Introduction to LangChain: Building AI-Powered Apps

1-minute read
langchain • ai • llm • python • nodejs

LangChain composes LLM calls with prompts, memory, tools, and retrieval. It standardizes patterns that every AI app eventually needs.

Chains and Prompts

from langchain_openai import ChatOpenAI
from langchain_core.prompts import ChatPromptTemplate

llm = ChatOpenAI(model="gpt-4")
prompt = ChatPromptTemplate.from_messages([
    ("system", "Answer as a senior engineer."),
    ("user", "{question}"),
])
chain = prompt | llm
response = chain.invoke({"question": "What is RAG?"})

Retrieval

Load documents, chunk text, embed with OpenAI or open models, store in a vector DB, and retrieve relevant chunks at query time-foundation for RAG systems.

Agents

Agents decide which tools to call (search, calculator, database). Use them when workflows are dynamic; use simple chains when the path is fixed.

Conclusion

LangChain accelerates prototyping. For production, extract stable abstractions and monitor latency-framework overhead matters at scale.