Computer Vision for LLMs

Computer Vision for Non-Vision Models

Deeptrain utilizes the Vision-to-Multimodal Transformation Protocol (VMTP) to bridge the gap between text-only Large Language Models and visual data. This capability allows you to integrate image-based intelligence into over 200+ models that do not natively support vision, such as legacy GPT versions, Llama-series base models, and private enterprise LLMs.

How it Works

The VMTP layer acts as a visual interpreter. Instead of sending raw pixels directly to a model that cannot process them, Deeptrain transforms visual inputs into high-dimensional semantic embeddings or structured descriptive contexts that the LLM can interpret within its standard text-based context window.

Implementation Guide

To enable vision capabilities, you interface with the VisionConnector module. This module handles image ingestion, preprocessing, and the transformation logic required for your specific target model.

Basic Image Integration

from deeptrain import VisionConnector, ModelConfig

# Initialize the connector for a non-vision model (e.g., Llama-3-70b)
vision_bridge = VisionConnector(
    model_id="meta-llama/Llama-3-70b",
    api_key="your_deeptrain_api_key"
)

# Process a local image or a URL
context_output = vision_bridge.process_image(
    source="path/to/invoice_image.jpg",
    detail_level="high"
)

# The output can now be passed directly to your LLM's prompt
response = llm.generate(f"Based on this visual data: {context_output}, what is the total amount due?")

Visual Logic: Flowcharts and Graphs

Standard OCR often fails to capture the logic of structured diagrams. VMTP includes specialized handlers for flowcharts and graphs, translating spatial relationships and directional arrows into logical sequences.

• Flowcharts: Converts nodes and edges into a step-by-step logic map.
• Graphs/Charts: Extracts data points and trends into structured JSON or Markdown tables.

Example: Analyzing a Flowchart

# Enable specialized flowchart processing
diagram_data = vision_bridge.analyze_diagram(
    source="architecture_diagram.png",
    diagram_type="flowchart"
)

print(diagram_data.logic_steps) 
# Output: ["Step 1: User Login", "Step 2: Auth Validation", "Step 3: Redirect to Dashboard"]

API Reference

VisionConnector.process_image()

Converts standard image files into LLM-readable context.

| Parameter | Type | Description | | :--- | :--- | :--- | | source | str | Path to local file, URL, or Base64 encoded string. | | detail_level | str | low, medium, or high. Higher levels consume more context tokens. | | output_format | str | text (default) or embedding. |

Returns: VisionResponse object containing the transformed semantic data.

VisionConnector.analyze_diagram()

Specialized extraction for structured visual logic.

| Parameter | Type | Description | | :--- | :--- | :--- | | source | str | Image source containing a chart, graph, or flowchart. | | diagram_type | str | One of: flowchart, graph, sequence_diagram, hierarchy. |

Returns: DiagramResult containing node-edge relationships and extracted text labels.

Best Practices

• Context Management: When using non-vision models, the transformed image context consumes part of your LLM's token limit. Use detail_level="low" for simple objects to preserve space for reasoning.
• Model Agnosticism: VMTP handles the formatting automatically. Whether your model expects prose or structured data, the VisionConnector adjusts the output to match the target model's optimal input format.