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Theia SDK Search and Data Extraction

Project description

qtcurate-sdk-python

Python SKD for Search and Data extraction

Theia documentation; Overview and Definitions

Theia is a managed solution to mine and retrieve Entities* or Typed Entities** out of unstructured data. In the following we cover details of configuring and submitting data mining jobs via Python SDK

*Entity : An entity is a word or phrase and can be associated with a meaning. For example, "AWS" is an entity and it can be associated with "Amazon Inc."

**Typed Entity : An entity that is meaningful only if it co-occurs with a number, date or another entity. For example, "Revenue" is considered a Typed Entity only if it is found in a context where the actual dollar value is reported:

Amazon Inc reported $68.8 billion in revenue

Table of content

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.

Request

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:

Response

{
"error": "unauthorized",
"error_description": "Full authentication is required to access this resource"
}

"Net Revenue" is a Typed Entity:

"Apple reported net revenue of $53.3 billion in 2018."

"Net Revenue" is not a Typed Entity:

"Tim Cook will go over net revenue of Apple in the upcoming conference call."

Entity Dictionaries

Entity dictionaries are lists of entities that can be used in a data mining task. Each item of a entity dictionary has a key and a value. The key is the associated or normalized phrase and the value is the actual word or phrase that represents the entity.

Theia scans all input utterances for every word or phrase in the dictionary and if found, map it to the associated value:

Dictionary (one item): M&A => merger and acquisition

Input utterance:

Merger and acquisitions report published in 2019.

The above will be labeled with M&A

Theia uses various strategies for matching of dictionary values allowing users to configure the fuzziness of search. User can also provide a list of synonyms and stop phrases for the value matching. For example, you can only have "Apple Inc" as one entity in your 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.

Entity dictionaries can be created in two ways:

  • By providing dictionary entries in the request payload
  • By uploading a TSV file containing dictionary entries in the format of key<TAB>value.

Data Dictionaries

Authentication with API KEY

dic = QtDict(api_key='1234567')

Create A New QtDict

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

dic.name("Name of QtDict)

Two ways to create entries

key = "some key"
value = "some value"
dic.add_entry(key, value)

or

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

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

dic.create()

Response

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

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. Response is the same as create function response.

Update an Existing QtDict

To update an existing QtDict:

dic.update("58608b1f-a0ff-45d0-b12a-2fb93af1a9ad")

We use ID as argument of function.

Delete a QtDict

To delete an existing QtDict:

dic.delete("58608b1f-a0ff-45d0-b12a-2fb93af1a9ad")

List Available Dictionaries:

To list all of your existing dictionaries:

dic.list()

Response 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("58608b1f-a0ff-45d0-b12a-2fb93af1a9ad")

Response

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

Extraction and Mining

Data extraction and mining is the process of identifying entities found in the unstructured content using entity dictionaries and formatting them it into structured format. Unstructured data can be streamed from content files, data APIs or directly from public URLs.

Authenticate with API_KEY

tag = DataProcess(api_key="1234")

Mining Content Files

First, upload all content files for DataProcess:

tag.upload("file.pdf")

PDF, TXT and HTML formats are supported.

Response

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

Now you can mine data via dictionaries. First you have to prepare additional options. title is optional but it is highly recommended for easier distinction between different tagging jobs.

vocabValueType (Optional): can have one of the following values: NONE, STRING, DOUBLE, DATETIME. If set, the engine will extract only entities that are associated with an entity of this type. vocabPath (Required): The path to entity QtDict. Path is returned either after creation of a new QtDict or via listing of existing dictionaries.There is no limit on the number of files and dictionaries that can be processed via tagging_files function

tag.files("c351283c-330c-418b-8fb7-44cf3c7a09d5")
tag.title("My data mining with files and dictionaries")
tag.search_rule("user-example-com/58608b1f-a0ff-45d0-b12a-2fb93af1a9ad.csv.gz", DictionaryType.NONE)

tag.tagging_files()

Response

{
"index": "puvqrjfhqq",
"title": "My data mining with files and dictionaries",
"get_phrases": true,
"maxTokenPerUtt": 35,
"minTokenPerUtt": 6,
"excludeUttWithoutEntities": false,
"stitle": null,
"files": ["c351283c-330c-418b-8fb7-44cf3c7a09d5"],
"searchDictionaries": [
{
"vocabPath": "user-example-com/58608b1f-a0ff-45d0-b12a-2fb93af1a9ad.csv.gz",
"vocabValueType": "NONE"
}
]
}

index represents the unique identification for the container that holds output labeled data. You can set index, autotag, maxTokenPerUtt, minTokenPerUtt, excludeUttWithoutEntities with following methods:

tag.index("some index")
tag.autotag(True)
tag.exclude_utt_without_entities(True)
tag.max_token_per_utt(101)
tag.min_token_per_utt(101)
tag.stitle("some stitle string")

Request parameters:

chunk (Optional, string) can be SENTENCE or PARAGRAPH 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.

get_phrases (Optional, boolean) if true auto tagging will be performed.

minTokenPerUtt (Optional, int)

maxTokenPerUtt (Optional, int)

stitle (Optional, string) Override command.

To delete a data container:

tag.delete("puvqrjfhqq")

Mining Web URLs:

Mining can be performed on a list of URLs. All parameters used in tagging files are applicable here.

tag.mining_url()

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

Mining Data Streams

Tagging data from data streams such as third party APIs is supported. Please contact support@quantxt.com for details.

Extracting Typed Entities

Entity dictionaries allow the user to quickly search and label thousands of phrases in unstructured content. There are cases when users want to label a keyword or phrase as an entity only if it is associated with a value. For example:

A "release => "Manufactured" dictionary item will label both of the following utterances:

The first automobile in the US released by Ford. The first automobile in the US released in 1908 by Ford.

However, if the user is looking for only release year of the car makers, the first utterance won't have much use for him. He can only label the second utterance using a Typed Entities.

Supported types for associated entities are:

REGEX, DOUBLE, DATETIME

types are passed via vocabValueType parameter.

If a type is set, a dictionary item will be extracted only if a type is found in its close proximity. In the above example, user can set vocabValueType in the request to DOUBLE to identify 1908 only if it is associated with the entity released

Status Monitoring

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

Request

tag.progress()

The search result is an array of active data mining jobs:

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

index 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:

Request

tag.progress("cjaejhvtao")

Response

{
"index": "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.

Request

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

Request 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.

Response

{
"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

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

Exporting in JSON

tag.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()
tag.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 support@quantxt.com

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