qtcurate 1.4.7

Theia SDK Search and Data Extraction

Qtcurate-SDK-Python

Python SDK for Search and Data extraction

Overview and Definitions

Theia is a semantic data extraction tool that can read PDF, HTML, Ms Excel, CSV and plain text documents and extract information in structured format using dictionaries. It is essential to understand the utility of dictionaries in extraction context:

Dictionary

In the simplest case, a dictionary is a list of phrases. Theia searches for every phrase in the dictionary in the input documents and based on the extraction type decides to extract data. At a minimum, a dictionary must have a Name and at least one entry, one search phrase. Users can also assign categories to dictionary entries.

Searching for dictionary phrases in the content is based on the techniques used in modern full-text search engines. Users can use various text analyzers, synonyms, stop words and fuzziness.

Extraction Types

Each dictionary can have one optional extraction type:

1. Number: Search for the phrases AND a number in proximity to the found phrases.
2. Date: Search for the phrases AND a date in proximity to the found phrases.
3. Regex: Search for the phrases AND a custom regular expression in proximity to the found phrases.

Without setting an extraction type the dictionary will essentially be used for tagging documentnts.

Search phrases and types should appear in reading order, either in a sentence or in a table. By default Theia expects the phrase and the type to appear close (but not necessarily next) to each other. Users can configure the allowable gap between dictionary phrases and types using regular expressions.

Examples

Input	Dictionary Phrase List	Extraction Type	Output
Revenue was $201.5 million , an increase of 36% year-over-year.	revenue	Number	201,500,000
Activity Date Due Date Amount July 25, 2019 October 23, 2010 $117,000	Activity Date	Date	07/25/2019
Fuel Consumption: 18.1 L/100km	Consumption	Regex ([\d\.]+) .*?100km	18.1
Gas Consumption is 23.1 L per 100km	Consumption	Regex ([\d\.]+) .*?100km	23.1
The car consumes 24 litre of gas per 100km	Consumption	Regex ([\d\.]+) .*?100km	24

In the following we will cover the details of configuring and submitting extraction jobs via our REST API. Extensive end-to-end examples can be found in our Java and Python SDK repositories.

Table of content

• Installation
• Authentication
• Data Dictionaries
  • Create a New QtDictionary
  • Upload a TSV Dictionary File
  • Update an Existing Dictionary
  • Delete a Dictionary
  • List Available Dictionaries
  • Fetch dictionary
• Data Extraction
  • Processing Files
  • Processing Web URLs
  • Processing Data Streams
  • Extracting Typed Entities
  • Status Monitoring
• Searching in the Results
• Exporting the Results
  • Exporting in Excel Format
  • Exporting in JSON
• Clear temporary data

Installation

You can install it with pip. You have to have Python 3.5 or above

pip install qtcurate

Authentication

Valid API key is required for all operations.

Qt.init("YOUR-API-KEY")

If API key is valid, than the response will be HTTP 200 containing user profile data.

If API key is missing or not valid, the endpoint will return HTTP 401

Data Dictionaries

Dictionaries are a list of phrases used for searching in input documents. Each entry of a dictionary has a str and an optional category. Once a phrase is found Theia produces an extraction object:Dictionaries are a list of phrases used for searching in input documents. Each entry of a dictionary has a str and an optional category. Once a phrase is found Theia produces an extraction object:

Dictionaries are a list of phrases used for searching in input documents. Each entry of a dictionary has a str and an optional category. Once a phrase is found Theia produces an extraction object:

{
	"start": 6188,
	"end": 6196,
	"str": "sales of equipment",
	"line": 309,
	"category": "Sales",
	"dict_name": "Revenue",
	"dict_id": "92e7e423-304a-421c-a612-b6dc4215fd09"
}

str is the found phrase. start, end and line are the positions of the found phrase. category (optional) and is only produced if the found phrase was associated with a category. dict_name assigned by user when creating a dictionary dict_id returned by Theia once a dictionary is created.

Theia uses various strategies for matching on dictionary phrases allowing users to configure the fuzziness of search. User can also provide a list synonyms and stop phrases for the value matching. For example, user can only have "Apple Inc" as one phrase in the dictionary and provide "inc", "corp", "corporation" and "company" as synonyms, allowing you to find all occurrences of "Apple the Company", "Apple Corporations" and "Apple Corp" in the content.

Dictionaries can be created in two ways:

• By providing dictionary entries in the request payload
• By uploading a TSV file

Create a New QtDictionary

Create data dictionaries with create() function First we prepare data Name is required.

dic.name("Name of QtDict)

Two ways to create entries

qt_str = "some str"
category = "some category"
dic.add_entry(str, category)

or

tmp_dictionary = {"str": "some key", "category": "some value"}
dic.entries(tmp_dictionary)

where method entries take a Python dictionary with 2 elements where KEYS have string value "str" and "category" After that we can create our QtDict

dic.create()

Return

{
  "id": "58608b1f-a0ff-45d0-b12a-2fb93af1a9ad",
  "name": "Name of dictionary",
  "global": false,
  "entries": [
  {
     "str": "some str",
     "category": "some category"
  }
  ]
}

Upload a TSV Dictionary File

Upload TSV data dictionaries with upload function:

dic.upload("file-path.tsv", "Name of dictionary")

TSV is required file format. Return is the same as create function Return.

Update an Existing QtDict

To update an existing QtDict:

dic.update(ID)

We use ID as argument of function.

Delete a QtDict

To delete an existing QtDict:

dic.delete(ID)

We use ID as argument of function.

List Available Dictionaries:

To list all of your existing dictionaries:

dic.list()

Return is a list of dictionaries

[
{
"id": "58608b1f-a0ff-45d0-b12a-2fb93af1a9ad",
"key": "user-example-com/58608b1f-a0ff-45d0-b12a-2fb93af1a9ad.csv.gz",
"name": "My dictionary",
"global": false,
"entries": []
}
{
...
}
]

Fetch QtDict:

To fetch data from the QtDict

dic.fetch(ID)

We use ID as argument of function.

Return

{
"id": "58608b1f-a0ff-45d0-b12a-2fb93af1a9ad",
"key": "user-example-com/58608b1f-a0ff-45d0-b12a-2fb93af1a9ad.csv.gz",
"name": "My dictionary",
"global": false,
"entries": []
}

Data Extraction

Data Extraction is the process of identifying search phrases found in the input documents along with extraction types (date, number or regex) and producing structured data. Input documents can be streamed from content files, data APIs or directly from public URLs.

Processing Files

First, upload all content files for DataProcess:

dp.upload("file.pdf")

PDF, TXT, XLS, XLSX, CSV and HTML formats are supported. Image documents, such as TIFF, PNG or scanned PDF, will automatically run through OCR before data extraction.

Return

{
"uuid": "c351283c-330c-418b-8fb7-44cf3c7a09d5",
"fileName": "file.pdf",
"link": "http://portal.document.quantxt.amazonaws.com/[e-mail]/c351283c-330c-418b-8fb7-44cf3c7a09d5",
"date": "2019-10-25T20:14:41.925+02:00",
"contentType": "application/pdf",
"source": "file.pdf"
}

uuids along with dictionaries are provided for the exraction engine.

Now you can mine data via dictionaries. First you have to prepare additional options. vocabId (required) id of the dictionary. title is optional but it is highly recommended for easier distinction between different tagging jobs.

vocabValueType (Optional): can have one of the following values: NUMBER, DATETIME, REGEX. If no extraction type is set, the dictionary will be used for tagging documents. If set, the engine will extract only entities that are associated with an entity of this type.

If REGEX is set user will also need to provide the look up regular expression via phraseMatchingPattern. The following pattern matches on social securities numbers:

dp.files(uuid)
dp.title("My data mining with files and dictionaries")
dp.search_rule("758345h-a0ff-45d0-b12a-2fb93af1a9ad", vocab_value_type_input=DictionaryType.REGEX, 
                phrase_matching_pattern="(\d{3}-\d{2}-\d{4})")

dp.create()

Return

{
    "id": "puvqrjfhqq",
    "title": "My data mining with files and dictionaries",
    "excludeUttWithoutEntities": true,
    "files": ["c351283c-330c-418b-8fb7-44cf3c7a09d5"],
    "searchDictionaries": [
        { 
            "vocabId": "58608b1f-a0ff-45d0-b12a-2fb93af1a9ad",
            "vocabValueType": "NUMBER"
        }
    ]
}

id is the extraction job id. You can use it to monitor status of the job or retrieve the results once completed. You can set sortByPosition, excludeUttWithoutEntities, stitle, sources, query with following methods:

dp.sort_by_position(True)
dp.exclude_utt_without_entities(True)
dp.stitle("some stitle string")
dp.sources("id of source files")
dp.query("some query string")

Function parameters:

chunk (Optional, string) can be SENTENCE or PAGE or NONE. This will result in splitting of data into semantic chunks before processing. For example, this allows user to split the content of an article in semantic sentences and apply entity dictionaries at sentence level.

excludeUttWithoutEntities (Optional, boolean) if true the output will only include chunks that have at least one label from the input dictionaries.

stitle (Optional, string) Override command.

query (Optional, string) Set query for source.

To delete a data container:

dp.delete("puvqrjfhqq")

Processing Web URLs:

Processing can be performed on a list of URLs. All parameters in processing files are applicable here.

dp.search_rule("758345h-a0ff-45d0-b12a-2fb93af1a9ad")
dp.urls(["url1", "url2"])
dp.create()

Theia can process both static and dynamic web pages. However, a number of websites use mechanisms to block web page scrapping. Theia built-in Web parser is not designed to bypass such blocking mechanisms

Processing Data Streams

Extraction data from streams or third party data APIs is supported. For example, user can stream documents directly from Amazon S3, Google Drive, DropBox and many other document repositories that are available via an API. Please contact [e-mail] for details.

Re-Use Data Processing Pipelines

A project is essentially a collection of data dictionaries along with extraction parameters. Once a project is completed, user can re-use the data processing piepline to process more documents. User can either append more documents to a current project or clone the project into a new project and process new documents.

To append fresh data to an already completed project:

Update function

dp.update(ID, list_of_files)

Return

{
    "index": "cjaejhvtao",
    "progress": 36,
    "progress_msg": "Collecting data..."
}

Re-Use Data Processing Pipelines

A project is essentially a collection of data dictionaries along with extraction parameters. Once a project is completed, user can re-use the data processing piepline to process more documents. User can either append more documents to a current project or clone the project into a new project and process new documents.

To append fresh data to an already completed project:

Request

dp.clone(ID, list_of_files)

The above will use dictionaries and extraction parameters from cjaejhvtao project to process uuid_1 and uuid_2. The call above will return a new id.

Status Monitoring

The progress functions allows user to check the progress of a submitted data mining job:

Function

dp.progress()

The search result is an array of all active data processing jobs:

[
{
"dp_id": "cjaejhvtao",
"progress": 36,
"progress_msg": "Collecting data..."
}
]

dp_id Unique ID of the running job

progress Progress in % (a number between 0 to 100).

progress_msg (Optional) Progress message.

It is also possible to check the progress of a specific data mining job:

Function

dp.progress("cjaejhvtao")

Return

{
"dp_id": "cjaejhvtao",
"progress": 36,
"progress_msg": "Collecting data..."
}

Searching in the Results

The search endpoint allows user to run full-text and faceted search in the extracted data.

Function

dp.search("puvqrjfhqq", param_from, size, f1, f2)

Function parameters:

q (Optional, string) Search query that filters the main content title field. It supports boolean OR, AND and NOT parameters.

f (Optional, string) Query filters must be used in pairs. Filters are created for each input dictionary. For example, to include results that have one or more label from Vehicle dictionary the request should look like: &f=Vehicle&f=*. To include results that are labeled with Ford or BMW from the Vehicle dictionary, the request would be &f=Vehicle&f=BMW&f=Vehicle&f=Ford

from (Optional, int) Offset for paging results. Defaults to 0. Each page contains 20 items.

size (Optional, int) Number of results to return. Maximum is 200.

Return

{
"Total": 2610,
"results": [
{
"title": "The Federal Reserve Bank of New York provides gold custody to several central banks, governments and official international organizations on behalf of the Federal Reserve System.",
"id": "Wv8fBG4Bc3WI8L9MbaO2",
"link": "https://www.hamilton.edu/news/story/hamilton-nyc-program-tours-federal-reserve-museum",
"score": 0.10268514747565982,
"source": "abc15.com",
"date": "2018-05-24T00:00:00.000Z",
"tags": [
"Federal Reserve",
"New York"
]
}
],
"aggs": {
"Tag": [{
"key": "Central bank",
"count": 878
}, {
"key": "Gold",
"count": 523
}
}
}

Total: Number of results.

result []: The array that contains result items.

aggs : Facets over the results with count of items for each facet.

Exporting the Results

Results can be exported in XLSX or JSON format:

Exporting in Excel Format

dp.report_to_xlsx("puvqrjfhqq", "name-of-output-file.xlsx")

Exporting in JSON

dp.report_to_json("puvqrjfhqq", "name-of-output-file.json")

Clear temporary data

Both classes QtDict and DataProcess have a method clear() to delete all used variable.

dic.clear()
dp.clear()

The export output is limited to 5000. All /search parameters can be passed here to export the desired slice of the data.

For technical questions please contact [e-mail]