LangChain Crash Course with Easy & Working Codes | Prompts + Memory + RAG + Agents
In this crash course, we introduce LangChain and explain its most important components with very easy and working codes in Python in just 15 minutes. The course covers Prompts and Prompt Templates, Memory and Chat History, RAG (Retrieval-Augmented Generation), Agents and Tools. We implement our code with an open-source Large Language Model (LLM) called Falcon from Hugging Face.
LangChain is an open-source framework designed to simplify the development of applications using large language models (LLMs). It provides a set of tools, APIs, and libraries that enable software developers to build LLM-driven applications such as chatbots and virtual agents. LangChain supports both Python- and Javascript-based libraries, making it accessible to developers working in different programming languages.
The framework allows developers to combine LLMs with other external components, such as vector databases and cloud technologies, to create powerful and context-aware applications. It provides a standard interface for chains, which are logical connections that help bridge one or multiple LLMs. These chains can be used for various purposes, including chatbots, generative question-answering, summarization, and more.
LangChain goes beyond standard API calls by being data-aware and agentic, enabling connections with various data sources for richer and personalized experiences. It offers features like memory, which allows the model to be context-aware and remember past chats, making conversations flow smoothly and feel more personal.
The framework has gained popularity in the AI community due to its ability to simplify interactions with different LLM providers, including OpenAI, Cohere, Bloom, Huggingface, and others. It provides a generic interface to a variety of foundation models and helps manage prompts effectively.
Overall, LangChain is a versatile framework that empowers developers to leverage the capabilities of large language models and build sophisticated and context-aware applications. Its flexibility, extensive toolkit, and support for various integrations make it a valuable tool in the AI development landscape.
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