Merge branch 'master' into workforce_pipeline

Author: hesamsheikh

.github/ISSUE_TEMPLATE/bug_report.yml

@@ -26,7 +26,7 @@ body:
     attributes:
       label: What version of camel are you using?
       description: Run command `python3 -c 'print(__import__("camel").__version__)'` in your shell and paste the output here.
-      placeholder: E.g., 0.2.78
+      placeholder: E.g., 0.2.79a0
     validations:
       required: true
 

.pre-commit-config.yaml

@@ -4,7 +4,7 @@ repos:
     hooks:
       - id: ruff
         args: [--fix, --exit-non-zero-on-fix, --show-fixes]
-        exclude: ^(docs/cookbooks/|examples/usecases/)  # Ignore files under docs/cookbooks and examples/usecases
+        exclude: ^(docs/cookbooks/|examples/usecases/|examples/custom_client_usage\.py)  # Ignore files under docs/cookbooks and examples/usecases
       - id: ruff-format
         exclude: ^(docs/cookbooks/|examples/usecases/)  # Ignore files under docs/cookbooks and examples/usecases
 

camel/__init__.py

@@ -14,7 +14,7 @@
 
 from camel.logger import disable_logging, enable_logging, set_log_level
 
-__version__ = '0.2.78'
+__version__ = '0.2.79a0'
 
 __all__ = [
     '__version__',

camel/agents/chat_agent.py

@@ -491,9 +491,7 @@ def __init__(
 
         # Set up system message and initialize messages
         self._original_system_message = (
-            BaseMessage.make_assistant_message(
-                role_name="Assistant", content=system_message
-            )
+            BaseMessage.make_system_message(system_message)
             if isinstance(system_message, str)
             else system_message
         )
@@ -1629,10 +1627,7 @@ def _generate_system_message_for_output_language(
             content = self._original_system_message.content + language_prompt
             return self._original_system_message.create_new_instance(content)
         else:
-            return BaseMessage.make_assistant_message(
-                role_name="Assistant",
-                content=language_prompt,
-            )
+            return BaseMessage.make_system_message(language_prompt)
 
     def init_messages(self) -> None:
         r"""Initializes the stored messages list with the current system
@@ -1650,6 +1645,62 @@ def init_messages(self) -> None:
                 )
             )
 
+    def update_system_message(
+        self,
+        system_message: Union[BaseMessage, str],
+        reset_memory: bool = True,
+    ) -> None:
+        r"""Update the system message.
+        It will reset conversation with new system message.
+
+        Args:
+            system_message (Union[BaseMessage, str]): The new system message.
+                Can be either a BaseMessage object or a string.
+                If a string is provided, it will be converted
+                into a BaseMessage object.
+            reset_memory (bool):
+                Whether to reinitialize conversation messages after updating
+                the system message. Defaults to True.
+        """
+        if system_message is None:
+            raise ValueError("system_message is required and cannot be None. ")
+        self._original_system_message = (
+            BaseMessage.make_system_message(system_message)
+            if isinstance(system_message, str)
+            else system_message
+        )
+        self._system_message = (
+            self._generate_system_message_for_output_language()
+        )
+        if reset_memory:
+            self.init_messages()
+
+    def append_to_system_message(
+        self, content: str, reset_memory: bool = True
+    ) -> None:
+        """Append additional context to existing system message.
+
+        Args:
+            content (str): The additional system message.
+            reset_memory (bool):
+                Whether to reinitialize conversation messages after appending
+                additional context. Defaults to True.
+        """
+        original_content = (
+            self._original_system_message.content
+            if self._original_system_message
+            else ""
+        )
+        new_system_message = original_content + '\n' + content
+        self._original_system_message = BaseMessage.make_system_message(
+            new_system_message
+        )
+        self._system_message = (
+            self._generate_system_message_for_output_language()
+        )
+        if reset_memory:
+            self.init_messages()
+
     def reset_to_original_system_message(self) -> None:
         r"""Reset system message to original, removing any appended context.
 
@@ -4468,23 +4519,29 @@ def _clone_tools(
                         # Toolkit doesn't support cloning, use original
                         cloned_toolkits[toolkit_id] = toolkit_instance
 
-                if getattr(
-                    tool.func, "__message_integration_enhanced__", False
-                ):
-                    cloned_tools.append(
-                        FunctionTool(
-                            func=tool.func,
-                            openai_tool_schema=tool.get_openai_tool_schema(),
-                        )
-                    )
-                    continue
-
                 # Get the method from the cloned (or original) toolkit
                 toolkit = cloned_toolkits[toolkit_id]
                 method_name = tool.func.__name__
 
+                # Check if toolkit was actually cloned or just reused
+                toolkit_was_cloned = toolkit is not toolkit_instance
+
                 if hasattr(toolkit, method_name):
                     new_method = getattr(toolkit, method_name)
+
+                    # If toolkit wasn't cloned (stateless), preserve the
+                    # original function to maintain any enhancements/wrappers
+                    if not toolkit_was_cloned:
+                        # Toolkit is stateless, safe to reuse original function
+                        cloned_tools.append(
+                            FunctionTool(
+                                func=tool.func,
+                                openai_tool_schema=tool.get_openai_tool_schema(),
+                            )
+                        )
+                        continue
+
+                    # Toolkit was cloned, use the new method
                     # Wrap cloned method into a new FunctionTool,
                     # preserving schema
                     try:

camel/messages/base.py

@@ -178,6 +178,32 @@ def make_assistant_message(
             OpenAIVisionDetailType(video_detail).value,
         )
 
+    @classmethod
+    def make_system_message(
+        cls,
+        content: str,
+        role_name: str = "System",
+        meta_dict: Optional[Dict[str, str]] = None,
+    ) -> "BaseMessage":
+        r"""Create a new system message.
+
+        Args:
+            content (str): The content of the system message.
+            role_name (str): The name of the system role.
+                (default: :obj:`"System"`)
+            meta_dict (Optional[Dict[str, str]]): Additional metadata
+                dictionary for the message.
+
+        Returns:
+            BaseMessage: The new system message.
+        """
+        return cls(
+            role_name,
+            RoleType.SYSTEM,
+            meta_dict,
+            content,
+        )
+
     def create_new_instance(self, content: str) -> "BaseMessage":
         r"""Create a new instance of the :obj:`BaseMessage` with updated
         content.

camel/models/aws_bedrock_model.py

@@ -13,17 +13,11 @@
 # ========= Copyright 2023-2024 @ CAMEL-AI.org. All Rights Reserved. =========
 
 import os
-from typing import Any, Dict, List, Optional, Type, Union
-
-from openai import AsyncStream
-from pydantic import BaseModel
+from typing import Any, Dict, Optional, Union
 
 from camel.configs import BedrockConfig
-from camel.messages import OpenAIMessage
 from camel.models.openai_compatible_model import OpenAICompatibleModel
 from camel.types import (
-    ChatCompletion,
-    ChatCompletionChunk,
     ModelType,
 )
 from camel.utils import BaseTokenCounter, api_keys_required
@@ -93,13 +87,3 @@ def __init__(
             max_retries=max_retries,
             **kwargs,
         )
-
-    async def _arun(
-        self,
-        messages: List[OpenAIMessage],
-        response_format: Optional[Type[BaseModel]] = None,
-        tools: Optional[List[Dict[str, Any]]] = None,
-    ) -> Union[ChatCompletion, AsyncStream[ChatCompletionChunk]]:
-        raise NotImplementedError(
-            "AWS Bedrock does not support async inference."
-        )

camel/models/azure_openai_model.py

@@ -88,8 +88,19 @@ class AzureOpenAIModel(BaseModelBackend):
             (default: :obj:`None`)
         max_retries (int, optional): Maximum number of retries for API calls.
             (default: :obj:`3`)
+        client (Optional[Any], optional): A custom synchronous AzureOpenAI
+            client instance. If provided, this client will be used instead of
+            creating a new one. Useful for RL frameworks like AReaL or rLLM
+            that provide Azure OpenAI-compatible clients. The client should
+            implement the AzureOpenAI client interface with
+            `.chat.completions.create()` and `.beta.chat.completions.parse()`
+            methods. (default: :obj:`None`)
+        async_client (Optional[Any], optional): A custom asynchronous
+            AzureOpenAI client instance. If provided, this client will be
+            used instead of creating a new one. The client should implement
+            the AsyncAzureOpenAI client interface. (default: :obj:`None`)
         **kwargs (Any): Additional arguments to pass to the client
-            initialization.
+            initialization. Ignored if custom clients are provided.
 
     References:
         https://learn.microsoft.com/en-us/azure/ai-services/openai/
@@ -108,6 +119,8 @@ def __init__(
         azure_ad_token_provider: Optional["AzureADTokenProvider"] = None,
         azure_ad_token: Optional[str] = None,
         max_retries: int = 3,
+        client: Optional[Any] = None,
+        async_client: Optional[Any] = None,
         **kwargs: Any,
     ) -> None:
         if model_config_dict is None:
@@ -138,56 +151,72 @@ def __init__(
                 "or `AZURE_DEPLOYMENT_NAME` environment variable."
             )
 
-        if is_langfuse_available():
-            from langfuse.openai import AsyncAzureOpenAI as LangfuseAsyncOpenAI
-            from langfuse.openai import AzureOpenAI as LangfuseOpenAI
-
-            self._client = LangfuseOpenAI(
-                azure_endpoint=str(self._url),
-                azure_deployment=self._azure_deployment_name,
-                api_version=self.api_version,
-                api_key=self._api_key,
-                azure_ad_token=self._azure_ad_token,
-                azure_ad_token_provider=self.azure_ad_token_provider,
-                timeout=self._timeout,
-                max_retries=max_retries,
-                **kwargs,
-            )
-            self._async_client = LangfuseAsyncOpenAI(
-                azure_endpoint=str(self._url),
-                azure_deployment=self._azure_deployment_name,
-                api_version=self.api_version,
-                api_key=self._api_key,
-                azure_ad_token=self._azure_ad_token,
-                azure_ad_token_provider=self.azure_ad_token_provider,
-                timeout=self._timeout,
-                max_retries=max_retries,
-                **kwargs,
-            )
+        # Use custom clients if provided, otherwise create new ones
+        if client is not None:
+            # Use the provided custom sync client
+            self._client = client
         else:
-            self._client = AzureOpenAI(
-                azure_endpoint=str(self._url),
-                azure_deployment=self._azure_deployment_name,
-                api_version=self.api_version,
-                api_key=self._api_key,
-                azure_ad_token=self._azure_ad_token,
-                azure_ad_token_provider=self.azure_ad_token_provider,
-                timeout=self._timeout,
-                max_retries=max_retries,
-                **kwargs,
-            )
+            # Create default sync client
+            if is_langfuse_available():
+                from langfuse.openai import AzureOpenAI as LangfuseOpenAI
+
+                self._client = LangfuseOpenAI(
+                    azure_endpoint=str(self._url),
+                    azure_deployment=self._azure_deployment_name,
+                    api_version=self.api_version,
+                    api_key=self._api_key,
+                    azure_ad_token=self._azure_ad_token,
+                    azure_ad_token_provider=self.azure_ad_token_provider,
+                    timeout=self._timeout,
+                    max_retries=max_retries,
+                    **kwargs,
+                )
+            else:
+                self._client = AzureOpenAI(
+                    azure_endpoint=str(self._url),
+                    azure_deployment=self._azure_deployment_name,
+                    api_version=self.api_version,
+                    api_key=self._api_key,
+                    azure_ad_token=self._azure_ad_token,
+                    azure_ad_token_provider=self.azure_ad_token_provider,
+                    timeout=self._timeout,
+                    max_retries=max_retries,
+                    **kwargs,
+                )
 
-            self._async_client = AsyncAzureOpenAI(
-                azure_endpoint=str(self._url),
-                azure_deployment=self._azure_deployment_name,
-                api_version=self.api_version,
-                api_key=self._api_key,
-                azure_ad_token=self._azure_ad_token,
-                azure_ad_token_provider=self.azure_ad_token_provider,
-                timeout=self._timeout,
-                max_retries=max_retries,
-                **kwargs,
-            )
+        if async_client is not None:
+            # Use the provided custom async client
+            self._async_client = async_client
+        else:
+            # Create default async client
+            if is_langfuse_available():
+                from langfuse.openai import (
+                    AsyncAzureOpenAI as LangfuseAsyncOpenAI,
+                )
+
+                self._async_client = LangfuseAsyncOpenAI(
+                    azure_endpoint=str(self._url),
+                    azure_deployment=self._azure_deployment_name,
+                    api_version=self.api_version,
+                    api_key=self._api_key,
+                    azure_ad_token=self._azure_ad_token,
+                    azure_ad_token_provider=self.azure_ad_token_provider,
+                    timeout=self._timeout,
+                    max_retries=max_retries,
+                    **kwargs,
+                )
+            else:
+                self._async_client = AsyncAzureOpenAI(
+                    azure_endpoint=str(self._url),
+                    azure_deployment=self._azure_deployment_name,
+                    api_version=self.api_version,
+                    api_key=self._api_key,
+                    azure_ad_token=self._azure_ad_token,
+                    azure_ad_token_provider=self.azure_ad_token_provider,
+                    timeout=self._timeout,
+                    max_retries=max_retries,
+                    **kwargs,
+                )
 
     @property
     def token_counter(self) -> BaseTokenCounter: