LangChain Fundamentals

This lesson provides an overview of the course structure, learning objectives, and the LangChain ecosystem. Learners will understand how the various tools covered in the course work together to support the development of AI-powered applications.

This lesson introduces LangChain and explains how it simplifies the development of applications powered by Large Language Models. Learners will explore its core components and understand its role in building intelligent AI workflows.

This lesson introduces LangGraph and explains how graph-based workflows enable the development of advanced, stateful AI applications with multiple decision paths.

This lesson introduces LangSmith and demonstrates how it helps developers monitor, debug, evaluate, and improve LangChain applications.

This lesson introduces LangServe and explains how LangChain applications can be deployed as scalable APIs for real-world usage.

This lesson guides learners through the setup and configuration process required to begin building applications with LangChain and Large Language Models.

This lesson introduces the legacy syntax of LangChain and explains how developers previously built chains, prompts, and workflows before the introduction of newer frameworks and abstractions.

This lesson introduces LangChain Expression Language (LCEL) and explains how it simplifies the creation of flexible, modular, and maintainable AI workflows.

This lesson provides a practical demonstration of LCEL and shows how to build modern LangChain workflows using the expression-based approach.

This lesson compares legacy LangChain development approaches with modern LCEL-based workflows, highlighting the improvements and benefits of the latest framework design.

This lesson introduces Large Language Models (LLMs) and Chat Models, explaining how they process inputs and generate intelligent, context-aware responses in LangChain applications.

This lesson explains how Prompt Templates are used in LangChain to structure, standardize, and dynamically generate inputs for Large Language Models.

This lesson explains how Output Parsers are used to structure and transform raw LLM responses into usable, machine-readable formats for applications.

This lesson introduces advanced LangChain operations that enhance LLM capabilities, enabling more controlled, efficient, and production-ready AI workflows.

This lesson introduces the concept of retrieval in LangChain and explains how external knowledge sources are used to enhance the capabilities of Large Language Models.

This lesson explains how Document Loaders are used to import and process data from different sources into LangChain applications.

This lesson explains how large documents are split into smaller chunks to improve processing efficiency and retrieval accuracy in LangChain applications.

This lesson introduces embedding models and explains how they convert text into numerical representations for semantic search and retrieval tasks.

This lesson explains how vector databases store and manage embeddings for efficient similarity search in LangChain applications.

This lesson explains how retrievers work in LangChain to fetch the most relevant information from vector databases based on user queries.