[WIP]Feat/progressive enhancement agent

.gitignore

@@ -16,6 +16,12 @@ llm_contents_report.json
 /user_questions.txt
 /traces/
 
-evalate_threads/evaluate.py
-evalate_threads/quick_test.py
-evalate_threads/test_adk_intergration.py
+/evaluate_threads/evaluate.py
+/evaluate_threads/quick_test.py
+/evaluate_threads/test_adk_intergration.py
+
+agents/matmaster_agent/.adk/
+
+agents/matmaster_agent/test_mat.evalset.json
+evaluate_threads/test_adk_integration.py
+tests/

agents/langgraph/progressive_agent_demo/agent.py

@@ -0,0 +1,41 @@
+import os
+from dotenv import load_dotenv
+from loguru import logger
+from langchain.chat_models import init_chat_model
+
+from typing import Annotated
+
+from typing_extensions import TypedDict
+
+from langgraph.graph import StateGraph, START, END
+from langgraph.graph.message import add_messages
+
+
+class State(TypedDict):
+    # Messages have the type "list". The `add_messages` function
+    # in the annotation defines how this state key should be updated
+    # (in this case, it appends messages to the list, rather than overwriting them)
+    messages: Annotated[list, add_messages]
+
+
+graph_builder = StateGraph(State)
+
+load_dotenv()
+
+llm = init_chat_model(
+    "azure_openai:gpt-4.1",
+    azure_deployment=os.environ["AZURE_OPENAI_DEPLOYMENT_NAME"],
+)
+
+def chatbot(state: State):
+    return {"messages": [llm.invoke(state["messages"])]}
+
+graph_builder.add_node("chatbot", chatbot)
+graph_builder.add_edge(START, "chatbot")
+graph_builder.add_edge("chatbot", END)
+
+graph = graph_builder.compile()
+
+if __name__ == "__main__":
+    res = graph.invoke({"messages": [{"role": "user", "content": "Hello, how are you?"}]})
+    print(res)

agents/langgraph/progressive_agent_demo/main.py

@@ -0,0 +1,110 @@
+"""
+agent 调用一个复杂参数的工具 abacus_prepare，然后用 progressive 的方式进行优化参数填写的例子
+
+"""
+
+import os
+from dotenv import load_dotenv
+from pydantic import BaseModel
+from pathlib import Path
+from loguru import logger
+
+load_dotenv()
+
+from langchain_openai import AzureChatOpenAI
+from mem0 import MemoryClient
+
+memory_client = MemoryClient()
+
+
+
+class AbacusPrepareInput(BaseModel):
+    stru_file: Path
+    stru_type: str
+    job_type: str
+    lcao: bool
+    nspin: int
+    soc: bool
+    dftu: bool
+    dftu_param: dict
+    init_mag: dict
+    afm: bool
+    extra_input: dict
+    name: str
+
+def abacus_prepare(input: AbacusPrepareInput):
+    logger.info(input)
+    return "abacus_prepare"
+
+def test_llm():
+    deployment_name = os.environ.get("AZURE_OPENAI_DEPLOYMENT_NAME")
+    print(deployment_name)
+
+    client = AzureChatOpenAI(
+        deployment_name=deployment_name,
+    )
+
+    response = client.invoke("你是什么模型,告诉我你是 gpt-4 还是 gpt-5，精确版本号")
+
+    print(response)
+
+
+def test_bare_toolcall():
+    data = {
+        "stru_file": "./demo.cif",
+        "stru_type": "cif",
+        "job_type": "scf",
+        "lcao": True,
+        "nspin": 1,
+        "soc": False,
+        "dftu": True,
+        "dftu_param": {"Fe": 7.0, "Ni": ("d", 4.0)},
+        "init_mag": {"Fe": 2.0, "Ni": 2.0},
+        "afm": False,
+        "extra_input": {"smearing_sigma": 0.01, "dft_functional": "pbe"}
+    }
+
+    abacus_prepare(data) 
+
+
+def test_mem0():
+    # messages = [
+    #     { "role": "user", "content": "Hi, I'm Ting. 我是中国人，我早晨习惯喝粥" },
+    #     { "role": "assistant", "content": "你好，你是中国人，你早晨习惯喝粥，那我早晨会推荐你喝小米粥和八宝粥" }
+    # ]
+
+    # add_result = memory_client.add(messages, user_id="ting",  metadata={"category": "breakfast"}, output_format="v1.1")
+    # print("add_result")
+    # print(add_result)
+
+    query = "What can I cook?"
+
+    search_result = memory_client.search(
+        query, 
+        user_id="ting",
+        metadata={"category": "breakfast"}
+    )
+    print("search_result")
+    print(search_result)
+
+    memories = memory_client.get_all(user_id="ting")
+    print("all memories")
+    print(memories)
+
+
+
+def main():
+
+    # test_llm()
+
+    # test_bare_toolcall()
+
+    test_mem0()
+
+
+
+
+if __name__ == "__main__":
+    main()
+
+

agents/langgraph/progressive_agent_demo/tools.py

@@ -0,0 +1,71 @@
+from pydantic import BaseModel
+from pathlib import Path
+from langchain_core.tools import tool
+from loguru import logger
+from pprint import pprint
+from typing import Optional
+
+class AbacusPrepareInput(BaseModel):
+    stru_file: Path
+    stru_type: str
+    job_type: str
+    lcao: bool
+    nspin: int
+    soc: bool
+    dftu: bool
+    dftu_param: Optional[dict]
+    init_mag: Optional[dict]
+    afm: Optional[bool]
+    extra_input: Optional[dict]
+    name: Optional[str]
+
+def abacus_prepare(input: AbacusPrepareInput):
+    pprint(input.model_dump())
+    return "abacus_prepare"
+
+@tool
+def abacus_prepare_tool(
+    stru_file: Path,
+    stru_type: str,
+    job_type: str,
+    lcao: bool,
+    nspin: int,
+    soc: bool,
+    dftu: bool,
+    dftu_param: dict = {},
+    init_mag: dict = {},
+    afm: bool = False,
+    extra_input: dict = {},
+    name: str = "",
+):
+    """
+    Prepare ABACUS input file directory from structure file and provided information.
+    """
+    input = AbacusPrepareInput(
+        stru_file=stru_file,
+        stru_type=stru_type,
+        job_type=job_type,
+        lcao=lcao,
+        nspin=nspin,
+        soc=soc,
+        dftu=dftu,
+        dftu_param=dftu_param,
+        init_mag=init_mag,
+        afm=afm,
+        extra_input=extra_input,
+        name=name,
+    )
+    return abacus_prepare(input)
+
+if __name__ == "__main__":
+    input_dict = {
+        "stru_file": "./demo.cif",
+        "stru_type": "cif",
+        "job_type": "scf",
+        "lcao": True,
+        "nspin": 1,
+        "soc": False,
+        "dftu": True,
+    }
+    res = abacus_prepare_tool.invoke(input_dict)
+    print(res)

pyproject.toml

@@ -14,6 +14,16 @@ dependencies = [
     "anyio>=4.10.0",
     "pytest-asyncio>=1.1.0",
     "litellm>=1.74.15.post2",
+    "langgraph>=0.6.6",
+    "langchain-azure-ai>=0.1.4",
+    "langchain-litellm>=0.2.2",
+    "loguru>=0.7.3",
+    "mem0ai>=0.1.116",
+    "langsmith>=0.4.20",
+    "langchain[azure-ai,openai]>=0.3.27",
+    "ipykernel>=6.30.1",
+    "pip>=25.2",
+    "langchain-tavily>=0.2.11",
 ]
 
 [tool.setuptools]