Revert "feat: enhance OpenAI-compatible model support with role-specific configurations (#356)"

This reverts commit bd8f220416c1bbaa06ed81909dfcef083ed0be45, reversing
changes made to 106a169ee93a97e54cee672d6ee4ade5df409cdf.

.gitignore

@@ -27,7 +27,6 @@ venv/
 env/
 ENV/
 .env
-.venv
 
 # IDE
 .idea/
@@ -59,4 +58,3 @@ coverage.xml
 owl/camel/types/__pycache__/
 owl/camel/__pycache__/
 owl/camel/utils/__pycache_/
-tmp/

README.md

@@ -364,10 +364,8 @@ python examples/run_qwen_zh.py
 # Run with Deepseek model
 python examples/run_deepseek_zh.py
 
-# Run with other OpenAI-compatible models, supporting different models for different roles
+# Run with other OpenAI-compatible models
 python examples/run_openai_compatiable_model.py
-# Example with question
-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."
 
 # Run with Azure OpenAI
 python examples/run_azure_openai.py

README_zh.md

@@ -363,10 +363,8 @@ python examples/run_qwen_zh.py
 # 使用 Deepseek 模型运行
 python examples/run_deepseek_zh.py
 
-# 使用其他 OpenAI 兼容模型运行，支持不同的 role 使用不同的模型
+# 使用其他 OpenAI 兼容模型运行
 python examples/run_openai_compatiable_model.py
-# 带问题的示例
-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。"
 
 # 使用 Azure OpenAI模型运行
 python examples/run_azure_openai.py

examples/run_openai_compatiable_model.py

@@ -53,56 +53,38 @@ def construct_society(question: str) -> RolePlaying:
     models = {
         "user": ModelFactory.create(
             model_platform=ModelPlatformType.OPENAI_COMPATIBLE_MODEL,
-
-            model_type=os.getenv("USER_ROLE_API_MODEL_TYPE", os.getenv("LLM_ROLE_API_MODEL_TYPE", "qwen-max")),
-            api_key=os.getenv("USER_ROLE_API_KEY", os.getenv("LLM_ROLE_API_KEY", os.getenv("QWEN_API_KEY", "Your_Key"))),
-            url=os.getenv("USER_ROLE_API_BASE_URL", os.getenv("LLM_ROLE_API_BASE_URL", "https://dashscope.aliyuncs.com/compatible-mode/v1")),
-            model_config_dict={
-                "temperature": float(os.getenv("USER_ROLE_API_MODEL_TEMPERATURE", os.getenv("LLM_ROLE_API_MODEL_TEMPERATURE", "0.4"))), 
-                "max_tokens": int(os.getenv("USER_ROLE_API_MODEL_MAX_TOKENS", os.getenv("LLM_ROLE_API_MODEL_MAX_TOKENS", "4096")))
-            },
+            model_type="qwen-max",
+            api_key=os.getenv("QWEN_API_KEY"),
+            url="https://dashscope.aliyuncs.com/compatible-mode/v1",
+            model_config_dict={"temperature": 0.4, "max_tokens": 128000},
         ),
         "assistant": ModelFactory.create(
             model_platform=ModelPlatformType.OPENAI_COMPATIBLE_MODEL,
-            model_type=os.getenv("ASSISTANT_ROLE_API_MODEL_TYPE", os.getenv("LLM_ROLE_API_MODEL_TYPE", "qwen-max")),
-            api_key=os.getenv("ASSISTANT_ROLE_API_KEY", os.getenv("LLM_ROLE_API_KEY", os.getenv("QWEN_API_KEY", "Your_Key"))),
-            url=os.getenv("ASSISTANT_ROLE_API_BASE_URL", os.getenv("LLM_ROLE_API_BASE_URL", "https://dashscope.aliyuncs.com/compatible-mode/v1")),
-            model_config_dict={
-                "temperature": float(os.getenv("ASSISTANT_ROLE_API_MODEL_TEMPERATURE", os.getenv("LLM_ROLE_API_MODEL_TEMPERATURE", "0.4"))), 
-                "max_tokens": int(os.getenv("ASSISTANT_ROLE_API_MODEL_MAX_TOKENS", os.getenv("LLM_ROLE_API_MODEL_MAX_TOKENS", "4096")))
-            },
-
+            model_type="qwen-max",
+            api_key=os.getenv("QWEN_API_KEY"),
+            url="https://dashscope.aliyuncs.com/compatible-mode/v1",
+            model_config_dict={"temperature": 0.4, "max_tokens": 128000},
         ),
         "browsing": ModelFactory.create(
             model_platform=ModelPlatformType.OPENAI_COMPATIBLE_MODEL,
-
-            model_type=os.getenv("WEB_ROLE_API_BASE_URL", os.getenv("VLLM_ROLE_API_MODEL_TYPE", "qwen-vl-max")),
-            api_key=os.getenv("WEB_ROLE_API_KEY", os.getenv("VLLM_ROLE_API_KEY", os.getenv("QWEN_API_KEY", "Your_Key"))),
-            url=os.getenv("USER_ROLE_API_BASE_URL", os.getenv("VLLM_ROLE_API_BASE_URL", "https://dashscope.aliyuncs.com/compatible-mode/v1")),
-            model_config_dict={
-                "temperature": float(os.getenv("WEB_ROLE_API_MODEL_TEMPERATURE", os.getenv("VLLM_ROLE_API_MODEL_TEMPERATURE", "0.4"))), 
-                "max_tokens": int(os.getenv("WEB_ROLE_API_MODEL_MAX_TOKENS", os.getenv("VLLM_ROLE_API_MODEL_MAX_TOKENS", "4096")))
-            },
+            model_type="qwen-vl-max",
+            api_key=os.getenv("QWEN_API_KEY"),
+            url="https://dashscope.aliyuncs.com/compatible-mode/v1",
+            model_config_dict={"temperature": 0.4, "max_tokens": 128000},
         ),
         "planning": ModelFactory.create(
             model_platform=ModelPlatformType.OPENAI_COMPATIBLE_MODEL,
-            model_type=os.getenv("PLANNING_ROLE_API_MODEL_TYPE", os.getenv("LLM_ROLE_API_MODEL_TYPE", "qwen-max")),
-            api_key=os.getenv("PLANNING_ROLE_API_KEY", os.getenv("LLM_ROLE_API_KEY", os.getenv("QWEN_API_KEY", "Your_Key"))),
-            url=os.getenv("PLANNING_ROLE_API_BASE_URL", os.getenv("LLM_ROLE_API_BASE_URL", "https://dashscope.aliyuncs.com/compatible-mode/v1")),
-            model_config_dict={
-                "temperature": float(os.getenv("PLANNING_ROLE_API_MODEL_TEMPERATURE", os.getenv("LLM_ROLE_API_MODEL_TEMPERATURE", "0.4"))), 
-                "max_tokens": int(os.getenv("PLANNING_ROLE_API_MODEL_MAX_TOKENS", os.getenv("LLM_ROLE_API_MODEL_MAX_TOKENS", "4096")))
-            },
+            model_type="qwen-max",
+            api_key=os.getenv("QWEN_API_KEY"),
+            url="https://dashscope.aliyuncs.com/compatible-mode/v1",
+            model_config_dict={"temperature": 0.4, "max_tokens": 128000},
         ),
         "image": ModelFactory.create(
             model_platform=ModelPlatformType.OPENAI_COMPATIBLE_MODEL,
-            model_type=os.getenv("IMAGE_ROLE_API_MODEL_TYPE", os.getenv("VLLM_ROLE_API_MODEL_TYPE", "qwen-vl-max")),
-            api_key=os.getenv("IMAGE_ROLE_API_KEY", os.getenv("VLLM_ROLE_API_KEY", os.getenv("QWEN_API_KEY", "Your_Key"))),
-            url=os.getenv("IMAGE_ROLE_API_BASE_URL", os.getenv("VLLM_ROLE_API_BASE_URL", "https://dashscope.aliyuncs.com/compatible-mode/v1")),
-            model_config_dict={
-                "temperature": float(os.getenv("IMAGE_ROLE_API_MODEL_TEMPERATURE", os.getenv("VLLM_ROLE_API_MODEL_TEMPERATURE", "0.4"))), 
-                "max_tokens": int(os.getenv("IMAGE_ROLE_API_MODEL_MAX_TOKENS", os.getenv("VLLM_ROLE_API_MODEL_MAX_TOKENS", "4096")))
-            },
+            model_type="qwen-vl-max",
+            api_key=os.getenv("QWEN_API_KEY"),
+            url="https://dashscope.aliyuncs.com/compatible-mode/v1",
+            model_config_dict={"temperature": 0.4, "max_tokens": 128000},
         ),
     }
 
@@ -144,16 +126,13 @@ def construct_society(question: str) -> RolePlaying:
     return society
 
 
+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."
 
-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."):
-    r"""Main function to run the OWL system with an example question.
-    Args:
-        question (str): The task or question to be addressed by the society.
-            If not provided, a default question will be used.
-            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."
-    Returns:
-        None
-    """
+    # 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)
@@ -162,9 +141,7 @@ def main(question: str = "Navigate to Amazon.com and identify one product that i
 
     # Output the result
     print(f"\033[94mAnswer: {answer}\033[0m")
-    # Output the token count
-    print(f"\033[94mToken count: {token_count}\033[0m")
 
 
 if __name__ == "__main__":
-    main(sys.argv[1] if len(sys.argv) > 1 else "")
+    main()

owl/.env_template

@@ -24,45 +24,6 @@ QWEN_API_KEY='Your_Key'
 # DeepSeek API (https://platform.deepseek.com/api_keys)
 DEEPSEEK_API_KEY='Your_Key'
 
-# Multi-platform LLM/VLLM API, default values for user assistant planning web image roles
-#  LLM_ROLE_API_BASE_URL=''
-#  LLM_ROLE_API_KEY='Your_Key'
-#  LLM_ROLE_API_MODEL_TYPE=''
-#  LLM_ROLE_API_MODEL_TEMPERATURE='0.0'
-#  LLM_ROLE_API_MODEL_MAX_TOKENS='0'
-#  VLLM_ROLE_API_BASE_URL=''
-#  VLLM_ROLE_API_KEY='Your_Key'
-#  VLLM_ROLE_API_MODEL_TYPE=''
-#  VLLM_ROLE_API_MODEL_TEMPERATURE='0.0'
-#  VLLM_ROLE_API_MODEL_MAX_TOKENS='0'
-
-# Multi-platform LLM/VLLM API for user assistant planning web image roles
-# USER_ROLE_API_BASE_URL=''
-# USER_ROLE_API_KEY='Your_Key'
-# USER_ROLE_API_MODEL_TYPE=''
-# USER_ROLE_API_MODEL_TEMPERATURE='0.8'
-# USER_ROLE_API_MODEL_MAX_TOKENS='4096'
-# ASSISTANT_ROLE_API_BASE_URL=''
-# ASSISTANT_ROLE_API_KEY='Your_Key'
-# ASSISTANT_ROLE_API_MODEL_TYPE=''
-# ASSISTANT_ROLE_API_MODEL_TEMPERATURE='0.2'
-# ASSISTANT_ROLE_API_MODEL_MAX_TOKENS='4096'
-# PLANNING_ROLE_API_BASE_URL=''
-# PLANNING_ROLE_API_KEY='Your_Key'
-# PLANNING_ROLE_API_MODEL_TYPE=''
-# PLANNING_ROLE_API_MODEL_TEMPERATURE='0.4'
-# PLANNING_ROLE_API_MODEL_MAX_TOKENS='8192'
-# WEB_ROLE_API_BASE_URL=''
-# WEB_ROLE_API_KEY='Your_Key'
-# WEB_ROLE_API_MODEL_TYPE=''
-# WEB_ROLE_API_MODEL_TEMPERATURE='0.0'
-# WEB_ROLE_API_MODEL_MAX_TOKENS='0'
-# IMAGE_ROLE_API_BASE_URL=''
-# IMAGE_ROLE_API_KEY='Your_Key'
-# IMAGE_ROLE_API_MODEL_TYPE=''
-# IMAGE_ROLE_API_MODEL_TEMPERATURE='0.0'
-# IMAGE_ROLE_API_MODEL_MAX_TOKENS='0'
-
 #===========================================
 # Tools & Services API
 #===========================================

owl/webapp.py

@@ -245,7 +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_deepseek_zh": "Using deepseek model to process Chinese tasks",
-    "run_openai_compatiable_model": "Using multiple openai compatible model to process tasks",
+    "run_openai_compatiable_model": "Using openai compatible model to process tasks",
     "run_ollama": "Using local ollama model to process tasks",
     "run_qwen_mini_zh": "Using qwen model with minimal configuration to process tasks",
     "run_qwen_zh": "Using qwen model to process tasks",

owl/webapp_zh.py

@@ -245,7 +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_deepseek_zh": "使用deepseek模型处理中文任务",
-    "run_openai_compatiable_model": "使用多个openai兼容模型处理任务",
+    "run_openai_compatiable_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",
     "run_qwen_mini_zh": "使用qwen模型最小化配置处理任务",
     "run_qwen_zh": "使用qwen模型处理任务",