FastAPI integration patterns in LangChain - Interactive Code Practice

Practice - 5 Tasks

1fill in blank

easy

Complete the code to import FastAPI and create an app instance.

LangChain

from fastapi import [1]
app = [1]()

Drag options to blanks, or click blank then click option'

AResponse

BRequest

CFastAPI

DDepends

Attempts:

3 left

💡 Hint

Common Mistakes

Importing Request or Response instead of FastAPI

Not creating an instance of FastAPI

You need to import FastAPI and create an instance of it to start your app.

2fill in blank

medium

Complete the code to define a GET endpoint that returns a greeting.

LangChain

@app.[1]("/")
async def read_root():
    return {"message": "Hello World"}

Drag options to blanks, or click blank then click option'

Apost

Bdelete

Cput

Dget

Attempts:

3 left

💡 Hint

Common Mistakes

Using POST or PUT instead of GET for a read endpoint

Forgetting the decorator

The decorator @app.get defines a GET HTTP method endpoint.

3fill in blank

hard

Fix the error in the code to correctly inject a dependency using Depends.

LangChain

from fastapi import Depends

def get_token():
    return "token123"

@app.get("/items")
async def read_items(token: str = [1](get_token)):
    return {"token": token}

Drag options to blanks, or click blank then click option'

ADependsWith

BDepends

CDependsOn

DRequest

Attempts:

3 left

💡 Hint

Common Mistakes

Using incorrect names like DependsOn or DependsWith

Not importing Depends

Use Depends to declare dependencies in FastAPI endpoints.

4fill in blank

hard

Fill both blanks to create a POST endpoint that accepts a Pydantic model and returns it.

LangChain

from pydantic import BaseModel

class Item(BaseModel):
    name: str
    price: float

@app.[1]("/items")
async def create_item(item: [2]):
    return item

Drag options to blanks, or click blank then click option'

Apost

BItem

Cget

Dstr

Attempts:

3 left

💡 Hint

Common Mistakes

Using GET instead of POST for creation

Using wrong parameter type like str

Use @app.post to define a POST endpoint and use the Pydantic model Item as the parameter type.

5fill in blank

hard

Fill all three blanks to create a FastAPI app with a startup event and a simple GET endpoint.

LangChain

from fastapi import [1]

app = [2]()

@app.[3]("/status")
async def status():
    return {"status": "ok"}

@app.on_event("startup")
async def startup_event():
    print("App is starting")

Drag options to blanks, or click blank then click option'

AFastAPI

Cget

DDepends

Attempts:

3 left

💡 Hint

Common Mistakes

Using wrong import or instance name

Using wrong decorator for the endpoint

Import FastAPI, create an app instance, and define a GET endpoint with @app.get.

Practice

1. What is the main benefit of using async routes in FastAPI when integrating with LangChain AI models?

easy

A. They convert Python code to JavaScript for frontend use.

B. They allow handling multiple requests without blocking, improving performance.

C. They automatically generate HTML pages for AI responses.

D. They disable input validation to speed up processing.

Solution

1. Step 1: Understand async routes in FastAPI

   Async routes let the server handle many requests at once without waiting for each to finish.

2. Step 2: Connect async behavior to LangChain integration

   Since AI calls can take time, async routes prevent blocking other users, improving app speed.

3. Final Answer:

   They allow handling multiple requests without blocking, improving performance. -> Option B

4. Quick Check:

   Async routes = non-blocking requests [OK]

Hint: Async means non-blocking, so multiple requests run smoothly [OK]

Common Mistakes:

• Thinking async auto-generates HTML output
• Believing async disables input validation
• Confusing async with frontend code conversion

2. Which of the following is the correct way to define a FastAPI route that accepts JSON input and returns JSON output asynchronously?

easy

A. @app.get('/predict')
def predict():
return 'ok'

B. @app.get('/predict')
async def predict():
return {'result': 'ok'}

C. @app.post('/predict')
def predict(data: dict):
return {'result': data}

D. @app.post('/predict')
async def predict(data: dict):
return {'result': data}

Solution

1. Step 1: Identify correct HTTP method and async usage

   POST is used for sending JSON data; async def enables asynchronous handling.

2. Step 2: Check input and output format

   Function accepts a dict parameter (JSON input) and returns a dict (JSON output).

3. Final Answer:

   @app.post('/predict') async def predict(data: dict): return {'result': data} -> Option D

4. Quick Check:

   POST + async + JSON input/output = @app.post('/predict') async def predict(data: dict): return {'result': data} [OK]

Hint: Use @app.post with async def and dict parameter for JSON [OK]

Common Mistakes:

• Using GET instead of POST for JSON input
• Missing async keyword for async routes
• Returning plain string instead of JSON dict

3. Given this FastAPI route using LangChain, what will be the output when sending POST request with JSON {"text": "Hello"}?

@app.post('/chat')
async def chat_endpoint(input: dict):
    response = await chain.acall(input["text"])
    return {"reply": response}

medium

A. 500 Internal Server Error

B. {"reply": "Processed: Hello"}

C. {"reply": "Hello"}

D. {"error": "Missing 'text' key"}

Solution

1. Step 1: Analyze the route code and input

   The route expects input dict with key "text" and calls async method chain.acall with input["text"].

2. Step 2: Identify missing chain definition causing error

   Since chain is not defined or imported, calling chain.acall will raise an error causing 500 response.

3. Final Answer:

   500 Internal Server Error -> Option A

4. Quick Check:

   Undefined chain causes server error [OK]

Hint: Undefined variables in async calls cause 500 errors [OK]

Common Mistakes:

• Assuming chain is predefined and returns processed text
• Expecting plain echo output
• Ignoring async call errors

4. Identify the error in this FastAPI route integrating LangChain and how to fix it:

@app.post('/process')
async def process(data: dict):
    result = chain.run(data['input'])
    return {'output': result}

medium

A. chain.run is synchronous; should use await chain.arun for async call.

B. Missing type annotation for data parameter.

C. Route should use @app.get instead of @app.post.

D. Return statement should return a string, not a dict.

Solution

1. Step 1: Check method call type in async function

   Function is async but calls chain.run which is synchronous, causing blocking or errors.

2. Step 2: Fix by using async method

   Replace chain.run with await chain.arun to properly await the async call.

3. Final Answer:

   chain.run is synchronous; should use await chain.arun for async call. -> Option A

4. Quick Check:

   Async function must await async calls [OK]

Hint: Async functions must await async methods, not call sync ones [OK]

Common Mistakes:

• Calling sync methods inside async functions without await
• Confusing HTTP methods for routes
• Returning wrong data types

5. You want to build a FastAPI app integrating LangChain that validates input text length before calling the AI model asynchronously. Which pattern best ensures modularity, validation, and async integration?

hard

A. Skip input validation and call chain.arun directly in a blocking route.

B. Write all logic inside the route function synchronously without validation.

C. Use Pydantic models for input validation, async route functions, and separate LangChain call in a helper async function.

D. Use global variables for input data and call chain.run synchronously.

Solution

1. Step 1: Identify best practice for input validation

   Pydantic models provide clear, reusable input validation in FastAPI.

2. Step 2: Combine async route with modular LangChain call

   Async route with a separate async helper function keeps code clean and non-blocking.

3. Final Answer:

   Use Pydantic models for input validation, async route functions, and separate LangChain call in a helper async function. -> Option C

4. Quick Check:

   Validation + async + modular code = Use Pydantic models for input validation, async route functions, and separate LangChain call in a helper async function. [OK]

Hint: Validate input with Pydantic, keep async calls modular [OK]

Common Mistakes:

• Putting all logic in one blocking function
• Ignoring input validation
• Using synchronous calls in async routes