Advanced Metadata Generation Through Video Transcription Integration

We developed and optimized AI-based solutions, leveraging cutting-edge AI platforms to improve the product’s functionalities. Additionally, we designed APIs to enhance features and collaborated on integrating a vector database into customers systems.

Scope

The scope involved designing, developing, and optimizing AI solutions for the product. Key tasks included utilizing AI platforms to their full potential, enhancing chatbot functionalities through prompt engineering, implementing video transcription features, and integrating APIs to improve product capabilities. We also facilitated the migration from one vector database to another, ensuring the new database structure met the required functionality and optimizing the data migration process.

Our Contribution

We made significant strides in enhancing the product with advanced AI features and backend optimizations. Our team integrated OpenAI’s LLM with Langchain to develop a robust system for video transcription. This system not only generates detailed summaries and titles but also creates chapter names with timestamps, all from video content.

We also took on the challenge of migrating the vector database from Pinecone to Qdrant. Our approach ensured that the new database met all functional requirements while optimizing the data migration process. To address technical hurdles, such as handling large transcription datasets and dealing with Qdrant cluster issues, we optimized our batch updates and migration pipeline, and fine-tuned our prompt engineering strategies.

Additionally, we implemented Langfuse for LLM observability, which helped us gather valuable user feedback and continuously improve the system. By developing and integrating new APIs, we enhanced the product’s capabilities and streamlined client interactions, ultimately saving time for content creators and making video metadata management more efficient.