RAG-Based Retrieval System for Spiritual Content

Overview

JashDS transformed spiritual content accessibility for a leading spiritual foundation by developing an advanced RAG-powered question-answering system that processes thousands of hours of video and audio teachings. The solution features intelligent multilingual transcription, hybrid search capabilities, and sophisticated content filtering, enabling devotees to instantly discover precise spiritual guidance with timestamp accuracy. Through innovative implementation of vector embeddings, multi-modal search, and LLM-based reranking, the system revolutionized how seekers interact with vast spiritual knowledge repositories, reducing content discovery time from hours to seconds.

About the Client

A non-profit spiritual and educational organization dedicated to teaching Vedic knowledge and promoting spiritual awareness in society. The organization focuses on spreading eternal knowledge in accessible formats while organizing spiritual camps and retreats. Their core mission involves guiding spiritual seekers toward God-realization through practical devotion and charitable activities.

The Challenge

The foundation faced significant challenges in making vast spiritual teachings accessible and interactive for devotees across diverse backgrounds and languages. They possessed thousands of hours of video content, audio files, and books containing invaluable spiritual knowledge, but lacked an effective methodology to process and retrieve information from these unstructured data sources.

Content Accessibility Crisis: Manual searching through thousands of hours of spiritual content took hours, creating barriers for seekers needing immediate guidance
Unstructured Data Processing: Diverse multimedia content formats (videos, audio, transcripts) require sophisticated processing to become searchable and retrievable
Language Barriers: Hindi content needed translation and multilingual support to reach broader audiences
Technical Implementation Gap: While recognizing the potential of RAG-based solutions, they lacked a proper methodology to ingest massive volumes of diverse content into vector databases for effective querying

Without a solution, the organization risked losing engagement from spiritual seekers who needed instant access to relevant teachings, potentially limiting their mission's reach and impact.

Key Results

Processed 3,000+ hours of spiritual content into a searchable format with complete metadata preservation
Reduced content discovery time from hours of manual searching to seconds of intelligent retrieval
Achieved 99%+ system reliability through robust fallback systems across multiple LLM providers
Decreased manual curation effort by 70% through an automated content processing pipeline
Enabled exact document chunk retrieval with complete source attribution and timestamp accuracy

Our Solution

RAG-Based Retrieval System for Spiritual Content — solution architecture diagram

Content Processing Pipeline Implementation: JashDS developed a comprehensive ingestion system to handle video, audio, and transcript files from S3 or local storage. The solution converted videos to audio format using FFMpeg for optimized downstream processing, then generated accurate transcripts using AssemblyAI's sentence API for enhanced quality. LLM-based translation converted Hindi transcripts to English for broader accessibility, while multiple chunking strategies, including fixed sentence chunks with overlap and semantic chunking, ensured optimal information retrieval.

Advanced Indexing and Metadata Management: The system built an indexing pipeline to store rich metadata alongside vector embeddings in Pinecone, including start/end timestamps, file names, batch information, and downloadable S3 links for video/audio/SRT files. This comprehensive metadata preservation enabled precise source attribution and seamless user experience.

Intelligent RAG Query Pipeline: JashDS implemented query translation to Hindi using LLM for multilingual support, integrated OpenAI content moderation to filter irrelevant queries, and developed a multi-index data retrieval system with similarity search, keyword search, and hybrid search capabilities. Advanced reranking using Cohere Rerank, GTE-base, Cross-encoder, and LLM-based options ensured high-quality results, while configurable threshold-based filtering maintained response relevance and quality.

Reliability and Fallback Systems: The solution incorporated LLM fallback mechanisms across OpenAI GPT, Claude, and Gemini models to ensure consistent system availability and performance, guaranteeing spiritual seekers could access guidance regardless of individual model availability.

Technologies Used

AWS S3 - Cloud storage and content managementFFmpeg - Video to audio conversion and media processingAssemblyAI Sentence API - High-quality speech-to-text transcriptionPinecone - Vector database for embeddings and retrievalCohere embed-multilingual-v3.0 - Multilingual embedding generationOpenAI GPT, Claude, Gemini - Language models with fallback architectureCohere Rerank, GTE-base, Cross-encoder - Advanced reranking systemsOpenAI Content Moderation API - Query filtering and content safety

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