Generative AI with LangChain

Generative AI with LangChain is the practice of pairing large language models (LLMs) like GPT-4, Claude, or Gemini with LangChain’s modular toolkit to build data-driven applications — chatbots, co-pilots, and content engines — in a few dozen lines of Python. The workflow starts with document loaders that extract PDFs, HTML, or databases into document objects, then text delimiters and embeddings vectorize the text and store it in a vector database like Chroma or Milvus. A rich generation chain (RAG) injects the most relevant snippets into a prompt template, and an LLM wrapper generates an informed response. For multi-step tasks, agents running the ReAct template select tools — APIs, calculators, code interpreters — on the fly, while memory preserves the context of the conversation. Callback handlers pass tokens to UIs or dashboards to enable real-time observability. Because each layer follows a plug-and-play interface, teams can change models, databases, or constraints without affecting business logic, speeding the journey from prototype to production-grade generative AI.