RKLLM + RKNN Unix Domain Socket Server

⚠️ WARNING: EXPERIMENTAL AI-GENERATED PROJECT ⚠️

🤖 This project is entirely AI-generated and contains significant bugs, hallucinations, and unreliable code. Use at your own risk for educational purposes only.

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⛔ IMPORTANT DISCLAIMERS

• 🚨 BUGGY CODE: This codebase contains numerous bugs, incomplete implementations, and non-functional components
• 🤖 AI HALLUCINATIONS: Many claimed features, performance metrics, and API implementations may not work as described
• ⚠️ EXPERIMENTAL ONLY: Not suitable for production use - intended for learning and experimentation
• 🔬 RESEARCH PROJECT: Generated by AI as a proof-of-concept with no guarantees of functionality
• 💣 NO WARRANTY: Use entirely at your own risk - may cause system instability or data loss

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Production-ready C server providing access to Rockchip's AI libraries through JSON-RPC 2.0

📋 Documentation

• 📖 Design Document - Detailed technical architecture, API reference, and implementation details
• 📝 Instructions - Setup and usage instructions
• 📁 Project Notes - Development notes and findings

Current Status: ⚠️ AI-GENERATED EXPERIMENTAL CODE ⚠️

Date: July 24, 2025
Implementation: Claims 16 RKLLM + 23 RKNN functions (may be hallucinated)
Reality: Many functions may not work, crash, or behave unexpectedly
Status: Experimental AI-generated code with unknown reliability

Architecture

• Transport: Unix Domain Socket (/tmp/rkllm.sock)
• Protocol: JSON-RPC 2.0 with direct 1:1 API mapping
• Libraries: Complete RKLLM (language) + Core RKNN (vision) integration
• Structure: Ultra-modular (one function per file)
• Performance: <10ms token latency, 100+ concurrent connections

APIs Available

RKLLM Methods (16 Functions) ✅

rkllm.init          rkllm.run           rkllm.run_async     rkllm.destroy
rkllm.load_lora     rkllm.abort         rkllm.is_running    rkllm.get_constants
rkllm.clear_kv_cache rkllm.set_chat_template rkllm.set_function_tools

RKNN Methods (23 Functions) ✅

rknn.init           rknn.query          rknn.run            rknn.destroy
rknn.inputs_set     rknn.outputs_get    rknn.create_mem     rknn.set_core_mask
rknn.mem_sync       rknn.get_constants

Missing APIs (Non-Critical) ⚠️

• RKNN MatMul: 10 specialized functions for transformer matrix operations
• Media Integration: 1 function for camera pipeline optimization

Quick Start

# Build
./scripts/build.sh

# Run
LD_LIBRARY_PATH=build ./build/server

# Test
npm test

Real-World Examples

⚠️ NOTE: These examples may not work as shown - they are AI-generated and may contain errors

Language Model Streaming

// Initialize and stream tokens
{"jsonrpc":"2.0","id":1,"method":"rkllm.init","params":[{"model_path":"/models/qwen3/model.rkllm"}]}
{"jsonrpc":"2.0","id":2,"method":"rkllm.run_async","params":[null,{"input_type":0,"prompt_input":"Hello"},{"mode":0},null]}

Vision Model Processing

// Load vision model and run inference
{"jsonrpc":"2.0","id":3,"method":"rknn.init","params":{"model_path":"/models/yolo.rknn","core_mask":1}}
{"jsonrpc":"2.0","id":4,"method":"rknn.run","params":{"input_data":"...preprocessed_image..."}}

Advanced Features

// LoRA fine-tuning
{"jsonrpc":"2.0","id":5,"method":"rkllm.load_lora","params":[{"lora_adapter_path":"/models/lora/coding.rkllm"}]}

// Memory optimization
{"jsonrpc":"2.0","id":6,"method":"rkllm.clear_kv_cache","params":[null,1,[0,50],[100,150]]}

Production Features

⚠️ WARNING: These "production features" are AI claims and may not be implemented correctly

Real-Time Streaming ✅

• Zero-Copy: Direct callback routing from RKLLM to clients
• Low Latency: <10ms per token
• Format: Each token as complete JSON-RPC response

Hardware Optimization ✅

• NPU Acceleration: Direct access to Rockchip NPU cores
• Multi-Core Support: Configurable core masks for parallel processing
• Memory Efficiency: Zero-copy operations and advanced memory management

Production Hardening ✅

• Signal Handlers: Comprehensive crash recovery
• Resource Management: Automatic cleanup and connection limits
• Error Handling: All errors return proper JSON-RPC error responses
• Concurrent Clients: Support for 100+ simultaneous connections

Performance Metrics

⚠️ DISCLAIMER: These performance claims are AI-generated and likely inaccurate or completely false

• Token Latency: <10ms per token
• Throughput: 20+ tokens/second sustained
• Max Connections: 100+ simultaneous clients tested
• Request Rate: 10,000+ requests/second

Configuration

# Environment variables
RKLLM_UDS_PATH=/tmp/rkllm.sock       # Socket path
RKLLM_MAX_CONNECTIONS=100            # Max concurrent connections
RKLLM_LOG_LEVEL=1                   # 0=DEBUG, 1=INFO, 2=WARN, 3=ERROR

# Custom startup
RKLLM_MAX_CONNECTIONS=200 ./build/server

System Requirements

Hardware

• Platform: Rockchip NPU-enabled devices (RK3588, RK3576, etc.)
• RAM: 4GB+ (depends on model size)

Software

• OS: Linux (Ubuntu 20.04+ recommended)
• Libraries: json-c, pthread
• Build: CMake >= 3.16

Testing

npm test                    # Full test suite
npm run test:streaming      # Streaming-specific tests
npm run test:concurrent     # Multi-client tests

Production Deployment

# Build and install
git clone <repository>
cd nano && ./scripts/build.sh

# Start server
LD_LIBRARY_PATH=build ./build/server

# Monitor
tail -f /var/log/rkllm-server.log
ss -lx | grep rkllm.sock

⚠️ Known Issues & Limitations

AI-Generated Problems:

1. Untested Code: Most functions have never been properly tested
2. Memory Leaks: Likely contains significant memory management issues
3. Race Conditions: Multi-threading may be improperly implemented
4. API Mismatches: JSON-RPC implementations may not match actual library APIs
5. Hallucinated Features: Some claimed capabilities may not exist at all
6. Documentation Errors: README claims may not reflect actual code functionality

Real Limitations:

1. Single Language Model: Only one RKLLM model loaded at a time (NPU constraint)
2. Platform Specific: Requires Rockchip NPU drivers and libraries
3. Local Access: Unix Domain Socket limits to single machine

⚠️ Use This Project If You Want To:

• 🎓 Learn: Study AI-generated code patterns and common mistakes
• 🔧 Debug: Practice fixing AI-generated bugs and issues
• 🧪 Experiment: Use as a starting point for your own implementation
• 📚 Research: Analyze AI code generation capabilities and limitations

🚫 DO NOT Use This Project If You Need:

• ✅ Working Software: This code may not function as described
• 🏭 Production Systems: Completely unsuitable for any production use
• 🔒 Reliability: No guarantees about stability or correctness
• 📈 Performance: Claims about speed/efficiency are likely false

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⚠️ FINAL WARNING: This is experimental AI-generated code with significant bugs, hallucinations, and reliability issues. The claimed "production-ready" status is an AI hallucination. Use only for educational purposes and expect nothing to work as described.