LangServe for API deployment in LangChain - Practice Problems & Coding Challenges

Challenge - 5 Problems

❓ component_behavior

intermediate

What is the output of this LangServe API call?

Consider a LangServe API deployed with a simple chain that returns the input text reversed. What will be the response when sending the input 'hello'?

LangChain

from langchain.chains import LLMChain
from langchain.llms import OpenAI
from langchain.server import LangServe

llm = OpenAI(temperature=0)
chain = LLMChain(llm=llm, prompt_template="{input_text[::-1]}")
app = LangServe()
app.add_endpoint("/reverse", chain)

# Simulate API call with input_text='hello'

A "hello"

B "olleh"

C "HELLO"

D "error"

💡 Hint

Think about what reversing the string 'hello' produces.

The chain reverses the input text, so 'hello' becomes 'olleh'.

📝 Syntax

intermediate

Which option correctly adds a POST endpoint in LangServe?

You want to add a POST endpoint '/summarize' to your LangServe app that uses a chain named 'summary_chain'. Which code snippet is correct?

LangChain

from langchain.server import LangServe
app = LangServe()

# summary_chain is defined elsewhere

A app.add_endpoint('/summarize', summary_chain, verbs=['POST'])

B app.add_endpoint('/summarize', summary_chain, method='POST')

C app.add_endpoint('/summarize', summary_chain, methods=['POST'])

D app.add_endpoint('/summarize', summary_chain, http_methods=['POST'])

💡 Hint

Check the parameter name for HTTP methods in add_endpoint.

The correct parameter is 'methods' as a list of HTTP methods.

🔧 Debug

advanced

Why does this LangServe app raise a runtime error?

Given this code snippet, why does the LangServe app fail when calling the '/chat' endpoint? from langchain.chains import ConversationChain from langchain.llms import OpenAI from langchain.server import LangServe llm = OpenAI() chain = ConversationChain(llm=llm) app = LangServe() app.add_endpoint('/chat', chain) # The client sends JSON: {"message": "Hello"}

A The chain expects input key 'input' but client sends 'message', causing a KeyError.

B The OpenAI LLM is not initialized with temperature=0 causing instability.

C LangServe requires endpoints to be async functions, but chain is sync.

D The ConversationChain does not support JSON input format.

💡 Hint

Check the expected input key names for ConversationChain.

ConversationChain expects input with key 'input'. Sending 'message' causes a KeyError.

❓ state_output

advanced

What is the state of the LangServe app after adding two endpoints?

You add two chains as endpoints: app.add_endpoint('/translate', translate_chain) app.add_endpoint('/summarize', summarize_chain) What does app.endpoints contain?

A A set containing the endpoint paths only.

B A list containing the two chain objects in order added.

C An empty dictionary because endpoints are not stored.

D A dictionary with keys '/translate' and '/summarize' mapping to their respective chains.

💡 Hint

Think about how LangServe stores endpoints internally.

LangServe stores endpoints in a dictionary mapping paths to chains.

🧠 Conceptual

expert

Which statement best describes LangServe's role in API deployment?

Select the most accurate description of what LangServe provides for deploying LangChain chains as APIs.

A LangServe acts as a lightweight server framework that exposes LangChain chains as HTTP endpoints with minimal setup.

B LangServe is a cloud service that automatically scales LangChain chains without any local setup.

C LangServe is a GUI tool for designing LangChain chains visually before deployment.

D LangServe replaces LangChain's LLMs with custom models optimized for API use.

💡 Hint

Focus on LangServe's function in deployment and API exposure.

LangServe provides a simple server to expose LangChain chains as HTTP endpoints easily.