Semantic Video Search Using LLM Tags and Vector Similarity
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Solution Overview
Problem
Existing digital media search systems, relying on third-party metadata, often fail to capture the full essence of digital media content, leading to incomplete and inaccurate search results.
Innovation Solution
Enhance digital media databases by extracting audio and closed caption data from videos, using a large language model to generate additional tags, and employing multidimensional vectors to represent metadata, enabling semantic searches and proactive video suggestions.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If traditional metadata provided by third-party providers is used for search, then the search system is simple to operate, but the search accuracy and completeness are limited
Solution Approach 1:
The system performs preliminary actions by extracting audio and closed caption data from videos before search operations, and pre-generates tags using a large language model. This prepares enriched metadata in advance, so that when users perform searches, they benefit from comprehensive, pre-processed content descriptors without experiencing the complexity of the underlying processing steps.
Solution Approach 2:
A large language model acts as an intermediary between the raw video content and the search system. The LLM processes audio and closed caption data to generate descriptive tags, serving as a mediator that translates unstructured media content into structured, searchable metadata. This intermediary handles the complexity of content analysis, leaving the search interface simple for users.
2Loss of information
If additional tags generated by large language model are added to metadata, then the search results become more accurate and comprehensive, but the data processing time and computational resources increase
Solution Approach 1:
The system generates tags using a large language model in advance, before users perform searches. By performing this computationally intensive tagging operation as a preliminary action, the enriched metadata is ready for immediate retrieval during search operations, reducing the perceived processing time for end users while still achieving comprehensive content description.
Solution Approach 2:
Instead of processing the entire video content during search operations, the system creates a copy in the form of generated tags that capture the essential content descriptors. These tag copies serve as proxies for the full video content, enabling accurate searches without requiring real-time analysis of the original media files.
3Adaptability or versatility
If only traditional metadata fields are used, then the database structure is simple, but the semantic understanding and relevance of search results are insufficient
Solution Approach 1:
The system adds another dimension to the metadata structure by incorporating LLM-generated tags alongside traditional metadata fields. This creates a multi-dimensional metadata framework where conventional fields (title, description, genre) coexist with semantically rich tags derived from audio and closed caption analysis, enabling both traditional and semantic search capabilities without disrupting the existing database structure.
Solution Approach 2:
The extended metadata structure serves multiple functions: it maintains compatibility with traditional search operations while simultaneously enabling semantic search, content recommendation, and accessibility features. The additional tags provide universal value across different search and content delivery scenarios, justifying the increased structural complexity through enhanced versatility.
Data Source
AI summary
The system obtains, from a database, multiple videos and multiple metadata corresponding to the multiple videos. The metadata includes tags generated by a large language model to describe a video. The system converts the metadata into a first vector in a multidimensional space. The first vector encodes information included in the tags as a numerical representation in the multidimensional space. A distance between the first vector and a second vector indicates similarity between the metadata represented by the first vector and a second metadata represented by the second vector. The system obtains a natural language query (NLQ) associated with the database, and converts the NLQ into a third vector in the multidimensional space. The system determines whether the first vector satisfies a distance threshold to the third vector. Upon determining that the first vector satisfies the distance threshold, the system presents the video as a result to the NLQ.