PG Vector Embedding and Query Rust

This repository contains a Rust-based system for managing vector embeddings and querying them using a PostgreSQL-backed vector database. The system is designed to handle embedding generation, storage, and querying.

Table of Contents

• Overview
• Features
• Directory Structure
• Getting Started
  • Prerequisites
  • Installation
  • Running the Application
• Usage
  • Commands
  • Configuration
  • Embedding and Querying
• Testing
• Contributing
• License

Overview

The system is composed of several modules that handle different aspects of the embedding and querying process:

• Commands: Handles command-line arguments and subcommands.
• Config: Manages configuration settings for embedding requests and database connections.
• Constants: Provides constant values used throughout the application.
• Embedding: Contains logic for generating embeddings and persisting them to the database.
• VectorDB: Handles interactions with the PostgreSQL database for storing and querying vector embeddings.

Features

• Embedding Generation: Generate vector embeddings from input data.
• Database Persistence: Store embeddings in a PostgreSQL database.
• Querying: Query the database to find nearest neighbors based on vector embeddings.

Directory Structure

src/
├── app/
│   ├── commands.rs
│   ├── config.rs
│   └── constants.rs
├── embedding/
│   ├── run_embedding.rs
│   └── vector_embedding.rs
├── main.rs
├── tests/
│   ├── setup_docker.rs
│   ├── test_pgclient.rs
│   ├── test_query_vector.rs
│   ├── test_run_embedding.rs
│   └── test_vector_embedding.rs
├── vectordb/
│   ├── pg_vector.rs
│   └── query_vector.rs
├── lib.rs
├── vectordb/mod.rs
├── tests/mod.rs
├── embedding/mod.rs
└── app/mod.rs

Getting Started

Prerequisites

• Rust (latest stable version)
• PostgreSQL Vector Db.
• Docker (for running tests)
• Active Ollama Service with nomic-embed-text or similar model.

Installation

1. Clone the repository:

   git clone https://github.com/rupeshtr78/pg-vector-embed-rust.git
   cd pg-vector-embed-rust

2. Install dependencies:

   cargo build

Running the Application

1. Start the PostgreSQL vector database (if not already running).

2. Ollama service should be running with the specified model.

3. Run the application:

   cargo run
      
   

Usage

Commands

The application supports various commands and subcommands. Use the --help flag to see available options:

   cargo run -- --help
   cargo run -- write --input "dog sound is called bark" --input "cat sounds is called purr" --model "nomic-embed-text" --table "from_rust2" --dim 768 --log-level "debug"
   cargo run -- query --input "who is barking" --model "nomic-embed-text" --table "from_rust2"  

Configuration

Configuration settings for embedding requests and database connections are managed in src/app/config.rs. You can modify these settings as needed.

Embedding and Querying

• Generate Embeddings: Use the run_embedding function to generate embeddings and persist them to the database.
• Query Embeddings: Use the run_query function to query the database for nearest neighbors based on vector embeddings.

Testing

The test suite requires postgres vectordb and ollama with embedding model to be running in the correct configuration.

cargo test

Contributing

Contributions are welcome! Please read the CONTRIBUTING.md file for details on how to contribute to this project.

License

This project is licensed under the MIT License - see the LICENSE file for details.

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