Vector Visualizer
An NLP-based interactive tool to explore word embeddings, solve analogies, and visualize vector relationships in 2D space.
Timeline
2-3 Days
Role
Full Stack Developer
Team
Solo
Status
CompletedTechnology Stack
Key Challenges
- Vector Arithmetic Accuracy
- Efficient Similarity Search
- 2D Projection of High-Dimensional Data
- Caching Optimization
- Real-time Response Handling
Key Learnings
- Word Embeddings & Vector Math
- Semantic Relationships in NLP
- FastAPI Optimization
- Frontend Visualization Techniques
- Caching with Redis
Overview
Vector Visualizer is an interactive NLP tool that allows users to explore relationships between words using vector embeddings. It supports analogy solving (e.g., king - man + woman ≈ queen) and visualizes word vectors in a 2D space for intuitive understanding.
The backend is built with FastAPI and leverages pre-trained embedding models along with Redis caching for fast responses. The frontend, built with Next.js, provides a clean interface for interacting with vector operations in real time.
Key Features
Core Functionalities
- Word Analogy Solver: Solve semantic analogies using vector arithmetic
- Expression Parsing: Supports inputs like
king - man + woman - 2D Vector Visualization: Plot word relationships in a visual space
- Word Existence Check: Validate if a word exists in the vocabulary
- Real-time Processing: Fast responses using optimized backend + caching
- Interactive UI: Easy-to-use interface for experimentation
Core Concept
At the heart of the system is vector arithmetic on word embeddings:
This allows the model to capture semantic relationships and return the closest matching word.
API Endpoints
Core Endpoints
# Health check
GET / - API status
# Solve analogy (vector-based)
POST /analogy - Input positive & negative word lists
# Solve analogy via text expression
POST /analogy/text - Input expression like "king - man + woman"
# Check if word exists
GET /exists?word=xyz
# Visualize vectors
POST /visualize - Plot words in 2D space