### Quick Start Benchmark Setup (Bash)

Source: https://github.com/cyberlife-coder/velesdb/blob/main/benchmarks/README.md

A quick setup guide to run the Docker-based benchmark. It includes starting the services, installing Python dependencies, and initiating the benchmark script. This is the recommended starting point for users wanting to quickly test the comparison.

```bash
# 1. Start both servers (Docker required)
docker-compose up -d --build

# 2. Install dependencies
pip install -r requirements.txt

# 3. Run fair Docker benchmark
python benchmark_docker.py --vectors 5000 --clusters 25
```

--------------------------------

### Quick Start: Project Setup and Development

Source: https://github.com/cyberlife-coder/velesdb/blob/main/demos/tauri-rag-app/README.md

Clones the example project, installs frontend dependencies using npm, and starts the development server with `cargo tauri dev`. This allows for rapid iteration during development.

```bash
# Clone this example
cd demos/tauri-rag-app

# Install frontend dependencies
npm install

# Run in development
cargo tauri dev
```

--------------------------------

### Install VelesDB using Cargo

Source: https://github.com/cyberlife-coder/velesdb/blob/main/docs/getting-started.md

These commands show how to install the VelesDB server using Cargo, either from crates.io or by building from source. It includes installing from crates.io and building from a git repository.

```bash
# Install from crates.io
cargo install velesdb-server

# Or build from source
git clone https://github.com/cyberlife-coder/VelesDB.git
cd velesdb
cargo build --release
./target/release/velesdb-server
```

--------------------------------

### Configure VelesDB Server with Command Line Options

Source: https://github.com/cyberlife-coder/velesdb/blob/main/docs/guides/INSTALLATION.md

Shows the available command-line options for starting the VelesDB server, including data directory, host, and port configuration. Also lists corresponding environment variables.

```bash
velesdb-server [OPTIONS]

Options:
  -d, --data-dir <PATH>   Data directory [default: ./data]
  -h, --host <HOST>       Host address [default: 0.0.0.0]
  -p, --port <PORT>       Port number [default: 8080]

Environment variables:
- VELESDB_DATA_DIR - Data directory path
- VELESDB_HOST - Bind address
- VELESDB_PORT - Port number
- RUST_LOG - Logging level (debug, info, warn, error)
```

--------------------------------

### Quick Start: Index Documents with VelesDB

Source: https://github.com/cyberlife-coder/velesdb/blob/main/integrations/llamaindex/README.md

Demonstrates how to initialize VelesDB, load documents from a directory, and create a LlamaIndex VectorStoreIndex. This example shows the basic setup for indexing and querying.

```python
from llama_index.core import VectorStoreIndex, SimpleDirectoryReader
from llamaindex_velesdb import VelesDBVectorStore

# Create vector store
vector_store = VelesDBVectorStore(
    path="./velesdb_data",
    collection_name="my_docs",
    metric="cosine",
)

# Load and index documents
documents = SimpleDirectoryReader("./data").load_data()
index = VectorStoreIndex.from_documents(
    documents,
    vector_store=vector_store,
)

# Query
query_engine = index.as_query_engine()
response = query_engine.query("What is VelesDB?")
print(response)
```

--------------------------------

### Kotlin Quick Start for VelesDB on Android

Source: https://github.com/cyberlife-coder/velesdb/blob/main/crates/velesdb-mobile/README.md

Provides a quick start guide for using VelesDB on Android with Kotlin. This example shows database opening, collection creation, vector insertion, and similarity search operations. Asynchronous operations like search should be handled using coroutines (e.g., Dispatchers.IO).

```kotlin
import com.velesdb.mobile.*

// Open database
val db = VelesDatabase.open("${context.filesDir}/velesdb")

// Create collection
db.createCollection("documents", 384u, DistanceMetric.COSINE)

// Get collection
val collection = db.getCollection("documents") 
    ?: throw Exception("Collection not found")

// Insert vectors
val point = VelesPoint(
    id = 1uL,
    vector = embedding,  // List<Float> from your embedding model
    payload = """{"title": "Hello World"}"""
)
collection.upsert(point)

// Search (use Dispatchers.IO for async)
val results = withContext(Dispatchers.IO) {
    collection.search(queryEmbedding, 10u)
}
results.forEach { result ->
    println("ID: ${result.id}, Score: ${result.score}")
}
```

--------------------------------

### Use VelesDB in Swift (iOS)

Source: https://github.com/cyberlife-coder/velesdb/blob/main/docs/guides/INSTALLATION.md

Example of how to use the VelesDB Swift bindings to open a database, create a collection, and perform vector searches. Assumes VelesDB Swift bindings are generated and imported.

```swift
import VelesDB

let db = try VelesDatabase.open(path: documentsPath + "/velesdb")
try db.createCollection(name: "docs", dimension: 384, metric: .cosine)

let collection = try db.getCollection(name: "docs")!
let results = try collection.search(vector: embedding, limit: 10)
```

--------------------------------

### Install VelesDB using One-liner Script (Linux/macOS)

Source: https://github.com/cyberlife-coder/velesdb/blob/main/README.md

Installs VelesDB on Linux or macOS by downloading and executing an installation script from a GitHub raw URL. This is a convenient way to get VelesDB up and running quickly.

```bash
curl -fsSL https://raw.githubusercontent.com/cyberlife-coder/VelesDB/main/scripts/install.sh | bash
```

--------------------------------

### Install VelesDB using Docker

Source: https://github.com/cyberlife-coder/velesdb/blob/main/docs/getting-started.md

This command runs VelesDB as a Docker container. It maps port 8080 and mounts a volume for data persistence. This is the recommended installation method for ease of use.

```bash
docker run -d \
  --name velesdb \
  -p 8080:8080 \
  -v velesdb_data:/data \
  langju/velesdb:latest
```

--------------------------------

### Install VelesDB using One-liner Script (Windows PowerShell)

Source: https://github.com/cyberlife-coder/velesdb/blob/main/README.md

Installs VelesDB on Windows using PowerShell by downloading and executing an installation script. This method provides a quick setup for Windows users.

```powershell
irm https://raw.githubusercontent.com/cyberlife-coder/VelesDB/main/scripts/install.ps1 | iex
```

--------------------------------

### Use VelesDB in Kotlin (Android)

Source: https://github.com/cyberlife-coder/velesdb/blob/main/docs/guides/INSTALLATION.md

Example of how to use the VelesDB Kotlin bindings to open a database, create a collection, and perform vector searches. Assumes VelesDB Kotlin bindings are generated and available.

```kotlin
val db = VelesDatabase.open("${context.filesDir}/velesdb")
db.createCollection("docs", 384u, DistanceMetric.COSINE)

val collection = db.getCollection("docs")!!
val results = collection.search(embedding, 10u)
```

--------------------------------

### Start Local Server with Node.js

Source: https://github.com/cyberlife-coder/velesdb/blob/main/examples/wasm-browser-demo/README.md

Starts a local HTTP server using the 'serve' package, commonly used with Node.js. This command serves the current directory, making the VelesDB WASM demo accessible locally. It requires Node.js and the 'serve' package to be installed.

```bash
npx serve .
```

--------------------------------

### Install VelesDB on Linux using One-liner Script

Source: https://github.com/cyberlife-coder/velesdb/blob/main/docs/guides/INSTALLATION.md

Installs VelesDB on Linux using a convenient one-liner script. This script downloads and executes the installation process.

```bash
curl -fsSL https://raw.githubusercontent.com/cyberlife-coder/VelesDB/main/scripts/install.sh | bash
```

--------------------------------

### Start API Server (Bash)

Source: https://github.com/cyberlife-coder/velesdb/blob/main/demos/rag-pdf-demo/README.md

Starts the FastAPI application server for the demo. It reloads automatically on code changes and runs on port 8000. This command is typically run after installing dependencies.

```bash
uvicorn src.main:app --reload --port 8000
```

--------------------------------

### Install VelesDB SDKs for Browser

Source: https://github.com/cyberlife-coder/velesdb/blob/main/docs/guides/INSTALLATION.md

Installs the necessary packages for using VelesDB in a browser environment. This includes the WASM module, the full TypeScript SDK, and Tauri plugin bindings.

```bash
# WASM module for browser
npm install @wiscale/velesdb-wasm

# Full TypeScript SDK
npm install @wiscale/velesdb-sdk

# Tauri plugin bindings
npm install @wiscale/tauri-plugin-velesdb
```

--------------------------------

### Start VelesDB Server (Bash)

Source: https://github.com/cyberlife-coder/velesdb/blob/main/demos/rag-pdf-demo/README.md

Command to start the VelesDB server. It requires a data directory for storage. Ensure VelesDB is installed and accessible in your PATH.

```bash
velesdb-server --data-dir ./rag-data
```

--------------------------------

### Install and Use VelesDB

Source: https://github.com/cyberlife-coder/velesdb/blob/main/crates/velesdb-migrate/README.md

Provides commands to install VelesDB using Cargo, migrate data from a source like Qdrant, query data using VelesQL, and start the VelesDB REST API server.

```bash
cargo install velesdb
velesdb-migrate detect --source qdrant --url http://localhost:6333 --collection my_data
velesdb-migrate run --config migration.yaml
velesdb query "SELECT id, title FROM my_data ORDER BY vector <-> [0.1, 0.2, ...] LIMIT 10"
velesdb serve --port 8080
```

--------------------------------

### Start Docker Compose and Run Benchmark (Bash)

Source: https://github.com/cyberlife-coder/velesdb/blob/main/benchmarks/README.md

This command sequence starts the VelesDB and pgvector services using Docker Compose and then executes a benchmark script. It's useful for setting up a fair client-server comparison environment. Ensure Docker and Docker Compose are installed and the necessary images are built.

```bash
docker-compose up -d --build
python benchmark_docker.py --vectors 5000 --clusters 25
```

--------------------------------

### Set up Rust Targets for Mobile Development

Source: https://github.com/cyberlife-coder/velesdb/blob/main/docs/guides/INSTALLATION.md

Configures the Rust toolchain with necessary targets for building VelesDB mobile applications for both iOS and Android platforms. Includes device, simulator, and various Android architectures.

```bash
# iOS targets
rustup target add aarch64-apple-ios        # Device
rustup target add aarch64-apple-ios-sim    # Simulator (ARM)
rustup target add x86_64-apple-ios         # Simulator (Intel)

# Android targets
rustup target add aarch64-linux-android    # ARM64
rustup target add armv7-linux-androideabi  # ARMv7
rustup target add x86_64-linux-android     # x86_64

# Android NDK tool
cargo install cargo-ndk
```

--------------------------------

### Quick Start with VelesDB and Langchain

Source: https://github.com/cyberlife-coder/velesdb/blob/main/integrations/langchain/README.md

Initialize a VelesDB vector store, add text documents, and perform a similarity search. This example demonstrates the basic workflow for using VelesDB with Langchain for semantic search.

```python
from langchain_velesdb import VelesDBVectorStore
from langchain_openai import OpenAIEmbeddings

# Initialize vector store
vectorstore = VelesDBVectorStore(
    path="./my_vectors",
    collection_name="documents",
    embedding=OpenAIEmbeddings()
)

# Add documents
vectorstore.add_texts([
    "VelesDB is a high-performance vector database",
    "Built entirely in Rust for speed and safety",
    "Perfect for RAG applications and semantic search"
])

# Search
results = vectorstore.similarity_search("fast database", k=2)
for doc in results:
    print(doc.page_content)
```

--------------------------------

### VelesDB Migration CLI: Command Examples

Source: https://github.com/cyberlife-coder/velesdb/blob/main/crates/velesdb-migrate/README.md

This provides practical examples of using the VelesDB migration CLI. It demonstrates how to generate configuration files for various source types (Supabase, Qdrant, ChromaDB, etc.), check source schemas, perform dry runs before actual migration, execute full migrations with verbose logging, and override the batch size.

```bash
# Generate config for each source type
velesdb-migrate init --source supabase --output supabase.yaml
velesdb-migrate init --source pgvector --output pgvector.yaml
velesdb-migrate init --source qdrant --output qdrant.yaml
velesdb-migrate init --source pinecone --output pinecone.yaml
velesdb-migrate init --source weaviate --output weaviate.yaml
velesdb-migrate init --source milvus --output milvus.yaml
velesdb-migrate init --source chromadb --output chromadb.yaml

# Check source schema before migration
velesdb-migrate schema --config migration.yaml

# Dry run (recommended before actual migration)
velesdb-migrate run --config migration.yaml --dry-run

# Full migration with verbose output
velesdb-migrate run --config migration.yaml --verbose

# Override batch size for testing
velesdb-migrate run --config migration.yaml --batch-size 100
```

--------------------------------

### Run Mini Recommender System (Rust)

Source: https://github.com/cyberlife-coder/velesdb/blob/main/examples/README.md

A complete product recommendation system implemented in Rust. This example covers collection creation, product ingestion, similarity search, filtered recommendations, VelesQL query parsing, and catalog analytics.

```bash
cd examples/mini_recommender
cargo run
```

--------------------------------

### Install VelesDB Python Package

Source: https://github.com/cyberlife-coder/velesdb/blob/main/crates/velesdb-python/README.md

Installs the velesdb Python package using pip. This is the primary method for getting started with the VelesDB Python bindings.

```bash
pip install velesdb
```

--------------------------------

### Start VelesDB Server with Custom Options

Source: https://github.com/cyberlife-coder/velesdb/blob/main/crates/velesdb-server/README.md

Demonstrates how to start the VelesDB server with a custom port and data directory, and how to enable logging. These options allow for flexible configuration of the server's runtime environment.

```bash
# Start server on default port 8080
velesdb-server

# Custom port and data directory
velesdb-server --port 9000 --data ./my_vectors

# With logging
RUST_LOG=info velesdb-server
```

--------------------------------

### Run Multi-model Search Example in Rust

Source: https://github.com/cyberlife-coder/velesdb/blob/main/README.md

Navigates to the Rust examples directory and runs the multi-model search example. This demonstrates VelesDB's capabilities within a Rust environment.

```bash
cd examples/rust && cargo run
```

--------------------------------

### Verify VelesDB Installation

Source: https://github.com/cyberlife-coder/velesdb/blob/main/docs/getting-started.md

This command uses curl to check if the VelesDB server is running by sending a request to the /health endpoint. A successful response indicates the server is operational.

```bash
curl http://localhost:8080/health
```

--------------------------------

### VelesDB Development Setup and Testing in Bash

Source: https://github.com/cyberlife-coder/velesdb/blob/main/README.md

Provides bash commands for setting up the VelesDB development environment. This includes cloning the repository, running tests, and performing code formatting and linting checks to ensure code quality before committing.

```bash
# Clone the repo
git clone https://github.com/cyberlife-coder/VelesDB.git
cd VelesDB

# Run tests
cargo test --all-features

# Run with checks (before committing)
cargo fmt --all
cargo clippy --all-targets --all-features -- -D warnings
```

--------------------------------

### Start VelesDB Server and CLI

Source: https://github.com/cyberlife-coder/velesdb/blob/main/README.md

Provides commands to start the VelesDB REST API server and interact with the VelesQL REPL. It also shows how to verify the server's health status.

```bash
# Start the REST API server (data persisted in ./data)
velesdb-server --data-dir ./my_data

# Or use the interactive CLI with VelesQL REPL
velesdb repl

# Verify server is running
curl http://localhost:8080/health
# {"status":"healthy","version":"1.1.0"}
```

--------------------------------

### GitHub Actions configuration for nightly fuzzing

Source: https://github.com/cyberlife-coder/velesdb/blob/main/docs/FUZZING.md

An example GitHub Actions workflow that runs VelesDB fuzzers nightly. It installs cargo-fuzz, runs the VelesQL parser fuzzer for 5 minutes, and uploads any found crashes.

```yaml
fuzz:
  name: Fuzzing (Nightly)
  runs-on: ubuntu-latest
  if: github.event_name == 'schedule'
  steps:
    - uses: actions/checkout@v4
    
    - name: Install cargo-fuzz
      run: cargo install cargo-fuzz
    
    - name: Run VelesQL parser fuzzer
      run: |
        cd fuzz
        cargo +nightly fuzz run fuzz_velesql_parser -- -max_total_time=300
    
    - name: Upload crashes
      if: failure()
      uses: actions/upload-artifact@v4
      with:
        name: fuzz-crashes
        path: fuzz/artifacts/
```

--------------------------------

### Install Frontend Dependencies and Tailwind CSS

Source: https://github.com/cyberlife-coder/velesdb/blob/main/docs/tutorials/tauri-rag-app/README.md

Sets up the React frontend by installing core dependencies, icon library (lucide-react), and configuring Tailwind CSS for styling. This involves installing packages and initializing Tailwind.

```bash
npm install
npm install lucide-react

npm install -D tailwindcss postcss autoprefixer
npx tailwindcss init -p
```

--------------------------------

### Resolve 'Port already in use' Error for VelesDB

Source: https://github.com/cyberlife-coder/velesdb/blob/main/docs/guides/INSTALLATION.md

Offers solutions for the 'Port already in use' error when starting VelesDB. Includes options to use a different port or find and kill the process occupying the port.

```bash
# Use different port
velesdb-server --port 8081

# Or find and kill existing process
lsof -i :8080
kill <PID>
```

--------------------------------

### Installing VelesDB CLI (Rust)

Source: https://github.com/cyberlife-coder/velesdb/blob/main/README.md

This command shows how to install the VelesDB command-line interface (CLI) tool. The CLI provides an interactive REPL for executing VelesQL queries, allowing for direct interaction with the database.

```bash
cargo install velesdb-cli
```

--------------------------------

### Run E-commerce Recommendation Demo (Rust)

Source: https://github.com/cyberlife-coder/velesdb/blob/main/examples/README.md

This example demonstrates VelesDB's combined Vector, Graph, and MultiColumn capabilities with a large e-commerce dataset. It involves setting up a demo with 5,000 products, graph edges, simulated users, and showcases four query types: Vector, Filtered, Graph, and Combined.

```bash
cd examples/ecommerce_recommendation
cargo run --release
```

--------------------------------

### Initialize Tauri Application with Plugins and Commands

Source: https://github.com/cyberlife-coder/velesdb/blob/main/docs/tutorials/tauri-rag-app/README.md

Sets up the main Rust entry point for the Tauri application. It initializes the shell and VelesDB plugins and registers command handlers for ingestion, search, stats, and index clearing.

```rust
#![cfg_attr(not(debug_assertions), windows_subsystem = "windows")]

mod commands;

fn main() {
    tauri::Builder::default()
        .plugin(tauri_plugin_shell::init())
        .plugin(tauri_plugin_velesdb::init())
        .invoke_handler(tauri::generate_handler![
            commands::ingest_text,
            commands::search,
            commands::get_stats,
            commands::clear_index,
        ])
        .run(tauri::generate_context!())
        .expect("error while running tauri application");
}

```

--------------------------------

### Install VelesDB on Windows using MSI (Silent)

Source: https://github.com/cyberlife-coder/velesdb/blob/main/docs/guides/INSTALLATION.md

Installs VelesDB on Windows silently using the MSI installer. Supports options for PATH modification and custom installation directories. Requires administrator privileges.

```powershell
msiexec /i velesdb-0.6.0-x86_64.msi /quiet ADDTOPATH=1
msiexec /i velesdb-0.6.0-x86_64.msi /quiet ADDTOPATH=0
msiexec /i velesdb-0.6.0-x86_64.msi /quiet APPLICATIONFOLDER="D:\VelesDB"
```

--------------------------------

### Install VelesDB Rust Crates

Source: https://github.com/cyberlife-coder/velesdb/blob/main/docs/guides/INSTALLATION.md

Installs VelesDB components in a Rust project. Includes instructions for adding the core library as a dependency and installing CLI tools via Cargo.

```toml
# Cargo.toml
[dependencies]
velesdb-core = "0.3"
```

```bash
# Install CLI (includes REPL)
cargo install velesdb-cli

# Install Server
cargo install velesdb-server
```

--------------------------------

### Install VelesDB Python Package

Source: https://github.com/cyberlife-coder/velesdb/blob/main/docs/guides/INSTALLATION.md

Installs the VelesDB Python package using pip. This allows for programmatic interaction with VelesDB within Python applications.

```bash
pip install velesdb
```

```python
import velesdb

# Open or create database
db = velesdb.Database("./my_vectors")

# Create collection
collection = db.create_collection("documents", dimension=768, metric="cosine")

# Insert vectors
collection.upsert([
    {"id": 1, "vector": [...], "payload": {"title": "Hello World"}}
])

# Search
results = collection.search(query_vector, top_k=10)
```

--------------------------------

### Install VelesDB on Linux using DEB Package

Source: https://github.com/cyberlife-coder/velesdb/blob/main/docs/guides/INSTALLATION.md

Installs VelesDB on Debian-based Linux distributions using a DEB package. This method installs the CLI and server binaries to standard system locations.

```bash
# Download
wget https://github.com/cyberlife-coder/VelesDB/releases/download/v0.6.0/velesdb-0.6.0-amd64.deb

# Install
sudo dpkg -i velesdb-0.6.0-amd64.deb

# Verify
velesdb --version
velesdb-server --version
```

--------------------------------

### Installing VelesDB Migration Tool (Rust)

Source: https://github.com/cyberlife-coder/velesdb/blob/main/README.md

This command installs the VelesDB migration tool, which assists in migrating data from other vector databases like Qdrant, Pinecone, and Supabase to VelesDB. This simplifies the transition process for users.

```bash
cargo install velesdb-migrate
```

--------------------------------

### Run Python REST API Client Example

Source: https://github.com/cyberlife-coder/velesdb/blob/main/examples/README.md

A simple HTTP client example for interacting with the VelesDB server via its REST API. Requires a VelesDB server to be running beforehand.

```bash
# Start VelesDB server first
velesdb-server -d ./data

# Run example
python examples/python_example.py
```

--------------------------------

### VelesQL Query Examples

Source: https://github.com/cyberlife-coder/velesdb/blob/main/crates/velesdb-cli/README.md

Demonstrates various ways to perform combined vector, metadata, and text searches using VelesQL.

```APIDOC
## VelesQL Query Examples

### Description

This section provides examples of how to perform combined searches using VelesQL, including vector search with metadata filters, hybrid search (vector + full-text), and complex conditional queries.

### Examples

**Vector Search + Metadata Filter**
```sql
-- Vector search + metadata filter
SELECT * FROM documents 
WHERE VECTOR NEAR [0.15, 0.25, ...] 
AND category = 'tech' 
AND views > 100
LIMIT 5;
```

**Hybrid Search (Vector + Full-Text)**
```sql
-- Hybrid search (vector + full-text)
SELECT * FROM documents
WHERE VECTOR NEAR $query 
AND content MATCH 'rust'
LIMIT 5;
```

**Complex Conditions**
```sql
-- Complex conditions
SELECT * FROM products
WHERE VECTOR NEAR COSINE [0.1, 0.2, ...] 
AND (category = 'electronics' OR category = 'gadgets')
AND price BETWEEN 100 AND 500
AND in_stock = true
LIMIT 10;
```

**Semantic Search Examples**
```sql
-- Search with metadata filter
SELECT id, score, payload->title FROM articles
WHERE VECTOR NEAR $query_embedding
AND category = 'technology'
LIMIT 5;

-- Search with multiple conditions
SELECT * FROM documents
WHERE VECTOR NEAR [0.1, 0.2, 0.3, ...] 
AND status = 'published'
AND views > 1000
LIMIT 10;
```

**Binary Vector Search**
```sql
-- Find similar binary vectors (fingerprints, hashes)
SELECT * FROM images
WHERE VECTOR NEAR [1.0, 0.0, 1.0, 1.0, 0.0, ...] 
LIMIT 10;
```
```

--------------------------------

### Install VelesDB on Linux using Portable Tarball

Source: https://github.com/cyberlife-coder/velesdb/blob/main/docs/guides/INSTALLATION.md

Installs VelesDB on Linux using a portable tarball. This method extracts VelesDB to a specified directory and requires manual PATH configuration.

```bash
# Download and extract
wget https://github.com/cyberlife-coder/VelesDB/releases/download/v0.6.0/velesdb-linux-x86_64.tar.gz
tar -xzf velesdb-linux-x86_64.tar.gz -C /opt/velesdb

# Add to PATH
echo 'export PATH=$PATH:/opt/velesdb' >> ~/.bashrc
source ~/.bashrc
```

--------------------------------

### Rust Project Setup for VelesDB Recommender

Source: https://github.com/cyberlife-coder/velesdb/blob/main/docs/guides/TUTORIALS/MINI_RECOMMENDER.md

This snippet demonstrates how to set up a new Rust project and add necessary dependencies for VelesDB integration. It includes creating a new cargo project and modifying the Cargo.toml file to include 'velesdb-core' and 'serde_json'.

```bash
cargo new mini_recommender
cd mini_recommender
```

```toml
[package]
name = "mini_recommender"
version = "0.1.0"
edition = "2021"

[dependencies]
velesdb-core = "1.3"
serde_json = "1.0"
```

--------------------------------

### Install VelesDB on Windows using Portable ZIP

Source: https://github.com/cyberlife-coder/velesdb/blob/main/docs/guides/INSTALLATION.md

Installs VelesDB on Windows using a portable ZIP archive. This method does not require administrator privileges and allows for manual PATH configuration.

```powershell
# Download and extract
Invoke-WebRequest -Uri "https://github.com/cyberlife-coder/VelesDB/releases/download/v0.6.0/velesdb-windows-x86_64.zip" -OutFile velesdb.zip
Expand-Archive velesdb.zip -DestinationPath C:\VelesDB

# Add to PATH (optional, current session only)
$env:PATH += ";C:\VelesDB"

# Or add permanently via System Properties > Environment Variables
```

--------------------------------

### Run Multi-Model Search Example (Rust)

Source: https://github.com/cyberlife-coder/velesdb/blob/main/examples/README.md

Demonstrates advanced multi-model search queries in Rust, combining vector similarity search, graph traversal, custom ORDER BY expressions, and hybrid search (vector + BM25 text).

```bash
cd examples/rust
cargo run --bin multimodel_search
```

--------------------------------

### Install Rust, Node.js, and Tauri CLI

Source: https://github.com/cyberlife-coder/velesdb/blob/main/docs/tutorials/tauri-rag-app/README.md

Installs the necessary tools for building the Tauri application. Requires Rust (1.70+), Node.js (18+), and the Tauri CLI. System dependencies vary by OS.

```bash
curl --proto '=https' --tlsv1.2 -sSf https://sh.rustup.rs | sh

# Node.js (18+)
# Download from https://nodejs.org/

cargo install tauri-cli
```

--------------------------------

### Install cargo-fuzz and Rust Nightly

Source: https://github.com/cyberlife-coder/velesdb/blob/main/docs/FUZZING.md

Installs the necessary Rust toolchain and the cargo-fuzz utility, which is required for running fuzz tests with libFuzzer.

```bash
# Install Rust nightly (required for cargo-fuzz)
rustup install nightly

# Install cargo-fuzz
cargo install cargo-fuzz
```

--------------------------------

### Uninstall VelesDB on Linux using DEB Package

Source: https://github.com/cyberlife-coder/velesdb/blob/main/docs/guides/INSTALLATION.md

Uninstalls VelesDB from Debian-based Linux distributions when installed via a DEB package.

```bash
sudo dpkg -r velesdb
```

--------------------------------

### Install VelesDB CLI from Source

Source: https://github.com/cyberlife-coder/velesdb/blob/main/crates/velesdb-cli/README.md

Installs the VelesDB CLI by cloning the repository and building from source. This method is useful for development or when you need the latest unreleased features.

```bash
git clone https://github.com/cyberlife-coder/VelesDB
cd VelesDB
cargo install --path crates/velesdb-cli
```

--------------------------------

### Uninstall VelesDB on Windows using MSI

Source: https://github.com/cyberlife-coder/velesdb/blob/main/docs/guides/INSTALLATION.md

Uninstalls VelesDB on Windows silently using the MSI installer. This command can be run from the command line or a script.

```powershell
msiexec /x velesdb-0.6.0-x86_64.msi /quiet
```

--------------------------------

### Collection Management

Source: https://github.com/cyberlife-coder/velesdb/blob/main/docs/getting-started.md

Endpoints for creating and managing collections in VelesDB.

```APIDOC
## POST /collections

### Description
Creates a new collection in VelesDB. Collections are containers for vectors with the same dimension.

### Method
POST

### Endpoint
/collections

### Parameters
#### Request Body
- **name** (string) - Required - The name of the collection.
- **dimension** (integer) - Required - The dimension of the vectors in this collection.
- **metric** (string) - Required - The distance metric to use for similarity searches (e.g., "cosine", "euclidean").

### Request Example
```json
{
  "name": "my_documents",
  "dimension": 384,
  "metric": "cosine"
}
```

### Response
#### Success Response (200)
(Details not provided in source text, typically returns confirmation of creation or collection details.)

#### Response Example
(Not provided in source text)
```

--------------------------------

### Health Check

Source: https://github.com/cyberlife-coder/velesdb/blob/main/docs/getting-started.md

Check the health status of the VelesDB server.

```APIDOC
## GET /health

### Description
Checks if the VelesDB server is running and healthy.

### Method
GET

### Endpoint
/health

### Parameters
None

### Request Example
None

### Response
#### Success Response (200)
- **status** (string) - The health status of the server (e.g., "healthy").
- **version** (string) - The current version of VelesDB.

#### Response Example
```json
{
  "status": "healthy",
  "version": "1.3.1"
}
```
```

--------------------------------

### VelesQL Query Example

Source: https://github.com/cyberlife-coder/velesdb/blob/main/README.md

Shows how to execute a VelesQL query to search for vectors within a collection, using a `NEAR` clause and parameters. This allows for more complex filtering and retrieval logic. The response includes search results and query performance metrics.

```bash
curl -X POST http://localhost:8080/query \
  -H "Content-Type: application/json" \
  -d '{
    "query": "SELECT * FROM my_vectors WHERE vector NEAR $v LIMIT 10",
    "params": {"v": [0.1, 0.2, 0.3, ...]}
  }'
```

--------------------------------

### Vector Operations

Source: https://github.com/cyberlife-coder/velesdb/blob/main/docs/getting-started.md

Endpoints for inserting and searching vectors within a collection.

```APIDOC
## POST /collections/{collection_name}/points

### Description
Inserts one or more vectors (points) into a specified collection. Each point can include an ID, the vector data, and associated metadata (payload).

### Method
POST

### Endpoint
/collections/{collection_name}/points

### Parameters
#### Path Parameters
- **collection_name** (string) - Required - The name of the collection to insert points into.

#### Request Body
- **points** (array) - Required - An array of point objects to insert.
  - **id** (integer or string) - Required - A unique identifier for the point.
  - **vector** (array of floats) - Required - The vector data.
  - **payload** (object) - Optional - Metadata associated with the vector.

### Request Example
```json
{
  "points": [
    {
      "id": 1,
      "vector": [0.1, 0.2, 0.3, ...],
      "payload": {"title": "Introduction to AI", "category": "tech"}
    },
    {
      "id": 2,
      "vector": [0.4, 0.5, 0.6, ...],
      "payload": {"title": "Machine Learning Guide", "category": "tech"}
    }
  ]
}
```

### Response
#### Success Response (200)
(Details not provided in source text, typically returns confirmation of insertion.)

#### Response Example
(Not provided in source text)
```

```APIDOC
## POST /collections/{collection_name}/search

### Description
Searches a collection for vectors similar to a given query vector. Returns the top K most similar results based on the collection's defined metric.

### Method
POST

### Endpoint
/collections/{collection_name}/search

### Parameters
#### Path Parameters
- **collection_name** (string) - Required - The name of the collection to search within.

#### Request Body
- **vector** (array of floats) - Required - The query vector to find similar vectors to.
- **top_k** (integer) - Required - The number of most similar results to return.

### Request Example
```json
{
  "vector": [0.15, 0.25, 0.35, ...],
  "top_k": 5
}
```

### Response
#### Success Response (200)
- **results** (array) - An array of search results.
  - **id** (integer or string) - The ID of the matching point.
  - **score** (float) - The similarity score between the query vector and the found vector.
  - **payload** (object) - The payload associated with the found vector.

#### Response Example
```json
{
  "results": [
    {"id": 1, "score": 0.98, "payload": {"title": "Introduction to AI"}},
    {"id": 2, "score": 0.85, "payload": {"title": "Machine Learning Guide"}}
  ]
}
```
```

--------------------------------

### VelesDB Python Client Usage

Source: https://github.com/cyberlife-coder/velesdb/blob/main/README.md

Demonstrates basic usage of the VelesDB Python client. It shows how to initialize a database, create a collection with specified dimensions and metric, upsert data with vectors and payloads, and perform a vector search.

```python
import velesdb

db = velesdb.Database("./my_vectors")
collection = db.create_collection("docs", dimension=768, metric="cosine")
collection.upsert([{"id": 1, "vector": [...], "payload": {"title": "Hello"}}])
results = collection.search([...], top_k=10)
```

--------------------------------

### Install VelesDB CLI from Crates.io

Source: https://github.com/cyberlife-coder/velesdb/blob/main/crates/velesdb-cli/README.md

Installs the VelesDB CLI using Cargo, the Rust package manager. This is the simplest way to get the CLI if you have Rust and Cargo set up.

```bash
cargo install velesdb-cli
```

--------------------------------

### Sequential vs. Parallel Benchmark Execution

Source: https://github.com/cyberlife-coder/velesdb/blob/main/docs/contributing/BENCHMARKING_GUIDE.md

Illustrates the correct method for running multiple benchmarks sequentially to avoid resource contention and ensure accurate measurements. Running benchmarks in parallel using '&' is shown as an anti-pattern that can lead to inaccurate results due to interference between benchmark processes.

```bash
# ✅ Good: Sequential
cargo bench --bench simd_benchmark
cargo bench --bench filter_benchmark

# ❌ Bad: Parallel (causes resource contention)
cargo bench --bench simd_benchmark &
cargo bench --bench filter_benchmark &
```

--------------------------------

### Text and Hybrid Search

Source: https://github.com/cyberlife-coder/velesdb/blob/main/docs/getting-started.md

Endpoints for performing text-based and combined vector/text searches.

```APIDOC
## POST /collections/{collection_name}/search/text

### Description
Performs a full-text search (using BM25) on a collection to find documents relevant to a given text query.

### Method
POST

### Endpoint
/collections/{collection_name}/search/text

### Parameters
#### Path Parameters
- **collection_name** (string) - Required - The name of the collection to search within.

#### Request Body
- **query** (string) - Required - The text query to search for.
- **top_k** (integer) - Required - The number of top results to return.

### Request Example
```json
{
  "query": "machine learning",
  "top_k": 5
}
```

### Response
#### Success Response (200)
(Details not provided in source text, expected to return search results similar to vector search.)

#### Response Example
(Not provided in source text)
```

```APIDOC
## POST /collections/{collection_name}/search/hybrid

### Description
Performs a hybrid search, combining vector similarity search with text relevance (BM25) to find the most relevant results.

### Method
POST

### Endpoint
/collections/{collection_name}/search/hybrid

### Parameters
#### Path Parameters
- **collection_name** (string) - Required - The name of the collection to search within.

#### Request Body
- **vector** (array of floats) - Required - The query vector for similarity search.
- **query** (string) - Required - The text query for full-text search.
- **top_k** (integer) - Required - The number of top results to return.
- **vector_weight** (float) - Optional - The weight to give to the vector similarity score in the combined ranking (default might be 0.5).

### Request Example
```json
{
  "vector": [0.15, 0.25, 0.35, ...],
  "query": "machine learning",
  "top_k": 5,
  "vector_weight": 0.7
}
```

### Response
#### Success Response (200)
(Details not provided in source text, expected to return search results combining both search types.)

#### Response Example
(Not provided in source text)
```

--------------------------------

### Knowledge Graph Operations

Source: https://github.com/cyberlife-coder/velesdb/blob/main/docs/getting-started.md

Endpoints for managing and traversing graph data within VelesDB.

```APIDOC
## POST /collections/{collection_name}/graph/edges

### Description
Adds an edge to the knowledge graph within a specified collection. Edges represent relationships between vectors (nodes).

### Method
POST

### Endpoint
/collections/{collection_name}/graph/edges

### Parameters
#### Path Parameters
- **collection_name** (string) - Required - The name of the collection containing the graph data.

#### Request Body
- **id** (integer or string) - Required - The ID of the edge.
- **source** (integer or string) - Required - The ID of the source node (vector).
- **target** (integer or string) - Required - The ID of the target node (vector).
- **label** (string) - Required - The type or label of the relationship (e.g., "RELATES_TO").
- **properties** (object) - Optional - Additional properties for the edge (e.g., weight).

### Request Example
```json
{
  "id": 1,
  "source": 100,
  "target": 200,
  "label": "RELATES_TO",
  "properties": {"weight": 0.8}
}
```

### Response
#### Success Response (200)
(Details not provided in source text, typically returns confirmation of edge creation.)

#### Response Example
(Not provided in source text)
```

```APIDOC
## POST /collections/{collection_name}/graph/traverse

### Description
Traverses the knowledge graph starting from a given source node, following defined relationships up to a specified depth.

### Method
POST

### Endpoint
/collections/{collection_name}/graph/traverse

### Parameters
#### Path Parameters
- **collection_name** (string) - Required - The name of the collection containing the graph data.

#### Request Body
- **source** (integer or string) - Required - The ID of the node to start the traversal from.
- **strategy** (string) - Required - The traversal strategy (e.g., "bfs" for Breadth-First Search).
- **max_depth** (integer) - Required - The maximum depth to traverse.
- **limit** (integer) - Optional - The maximum number of results to return.

### Request Example
```json
{
  "source": 100,
  "strategy": "bfs",
  "max_depth": 3,
  "limit": 50
}
```

### Response
#### Success Response (200)
- **results** (array) - An array of traversal results.
  - **target_id** (integer or string) - The ID of the node reached.
  - **depth** (integer) - The depth at which the node was reached.
  - **path** (array) - The sequence of node IDs forming the path from the source.
- **stats** (object) - Statistics about the traversal.
  - **visited** (integer) - The number of nodes visited.
  - **depth_reached** (integer) - The maximum depth reached during traversal.

#### Response Example
```json
{
  "results": [
    {"target_id": 200, "depth": 1, "path": [100, 200]},
    {"target_id": 300, "depth": 2, "path": [100, 200, 300]}
  ],
  "stats": {"visited": 2, "depth_reached": 2}
}
```
```