The Complete Production SOP: How to Deploy and Run LangChain Expression Language (LCEL) Pipelines in Autonomous AI Agents

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

prompt = ChatPromptTemplate.from_template("Summarize the following topic concisely: {topic}")
model = ChatOpenAI(model="gpt-4o-mini", temperature=0).with_retry(stop_after_attempt=3)
parser = StrOutputParser()

# Idiomatic LCEL pipe composition
chain = (
    {"topic": RunnablePassthrough()}
    | prompt
    | model
    | parser
)

from langchain_core.runnables import RunnableParallel, RunnablePassthrough
from langchain_core.prompts import ChatPromptTemplate
from langchain_core.output_parsers import StrOutputParser
from langchain_openai import ChatOpenAI

def format_docs(docs):
    return "\n\n".join(doc.page_content for doc in docs)

def setup_rag_chain(retriever, model=None):
    llm = model or ChatOpenAI(model="gpt-4o", temperature=0)
    prompt = ChatPromptTemplate.from_messages([
        ("system", "Answer using only the supplied context:\n{context}"),
        ("human", "{question}")
    ])
    
    rag_chain = (
        RunnableParallel(
            context=retriever | format_docs,
            question=RunnablePassthrough()
        )
        | prompt
        | llm
        | StrOutputParser()
    )
    return rag_chain