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yifeng.wang
@@ -370,6 +370,9 @@ For information on configuring AI models, please refer to our [CAMEL models docu
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OWL supports various LLM backends, though capabilities may vary depending on the model's tool calling and multimodal abilities. You can use the following scripts to run with different models:
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```bash
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# Run with Claude model
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python examples/run_claude.py
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# Run with Qwen model
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python examples/run_qwen_zh.py
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@@ -362,6 +362,9 @@ python examples/run_mini.py
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OWL 支持多种 LLM 后端，但功能可能因模型的工具调用和多模态能力而异。您可以使用以下脚本来运行不同的模型：
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```bash
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# 使用 Claude 模型运行
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python examples/run_claude.py
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# 使用 Qwen 模型运行
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python examples/run_qwen_zh.py
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142
community_usecase/qwen3_mcp/README.md
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142
community_usecase/qwen3_mcp/README.md
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@@ -0,0 +1,142 @@
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# 🚀 OWL with Qwen3 MCP Integration
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This project demonstrates how to use the [CAMEL-AI OWL framework](https://github.com/camel-ai/owl) with **Qwen3** large language model through MCP (Model Context Protocol). The example showcases improved terminal output formatting, markdown log generation, and seamless integration with MCP servers.
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|
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## ✨ Features
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- **Enhanced Terminal Output**: Colorful, well-formatted console output for better readability
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- **Automatic Log Generation**: Creates detailed markdown logs of agent conversations with timestamps
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- **Qwen3 Integration**: Seamlessly uses Qwen3-Plus for both user and assistant agents
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- **MCP Server Support**: Connects to MCP servers including EdgeOne Pages and Playwright
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- **Robust Error Handling**: Graceful cleanup and exception management
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|
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## 📋 Prerequisites
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|
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- Python 3.10+
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- OWL framework installed
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- Qwen API key
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- Node.js (for Playwright MCP)
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|
||||
## 🛠️ Setup
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1. Clone the OWL repository:
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```bash
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git clone https://github.com/camel-ai/owl
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cd owl
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```
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2. Install dependencies:
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```bash
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pip install -r requirements.txt
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```
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3. Add your Qwen API key to the `.env` file:
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```
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QWEN_API_KEY=your_api_key_here
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```
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|
||||
4. Configure MCP servers in `mcp_sse_config.json`
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|
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5. (Optional) Install Playwright MCP manually:
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```bash
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npm install -D @playwright/mcp
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```
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Note: This step is optional as the config will auto-install Playwright MCP through npx.
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|
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## 🧩 MCP Servers Included
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|
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This example integrates with two powerful MCP servers:
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|
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### 1. EdgeOne Pages MCP (`edgeone-pages-mcp`)
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EdgeOne Pages MCP is a specialized service that enables:
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- **Instant HTML Deployment**: Deploy AI-generated HTML content to EdgeOne Pages with a single call
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- **Public Access URLs**: Generate publicly accessible links for deployed content
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- **No Setup Required**: Uses an SSE (Server-Sent Events) endpoint, so no local installation is needed
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|
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Configuration in `mcp_sse_config.json`:
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```json
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"edgeone-pages-mcp": {
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"type": "sse",
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"url": "https://mcp.api-inference.modelscope.cn/sse/fcbc9ff4e9704d"
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}
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```
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### 2. Playwright MCP (`playwright`)
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|
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Playwright MCP is a powerful browser automation tool that allows:
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- **Web Navigation**: Browse websites and interact with web pages
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- **Screen Capture**: Take screenshots and extract page content
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- **Element Interaction**: Click, type, and interact with page elements
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- **Web Scraping**: Extract structured data from web pages
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|
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Configuration in `mcp_sse_config.json`:
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```json
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"playwright": {
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"command": "npx",
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"args": [
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"@playwright/mcp@latest"
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]
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}
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```
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|
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**Installation Options**:
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1. **Auto-installation**: The configuration above automatically installs and runs Playwright MCP through `npx`.
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2. **Manual installation**: If you prefer to install it permanently in your project:
|
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```bash
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npm install -D @playwright/mcp
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```
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Then you can update the config to use the local installation:
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```json
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"playwright": {
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"command": "npx",
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"args": [
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"@playwright/mcp"
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]
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}
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```
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|
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## 🚀 Usage
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Run the script with a task prompt:
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```bash
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python run_mcp_qwen3.py "Your task description here"
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```
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If no task is provided, a default task will be used.
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## 📊 Output
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1. **Terminal Output**: Colorful, formatted output showing:
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- System messages
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- Task specifications
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- Agent conversations
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- Tool calls
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- Task completion status
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|
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2. **Markdown Logs**: Detailed conversation logs saved to `conversation_logs/` directory
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|
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## 🔧 Customization
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- Modify `mcp_sse_config.json` to add or remove MCP servers
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- Adjust model parameters in the `construct_society` function
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- Change the maximum conversation rounds with the `round_limit` parameter
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## 📝 Technical Details
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This implementation extends the standard OWL run_mcp.py script with:
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1. Colorized terminal output using Colorama
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2. Structured markdown log generation
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3. Improved error handling and graceful termination
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4. Enhanced formatting for tool calls and agent messages
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## 🤝 Acknowledgements
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- [CAMEL-AI Organization](https://github.com/camel-ai)
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- [OWL Framework](https://github.com/camel-ai/owl)
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- [Model Context Protocol (MCP)](https://modelcontextprotocol.io/introduction)
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- [Qwen API](https://help.aliyun.com/zh/dashscope/developer-reference/api-details)
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- [EdgeOne Pages](https://edgeone.cloud.tencent.com/pages)
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- [Microsoft Playwright](https://github.com/microsoft/playwright-mcp)
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14
community_usecase/qwen3_mcp/mcp_sse_config.json
Normal file
14
community_usecase/qwen3_mcp/mcp_sse_config.json
Normal file
@@ -0,0 +1,14 @@
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{
|
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"mcpServers": {
|
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"edgeone-pages-mcp": {
|
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"type": "sse",
|
||||
"url": "https://mcp.api-inference.modelscope.cn/sse/fcbc9ff4e9704d"
|
||||
},
|
||||
"playwright": {
|
||||
"command": "npx",
|
||||
"args": [
|
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"@playwright/mcp@latest"
|
||||
]
|
||||
}
|
||||
}
|
||||
}
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||||
332
community_usecase/qwen3_mcp/run_mcp_qwen3.py
Normal file
332
community_usecase/qwen3_mcp/run_mcp_qwen3.py
Normal file
@@ -0,0 +1,332 @@
|
||||
# ========= Copyright 2023-2024 @ CAMEL-AI.org. All Rights Reserved. =========
|
||||
# Licensed under the Apache License, Version 2.0 (the "License");
|
||||
# you may not use this file except in compliance with the License.
|
||||
# You may obtain a copy of the License at
|
||||
#
|
||||
# http://www.apache.org/licenses/LICENSE-2.0
|
||||
#
|
||||
# Unless required by applicable law or agreed to in writing, software
|
||||
# distributed under the License is distributed on an "AS IS" BASIS,
|
||||
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
|
||||
# See the License for the specific language governing permissions and
|
||||
# limitations under the License.
|
||||
# ========= Copyright 2023-2024 @ CAMEL-AI.org. All Rights Reserved. =========
|
||||
import asyncio
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import sys
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||||
import contextlib
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import time
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||||
from pathlib import Path
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||||
from typing import List, Dict, Any
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import os
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|
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from colorama import Fore, init
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from dotenv import load_dotenv
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|
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from camel.agents.chat_agent import ToolCallingRecord
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from camel.models import ModelFactory
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from camel.toolkits import FunctionTool, MCPToolkit
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from camel.types import ModelPlatformType, ModelType
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from camel.logger import set_log_level
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from camel.utils import print_text_animated
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from owl.utils.enhanced_role_playing import OwlRolePlaying, arun_society
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import pathlib
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# Initialize colorama for cross-platform colored terminal output
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init()
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base_dir = pathlib.Path(__file__).parent.parent
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env_path = base_dir / "owl" / ".env"
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load_dotenv(dotenv_path=str(env_path))
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set_log_level(level="INFO")
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|
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async def construct_society(
|
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question: str,
|
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tools: List[FunctionTool],
|
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) -> OwlRolePlaying:
|
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r"""Build a multi-agent OwlRolePlaying instance for task completion.
|
||||
|
||||
Args:
|
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question (str): The task to perform.
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tools (List[FunctionTool]): The MCP tools to use for interaction.
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|
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Returns:
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OwlRolePlaying: The configured society of agents.
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"""
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models = {
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"user": ModelFactory.create(
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model_platform=ModelPlatformType.QWEN,
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model_type=ModelType.QWEN_PLUS_LATEST,
|
||||
model_config_dict={"temperature": 0},
|
||||
),
|
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"assistant": ModelFactory.create(
|
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model_platform=ModelPlatformType.QWEN,
|
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model_type=ModelType.QWEN_PLUS_LATEST,
|
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model_config_dict={"temperature": 0},
|
||||
),
|
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}
|
||||
|
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user_agent_kwargs = {"model": models["user"]}
|
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assistant_agent_kwargs = {
|
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"model": models["assistant"],
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"tools": tools,
|
||||
}
|
||||
|
||||
task_kwargs = {
|
||||
"task_prompt": question,
|
||||
"with_task_specify": False,
|
||||
}
|
||||
|
||||
society = OwlRolePlaying(
|
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**task_kwargs,
|
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user_role_name="user",
|
||||
user_agent_kwargs=user_agent_kwargs,
|
||||
assistant_role_name="assistant",
|
||||
assistant_agent_kwargs=assistant_agent_kwargs,
|
||||
)
|
||||
return society
|
||||
|
||||
|
||||
def create_md_file(task: str) -> str:
|
||||
"""Create a markdown file for the conversation with timestamp in filename.
|
||||
|
||||
Args:
|
||||
task (str): The task being performed.
|
||||
|
||||
Returns:
|
||||
str: Path to the created markdown file.
|
||||
"""
|
||||
timestamp = time.strftime("%Y%m%d_%H%M%S")
|
||||
# Create logs directory if it doesn't exist
|
||||
logs_dir = Path("conversation_logs")
|
||||
logs_dir.mkdir(exist_ok=True)
|
||||
|
||||
# Create a shortened task name for the filename
|
||||
task_short = task[:30].replace(" ", "_").replace("/", "_")
|
||||
filename = f"{logs_dir}/conversation_{timestamp}_{task_short}.md"
|
||||
|
||||
# Initialize the file with header
|
||||
with open(filename, "w", encoding="utf-8") as f:
|
||||
f.write(f"# Agent Conversation: {task}\n\n")
|
||||
f.write(f"*Generated on: {time.strftime('%Y-%m-%d %H:%M:%S')}*\n\n")
|
||||
f.write("## Task Details\n\n")
|
||||
f.write(f"**Task:** {task}\n\n")
|
||||
f.write("## Conversation\n\n")
|
||||
|
||||
return filename
|
||||
|
||||
|
||||
def write_to_md(filename: str, content: Dict[str, Any]) -> None:
|
||||
"""Write content to the markdown file.
|
||||
|
||||
Args:
|
||||
filename (str): Path to the markdown file.
|
||||
content (Dict[str, Any]): Content to write to the file.
|
||||
"""
|
||||
with open(filename, "a", encoding="utf-8") as f:
|
||||
if "system_info" in content:
|
||||
f.write(f"### System Information\n\n")
|
||||
for key, value in content["system_info"].items():
|
||||
f.write(f"**{key}:** {value}\n\n")
|
||||
|
||||
if "assistant" in content:
|
||||
f.write(f"### 🤖 Assistant\n\n")
|
||||
if "tool_calls" in content:
|
||||
f.write("**Tool Calls:**\n\n")
|
||||
for tool_call in content["tool_calls"]:
|
||||
f.write(f"```\n{tool_call}\n```\n\n")
|
||||
f.write(f"{content['assistant']}\n\n")
|
||||
|
||||
if "user" in content:
|
||||
f.write(f"### 👤 User\n\n")
|
||||
f.write(f"{content['user']}\n\n")
|
||||
|
||||
if "summary" in content:
|
||||
f.write(f"## Summary\n\n")
|
||||
f.write(f"{content['summary']}\n\n")
|
||||
|
||||
if "token_count" in content:
|
||||
f.write(f"**Total tokens used:** {content['token_count']}\n\n")
|
||||
|
||||
|
||||
async def run_society_with_formatted_output(society: OwlRolePlaying, md_filename: str, round_limit: int = 15):
|
||||
"""Run the society with nicely formatted terminal output and write to markdown.
|
||||
|
||||
Args:
|
||||
society (OwlRolePlaying): The society to run.
|
||||
md_filename (str): Path to the markdown file for output.
|
||||
round_limit (int, optional): Maximum number of conversation rounds. Defaults to 15.
|
||||
|
||||
Returns:
|
||||
tuple: (answer, chat_history, token_count)
|
||||
"""
|
||||
print(Fore.GREEN + f"AI Assistant sys message:\n{society.assistant_sys_msg}\n")
|
||||
print(Fore.BLUE + f"AI User sys message:\n{society.user_sys_msg}\n")
|
||||
|
||||
print(Fore.YELLOW + f"Original task prompt:\n{society.task_prompt}\n")
|
||||
print(Fore.CYAN + "Specified task prompt:" + f"\n{society.specified_task_prompt}\n")
|
||||
print(Fore.RED + f"Final task prompt:\n{society.task_prompt}\n")
|
||||
|
||||
# Write system information to markdown
|
||||
write_to_md(md_filename, {
|
||||
"system_info": {
|
||||
"AI Assistant System Message": society.assistant_sys_msg,
|
||||
"AI User System Message": society.user_sys_msg,
|
||||
"Original Task Prompt": society.task_prompt,
|
||||
"Specified Task Prompt": society.specified_task_prompt,
|
||||
"Final Task Prompt": society.task_prompt
|
||||
}
|
||||
})
|
||||
|
||||
input_msg = society.init_chat()
|
||||
chat_history = []
|
||||
token_count = {"total": 0}
|
||||
n = 0
|
||||
|
||||
while n < round_limit:
|
||||
n += 1
|
||||
assistant_response, user_response = await society.astep(input_msg)
|
||||
|
||||
md_content = {}
|
||||
|
||||
if assistant_response.terminated:
|
||||
termination_msg = f"AI Assistant terminated. Reason: {assistant_response.info['termination_reasons']}."
|
||||
print(Fore.GREEN + termination_msg)
|
||||
md_content["summary"] = termination_msg
|
||||
write_to_md(md_filename, md_content)
|
||||
break
|
||||
|
||||
if user_response.terminated:
|
||||
termination_msg = f"AI User terminated. Reason: {user_response.info['termination_reasons']}."
|
||||
print(Fore.GREEN + termination_msg)
|
||||
md_content["summary"] = termination_msg
|
||||
write_to_md(md_filename, md_content)
|
||||
break
|
||||
|
||||
# Handle tool calls for both terminal and markdown
|
||||
if "tool_calls" in assistant_response.info:
|
||||
tool_calls: List[ToolCallingRecord] = [
|
||||
ToolCallingRecord(**call.as_dict())
|
||||
for call in assistant_response.info['tool_calls']
|
||||
]
|
||||
md_content["tool_calls"] = tool_calls
|
||||
|
||||
# Print to terminal
|
||||
print(Fore.GREEN + "AI Assistant:")
|
||||
for func_record in tool_calls:
|
||||
print(f"{func_record}")
|
||||
else:
|
||||
print(Fore.GREEN + "AI Assistant:")
|
||||
|
||||
# Print assistant response to terminal
|
||||
print(f"{assistant_response.msg.content}\n")
|
||||
|
||||
# Print user response to terminal
|
||||
print(Fore.BLUE + f"AI User:\n\n{user_response.msg.content}\n")
|
||||
|
||||
# Build content for markdown file
|
||||
md_content["assistant"] = assistant_response.msg.content
|
||||
md_content["user"] = user_response.msg.content
|
||||
|
||||
# Write to markdown
|
||||
write_to_md(md_filename, md_content)
|
||||
|
||||
# Update chat history
|
||||
chat_history.append({
|
||||
"assistant": assistant_response.msg.content,
|
||||
"user": user_response.msg.content,
|
||||
})
|
||||
|
||||
# Update token count
|
||||
if "token_count" in assistant_response.info:
|
||||
token_count["total"] += assistant_response.info["token_count"]
|
||||
|
||||
if "TASK_DONE" in user_response.msg.content:
|
||||
task_done_msg = "Task completed successfully!"
|
||||
print(Fore.YELLOW + task_done_msg + "\n")
|
||||
write_to_md(md_filename, {"summary": task_done_msg})
|
||||
break
|
||||
|
||||
input_msg = assistant_response.msg
|
||||
|
||||
# Write token count information
|
||||
write_to_md(md_filename, {"token_count": token_count["total"]})
|
||||
|
||||
# Extract final answer
|
||||
answer = assistant_response.msg.content if assistant_response and assistant_response.msg else ""
|
||||
|
||||
return answer, chat_history, token_count
|
||||
|
||||
|
||||
@contextlib.asynccontextmanager
|
||||
async def mcp_toolkit_context(config_path):
|
||||
"""Context manager for safely handling MCP Toolkit connection/disconnection.
|
||||
|
||||
Args:
|
||||
config_path (str): Path to the MCP configuration file.
|
||||
|
||||
Yields:
|
||||
MCPToolkit: The connected MCPToolkit instance.
|
||||
"""
|
||||
toolkit = MCPToolkit(config_path=str(config_path))
|
||||
try:
|
||||
await toolkit.connect()
|
||||
print(Fore.GREEN + "Successfully connected to SSE server")
|
||||
yield toolkit
|
||||
finally:
|
||||
# Use a separate try/except to ensure we always attempt to disconnect
|
||||
try:
|
||||
await toolkit.disconnect()
|
||||
print(Fore.GREEN + "Successfully disconnected from SSE server")
|
||||
except Exception as e:
|
||||
# Just log the error but don't re-raise as we're in cleanup
|
||||
print(Fore.RED + f"Warning: Error during disconnect: {e}")
|
||||
|
||||
|
||||
async def main():
|
||||
# Load SSE server configuration
|
||||
config_path = Path(__file__).parent / "mcp_sse_config.json"
|
||||
|
||||
# Set default task - a simple example query
|
||||
default_task = (
|
||||
"Visit the Qwen3 GitHub repository, summarize the introduction of the repository."
|
||||
"Write a comprehensive HTML documentation site with the following features:"
|
||||
"A clear introduction to Qwen3"
|
||||
"Well-organized sections of the technical documentation"
|
||||
"Practical code examples"
|
||||
"A visually appealing purple technology theme (e.g. modern, clean, purple-accented design)"
|
||||
"Finally, deploy the HTML site and open it in the browser."
|
||||
)
|
||||
|
||||
# Use command line argument if provided, otherwise use default task
|
||||
task = sys.argv[1] if len(sys.argv) > 1 else default_task
|
||||
|
||||
try:
|
||||
# Create markdown file for conversation export
|
||||
md_filename = create_md_file(task)
|
||||
print(Fore.CYAN + f"Conversation will be saved to: {md_filename}")
|
||||
|
||||
async with mcp_toolkit_context(config_path) as mcp_toolkit:
|
||||
# Get available tools
|
||||
tools = [*mcp_toolkit.get_tools()]
|
||||
|
||||
# Build and run society
|
||||
print(Fore.YELLOW + f"Starting task: {task}\n")
|
||||
society = await construct_society(task, tools)
|
||||
answer, chat_history, token_count = await run_society_with_formatted_output(society, md_filename)
|
||||
|
||||
print(Fore.GREEN + f"\nFinal Result: {answer}")
|
||||
print(Fore.CYAN + f"Total tokens used: {token_count['total']}")
|
||||
print(Fore.CYAN + f"Full conversation log saved to: {md_filename}")
|
||||
|
||||
except KeyboardInterrupt:
|
||||
print(Fore.YELLOW + "\nReceived exit signal, shutting down...")
|
||||
except Exception as e:
|
||||
print(Fore.RED + f"Error occurred: {e}")
|
||||
|
||||
|
||||
if __name__ == "__main__":
|
||||
asyncio.run(main())
|
||||
@@ -131,7 +131,7 @@ def construct_society(question: str) -> RolePlaying:
|
||||
def main():
|
||||
r"""Main function to run the OWL system with an example question."""
|
||||
# Default research question
|
||||
default_task = "Navigate to Amazon.com and identify one product that is attractive to coders. Please provide me with the product name and price. No need to verify your answer."
|
||||
default_task = "Open Brave search, summarize the github stars, fork counts, etc. of camel-ai's camel framework, and write the numbers into a python file using the plot package, save it locally, and run the generated python file. Note: You have been provided with the necessary tools to complete this task."
|
||||
|
||||
# Override default task if command line argument is provided
|
||||
task = sys.argv[1] if len(sys.argv) > 1 else default_task
|
||||
|
||||
@@ -105,7 +105,7 @@ def construct_society(question: str) -> OwlRolePlaying:
|
||||
def main():
|
||||
r"""Main function to run the OWL system with Azure OpenAI."""
|
||||
# Example question
|
||||
default_task = "Navigate to Amazon.com and identify one product that is attractive to coders. Please provide me with the product name and price. No need to verify your answer."
|
||||
default_task = "Open Brave search, summarize the github stars, fork counts, etc. of camel-ai's camel framework, and write the numbers into a python file using the plot package, save it locally, and run the generated python file. Note: You have been provided with the necessary tools to complete this task."
|
||||
|
||||
# Override default task if command line argument is provided
|
||||
task = sys.argv[1] if len(sys.argv) > 1 else default_task
|
||||
|
||||
190
examples/run_claude.py
Normal file
190
examples/run_claude.py
Normal file
@@ -0,0 +1,190 @@
|
||||
# ========= Copyright 2023-2024 @ CAMEL-AI.org. All Rights Reserved. =========
|
||||
# Licensed under the Apache License, Version 2.0 (the "License");
|
||||
# you may not use this file except in compliance with the License.
|
||||
# You may obtain a copy of the License at
|
||||
#
|
||||
# http://www.apache.org/licenses/LICENSE-2.0
|
||||
#
|
||||
# Unless required by applicable law or agreed to in writing, software
|
||||
# distributed under the License is distributed on an "AS IS" BASIS,
|
||||
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
|
||||
# See the License for the specific language governing permissions and
|
||||
# limitations under the License.
|
||||
# ========= Copyright 2023-2024 @ CAMEL-AI.org. All Rights Reserved. =========
|
||||
import sys
|
||||
import pathlib
|
||||
from dotenv import load_dotenv
|
||||
from camel.configs import MistralConfig
|
||||
from camel.models import ModelFactory
|
||||
from camel.toolkits import (
|
||||
SearchToolkit,
|
||||
BrowserToolkit,
|
||||
FileWriteToolkit,
|
||||
)
|
||||
from camel.types import ModelPlatformType, ModelType
|
||||
from camel.logger import set_log_level
|
||||
from camel.societies import RolePlaying
|
||||
|
||||
from owl.utils import run_society, DocumentProcessingToolkit
|
||||
|
||||
base_dir = pathlib.Path(__file__).parent.parent
|
||||
env_path = base_dir / "owl" / ".env"
|
||||
load_dotenv(dotenv_path=str(env_path))
|
||||
|
||||
set_log_level(level="DEBUG")
|
||||
|
||||
|
||||
def construct_society(question: str) -> RolePlaying:
|
||||
r"""Construct a society of agents based on the given question.
|
||||
|
||||
Args:
|
||||
question (str): The task or question to be addressed by the society.
|
||||
|
||||
Returns:
|
||||
RolePlaying: A configured society of agents ready to address the question.
|
||||
"""
|
||||
|
||||
# Create models for different components
|
||||
models = {
|
||||
"user": ModelFactory.create(
|
||||
<<<<<<<< HEAD:community_usecase/Smart_city_research/main.py
|
||||
model_platform=ModelPlatformType.MISTRAL,
|
||||
model_type=ModelType.MISTRAL_LARGE,
|
||||
model_config_dict=MistralConfig(temperature=0.0).as_dict(),
|
||||
),
|
||||
"assistant": ModelFactory.create(
|
||||
model_platform=ModelPlatformType.MISTRAL,
|
||||
model_type=ModelType.MISTRAL_LARGE,
|
||||
model_config_dict=MistralConfig(temperature=0.0).as_dict(),
|
||||
),
|
||||
"browsing": ModelFactory.create(
|
||||
model_platform=ModelPlatformType.MISTRAL,
|
||||
model_type=ModelType.MISTRAL_LARGE,
|
||||
model_config_dict=MistralConfig(temperature=0.0).as_dict(),
|
||||
),
|
||||
"planning": ModelFactory.create(
|
||||
model_platform=ModelPlatformType.MISTRAL,
|
||||
model_type=ModelType.MISTRAL_LARGE,
|
||||
model_config_dict=MistralConfig(temperature=0.0).as_dict(),
|
||||
========
|
||||
model_platform=ModelPlatformType.ANTHROPIC,
|
||||
model_type=ModelType.CLAUDE_3_7_SONNET,
|
||||
model_config_dict={"temperature": 0},
|
||||
),
|
||||
"assistant": ModelFactory.create(
|
||||
model_platform=ModelPlatformType.ANTHROPIC,
|
||||
model_type=ModelType.CLAUDE_3_7_SONNET,
|
||||
model_config_dict={"temperature": 0},
|
||||
),
|
||||
"browsing": ModelFactory.create(
|
||||
model_platform=ModelPlatformType.ANTHROPIC,
|
||||
model_type=ModelType.CLAUDE_3_7_SONNET,
|
||||
model_config_dict={"temperature": 0},
|
||||
),
|
||||
"planning": ModelFactory.create(
|
||||
model_platform=ModelPlatformType.ANTHROPIC,
|
||||
model_type=ModelType.CLAUDE_3_7_SONNET,
|
||||
model_config_dict={"temperature": 0},
|
||||
),
|
||||
"video": ModelFactory.create(
|
||||
model_platform=ModelPlatformType.ANTHROPIC,
|
||||
model_type=ModelType.CLAUDE_3_7_SONNET,
|
||||
model_config_dict={"temperature": 0},
|
||||
),
|
||||
"image": ModelFactory.create(
|
||||
model_platform=ModelPlatformType.ANTHROPIC,
|
||||
model_type=ModelType.CLAUDE_3_7_SONNET,
|
||||
model_config_dict={"temperature": 0},
|
||||
),
|
||||
"document": ModelFactory.create(
|
||||
model_platform=ModelPlatformType.ANTHROPIC,
|
||||
model_type=ModelType.CLAUDE_3_7_SONNET,
|
||||
model_config_dict={"temperature": 0},
|
||||
>>>>>>>> main:examples/run_claude.py
|
||||
),
|
||||
|
||||
|
||||
}
|
||||
|
||||
# Configure toolkits
|
||||
tools = [
|
||||
*BrowserToolkit(
|
||||
headless=False, # Set to True for headless mode (e.g., on remote servers)
|
||||
web_agent_model=models["browsing"],
|
||||
planning_agent_model=models["planning"],
|
||||
).get_tools(),
|
||||
<<<<<<<< HEAD:community_usecase/Smart_city_research/main.py
|
||||
*PyAutoGUIToolkit().get_tools(),
|
||||
*TerminalToolkit(working_dir=workspace_dir).get_tools(),
|
||||
# SearchToolkit().search_duckduckgo,
|
||||
SearchToolkit().search_google, # Comment this out if you don't have google search
|
||||
========
|
||||
*VideoAnalysisToolkit(model=models["video"]).get_tools(),
|
||||
*CodeExecutionToolkit(sandbox="subprocess", verbose=True).get_tools(),
|
||||
*ImageAnalysisToolkit(model=models["image"]).get_tools(),
|
||||
SearchToolkit().search_duckduckgo,
|
||||
SearchToolkit().search_wiki,
|
||||
*ExcelToolkit().get_tools(),
|
||||
>>>>>>>> main:examples/run_claude.py
|
||||
*DocumentProcessingToolkit(model=models["document"]).get_tools(),
|
||||
*FileWriteToolkit(output_dir="./").get_tools(),
|
||||
]
|
||||
|
||||
# Configure agent roles and parameters
|
||||
user_agent_kwargs = {"model": models["user"]}
|
||||
assistant_agent_kwargs = {"model": models["assistant"], "tools": tools}
|
||||
|
||||
# Configure task parameters
|
||||
task_kwargs = {
|
||||
"task_prompt": question,
|
||||
"with_task_specify": False,
|
||||
}
|
||||
|
||||
# Create and return the society
|
||||
society = RolePlaying(
|
||||
**task_kwargs,
|
||||
user_role_name="user",
|
||||
user_agent_kwargs=user_agent_kwargs,
|
||||
assistant_role_name="assistant",
|
||||
assistant_agent_kwargs=assistant_agent_kwargs,
|
||||
)
|
||||
|
||||
return society
|
||||
|
||||
|
||||
def main():
|
||||
r"""Main function to run the OWL system with an example question."""
|
||||
# Default research question
|
||||
<<<<<<<< HEAD:community_usecase/Smart_city_research/main.py
|
||||
default_task = """Conduct a comprehensive research on smart city technologies and implementations:
|
||||
|
||||
1. Search for the latest smart city initiatives in major global cities and identify common technologies they use.
|
||||
2. Browse official websites of 2-3 leading smart city technology providers to understand their key solutions.
|
||||
3. Analyze how IoT sensors, AI, and data analytics are integrated in traffic management and public transportation systems.
|
||||
4. Research case studies of successful smart city implementations that reduced energy consumption and carbon emissions.
|
||||
5. Investigate privacy and security concerns in smart city data collection.
|
||||
6. Create a brief report documenting your findings, including:
|
||||
- Top 5 emerging smart city technologies
|
||||
- Success metrics used to evaluate smart city projects
|
||||
- Implementation challenges and solutions
|
||||
- Future trends in smart urban planning
|
||||
|
||||
Save the report as 'smart_city_research.md' in the current directory with properly formatted sections.
|
||||
"""
|
||||
========
|
||||
default_task = "Open Brave search, summarize the github stars, fork counts, etc. of camel-ai's camel framework, and write the numbers into a python file using the plot package, save it locally, and run the generated python file. Note: You have been provided with the necessary tools to complete this task."
|
||||
>>>>>>>> main:examples/run_claude.py
|
||||
|
||||
# Override default task if command line argument is provided
|
||||
task = sys.argv[1] if len(sys.argv) > 1 else default_task
|
||||
|
||||
# Construct and run the society
|
||||
society = construct_society(task)
|
||||
answer, chat_history, token_count = run_society(society)
|
||||
|
||||
# Output the result
|
||||
print(f"\033[94mAnswer: {answer}\033[0m")
|
||||
|
||||
|
||||
if __name__ == "__main__":
|
||||
main()
|
||||
@@ -127,7 +127,7 @@ def construct_society(question: str) -> RolePlaying:
|
||||
def main():
|
||||
r"""Main function to run the OWL system with an example question."""
|
||||
# Default research question
|
||||
default_task = "Navigate to Amazon.com and identify one product that is attractive to coders. Please provide me with the product name and price. No need to verify your answer."
|
||||
default_task = "Open Brave search, summarize the github stars, fork counts, etc. of camel-ai's camel framework, and write the numbers into a python file using the plot package, save it locally, and run the generated python file. Note: You have been provided with the necessary tools to complete this task."
|
||||
|
||||
# Override default task if command line argument is provided
|
||||
task = sys.argv[1] if len(sys.argv) > 1 else default_task
|
||||
|
||||
@@ -142,7 +142,7 @@ def construct_society(question: str) -> OwlRolePlaying:
|
||||
def main():
|
||||
r"""Main function to run the OWL system with an example question."""
|
||||
# Example research question
|
||||
default_task = "Navigate to Amazon.com and identify one product that is attractive to coders. Please provide me with the product name and price. No need to verify your answer."
|
||||
default_task = "Open Brave search, summarize the github stars, fork counts, etc. of camel-ai's camel framework, and write the numbers into a python file using the plot package, save it locally, and run the generated python file. Note: You have been provided with the necessary tools to complete this task."
|
||||
|
||||
# Construct and run the society
|
||||
# Note: This configuration uses GROQ_LLAMA_3_3_70B for tool-intensive roles (assistant, web, planning, video, image)
|
||||
|
||||
@@ -19,6 +19,7 @@ from camel.toolkits import (
|
||||
SearchToolkit,
|
||||
BrowserToolkit,
|
||||
FileWriteToolkit,
|
||||
CodeExecutionToolkit,
|
||||
)
|
||||
from camel.types import ModelPlatformType, ModelType
|
||||
from camel.logger import set_log_level
|
||||
@@ -78,6 +79,7 @@ def construct_society(question: str) -> RolePlaying:
|
||||
web_agent_model=models["browsing"],
|
||||
planning_agent_model=models["planning"],
|
||||
).get_tools(),
|
||||
*CodeExecutionToolkit(sandbox="subprocess", verbose=True).get_tools(),
|
||||
SearchToolkit().search_duckduckgo,
|
||||
SearchToolkit().search_wiki,
|
||||
*FileWriteToolkit(output_dir="./").get_tools(),
|
||||
@@ -108,7 +110,7 @@ def construct_society(question: str) -> RolePlaying:
|
||||
def main():
|
||||
r"""Main function to run the OWL system with an example question."""
|
||||
# Default research question
|
||||
default_task = "Navigate to Amazon.com and identify one product that is attractive to coders. Please provide me with the product name and price. No need to verify your answer."
|
||||
default_task = "Open Brave search, summarize the github stars, fork counts, etc. of camel-ai's camel framework, and write the numbers into a python file using the plot package, save it locally, and run the generated python file. Note: You have been provided with the necessary tools to complete this task."
|
||||
|
||||
# Override default task if command line argument is provided
|
||||
task = sys.argv[1] if len(sys.argv) > 1 else default_task
|
||||
|
||||
@@ -131,7 +131,7 @@ def construct_society(question: str) -> RolePlaying:
|
||||
def main():
|
||||
r"""Main function to run the OWL system with an example question."""
|
||||
# Default research question
|
||||
default_task = "Navigate to Amazon.com and identify one product that is attractive to coders. Please provide me with the product name and price. No need to verify your answer."
|
||||
default_task = "Open Brave search, summarize the github stars, fork counts, etc. of camel-ai's camel framework, and write the numbers into a python file using the plot package, save it locally, and run the generated python file. Note: You have been provided with the necessary tools to complete this task."
|
||||
|
||||
# Override default task if command line argument is provided
|
||||
task = sys.argv[1] if len(sys.argv) > 1 else default_task
|
||||
|
||||
135
examples/run_novita_ai.py
Normal file
135
examples/run_novita_ai.py
Normal file
@@ -0,0 +1,135 @@
|
||||
# ========= Copyright 2023-2024 @ CAMEL-AI.org. All Rights Reserved. =========
|
||||
# Licensed under the Apache License, Version 2.0 (the "License");
|
||||
# you may not use this file except in compliance with the License.
|
||||
# You may obtain a copy of the License at
|
||||
#
|
||||
# http://www.apache.org/licenses/LICENSE-2.0
|
||||
#
|
||||
# Unless required by applicable law or agreed to in writing, software
|
||||
# distributed under the License is distributed on an "AS IS" BASIS,
|
||||
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
|
||||
# See the License for the specific language governing permissions and
|
||||
# limitations under the License.
|
||||
# ========= Copyright 2023-2024 @ CAMEL-AI.org. All Rights Reserved. =========
|
||||
import sys
|
||||
import pathlib
|
||||
from dotenv import load_dotenv
|
||||
from camel.models import ModelFactory
|
||||
from camel.toolkits import (
|
||||
CodeExecutionToolkit,
|
||||
ExcelToolkit,
|
||||
ImageAnalysisToolkit,
|
||||
BrowserToolkit,
|
||||
FileWriteToolkit,
|
||||
)
|
||||
from camel.types import ModelPlatformType, ModelType
|
||||
from camel.logger import set_log_level
|
||||
from camel.societies import RolePlaying
|
||||
|
||||
from owl.utils import run_society, DocumentProcessingToolkit
|
||||
|
||||
base_dir = pathlib.Path(__file__).parent.parent
|
||||
env_path = base_dir / "owl" / ".env"
|
||||
load_dotenv(dotenv_path=str(env_path))
|
||||
|
||||
set_log_level(level="DEBUG")
|
||||
|
||||
|
||||
def construct_society(question: str) -> RolePlaying:
|
||||
r"""Construct a society of agents based on the given question.
|
||||
|
||||
Args:
|
||||
question (str): The task or question to be addressed by the society.
|
||||
|
||||
Returns:
|
||||
RolePlaying: A configured society of agents ready to address the question.
|
||||
"""
|
||||
|
||||
# Create models for different components
|
||||
models = {
|
||||
"user": ModelFactory.create(
|
||||
model_platform=ModelPlatformType.NOVITA,
|
||||
model_type=ModelType.NOVITA_LLAMA_4_MAVERICK_17B,
|
||||
model_config_dict={"temperature": 0},
|
||||
),
|
||||
"assistant": ModelFactory.create(
|
||||
model_platform=ModelPlatformType.NOVITA,
|
||||
model_type=ModelType.NOVITA_LLAMA_4_MAVERICK_17B,
|
||||
model_config_dict={"temperature": 0},
|
||||
),
|
||||
"browsing": ModelFactory.create(
|
||||
model_platform=ModelPlatformType.NOVITA,
|
||||
model_type=ModelType.NOVITA_LLAMA_4_MAVERICK_17B,
|
||||
model_config_dict={"temperature": 0},
|
||||
),
|
||||
"planning": ModelFactory.create(
|
||||
model_platform=ModelPlatformType.NOVITA,
|
||||
model_type=ModelType.NOVITA_LLAMA_4_MAVERICK_17B,
|
||||
model_config_dict={"temperature": 0},
|
||||
),
|
||||
"image": ModelFactory.create(
|
||||
model_platform=ModelPlatformType.NOVITA,
|
||||
model_type=ModelType.NOVITA_LLAMA_4_MAVERICK_17B,
|
||||
model_config_dict={"temperature": 0},
|
||||
),
|
||||
"document": ModelFactory.create(
|
||||
model_platform=ModelPlatformType.NOVITA,
|
||||
model_type=ModelType.NOVITA_LLAMA_4_MAVERICK_17B,
|
||||
model_config_dict={"temperature": 0},
|
||||
),
|
||||
}
|
||||
|
||||
# Configure toolkits
|
||||
tools = [
|
||||
*BrowserToolkit(
|
||||
headless=False, # Set to True for headless mode (e.g., on remote servers)
|
||||
web_agent_model=models["browsing"],
|
||||
planning_agent_model=models["planning"],
|
||||
).get_tools(),
|
||||
*CodeExecutionToolkit(sandbox="subprocess", verbose=True).get_tools(),
|
||||
*ImageAnalysisToolkit(model=models["image"]).get_tools(),
|
||||
*ExcelToolkit().get_tools(),
|
||||
*DocumentProcessingToolkit(model=models["document"]).get_tools(),
|
||||
*FileWriteToolkit(output_dir="./").get_tools(),
|
||||
]
|
||||
|
||||
# Configure agent roles and parameters
|
||||
user_agent_kwargs = {"model": models["user"]}
|
||||
assistant_agent_kwargs = {"model": models["assistant"], "tools": tools}
|
||||
|
||||
# Configure task parameters
|
||||
task_kwargs = {
|
||||
"task_prompt": question,
|
||||
"with_task_specify": False,
|
||||
}
|
||||
|
||||
# Create and return the society
|
||||
society = RolePlaying(
|
||||
**task_kwargs,
|
||||
user_role_name="user",
|
||||
user_agent_kwargs=user_agent_kwargs,
|
||||
assistant_role_name="assistant",
|
||||
assistant_agent_kwargs=assistant_agent_kwargs,
|
||||
)
|
||||
|
||||
return society
|
||||
|
||||
|
||||
def main():
|
||||
r"""Main function to run the OWL system with an example question."""
|
||||
# Default research question
|
||||
default_task = "Open Brave search, summarize the github stars, fork counts, etc. of camel-ai's camel framework, and write the numbers into a python file using the plot package, save it locally, and run the generated python file. Note: You have been provided with the necessary tools to complete this task."
|
||||
|
||||
# Override default task if command line argument is provided
|
||||
task = sys.argv[1] if len(sys.argv) > 1 else default_task
|
||||
|
||||
# Construct and run the society
|
||||
society = construct_society(task)
|
||||
answer, chat_history, token_count = run_society(society)
|
||||
|
||||
# Output the result
|
||||
print(f"\033[94mAnswer: {answer}\033[0m")
|
||||
|
||||
|
||||
if __name__ == "__main__":
|
||||
main()
|
||||
@@ -126,7 +126,7 @@ def construct_society(question: str) -> RolePlaying:
|
||||
def main():
|
||||
r"""Main function to run the OWL system with an example question."""
|
||||
# Default research question
|
||||
default_task = "Navigate to Amazon.com and identify one product that is attractive to coders. Please provide me with the product name and price. No need to verify your answer."
|
||||
default_task = "Open Brave search, summarize the github stars, fork counts, etc. of camel-ai's camel framework, and write the numbers into a python file using the plot package, save it locally, and run the generated python file. Note: You have been provided with the necessary tools to complete this task."
|
||||
|
||||
# Override default task if command line argument is provided
|
||||
task = sys.argv[1] if len(sys.argv) > 1 else default_task
|
||||
|
||||
@@ -129,7 +129,7 @@ def construct_society(question: str) -> RolePlaying:
|
||||
def main():
|
||||
r"""Main function to run the OWL system with an example question."""
|
||||
# Example research question
|
||||
default_task = "Navigate to Amazon.com and identify one product that is attractive to coders. Please provide me with the product name and price. No need to verify your answer."
|
||||
default_task = "Open Brave search, summarize the github stars, fork counts, etc. of camel-ai's camel framework, and write the numbers into a python file using the plot package, save it locally, and run the generated python file. Note: You have been provided with the necessary tools to complete this task."
|
||||
|
||||
# Override default task if command line argument is provided
|
||||
task = sys.argv[1] if len(sys.argv) > 1 else default_task
|
||||
|
||||
@@ -118,7 +118,7 @@ def construct_society(question: str) -> RolePlaying:
|
||||
def main():
|
||||
r"""Main function to run the OWL system with an example question."""
|
||||
# Default research question
|
||||
default_task = "Open Brave search, summarize the github stars, fork counts, etc. of camel-ai's camel framework, and write the numbers into a python file using the plot package, save it locally, and run the generated python file."
|
||||
default_task = "Open Brave search, summarize the github stars, fork counts, etc. of camel-ai's camel framework, and write the numbers into a python file using the plot package, save it locally, and run the generated python file. Note: You have been provided with the necessary tools to complete this task."
|
||||
|
||||
# Override default task if command line argument is provided
|
||||
task = sys.argv[1] if len(sys.argv) > 1 else default_task
|
||||
|
||||
@@ -32,6 +32,9 @@ OPENAI_API_KEY="Your_Key"
|
||||
#GOOGLE GEMINI API (https://ai.google.dev/gemini-api/docs/api-key)
|
||||
# GEMINI_API_KEY ="Your_Key"
|
||||
|
||||
# NOVITA API (https://novita.ai/settings/key-management?utm_source=github_owl&utm_medium=github_readme&utm_campaign=github_link)
|
||||
# NOVITA_API_KEY="Your_Key"
|
||||
|
||||
#===========================================
|
||||
# Tools & Services API
|
||||
#===========================================
|
||||
|
||||
@@ -12,13 +12,13 @@
|
||||
# limitations under the License.
|
||||
# ========= Copyright 2023-2024 @ CAMEL-AI.org. All Rights Reserved. =========
|
||||
|
||||
from camel.loaders import UnstructuredIO
|
||||
from camel.toolkits.base import BaseToolkit
|
||||
from camel.toolkits.function_tool import FunctionTool
|
||||
from camel.toolkits import ImageAnalysisToolkit, ExcelToolkit
|
||||
from camel.utils import retry_on_error
|
||||
from camel.logger import get_logger
|
||||
from camel.models import BaseModelBackend
|
||||
from docx2markdown._docx_to_markdown import docx_to_markdown
|
||||
from chunkr_ai import Chunkr
|
||||
import requests
|
||||
import mimetypes
|
||||
@@ -29,6 +29,7 @@ import os
|
||||
import subprocess
|
||||
import xmltodict
|
||||
import nest_asyncio
|
||||
import traceback
|
||||
|
||||
nest_asyncio.apply()
|
||||
|
||||
@@ -52,6 +53,8 @@ class DocumentProcessingToolkit(BaseToolkit):
|
||||
if cache_dir:
|
||||
self.cache_dir = cache_dir
|
||||
|
||||
self.uio = UnstructuredIO()
|
||||
|
||||
@retry_on_error()
|
||||
def extract_document_content(self, document_path: str) -> Tuple[bool, str]:
|
||||
r"""Extract the content of a given document (or url) and return the processed text.
|
||||
@@ -63,7 +66,6 @@ class DocumentProcessingToolkit(BaseToolkit):
|
||||
Returns:
|
||||
Tuple[bool, str]: A tuple containing a boolean indicating whether the document was processed successfully, and the content of the document (if success).
|
||||
"""
|
||||
import asyncio
|
||||
|
||||
logger.debug(
|
||||
f"Calling extract_document_content function with document_path=`{document_path}`"
|
||||
@@ -115,71 +117,35 @@ class DocumentProcessingToolkit(BaseToolkit):
|
||||
return True, content
|
||||
|
||||
if self._is_webpage(document_path):
|
||||
extracted_text = self._extract_webpage_content(document_path)
|
||||
return True, extracted_text
|
||||
try:
|
||||
extracted_text = self._extract_webpage_content(document_path)
|
||||
return True, extracted_text
|
||||
except Exception:
|
||||
try:
|
||||
elements = self.uio.parse_file_or_url(document_path)
|
||||
if elements is None:
|
||||
logger.error(f"Failed to parse the document: {document_path}.")
|
||||
return False, f"Failed to parse the document: {document_path}."
|
||||
else:
|
||||
# Convert elements list to string
|
||||
elements_str = "\n".join(str(element) for element in elements)
|
||||
return True, elements_str
|
||||
except Exception:
|
||||
return False, "Failed to extract content from the webpage."
|
||||
|
||||
else:
|
||||
# judge if url
|
||||
parsed_url = urlparse(document_path)
|
||||
is_url = all([parsed_url.scheme, parsed_url.netloc])
|
||||
if not is_url:
|
||||
if not os.path.exists(document_path):
|
||||
return False, f"Document not found at path: {document_path}."
|
||||
|
||||
# if is docx file, use docx2markdown to convert it
|
||||
if document_path.endswith(".docx"):
|
||||
if is_url:
|
||||
tmp_path = self._download_file(document_path)
|
||||
else:
|
||||
tmp_path = document_path
|
||||
|
||||
file_name = os.path.basename(tmp_path)
|
||||
md_file_path = f"{file_name}.md"
|
||||
docx_to_markdown(tmp_path, md_file_path)
|
||||
|
||||
# load content of md file
|
||||
with open(md_file_path, "r") as f:
|
||||
extracted_text = f.read()
|
||||
f.close()
|
||||
return True, extracted_text
|
||||
try:
|
||||
result = asyncio.run(self._extract_content_with_chunkr(document_path))
|
||||
return True, result
|
||||
elements = self.uio.parse_file_or_url(document_path)
|
||||
if elements is None:
|
||||
logger.error(f"Failed to parse the document: {document_path}.")
|
||||
return False, f"Failed to parse the document: {document_path}."
|
||||
else:
|
||||
# Convert elements list to string
|
||||
elements_str = "\n".join(str(element) for element in elements)
|
||||
return True, elements_str
|
||||
|
||||
except Exception as e:
|
||||
logger.warning(
|
||||
f"Error occurred while using Chunkr to process document: {e}"
|
||||
)
|
||||
if document_path.endswith(".pdf"):
|
||||
# try using pypdf to extract text from pdf
|
||||
try:
|
||||
from PyPDF2 import PdfReader
|
||||
|
||||
if is_url:
|
||||
tmp_path = self._download_file(document_path)
|
||||
document_path = tmp_path
|
||||
|
||||
# Open file in binary mode for PdfReader
|
||||
f = open(document_path, "rb")
|
||||
reader = PdfReader(f)
|
||||
extracted_text = ""
|
||||
for page in reader.pages:
|
||||
extracted_text += page.extract_text()
|
||||
f.close()
|
||||
|
||||
return True, extracted_text
|
||||
|
||||
except Exception as pdf_error:
|
||||
logger.error(
|
||||
f"Error occurred while processing pdf: {pdf_error}"
|
||||
)
|
||||
return (
|
||||
False,
|
||||
f"Error occurred while processing pdf: {pdf_error}",
|
||||
)
|
||||
|
||||
# If we get here, either it's not a PDF or PDF processing failed
|
||||
logger.error(f"Error occurred while processing document: {e}")
|
||||
logger.error(traceback.format_exc())
|
||||
return False, f"Error occurred while processing document: {e}"
|
||||
|
||||
def _is_webpage(self, url: str) -> bool:
|
||||
|
||||
@@ -245,6 +245,7 @@ MODULE_DESCRIPTIONS = {
|
||||
"run": "Default mode: Using OpenAI model's default agent collaboration mode, suitable for most tasks.",
|
||||
"run_mini": "Using OpenAI model with minimal configuration to process tasks",
|
||||
"run_gemini": "Using Gemini model to process tasks",
|
||||
"run_claude": "Using Claude model to process tasks",
|
||||
"run_deepseek_zh": "Using deepseek model to process Chinese tasks",
|
||||
"run_mistral": "Using Mistral models to process tasks",
|
||||
"run_openai_compatible_model": "Using openai compatible model to process tasks",
|
||||
@@ -255,6 +256,7 @@ MODULE_DESCRIPTIONS = {
|
||||
"run_groq": "Using groq model to process tasks",
|
||||
"run_ppio": "Using ppio model to process tasks",
|
||||
"run_together_ai": "Using together ai model to process tasks",
|
||||
"run_novita_ai": "Using novita ai model to process tasks",
|
||||
}
|
||||
|
||||
|
||||
@@ -640,6 +642,8 @@ def get_api_guide(key: str) -> str:
|
||||
return "https://chunkr.ai/"
|
||||
elif "firecrawl" in key_lower:
|
||||
return "https://www.firecrawl.dev/"
|
||||
elif "novita" in key_lower:
|
||||
return "https://novita.ai/settings/key-management?utm_source=github_owl&utm_medium=github_readme&utm_campaign=github_link"
|
||||
else:
|
||||
return ""
|
||||
|
||||
@@ -1089,7 +1093,7 @@ def create_ui():
|
||||
label="Question",
|
||||
elem_id="question_input",
|
||||
show_copy_button=True,
|
||||
value="Open Google search, summarize the github stars, fork counts, etc. of camel-ai's camel framework, and write the numbers into a python file using the plot package, save it locally, and run the generated python file.",
|
||||
value="Open Brave search, summarize the github stars, fork counts, etc. of camel-ai's camel framework, and write the numbers into a python file using the plot package, save it locally, and run the generated python file. Note: You have been provided with the necessary tools to complete this task.",
|
||||
)
|
||||
|
||||
# Enhanced module selection dropdown
|
||||
@@ -1124,7 +1128,7 @@ def create_ui():
|
||||
|
||||
# Example questions
|
||||
examples = [
|
||||
"Open Google search, summarize the github stars, fork counts, etc. of camel-ai's camel framework, and write the numbers into a python file using the plot package, save it locally, and run the generated python file.",
|
||||
"Open Brave search, summarize the github stars, fork counts, etc. of camel-ai's camel framework, and write the numbers into a python file using the plot package, save it locally, and run the generated python file. Note: You have been provided with the necessary tools to complete this task.",
|
||||
"Browse Amazon and find a product that is attractive to programmers. Please provide the product name and price",
|
||||
"Write a hello world python file and save it locally",
|
||||
]
|
||||
|
||||
@@ -245,6 +245,7 @@ MODULE_DESCRIPTIONS = {
|
||||
"run": "默认模式：使用OpenAI模型的默认的智能体协作模式，适合大多数任务。",
|
||||
"run_mini": "使用使用OpenAI模型最小化配置处理任务",
|
||||
"run_gemini": "使用 Gemini模型处理任务",
|
||||
"run_claude": "使用 Claude模型处理任务",
|
||||
"run_deepseek_zh": "使用eepseek模型处理中文任务",
|
||||
"run_openai_compatible_model": "使用openai兼容模型处理任务",
|
||||
"run_ollama": "使用本地ollama模型处理任务",
|
||||
@@ -254,6 +255,7 @@ MODULE_DESCRIPTIONS = {
|
||||
"run_groq": "使用groq模型处理任务",
|
||||
"run_ppio": "使用ppio模型处理任务",
|
||||
"run_together_ai": "使用together ai模型处理任务",
|
||||
"run_novita_ai": "使用novita ai模型处理任务",
|
||||
}
|
||||
|
||||
|
||||
@@ -623,6 +625,8 @@ def get_api_guide(key: str) -> str:
|
||||
return "https://chunkr.ai/"
|
||||
elif "firecrawl" in key_lower:
|
||||
return "https://www.firecrawl.dev/"
|
||||
elif "novita" in key_lower:
|
||||
return "https://novita.ai/settings/key-management?utm_source=github_owl&utm_medium=github_readme&utm_campaign=github_link"
|
||||
else:
|
||||
return ""
|
||||
|
||||
|
||||
68
pyproject.toml
Normal file
68
pyproject.toml
Normal file
@@ -0,0 +1,68 @@
|
||||
[build-system]
|
||||
requires = ["hatchling"]
|
||||
build-backend = "hatchling.build"
|
||||
|
||||
[project]
|
||||
name = "owl"
|
||||
version = "0.0.1"
|
||||
description = "Optimized Workforce Learning for General Multi-Agent Assistance in Real-World Task Automation"
|
||||
authors = [{ name = "CAMEL-AI.org" }]
|
||||
requires-python = ">=3.10,<3.13"
|
||||
readme = "README.md"
|
||||
license = "Apache-2.0"
|
||||
keywords = [
|
||||
"optimized-workforce-learning",
|
||||
"multi-agent-assistance",
|
||||
"task-automation",
|
||||
"real-world-tasks",
|
||||
"artificial-intelligence",
|
||||
"agent-collaboration",
|
||||
"workforce-optimization",
|
||||
"learning-systems"
|
||||
]
|
||||
dependencies = [
|
||||
"camel-ai[owl]==0.2.52",
|
||||
"docx2markdown>=0.1.1",
|
||||
"gradio>=3.50.2",
|
||||
"mcp-simple-arxiv==0.2.2",
|
||||
"mcp-server-fetch==2025.1.17",
|
||||
"xmltodict>=0.14.2",
|
||||
]
|
||||
|
||||
[project.urls]
|
||||
Homepage = "https://www.camel-ai.org/"
|
||||
Repository = "https://github.com/camel-ai/owl"
|
||||
Documentation = "https://docs.camel-ai.org"
|
||||
|
||||
[tool.hatch.build.targets.wheel]
|
||||
packages = ["owl"]
|
||||
|
||||
[tool.mypy]
|
||||
python_version = "3.11"
|
||||
warn_return_any = false
|
||||
warn_unused_configs = true
|
||||
disallow_untyped_defs = false
|
||||
disallow_incomplete_defs = false
|
||||
check_untyped_defs = false
|
||||
disallow_untyped_decorators = false
|
||||
no_implicit_optional = false
|
||||
strict_optional = false
|
||||
ignore_missing_imports = true
|
||||
allow_redefinition = true
|
||||
disable_error_code = ["assignment", "arg-type", "return-value"]
|
||||
|
||||
[[tool.mypy.overrides]]
|
||||
module = "camel.*"
|
||||
ignore_missing_imports = true
|
||||
follow_imports = "skip"
|
||||
|
||||
[[tool.mypy.overrides]]
|
||||
module = "utils"
|
||||
ignore_missing_imports = true
|
||||
|
||||
[tool.codespell]
|
||||
# Ref: https://github.com/codespell-project/codespell#using-a-config-file
|
||||
skip = '.git*,*.pdf,*.lock'
|
||||
check-hidden = true
|
||||
ignore-regex = '\bBrin\b'
|
||||
ignore-words-list = 'datas'
|
||||
5
requirements.txt
Normal file
5
requirements.txt
Normal file
@@ -0,0 +1,5 @@
|
||||
camel-ai[owl]==0.2.52
|
||||
docx2markdown>=0.1.1
|
||||
gradio>=3.50.2
|
||||
mcp-simple-arxiv==0.2.2
|
||||
mcp-server-fetch==2025.1.17
|
||||
Reference in New Issue
Block a user