Multimedia Content Parsing for Accurate AI Answer Generation
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Solution Overview
Problem
Existing artificial intelligence models struggle to provide accurate and relevant answer results when users ask questions about uploaded multimedia content, as the models often focus on large-scale training samples rather than the specific content, leading to low accuracy and poor answer effects.
Innovation Solution
A content generation method and apparatus that parses multimedia content to extract text information, stores it in a vector database based on text association degrees, and uses this database to quickly find relevant vector data for user questions, which are then input into an AI model to generate accurate and relevant answer results.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If existing AI models are used to answer questions about uploaded multimedia content, then the model can provide answers based on large-scale training samples, but the accuracy and relevance of answers deteriorate because the model focuses on training samples rather than specific content
Solution Approach 1:
The system performs preliminary parsing of the uploaded multimedia content to extract text information and stores it in a vector database before answering questions. This preliminary action ensures that the specific content is prepared and indexed in advance, allowing the AI model to accurately retrieve and reference the actual content rather than relying solely on general training samples.
Solution Approach 2:
A vector database is introduced as an intermediary between the uploaded content and the AI model. The vector database stores embedded representations of the content text information and enables efficient retrieval of relevant information based on query questions. This intermediary mechanism allows the AI model to access specific content information quickly and accurately, resolving the contradiction between using general training data and adapting to specific uploaded content.
2Productivity
If the AI model processes uploaded multimedia content directly, then the system structure remains simple, but the answer generation efficiency and accuracy deteriorate due to lack of content indexing
Solution Approach 1:
The system segments the content processing into distinct stages: parsing the uploaded content, extracting text information, embedding the text into vector data, storing in a vector database, and finally generating answers. This segmentation improves productivity by allowing parallel processing of content indexing and query answering, while the modular structure manages complexity through clear separation of concerns.
Solution Approach 2:
The system replaces direct mechanical processing of multimedia content by the AI model with an information retrieval mechanism using vector databases and embedded representations. Instead of processing raw content directly, the system substitutes this with converting content to vector embeddings, storing them, and retrieving relevant vectors for answer generation, which significantly improves efficiency.
3Loss of information
If the system stores all content text information in the vector database, then the content retrieval completeness improves, but the data storage volume and query processing time increase
Solution Approach 1:
The system extracts only the text information from the uploaded multimedia content and stores it in the vector database, rather than storing the entire original content. This extraction approach maintains retrieval completeness for text-based queries while significantly reducing storage volume and improving query processing efficiency by working with condensed text representations rather than raw multimedia data.
Data Source
AI summary
The present disclosure provides a content generation method, a computer device, and a storage medium; and the method includes: parsing a multimedia content, which is acquired, to obtain multiple pieces of content text information; storing the content text information into a vector database in a vector data form according to a text association degree between the content text information, wherein vector data in the vector database have an index relationship related to the text association degree; in response to receiving question information which is input, querying target vector data matching the question information from the vector database based on the index relationship between the vector data in the vector database; and inputting target content text information corresponding to the target vector data and the question information into an artificial intelligence model to obtain an answer result associated with the multimedia content.