How to stream custom data¶
Prerequisites
This guide assumes familiarity with the following:
The most common use case for streaming from inside a node is to stream LLM tokens, but you may also want to stream custom data.
For example, if you have a long-running tool call, you can dispatch custom events between the steps and use these custom events to monitor progress. You could also surface these custom events to an end user of your application to show them how the current task is progressing.
You can do so in two ways:
* using graph's .stream / .astream methods with stream_mode="custom"
* emitting custom events using adispatch_custom_events.
Below we'll see how to use both APIs.
Setup¶
First, let's install our required packages
Set up LangSmith for LangGraph development
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Stream custom data using .stream / .astream¶
Define the graph¶
from langchain_core.messages import AIMessage
from langgraph.graph import START, StateGraph, MessagesState, END
from langgraph.types import StreamWriter
async def my_node(
state: MessagesState,
writer: StreamWriter, # <-- provide StreamWriter to write chunks to be streamed
):
chunks = [
"Four",
"score",
"and",
"seven",
"years",
"ago",
"our",
"fathers",
"...",
]
for chunk in chunks:
# write the chunk to be streamed using stream_mode=custom
writer(chunk)
return {"messages": [AIMessage(content=" ".join(chunks))]}
# Define a new graph
workflow = StateGraph(MessagesState)
workflow.add_node("model", my_node)
workflow.add_edge(START, "model")
workflow.add_edge("model", END)
app = workflow.compile()
Stream content¶
from langchain_core.messages import HumanMessage
inputs = [HumanMessage(content="What are you thinking about?")]
async for chunk in app.astream({"messages": inputs}, stream_mode="custom"):
print(chunk, flush=True)
You will likely need to use multiple streaming modes as you will want access to both the custom data and the state updates.
from langchain_core.messages import HumanMessage
inputs = [HumanMessage(content="What are you thinking about?")]
async for chunk in app.astream({"messages": inputs}, stream_mode=["custom", "updates"]):
print(chunk, flush=True)
('custom', 'Four')
('custom', 'score')
('custom', 'and')
('custom', 'seven')
('custom', 'years')
('custom', 'ago')
('custom', 'our')
('custom', 'fathers')
('custom', '...')
('updates', {'model': {'messages': [AIMessage(content='Four score and seven years ago our fathers ...', additional_kwargs={}, response_metadata={})]}})
Stream custom data using .astream_events¶
If you are already using graph's .astream_events method in your workflow, you can also stream custom data by emitting custom events using adispatch_custom_event
ASYNC IN PYTHON<=3.10
LangChain cannot automatically propagate configuration, including callbacks necessary for `astream_events()`, to child runnables if you are running async code in python<=3.10. This is a common reason why you may fail to see events being emitted from custom runnables or tools. If you are running python<=3.10, you will need to manually propagate the `RunnableConfig` object to the child runnable in async environments. For an example of how to manually propagate the config, see the implementation of the node below with `adispatch_custom_event`. If you are running python>=3.11, the `RunnableConfig` will automatically propagate to child runnables in async environment. However, it is still a good idea to propagate the `RunnableConfig` manually if your code may run in other Python versions.
Define the graph¶
from langchain_core.runnables import RunnableConfig, RunnableLambda
from langchain_core.callbacks.manager import adispatch_custom_event
async def my_node(state: MessagesState, config: RunnableConfig):
chunks = [
"Four",
"score",
"and",
"seven",
"years",
"ago",
"our",
"fathers",
"...",
]
for chunk in chunks:
await adispatch_custom_event(
"my_custom_event",
{"chunk": chunk},
config=config, # <-- propagate config
)
return {"messages": [AIMessage(content=" ".join(chunks))]}
# Define a new graph
workflow = StateGraph(MessagesState)
workflow.add_node("model", my_node)
workflow.add_edge(START, "model")
workflow.add_edge("model", END)
app = workflow.compile()
Stream content¶
from langchain_core.messages import HumanMessage
inputs = [HumanMessage(content="What are you thinking about?")]
async for event in app.astream_events({"messages": inputs}, version="v2"):
tags = event.get("tags", [])
if event["event"] == "on_custom_event" and event["name"] == "my_custom_event":
data = event["data"]
if data:
print(data["chunk"], end="|", flush=True)