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Application APIs

Lets documents, content and metadata be stored in MARCIE for further API processing. To get this data into the MARCIE store, document decomposition and ingestion, as well as manual document/content submission APIs can be used. Once populated, application APIs provide semantic/syntactic analysis, semantic search and content related querying to be executed at scale. See APPS from the navigation panel for more details.

Content APIs

Content is submitted to endpoints with associated control attributes and results are synchronously returned. Primary examples of these APIs include: content comparison, enrichment, transformation, and analysis (spellcheck, grammar, sentiment, readability). See DOCUMENT and CONTENT from the navigation panel for more details. In this scenario, content is not stored or persisted.

Download OpenAPI description
Overview
MARCIE Support

info@messagepoint.com

Languages
Servers
Mock server

https://marcie.redocly.app/_mock/openapi/

https://w1waoh1clk.execute-api.us-east-1.amazonaws.com/{basePath}/

Text Generative AI

Text focused APIs utilizing generative large language models (LLMs)

Operations

Plain Language Rewrite (EXPERIMENTAL)

Request

Rewrite the given text to be more easier to read and comprehend.

Security
api_key
Bodyapplication/jsonrequired
textstringnon-emptyrequired

The text content with UTF-8 text representation

ishtmlboolean

A boolean flag that should be set to true if text is in HTML markup.

Default false
randomnumber[ 0 .. 1 ]

Controls the amount of randomness in the generated text. Lowering this will result in the output to be more deterministic.

Default 0
modelstring

The generative model to use.

Default "gpt4-o"
Enum"gpt4-o""gpt4-o-mini""gpt4""gpt4-t""o1""o1-mini"
curl -i -X POST \
  https://marcie.redocly.app/_mock/openapi/text/enrichment/plainlang/rewrite \
  -H 'Content-Type: application/json' \
  -H 'x-api-key: YOUR_API_KEY_HERE' \
  -d '{
    "text": "Quantum mechanics is a basic theory in physics that explains how things work at the tiny scale of atoms and subatomic particles. It is the basis for all quantum physics, like quantum chemistry, quantum field theory, quantum technology, and quantum information science."
  }'

Responses

200 response

Headers
Access-Control-Allow-Originstring
Bodyapplication/json
object(Empty Schema)
Response
application/json
{}

Readability Rewrite (EXPERIMENTAL)

Request

Rewrite the given text to be suitable for an 8th Grader.

Security
api_key
Bodyapplication/jsonrequired
textstringnon-emptyrequired

The text content with UTF-8 text representation

ishtmlboolean

A boolean flag that should be set to true if text is in HTML markup.

Default false
targetstringrequired

The target grade level.

Enum"5""6""7""8-9""10-12""college student""college graduate"
randomnumber[ 0 .. 1 ]

Controls the amount of randomness in the generated text. Lowering this will result in the output to be more deterministic.

Default 0
modelstring

The generative model to use.

Default "gpt4-o"
Enum"gpt4-o""gpt4-o-mini""gpt4""gpt4-t""o1""o1-mini"
curl -i -X POST \
  https://marcie.redocly.app/_mock/openapi/text/enrichment/readability/rewrite \
  -H 'Content-Type: application/json' \
  -H 'x-api-key: YOUR_API_KEY_HERE' \
  -d '{
    "text": "These methods still require supervised training in order to perform a task. When only minimal or no supervised data is available, another line of work has demonstrated the promise of language models to perform specific tasks, such as common sense reasoning (Schwartz et al., 2017) and sentiment analysis (Radford et al., 2017). In this paper, we connect these two lines of work and continue the trend of more general methods of transfer. We demonstrate language models can perform down-stream tasks in a zero-shot setting – without any parameter or architecture modification. We demonstrate this approach shows potential by highlighting the ability of language models to perform a wide range of tasks in a zero-shot setting. We achieve promising, competitive, and state of the art results depending on the task.",
    "target": "5"
  }'

Responses

200 response

Headers
Access-Control-Allow-Originstring
Bodyapplication/json
object(Empty Schema)
Response
application/json
{}

Request

Perform text summarization.

Security
api_key
Bodyapplication/jsonrequired
textstringnon-emptyrequired

The text content with UTF-8 text representation

ishtmlboolean

A boolean flag that should be set to true if text is in HTML markup.

Default false
limitinteger>= 1

Attempt to restrict the summary to these many words or less.

randomnumber[ 0 .. 1 ]

Controls the amount of randomness in the generated text. Lowering this will result in the output to be more deterministic.

Default 0
modelstring

The generative model to use.

Default "gpt4-o"
Enum"gpt4-o""gpt4-o-mini""gpt4""gpt4-t""o1""o1-mini"
curl -i -X POST \
  https://marcie.redocly.app/_mock/openapi/text/summary \
  -H 'Content-Type: application/json' \
  -H 'x-api-key: YOUR_API_KEY_HERE' \
  -d '{
    "text": "Jupiter is the fifth planet from the Sun and the largest in the Solar System. It is a gas giant with a mass one-thousandth that of the Sun, but two-and-a-half times that of all the other planets in the Solar System combined. Jupiter is one of the brightest objects visible to the naked eye in the night sky, and has been known to ancient civilizations since before recorded history. It is named after the Roman god Jupiter.[19] When viewed from Earth, Jupiter can be bright enough for its reflected light to cast visible shadows,[20] and is on average the third-brightest natural object in the night sky after the Moon and Venus."
  }'

Responses

200 response

Headers
Access-Control-Allow-Originstring
Bodyapplication/json
object(Empty Schema)
Response
application/json
{}

Text Similarity

Provides multiple text-based similarity algorithms to measure the similarity of input text pairs. The provided algorithms are tuned to measure similarity both in the representation (syntax) and the meaning (semantics) of the text content.

Operations

Text Summary

Generates a summary for the given text.

Operations

Natural Language Processing

MARCIE NLP operations' request on provided content

Operations

Enrichments/Classification

Text enrichment APIs offer various enrichment functions that take the raw text as its input and provides a specific enrichment/feature corresponding to the input text. An enrichment function is idempotent and its output is determined by the input text and the underlying predictive (deep learning based) linguistic model. Some examples of these include text based sentiment, readability calculation etc. Most of the underlying methods can be used either using a "GET" or a "POST" HTTP method. For smaller text, the GET method offers better performance and allows for network optimizations such as caching.

Operations

Text Transformers

MARCIE text transformers operations' request on provided content

Operations

Spelling & Grammar

MARCIE spell and grammar operations' request on provided content

Operations

Content Moderation

MARCIE API Content Moderation

Operations

Translation

MARCIE translation operations

Operations

Application

Root resource for all application APIs

Operations

Content

Root resource for all content APIs

Operations

Document

Root resource for all document APIs

Operations

PDF Document

PDF document parsing & processing APIs

Operations

Word Document

Microsoft Word document parsing.

Operations

XHTML Email

XHTML Email template parsing.

Operations

Self Service

MARCIE API Self Service

Operations