The Problem
Extracting specific insights from long-form YouTube videos—especially in foreign languages—is incredibly tedious. Standard LLMs hallucinate because they lack the video's direct context, and manually reading auto-generated transcripts is unscalable.
The Hard Constraint
The pipeline needed to seamlessly extract, chunk, and embed YouTube transcripts across 10 different languages on-the-fly, storing them in an ephemeral vector database for instantaneous retrieval-augmented generation.
Architecture
Results & Caveats
The Outcome
Successfully engineered a multi-lingual RAG pipeline. The local FAISS database provides lightning-fast semantic retrieval, allowing Qwen 2.5 to generate accurate, context-bound answers directly from the video source.
Honest Caveats
The initial transcript extraction and vector embedding phase introduces a bottleneck for extremely long videos (e.g., 3-hour podcasts), causing a slight delay before the chat interface becomes fully interactive.