Project 42
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Module 3 of 3 · 45 min

Pixtral Vision & Mistral Embeddings

Process complex document images, charts, and technical diagrams with Pixtral and index documents with Mistral Embeddings.

Core concept

By the end

You will be able to

  • Pass image URLs and Base64 payloads to Pixtral 12B / Pixtral Large vision models.
  • Extract structured key-value data from complex scanned invoices and engineering diagrams.
  • Generate high-dimensional embeddings using `mistral-embed` for semantic search indexing.
01

Multimodal Vision with Pixtral

Pixtral is natively trained on arbitrary image resolutions and aspect ratios, avoiding distortion when analyzing widescreen architectural blueprints or tall financial receipts.

Pixtral Image Understanding Call
python
from mistralai import Mistral

client = Mistral(api_key="your-api-key")
response = client.chat.complete(
    model="pixtral-12b-2409",
    messages=[
        {
            "role": "user",
            "content": [
                {"type": "text", "text": "Extract table values from this image."},
                {"type": "image_url", "image_url": "https://example.com/invoice.png"}
            ]
        }
    ]
)

Practice activity

Extract Structured JSON from an Architecture Diagram

  1. Pass a technical system diagram image to Pixtral.
  2. Prompt for strict JSON extracting all services, databases, and network connections.
  3. Validate extracted JSON against a target schema.

What to produce

  • JSON extraction output file matching diagram entities.

Reflect before continuing

How does native image aspect ratio handling improve OCR precision on technical schematics?

Evidence

Sources and verification

Knowledge check

Make it stick.

Pass at 80%

Choose the strongest answer for each question. Your attempts become part of your account transcript.

01What advantage does Pixtral offer for processing technical diagrams and invoices?