Monitoring - Hamming

Before you begin

Follow the Setting up guide to make sure that you have access to the Hamming dashboard and you have created a secret key.

Quickstart - Node.js

Learn how to monitor your AI application with our Hamming TypeScript SDK.

Install Hamming SDK TypeScript library

Create a script for a simple AI application

Make sure to replace the placeholders with your actual keys and dataset ID created in the previous step.

Create a file named ai-app.ts or ai-app.js and add the following code:

import { Hamming } from "@hamming/hamming-sdk";
import { OpenAI } from "openai";

const HAMMING_API_KEY = "<your-secret-key>";
const OPENAI_API_KEY = "<your-openai-key>";

const openai = new OpenAI({ apiKey: OPENAI_API_KEY });
const hamming = new Hamming({ apiKey: HAMMING_API_KEY });

hamming.monitoring.start();
const trace = hamming.tracing;

async function run() {
  const question = "What is the capital of France?";
  console.log("Question:", question);

  const output = await hamming.monitoring.runItem(async (item) => {
    item.setInput({ question });

    const response = await openai.chat.completions.create({
      model: "gpt-4-turbo",
      messages: [{ role: "user", content: question }],
      max_tokens: 1000,
      temperature: 0.7,
    });
    const answer = response.choices[0].message.content;

    trace.logGeneration({
      input: question,
      output: answer,
      metadata: {
        provider: "openai",
        model: "gpt-4-turbo",
        max_tokens: 1000,
        temperature: 0.7,
        error: false,
      },
    });
    return { answer };
  });
  console.log("Answer:", output.answer);
}

run().catch(console.error);

Run the AI application script

Install dependencies:

npm install openai

Run the script by executing the following command in your terminal:

npx tsx ai-app.ts

This will create a monitoring item in Hamming with the corresponding LLM trace. You can view the item on the Monitoring page.

Quickstart - Python

Learn how to monitor your AI application with our Hamming Python SDK.

Install Hamming SDK Python library

Create a script for a simple AI application

Create a file named ai-app.py and add the following code:

ai-app.py

from hamming import Hamming, ClientOptions, GenerationParams
from openai import OpenAI

HAMMING_API_KEY = "<your-secret-key>"
OPENAI_API_KEY = "<your-openai-key>"

openai_client = OpenAI(api_key=OPENAI_API_KEY)
hamming = Hamming(ClientOptions(api_key=HAMMING_API_KEY))

hamming.monitoring.start()
trace = hamming.tracing


def run():
    question = "What is the capital of France?"
    print("Question:", question)

    with hamming.monitoring.start_item() as item:
        item.set_input({"question": question})
        response = openai_client.chat.completions.create(
            model="gpt-3.5-turbo",
            messages=[{"role": "user", "content": question}],
            max_tokens=1000,
            temperature=0.7,
        )
        answer = response.choices[0].message.content
        trace.log_generation(
            GenerationParams(
                input=question,
                output=answer,
                metadata=GenerationParams.Metadata(
                    provider="openai",
                    model="gpt-3.5-turbo",
                    max_tokens=1000,
                    temperature=0.7,
                    error=False,
                ),
            ),
        )
        print("Answer:", answer)
        item.set_output({"answer": answer})


if __name__ == "__main__":
    run()

Run the AI application script

Install dependencies:

pip install openai

Run the script by executing the following command in your terminal:

python ai-app.py

This will create a monitoring item in Hamming with the corresponding LLM trace. You can view the item in the Monitoring dashboard.

On this page

Before you begin
Quickstart - Node.js
Quickstart - Python

Before you begin

Follow the Setting up guide to make sure that you have access to the Hamming dashboard and you have created a secret key.

Quickstart - Node.js

Learn how to monitor your AI application with our Hamming TypeScript SDK.

Install Hamming SDK TypeScript library

Create a script for a simple AI application

Make sure to replace the placeholders with your actual keys and dataset ID created in the previous step.

Create a file named ai-app.ts or ai-app.js and add the following code:

import { Hamming } from "@hamming/hamming-sdk";
import { OpenAI } from "openai";

const HAMMING_API_KEY = "<your-secret-key>";
const OPENAI_API_KEY = "<your-openai-key>";

const openai = new OpenAI({ apiKey: OPENAI_API_KEY });
const hamming = new Hamming({ apiKey: HAMMING_API_KEY });

hamming.monitoring.start();
const trace = hamming.tracing;

async function run() {
  const question = "What is the capital of France?";
  console.log("Question:", question);

  const output = await hamming.monitoring.runItem(async (item) => {
    item.setInput({ question });

    const response = await openai.chat.completions.create({
      model: "gpt-4-turbo",
      messages: [{ role: "user", content: question }],
      max_tokens: 1000,
      temperature: 0.7,
    });
    const answer = response.choices[0].message.content;

    trace.logGeneration({
      input: question,
      output: answer,
      metadata: {
        provider: "openai",
        model: "gpt-4-turbo",
        max_tokens: 1000,
        temperature: 0.7,
        error: false,
      },
    });
    return { answer };
  });
  console.log("Answer:", output.answer);
}

run().catch(console.error);

Run the AI application script

Install dependencies:

npm install openai

Run the script by executing the following command in your terminal:

npx tsx ai-app.ts

This will create a monitoring item in Hamming with the corresponding LLM trace. You can view the item on the Monitoring page.

Quickstart - Python

Learn how to monitor your AI application with our Hamming Python SDK.

Install Hamming SDK Python library

Create a script for a simple AI application

Create a file named ai-app.py and add the following code:

ai-app.py

from hamming import Hamming, ClientOptions, GenerationParams
from openai import OpenAI

HAMMING_API_KEY = "<your-secret-key>"
OPENAI_API_KEY = "<your-openai-key>"

openai_client = OpenAI(api_key=OPENAI_API_KEY)
hamming = Hamming(ClientOptions(api_key=HAMMING_API_KEY))

hamming.monitoring.start()
trace = hamming.tracing


def run():
    question = "What is the capital of France?"
    print("Question:", question)

    with hamming.monitoring.start_item() as item:
        item.set_input({"question": question})
        response = openai_client.chat.completions.create(
            model="gpt-3.5-turbo",
            messages=[{"role": "user", "content": question}],
            max_tokens=1000,
            temperature=0.7,
        )
        answer = response.choices[0].message.content
        trace.log_generation(
            GenerationParams(
                input=question,
                output=answer,
                metadata=GenerationParams.Metadata(
                    provider="openai",
                    model="gpt-3.5-turbo",
                    max_tokens=1000,
                    temperature=0.7,
                    error=False,
                ),
            ),
        )
        print("Answer:", answer)
        item.set_output({"answer": answer})


if __name__ == "__main__":
    run()

Run the AI application script

Install dependencies:

pip install openai

Run the script by executing the following command in your terminal:

python ai-app.py

This will create a monitoring item in Hamming with the corresponding LLM trace. You can view the item in the Monitoring dashboard.

On this page

Before you begin
Quickstart - Node.js
Quickstart - Python

​Before you begin

​Quickstart - Node.js

​Quickstart - Python

API Reference

​Before you begin

​Quickstart - Node.js

​Quickstart - Python

Before you begin

Quickstart - Node.js

Quickstart - Python

Before you begin

Quickstart - Node.js

Quickstart - Python