🚀 RAG-Ready Extractor

The missing ingestion layer for modern RAG systems.

Turn messy webpages and PDFs into clean, structured knowledge for AI pipelines in one API call.

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⚡ TL;DR

RAG-Ready Extractor is a high-performance ingestion API that converts raw web pages and PDFs into RAG-optimized structured data.

Input

• URL • PDF

Output

• semantic chunks • importance scoring • structured tables • token usage metadata

Designed for

• Pinecone • Weaviate • Qdrant • LangChain • LlamaIndex

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🚨 The Problem

Most RAG systems index raw HTML or PDF text.

This introduces a large amount of noise into the knowledge base:

• navigation menus • cookie banners • footers • related links • ads

In many real pipelines 30-40% of indexed tokens are useless.

This causes:

Hallucinations The model confuses navigation elements with actual knowledge.

High Costs You pay for tokens that add zero value.

Slow Performance Larger contexts increase latency.

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💸 Cost Savings Example

Based on benchmarks using large technical documents (e.g., the BloombergGPT research paper):

Metric	Standard Pipeline	RAG-Ready Pipeline
Input Tokens	100k	~65k
Noise Removed	0%	~35%
Retrieval Quality	Variable	Improved via importance filtering
Estimated LLM Cost	100%	30-70% reduction

Filtering noise before embedding dramatically reduces token usage.

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🛠 Practical Integration (Pinecone Example)

Extract clean content and index only meaningful chunks.

import requests
from pinecone import Pinecone

# 1. Extract structured content
data = requests.post(
    "API_URL",
    json={"url": "https://example.com/blog"}
).json()

# 2. Connect to vector database
pc = Pinecone(api_key="YOUR_KEY")
index = pc.Index("rag-knowledge")

# 3. Index only high-importance chunks
for chunk in data["content"]["chunks"]:
    if chunk["importance_score"] > 6.5:

        index.upsert(vectors=[{
            "id": str(hash(chunk["text"])),
            "values": embed(chunk["text"]),
            "metadata": {
                "section": chunk["heading_context"]
            }
        }])

The API returns content that is ready to be embedded and indexed.

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🧱 Supported Inputs

The API currently supports:

Web Pages

• blogs • documentation sites • landing pages

PDF Documents

• research papers (ArXiv) • technical manuals • financial filings

Structured Data

HTML tables are automatically converted into clean JSON objects.

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📦 API Response

Example response structure:

{
  "metadata": {
    "title": "BloombergGPT...",
    "word_count": 27938
  },
  "content": {
    "chunks": [
      {
        "text": "The use of NLP in finance...",
        "importance_score": 7.06,
        "heading_context": "Introduction"
      }
    ],
    "tables": [
      {
        "table_type": "pricing",
        "headers": ["Feature", "Basic", "Pro"],
        "rows": []
      }
    ]
  },
  "usage": {
    "latency_ms": 6900,
    "token_count": 57212
  }
}

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⚡ Benchmarks

Example benchmark using the BloombergGPT research paper.

Metric	Result
Tokens processed	57,212
Word count	27,938
Processing latency	6.9 seconds
Extracted chunks	3,481

Large documents can be processed in a few seconds while preserving semantic structure.

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🏗 Architecture

Client
   ↓
RapidAPI Gateway
   ↓
Extraction Engine
   ↓
Structure Analyzer
   ↓
Importance Scoring
   ↓
Structured JSON Output

The API is designed as a pre-vectorization ingestion layer for AI pipelines.

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🎯 Ideal Use Cases

RAG-Ready Extractor is designed for:

• RAG pipelines • AI documentation chatbots • research assistants • knowledge base ingestion • web → vector database pipelines

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🚀 Get Started

Access the API through RapidAPI.

Step 1 — Get your API key

👉 RapidAPI: https://rapidapi.com/cdd-it-cdd-lab-it/api/rag-ready-markdown-pdf-extractor

2. Send Request

Use your preferred HTTP client.

Example using Python:

import requests

response = requests.post(
    API_URL,
    json={"url": "https://example.com"}
)
print(response.json())

Step 3 — Extract structured knowledge

Send a URL or PDF and receive clean data ready for vector databases.

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❤️ Built for AI Developers

Created by Carlos Manuel Díaz.

Helping developers build cleaner and more efficient RAG pipelines.