chore: add multi-turn example code (#3335)

Author: Wendong-Fan

examples/workforce/multi_turn_conversation.py

@@ -0,0 +1,131 @@
+# ========= Copyright 2023-2024 @ CAMEL-AI.org. All Rights Reserved. =========
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+#     http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+# ========= Copyright 2023-2024 @ CAMEL-AI.org. All Rights Reserved. =========
+
+"""
+Multi-turn Workforce Conversation Example
+
+This example demonstrates a multi-turn conversation where:
+- User provides input via terminal
+- Previous task and summary are passed as context to each new turn
+- Two agents (Researcher and Writer) collaborate on tasks
+
+Example conversation to try:
+    Turn 1: "What is machine learning?"
+    Turn 2: "Can you give me an example application?"
+    Turn 3: "How does that relate to what you explained earlier?"
+
+Or try:
+    Turn 1: "Tell me about Paris"
+    Turn 2: "What are the top 3 attractions there?"
+    Turn 3: "Which one would you recommend based on our discussion?"
+"""
+
+from camel.agents.chat_agent import ChatAgent
+from camel.messages.base import BaseMessage
+from camel.models import ModelFactory
+from camel.societies.workforce import Workforce
+from camel.tasks.task import Task
+from camel.types import ModelPlatformType, ModelType
+
+# Set up agents
+research_agent = ChatAgent(
+    system_message=BaseMessage.make_assistant_message(
+        role_name="Researcher",
+        content="You are a research specialist who gathers and "
+        "analyzes information. You focus on finding facts and "
+        "providing detailed context.",
+    ),
+    model=ModelFactory.create(
+        model_platform=ModelPlatformType.DEFAULT,
+        model_type=ModelType.DEFAULT,
+    ),
+)
+
+writer_agent = ChatAgent(
+    system_message=BaseMessage.make_assistant_message(
+        role_name="Writer",
+        content="You are a professional writer who creates clear, "
+        "concise responses. You synthesize information into "
+        "well-structured answers.",
+    ),
+    model=ModelFactory.create(
+        model_platform=ModelPlatformType.DEFAULT,
+        model_type=ModelType.DEFAULT,
+    ),
+)
+
+# Create workforce with 2 agents
+workforce = Workforce('Multi-turn Assistant Team')
+workforce.add_single_agent_worker(
+    "A researcher who gathers and analyzes information", worker=research_agent
+).add_single_agent_worker(
+    "A writer who synthesizes information into clear responses",
+    worker=writer_agent,
+)
+
+# Initialize conversation history for all rounds
+conversation_history = []
+turn_number = 0
+
+print("=== Multi-turn Workforce Conversation ===")
+print("Type your task/question (or 'quit' to exit)")
+print("=" * 50)
+
+# Multi-turn conversation loop
+while True:
+    turn_number += 1
+    print(f"\n--- Turn {turn_number} ---")
+
+    # Get user input from terminal
+    user_input = input("You: ").strip()
+
+    if user_input.lower() in ['quit', 'exit', 'q']:
+        print("Exiting conversation. Goodbye!")
+        break
+
+    if not user_input:
+        print("Please enter a valid task or question.")
+        continue
+
+    # Build task content with context from previous rounds
+    history_context = ""
+    for i in range(len(conversation_history)):
+        item = conversation_history[i]
+        history_context += f"Round {i+1}:\n"
+        history_context += f"Task: {item['task']}\n"
+        history_context += f"Result: {item['result']}\n\n"
+
+    task_content = (
+        f"{history_context}{'='*50}\nNew task: {user_input}"
+        if history_context
+        else user_input
+    )
+
+    # Create and process task
+    task = Task(content=task_content, id=str(turn_number))
+    task_result = workforce.process_task(task)
+
+    # Display response
+    print(f"\nAssistant: {task_result.result}")
+
+    # Store all information from this round for future context
+    round_info = {
+        'task': user_input,
+        'result': task_result.result,
+    }
+
+    conversation_history.append(round_info)
+
+print("\n--- Conversation Complete ---")
+print(f"Total turns: {turn_number}")