Why observability is essential for LLM apps in LangChain - Test Your Understanding

Practice - 5 Tasks

1fill in blank

easy

Complete the code to import the observability tool in LangChain.

LangChain

from langchain.[1] import get_openai_callback

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

Achains

Bmemory

Ccallbacks

Dprompts

Attempts:

3 left

💡 Hint

Common Mistakes

Importing from memory or chains instead of callbacks

Trying to import a non-existent module

The callbacks module in LangChain provides observability tools like get_openai_callback to monitor LLM usage.

2fill in blank

medium

Complete the code to start tracking LLM usage with a context manager.

LangChain

with get_openai_callback() as [1]:
    response = llm(prompt)

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

Asession

Btracker

Cmonitor

Dcallback

Attempts:

3 left

💡 Hint

Common Mistakes

Using unrelated variable names like 'session' or 'tracker'

Not using a variable to capture the callback

The variable name inside the with block is commonly callback to track usage.

3fill in blank

hard

Fix the error in printing the total tokens used after the LLM call.

LangChain

print(f"Total tokens used: { [1].total_tokens }")

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

Acallback().total_tokens

Bcallback

Ccallback.total_tokens

Dget_openai_callback.total_tokens

Attempts:

3 left

💡 Hint

Common Mistakes

Using the method name instead of the variable

Adding parentheses incorrectly

Putting the attribute outside the braces

The variable callback holds the context manager object, so callback.total_tokens accesses the attribute. Inside an f-string, use {callback.total_tokens} without quotes.

4fill in blank

hard

Fill the blanks to create a dictionary that logs prompt and token count.

LangChain

log = { [1]: [2] for [3], [4] in data.items() }

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

Aprompt

Btokens

Ckey

Dvalue

Attempts:

3 left

💡 Hint

Common Mistakes

Using prompt and tokens as loop variables

Swapping key and value

When iterating over data.items(), key and value are the standard variable names for keys and values.

5fill in blank

hard

Fill all three blanks to filter logs for tokens greater than 100.

LangChain

filtered = {k: v for k, v in logs.items() if v [1] [2] and k [3] 'prompt'}

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

A>

B100

C!=

D==

Attempts:

3 left

💡 Hint

Common Mistakes

Using wrong comparison operators

Confusing equality and inequality

This dictionary comprehension filters entries where the token count is greater than 100 and the key is not 'prompt'.

Practice

1. Why is observability important in LangChain apps that use large language models (LLMs)?

easy

A. It makes the app run faster by skipping API calls.

B. It automatically writes the code for the app without user input.

C. It replaces the need for training the language model.

D. It helps track what happens inside the app to find errors and improve responses.

Solution

1. Step 1: Understand observability's role in LLM apps

   Observability means seeing inside the app's processes to understand behavior and issues.

2. Step 2: Connect observability to error detection and improvement

   By tracking app actions, developers can find errors and improve responses effectively.

3. Final Answer:

   It helps track what happens inside the app to find errors and improve responses. -> Option D

4. Quick Check:

   Observability = Track and improve app behavior [OK]

Hint: Observability means watching app actions to fix and improve [OK]

Common Mistakes:

• Thinking observability writes code automatically
• Believing observability replaces model training
• Assuming observability speeds up API calls

2. Which of the following is the correct way to add a callback for observability in a LangChain LLM chain?

easy

A. chain = LLMChain(llm=llm, prompt=prompt, handlers=MyCallbackHandler())

B. chain = LLMChain(llm=llm, prompt=prompt, callbacks=[MyCallbackHandler()])

C. chain = LLMChain(llm=llm, prompt=prompt, callback=MyCallbackHandler())

D. chain = LLMChain(llm=llm, prompt=prompt, observers=[MyCallbackHandler()])

Solution

1. Step 1: Recall LangChain callback syntax

   LangChain expects a list of callback handlers passed as the 'callbacks' parameter.

2. Step 2: Match correct parameter and value type

   chain = LLMChain(llm=llm, prompt=prompt, callbacks=[MyCallbackHandler()]) uses 'callbacks' with a list containing the handler instance, which is correct.

3. Final Answer:

   chain = LLMChain(llm=llm, prompt=prompt, callbacks=[MyCallbackHandler()]) -> Option B

4. Quick Check:

   Callbacks param = list of handlers [OK]

Hint: Callbacks parameter takes a list of handler instances [OK]

Common Mistakes:

• Using 'callback' instead of 'callbacks'
• Passing a single handler without list brackets
• Using wrong parameter names like 'handlers' or 'observers'

3. Given this LangChain code snippet, what will be printed when the chain runs?

from langchain.callbacks.base import BaseCallbackHandler

class PrintCallback(BaseCallbackHandler):
    def on_llm_start(self, serialized, prompts, **kwargs):
        print(f"LLM started with prompt: {prompts[0]}")

chain = LLMChain(llm=llm, prompt=prompt, callbacks=[PrintCallback()])
chain.run("Hello")

medium

A. LLM started with prompt: Hello

B. LLM started with prompt: ["Hello"]

C. No output printed

D. Error: on_llm_start method missing required arguments

Solution

1. Step 1: Understand the on_llm_start callback parameter

   The 'prompts' argument is a list of prompt strings, so prompts[0] is the first prompt string.

2. Step 2: Analyze the print statement output

   The print outputs the string with prompts[0], which is the string "Hello" passed to run, but wrapped in a list originally.

3. Final Answer:

   LLM started with prompt: Hello -> Option A

4. Quick Check:

   Print prompt string = "Hello" [OK]

Hint: Callbacks receive prompts as list; print first item for prompt text [OK]

Common Mistakes:

• Thinking prompts is a string, not a list
• Expecting no output from callback
• Confusing method parameters causing errors

4. You added a callback to your LangChain app but no logs appear when running the chain. What is the most likely cause?

medium

A. The prompt variable is empty.

B. The callback was added as a single object, not inside a list.

C. The callback class does not implement any event methods like on_llm_start.

D. The LLM model is not connected to the internet.

Solution

1. Step 1: Check callback implementation

   If the callback class lacks event methods like on_llm_start, no logs will be triggered.

2. Step 2: Verify callback registration

   Even if callbacks are registered correctly, without event methods, no output occurs.

3. Final Answer:

   The callback class does not implement any event methods like on_llm_start. -> Option C

4. Quick Check:

   Callbacks need event methods to log [OK]

Hint: Callbacks must implement event methods to produce logs [OK]

Common Mistakes:

• Assuming single object instead of list stops logs
• Blaming internet connection for no logs
• Thinking empty prompt causes no callback logs

5. You want to monitor both the input prompts and the cost of API calls in your LangChain app. Which observability approach best achieves this?

hard

A. Use a callback handler that logs prompts on on_llm_start and tracks token usage on on_llm_end.

B. Add print statements inside the prompt template and ignore callbacks.

C. Only log the final output text after the chain finishes.

D. Use a callback that only tracks errors during chain execution.

Solution

1. Step 1: Identify observability needs

   You want to see input prompts and monitor API call costs (token usage).

2. Step 2: Match callback events to needs

   on_llm_start can log prompts; on_llm_end can provide token usage info for cost tracking.

3. Step 3: Evaluate options

   Use a callback handler that logs prompts on on_llm_start and tracks token usage on on_llm_end. covers both prompt logging and cost monitoring via callbacks, which fits best.

4. Final Answer:

   Use a callback handler that logs prompts on on_llm_start and tracks token usage on on_llm_end. -> Option A

5. Quick Check:

   Callbacks on start/end = prompt + cost tracking [OK]

Hint: Use callbacks on start and end to log prompts and costs [OK]

Common Mistakes:

• Ignoring callbacks and using print statements only
• Logging only outputs misses input and cost info
• Tracking only errors misses prompt and cost data