How to stream responses from an LLM

All LLMs implement the Runnable interface, which comes with default implementations of standard runnable methods (i.e. ainvoke, batch, abatch, stream, astream, astream_events).

The default streaming implementations provide anIterator (or AsyncIterator for asynchronous streaming) that yields a single value: the final output from the underlying chat model provider.

The ability to stream the output token-by-token depends on whether the provider has implemented proper streaming support.

See which integrations support token-by-token streaming here.

note

The default implementation does not provide support for token-by-token streaming, but it ensures that the model can be swapped in for any other model as it supports the same standard interface.

Sync stream

Below we use a | to help visualize the delimiter between tokens.

from langchain_openai import OpenAI

llm = OpenAI(model="gpt-3.5-turbo-instruct", temperature=0, max_tokens=512)
for chunk in llm.stream("Write me a 1 verse song about sparkling water."):
    print(chunk, end="|", flush=True)

API Reference:

• OpenAI

|Spark|ling| water|,| oh| so clear|
|Bubbles dancing|,| without| fear|
|Refreshing| taste|,| a| pure| delight|
|Spark|ling| water|,| my| thirst|'s| delight||

Async streaming

Let's see how to stream in an async setting using astream.

from langchain_openai import OpenAI

llm = OpenAI(model="gpt-3.5-turbo-instruct", temperature=0, max_tokens=512)
async for chunk in llm.astream("Write me a 1 verse song about sparkling water."):
    print(chunk, end="|", flush=True)

API Reference:

• OpenAI

|Spark|ling| water|,| oh| so clear|
|Bubbles dancing|,| without| fear|
|Refreshing| taste|,| a| pure| delight|
|Spark|ling| water|,| my| thirst|'s| delight||

Async event streaming

LLMs also support the standard astream events method.

tip

astream_events is most useful when implementing streaming in a larger LLM application that contains multiple steps (e.g., an application that involves an agent).

from langchain_openai import OpenAI

llm = OpenAI(model="gpt-3.5-turbo-instruct", temperature=0, max_tokens=512)

idx = 0

async for event in llm.astream_events(
    "Write me a 1 verse song about goldfish on the moon", version="v1"
):
    idx += 1
    if idx >= 5:  # Truncate the output
        print("...Truncated")
        break
    print(event)

API Reference:

• OpenAI