LCEL Demo 3

from langchain_core.prompts import ChatPromptTemplate
from langchain_core.output_parsers import StrOutputParser
from langchain.chat_models import ChatOpenAI

# Prompt template expects a variable named `question`
prompt = ChatPromptTemplate.from_template(
    """
    You are a helpful assistant.
    Answer the following question: {question}
    """
)

llm = ChatOpenAI()
output_parser = StrOutputParser()

# Compose the chain
chain = prompt | llm | output_parser

# A list of input dictionaries; each dict maps the prompt variable `question`
questions = [
    {"question": "Tell me about The Godfather Movie"},
    {"question": "Tell me about Avatar Movie"}
]

# Batch execution: the runtime will unpack the list and execute the chain for each item,
# typically in parallel (concurrency settings may depend on the runtime and client).
response = chain.batch(questions)

# `response` is a list where each element corresponds to one input in `questions`
print(response[0])  # Answer for the first question
print(response[1])  # Answer for the second question

import time

# Prepare inputs
questions = [
    {"question": "Tell me about The Godfather Movie"},
    {"question": "Tell me about Avatar Movie"}
]

# Single synchronous runs (run each input individually)
start = time.perf_counter()
single_responses = [chain.run(q) for q in [d["question"] for d in questions]]
single_elapsed = time.perf_counter() - start

# Batched run
start = time.perf_counter()
batch_response = chain.batch(questions)
batch_elapsed = time.perf_counter() - start

print(f"Single-run total time: {single_elapsed:.2f}s")
print(f"Batch-run total time:  {batch_elapsed:.2f}s")