Introduction
In the ever-evolving realm of artificial intelligence, Large Language Models (LLMs) have emerged as transformative tools, enabling developers to craft applications with unprecedented capabilities. From intelligent chatbots to sophisticated content generators, these models are reshaping industries. LangChain, a robust framework tailored for LLMs, simplifies the integration of these models into practical solutions. This tutorial explores the essential steps to harness LangChain effectively, guiding you through its setup, concepts, and advanced applications.
What are Large Language Models (LLMs)?
Large Language Models (LLMs) are sophisticated AI systems capable of understanding and generating text with remarkable human-like accuracy. Trained on extensive datasets, these models excel at recognizing intricate language patterns, interpreting subtle nuances, and producing coherent outputs. LLMs can handle a wide range of language-based tasks, such as translation, text completion, summarization, and conversational dialogue. A prominent example of an LLM is GPT, which showcases these capabilities across diverse applications.
LLM is a type of Generative AI. If you would like to learn about Generative AI and how it can boost your creativity, check our blogs on Using Generative AI to Boost Your Creativity and our podcast, Inside the Generative AI Revolution. You can also register for our upcoming course on Large Language Models Concepts.
Also Read : Step-by-Step: PDF Chatbots with Langchain and Ollama
Introduction to LangChain
LangChain is an open-source framework designed to streamline the creation of applications powered by large language models (LLMs). It provides a comprehensive suite of tools, components, and interfaces that simplify the development of LLM-driven solutions. With LangChain, developers can effortlessly manage interactions with language models, connect various components seamlessly, and integrate resources such as APIs and databases. For those interested in data engineering and data-focused applications, LangChain offers additional specialized capabilities explored in separate discussions.
The platform includes a robust collection of APIs that developers can embed into their applications, enabling advanced language processing functionalities without the need to build these features from scratch. This efficiency makes LangChain an ideal choice for developers of all expertise levels, whether creating simple prototypes or complex, enterprise-grade applications.
Applications like chatbots, virtual assistants, language translators, and sentiment analysis tools highlight the versatility of LLM-powered solutions. LangChain enables developers to design customized language model-based applications tailored to unique requirements, offering flexibility and precision in addressing specific challenges.
As natural language processing continues to evolve and gain broader adoption, the potential use cases for LLM-based applications are virtually boundless. Below are some key features that make LangChain a standout framework:
- Customizable prompts to align with unique application needs.
- Chain-building components for handling complex workflows and advanced scenarios.
- Model integration to enhance data enrichment and leverage leading LLM capabilities such as GPT and HuggingFace Hub.
- Flexible components designed for mixing and matching to fit specific project requirements.
- Context management to refine understanding and guide interactions, boosting accuracy and user satisfaction.
LangChain’s modular design and powerful features make it a go-to framework for developers looking to harness the transformative potential of LLMs.
Setting up LangChain in Python
Installing LangChain in Python is a simple process. You can use either pip or conda to get started quickly.
Install using pip
pip install langchain
Install using conda
install langchain -c conda-forge
This setup covers the fundamental requirements for using LangChain. However, the true potential of LangChain emerges when it is integrated with various model providers, data stores, and other tools.
By default, these integration-specific dependencies are not included in the standard installation. To install all necessary dependencies, use the following command:
pip install langchain[all]
Another option is to build the library directly from the source. To do this, you can clone the project from its GitHub repo.
Environment setup
Using LangChain typically involves integrating with various model providers, data stores, APIs, and similar components. As with any integration, providing the necessary API keys is essential for LangChain to operate properly. There are two methods to accomplish this:
1. Setting up key as an environment variable
OPENAI_API_KEY="..."
If you prefer not to set an environment variable, you can pass the API key directly using the openai_api_key parameter when initializing the OpenAI LLM class:
2. Directly set up the key in the relevant class
from langchain.llms import OpenAIllm = OpenAI(openai_api_key="...")
Key Components of LangChain
LangChain distinguishes itself with a focus on flexibility and modularity. By breaking down the natural language processing pipeline into individual components, it allows developers to customize workflows to suit their specific needs. This adaptability makes LangChain a perfect choice for building AI applications across a wide range of use cases and industries.
Components and chains
In LangChain, components are modules performing specific functions in the language processing pipeline. These components can be linked into "chains" for tailored workflows, such as a customer service chatbot chain with sentiment analysis, intent recognition, and response generation modules.
Prompt templates
In LangChain, components are specialized modules that carry out specific tasks within the language processing pipeline. These components can be connected into "chains" to create customized workflows, such as a customer service chatbot chain that includes sentiment analysis, intent recognition, and response generation modules.
Vector stores
These are utilized to store and search information through embeddings, which involve analyzing numerical representations of document meanings. The VectorStore acts as a repository for these embeddings, enabling efficient searches based on semantic similarity.
Indexes and retrievers
Indexes function as databases that store information and metadata about the model's training data, while retrievers quickly search these indexes for relevant details. This enhances the model's responses by supplying context and related information.
Output parsers
Output parsers are used to manage and refine the responses generated by the model. They can remove irrelevant content, adjust the output format, or add additional data to the response. In this way, output parsers help transform the language model's output into structured results, such as JSON objects.
Example selectors
Example selectors in LangChain are used to identify relevant instances from the model's training data, enhancing the accuracy and relevance of the generated responses. These selectors can be customized to prioritize specific types of examples or filter out irrelevant ones, ensuring a more tailored AI response based on user input.
Agents
Agents are distinct LangChain instances, each designed with specific prompts, memory, and chains tailored to a particular use case. They can be deployed across multiple platforms, such as web, mobile, and chatbots, serving a diverse range of users.
Also Read : 7 Steps to Master Large Language Models
How to Build A Language Model Application in LangChain
LangChain offers an LLM class that facilitates interaction with multiple language model providers, including OpenAI, Cohere, and Hugging Face. The core functionality of an LLM is text generation, and it is simple to create an application with LangChain that takes a string prompt and returns the generated output.
import os from langchain.llms import OpenAI # Set up the OpenAI API key os.environ["OPENAI_API_KEY"] = "your-api-key" # Initialize the OpenAI LLM with the model llm = OpenAI(model_name="text-ada-001") # Prompt the model response = llm("What is the meaning of life?") # Print the response from the model print(response)
Output:
>>> "The meaning of life is subjective and can vary depending on individual perspectives. Some believe it's about finding happiness, others about seeking knowledge or purpose, while some may turn to philosophy or religion for guidance."
In the example above, we are using the text-ada-001 model from OpenAI. If you'd like to switch to any open-source models from HuggingFace, the change is straightforward:
from langchain.llms import HuggingFace # Initialize the HuggingFace model (replace with any model you prefer) llm = HuggingFace(model_name="distilgpt2") # Prompt the model response = llm("Tell me a joke about data scientists") # Print the response from the model print(response)
If you have multiple prompts, you can send them all at once by passing a list of prompts to the generate method:
llm_response = llm.generate([ "Tell me a joke about data scientists", "Tell me a joke about recruiters", "Tell me a joke about psychologists" ]) # Print the responses from the model for response in llm_response: print(response)
Output:
>>> "Data scientists walk into a bar… but only after performing a data-driven analysis of the best bar to go to.
Recruiters are like software updates—they promise to improve things, but you never quite know when they’ll show up!
A psychologist walks into a bar... but only after analyzing the emotional state of everyone inside first!"
Managing Prompt Templates for LLMs in LangChain
LLMs have unique APIs that may not always behave as expected. Although it might seem straightforward to input prompts in natural language, achieving the desired output often requires adjusting the prompt. This adjustment is called prompt engineering. Once a well-crafted prompt is established, it can be reused as a template for various purposes.
In LangChain, a PromptTemplate allows you to define a prompt template that can be dynamically populated with different values. This is particularly useful when you want to maintain a consistent prompt structure across multiple use cases, while only changing specific parts of the input.
USER_INPUT = 'New York' from langchain.llms import OpenAI from langchain import PromptTemplate llm = OpenAI(model_name="text-davinci-003", openai_api_key=API_KEY) template = """I am visiting {city}. What are the must-see landmarks and hidden gems in the area? Please list them as bullet points with brief descriptions.""" prompt = PromptTemplate( input_variables=["city"], template=template, ) final_prompt = prompt.format(city=USER_INPUT) print(f"LLM Output: {llm(final_prompt)}")
Output:
LLM Output:
- Statue of Liberty: An iconic symbol of freedom, offering panoramic views of New York Harbor.
- Central Park: A vast, green oasis in the heart of Manhattan, perfect for a relaxing stroll or bike ride.
- The High Line: A former rail line turned into a park, offering a unique view of the city’s architecture and green spaces.
Also Read : Step-by-Step: Your Own YouTube and Web Summarizer with LangChain
Combining LLMs and Prompts in Multi-Step Workflows
In the context of LangChain, chaining refers to the process of linking multiple elements, such as LLMs, to create a cohesive application. Some common examples of chaining include:
- Sequentially connecting multiple LLMs, where the output from the first LLM serves as input for the next.
- Integrating LLMs with prompt templates to streamline the process.
- Combining LLMs with external data sources, such as for answering complex questions.
- Utilizing long-term memory to incorporate chat history or other contextual information.
Let’s explore an example of the first scenario, where we take the output of one LLM and use it as input for the next.
from langchain.llms import OpenAI from langchain.chains import LLMChain, SimpleSequentialChain from langchain import PromptTemplate llm = OpenAI(model_name="text-davinci-003", openai_api_key=API_KEY) # First step in the chain template_one = "What is the title of a popular book written by {author}? Just return the book title." first_prompt = PromptTemplate( input_variables=["author"], template=template_one ) chain_one = LLMChain(llm=llm, prompt=first_prompt) # Second step in the chain template_two = "Provide a brief summary of the book titled {book_title}. Just return a concise summary." second_prompt = PromptTemplate( input_variables=["book_title"], template=template_two ) chain_two = LLMChain(llm=llm, prompt=second_prompt) # Combine the first and second chain overall_chain = SimpleSequentialChain(chains=[chain_one, chain_two], verbose=True) # Running the chain with a specific author as input final_answer = overall_chain.run("J.K. Rowling")
Output:
> Entering new chain...
What is the title of a popular book written by J.K. Rowling? Just return the book title.
Output: Harry Potter and the Sorcerer's Stone
> Entering new chain...
Provide a brief summary of the book titled Harry Potter and the Sorcerer's Stone. Just return a concise summary.
Output: A young boy named Harry Potter discovers he is a wizard and attends Hogwarts School of Witchcraft and Wizardry. There, he uncovers the mystery of a magical artifact, the Sorcerer's Stone, and confronts the dark wizard who killed his parents.
Conclusion and Further Learning
Not long ago, we were all amazed by the incredible capabilities of ChatGPT. Yet, the field has rapidly advanced, and today, we have powerful new tools like LangChain that allow us to create equally impressive prototypes on our personal computers in just hours.
LangChain is an open-source Python framework that enables developers to build applications powered by Large Language Models (LLMs). This versatile framework provides an interface to various foundational models, streamlining prompt management and acting as a central hub for components like prompt templates, multiple LLMs, external data, and tools via agents (at the time of writing).
If you're eager to stay ahead in the world of Generative AI and LLMs, don't miss our *Building AI Applications with LangChain and GPT* webinar. In this session, you'll learn the essentials of using LangChain to develop AI applications, discover how to structure your AI projects, and see how to embed text data for optimal performance. We also offer a cheat sheet on the generative AI tools landscape, which covers the different categories of generative AI tools, their use cases, and their impact across industries. Additionally, explore our curated list of the top open-source LLMs to uncover even more powerful resources.
