LangChain helps turn a language-model chatbot into a more capable application. It supplies reusable components for retrieval, conversation memory, tools, and multistep workflows, so you do not have to write every feature from scratch. section 16.4
from langchain_ollama import OllamaLLM
# Create an LLM instance connected to your local Ollama
llm = OllamaLLM(model="gemma3:4b")
# Simple invocation
response = llm.invoke("What is the capital of France?")
print(response)
This Python example connects LangChain to a local Ollama model and asks one question. A single invocation looks much like using Ollama’s Python library directly; the benefit appears when you combine components. section 16.4.2
Choose components according to what your application needs:
- Prompts provide reusable templates; chains link steps such as a prompt, model, and parser. section 16.4.4
- Memory manages conversation history; tools let the AI call functions to search, calculate, or access databases. section 16.4.4
- Agents choose tools based on a task; retrievers connect to vector databases for retrieval-augmented generation (RAG). section 16.4.4
You need not learn every component at once. After the basic connection works, a practical next step is one complete project, such as a document question-answering system. More advanced extensions include branching chains, different memory strategies, custom tools, and connections to external APIs or databases. section 16.4.4 section 18.2.2
