> For the complete documentation index, see [llms.txt](https://infronai.gitbook.io/docs/llms.txt). Markdown versions of documentation pages are available by appending `.md` to page URLs; this page is available as [Markdown](https://infronai.gitbook.io/docs/features/multimodal-input/video-inputs.md). # Video Inputs Infron.AI supports sending video files to compatible models via the API. This guide will show you how to work with video using our API. Infron.AI supports both **direct URLs** and **base64-encoded data URLs** for videos: * **URLs**: Efficient for publicly accessible videos as they don't require local encoding * **Base64 Data URLs**: Required for local files or private videos that aren't publicly accessible {% hint style="info" %} **Important**: Video URL support varies by provider. Infron.AI only sends video URLs to providers that explicitly support them. For example, Google Gemini on AI Studio only supports YouTube links (not Vertex AI). {% endhint %} {% hint style="info" %} **API Only**: Video inputs are currently only supported via the API. {% endhint %} ### Video Inputs Requests with video files to compatible models are available via the `/v1/chat/completions` API with the `video_url` content type. The `url` can either be a **URL** or a **base64-encoded data** URL. Note that only models with video processing capabilities will handle these requests. #### Using Video URLs Here's how to send a video using a URL. Note that for Google Gemini on AI Studio, only YouTube links are supported: {% tabs %} {% tab title="Python" %} ```python import requests import json url = "https://llm.onerouter.pro/v1/chat/completions" headers = { "Authorization": f"Bearer {API_KEY_REF}", "Content-Type": "application/json" } messages = [ { "role": "user", "content": [ { "type": "text", "text": "Please describe what's happening in this video." }, { "type": "video_url", "video_url": { "url": "https://www.youtube.com/watch?v=dQw4w9WgXcQ" } } ] } ] payload = { "model": "{{MODEL}}", "messages": messages } response = requests.post(url, headers=headers, json=payload) print(response.json()) ``` {% endtab %} {% endtabs %} #### Using Base64 Encoded Videos For locally stored videos, you can send them using base64 encoding as data URLs: {% tabs %} {% tab title="Python" %} ```python import requests import json import base64 from pathlib import Path def encode_video_to_base64(video_path): with open(video_path, "rb") as video_file: return base64.b64encode(video_file.read()).decode('utf-8') url = "https://llm.onerouter.pro/v1/chat/completions" headers = { "Authorization": f"Bearer {API_KEY_REF}", "Content-Type": "application/json" } # Read and encode the video video_path = "path/to/your/video.mp4" base64_video = encode_video_to_base64(video_path) data_url = f"data:video/mp4;base64,{base64_video}" messages = [ { "role": "user", "content": [ { "type": "text", "text": "What's in this video?" }, { "type": "video_url", "video_url": { "url": data_url } } ] } ] payload = { "model": "{{MODEL}}", "messages": messages } response = requests.post(url, headers=headers, json=payload) print(response.json()) ``` {% endtab %} {% endtabs %} ### Supported Video Formats Infron.AI supports the following video formats: * `video/mp4` * `video/mpeg` * `video/mov` * `video/webm` ### Common Use Cases Video inputs enable a wide range of applications: * **Video Summarization**: Generate text summaries of video content * **Object and Activity Recognition**: Identify objects, people, and actions in videos * **Scene Understanding**: Describe settings, environments, and contexts * **Sports Analysis**: Analyze gameplay, movements, and tactics * **Surveillance**: Monitor and analyze security footage * **Educational Content**: Analyze instructional videos and provide insights ### Best Practices #### File Size Considerations Video files can be large, which affects both upload time and processing costs: * **Compress videos** when possible to reduce file size without significant quality loss * **Trim videos** to include only relevant segments * **Consider resolution**: Lower resolutions (e.g., 720p vs 4K) reduce file size while maintaining usability for most analysis tasks * **Frame rate**: Lower frame rates can reduce file size for videos where high temporal resolution isn't critical #### Optimal Video Length Different models may have different limits on video duration: * Check model-specific documentation for maximum video length * For long videos, consider splitting into shorter segments * Focus on key moments rather than sending entire long-form content #### Quality vs. Size Trade-offs Balance video quality with practical considerations: * **High quality** (1080p+, high bitrate): Best for detailed visual analysis, object detection, text recognition * **Medium quality** (720p, moderate bitrate): Suitable for most general analysis tasks * **Lower quality** (480p, lower bitrate): Acceptable for basic scene understanding and action recognition ### Provider-Specific Video URL Support Video URL support varies significantly by provider: * **Google Gemini (AI Studio)**: Only supports YouTube links (e.g., `https://www.youtube.com/watch?v=...`) * **Google Gemini (Vertex AI)**: Does not support video URLs - use base64-encoded data URLs instead * **Other providers**: Check model-specific documentation for video URL support --- # Agent Instructions This documentation is published with GitBook. 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