Build a FREE Chatbot using LangChain with a Very Simple & Working Code | A Quick Tutorial!

Quick Tutorials · Intermediate ·🧠 Large Language Models ·2y ago

Key Takeaways

This video demonstrates how to build a free chatbot using LangChain and the Falcon large language model (LLM) in Google Colab, with a simple Python code and retrieval-augmented generation (RAG) ability.

Full Transcript

[Music] lank chain is an open source framework by which you can build AI applications by large language models or llms in this video we show you how you can use Link Chain to create a simple free chatbot like chat GPT we will write a simple python code in Google collab and explain it line by line to learn more about L chain please watch our other videos to start create a new notebook in Google collab and choose any name for it then go to the runtime menu and change the runtime type to use a GPU the next step is to install link chain which can be done by this line it takes a few seconds to install it we then import three modules from link chain which include hugging face Hub prompt template and llm chain we then need to create our llm model this can be done using hugging face hub Hub it requires an API token from hugging face which you can get it for free from your hugging face account just go to the settings of your hugging face account and create a new access token and copy it to your code note that the API key shown here will not work by the time you are watching this video so make sure you will get your own API key we then need to specify the address of our llm model on hugging face in this example we use the 7 billion parameter Falcon model from this repo from hugging face you can find find the detail of this model on the hugging face website also you can copy the address of this repo easily we then specify some optional input parameters such as the temperature value which defines the randomness level of the output responses the other parameter sets the maximum number of tokens to generate using this parameter you can control the length of the output response next we need to Define our prompt template our template consists of a question for which we Define a placeholder variable in curly brackets we also instruct the model on how to answer our questions we here instruct it to give us detailed answers we then generate our prompt using this template by specifying our input variable in our template using this template our input question will be replaced by our placeholder in our template to create our prompt after this step we create our Chain by feeding our prompt and our llm model to llm chain and finally we can run our created Chain by passing our input question to the Run function of our llm chain this generated our output response and we can simply print it here we asked the model to give us a recipe to make a cupcake as you can see the output response is a detailed response you can change the temperature value to get more variety in the output responses as an alternative you can change the prompt template to get different responses for example let's instruct the model to give us short answers rather than detailed answers so just go and change the template now let's run the chain again and see the result as you can see this time we have a shorter response this was a short video on how to use Lang chain and a free llm hope you liked it please don't forget to like And subscribe to our Channel

Original Description

In this tutorial, we show you how you can build a FREE chatbot like ChatGPT using LangChain through a simple Python code in Google Colab! Specifically, we use the Falcon large language model (LLM) to implement our chatbot. Also, we show you how to add RAG (Retrieval-Augmented Generation) ability to the chatbot so that it can access external data sources such as PDF files to provide more specific responses. LangChain is an open-source framework designed to simplify the development of applications using large language models (LLMs). It provides a comprehensive toolkit and APIs that enable developers to create context-aware and reasoning-driven applications. LangChain connects LLMs to various sources of context, such as prompt instructions, few-shot examples, or content, allowing applications to generate responses based on the provided context. The framework offers a generic interface for building LLM-driven applications like chatbots, virtual agents, code analysis tools, document analysis and summarization tools, and more. It supports integration with popular LLM providers such as OpenAI, Hugging Face, Cohere, and Bloom, allowing developers to leverage the capabilities of these models seamlessly. One of the key features of LangChain is its ability to create chains, which are logical links between one or more LLMs. These chains enable developers to bridge multiple LLMs and create more advanced use cases. LangChain also provides prompt management, allowing developers to easily manage and customize prompts for their applications. LangChain's flexibility and extensive toolkit make it a powerful tool for developers. It simplifies interactions with LLM providers, enables connections with external data sources, and facilitates the creation of complex applications. The framework is available in Python and JavaScript (TypeScript) packages, making it accessible to developers proficient in these languages. Overall, LangChain empowers developers to harness the capabilities o
Watch on YouTube ↗ (saves to browser)
Sign in to unlock AI tutor explanation · ⚡30

This video teaches how to build a free chatbot using LangChain and the Falcon LLM in Google Colab, with a focus on creating a simple Python code and implementing retrieval-augmented generation (RAG) ability. The tutorial covers installing LangChain, importing necessary modules, creating an LLM model, defining a prompt template, and generating output responses.

Key Takeaways
  1. Create a new notebook in Google Colab
  2. Install LangChain
  3. Import necessary modules
  4. Create an LLM model using Hugging Face Hub
  5. Define a prompt template
  6. Generate output responses
💡 The Falcon LLM can be used to implement a chatbot with retrieval-augmented generation (RAG) ability, allowing for more detailed and informative responses.

Related Reads

📰
Building Your First Model Context Protocol (MCP) Server with TypeScript and Zod
Learn to build a Model Context Protocol (MCP) server with TypeScript and Zod to integrate Large Language Models with external environments
Dev.to AI
 📰
Introducing Claude Opus 5 on AWS: Anthropic’s most capable Opus model
Learn about Claude Opus 5, Anthropic's most capable Opus model, and how to integrate it into agentic systems on AWS
AWS Machine Learning
 📰
Can AI Keep a Great Mind Alive?
Learn how AI can preserve a great mind's thinking through persona fine-tuning, first-principles reasoning, and mechanistic interpretability
Dev.to AI
 📰
Anthropic launches Opus 5
Anthropic's Opus 5 offers a cheaper and less restrictive alternative to Fable, making it a preferable choice for most use cases
TechCrunch AI
Up next
5 Levels of AI Agents - From Simple LLM Calls to Multi-Agent Systems
Dave Ebbelaar (LLM Eng)
Watch →