AI Research Assistant
An AI research workspace that ingests PDF documents, performs vector semantic search, and provides streaming answers with citations.
LIKELYintermediate4-6 hoursIntermediate
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What it does
AI generators effortlessly build front-end chat workspaces, document libraries, and UI layout components. Custom backend tuning for PDF text parsing, chunk overlap, and vector similarity retrieval requires technical guidance.
LIKELYThe verdict
AI generators effortlessly build front-end chat workspaces, document libraries, and UI layout components. Custom backend tuning for PDF text parsing, chunk overlap, and vector similarity retrieval requires technical guidance.
MVP features
PDF document upload and automated text chunk extraction
Vector embeddings generation and similarity indexing
Interactive chat workspace with streaming AI response output
Inline citations pointing directly to source PDF page numbers
Document summary card generator with key takeaway bullet points
Multi-document side-by-side comparative context chat
Export conversation logs and research notes as Markdown or PDF
Build prompt
build-prompt.txt
Create a full-stack AI Research Assistant web application using React, Node.js, Express, and Tailwind CSS. Implement secure file uploading for PDF and plain text research documents, extracting raw text content for vector embedding generation. Connect to the OpenAI API to generate dense text embeddings and store vector embeddings in Supabase Vector or Pinecone for rapid semantic search retrieval. Build an interactive chat workspace where users can submit complex research questions and receive real-time streaming answers derived directly from uploaded documents, complete with inline source citations and page reference links. Include an automated web search fallback mechanism using Tavily search API whenever uploaded documents lack sufficient context. Add convenient export options allowing users to save conversation logs and synthesized research summaries as downloadable Markdown files or styled PDF reports. Design an intuitive multi-tab interface featuring document library management, prompt engineering selection presets, and real-time LLM token usage tracking statistics.
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Last reviewed: Aug 8, 2026