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How to Stream with LangChain: Complete Tutorials

How to Stream with LangChain: Complete Tutorials

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Unleash your creativity with LangChain's streaming capabilities and innovative chat models in this article showcasing its unique song generation feature!

Once upon a time, in the farthest corners of the Universe, there lived a goldfish. Now, this was no ordinary goldfish. It had a peculiar ability to sing, and not just any random tune, but songs that told tales of mesmerizing worlds and celestial wonders. One day, the goldfish crooned a ballad that revolved around a unique language streaming platform called LangChain.

Just as the goldfish's song echoed the thrill of space adventures, LangChain streams languages in a way that opens up new frontiers in the world of programming. Developed as a versatile solution to language processing, LangChain's streaming capability offers a variety of benefits and a few challenges, with its connection to the Magical Goldfish Song from the Moon being an intriguing part of the story.

Article Summary

  • LangChain is a revolutionary language processing platform that supports streaming.
  • The platform's streaming function is used in applications like the ChatAnthropic model, despite some limitations.
  • This article delves into the workings of LangChain streaming and how it connects to the magical goldfish song from the moon.
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What is LangChain and how does it relate to the magical goldfish song from the moon?

LangChain is an innovative platform that brings the power of language processing to a wide array of applications. It boasts impressive features such as powerful integrations, extensive documentation, and cutting-edge language models. But the standout feature is its streaming capability, a concept that aligns well with a certain goldfish's song echoing from the moon.

The goldfish's song tells a tale of continuous, flowing information, much like a stream. This resonates with how LangChain's streaming works - it continuously processes and delivers language data. This concept is symbolically represented in the goldfish's continuous, flowing melody. Through the goldfish's song, we get a glimpse into the heart of LangChain streaming - a stream of words, sentences, and entire narratives that unfold in the same manner as the captivating song of our lunar goldfish.

What is LangChain streaming?

Streaming, in the context of LangChain, refers to the continuous processing and delivery of language data. All ChatModels in LangChain implement the Runnable interface, which lends itself to a streaming functionality. However, it's worth noting that LangChain's streaming support is somewhat limited.

LangChain's streaming does not support token-by-token streaming. In other words, the streaming function returns an iterator of the final result, which you can loop through to get the chunks of processed data. This is akin to listening to the goldfish's song in its entirety, instead of note by note.

How is LangChain streaming used in practice?

In practice, LangChain's streaming capability is a key part of its functionality. It's used in diverse scenarios, such as when working with a ChatAnthropic model. Here's an example to demonstrate this:

from langchain_community.chat_models import ChatAnthropic
# Set up a chat object with a model called "claude-2"
chat = ChatAnthropic(model="claude-2")
# Use the chat.stream() method to send a prompt and iterate over the resulting chunks
for chunk in chat.stream(prompt="Tell me a story about a goldfish on the moon."):

In this example, the chat.stream() method sends a prompt to the ChatAnthropic model. The model then processes this prompt and yields the resulting chunks, which are printed out. The output is an improvised song by the ChatAnthropic model about a goldfish's adventures on the moon.

This process is a prime illustration of LangChain's streaming in action. You send a sequence to the model, which then processes the sequence and returns an improvised response in the form of chunks. This gives you the flexibility to handle each chunk individually and process them according to your application's needs.

What are the Pros and Cons of LangChain streaming?

LangChain's streaming undoubtedly offers a number of benefits:

  • Integration: LangChain's streaming function integrates seamlessly with various other platforms, allowing you to easily use it with other services or applications.
  • Flexibility: The platform's streaming allows you to handle and process chunks of data individually, giving you greater control over data handling.
  • Efficiency: Streaming in LangChain can lead to more efficient data processing as it allows for continuous, uninterrupted operations.

However, as with any technology, LangChain's streaming also has its limitations:

  • Limited Streaming: LangChain does not support token-by-token streaming. This means that you only get an iterator of the final result, rather than a continuous stream of tokens.
  • Learning Curve: Understanding how to effectively use LangChain's streaming can require a learning curve, especially for those new to the concept of streaming in language processing.

Despite these challenges, the benefits of using LangChain streaming are substantial and its use in applications such as the ChatAnthropic model is a testament to its practical utility. As with the enchanting goldfish song from the moon, LangChain streaming has its own unique rhythm, with each chunk of data adding a new note to the melody.

Examples of LangChain Streaming Applications

LangChain Streaming

To further understand LangChain's streaming mechanism, let's take a look at a few practical applications of LangChain streaming API, LangChain streaming OpenAI, and LangChain streaming FastAPI.

LangChain Streaming API

The LangChain Streaming API provides developers with an interface to work with language data continuously for extended periods. It's made to handle long conversations in a manageable way. Here's a simplified example of using the LangChain Streaming API:

from langchain_community.stream import TextStream 
# Initialize a TextStream
stream = TextStream()
# Send a message and return an iterator of results
for result in stream.send("Tell me a story about a goldfish on the moon."):

Here, after initializing a TextStream, the send method streams a message and yields an iterator of results. You can loop over this iterator to get each result as it comes in.

LangChain Streaming OpenAI

LangChain also allows for easy integration with OpenAI, enabling developers to make the most of OpenAI's powerful machine learning models. Let's look at how you might use LangChain Streaming with OpenAI:

from langchain_community.openai_api import OpenAIStream
# Initialize an OpenAIStream
openai_stream = OpenAIStream()
# Send a prompt and return an iterator of responses
for response in openai_stream.send_prompt("Tell me a story about a goldfish on the moon."):

Here, after initializing an OpenAIStream, the send_prompt method streams a prompt to the OpenAI model and returns an iterator of responses which you can loop through for processing.

LangChain Streaming FastAPI

FastAPI is a modern, fast web framework for building APIs with Python that can be integrated with LangChain to use its streaming feature. Here is a demo of how it can be done:

from fastapi import FastAPI
from langchain_community.fastapi_integration import LangChainStream
app = FastAPI()
async def read_item(prompt: str):
    stream = LangChainStream()
    return stream.send(prompt)

In this example, the send method of LangChainStream is called inside a FastAPI route handler, which streams the given prompt to the LangChain model and returns the responses as a HTTP stream.


When the goldfish sang its magical song from the moon, it gifted us with a melody that flowed uninterrupted, similar to the essence of LangChain streaming. Much like the continuing verses of the song, LangChain allows for a continuous stream of language data to be processed and delivered, thereby adding a fascinating dynamic to language processing.

Despite the few challenges such as no support for token-by-token streaming and a steep learning curve, LangChain's streaming, in essence, provides benefits like seamless integration, flexibility, and increased efficiency that make it an excellent option for language processing applications. Whether it's LangChain Streaming API, LangChain Streaming OpenAI, or LangChain Streaming FastAPI, each instance significantly exemplifies LangChain's capabilities in different scenarios and demonstrates its impressive versatility.

Bearing similar threads to the enchanting saga of the moon-locked goldfish, LangChain's journey of streaming feels like an ethereal song in the midst of the colossal cosmic space. So, as you unravel and explore the magical universe of language processing through LangChain's streaming, be ready to discover thrilling novelties just like the tales crooned by the goldfish, hushed and glowing, under the moon's silver glow.

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