Streaming in production in LangChain

Introduction

Streaming lets your app show results bit by bit as they come. This makes users feel the app is faster and more responsive.

When you want to display partial answers from a language model as soon as they are ready.

When handling long responses that take time to generate, so users see progress.

When building chatbots or assistants that reply in real-time.

When you want to reduce waiting time and improve user experience in production apps.

Syntax

LangChain

from langchain.llms import OpenAI
from langchain.callbacks.streaming_stdout import StreamingStdOutCallbackHandler

llm = OpenAI(streaming=True, callbacks=[StreamingStdOutCallbackHandler()])
response = llm("Tell me a story.")

Set streaming=True to enable streaming mode.

Pass a list of callbacks to handle streamed tokens as they arrive.

Examples

This example streams the poem tokens to the console as they arrive.

LangChain

from langchain.llms import OpenAI
from langchain.callbacks.streaming_stdout import StreamingStdOutCallbackHandler

llm = OpenAI(streaming=True, callbacks=[StreamingStdOutCallbackHandler()])
response = llm("Write a poem about spring.")

Custom callback class to handle each new token received from the model.

LangChain

from langchain.llms import OpenAI

class MyStreamHandler:
    def on_llm_new_token(self, token: str):
        print(f"Received token: {token}")

handler = MyStreamHandler()
llm = OpenAI(streaming=True, callbacks=[handler])
llm("Explain photosynthesis.")

Sample Program

This program streams the answer token by token to the console, then prints the full answer.

LangChain

from langchain.llms import OpenAI
from langchain.callbacks.streaming_stdout import StreamingStdOutCallbackHandler

# Enable streaming to print tokens as they come
llm = OpenAI(streaming=True, callbacks=[StreamingStdOutCallbackHandler()])

# Ask the model a question
response = llm("What is the capital of France?")

print(f"\nFull response: {response}")

Important Notes

Streaming requires your model provider to support token streaming.

Callbacks let you customize what happens with each token (e.g., update UI, log, or process).

Streaming improves user experience by showing partial results early.

Summary

Streaming sends tokens as soon as they are generated.

Use streaming=True and callbacks to enable streaming in LangChain.

Streaming is great for chatbots and apps needing fast, live feedback.

Practice

1. What does enabling streaming=True in LangChain do?

easy

A. It sends tokens immediately as they are generated.

B. It delays token sending until the entire response is ready.

C. It disables callbacks for token processing.

D. It caches all tokens before sending them.

Solution

1. Step 1: Understand streaming behavior in LangChain

   Streaming means tokens are sent one by one as soon as they are generated, not waiting for the full response.

2. Step 2: Match streaming=True effect

   Setting streaming=True activates this immediate token sending behavior.

3. Final Answer:

   It sends tokens immediately as they are generated. -> Option A

4. Quick Check:

   Streaming = immediate token sending [OK]

Hint: Streaming means tokens flow out live, not delayed [OK]

Common Mistakes:

• Thinking streaming buffers all tokens first
• Confusing streaming with disabling callbacks
• Assuming streaming delays output

2. Which of the following is the correct way to enable streaming with callbacks in LangChain?

easy

A. llm = OpenAI(streaming=True, callbacks=[MyCallbackHandler()])

B. llm = OpenAI(streaming=False, callbacks=MyCallbackHandler)

C. llm = OpenAI(callbacks=True, streaming=[MyCallbackHandler()])

D. llm = OpenAI(stream=True, callback=[MyCallbackHandler()])

Solution

1. Step 1: Recall correct parameter names

   LangChain's OpenAI class uses 'streaming=True' and 'callbacks' as a list of handlers.

2. Step 2: Check each option's syntax

   llm = OpenAI(streaming=True, callbacks=[MyCallbackHandler()]) correctly uses streaming=True and callbacks as a list. Others misuse parameter names or types.

3. Final Answer:

   llm = OpenAI(streaming=True, callbacks=[MyCallbackHandler()]) -> Option A

4. Quick Check:

   Correct params: streaming=True, callbacks=[handler] [OK]

Hint: Use streaming=True and callbacks as a list [OK]

Common Mistakes:

• Using streaming=False to try enabling streaming
• Passing callbacks as a single object, not a list
• Misspelling parameter names like 'stream' or 'callback'

3. Given this code snippet:

from langchain.callbacks.base import BaseCallbackHandler

class PrintTokens(BaseCallbackHandler):
    def on_llm_new_token(self, token: str, **kwargs):
        print(token, end='')

llm = OpenAI(streaming=True, callbacks=[PrintTokens()])
llm('Hello world')

What will be the output behavior?

medium

A. Prints 'Hello world' all at once after generation completes.

B. Raises a syntax error due to missing imports.

C. Prints nothing because callbacks are not supported.

D. Prints each token of 'Hello world' immediately as it is generated.

Solution

1. Step 1: Understand the callback handler

   The PrintTokens class prints each token immediately when on_llm_new_token is called.

2. Step 2: Streaming enabled triggers token callbacks live

   With streaming=True, tokens are sent and printed one by one as generated.

3. Final Answer:

   Prints each token of 'Hello world' immediately as it is generated. -> Option D

4. Quick Check:

   Streaming + on_llm_new_token = live token print [OK]

Hint: Streaming with on_llm_new_token prints tokens live [OK]

Common Mistakes:

• Expecting full output after completion
• Assuming callbacks don't work with streaming
• Missing that print uses end='' to avoid newlines

4. What is the main issue with this code snippet for streaming in LangChain?

llm = OpenAI(streaming=True, callbacks=PrintTokens())
llm('Test')

medium

A. The PrintTokens class is missing required methods.

B. streaming=True is not a valid parameter for OpenAI.

C. Callbacks must be passed as a list, not a single instance.

D. The llm call should be awaited with async syntax.

Solution

1. Step 1: Check callback parameter type

   LangChain expects callbacks as a list, even if only one handler is used.

2. Step 2: Identify error cause

   Passing callbacks=PrintTokens() (not in a list) causes a type error or unexpected behavior.

3. Final Answer:

   Callbacks must be passed as a list, not a single instance. -> Option C

4. Quick Check:

   Callbacks = list of handlers [OK]

Hint: Always wrap callbacks in a list, even if one [OK]

Common Mistakes:

• Passing a single callback object directly
• Assuming streaming=True is invalid
• Forgetting to implement callback methods

5. You want to build a chatbot that shows user responses token-by-token as they are generated. Which combination of LangChain features should you use in production?

hard

A. Use streaming=True with callbacks, but disable token printing to improve speed.

B. Use streaming=True with a callback handler implementing on_llm_new_token to display tokens live.

C. Use streaming=True but no callbacks, then print the final output after completion.

D. Use streaming=False and collect all tokens before displaying the full response.

Solution

1. Step 1: Identify streaming usage for live token display

   Streaming must be enabled to get tokens as they generate, not after full response.

2. Step 2: Use callback handler to process tokens live

   Implementing on_llm_new_token in a callback lets you display tokens immediately.

3. Step 3: Confirm best practice for production chatbot

   Combining streaming=True with a callback that prints tokens live is the correct approach.

4. Final Answer:

   Use streaming=True with a callback handler implementing on_llm_new_token to display tokens live. -> Option B

5. Quick Check:

   Streaming + on_llm_new_token = live chatbot tokens [OK]

Hint: Streaming plus on_llm_new_token callback shows tokens live [OK]

Common Mistakes:

• Disabling streaming and expecting live tokens
• Not using callbacks to handle tokens
• Printing tokens only after full response