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Merge pull request #1751 from langchain-ai/nc/17sep/stream-messages

Browse Source 
Add stream_mode=messages
This commit is contained in:
Nuno Campos
2024-09-18 14:01:12 -07:00
committed by GitHub
parent 22c78050dd 9235178c88
commit 4ba803b415
8 changed files with 711 additions and 140 deletions
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libs/langgraph/langgraph/pregel/__init__.py
+8 -1
View File
@@ -78,6 +78,7 @@ from langgraph.pregel.debug import tasks_w_writes
from langgraph.pregel.io import read_channels
from langgraph.pregel.loop import AsyncPregelLoop, StreamProtocol, SyncPregelLoop
from langgraph.pregel.manager import AsyncChannelsManager, ChannelsManager
from langgraph.pregel.messages import StreamMessagesHandler
from langgraph.pregel.read import PregelNode
from langgraph.pregel.retry import RetryPolicy
from langgraph.pregel.runner import PregelRunner
@@ -1213,7 +1214,11 @@ class Pregel(Runnable[Union[dict[str, Any], Any], Union[dict[str, Any], Any]]):
interrupt_after=interrupt_after,
debug=debug,
)
# set up messages stream mode
if "messages" in stream_modes:
run_manager.inheritable_handlers.append(
StreamMessagesHandler(stream.put)
)
with SyncPregelLoop(
input,
stream=StreamProtocol(stream.put, stream_modes),
@@ -1234,6 +1239,8 @@ class Pregel(Runnable[Union[dict[str, Any], Any], Union[dict[str, Any], Any]]):
# enable subgraph streaming
if subgraphs:
loop.config["configurable"][CONFIG_KEY_STREAM] = loop.stream
# enable concurrent streaming
if subgraphs or "messages" in stream_modes:
# we are careful to have a single waiter live at any one time
# because on exit we increment semaphore count by exactly 1
waiter: Optional[concurrent.futures.Future] = None
libs/langgraph/langgraph/pregel/algo.py
+16 -15
View File
@@ -350,12 +350,6 @@ def prepare_single_task(
return
# create task id
triggers = [PUSH]
metadata = {
"langgraph_step": step,
"langgraph_node": packet.node,
"langgraph_triggers": triggers,
"langgraph_path": task_path,
}
checkpoint_ns = (
f"{parent_ns}{NS_SEP}{packet.node}" if parent_ns else packet.node
)
@@ -367,6 +361,14 @@ def prepare_single_task(
PUSH,
str(idx),
)
task_checkpoint_ns = f"{checkpoint_ns}:{task_id}"
metadata = {
"langgraph_step": step,
"langgraph_node": packet.node,
"langgraph_triggers": triggers,
"langgraph_path": task_path,
"langgraph_checkpoint_ns": task_checkpoint_ns,
}
if task_id_checksum is not None:
assert task_id == task_id_checksum
if for_execution:
@@ -376,7 +378,6 @@ def prepare_single_task(
if proc.metadata:
metadata.update(proc.metadata)
writes = deque()
task_checkpoint_ns = f"{checkpoint_ns}:{task_id}"
return PregelExecutableTask(
packet.node,
packet.arg,
@@ -461,12 +462,6 @@ def prepare_single_task(
return
# create task id
metadata = {
"langgraph_step": step,
"langgraph_node": name,
"langgraph_triggers": triggers,
"langgraph_path": task_path,
}
checkpoint_ns = f"{parent_ns}{NS_SEP}{name}" if parent_ns else name
task_id = _uuid5_str(
checkpoint_id,
@@ -476,15 +471,21 @@ def prepare_single_task(
PULL,
*triggers,
)
task_checkpoint_ns = f"{checkpoint_ns}:{task_id}"
metadata = {
"langgraph_step": step,
"langgraph_node": name,
"langgraph_triggers": triggers,
"langgraph_path": task_path,
"langgraph_checkpoint_ns": task_checkpoint_ns,
}
if task_id_checksum is not None:
assert task_id == task_id_checksum
if for_execution:
if node := proc.node:
if proc.metadata:
metadata.update(proc.metadata)
writes = deque()
task_checkpoint_ns = f"{checkpoint_ns}:{task_id}"
return PregelExecutableTask(
name,
val,
libs/langgraph/langgraph/pregel/loop.py
+2 -3
View File
@@ -8,7 +8,6 @@ from typing import (
AsyncContextManager,
Callable,
ContextManager,
Iterable,
Iterator,
List,
Literal,
@@ -112,11 +111,11 @@ class StreamProtocol:
modes: Sequence[Literal["values", "updates", "debug"]]
__call__: Callable[[Iterable[Tuple[str, str, Any]]], None]
__call__: Callable[[Tuple[str, str, Any]], None]
def __init__(
self,
__call__: Callable[[Iterable[Tuple[str, str, Any]]], None],
__call__: Callable[[Tuple[str, str, Any]], None],
modes: Sequence[Literal["values", "updates", "debug"]],
) -> None:
self.__call__ = __call__
libs/langgraph/langgraph/pregel/messages.py
+167
View File
@@ -0,0 +1,167 @@
from typing import (
Any,
AsyncIterator,
Callable,
Dict,
Iterator,
List,
Optional,
Sequence,
Tuple,
)
from uuid import UUID, uuid4
from langchain_core.callbacks import BaseCallbackHandler
from langchain_core.messages import BaseMessage
from langchain_core.outputs import ChatGenerationChunk, LLMResult
from langchain_core.tracers._streaming import T, _StreamingCallbackHandler
from langgraph.constants import NS_SEP
class StreamMessagesHandler(BaseCallbackHandler, _StreamingCallbackHandler):
def __init__(self, stream: Callable[[Tuple[str, str, Any]], None]):
self.stream = stream
self.metadata: dict[str, tuple[str, dict[str, Any]]] = {}
self.seen = set()
def _emit(
self,
meta: Tuple[str, dict[str, Any]],
message: BaseMessage,
*,
dedupe: bool = False,
):
ident = id(message)
if dedupe and message.id in self.seen:
return
elif ident in self.seen:
return
else:
if message.id is None:
message.id = str(uuid4())
self.seen.add(ident)
self.seen.add(message.id)
self.stream((meta[0], "messages", (message, meta[1])))
def tap_output_aiter(
self, run_id: UUID, output: AsyncIterator[T]
) -> AsyncIterator[T]:
return output
def tap_output_iter(self, run_id: UUID, output: Iterator[T]) -> Iterator[T]:
return output
def on_chat_model_start(
self,
serialized: dict[str, Any],
messages: list[list[BaseMessage]],
*,
run_id: UUID,
parent_run_id: Optional[UUID] = None,
tags: Optional[list[str]] = None,
metadata: Optional[dict[str, Any]] = None,
**kwargs: Any,
) -> Any:
if metadata:
self.metadata[run_id] = (
tuple(metadata["langgraph_checkpoint_ns"].split(NS_SEP)),
metadata,
)
def on_llm_new_token(
self,
token: str,
*,
chunk: Optional[ChatGenerationChunk] = None,
run_id: UUID,
parent_run_id: Optional[UUID] = None,
**kwargs: Any,
) -> Any:
if not isinstance(chunk, ChatGenerationChunk):
return
if meta := self.metadata.get(run_id):
self._emit(meta, chunk.message)
def on_llm_end(
self,
response: LLMResult,
*,
run_id: UUID,
parent_run_id: Optional[UUID] = None,
**kwargs: Any,
) -> Any:
self.metadata.pop(run_id, None)
def on_llm_error(
self,
error: BaseException,
*,
run_id: UUID,
parent_run_id: Optional[UUID] = None,
**kwargs: Any,
) -> Any:
self.metadata.pop(run_id, None)
def on_chain_start(
self,
serialized: Dict[str, Any],
inputs: Dict[str, Any],
*,
run_id: UUID,
parent_run_id: Optional[UUID] = None,
tags: Optional[List[str]] = None,
metadata: Optional[Dict[str, Any]] = None,
**kwargs: Any,
) -> Any:
if metadata and kwargs.get("name") == metadata.get("langgraph_node"):
self.metadata[run_id] = (
tuple(metadata["langgraph_checkpoint_ns"].split(NS_SEP)),
metadata,
)
def on_chain_end(
self,
response: Any,
*,
run_id: UUID,
parent_run_id: Optional[UUID] = None,
**kwargs: Any,
) -> Any:
if meta := self.metadata.pop(run_id, None):
if isinstance(response, BaseMessage):
self._emit(meta, response, dedupe=True)
elif isinstance(response, Sequence):
for value in response:
if isinstance(value, BaseMessage):
self._emit(meta, value, dedupe=True)
elif isinstance(response, dict):
for value in response.values():
if isinstance(value, BaseMessage):
self._emit(meta, value, dedupe=True)
elif isinstance(value, Sequence):
for item in value:
if isinstance(item, BaseMessage):
self._emit(meta, item, dedupe=True)
elif hasattr(response, "__dir__") and callable(response.__dir__):
for key in dir(response):
try:
value = getattr(response, key)
if isinstance(value, BaseMessage):
self._emit(meta, value, dedupe=True)
elif isinstance(value, Sequence):
for item in value:
if isinstance(item, BaseMessage):
self._emit(meta, item, dedupe=True)
except AttributeError:
pass
def on_chain_error(
self,
error: BaseException,
*,
run_id: UUID,
parent_run_id: Optional[UUID] = None,
**kwargs: Any,
) -> Any:
self.metadata.pop(run_id, None)
libs/langgraph/langgraph/pregel/types.py
+2 -1
View File
@@ -107,11 +107,12 @@ class StateSnapshot(NamedTuple):
All = Literal["*"]
StreamMode = Literal["values", "updates", "debug"]
StreamMode = Literal["values", "updates", "debug", "messages"]
"""How the stream method should emit outputs.
- 'values': Emit all values of the state for each step.
- 'updates': Emit only the node name(s) and updates
that were returned by the node(s) **after** each step.
- 'debug': Emit debug events for each step.
- 'messages': Emit LLM messages token-by-token.
"""
libs/langgraph/tests/fake_chat.py
+86
View File
@@ -0,0 +1,86 @@
import re
from typing import Any, Iterator, List, Optional, cast
from langchain_core.callbacks import CallbackManagerForLLMRun
from langchain_core.language_models.fake_chat_models import GenericFakeChatModel
from langchain_core.messages import AIMessage, AIMessageChunk, BaseMessage
from langchain_core.outputs import ChatGeneration, ChatGenerationChunk, ChatResult
class FakeChatModel(GenericFakeChatModel):
messages: list[BaseMessage]
i: int = 0
def bind_tools(self, functions: list):
return self
def _generate(
self,
messages: List[BaseMessage],
stop: Optional[List[str]] = None,
run_manager: Optional[CallbackManagerForLLMRun] = None,
**kwargs: Any,
) -> ChatResult:
"""Top Level call"""
if self.i >= len(self.messages):
self.i = 0
message = self.messages[self.i]
self.i += 1
if isinstance(message, str):
message_ = AIMessage(content=message)
else:
if hasattr(message, "model_copy"):
message_ = message.model_copy()
else:
message_ = message.copy()
generation = ChatGeneration(message=message_)
return ChatResult(generations=[generation])
def _stream(
self,
messages: List[BaseMessage],
stop: Optional[List[str]] = None,
run_manager: Optional[CallbackManagerForLLMRun] = None,
**kwargs: Any,
) -> Iterator[ChatGenerationChunk]:
"""Stream the output of the model."""
chat_result = self._generate(
messages, stop=stop, run_manager=run_manager, **kwargs
)
if not isinstance(chat_result, ChatResult):
raise ValueError(
f"Expected generate to return a ChatResult, "
f"but got {type(chat_result)} instead."
)
message = chat_result.generations[0].message
if not isinstance(message, AIMessage):
raise ValueError(
f"Expected invoke to return an AIMessage, "
f"but got {type(message)} instead."
)
content = message.content
if content:
# Use a regular expression to split on whitespace with a capture group
# so that we can preserve the whitespace in the output.
assert isinstance(content, str)
content_chunks = cast(list[str], re.split(r"(\s)", content))
for token in content_chunks:
chunk = ChatGenerationChunk(
message=AIMessageChunk(content=token, id=message.id)
)
if run_manager:
run_manager.on_llm_new_token(token, chunk=chunk)
yield chunk
else:
args = message.__dict__
args.pop("type")
chunk = ChatGenerationChunk(message=AIMessageChunk(**args))
if run_manager:
run_manager.on_llm_new_token("", chunk=chunk)
yield chunk
libs/langgraph/tests/test_pregel.py
+237 -80
View File
@@ -74,9 +74,15 @@ from langgraph.pregel.types import PregelTask
from langgraph.store.memory import MemoryStore
from tests.any_str import AnyDict, AnyStr, AnyVersion, UnsortedSequence
from tests.conftest import ALL_CHECKPOINTERS_SYNC, SHOULD_CHECK_SNAPSHOTS
from tests.fake_chat import FakeChatModel
from tests.fake_tracer import FakeTracer
from tests.memory_assert import MemorySaverAssertCheckpointMetadata
from tests.messages import _AnyIdAIMessage, _AnyIdHumanMessage, _AnyIdToolMessage
from tests.messages import (
_AnyIdAIMessage,
_AnyIdAIMessageChunk,
_AnyIdHumanMessage,
_AnyIdToolMessage,
)
# define these objects to avoid importing langchain_core.agents
@@ -3926,16 +3932,9 @@ def test_conditional_entrypoint_graph_state(snapshot: SnapshotAssertion) -> None
def test_prebuilt_tool_chat(snapshot: SnapshotAssertion) -> None:
from langchain_core.language_models.fake_chat_models import (
FakeMessagesListChatModel,
)
from langchain_core.messages import AIMessage, HumanMessage
from langchain_core.tools import tool
class FakeFuntionChatModel(FakeMessagesListChatModel):
def bind_tools(self, functions: list):
return self
@tool()
def search_api(query: str) -> str:
"""Searches the API for the query."""
@@ -3943,8 +3942,8 @@ def test_prebuilt_tool_chat(snapshot: SnapshotAssertion) -> None:
tools = [search_api]
model = FakeFuntionChatModel(
responses=[
model = FakeChatModel(
messages=[
AIMessage(
content="",
tool_calls=[
@@ -4032,6 +4031,161 @@ def test_prebuilt_tool_chat(snapshot: SnapshotAssertion) -> None:
]
}
assert [
c
for c in app.stream(
{"messages": [HumanMessage(content="what is weather in sf")]},
stream_mode="messages",
)
] == [
(
_AnyIdHumanMessage(
content="what is weather in sf",
),
{
"langgraph_step": 0,
"langgraph_node": "__start__",
"langgraph_triggers": ["__start__"],
"langgraph_path": ("__pregel_pull", "__start__"),
"langgraph_checkpoint_ns": AnyStr("__start__:"),
},
),
(
_AnyIdAIMessageChunk(
content="",
tool_calls=[
{
"name": "search_api",
"args": {"query": "query"},
"id": "tool_call123",
"type": "tool_call",
}
],
tool_call_chunks=[
{
"name": "search_api",
"args": '{"query": "query"}',
"id": "tool_call123",
"index": None,
"type": "tool_call_chunk",
}
],
),
{
"langgraph_step": 1,
"langgraph_node": "agent",
"langgraph_triggers": ["start:agent"],
"langgraph_path": ("__pregel_pull", "agent"),
"langgraph_checkpoint_ns": AnyStr("agent:"),
"checkpoint_ns": AnyStr("agent:"),
"ls_provider": "fakechatmodel",
"ls_model_type": "chat",
},
),
(
_AnyIdToolMessage(
content="result for query",
name="search_api",
tool_call_id="tool_call123",
),
{
"langgraph_step": 2,
"langgraph_node": "tools",
"langgraph_triggers": ["branch:agent:should_continue:tools"],
"langgraph_path": ("__pregel_pull", "tools"),
"langgraph_checkpoint_ns": AnyStr("tools:"),
},
),
(
_AnyIdAIMessageChunk(
content="",
tool_calls=[
{
"name": "search_api",
"args": {"query": "another"},
"id": "tool_call234",
"type": "tool_call",
},
{
"name": "search_api",
"args": {"query": "a third one"},
"id": "tool_call567",
"type": "tool_call",
},
],
tool_call_chunks=[
{
"name": "search_api",
"args": '{"query": "another"}',
"id": "tool_call234",
"index": None,
"type": "tool_call_chunk",
},
{
"name": "search_api",
"args": '{"query": "a third one"}',
"id": "tool_call567",
"index": None,
"type": "tool_call_chunk",
},
],
),
{
"langgraph_step": 3,
"langgraph_node": "agent",
"langgraph_triggers": ["tools"],
"langgraph_path": ("__pregel_pull", "agent"),
"langgraph_checkpoint_ns": AnyStr("agent:"),
"checkpoint_ns": AnyStr("agent:"),
"ls_provider": "fakechatmodel",
"ls_model_type": "chat",
},
),
(
_AnyIdToolMessage(
content="result for another",
name="search_api",
tool_call_id="tool_call234",
),
{
"langgraph_step": 4,
"langgraph_node": "tools",
"langgraph_triggers": ["branch:agent:should_continue:tools"],
"langgraph_path": ("__pregel_pull", "tools"),
"langgraph_checkpoint_ns": AnyStr("tools:"),
},
),
(
_AnyIdToolMessage(
content="result for a third one",
name="search_api",
tool_call_id="tool_call567",
),
{
"langgraph_step": 4,
"langgraph_node": "tools",
"langgraph_triggers": ["branch:agent:should_continue:tools"],
"langgraph_path": ("__pregel_pull", "tools"),
"langgraph_checkpoint_ns": AnyStr("tools:"),
},
),
(
_AnyIdAIMessageChunk(
content="answer",
),
{
"langgraph_step": 5,
"langgraph_node": "agent",
"langgraph_triggers": ["tools"],
"langgraph_path": ("__pregel_pull", "agent"),
"langgraph_checkpoint_ns": AnyStr("agent:"),
"checkpoint_ns": AnyStr("agent:"),
"ls_provider": "fakechatmodel",
"ls_model_type": "chat",
},
),
]
assert app.invoke(
{"messages": [HumanMessage(content="what is weather in sf")]},
{"recursion_limit": 2},
@@ -4045,76 +4199,79 @@ def test_prebuilt_tool_chat(snapshot: SnapshotAssertion) -> None:
model.i = 0 # reset the model
assert app.invoke(
{"messages": [HumanMessage(content="what is weather in sf")]},
stream_mode="updates",
) == [
{
"agent": {
"messages": [
_AnyIdAIMessage(
content="",
tool_calls=[
{
"id": "tool_call123",
"name": "search_api",
"args": {"query": "query"},
},
],
)
]
}
},
{
"tools": {
"messages": [
_AnyIdToolMessage(
content="result for query",
name="search_api",
tool_call_id="tool_call123",
)
]
}
},
{
"agent": {
"messages": [
_AnyIdAIMessage(
content="",
tool_calls=[
{
"id": "tool_call234",
"name": "search_api",
"args": {"query": "another"},
},
{
"id": "tool_call567",
"name": "search_api",
"args": {"query": "a third one"},
},
],
)
]
}
},
{
"tools": {
"messages": [
_AnyIdToolMessage(
content="result for another",
name="search_api",
tool_call_id="tool_call234",
),
_AnyIdToolMessage(
content="result for a third one",
name="search_api",
tool_call_id="tool_call567",
),
]
}
},
{"agent": {"messages": [_AnyIdAIMessage(content="answer")]}},
]
assert (
app.invoke(
{"messages": [HumanMessage(content="what is weather in sf")]},
stream_mode="updates",
)[0]["agent"]["messages"]
== [
{
"agent": {
"messages": [
_AnyIdAIMessage(
content="",
tool_calls=[
{
"id": "tool_call123",
"name": "search_api",
"args": {"query": "query"},
},
],
)
]
}
},
{
"tools": {
"messages": [
_AnyIdToolMessage(
content="result for query",
name="search_api",
tool_call_id="tool_call123",
)
]
}
},
{
"agent": {
"messages": [
_AnyIdAIMessage(
content="",
tool_calls=[
{
"id": "tool_call234",
"name": "search_api",
"args": {"query": "another"},
},
{
"id": "tool_call567",
"name": "search_api",
"args": {"query": "a third one"},
},
],
)
]
}
},
{
"tools": {
"messages": [
_AnyIdToolMessage(
content="result for another",
name="search_api",
tool_call_id="tool_call234",
),
_AnyIdToolMessage(
content="result for a third one",
name="search_api",
tool_call_id="tool_call567",
),
]
}
},
{"agent": {"messages": [_AnyIdAIMessage(content="answer")]}},
][0]["agent"]["messages"]
)
assert [
*app.stream({"messages": [HumanMessage(content="what is weather in sf")]})
libs/langgraph/tests/test_pregel_async.py
+193 -40
View File
@@ -77,12 +77,18 @@ from tests.conftest import (
SHOULD_CHECK_SNAPSHOTS,
awith_checkpointer,
)
from tests.fake_chat import FakeChatModel
from tests.fake_tracer import FakeTracer
from tests.memory_assert import (
MemorySaverAssertCheckpointMetadata,
MemorySaverNoPending,
)
from tests.messages import _AnyIdAIMessage, _AnyIdHumanMessage, _AnyIdToolMessage
from tests.messages import (
_AnyIdAIMessage,
_AnyIdAIMessageChunk,
_AnyIdHumanMessage,
_AnyIdToolMessage,
)
pytestmark = pytest.mark.anyio
@@ -3861,15 +3867,39 @@ async def test_conditional_entrypoint_graph_state() -> None:
async def test_prebuilt_tool_chat() -> None:
from langchain_core.language_models.fake_chat_models import (
FakeMessagesListChatModel,
)
from langchain_core.messages import AIMessage, HumanMessage
from langchain_core.tools import tool
class FakeFuntionChatModel(FakeMessagesListChatModel):
def bind_tools(self, functions: list):
return self
model = FakeChatModel(
messages=[
AIMessage(
content="",
tool_calls=[
{
"id": "tool_call123",
"name": "search_api",
"args": {"query": "query"},
},
],
),
AIMessage(
content="",
tool_calls=[
{
"id": "tool_call234",
"name": "search_api",
"args": {"query": "another"},
},
{
"id": "tool_call567",
"name": "search_api",
"args": {"query": "a third one"},
},
],
),
AIMessage(content="answer"),
]
)
@tool()
def search_api(query: str) -> str:
@@ -3878,39 +3908,7 @@ async def test_prebuilt_tool_chat() -> None:
tools = [search_api]
app = create_tool_calling_executor(
FakeFuntionChatModel(
responses=[
AIMessage(
content="",
tool_calls=[
{
"id": "tool_call123",
"name": "search_api",
"args": {"query": "query"},
},
],
),
AIMessage(
content="",
tool_calls=[
{
"id": "tool_call234",
"name": "search_api",
"args": {"query": "another"},
},
{
"id": "tool_call567",
"name": "search_api",
"args": {"query": "a third one"},
},
],
),
AIMessage(content="answer"),
]
),
tools,
)
app = create_tool_calling_executor(model, tools)
assert await app.ainvoke(
{"messages": [HumanMessage(content="what is weather in sf")]}
@@ -3962,6 +3960,161 @@ async def test_prebuilt_tool_chat() -> None:
]
}
assert [
c
for c in app.stream(
{"messages": [HumanMessage(content="what is weather in sf")]},
stream_mode="messages",
)
] == [
(
_AnyIdHumanMessage(
content="what is weather in sf",
),
{
"langgraph_step": 0,
"langgraph_node": "__start__",
"langgraph_triggers": ["__start__"],
"langgraph_path": ("__pregel_pull", "__start__"),
"langgraph_checkpoint_ns": AnyStr("__start__:"),
},
),
(
_AnyIdAIMessageChunk(
content="",
tool_calls=[
{
"name": "search_api",
"args": {"query": "query"},
"id": "tool_call123",
"type": "tool_call",
}
],
tool_call_chunks=[
{
"name": "search_api",
"args": '{"query": "query"}',
"id": "tool_call123",
"index": None,
"type": "tool_call_chunk",
}
],
),
{
"langgraph_step": 1,
"langgraph_node": "agent",
"langgraph_triggers": ["start:agent"],
"langgraph_path": ("__pregel_pull", "agent"),
"langgraph_checkpoint_ns": AnyStr("agent:"),
"checkpoint_ns": AnyStr("agent:"),
"ls_provider": "fakechatmodel",
"ls_model_type": "chat",
},
),
(
_AnyIdToolMessage(
content="result for query",
name="search_api",
tool_call_id="tool_call123",
),
{
"langgraph_step": 2,
"langgraph_node": "tools",
"langgraph_triggers": ["branch:agent:should_continue:tools"],
"langgraph_path": ("__pregel_pull", "tools"),
"langgraph_checkpoint_ns": AnyStr("tools:"),
},
),
(
_AnyIdAIMessageChunk(
content="",
tool_calls=[
{
"name": "search_api",
"args": {"query": "another"},
"id": "tool_call234",
"type": "tool_call",
},
{
"name": "search_api",
"args": {"query": "a third one"},
"id": "tool_call567",
"type": "tool_call",
},
],
tool_call_chunks=[
{
"name": "search_api",
"args": '{"query": "another"}',
"id": "tool_call234",
"index": None,
"type": "tool_call_chunk",
},
{
"name": "search_api",
"args": '{"query": "a third one"}',
"id": "tool_call567",
"index": None,
"type": "tool_call_chunk",
},
],
),
{
"langgraph_step": 3,
"langgraph_node": "agent",
"langgraph_triggers": ["tools"],
"langgraph_path": ("__pregel_pull", "agent"),
"langgraph_checkpoint_ns": AnyStr("agent:"),
"checkpoint_ns": AnyStr("agent:"),
"ls_provider": "fakechatmodel",
"ls_model_type": "chat",
},
),
(
_AnyIdToolMessage(
content="result for another",
name="search_api",
tool_call_id="tool_call234",
),
{
"langgraph_step": 4,
"langgraph_node": "tools",
"langgraph_triggers": ["branch:agent:should_continue:tools"],
"langgraph_path": ("__pregel_pull", "tools"),
"langgraph_checkpoint_ns": AnyStr("tools:"),
},
),
(
_AnyIdToolMessage(
content="result for a third one",
name="search_api",
tool_call_id="tool_call567",
),
{
"langgraph_step": 4,
"langgraph_node": "tools",
"langgraph_triggers": ["branch:agent:should_continue:tools"],
"langgraph_path": ("__pregel_pull", "tools"),
"langgraph_checkpoint_ns": AnyStr("tools:"),
},
),
(
_AnyIdAIMessageChunk(
content="answer",
),
{
"langgraph_step": 5,
"langgraph_node": "agent",
"langgraph_triggers": ["tools"],
"langgraph_path": ("__pregel_pull", "agent"),
"langgraph_checkpoint_ns": AnyStr("agent:"),
"checkpoint_ns": AnyStr("agent:"),
"ls_provider": "fakechatmodel",
"ls_model_type": "chat",
},
),
]
assert [
c
async for c in app.astream(