Getting Started with EngramDB

Installation, setup, and basic usage of EngramDB

Getting Started with EngramDB

This guide will help you get started with EngramDB, covering installation, basic setup, and simple usage examples.

Prerequisites

• Rust 2021 edition (for Rust development)
• Cargo (for building from source)
• Python 3.7+ (for Python bindings)
• Flask (for web interface)

Installation

Rust Library

Clone the repository and build the project:

git clone https://github.com/nkkko/engramdb.git
cd engramdb
cargo build

To run tests:

cargo test

Python Package

Install using pip:

pip install engramdb-py

Or build from source:

cd engramdb/python
pip install maturin
maturin develop

Web Interface

The web interface provides a visual way to interact with the database:

# Quick start with the provided script
cd web
./run_web.sh

# Or manual setup
cd web
python -m venv venv_app
source venv_app/bin/activate
pip install flask==2.3.3 werkzeug==2.3.7 flask-wtf==1.2.1
python app_full.py

Once running, access the web interface at: http://localhost:8082

Basic Usage

Rust

use engramdb::{Database, MemoryNode};
use engramdb::core::AttributeValue;

// Create an in-memory database
let mut db = Database::in_memory();

// Create a memory node
let mut memory = MemoryNode::new(vec![0.1, 0.2, 0.3, 0.4]);
memory.set_attribute("title".to_string(),
    AttributeValue::String("Important information".to_string()));

// Save to database
db.save(&memory)?;

// Search for similar memories
let query_vector = vec![0.15, 0.25, 0.35, 0.45];
let results = db.search_similar(&query_vector, 5, 0.0)?;

// Process results
for (memory_id, similarity) in results {
    let memory = db.load(memory_id)?;
    println!("Memory: {:?}, Similarity: {}", memory, similarity);
}

Python

import engramdb
import numpy as np

# Create an in-memory database
db = engramdb.Database.in_memory()

# Create a memory node with numpy embeddings
embeddings = np.array([0.1, 0.2, 0.3, 0.4], dtype=np.float32)
memory = engramdb.MemoryNode(embeddings)
memory.set_attribute("title", "Important information")
memory.set_attribute("importance", 0.8)

# Save to database
memory_id = db.save(memory)

# Search for similar memories
query_vector = np.array([0.15, 0.25, 0.35, 0.45], dtype=np.float32)
results = db.search_similar(query_vector, limit=5, threshold=0.0)

# Process results
for memory_id, similarity in results:
    memory = db.load(memory_id)
    print(f"Memory: {memory.get_attribute('title')}, Similarity: {similarity:.4f}")

Next Steps

• Explore the Core Concepts to understand the fundamental building blocks of EngramDB
• Check out the API Reference for detailed information on the available APIs
• See the Examples for more complex usage scenarios