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Roadmap Features Documentation

This document provides comprehensive documentation for all newly implemented roadmap features.

Table of Contents

  1. Enhanced GraphRAG Integration
  2. S3-Compatible Gateway
  3. WebAssembly Support
  4. Mobile SDK (iOS/Android)
  5. Enhanced Analytics Dashboard
  6. Multi-Region Cluster Support

Enhanced GraphRAG Integration

Overview

The enhanced GraphRAG integration provides advanced search capabilities for IPFS content using knowledge graphs, vector embeddings, and SPARQL queries.

Features

  • Entity Extraction: Automatically extract entities (CIDs, paths, keywords) from content
  • Graph Search: Traverse knowledge graphs to find related content
  • Vector Search: Semantic similarity search using embeddings
  • SPARQL Queries: Structured queries on RDF knowledge graphs
  • Relationship Mapping: Track relationships between content items

Usage

from ipfs_kit_py.graphrag import GraphRAGSearchEngine

# Initialize search engine
engine = GraphRAGSearchEngine()

# Index content
await engine.index_content(
    cid="QmTest123",
    path="/docs/readme.md",
    content="Documentation about IPFS"
)

# Add relationships
await engine.add_relationship(
    source_cid="QmTest123",
    target_cid="QmTest456",
    relationship_type="references"
)

# Vector search
results = await engine.search(
    query="IPFS documentation",
    search_type="vector",
    limit=10
)

# Graph search
results = await engine.search(
    query="related documents",
    search_type="graph",
    max_depth=2
)

# SPARQL query
sparql_query = """
    SELECT ?doc ?title
    WHERE {
        ?doc rdf:type :Document .
        ?doc :title ?title .
    }
"""
results = await engine.search(sparql_query, search_type="sparql")

# Extract entities
entities = await engine.extract_entities(content_text)

# Get statistics
stats = engine.get_stats()
print(f"Documents: {stats['stats']['document_count']}")
print(f"Relationships: {stats['stats']['relationship_count']}")

Dependencies

  • numpy - For vector operations
  • sentence-transformers - For embeddings (optional)
  • scikit-learn - For similarity calculations (optional)
  • networkx - For graph operations (optional)
  • rdflib - For SPARQL queries (optional)

S3-Compatible Gateway

Overview

The S3-compatible gateway exposes an S3 API interface, allowing S3-compatible tools and applications to interact with IPFS Kit as if it were an S3 service.

Features

  • S3 API Compatibility: Full REST API compatible with S3 clients
  • Bucket Operations: List, create, and manage buckets
  • Object Operations: Get, put, delete objects
  • VFS Integration: Maps S3 operations to IPFS VFS
  • Standard Headers: ETag, Content-Length, etc.

Usage

Starting the Gateway:

from ipfs_kit_py.s3_gateway import S3Gateway
from ipfs_kit_py.high_level_api import IPFSSimpleAPI

# Initialize IPFS API
ipfs_api = IPFSSimpleAPI()

# Create and start S3 gateway
gateway = S3Gateway(ipfs_api=ipfs_api, host="0.0.0.0", port=9000)
gateway.run()  # Blocking

# Or start asynchronously
await gateway.start()

Using with AWS CLI:

# Configure AWS CLI to use the gateway
aws configure set aws_access_key_id "ipfs-kit"
aws configure set aws_secret_access_key "not-needed"
aws configure set default.s3.signature_version s3v4

# List buckets
aws --endpoint-url=http://localhost:9000 s3 ls

# Upload file
aws --endpoint-url=http://localhost:9000 s3 cp file.txt s3://my-bucket/

# Download file
aws --endpoint-url=http://localhost:9000 s3 cp s3://my-bucket/file.txt .

# List objects
aws --endpoint-url=http://localhost:9000 s3 ls s3://my-bucket/

Using with boto3:

import boto3

s3 = boto3.client(
    's3',
    endpoint_url='http://localhost:9000',
    aws_access_key_id='ipfs-kit',
    aws_secret_access_key='not-needed'
)

# List buckets
response = s3.list_buckets()
print(response['Buckets'])

# Upload object
s3.put_object(
    Bucket='my-bucket',
    Key='file.txt',
    Body=b'Hello, IPFS!'
)

# Download object
obj = s3.get_object(Bucket='my-bucket', Key='file.txt')
content = obj['Body'].read()

Dependencies

  • fastapi - Web framework
  • uvicorn - ASGI server

WebAssembly Support

Overview

WebAssembly support enables IPFS Kit operations to run in browsers and edge computing environments through WASM modules.

Features

  • WASM Runtime Support: Wasmtime and Wasmer runtimes
  • Module Registry: Store and retrieve WASM modules from IPFS
  • Host Functions: IPFS operations callable from WASM
  • JavaScript Bindings: Generated bindings for browser usage

Usage

Loading WASM Modules:

from ipfs_kit_py.wasm_support import WasmIPFSBridge

# Initialize bridge
bridge = WasmIPFSBridge(ipfs_api=ipfs_api, runtime="wasmtime")

# Load WASM module from IPFS
module = await bridge.load_wasm_module("QmWasmModuleCID")

# Execute WASM function
result = await bridge.execute_wasm_function(
    module,
    "process_data",
    args=[42, 100]
)

# Store new WASM module
cid = await bridge.store_wasm_module(
    wasm_bytes,
    metadata={"name": "data_processor", "version": "1.0.0"}
)

Module Registry:

from ipfs_kit_py.wasm_support import WasmModuleRegistry

registry = WasmModuleRegistry(ipfs_api=ipfs_api)

# Register module
await registry.register_module(
    name="image_processor",
    cid="QmImageProcessorWASM",
    metadata={"version": "2.1.0"}
)

# Get module
module_info = await registry.get_module("image_processor")
print(f"Module CID: {module_info['cid']}")

# List all modules
modules = registry.list_modules()

Browser Usage:

from ipfs_kit_py.wasm_support import WasmJSBindings

# Generate JavaScript bindings
js_code = WasmJSBindings.generate_js_bindings(
    "DataProcessor",
    ["encode", "decode", "hash"]
)

# Save to file for browser use
with open("data_processor.js", "w") as f:
    f.write(js_code)
// In browser
import DataProcessorWASM from './data_processor.js';

const processor = new DataProcessorWASM();
await processor.load('ipfs://QmWasmModuleCID');

const result = processor.encode(data);

Dependencies

  • wasmtime - Python bindings for Wasmtime (optional)
  • wasmer - Python bindings for Wasmer (optional)

Mobile SDK (iOS/Android)

Overview

The Mobile SDK provides native bindings for iOS and Android platforms, enabling mobile applications to interact with IPFS.

Features

  • iOS Swift Bindings: Native Swift API with async/await support
  • Android Kotlin Bindings: Native Kotlin API with coroutines
  • Swift Package Manager: SPM support for iOS
  • CocoaPods: Alternative iOS installation
  • Gradle: Android library distribution

Usage

Generating SDKs:

from ipfs_kit_py.mobile_sdk import MobileSDKGenerator

generator = MobileSDKGenerator(output_dir="./mobile_sdk")

# Generate iOS SDK
ios_result = generator.generate_ios_sdk()
print(f"iOS SDK created at: {ios_result['output_dir']}")

# Generate Android SDK
android_result = generator.generate_android_sdk()
print(f"Android SDK created at: {android_result['output_dir']}")

iOS Usage (Swift):

import IPFSKit

// Initialize
let ipfs = IPFSKit(endpoint: "http://192.168.1.100", port: 5001)

// Add content (async/await)
let data = "Hello, IPFS!".data(using: .utf8)!
do {
    let cid = try await ipfs.add(data)
    print("Added with CID: \(cid)")
    
    // Retrieve content
    let retrieved = try await ipfs.get(cid)
    print("Retrieved: \(String(data: retrieved, encoding: .utf8) ?? "")")
    
    // Pin content
    let pinned = try await ipfs.pin(cid)
    print("Pinned: \(pinned)")
} catch {
    print("Error: \(error)")
}

Android Usage (Kotlin):

import org.ipfskit.mobile.IPFSKit
import kotlinx.coroutines.launch

// Initialize
val ipfs = IPFSKit(endpoint = "http://192.168.1.100", port = 5001)

// Add content (coroutines)
lifecycleScope.launch {
    try {
        val data = "Hello, IPFS!".toByteArray()
        val cid = ipfs.addAsync(data)
        println("Added with CID: $cid")
        
        // Retrieve content
        val retrieved = ipfs.getAsync(cid)
        println("Retrieved: ${String(retrieved)}")
        
        // Pin content
        val pinned = ipfs.pin(cid) { success, error ->
            println("Pinned: $success")
        }
    } catch (e: Exception) {
        println("Error: $e")
    }
}

Installation

iOS (Swift Package Manager):

dependencies: [
    .package(url: "https://github.com/endomorphosis/ipfs_kit_py.git", from: "0.3.0")
]

iOS (CocoaPods):

pod 'IPFSKit', '~> 0.3.0'

Android (Gradle):

dependencies {
    implementation 'org.ipfskit:mobile:0.3.0'
}

Enhanced Analytics Dashboard

Overview

The enhanced analytics dashboard provides real-time monitoring, metrics collection, and visualization for IPFS Kit operations.

Features

  • Metrics Collection: Track operations, latency, bandwidth, errors
  • Real-time Monitoring: Continuous monitoring with configurable intervals
  • Visualization: Generate charts for operations, latency, bandwidth
  • Statistics: Aggregated metrics and performance indicators
  • Cluster Analytics: Multi-node cluster monitoring

Usage

Basic Analytics:

from ipfs_kit_py.analytics_dashboard import AnalyticsCollector, AnalyticsDashboard

# Initialize collector
collector = AnalyticsCollector(window_size=1000)

# Record operations
collector.record_operation(
    operation_type="add",
    duration=0.5,
    bytes_transferred=1024,
    success=True,
    peer_id="peer123"
)

# Get metrics
metrics = collector.get_metrics()
print(f"Operations/sec: {metrics['ops_per_second']:.2f}")
print(f"Avg latency: {metrics['latency']['mean']:.3f}s")
print(f"Error rate: {metrics['error_rate']:.2%}")

Dashboard:

from ipfs_kit_py.analytics_dashboard import AnalyticsDashboard

# Initialize dashboard
dashboard = AnalyticsDashboard(ipfs_api=ipfs_api, collector=collector)

# Get dashboard data
data = dashboard.get_dashboard_data()
print(f"Storage: {data['storage']}")
print(f"Network: {data['network']}")
print(f"Cluster: {data['cluster']}")

# Generate charts
charts = dashboard.generate_charts(output_dir="/tmp/charts")
print(f"Generated charts: {list(charts.keys())}")

# Start real-time monitoring
await dashboard.start_monitoring()

Monitoring Loop:

import asyncio

# Start monitoring in background
async def monitor():
    dashboard = AnalyticsDashboard(ipfs_api=ipfs_api)
    await dashboard.start_monitoring()

# Run monitoring
asyncio.create_task(monitor())

Dependencies

  • matplotlib - For chart generation (optional)
  • numpy - For statistical calculations (optional)

Multi-Region Cluster Support

Overview

Multi-region cluster support enables deployment and management of IPFS clusters across multiple geographic regions with intelligent routing and failover.

Features

  • Region Management: Register and manage multiple regions
  • Health Monitoring: Continuous health checks across regions
  • Intelligent Routing: Latency-optimized, geo-distributed, cost-optimized
  • Cross-Region Replication: Automatic content replication
  • Failover: Automatic region failover on failures

Usage

Basic Setup:

from ipfs_kit_py.multi_region_cluster import MultiRegionCluster

# Initialize cluster manager
cluster = MultiRegionCluster(ipfs_api=ipfs_api)

# Add regions
cluster.add_region(
    name="us-west-1",
    location="Oregon, USA",
    latency_zone="us-west",
    endpoints=["http://node1:5001", "http://node2:5001"]
)

cluster.add_region(
    name="eu-central-1",
    location="Frankfurt, Germany",
    latency_zone="eu-central",
    endpoints=["http://node3:5001", "http://node4:5001"]
)

cluster.add_region(
    name="ap-southeast-1",
    location="Singapore",
    latency_zone="ap-southeast",
    endpoints=["http://node5:5001", "http://node6:5001"]
)

Health Monitoring:

# Check all regions
results = await cluster.health_check()
for region, status in results.items():
    print(f"{region}: {status['status']} - {status['average_latency']:.1f}ms")

# Start continuous monitoring
await cluster.start_monitoring()

Region Selection:

# Select region by strategy
region = cluster.select_region(strategy="latency_optimized")
print(f"Selected region: {region.name} ({region.location})")

# Exclude specific regions
region = cluster.select_region(exclude_regions={"us-west-1"})

# Get closest region
region = await cluster.get_closest_region(client_location="us-east")

Cross-Region Replication:

# Replicate content to multiple regions
result = await cluster.replicate_to_regions(
    cid="QmTestContent",
    target_regions=["us-west-1", "eu-central-1", "ap-southeast-1"],
    min_replicas=2
)

print(f"Replication success: {result['success']}")
for region, status in result['regions'].items():
    print(f"  {region}: {status['success']}")

# Auto-select regions for replication
result = await cluster.replicate_to_regions(
    cid="QmTestContent",
    min_replicas=3  # Will auto-select 3 regions
)

Failover:

# Handle region failure
result = await cluster.failover("us-west-1")
print(f"Failover to: {result['backup_regions']}")

Cluster Statistics:

stats = cluster.get_cluster_stats()
print(f"Total regions: {stats['total_regions']}")
print(f"Healthy regions: {stats['regions_by_status']['healthy']}")
print(f"Total nodes: {stats['total_nodes']}")
print(f"Utilization: {stats['utilization']:.1%}")

Routing Strategies

  1. latency_optimized: Select region with lowest latency
  2. geo_distributed: Distribute across geographic zones
  3. cost_optimized: Optimize for storage/bandwidth costs

Region Status

  • HEALTHY: All endpoints operational
  • DEGRADED: Some endpoints down
  • UNAVAILABLE: Region unreachable

Testing

All features include comprehensive tests:

# Run all roadmap feature tests
pytest tests/test_roadmap_features.py -v

# Run specific feature tests
pytest tests/test_roadmap_features.py::TestGraphRAGEnhancements -v
pytest tests/test_roadmap_features.py::TestS3Gateway -v
pytest tests/test_roadmap_features.py::TestWASMSupport -v
pytest tests/test_roadmap_features.py::TestMobileSDK -v
pytest tests/test_roadmap_features.py::TestAnalyticsDashboard -v
pytest tests/test_roadmap_features.py::TestMultiRegionCluster -v

Contributing

Contributions to these features are welcome! Please see CONTRIBUTING.md for guidelines.

License

All features are licensed under AGPL-3.0. See LICENSE for details.