Semantic Book Search with Go, pgvector, and Gemini API

A Golang-based API for semantic search over a book dataset using vector embeddings. Books are embedded using the Gemini API and stored in PostgreSQL with pgvector, enabling fast, meaningful similarity search via approximate nearest neighbor indexing (IVFFLAT). Built with Echo, sqlc, go-migrate, and pgx.

C4Context
  title System Context Diagram for Book Semantic Search
  Enterprise_Boundary(b0, "Book Search Platform") {
    Person(user, "Book Searcher", "User searching for books")
    System(api, "Semantic Search API", "Go service for searching books via embeddings and semantic queries")
    SystemDb(db, "PostgreSQL + pgvector", "Stores books and vector embeddings")
    System_Ext(gemini, "Gemini API", "External API for generating text embeddings")

    Rel(user, api, "Uses", "HTTP\n/Books (POST)\n/Search (GET)")
    Rel(api, db, "Reads & writes", "pgx (SQL)")
    Rel(api, gemini, "Requests text embeddings", "Embed API")
    Rel(gemini, api, "Returns embeddings", "Embedding Response")
    Rel(db, api, "Returns Books/Search Results")
  }
  UpdateRelStyle(user, api, $offsetY="-40", $offsetX="30")
  UpdateRelStyle(api, db, $offsetY="50", $offsetX="-35")
  UpdateRelStyle(api, gemini, $offsetY="-30", $offsetX="-40")
  UpdateRelStyle(gemini, api, $offsetY="0", $offsetX="80")
  UpdateRelStyle(db, api, $offsetY="40", $offsetX="50")
  UpdateLayoutConfig($c4ShapeInRow="3", $c4BoundaryInRow="1")

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Caution

This project is intended for learning and demonstration purposes only. While it tries to follow best and security practices, it may contain errors or incomplete implementations.

Features

• Semantic Search — Search books by semantic similarity using vector embeddings
• Gemini API Integration — Generates high-quality embeddings via Google's Gemini API
• PostgreSQL + pgvector — Efficient storage and approximate nearest neighbor search
• Redis-powered Cache — Speeds up repeated search queries with vector caching
• Run Migrations via CLI — Run -migrate to apply database schema changes at startup
• Multi-Platform Support — Build and release for Linux, macOS, Windows, amd64, and arm64
• Docker & GitHub Container Registry — Easy deployment with multi-arch Docker images
• Automated Releases — GitHub Actions + GoReleaser for continuous delivery

Getting Started

Prerequisites

• Go 1.24+
• PostgreSQL with pgvector extension installed
• Gemini API Key (Get API Key Here)
• Redis (for vector caching)
• Docker (optional, for containerized deployment)

API Endpoints

• POST /books — Add a book with title and description; stores embedding in DB
• GET /search?q=your+query — Search books semantically by query text
• GET /ping — Health check endpoint

Setup PostgreSQL

1. Create your database:

Run migrations:

make migrate-up

Or via the binary:

semantic-search-api -migrate=true

Environment Variables

DATABASE_URL=postgres://user:password@localhost:5432/semantic_search?sslmode=disable
GEMINI_API_KEY=your_gemini_api_key_here
REDIS_URL=localhost:6379

Running Locally

go run ./cmd/main.go

API will be available at http://localhost:8080.

Docker

Run Database migrations:

docker run --rm \
  --network=host \
  ghcr.io/nmdra/semantic-search:latest \
  -apikey="$GEMINI_API_KEY" \
  -db-dsn="$DATABASE_URL" \
  -migrate

Run container:

docker run --rm \
  --network=host \
  ghcr.io/nmdra/semantic-search:latest \
  -apikey="$GEMINI_API_KEY" \
  -db-dsn="$DATABASE_URL" \
  -redis="localhost:6379" \
  -loglevel="warn"