Mastering LlamaIndex: From Fundamentals to Building AI Apps/

...

/

Tracing and Debugging AI Systems in LlamaIndex

Tracing and Debugging AI Systems in LlamaIndex

Learn how to trace and debug LLM applications using LlamaIndex’s built-in tools for logging, performance profiling, and system optimization.

We'll cover the following...

When we build AI systems with LlamaIndex or any other framework, it’s easy to focus on inputs and outputs—a question goes in, and an answer emerges. But much happens beneath the surface: documents are embedded and retrieved, prompts are dynamically constructed, and language models generate results.

In more complex setups, agents may call tools, and workflows may branch based on logic or user interaction. When something goes wrong—a poor answer, a missed tool call, or a slow response—we must understand what happened.

Tracing and debugging help us make the invisible visible. They let us follow how data flows through the system and diagnose exactly where things break down. With LlamaIndex, we can inspect each step—from retrieval and prompt construction to final response generation.

Press + to interact

Enabling basic logging

Basic logging refers to printing key events or information to the console during runtime, like the queries we send, the responses we get back, or the progress of certain steps in our application.

This is often done using Python’s built-in logging module. It gives us immediate visibility into high-level events without special tooling or tracing setup.

For example:

Press + to interact
import logging
# Configure logging
logging.basicConfig(level=logging.INFO)
logger = logging.getLogger(__name__)
# Example function to simulate response retrieval
def retrieve_response(query):
    logger.info(f"Processing query: {query}")
    response = "This is a sample response."
    logger.info(f"Generated response: {response}")
    return response
# Test logging
response = retrieve_response("What is LlamaIndex?")
print(response)

  • Line 1: We import Python’s built-in logging module.

  • Line 4: We configure the logging system to show messages at the INFO level or higher.

  • Line 5: logger = logging.getLogger(__name__) creates a logger instance. Using __name__ ensures the logger is properly scoped to the current module.

  • Lines 8–12: We define a function retrieve_response(query) that simulates generating a response to a user query. Before generating the response, we log the received query. After generating the (simulated) response, we log the output.

  • Lines 15–16: We test the function with a sample query and print the returned response to the console.

This helps us confirm that our query logic is working as expected, verifying that queries are received ...