DEV Community: NFC

How to Automate PII Redaction and AI Data Extraction with Python (Serverless API)

NFC — Wed, 03 Jun 2026 14:16:47 +0000

Handling unstructured documents is one of the most tedious tasks in software engineering. Whether you are building an HR platform, an accounting ERP, or a legal tech app, you eventually face two massive headaches: Data Privacy (GDPR/LGPD compliance) and Structured Data Extraction.

How do you safely share a resume without leaking the candidate's phone number? How do you extract the total tax amount from a messy PDF invoice without writing hundreds of fragile Regex rules?

Enter the NeoPII & Extract API. In this article, I’ll walk you through how to use this serverless, GPT-4o-powered API to sanitize documents and extract structured JSON data using Python.

🌟 The Opportunities: What Can You Build?
The API is divided into two main engines:

NeoShield (Data Anonymization) This engine takes a file, scans it using NLP and advanced Regex, and replaces Personally Identifiable Information (PII) — like emails, credit cards, and social security numbers — with safe tags (e.g., [EMAIL], [CPF]). The killer feature? It natively supports and reconstructs binary files like .DOCX, .XLSX, and .PDF. It doesn't just return flat text; it returns your original Excel sheet or Word document with the exact same formatting, just safely redacted.

Use cases: Unbiased recruitment pipelines, public legal document filings, and secure data sharing.

Neo-Struct (AI Data Extraction) Powered by Azure OpenAI (GPT-4o), this endpoint reads unstructured files and forces the output into a strict JSON schema that you define. No AI hallucinations, no broken JSON.

Use cases: Automating invoice data entry, extracting clauses from contracts, and digitizing messy historical records.

💻 Tutorial: Python Implementation
Let’s get our hands dirty. First, make sure you have the requests library installed:

pip install requests

Scenario A: Masking a Confidential Document
When working with APIs that accept files, the biggest pitfall in Python is how the requests library handles multipart/form-data. To ensure the serverless backend correctly parses your file, always send the file as a tuple (filename, file_object, mime_type).

Here is how you mask a .docx file:

import requests
import os

API_URL = "https://neopii-extract.p.rapidapi.com/mask_file"
HEADERS = {
    "X-RapidAPI-Host": "neopii-extract.p.rapidapi.com",
    "X-RapidAPI-Key": "YOUR_RAPIDAPI_KEY"
}

file_path = "confidential_contract.docx"

with open(file_path, "rb") as f:
    # Pro-tip: Use the tuple format to ensure boundary integrity
    files = {"file": (os.path.basename(file_path), f, "application/octet-stream")}

    print("Anonymizing document...")
    response = requests.post(API_URL, files=files, headers=HEADERS)

    if response.status_code == 200:
        with open("masked_contract.docx", "wb") as out:
            out.write(response.content)
        print("Success! The document was sanitized and saved.")
    else:
        print(f"Error: {response.text}")

Scenario B: Extracting Structured JSON from a PDF
Imagine you have a messy PDF invoice and you only care about the vendor's name, the total amount, and the due date. You can simply declare a JSON schema and let the AI do the heavy lifting.

import requests
import json
import os

API_URL = "https://neopii-extract.p.rapidapi.com/extract"
HEADERS = {
    "X-RapidAPI-Host": "neopii-extract.p.rapidapi.com",
    "X-RapidAPI-Key": "YOUR_RAPIDAPI_KEY"
}

file_path = "vendor_invoice.pdf"

# Define the exact data structure you want the AI to return
my_schema = json.dumps({
    "vendor_name": "string",
    "total_amount": "number",
    "due_date": "string (ISO format)"
})

with open(file_path, "rb") as f:
    files = {"file": (os.path.basename(file_path), f, "application/octet-stream")}
    data = {"schema": my_schema}

    print("Extracting data via AI...")
    response = requests.post(API_URL, files=files, data=data, headers=HEADERS)

    if response.status_code == 200:
        structured_data = response.json()
        print("\nExtraction Complete:")
        print(json.dumps(structured_data, indent=2))
    else:
        print(f"Error: {response.text}")

⚠️ Technical Limitations & Best Practices
Before you push to production, there are a few architectural constraints you should keep in mind:

Serverless "Cold Starts" The API is built on a serverless architecture to ensure scalability. If the API hasn't been called in a while, the very first request might take around 10 to 15 seconds to load the heavy NLP libraries (like SpaCy) into memory. Subsequent requests, however, will be lightning-fast (usually under 2 seconds).

Best Practice: Implement a retry mechanism or a simple background ping to the /health endpoint to keep the engine warm during business hours.

Context Window (Token Limits) The AI extraction endpoint (/extract) utilizes GPT-4o with an operational limit of around 15,000 tokens. If you send a highly dense 50-page PDF, the text will be truncated to fit the context window, which might lead to missing data.

Best Practice: If you have massive documents, split them into smaller chunks or ensure the file size remains reasonable.

File Size Limits To prevent timeouts and ensure low latency, the API enforces strict size limits:

PDF: 10 MB

DOCX: 5 MB

XLSX: 3 MB

TXT / JSON / CSV: 1-2 MB

Final Thoughts
The days of writing endless Regex to parse PDFs or risking data leaks in file processing are over. By combining native binary reconstruction with LLM-based extraction, you can build highly secure, enterprise-grade applications in minutes.

Ready to give it a try? You can grab your API key and test it out for free on the RapidAPI Hub. Happy coding! 🚀

Build a Production-Ready RAG Engine in Minutes

NFC — Thu, 28 May 2026 20:29:22 +0000

As AI developers, we all know the struggle: building a high-quality RAG (Retrieval-Augmented Generation) pipeline is hard. You have to handle document parsing, chunking strategies, vector embeddings, database management, and the constant threat of "hallucinations."

What if you could offload the entire infrastructure to a robust, scalable API and focus strictly on the AI experience?

Meet NeoDoc API, a serverless Document Intelligence API that turns any complex file format into an intelligent chat partner.

Why NeoDoc?
NeoDoc isn't just another text extractor. It’s an Enterprise-Grade ETL (Extract, Transform, Load) pipeline built to handle the chaos of real-world documents:

Native Support for Everything: Don't just settle for PDFs. NeoDoc natively digests .docx, .xlsx, .pptx, .csv, .md, and more.

Zero-Overhead: It’s stateless. You upload, query, and manage via API—no need to maintain vector indices or manage database connections.

Privacy-First: We treat your data as transient. It’s processed in memory and discarded. You are the only owner of your index.

CRUD Compliance: Manage your document lifecycle with built-in list and delete endpoints—essential for GDPR and LGPD compliance.

Getting Started: The NeoDoc Workflow
Integration is straightforward. You only need the Python requests library.

Ingest Your Documents Send your file to the /ingest endpoint. NeoDoc will automatically handle the text extraction, splitting, and vectorization.

import requests

url = "https://neodoc-ai-document-intelligence-api.p.rapidapi.com/ingest"
headers = {"X-RapidAPI-Key": "YOUR_KEY", "X-RapidAPI-Host": "neodoc-ai-document-intelligence-api.p.rapidapi.com"}

with open("financial_report.xlsx", "rb") as f:
    response = requests.post(url, headers=headers, 
                             files={"file": ("financial_report.xlsx", f)}, 
                             data={"client_id": "my_unique_user_id"})
print(response.json())

Querying with AI Once indexed, you can query your data. Because we use Azure OpenAI (GPT-4o) under the hood, you get industry-leading reasoning.

url = "https://neodoc-ai-document-intelligence-api.p.rapidapi.com/query"
payload = {
    "client_id": "my_unique_user_id",
    "question": "What are the Q3 growth projections?",
    "custom_prompt": "Answer in a professional tone for a C-level executive."
}

response = requests.post(url, json=payload, headers=headers)
print(response.json()['answer'])

Managing the Lifecycle (List & Delete) This is where NeoDoc shines for B2B SaaS. Need to remove a document? Just call the delete endpoint.

# List what the user has indexed
list_res = requests.post(".../list", json={"client_id": "my_unique_user_id"}, headers=headers)

# Delete a specific file to maintain privacy
del_res = requests.post(".../delete", json={"client_id": "my_unique_user_id", "filename": "financial_report.xlsx"}, headers=headers)

Use Cases
Legal Tech: Querying hundreds of contracts instantly without a single hallucination.

FinTech: Ingesting complex Excel spreadsheets and PowerPoint presentations to extract key financial KPIs.

EdTech: Creating study assistants that answer questions based on uploaded lecture materials.

Ready to Launch?
Stop reinventing the wheel. Start building AI applications that actually deliver value.

Check out the full documentation and get your API Key at:
👉 NeoDoc on RapidAPI

Found this useful? Let me know in the comments how you plan to use NeoDoc in your next AI project!

How to Price Options at an Institutional Level Using AI (PINNs)

NFC — Mon, 18 May 2026 12:11:41 +0000

If you work in or study the derivatives market, you know that speed and accuracy in option pricing are not just technical goals — they are competitive advantages.

Traditionally, we rely on the Black-Scholes model or Monte Carlo simulations structured in legacy code to approximate the fair price of a contract. However, when we need to scale these calculations for thousands of simultaneous requests or handle complex boundary conditions, processing bottlenecks appear.

This is where the fusion between Artificial Intelligence and Financial Physics comes in: PINNs (Physics-Informed Neural Networks).

In this article, I will show you how to consume an institutional-grade infrastructure based on physics-informed neural networks to price options in milliseconds using Python.

What are PINNs and why do they matter in finance? Unlike traditional neural networks that need billions of historical data points to "learn" a trend (and frequently fail when trying to extrapolate), PINNs integrate mathematical laws directly into their loss function. In our context, the network is trained while respecting the famous Black-Scholes Partial Differential Equation (PDE):

By forcing the AI model to obey the constraints of financial physics, we eliminate mathematical hallucinations and achieve an incredibly fast inference power, ideal for high-frequency trading (HFT) systems and real-time risk management.

Hands-on: Consuming PINN Master in Python
To avoid having to build, train, and host a GPU cluster to run this network from scratch, we will use PINN Master - Institutional Option Pricing, a robust API hosted on AZURE that exposes this production-ready model.

The best part? It has a 100% free tier for testing.

Step 1: Obtain your credentials
Before running the script, you just need to access the official PINN Master page on RapidAPI and subscribe to the free plan to unlock your access token. If you have any questions about the first steps, there is a very simple to follow Official PINN Master Getting Started Tutorial.

Step 2: The Code
With your key in hand, use the code below to make a pricing call for a call option (Call):

import requests

# Endpoint de alta performance da API
url = "https://pinn-master-institutional-option-pricing.p.rapidapi.com/v1/price"

# Parâmetros de precificação do contrato
querystring = {
    "spot": "100.0",        # Preço atual do ativo subjacente
    "strike": "100.0",      # Preço de exercício da opção
    "volatility": "0.20",   # Volatilidade implícita (20%)
    "rate": "0.05",         # Taxa de juros livre de risco (5%)
    "maturity": "1.0",      # Tempo até o vencimento (1 ano)
    "type": "call"          # Tipo do contrato: call ou put
}

headers = {
    "X-RapidAPI-Key": "SUA_CHAVE_GRATUITA_AQUI",
    "X-RapidAPI-Host": "pinn-master-institutional-option-pricing.p.rapidapi.com"
}

try:
    response = requests.get(url, headers=headers, params=querystring)
    response.raise_for_status()

    dados_precificacao = response.json()
    print("--- Resultado da Invocação PINN Master ---")
    print(dados_precificacao)

except requests.exceptions.RequestException as e:
    print(f"Erro ao conectar com a infraestrutura quant: {e}")

Why does this approach change the game?

Predictable Latency: By transferring the complexity of the mathematical calculation to an optimized neural network inference in the cloud, you gain a consistent response time.

Infrastructure Abstraction: The entire scalability architecture on AZURE is hidden behind a clean GET method.

Easy Integration: You can plug this return directly into trading dashboards, dynamic spreadsheets, or order execution bots.