mirror of
https://github.com/camel-ai/owl.git
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feat: 通过角色专属配置增强 OpenAI 兼容模型支持
- 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
This commit is contained in:
tj-scripts
2
.gitignore
vendored
2
.gitignore
vendored
@@ -27,6 +27,7 @@ venv/
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env/
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ENV/
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.env
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.venv
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# IDE
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.idea/
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@@ -58,3 +59,4 @@ coverage.xml
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owl/camel/types/__pycache__/
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owl/camel/__pycache__/
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owl/camel/utils/__pycache_/
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tmp/
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@@ -364,8 +364,10 @@ python examples/run_qwen_zh.py
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# Run with Deepseek model
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python examples/run_deepseek_zh.py
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# Run with other OpenAI-compatible models
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# Run with other OpenAI-compatible models, supporting different models for different roles
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python examples/run_openai_compatiable_model.py
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# Example with question
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python examples/run_openai_compatiable_model.py "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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# Run with Azure OpenAI
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python examples/run_azure_openai.py
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@@ -363,8 +363,10 @@ python examples/run_qwen_zh.py
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# 使用 Deepseek 模型运行
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python examples/run_deepseek_zh.py
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# 使用其他 OpenAI 兼容模型运行
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# 使用其他 OpenAI 兼容模型运行，支持不同的 role 使用不同的模型
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python examples/run_openai_compatiable_model.py
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# 带问题的示例
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python examples/run_openai_compatiable_model.py "浏览京东并找出一款对程序员有吸引力的产品。请提供产品名称和价格。"
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# 使用 Azure OpenAI模型运行
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python examples/run_azure_openai.py
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@@ -12,6 +12,7 @@
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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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@@ -52,38 +53,53 @@ def construct_society(question: str) -> RolePlaying:
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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="qwen-max",
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api_key=os.getenv("QWEN_API_KEY"),
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url="https://dashscope.aliyuncs.com/compatible-mode/v1",
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model_config_dict={"temperature": 0.4, "max_tokens": 4096},
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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="qwen-max",
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api_key=os.getenv("QWEN_API_KEY"),
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url="https://dashscope.aliyuncs.com/compatible-mode/v1",
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model_config_dict={"temperature": 0.4, "max_tokens": 4096},
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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="qwen-vl-max",
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api_key=os.getenv("QWEN_API_KEY"),
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url="https://dashscope.aliyuncs.com/compatible-mode/v1",
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model_config_dict={"temperature": 0.4, "max_tokens": 4096},
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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="qwen-max",
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api_key=os.getenv("QWEN_API_KEY"),
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url="https://dashscope.aliyuncs.com/compatible-mode/v1",
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model_config_dict={"temperature": 0.4, "max_tokens": 4096},
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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="qwen-vl-max",
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api_key=os.getenv("QWEN_API_KEY"),
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url="https://dashscope.aliyuncs.com/compatible-mode/v1",
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model_config_dict={"temperature": 0.4, "max_tokens": 4096},
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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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@@ -125,18 +141,24 @@ def construct_society(question: str) -> RolePlaying:
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return society
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def main():
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r"""Main function to run the OWL system with an example question."""
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# Example research question
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question = "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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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()
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main(sys.argv[1] if len(sys.argv) > 1 else "")
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@@ -24,6 +24,45 @@ QWEN_API_KEY='Your_Key'
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# DeepSeek API (https://platform.deepseek.com/api_keys)
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DEEPSEEK_API_KEY='Your_Key'
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# Multi-platform LLM/VLLM API, default values for user assistant planning web image roles
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# LLM_ROLE_API_BASE_URL=''
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# LLM_ROLE_API_KEY='Your_Key'
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# LLM_ROLE_API_MODEL_TYPE=''
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# LLM_ROLE_API_MODEL_TEMPERATURE='0.0'
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# LLM_ROLE_API_MODEL_MAX_TOKENS='0'
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# VLLM_ROLE_API_BASE_URL=''
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# VLLM_ROLE_API_KEY='Your_Key'
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# VLLM_ROLE_API_MODEL_TYPE=''
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# VLLM_ROLE_API_MODEL_TEMPERATURE='0.0'
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# VLLM_ROLE_API_MODEL_MAX_TOKENS='0'
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# Multi-platform LLM/VLLM API for user assistant planning web image roles
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# USER_ROLE_API_BASE_URL=''
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# USER_ROLE_API_KEY='Your_Key'
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# USER_ROLE_API_MODEL_TYPE=''
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# USER_ROLE_API_MODEL_TEMPERATURE='0.8'
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# USER_ROLE_API_MODEL_MAX_TOKENS='4096'
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# ASSISTANT_ROLE_API_BASE_URL=''
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# ASSISTANT_ROLE_API_KEY='Your_Key'
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# ASSISTANT_ROLE_API_MODEL_TYPE=''
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# ASSISTANT_ROLE_API_MODEL_TEMPERATURE='0.2'
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# ASSISTANT_ROLE_API_MODEL_MAX_TOKENS='4096'
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# PLANNING_ROLE_API_BASE_URL=''
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# PLANNING_ROLE_API_KEY='Your_Key'
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# PLANNING_ROLE_API_MODEL_TYPE=''
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# PLANNING_ROLE_API_MODEL_TEMPERATURE='0.4'
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# PLANNING_ROLE_API_MODEL_MAX_TOKENS='8192'
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# WEB_ROLE_API_BASE_URL=''
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# WEB_ROLE_API_KEY='Your_Key'
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# WEB_ROLE_API_MODEL_TYPE=''
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# WEB_ROLE_API_MODEL_TEMPERATURE='0.0'
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# WEB_ROLE_API_MODEL_MAX_TOKENS='0'
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# IMAGE_ROLE_API_BASE_URL=''
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# IMAGE_ROLE_API_KEY='Your_Key'
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# IMAGE_ROLE_API_MODEL_TYPE=''
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# IMAGE_ROLE_API_MODEL_TEMPERATURE='0.0'
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# IMAGE_ROLE_API_MODEL_MAX_TOKENS='0'
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#===========================================
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# Tools & Services API
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#===========================================
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@@ -245,7 +245,7 @@ MODULE_DESCRIPTIONS = {
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"run": "Default mode: Using OpenAI model's default agent collaboration mode, suitable for most tasks.",
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"run_mini": "Using OpenAI model with minimal configuration to process tasks",
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"run_deepseek_zh": "Using deepseek model to process Chinese tasks",
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"run_openai_compatiable_model": "Using openai compatible model to process tasks",
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"run_openai_compatiable_model": "Using multiple openai compatible model to process tasks",
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"run_ollama": "Using local ollama model to process tasks",
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"run_qwen_mini_zh": "Using qwen model with minimal configuration to process tasks",
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"run_qwen_zh": "Using qwen model to process tasks",
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@@ -245,7 +245,7 @@ MODULE_DESCRIPTIONS = {
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"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。",
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"run_mini": "使用使用OpenAI模型最小化配置处理任务",
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"run_deepseek_zh": "使用deepseek模型处理中文任务",
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"run_openai_compatiable_model": "使用openai兼容模型处理任务",
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"run_openai_compatiable_model": "使用多个openai兼容模型处理任务",
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"run_ollama": "使用本地ollama模型处理任务",
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"run_qwen_mini_zh": "使用qwen模型最小化配置处理任务",
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"run_qwen_zh": "使用qwen模型处理任务",
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