Intro

# ===== The values =====
# - Provider-agnostic LLM interface (OpenAI, Anthropic, Google, local)
# - Prompt + model + parser as composable building blocks
# - Retrieval (RAG), tools / agents, memory, evaluation
# - Big ecosystem: LangSmith for tracing, LangServe for deploys

# ===== Hello, chain =====
from langchain_openai import ChatOpenAI
from langchain_core.prompts import ChatPromptTemplate
from langchain_core.output_parsers import StrOutputParser

llm    = ChatOpenAI(model='gpt-4o-mini', temperature=0)
prompt = ChatPromptTemplate.from_messages([
    ('system', 'You are a concise editor.'),
    ('human',  'Rewrite: {text}'),
])
chain = prompt | llm | StrOutputParser()
print(chain.invoke({'text': 'their are 5 cars'}))

# ===== Retrieval-Augmented Generation (RAG) =====
from langchain_community.vectorstores import Chroma
from langchain_openai import OpenAIEmbeddings
emb = OpenAIEmbeddings(model='text-embedding-3-small')
store = Chroma.from_texts(['cats meow', 'dogs bark'], emb)
retriever = store.as_retriever()
docs = retriever.invoke('what do dogs do?')

# ===== Tools / agents =====
from langchain_core.tools import tool

@tool
def calculator(expr: str) -> str:
    """Evaluate a math expression."""
    return str(eval(expr))    # demo only

# Bind tools to model:
llm_with_tools = llm.bind_tools([calculator])
res = llm_with_tools.invoke('what is 12 * 7?')

# ===== When LangChain wins =====
# - You need to swap providers easily
# - Chains have many stages (prompt + model + parser + retriever + ...)
# - You want LangSmith for tracing + eval
# - Standard patterns (RAG, agents, summarisation)

# ===== When LangChain hurts =====
# - Single-call apps where direct SDK is simpler
# - You need maximum control + minimum abstractions
# - Rapidly changing APIs cause version churn

# ===== Patterns to internalise =====
# - Compose with the pipe operator (prompt | llm | parser)
# - Use structured output for non-prose responses
# - Cache during dev to save money
# - Trace with LangSmith from day one

# ===== Pitfalls =====
# - Wrapping too much in custom subclasses -> upgrade pain
# - No timeouts -> stuck requests stall pipelines
# - Memory and tool calls without cost tracking
# - Treating chains as static; expect to rewrite as the use case evolves

# LangChain: a framework for composing LLM apps.
# Chains, agents, RAG, tools, memory — all behind one interface.