How Ekantik Vartalap Archive Works
यह प्रोजेक्ट तकनीकी रूप से कैसे काम करता है?
Learn how this project uses semantic search to help you find relevant questions and moments from Maharaj Ji's original videos.
It does not generate spiritual answers using an AI chatbot. Instead, it searches existing questions and connects them with the corresponding YouTube videos and timestamps.
How Search Works
सर्च कैसे काम करता है?
You ask a question
You enter a question in Hindi, English, or Hinglish.
Your question is converted into an embedding
A Python background worker uses the embedding model to convert your question into a numerical representation of its meaning. (Currently it is being run on my laptop.)
Similar questions are found
The embedding is compared with embeddings of existing questions stored in the database.
Relevant video moments are matched
The most relevant questions are connected to their corresponding videos and timestamps.
Results are shown to you
The relevant YouTube video moments are returned with their timestamps so you can directly watch the original content.
Technology Stack
तकनीकी संरचना
Next.js
Full-Stack Framework
Next.js is used as the full-stack framework for the project. It handles both the frontend website and the backend APIs.
PostgreSQL + pg_vector
Database & Semantic Search
PostgreSQL is used as the database, with the pg_vector extension enabled to store and search embeddings.
Python Background Worker
Embedding & Matching
A Python background worker creates embeddings for user queries, finds similar questions, and stores the search results in the database.
Ingestion Pipeline
Preparing the Search Data
An ingestion pipeline processes the existing questions, creates their embeddings, and stores them in the database for future searches.
How Questions Are Added
प्रश्नों को कैसे तैयार किया जाता है?
Before users search, the existing questions need to be prepared for semantic search. This is done using an ingestion pipeline.
Collect questions from the available Ekantik Vartalap content.
Process and prepare the questions.
Create embeddings for the questions.
Store the embeddings in PostgreSQL.
Use these embeddings for future searches.
Python Background Worker
बैकग्राउंड प्रोसेसिंग
The heavier embedding and matching work is handled by a Python background worker. This keeps the web application separate from the processing work.
Create embeddings for user queries
Find similar existing questions
Match questions with relevant content
Store search results in PostgreSQL
Embedding Model
एम्बेडिंग मॉडल
Model
bhasha-embed-v0
The project currently uses bhasha-embed-v0 to create embeddings for questions and search queries.
Created by
Akshita SukhlechaA special thanks to the creator for making this embedding model available.
Simple Architecture
प्रोजेक्ट की संरचना
Why It Works This Way
इसका उद्देश्य
The goal of this project is simple: help people find original teachings that are relevant to their questions.
The website does not try to create new spiritual answers. Instead, it uses semantic search to help people discover relevant moments from the original videos.