Why observability is essential for LLM apps in LangChain

Introduction

Observability helps you understand how your large language model (LLM) app works and find problems quickly. It shows what is happening inside your app so you can fix issues and improve it.

When you want to see how users interact with your LLM app in real time.

When you need to find and fix errors or unexpected results from the LLM.

When you want to improve the app's responses by analyzing past behavior.

When you want to monitor the app's performance and resource use.

When you want to keep your app reliable and trustworthy for users.

Syntax

LangChain

from langchain.callbacks import get_openai_callback

with get_openai_callback() as cb:
    response = llm('Your prompt here')
    print(cb)

This example shows how to use a callback to observe LLM usage like tokens and cost.

Observability tools often include logging, tracing, and metrics to track app behavior.

Examples

This example prints how many tokens the LLM used for a prompt.

LangChain

from langchain.callbacks import get_openai_callback

with get_openai_callback() as cb:
    response = llm('Hello, how are you?')
    print(f'Tokens used: {cb.total_tokens}')

This example shows how to print LLM events directly to the console for easy monitoring.

LangChain

from langchain.llms import OpenAI
from langchain.callbacks import StdOutCallbackHandler

llm = OpenAI(callbacks=[StdOutCallbackHandler()])
response = llm('Tell me a joke')

Sample Program

This program sends a question to the LLM and uses observability to print the response, tokens used, and cost. It helps you see how much your app is using the LLM and what it returns.

LangChain

from langchain.llms import OpenAI
from langchain.callbacks import get_openai_callback

llm = OpenAI()

with get_openai_callback() as cb:
    response = llm('What is the capital of France?')
    print('Response:', response)
    print(f'Total tokens used: {cb.total_tokens}')
    print(f'Total cost: ${cb.total_cost:.6f}')

Important Notes

Observability helps catch issues early before users notice them.

Tracking tokens and cost helps manage your budget when using paid LLM services.

Use callbacks and logging to get detailed insights into your app's behavior.

Summary

Observability shows what happens inside your LLM app.

It helps find errors, improve responses, and monitor costs.

Using callbacks in LangChain is an easy way to add observability.

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