Build a FREE Chatbot using LangChain with a Very Simple & Working Code | A Quick Tutorial!
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 of large language models and build sophisticated applications that are context-aware, reasoning-driven, and capable of interacting with their environment. Its open-source nature and integration with popular LLM providers make it a valuable tool in the AI development landscape.
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