add misinfo vllm example

Author: zhangzaibin

examples/experiment_misinformation/twitter_simulation_misinfo_vllm.py

@@ -0,0 +1,161 @@
+# =========== Copyright 2023 @ 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 @ CAMEL-AI.org. All Rights Reserved. ===========
+import asyncio
+import os
+import random
+
+from camel.models import ModelFactory
+from camel.types import ModelPlatformType, ModelType
+
+import oasis
+from oasis import ActionType, EnvAction, SingleAction
+
+
+async def main():
+    # NOTE: You need to deploy the vllm server first
+    vllm_model_1 = ModelFactory.create(
+        model_platform=ModelPlatformType.VLLM,
+        model_type="qwen-2",
+        url="http://10.109.28.7:8080/v1",
+    )
+    vllm_model_2 = ModelFactory.create(
+        model_platform=ModelPlatformType.VLLM,
+        model_type="qwen-2",
+        url="http://10.109.27.103:8080/v1",
+    )
+    # Define the models for agents. Agents will select models based on
+    # pre-defined scheduling strategies
+    models = [vllm_model_1, vllm_model_2]
+
+    # Define the available actions for the agents
+    available_actions = [
+        ActionType.CREATE_POST,
+        ActionType.LIKE_POST,
+        ActionType.REPOST,
+        ActionType.FOLLOW,
+        ActionType.DO_NOTHING,
+        ActionType.QUOTE_POST,
+    ]
+
+    # Define the path to the database
+    db_path = "./data/twitter_simulation.db"
+
+    # Delete the old database
+    if os.path.exists(db_path):
+        os.remove(db_path)
+
+    # Make the environment
+    env = oasis.make(
+        platform=oasis.DefaultPlatformType.TWITTER,
+        database_path=db_path,
+        agent_profile_path=("tmp/random_network.csv"),
+        agent_models=models,
+        available_actions=available_actions,
+    )
+
+    # Run the environment
+    await env.reset()
+
+    # inject truth and misinformation across different topics
+    business_action_truth = SingleAction(
+        agent_id=0,
+        action=ActionType.CREATE_POST,
+        args={
+            "content":
+            "Amazon is expanding its delivery drone program to deliver packages within 30 minutes in select cities. This initiative aims to improve efficiency and reduce delivery times."
+        })
+    business_action_misinfo = SingleAction(
+        agent_id=0,
+        action=ActionType.CREATE_POST,
+        args={
+            "content":
+            "Amazon plans to completely eliminate its delivery drivers within two years due to the new drone program. #Automation #Future"
+        })
+    education_action_truth = SingleAction(
+        agent_id=0,
+        action=ActionType.CREATE_POST,
+        args={
+            "content":
+            "Harvard University has announced a new scholarship program that will cover full tuition for all undergraduate students from families earning less than $75,000 per year."
+        })
+    education_action_misinfo = SingleAction(
+        agent_id=0,
+        action=ActionType.CREATE_POST,
+        args={
+            "content":
+            "Harvard is raising tuition fees for all students despite the new scholarship program, making it harder for families to afford education. #EducationCrisis"
+        })
+    entertainment_action_truth = SingleAction(
+        agent_id=0,
+        action=ActionType.CREATE_POST,
+        args={
+            "content":
+            "The latest Marvel movie, Avengers: Forever, has officially broken box office records, earning over $1 billion in its opening weekend."
+        })
+    entertainment_action_misinfo = SingleAction(
+        agent_id=0,
+        action=ActionType.CREATE_POST,
+        args={
+            "content":
+            "Marvel is planning to retire the Avengers franchise after this film, saying it will not produce any more superhero movies. #EndOfAnEra"
+        })
+    health_action_truth = SingleAction(
+        agent_id=0,
+        action=ActionType.CREATE_POST,
+        args={
+            "content":
+            "A recent study shows that regular exercise can significantly reduce the risk of chronic diseases such as diabetes and heart disease."
+        })
+    health_action_misinfo = SingleAction(
+        agent_id=0,
+        action=ActionType.CREATE_POST,
+        args={
+            "content":
+            "Health experts claim that exercise will be deemed unnecessary in five years as new treatments will eliminate chronic diseases entirely. #HealthRevolution"
+        })
+
+    init_env_action = EnvAction(
+        activate_agents=[0],
+        intervention=[
+            business_action_truth, business_action_misinfo,
+            education_action_truth, education_action_misinfo,
+            entertainment_action_truth, entertainment_action_misinfo,
+            health_action_truth, health_action_misinfo
+        ])
+
+    env_simulation_actions = [init_env_action]
+    for timestep in range(3):
+        # Randomly select 1% of agents to activate. This is the active probability in the paper.
+        total_agents = env.agent_graph.get_num_nodes()
+        num_agents_to_activate = max(1, int(
+            total_agents * 0.01))  # Ensure at least 1 agent is activated
+        agents_to_activate = random.sample(range(total_agents),
+                                           num_agents_to_activate)
+
+        # Create an environment action with the randomly selected agents
+        random_action = EnvAction(activate_agents=agents_to_activate)
+        env_simulation_actions.append(random_action)
+
+    # Simulate 3 timesteps
+    for i in range(3):
+        env_actions = env_simulation_actions[i]
+        # Perform the actions
+        await env.step(env_actions)
+
+    # Close the environment
+    await env.close()
+
+
+if __name__ == "__main__":
+    asyncio.run(main())