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Vision SDK - Document Processing Pipeline

Status: Planning Timeline: Q2-Q3 2026 Vote with 👍

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Executive Summary

A comprehensive Vision Pipeline SDK for document processing, OCR, and structured data extraction. Consolidates fragmented vision capabilities across GAIA into a unified, developer-friendly API. Value: Transform any document (medical forms, legal logs, technical manuals) into structured, searchable data using VLM-powered OCR—100% locally on AMD hardware. Impact: Reduce vision agent code by 60%, enable document automation workflows, power legal/medical/technical document processing.

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The Problem

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Fragmented Vision Capabilities

Current state:
  • Image processing duplicated across agents (EMR has 400+ lines of boilerplate)
  • No unified document processing API
  • Vision capabilities scattered across llm/, vlm/, rag/, utils/
  • Each agent reimplements the same patterns
Missing features:
  • No multi-page document processing
  • No table extraction from documents
  • No visual element extraction (charts, timelines)
  • No structured extraction with flexible schemas
  • No seamless RAG integration
Impact:
  • Developers write 400+ lines for basic vision tasks
  • Cannot process large legal documents (1,200+ pages)
  • Technical manuals require manual preprocessing for RAG
  • Each vision agent starts from scratch

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The Solution

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Vision SDK - One API for All Documents

from gaia.vision import VisionSDK, ExtractionSchema

vision = VisionSDK()

# Medical forms → structured data
result = vision.extract("form.pdf", schema=medical_schema)

# Legal logs → text + tables + visuals
result = vision.extract("logs.pdf", extract_tables=True, extract_visuals=True)

# Technical manuals → RAG indexing
result = vision.extract("manual.pdf", pages="all")
Key capabilities:
  • Multi-page processing (1 to 1,200+ pages)
  • Table and visual element extraction
  • Handwriting recognition (validated, production-ready)
  • Structured data with flexible schemas
  • Batch processing and multi-document detection
  • Seamless RAG integration
  • Agent mixin for vision-powered tools
  • Production-ready with checkpointing and parallel processing

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Use Cases

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Medical Forms (EMR Agent)

Extract patient data from intake forms with validation. 
from gaia.vision import VisionSDK, ExtractionSchema

schema = ExtractionSchema.from_dict({
    "required": ["first_name", "last_name"],
    "fields": {
        "date_of_birth": {"type": "date"},
        "allergies": {"type": "string", "critical": True},
    },
    "allow_additional": True,
})

vision = VisionSDK()
result = vision.extract("intake_form.pdf", schema=schema)
Impact: EMR agent code reduces from 1,500 → 600 lines (60% reduction)
Process multi-page driver logs with timeline charts and violation detection. 
vision = VisionSDK()

result = vision.extract(
    "driver_logs.pdf",
    pages="all",  # 1,200 pages
    extract_tables=True,
    extract_visuals=True,
    on_progress=callback,
)

# Access timeline charts
for page in result.pages:
    for visual in page.visuals:
        if visual.type == "timeline":
            print(visual.data)
Impact: Automate $$$+ legal expert analysis work

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Technical Manuals (RAG Q&A)

Index operations manuals for accurate Q&A. 
from gaia.vision import VisionSDK
from gaia.rag import RAGSDK

# Extract with tables
vision = VisionSDK()
doc = vision.extract("manual.pdf", pages="all", extract_tables=True)

# Index with RAG
rag = RAGSDK()
rag.add_document(doc.to_markdown())

# Query
answer = rag.query("What are the safety procedures?")
Impact: Enable precise answers from complex technical documents

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Batch Receipts (Expense Automation)

Process multiple receipts and generate expense reports. 
vision = VisionSDK()

# Batch process receipts
results = vision.extract_batch(
    files=["receipt1.jpg", "receipt2.jpg", ...],
    schema=receipt_schema,
)

# Export as expense report
results.to_excel("expenses.xlsx")

# Or single image with multiple receipts
result = vision.extract(
    "three_receipts.jpg",
    detect_multiple=True,  # Detect 3 receipts in one image
    schema=receipt_schema,
)
Impact: Expense reporting automation, receipt digitization

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Business Cards (CRM Integration)

Extract contact information for CRM import. 
vision = VisionSDK()

result = vision.extract("business_card.jpg", schema=contact_schema)

# Export to VCF/CSV
result.to_vcard("contact.vcf")
# Fields: name, title, company, email, phone, linkedin
Impact: Networking automation, contact management

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ID Cards (KYC Compliance)

Identity verification for compliance and onboarding. 
vision = VisionSDK()

result = vision.extract(
    "drivers_license.jpg",
    schema=id_card_schema,
    detect_face=True,  # Face detection
)

# Validate
age = calculate_age(result.data["date_of_birth"])
is_valid = result.data["expiration_date"] > today()
Impact: KYC automation, age verification, identity fraud prevention

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Bank Statements (Financial Analysis)

Extract transactions for accounting and reconciliation. 
vision = VisionSDK()

result = vision.extract(
    "bank_statement.pdf",
    pages="all",
    extract_tables=True,
)

# Transaction table
transactions = result.tables[0].to_dataframe()
transactions.to_csv("transactions.csv")
Impact: Accounting automation, expense tracking, reconciliation

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Handwritten Forms & Notes

Digitize handwritten content with high accuracy. 
vision = VisionSDK()

# Handwritten meeting notes
result = vision.extract(
    "meeting_notes.jpg",
    handwriting_mode=True,  # Optimized for handwriting
)

# Filled forms (mixed print + handwriting)
result = vision.extract(
    "filled_application.pdf",
    schema=application_schema,
    handwriting_mode=True,
)
Impact: Form digitization, note-taking automation, historical document preservation
Validated: VLM handwriting recognition performs well on real-world forms. Included as core capability.

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Architecture

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Module Structure

src/gaia/vision/
├── sdk.py               # Main VisionSDK class
├── document.py          # Document/Page models
├── schema.py            # ExtractionSchema, ValidationRule
├── preprocessor.py      # Image optimization
├── loaders.py           # PDF/image loading
├── extractors/
│   ├── text.py          # OCR extraction
│   ├── table.py         # Table extraction
│   ├── visual.py        # Visual element extraction
│   └── structured.py    # Schema-based extraction
└── mixin.py             # Agent integration

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Implementation Milestones

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M1: Foundation (1 week)

  • Core architecture
  • Image preprocessing (EXIF, resize, optimize)
  • Basic single-page OCR
  • Document/Page models

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M2: Structured Extraction (1-1.5 weeks)

  • Multi-page support
  • ExtractionSchema with validation
  • Progress tracking
  • Deliverable: EMR agent refactored

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M3: Tables & Visuals (1.5-2 weeks)

  • Table extraction
  • Visual element extraction
  • RAG integration
  • Deliverable: Driver logs prototype, manual RAG indexing

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M4: Optimization (1-1.5 weeks)

  • Parallel processing
  • Checkpoint/resume
  • Production performance
  • Deliverable: Full 1,200-page driver logs processing

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M5: Superset (1.5-2 weeks)

  • Advanced analysis tools
  • Report generation
  • Specialized processors
  • Deliverable: Complete SDK

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Success Criteria

  • ✅ EMR agent refactored with 60% code reduction
  • ✅ Process 1,200-page documents successfully
  • ✅ Table/visual extraction working (70%+ accuracy)
  • ✅ RAG integration seamless
  • ✅ 95%+ test coverage
  • ✅ Production-ready performance

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Dependencies

Required:
  • VLMClient (Qwen3-VL-4B-Instruct-GGUF)
  • Lemonade Server
  • PyMuPDF (PDF processing)
  • PIL/Pillow (image processing)
Vote on this plan: GitHub Issue #325
Last Updated: February 9, 2026