How to use LangChain
How to use LangChain is a practical five-step roadmap:
1) Install the packages you need: pip install langchain langchain-openai covers the core path, and pip install langchain-community adds the community integrations. 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 an LCEL pipeline (prompt | model | parser) for single-query tasks, then compose a retriever with a chat model for retrieval-augmented generation, or use create_agent from langchain.agents for tool calling. The older LLMChain, RetrievalQA and initialize_agent APIs were deprecated and now live in langchain-classic.
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 swap providers (OpenAI ↔ Anthropic), databases, or prompt templates without rewriting the core code, allowing you to move prototypes into production in days.
Related terms
Related services: LangChain development company, Agentic AI consulting.
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