LangGraphでOceanBaseデータベースのMySQLモードをバックエンドとして使用する

obclient -h$host -P$port -u$user_name -p$password -D$database_name

obclient -hxxx.xxx.xxx.xxx -P2881 -utest_user001@mysql001 -p****** -Dtest

pip install langgraph-checkpoint-oceanbase pymysql langgraph langchain[openai] aiomysql asyncmy

from langgraph.checkpoint.oceanbase.pyoceanbase import PyOceanBaseSaver
DB_URI = "mysql://$user_name:$password@$host:$port/$database_name"
with PyOceanBaseSaver.from_conn_string(DB_URI) as checkpointer:
    checkpointer.setup()

from langchain.chat_models import init_chat_model
from langgraph.graph import StateGraph, MessagesState, START
from langgraph.checkpoint.oceanbase.pyoceanbase import PyOceanBaseSaver
from langchain_core.runnables.config import RunnableConfig
from langchain_core.messages import HumanMessage

model = init_chat_model(model="qwen-max-latest", api_key="$your_api_key",
                        base_url="https://dashscope.aliyuncs.com/compatible-mode/v1", model_provider="openai",
                        temperature=0)

def call_model(state: MessagesState):
    response = model.invoke(state["messages"])
    return {"messages": response}

DB_URI = "mysql://$user_name:$password@$host:$port/$database_name"
with PyOceanBaseSaver.from_conn_string(DB_URI) as checkpointer:
    checkpointer.setup()
    builder = StateGraph(MessagesState)
    builder.add_node(call_model)
    builder.add_edge(START, "call_model")
    graph = builder.compile(checkpointer=checkpointer)
    config: RunnableConfig = {
        "configurable": {
            "thread_id": "1"
        }
    }
    # First conversation
    print("First conversation:")
    for chunk in graph.stream(
        {"messages": [HumanMessage(content="hi! I'm bob")]},
        config,
        stream_mode="values"
    ):
        chunk["messages"][-1].pretty_print()
    # Second conversation (testing memory function)
    print("\nSecond conversation:")
    for chunk in graph.stream(
        {"messages":[HumanMessage(content="what's my name?")]},
        config,
        stream_mode="values"
    ):
        chunk["messages"][-1].pretty_print()

First conversation:
================================ Human Message =================================

hi! I'm bob
================================== Ai Message ==================================

Hi Bob! Great to meet you again. How can I assist you today? 😊

Second conversation:
================================ Human Message =================================

what's my name?
================================== Ai Message ==================================

Your name is Bob! 😊

from langgraph.store.oceanbase import PyOceanBaseStore
DB_URI = "mysql://$user_name:$password@$host:$port/$database_name"
with PyOceanBaseStore.from_conn_string(DB_URI) as store:
    store.setup()

from langchain_core.runnables import RunnableConfig
from langgraph.config import get_store
from langgraph.prebuilt import create_react_agent
from langgraph.store.oceanbase import PyOceanBaseStore
from langchain.chat_models import init_chat_model
from langchain_core.messages import HumanMessage

DB_URI = "mysql://$user_name:$password@$host:$port/$database_name"
model = init_chat_model(model="qwen-max-latest", api_key="$your_api_key",
                        base_url="https://dashscope.aliyuncs.com/compatible-mode/v1", model_provider="openai", temperature=0)

def get_user_info(config: RunnableConfig) -> str:
    """Look up user info"""
    store = get_store()
    user_id = config.get("configurable", {}).get("user_id")
    if user_id is None:
        raise ValueError("user_id must be provided in config['configurable']")
    user_info = store.get(("users",), user_id)
    return str(user_info.value) if user_info else "Unknown user"

with PyOceanBaseStore.from_conn_string(DB_URI) as store:
    store.put(
        ("users",),
        "user_123",
        {
            "name": "John Smith",
            "language": "English"
        }
    )
    agent = create_react_agent(
        model=model,
        tools=[get_user_info],
        store=store
    )
    # Run the agent
    result = agent.invoke(
        {"messages": HumanMessage(content="look up user information")},
        config={"configurable": {"user_id": "user_123"}}
    )
    print(result["messages"][-1].content)

The user's name is John Smith and his preferred language is English.

from langchain_core.runnables import RunnableConfig
from langgraph.config import get_store
from langgraph.prebuilt import create_react_agent
from langgraph.store.oceanbase import PyOceanBaseStore
from langchain.chat_models import init_chat_model
from langchain_core.messages import HumanMessage
from typing_extensions import TypedDict

DB_URI = "mysql://$user_name:$password@$host:$port/$database_name"
model = init_chat_model(model="qwen-max-latest", api_key="$your_api_key",
                        base_url="https://dashscope.aliyuncs.com/compatible-mode/v1", model_provider="openai", temperature=0)

class UserInfo(TypedDict):
    name: str

def save_user_info(user_info: UserInfo, config: RunnableConfig) -> str:
    """Save user info"""
    store = get_store()
    user_id = config.get("configurable", {}).get("user_id")
    if user_id is None:
        raise ValueError("user_id must be provided in config['configurable']")
    store.put(("users",), user_id, dict(user_info))
    return "Successfully saved user info."

with PyOceanBaseStore.from_conn_string(DB_URI) as store:
    agent = create_react_agent(
        model=model,
        tools=[save_user_info],
        store=store
    )
    # Run the agent
    _ = agent.invoke(
        {"messages": [HumanMessage(content="My name is Tom and save my information")]},
        config={"configurable": {"user_id": "user_1"}}
    )
    # You can access the store directly to get the value
    result = store.get(("users",), "user_1")
    if result is not None:
        print(result.value)
    else:
        print("No value found for user_1")

{'name': 'Tom'}