Evaluate - Maitai

import maitai

async def on_eval(eval_response):
    # handle evaluate response (EvaluateResponse)
    print(eval_response)

messages = [
    {"role": "system", "content": "You are a helpful assistant"},
    {"role": "user", "content": "Generate numbers 1-10"},
]

client = maitai.MaitaiAsync()

response = await client.chat.completions.create(
    messages=messages,
    model="gpt-4o",
    session_id="YOUR_SESSION_ID",
    intent="NUMBER_GENERATOR",
    application="demo_app",
    callback=on_eval,
)

import maitai

def on_eval(eval_response):
    # handle evaluate response (EvaluateResponse)
    print(eval_response)

messages = [
    {"role": "system", "content": "You are a helpful assistant"},
    {"role": "user", "content": "Generate numbers 1-10"},
]

client = maitai.Maitai()

response = client.chat.completions.create(
    messages=messages,
    model="gpt-4o",
    session_id="YOUR_SESSION_ID",
    intent="NUMBER_GENERATOR",
    application="demo_app",
    callback=on_eval,
)

import Maitai from "maitai";

const maitai = new Maitai();

const onEval = (evalResponse) => {
  // handle evaluate response (EvaluateResponse)
  console.log(evalResponse);
};

const messages = [
  { role: "system", content: "You are a helpful assistant" },
  { role: "user", content: "Generate numbers 1-10" },
];

const response = await maitai.chat.completions.create({
  messages: messages,
  model: "gpt-4o",
  session_id: "YOUR_SESSION_ID",
  intent: "NUMBER_GENERATOR",
  application: "demo_app",
  callback: onEval,
});

{
  "application_id": 16,
  "session_id": "xxx",
  "evaluation_results": [
    {
      "status": "FAULT",
      "description": "Test with random number",
      "confidence": 100.0,
      "correction": "Test with random number 42.",
      "sentinel_id": 41,
      "eval_time": 489,
      "date_created": 1716682520088,
      "usage": {
        "prompt_tokens": 380,
        "completion_tokens": 54,
        "total_tokens": 434
      }
    }
  ],
  "evaluation_request_id": "b84f0d55-7c7e-4af4-8d84-106385682250"
}

import maitai

async def on_eval(eval_response):
    # handle evaluate response (EvaluateResponse)
    print(eval_response)

messages = [
    {"role": "system", "content": "You are a helpful assistant"},
    {"role": "user", "content": "Generate numbers 1-10"},
]

client = maitai.MaitaiAsync()

response = await client.chat.completions.create(
    messages=messages,
    model="gpt-4o",
    session_id="YOUR_SESSION_ID",
    intent="NUMBER_GENERATOR",
    application="demo_app",
    callback=on_eval,
)

import maitai

def on_eval(eval_response):
    # handle evaluate response (EvaluateResponse)
    print(eval_response)

messages = [
    {"role": "system", "content": "You are a helpful assistant"},
    {"role": "user", "content": "Generate numbers 1-10"},
]

client = maitai.Maitai()

response = client.chat.completions.create(
    messages=messages,
    model="gpt-4o",
    session_id="YOUR_SESSION_ID",
    intent="NUMBER_GENERATOR",
    application="demo_app",
    callback=on_eval,
)

import Maitai from "maitai";

const maitai = new Maitai();

const onEval = (evalResponse) => {
  // handle evaluate response (EvaluateResponse)
  console.log(evalResponse);
};

const messages = [
  { role: "system", content: "You are a helpful assistant" },
  { role: "user", content: "Generate numbers 1-10" },
];

const response = await maitai.chat.completions.create({
  messages: messages,
  model: "gpt-4o",
  session_id: "YOUR_SESSION_ID",
  intent: "NUMBER_GENERATOR",
  application: "demo_app",
  callback: onEval,
});

{
  "application_id": 16,
  "session_id": "xxx",
  "evaluation_results": [
    {
      "status": "FAULT",
      "description": "Test with random number",
      "confidence": 100.0,
      "correction": "Test with random number 42.",
      "sentinel_id": 41,
      "eval_time": 489,
      "date_created": 1716682520088,
      "usage": {
        "prompt_tokens": 380,
        "completion_tokens": 54,
        "total_tokens": 434
      }
    }
  ],
  "evaluation_request_id": "b84f0d55-7c7e-4af4-8d84-106385682250"
}

Create evaluations

Evaluations are created when you call Chat with evaluations enabled (via Portal configuration or SDK parameters). The model output is what gets evaluated, with the request used as context. If a callback is provided, the evaluation is passed to that function asynchronously. If no callback is provided, and stream is true, then the evaluation is available on the last chunk. If no callback is provided and stream is false, then the evaluation can be found on the completion response.

Example (callback)

import maitai

async def on_eval(eval_response):
    # handle evaluate response (EvaluateResponse)
    print(eval_response)

messages = [
    {"role": "system", "content": "You are a helpful assistant"},
    {"role": "user", "content": "Generate numbers 1-10"},
]

client = maitai.MaitaiAsync()

response = await client.chat.completions.create(
    messages=messages,
    model="gpt-4o",
    session_id="YOUR_SESSION_ID",
    intent="NUMBER_GENERATOR",
    application="demo_app",
    callback=on_eval,
)

import maitai

def on_eval(eval_response):
    # handle evaluate response (EvaluateResponse)
    print(eval_response)

messages = [
    {"role": "system", "content": "You are a helpful assistant"},
    {"role": "user", "content": "Generate numbers 1-10"},
]

client = maitai.Maitai()

response = client.chat.completions.create(
    messages=messages,
    model="gpt-4o",
    session_id="YOUR_SESSION_ID",
    intent="NUMBER_GENERATOR",
    application="demo_app",
    callback=on_eval,
)

import Maitai from "maitai";

const maitai = new Maitai();

const onEval = (evalResponse) => {
  // handle evaluate response (EvaluateResponse)
  console.log(evalResponse);
};

const messages = [
  { role: "system", content: "You are a helpful assistant" },
  { role: "user", content: "Generate numbers 1-10" },
];

const response = await maitai.chat.completions.create({
  messages: messages,
  model: "gpt-4o",
  session_id: "YOUR_SESSION_ID",
  intent: "NUMBER_GENERATOR",
  application: "demo_app",
  callback: onEval,
});

{
  "application_id": 16,
  "session_id": "xxx",
  "evaluation_results": [
    {
      "status": "FAULT",
      "description": "Test with random number",
      "confidence": 100.0,
      "correction": "Test with random number 42.",
      "sentinel_id": 41,
      "eval_time": 489,
      "date_created": 1716682520088,
      "usage": {
        "prompt_tokens": 380,
        "completion_tokens": 54,
        "total_tokens": 434
      }
    }
  ],
  "evaluation_request_id": "b84f0d55-7c7e-4af4-8d84-106385682250"
}

integer

The Maitai identifier for the application.

string

A unique identifier for the session, passed in from the Chat endpoint.

string

The identifier of the evaluated chat completion request.

array

A list of individual Sentinel results.

Show properties

integer

A unique identifier for the evaluation result.

string

The status of the evaluation. FAULT means a fault was detected. PASS means the LLM output passed testing. NA means an evaluation wasn’t performed.

string

A detailed description of the evaluation outcome.

float

The confidence level of the evaluation result.

string

Optional suggested correction text when a FAULT is detected.

object

Additional metadata associated with the evaluation result.

integer

An identifier for the Sentinel associated with this result.

integer

The evaluation time in milliseconds.

integer

The Unix timestamp marking the creation of the evaluation result.

object

Usage statistics for the Sentinel

Show properties

integer

Number of tokens in the generated completion.

integer

Number of tokens in the prompt.

integer

Total number of tokens used in the request (prompt + completion).

string

The unique identifier for the evaluation request.

​Create evaluations

​Example (callback)

Create evaluations

Example (callback)