# Implementation Guide: Bi-Directional Data Bridge

## Overview

This document describes the implementation of the high-performance, bi-directional data bridge between **Julia**, **JavaScript**, and **Python/Micropython** applications using NATS (Core & JetStream), implementing the Claim-Check pattern for large payloads.

The system enables seamless communication across all three platforms:
- **Julia ↔ JavaScript** bi-directional messaging
- **JavaScript ↔ Python/Micropython** bi-directional messaging
- **Julia ↔ Python/Micropython** bi-directional messaging (via JSON serialization)

### Implementation Files

NATSBridge is implemented in three languages, each providing the same API:

| Language | Implementation File | Description |
|----------|---------------------|-------------|
| **Julia** | [`src/NATSBridge.jl`](../src/NATSBridge.jl) | Full Julia implementation with Arrow IPC support |
| **JavaScript** | [`src/NATSBridge.js`](../src/NATSBridge.js) | JavaScript implementation for Node.js and browsers |
| **Python/Micropython** | [`src/nats_bridge.py`](../src/nats_bridge.py) | Python implementation for desktop and microcontrollers |

### Multi-Payload Support

The implementation uses a **standardized list-of-tuples format** for all payload operations. **Even when sending a single payload, the user must wrap it in a list.**

**API Standard:**
```julia
# Input format for smartsend (always a list of tuples with type info)
[(dataname1, data1, type1), (dataname2, data2, type2), ...]

# Output format for smartreceive (returns envelope dictionary with payloads field)
# Returns: Dict with envelope metadata and payloads field containing list of tuples
# {
#   "correlationId": "...",
#   "msgId": "...",
#   "timestamp": "...",
#   "sendTo": "...",
#   "msgPurpose": "...",
#   "senderName": "...",
#   "senderId": "...",
#   "receiverName": "...",
#   "receiverId": "...",
#   "replyTo": "...",
#   "replyToMsgId": "...",
#   "brokerURL": "...",
#   "metadata": {...},
#   "payloads": [(dataname1, data1, type1), (dataname2, data2, type2), ...]
# }
```

Where `type` can be: `"text"`, `"dictionary"`, `"table"`, `"image"`, `"audio"`, `"video"`, `"binary"`

**Examples:**
```julia
# Single payload - still wrapped in a list (type is required as third element)
smartsend("/test", [(dataname1, data1, "text")], ...)

# Multiple payloads in one message (each payload has its own type)
smartsend("/test", [(dataname1, data1, "dictionary"), (dataname2, data2, "table")], ...)

# Receive returns a dictionary envelope with all metadata and deserialized payloads
envelope = smartreceive(msg, ...)
# envelope["payloads"] = [(dataname1, data1, "text"), (dataname2, data2, "table"), ...]
# envelope["correlationId"], envelope["msgId"], etc.
```

## Cross-Platform Interoperability

NATSBridge is designed for seamless communication between Julia, JavaScript, and Python/Micropython applications. All three implementations share the same interface and data format, ensuring compatibility across platforms.

### Platform-Specific Features

| Feature | Julia | JavaScript | Python/Micropython |
|---------|-------|------------|-------------------|
| Direct NATS transport | ✅ | ✅ | ✅ |
| HTTP file server (Claim-Check) | ✅ | ✅ | ✅ |
| Arrow IPC tables | ✅ | ✅ | ✅ |
| Base64 encoding | ✅ | ✅ | ✅ |
| Exponential backoff | ✅ | ✅ | ✅ |
| Correlation ID tracking | ✅ | ✅ | ✅ |
| Reply-to support | ✅ | ✅ | ✅ |

### Data Type Mapping

| Type | Julia | JavaScript | Python/Micropython |
|------|-------|------------|-------------------|
| `text` | `String` | `String` | `str` |
| `dictionary` | `Dict` | `Object` | `dict` |
| `table` | `DataFrame` | `Array<Object>` | `DataFrame` / `list` |
| `image` | `Vector{UInt8}` | `ArrayBuffer/Uint8Array` | `bytes` |
| `audio` | `Vector{UInt8}` | `ArrayBuffer/Uint8Array` | `bytes` |
| `video` | `Vector{UInt8}` | `ArrayBuffer/Uint8Array` | `bytes` |
| `binary` | `Vector{UInt8}` | `ArrayBuffer/Uint8Array` | `bytes` |

### Example: Julia ↔ Python ↔ JavaScript

```julia
# Julia sender
using NATSBridge
data = [("message", "Hello from Julia!", "text")]
smartsend("/cross_platform", data, nats_url="nats://localhost:4222")
```

```javascript
// JavaScript receiver
const { smartreceive } = require('./src/NATSBridge');
const envelope = await smartreceive(msg);
// envelope.payloads[0].data === "Hello from Julia!"
```

```python
# Python sender
from nats_bridge import smartsend
data = [("response", "Hello from Python!", "text")]
smartsend("/cross_platform", data, nats_url="nats://localhost:4222")
```

All three platforms can communicate seamlessly using the same NATS subjects and data format.

## Architecture

All three implementations (Julia, JavaScript, Python/Micropython) follow the same Claim-Check pattern:

```
┌─────────────────────────────────────────────────────────────────────────┐
│                         SmartSend Function                              │
└─────────────────────────────────────────────────────────────────────────┘
                              │
                              ▼
┌─────────────────────────────────────────────────────────────────────────┐
│  Is payload size < 1MB?                                                 │
└─────────────────────────────────────────────────────────────────────────┘
                              │
            ┌─────────────────┴─────────────────┐
            ▼                                   ▼
      ┌─────────────────┐                 ┌─────────────────┐
      │  Direct Path    │                 │  Link Path      │
      │  (< 1MB)        │                 │  (> 1MB)        │
      │                 │                 │                 │
      │ • Serialize to  │                 │ • Serialize to  │
      │   Buffer        │                 │   Buffer        │
      │ • Base64 encode │                 │ • Upload to     │
      │ • Publish to    │                 │   HTTP Server   │
      │   NATS          │                 │ • Publish to    │
      │                 │                 │   NATS with URL │
      └─────────────────┘                 └─────────────────┘
```

## Files

### Julia Module: [`src/NATSBridge.jl`](../src/NATSBridge.jl)

The Julia implementation provides:

- **[`MessageEnvelope`](src/NATSBridge.jl)**: Struct for the unified JSON envelope
- **[`SmartSend()`](src/NATSBridge.jl)**: Handles transport selection based on payload size
- **[`SmartReceive()`](src/NATSBridge.jl)**: Handles both direct and link transport

### JavaScript Module: [`src/NATSBridge.js`](../src/NATSBridge.js)

The JavaScript implementation provides:

- **`MessageEnvelope` class**: For the unified JSON envelope
- **`MessagePayload` class**: For individual payload representation
- **[`smartsend()`](src/NATSBridge.js)**: Handles transport selection based on payload size
- **[`smartreceive()`](src/NATSBridge.js)**: Handles both direct and link transport

### Python/Micropython Module: [`src/nats_bridge.py`](../src/nats_bridge.py)

The Python/Micropython implementation provides:

- **`MessageEnvelope` class**: For the unified JSON envelope
- **`MessagePayload` class**: For individual payload representation
- **[`smartsend()`](src/nats_bridge.py)**: Handles transport selection based on payload size
- **[`smartreceive()`](src/nats_bridge.py)**: Handles both direct and link transport

## Installation

### Julia Dependencies

```julia
using Pkg
Pkg.add("NATS")
Pkg.add("Arrow")
Pkg.add("JSON3")
Pkg.add("HTTP")
Pkg.add("UUIDs")
Pkg.add("Dates")
```

### JavaScript Dependencies

```bash
npm install nats.js apache-arrow uuid base64-url
```

### Python/Micropython Dependencies

1. Copy [`src/nats_bridge.py`](../src/nats_bridge.py) to your device
2. Ensure you have the following dependencies:

   **For Python (desktop):**
   ```bash
   pip install nats-py
   ```

   **For Micropython:**
   - `urequests` for HTTP requests
   - `base64` for base64 encoding (built-in)
   - `json` for JSON handling (built-in)
   - `socket` for networking (built-in)
   - `uuid` for UUID generation (built-in)

## Usage Tutorial

### Step 1: Start NATS Server

```bash
docker run -p 4222:4222 nats:latest
```

### Step 2: Start HTTP File Server (optional)

```bash
# Create a directory for file uploads
mkdir -p /tmp/fileserver

# Use any HTTP server that supports POST for file uploads
# Example: Python's built-in server
python3 -m http.server 8080 --directory /tmp/fileserver
```

### Step 3: Run Test Scenarios

```bash
# Scenario 1: Command & Control (JavaScript sender)
node test/scenario1_command_control.js

# Scenario 2: Large Arrow Table (JavaScript sender)
node test/scenario2_large_table.js

# Scenario 3: Julia-to-Julia communication
# Run both Julia and JavaScript versions
julia test/scenario3_julia_to_julia.jl
node test/scenario3_julia_to_julia.js
```

## Usage

### Scenario 0: Basic Multi-Payload Example

#### Python/Micropython (Sender)
```python
from nats_bridge import smartsend

# Send multiple payloads in one message (type is required per payload)
smartsend(
    "/test",
    [("dataname1", data1, "dictionary"), ("dataname2", data2, "table")],
    nats_url="nats://localhost:4222",
    fileserver_url="http://localhost:8080"
)

# Even single payload must be wrapped in a list with type
smartsend("/test", [("single_data", mydata, "dictionary")])
```

#### Python/Micropython (Receiver)
```python
from nats_bridge import smartreceive

# Receive returns a list of (dataname, data, type) tuples
payloads = smartreceive(msg)
# payloads = [(dataname1, data1, "dictionary"), (dataname2, data2, "table"), ...]
```

#### JavaScript (Sender)
```javascript
const { smartsend } = require('./src/NATSBridge');

// Single payload wrapped in a list
const config = [{
    dataname: "config",
    data: { step_size: 0.01, iterations: 1000 },
    type: "dictionary"
}];

await smartsend("control", config, {
    correlationId: "unique-id"
});

// Multiple payloads
const configs = [
    {
        dataname: "config1",
        data: { step_size: 0.01 },
        type: "dictionary"
    },
    {
        dataname: "config2", 
        data: { iterations: 1000 },
        type: "dictionary"
    }
];

await smartsend("control", configs);
```

#### JavaScript (Receiver)
```javascript
const { smartreceive } = require('./src/NATSBridge');

// Subscribe to messages
const nc = await connect({ servers: ['nats://localhost:4222'] });
const sub = nc.subscribe("control");

for await (const msg of sub) {
    const envelope = await smartreceive(msg);
    
    // Process the payloads from the envelope
    for (const payload of envelope.payloads) {
        const { dataname, data, type } = payload;
        console.log(`Received ${dataname} of type ${type}`);
        console.log(`Data: ${JSON.stringify(data)}`);
    }
    
    // Also access envelope metadata
    console.log(`Correlation ID: ${envelope.correlationId}`);
    console.log(`Message ID: ${envelope.msgId}`);
}
```

### Scenario 2: Deep Dive Analysis (Large Arrow Table)

#### Julia (Sender)
```julia
using Arrow
using DataFrames

# Create large DataFrame
df = DataFrame(
    id = 1:10_000_000,
    value = rand(10_000_000),
    category = rand(["A", "B", "C"], 10_000_000)
)

# Send via SmartSend - wrapped in a list (type is part of each tuple)
await SmartSend("analysis_results", [("table_data", df, "table")]);
```

#### JavaScript (Receiver)
```javascript
const { smartreceive } = require('./src/NATSBridge');

const envelope = await smartreceive(msg);

// Use table data from the payloads field
// Note: Tables are sent as arrays of objects in JavaScript
const table = envelope.payloads;
```

### Scenario 3: Live Binary Processing

#### Python/Micropython (Sender)
```python
from nats_bridge import smartsend

# Binary data wrapped in a list
binary_data = [
    ("audio_chunk", binary_buffer, "binary")
]

smartsend(
    "binary_input",
    binary_data,
    nats_url="nats://localhost:4222",
    metadata={
        "sample_rate": 44100,
        "channels": 1
    }
)
```

#### JavaScript (Sender)
```javascript
const { smartsend } = require('./src/NATSBridge');

// Binary data wrapped in a list
const binaryData = [{
    dataname: "audio_chunk",
    data: binaryBuffer,  // ArrayBuffer or Uint8Array
    type: "binary"
}];

await smartsend("binary_input", binaryData, {
    metadata: {
        sample_rate: 44100,
        channels: 1
    }
});
```

#### Python/Micropython (Receiver)
```python
from nats_bridge import smartreceive

# Receive binary data
def process_binary(msg):
    envelope = smartreceive(msg)
    
    # Process the binary data from envelope.payloads
    for dataname, data, type in envelope["payloads"]:
        if type == "binary":
            # data is bytes
            print(f"Received binary data: {dataname}, size: {len(data)}")
            # Perform FFT or AI transcription here
```

#### JavaScript (Receiver)
```javascript
const { smartreceive } = require('./src/NATSBridge');

// Receive binary data
function process_binary(msg) {
    const envelope = await smartreceive(msg);
    
    // Process the binary data from envelope.payloads
    for (const payload of envelope.payloads) {
        if (payload.type === "binary") {
            // data is an ArrayBuffer or Uint8Array
            console.log(`Received binary data: ${payload.dataname}, size: ${payload.data.length}`);
            // Perform FFT or AI transcription here
        }
    }
}
```

### Scenario 4: Catch-Up (JetStream)

#### Julia (Producer)
```julia
using NATSBridge

function publish_health_status(nats_url)
    # Send status wrapped in a list (type is part of each tuple)
    status = Dict("cpu" => rand(), "memory" => rand())
    smartsend("health", [("status", status, "dictionary")], nats_url=nats_url)
    sleep(5)  # Every 5 seconds
end
```

#### JavaScript (Consumer)
```javascript
const { connect } = require('nats');
const { smartreceive } = require('./src/NATSBridge');

const nc = await connect({ servers: ['nats://localhost:4222'] });
const js = nc.jetstream();

// Request replay from last 10 minutes
const consumer = await js.pullSubscribe("health", { 
    durable_name: "catchup",
    max_batch: 100,
    max_ack_wait: 30000
});

// Process historical and real-time messages
for await (const msg of consumer) {
    const envelope = await smartreceive(msg);
    // envelope.payloads contains the list of payloads
    // Each payload has: dataname, data, type
    msg.ack();
}
```

### Scenario 4: Micropython Device Control

**Focus:** Sending configuration to a Micropython device over NATS. This demonstrates the lightweight nature of the Python implementation suitable for microcontrollers.

**Python/Micropython (Receiver/Device):**
```python
from nats_bridge import smartsend, smartreceive
import json

# Device configuration handler
def handle_device_config(msg):
    envelope = smartreceive(msg)
    
    # Process configuration from payloads
    for dataname, data, type in envelope["payloads"]:
        if type == "dictionary":
            print(f"Received configuration: {data}")
            # Apply configuration to device
            if "wifi_ssid" in data:
                wifi_ssid = data["wifi_ssid"]
                wifi_password = data["wifi_password"]
                update_wifi_config(wifi_ssid, wifi_password)
    
    # Send confirmation back
    config = {
        "status": "configured",
        "wifi_ssid": "MyNetwork",
        "ip": get_device_ip()
    }
    smartsend(
        "device/response",
        [("config", config, "dictionary")],
        nats_url="nats://localhost:4222",
        reply_to=envelope.get("replyTo")
    )
```

**JavaScript (Sender/Controller):**
```javascript
const { smartsend } = require('./src/NATSBridge');

// Send configuration to Micropython device
await smartsend("device/config", [
    {
        dataname: "config",
        data: {
            wifi_ssid: "MyNetwork",
            wifi_password: "password123",
            update_interval: 60,
            temperature_threshold: 30.0
        },
        type: "dictionary"
    }
]);
```

**Use Case:** A controller sends WiFi and operational configuration to a Micropython device (e.g., ESP32). The device receives the configuration, applies it, and sends back a confirmation with its current status.

### Scenario 5: Selection (Low Bandwidth)

**Focus:** Small Arrow tables, Julia to JavaScript. The Action: Julia wants to send a small DataFrame to show on a JavaScript dashboard for the user to choose.

**Julia (Sender):**
```julia
using NATSBridge
using DataFrames

# Create small DataFrame (e.g., 50KB - 500KB)
options_df = DataFrame(
    id = 1:10,
    name = ["Option A", "Option B", "Option C", "Option D", "Option E",
            "Option F", "Option G", "Option H", "Option I", "Option J"],
    description = ["Description A", "Description B", "Description C", "Description D", "Description E",
                   "Description F", "Description G", "Description H", "Description I", "Description J"]
)

# Convert to Arrow IPC stream
# Check payload size (< 1MB threshold)
# Publish directly to NATS with Base64-encoded payload
# Include metadata for dashboard selection context
smartsend(
    "dashboard.selection",
    [("options_table", options_df, "table")],
    nats_url="nats://localhost:4222",
    metadata=Dict("context" => "user_selection")
)
```

**JavaScript (Receiver):**
```javascript
const { smartreceive, smartsend } = require('./src/NATSBridge');

// Receive NATS message with direct transport
const envelope = await smartreceive(msg);

// Decode Base64 payload (for direct transport)
// For tables, data is in envelope.payloads
const table = envelope.payloads;  // Array of objects

// User makes selection
const selection = uiComponent.getSelectedOption();

// Send selection back to Julia
await smartsend("dashboard.response", [
    { dataname: "selected_option", data: selection, type: "dictionary" }
]);
```

**Use Case:** Julia server generates a list of available options (e.g., file selections, configuration presets) as a small DataFrame and sends to JavaScript dashboard for user selection. The selection is then sent back to Julia for processing.

### Scenario 6: Chat System

**Focus:** Every conversational message is composed of any number and any combination of components, spanning the full spectrum from small to large. This includes text, images, audio, video, tables, and files—specifically accommodating everything from brief snippets to high-resolution images, large audio files, extensive tables, and massive documents. Support for claim-check delivery and full bi-directional messaging.

**Multi-Payload Support:** The system supports mixed-payload messages where a single message can contain multiple payloads with different transport strategies. The `smartreceive` function iterates through all payloads in the envelope and processes each according to its transport type.

**Julia (Sender/Receiver):**
```julia
using NATSBridge
using DataFrames

# Build chat message with mixed payloads:
# - Text: direct transport (Base64)
# - Small images: direct transport (Base64)
# - Large images: link transport (HTTP URL)
# - Audio/video: link transport (HTTP URL)
# - Tables: direct or link depending on size
# - Files: link transport (HTTP URL)
# 
# Each payload uses appropriate transport strategy:
# - Size < 1MB → direct (NATS + Base64)
# - Size >= 1MB → link (HTTP upload + NATS URL)
# 
# Include claim-check metadata for delivery tracking
# Support bidirectional messaging with replyTo fields

# Example: Chat with text, small image, and large file
chat_message = [
    ("message_text", "Hello, this is a test message!", "text"),
    ("user_avatar", image_bytes, "image"),  # Small image, direct transport
    ("large_document", large_file_bytes, "binary")  # Large file, link transport
]

smartsend(
    "chat.room123",
    chat_message,
    nats_url="nats://localhost:4222",
    msg_purpose="chat",
    reply_to="chat.room123.responses"
)
```

**JavaScript (Sender/Receiver):**
```javascript
const { smartsend, smartreceive } = require('./src/NATSBridge');

// Build chat message with mixed content:
// - User input text: direct transport
// - Selected image: check size, use appropriate transport
// - Audio recording: link transport for large files
// - File attachment: link transport
// 
// Parse received message:
// - Direct payloads: decode Base64
// - Link payloads: fetch from HTTP with exponential backoff
// - Deserialize all payloads appropriately
// 
// Render mixed content in chat interface
// Support bidirectional reply with claim-check delivery confirmation

// Example: Send chat with mixed content
const message = [
    {
        dataname: "text",
        data: "Hello from JavaScript!",
        type: "text"
    },
    {
        dataname: "image",
        data: selectedImageBuffer,  // Small image (ArrayBuffer or Uint8Array)
        type: "image"
    },
    {
        dataname: "audio",
        data: audioUrl,  // Large audio, link transport
        type: "audio"
    }
];

await smartsend("chat.room123", message);
```

**Use Case:** Full-featured chat system supporting rich media. User can send text, small images directly, or upload large files that get uploaded to HTTP server and referenced via URLs. Claim-check pattern ensures reliable delivery tracking for all message components.

**Implementation Note:** The `smartreceive` function iterates through all payloads in the envelope and processes each according to its transport type. See the standard API format in Section 1: `msgEnvelope_v1` supports `AbstractArray{msgPayload_v1}` for multiple payloads.

## Configuration

### Environment Variables

| Variable | Default | Description |
|----------|---------|-------------|
| `NATS_URL` | `nats://localhost:4222` | NATS server URL |
| `FILESERVER_URL` | `http://localhost:8080` | HTTP file server URL (base URL without `/upload` suffix) |
| `SIZE_THRESHOLD` | `1_000_000` | Size threshold in bytes (1MB) |

### Message Envelope Schema

```json
{
  "correlationId": "uuid-v4-string",
  "msgId": "uuid-v4-string",
  "timestamp": "2024-01-15T10:30:00Z",
  
  "sendTo": "topic/subject",
  "msgPurpose": "ACK | NACK | updateStatus | shutdown | chat",
  "senderName": "agent-wine-web-frontend",
  "senderId": "uuid4",
  "receiverName": "agent-backend",
  "receiverId": "uuid4",
  "replyTo": "topic",
  "replyToMsgId": "uuid4",
  "BrokerURL": "nats://localhost:4222",
  
  "metadata": {
    "content_type": "application/octet-stream",
    "content_length": 123456
  },
  
  "payloads": [
    {
      "id": "uuid4",
      "dataname": "login_image",
      "type": "image",
      "transport": "direct",
      "encoding": "base64",
      "size": 15433,
      "data": "base64-encoded-string",
      "metadata": {
        "checksum": "sha256_hash"
      }
    }
  ]
}
```

## Performance Considerations

### Zero-Copy Reading
- Use Arrow's memory-mapped file reading
- Avoid unnecessary data copying during deserialization
- Use Apache Arrow's native IPC reader

### Exponential Backoff
- Maximum retry count: 5
- Base delay: 100ms, max delay: 5000ms
- Implemented in all three implementations (Julia, JavaScript, Python/Micropython)

### Correlation ID Logging
- Log correlation_id at every stage
- Include: send, receive, serialize, deserialize
- Use structured logging format

## Testing

Run the test scripts for each platform:

### Python/Micropython Tests

```bash
# Basic functionality test
python test/test_micropython_basic.py
```

### JavaScript Tests

```bash
# Text message exchange
node test/test_js_to_js_text_sender.js
node test/test_js_to_js_text_receiver.js

# Dictionary exchange
node test/test_js_to_js_dict_sender.js
node test/test_js_to_js_dict_receiver.js

# File transfer (direct transport)
node test/test_js_to_js_file_sender.js
node test/test_js_to_js_file_receiver.js

# Mixed payload types
node test/test_js_to_js_mix_payloads_sender.js
node test/test_js_to_js_mix_payloads_receiver.js

# Table (Arrow IPC) exchange
node test/test_js_to_js_table_sender.js
node test/test_js_to_js_table_receiver.js
```

### Julia Tests

```bash
# Text message exchange
julia test/test_julia_to_julia_text_sender.jl
julia test/test_julia_to_julia_text_receiver.jl

# Dictionary exchange
julia test/test_julia_to_julia_dict_sender.jl
julia test/test_julia_to_julia_dict_receiver.jl

# File transfer
julia test/test_julia_to_julia_file_sender.jl
julia test/test_julia_to_julia_file_receiver.jl

# Mixed payload types
julia test/test_julia_to_julia_mix_payloads_sender.jl
julia test/test_julia_to_julia_mix_payloads_receiver.jl

# Table exchange
julia test/test_julia_to_julia_table_sender.jl
julia test/test_julia_to_julia_table_receiver.jl
```

### Cross-Platform Tests

```bash
# Julia ↔ JavaScript communication
julia test/test_julia_to_julia_text_sender.jl
node test/test_js_to_js_text_receiver.js

# Python ↔ JavaScript communication
python test/test_micropython_basic.py
node test/test_js_to_js_text_receiver.js
```

## Troubleshooting

### Common Issues

1. **NATS Connection Failed**
   - **Julia/JavaScript/Python**: Ensure NATS server is running
   - **Python/Micropython**: Check `nats_url` parameter and network connectivity

2. **HTTP Upload Failed**
   - Ensure file server is running
   - Check `fileserver_url` configuration
   - Verify upload permissions
   - **Micropython**: Ensure `urequests` is available and network is connected

3. **Arrow IPC Deserialization Error**
   - Ensure data is properly serialized to Arrow format
   - Check Arrow version compatibility

4. **Python/Micropython Specific Issues**
   - **Import Error**: Ensure `nats_bridge.py` is in the correct path
   - **Memory Error (Micropython)**: Reduce payload size or use link transport for large payloads
   - **Unicode Error**: Ensure proper encoding when sending text data

## License

MIT