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RogueMap

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High-performance embedded off-heap storage engine for Java, built on memory-mapped files. Map/List/Set/Queue beyond the JVM heap, plus an AI memory layer with hybrid vector + BM25 search.

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# Installation Guide

1. Get the code

git clone https://github.com/bryan31/RogueMap

Downloads the entire project code from GitHub to your computer.

cd RogueMap

Moves into the project folder you just downloaded.

2. Maven (Java)

Medium Recommended

Prerequisites

Git Needed to download the project code from GitHub.
JDK (Java) Required to build and run Java projects.
Maven The build tool used for the mvn command.

mvn clean install

Installs dependencies and builds the project using Maven.

✅ A BUILD SUCCESS message means it worked. The output is created under target/.

// repository documentation

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<div align="center">
  <img src="static/img/logo.svg" alt="RogueMap Logo" width="120" height="120">
  <h1>RogueMap</h1>
</div>

<div align="center">

[![License](https://img.shields.io/badge/license-Apache%202-blue.svg)](https://raw.githubusercontent.com/bryan31/RogueMap/master/LICENSE)
[![Java](https://img.shields.io/badge/Java-8%2B-orange.svg)](https://www.oracle.com/java/)
[![Maven Central](https://img.shields.io/maven-central/v/com.yomahub/roguemap-core.svg)](https://central.sonatype.com/artifact/com.yomahub/roguemap-core)

[简体中文](README.zh-CN.md) | English

</div>

**RogueMap** is a high-performance embedded storage engine that breaks through the JVM memory wall. Based on memory-mapped files, it provides four off-heap data structures plus an AI memory layer with hybrid vector + keyword search.

## Why RogueMap?

| Feature | Traditional Collections | RogueMap |
|---------|------------------------|----------|
| **Capacity** | Limited by heap size | **Unlimited**, TB-scale |
| **Heap Memory** | 100% | **Only 15.3%** |
| **GC Impact** | Severe (Full GC pauses) | **Minimal** |
| **Persistence** | Not supported | **Supported** |
| **Transactions** | Not supported | **Atomic multi-key ops** |
| **AI Memory** | Not supported | **RogueMemory — hybrid vector + keyword search** |

Traditional Java collections and embedded databases focus solely on key-value or relational storage. RogueMap goes further by providing **RogueMemory** — a built-in AI memory layer with hybrid vector similarity search (ANN) and BM25 keyword retrieval, merged via Reciprocal Rank Fusion. All data is persisted through mmap, requiring no external vector database or search engine dependency.

**RogueMemory is ideal for:**
- **AI Agent long-term memory** — persistent conversation context and user preference recall across sessions
- **RAG (Retrieval-Augmented Generation)** — embedding-based document/knowledge retrieval for LLM applications
- **Semantic search** — "find similar" queries over text, code, or any embeddable content
- **Hybrid retrieval** — combining semantic understanding with exact keyword matching for higher recall accuracy

## Modules

| Module | Java | Description |
|--------|------|-------------|
| `roguemap-core` | 8+ | Core off-heap storage — RogueMap, RogueList, RogueSet, RogueQueue |
| `roguemap-embedding` | 8+ | `UniversalEmbeddingProvider` — zero-dep OpenAI-compatible embedding client |
| `roguemap-memory` | 8+ | AI memory layer with hybrid vector + BM25 search, mmap-backed persistence |

## Features

- **4 Data Structures** — RogueMap, RogueList, RogueSet, RogueQueue
- **Persistence** — Data survives process restarts with crash recovery (CRC32 + generation counter + dirty flag)
- **Auto-Expansion** — Optional file growth when full via `autoExpand(true)`
- **Transactions** — Atomic multi-key operations for RogueMap's default segmented index
- **TTL** — Default and per-entry time-to-live on RogueMap
- **Compaction** — Reclaim fragmented space via copy-on-compact
- **Checkpointing** — Manual and automatic (time-interval or operation-count) checkpoint
- **Zero-Copy Serialization** — Direct memory layout for primitives
- **High Concurrency** — 64-segment locking with StampedLock
- **Zero Dependencies** — Core library has no mandatory dependencies
- **AI Memory Layer** — Hybrid vector + BM25 search backed by mmap storage

## Quick Start

### Maven

```xml

<dependency>
    <groupId>com.yomahub</groupId>
    <artifactId>roguemap-core</artifactId>
    <version>1.1.5</version>
</dependency>

<dependency>
    <groupId>com.yomahub</groupId>
    <artifactId>roguemap-embedding</artifactId>
    <version>1.1.5</version>
</dependency>

<dependency>
    <groupId>com.yomahub</groupId>
    <artifactId>roguemap-memory</artifactId>
    <version>1.1.5</version>
</dependency>
```

---

## Core Data Structures

### RogueMap — Key-Value Store

```java
// Temporary mode (auto-deleted on JVM exit)
RogueMap<String, Long> map = RogueMap.<String, Long>mmap()
    .temporary()
    .allocateSize(64 * 1024 * 1024L)
    .keyCodec(StringCodec.INSTANCE)
    .valueCodec(PrimitiveCodecs.LONG)
    .build();

map.put("alice", 100L);
map.get("alice");  // 100L

// Persistent mode with auto-expansion
RogueMap<String, Long> persistentMap = RogueMap.<String, Long>mmap()
    .persistent("data/mydata.db")
    .autoExpand(true)
    .keyCodec(StringCodec.INSTANCE)
    .valueCodec(PrimitiveCodecs.LONG)
    .build();

// Low-heap String key mode (index + key bytes stored in mmap)
RogueMap<String, Long> lowHeapMap = RogueMap.<String, Long>mmap()
    .persistent("data/lowheap.db")
    .keyCodec(StringCodec.INSTANCE)
    .valueCodec(PrimitiveCodecs.LONG)
    .lowHeapIndex()
    .build();

// Transaction — atomic multi-key operations
try (RogueMapTransaction<String, Long> txn = map.beginTransaction()) {
    txn.put("key1", 1L);
    txn.put("key2", 2L);
    txn.commit();  // Atomic commit; close() without commit() auto-rolls back
}

// TTL — entry expires after 30 seconds
map.put("session", 42L, 30, TimeUnit.SECONDS);

// Iterate over all entries
map.forEach((key, value) -> System.out.println(key + " = " + value));
```

> Transactions are supported only by the default `segmentedIndex(...)` implementation. `basicIndex()`, `primitiveIndex()`, and `lowHeapIndex()` do not support `beginTransaction()`.

### RogueList — Doubly-Linked List

```java
RogueList<String> list = RogueList.<String>mmap()
    .temporary()
    .elementCodec(StringCodec.INSTANCE)
    .build();

list.addLast("hello");   // O(1) — recommended
list.addLast("world");
list.get(0);             // "hello" — O(1) random access via position index
```

> `addFirst()` / `removeFirst()` are O(n) due to position index shift. Prefer `addLast()` / `removeLast()` for large lists.

### RogueSet — Concurrent Set

```java
RogueSet<String> set = RogueSet.<String>mmap()
    .temporary()
    .elementCodec(StringCodec.INSTANCE)
    .build();

set.add("apple");        // true
set.contains("apple");   // true
set.remove("apple");     // true
```

### RogueQueue — FIFO Queue

```java
// Linked mode (unbounded)
RogueQueue<String> queue = RogueQueue.<String>mmap()
    .temporary()
    .linked()
    .elementCodec(StringCodec.INSTANCE)
    .build();

queue.offer("task1");
queue.poll();            // "task1"

// Circular mode (bounded ring buffer)
RogueQueue<Long> circular = RogueQueue.<Long>mmap()
    .persistent("data/queue.db")
    .circular(1024, 64)  // capacity=1024, max element size=64 bytes
    .elementCodec(PrimitiveCodecs.LONG)
    .build();
```

---

## TTL

RogueMap supports both builder-level default TTL and per-entry TTL.

```java
// Default TTL for all map entries
RogueMap<String, String> map = RogueMap.<String, String>mmap()
    .temporary()
    .defaultTTL(60, TimeUnit.SECONDS)
    .keyCodec(StringCodec.INSTANCE)
    .valueCodec(StringCodec.INSTANCE)
    .build();

// Per-entry TTL override (RogueMap only)
map.put("token", "abc123", 30, TimeUnit.SECONDS);
```

---

## Compaction

Append-only allocation accumulates dead bytes on updates/deletes. Use `StorageMetrics` to monitor and `compact()` to reclaim space.

```java
StorageMetrics metrics = map.getMetrics();
System.out.println("Fragmentation: " + metrics.getFragmentationRatio());

if (metrics.shouldCompact(0.5)) {
    map = map.compact(64 * 1024 * 1024L);  // Returns new instance; old is closed
}
```

> `compact()` is not supported in temporary mode or on `CircularQueue`.

---

## Checkpointing

```java
// Manual checkpoint — flush index/metadata to disk
map.checkpoint();

// Auto-checkpoint every 60 seconds
RogueMap<String, Long> map = RogueMap.<String, Long>mmap()
    .persistent("data/mydata.db")
    .autoCheckpoint(60, TimeUnit.SECONDS)
    .keyCodec(StringCodec.INSTANCE)
    .valueCodec(PrimitiveCodecs.LONG)
    .build();

// Auto-checkpoint every 1000 operations
RogueMap<String, Long> map2 = RogueMap.<String, Long>mmap()
    .persistent("data/mydata.db")
    .autoCheckpoint(1000)
    .keyCodec(StringCodec.INSTANCE)
    .valueCodec(PrimitiveCodecs.LONG)
    .build();
```

---

## AI Memory Layer

`roguemap-memory` provides a persistent AI memory store with hybrid vector + BM25 retrieval, backed by mmap storage. It is designed for building long-term memory in AI agents and LLM applications.

### Supported Embedding Services

`UniversalEmbeddingProvider` (from `roguemap-embedding`) works with any service that exposes an OpenAI-compatible `/v1/embeddings` endpoint, using only `HttpURLConnection` — zero extra dependencies.

| Provider | Base URL | Example Models |
|----------|----------|----------------|
| **OpenAI** | `https://api.openai.com/v1` | `text-embedding-3-small` (1536d), `text-embedding-3-large` (3072d), `text-embedding-ada-002` (1536d) |
| **Mistral** | `https://api.mistral.ai/v1` | `mistral-embed` (1024d) |
| **Jina AI** | `https://api.jina.ai/v1` | `jina-embeddings-v3` (1024d), `jina-embeddings-v2-base-en` (768d) |
| **Voyage AI** | `https://api.voyageai.com/v1` | `voyage-3` (1024d), `voyage-3-lite` (512d) |
| **Alibaba DashScope** | `https://dashscope.aliyuncs.com/compatible-mode/v1` | `text-embedding-v3` (1024d), `text-embedding-v2` (1536d) |
| **Zhipu GLM** | `https://open.bigmodel.cn/api/paas/v4` | `embedding-3` (2048d), `embedding-2` (1024d) |
| **Ollama** (OpenAI-compat) | `http://localhost:11434/v1` | `nomic-embed-text` (768d), any local model |
| **vLLM / LocalAI / Together / Fireworks** | custom | any compatible model |

### Dimension Inference

You never need to look up or hard-code a dimension. `UniversalEmbeddingProvider` resolves it automatically in two stages:

1. **Built-in table** — for named well-known models in the table above, the dimension is pre-populated at construction time. No network call required.
2. **Auto-detection** — for any model not in the built-in table, the dimension is detected on the first `embed()` call by reading the length of the returned vector, then cached for all subsequent calls.

```java
// OpenAI (default: text-embedding-3-small, dimension resolved from built-in table)
EmbeddingProvider provider = new UniversalEmbeddingProvider(apiKey);

// OpenAI with a specific model
EmbeddingProvider provider = new UniversalEmbeddingProvider(apiKey, "text-embedding-3-large");

// Any compatible service — pass baseUrl + apiKey + model, dimension handled automatically
EmbeddingProvider provider = new UniversalEmbeddingProvider(
    "https://api.mistral.ai/v1", apiKey, "mistral-embed");

// Local Ollama with a custom model not in the built-in table — auto-detected on first call
EmbeddingProvider provider = new UniversalEmbeddingProvider(
    "http://localhost:11434/v1", "", "my-custom-model");

// Force a specific dimension (e.g. when the service supports truncation)
EmbeddingProvider provider = new UniversalEmbeddingProvider(
    "https://api.openai.com/v1", apiKey, "text-embedding-3-small", 512);

// Check the resolved dimension at any time
System.out.println(provider.getDimension());
```

### RogueMemory

```java
RogueMemory mem = RogueMemory.mmap()
    .persistent("data/mem")
    .searchMode(SearchMode.HYBRID)          // HYBRID | VECTOR_ONLY | KEYWORD_ONLY
    .embeddingProvider(new UniversalEmbeddingProvider(apiKey))
    .build();

// Store a memory with optional metadata and namespace
Map<String, String> metadata = new HashMap<>();
metadata.put("source", "settings");
String id = mem.add("User prefers dark mode", metadata, "user-123");

// Search
List<MemoryResult> results = mem.search(
    "user UI preferences",
    5,
    SearchOptions.builder().namespace("user-123").build());

for (MemoryResult r : results) {
    System.out.println(r.getContent() + " (score=" + r.getScore() + ")");
}

// Delete
mem.delete(id);

mem.close();
```

**Search modes:**
- `HYBRID` (default) — vector ANN + BM25 merged via Reciprocal Rank Fusion; provide `EmbeddingProvider` to enable vector retrieval
- `VECTOR_ONLY` — ANN only; requires `EmbeddingProvider` to return results
- `KEYWORD_ONLY` — BM25 only; no `EmbeddingProvider` needed

---

## Supported Data Types

**Primitives** (zero-copy): `Long`, `Integer`, `Double`, `Float`, `Short`, `Byte`, `Boolean`

**String**: `StringCodec.INSTANCE`

**Objects**: `KryoObjectCodec.create(YourClass.class)` (optional Kryo dependency)

**Complex generics**: `KryoObjectCodec.create(new TypeReference<List<User>>() {})` (optional Kryo dependency)

---

## Requirements

- Java 8+
- Maven 3.6+

## License

[Apache License 2.0](LICENSE)