Overview

AI Search with LLM Embeddings is a prototype application that showcases the power of semantic search using vector embeddings. It enables users to search job descriptions using natural language queries, demonstrating how AI-powered search can understand intent and context rather than just matching keywords.

Video Tutorials

Quick Introduction

Watch this short introduction to get a quick overview of the AI search capabilities and see the demo in action.

Deep Dive Tutorial

This comprehensive tutorial walks through the entire implementation, explaining the concepts behind vector embeddings, semantic search, and how to build similar applications.

Features

• Semantic Search: Find relevant job descriptions based on meaning, not just keywords
• Natural Language Queries: Search using conversational descriptions of ideal candidates
• Vector Embeddings: Utilizes OpenAI embeddings for semantic understanding
• PostgreSQL Integration: Leverages pgvector extension for efficient vector similarity search
• Django Backend: Built on Django for robust web application framework
• Real-time Results: Fast search results with similarity scoring
• Scalable Architecture: Designed to handle large datasets efficiently

How It Works

1. Data Ingestion

Job descriptions are processed and converted into vector embeddings using OpenAI’s embedding models. These high-dimensional vectors capture the semantic meaning of the text.

2. Storage

Embeddings are stored in PostgreSQL using the pgvector extension, which provides efficient indexing and similarity search capabilities for vector data.

3. Query Processing

When a user enters a search query:

• The query is converted to an embedding vector
• pgvector performs a similarity search against stored embeddings
• Results are ranked by semantic similarity

4. Results

The most semantically similar job descriptions are returned, even if they don’t contain the exact keywords from the query.

Technical Architecture

┌─────────────┐     ┌─────────────┐     ┌──────────────┐
│   Frontend  │────▶│   Django    │────▶│  PostgreSQL  │
│    (Web)    │     │   Backend   │     │  + pgvector  │
└─────────────┘     └─────────────┘     └──────────────┘
                            │
                            ▼
                    ┌─────────────┐
                    │  OpenAI API │
                    │ (Embeddings)│
                    └─────────────┘

Contributing

This is a demonstration project designed for learning. We welcome contributions that:

• Improve documentation and tutorials
• Add new search features
• Optimize performance
• Extend to new use cases

Visit our GitHub repository to explore the code and contribute!