Viewing trace details and latency in LangChain - Mini Project: Build & Apply

Viewing trace details and latency in LangChain

📖 Scenario: You are building a simple LangChain application that calls an LLM to generate text. You want to see detailed trace information and latency for each step to understand how long each part takes.

🎯 Goal: Enable LangChain tracing to view detailed trace logs and latency information for your chain calls.

📋 What You'll Learn

Create a LangChain LLM chain with OpenAI

Enable tracing on the chain

Call the chain with a prompt

Access and print the trace details and latency

💡 Why This Matters

🌍 Real World

Developers use LangChain to build AI applications and need to monitor performance and costs by viewing trace details and latency.

💼 Career

Understanding how to trace and measure latency in LangChain is important for AI engineers and developers working with language models to optimize user experience and resource usage.

1

Set up the LangChain LLM chain

Import OpenAI and LLMChain from langchain. Create an llm variable as OpenAI(model_name='text-davinci-003'). Then create a simple LLMChain called chain with the llm and a prompt template 'Say hello to {name}'.

LangChain

# Import OpenAI and LLMChain
# Create llm = OpenAI(model_name='text-davinci-003')
# Create chain = LLMChain(llm=llm, prompt='Say hello to {name}')

Hint

Use OpenAI from langchain.llms and LLMChain from langchain.chains. The prompt can be a simple string with {name} as a variable.

2

Enable tracing on the chain

Set the verbose parameter of the LLMChain to True to enable tracing. Update the chain creation line to include verbose=True.

LangChain

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

llm = OpenAI(model_name='text-davinci-003')
# Enable verbose tracing on the chain
chain = LLMChain(llm=llm, prompt='Say hello to {name}', verbose=True)

Hint

Add verbose=True when creating the LLMChain to see trace details.

3

Call the chain with a prompt

Call the chain with name='Alice' and save the result in a variable called result. Use chain.run(name='Alice').

LangChain

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

llm = OpenAI(model_name='text-davinci-003')
chain = LLMChain(llm=llm, prompt='Say hello to {name}', verbose=True)

# Call the chain with name='Alice' and save to result
result = chain.run(name='Alice')

Hint

Use chain.run(name='Alice') to get the output for the prompt.

4

Access and print trace details and latency

Import get_openai_callback from langchain.callbacks. Use it as a context manager around the chain.run(name='Alice') call to capture trace details. After the call, assign the callback object to a variable callback. Then access callback.total_tokens and callback.total_time to get token usage and latency.

LangChain

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

llm = OpenAI(model_name='text-davinci-003')
chain = LLMChain(llm=llm, prompt='Say hello to {name}', verbose=True)

# Use get_openai_callback to capture trace details and latency
with get_openai_callback() as callback:
    result = chain.run(name='Alice')

# Access callback.total_tokens and callback.total_time below

Hint

Use get_openai_callback as a context manager to capture usage and latency info.

Practice

1. What is the main purpose of viewing trace details in a LangChain application?

easy

A. To change the output format of the application

B. To understand the internal steps and flow of the application

C. To increase the speed of the application automatically

D. To add new features without coding

Solution

1. Step 1: Understand what trace details represent

   Trace details show the internal steps and flow of the LangChain app during execution.

2. Step 2: Identify the purpose of viewing these details

   Viewing trace details helps developers see what happens inside, making debugging and optimization easier.

3. Final Answer:

   To understand the internal steps and flow of the application -> Option B

4. Quick Check:

   Trace details = internal flow insight [OK]

Hint: Trace details show what happens inside your app [OK]

Common Mistakes:

• Thinking trace changes output format
• Believing trace speeds up app automatically
• Confusing trace with adding features

2. Which of the following is the correct way to enable LangChainTracer to view trace details?

easy

A. from langchain.callbacks import tracer\ntracer = tracer()

B. import LangChainTracer from langchain.callbacks\ntracer = LangChainTracer()

C. from langchain import LangChainTracer\ntracer = LangChainTracer()

D. from langchain.callbacks import LangChainTracer\ntracer = LangChainTracer()

Solution

1. Step 1: Recall the correct import path for LangChainTracer

   LangChainTracer is imported from langchain.callbacks module.

2. Step 2: Check the correct instantiation syntax

   The correct way is to import LangChainTracer and then create an instance with LangChainTracer().

3. Final Answer:

   from langchain.callbacks import LangChainTracer\ntracer = LangChainTracer() -> Option D

4. Quick Check:

   Correct import and instantiation = from langchain.callbacks import LangChainTracer\ntracer = LangChainTracer() [OK]

Hint: Import from langchain.callbacks and instantiate with () [OK]

Common Mistakes:

• Wrong import path
• Using incorrect import syntax
• Calling a non-existent function

3. Given this code snippet using LangChainTracer, what will be the output regarding latency?

from langchain.callbacks import LangChainTracer
tracer = LangChainTracer()

with tracer:
    result = chain.run("Hello")

print(tracer.get_trace())

medium

A. A detailed trace including each step's latency in milliseconds

B. Only the final output without any timing information

C. An error because get_trace() is not a valid method

D. An empty trace because tracer was not started

Solution

1. Step 1: Understand what LangChainTracer does inside a with block

   LangChainTracer collects detailed trace info including latency for each step during the with block execution.

2. Step 2: Check the output of get_trace()

   get_trace() returns the collected trace data, which includes latency details for each step.

3. Final Answer:

   A detailed trace including each step's latency in milliseconds -> Option A

4. Quick Check:

   Tracer with block + get_trace() = detailed latency trace [OK]

Hint: Tracer inside with block captures latency info [OK]

Common Mistakes:

• Assuming get_trace() does not exist
• Expecting output without timing info
• Forgetting to use with block for tracing

4. You added LangChainTracer but see no trace output after running your chain. What is the most likely cause?

medium

A. The chain.run() method does not support tracing

B. You did not import LangChainTracer correctly

C. You forgot to wrap the chain execution inside the tracer's with block

D. You need to call tracer.start() before running the chain

Solution

1. Step 1: Check how LangChainTracer collects trace data

   LangChainTracer collects trace only when chain execution is inside its with block context.

2. Step 2: Identify the missing step causing no trace output

   If chain.run() is outside the with block, no trace is recorded, so output is empty.

3. Final Answer:

   You forgot to wrap the chain execution inside the tracer's with block -> Option C

4. Quick Check:

   Tracing requires with block wrapping chain run [OK]

Hint: Always run chain inside tracer's with block [OK]

Common Mistakes:

• Assuming import error causes no trace
• Thinking chain.run() disables tracing
• Trying to call non-existent start() method

5. You want to find the slowest step in your LangChain app using LangChainTracer. Which approach correctly identifies it?

hard

A. Extract latency from each trace step and find the maximum value

B. Check the total runtime of the app without step details

C. Use print statements inside the chain to guess slow parts

D. Restart the app multiple times to see when it feels slow

Solution

1. Step 1: Understand what latency data LangChainTracer provides

   LangChainTracer records latency for each individual step in the trace details.

2. Step 2: Identify how to find the slowest step

   By extracting latency values from each step and comparing them, you can find the maximum latency which indicates the slowest step.

3. Final Answer:

   Extract latency from each trace step and find the maximum value -> Option A

4. Quick Check:

   Find max latency in trace steps = slowest step [OK]

Hint: Find max latency value in trace steps to spot slowest [OK]

Common Mistakes:

• Ignoring step-level latency and checking only total time
• Using print statements instead of trace data
• Guessing speed without data