​
Overview

We’ll create a FastAPI application that:
  1. Accepts support ticket submissions
  2. Uses an LLM to classify the ticket
  3. Returns the classification result
  4. Traces the entire process with Laminar

​
Setup

You can also use the example app from our GitHub repo. Alternatively, for a clean install, follow the steps below:
1

Install Dependencies

First, let’s install the required packages:
pip install fastapi uvicorn openai lmnr python-dotenv
2

Environment Setup

Create a .env file with your API keys:
LMNR_PROJECT_API_KEY="your-laminar-project-api-key"
OPENAI_API_KEY="your-openai-api-key"
3

Project Structure

Create the following project structure:
fastapi-app/
├── .env
├── requirements.txt
└── src/
    ├── main.py
    ├── llm.py
    └── schemas.py

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Implementation

Let’s create our FastAPI application with Laminar tracing. We’ll split the code into three files:

​
1. schemas.py

This file will contain the Pydantic models for the request and response.
schemas.py
from enum import Enum
from pydantic import BaseModel

class TicketCategory(str, Enum):
    REFUND = "REFUND"
    BUG = "BUG"
    QUESTION = "QUESTION"
    OTHER = "OTHER"

class Ticket(BaseModel):
    title: str
    description: str
    customer_email: str

class TicketClassification(BaseModel):
    category: TicketCategory
    reasoning: str

​
2. llm.py

This file will contain the logic that handles the LLM. In a production app, this will likely be a much bigger module with classes, routing, etc. For now, we’ll just have a function that handles an OpenAI call.
llm.py
from dotenv import load_dotenv
from lmnr import observe
from openai import OpenAI
from schemas import Ticket, TicketCategory, TicketClassification

import os

load_dotenv(override=True)

client = OpenAI(api_key=os.getenv("OPENAI_API_KEY"))

@observe()
def model_classify_ticket(ticket: Ticket) -> TicketClassification:
    response = client.beta.chat.completions.parse(
        model="gpt-4.1-nano",
        response_format=TicketClassification,
        messages=[
            {
                "role": "system",
                "content": """You are a support ticket classifier.
Classify the ticket that a user sends you""",
            },
            {
                "role": "user",
                "content": f"""Title: {ticket.title}
Description: {ticket.description}
Customer Email: {ticket.customer_email}""",
            },
        ],
    )
    
    return response.choices[0].message.parsed or TicketClassification(
        category=TicketCategory.OTHER,
        reasoning=response.choices[0].message.content,
    )

​
3. main.py

This file will contain the logic that handles the FastAPI app.
Laminar must be initialized once at the entry point of the application. For FastAPI, this is typically main.py, right before creating app.
main.py
from dotenv import load_dotenv
from fastapi import FastAPI, HTTPException
from llm import model_classify_ticket
from lmnr import Laminar
from schemas import Ticket, TicketClassification

load_dotenv(override=True)

# Laminar must be initialized once, preferably at the entry point of the application
Laminar.initialize()

app = FastAPI()

@app.post("/api/v1/tickets/classify", response_model=TicketClassification)
async def classify_ticket(ticket: Ticket):
    try:
        classification = model_classify_ticket(ticket)
        print(classification)
        return classification
    except Exception as e:
        raise HTTPException(status_code=500, detail=str(e))

​
Running the Application

Start the FastAPI server: 
uvicorn src.main:app --reload --port 8011

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Testing the API

You can test the API using curl: 
curl --location 'localhost:8011/api/v1/tickets/classify' \
--header 'Content-Type: application/json' \
--data-raw '{
    "title": "Can'\''t access my account",
    "description": "I'\''ve been trying to log in for the past hour but keep getting an error message",
    "customer_email": "user@example.com"
}'

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Viewing Traces

After making a request, you can view the traces in your Laminar dashboard at https://www.lmnr.ai. The trace will show:
  1. The ticket classification function execution
  2. The OpenAI API call

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Key Features Demonstrated

  1. Automatic Tracing: Laminar automatically traces OpenAI calls and functions marked with @observe
  2. Tokens Usage: Laminar automatically calculates the tokens used for each OpenAI call
  3. Cost Estimation: Laminar automatically estimates the cost of each OpenAI call

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Troubleshooting

If you encounter issues:
  • Check that your API keys are correctly set in the .env file and that it is loaded correctly
  • Verify that Laminar is properly initialized
  • Ensure all dependencies are installed
  • Review the FastAPI logs for any application errors