Project 3: Text Embedding Generator & Visualizer

Generate embeddings for text samples and visualize them in 2D to understand semantic clustering.

Quick Reference

Attribute Value
Difficulty Level 2: Intermediate
Time Estimate 8-12 hours
Language Python
Prerequisites Embeddings basics, vector math
Key Topics embeddings, similarity, visualization

1. Learning Objectives

By completing this project, you will:

  1. Generate embeddings for text inputs.
  2. Compute similarity between phrases.
  3. Visualize embedding clusters in 2D.
  4. Identify outliers and semantic drift.
  5. Export embeddings for reuse.

2. Theoretical Foundation

2.1 Semantic Spaces

Embeddings map text into vector spaces where distance encodes meaning.

3. Project Specification

3.1 What You Will Build

A tool that creates embeddings for a dataset and visualizes clusters using UMAP or t-SNE.

3.2 Functional Requirements

  1. Embedding generator for text inputs.
  2. Similarity metrics (cosine).
  3. Dimensionality reduction for visualization.
  4. Plot output with labeled points.
  5. Export embeddings and metadata.

3.3 Non-Functional Requirements

  • Reproducible embeddings with fixed model.
  • Clear visualization with labels or hover tooltips.
  • Handle medium datasets (1k points).

4. Solution Architecture

4.1 Components

Component Responsibility
Embedder Generate vectors
Reducer Reduce to 2D
Visualizer Plot clusters
Exporter Save vectors

5. Implementation Guide

5.1 Project Structure

LEARN_LLM_MEMORY/P03-embedding-visualizer/
├── src/
│   ├── embed.py
│   ├── reduce.py
│   ├── plot.py
│   └── export.py

5.2 Implementation Phases

Phase 1: Embeddings (3-4h)

  • Generate embeddings for a sample set.
  • Checkpoint: embeddings stored with metadata.

Phase 2: Visualization (3-5h)

  • Apply UMAP/t-SNE and plot points.
  • Checkpoint: similar texts cluster.

Phase 3: Analysis (2-3h)

  • Measure similarity and identify outliers.
  • Checkpoint: report highlights clusters.

6. Testing Strategy

6.1 Test Categories

Category Purpose Examples
Unit embedding vector shape consistent
Integration reduction output stable
Regression plotting labels match points

6.2 Critical Test Cases

  1. Similar texts have high cosine similarity.
  2. Reduction output has correct shape.
  3. Plot labels match input texts.

7. Common Pitfalls & Debugging

Pitfall Symptom Fix
Poor clusters random scatter normalize data, tune UMAP
Slow reduction long runtime reduce dataset size
Misaligned labels wrong points track indices carefully

8. Extensions & Challenges

Beginner

  • Add category colors.
  • Add CSV import.

Intermediate

  • Add interactive plot (hover tooltips).
  • Add outlier detection.

Advanced

  • Add clustering metrics (silhouette score).
  • Compare multiple embedding models.

9. Real-World Connections

  • Semantic search relies on embedding quality.
  • Memory systems use embeddings for retrieval.

10. Resources

  • Sentence Transformers docs
  • UMAP/t-SNE references

11. Self-Assessment Checklist

  • I can generate embeddings for text.
  • I can visualize semantic clusters.
  • I can interpret similarity metrics.

12. Submission / Completion Criteria

Minimum Completion:

  • Embedding generation + 2D plot

Full Completion:

  • Exportable embeddings
  • Similarity analysis

Excellence:

  • Multi-model comparison
  • Interactive visualization

This guide was generated from project_based_ideas/AI_AGENTS_LLM_RAG/LEARN_LLM_MEMORY.md.