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ResearchBase

  • 5 Devlogs
  • 4 Total hours

ResearchBase, the better version of you trying to craft a perfect prompt

Ship #1 Changes requested

Problem Being Solved:
Researchers and students waste hours manually searching through multiple PDF papers to find specific information, cross-reference concepts, and synthesize knowledge across documents.

Quality-of-Life Improvements:
Instant Multi-Document Search - Upload multiple research papers and ask questions in natural language to get instant answers instead of manually reading through hundreds of pages.

Semantic Understanding - RAG pipeline with vector embeddings understands context and meaning, not just keywords, finding relevant information even when different terminology is used.

Chat-Based Interface - Simple conversation-style interaction eliminates the need to learn complex search syntax or navigate through document hierarchies.

Local Desktop App - Works offline with your documents staying private on your machine, no cloud uploads required for sensitive research.

Automatic Document Processing - Drag-and-drop PDFs and the app automatically extracts text, chunks intelligently, generates embeddings, and indexes everything for instant retrieval.

Technology Stack:
Electron + React + TypeScript for cross-platform desktop app
RAG (Retrieval-Augmented Generation) pipeline with HuggingFace embeddings
Vector similarity search with cosine similarity
LLM integration for natural language answer

Try project → See source code →
Open comments for this post

29m 39s logged

Learn along with me everyone!

I am almost done with the RAG pipeline for ResearchBase.
For ResearchBase to use the AI model, I needed the LLM to be hosted in the cloud as my GPU can’t handle ts shi😭.

But also I learnt really how a RAG pipeline is built and what are all the processes behind it. This is truly project based learning at its peak.

For those who don’t know what a RAG pipeline is

A RAG pipeline is an alternative method some applications use to have AI responses.

Usually, if you use hugging face, transformers and models are installed locally in the CPU. But if you start using it, it will fry your GPU if it’s not good enough.

So alternatively, most applications use a RAG pipeline with an API key to access a model that runs on a server.

Then with a little bit of data indexing, converting that data into vector db*, chunking the top K results, feeding those top K results into the LLM for context, and a bit of prompt engineering, you get a fully fledged response from the AI.

Now you are caught up with me!

The more you know.

*Vector DB: A vector database is a specialized database that stores and indexes data as high-dimensional vectors (arrays of numbers). Instead of using exact keyword matches, it allows AI applications to search for information based on semantic meaning, context, and similarity.

Check out more info on RAG pipelines from Nvidia

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Open comments for this post

1h 50m 10s logged

I have been working on this RAG pipeline for so long.

Just wanted to experience how a normal dev was in the BC era (before chat 😭 😭 ). I don’t know why the post is saying that I only have a 1h 50m logged even though on hacktime it says 2h 26m, but I am more than ready to ship this and just get started on another project omg..

I geniunely don’t know if this would run, I haven’t been vibecoding for the past 30 mins, js only using stackoverflow. This is prolly not going to work but if this does, I am the literal goat.

Follow me for more posts about my sanity 😭
Good Night Everyone!

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Open comments for this post

36m 6s logged

I am trying to get a research internship at universities, but I am not smart enough to understand the research papers over topics like biology and CS 😭 😭 😭

The main psychology behind landing a research internship from cold emailing a professor is to mention their work. Boost their pride by showing you know and have read their work.

But the hardest part is just that… I can’t understand the weird math symbols nor some of the concepts 😭

That’s why I am creating ResearchBase, an application that uses a RAG pipeline + highly specific prompt engineering so that you don’t have to go to Gemini or Chat and have multiple attempts in trying to get the answer you want.

UPDATES===========================

  • Created an Electron JS application
  • Have an upload feature that only takes in PDFs

Follow along for more updates!

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