LangChain

from langchain_openai import ChatOpenAI
from langchain_core.prompts import ChatPromptTemplate
from langchain_core.output_parsers import StrOutputParser

# Define components
llm = ChatOpenAI(model="gpt-4o-mini", temperature=0)
prompt = ChatPromptTemplate.from_messages([
    ("system", "You are a helpful assistant."),
    ("human", "{question}"),
])
parser = StrOutputParser()

# Chain with | operator (LCEL)
chain = prompt | llm | parser

# Invoke
response = chain.invoke({"question": "What is LangChain?"})

# Stream
for chunk in chain.stream({"question": "Explain RAG"}):
    print(chunk, end="", flush=True)

# Batch
responses = chain.batch([
    {"question": "What is Python?"},
    {"question": "What is TypeScript?"},
])

from langchain_community.document_loaders import WebBaseLoader
from langchain_text_splitters import RecursiveCharacterTextSplitter
from langchain_openai import OpenAIEmbeddings
from langchain_chroma import Chroma
from langchain_core.runnables import RunnablePassthrough

# 1. Load documents
loader = WebBaseLoader("https://docs.example.com")
docs = loader.load()

# 2. Split into chunks
splitter = RecursiveCharacterTextSplitter(chunk_size=1000, chunk_overlap=200)
chunks = splitter.split_documents(docs)

# 3. Embed and store
vectorstore = Chroma.from_documents(chunks, OpenAIEmbeddings())
retriever = vectorstore.as_retriever(search_kwargs={"k": 4})

# 4. RAG chain
def format_docs(docs):
    return "\n\n".join(d.page_content for d in docs)

rag_chain = (
    {"context": retriever | format_docs, "question": RunnablePassthrough()}
    | prompt
    | llm
    | StrOutputParser()
)

answer = rag_chain.invoke("What is the rate limit?")