Branch mk (#88)

create minimum example for running with owl agent.

README.md

@@ -137,7 +137,10 @@ playwright install
 In the `owl/.env_template` file, you will find all the necessary API keys along with the websites where you can register for each service. To use these API services, follow these steps:
 
 1. *Copy and Rename*: Duplicate the `.env_example` file and rename the copy to `.env`.
-2. *Fill in Your Keys*: Open the `.env` file and insert your API keys in the corresponding fields. 
+```bash
+cp owl/.env_template .env
+```
+2. *Fill in Your Keys*: Open the `.env` file and insert your API keys in the corresponding fields.  (For the minimal example (`run_mini.py`), you only need to configure the LLM API key (e.g., OPENAI_API_KEY).)
 3. *For using more other models*: please refer to our CAMEL models docs:https://docs.camel-ai.org/key_modules/models.html#supported-model-platforms-in-camel
 
 
@@ -145,11 +148,18 @@ In the `owl/.env_template` file, you will find all the necessary API keys along
 
 # 🚀 Quick Start
    
-Run the following minimal example:
+Run the following demo case:
 
 ```bash
 python owl/run.py
 ```
+
+For a simpler version that only requires an LLM API key, you can try our minimal example:
+
+```bash
+python owl/run_mini.py
+```
+
 You can run OWL agent with your own task by modifying the `run.py` script:
 
 ```python

README_zh.md

@@ -142,13 +142,19 @@ python -m pip install -r requirements.txt
 
 # 🚀 快速开始
    
-运行以下最小示例：
+运行以下示例：
 
 ```bash
 python owl/run.py
 ```
 
-你可以通过修改 `run.py` 来运行自定义任务的 OWL 智能体：
+我们还提供了一个最小化示例，只需配置LLM的API密钥即可运行：
+
+```bash
+python owl/run_mini.py
+```
+
+你可以通过修改 `run.py` 脚本来运行自己的任务：
 
 ```python
 # Define your own task

owl/run.py

@@ -2,7 +2,16 @@ from dotenv import load_dotenv
 load_dotenv()
 
 from camel.models import ModelFactory
-from camel.toolkits import *
+from camel.toolkits import (
+    WebToolkit, 
+    DocumentProcessingToolkit, 
+    VideoAnalysisToolkit, 
+    AudioAnalysisToolkit, 
+    CodeExecutionToolkit, 
+    ImageAnalysisToolkit, 
+    SearchToolkit,
+    ExcelToolkit
+    )
 from camel.types import ModelPlatformType, ModelType
 from camel.configs import ChatGPTConfig
 

owl/run_mini.py

@@ -0,0 +1,96 @@
+from dotenv import load_dotenv
+load_dotenv()
+
+from camel.models import ModelFactory
+from camel.toolkits import (
+    WebToolkit, 
+    DocumentProcessingToolkit, 
+    VideoAnalysisToolkit, 
+    AudioAnalysisToolkit, 
+    CodeExecutionToolkit, 
+    ImageAnalysisToolkit, 
+    SearchToolkit,
+    ExcelToolkit,
+    FunctionTool
+    )
+from camel.types import ModelPlatformType, ModelType
+from camel.configs import ChatGPTConfig
+
+from typing import List, Dict
+
+from retry import retry
+from loguru import logger
+
+from utils import OwlRolePlaying, run_society
+import os
+
+
+
+
+def construct_society(question: str) -> OwlRolePlaying:
+    r"""Construct the society based on the question."""
+
+    user_role_name = "user"
+    assistant_role_name = "assistant"
+    
+    user_model = ModelFactory.create(
+        model_platform=ModelPlatformType.OPENAI,
+        model_type=ModelType.GPT_4O,
+        model_config_dict=ChatGPTConfig(temperature=0, top_p=1).as_dict(), # [Optional] the config for model
+    )
+
+    assistant_model = ModelFactory.create(
+        model_platform=ModelPlatformType.OPENAI,
+        model_type=ModelType.GPT_4O,
+        model_config_dict=ChatGPTConfig(temperature=0, top_p=1).as_dict(), # [Optional] the config for model
+    )
+
+    tools_list = [
+        *WebToolkit(
+            headless=False, 
+            web_agent_model=assistant_model, 
+            planning_agent_model=assistant_model
+        ).get_tools(),
+        # *DocumentProcessingToolkit().get_tools(),
+        # *VideoAnalysisToolkit(model=assistant_model).get_tools(),  # This requires OpenAI Key
+        # *AudioAnalysisToolkit().get_tools(),  # This requires OpenAI Key
+        # *CodeExecutionToolkit().get_tools(),
+        # *ImageAnalysisToolkit(model=assistant_model).get_tools(),
+        FunctionTool(SearchToolkit(model=assistant_model).search_duckduckgo),
+        # *ExcelToolkit().get_tools()
+    ]
+
+    user_role_name = 'user'
+    user_agent_kwargs = dict(model=user_model)
+    assistant_role_name = 'assistant'
+    assistant_agent_kwargs = dict(model=assistant_model,
+    tools=tools_list)
+    
+    task_kwargs = {
+        'task_prompt': question,
+        'with_task_specify': False,
+    }
+
+    society = OwlRolePlaying(
+        **task_kwargs,
+        user_role_name=user_role_name,
+        user_agent_kwargs=user_agent_kwargs,
+        assistant_role_name=assistant_role_name,
+        assistant_agent_kwargs=assistant_agent_kwargs,
+    )
+    
+    return society
+
+
+# Example case
+question = "What was the volume in m^3 of the fish bag that was calculated in the University of Leicester paper `Can Hiccup Supply Enough Fish to Maintain a Dragon’s Diet?` "
+
+society = construct_society(question)
+answer, chat_history, token_count = run_society(society)
+
+logger.success(f"Answer: {answer}")
+
+
+
+
+