如何使用Redis创建自定义检查点器¶
在创建LangGraph代理时，您可以设置它们以持久化其状态。这允许您多次与代理交互，并使其能够记住之前的交互。
此参考实现演示了如何使用Redis作为持久化检查点状态的后端。请确保Redis在端口6379上运行，以便按照本指南进行操作。
注意
这是一个**参考**实现。您可以使用不同的数据库实现自己的检查点器，或者修改此实现，只要它符合BaseCheckpointSaver接口即可。
为了演示目的，我们向预构建的create react代理添加了持久化功能。
通常，您可以像这样向您构建的任何自定义图添加检查点器：
from langgraph.graph import StateGraph
builder = StateGraph(....)
# ... 定义图
checkpointer = # redis检查点器（见下面示例）
graph = builder.compile(checkpointer=checkpointer)
...
设置¶
首先，让我们安装所需的包并设置我们的API密钥
import getpass
import os
def _set_env(var: str):
if not os.environ.get(var):
os.environ[var] = getpass.getpass(f"{var}: ")
_set_env("OPENAI_API_KEY")
使用LangSmith进行LangGraph开发
注册LangSmith，可以快速发现并解决项目中的问题，提高LangGraph项目的性能。LangSmith允许你使用跟踪数据来调试、测试和监控使用LangGraph构建的LLM应用程序——更多关于如何开始的信息，请参阅这里。
Checkpointer实现¶
定义导入和辅助函数¶
首先，让我们为RedisSaver和AsyncRedisSaver定义一些导入和共享的工具类。
"""Implementation of a langgraph checkpoint saver using Redis."""
from contextlib import asynccontextmanager, contextmanager
from typing import (
Any,
AsyncGenerator,
AsyncIterator,
Iterator,
List,
Optional,
Tuple,
)
from langchain_core.runnables import RunnableConfig
from langgraph.checkpoint.base import (
WRITES_IDX_MAP,
BaseCheckpointSaver,
ChannelVersions,
Checkpoint,
CheckpointMetadata,
CheckpointTuple,
PendingWrite,
get_checkpoint_id,
)
from langgraph.checkpoint.serde.base import SerializerProtocol
from redis import Redis
from redis.asyncio import Redis as AsyncRedis
REDIS_KEY_SEPARATOR = "$"
# Utilities shared by both RedisSaver and AsyncRedisSaver
def _make_redis_checkpoint_key(
thread_id: str, checkpoint_ns: str, checkpoint_id: str
) -> str:
return REDIS_KEY_SEPARATOR.join(
["checkpoint", thread_id, checkpoint_ns, checkpoint_id]
)
def _make_redis_checkpoint_writes_key(
thread_id: str,
checkpoint_ns: str,
checkpoint_id: str,
task_id: str,
idx: Optional[int],
) -> str:
if idx is None:
return REDIS_KEY_SEPARATOR.join(
["writes", thread_id, checkpoint_ns, checkpoint_id, task_id]
)
return REDIS_KEY_SEPARATOR.join(
["writes", thread_id, checkpoint_ns, checkpoint_id, task_id, str(idx)]
)
def _parse_redis_checkpoint_key(redis_key: str) -> dict:
namespace, thread_id, checkpoint_ns, checkpoint_id = redis_key.split(
REDIS_KEY_SEPARATOR
)
if namespace != "checkpoint":
raise ValueError("Expected checkpoint key to start with 'checkpoint'")
return {
"thread_id": thread_id,
"checkpoint_ns": checkpoint_ns,
"checkpoint_id": checkpoint_id,
}
def _parse_redis_checkpoint_writes_key(redis_key: str) -> dict:
namespace, thread_id, checkpoint_ns, checkpoint_id, task_id, idx = redis_key.split(
REDIS_KEY_SEPARATOR
)
if namespace != "writes":
raise ValueError("Expected checkpoint key to start with 'checkpoint'")
return {
"thread_id": thread_id,
"checkpoint_ns": checkpoint_ns,
"checkpoint_id": checkpoint_id,
"task_id": task_id,
"idx": idx,
}
def _filter_keys(
keys: List[str], before: Optional[RunnableConfig], limit: Optional[int]
) -> list:
"""Filter and sort Redis keys based on optional criteria."""
if before:
keys = [
k
for k in keys
if _parse_redis_checkpoint_key(k.decode())["checkpoint_id"]
< before["configurable"]["checkpoint_id"]
]
keys = sorted(
keys,
key=lambda k: _parse_redis_checkpoint_key(k.decode())["checkpoint_id"],
reverse=True,
)
if limit:
keys = keys[:limit]
return keys
def _load_writes(
serde: SerializerProtocol, task_id_to_data: dict[tuple[str, str], dict]
) -> list[PendingWrite]:
"""Deserialize pending writes."""
writes = [
(
task_id,
data[b"channel"].decode(),
serde.loads_typed((data[b"type"].decode(), data[b"value"])),
)
for (task_id, _), data in task_id_to_data.items()
]
return writes
def _parse_redis_checkpoint_data(
serde: SerializerProtocol,
key: str,
data: dict,
pending_writes: Optional[List[PendingWrite]] = None,
) -> Optional[CheckpointTuple]:
"""Parse checkpoint data retrieved from Redis."""
if not data:
return None
parsed_key = _parse_redis_checkpoint_key(key)
thread_id = parsed_key["thread_id"]
checkpoint_ns = parsed_key["checkpoint_ns"]
checkpoint_id = parsed_key["checkpoint_id"]
config = {
"configurable": {
"thread_id": thread_id,
"checkpoint_ns": checkpoint_ns,
"checkpoint_id": checkpoint_id,
}
}
checkpoint = serde.loads_typed((data[b"type"].decode(), data[b"checkpoint"]))
metadata = serde.loads(data[b"metadata"].decode())
parent_checkpoint_id = data.get(b"parent_checkpoint_id", b"").decode()
parent_config = (
{
"configurable": {
"thread_id": thread_id,
"checkpoint_ns": checkpoint_ns,
"checkpoint_id": parent_checkpoint_id,
}
}
if parent_checkpoint_id
else None
)
return CheckpointTuple(
config=config,
checkpoint=checkpoint,
metadata=metadata,
parent_config=parent_config,
pending_writes=pending_writes,
)
API Reference: RunnableConfig | BaseCheckpointSaver | Checkpoint | CheckpointMetadata
RedisSaver¶
以下是RedisSaver的实现（用于图的同步使用，即.invoke()，.stream()）。RedisSaver实现了四个任何检查点器所需的函数：
.put- 存储一个检查点及其配置和元数据。.put_writes- 存储与检查点关联的中间写入（即待处理写入）。.get_tuple- 使用给定配置（thread_id和checkpoint_id）获取检查点元组。.list- 列出与给定配置和过滤标准匹配的检查点。
class RedisSaver(BaseCheckpointSaver):
"""Redis-based checkpoint saver implementation."""
conn: Redis
def __init__(self, conn: Redis):
super().__init__()
self.conn = conn
@classmethod
@contextmanager
def from_conn_info(cls, *, host: str, port: int, db: int) -> Iterator["RedisSaver"]:
conn = None
try:
conn = Redis(host=host, port=port, db=db)
yield RedisSaver(conn)
finally:
if conn:
conn.close()
def put(
self,
config: RunnableConfig,
checkpoint: Checkpoint,
metadata: CheckpointMetadata,
new_versions: ChannelVersions,
) -> RunnableConfig:
"""Save a checkpoint to Redis.
Args:
config (RunnableConfig): The config to associate with the checkpoint.
checkpoint (Checkpoint): The checkpoint to save.
metadata (CheckpointMetadata): Additional metadata to save with the checkpoint.
new_versions (ChannelVersions): New channel versions as of this write.
Returns:
RunnableConfig: Updated configuration after storing the checkpoint.
"""
thread_id = config["configurable"]["thread_id"]
checkpoint_ns = config["configurable"]["checkpoint_ns"]
checkpoint_id = checkpoint["id"]
parent_checkpoint_id = config["configurable"].get("checkpoint_id")
key = _make_redis_checkpoint_key(thread_id, checkpoint_ns, checkpoint_id)
type_, serialized_checkpoint = self.serde.dumps_typed(checkpoint)
serialized_metadata = self.serde.dumps(metadata)
data = {
"checkpoint": serialized_checkpoint,
"type": type_,
"metadata": serialized_metadata,
"parent_checkpoint_id": parent_checkpoint_id
if parent_checkpoint_id
else "",
}
self.conn.hset(key, mapping=data)
return {
"configurable": {
"thread_id": thread_id,
"checkpoint_ns": checkpoint_ns,
"checkpoint_id": checkpoint_id,
}
}
def put_writes(
self,
config: RunnableConfig,
writes: List[Tuple[str, Any]],
task_id: str,
) -> None:
"""Store intermediate writes linked to a checkpoint.
Args:
config (RunnableConfig): Configuration of the related checkpoint.
writes (Sequence[Tuple[str, Any]]): List of writes to store, each as (channel, value) pair.
task_id (str): Identifier for the task creating the writes.
"""
thread_id = config["configurable"]["thread_id"]
checkpoint_ns = config["configurable"]["checkpoint_ns"]
checkpoint_id = config["configurable"]["checkpoint_id"]
for idx, (channel, value) in enumerate(writes):
key = _make_redis_checkpoint_writes_key(
thread_id,
checkpoint_ns,
checkpoint_id,
task_id,
WRITES_IDX_MAP.get(channel, idx),
)
type_, serialized_value = self.serde.dumps_typed(value)
data = {"channel": channel, "type": type_, "value": serialized_value}
if all(w[0] in WRITES_IDX_MAP for w in writes):
# Use HSET which will overwrite existing values
self.conn.hset(key, mapping=data)
else:
# Use HSETNX which will not overwrite existing values
for field, value in data.items():
self.conn.hsetnx(key, field, value)
def get_tuple(self, config: RunnableConfig) -> Optional[CheckpointTuple]:
"""Get a checkpoint tuple from Redis.
This method retrieves a checkpoint tuple from Redis based on the
provided config. If the config contains a "checkpoint_id" key, the checkpoint with
the matching thread ID and checkpoint ID is retrieved. Otherwise, the latest checkpoint
for the given thread ID is retrieved.
Args:
config (RunnableConfig): The config to use for retrieving the checkpoint.
Returns:
Optional[CheckpointTuple]: The retrieved checkpoint tuple, or None if no matching checkpoint was found.
"""
thread_id = config["configurable"]["thread_id"]
checkpoint_id = get_checkpoint_id(config)
checkpoint_ns = config["configurable"].get("checkpoint_ns", "")
checkpoint_key = self._get_checkpoint_key(
self.conn, thread_id, checkpoint_ns, checkpoint_id
)
if not checkpoint_key:
return None
checkpoint_data = self.conn.hgetall(checkpoint_key)
# load pending writes
checkpoint_id = (
checkpoint_id
or _parse_redis_checkpoint_key(checkpoint_key)["checkpoint_id"]
)
pending_writes = self._load_pending_writes(
thread_id, checkpoint_ns, checkpoint_id
)
return _parse_redis_checkpoint_data(
self.serde, checkpoint_key, checkpoint_data, pending_writes=pending_writes
)
def list(
self,
config: Optional[RunnableConfig],
*,
# TODO: implement filtering
filter: Optional[dict[str, Any]] = None,
before: Optional[RunnableConfig] = None,
limit: Optional[int] = None,
) -> Iterator[CheckpointTuple]:
"""List checkpoints from the database.
This method retrieves a list of checkpoint tuples from Redis based
on the provided config. The checkpoints are ordered by checkpoint ID in descending order (newest first).
Args:
config (RunnableConfig): The config to use for listing the checkpoints.
filter (Optional[Dict[str, Any]]): Additional filtering criteria for metadata. Defaults to None.
before (Optional[RunnableConfig]): If provided, only checkpoints before the specified checkpoint ID are returned. Defaults to None.
limit (Optional[int]): The maximum number of checkpoints to return. Defaults to None.
Yields:
Iterator[CheckpointTuple]: An iterator of checkpoint tuples.
"""
thread_id = config["configurable"]["thread_id"]
checkpoint_ns = config["configurable"].get("checkpoint_ns", "")
pattern = _make_redis_checkpoint_key(thread_id, checkpoint_ns, "*")
keys = _filter_keys(self.conn.keys(pattern), before, limit)
for key in keys:
data = self.conn.hgetall(key)
if data and b"checkpoint" in data and b"metadata" in data:
# load pending writes
checkpoint_id = _parse_redis_checkpoint_key(key.decode())[
"checkpoint_id"
]
pending_writes = self._load_pending_writes(
thread_id, checkpoint_ns, checkpoint_id
)
yield _parse_redis_checkpoint_data(
self.serde, key.decode(), data, pending_writes=pending_writes
)
def _load_pending_writes(
self, thread_id: str, checkpoint_ns: str, checkpoint_id: str
) -> List[PendingWrite]:
writes_key = _make_redis_checkpoint_writes_key(
thread_id, checkpoint_ns, checkpoint_id, "*", None
)
matching_keys = self.conn.keys(pattern=writes_key)
parsed_keys = [
_parse_redis_checkpoint_writes_key(key.decode()) for key in matching_keys
]
pending_writes = _load_writes(
self.serde,
{
(parsed_key["task_id"], parsed_key["idx"]): self.conn.hgetall(key)
for key, parsed_key in sorted(
zip(matching_keys, parsed_keys), key=lambda x: x[1]["idx"]
)
},
)
return pending_writes
def _get_checkpoint_key(
self, conn, thread_id: str, checkpoint_ns: str, checkpoint_id: Optional[str]
) -> Optional[str]:
"""Determine the Redis key for a checkpoint."""
if checkpoint_id:
return _make_redis_checkpoint_key(thread_id, checkpoint_ns, checkpoint_id)
all_keys = conn.keys(_make_redis_checkpoint_key(thread_id, checkpoint_ns, "*"))
if not all_keys:
return None
latest_key = max(
all_keys,
key=lambda k: _parse_redis_checkpoint_key(k.decode())["checkpoint_id"],
)
return latest_key.decode()
异步Redis¶
以下是 AsyncRedisSaver 的参考实现（用于图的异步调用，即 .ainvoke() 和 .astream()）。AsyncRedisSaver 实现了四个异步检查点器所需的函数：
.aput- 存储检查点及其配置和元数据。.aput_writes- 存储与检查点关联的中间写入（即待处理写入）。.aget_tuple- 使用给定配置（thread_id和checkpoint_id）获取检查点元组。.alist- 列出与给定配置和过滤标准匹配的检查点。
class AsyncRedisSaver(BaseCheckpointSaver):
"""Async redis-based checkpoint saver implementation."""
conn: AsyncRedis
def __init__(self, conn: AsyncRedis):
super().__init__()
self.conn = conn
@classmethod
@asynccontextmanager
async def from_conn_info(
cls, *, host: str, port: int, db: int
) -> AsyncIterator["AsyncRedisSaver"]:
conn = None
try:
conn = AsyncRedis(host=host, port=port, db=db)
yield AsyncRedisSaver(conn)
finally:
if conn:
await conn.aclose()
async def aput(
self,
config: RunnableConfig,
checkpoint: Checkpoint,
metadata: CheckpointMetadata,
new_versions: ChannelVersions,
) -> RunnableConfig:
"""Save a checkpoint to the database asynchronously.
This method saves a checkpoint to Redis. The checkpoint is associated
with the provided config and its parent config (if any).
Args:
config (RunnableConfig): The config to associate with the checkpoint.
checkpoint (Checkpoint): The checkpoint to save.
metadata (CheckpointMetadata): Additional metadata to save with the checkpoint.
new_versions (ChannelVersions): New channel versions as of this write.
Returns:
RunnableConfig: Updated configuration after storing the checkpoint.
"""
thread_id = config["configurable"]["thread_id"]
checkpoint_ns = config["configurable"]["checkpoint_ns"]
checkpoint_id = checkpoint["id"]
parent_checkpoint_id = config["configurable"].get("checkpoint_id")
key = _make_redis_checkpoint_key(thread_id, checkpoint_ns, checkpoint_id)
type_, serialized_checkpoint = self.serde.dumps_typed(checkpoint)
serialized_metadata = self.serde.dumps(metadata)
data = {
"checkpoint": serialized_checkpoint,
"type": type_,
"checkpoint_id": checkpoint_id,
"metadata": serialized_metadata,
"parent_checkpoint_id": parent_checkpoint_id
if parent_checkpoint_id
else "",
}
await self.conn.hset(key, mapping=data)
return {
"configurable": {
"thread_id": thread_id,
"checkpoint_ns": checkpoint_ns,
"checkpoint_id": checkpoint_id,
}
}
async def aput_writes(
self,
config: RunnableConfig,
writes: List[Tuple[str, Any]],
task_id: str,
) -> None:
"""Store intermediate writes linked to a checkpoint asynchronously.
This method saves intermediate writes associated with a checkpoint to the database.
Args:
config (RunnableConfig): Configuration of the related checkpoint.
writes (Sequence[Tuple[str, Any]]): List of writes to store, each as (channel, value) pair.
task_id (str): Identifier for the task creating the writes.
"""
thread_id = config["configurable"]["thread_id"]
checkpoint_ns = config["configurable"]["checkpoint_ns"]
checkpoint_id = config["configurable"]["checkpoint_id"]
for idx, (channel, value) in enumerate(writes):
key = _make_redis_checkpoint_writes_key(
thread_id,
checkpoint_ns,
checkpoint_id,
task_id,
WRITES_IDX_MAP.get(channel, idx),
)
type_, serialized_value = self.serde.dumps_typed(value)
data = {"channel": channel, "type": type_, "value": serialized_value}
if all(w[0] in WRITES_IDX_MAP for w in writes):
# Use HSET which will overwrite existing values
await self.conn.hset(key, mapping=data)
else:
# Use HSETNX which will not overwrite existing values
for field, value in data.items():
await self.conn.hsetnx(key, field, value)
async def aget_tuple(self, config: RunnableConfig) -> Optional[CheckpointTuple]:
"""Get a checkpoint tuple from Redis asynchronously.
This method retrieves a checkpoint tuple from Redis based on the
provided config. If the config contains a "checkpoint_id" key, the checkpoint with
the matching thread ID and checkpoint ID is retrieved. Otherwise, the latest checkpoint
for the given thread ID is retrieved.
Args:
config (RunnableConfig): The config to use for retrieving the checkpoint.
Returns:
Optional[CheckpointTuple]: The retrieved checkpoint tuple, or None if no matching checkpoint was found.
"""
thread_id = config["configurable"]["thread_id"]
checkpoint_id = get_checkpoint_id(config)
checkpoint_ns = config["configurable"].get("checkpoint_ns", "")
checkpoint_key = await self._aget_checkpoint_key(
self.conn, thread_id, checkpoint_ns, checkpoint_id
)
if not checkpoint_key:
return None
checkpoint_data = await self.conn.hgetall(checkpoint_key)
# load pending writes
checkpoint_id = (
checkpoint_id
or _parse_redis_checkpoint_key(checkpoint_key)["checkpoint_id"]
)
pending_writes = await self._aload_pending_writes(
thread_id, checkpoint_ns, checkpoint_id
)
return _parse_redis_checkpoint_data(
self.serde, checkpoint_key, checkpoint_data, pending_writes=pending_writes
)
async def alist(
self,
config: Optional[RunnableConfig],
*,
# TODO: implement filtering
filter: Optional[dict[str, Any]] = None,
before: Optional[RunnableConfig] = None,
limit: Optional[int] = None,
) -> AsyncGenerator[CheckpointTuple, None]:
"""List checkpoints from Redis asynchronously.
This method retrieves a list of checkpoint tuples from Redis based
on the provided config. The checkpoints are ordered by checkpoint ID in descending order (newest first).
Args:
config (Optional[RunnableConfig]): Base configuration for filtering checkpoints.
filter (Optional[Dict[str, Any]]): Additional filtering criteria for metadata.
before (Optional[RunnableConfig]): If provided, only checkpoints before the specified checkpoint ID are returned. Defaults to None.
limit (Optional[int]): Maximum number of checkpoints to return.
Yields:
AsyncIterator[CheckpointTuple]: An asynchronous iterator of matching checkpoint tuples.
"""
thread_id = config["configurable"]["thread_id"]
checkpoint_ns = config["configurable"].get("checkpoint_ns", "")
pattern = _make_redis_checkpoint_key(thread_id, checkpoint_ns, "*")
keys = _filter_keys(await self.conn.keys(pattern), before, limit)
for key in keys:
data = await self.conn.hgetall(key)
if data and b"checkpoint" in data and b"metadata" in data:
checkpoint_id = _parse_redis_checkpoint_key(key.decode())[
"checkpoint_id"
]
pending_writes = await self._aload_pending_writes(
thread_id, checkpoint_ns, checkpoint_id
)
yield _parse_redis_checkpoint_data(
self.serde, key.decode(), data, pending_writes=pending_writes
)
async def _aload_pending_writes(
self, thread_id: str, checkpoint_ns: str, checkpoint_id: str
) -> List[PendingWrite]:
writes_key = _make_redis_checkpoint_writes_key(
thread_id, checkpoint_ns, checkpoint_id, "*", None
)
matching_keys = await self.conn.keys(pattern=writes_key)
parsed_keys = [
_parse_redis_checkpoint_writes_key(key.decode()) for key in matching_keys
]
pending_writes = _load_writes(
self.serde,
{
(parsed_key["task_id"], parsed_key["idx"]): await self.conn.hgetall(key)
for key, parsed_key in sorted(
zip(matching_keys, parsed_keys), key=lambda x: x[1]["idx"]
)
},
)
return pending_writes
async def _aget_checkpoint_key(
self, conn, thread_id: str, checkpoint_ns: str, checkpoint_id: Optional[str]
) -> Optional[str]:
"""Asynchronously determine the Redis key for a checkpoint."""
if checkpoint_id:
return _make_redis_checkpoint_key(thread_id, checkpoint_ns, checkpoint_id)
all_keys = await conn.keys(
_make_redis_checkpoint_key(thread_id, checkpoint_ns, "*")
)
if not all_keys:
return None
latest_key = max(
all_keys,
key=lambda k: _parse_redis_checkpoint_key(k.decode())["checkpoint_id"],
)
return latest_key.decode()
设置图形的模型和工具¶
from typing import Literal
from langchain_core.runnables import ConfigurableField
from langchain_core.tools import tool
from langchain_openai import ChatOpenAI
from langgraph.prebuilt import create_react_agent
@tool
def get_weather(city: Literal["nyc", "sf"]):
"""Use this to get weather information."""
if city == "nyc":
return "It might be cloudy in nyc"
elif city == "sf":
return "It's always sunny in sf"
else:
raise AssertionError("Unknown city")
tools = [get_weather]
model = ChatOpenAI(model_name="gpt-4o-mini", temperature=0)
API Reference: ConfigurableField | tool | ChatOpenAI | create_react_agent
使用同步连接¶
with RedisSaver.from_conn_info(host="localhost", port=6379, db=0) as checkpointer:
graph = create_react_agent(model, tools=tools, checkpointer=checkpointer)
config = {"configurable": {"thread_id": "1"}}
res = graph.invoke({"messages": [("human", "what's the weather in sf")]}, config)
latest_checkpoint = checkpointer.get(config)
latest_checkpoint_tuple = checkpointer.get_tuple(config)
checkpoint_tuples = list(checkpointer.list(config))
{'v': 1,
'ts': '2024-08-09T01:56:48.328315+00:00',
'id': '1ef55f2a-3614-69b4-8003-2181cff935cc',
'channel_values': {'messages': [HumanMessage(content="what's the weather in sf", id='f911e000-75a1-41f6-8e38-77bb086c2ecf'),
AIMessage(content='', additional_kwargs={'tool_calls': [{'id': 'call_l5e5YcTJDJYOdvi4scBy9n2I', 'function': {'arguments': '{"city":"sf"}', 'name': 'get_weather'}, 'type': 'function'}]}, response_metadata={'token_usage': {'completion_tokens': 14, 'prompt_tokens': 57, 'total_tokens': 71}, 'model_name': 'gpt-4o-mini', 'system_fingerprint': 'fp_48196bc67a', 'finish_reason': 'tool_calls', 'logprobs': None}, id='run-4f1531f1-067c-4e16-8b62-7a6b663e93bd-0', tool_calls=[{'name': 'get_weather', 'args': {'city': 'sf'}, 'id': 'call_l5e5YcTJDJYOdvi4scBy9n2I', 'type': 'tool_call'}], usage_metadata={'input_tokens': 57, 'output_tokens': 14, 'total_tokens': 71}),
ToolMessage(content="It's always sunny in sf", name='get_weather', id='e27bb3a1-1798-494a-b4ad-2deadda8b2bf', tool_call_id='call_l5e5YcTJDJYOdvi4scBy9n2I'),
AIMessage(content='The weather in San Francisco is always sunny!', response_metadata={'token_usage': {'completion_tokens': 10, 'prompt_tokens': 84, 'total_tokens': 94}, 'model_name': 'gpt-4o-mini', 'system_fingerprint': 'fp_48196bc67a', 'finish_reason': 'stop', 'logprobs': None}, id='run-ad546b5a-70ce-404e-9656-dcc6ecd482d3-0', usage_metadata={'input_tokens': 84, 'output_tokens': 10, 'total_tokens': 94})],
'agent': 'agent'},
'channel_versions': {'__start__': '00000000000000000000000000000002.',
'messages': '00000000000000000000000000000005.16e98d6f7ece7598829eddf1b33a33c4',
'start:agent': '00000000000000000000000000000003.',
'agent': '00000000000000000000000000000005.065d90dd7f7cd091f0233855210bb2af',
'branch:agent:should_continue:tools': '00000000000000000000000000000004.',
'tools': '00000000000000000000000000000005.'},
'versions_seen': {'__input__': {},
'__start__': {'__start__': '00000000000000000000000000000001.ab89befb52cc0e91e106ef7f500ea033'},
'agent': {'start:agent': '00000000000000000000000000000002.d6f25946c3108fc12f27abbcf9b4cedc',
'tools': '00000000000000000000000000000004.022986cd20ae85c77ea298a383f69ba8'},
'tools': {'branch:agent:should_continue:tools': '00000000000000000000000000000003.065d90dd7f7cd091f0233855210bb2af'}},
'pending_sends': [],
'current_tasks': {}}
CheckpointTuple(config={'configurable': {'thread_id': '1', 'checkpoint_ns': '', 'checkpoint_id': '1ef55f2a-3614-69b4-8003-2181cff935cc'}}, checkpoint={'v': 1, 'ts': '2024-08-09T01:56:48.328315+00:00', 'id': '1ef55f2a-3614-69b4-8003-2181cff935cc', 'channel_values': {'messages': [HumanMessage(content="what's the weather in sf", id='f911e000-75a1-41f6-8e38-77bb086c2ecf'), AIMessage(content='', additional_kwargs={'tool_calls': [{'id': 'call_l5e5YcTJDJYOdvi4scBy9n2I', 'function': {'arguments': '{"city":"sf"}', 'name': 'get_weather'}, 'type': 'function'}]}, response_metadata={'token_usage': {'completion_tokens': 14, 'prompt_tokens': 57, 'total_tokens': 71}, 'model_name': 'gpt-4o-mini', 'system_fingerprint': 'fp_48196bc67a', 'finish_reason': 'tool_calls', 'logprobs': None}, id='run-4f1531f1-067c-4e16-8b62-7a6b663e93bd-0', tool_calls=[{'name': 'get_weather', 'args': {'city': 'sf'}, 'id': 'call_l5e5YcTJDJYOdvi4scBy9n2I', 'type': 'tool_call'}], usage_metadata={'input_tokens': 57, 'output_tokens': 14, 'total_tokens': 71}), ToolMessage(content="It's always sunny in sf", name='get_weather', id='e27bb3a1-1798-494a-b4ad-2deadda8b2bf', tool_call_id='call_l5e5YcTJDJYOdvi4scBy9n2I'), AIMessage(content='The weather in San Francisco is always sunny!', response_metadata={'token_usage': {'completion_tokens': 10, 'prompt_tokens': 84, 'total_tokens': 94}, 'model_name': 'gpt-4o-mini', 'system_fingerprint': 'fp_48196bc67a', 'finish_reason': 'stop', 'logprobs': None}, id='run-ad546b5a-70ce-404e-9656-dcc6ecd482d3-0', usage_metadata={'input_tokens': 84, 'output_tokens': 10, 'total_tokens': 94})], 'agent': 'agent'}, 'channel_versions': {'__start__': '00000000000000000000000000000002.', 'messages': '00000000000000000000000000000005.16e98d6f7ece7598829eddf1b33a33c4', 'start:agent': '00000000000000000000000000000003.', 'agent': '00000000000000000000000000000005.065d90dd7f7cd091f0233855210bb2af', 'branch:agent:should_continue:tools': '00000000000000000000000000000004.', 'tools': '00000000000000000000000000000005.'}, 'versions_seen': {'__input__': {}, '__start__': {'__start__': '00000000000000000000000000000001.ab89befb52cc0e91e106ef7f500ea033'}, 'agent': {'start:agent': '00000000000000000000000000000002.d6f25946c3108fc12f27abbcf9b4cedc', 'tools': '00000000000000000000000000000004.022986cd20ae85c77ea298a383f69ba8'}, 'tools': {'branch:agent:should_continue:tools': '00000000000000000000000000000003.065d90dd7f7cd091f0233855210bb2af'}}, 'pending_sends': [], 'current_tasks': {}}, metadata={'source': 'loop', 'writes': {'agent': {'messages': [AIMessage(content='The weather in San Francisco is always sunny!', response_metadata={'token_usage': {'completion_tokens': 10, 'prompt_tokens': 84, 'total_tokens': 94}, 'model_name': 'gpt-4o-mini', 'system_fingerprint': 'fp_48196bc67a', 'finish_reason': 'stop', 'logprobs': None}, id='run-ad546b5a-70ce-404e-9656-dcc6ecd482d3-0', usage_metadata={'input_tokens': 84, 'output_tokens': 10, 'total_tokens': 94})]}}, 'step': 3}, parent_config={'configurable': {'thread_id': '1', 'checkpoint_ns': '', 'checkpoint_id': '1ef55f2a-306f-6252-8002-47c2374ec1f2'}}, pending_writes=[])
[CheckpointTuple(config={'configurable': {'thread_id': '1', 'checkpoint_ns': '', 'checkpoint_id': '1ef55f2a-3614-69b4-8003-2181cff935cc'}}, checkpoint={'v': 1, 'ts': '2024-08-09T01:56:48.328315+00:00', 'id': '1ef55f2a-3614-69b4-8003-2181cff935cc', 'channel_values': {'messages': [HumanMessage(content="what's the weather in sf", id='f911e000-75a1-41f6-8e38-77bb086c2ecf'), AIMessage(content='', additional_kwargs={'tool_calls': [{'id': 'call_l5e5YcTJDJYOdvi4scBy9n2I', 'function': {'arguments': '{"city":"sf"}', 'name': 'get_weather'}, 'type': 'function'}]}, response_metadata={'token_usage': {'completion_tokens': 14, 'prompt_tokens': 57, 'total_tokens': 71}, 'model_name': 'gpt-4o-mini', 'system_fingerprint': 'fp_48196bc67a', 'finish_reason': 'tool_calls', 'logprobs': None}, id='run-4f1531f1-067c-4e16-8b62-7a6b663e93bd-0', tool_calls=[{'name': 'get_weather', 'args': {'city': 'sf'}, 'id': 'call_l5e5YcTJDJYOdvi4scBy9n2I', 'type': 'tool_call'}], usage_metadata={'input_tokens': 57, 'output_tokens': 14, 'total_tokens': 71}), ToolMessage(content="It's always sunny in sf", name='get_weather', id='e27bb3a1-1798-494a-b4ad-2deadda8b2bf', tool_call_id='call_l5e5YcTJDJYOdvi4scBy9n2I'), AIMessage(content='The weather in San Francisco is always sunny!', response_metadata={'token_usage': {'completion_tokens': 10, 'prompt_tokens': 84, 'total_tokens': 94}, 'model_name': 'gpt-4o-mini', 'system_fingerprint': 'fp_48196bc67a', 'finish_reason': 'stop', 'logprobs': None}, id='run-ad546b5a-70ce-404e-9656-dcc6ecd482d3-0', usage_metadata={'input_tokens': 84, 'output_tokens': 10, 'total_tokens': 94})], 'agent': 'agent'}, 'channel_versions': {'__start__': '00000000000000000000000000000002.', 'messages': '00000000000000000000000000000005.16e98d6f7ece7598829eddf1b33a33c4', 'start:agent': '00000000000000000000000000000003.', 'agent': '00000000000000000000000000000005.065d90dd7f7cd091f0233855210bb2af', 'branch:agent:should_continue:tools': '00000000000000000000000000000004.', 'tools': '00000000000000000000000000000005.'}, 'versions_seen': {'__input__': {}, '__start__': {'__start__': '00000000000000000000000000000001.ab89befb52cc0e91e106ef7f500ea033'}, 'agent': {'start:agent': '00000000000000000000000000000002.d6f25946c3108fc12f27abbcf9b4cedc', 'tools': '00000000000000000000000000000004.022986cd20ae85c77ea298a383f69ba8'}, 'tools': {'branch:agent:should_continue:tools': '00000000000000000000000000000003.065d90dd7f7cd091f0233855210bb2af'}}, 'pending_sends': [], 'current_tasks': {}}, metadata={'source': 'loop', 'writes': {'agent': {'messages': [AIMessage(content='The weather in San Francisco is always sunny!', response_metadata={'token_usage': {'completion_tokens': 10, 'prompt_tokens': 84, 'total_tokens': 94}, 'model_name': 'gpt-4o-mini', 'system_fingerprint': 'fp_48196bc67a', 'finish_reason': 'stop', 'logprobs': None}, id='run-ad546b5a-70ce-404e-9656-dcc6ecd482d3-0', usage_metadata={'input_tokens': 84, 'output_tokens': 10, 'total_tokens': 94})]}}, 'step': 3}, parent_config={'configurable': {'thread_id': '1', 'checkpoint_ns': '', 'checkpoint_id': '1ef55f2a-306f-6252-8002-47c2374ec1f2'}}, pending_writes=None),
CheckpointTuple(config={'configurable': {'thread_id': '1', 'checkpoint_ns': '', 'checkpoint_id': '1ef55f2a-306f-6252-8002-47c2374ec1f2'}}, checkpoint={'v': 1, 'ts': '2024-08-09T01:56:47.736251+00:00', 'id': '1ef55f2a-306f-6252-8002-47c2374ec1f2', 'channel_values': {'messages': [HumanMessage(content="what's the weather in sf", id='f911e000-75a1-41f6-8e38-77bb086c2ecf'), AIMessage(content='', additional_kwargs={'tool_calls': [{'id': 'call_l5e5YcTJDJYOdvi4scBy9n2I', 'function': {'arguments': '{"city":"sf"}', 'name': 'get_weather'}, 'type': 'function'}]}, response_metadata={'token_usage': {'completion_tokens': 14, 'prompt_tokens': 57, 'total_tokens': 71}, 'model_name': 'gpt-4o-mini', 'system_fingerprint': 'fp_48196bc67a', 'finish_reason': 'tool_calls', 'logprobs': None}, id='run-4f1531f1-067c-4e16-8b62-7a6b663e93bd-0', tool_calls=[{'name': 'get_weather', 'args': {'city': 'sf'}, 'id': 'call_l5e5YcTJDJYOdvi4scBy9n2I', 'type': 'tool_call'}], usage_metadata={'input_tokens': 57, 'output_tokens': 14, 'total_tokens': 71}), ToolMessage(content="It's always sunny in sf", name='get_weather', id='e27bb3a1-1798-494a-b4ad-2deadda8b2bf', tool_call_id='call_l5e5YcTJDJYOdvi4scBy9n2I')], 'tools': 'tools'}, 'channel_versions': {'__start__': '00000000000000000000000000000002.', 'messages': '00000000000000000000000000000004.b16eb718f179ac1dcde54c5652768cf5', 'start:agent': '00000000000000000000000000000003.', 'agent': '00000000000000000000000000000004.', 'branch:agent:should_continue:tools': '00000000000000000000000000000004.', 'tools': '00000000000000000000000000000004.022986cd20ae85c77ea298a383f69ba8'}, 'versions_seen': {'__input__': {}, '__start__': {'__start__': '00000000000000000000000000000001.ab89befb52cc0e91e106ef7f500ea033'}, 'agent': {'start:agent': '00000000000000000000000000000002.d6f25946c3108fc12f27abbcf9b4cedc'}, 'tools': {'branch:agent:should_continue:tools': '00000000000000000000000000000003.065d90dd7f7cd091f0233855210bb2af'}}, 'pending_sends': [], 'current_tasks': {}}, metadata={'source': 'loop', 'writes': {'tools': {'messages': [ToolMessage(content="It's always sunny in sf", name='get_weather', id='e27bb3a1-1798-494a-b4ad-2deadda8b2bf', tool_call_id='call_l5e5YcTJDJYOdvi4scBy9n2I')]}}, 'step': 2}, parent_config={'configurable': {'thread_id': '1', 'checkpoint_ns': '', 'checkpoint_id': '1ef55f2a-305f-61cc-8001-efac33022ef7'}}, pending_writes=None),
CheckpointTuple(config={'configurable': {'thread_id': '1', 'checkpoint_ns': '', 'checkpoint_id': '1ef55f2a-305f-61cc-8001-efac33022ef7'}}, checkpoint={'v': 1, 'ts': '2024-08-09T01:56:47.729689+00:00', 'id': '1ef55f2a-305f-61cc-8001-efac33022ef7', 'channel_values': {'messages': [HumanMessage(content="what's the weather in sf", id='f911e000-75a1-41f6-8e38-77bb086c2ecf'), AIMessage(content='', additional_kwargs={'tool_calls': [{'id': 'call_l5e5YcTJDJYOdvi4scBy9n2I', 'function': {'arguments': '{"city":"sf"}', 'name': 'get_weather'}, 'type': 'function'}]}, response_metadata={'token_usage': {'completion_tokens': 14, 'prompt_tokens': 57, 'total_tokens': 71}, 'model_name': 'gpt-4o-mini', 'system_fingerprint': 'fp_48196bc67a', 'finish_reason': 'tool_calls', 'logprobs': None}, id='run-4f1531f1-067c-4e16-8b62-7a6b663e93bd-0', tool_calls=[{'name': 'get_weather', 'args': {'city': 'sf'}, 'id': 'call_l5e5YcTJDJYOdvi4scBy9n2I', 'type': 'tool_call'}], usage_metadata={'input_tokens': 57, 'output_tokens': 14, 'total_tokens': 71})], 'agent': 'agent', 'branch:agent:should_continue:tools': 'agent'}, 'channel_versions': {'__start__': '00000000000000000000000000000002.', 'messages': '00000000000000000000000000000003.4dd312547dcca1cf91a19adb620a18d6', 'start:agent': '00000000000000000000000000000003.', 'agent': '00000000000000000000000000000003.065d90dd7f7cd091f0233855210bb2af', 'branch:agent:should_continue:tools': '00000000000000000000000000000003.065d90dd7f7cd091f0233855210bb2af'}, 'versions_seen': {'__input__': {}, '__start__': {'__start__': '00000000000000000000000000000001.ab89befb52cc0e91e106ef7f500ea033'}, 'agent': {'start:agent': '00000000000000000000000000000002.d6f25946c3108fc12f27abbcf9b4cedc'}}, 'pending_sends': [], 'current_tasks': {}}, metadata={'source': 'loop', 'writes': {'agent': {'messages': [AIMessage(content='', additional_kwargs={'tool_calls': [{'id': 'call_l5e5YcTJDJYOdvi4scBy9n2I', 'function': {'arguments': '{"city":"sf"}', 'name': 'get_weather'}, 'type': 'function'}]}, response_metadata={'token_usage': {'completion_tokens': 14, 'prompt_tokens': 57, 'total_tokens': 71}, 'model_name': 'gpt-4o-mini', 'system_fingerprint': 'fp_48196bc67a', 'finish_reason': 'tool_calls', 'logprobs': None}, id='run-4f1531f1-067c-4e16-8b62-7a6b663e93bd-0', tool_calls=[{'name': 'get_weather', 'args': {'city': 'sf'}, 'id': 'call_l5e5YcTJDJYOdvi4scBy9n2I', 'type': 'tool_call'}], usage_metadata={'input_tokens': 57, 'output_tokens': 14, 'total_tokens': 71})]}}, 'step': 1}, parent_config={'configurable': {'thread_id': '1', 'checkpoint_ns': '', 'checkpoint_id': '1ef55f2a-2a52-6a7c-8000-27624d954d15'}}, pending_writes=None),
CheckpointTuple(config={'configurable': {'thread_id': '1', 'checkpoint_ns': '', 'checkpoint_id': '1ef55f2a-2a52-6a7c-8000-27624d954d15'}}, checkpoint={'v': 1, 'ts': '2024-08-09T01:56:47.095456+00:00', 'id': '1ef55f2a-2a52-6a7c-8000-27624d954d15', 'channel_values': {'messages': [HumanMessage(content="what's the weather in sf", id='f911e000-75a1-41f6-8e38-77bb086c2ecf')], 'start:agent': '__start__'}, 'channel_versions': {'__start__': '00000000000000000000000000000002.', 'messages': '00000000000000000000000000000002.52e8b0c387f50c28345585c088150464', 'start:agent': '00000000000000000000000000000002.d6f25946c3108fc12f27abbcf9b4cedc'}, 'versions_seen': {'__input__': {}, '__start__': {'__start__': '00000000000000000000000000000001.ab89befb52cc0e91e106ef7f500ea033'}}, 'pending_sends': [], 'current_tasks': {}}, metadata={'source': 'loop', 'writes': None, 'step': 0}, parent_config={'configurable': {'thread_id': '1', 'checkpoint_ns': '', 'checkpoint_id': '1ef55f2a-2a50-6812-bfff-34e3be35d6f2'}}, pending_writes=None),
CheckpointTuple(config={'configurable': {'thread_id': '1', 'checkpoint_ns': '', 'checkpoint_id': '1ef55f2a-2a50-6812-bfff-34e3be35d6f2'}}, checkpoint={'v': 1, 'ts': '2024-08-09T01:56:47.094575+00:00', 'id': '1ef55f2a-2a50-6812-bfff-34e3be35d6f2', 'channel_values': {'messages': [], '__start__': {'messages': [['human', "what's the weather in sf"]]}}, 'channel_versions': {'__start__': '00000000000000000000000000000001.ab89befb52cc0e91e106ef7f500ea033'}, 'versions_seen': {'__input__': {}}, 'pending_sends': [], 'current_tasks': {}}, metadata={'source': 'input', 'writes': {'messages': [['human', "what's the weather in sf"]]}, 'step': -1}, parent_config=None, pending_writes=None)]
使用异步连接¶
async with AsyncRedisSaver.from_conn_info(
host="localhost", port=6379, db=0
) as checkpointer:
graph = create_react_agent(model, tools=tools, checkpointer=checkpointer)
config = {"configurable": {"thread_id": "2"}}
res = await graph.ainvoke(
{"messages": [("human", "what's the weather in nyc")]}, config
)
latest_checkpoint = await checkpointer.aget(config)
latest_checkpoint_tuple = await checkpointer.aget_tuple(config)
checkpoint_tuples = [c async for c in checkpointer.alist(config)]
{'v': 1,
'ts': '2024-08-09T01:56:49.503241+00:00',
'id': '1ef55f2a-4149-61ea-8003-dc5506862287',
'channel_values': {'messages': [HumanMessage(content="what's the weather in nyc", id='5a106e79-a617-4707-839f-134d4e4b762a'),
AIMessage(content='', additional_kwargs={'tool_calls': [{'id': 'call_TvPLLyhuQQN99EcZc8SzL8x9', 'function': {'arguments': '{"city":"nyc"}', 'name': 'get_weather'}, 'type': 'function'}]}, response_metadata={'token_usage': {'completion_tokens': 15, 'prompt_tokens': 58, 'total_tokens': 73}, 'model_name': 'gpt-4o-mini', 'system_fingerprint': 'fp_48196bc67a', 'finish_reason': 'tool_calls', 'logprobs': None}, id='run-0d6fa3b4-cace-41a8-b025-d01d16f6bbe9-0', tool_calls=[{'name': 'get_weather', 'args': {'city': 'nyc'}, 'id': 'call_TvPLLyhuQQN99EcZc8SzL8x9', 'type': 'tool_call'}], usage_metadata={'input_tokens': 58, 'output_tokens': 15, 'total_tokens': 73}),
ToolMessage(content='It might be cloudy in nyc', name='get_weather', id='922124bd-d3b0-4929-a996-a75d842b8b44', tool_call_id='call_TvPLLyhuQQN99EcZc8SzL8x9'),
AIMessage(content='The weather in NYC might be cloudy.', response_metadata={'token_usage': {'completion_tokens': 9, 'prompt_tokens': 88, 'total_tokens': 97}, 'model_name': 'gpt-4o-mini', 'system_fingerprint': 'fp_48196bc67a', 'finish_reason': 'stop', 'logprobs': None}, id='run-69a10e66-d61f-475e-b7de-a1ecd08a6c3a-0', usage_metadata={'input_tokens': 88, 'output_tokens': 9, 'total_tokens': 97})],
'agent': 'agent'},
'channel_versions': {'__start__': '00000000000000000000000000000002.',
'messages': '00000000000000000000000000000005.2cb29d082da6435a7528b4c917fd0c28',
'start:agent': '00000000000000000000000000000003.',
'agent': '00000000000000000000000000000005.065d90dd7f7cd091f0233855210bb2af',
'branch:agent:should_continue:tools': '00000000000000000000000000000004.',
'tools': '00000000000000000000000000000005.'},
'versions_seen': {'__input__': {},
'__start__': {'__start__': '00000000000000000000000000000001.0e148ae3debe753278387e84f786e863'},
'agent': {'start:agent': '00000000000000000000000000000002.d6f25946c3108fc12f27abbcf9b4cedc',
'tools': '00000000000000000000000000000004.022986cd20ae85c77ea298a383f69ba8'},
'tools': {'branch:agent:should_continue:tools': '00000000000000000000000000000003.065d90dd7f7cd091f0233855210bb2af'}},
'pending_sends': [],
'current_tasks': {}}
CheckpointTuple(config={'configurable': {'thread_id': '2', 'checkpoint_ns': '', 'checkpoint_id': '1ef55f2a-4149-61ea-8003-dc5506862287'}}, checkpoint={'v': 1, 'ts': '2024-08-09T01:56:49.503241+00:00', 'id': '1ef55f2a-4149-61ea-8003-dc5506862287', 'channel_values': {'messages': [HumanMessage(content="what's the weather in nyc", id='5a106e79-a617-4707-839f-134d4e4b762a'), AIMessage(content='', additional_kwargs={'tool_calls': [{'id': 'call_TvPLLyhuQQN99EcZc8SzL8x9', 'function': {'arguments': '{"city":"nyc"}', 'name': 'get_weather'}, 'type': 'function'}]}, response_metadata={'token_usage': {'completion_tokens': 15, 'prompt_tokens': 58, 'total_tokens': 73}, 'model_name': 'gpt-4o-mini', 'system_fingerprint': 'fp_48196bc67a', 'finish_reason': 'tool_calls', 'logprobs': None}, id='run-0d6fa3b4-cace-41a8-b025-d01d16f6bbe9-0', tool_calls=[{'name': 'get_weather', 'args': {'city': 'nyc'}, 'id': 'call_TvPLLyhuQQN99EcZc8SzL8x9', 'type': 'tool_call'}], usage_metadata={'input_tokens': 58, 'output_tokens': 15, 'total_tokens': 73}), ToolMessage(content='It might be cloudy in nyc', name='get_weather', id='922124bd-d3b0-4929-a996-a75d842b8b44', tool_call_id='call_TvPLLyhuQQN99EcZc8SzL8x9'), AIMessage(content='The weather in NYC might be cloudy.', response_metadata={'token_usage': {'completion_tokens': 9, 'prompt_tokens': 88, 'total_tokens': 97}, 'model_name': 'gpt-4o-mini', 'system_fingerprint': 'fp_48196bc67a', 'finish_reason': 'stop', 'logprobs': None}, id='run-69a10e66-d61f-475e-b7de-a1ecd08a6c3a-0', usage_metadata={'input_tokens': 88, 'output_tokens': 9, 'total_tokens': 97})], 'agent': 'agent'}, 'channel_versions': {'__start__': '00000000000000000000000000000002.', 'messages': '00000000000000000000000000000005.2cb29d082da6435a7528b4c917fd0c28', 'start:agent': '00000000000000000000000000000003.', 'agent': '00000000000000000000000000000005.065d90dd7f7cd091f0233855210bb2af', 'branch:agent:should_continue:tools': '00000000000000000000000000000004.', 'tools': '00000000000000000000000000000005.'}, 'versions_seen': {'__input__': {}, '__start__': {'__start__': '00000000000000000000000000000001.0e148ae3debe753278387e84f786e863'}, 'agent': {'start:agent': '00000000000000000000000000000002.d6f25946c3108fc12f27abbcf9b4cedc', 'tools': '00000000000000000000000000000004.022986cd20ae85c77ea298a383f69ba8'}, 'tools': {'branch:agent:should_continue:tools': '00000000000000000000000000000003.065d90dd7f7cd091f0233855210bb2af'}}, 'pending_sends': [], 'current_tasks': {}}, metadata={'source': 'loop', 'writes': {'agent': {'messages': [AIMessage(content='The weather in NYC might be cloudy.', response_metadata={'token_usage': {'completion_tokens': 9, 'prompt_tokens': 88, 'total_tokens': 97}, 'model_name': 'gpt-4o-mini', 'system_fingerprint': 'fp_48196bc67a', 'finish_reason': 'stop', 'logprobs': None}, id='run-69a10e66-d61f-475e-b7de-a1ecd08a6c3a-0', usage_metadata={'input_tokens': 88, 'output_tokens': 9, 'total_tokens': 97})]}}, 'step': 3}, parent_config={'configurable': {'thread_id': '2', 'checkpoint_ns': '', 'checkpoint_id': '1ef55f2a-3d07-647e-8002-b5e4d28c00c9'}}, pending_writes=[])
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