Multilingual YouTube RAG: Architecture Notes
A practical retrieval pipeline for long-form video: transcript embedding, FAISS search, multilingual models, and session-aware Q&A with Gemini.
Keyword search over video transcripts misses context. Users ask natural questions, want summaries across segments, and often work in multiple languages. A RAG pipeline grounds answers in retrieved transcript chunks instead of model memory alone.
The core flow: fetch transcripts, chunk and embed with a multilingual embedding model, index with FAISS for fast similarity search, then pass top-k segments to an LLM for answer generation and summarization.
Why session context matters
Follow-up questions depend on prior turns. Session-aware retrieval lets users drill into a topic without restating the entire video context each time.
Grounding responses in retrieved segments reduces hallucination on factual questions about video content.
Stack choices
Flask provides a simple API layer. LangChain helps orchestrate retrieval and generation. Hugging Face multilingual embeddings handle cross-language queries. Gemini 2.5 Flash keeps latency reasonable for interactive use.
See the Multilingual YouTube RAG case study for implementation details and outcomes.