How to use LangChain

How to use LangChain is a practical five-step roadmap:

1) Install core langchain-community with pip install langchain and set your OPENAI_API_KEY (or other model key) as an environment variable.

2) Load data using your choice of DocumentLoader — PDF, web page, or database — and turn raw text into Document objects.

3) Split and embed that text with TextSplitter and the embedding model; store the vectors in Chroma, Qdrant, or another supported database.

4) Build logic with a chain or agent — start simple with LLMChain for single-query tasks, then upgrade to RetrievalQA for augmented search generation or the ReAct agent for tool invocation.

5) Observe and deploy by adding callback handlers for token streaming, cost tracking, and debugging, then wrap the chain in FastAPI, Streamlit, or AWS Lambda. Because each component is plug-and-play, you can change models (GPT-4 ↔ Claude), databases, or hint templates without rewriting the core code, allowing you to move prototypes into production in days.