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1017041b10
- 在 run_openai_compatiable_model.py 中新增不同角色使用不同模型的支持 - 在 .env_template 中添加角色专属 API 配置选项 - 新增命令行参数支持直接传入问题 - 在 README_zh.md 和 README.md 中更新使用示例文档 - 优化网页应用描述提升表述清晰度 - 在 .gitignore 中添加 `.venv` 和 `tmp/` 目录
165 lines
7.8 KiB
Python
165 lines
7.8 KiB
Python
# ========= Copyright 2023-2024 @ CAMEL-AI.org. All Rights Reserved. =========
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# Licensed under the Apache License, Version 2.0 (the "License");
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# you may not use this file except in compliance with the License.
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# You may obtain a copy of the License at
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#
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# http://www.apache.org/licenses/LICENSE-2.0
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#
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# Unless required by applicable law or agreed to in writing, software
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# distributed under the License is distributed on an "AS IS" BASIS,
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# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
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# See the License for the specific language governing permissions and
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# limitations under the License.
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# ========= Copyright 2023-2024 @ CAMEL-AI.org. All Rights Reserved. =========
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import os
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import sys
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from dotenv import load_dotenv
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from camel.models import ModelFactory
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from camel.toolkits import (
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CodeExecutionToolkit,
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ExcelToolkit,
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ImageAnalysisToolkit,
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SearchToolkit,
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BrowserToolkit,
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FileWriteToolkit,
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)
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from camel.types import ModelPlatformType
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from owl.utils import run_society
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from camel.societies import RolePlaying
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from camel.logger import set_log_level
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import pathlib
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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="DEBUG")
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def construct_society(question: str) -> RolePlaying:
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r"""Construct a society of agents based on the given question.
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Args:
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question (str): The task or question to be addressed by the society.
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Returns:
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RolePlaying: A configured society of agents ready to address the question.
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"""
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# Create models for different components
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models = {
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"user": ModelFactory.create(
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model_platform=ModelPlatformType.OPENAI_COMPATIBLE_MODEL,
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model_type=os.getenv("USER_ROLE_API_MODEL_TYPE", os.getenv("LLM_ROLE_API_MODEL_TYPE", "qwen-max")),
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api_key=os.getenv("USER_ROLE_API_KEY", os.getenv("LLM_ROLE_API_KEY", os.getenv("QWEN_API_KEY"))),
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url=os.getenv("USER_ROLE_API_BASE_URL", os.getenv("LLM_ROLE_API_BASE_URL", "https://dashscope.aliyuncs.com/compatible-mode/v1")),
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model_config_dict={
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"temperature": float(os.getenv("USER_ROLE_API_MODEL_TEMPERATURE", os.getenv("LLM_ROLE_API_MODEL_TEMPERATURE", "0.4"))),
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"max_tokens": int(os.getenv("USER_ROLE_API_MODEL_MAX_TOKENS", os.getenv("LLM_ROLE_API_MODEL_MAX_TOKENS", "4096")))
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},
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),
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"assistant": ModelFactory.create(
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model_platform=ModelPlatformType.OPENAI_COMPATIBLE_MODEL,
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model_type=os.getenv("ASSISTANT_ROLE_API_MODEL_TYPE", os.getenv("LLM_ROLE_API_MODEL_TYPE", "qwen-max")),
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api_key=os.getenv("ASSISTANT_ROLE_API_KEY", os.getenv("LLM_ROLE_API_KEY", os.getenv("QWEN_API_KEY"))),
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url=os.getenv("ASSISTANT_ROLE_API_BASE_URL", os.getenv("LLM_ROLE_API_BASE_URL", "https://dashscope.aliyuncs.com/compatible-mode/v1")),
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model_config_dict={
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"temperature": float(os.getenv("ASSISTANT_ROLE_API_MODEL_TEMPERATURE", os.getenv("LLM_ROLE_API_MODEL_TEMPERATURE", "0.4"))),
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"max_tokens": int(os.getenv("ASSISTANT_ROLE_API_MODEL_MAX_TOKENS", os.getenv("LLM_ROLE_API_MODEL_MAX_TOKENS", "4096")))
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},
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),
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"web": ModelFactory.create(
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model_platform=ModelPlatformType.OPENAI_COMPATIBLE_MODEL,
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model_type=os.getenv("WEB_ROLE_API_BASE_URL", os.getenv("VLLM_ROLE_API_MODEL_TYPE", "qwen-vl-max")),
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api_key=os.getenv("WEB_ROLE_API_KEY", os.getenv("VLLM_ROLE_API_KEY", os.getenv("QWEN_API_KEY"))),
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url=os.getenv("USER_ROLE_API_BASE_URL", os.getenv("VLLM_ROLE_API_BASE_URL", "https://dashscope.aliyuncs.com/compatible-mode/v1")),
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model_config_dict={
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"temperature": float(os.getenv("WEB_ROLE_API_MODEL_TEMPERATURE", os.getenv("VLLM_ROLE_API_MODEL_TEMPERATURE", "0.4"))),
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"max_tokens": int(os.getenv("WEB_ROLE_API_MODEL_MAX_TOKENS", os.getenv("VLLM_ROLE_API_MODEL_MAX_TOKENS", "4096")))
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},
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),
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"planning": ModelFactory.create(
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model_platform=ModelPlatformType.OPENAI_COMPATIBLE_MODEL,
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model_type=os.getenv("PLANNING_ROLE_API_MODEL_TYPE", os.getenv("LLM_ROLE_API_MODEL_TYPE", "qwen-max")),
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api_key=os.getenv("PLANNING_ROLE_API_KEY", os.getenv("LLM_ROLE_API_KEY", os.getenv("QWEN_API_KEY"))),
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url=os.getenv("PLANNING_ROLE_API_BASE_URL", os.getenv("LLM_ROLE_API_BASE_URL", "https://dashscope.aliyuncs.com/compatible-mode/v1")),
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model_config_dict={
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"temperature": float(os.getenv("PLANNING_ROLE_API_MODEL_TEMPERATURE", os.getenv("LLM_ROLE_API_MODEL_TEMPERATURE", "0.4"))),
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"max_tokens": int(os.getenv("PLANNING_ROLE_API_MODEL_MAX_TOKENS", os.getenv("LLM_ROLE_API_MODEL_MAX_TOKENS", "4096")))
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},
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),
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"image": ModelFactory.create(
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model_platform=ModelPlatformType.OPENAI_COMPATIBLE_MODEL,
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model_type=os.getenv("IMAGE_ROLE_API_MODEL_TYPE", os.getenv("VLLM_ROLE_API_MODEL_TYPE", "qwen-vl-max")),
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api_key=os.getenv("IMAGE_ROLE_API_KEY", os.getenv("VLLM_ROLE_API_KEY", os.getenv("QWEN_API_KEY"))),
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url=os.getenv("IMAGE_ROLE_API_BASE_URL", os.getenv("VLLM_ROLE_API_BASE_URL", "https://dashscope.aliyuncs.com/compatible-mode/v1")),
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model_config_dict={
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"temperature": float(os.getenv("IMAGE_ROLE_API_MODEL_TEMPERATURE", os.getenv("VLLM_ROLE_API_MODEL_TEMPERATURE", "0.4"))),
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"max_tokens": int(os.getenv("IMAGE_ROLE_API_MODEL_MAX_TOKENS", os.getenv("VLLM_ROLE_API_MODEL_MAX_TOKENS", "4096")))
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},
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),
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}
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# Configure toolkits
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tools = [
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*BrowserToolkit(
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headless=False, # Set to True for headless mode (e.g., on remote servers)
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web_agent_model=models["web"],
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planning_agent_model=models["planning"],
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).get_tools(),
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*CodeExecutionToolkit(sandbox="subprocess", verbose=True).get_tools(),
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*ImageAnalysisToolkit(model=models["image"]).get_tools(),
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SearchToolkit().search_duckduckgo,
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SearchToolkit().search_google, # Comment this out if you don't have google search
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SearchToolkit().search_wiki,
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*ExcelToolkit().get_tools(),
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*FileWriteToolkit(output_dir="./").get_tools(),
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]
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# Configure agent roles and parameters
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user_agent_kwargs = {"model": models["user"]}
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assistant_agent_kwargs = {"model": models["assistant"], "tools": tools}
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# Configure task parameters
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task_kwargs = {
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"task_prompt": question,
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"with_task_specify": False,
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}
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# Create and return the society
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society = RolePlaying(
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**task_kwargs,
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user_role_name="user",
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user_agent_kwargs=user_agent_kwargs,
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assistant_role_name="assistant",
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assistant_agent_kwargs=assistant_agent_kwargs,
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)
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return society
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def main(question: str = "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."):
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r"""Main function to run the OWL system with an example question.
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Args:
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question (str): The task or question to be addressed by the society.
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If not provided, a default question will be used.
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Defaults to "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."
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Returns:
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None
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"""
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# Construct and run the society
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society = construct_society(question)
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answer, chat_history, token_count = run_society(society)
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# Output the result
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print(f"\033[94mAnswer: {answer}\033[0m")
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# Output the token count
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print(f"\033[94mToken count: {token_count}\033[0m")
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if __name__ == "__main__":
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main(sys.argv[1] if len(sys.argv) > 1 else "")
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