notdiamond 1.5.0

The official Python library for the notdiamond API

Notdiamond Python API library

The Notdiamond Python library provides convenient access to the Notdiamond REST API from any Python 3.9+ application. The library includes type definitions for all request params and response fields, and offers both synchronous and asynchronous clients powered by httpx.

What is Prompt Optimization?

Not Diamond specializes in Prompt Optimization - automatically optimizing your prompts to work optimally across different LLMs. Each language model has unique characteristics, instruction-following patterns, and preferred prompt formats. A prompt that works perfectly for GPT-5 might perform poorly on Claude or Gemini. Manually rewriting prompts for each model is time-consuming and requires deep expertise in each model's quirks.

The Solution: Not Diamond automatically optimizes your prompts with:

• Automatic optimization of both system and user prompts
• Built-in evaluation metrics
• Minimum 25 training examples recommended
• Processing time: typically 10–30 minutes

Documentation

The REST API documentation can be found on docs.notdiamond.ai. The full API of this library can be found in api.md.

Installation

# install from PyPI
pip install notdiamond

Usage

Quick Start

import os
from notdiamond import NotDiamond

client = NotDiamond(
    api_key=os.environ.get("NOT_DIAMOND_API_KEY"),  # This is the default and can be omitted
)

# Step 1: Start a prompt optimization job with prototype mode
result = client.prompt_optimization.optimize(
    fields=["question"],
    system_prompt="You are a mathematical assistant that counts digits accurately.",
    target_models=[
        {
            "model": "claude-sonnet-4-5-20250929",
            "provider": "anthropic",
        },
        {
            "model": "gemini-2.5-flash",
            "provider": "google",
        },
    ],
    template="Question: {question}\nAnswer:",
    train_goldens=[
        {
            "fields": {"question": "How many digits are in (23874045494*2789392485)?"},
            "answer": "20",
        },
        {
            "fields": {"question": "How many odd digits are in (999*777*555*333*111)?"},
            "answer": "10",
        },
        {
            "fields": {"question": "How often does the number '17' appear in the digits of (287558*17)?"},
            "answer": "0",
        },
        {
            "fields": {"question": "How many even digits are in (222*444*666*888)?"},
            "answer": "16",
        },
        {
            "fields": {"question": "How many 0s are in (1234567890*1357908642)?"},
            "answer": "2",
        },
    ],
    test_goldens=[
        {
            "fields": {"question": "How many digits are in (9876543210*123456)?"},
            "answer": "15",
        },
        {
            "fields": {"question": "How many odd digits are in (135*579*246)?"},
            "answer": "8",
        },
        {
            "fields": {"question": "How often does the number '42' appear in the digits of (123456789*42)?"},
            "answer": "1",
        },
        {
            "fields": {"question": "How many even digits are in (1111*2222*3333)?"},
            "answer": "10",
        },
        {
            "fields": {"question": "How many 9s are in (999999*888888)?"},
            "answer": "11",
        },
    ],
    evaluation_metric="LLMaaJ:Sem_Sim_1",  # Or use custom evaluation
    prototype_mode=True,  # Enable faster prototype mode for quick experimentation
)

print(f"Optimization started: {result.optimization_run_id}")

# Step 2: Poll for completion (typically takes 10-30 minutes)
while True:
    status = client.prompt_optimization.get_optimziation_status(result.optimization_run_id)
    print(f"Status: {status.status}")
    
    if status.status == "queued":
        print(f"Queue position: {status.queue_position}")
    
    if status.status in ["completed", "failed"]:
        break
    
    time.sleep(30)  # Poll every 30 seconds

# Step 3: Get the optimized prompts
if status.status == "completed":
    results = client.prompt_optimization.get_optimization_results(result.optimization_run_id)
    
    print(f"\nOrigin model baseline: {results.origin_model.score:.2f}")
    
    for target in results.target_models:
        print(f"\n{'='*50}")
        print(f"Model: {target.api_model_name}")
        print(f"Optimized System Prompt:\n{target.system_prompt}")
        print(f"Optimized Template:\n{target.user_message_template}")
        print(f"Pre-optimization score: {target.pre_optimization_score:.2f}")
        print(f"Post-optimization score: {target.post_optimization_score:.2f}")
        print(f"Improvement: {((target.post_optimization_score / target.pre_optimization_score - 1) * 100):.1f}%")
        print(f"Cost: ${target.cost:.4f}")

For more details, see the Prompt Optimization documentation.

Model Routing

Select the best model automatically:

import os
from notdiamond import NotDiamond

client = NotDiamond(
    api_key=os.environ.get("NOT_DIAMOND_API_KEY"),  # This is the default and can be omitted
)

response = client.model_router.select_model(
    llm_providers=[
        {"model": "gpt-4o", "provider": "openai"},
        {"model": "claude-sonnet-4-5-20250929", "provider": "anthropic"},
        {"model": "gemini-2.5-flash", "provider": "google"},
    ],
    messages=[
        {"role": "system", "content": "You are a helpful assistant."},
        {"role": "user", "content": "Explain quantum computing in simple terms"},
    ],
)
print(response.providers)

Train Custom Router

For even better performance, you can train a custom router on your own dataset. This allows the router to learn the specific patterns and preferences of your use case:

from pathlib import Path
from notdiamond import NotDiamond

client = NotDiamond(
    api_key=os.environ.get("NOT_DIAMOND_API_KEY"),  # This is the default and can be omitted
)

client.custom_router.train_custom_router(
    dataset_file=Path("/path/to/file"),
    language="english",
    llm_providers='[{"provider": "openai", "model": "gpt-4o"}, {"provider": "anthropic", "model": "claude-sonnet-4-5-20250929"}]',
    maximize=True,
    prompt_column="prompt",
)

Using types

Nested request parameters are TypedDicts. Responses are Pydantic models which also provide helper methods for things like:

• Serializing back into JSON, model.to_json()
• Converting to a dictionary, model.to_dict()

Typed requests and responses provide autocomplete and documentation within your editor. If you would like to see type errors in VS Code to help catch bugs earlier, set python.analysis.typeCheckingMode to basic.

Handling errors

When the library is unable to connect to the API (for example, due to network connection problems or a timeout), a subclass of notdiamond.APIConnectionError is raised.

When the API returns a non-success status code (that is, 4xx or 5xx response), a subclass of notdiamond.APIStatusError is raised, containing status_code and response properties.

All errors inherit from notdiamond.APIError.

import notdiamond
from notdiamond import NotDiamond

client = NotDiamond()

try:
    client.prompt_optimization.optimize(
        fields=["question"],
        system_prompt="You are a helpful assistant.",
        target_models=[
            {
                "model": "claude-sonnet-4-5-20250929",
                "provider": "anthropic",
            },
            {
                "model": "gemini-2.5-flash",
                "provider": "google",
            },
        ],
        template="Question: {question}\nAnswer:",
        train_goldens=[
            {"fields": {"question": "What is 2+2?"}, "answer": "4"},
            # Add at least 25 examples...
        ],
        test_goldens=[
            {"fields": {"question": "What is 3*3?"}, "answer": "9"},
        ],
    )
except notdiamond.APIConnectionError as e:
    print("The server could not be reached")
    print(e.__cause__)  # an underlying Exception, likely raised within httpx.
except notdiamond.RateLimitError as e:
    print("A 429 status code was received; we should back off a bit.")
except notdiamond.APIStatusError as e:
    print("Another non-200-range status code was received")
    print(e.status_code)
    print(e.response)

Error codes are as follows:

Status Code	Error Type
400	BadRequestError
401	AuthenticationError
403	PermissionDeniedError
404	NotFoundError
422	UnprocessableEntityError
429	RateLimitError
>=500	InternalServerError
N/A	APIConnectionError

Timeouts

By default requests time out after 1 minute. You can configure this with a timeout option, which accepts a float or an httpx.Timeout object:

from notdiamond import NotDiamond

# Configure the default for all requests:
client = NotDiamond(
    # 20 seconds (default is 1 minute)
    timeout=20.0,
)

# More granular control:
client = NotDiamond(
    timeout=httpx.Timeout(60.0, read=5.0, write=10.0, connect=2.0),
)

# Override per-request (note: prompt optimization may take 10-30 minutes, so increase timeout accordingly):
client.with_options(timeout=120.0).prompt_optimization.get_optimziation_status(
    optimization_run_id="your-optimization-run-id"
)

On timeout, an APITimeoutError is thrown.

Note that requests that time out are retried twice by default.

Advanced

These methods return an APIResponse object.

Making custom/undocumented requests

This library is typed for convenient access to the documented API.

If you need to access undocumented endpoints, params, or response properties, the library can still be used.

Undocumented endpoints

To make requests to undocumented endpoints, you can make requests using client.get, client.post, and other http verbs. Options on the client will be respected (such as retries) when making this request.

import httpx

response = client.post(
    "/foo",
    cast_to=httpx.Response,
    body={"my_param": True},
)

print(response.headers.get("x-foo"))

Undocumented request params

If you want to explicitly send an extra param, you can do so with the extra_query, extra_body, and extra_headers request options.

Undocumented response properties

To access undocumented response properties, you can access the extra fields like response.unknown_prop. You can also get all the extra fields on the Pydantic model as a dict with response.model_extra.

Configuring the HTTP client

You can directly override the httpx client to customize it for your use case, including:

• Support for proxies
• Custom transports
• Additional advanced functionality

import httpx
from notdiamond import NotDiamond, DefaultHttpxClient

client = NotDiamond(
    # Or use the `NOTDIAMOND_BASE_URL` env var
    base_url="http://my.test.server.example.com:8083",
    http_client=DefaultHttpxClient(
        proxy="http://my.test.proxy.example.com",
        transport=httpx.HTTPTransport(local_address="0.0.0.0"),
    ),
)

Managing HTTP resources

By default the library closes underlying HTTP connections whenever the client is garbage collected. You can manually close the client using the .close() method if desired, or with a context manager that closes when exiting.

from notdiamond import NotDiamond

with NotDiamond() as client:
  # make requests here
  ...

# HTTP client is now closed

Versioning

This package generally follows SemVer conventions, though certain backwards-incompatible changes may be released as minor versions:

1. Changes that only affect static types, without breaking runtime behavior.
2. Changes to library internals which are technically public but not intended or documented for external use. (Please open a GitHub issue to let us know if you are relying on such internals.)
3. Changes that we do not expect to impact the vast majority of users in practice.

We take backwards-compatibility seriously and work hard to ensure you can rely on a smooth upgrade experience.

We are keen for your feedback; please open an issue with questions, bugs, or suggestions.

Determining the installed version

If you've upgraded to the latest version but aren't seeing any new features you were expecting then your python environment is likely still using an older version.

You can determine the version that is being used at runtime with:

import notdiamond
print(notdiamond.__version__)

Requirements

Python 3.9 or higher.

Contributing

See the contributing documentation.