I’m working on integrating Google’s Gemini API into my CapCut-related website to enhance AI-powered video editing features, such as auto-captioning, AI-generated video summaries, and smart effects recommendations. However, I’m facing a few technical challenges:
Token Limits & Performance: Since video editing requires processing large amounts of data (e.g., extracting frames, analyzing speech for captions), I’m encountering rate limits and slow response times. Has anyone optimized Gemini API calls for handling media-heavy workflows?
Streaming vs. Batch Processing: I initially considered using Gemini API for real-time AI-powered edits (e.g., suggesting effects while a user uploads a video), but the latency makes it impractical. Would batch processing be a better approach, or are there alternative ways to reduce delays?
Multi-Modal Inputs Handling: CapCut edits involve both text and video/image inputs. While Gemini API supports multi-modal inputs, I’m unsure about the best way to structure requests for processing video metadata, extracted text, and user prompts efficiently. Any best practices?
Fine-Tuning for Video Editing Context: Since Gemini models are general-purpose, I want to fine-tune responses for better video editing insights (e.g., automatically suggesting CapCut templates based on content). What’s the best approach to fine-tune or guide the API for domain-specific outputs?
If anyone has experience integrating Gemini API for video-related applications, I’d appreciate any insights or recommendations. Thanks!