Mock sample for your project: API v1
Integrate with "API v1" from formapi.io in no time with Mockoon's ready to use mock sample
API v1
formapi.io
Version: v1
Start working with "API v1" right away by using this ready-to-use mock sample. API mocking can greatly speed up your application development by removing all the tedious tasks or issues: API key provisioning, account creation, unplanned downtime, etc.
It also helps reduce your dependency on third-party APIs and improves your integration tests' quality and reliability by accounting for random failures, slow response time, etc.
Description
DocSpring is a service that helps you fill out and sign PDF templates.
Other APIs in the same category
Random Lottery Number generator API
fungenerators.com
Below is the documentation for the API calls. You can try them out right here.
Psycholinguistic Text Analytics
We aim to provide the deepest understanding of people through psychology & AI
Name Generation API
Fungenerators name generation API generates random names relevant to the given category. Lots of categories are supported with many variations supported. Click here to subscribe
Palabras aleatorias
An API Rest to get random words
LibreTranslate
libretranslate.local
Barcode API
Generate Barcode images for a given barcode number. You can decode Barcode images and get the barcodes in a numberic form as well. Many industry standard barcode types are supported. The best and complete Barcode API on the cloud. Click here to subscribe
Geneea Natural Language Processing
geneea.com
Authentication
For all calls, supply your API key. Sign up to obtain the key .
Our API supports both unencrypted (HTTP) and encrypted (HTTPS) protocols.
However, for security reasons, we strongly encourage using only the encrypted version.
The API key should be supplied as either a request parameter user_key or in Authorization header.
Authorization: user_key
API operations
All API operations can perform analysis on supplied raw text or on text extracted from a given URL.
Optionally, one can supply additional information which can make the result more precise. An example
of such information would be the language of text or a particular text extractor for URL resources.
The supported types of analyses are:
lemmatization ⟶
Finds out lemmata (basic forms) of all the words in the document.
correction ⟶
Performs correction (diacritization) on all the words in the document.
topic detection ⟶
Determines a topic of the document, e.g. finance or sports.
sentiment analysis ⟶
Determines a sentiment of the document, i.e. how positive or negative the document is.
named entity recognition ⟶
Finds named entities (like person, location, date etc.) mentioned the the document.
Encoding
The supplied text is expected to be in UTF-8 encoding, this is especially important for non-english texts.
Returned values
The API calls always return objects in serialized JSON format in UTF-8 encoding.
If any error occurs, the HTTP response code will be in the range 4xx (client-side error) or
5xx (server-side error). In this situation, the body of the response will contain information
about the error in JSON format, with exception and message values.
URL limitations
All the requests are semantically GET. However, for longer texts, you may run into issues
with URL length limit. Therefore, it's possible to always issue a POST request with all
the parameters encoded as a JSON in the request body.
Example:
POST /s1/sentiment
Content-Type: application/json
{"text":"There is no harm in being sometimes wrong - especially if one is promptly found out."}
This is equivalent to GET /s1/sentiment?text=There%20is%20no%20harm...
Request limitations
The API has other limitations concerning the size of the HTTP requests. The maximum allowed size of any
POST request body is 512 KiB. For request with a URL resource, the maximum allowed number of
extracted characters from each such resource is 100,000.
Terms of Service
By using the API, you agree to our
Terms of Service Agreement.
More information
The Interpretor Public Documentation
For all calls, supply your API key. Sign up to obtain the key .
Our API supports both unencrypted (HTTP) and encrypted (HTTPS) protocols.
However, for security reasons, we strongly encourage using only the encrypted version.
The API key should be supplied as either a request parameter user_key or in Authorization header.
Authorization: user_key
API operations
All API operations can perform analysis on supplied raw text or on text extracted from a given URL.
Optionally, one can supply additional information which can make the result more precise. An example
of such information would be the language of text or a particular text extractor for URL resources.
The supported types of analyses are:
lemmatization ⟶
Finds out lemmata (basic forms) of all the words in the document.
correction ⟶
Performs correction (diacritization) on all the words in the document.
topic detection ⟶
Determines a topic of the document, e.g. finance or sports.
sentiment analysis ⟶
Determines a sentiment of the document, i.e. how positive or negative the document is.
named entity recognition ⟶
Finds named entities (like person, location, date etc.) mentioned the the document.
Encoding
The supplied text is expected to be in UTF-8 encoding, this is especially important for non-english texts.
Returned values
The API calls always return objects in serialized JSON format in UTF-8 encoding.
If any error occurs, the HTTP response code will be in the range 4xx (client-side error) or
5xx (server-side error). In this situation, the body of the response will contain information
about the error in JSON format, with exception and message values.
URL limitations
All the requests are semantically GET. However, for longer texts, you may run into issues
with URL length limit. Therefore, it's possible to always issue a POST request with all
the parameters encoded as a JSON in the request body.
Example:
POST /s1/sentiment
Content-Type: application/json
{"text":"There is no harm in being sometimes wrong - especially if one is promptly found out."}
This is equivalent to GET /s1/sentiment?text=There%20is%20no%20harm...
Request limitations
The API has other limitations concerning the size of the HTTP requests. The maximum allowed size of any
POST request body is 512 KiB. For request with a URL resource, the maximum allowed number of
extracted characters from each such resource is 100,000.
Terms of Service
By using the API, you agree to our
Terms of Service Agreement.
More information
The Interpretor Public Documentation
Semantria
Semantria applies Text and Sentiment Analysis to tweets, facebook posts, surveys, reviews or enterprise content.
DynamicDocs
ADVICEment's DynamicDocs API automates your document generation and creates dynamic, optimized, interactive PDFs. Write your templates in LaTeX and call the API with JSON data to get your PDFs in seconds.
The template files are stored in your dashboard and can be edited, tested and published online. Document templates can contain dynamic text using logic statements, include tables stretching multiple pages and show great-looking charts based on the underlying data. LaTeX creates crisp, high-quality documents where every detail is well-positioned and styled.
Integrate with ADVICEment DynamicDocs API in minutes and start creating beautiful dynamic PDF documents for your needs.
For more information, visit DynamicDocs API Home page.
The template files are stored in your dashboard and can be edited, tested and published online. Document templates can contain dynamic text using logic statements, include tables stretching multiple pages and show great-looking charts based on the underlying data. LaTeX creates crisp, high-quality documents where every detail is well-positioned and styled.
Integrate with ADVICEment DynamicDocs API in minutes and start creating beautiful dynamic PDF documents for your needs.
For more information, visit DynamicDocs API Home page.
PdfBroker.io API
PdfBroker.io is an api for creating pdf files from Xsl-Fo or Html and other useful pdf utilities.
NamSor API v2
NamSor API v2 : enpoints to process personal names (gender, cultural origin or ethnicity) in all alphabets or languages. By default, enpoints use 1 unit per name (ex. Gender), but Ethnicity classification uses 10 to 20 units per name depending on taxonomy. Use GET methods for small tests, but prefer POST methods for higher throughput (batch processing of up to 100 names at a time). Need something you can't find here? We have many more features coming soon. Let us know, we'll do our best to add it!