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Getting Started with EngramDB

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

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Prerequisites

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

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Installation

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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

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Python Package

Install using pip: 
pip install engramdb-py
Or build from source: 
cd engramdb/python
pip install maturin
maturin develop

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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

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Basic Usage

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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);
}

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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}")

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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