Replace create_completion_client_from_env with component config (#4928)

* Replace create_completion_client_from_env with component config

* json load

python/packages/agbench/benchmarks/AssistantBench/Templates/MagenticOne/scenario.py

@@ -1,4 +1,5 @@
 import asyncio
+import json
 import logging
 import os
 import re
@@ -10,7 +11,7 @@
 
 from autogen_core import AgentId, AgentProxy, TopicId
 from autogen_core import SingleThreadedAgentRuntime
-from autogen_core.logging import EVENT_LOGGER_NAME
+from autogen_core import EVENT_LOGGER_NAME
 from autogen_core.models import (
     ChatCompletionClient,
     UserMessage,
@@ -26,7 +27,7 @@
 from autogen_magentic_one.messages import BroadcastMessage
 from autogen_magentic_one.agents.multimodal_web_surfer import MultimodalWebSurfer
 from autogen_magentic_one.agents.file_surfer import FileSurfer
-from autogen_magentic_one.utils import LogHandler, message_content_to_str, create_completion_client_from_env
+from autogen_magentic_one.utils import LogHandler, message_content_to_str
 
 encoding = None
 def count_token(value: str) -> int:
@@ -123,10 +124,8 @@ async def main() -> None:
     runtime = SingleThreadedAgentRuntime()
 
     # Create the AzureOpenAI client from the environment file
-    client = create_completion_client_from_env()
-
-
-    mlm_client = create_completion_client_from_env()
+    client = ChatCompletionClient.load_component(json.loads(os.environ["CHAT_COMPLETION_CLIENT_CONFIG"]))
+    mlm_client = ChatCompletionClient.load_component(json.loads(os.environ["MLM_CHAT_COMPLETION_CLIENT_CONFIG"]))
 
 
     # Register agents.

python/packages/agbench/benchmarks/GAIA/Templates/MagenticOne/scenario.py

@@ -1,4 +1,5 @@
 import asyncio
+import json
 import logging
 import os
 import re
@@ -27,7 +28,7 @@
 from autogen_magentic_one.messages import BroadcastMessage
 from autogen_magentic_one.agents.multimodal_web_surfer import MultimodalWebSurfer
 from autogen_magentic_one.agents.file_surfer import FileSurfer
-from autogen_magentic_one.utils import LogHandler, message_content_to_str, create_completion_client_from_env
+from autogen_magentic_one.utils import LogHandler, message_content_to_str
 
 encoding = None
 def count_token(value: str) -> int:
@@ -124,11 +125,10 @@ async def main() -> None:
     runtime = SingleThreadedAgentRuntime()
 
     # Create the AzureOpenAI client, with AAD auth, from environment
-    client = create_completion_client_from_env()
+    client = ChatCompletionClient.load_component(json.loads(os.environ["CHAT_COMPLETION_CLIENT_CONFIG"]))
+    mlm_client = ChatCompletionClient.load_component(json.loads(os.environ["MLM_CHAT_COMPLETION_CLIENT_CONFIG"]))
 
 
-    mlm_client = create_completion_client_from_env()
-
     # Register agents.
     await runtime.register(
         "Assistant",

python/packages/agbench/benchmarks/HumanEval/Templates/MagenticOne/scenario.py

@@ -13,15 +13,15 @@
 from autogen_magentic_one.agents.coder import Coder, Executor
 from autogen_magentic_one.agents.orchestrator import RoundRobinOrchestrator
 from autogen_magentic_one.messages import BroadcastMessage, OrchestrationEvent
-from autogen_magentic_one.utils import create_completion_client_from_env
 
 
 async def main() -> None:
     # Create the runtime.
     runtime = SingleThreadedAgentRuntime()
 
     # Create the AzureOpenAI client
-    client = create_completion_client_from_env()
+    client = ChatCompletionClient.load_component(json.loads(os.environ["CHAT_COMPLETION_CLIENT_CONFIG"]))
+
 
     # Register agents.
     await runtime.register(

python/packages/agbench/benchmarks/WebArena/Templates/MagenticOne/scenario.py

@@ -24,7 +24,7 @@
 from autogen_magentic_one.messages import BroadcastMessage, OrchestrationEvent, RequestReplyMessage, ResetMessage, DeactivateMessage
 from autogen_magentic_one.agents.multimodal_web_surfer import MultimodalWebSurfer
 from autogen_magentic_one.agents.file_surfer import FileSurfer
-from autogen_magentic_one.utils import LogHandler, message_content_to_str, create_completion_client_from_env
+from autogen_magentic_one.utils import LogHandler, message_content_to_str
 
 
 import evaluation_harness
@@ -120,7 +120,8 @@ async def main() -> None:
     runtime = SingleThreadedAgentRuntime()
 
     # Create the AzureOpenAI client, with AAD auth
-    client = create_completion_client_from_env()
+    client = ChatCompletionClient.load_component(json.loads(os.environ["CHAT_COMPLETION_CLIENT_CONFIG"]))
+
     # Login assistant
     await runtime.register(
         "LoginAssistant",

python/packages/autogen-magentic-one/README.md

@@ -119,28 +119,39 @@ playwright install --with-deps chromium
 
 ## Environment Configuration for Chat Completion Client
 
-This guide outlines how to configure your environment to use the `create_completion_client_from_env` function, which reads environment variables to return an appropriate `ChatCompletionClient`.
+This guide outlines how to structure the config to load a ChatCompletionClient for Magentic-One.
+
+```python
+from autogen_core.models import ChatCompletionClient
+
+config = {}
+client = ChatCompletionClient.load_component(config)
+```
 
 Currently, Magentic-One only supports OpenAI's GPT-4o as the underlying LLM.
 
 ### Azure OpenAI service
 
-To configure for Azure OpenAI service, set the following environment variables:
-
-- `CHAT_COMPLETION_PROVIDER='azure'`
-- `CHAT_COMPLETION_KWARGS_JSON` with the following JSON structure:
+To configure for Azure OpenAI service, use the following config:
 
 ```json
 {
-  "api_version": "2024-02-15-preview",
-  "azure_endpoint": "REPLACE_WITH_YOUR_ENDPOINT",
-  "model_capabilities": {
-    "function_calling": true,
-    "json_output": true,
-    "vision": true
-  },
-  "azure_ad_token_provider": "DEFAULT",
-  "model": "gpt-4o-2024-05-13"
+    "provider": "AzureOpenAIChatCompletionClient",
+    "config": {
+        "model": "gpt-4o-2024-05-13",
+        "azure_endpoint": "https://{your-custom-endpoint}.openai.azure.com/",
+        "azure_deployment": "{your-azure-deployment}",
+        "api_version": "2024-06-01",
+        "azure_ad_token_provider": {
+            "provider": "autogen_ext.models.openai.AzureTokenProvider",
+            "config": {
+                "provider_kind": "DefaultAzureCredential",
+                "scopes": [
+                    "https://cognitiveservices.azure.com/.default"
+                ]
+            }
+        }
+    }
 }
 ```
 
@@ -150,19 +161,34 @@ Log in to Azure using `az login`, and then run the examples. The account used mu
 
 Note that even if you are the owner of the subscription, you still need to grant the necessary Azure Cognitive Services OpenAI permissions to call the API.
 
-### With OpenAI
+Or, to use an API key:
+```json
+{
+    "provider": "AzureOpenAIChatCompletionClient",
+    "config": {
+        "model": "gpt-4o-2024-05-13",
+        "azure_endpoint": "https://{your-custom-endpoint}.openai.azure.com/",
+        "azure_deployment": "{your-azure-deployment}",
+        "api_version": "2024-06-01",
+        "api_key": "REPLACE_WITH_YOUR_API_KEY"
+    }
+}
+```
 
-To configure for OpenAI, set the following environment variables:
+### With OpenAI
 
-- `CHAT_COMPLETION_PROVIDER='openai'`
-- `CHAT_COMPLETION_KWARGS_JSON` with the following JSON structure:
+To configure for OpenAI, use the following config:
 
 ```json
 {
-  "api_key": "REPLACE_WITH_YOUR_API",
-  "model": "gpt-4o-2024-05-13"
+  "provider": "OpenAIChatCompletionClient",
+  "config": {
+      "model": "gpt-4o-2024-05-13",
+      "api_key": "REPLACE_WITH_YOUR_API_KEY"
+  }
 }
 ```
+
 Feel free to replace the model with newer versions of gpt-4o if needed.
 
 ### Other Keys (Optional)

python/packages/autogen-magentic-one/examples/example.py

@@ -2,19 +2,21 @@
 
 import argparse
 import asyncio
+import json
 import logging
 import os
 
 from autogen_core import EVENT_LOGGER_NAME, AgentId, AgentProxy, SingleThreadedAgentRuntime
 from autogen_core.code_executor import CodeBlock
+from autogen_core.models._model_client import ChatCompletionClient
 from autogen_ext.code_executors.docker import DockerCommandLineCodeExecutor
 from autogen_magentic_one.agents.coder import Coder, Executor
 from autogen_magentic_one.agents.file_surfer import FileSurfer
 from autogen_magentic_one.agents.multimodal_web_surfer import MultimodalWebSurfer
 from autogen_magentic_one.agents.orchestrator import LedgerOrchestrator
 from autogen_magentic_one.agents.user_proxy import UserProxy
 from autogen_magentic_one.messages import RequestReplyMessage
-from autogen_magentic_one.utils import LogHandler, create_completion_client_from_env
+from autogen_magentic_one.utils import LogHandler
 
 # NOTE: Don't forget to 'playwright install --with-deps chromium'
 
@@ -32,7 +34,8 @@ async def main(logs_dir: str, hil_mode: bool, save_screenshots: bool) -> None:
     runtime = SingleThreadedAgentRuntime()
 
     # Create an appropriate client
-    client = create_completion_client_from_env(model="gpt-4o")
+    client = ChatCompletionClient.load_component(json.loads(os.environ["CHAT_COMPLETION_CLIENT_CONFIG"]))
+    assert client.model_info["family"] == "gpt-4o", "This example requires the gpt-4o model"
 
     async with DockerCommandLineCodeExecutor(work_dir=logs_dir) as code_executor:
         # Register agents.

python/packages/autogen-magentic-one/examples/example_coder.py

@@ -5,16 +5,19 @@
 """
 
 import asyncio
+import json
 import logging
+import os
 
 from autogen_core import EVENT_LOGGER_NAME, AgentId, AgentProxy, SingleThreadedAgentRuntime
 from autogen_core.code_executor import CodeBlock
+from autogen_core.models._model_client import ChatCompletionClient
 from autogen_ext.code_executors.docker import DockerCommandLineCodeExecutor
 from autogen_magentic_one.agents.coder import Coder, Executor
 from autogen_magentic_one.agents.orchestrator import RoundRobinOrchestrator
 from autogen_magentic_one.agents.user_proxy import UserProxy
 from autogen_magentic_one.messages import RequestReplyMessage
-from autogen_magentic_one.utils import LogHandler, create_completion_client_from_env
+from autogen_magentic_one.utils import LogHandler
 
 
 async def confirm_code(code: CodeBlock) -> bool:
@@ -29,9 +32,10 @@ async def main() -> None:
     # Create the runtime.
     runtime = SingleThreadedAgentRuntime()
 
+    model_client = ChatCompletionClient.load_component(json.loads(os.environ["CHAT_COMPLETION_CLIENT_CONFIG"]))
     async with DockerCommandLineCodeExecutor() as code_executor:
         # Register agents.
-        await Coder.register(runtime, "Coder", lambda: Coder(model_client=create_completion_client_from_env()))
+        await Coder.register(runtime, "Coder", lambda: Coder(model_client=model_client))
         coder = AgentProxy(AgentId("Coder", "default"), runtime)
 
         await Executor.register(

python/packages/autogen-magentic-one/examples/example_file_surfer.py

@@ -3,22 +3,25 @@
 to write input or perform actions, orchestrated by an round-robin orchestrator agent."""
 
 import asyncio
+import json
 import logging
+import os
 
 from autogen_core import EVENT_LOGGER_NAME, AgentId, AgentProxy, SingleThreadedAgentRuntime
+from autogen_core.models._model_client import ChatCompletionClient
 from autogen_magentic_one.agents.file_surfer import FileSurfer
 from autogen_magentic_one.agents.orchestrator import RoundRobinOrchestrator
 from autogen_magentic_one.agents.user_proxy import UserProxy
 from autogen_magentic_one.messages import RequestReplyMessage
-from autogen_magentic_one.utils import LogHandler, create_completion_client_from_env
+from autogen_magentic_one.utils import LogHandler
 
 
 async def main() -> None:
     # Create the runtime.
     runtime = SingleThreadedAgentRuntime()
 
     # Get an appropriate client
-    client = create_completion_client_from_env()
+    client = ChatCompletionClient.load_component(json.loads(os.environ["CHAT_COMPLETION_CLIENT_CONFIG"]))
 
     # Register agents.
     await FileSurfer.register(runtime, "file_surfer", lambda: FileSurfer(model_client=client))

python/packages/autogen-magentic-one/examples/example_userproxy.py

@@ -5,24 +5,27 @@
 The code snippets are not executed in this example."""
 
 import asyncio
+import json
 import logging
+import os
 
 from autogen_core import EVENT_LOGGER_NAME, AgentId, AgentProxy, SingleThreadedAgentRuntime
 
 # from typing import Any, Dict, List, Tuple, Union
+from autogen_core.models._model_client import ChatCompletionClient
 from autogen_magentic_one.agents.coder import Coder
 from autogen_magentic_one.agents.orchestrator import RoundRobinOrchestrator
 from autogen_magentic_one.agents.user_proxy import UserProxy
 from autogen_magentic_one.messages import RequestReplyMessage
-from autogen_magentic_one.utils import LogHandler, create_completion_client_from_env
+from autogen_magentic_one.utils import LogHandler
 
 
 async def main() -> None:
     # Create the runtime.
     runtime = SingleThreadedAgentRuntime()
 
     # Get an appropriate client
-    client = create_completion_client_from_env()
+    client = ChatCompletionClient.load_component(json.loads(os.environ["CHAT_COMPLETION_CLIENT_CONFIG"]))
 
     # Register agents.
     await Coder.register(runtime, "Coder", lambda: Coder(model_client=client))

python/packages/autogen-magentic-one/examples/example_websurfer.py

@@ -4,15 +4,17 @@
 orchestrated by an round-robin orchestrator agent."""
 
 import asyncio
+import json
 import logging
 import os
 
 from autogen_core import EVENT_LOGGER_NAME, AgentId, AgentProxy, SingleThreadedAgentRuntime
+from autogen_core.models import ChatCompletionClient
 from autogen_magentic_one.agents.multimodal_web_surfer import MultimodalWebSurfer
 from autogen_magentic_one.agents.orchestrator import RoundRobinOrchestrator
 from autogen_magentic_one.agents.user_proxy import UserProxy
 from autogen_magentic_one.messages import RequestReplyMessage
-from autogen_magentic_one.utils import LogHandler, create_completion_client_from_env
+from autogen_magentic_one.utils import LogHandler
 
 # NOTE: Don't forget to 'playwright install --with-deps chromium'
 
@@ -22,7 +24,8 @@ async def main() -> None:
     runtime = SingleThreadedAgentRuntime()
 
     # Create an appropriate client
-    client = create_completion_client_from_env(model="gpt-4o")
+    client = ChatCompletionClient.load_component(json.loads(os.environ["CHAT_COMPLETION_CLIENT_CONFIG"]))
+    assert client.model_info["family"] == "gpt-4o", "This example requires the gpt-4o model"
 
     # Register agents.
     await MultimodalWebSurfer.register(runtime, "WebSurfer", MultimodalWebSurfer)

python/packages/autogen-magentic-one/interface/magentic_one_helper.py

@@ -10,6 +10,7 @@
 from autogen_core import EVENT_LOGGER_NAME
 from autogen_core import AgentId, AgentProxy
 from autogen_core import DefaultTopicId
+from autogen_core.models._model_client import ChatCompletionClient
 from autogen_ext.code_executors.local import LocalCommandLineCodeExecutor
 from autogen_ext.code_executors.docker import DockerCommandLineCodeExecutor
 from autogen_core.code_executor import CodeBlock
@@ -19,7 +20,7 @@
 from autogen_magentic_one.agents.orchestrator import LedgerOrchestrator
 from autogen_magentic_one.agents.user_proxy import UserProxy
 from autogen_magentic_one.messages import BroadcastMessage
-from autogen_magentic_one.utils import LogHandler, create_completion_client_from_env
+from autogen_magentic_one.utils import LogHandler
 from autogen_core.models import UserMessage
 from threading import Lock
 
@@ -60,7 +61,9 @@ async def initialize(self) -> None:
         logger.handlers = [self.log_handler]
 
         # Create client
-        client = create_completion_client_from_env(model="gpt-4o")
+        client = ChatCompletionClient.load_component(json.loads(os.environ["CHAT_COMPLETION_CLIENT_CONFIG"]))
+        assert client.model_info["family"] == "gpt-4o", "This example requires the gpt-4o model"
+
 
         # Set up code executor
         self.code_executor = DockerCommandLineCodeExecutor(work_dir=self.logs_dir)