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Web Shop Payment Use Case Diagram Vectorize Iris is a model-based extraction solution that transforms how RAG systems handle PDFs. It combines extraction and chunking into one streamlined process, making it easier than ever to get clean, usable text from complex documents. Image For My Blog Website

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Example Of Blog Or Blogging Traditional OCR tools struggle with complex layouts, poor scans, and structured data. Iris uses advanced AI to understand document structure and context, delivering: Vincenzo Memes

• 📄 Universal format support - Works with all unstructured document types (PDFs, images, scans, and more)
• ✨ High accuracy - Handles poor quality scans and complex layouts
• 📊 Structure preservation - Maintains tables, lists, and formatting
• 🎯 Smart chunking - Semantic splitting for RAG pipelines
• 🔍 Metadata extraction - Extract specific fields using natural language
• 🚀 Simple API - One function call to extract text
• ⚡ Parallel processing - Process multiple documents simultaneously
• 🌐 URL support - Extract directly from HTTP/HTTPS URLs
• 📂 Batch processing - Process entire directories automatically
• 🔧 Multiple formats - Output as JSON, YAML, or plain text
• 🪶 Lightweight - Single binary CLI with no dependencies
• ☁️ Cloud-native - Serverless-ready APIs
• 🌍 Multi-lingual - 100+ languages including Hindi, Arabic, Chinese
• 🔌 Multi-platform - Python, Node.js, and CLI support

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from vectorize_iris import extract_text_from_file result = extract_text_from_file('document.pdf') print(result.text)

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import { extractTextFromFile } from '@vectorize-io/iris'; const result = await extractTextFromFile('document.pdf'); console.log(result.text);

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vectorize-iris document.pdf

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curl -fsSL https://get-iris.vectorize.io | sh

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pip install vectorize-iris

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npm install @vectorize-io/iris

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• Markdown-aware chunking
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• Preserves context across chunks

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result = extract_text_from_file('invoice.pdf', options=ExtractionOptions( metadata_schemas=[{ 'id': 'invoice-data', 'schema': { 'invoice_number': 'string', 'date': 'string', 'total_amount': 'number', 'vendor_name': 'string' } }] )) # Returns structured JSON metadata

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result = extract_text_from_file('document.pdf', options=ExtractionOptions( parsing_instructions='Focus on extracting tables and ignore headers/footers' ))

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vectorize-iris document.pdf

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✨ Vectorize Iris Extraction ────────────────────────────────────────────────── ✓ Upload prepared ✓ File uploaded successfully ✓ Extraction started ✓ Extraction completed in 7s ───────────────────────────────────────────────────────── 📄 Extracted Text ───────────────────────────────────────────────────────── Stats: 5536 chars • 1245 words • 89 lines This is the extracted text from your PDF document. All formatting and structure is preserved. Tables, lists, and other elements are properly extracted. 

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vectorize-iris https://arxiv.org/pdf/2206.01062

vectorize-iris document.pdf -o json

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{ "success": true, "text": "This is the extracted text from your PDF document...", "chunks": null, "metadata": null }

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vectorize-iris document.pdf -o json | jq -r '.text' > output.txt

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vectorize-iris document.pdf -o text

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vectorize-iris document.pdf -o text > output.txt

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vectorize-iris document.pdf -o json -f output.json

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✨ Vectorize Iris Extraction ────────────────────────────────────────────────── ✓ Upload prepared ✓ File uploaded successfully ✓ Extraction started ✓ Extraction completed in 7s ✓ Output written to output.json 

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vectorize-iris ./documents -f ./output

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📦 Processing Directory ────────────────────────────────────────────────── 💡 Found 5 files to process ⚙️ Processing 1/5 - report-q1.pdf ✨ Vectorize Iris Extraction ────────────────────────────────────────────────── ✓ Upload prepared ✓ File uploaded successfully ✓ Extraction started ✓ Extraction completed in 8s ✓ Output written to output/report-q1.txt ⚙️ Processing 2/5 - report-q2.pdf ... ────────────────────────────────────────────────── ✨ Batch Processing Complete ✓ Successful: 5 

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# Extract all PDFs to JSON vectorize-iris ./documents -o json -f ./output # Extract all files to plain text vectorize-iris ./scans -o text -f ./extracted

vectorize-iris long-document.pdf --chunk-size 512

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vectorize-iris report.pdf --parsing-instructions "Extract only tables and numerical data, ignore narrative text"

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vectorize-iris invoice.pdf \ --metadata-schema 'invoice:{"invoice_number":"string","date":"string","total_amount":"number","vendor":"string"}' \ --metadata-schema 'receipt:{"store_name":"string","date":"string","items":"array","total":"number"}' \ --metadata-schema 'contract:{"parties":"array","effective_date":"string","terms":"string"}' \ --metadata-schema 'cv:{"name":"string","contact_info":"object","skills":"array","experience":"array"}' \ -o json

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{ "success": true, "text": "...", "metadata": "{\"invoice_number\":\"INV-2024-001\",\"date\":\"2024-01-15\",\"total_amount\":1250.00,\"vendor\":\"Acme Corp\"}", "metadataSchema": "invoice" }

How To Write A Assignment For College Iris automatically detected this was an invoice and extracted the relevant fields using the matching schema. Making Your Business Cards

# Custom chunk size with metadata extraction vectorize-iris document.pdf \ --chunk-size 256 \ --infer-metadata-schema \ --parsing-instructions "Focus on extracting structured data" \ -o yaml -f output.yaml # Longer timeout for large documents vectorize-iris large-document.pdf \ --timeout 600 \ --poll-interval 5

New Product Launch Social Media Post The CLI offers multiple ways to configure your credentials: Facebook Story Message

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vectorize-iris configure

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1. Opens your browser to the Vectorize platform
2. Click "Authorize" to grant access
3. Credentials are automatically saved to ~/.vectorize-iris/credentials
4. Done! You're ready to extract

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vectorize-iris configure --manual

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• Access Token
• Organization ID

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vectorize-iris configure --api-token "your-token" --org-id "your-org-id"

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export VECTORIZE_TOKEN="your-token" export VECTORIZE_ORG_ID="your-org-id"

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export VECTORIZE_TOKEN="your-token" export VECTORIZE_ORG_ID="your-org-id"

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from vectorize_iris import VectorizeIrisClient client = VectorizeIrisClient( api_token="your-token", org_id="your-org-id" )

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import { extractTextFromFile } from '@vectorize-io/iris'; const result = await extractTextFromFile('document.pdf', { apiToken: 'your-token', orgId: 'your-org-id' });

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