Structured Data
1/* Clean and extract your desired fields */2payload = {3"schema": "date_purchased (mm-dd-yyyy)"4"content": [5"My purchase was made back in janary 12, 2012. i am not sure if i am eligible for a refund, but i would like to know."6]7"output": "json": {8"date_purchased": "01-12-2012"9}10}
Our Advantages
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Step By Step
JSON Scout saves development time and maintenance by leveraging the power of LLMs to extract the data you need with human-like precision.
Define Your Output
Tell JSON Scout what information you are looking to extract
Input Your Content
Provide your content
Fetch Insights
JSON Scout processes your input and returns the structured data you need
How It Works
Click through the different examples of data that JSON Scout is able to process
1payload = {2"schema": "date_purchased (mm-dd-yyyy)"3"content": [4"My purchase was made back in january 12, 2012. i am not sure if i am eligible for a refund, but i would like to know.",5"Date submitted: 01/01/2024, i made my purchase 2 weeks ago and wanted to know about warranty information.",6]7}
1{2"data": [3{4"date_purchased": "01-12-2012"5},6{7"date_purchased": "12-18-2023"8}9],10"tokens_remaining": 99911}
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Old way of doing it;
The old way of extracting specific data requires you to create a REGEX pattern for a specific data format.
This process is time-consuming and requires constant maintenance to keep up with changing data formats. Keep in mind, it won't consider typos.
JSON Scout eliminates the need for REGEX patterns, saving you time and effort.
1import re, datetime2content = "My purchase was made back in january 12, 2012. I am not sure if I am eligible for a refund, but I would like to know.",3/* Create the REGEX pattern */4date_pattern = r'[a-zA-Z]+sd{1,2},sd{4}'5/* Grab the date from the content*/6date = re.search(date_pattern, content).group(0)7/* Convert the date to a datetime object */8date_object = datetime.datetime.strptime(date, '%B %d, %Y').date()9date_string_1 = date_object.strftime('%m-%d-%Y')
New way using JSON Scout;
With JSON Scout you can extract data without the need for REGEX patterns.
Simply provide the desired output and JSON Scout will handle the rest.
JSON Scout is more accurate and efficient than REGEX, saving you time and effort.
1import requests, json2url = "https://fetch.jsonscout.com/"3payload = json.dumps({4"schema": "date_purchased (mm-dd-yyyy)"5"content": "My purchase was made back in january 12, 2012. I am not sure if I am eligible for a refund, but I would like to know."6})7headers = {8'Content-Type': 'application/json',9'api_key': 'secret_key'10}11response = requests.request("POST", url, headers=headers, data=payload)
With JSON Scout You Can
Stop the endless cycle of writing and rewriting complex regex. JSON Scout intuitively understands and locates the data you need with human-like precision.
Expand beyond limits
Regex has its limits. JSON Scout adapts to your evolving data needs, understanding context and semantics, not just patterns.
Boost Productivity
Redirect your valuable time and talent from the tedium of pattern matching to strategic initiatives that drive your business forward.
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Future-Proof
As your data grows and changes, JSON Scout evolves with it, continuously learning from interactions to provide more accurate extractions.
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API Integration
Seamlessly integrate JSON Scout into your existing data pipeline, whether you're using Python, Node.js, or any other language.
Batch Processing
Our API is built for scale, whether you're processing a few requests or hundreds. JSON Scout is designed to fit seamlessly into your existing data pipeline.
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Frequently Asked Questions
How do you determine the correct data to extract?
Is it possible to specific an output format?
How many tokens is an average request?
What happens if I go over my token limit?
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