The world of AI and machine learning is vast, and with the advent of tools like LangChain, it’s becoming more accessible to developers and enthusiasts alike.
So you want to be an LLM Developer? LangChain: Bridging the Gap for Beginners
The LangChain Course by SamurAiGPT for Beginners, hosted on GitHub, offers a deep dive into this powerful open-source framework. However, for those just dipping their toes into this realm, the course might seem a tad overwhelming.
Before we dive in, ensure you have a local copy of the LangChain Course for Beginners repository on your machine. If you’re unfamiliar with how to clone a GitHub repository, here’s a quick guide to help you out.
To understand, the power and potential of LangChain we have to first understand what this tool is used for. In a nutshell, the framework is primarily used for tasks like document analysis and summarization, chatbots, and code analysis.
LangChain stands out in the AI landscape for its versatility and user-friendly approach to building applications using large language models.
Whether you’re looking to develop chatbots, AI-driven apps, or any tool harnessing the power of language models, LangChain offers a robust framework to bring your ideas to life.
Its open-source nature means a community of developers continually refine and expand it, ensuring it remains at the forefront of AI development.
Setting the Stage
Building a Strong Foundation: Before diving into LangChain, it’s essential to have a foundational understanding of certain tools and concepts:
- Machine Learning Concepts: While not mandatory, a basic grasp of machine learning can enhance your experience with LangChain. Websites like Coursera offer beginner courses that can set you on the right path.
Diving into the Course
Your First Steps with LangChain: With the basics in place, let’s delve into the course.
🦜🔗 LangChain 0.0.177 by Ankur Singh
- What is LangChain?
- Key Components of LangChain
- Applications and Integrations
- Getting Started: Basic LangChain Setup
- Building a Simple Application with LangChain
- Resources and Further Learning
LangChain Integration with LLM: The document mentions the integration of LangChain with LLM (Large Language Models) and how it can be combined with external data and applications.
Building an Application: There’s a reference to a Colab notebook that provides guidance on building an application using LangChain.
Resources for Further Learning:
LLM Integration: The document highlights the process of question similarity search, providing relevant context, and generating answers using integrations and pipelines.
LangChain is a software development framework designed to simplify the creation of applications that use large language models (LLMs) like OpenAI’s GPT.
It was launched in October 2022 by Harrison Chase.
- Ease of Use: Simplifies the integration of LLMs into applications.
- Flexibility: Integrates with various systems and services.
- Chain Mechanism: Allows for complex, multi-step processes.
- Memory Functionality: Supports the concept of a memory for chain objects.
- Customizable Chains: Developers can create custom chains.
- Debugging: Facilitated by setting the verbose mode to true.
Usage of LangChain:
- LangChain is a Python library that helps interact with LLMs like ChatGPT.
- It can be used to generate text, similar to applications like JasperAI and CopyAI.
- Libraries required:
- Demonstrates how to generate an article outline using LangChain and GPT.
Prompts and Prompt Templates:
- Explains the concept of prompts and prompt templates.
- Demonstrates how to create dynamic prompts using the
- Chains are responsible for the entire data flow inside LangChain.
- Demonstrates how to use the
LLMChainto generate content based on dynamic input.
- Shows how to extend it for multi-input prompts.
- Describes how to combine multiple chains using the
- Demonstrates how to generate a blog article based on a given outline.
Chains in LangChain:
- Chains are the foundational elements of LangChain.
- They represent a sequence of components executed in a specific order.
- Different chains have distinct functionalities, with LLMChain being the most basic.
- It uses a PromptTemplate to format user input into a specific prompt.
- The advantage is the dynamic input change using templates, allowing direct use without starting from scratch.
This chain combines multiple chains, allowing the output of one chain to be the input for the next.
It’s useful for multi-step processes, like creating a blog outline and then generating a full article based on that outline.
Retrieval QA Chain:
- This chain is essential for performing QA over various document types, including PDFs, web pages, videos, etc.
- It allows for querying specific data from large documents.
- This chain is designed for summarizing documents.
- It breaks the input into smaller chunks, summarizes each chunk, and then creates a final summary of those summaries.
- Useful when there are multiple chains for different tasks.
- It determines the appropriate chain to use based on the input provided.
- The text offers practical examples of how to use each chain, including code snippets for setting up and running the chains.
- For instance, the LLMChain example demonstrates how to generate tweet ideas based on given topics, while the Sequential Chain example shows how to create a blog outline and then a full article based on that outline.
LangChain provides a versatile framework for chaining together different processes, making it easier to handle complex tasks and workflows in natural language processing.
The section provides a comprehensive guide on how to create a chatbot using ChatGPT, trained on custom data. The primary challenge addressed is the character input limit of ChatGPT. To overcome this, the data is split into chunks, and only relevant chunks are passed to ChatGPT when a query is made. The process involves:
Computers understand numbers, not text. To make text understandable to computers, it’s converted into numbers while preserving the text’s meaning. This conversion is called embeddings. OpenAI’s Ada is used to create these embeddings.
Vector Database (db)
Once embeddings are created, they need to be stored for quick retrieval. Traditional databases aren’t efficient for this, so specialized databases called vector dbs are used. Langchain uses Chroma db.
Flow for Question Answering:
- User poses a question.
- A similarity search identifies similar text from the vector store/db.
- The question, combined with relevant documents, is passed to ChatGPT to generate an answer.
It’s an abstract in Langchain that fetches training data from various sources. In this tutorial, a text document is loaded using TextLoader.
The input data is split into chunks using document splitters. The tutorial uses a TextSplitter that splits documents based on character limits, allowing some overlap to maintain context.
The chunks are then converted into embeddings and stored in Chroma db.
A Q&A chain is created, which can be queried to get answers based on the training data. The chain uses a retriever created from the index.
Key Takeaways for Developers
Scalability Challenge: Directly feeding all document data to ChatGPT isn’t scalable due to its input character limit. Splitting data into chunks is the solution.
Importance of Embeddings: Converting text into meaningful numbers (embeddings) is crucial for computers to understand and process the text.
Vector Database: Use specialized databases like Chroma db for storing and quickly retrieving embeddings.
Document Handling: Understand the importance of document loaders and splitters. Ensure data is split in a way that context isn’t lost.
Q&A Chain: The final step involves creating a Q&A chain that uses ChatGPT to provide answers based on the training data.
Customization: The entire process allows developers to train ChatGPT on their custom data, making the chatbot more tailored to specific use-cases.
Dependencies: Ensure all required dependencies (like langchain, openai, tiktoken, etc.) are installed.
Security: When using API keys (like OpenAI key), ensure they are securely stored and not hard-coded in the script.
By the end of the tutorial, developers should be equipped to create a chatbot that can answer questions based on custom data, leveraging the power of ChatGPT and Langchain.
The memory section of the tutorial delves into the importance of memory in creating a chatbot using Langchain.
While the RetrievalQA method is effective, it lacks the ability to maintain a conversational context, which is crucial for follow-up questions.
To address this, the tutorial introduces the concept of memory and its various types in Langchain.
Importance of Memory
A chatbot without memory cannot answer in a conversational manner, making it challenging to handle follow-up questions.
Types of Memory in Langchain
Stores every chat interaction directly in the buffer. While effective, it can lead to high costs and potential errors due to ChatGPT’s input context limit.
Similar to ConversationBufferMemory but only stores a specified number of recent interactions, discarding the rest.
Uses a token limit based on the number of words in stored messages, rather than the number of turns.
Summarizes interactions between the user and the AI, creating a “running summary” of all past interactions instead of storing raw conversations.
Drawbacks of Direct Buffer Storage: Storing every message in ConversationBufferMemory can result in higher costs, slower response times, and potential errors due to ChatGPT’s input context limit.
Benefits of Window Memory: ConversationBufferWindowMemory maintains a window of recent interactions, ensuring that only relevant context is sent to ChatGPT, optimizing performance and costs.
Token-based Memory: ConversationTokenBufferMemory focuses on the number of words or tokens in stored messages, offering a different approach to managing memory.
Summarized Memory: ConversationSummaryMemory provides a summarized version of interactions, allowing for longer conversations with fewer tokens.
Practical Implementation: The tutorial provides code examples for each type of memory, demonstrating how to integrate them into a chatbot using Langchain.
By understanding and implementing the right type of memory, developers can create more efficient and conversational chatbots using Langchain.
In the course on Langchain, after introducing the basics, the focus shifts to various models, primarily language models. A model is essentially a program trained for a specific task. In this context, the models are trained for language tasks using vast amounts of data. The course doesn’t delve into the training process but uses pre-trained models, especially Large Language Models (LLM).
- LLM (Large Language Model): LLMs are designed for language tasks like text generation. The course emphasizes OpenAI’s LLMs, which include Davinci, Curie, Ada, and Babbage. These models require the installation of specific libraries and an API key. They have a context length, and users need to ensure the input text is within this limit. Streaming is a significant concept in LLM, allowing real-time output display.
- Chat Models: These models, like ChatGPT, differ from LLMs in cost and functionality. They are cheaper and can hold conversations. They take a list of chat messages as input. The course explains how to use these models, emphasizing the difference between system, human, and AI messages. Templates can be used in Chat Models for dynamic inputs, and they can also be chained for multiple tasks. Streaming is also applicable here.
- Embedding Models: These are different from text generation models. Embeddings represent the properties of text. Words with similar meanings have similar embeddings. An interesting property of embeddings is their ability to understand complex relations between words. These models are used for tasks like semantic search. The course provides an example of how to use OpenAIEmbeddings to generate embeddings for text.
In essence, the course covers the use and functionalities of different models, emphasizing their application in language tasks.
LangChain is a powerful tool designed to streamline and enhance the process of language processing. At its core, LangChain utilizes chains, which are sequences of components executed in a specific order. The article delves into various types of chains, such as LLMChain, Sequential Chain, Retrieval QA chain, LoadSummarize chain, and Router Chain.
Each chain has its unique functionality and application, from basic input formatting to complex tasks like document summarization and routing based on input.
- LangChain Official Documentation: Dive deep into the intricacies of LangChain with the official documentation. It provides detailed explanations, examples, and best practices.
- LangChain Community Forum: Join the community to discuss, share, and learn from fellow LangChain enthusiasts.
- Tutorials & Workshops: Look for online tutorials and workshops that offer hands-on experience with LangChain.
Here are some books available on Amazon related to LangChain, Python, and ChatGPT, using these links can help to support this website.
- Learn Python Programming: The no-nonsense, beginner’s guide to programming, data science, and web development with Python 3.7, 2nd Edition.
- Clean Code in Python: Refactor your legacy code base.
- Python Programming Blueprints: Build nine projects by leveraging powerful frameworks such as Flask, Nameko, and Django.
- LangChain Crash Course: Build OpenAI LLM powered Apps: Fast track to building OpenAI LLM powered Apps using Python.
- Generative AI with LangChain: Build large language model (LLM) apps with Python, ChatGPT, and other models.
- ChatGPT and Python Programming: Python Programming Made Easy with ChatGPT
Advice for Your Learning Journey:
- Start Small: Begin with understanding the basic chains before diving into the more complex ones. This will give you a solid foundation.
- Practice Regularly: The more you work with LangChain, the more proficient you’ll become. Try building small projects to reinforce your learning.
- Stay Updated: LangChain, like many tech tools, is continuously evolving. Regularly check for updates and new features.
- Engage with the Community: Don’t hesitate to ask questions or share your knowledge in community forums. Collaboration accelerates learning.
- Apply Real-World Scenarios: Think of real-world applications for LangChain in your field or area of interest. This will make your learning more relevant and exciting.
Remember, every expert was once a beginner.
Stay curious, keep experimenting, and enjoy the journey with LangChain!