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Mastering LlamaIndex: From Fundamentals to Building AI Apps

Tracing and Debugging AI Systems in LlamaIndex

Explore how to trace and debug AI systems built with LlamaIndex. Learn to implement basic logging, use callback handlers for structured tracing, and integrate advanced observability tools like LlamaTrace. Understand workflow execution, identify performance bottlenecks, and improve the reliability of your LLM-based applications through step-by-step tracing and monitoring.

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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.

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:

Python 3.10.4
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. ...