# pip install langchain langchain-openai
from langchain_openai import ChatOpenAI
from langchain_core.prompts import ChatPromptTemplate
# 1) Model
llm = ChatOpenAI(model="gpt-4o-mini", temperature=0)
# 2) Prompt
prompt = ChatPromptTemplate.from_messages([("system", "You are a world class poet"),("user", "Write a 5 line poem about {topic}")])
# 3) Chain
chain = prompt | llm
# 4) Run
for chunk in chain.stream({"topic": "ducks"}):
print(chunk.content, end="", flush=True)
# Output: Quacking friends in a row,
# Across the pond, they softly go,
# Yellow bills in the sun,
# A peaceful sight, second to none,
# Ducks in harmony, to and fro.

# pip install faiss-cpu tiktoken
from langchain_openai import OpenAIEmbeddings, ChatOpenAI
from langchain_community.vectorstores import FAISS
from langchain_text_splitters import RecursiveCharacterTextSplitter
from langchain_core.prompts import ChatPromptTemplate
# Prepare documents
texts = ["LangChain is great for RAG", "LangChain also does agents"]
splitter = RecursiveCharacterTextSplitter(chunk_size=300, chunk_overlap=20)
chunks = splitter.create_documents(texts)
# Embed and index
embeddings = OpenAIEmbeddings
db = FAISS.from_documents(chunks, embeddings)
retriever = db.as_retriever
# Create chain
prompt = ChatPromptTemplate.from_template("Answer this: {question}\nContext: {context}")
model = ChatOpenAI
chain = prompt | model
# Run
chain.invoke({"question": "What is LangChain good for?", "context": retriever.get_relevant_documents("What is LangChain good for?")})