LangChain — Building LLM Applications
LangChain is a framework for building applications powered by language models. It provides abstractions for chains (sequences of LLM calls), tools, memory, and agents that work across any LLM provider.
Core Concepts
python
# pip install langchain langchain-anthropic langchain-openai
from langchain_anthropic import ChatAnthropic
from langchain.prompts import ChatPromptTemplate
from langchain.schema.output_parser import StrOutputParser
# 1. The LLM
llm = ChatAnthropic(model="claude-sonnet-4-6")
# 2. Prompt template
prompt = ChatPromptTemplate.from_messages([
("system", "You are a {role}. Be concise."),
("human", "{question}")
])
# 3. Chain (LCEL - LangChain Expression Language)
chain = prompt | llm | StrOutputParser()
# 4. Invoke
result = chain.invoke({"role": "DevOps expert", "question": "What is Kubernetes?"})
print(result)
# Streaming
for chunk in chain.stream({"role": "DevOps expert", "question": "Explain Docker"}):
print(chunk, end="", flush=True)
# Batch
results = chain.batch([
{"role": "expert", "question": "What is Terraform?"},
{"role": "expert", "question": "What is Ansible?"},
])RAG with LangChain
python
from langchain_anthropic import ChatAnthropic
from langchain_openai import OpenAIEmbeddings
from langchain_community.vectorstores import Chroma
from langchain.text_splitter import RecursiveCharacterTextSplitter
from langchain.chains import RetrievalQA
from langchain.document_loaders import DirectoryLoader, TextLoader
# Load documents
loader = DirectoryLoader("./docs", glob="**/*.md", loader_cls=TextLoader)
documents = loader.load()
# Split into chunks
splitter = RecursiveCharacterTextSplitter(chunk_size=500, chunk_overlap=50)
chunks = splitter.split_documents(documents)
# Create vector store
embeddings = OpenAIEmbeddings(model="text-embedding-3-small")
vectorstore = Chroma.from_documents(chunks, embeddings, persist_directory="./chroma_db")
# Build RAG chain
llm = ChatAnthropic(model="claude-sonnet-4-6")
qa_chain = RetrievalQA.from_chain_type(
llm=llm,
retriever=vectorstore.as_retriever(search_kwargs={"k": 5}),
return_source_documents=True
)
result = qa_chain.invoke({"query": "How do I deploy to Kubernetes?"})
print(result["result"])
print("Sources:", [d.metadata["source"] for d in result["source_documents"]])Memory and Conversation History
python
from langchain.memory import ConversationBufferWindowMemory
from langchain.chains import ConversationChain
memory = ConversationBufferWindowMemory(k=10) # Keep last 10 turns
conversation = ConversationChain(
llm=ChatAnthropic(model="claude-sonnet-4-6"),
memory=memory,
verbose=False
)
conversation.predict(input="What is Docker?")
conversation.predict(input="How is it different from Kubernetes?")
conversation.predict(input="Which should I learn first?")
# Model remembers the entire conversation context