Vector-Store

Vector-Store

A lightweight, disk-persistent vector storage and search service using KD-Trees. Built with Rust and Actix-web, providing efficient nearest neighbor search capabilities with automatic memory management.

Features

• 🚀 Fast nearest neighbor search using KD-Trees
• 💾 Disk persistence for large datasets
• 🔄 LRU-based memory management
• 🌲 Multiple independent trees support
• 🔌 RESTful API interface
• ⚙️ Configurable memory limits

Configuration

Create a .env file in the project root:

HOST=127.0.0.1
PORT=8080
MAX_MEMORY_MB=1024
BIN_DIRECTORY=bin

API Reference

Insert Vector

Adds a vector to a specified tree.

POST /insert?tree_name={tree_name}
Content-Type: application/json

# Request Body: Array of numbers representing the vector
[0.5, 0.3, 0.8]

# Response: 200 OK
"Point inserted into KD-Tree and saved to disk"

Find Nearest Neighbors

Finds the n-nearest neighbors for a given vector.

POST /nearesttop?tree_name={tree_name}&n={number_of_neighbors}
Content-Type: application/json

# Request Body: Array of numbers representing the query vector
[0.5, 0.3, 0.8]

# Response: 200 OK
[
  [0.51, 0.31, 0.79],
  [0.49, 0.32, 0.81]
]

Get Status

Retrieves the current status of all trees.

GET /status

# Response: 200 OK
{
  "active_trees": 1,
  "trees": [
    {
      "tree_name": "example_tree",
      "num_records": 1000,
      "in_memory": true,
      "last_accessed": 60
    }
  ]
}

Error Codes

• 200: Success
• 400: Invalid request
• 404: Tree/points not found
• 500: Internal server error