volcengine/ai-app-lab
Jun 2025 – Jun 2025
1 Month active
Languages Used
MarkdownPythonShellTypeScript
Technical Skills
AI Integration (VLM)API DevelopmentBackend DevelopmentCloud Services (TOS, VikingDB)Frontend DevelopmentObject Storage
lujiakun
PROFILE
Lujiakun
Developed and delivered an end-to-end Intelligent Animal Image Retrieval Application in the volcengine/ai-app-lab repository, enabling users to search for animals by image or text. The solution integrated VikingDB for vector storage, TOS for object storage, and Doubao VLM to provide animal recognition and descriptive科普 information. Leveraging TypeScript, React, and Python, the developer implemented robust backend and frontend components, exposing animal recognition capabilities through a stable API. The work focused on seamless cross-stack integration of AI, storage, and data pipelines, resulting in a user-facing retrieval experience and laying the foundation for scalable, content-rich animal discovery features.
Overall Statistics
Feature vs Bugs
100%Features
Repository Contributions
1Total
Bugs
0
Commits
1
Features
1
Lines of code
15,184
Activity Months1
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Work History
June 2025
1 Commits • 1 Features
Jun 1, 2025June 2025 — Delivered an end-to-end Intelligent Animal Image Retrieval Application in volcengine/ai-app-lab, enabling search by image or text. The feature integrates VikingDB for vector storage, TOS for object storage, and Doubao VLM for understanding and describing animals, with科普 information provided to users. The release focused on a robust, user-facing retrieval experience and demonstrates cross-stack integration of AI, storage, and data pipelines.
1 Commits • 1 Features
Jun 1, 2025June 2025 — Delivered an end-to-end Intelligent Animal Image Retrieval Application in volcengine/ai-app-lab, enabling search by image or text. The feature integrates VikingDB for vector storage, TOS for object storage, and Doubao VLM for understanding and describing animals, with科普 information provided to users. The release focused on a robust, user-facing retrieval experience and demonstrates cross-stack integration of AI, storage, and data pipelines.
June 2025
Activity
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Quality Metrics
Correctness90.0%
Maintainability80.0%
Architecture90.0%
Performance80.0%
AI Usage90.0%
Skills & Technologies
Programming Languages
MarkdownPythonShellTypeScript
Technical Skills
AI Integration (VLM)API DevelopmentBackend DevelopmentCloud Services (TOS, VikingDB)Frontend DevelopmentObject StorageReactShell ScriptingTypeScriptVector Databases
Repositories Contributed To
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