000..c59f511 --- /dev/null +++ b/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 \ No newline at end of file diff --git a/fastapi_server/README.md b/fastapi_server/README.md new file mode 100644 index 0000000..e767853 --- /dev/null +++ b/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） \ No newline at end of file diff --git a/fastapi_server/docker-compose.api.yml b/fastapi_server/docker-compose.api.yml new file mode 100644 index 0000000..8c0ae26 --- /dev/null +++ b/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 \ No newline at end of file diff --git a/fastapi_server/openai_client_example.py b/fastapi_server/openai_client_example.py new file mode 100644 index 0000000..5622129 --- /dev/null +++ b/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() \ No newline at end of file diff --git a/fastapi_server/requirements.txt b/fastapi_server/requirements.txt new file mode 100644 index 0000000..c7f0a7e --- /dev/null +++ b/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 diff --git a/fastapi_server/server.py b/fastapi_server/server.py new file mode 100644 index 0000000..394d404 --- /dev/null +++ b/fastapi_server/server.py @@ -0,0 +1,163 @@ +from fastapi import FastAPI, HTTPException, Depends, Security +from fastapi.middleware.cors import CORSMiddleware +from fastapi.security import HTTPBearer, HTTPAuthorizationCredentials +from pydantic import BaseModel, Field +from typing import List, Optional, Dict, Any, Union +import os +import sys +import time +import uvicorn +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") + +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 + +# 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) + +@app.post("/v1/chat/completions", response_model=ChatCompletionResponse) +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 + + for message in request.messages: + if message.role == "user": + user_message = message.content + elif message.role == "system": + 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 + response_content = pipeline.chat( + inp=user_message, + as_stream=request.stream, + thread_id=request.thread_id + ) + + # 构建响应 + response = ChatCompletionResponse( + id=f"chatcmpl-{os.urandom(12).hex()}", + 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 + ) \ No newline at end of file diff --git a/fastapi_server/start_server.sh b/fastapi_server/start_server.sh new file mode 100755 index 0000000..852ab95 --- /dev/null +++ b/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 \ No newline at end of file diff --git a/lang_agent/rag/simple.py b/lang_agent/rag/simple.py index bb64487..f7724cd 100644 --- a/lang_agent/rag/simple.py +++ b/lang_agent/rag/simple.py @@ -21,8 +21,21 @@ 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" - """path to local database""" + folder_path:str = "/Users/jeremygan/Desktop/TangledupAI/lang-agent/assets/xiaozhan_emb" + """path to docker database""" + + # @property + # def folder_path(self) -> str: + # """Dynamically determine the folder path for the vector store""" + # # Check if environment variable is set + # env_path = os.environ.get("RAG_FOLDER_PATH") + # if env_path: + # return env_path + + # # Default to relative path from current working directory + # return os.path.join(os.getcwd(), "assets", "xiaozhan_emb") + + @@ -31,8 +44,19 @@ class SimpleRag(LangToolBase): self.config = config self.emb = QwenEmbeddings(self.config.api_key, self.config.model_name) + + # Determine the folder path dynamically + # folder_path = os.environ.get("RAG_FOLDER_PATH") + # if not folder_path: + # # Default to relative path from current working directory + # folder_path = os.path.join(os.getcwd(), "assets", "xiaozhan_emb") + + # logger.info(f"Loading FAISS index from: {folder_path}") + + folder_path = "/Users/jeremygan/Desktop/TangledupAI/lang-agent/assets/xiaozhan_emb" + self.vec_store = FAISS.load_local( - folder_path=self.config.folder_path, + folder_path=folder_path, embeddings=self.emb, allow_dangerous_deserialization=True # Required for LangChain >= 0.1.1 ) diff --git a/lang_agent/test.py b/lang_agent/test.py new file mode 100644 index 0000000..e69de29 diff --git a/lang_agent/tool_manager.py b/lang_agent/tool_manager.py index c63d36b..73f0ef8 100644 --- a/lang_agent/tool_manager.py +++ b/lang_agent/tool_manager.py @@ -6,14 +6,14 @@ 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 from lang_agent.rag.simple import SimpleRagConfig from lang_agent.dummy.calculator import CalculatorConfig -from catering_end.lang_tool import CartToolConfig, CartTool +# from catering_end.lang_tool import CartToolConfig, CartTool from langchain_core.tools.structured import StructuredTool import jax @@ -26,7 +26,7 @@ class ToolManagerConfig(InstantiateConfig): # tool configs here; MUST HAVE 'config' in name and must be dataclass rag_config: SimpleRagConfig = field(default_factory=SimpleRagConfig) - cart_config: CartToolConfig = field(default_factory=CartToolConfig) + # cart_config: CartToolConfig = field(default_factory=CartToolConfig) calc_config: CalculatorConfig = field(default_factory=CalculatorConfig) @@ -78,7 +78,7 @@ 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)