> ## Documentation Index
> Fetch the complete documentation index at: https://engramdb.mintlify.site/llms.txt
> Use this file to discover all available pages before exploring further.

# 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:

```bash theme={null}
git clone https://github.com/nkkko/engramdb.git
cd engramdb
cargo build
```

To run tests:

```bash theme={null}
cargo test
```

### Python Package

Install using pip:

```bash theme={null}
pip install engramdb-py
```

Or build from source:

```bash theme={null}
cd engramdb/python
pip install maturin
maturin develop
```

### Web Interface

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

```bash theme={null}
# 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](http://localhost:8082)

## Basic Usage

### Rust

```rust theme={null}
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

```python theme={null}
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](/core-concepts) to understand the fundamental building blocks of EngramDB
* Check out the [API Reference](/api-reference/memory-node) for detailed information on the available APIs
* See the [Examples](/examples/rust-examples) for more complex usage scenarios