LCEL Interface
Last updated
Last updated
Author: JeongGi Park
Design:
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This is a part of LangChain Open Tutorial
The LangChain Expression Language (LCEL) is a powerful interface designed to simplify the creation and management of custom chains in LangChain. It implements the Runnable protocol, providing a standardized way to build and execute language model chains.
Set up the environment. You may refer to Environment Setup for more details.
[Note]
langchain-opentutorial is a package that provides a set of easy-to-use environment setup, useful functions and utilities for tutorials.
You can checkout the langchain-opentutorial for more details.
set environment variables is in .env.
Copy the contents of .env_sample and load it into your .env with the key you set.
To make it as easy as possible to create custom chains, we've implemented the Runnable protocol.
The Runnable protocol is implemented in most components.
It is a standard interface that makes it easy to define custom chains and call them in a standard way. The standard interface includes
stream: Streams a chunk of the response.
invoke: Invoke a chain on an input.
batch: Invoke a chain against a list of inputs.
There are also asynchronous methods
astream: Stream chunks of the response asynchronously.
ainvoke: Invoke a chain asynchronously on an input.
abatch: Asynchronously invoke a chain against a list of inputs.
astream_log: Streams the final response as well as intermediate steps as they occur.
We provide multiple ways to log traces to LangSmith. Below, we'll highlight how to use traceable().
Use the code below to record a trace in LangSmith
Create a chain using LCEL syntax.
This function uses the chain.stream method to create a stream of data for a given topic, iterating over it and immediately outputting the content of each piece of data. The end="" argument disables newlines after output, and the flush=True argument causes the output buffer to be emptied immediately.
The multimodal approach involves using multiple modes of communication, such as visual, auditory, and kinesthetic, to enhance learning and understanding. By incorporating different sensory inputs, learners are able to engage with material in a more holistic and immersive way. This approach is especially effective in catering to diverse learning styles and preferences.
The invoke method of a chain object takes a topic as an argument and performs processing on that topic.
'ChatGPT is an AI-powered chatbot that uses natural language processing to engage in conversations with users. It is trained on a vast amount of text data to generate human-like responses and provide helpful information. ChatGPT can be used for customer support, virtual assistance, and general chit-chat.'
The function chain.batch takes a list containing multiple dictionaries as arguments and performs batch processing using the values of the topic key in each dictionary.
['ChatGPT is a state-of-the-art conversational AI developed by OpenAI, capable of generating human-like responses in natural language conversations. It can understand and respond to a wide range of topics, providing engaging and coherent interactions with users. ChatGPT has been trained on massive amounts of text data to improve its language understanding and generate more contextually relevant responses.', 'Instagram is a social media platform where users can share photos and videos with their followers. It allows users to edit and filter their photos before posting them, as well as engage with other users through likes, comments, and direct messages. With over a billion active users, Instagram has become a popular way for people to connect, discover new content, and express themselves creatively.']
You can use the max_concurrency parameter to set the number of concurrent requests | The config dictionary uses the max_concurrency key to set the maximum number of operations that can be processed concurrently. Here, it is set to process up to three jobs concurrently.
['ChatGPT is an AI-powered chatbot developed by OpenAI that is capable of engaging in natural and human-like conversations. It uses a deep learning model trained on a vast amount of text data to generate responses and hold meaningful interactions with users. ChatGPT can assist with answering questions, providing recommendations, and engaging in casual conversation on a wide range of topics.', 'Instagram is a popular social media platform that allows users to share photos and videos with their followers. Users can also interact with each other by liking, commenting, and sharing posts. The platform also offers various filters and editing tools to enhance the visual appeal of the content.', 'Multimodal refers to the use of multiple modes of communication or expression, such as text, images, sound, and video, to convey information. It emphasizes the importance of utilizing different sensory channels to enhance understanding and engagement. By incorporating various modes of communication, multimodal approaches can cater to diverse learning styles and preferences.', 'Programming is the process of writing instructions for a computer to follow in order to perform a specific task. It involves using a programming language to create algorithms and code that can be executed by a computer. Programmers use their problem-solving skills and logical thinking to create efficient and effective solutions to various problems.', 'Machine learning is a type of artificial intelligence that involves training algorithms to learn from data and make predictions or decisions without being explicitly programmed. It uses statistical techniques to enable computers to improve their performance on a specific task through experience. Machine learning is used in a wide range of applications, from image and speech recognition to recommendation systems and autonomous vehicles.']
The function chain.astream creates an asynchronous stream and processes messages for a given topic asynchronously.
It uses an asynchronous for loop (async for) to sequentially receive messages from the stream, and the print function to immediately print the contents of the messages (s.content). end="" disables line wrapping after printing, and flush=True forces the output buffer to be emptied to ensure immediate printing.
YouTube is a popular video-sharing platform where users can upload, view, and share videos on a wide range of topics. It has become a go-to source for entertainment, education, and news, with millions of videos being watched daily by users all around the world. Content creators can monetize their videos through advertising, sponsorships, and merchandise sales.
The ainvoke method of a chain object performs an operation asynchronously with the given arguments. Here, we are passing a dictionary with a key named topic and a value named NVDA (NVIDIA's ticker) as arguments. This method can be used to asynchronously request processing for a specific topic.
'The National Vision Doctor of Optometry Association (NVDA) is a professional organization representing optometrists across the United States. They provide resources, support, and advocacy for optometrists to ensure high-quality eye care for patients. The NVDA also works to promote the importance of regular eye exams and vision health awareness.'
The function abatch batches a series of actions asynchronously.
In this example, we are using the abatch method of the chain object to asynchronously process actions on topic .
The await keyword is used to wait for those asynchronous tasks to complete.
['YouTube is a popular video-sharing platform where users can upload, share, and view a wide variety of content. It has millions of users worldwide and offers a vast array of videos, including music, tutorials, vlogs, and more. Users can also subscribe to channels, create playlists, and engage with other viewers through comments and likes.', 'Instagram is a popular social media platform where users can share photos and videos with their followers. It allows users to apply filters and editing tools to enhance their content before posting. Users can also interact with others by liking, commenting, and messaging on their posts.', 'Facebook is a social media platform that allows users to connect with friends and family, share photos and updates, and discover news and events. Users can create personal profiles, join groups, and interact with others through likes, comments, and messages. Mark Zuckerberg founded Facebook in 2004, and it has since become one of the most popular and influential social networking sites in the world.']
Let's take a look at how the LangChain Expression Language supports parallel requests. For example, when you use RunnableParallel (often written in dictionary form), you execute each element in parallel.
Here's an example of running two tasks in parallel using the RunnableParallel class in the langchain_core.runnables module.
Create two chains (chain1, chain2) that use the ChatPromptTemplate.from_template method to get the capital and area for a given country.
These chains are connected via the model and pipe (|) operators, respectively. Finally, we use the RunnableParallel class to combine these two chains with the keys capital and area to create a combined object that can be run in parallel.
The chain1.invoke() function calls the invoke method of the chain1 object.
As an argument, it passes a dictionary with the value Canada in the key named country.
'The capital of Canada is Ottawa.'
Call chain2.invoke(), this time passing a different country, the United States, for the country key.
'The total area of the United States is approximately 3.8 million square miles.'
The invoke method of the combined object performs the processing for the given country.
In this example, the topic USA is passed to the invoke method to run.
{'capital': 'Washington, D.C.', 'area': 'The total area of the United States is approximately 3.8 million square miles.'}
Parallelism can be combined with other executable code. Let's try using parallelism with batch.
The chain1.batch function takes a list containing multiple dictionaries as an argument, and processes the values corresponding to the "topic" key in each dictionary. In this example, we're batch processing two topics, "Canada" and "United States".
['Ottawa', 'The capital of the United States of America is Washington, D.C.']
The chain2.batch function takes in multiple dictionaries as a list and performs batch processing.
In this example, we request processing for two countries, Canada and the United States.
['The total area of Canada is approximately 9.98 million square kilometers.', 'The total area of the United States of America is approximately 3.8 million square miles.']
The combined.batch function is used to process the given data in batches.
In this example, it takes a list containing two dictionary objects as arguments and batches data for two countries, Canada and the United States, respectively.
[{'capital': 'The capital of Canada is Ottawa.', 'area': 'The total land area of Canada is approximately 9.98 million square kilometers.'}, {'capital': 'The capital of USA is Washington, D.C.', 'area': 'The total land area of the United States is approximately 3.8 million square miles (9.8 million square kilometers).'}]
