Beyond Text: How to Leverage Onboarding Buddy for Image and Video Search

In today's content-rich digital landscape, efficiently managing and retrieving multimedia assets like images and videos has become a critical challenge for businesses. Traditional search methods, often reliant on filenames or manually applied tags, fall short when dealing with vast and complex content libraries. Imagine a marketer needing to find all images related to 'sustainable urban development' or a video editor searching for 'joyful moments in a park' across thousands of unsorted files. This is where AI-powered semantic search, like that offered by Onboarding Buddy's File Management API, revolutionizes asset discovery.

A recent study by Statista highlighted that enterprise data is growing at an exponential rate, with unstructured data, including multimedia, making up a significant portion. Effectively navigating this data deluge requires intelligent tools that understand content beyond simple keywords.

The Power of Semantic Search with Onboarding Buddy

Onboarding Buddy's File Management API transcends basic keyword matching. By leveraging advanced AI and machine learning, it enables semantic search for your images and videos. This means you can query your content using natural language, and the API will return results based on the *meaning* and *context* of your request, even if those exact words aren't present in the metadata. This dramatically reduces the time and effort required to locate specific assets, boosting productivity and ensuring brand consistency.

Let's explore how to use the /file-service/search endpoint for semantic file search and the /file-service/query/rag endpoint for document RAG (Retrieval Augmented Generation) on your indexed files.

Semantic Search for Multimedia Files

The /file-service/search endpoint allows you to search your uploaded multimedia files using a natural language query. The API processes your query semantically, comparing it against the content and contextual understanding of your stored images and videos.

Python Example for Semantic Search

Here's how you can perform a semantic search using Python:

import requests
import uuid

headers = {
    "ob-app-key": "<your-app-key>",
    "ob-api-key": "<your-api-key>",
    "ob-api-secret": "<your-api-secret>",
    "Content-Type": "application/json"
}

payload = {
    "correlationId": str(uuid.uuid4()),
    "searchString": "images of vibrant cityscapes at sunset"
}

response = requests.post(
    "https://api.onboardingbuddy.co/file-service/search",
    headers=headers,
    json=payload
)
response.raise_for_status()
print(response.json())

This example searches for images depicting "vibrant cityscapes at sunset." The API will intelligently find relevant files, even if they're only tagged with broader terms like "city" or "evening."

Document RAG for Detailed Information

Beyond simple search, Onboarding Buddy offers a powerful Document RAG (Retrieval Augmented Generation) capability via the /file-service/query/rag endpoint. While typically associated with text documents, this feature can be incredibly useful when combined with the rich metadata and AI understanding of your multimedia assets. For instance, if you have a video accompanied by a detailed script or a whitepaper embedded within an image file (e.g., as part of a presentation slide), you can ask specific questions about the content, and the API will generate a coherent response based on the retrieved information.

Python Example for Document RAG Query

Here's a Python example for querying an indexed file using RAG:

import requests
import uuid

headers = {
    "ob-app-key": "<your-app-key>",
    "ob-api-key": "<your-api-key>",
    "ob-api-secret": "<your-api-secret>",
    "Content-Type": "application/json"
}

payload = {
    "correlationId": str(uuid.uuid4()),
    "fileGlobalId": "<your-file-global-id>", # Replace with the ID of your indexed multimedia file
    "query": "Summarize the key themes discussed in this presentation slide"
}

response = requests.post(
    "https://api.onboardingbuddy.co/file-service/query/rag",
    headers=headers,
    json=payload
)
response.raise_for_status()
print(response.json())

This allows you to extract specific insights and summaries directly from your multimedia content's associated data, making your content library an active source of information.

Future Trends in AI Multimedia Search

The future of multimedia search is undeniably linked to advancements in AI. We can expect even more sophisticated capabilities:

• **Cross-modal Search:** The ability to search across different media types seamlessly, e.g., finding a video clip based on a spoken description or an image based on a musical theme.
• **Personalized Content Discovery:** AI systems learning individual preferences to deliver highly relevant image and video suggestions.
• **Generative Search:** Beyond retrieving existing content, AI might soon generate variations or entirely new images/videos based on search queries, blending search with content creation.
• **Real-time Analysis:** Instantaneous indexing and semantic understanding of live streams or newly uploaded content, enabling immediate search and RAG capabilities.

These trends underscore the importance of integrating robust AI-powered file management solutions like Onboarding Buddy's API into your workflow.

Revolutionize your media search. Explore our File Management APIs.