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Build a Production-Ready RAG Engine in Minutes

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.

  1. 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())
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  1. 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'])
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  1. 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)
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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!

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