Mantium API Client Library

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Table of Contents

• Quickstart
• Authentication
• Installation
• Usage
  • Initializing
  • Auth
    • Login
    • Logout
  • AI Methods
    • List Methods
  • AI Engines
    • Get All AI Engines
    • Get Ai Engines By Provider
    • Get Ai Engine By Name
  • Tags
    • List Tags
    • Get Tag by ID
    • Create Tag
    • Update Tag
    • Delete Tag
  • Prompts
    • List Prompts
    • Create Prompt
    • Update Prompt
    • Get Prompt by ID
    • Delete Prompt
    • Execute Prompt
  • Intelets
    • List Intelets
    • Create Intelet
    • Update Intelet
    • Get Intelet by ID
    • Delete Intelet
    • Execute Intelet
  • Logs
    • List Logs
    • Get Log by ID

Quickstart:

Read the getting started guide for more information on how to use Mantium.

Authentication

• Make an account by visiting app.mantiumai.com and select Register.
• Enter your email address and create a password. After you've verified the email, you'll be able to sign in to the Mantium application. You'll also need your username and password to obtain a token for API use.

Installation

To install the python Library please use the following command.

pip install mantiumapi

Usage

Set authentication credentials in your environment. Setting MANTIUM_USER and MANTIUM_PASSWORD will allow the client to obtain the authentication token, which will re-authenticate when the token expires.

It's also possible to directly set the token through the MANTIUM_TOKEN environment variable. Documentation for authenticating can be found here

• Linux/MacOS

export MANTIUM_USER='<username>'
export MANTIUM_PASSWORD='<password>'

• Windows

set MANTIUM_USER="<username>"
set MANTIUM_PASSWORD="<password>"

• Windows Powershell

$env:MANTIUM_USER="<username>"
$env:MANTIUM_PASSWORD="<password>"

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AI Methods

Get all of the supported ai_methods for a provider

List Methods

Requires AI Provider name

>>> from mantiumapi import AiMethods
>>> ai_methods = AiMethods.get_list("openai")
>>> print([method.name for method in ai_methods])
['answers', 'classifications', 'completion', 'search']

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AI Engines

Get available AI engines

Get All AI Engines

Get all of the configured and available AI engines

>>> from mantiumapi import AiEngine
>>> ai_engines = AiEngine.get_list()
>>> print([(engine.ai_provider, engine.name) for engine in ai_engines])
[('OpenAI', 'content-filter-alpha-c4'), ...]

Get Ai Engines for a single provider

>>> ai_engines = AiEngine.from_provider("Cohere")
>>> print([(engine.ai_provider, engine.name) for engine in ai_engines])
[('Cohere', 'baseline-otter'), ...]

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Tags

List Tags

Get all of the tags for your selected organization.

Query Params Page Page number Size Page size. If not supplied, returns all the results in a single page for certain APIs.

>>> from mantiumapi import Tag
>>> tags = Tag.get_list()
>>> print([tag.name for tag in tags])
['Tag 1', 'Tag 2']

Passing URL parameters

>>> tags = Tag.get_list(params={"page":1,"size":1})
>>> print([tag.name for tag in tags])
['Tag 1']

Get Tag by ID

>>> tag = Tag.from_id('8c809efe-4738-4c7a-93e1-ea933ef84172')
>>> tag.name
['Tag 1']

Create Tag

>>> new_tag = Tag()
>>> new_tag.name = "My new tag"
>>> new_tag.description = "This is a new tag"
>>> new_tag.create()

Update Tag

>>> tag = Tag.from_id('8c809efe-4738-4c7a-93e1-ea933ef84172')
>>> tag.name = "New tag name"
>>> tag.save()

Delete Tag

>>> tag = Tag.from_id('8c809efe-4738-4c7a-93e1-ea933ef84172')
>>> tag.refres()
>>> tag.delete()

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Prompts

List Prompts

List all of your organization's prompts.

Optional Query String Parameters

• page - The page of records to return. Optional, defaults to page 1.
• size - the number of records to return for each page. Optional, defaults to 20 - prompts a page.
• schema_class - not used, exclude.
• tags - A list of Tag IDs separated by comma used to filter the results, optional.

Document link

>>> from mantiumapi import Prompt
>>> prompts = Prompt.get_list(params={"size":2})
>>> print([prompt.name for prompt in prompts])
['My first prompt', 'Test prompt']

Get Prompt by ID

>>> prompt = Prompt.from_id('3184ba90-c2b1-4604-bbd2-6ed436ca5f52')
>>> prompt.name
'My first prompt'

Create Prompt

>>> new_prompt.name = "A new prompt"
>>> new_prompt.prompt_text = "Prompt text"
>>> new_prompt.ai_provider = "OpenAI"
>>> new_prompt.default_engine = "davinci"
>>> new_prompt.ai_method = "completion"
>>> new_prompt.ai_engine_id = "b2ffecf7-4fee-42e7-b85d-a5e28d939396"
>>> new_prompt.text = "Prompt text"
>>> new_prompt.prompt_parameters = {"basic_settings": {"temperature": 1,"max_tokens": 16,"frequency_penalty": 0,"presence_penalty": 0,"top_p": 1},"advanced_settings": {"best_of": 1,"n": 1,"echo": "false","stream": "false"}}
>>> new_prompt.create()

Update Prompt

>>> prompt = Prompt.from_id('3184ba90-c2b1-4604-bbd2-6ed436ca5f52')
>>> prompt.name = "New Prompt name"
>>> prompt.update()

Delete Prompt

>>> prompt = Prompt.from_id('3184ba90-c2b1-4604-bbd2-6ed436ca5f52')
>>> prompt.refresh()
>>> prompt.delete()

Execute Prompt

Asynchronously submit input to a Prompt for execution. Returns a PromptExecute object to manage the result.

As this is an asynchronous endpoint, the first result returned will not be a finished result. The result can be updated by calling .refresh() on the PromptExecute object.

• Input (string)- Data to append to Prompt for execution

>>> prompt = Prompt.from_id('0aab4c37-d931-4726-8768-b7ff91776ce6')
>>> result = Prompt.execute('Data for the Prompt')
>>> result.__dict__
{'prompt_execution_id': '221202da-9d76-4711-91f0-e0d8b50a57d5', 'prompt_id': '0aab4c37-d931-4726-8768-b7ff91776ce6', 'input': 'Data for the Prompt', 'output': '', 'reason': '', 'status': 'RUNNING', 'error': '', 'warning_message': '', 'hitl_info': None}
>>> result.refresh()
>>> result.refresh()
>>> result.__dict__
{'prompt_execution_id': '221202da-9d76-4711-91f0-e0d8b50a57d5', 'prompt_id': '0aab4c37-d931-4726-8768-b7ff91776ce6', 'input': 'Data for the Prompt', 'output': 'Output from the Prompt Exeuction', 'reason': '', 'status': 'COMPLETED', 'error': '', 'warning_message': '', 'hitl_info': None}

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Intelets

Intelets organize multiple prompts by grouping them together sequentially so that the output of one prompt feeds into the input of the next - this enables the creation of complex AI data pipelines for processing text.

List Intelets

List all of your organization's prompts.

Optional Query String Parameters

• page - The page of records to return. Optional, defaults to page 1.
• size - the number of records to return for each page. Optional, defaults to 20 - prompts a page.

>>> from mantiumapi import Intelet
>>> intelets = Intelet.get_list()
>>> print([i.name for i in intelets])
['My first Intelet', 'Processing pipeline', ...]

Get Intelet by ID

>>> intelet = Intelet.from_id('0aab4c37-d931-4726-8768-b7ff91776ce6')
>>> print(intelet.name)
'My first Intelet'

Create Intelet

The order of Prompts in the prompts list dictate in which order they will be executed in the pipeline.

>>> new_intelet=Intelet()
>>> new_intelet.name = "Name of new Intelet"
>>> new_intelet.description = "Description of the Intelet"
>>> new_intelet.prompts = ['3184ba90-c2b1-4604-bbd2-6ed436ca5f52']
>>> new_intelet.save()

Update Intelet

>>> intelet = Intelet.from_id('3184ba90-c2b1-4604-bbd2-6ed436ca5f52')
>>> intelet.name = "New Intelet name"
>>> intelet.update()

Delete Intelet

>>> intelet = intelet.from_id('3184ba90-c2b1-4604-bbd2-6ed436ca5f52')
>>> intelet.refresh()
>>> intelet.delete()

Execute Intelet

Asynchronously submit input to an Intelet for execution. Returns a InteletExecute object to manage the result.

As this is an asynchronous endpoint, the first result returned will not be a finished result. The result can be updated by calling .refresh() on the InteletExecute object.

• Input (string)- Data to append to Prompt for execution

>>> intelet = Intelet.from_id('3c3a14f3-aeaa-468b-8b8f-8d8a053f1719')
>>> result = Intelet.execute('Sample input text')
>>> result.__dict__
{'intelet_execution_id': 'd81fba13-d3fc-41af-9f0b-10816b7ca5df', 'intelet_id': '3c3a14f3-aeaa-468b-8b8f-8d8a053f1719', 'input': 'Sample input text', 'output': '', 'reason': '', 'status': 'QUEUED', 'error': '', 'executed_prompts': [], 'pending_prompts': ['760311e5-9137-4ed6-dc2a-34f077b44131', '3a063314-1fe5-42f2-a276-c25404071c3d', '6004bfd0-5bec-4f41-bd89-1e4a6ca3f76b'], 'results': []}
>>> result.refresh()
>>> result.__dict__
{'intelet_execution_id': 'd81fba13-d3fc-41af-9f0b-10816b7ca5df', 'intelet_id': '3c3a14f3-aeaa-468b-8b8f-8d8a053f1719', 'input': 'Sample input text', 'output': 'Output of Intelet', 'reason': '', 'status': 'COMPLETED', 'error': '', 'executed_prompts': ['760311e5-9137-4ed6-dc2a-34f077b44131', '3a063314-1fe5-42f2-a276-c25404071c3d', '6004bfd0-5bec-4f41-bd89-1e4a6ca3f76b'], 'pending_prompts': [], 'results': []}

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Logs

List Logs

Query Params

• page (int) - Page number
• size (int) - Page size. If not supplied, returns all the results in a single page for certain APIs.
• after_date (string) - After Date
• before_date (string) Before Date
• log_type (string) LogType, An enumeration. [AUTH | DEFAULT | PROMPT | INTELET FILE]
• log_level (string) Log Level
• log_status (string) Log Status

>>> from mantiumapi import Log
>>> logs = Log.get_list(params={"size":5, "log_type":"PROMPT"})
>>> print([(l.log_type, l.log_payload) for l in logs])
[('PROMPT', {'to': 'completion', 'name': 'OpenAI Completion', 'error': '', 'input': ...]

Get Log by ID

>>> log = Log.from_id('b372d92c-028d-463b-98ba-3cec3f170af5')
>>> log.id
'b372d92c-028d-463b-98ba-3cec3f170af5'
>>> log.log_type
'PROMPT'

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