Reading
A RAG system that converts long video courses into a searchable knowledge base, supporting natural language queries and returning precise video timestamp locations.
Section 01
This open-source project builds a Retrieval-Augmented Generation (RAG) system to address the pain point of low retrieval efficiency for long video courses. It converts videos into a searchable knowledge base, supports natural language queries and returns precise timestamps, enables local deployment to protect privacy, and uses a tech stack including Whisper, Ollama, LLaMA 3.2, etc.
Section 02
The popularity of online education brings convenience, but long video content has low retrieval efficiency with primitive traditional navigation methods; video content is unstructured, so pure text search struggles to understand intent and related concepts.
Section 03
Build a RAG system tailored for long video scenarios, aiming to convert videos into a searchable Q&A knowledge base, support natural language questions and return accurate answers with timestamps, designed for production environments, and integrate semantic retrieval with LLM reasoning.
Section 04
Use FFmpeg to extract audio, addressing details like filename conflicts.
Use Whisper to generate transcribed text with timestamps, accelerate batch processing via distributed Colab instances, and produce structured JSON.
Intelligently merge short segments into semantic units to avoid context loss.
Deploy bge-m3 locally via Ollama to generate vectors, store in Pandas and persist with Joblib, and use cosine similarity for query matching.
LLaMA 3.2 combines retrieved segments to generate answers with precise timestamp locations.
Section 05
Answers link to specific positions in the video, changing the way retrieval works.
Local deployment based on Ollama, no external API dependencies, protecting data privacy.
Modular and loosely coupled design, easy for customization and expansion.
Section 06
Application Scenarios: Integration with online education platforms, enterprise training retrieval, personal learning organization.
Future Directions: Introduce vector databases, develop Web UI, support multiple disciplines, optimize retrieval ranking strategies.
Section 07
RAG technology successfully converts unstructured videos into a searchable knowledge base, runs locally without external dependencies, and provides a practical and scalable solution for the intelligentization of educational content.
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