Multi-modal Framework

Multi-modal Framework Overview

The VMTP (Deeptrain) Multi-modal Framework serves as a translation layer between unstructured, high-dimensional data—such as video, audio, and complex imagery—and the text-based context windows of Large Language Models.

By normalizing diverse data formats into a unified knowledge stream, Deeptrain allows developers to build agents that "see," "hear," and "understand" content that is traditionally inaccessible to standard LLMs.

Data Processing Modalities

Text & Context Expansion

Deeptrain bypasses the physical token limits of LLMs by utilizing a localized embedding database. Instead of stuffing raw text into a prompt, the framework retrieves relevant context in real-time.

Usage: Ideal for large document sets or live data feeds.
Key Functionality: Automatically segments and embeds text for semantic retrieval.

Computer Vision for Non-Vision Models

This module bridges the gap for models that lack native vision capabilities. It processes images, flowcharts, and graphs, converting visual logic into a structured format the LLM can interpret.

# Example: Integrating visual logic into a standard LLM
response = deeptrain.process_visual(
    source="path/to/flowchart.png",
    type="flowchart",
    model_id="your-non-vision-llm-id"
)

Audio & Video Intelligence

Deeptrain handles both local and hosted video content (YouTube, Vimeo), extracting knowledge through the Transcribe API. This allows agents to utilize video tutorials, meetings, or podcasts as primary training data.

The Transcribe API

The Transcribe API is the primary interface for converting multi-dimensional media into LLM-readable context.

API Specification:

Usage Example:

# Processing a remote video for agent training
video_data = deeptrain.transcribe(
    source="https://www.youtube.com/watch?v=example",
    mode="knowledge_extraction"
)

# Integration with the agent's memory
agent.update_knowledge_base(video_data)

Bridging the Context Gap

The framework utilizes a "Retrieve-and-Process" architecture to ensure that multi-modal data does not overwhelm the model's context window:

Ingestion: Raw data (Video, Image, Audio) is sent to the Deeptrain processing engine.
Transformation: Data is converted into high-fidelity embeddings or structured text descriptions.
Localization: Processed data is stored in a localized database rather than the prompt.
Just-in-Time Retrieval: Only the specific segments of data relevant to the current user query are injected into the LLM context.

Model Agnostic Integration

The Multi-modal Framework is designed to be model-agnostic, supporting over 200 private and open-source models.

To configure a model for multi-modal operations:

from deeptrain import MultiModalConnector

connector = MultiModalConnector(
    model="your-chosen-model",
    api_key="your_api_key"
)

# Enable specific multi-modal features
connector.enable_feature("video_processing")
connector.enable_feature("flowchart_interpretation")

By offloading the heavy lifting of data normalization to the VMTP framework, your AI agents can interact with the physical and digital world through a single, unified interface.