chore: import upstream snapshot with attribution

python/samples/demos/document_generator/main.py

@@ -0,0 +1,138 @@
+# Copyright (c) Microsoft. All rights reserved.
+
+import asyncio
+import logging
+import os
+
+from dotenv import load_dotenv
+from opentelemetry import trace
+from opentelemetry.sdk.resources import Resource
+from opentelemetry.semconv.resource import ResourceAttributes
+
+from samples.demos.document_generator.agents.code_validation_agent import CodeValidationAgent
+from samples.demos.document_generator.agents.content_creation_agent import ContentCreationAgent
+from samples.demos.document_generator.agents.user_agent import UserAgent
+from samples.demos.document_generator.custom_selection_strategy import CustomSelectionStrategy
+from samples.demos.document_generator.custom_termination_strategy import CustomTerminationStrategy
+from semantic_kernel.agents import AgentGroupChat
+from semantic_kernel.contents import AuthorRole, ChatMessageContent
+
+"""
+Note: This sample use the `AgentGroupChat` feature of Semantic Kernel, which is
+no longer maintained. For a replacement, consider using the `GroupChatOrchestration`.
+Read more about the `GroupChatOrchestration` here:
+https://learn.microsoft.com/semantic-kernel/frameworks/agent/agent-orchestration/group-chat?pivots=programming-language-python
+Here is a migration guide from `AgentGroupChat` to `GroupChatOrchestration`:
+https://learn.microsoft.com/semantic-kernel/support/migration/group-chat-orchestration-migration-guide?pivots=programming-language-python
+"""
+
+TASK = """
+Create a blog post to share technical details about the Semantic Kernel AI connectors.
+The content of the blog post should include the following:
+1. What are AI connectors in Semantic Kernel?
+2. How do people use AI connectors in Semantic Kernel?
+3. How do devs create custom AI connectors in Semantic Kernel?
+    - Include a walk through of creating a custom AI connector. 
+      The connector may not connect to a real service, but should demonstrate the process.
+    - Include a sample on how to use the connector.
+    - If a reader follows the walk through and the sample, they should be able to run the connector.
+
+
+Here is the file that contains the source code for the base class of the AI connectors:
+semantic_kernel/connectors/ai/chat_completion_client_base.py
+semantic_kernel/services/ai_service_client_base.py
+
+Here are some files containing the source code that may be useful:
+semantic_kernel/connectors/ai/ollama/services/ollama_chat_completion.py
+semantic_kernel/connectors/ai/open_ai/services/open_ai_chat_completion_base.py
+semantic_kernel/contents/chat_history.py
+
+If you want to reference the implementations of other AI connectors, you can find them under the following directory:
+semantic_kernel/connectors/ai
+"""
+
+load_dotenv()
+AZURE_APP_INSIGHTS_CONNECTION_STRING = os.getenv("AZURE_APP_INSIGHTS_CONNECTION_STRING")
+
+resource = Resource.create({ResourceAttributes.SERVICE_NAME: "Document Generator"})
+
+
+def set_up_tracing():
+    from azure.monitor.opentelemetry.exporter import AzureMonitorTraceExporter
+    from opentelemetry.sdk.trace import TracerProvider
+    from opentelemetry.sdk.trace.export import BatchSpanProcessor
+    from opentelemetry.trace import set_tracer_provider
+
+    # Initialize a trace provider for the application. This is a factory for creating tracers.
+    tracer_provider = TracerProvider(resource=resource)
+    tracer_provider.add_span_processor(
+        BatchSpanProcessor(AzureMonitorTraceExporter(connection_string=AZURE_APP_INSIGHTS_CONNECTION_STRING))
+    )
+    # Sets the global default tracer provider
+    set_tracer_provider(tracer_provider)
+
+
+def set_up_logging():
+    from azure.monitor.opentelemetry.exporter import AzureMonitorLogExporter
+    from opentelemetry._logs import set_logger_provider
+    from opentelemetry.sdk._logs import LoggerProvider, LoggingHandler
+    from opentelemetry.sdk._logs.export import BatchLogRecordProcessor
+
+    # Create and set a global logger provider for the application.
+    logger_provider = LoggerProvider(resource=resource)
+    logger_provider.add_log_record_processor(
+        BatchLogRecordProcessor(AzureMonitorLogExporter(connection_string=AZURE_APP_INSIGHTS_CONNECTION_STRING))
+    )
+    # Sets the global default logger provider
+    set_logger_provider(logger_provider)
+
+    # Create a logging handler to write logging records, in OTLP format, to the exporter.
+    handler = LoggingHandler()
+    # Attach the handler to the root logger. `getLogger()` with no arguments returns the root logger.
+    # Events from all child loggers will be processed by this handler.
+    logger = logging.getLogger()
+    logger.addHandler(handler)
+    logger.setLevel(logging.INFO)
+
+
+async def main():
+    if AZURE_APP_INSIGHTS_CONNECTION_STRING:
+        set_up_tracing()
+        set_up_logging()
+
+    tracer = trace.get_tracer(__name__)
+    with tracer.start_as_current_span("main"):
+        agents = [
+            ContentCreationAgent(),
+            UserAgent(),
+            CodeValidationAgent(),
+        ]
+
+        group_chat = AgentGroupChat(
+            agents=agents,
+            termination_strategy=CustomTerminationStrategy(agents=agents),
+            selection_strategy=CustomSelectionStrategy(),
+        )
+        await group_chat.add_chat_message(
+            ChatMessageContent(
+                role=AuthorRole.USER,
+                content=TASK.strip(),
+            )
+        )
+
+        async for response in group_chat.invoke():
+            print(f"==== {response.name} just responded ====")
+            # print(response.content)
+
+        content_history: list[ChatMessageContent] = []
+        async for message in group_chat.get_chat_messages(agent=agents[0]):
+            if message.name == agents[0].name:
+                # The chat history contains responses from other agents.
+                content_history.append(message)
+        # The chat history is in descending order.
+        print("Final content:")
+        print(content_history[0].content)
+
+
+if __name__ == "__main__":
+    asyncio.run(main())