Debugging and Tracing LLMs Like a Professional

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# Introduction

 
Conventional debugging with print() or logging works, nevertheless it’s sluggish and clunky with LLMs. Phoenix gives a timeline view of each step, immediate, and response inspection, error detection with retries, visibility into latency and prices, and a whole visible understanding of your app. Phoenix by Arize AI is a robust open-source observability and tracing device particularly designed for LLM purposes. It helps you monitor, debug, and hint all the pieces taking place in your LLM pipelines visually. On this article, we’ll stroll by what Phoenix does and why it issues, learn how to combine Phoenix with LangChain step-by-step, and learn how to visualize traces within the Phoenix UI.

# What’s Phoenix?

 
Phoenix is an open-source observability and debugging device made for big language mannequin purposes. It captures detailed telemetry knowledge out of your LLM workflows, together with prompts, responses, latency, errors, and power utilization, and presents this info in an intuitive, interactive dashboard. Phoenix permits builders to deeply perceive how their LLM pipelines behave contained in the system, determine and debug points with immediate outputs, analyze efficiency bottlenecks, monitor utilizing tokens and related prices, and hint any errors/retry logic throughout execution section. It helps constant integrations with fashionable frameworks like LangChain and LlamaIndex, and in addition provides OpenTelemetry assist for extra custom-made setups.

# Step-by-Step Setup

// 1. Putting in Required Libraries

Be sure you have Python 3.8+ and set up the dependencies:

pip set up arize-phoenix langchain langchain-together openinference-instrumentation-langchain langchain-community

// 2. Launching Phoenix

Add this line to launch the Phoenix dashboard:

import phoenix as px
px.launch_app()

 
This begins an area dashboard at http://localhost:6006.

// 3. Constructing the LangChain Pipeline with Phoenix Callback

Let’s perceive Phoenix utilizing a use case. We’re constructing a easy LangChain-powered chatbot. Now, we need to:

• Debug if the immediate is working
• Monitor how lengthy the mannequin takes to reply
• Monitor immediate construction, mannequin utilization, and outputs
• See all this visually as a substitute of logging all the pieces manually

// Step 1: Launch the Phoenix Dashboard within the Background

import threading
import phoenix as px

# Launch Phoenix app regionally (entry at http://localhost:6006)
def run_phoenix():
    px.launch_app()

threading.Thread(goal=run_phoenix, daemon=True).begin()

// Step 2: Register Phoenix with OpenTelemetry & Instrument LangChain

from phoenix.otel import register
from openinference.instrumentation.langchain import LangChainInstrumentor

# Register OpenTelemetry tracer
tracer_provider = register()

# Instrument LangChain with Phoenix
LangChainInstrumentor().instrument(tracer_provider=tracer_provider)

// Step 3: Initialize the LLM (Collectively API)

from langchain_together import Collectively

llm = Collectively(
    mannequin="meta-llama/Llama-3-8b-chat-hf",
    temperature=0.7,
    max_tokens=256,
    together_api_key="your-api-key",  # Substitute together with your precise API key
)

 
Please don’t overlook to exchange the “your-api-key” together with your precise collectively.ai API key. You may get it utilizing this hyperlink.

// Step 4: Outline the Immediate Template

from langchain.prompts import ChatPromptTemplate

immediate = ChatPromptTemplate.from_messages([
    ("system", "You are a helpful assistant."),
    ("human", "{question}"),
])

// Step 5: Mix Immediate and Mannequin right into a Chain

// Step 6: Ask A number of Questions and Print Responses

questions = [
    "What is the capital of France?",
    "Who discovered gravity?",
    "Give me a motivational quote about perseverance.",
    "Explain photosynthesis in one sentence.",
    "What is the speed of light?",
]

print("Phoenix operating at http://localhost:6006n")

for q in questions:
    print(f" Query: {q}")
    response = chain.invoke({"query": q})
    print(" Reply:", response, "n")

// Step 7: Maintain the App Alive for Monitoring

strive:
    whereas True:
        go
besides KeyboardInterrupt:
    print(" Exiting.")

# Understanding Phoenix Traces & Metrics

 
Earlier than seeing the output, we should always first perceive Phoenix metrics. You will have to first perceive what traces and spans are:
Hint: Every hint represents one full run of your LLM pipeline. For instance, every query like “What’s the capital of France?” generates a brand new hint.
Spans: Every hint is combined of a number of spans, every representing a stage in your chain:

• ChatPromptTemplate.format: Immediate formatting
• TogetherLLM.invoke: LLM name
• Any customized parts you add

Metrics Proven per Hint
 

Now go to http://localhost:6006 to view the Phoenix dashboard. You will note one thing like:
 

 
Open the primary hint to view its particulars.
 

# Wrapping Up

 
To wrap it up, Arize Phoenix makes it extremely straightforward to debug, hint, and monitor your LLM purposes. You don’t must guess what went mistaken or dig by logs. Every little thing’s proper there: prompts, responses, timings, and extra. It helps you notice points, perceive efficiency, and simply construct higher AI experiences with manner much less stress.
 
 

Kanwal Mehreen is a machine studying engineer and a technical author with a profound ardour for knowledge science and the intersection of AI with medication. She co-authored the e-book “Maximizing Productiveness with ChatGPT”. As a Google Technology Scholar 2022 for APAC, she champions range and tutorial excellence. She’s additionally acknowledged as a Teradata Range in Tech Scholar, Mitacs Globalink Analysis Scholar, and Harvard WeCode Scholar. Kanwal is an ardent advocate for change, having based FEMCodes to empower ladies in STEM fields.