Merge branch 'dynamic_think' into 'mgx_ops'

Add data analyst, explore agent controlling its own plan by thinking

See merge request pub/MetaGPT!83

tests/metagpt/environment/mgx_env/run_mgx_env.py

@@ -0,0 +1,78 @@
+import asyncio
+import threading
+
+from metagpt.environment.mgx.mgx_env import MGXEnv
+from metagpt.roles import (
+    Architect,
+    Engineer,
+    ProductManager,
+    ProjectManager,
+    QaEngineer,
+)
+from metagpt.roles.di.data_analyst import DataAnalyst
+from metagpt.roles.di.team_leader import TeamLeader
+from metagpt.schema import Message
+
+
+async def main(requirement, enable_human_input=False):
+    env = MGXEnv()
+    env.add_roles(
+        [
+            TeamLeader(),
+            ProductManager(),
+            Architect(),
+            ProjectManager(),
+            Engineer(n_borg=5, use_code_review=False),
+            QaEngineer(),
+            DataAnalyst(tools=["<all>"]),
+        ]
+    )
+
+    if enable_human_input:
+        # simulate human sending messages in chatbox
+        send_human_input(env)
+
+    env.publish_message(Message(content=requirement))
+
+    while not env.is_idle:
+        await env.run()
+
+
+def send_human_input(env):
+    """
+    Simulate sending message in chatbox
+    Note in local environment, the message is consumed only after current round of env.run is finished
+    """
+
+    def send_messages():
+        while True:
+            message = input("Enter a message any time: ")
+            env.publish_message(Message(content=message))
+
+    # Start a thread for sending messages
+    send_thread = threading.Thread(target=send_messages, args=())
+    send_thread.start()
+
+
+GAME_REQ = "create a 2048 game"
+SIMPLE_REQ = "print statistic summary of sklearn iris dataset"
+WINE_REQ = "Run data analysis on sklearn Wine recognition dataset, include a plot, and train a model to predict wine class (20% as validation), and show validation accuracy."
+PAPER_LIST_REQ = """
+Get data from `paperlist` table in https://papercopilot.com/statistics/iclr-statistics/iclr-2024-statistics/,
+and save it to a csv file. paper title must include `multiagent` or `large language model`. *notice: print key variables*
+"""
+ECOMMERCE_REQ = """
+Get products data from website https://scrapeme.live/shop/ and save it as a csv file.
+**Notice: Firstly parse the web page encoding and the text HTML structure;
+The first page product name, price, product URL, and image URL must be saved in the csv;**
+"""
+data_path = "data/titanic"
+train_path = f"{data_path}/split_train.csv"
+eval_path = f"{data_path}/split_eval.csv"
+TITANIC_REQ = f"This is a titanic passenger survival dataset, your goal is to predict passenger survival outcome. The target column is Survived. Perform data analysis, data preprocessing, feature engineering, and modeling to predict the target. Report accuracy on the eval data. Train data path: '{train_path}', eval data path: '{eval_path}'."
+
+
+if __name__ == "__main__":
+    # NOTE: Change the requirement to the one you want to test
+    #       Set enable_human_input to True if you want to simulate sending messages in chatbox
+    asyncio.run(main(requirement=SIMPLE_REQ, enable_human_input=False))