如何使用自定义存储

langgraph new --template=new-langgraph-project-python my_new_project

# ./src/agent/store.py
import contextlib

from langchain.embeddings import init_embeddings
from langgraph.store.base import IndexConfig
from langgraph.store.sqlite import AsyncSqliteStore

embeddings = init_embeddings("openai:text-embedding-3-small")


@contextlib.asynccontextmanager
async def generate_store():
    """生成一个 BaseStore，在服务器运行期间保持打开。"""
    async with AsyncSqliteStore.from_conn_string(
        "./custom_store.sql",
        index=IndexConfig(
            dims=1536,
            embed=embeddings,
            fields=["$"],
        ),
    ) as store:
        await store.setup()
        yield store

{
  "dependencies": ["."],
  "graphs": {
    "agent": "./src/agent/graph.py:graph"
  },
  "env": ".env",
  "store": {
    "path": "./src/agent/store.py:generate_store"
  }
}

langgraph dev --no-browser

Using custom store. Skipping store TTL sweeper.