Vision Bridge Skills: Building a Visual Understanding Bridge for Text-Only Large Models

Vision Bridge Skills is an innovative open-source tool designed to address the pain point that text-only large models cannot handle image tasks. Through its two-stage workflow design, it enables text-only models (without visual support) to indirectly gain visual understanding capabilities, achieving seamless bridging between visual and text models. This tool has advantages such as modularity, high flexibility, and controllable costs, and is suitable for various scenarios like existing system enhancement and cost optimization.

In the application of large language models, many excellent text-only models (such as GPT-3.5 and early versions of Claude Instant) perform well in language understanding and generation but cannot directly process image inputs. This causes text-only models to fail to understand the content when users upload images, limiting application scenarios. The Vision Bridge Skills project is designed precisely to address this pain point.

Two-Stage Workflow

1. Visual Analysis Stage: Route the image to a vision-supported model (e.g., Claude 3, GPT-4V) to extract information such as object recognition, scene description, OCR text, and sentiment analysis.
2. Action Mapping Stage: Pass the text analysis results from the vision model to the text-only main model, which then decides the response or action based on the user's question.

Anthropic Messages API Compatibility

Supports Anthropic-compatible multimodal models (e.g., Claude 3 series), making it easy to integrate into the Anthropic ecosystem. The standardized interface lowers the entry barrier for access.

• Intelligent Routing Mechanism: Automatically detects image inputs and coordinates data flow between the vision model and the main model.
• Configurability: Supports selecting the vision model, retaining the main model, and customizing the processing flow.
• Lightweight Design: As a skill rather than a complete framework, it has few dependencies, simple configuration, and is easy to integrate into existing systems.

1. Existing System Enhancement: Without replacing the main model, add visual capabilities to systems that have already deployed text-only models, suitable for teams undergoing progressive upgrades.
2. Cost Optimization: Only call expensive multimodal models when necessary; handle simple queries with text-only models to achieve fine-grained cost control.
3. Multi-Model Collaboration: Provide a standardized multi-model collaboration mode for complex systems.

• Bridging Heterogeneous Capabilities: Combine the advantages of different models, make up for the deficiencies of a single model, and provide inspiration for AI architecture design.
• Progressive Upgrade: Allow organizations to gain visual capabilities while protecting existing investments, reducing the cost of replacing models.
• Modular Architecture: Separate visual understanding and language reasoning; each part can be independently optimized or replaced.

Vision Bridge Skills is a practical and innovative open-source project that enables text-only models to handle visual tasks through a two-stage workflow, providing new possibilities for AI application development. It is worth trying for developers who want to add visual capabilities to text-only models.

Usage Flow: User uploads image → Detection → Call vision model for analysis → Obtain text description → Pass to main model → Generate response (transparent to the user).

Project URL: https://github.com/Guavafsl/vision-bridge-skills