first update

backend/matagent_main.py

@@ -0,0 +1,128 @@
+from autogen.code_utils import create_virtual_env
+from autogen.coding import LocalCommandLineCodeExecutor
+from autogen.agentchat.contrib.capabilities.teachability import Teachability
+from autogen.agentchat.contrib.capabilities.vision_capability import VisionCapability
+from autogen.agentchat.contrib.multimodal_conversable_agent import MultimodalConversableAgent
+from pathlib import Path
+import autogen
+import os
+from .constant import config_list, STREAM, SILENT, WORK_DIR
+from .utils import load_agent_configs
+from .retrieval_group import init_retrieval_group
+from .generate_group import init_generate_group
+from .converter_group import init_converter_group
+from .executor_group import init_executor_group
+from .optimize_group import init_optimize_group
+
+
+venv_context = create_virtual_env(WORK_DIR)
+llm_config = {"config_list": config_list, "stream": STREAM}
+
+
+def main():
+    agent_configs = load_agent_configs(os.path.join(os.path.dirname(os.path.abspath(__file__)), "config/plan_group.yaml"))
+    user = autogen.UserProxyAgent(
+        name="User",
+        human_input_mode="ALWAYS",
+        code_execution_config={
+            "work_dir": WORK_DIR,
+            "use_docker": False,
+        },
+        is_termination_msg=lambda x: x.get("content", "").find("TERMINATE") >= 0,
+        description="User",
+    )
+    
+    inner_retrieval_admin, outer_retrieval_agent = init_retrieval_group(WORK_DIR, venv_context)
+    inner_generate_admin, outer_generate_agent = init_generate_group(outer_retrieval_agent, inner_retrieval_admin)
+    inner_converter_admin, outer_converter_agent = init_converter_group()
+    inner_executor_admin, outer_executor_agent = init_executor_group(WORK_DIR, venv_context)
+    inner_analysis_admin, outer_analysis_agent, optimizer = init_optimize_group(WORK_DIR, venv_context)
+
+    def state_transition(last_speaker, groupchat):
+        messages = groupchat.messages
+
+        if last_speaker is user:
+            if len(messages) <= 1:
+                return outer_generate_agent
+            else:
+                return "auto"
+        elif last_speaker is outer_generate_agent:
+            if "synthesis" in messages[-1]["content"].lower():
+                return outer_converter_agent
+            else:
+                return user
+        elif last_speaker is outer_converter_agent:
+            return outer_executor_agent
+        elif last_speaker is outer_executor_agent:
+            return outer_analysis_agent
+        elif last_speaker is outer_analysis_agent:
+            return optimizer
+        else:
+            return user
+        
+    matagent_group = autogen.GroupChat(
+        agents=[user, outer_generate_agent, outer_converter_agent, outer_executor_agent, outer_analysis_agent, optimizer],
+        messages=[],
+        speaker_selection_method=state_transition,
+        max_round=50,
+    )
+
+    matagent_admin_name = "Planer"
+    matagent_admin = autogen.GroupChatManager(
+        name=matagent_admin_name,
+        groupchat=matagent_group,
+        # is_termination_msg=lambda x: x.get("content", "").find("TERMINATE") >= 0,
+        llm_config=llm_config,
+        system_message=agent_configs[matagent_admin_name]['system_message'],
+        description=matagent_admin_name
+    )
+
+    outer_generate_agent.register_nested_chats(
+        [
+            {"recipient": inner_generate_admin, "max_turn": 1, "summary_method": "last_msg", "silent": SILENT},
+        ],
+        trigger=matagent_admin,
+    )
+
+    outer_converter_agent.register_nested_chats(
+        [
+            {"recipient": inner_converter_admin, "max_turn": 1, "summary_method": "last_msg", "silent": SILENT},
+        ],
+        trigger=matagent_admin,
+    )
+
+    outer_executor_agent.register_nested_chats(
+        [
+            {"recipient": inner_executor_admin, "max_turn": 1, "summary_method": "last_msg", "silent": SILENT},
+        ],
+        trigger=matagent_admin,
+    )
+
+    outer_analysis_agent.register_nested_chats(
+        [
+            {"recipient": inner_analysis_admin, "max_turn": 1, "summary_method": "last_msg", "silent": SILENT},
+        ],
+        trigger=matagent_admin,
+    )
+
+
+    user.initiate_chat(
+        matagent_admin,
+        # message="如何在常温条件下制备CsPbBr3纳米立方体",
+        message="how to synthesis of CsPbBr3 Perovskite NCs at room temperature?"
+        # message="how to synthesis CsPbBr3 nanocubes at room temperature?"
+        # message="什么是钙钛矿？"
+        # message="Please prepare few layers graphene from graphite powder.",
+        # message="Can you please prepare black phosphorusene with improved stability from black phosphorus crystals powder?",
+        # message="Can you synthesize gold nanorods by seed-mediated method with absorption peaks at 820 nm?",
+        # message="Please synthesize CsPbBr3 nanocubes with a fluorescence emission wavelength of 520 nm at room temperature?",
+        # message="Please design a new hybridized halide perovskite composite material that is biocompatible and water-stable",
+        # message="please use phospholipid membrane as shell to encapsulate hybrid perovskite"
+        # Now I want a novel bright perovskite composite based CH3NH3PbBr3 and phospholipid membrane(PM) to improve the stability and biocompatibility, please synthesis this materials under room temperature
+    )
+    print(outer_generate_agent.last_message(matagent_admin))
+    print(matagent_admin)
+
+
+if __name__ == "__main__":
+    main()