YouTube RAG Pipeline
Chat with Any YouTube Video
Minimal RAG pipeline transforming YouTube videos into intelligent Q&A systems. Extracts transcripts, builds vector embeddings with Google's embedding-001 model, and indexes with FAISS. Uses LangChain for orchestration and Gemini 1.5 Flash for answer generation. Jupyter-based, Colab-friendly, and Docker-ready.
Why it mattered
Processes 30-min video transcripts in 2-3 minutes; Powers natural language Q&A on captioned content
What shipped
Minimal RAG pipeline transforming YouTube videos into intelligent Q&A systems. Extracts transcripts, builds vector embeddings with Google's embedding-001 model, and indexes with FAISS. Uses LangChain for orchestration and Gemini 1.5 Flash for answer generation. Jupyter-based, Colab-friendly, and Docker-ready.
Proof signal
Processes 30-min video transcripts in 2-3 minutes; Powers natural language Q&A on captioned content
System flow
Extract Transcripts -> Split Text -> Generate Embeddings -> Build Index -> Retrieve Context -> Generate Answers.
Honest maturity snapshot
Raw datasets, disconnected metrics, and hard-to-scan findings.
A structured analysis or dashboard with clear KPIs, visual patterns, and decision-ready insights.
Features
- Python integrated into the workflow.
- LangChain integrated into the workflow.
- Gemini API integrated into the workflow.
- FAISS integrated into the workflow.
- Extract Transcripts stage documented in the delivery flow.
- Split Text stage documented in the delivery flow.
Engineering challenges
- Keeping the implementation clear enough to explain while still solving the core technical problem.
- Choosing a scope that could be shipped, tested, and documented.
- Turning technical work into proof a visitor can evaluate quickly.
Results
- Processes 30-min video transcripts in 2-3 minutes; Powers natural language Q&A on captioned content
- Documented the engineering path and important learnings.
- Made source code or technical proof available for review.
What I learned
- Tradeoff learned: Keeping the implementation clear enough to explain while still solving the core technical problem.
- Proof learned: Processes 30-min video transcripts in 2-3 minutes; Powers natural language Q&A on captioned content
- Next iteration: Add richer screenshots or a narrated demo.