fix another cersion number

Reviewed-on: #9

.gitignore

@@ -0,0 +1,3 @@
+node_modules/
+package.json
+package-lock.json

AI_prompt.txt

@@ -12,3 +12,92 @@ Role: Principal Systems Architect & Lead Software Engineer.Objective: Implement
 
 
 
+Create a walkthrough for Julia service-A service sending a mix-content chat message to Julia service-B. the chat message must includes 
+
+
+
+
+
+I updated the following:
+- NATSBridge.jl. Essentially I add NATS_connection keyword and new publish_message function to support the keyword. 
+Use them and ONLY them as ground truth.
+Then update the following files accordingly:
+- architecture.md
+- implementation.md
+
+All API should be semantically consistent and naming should be consistent across the board.
+
+
+
+
+
+
+
+
+
+Task: Update NATSBridge.js to reflect recent changes in NATSBridge.jl and docs
+Context: NATSBridge.jl and docs has been updated.
+Requirements:
+Source of Truth: Treat the updated NATSBridge.jl and docs as the definitive source.
+API Consistency: Ensure the Main Package API (e.g., smartsend(), publish_message()) uses consistent naming across all three supported languages.
+Ecosystem Variance: Low-level native functions (e.g., NATS.connect(), JSON.read()) should follow the conventions of the specific language ecosystem and do not require cross-language consistency.
+
+
+
+
+
+
+
+I'm expanding this Julia package (NATSBridge) into a cross-platform project by adding a JavaScript and Python/MicroPython implementation. To ensure accuracy, the Julia src directory will serve as the ground truth, as the documentation may be outdated.
+
+My goal is to maintain interface parity at the high-level API for a consistent user experience, while ensuring the low-level implementation adheres strictly to the idiomatic conventions of each respective language (e.g., multiple dispatch in Julia vs. asynchronous, prototype, or class-based patterns in JS and Python/MicroPython)
+
+Now, help me do the following:
+1) check architecture.md for any mistake.
+
+
+
+
+Help me expands this Julia package (NATSBridge) into a cross-platform project by adding a JavaScript and Python/MicroPython implementation. To ensure accuracy,  NATSBridge.jl will serve as the ground truth, as the documentation may be outdated.
+
+My goal is to maintain interface parity at the high-level API for a consistent user experience, while ensuring the low-level implementation adheres strictly to the idiomatic conventions of each respective language (e.g., multiple dispatch in Julia vs. asynchronous, prototype, or class-based patterns in JS and Python/MicroPython)
+
+Now do the following:
+1) check docs to see if there is any mistake.
+
+
+
+
+
+I'm expanding this Julia package (NATSBridge) into a cross-platform project by adding 
+a JavaScript, Python and MicroPython implementation. 
+The following will serve as the ground truth:
+- test_julia_mix_payloads_sender.jl
+- NATSBridge.jl
+- test_julia_mix_payloads_receiver.jl
+- architecture.md
+
+My goal is to maintain interface parity at the high-level API for a consistent user experience, 
+while ensuring the low-level implementation adheres strictly to the idiomatic conventions of each 
+respective language (e.g., multiple dispatch in Julia vs. asynchronous, prototype, or class-based 
+patterns in JS, Python and MicroPython)
+
+Now, help me do the following:
+1) Check whether natsbridge.js needs update or it already up to date.
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+

Manifest.toml

@@ -1,8 +1,8 @@
 # This file is machine-generated - editing it directly is not advised
 
-julia_version = "1.12.4"
+julia_version = "1.12.5"
 manifest_format = "2.0"
-project_hash = "be1e3c2d8b7f4f0ee7375c94aaf704ce73ba57b9"
+project_hash = "b632f853bcf5355f5c53ad3efa7a19f70444dc6c"
 
 [[deps.AliasTables]]
 deps = ["PtrArrays", "Random"]
@@ -436,6 +436,12 @@ git-tree-sha1 = "d9d9a189fb9155a460e6b5e8966bf6a66737abf8"
 uuid = "55e73f9c-eeeb-467f-b4cc-a633fde63d2a"
 version = "0.1.0"
 
+[[deps.NATSBridge]]
+deps = ["Arrow", "DataFrames", "Dates", "GeneralUtils", "HTTP", "JSON", "NATS", "PrettyPrinting", "Revise", "UUIDs"]
+path = "."
+uuid = "f2724d33-f338-4a57-b9f8-1be882570d10"
+version = "0.4.1"
+
 [[deps.NanoDates]]
 deps = ["Dates", "Parsers"]
 git-tree-sha1 = "850a0557ae5934f6e67ac0dc5ca13d0328422d1f"

Plik_API_document.md

@@ -1,194 +0,0 @@
-### API
-Plik server expose a REST-full API to manage uploads and get files :
-
-Get and create upload :
- 
-   - **POST**        /upload
-     - Params (json object in request body) :
-      - oneshot (bool)
-      - stream (bool)
-      - removable (bool)
-      - ttl (int)
-      - login (string)
-      - password (string)
-      - files (see below)
-     - Return :
-         JSON formatted upload object.
-         Important fields :
-           - id (required to upload files)
-           - uploadToken (required to upload/remove files)
-           - files (see below)
-
-   For stream mode you need to know the file id before the upload starts as it will block.
-   File size and/or file type also need to be known before the upload starts as they have to be printed 
-   in HTTP response headers.
-   To get the file ids pass a "files" json object with each file you are about to upload.
-   Fill the reference field with an arbitrary string to avoid matching file ids using the fileName field.
-   This is also used to notify of MISSING files when file upload is not yet finished or has failed.
-  ```
-  "files" : [
-    {
-      "fileName": "file.txt",
-      "fileSize": 12345,
-      "fileType": "text/plain",
-      "reference": "0"
-    },...
-  ]
-  ```
-  
-   - **GET** /upload/:uploadid:
-     - Get upload metadata (files list, upload date, ttl,...)
-
-Upload file :
-
-   - **POST** /$mode/:uploadid:/:fileid:/:filename:
-     - Request body must be a multipart request with a part named "file" containing file data.
-
-   - **POST** /file/:uploadid:
-     - Same as above without passing file id, won't work for stream mode.
-     
-   - **POST** /:
-     - Quick mode, automatically create an upload with default parameters and add the file to it.
-
-Get file :
-
-  - **HEAD** /$mode/:uploadid:/:fileid:/:filename:
-    - Returns only HTTP headers. Useful to know Content-Type and Content-Length without downloading the file. Especially if upload has OneShot option enabled.
-
-  - **GET**  /$mode/:uploadid:/:fileid:/:filename:
-    - Download file. Filename **MUST** match. A browser, might try to display the file if it's a jpeg for example. You may try to force download with ?dl=1 in url.
-
-  - **GET**  /archive/:uploadid:/:filename:
-    - Download uploaded files in a zip archive. :filename: must end with .zip
-
-Remove file :
-
-   - **DELETE** /$mode/:uploadid:/:fileid:/:filename:
-     - Delete file. Upload **MUST** have "removable" option enabled.
-
-Show server details :
-
-   - **GET** /version
-     - Show plik server version, and some build information (build host, date, git revision,...)
-
-   - **GET** /config
-     - Show plik server configuration (ttl values, max file size, ...)
-
-   - **GET** /stats
-     - Get server statistics ( upload/file count, user count, total size used )
-     - Admin only
-
-User authentication :
-
-   - 
-   Plik can authenticate users using Google and/or OVH third-party API.   
-   The /auth API is designed for the Plik web application nevertheless if you want to automatize it be sure to provide a valid
-   Referrer HTTP header and forward all session cookies.   
-   Plik session cookies have the "secure" flag set, so they can only be transmitted over secure HTTPS connections.   
-   To avoid CSRF attacks the value of the plik-xsrf cookie MUST be copied in the X-XSRFToken HTTP header of each
-   authenticated request.   
-   Once authenticated a user can generate upload tokens. Those tokens can be used in the X-PlikToken HTTP header used to link
-   an upload to the user account. It can be put in the ~/.plikrc file of the Plik command line client.   
-   
-   - **Local** :
-      - You'll need to create users using the server command line
-   
-   - **Google** :
-      - You'll need to create a new application in the [Google Developper Console](https://console.developers.google.com)
-      - You'll be handed a Google API ClientID and a Google API ClientSecret that you'll need to put in the plikd.cfg file
-      - Do not forget to whitelist valid origin and redirect url ( https://yourdomain/auth/google/callback ) for your domain
-   
-   - **OVH** :
-      - You'll need to create a new application in the OVH API : https://eu.api.ovh.com/createApp/
-      - You'll be handed an OVH application key and an OVH application secret key that you'll need to put in the plikd.cfg file
-
-   - **GET** /auth/google/login
-      - Get Google user consent URL. User have to visit this URL to authenticate
-
-   - **GET** /auth/google/callback
-     - Callback of the user consent dialog
-     - The user will be redirected back to the web application with a Plik session cookie at the end of this call
-
-   - **GET** /auth/ovh/login
-     - Get OVH user consent URL. User have to visit this URL to authenticate
-     - The response will contain a temporary session cookie to forward the API endpoint and OVH consumer key to the callback
-
-   - **GET** /auth/ovh/callback
-     - Callback of the user consent dialog. 
-     - The user will be redirected back to the web application with a Plik session cookie at the end of this call
-
-   - **POST** /auth/local/login
-     - Params :
-       - login : user login
-       - password : user password
-
-   - **GET** /auth/logout
-     - Invalidate Plik session cookies
-
-   - **GET** /me
-     - Return basic user info ( ID, name, email ) and tokens
-
-   - **DELETE** /me
-     - Remove user account.
-
-   - **GET** /me/token
-     - List user tokens
-      - This call use pagination
-
-   - **POST** /me/token
-     - Create a new upload token
-     - A comment can be passed in the json body
-
-   - **DELETE** /me/token/{token}
-     - Revoke an upload token
-
-   - **GET** /me/uploads
-     - List user uploads
-     - Params :
-        - token : filter by token
-      - This call use pagination
-
-   - **DELETE** /me/uploads
-     - Remove all uploads linked to a user account
-     - Params :
-        - token : filter by token
-
-   - **GET** /me/stats
-     - Get user statistics ( upload/file count, total size used )
-
-   - **GET** /users
-     - List all users
-     - This call use pagination
-     - Admin only 
-
-QRCode :
-
-   - **GET** /qrcode
-     - Generate a QRCode image from an url
-     - Params :
-        - url  : The url you want to store in the QRCode
-        - size : The size of the generated image in pixels (default: 250, max: 1000)
-
-
-$mode can be "file" or "stream" depending if stream mode is enabled. See FAQ for more details.
-
-Examples :
-```sh
-Create an upload (in the json response, you'll have upload id and upload token)
-$ curl -X POST http://127.0.0.1:8080/upload
-
-Create a OneShot upload
-$ curl -X POST -d '{ "OneShot" : true }' http://127.0.0.1:8080/upload
-
-Upload a file to upload
-$ curl -X POST --header "X-UploadToken: M9PJftiApG1Kqr81gN3Fq1HJItPENMhl" -F "file=@test.txt" http://127.0.0.1:8080/file/IsrIPIsDskFpN12E
-
-Get headers
-$ curl -I http://127.0.0.1:8080/file/IsrIPIsDskFpN12E/sFjIeokH23M35tN4/test.txt
-HTTP/1.1 200 OK
-Content-Disposition: filename=test.txt
-Content-Length: 3486
-Content-Type: text/plain; charset=utf-8
-Date: Fri, 15 May 2015 09:16:20 GMT
-
-```

Project.toml

@@ -1,8 +1,21 @@
+name = "NATSBridge"
+uuid = "f2724d33-f338-4a57-b9f8-1be882570d10"
+version = "0.5.2"
+authors = ["narawat <narawat@gmail.com>"]
+
 [deps]
 Arrow = "69666777-d1a9-59fb-9406-91d4454c9d45"
+Base64 = "2a0f44e3-6c83-55bd-87e4-b1978d98bd5f"
+DataFrames = "a93c6f00-e57d-5684-b7b6-d8193f3e46c0"
 Dates = "ade2ca70-3891-5945-98fb-dc099432e06a"
 GeneralUtils = "c6c72f09-b708-4ac8-ac7c-2084d70108fe"
 HTTP = "cd3eb016-35fb-5094-929b-558a96fad6f3"
 JSON = "682c06a0-de6a-54ab-a142-c8b1cf79cde6"
 NATS = "55e73f9c-eeeb-467f-b4cc-a633fde63d2a"
+PrettyPrinting = "54e16d92-306c-5ea0-a30b-337be88ac337"
+Revise = "295af30f-e4ad-537b-8983-00126c2a3abe"
 UUIDs = "cf7118a7-6976-5b1a-9a39-7adc72f591a4"
+
+[compat]
+Base64 = "1.11.0"
+JSON = "1.4.0"

README.md

@@ -0,0 +1,670 @@
+# NATSBridge - Cross-Platform Bi-Directional Data Bridge
+
+A high-performance, bi-directional data bridge for **Julia, JavaScript, Python, and MicroPython** applications using NATS (Core & JetStream), implementing the Claim-Check pattern for large payloads.
+
+[![License: MIT](https://img.shields.io/badge/License-MIT-blue.svg)](https://opensource.org/licenses/MIT)
+[![NATS](https://img.shields.io/badge/NATS-Enabled-green.svg)](https://nats.io)
+
+---
+
+## Table of Contents
+
+- [Overview](#overview)
+- [Cross-Platform Support](#cross-platform-support)
+- [Features](#features)
+- [Quick Start](#quick-start)
+- [API Reference](#api-reference)
+- [Payload Types](#payload-types)
+- [Cross-Platform Examples](#cross-platform-examples)
+- [Testing](#testing)
+- [Documentation](#documentation)
+- [License](#license)
+
+---
+
+## Overview
+
+NATSBridge enables seamless communication across multiple platforms through NATS, with intelligent transport selection based on payload size:
+
+| Transport | Payload Size | Method |
+|-----------|--------------|--------|
+| **Direct** | < 1MB | Sent directly via NATS (Base64 encoded) |
+| **Link** | >= 1MB | Uploaded to HTTP file server, URL sent via NATS |
+
+### Use Cases
+
+- **Chat Applications**: Text, images, audio, video in a single message
+- **File Transfer**: Efficient transfer of large files using claim-check pattern
+- **IoT/Embedded**: Sensor data, telemetry, and analytics pipelines (MicroPython)
+- **Cross-Platform Communication**: Interoperability between Julia, JavaScript, Python, and MicroPython systems
+
+---
+
+## Cross-Platform Support
+
+| Platform | Implementation | Features |
+|----------|----------------|----------|
+| **Julia** | [`src/NATSBridge.jl`](src/NATSBridge.jl) | Full feature set, Arrow IPC, multiple dispatch |
+| **JavaScript** | [`src/natsbridge.js`](src/natsbridge.js) | Node.js & browser, async/await |
+| **Python** | [`src/natsbridge.py`](src/natsbridge.py) | Desktop Python, asyncio, type hints |
+| **MicroPython** | [`src/natsbridge_mpy.py`](src/natsbridge_mpy.py) | Memory-constrained, synchronous API |
+
+### Platform Comparison
+
+| Feature | Julia | JavaScript | Python | MicroPython |
+|---------|-------|------------|--------|-------------|
+| Multiple Dispatch | ✅ Native | ❌ | ❌ | ❌ |
+| Async/Await | ❌ | ✅ Native | ✅ Native | ⚠️ (uasyncio) |
+| Type Safety | ✅ Strong | ⚠️ (TypeScript) | ✅ (Type hints) | ❌ |
+| Arrow IPC | ✅ Native | ✅ | ✅ | ❌ |
+| Direct Transport | ✅ | ✅ | ✅ | ✅ |
+| Link Transport | ✅ | ✅ | ✅ | ⚠️ (Limited) |
+| Handler Functions | ✅ | ✅ | ✅ | ✅ |
+| Cross-Platform API | ✅ | ✅ | ✅ | ✅ |
+
+---
+
+## Features
+
+- ✅ **Cross-platform messaging** for Julia, JavaScript, Python, and MicroPython applications
+- ✅ **Bi-directional messaging** with request-reply patterns
+- ✅ **Multi-payload support** - send multiple payloads with different types in one message
+- ✅ **Automatic transport selection** - direct vs link based on payload size
+- ✅ **Claim-Check pattern** for payloads > 1MB
+- ✅ **Apache Arrow IPC** support for tabular data (zero-copy reading)
+- ✅ **Exponential backoff** for reliable file server downloads
+- ✅ **Correlation ID tracking** for message tracing
+- ✅ **Reply-to support** for request-response patterns
+- ✅ **Handler function abstraction** - pluggable file server implementations (Plik, AWS S3, custom)
+
+---
+
+## Quick Start
+
+### 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
+
+# Start HTTP file server
+python3 -m http.server 8080 --directory /tmp/fileserver
+```
+
+### Step 3: Send Your First Message
+
+#### Julia
+
+```julia
+using NATSBridge
+
+data = [("message", "Hello World", "text")]
+env, env_json_str = smartsend("/chat/room1", data, broker_url="nats://localhost:4222")
+println("Message sent!")
+```
+
+#### JavaScript
+
+```javascript
+const NATSBridge = require('./src/natsbridge.js');
+
+const data = [["message", "Hello World", "text"]];
+const [env, env_json_str] = await NATSBridge.smartsend(
+    "/chat/room1",
+    data,
+    { broker_url: "nats://localhost:4222" }
+);
+console.log("Message sent!");
+```
+
+#### Python
+
+```python
+from natsbridge import smartsend
+
+data = [("message", "Hello World", "text")]
+env, env_json_str = await smartsend(
+    "/chat/room1",
+    data,
+    broker_url="nats://localhost:4222"
+)
+print("Message sent!")
+```
+
+---
+
+## API Reference
+
+### Unified API Standard
+
+All platforms use the same input/output format for payloads:
+
+**Input format for smartsend:**
+```
+[(dataname1, data1, type1), (dataname2, data2, type2), ...]
+```
+
+**Output format for smartreceive:**
+```
+{
+  "correlation_id": "...",
+  "msg_id": "...",
+  "timestamp": "...",
+  "send_to": "...",
+  "msg_purpose": "...",
+  "sender_name": "...",
+  "sender_id": "...",
+  "receiver_name": "...",
+  "receiver_id": "...",
+  "reply_to": "...",
+  "reply_to_msg_id": "...",
+  "broker_url": "...",
+  "metadata": {...},
+  "payloads": [(dataname1, data1, type1), (dataname2, data2, type2), ...]
+}
+```
+
+### smartsend
+
+Sends data either directly via NATS or via a fileserver URL, depending on payload size.
+
+#### Julia
+
+```julia
+using NATSBridge
+
+env, env_json_str = NATSBridge.smartsend(
+    subject::String,
+    data::AbstractArray{Tuple{String, Any, String}};
+    broker_url::String = "nats://localhost:4222",
+    fileserver_url = "http://localhost:8080",
+    fileserver_upload_handler::Function = plik_oneshot_upload,
+    size_threshold::Int = 1_000_000,
+    correlation_id::String = string(uuid4()),
+    msg_purpose::String = "chat",
+    sender_name::String = "NATSBridge",
+    receiver_name::String = "",
+    receiver_id::String = "",
+    reply_to::String = "",
+    reply_to_msg_id::String = "",
+    is_publish::Bool = true,
+    NATS_connection::Union{NATS.Connection, Nothing} = nothing,
+    msg_id::String = string(uuid4()),
+    sender_id::String = string(uuid4())
+)
+# Returns: ::Tuple{msg_envelope_v1, String}
+```
+
+#### JavaScript
+
+```javascript
+const NATSBridge = require('natsbridge');
+
+const [env, env_json_str] = await NATSBridge.smartsend(
+    subject,
+    data,  // Array of [dataname, data, type] tuples
+    {
+        broker_url: 'nats://localhost:4222',
+        fileserver_url: 'http://localhost:8080',
+        fileserver_upload_handler: NATSBridge.plikOneshotUpload,
+        size_threshold: 1_000_000,
+        correlation_id: uuidv4(),
+        msg_purpose: 'chat',
+        sender_name: 'NATSBridge',
+        receiver_name: '',
+        receiver_id: '',
+        reply_to: '',
+        reply_to_msg_id: '',
+        is_publish: true,
+        nats_connection: null,
+        msg_id: uuidv4(),
+        sender_id: uuidv4()
+    }
+);
+// Returns: Promise<[env, env_json_str]>
+```
+
+#### Python
+
+```python
+from natsbridge import NATSBridge
+
+env, env_json_str = await NATSBridge.smartsend(
+    subject: str,
+    data: List[Tuple[str, Any, str]],
+    broker_url: str = "nats://localhost:4222",
+    fileserver_url: str = "http://localhost:8080",
+    fileserver_upload_handler: Callable = plik_oneshot_upload,
+    size_threshold: int = 1_000_000,
+    correlation_id: str = None,
+    msg_purpose: str = "chat",
+    sender_name: str = "NATSBridge",
+    receiver_name: str = "",
+    receiver_id: str = "",
+    reply_to: str = "",
+    reply_to_msg_id: str = "",
+    is_publish: bool = True,
+    nats_connection: Any = None,
+    msg_id: str = None,
+    sender_id: str = None
+)
+# Returns: Tuple[Dict, str]
+```
+
+#### MicroPython
+
+```python
+from natsbridge import NATSBridge
+
+# Limited to direct transport (< 100KB threshold)
+env, env_json_str = NATSBridge.smartsend(
+    subject,
+    data,  # List of (dataname, data, type) tuples
+    broker_url="nats://localhost:4222",
+    size_threshold=100000  # Lower threshold for memory constraints
+)
+# Returns: Tuple[Dict, str]
+```
+
+### smartreceive
+
+Receives and processes messages from NATS, handling both direct and link transport.
+
+#### Julia
+
+```julia
+using NATSBridge
+
+env = NATSBridge.smartreceive(
+    msg::NATS.Msg;
+    fileserver_download_handler::Function = _fetch_with_backoff,
+    max_retries::Int = 5,
+    base_delay::Int = 100,
+    max_delay::Int = 5000
+)
+# Returns: ::JSON.Object{String, Any}
+```
+
+#### JavaScript
+
+```javascript
+const env = await NATSBridge.smartreceive(
+    msg,
+    {
+        fileserver_download_handler: NATSBridge.fetchWithBackoff,
+        max_retries: 5,
+        base_delay: 100,
+        max_delay: 5000
+    }
+);
+// Returns: Promise<env_object>
+```
+
+#### Python
+
+```python
+env = await NATSBridge.smartreceive(
+    msg,
+    fileserver_download_handler=fetch_with_backoff,
+    max_retries=5,
+    base_delay=100,
+    max_delay=5000
+)
+# Returns: Dict with "payloads" key
+```
+
+#### MicroPython
+
+```python
+env = NATSBridge.smartreceive(
+    msg,
+    fileserver_download_handler=_sync_fileserver_download,
+    max_retries=3,
+    base_delay=100,
+    max_delay=1000
+)
+# Returns: Dict with "payloads" key
+```
+
+---
+
+## Payload Types
+
+| Type | Julia | JavaScript | Python | MicroPython | Description |
+|------|-------|------------|--------|-------------|-------------|
+| `text` | `String` | `string` | `str` | `str` | Plain text strings |
+| `dictionary` | `Dict`, `NamedTuple` | `Object`, `Array` | `dict`, `list` | `dict` | JSON-serializable dictionaries |
+| `arrowtable` | `DataFrame`, `Arrow.Table` | `Array<Object>` | `pandas.DataFrame` | ❌ | Tabular data (Arrow IPC) |
+| `jsontable` | `Vector{NamedTuple}` | `Array<Object>` | `list[dict]` | ❌ | Tabular data (JSON) |
+| `image` | `Vector{UInt8}` | `Uint8Array`, `Buffer` | `bytes` | `bytearray` | Image data (PNG, JPG) |
+| `audio` | `Vector{UInt8}` | `Uint8Array`, `Buffer` | `bytes` | `bytearray` | Audio data (WAV, MP3) |
+| `video` | `Vector{UInt8}` | `Uint8Array`, `Buffer` | `bytes` | `bytearray` | Video data (MP4, AVI) |
+| `binary` | `Vector{UInt8}`, `IOBuffer` | `Uint8Array`, `Buffer` | `bytes`, `bytearray` | `bytearray` | Generic binary data |
+
+---
+
+## Cross-Platform Examples
+
+### Example 1: Chat with Mixed Content
+
+Send text, image, and large file in one message.
+
+#### Julia
+
+```julia
+using NATSBridge
+
+data = [
+    ("message_text", "Hello!", "text"),
+    ("user_avatar", image_data, "image"),
+    ("large_document", large_file_data, "binary")
+]
+
+env, env_json_str = NATSBridge.smartsend("/chat/room1", data; fileserver_url="http://localhost:8080")
+```
+
+#### JavaScript
+
+```javascript
+const NATSBridge = require('natsbridge');
+
+const data = [
+    ["message_text", "Hello!", "text"],
+    ["user_avatar", imageData, "image"],
+    ["large_document", largeFileData, "binary"]
+];
+
+const [env, env_json_str] = await NATSBridge.smartsend(
+    "/chat/room1",
+    data,
+    { fileserver_url: 'http://localhost:8080' }
+);
+```
+
+#### Python
+
+```python
+from natsbridge import NATSBridge
+
+data = [
+    ("message_text", "Hello!", "text"),
+    ("user_avatar", image_data, "image"),
+    ("large_document", large_file_data, "binary")
+]
+
+env, env_json_str = await NATSBridge.smartsend(
+    "/chat/room1",
+    data,
+    fileserver_url="http://localhost:8080"
+)
+```
+
+### Example 2: Dictionary Exchange
+
+Send configuration data between platforms.
+
+#### Julia
+
+```julia
+using NATSBridge
+
+config = Dict(
+    "wifi_ssid" => "MyNetwork",
+    "wifi_password" => "password123",
+    "update_interval" => 60
+)
+
+data = [("config", config, "dictionary")]
+env, env_json_str = NATSBridge.smartsend("/device/config", data)
+```
+
+#### JavaScript
+
+```javascript
+const NATSBridge = require('natsbridge');
+
+const config = {
+    wifi_ssid: "MyNetwork",
+    wifi_password: "password123",
+    update_interval: 60
+};
+
+const [env, env_json_str] = await NATSBridge.smartsend(
+    "/device/config",
+    [["config", config, "dictionary"]]
+);
+```
+
+#### Python
+
+```python
+from natsbridge import NATSBridge
+
+config = {
+    "wifi_ssid": "MyNetwork",
+    "wifi_password": "password123",
+    "update_interval": 60
+}
+
+data = [("config", config, "dictionary")]
+env, env_json_str = await NATSBridge.smartsend("/device/config", data)
+```
+
+### Example 3: Table Data (Arrow IPC)
+
+Send tabular data using Apache Arrow IPC format.
+
+#### Julia
+
+```julia
+using NATSBridge
+using DataFrames
+
+df = DataFrame(
+    id = [1, 2, 3],
+    name = ["Alice", "Bob", "Charlie"],
+    score = [95, 88, 92]
+)
+
+data = [("students", df, "arrowtable")]
+env, env_json_str = NATSBridge.smartsend("/data/analysis", data)
+```
+
+#### JavaScript
+
+```javascript
+const NATSBridge = require('natsbridge');
+
+const df = [
+    { id: 1, name: "Alice", score: 95 },
+    { id: 2, name: "Bob", score: 88 },
+    { id: 3, name: "Charlie", score: 92 }
+];
+
+const [env, env_json_str] = await NATSBridge.smartsend(
+    "/data/analysis",
+    [["students", df, "arrowtable"]]
+);
+```
+
+#### Python
+
+```python
+from natsbridge import NATSBridge
+import pandas as pd
+
+df = pd.DataFrame({
+    "id": [1, 2, 3],
+    "name": ["Alice", "Bob", "Charlie"],
+    "score": [95, 88, 92]
+})
+
+data = [("students", df, "arrowtable")]
+env, env_json_str = await NATSBridge.smartsend("/data/analysis", data)
+```
+
+### Example 4: Request-Response Pattern
+
+Bi-directional communication with reply-to support.
+
+#### Julia
+
+```julia
+using NATSBridge
+
+# Requester
+env, env_json_str = NATSBridge.smartsend(
+    "/device/command",
+    [("command", Dict("action" => "read_sensor"), "dictionary")];
+    broker_url="nats://localhost:4222",
+    reply_to="/device/response"
+)
+```
+
+#### JavaScript
+
+```javascript
+const NATSBridge = require('natsbridge');
+
+// Requester
+const [env, env_json_str] = await NATSBridge.smartsend(
+    "/device/command",
+    [["command", { action: "read_sensor" }, "dictionary"]],
+    { broker_url: 'nats://localhost:4222', reply_to: '/device/response' }
+);
+```
+
+#### Python
+
+```python
+from natsbridge import NATSBridge
+
+# Requester
+env, env_json_str = await NATSBridge.smartsend(
+    "/device/command",
+    [("command", {"action": "read_sensor"}, "dictionary")],
+    broker_url="nats://localhost:4222",
+    reply_to="/device/response"
+)
+```
+
+---
+
+## Testing
+
+### Test File Organization
+
+| Platform | Sender Tests | Receiver Tests |
+|----------|--------------|----------------|
+| **Julia** | `test/test_julia_*_sender.jl` | `test/test_julia_*_receiver.jl` |
+| **JavaScript** | `test/test_js_*_sender.js` | `test/test_js_*_receiver.js` |
+| **Python** | `test/test_py_*_sender.py` | `test/test_py_*_receiver.py` |
+
+### Run Tests
+
+#### Julia
+
+```bash
+# Text message exchange
+julia test/test_julia_text_sender.jl
+julia test/test_julia_text_receiver.jl
+
+# Dictionary exchange
+julia test/test_julia_dict_sender.jl
+julia test/test_julia_dict_receiver.jl
+
+# File transfer
+julia test/test_julia_file_sender.jl
+julia test/test_julia_file_receiver.jl
+
+# Mixed payload types
+julia test/test_julia_mix_payloads_sender.jl
+julia test/test_julia_mix_payloads_receiver.jl
+
+# Table exchange
+julia test/test_julia_table_sender.jl
+julia test/test_julia_table_receiver.jl
+```
+
+#### JavaScript (Node.js)
+
+```bash
+# Text message exchange
+node test/test_js_text_sender.js
+node test/test_js_text_receiver.js
+
+# Dictionary exchange
+node test/test_js_dictionary_sender.js
+node test/test_js_dictionary_receiver.js
+
+# Binary transfer
+node test/test_js_binary_sender.js
+node test/test_js_binary_receiver.js
+
+# Table exchange
+node test/test_js_table_sender.js
+node test/test_js_table_receiver.js
+```
+
+#### Python
+
+```bash
+# Text message exchange
+python3 test/test_py_text_sender.py
+python3 test/test_py_text_receiver.py
+
+# Dictionary exchange
+python3 test/test_py_dictionary_sender.py
+python3 test/test_py_dictionary_receiver.py
+
+# Binary transfer
+python3 test/test_py_binary_sender.py
+python3 test/test_py_binary_receiver.py
+
+# Table exchange
+python3 test/test_py_table_sender.py
+python3 test/test_py_table_receiver.py
+```
+
+---
+
+## Documentation
+
+For detailed architecture and implementation information, see:
+
+- [Architecture Documentation](docs/architecture_updated.md) - Cross-platform architecture, API parity, platform-specific patterns
+- [Implementation Guide](docs/implementation_updated.md) - Detailed implementation for each platform, handler functions, testing
+- [Tutorial](docs/tutorial_updated.md) - Step-by-step getting started guide
+- [Walkthrough](docs/walkthrough_updated.md) - Real-world application building guides
+
+---
+
+## License
+
+MIT License
+
+Copyright (c) 2026 NATSBridge Contributors
+
+Permission is hereby granted, free of charge, to any person obtaining a copy
+of this software and associated documentation files (the "Software"), to deal
+in the Software without restriction, including without limitation the rights
+to use, copy, modify, merge, publish, distribute, sublicense, and/or sell
+copies of the Software, and to permit persons to whom the Software is
+furnished to do so, subject to the following conditions:
+
+The above copyright notice and this permission notice shall be included in all
+copies or substantial portions of the Software.
+
+THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR
+IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,
+FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE
+AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER
+LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM,
+OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE
+SOFTWARE.

docs/IMPLEMENTATION.md

@@ -1,321 +0,0 @@
-# Implementation Guide: Bi-Directional Data Bridge
-
-## Overview
-
-This document describes the implementation of the high-performance, bi-directional data bridge between Julia and JavaScript services using NATS (Core & JetStream), implementing the Claim-Check pattern for large payloads.
-
-## Architecture
-
-The implementation follows the Claim-Check pattern:
-
-```
-┌─────────────────────────────────────────────────────────────────────────┐
-│                         SmartSend Function                              │
-└─────────────────────────────────────────────────────────────────────────┘
-                             │
-                             ▼
-┌─────────────────────────────────────────────────────────────────────────┐
-│  Is payload size < 1MB?                                                 │
-└─────────────────────────────────────────────────────────────────────────┘
-                             │
-           ┌─────────────────┴─────────────────┐
-           ▼                                   ▼
-     ┌─────────────────┐                 ┌─────────────────┐
-     │  Direct Path    │                 │  Link Path      │
-     │  (< 1MB)        │                 │  (> 1MB)        │
-     │                 │                 │                 │
-     │ • Serialize to  │                 │ • Serialize to  │
-     │   IOBuffer      │                 │   IOBuffer      │
-     │ • Base64 encode │                 │ • Upload to     │
-     │ • Publish to    │                 │   HTTP Server   │
-     │   NATS          │                 │ • Publish to    │
-     │                 │                 │   NATS with URL │
-     └─────────────────┘                 └─────────────────┘
-```
-
-## Files
-
-### Julia Module: [`src/julia_bridge.jl`](../src/julia_bridge.jl)
-
-The Julia implementation provides:
-
-- **[`MessageEnvelope`](../src/julia_bridge.jl)**: Struct for the unified JSON envelope
-- **[`SmartSend()`](../src/julia_bridge.jl)**: Handles transport selection based on payload size
-- **[`SmartReceive()`](../src/julia_bridge.jl)**: Handles both direct and link transport
-
-### JavaScript Module: [`src/js_bridge.js`](../src/js_bridge.js)
-
-The JavaScript implementation provides:
-
-- **`MessageEnvelope` class**: For the unified JSON envelope
-- **[`SmartSend()`](../src/js_bridge.js)**: Handles transport selection based on payload size
-- **[`SmartReceive()`](../src/js_bridge.js)**: 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
-```
-
-## 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 1: Command & Control (Small JSON)
-
-#### JavaScript (Sender)
-```javascript
-const { SmartSend } = require('./js_bridge');
-
-const config = {
-    step_size: 0.01,
-    iterations: 1000
-};
-
-await SmartSend("control", config, "json", {
-    correlationId: "unique-id"
-});
-```
-
-#### Julia (Receiver)
-```julia
-using NATS
-using JSON3
-
-# Subscribe to control subject
-subscribe(nats, "control") do msg
-    env = MessageEnvelope(String(msg.data))
-    config = JSON3.read(env.payload)
-    
-    # Execute simulation with parameters
-    step_size = config.step_size
-    iterations = config.iterations
-    
-    # Send acknowledgment
-    response = Dict("status" => "Running", "correlation_id" => env.correlation_id)
-    publish(nats, "control_response", JSON3.stringify(response))
-end
-```
-
-### 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 with type="table"
-await SmartSend("analysis_results", df, "table");
-```
-
-#### JavaScript (Receiver)
-```javascript
-const { SmartReceive } = require('./js_bridge');
-
-const result = await SmartReceive(msg);
-
-// Use table data for visualization with Perspective.js or D3
-const table = result.data;
-```
-
-### Scenario 3: Live Binary Processing
-
-#### JavaScript (Sender)
-```javascript
-const { SmartSend } = require('./js_bridge');
-
-// Capture binary chunk
-const binaryData = await navigator.mediaDevices.getUserMedia({ binary: true });
-
-await SmartSend("binary_input", binaryData, "binary", {
-    metadata: {
-        sample_rate: 44100,
-        channels: 1
-    }
-});
-```
-
-#### Julia (Receiver)
-```julia
-using WAV
-using DSP
-
-# Receive binary data
-function process_binary(data)
-    # Perform FFT or AI transcription
-    spectrum = fft(data)
-    
-    # Send results back (JSON + Arrow table)
-    results = Dict("transcription" => "sample text", "spectrum" => spectrum)
-    await SmartSend("binary_output", results, "json")
-end
-```
-
-### Scenario 4: Catch-Up (JetStream)
-
-#### Julia (Producer)
-```julia
-using NATS
-
-function publish_health_status(nats)
-    jetstream = JetStream(nats, "health_updates")
-    
-    while true
-        status = Dict("cpu" => rand(), "memory" => rand())
-        publish(jetstream, "health", status)
-        sleep(5)  # Every 5 seconds
-    end
-end
-```
-
-#### JavaScript (Consumer)
-```javascript
-const { connect } = require('nats');
-
-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 result = await SmartReceive(msg);
-    // Process the data
-    msg.ack();
-}
-```
-
-## Configuration
-
-### Environment Variables
-
-| Variable | Default | Description |
-|----------|---------|-------------|
-| `NATS_URL` | `nats://localhost:4222` | NATS server URL |
-| `FILESERVER_URL` | `http://localhost:8080/upload` | HTTP file server URL |
-| `SIZE_THRESHOLD` | `1_000_000` | Size threshold in bytes (1MB) |
-
-### Message Envelope Schema
-
-```json
-{
-  "correlation_id": "uuid-v4-string",
-  "type": "json|table|binary",
-  "transport": "direct|link",
-  "payload": "base64-encoded-string",  // Only if transport=direct
-  "url": "http://fileserver/path/to/data",  // Only if transport=link
-  "metadata": {
-    "content_type": "application/octet-stream",
-    "content_length": 123456,
-    "format": "arrow_ipc_stream"
-  }
-}
-```
-
-## 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 both Julia and JavaScript implementations
-
-### Correlation ID Logging
-- Log correlation_id at every stage
-- Include: send, receive, serialize, deserialize
-- Use structured logging format
-
-## Testing
-
-Run the test scripts:
-
-```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
-```
-
-## Troubleshooting
-
-### Common Issues
-
-1. **NATS Connection Failed**
-   - Ensure NATS server is running
-   - Check NATS_URL configuration
-
-2. **HTTP Upload Failed**
-   - Ensure file server is running
-   - Check FILESERVER_URL configuration
-   - Verify upload permissions
-
-3. **Arrow IPC Deserialization Error**
-   - Ensure data is properly serialized to Arrow format
-   - Check Arrow version compatibility
-
-## License
-
-MIT

docs/architecture.md

@@ -1,294 +1,475 @@
-# Architecture Documentation: Bi-Directional Data Bridge (Julia ↔ JavaScript)
+# Cross-Platform Architecture Documentation: Bi-Directional Data Bridge
 
 ## Overview
 
-This document describes the architecture for a high-performance, bi-directional data bridge between a Julia service and a JavaScript (Node.js) service using NATS (Core & JetStream), implementing the Claim-Check pattern for large payloads.
+This document describes the architecture for a high-performance, bi-directional data bridge using **NATS (Core & JetStream)**, implementing the Claim-Check pattern for large payloads. The system is implemented across three platforms with **high-level API parity** while maintaining **idiomatic implementations** for each language.
 
-## Architecture Diagram
+**Supported Platforms:**
+- **Julia** - Ground truth implementation with full feature set
+- **JavaScript** - Node.js and browser-compatible implementation
+- **Python/MicroPython** - Desktop and embedded-compatible implementation
+
+### Cross-Platform Design Principles
+
+1. **High-Level API Parity**: All three platforms expose the same `smartsend()` and `smartreceive()` functions with identical signatures and behavior
+2. **Idiomatic Implementations**: Each platform uses its native patterns (multiple dispatch in Julia, async/prototype in JS, class-based in Python)
+3. **Message Format Consistency**: The `msg_envelope_v1` and `msg_payload_v1` JSON schemas are identical across all platforms
+4. **Handler Function Abstraction**: File server operations are abstracted through handler functions for backend flexibility
+
+---
+
+## High-Level API Standard (Cross-Platform)
+
+### Unified API Signature
+
+All three platforms expose the same high-level API:
+
+**Input Format (smartsend):**
+```
+[(dataname1, data1, type1), (dataname2, data2, type2), ...]
+```
+
+**Output Format (smartreceive):**
+```
+{
+  "correlation_id": "...",
+  "msg_id": "...",
+  "timestamp": "...",
+  "send_to": "...",
+  "msg_purpose": "...",
+  "sender_name": "...",
+  "sender_id": "...",
+  "receiver_name": "...",
+  "receiver_id": "...",
+  "reply_to": "...",
+  "reply_to_msg_id": "...",
+  "broker_url": "...",
+  "metadata": {...},
+  "payloads": [(dataname1, data1, type1), (dataname2, data2, type2), ...]
+}
+```
+
+### Supported Payload Types
+
+| Type | Julia | JavaScript | Python/MicroPython |
+|------|-------|------------|-------------------|
+| `text` | `String` | `string` | `str` |
+| `dictionary` | `Dict`, `NamedTuple` | `Object`, `Array` | `dict`, `list` |
+| `arrowtable` | `DataFrame`, `Arrow.Table` | `Array<Object>` (input) → `Buffer` (Arrow IPC) | `pandas.DataFrame`, `bytes` (Arrow IPC) |
+| `jsontable` | `Vector{NamedTuple}`, `Vector{Dict}` | `Array<Object>` | `list[dict]`, `list` |
+| `table` | ❌ | ❌ | `pandas.DataFrame`, `bytes` (Arrow IPC) |
+| `image` | `Vector{UInt8}` | `Uint8Array`, `Buffer` | `bytes`, `bytearray` |
+| `audio` | `Vector{UInt8}` | `Uint8Array`, `Buffer` | `bytes`, `bytearray` |
+| `video` | `Vector{UInt8}` | `Uint8Array`, `Buffer` | `bytes`, `bytearray` |
+| `binary` | `Vector{UInt8}`, `IOBuffer` | `Uint8Array`, `Buffer` | `bytes`, `bytearray`, `io.BytesIO` |
+
+**Note on MicroPython:** MicroPython does not support table types (`arrowtable` or `jsontable`) due to memory constraints. Use `dictionary` or `binary` instead.
+
+### Cross-Platform API Examples
+
+**Julia:**
+```julia
+using NATSBridge
+
+# Send
+env, env_json_str = smartsend(
+    "/chat",
+    [("message", "Hello!", "text"), ("image", image_bytes, "image")],
+    broker_url="nats://localhost:4222"
+)
+
+# Receive - returns JSON.Object{String, Any}
+env = smartreceive(msg; fileserver_download_handler=_fetch_with_backoff)
+# env is a JSON.Object{String, Any} with "payloads" field containing Vector{Tuple{String, Any, String}}
+# Access payloads: for (dataname, data, type) in env["payloads]
+```
+
+**JavaScript:**
+```javascript
+const NATSBridge = require('natsbridge');
+
+// Send
+const [env, env_json_str] = await NATSBridge.smartsend(
+    "/chat",
+    [
+        ["message", "Hello!", "text"],
+        ["image", imageBuffer, "image"]
+    ],
+    { broker_url: "nats://localhost:4222" }
+);
+
+// Receive - returns Promise<object>
+const env = await NATSBridge.smartreceive(msg, {
+    fileserver_download_handler: fetchWithBackoff
+});
+// env is an object with "payloads" field containing Array of arrays
+// Access payloads: for (const [dataname, data, type] of env.payloads)
+```
+
+**Python:**
+```python
+from natsbridge import NATSBridge
+
+# Send
+env, env_json_str = NATSBridge.smartsend(
+    "/chat",
+    [("message", "Hello!", "text"), ("image", image_bytes, "image")],
+    broker_url="nats://localhost:4222"
+)
+
+# Receive - returns Tuple[Dict, str]
+env = NATSBridge.smartreceive(
+    msg,
+    fileserver_download_handler=fetch_with_backoff
+)
+# env is a Dict with "payloads" key containing List[Tuple[str, Any, str]]
+# Access payloads: for dataname, data, type_ in env["payloads"]
+```
+
+**MicroPython:**
+```python
+from natsbridge import NATSBridge
+
+# Send (limited to direct transport due to memory constraints)
+env, env_json_str = NATSBridge.smartsend(
+    "/chat",
+    [("message", "Hello!", "text")],
+    broker_url="nats://localhost:4222"
+)
+```
+
+---
+
+## Architecture Diagram (Cross-Platform)
 
 ```mermaid
 flowchart TD
     subgraph Client
-        JS[JavaScript Client]
+        App[Julia/JS/Python/MicroPython Application]
-        JSApp[Application Logic]
     end
 
     subgraph Server
-        Julia[Julia Service]
+        Julia/JS/Python/MicroPython[Julia/JS/Python/MicroPython Service]
         NATS[NATS Server]
         FileServer[HTTP File Server]
     end
 
-    JS -->|Control/Small Data| JSApp
+    App -->|NATS| NATS
-    JSApp -->|NATS| NATS
+    NATS -->|NATS| Julia/JS/Python/MicroPython
-    NATS -->|NATS| Julia
+    Julia/JS/Python/MicroPython -->|NATS| NATS
-    Julia -->|NATS| NATS
+    Julia/JS/Python/MicroPython -->|HTTP POST| FileServer
-    Julia -->|HTTP POST| FileServer
-    JS -->|HTTP GET| FileServer
 
-    style JS fill:#e1f5fe
+    style App fill:#e8f5e9
-    style Julia fill:#e8f5e9
+    style Julia/JS/Python/MicroPython fill:#e8f5e9
     style NATS fill:#fff3e0
     style FileServer fill:#f3e5f5
 ```
 
+---
+
 ## System Components
 
-### 1. Unified JSON Envelope Schema
+### 1. msg_envelope_v1 - Message Envelope
-
-All messages use a standardized envelope format:
 
+**JSON Schema (Identical Across All Platforms):**
 ```json
 {
   "correlation_id": "uuid-v4-string",
-  "type": "json|table|binary",
+  "msg_id": "uuid-v4-string",
-  "transport": "direct|link",
+  "timestamp": "2024-01-15T10:30:00Z",
-  "payload": "base64-encoded-string",  // Only if transport=direct
+  
-  "url": "http://fileserver/path/to/data",  // Only if transport=link
+  "send_to": "topic/subject",
+  "msg_purpose": "ACK | NACK | updateStatus | shutdown | chat",
+  "sender_name": "agent-wine-web-frontend",
+  "sender_id": "uuid4",
+  "receiver_name": "agent-backend",
+  "receiver_id": "uuid4",
+  "reply_to": "topic",
+  "reply_to_msg_id": "uuid4",
+  "broker_url": "nats://localhost:4222",
+  
   "metadata": {
     "content_type": "application/octet-stream",
-    "content_length": 123456,
+    "content_length": 123456
-    "format": "arrow_ipc_stream"
+  },
+  
+  "payloads": [
+    {
+      "id": "uuid4",
+      "dataname": "login_image",
+      "payload_type": "image",
+      "transport": "direct",
+      "encoding": "base64",
+      "size": 15433,
+      "data": "base64-encoded-string",
+      "metadata": {
+        "checksum": "sha256_hash"
+      }
+    },
+    {
+      "id": "uuid4",
+      "dataname": "large_arrow_table",
+      "payload_type": "arrowtable",
+      "transport": "link",
+      "encoding": "arrow-ipc",
+      "size": 524288,
+      "data": "http://localhost:8080/file/UPLOAD_ID/FILE_ID/data.arrow",
+      "metadata": {}
+    }
+  ]
+}
+```
+
+### 2. msg_payload_v1 - Payload Structure
+
+**JSON Schema (Identical Across All Platforms):**
+```json
+{
+  "id": "uuid4",
+  "dataname": "login_image",
+  "payload_type": "image | dictionary | arrowtable | jsontable | table | text | audio | video | binary",
+  "transport": "direct | link",
+  "encoding": "none | json | base64 | arrow-ipc",
+  "size": 15433,
+  "data": "base64-encoded-string | http-url | json-string",
+  "metadata": {
+    "checksum": "sha256_hash"
   }
 }
 ```
 
-### 2. Transport Strategy Decision Logic
+### 3. Transport Strategy Decision Logic (Cross-Platform)
 
 ```
 ┌─────────────────────────────────────────────────────────────┐
-│                     SmartSend Function                        │
+│              smartsend Function (All Platforms)             │
+│  Accepts: [(dataname1, data1, type1), ...]                  │
+│  (Type is per payload, not standalone)                      │
 └─────────────────────────────────────────────────────────────┘
-                            │
+                         │
-                            ▼
+                         ▼
 ┌─────────────────────────────────────────────────────────────┐
-│  Is payload size < 1MB?                                     │
+│  For each payload:                                          │
+│  1. Extract type from tuple/array                           │
+│  2. Serialize based on type                                 │
+│  3. Check payload size                                      │
 └─────────────────────────────────────────────────────────────┘
-                            │
+                         │
-          ┌─────────────────┴─────────────────┐
+              ┌───────────┴────────────┐
-          ▼                                   ▼
+              ▼                        ▼
-    ┌─────────────────┐                 ┌─────────────────┐
+        ┌──────────────┐         ┌──────────────┐
-    │  Direct Path    │                 │  Link Path      │
+        │  Direct Path │         │  Link Path   │
-    │  (< 1MB)        │                 │  (> 1MB)        │
+        │  (< 1MB)     │         │  (>= 1MB)    │
-    │                 │                 │                 │
+        │              │         │              │
-    │ • Serialize to  │                 │ • Serialize to  │
+        │ • Serialize  │         │ • Serialize  │
-    │   IOBuffer      │                 │   IOBuffer      │
+        │   to buffer  │         │   to buffer  │
-    │ • Base64 encode │                 │ • Upload to     │
+        │ • Base64/JSON│         │ • Upload to  │
-    │ • Publish to    │                 │   HTTP Server   │
+        │   encode     │         │   HTTP Server│
-    │   NATS          │                 │ • Publish to    │
+        │ • Publish to │         │ • Publish to │
-    │                 │                 │   NATS with URL │
+        │   NATS       │         │   NATS with  │
-    └─────────────────┘                 └─────────────────┘
+        │   (in msg)   │         │   URL        │
+        └──────────────┘         └──────────────┘
 ```
 
-### 3. Julia Module Architecture
+---
 
-```mermaid
+## Platform Comparison Matrix
-graph TD
-    subgraph JuliaModule
-        SmartSendJulia[SmartSend Julia]
-        SizeCheck[Size Check]
-        DirectPath[Direct Path]
-        LinkPath[Link Path]
-        HTTPClient[HTTP Client]
-    end
 
-    SmartSendJulia --> SizeCheck
+| Feature | Julia | JavaScript | Python | MicroPython |
-    SizeCheck -->|< 1MB| DirectPath
+|---------|-------|------------|--------|-------------|
-    SizeCheck -->|>= 1MB| LinkPath
+| **Multiple Dispatch** | ✅ Native | ❌ (Prototypes) | ❌ (Overload via `@overload`) | ❌ |
-    LinkPath --> HTTPClient
+| **Async/Await** | ❌ (Tasks) | ✅ Native | ✅ Native | ⚠️ (uasyncio) |
+| **Type Safety** | ✅ Strong | ⚠️ (TypeScript) | ✅ (Type hints) | ❌ |
+| **Memory Management** | ✅ GC | ✅ GC | ✅ GC | ⚠️ (Manual) |
+| **Arrow IPC** | ✅ Native | ✅ (arrow package) | ✅ (pyarrow) | ❌ |
+| **JSON Serialization** | ✅ (JSON.jl) | ✅ (native) | ✅ (json) | ✅ (json) |
+| **arrowtable Support** | ✅ | ✅ | ✅ | ❌ |
+| **jsontable Support** | ✅ | ✅ | ✅ | ❌ |
+| **Direct Transport** | ✅ | ✅ | ✅ | ✅ |
+| **Link Transport** | ✅ | ✅ | ✅ | ⚠️ (Limited) |
+| **Handler Functions** | ✅ | ✅ | ✅ | ✅ |
+| **Cross-Platform API** | ✅ | ✅ | ✅ | ✅ |
 
-    style JuliaModule fill:#c5e1a5
+---
+
+## Platform-Specific Architecture Patterns
+
+### Julia: Multiple Dispatch Pattern
+
+Julia leverages multiple dispatch for type-specific implementations:
+
+- **Function overloading** based on argument types
+- **Struct-based data models** with explicit types
+- **Native Arrow IPC** support via Arrow.jl
+
+### JavaScript: Prototype + Async Pattern
+
+JavaScript uses async/await for non-blocking I/O:
+
+- **Class-based NATS client** for connection management
+- **Module-level utility functions** for serialization
+- **Native ArrayBuffer** for binary data handling
+
+### Python: Class-Based Pattern
+
+Python uses classes for stateful operations:
+
+- **Class-based NATSBridge** with type hints
+- **Dataclasses** for structured data (MsgPayloadV1, MsgEnvelopeV1)
+- **Async/await** for I/O operations
+
+### MicroPython: Synchronous Pattern
+
+MicroPython has significant constraints:
+
+- **Synchronous API** (no async/await)
+- **Memory-constrained** (256KB - 1MB)
+- **Limited payload support** (no tables, max 50KB)
+
+---
+
+## Cross-Platform Compatibility Notes
+
+### 1. Payload Type Consistency
+
+All platforms use the same payload type values for tabular data:
+
+| Platform | Table Types |
+|----------|-------------|
+| Julia | `"arrowtable"`, `"jsontable"` |
+| JavaScript | `"arrowtable"`, `"jsontable"` |
+| Python | `"arrowtable"`, `"jsontable"` |
+| MicroPython | Not supported |
+
+
+### 2. Direct Transport Encoding Field
+
+The encoding field in direct transport payloads differs between platforms:
+
+| Platform | Encoding for Direct Transport |
+|----------|-------------------------------|
+| Julia | Preserves original type: `"base64"`, `"json"`, or `"arrow-ipc"` |
+| JavaScript | Preserves original type: `"base64"`, `"json"`, or `"arrow-ipc"` |
+| Python | Always `"base64"` for all direct transport payloads |
+| MicroPython | Always `"base64"` for all direct transport payloads |
+
+**Impact:** The encoding field may not accurately reflect the original serialization format when using Python or MicroPython.
+
+### 3. MicroPython Limitations
+
+MicroPython has significant constraints that affect feature support:
+
+| Feature | Desktop Platforms | MicroPython |
+|---------|-------------------|-------------|
+| `arrowtable` | ✅ | ❌ (not supported - memory constraints) |
+| `jsontable` | ✅ | ❌ (not supported - memory constraints) |
+| `table` | ✅ | ❌ (not supported - memory constraints) |
+| Async/await | ✅ | ❌ (synchronous only) |
+| File upload/download | ✅ | ⚠️ (placeholder implementations) |
+| MAX_PAYLOAD_SIZE | 1MB+ | 50KB (hard limit) |
+| DEFAULT_SIZE_THRESHOLD | 1MB | 100KB |
+
+**Impact:** MicroPython should only be used for small payloads with direct transport. File server operations are not fully implemented.
+
+---
+
+## Configuration
+
+### Environment Variables
+
+| Variable | Default | Description |
+|----------|---------|-------------|
+| `NATS_URL` | `nats://localhost:4222` | NATS server URL |
+| `FILESERVER_URL` | `http://localhost:8080` | HTTP file server URL |
+| `SIZE_THRESHOLD` | `1000000` | Size threshold in bytes (1MB) |
+
+### MicroPython-Specific Configuration
+
+```python
+# micropython.conf
+NATS_URL = "nats://broker.local:4222"
+FILESERVER_URL = "http://fileserver.local:8080"
+SIZE_THRESHOLD = 100000  # Lower threshold for memory-constrained devices
+MAX_PAYLOAD_SIZE = 50000  # Hard limit for MicroPython
 ```
 
-### 4. JavaScript Module Architecture
+---
-
-```mermaid
-graph TD
-    subgraph JSModule
-        SmartSendJS[SmartSend JS]
-        SmartReceiveJS[SmartReceive JS]
-        JetStreamConsumer[JetStream Pull Consumer]
-        ApacheArrow[Apache Arrow]
-    end
-
-    SmartSendJS --> NATS
-    SmartReceiveJS --> JetStreamConsumer
-    JetStreamConsumer --> ApacheArrow
-
-    style JSModule fill:#f3e5f5
-```
-
-## Implementation Details
-
-### Julia Implementation
-
-#### Dependencies
-- `NATS.jl` - Core NATS functionality
-- `Arrow.jl` - Arrow IPC serialization
-- `JSON3.jl` - JSON parsing
-- `HTTP.jl` - HTTP client for file server
-- `Dates.jl` - Timestamps for logging
-
-#### SmartSend Function
-
-```julia
-function SmartSend(
-    subject::String,
-    data::Any,
-    type::String = "json";
-    nats_url::String = "nats://localhost:4222",
-    fileserver_url::String = "http://localhost:8080/upload",
-    size_threshold::Int = 1_000_000  # 1MB
-)
-```
-
-**Flow:**
-1. Serialize data to Arrow IPC stream (if table)
-2. Check payload size
-3. If < threshold: publish directly to NATS with Base64-encoded payload
-4. If >= threshold: upload to HTTP server, publish NATS with URL
-
-#### SmartReceive Handler
-
-```julia
-function SmartReceive(msg::NATS.Message)
-    # Parse envelope
-    # Check transport type
-    # If direct: decode Base64 payload
-    # If link: fetch from URL with exponential backoff
-    # Deserialize Arrow IPC to DataFrame
-end
-```
-
-### JavaScript Implementation
-
-#### Dependencies
-- `nats.js` - Core NATS functionality
-- `apache-arrow` - Arrow IPC serialization
-- `uuid` - Correlation ID generation
-
-#### SmartSend Function
-
-```javascript
-async function SmartSend(subject, data, type = 'json', options = {})
-```
-
-**Flow:**
-1. Serialize data to Arrow IPC buffer (if table)
-2. Check payload size
-3. If < threshold: publish directly to NATS
-4. If >= threshold: upload to HTTP server, publish NATS with URL
-
-#### SmartReceive Handler
-
-```javascript
-async function SmartReceive(msg, options = {})
-```
-
-**Flow:**
-1. Parse envelope
-2. Check transport type
-3. If direct: decode Base64 payload
-4. If link: fetch with exponential backoff
-5. Deserialize Arrow IPC with zero-copy
-
-## Scenario Implementations
-
-### Scenario 1: Command & Control (Small JSON)
-
-**Julia (Receiver):**
-```julia
-# Subscribe to control subject
-# Parse JSON envelope
-# Execute simulation with parameters
-# Send acknowledgment
-```
-
-**JavaScript (Sender):**
-```javascript
-// Create small JSON config
-// Send via SmartSend with type="json"
-```
-
-### Scenario 2: Deep Dive Analysis (Large Arrow Table)
-
-**Julia (Sender):**
-```julia
-# Create large DataFrame
-# Convert to Arrow IPC stream
-# Check size (> 1MB)
-# Upload to HTTP server
-# Publish NATS with URL
-```
-
-**JavaScript (Receiver):**
-```javascript
-// Receive NATS message with URL
-// Fetch data from HTTP server
-// Parse Arrow IPC with zero-copy
-// Load into Perspective.js or D3
-```
-
-### Scenario 3: Live Audio Processing
-
-**JavaScript (Sender):**
-```javascript
-// Capture audio chunk
-// Send as binary with metadata headers
-// Use SmartSend with type="audio"
-```
-
-**Julia (Receiver):**
-```julia
-// Receive audio data
-// Perform FFT or AI transcription
-// Send results back (JSON + Arrow table)
-```
-
-### Scenario 4: Catch-Up (JetStream)
-
-**Julia (Producer):**
-```julia
-# Publish to JetStream
-# Include metadata for temporal tracking
-```
-
-**JavaScript (Consumer):**
-```javascript
-// Connect to JetStream
-// Request replay from last 10 minutes
-// Process historical and real-time messages
-```
 
 ## Performance Considerations
 
 ### Zero-Copy Reading
-- Use Arrow's memory-mapped file reading
+
-- Avoid unnecessary data copying during deserialization
+| Platform | Strategy |
-- Use Apache Arrow's native IPC reader
+|----------|----------|
+| **Julia** | `Arrow.read()` with memory-mapped files |
+| **JavaScript** | `ArrayBuffer` with `DataView` |
+| **Python** | `pyarrow` memory mapping |
+| **MicroPython** | Not available (streaming only) |
 
 ### Exponential Backoff
-- Implement exponential backoff for HTTP link fetching
+
-- Maximum retry count: 5
+All platforms implement exponential backoff for HTTP downloads:
-- Base delay: 100ms, max delay: 5000ms
+
+```
+delay = base_delay
+for attempt in 1:max_retries:
+    try:
+        response = fetch(url)
+        if success: return response
+    except:
+        if attempt < max_retries:
+            sleep(delay)
+            delay = min(delay * 2, max_delay)
+```
 
 ### Correlation ID Logging
-- Log correlation_id at every stage
-- Include: send, receive, serialize, deserialize
-- Use structured logging format
 
-## Testing Strategy
+All platforms use correlation IDs for distributed tracing:
 
-### Unit Tests
+```
-- Test SmartSend with various payload sizes
+[timestamp] [Correlation: abc123] Message published to subject
-- Test SmartReceive with direct and link transport
+```
-- Test Arrow IPC serialization/deserialization
 
-### Integration Tests
+### Serialization Performance Comparison
-- Test full flow with NATS server
-- Test large data transfer (> 100MB)
-- Test audio processing pipeline
 
-### Performance Tests
+| Format | Use Case | Pros | Cons |
-- Measure throughput for small payloads
+|--------|----------|------|------|
-- Measure throughput for large payloads
+| `arrowtable` | Large tabular data | Fast, zero-copy, schema-preserving | Binary format, requires Arrow library |
+| `jsontable` | Small/medium tabular data | Human-readable, universal support | Slower, larger size, no schema |
+| `table` (Python) | Large tabular data | Fast, zero-copy, schema-preserving | Python-specific, requires pyarrow |
+
+---
+
+## Summary
+
+This cross-platform NATS bridge provides:
+
+1. **High-Level API Parity**: Identical `smartsend()` and `smartreceive()` signatures across Julia, JavaScript, and Python/MicroPython
+2. **Idiomatic Implementations**: 
+   - Julia: Multiple dispatch and struct-based design
+   - JavaScript: Async/await and prototype-based utilities
+   - Python: Class-based design with type hints
+   - MicroPython: Synchronous API with memory constraints
+3. **Message Format Consistency**: Identical `msg_envelope_v1` and `msg_payload_v1` JSON schemas
+4. **Handler Abstraction**: File server operations abstracted through configurable handlers
+5. **Platform-Specific Optimizations**: 
+   - **Arrow IPC** (`arrowtable`): Efficient binary format for large tabular data
+   - **JSON** (`jsontable`): Universal human-readable format for smaller tables
+   - **Python table**: Unified table type for Python-specific implementations
+   - Streaming support in MicroPython
+
+The Julia implementation serves as the **ground truth** for API design and behavior, while JavaScript and Python implementations maintain interface parity while leveraging their respective language idioms.
+
+### Datatype Summary
+
+| Datatype | Serialization | Use Case | Encoding | Supported Platforms |
+|----------|---------------|----------|----------|---------------------|
+| `text` | UTF-8 bytes | Text messages, chat content | `utf-8` → `base64` | All |
+| `dictionary` | JSON | Structured key-value data, config | `json` → `base64` | All |
+| `arrowtable` | Apache Arrow IPC | Large tabular data, schema-preserving | `arrow-ipc` → `base64` | Julia, JavaScript, Python |
+| `jsontable` | JSON | Small/medium tabular data, human-readable | `json` → `base64` | Julia, JavaScript, Python |
+| `table` | Apache Arrow IPC | Python's unified table type | `arrow-ipc` → `base64` | Python |
+| `image` | Binary | Image files (JPEG, PNG, etc.) | `binary` → `base64` | All |
+| `audio` | Binary | Audio files (WAV, MP3, etc.) | `binary` → `base64` | All |
+| `video` | Binary | Video files (MP4, AVI, etc.) | `binary` → `base64` | All |
+| `binary` | Binary | Generic binary data, files | `binary` → `base64` | All |

docs/tutorial.md

@@ -0,0 +1,741 @@
+# Cross-Platform NATSBridge Tutorial
+
+A step-by-step guide to get started with NATSBridge across **Julia**, **JavaScript**, and **Python/MicroPython**.
+
+## Table of Contents
+
+1. [Overview](#overview)
+2. [Prerequisites](#prerequisites)
+3. [Installation](#installation)
+4. [Quick Start](#quick-start)
+5. [Basic Examples](#basic-examples)
+6. [Advanced Usage](#advanced-usage)
+
+---
+
+## Overview
+
+NATSBridge enables seamless communication across platforms through NATS, with automatic transport selection based on payload size:
+
+- **Direct Transport**: Payloads < 1MB are sent directly via NATS (Base64 encoded)
+- **Link Transport**: Payloads >= 1MB are uploaded to an HTTP file server and referenced via URL
+
+### Cross-Platform API Parity
+
+All three platforms use the same high-level API:
+
+```
+# Input format
+smartsend(subject, [(dataname, data, type), ...], options)
+
+# Output format
+(env, env_json_str) = smartsend(...)
+env = smartreceive(msg, options)
+```
+
+**Important Platform Differences:**
+
+1. **Encoding field:** Julia and JavaScript preserve the original serialization format in the encoding field (`"base64"`, `"json"`, or `"arrow-ipc"`), while Python and MicroPython always use `"base64"` for all direct transport payloads.
+
+2. **Async vs Sync:** JavaScript and Python desktop use async/await, while MicroPython uses synchronous API.
+
+### Supported Payload Types
+
+| Type | Julia | JavaScript | Python | MicroPython |
+|------|-------|------------|--------|-------------|
+| `text` | `String` | `string` | `str` | `str` |
+| `dictionary` | `Dict` | `Object` | `dict` | `dict` |
+| `arrowtable` | `DataFrame` | `Array<Object>` | `pandas.DataFrame` | ❌ |
+| `jsontable` | `Vector{NamedTuple}` | `Array<Object>` | `list[dict]` | ❌ |
+| `table` | ❌ | ❌ | `pandas.DataFrame` | ❌ |
+| `image` | `Vector{UInt8}` | `Uint8Array` | `bytes` | `bytearray` |
+| `audio` | `Vector{UInt8}` | `Uint8Array` | `bytes` | `bytearray` |
+| `video` | `Vector{UInt8}` | `Uint8Array` | `bytes` | `bytearray` |
+| `binary` | `Vector{UInt8}` | `Uint8Array` | `bytes` | `bytearray` |
+
+**Note on MicroPython:** MicroPython does not support table types (`arrowtable`, `jsontable`, or `table`) due to memory constraints. Use `dictionary` or `binary` instead.
+
+---
+
+## Prerequisites
+
+Before you begin, ensure you have:
+
+1. **NATS Server** running (or accessible)
+2. **HTTP File Server** (optional, for large payloads > 1MB)
+3. **Platform-specific packages** installed
+
+---
+
+## Installation
+
+### Julia
+
+```julia
+using Pkg
+Pkg.add("NATS")
+Pkg.add("Arrow")
+Pkg.add("JSON3")
+Pkg.add("HTTP")
+Pkg.add("UUIDs")
+Pkg.add("Dates")
+```
+
+### JavaScript (Node.js)
+
+```bash
+npm install nats uuid apache-arrow node-fetch
+```
+
+### JavaScript (Browser)
+
+```html
+<script src="https://unpkg.com/nats-js/dist/bundle/nats.min.js"></script>
+<script src="https://unpkg.com/apache-arrow/arrow.min.js"></script>
+```
+
+### Python (Desktop)
+
+```bash
+pip install nats-py aiohttp pyarrow pandas
+```
+
+### MicroPython
+
+Uses built-in modules: `network`, `socket`, `time`, `json`, `base64`
+
+---
+
+## Quick Start
+
+### Step 1: Start NATS Server
+
+```bash
+docker run -p 4222:4222 nats:latest
+```
+
+### Step 2: Start HTTP File Server (Optional)
+
+```bash
+mkdir -p /tmp/fileserver
+python3 -m http.server 8080 --directory /tmp/fileserver
+```
+
+### Step 3: Send Your First Message
+
+#### Julia
+
+```julia
+using NATSBridge
+
+# Send a text message
+data = [("message", "Hello World", "text")]
+env, env_json_str = smartsend("/chat/room1", data, broker_url="nats://localhost:4222")
+# env: msg_envelope_v1 struct with all metadata and payloads
+# env_json_str: JSON string representation of the envelope for publishing
+println("Message sent!")
+
+# Or use is_publish=false to get envelope and JSON without publishing
+env, env_json_str = smartsend("/chat/room1", data, broker_url="nats://localhost:4222", is_publish=false)
+# env: msg_envelope_v1 struct
+# env_json_str: JSON string for publishing to NATS
+```
+
+#### JavaScript
+
+```javascript
+const NATSBridge = require('./src/natsbridge.js');
+
+// Send a text message
+const data = [["message", "Hello World", "text"]];
+const [env, env_json_str] = await NATSBridge.smartsend(
+    "/chat/room1",
+    data,
+    { broker_url: "nats://localhost:4222" }
+);
+// env: Object with all metadata and payloads
+// env_json_str: JSON string for publishing
+console.log("Message sent!");
+
+// Or use is_publish=false
+const [env, env_json_str] = await NATSBridge.smartsend(
+    "/chat/room1",
+    data,
+    { broker_url: "nats://localhost:4222", is_publish: false }
+);
+```
+
+#### Python
+
+```python
+from natsbridge import smartsend
+
+# Send a text message
+data = [("message", "Hello World", "text")]
+env, env_json_str = await smartsend(
+    "/chat/room1",
+    data,
+    broker_url="nats://localhost:4222"
+)
+# env: Dict with all metadata and payloads
+# env_json_str: JSON string for publishing
+print("Message sent!")
+
+# Or use is_publish=False
+env, env_json_str = await smartsend(
+    "/chat/room1",
+    data,
+    broker_url="nats://localhost:4222",
+    is_publish=False
+)
+# env: Dict with all metadata and payloads
+# env_json_str: JSON string for publishing to NATS
+```
+
+#### MicroPython
+
+```python
+from natsbridge_mpy import NATSBridge
+
+bridge = NATSBridge()
+
+# Send a text message (limited to small payloads)
+data = [("message", "Hello World", "text")]
+env, env_json_str = bridge.smartsend(
+    "/chat/room1",
+    data,
+    size_threshold=100000  # Lower threshold for MicroPython
+)
+print("Message sent!")
+```
+
+### Step 4: Receive Messages
+
+#### Julia
+
+```julia
+using NATSBridge
+
+# Receive and process message
+env = smartreceive(msg; fileserver_download_handler=_fetch_with_backoff)
+# Returns: ::JSON.Object{String, Any} with "payloads" field containing Vector{Tuple{String, Any, String}}
+# Access payloads: for (dataname, data, type) in env["payloads"]
+for (dataname, data, type) in env["payloads"]
+    println("Received $dataname: $data")
+end
+```
+
+#### JavaScript
+
+```javascript
+const NATSBridge = require('./src/natsbridge.js');
+
+// Receive and process message
+const env = await NATSBridge.smartreceive(msg, {
+    fileserver_download_handler: NATSBridge.fetchWithBackoff
+});
+// env.payloads = [[dataname, data, type], ...]
+for (const [dataname, data, type] of env.payloads) {
+    console.log(`Received ${dataname}:`, data);
+}
+```
+
+#### Python
+
+```python
+from natsbridge import smartreceive, fetch_with_backoff
+
+# Receive and process message
+env = await smartreceive(
+    msg,
+    fileserver_download_handler=fetch_with_backoff
+)
+# env["payloads"] = [(dataname, data, type), ...]
+for dataname, data, type_ in env["payloads"]:
+    print(f"Received {dataname}: {data}")
+```
+
+---
+
+## Basic Examples
+
+### Example 1: Sending a Dictionary
+
+#### Julia
+
+```julia
+using NATSBridge
+
+config = Dict(
+    "wifi_ssid" => "MyNetwork",
+    "wifi_password" => "password123",
+    "update_interval" => 60
+)
+
+data = [("config", config, "dictionary")]
+env, env_json_str = smartsend("/device/config", data, broker_url="nats://localhost:4222")
+```
+
+#### JavaScript
+
+```javascript
+const NATSBridge = require('./src/natsbridge.js');
+
+const config = {
+    wifi_ssid: "MyNetwork",
+    wifi_password: "password123",
+    update_interval: 60
+};
+
+const data = [["config", config, "dictionary"]];
+const [env, env_json_str] = await NATSBridge.smartsend(
+    "/device/config",
+    data,
+    { broker_url: "nats://localhost:4222" }
+);
+```
+
+#### Python
+
+```python
+from natsbridge import smartsend
+
+config = {
+    "wifi_ssid": "MyNetwork",
+    "wifi_password": "password123",
+    "update_interval": 60
+}
+
+data = [("config", config, "dictionary")]
+env, env_json_str = await smartsend(
+    "/device/config",
+    data,
+    broker_url="nats://localhost:4222"
+)
+```
+
+#### MicroPython
+
+```python
+from natsbridge_mpy import NATSBridge
+
+bridge = NATSBridge()
+
+config = {
+    "wifi_ssid": "MyNetwork",
+    "wifi_password": "password123",
+    "update_interval": 60
+}
+
+data = [("config", config, "dictionary")]
+env, env_json_str = bridge.smartsend(
+    "/device/config",
+    data,
+    size_threshold=100000
+)
+```
+
+### Example 2: Sending Binary Data (Image)
+
+#### Julia
+
+```julia
+using NATSBridge
+
+# Read image file
+image_data = read("image.png")
+
+data = [("user_image", image_data, "binary")]
+env, env_json_str = smartsend("/chat/image", data, broker_url="nats://localhost:4222")
+```
+
+#### JavaScript
+
+```javascript
+const NATSBridge = require('./src/natsbridge.js');
+const fs = require('fs');
+
+// Read image file
+const image_data = fs.readFileSync('image.png');
+
+const data = [["user_image", image_data, "binary"]];
+const [env, env_json_str] = await NATSBridge.smartsend(
+    "/chat/image",
+    data,
+    { broker_url: "nats://localhost:4222" }
+);
+```
+
+#### Python
+
+```python
+from natsbridge import smartsend
+
+# Read image file
+with open("image.png", "rb") as f:
+    image_data = f.read()
+
+data = [("user_image", image_data, "binary")]
+env, env_json_str = await smartsend(
+    "/chat/image",
+    data,
+    broker_url="nats://localhost:4222"
+)
+```
+
+#### MicroPython
+
+```python
+from natsbridge_mpy import NATSBridge
+
+bridge = NATSBridge()
+
+# Read image file
+with open("image.png", "rb") as f:
+    image_data = f.read()
+
+data = [("user_image", image_data, "binary")]
+env, env_json_str = bridge.smartsend(
+    "/chat/image",
+    data,
+    size_threshold=100000
+)
+```
+
+### Example 3: Request-Response Pattern
+
+#### Julia (Requester)
+
+```julia
+using NATSBridge
+
+# Send command with reply-to
+data = [("command", Dict("action" => "read_sensor"), "dictionary")]
+env, env_json_str = smartsend(
+    "/device/command",
+    data,
+    broker_url="nats://localhost:4222",
+    reply_to="/device/response",
+    reply_to_msg_id="cmd-001"
+)
+```
+
+#### JavaScript (Requester)
+
+```javascript
+const NATSBridge = require('./src/natsbridge.js');
+
+// Send command with reply-to
+const data = [["command", { action: "read_sensor" }, "dictionary"]];
+const [env, env_json_str] = await NATSBridge.smartsend(
+    "/device/command",
+    data,
+    {
+        broker_url: "nats://localhost:4222",
+        reply_to: "/device/response",
+        reply_to_msg_id: "cmd-001"
+    }
+);
+```
+
+#### Python (Requester)
+
+```python
+from natsbridge import smartsend
+
+# Send command with reply-to
+data = [("command", {"action": "read_sensor"}, "dictionary")]
+env, env_json_str = await smartsend(
+    "/device/command",
+    data,
+    broker_url="nats://localhost:4222",
+    reply_to="/device/response",
+    reply_to_msg_id="cmd-001"
+)
+```
+
+#### Julia (Responder)
+
+```julia
+using NATSBridge, NATS
+
+const SUBJECT = "/device/command"
+const NATS_URL = "nats://localhost:4222"
+
+function test_responder()
+    conn = NATS.connect(NATS_URL)
+    NATS.subscribe(conn, SUBJECT) do msg
+        env = smartreceive(msg, fileserver_download_handler=_fetch_with_backoff)
+        
+        reply_to = env["reply_to"]
+        
+        for (dataname, data, type) in env["payloads"]
+            if dataname == "command" && data["action"] == "read_sensor"
+                response = Dict("sensor_id" => "sensor-001", "value" => 42.5)
+                if !isempty(reply_to)
+                    smartsend(reply_to, [("data", response, "dictionary")])
+                end
+            end
+        end
+    end
+    
+    sleep(120)
+    NATS.drain(conn)
+end
+
+test_responder()
+```
+
+---
+
+## Advanced Usage
+
+### Example 4: Large Payloads (File Server)
+
+For payloads larger than 1MB, NATSBridge automatically uses the file server:
+
+#### Julia
+
+```julia
+using NATSBridge
+
+# Create large data (> 1MB)
+large_data = rand(UInt8, 2_000_000)
+
+env, env_json_str = smartsend(
+    "/data/large",
+    [("large_file", large_data, "binary")],
+    broker_url="nats://localhost:4222",
+    fileserver_url="http://localhost:8080"
+)
+
+println("File uploaded to: $(env.payloads[1].data)")
+# Note: For link transport, data field contains the URL string
+```
+
+#### JavaScript
+
+```javascript
+const NATSBridge = require('./src/natsbridge.js');
+
+// Create large data (> 1MB)
+const large_data = Buffer.alloc(2_000_000);
+for (let i = 0; i < large_data.length; i++) {
+    large_data[i] = Math.floor(Math.random() * 256);
+}
+
+const [env, env_json_str] = await NATSBridge.smartsend(
+    "/data/large",
+    [["large_file", large_data, "binary"]],
+    {
+        broker_url: "nats://localhost:4222",
+        fileserver_url: "http://localhost:8080"
+    }
+);
+
+console.log("File uploaded to:", env.payloads[0].data);
+// Note: For link transport, data field contains the URL string
+```
+
+#### Python
+
+```python
+from natsbridge import smartsend
+
+# Create large data (> 1MB)
+import os
+large_data = os.urandom(2_000_000)
+
+env, env_json_str = await smartsend(
+    "/data/large",
+    [("large_file", large_data, "binary")],
+    broker_url="nats://localhost:4222",
+    fileserver_url="http://localhost:8080"
+)
+
+print(f"File uploaded to: {env['payloads'][0]['data']}")
+# Note: For link transport, data field contains the URL string
+```
+
+#### MicroPython
+
+MicroPython enforces a hard limit of 50KB per payload:
+
+```python
+from natsbridge_mpy import NATSBridge
+
+bridge = NATSBridge()
+
+# MicroPython has a hard limit of 50KB per payload
+# Use streaming or chunking for larger data
+small_data = bytes(1000)  # 1KB
+
+data = [("small_file", small_data, "binary")]
+env, env_json_str = bridge.smartsend(
+    "/data/small",
+    data,
+    size_threshold=100000  # Enforced max: 50000 bytes
+)
+```
+
+### Example 5: Mixed Content (Chat with Text + Image)
+
+NATSBridge supports sending multiple payloads with different types in a single message:
+
+#### Julia
+
+```julia
+using NATSBridge
+
+image_data = read("avatar.png")
+
+data = [
+    ("message_text", "Hello with image!", "text"),
+    ("user_avatar", image_data, "image")
+]
+
+env, env_json_str = smartsend("/chat/mixed", data, broker_url="nats://localhost:4222")
+```
+
+#### JavaScript
+
+```javascript
+const NATSBridge = require('./src/natsbridge.js');
+const fs = require('fs');
+
+const image_data = fs.readFileSync('avatar.png');
+
+const data = [
+    ["message_text", "Hello with image!", "text"],
+    ["user_avatar", image_data, "image"]
+];
+
+const [env, env_json_str] = await NATSBridge.smartsend(
+    "/chat/mixed",
+    data,
+    { broker_url: "nats://localhost:4222" }
+);
+```
+
+#### Python
+
+```python
+from natsbridge import smartsend
+
+with open("avatar.png", "rb") as f:
+    image_data = f.read()
+
+data = [
+    ("message_text", "Hello with image!", "text"),
+    ("user_avatar", image_data, "image")
+]
+
+env, env_json_str = await smartsend(
+    "/chat/mixed",
+    data,
+    broker_url="nats://localhost:4222"
+)
+# env: Dict with all metadata and payloads
+```
+
+### Example 6: Table Data (Arrow IPC)
+
+For tabular data, NATSBridge uses Apache Arrow IPC format:
+
+#### Julia
+
+```julia
+using NATSBridge
+using DataFrames
+
+# Create DataFrame
+df = DataFrame(
+    id = [1, 2, 3],
+    name = ["Alice", "Bob", "Charlie"],
+    score = [95, 88, 92]
+)
+
+data = [("students", df, "arrowtable")]
+env, env_json_str = smartsend("/data/students", data, broker_url="nats://localhost:4222")
+```
+
+#### JavaScript
+
+```javascript
+const NATSBridge = require('./src/natsbridge.js');
+
+// Create table data (array of objects)
+const table_data = [
+    { id: 1, name: "Alice", score: 95 },
+    { id: 2, name: "Bob", score: 88 },
+    { id: 3, name: "Charlie", score: 92 }
+];
+
+const data = [["students", table_data, "arrowtable"]];
+const [env, env_json_str] = await NATSBridge.smartsend(
+    "/data/students",
+    data,
+    { broker_url: "nats://localhost:4222" }
+);
+```
+
+#### Python
+
+```python
+from natsbridge import smartsend
+import pandas as pd
+
+# Create DataFrame
+df = pd.DataFrame({
+    'id': [1, 2, 3],
+    'name': ['Alice', 'Bob', 'Charlie'],
+    'score': [95, 88, 92]
+})
+
+data = [("students", df, "table")]
+env, env_json_str = await smartsend(
+    "/data/students",
+    data,
+    broker_url="nats://localhost:4222"
+)
+```
+
+#### MicroPython
+
+MicroPython does not support table type due to memory constraints. Use dictionary or binary instead.
+
+---
+
+## Next Steps
+
+1. **Explore the test directory** for more examples
+2. **Check the documentation** for advanced configuration options
+3. **Read the walkthrough** for building real-world applications
+
+---
+
+## Troubleshooting
+
+### Connection Issues
+
+- Ensure NATS server is running: `docker ps | grep nats`
+- Check firewall settings
+- Verify NATS URL configuration
+
+### File Server Issues
+
+- Ensure file server is running and accessible
+- Check upload permissions
+- Verify file server URL configuration
+
+### Serialization Errors
+
+- Verify data type matches the specified type
+- Check that binary data is in the correct format
+- MicroPython: Ensure payload size < 50KB
+
+---
+
+## License
+
+MIT

etc.jl

@@ -1,42 +0,0 @@
-
-""" fileServerURL = "http://192.168.88.104:8080"
-filepath = "/home/ton/docker-apps/sendreceive/image/test.zip"
-filename = basename(filepath)
-filebytes = read(filepath)
-
-plik_oneshot_upload - Upload a single file to a plik server using one-shot mode
-
-This function uploads a raw byte array to a plik server in one-shot mode (no upload session).
-It first creates a one-shot upload session by sending a POST request with `{"OneShot": true}`,
-retrieves an upload ID and token, then uploads the file data as multipart form data using the token.
-
-The function handles the entire flow:
-1. Obtains an upload ID and token from the server
-2. Uploads the provided binary data as a file using the `X-UploadToken` header
-3. Returns identifiers and download URL for the uploaded file
-
-# Arguments:
-  - `fileServerURL::String` - Base URL of the plik server (e.g., `"http://192.168.88.104:8080"`)
-  - `filename::String` - Name of the file being uploaded
-  - `data::Vector{UInt8}` - Raw byte data of the file content
-
-# Return:
-  - A named tuple with fields:
-    - `uploadid::String` - ID of the one-shot upload session
-    - `fileid::String` - ID of the uploaded file within the session
-    - `downloadurl::String` - Full URL to download the uploaded file
-
-# Example
-```jldoctest
-using HTTP, JSON
-
-# Example data: "Hello" as bytes
-data = collect("Hello World!" |> collect |> CodeUnits |> collect)
-
-# Upload to local plik server
-result = plik_oneshot_upload("http://192.168.88.104:8080", "hello.txt", data)
-
-# Download URL for the uploaded file
-println(result.downloadurl)
-```
-"""

etc.txt

@@ -0,0 +1,310 @@
+#!/usr/bin/env julia
+# Test script for mixed-content message testing
+# Tests receiving a mix of text, json, table, image, audio, video, and binary data
+# from Julia serviceA to Julia serviceB using NATSBridge.jl smartreceive
+#
+# This test demonstrates that any combination and any number of mixed content
+# can be sent and received correctly.
+
+using NATS, JSON, UUIDs, Dates, PrettyPrinting, DataFrames, Arrow, HTTP, Base64
+
+# Include the bridge module
+include("./src/NATSBridge.jl")
+using .NATSBridge
+
+# Configuration
+const SUBJECT = "/test/mix"
+const NATS_URL = "nats.yiem.cc"
+const FILESERVER_URL = "http://192.168.88.104:8080"
+
+
+# ------------------------------------------------------------------------------------------------ #
+#                                      test mixed content transfer                                 #
+# ------------------------------------------------------------------------------------------------ #
+
+
+# Helper: Log with correlation ID
+function log_trace(message)
+    timestamp = Dates.now()
+    println("[$timestamp] $message")
+end
+
+
+# Receiver: Listen for messages and verify mixed content handling
+function test_mix_receive()
+    conn = NATS.connect(NATS_URL)
+    incoming_msg = nothing
+    NATS.subscribe(conn, SUBJECT) do msg
+        log_trace("Received message on $(msg.subject)")
+        incoming_msg = msg
+        
+        # # Use NATSBridge.smartreceive to handle the data
+        # # API: smartreceive(msg, download_handler; max_retries, base_delay, max_delay)
+        # result = NATSBridge.smartreceive(
+        #     msg;
+        #     max_retries = 5,
+        #     base_delay = 100,
+        #     max_delay = 5000
+        # )
+        
+        # log_trace("Received $(length(result["payloads"])) payloads")
+        
+        
+        # # Result is an envelope dictionary with payloads field containing list of (dataname, data, data_type) tuples
+        # for (dataname, data, data_type) in result["payloads"]
+        #     log_trace("\n=== Payload: $dataname (type: $data_type) ===")
+            
+        #     # Handle different data types
+        #     if data_type == "text"
+        #         # Text data - should be a String
+        #         if isa(data, String)
+        #             log_trace("  Type: String")
+        #             log_trace("  Length: $(length(data)) characters")
+                    
+        #             # Display first 200 characters
+        #             if length(data) > 200
+        #                 log_trace("  First 200 chars: $(data[1:200])...")
+        #             else
+        #                 log_trace("  Content: $data")
+        #             end
+                    
+        #             # Save to file
+        #             output_path = "./received_$dataname.txt"
+        #             write(output_path, data)
+        #             log_trace("  Saved to: $output_path")
+        #         else
+        #             log_trace("  ERROR: Expected String, got $(typeof(data))")
+        #         end
+                
+        #     elseif data_type == "dictionary"
+        #         # Dictionary data - should be JSON object
+        #         if isa(data, JSON.Object{String, Any})
+        #             log_trace("  Type: Dict")
+        #             log_trace("  Keys: $(keys(data))")
+                    
+        #             # Display nested content
+        #             for (key, value) in data
+        #                 log_trace("  $key => $value")
+        #             end
+                    
+        #             # Save to JSON file
+        #             output_path = "./received_$dataname.json"
+        #             json_str = JSON.json(data, 2)
+        #             write(output_path, json_str)
+        #             log_trace("  Saved to: $output_path")
+        #         else
+        #             log_trace("  ERROR: Expected Dict, got $(typeof(data))")
+        #         end
+                
+        #     elseif data_type == "table"
+        #         # Table data - should be a DataFrame
+        #         tabledata = deepcopy(data)
+        #         println("found table data")
+        #         break
+        #         # return data
+        #         # if isa(data, DataFrame)
+        #         #     log_trace("  Type: DataFrame")
+        #         #     log_trace("  Dimensions: $(size(data, 1)) rows x $(size(data, 2)) columns")
+        #         #     log_trace("  Columns: $(names(data))")
+                    
+        #         #     # Display first few rows
+        #         #     log_trace("  First 5 rows:")
+        #         #     display(data[1:min(5, size(data, 1)), :])
+                    
+        #         #     # Save to Arrow file
+        #         #     output_path = "./received_$dataname.arrow"
+        #         #     io = IOBuffer()
+        #         #     Arrow.write(io, data)
+        #         #     write(output_path, take!(io))
+        #         #     log_trace("  Saved to: $output_path")
+        #         # else
+        #         #     log_trace("  ERROR: Expected DataFrame, got $(typeof(data))")
+        #         # end
+                
+        #     elseif data_type == "image"
+        #         # Image data - should be Vector{UInt8}
+        #         if isa(data, Vector{UInt8})
+        #             log_trace("  Type: Vector{UInt8} (binary)")
+        #             log_trace("  Size: $(length(data)) bytes")
+                    
+        #             # Save to file
+        #             output_path = "./received_$dataname.bin"
+        #             write(output_path, data)
+        #             log_trace("  Saved to: $output_path")
+        #         else
+        #             log_trace("  ERROR: Expected Vector{UInt8}, got $(typeof(data))")
+        #         end
+                
+        #     elseif data_type == "audio"
+        #         # Audio data - should be Vector{UInt8}
+        #         if isa(data, Vector{UInt8})
+        #             log_trace("  Type: Vector{UInt8} (binary)")
+        #             log_trace("  Size: $(length(data)) bytes")
+                    
+        #             # Save to file
+        #             output_path = "./received_$dataname.bin"
+        #             write(output_path, data)
+        #             log_trace("  Saved to: $output_path")
+        #         else
+        #             log_trace("  ERROR: Expected Vector{UInt8}, got $(typeof(data))")
+        #         end
+                
+        #     elseif data_type == "video"
+        #         # Video data - should be Vector{UInt8}
+        #         if isa(data, Vector{UInt8})
+        #             log_trace("  Type: Vector{UInt8} (binary)")
+        #             log_trace("  Size: $(length(data)) bytes")
+                    
+        #             # Save to file
+        #             output_path = "./received_$dataname.bin"
+        #             write(output_path, data)
+        #             log_trace("  Saved to: $output_path")
+        #         else
+        #             log_trace("  ERROR: Expected Vector{UInt8}, got $(typeof(data))")
+        #         end
+                
+        #     elseif data_type == "binary"
+        #         # Binary data - should be Vector{UInt8}
+        #         if isa(data, Vector{UInt8})
+        #             log_trace("  Type: Vector{UInt8} (binary)")
+        #             log_trace("  Size: $(length(data)) bytes")
+                    
+        #             # Save to file
+        #             output_path = "./received_$dataname.bin"
+        #             write(output_path, data)
+        #             log_trace("  Saved to: $output_path")
+        #         else
+        #             log_trace("  ERROR: Expected Vector{UInt8}, got $(typeof(data))")
+        #         end
+                
+        #     else
+        #         log_trace("  ERROR: Unknown data type '$data_type'")
+        #     end
+        # end
+        
+        # Summary
+        # println("\n=== Verification Summary ===")
+        # text_count = count(x -> x[3] == "text", result["payloads"])
+        # dict_count = count(x -> x[3] == "dictionary", result["payloads"])
+        # table_count = count(x -> x[3] == "table", result["payloads"])
+        # image_count = count(x -> x[3] == "image", result["payloads"])
+        # audio_count = count(x -> x[3] == "audio", result["payloads"])
+        # video_count = count(x -> x[3] == "video", result["payloads"])
+        # binary_count = count(x -> x[3] == "binary", result["payloads"])
+        
+        # log_trace("Text payloads: $text_count")
+        # log_trace("Dictionary payloads: $dict_count")
+        # log_trace("Table payloads: $table_count")
+        # log_trace("Image payloads: $image_count")
+        # log_trace("Audio payloads: $audio_count")
+        # log_trace("Video payloads: $video_count")
+        # log_trace("Binary payloads: $binary_count")
+        
+        # # Print transport type info for each payload if available
+        # println("\n=== Payload Details ===")
+        # for (dataname, data, data_type) in result["payloads"]
+        #     if data_type in ["image", "audio", "video", "binary"]
+        #         log_trace("$dataname: $(length(data)) bytes (binary)")
+        #     elseif data_type == "table"
+        #       data = DataFrame(data)
+        #         log_trace("$dataname: $(size(data, 1)) rows x $(size(data, 2)) columns (DataFrame)")
+        #     elseif data_type == "dictionary"
+        #         log_trace("$dataname: $(length(JSON.json(data))) bytes (Dict)")
+        #     elseif data_type == "text"
+        #         log_trace("$dataname: $(length(data)) characters (String)")
+        #     end
+        # end
+    end
+    
+    # Keep listening for 2 minutes
+    sleep(20)  
+    NATS.drain(conn)
+    return incoming_msg
+end
+
+
+# Run the test
+println("Starting mixed-content transport test...")
+println("Note: This receiver will wait for messages from the sender.")
+println("Run test_julia_to_julia_mix_sender.jl first to send test data.")
+
+# Run receiver
+println("\ntesting smartreceive for mixed content")
+incoming_msg = test_mix_receive()
+
+println("\nTest completed.")
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+Check architecture.md. For sending table I want to add JSON in addition to Apache Arrow. 
+Currently I use "table" datatype when sending table data using Arrow. Now table that I want to send using JSON 
+I will use "jsontable" as datatype while sending table using Arrow I will use "arrowtable" as datatype. 
+This will select how smartsend and smartreceive serialize/deserialize the table.
+
+Can you help me do this? Save the updated architecture.md into updated_architecture.md file. I will deal with source code later.
+
+
+
+
+Now update implementation.md and save into updated_implementation.md
+Keep in mind that Julia DataFrame and Python Pandas rely on columnar-oriented dictionary to create as the following example:
+julia> dict = Dict("customer age" => [15, 20, 25],
+                   "first name" => ["Rohit", "Rahul", "Akshat"])
+julia> DataFrame(dict)
+
+python> data = {
+    "Name": ["Alice", "Bob", "Charlie"],
+    "Age": [25, 30, 35],
+    "Score": [88.5, 92.0, 79.5]
+}
+
+python> df = pd.DataFrame(data)
+
+
+But JS use Array of Objects while MicroPython use list of lists. Both are row-oriented structure.
+So use row-oriented JSON to send across these languages. For Julia and Python, only convert 
+row-oriented JSON to columnar-oriented dictionary for "going-into" and vise versa for "coming-out" 
+a dataframe while JS and MicroPython won't require such process. 
+You may add these info into architecture.md if you see fit.
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+

plik_fileserver/docker-compose.yml

@@ -0,0 +1,14 @@
+services:
+  plik:
+    image: rootgg/plik:latest
+    container_name: plik-server
+    restart: unless-stopped
+    ports:
+      - "8080:8080"
+    volumes:
+      # # Mount the config file (created below)
+      # - ./plikd.cfg:/home/plik/server/plikd.cfg
+      # Mount local folder for uploads and database
+      - ./plik-data:/data
+    # Set user to match your host UID to avoid permission issues
+    user: "1000:1000"

src/NATSBridge.js

@@ -1,245 +0,0 @@
-/**
- * Bi-Directional Data Bridge - JavaScript Module
- * Implements SmartSend and SmartReceive for NATS communication
- */
-
-const { v4: uuidv4 } = require('uuid');
-const { decode, encode } = require('base64-url');
-const Arrow = require('apache-arrow');
-
-// Constants
-const DEFAULT_SIZE_THRESHOLD = 1_000_000; // 1MB
-const DEFAULT_NATS_URL = 'nats://localhost:4222';
-const DEFAULT_FILESERVER_URL = 'http://localhost:8080/upload';
-
-// Logging helper
-function logTrace(correlationId, message) {
-    const timestamp = new Date().toISOString();
-    console.log(`[${timestamp}] [Correlation: ${correlationId}] ${message}`);
-}
-
-// Message Envelope Class
-class MessageEnvelope {
-    constructor(options = {}) {
-        this.correlation_id = options.correlation_id || uuidv4();
-        this.type = options.type || 'json';
-        this.transport = options.transport || 'direct';
-        this.payload = options.payload || null;
-        this.url = options.url || null;
-        this.metadata = options.metadata || {};
-    }
-
-    static fromJSON(jsonStr) {
-        const data = JSON.parse(jsonStr);
-        return new MessageEnvelope({
-            correlation_id: data.correlation_id,
-            type: data.type,
-            transport: data.transport,
-            payload: data.payload || null,
-            url: data.url || null,
-            metadata: data.metadata || {}
-        });
-    }
-
-    toJSON() {
-        const obj = {
-            correlation_id: this.correlation_id,
-            type: this.type,
-            transport: this.transport
-        };
-
-        if (this.payload) {
-            obj.payload = this.payload;
-        }
-
-        if (this.url) {
-            obj.url = this.url;
-        }
-
-        if (Object.keys(this.metadata).length > 0) {
-            obj.metadata = this.metadata;
-        }
-
-        return JSON.stringify(obj);
-    }
-}
-
-// SmartSend for JavaScript - Handles transport selection based on payload size
-async function SmartSend(subject, data, type = 'json', options = {}) {
-    const {
-        natsUrl = DEFAULT_NATS_URL,
-        fileserverUrl = DEFAULT_FILESERVER_URL,
-        sizeThreshold = DEFAULT_SIZE_THRESHOLD,
-        correlationId = uuidv4()
-    } = options;
-
-    logTrace(correlationId, `Starting SmartSend for subject: ${subject}`);
-
-    // Serialize data based on type
-    const payloadBytes = _serializeData(data, type, correlationId);
-    const payloadSize = payloadBytes.length;
-
-    logTrace(correlationId, `Serialized payload size: ${payloadSize} bytes`);
-
-    // Decision: Direct vs Link
-    if (payloadSize < sizeThreshold) {
-        // Direct path - Base64 encode and send via NATS
-        const payloadBase64 = encode(payloadBytes);
-        logTrace(correlationId, `Using direct transport for ${payloadSize} bytes`);
-
-        const env = new MessageEnvelope({
-            correlation_id: correlationId,
-            type: type,
-            transport: 'direct',
-            payload: payloadBase64,
-            metadata: {
-                content_length: payloadSize.toString(),
-                format: 'arrow_ipc_stream'
-            }
-        });
-
-        await publishMessage(natsUrl, subject, env.toJSON(), correlationId);
-        return env;
-    } else {
-        // Link path - Upload to HTTP server, send URL via NATS
-        logTrace(correlationId, `Using link transport, uploading to fileserver`);
-
-        const url = await uploadToServer(payloadBytes, fileserverUrl, correlationId);
-
-        const env = new MessageEnvelope({
-            correlation_id: correlationId,
-            type: type,
-            transport: 'link',
-            url: url,
-            metadata: {
-                content_length: payloadSize.toString(),
-                format: 'arrow_ipc_stream'
-            }
-        });
-
-        await publishMessage(natsUrl, subject, env.toJSON(), correlationId);
-        return env;
-    }
-}
-
-// Helper: Serialize data based on type
-function _serializeData(data, type, correlationId) {
-    if (type === 'json') {
-        const jsonStr = JSON.stringify(data);
-        return Buffer.from(jsonStr, 'utf8');
-    } else if (type === 'table') {
-        // Table data - convert to Arrow IPC stream
-        const writer = new Arrow.Writer();
-        writer.writeTable(data);
-        return writer.toByteArray();
-    } else if (type === 'binary') {
-        // Binary data - treat as binary
-        if (data instanceof Buffer) {
-            return data;
-        } else if (Array.isArray(data)) {
-            return Buffer.from(data);
-        } else {
-            throw new Error('Binary data must be binary (Buffer or Array)');
-        }
-    } else {
-        throw new Error(`Unknown type: ${type}`);
-    }
-}
-
-// Helper: Publish message to NATS
-async function publishMessage(natsUrl, subject, message, correlationId) {
-    const { connect } = require('nats');
-    
-    try {
-        const nc = await connect({ servers: [natsUrl] });
-        await nc.publish(subject, message);
-        logTrace(correlationId, `Message published to ${subject}`);
-        nc.close();
-    } catch (error) {
-        logTrace(correlationId, `Failed to publish message: ${error.message}`);
-        throw error;
-    }
-}
-
-// SmartReceive for JavaScript - Handles both direct and link transport
-async function SmartReceive(msg, options = {}) {
-    const {
-        fileserverUrl = DEFAULT_FILESERVER_URL,
-        maxRetries = 5,
-        baseDelay = 100,
-        maxDelay = 5000
-    } = options;
-
-    const env = MessageEnvelope.fromJSON(msg.data);
-
-    logTrace(env.correlation_id, `Processing received message`);
-
-    if (env.transport === 'direct') {
-        logTrace(env.correlation_id, `Direct transport - decoding payload`);
-
-        const payloadBytes = decode(env.payload);
-        const data = _deserializeData(payloadBytes, env.type, env.correlation_id, env.metadata);
-
-        return { data, envelope: env };
-    } else if (env.transport === 'link') {
-        logTrace(env.correlation_id, `Link transport - fetching from URL`);
-
-        const data = await _fetchWithBackoff(env.url, maxRetries, baseDelay, maxDelay, env.correlation_id);
-        const result = _deserializeData(data, env.type, env.correlation_id, env.metadata);
-
-        return { data: result, envelope: env };
-    } else {
-        throw new Error(`Unknown transport type: ${env.transport}`);
-    }
-}
-
-// Helper: Fetch with exponential backoff
-async function _fetchWithBackoff(url, maxRetries, baseDelay, maxDelay, correlationId) {
-    let delay = baseDelay;
-
-    for (let attempt = 1; attempt <= maxRetries; attempt++) {
-        try {
-            const response = await fetch(url);
-            if (response.ok) {
-                const buffer = await response.arrayBuffer();
-                logTrace(correlationId, `Successfully fetched data from ${url} on attempt ${attempt}`);
-                return new Uint8Array(buffer);
-            } else {
-                throw new Error(`Failed to fetch: ${response.status}`);
-            }
-        } catch (error) {
-            logTrace(correlationId, `Attempt ${attempt} failed: ${error.message}`);
-
-            if (attempt < maxRetries) {
-                await new Promise(resolve => setTimeout(resolve, delay));
-                delay = Math.min(delay * 2, maxDelay);
-            }
-        }
-    }
-
-    throw new Error(`Failed to fetch data after ${maxRetries} attempts`);
-}
-
-// Helper: Deserialize data based on type
-async function _deserializeData(data, type, correlationId, metadata) {
-    if (type === 'json') {
-        const jsonStr = new TextDecoder().decode(data);
-        return JSON.parse(jsonStr);
-    } else if (type === 'table') {
-        // Deserialize Arrow IPC stream to Table
-        const table = Arrow.Table.from(data);
-        return table;
-    } else if (type === 'binary') {
-        // Return binary binary data
-        return data;
-    } else {
-        throw new Error(`Unknown type: ${type}`);
-    }
-}
-
-// Export functions
-module.exports = {
-    SmartSend,
-    SmartReceive,
-    MessageEnvelope
-};

src/natsbridge.js

@@ -0,0 +1,788 @@
+/**
+ * NATSBridge - Cross-Platform Bi-Directional Data Bridge
+ * JavaScript/Node.js Implementation
+ * 
+ * This module provides functionality for sending and receiving data across network boundaries
+ * using NATS as the message bus, with support for both direct payload transport and
+ * URL-based transport for larger payloads.
+ * 
+ * Supported payload types: "text", "dictionary", "arrowtable", "jsontable", "image", "audio", "video", "binary"
+ * 
+ * @module NATSBridge
+ */
+
+const nats = require('nats');
+const crypto = require('crypto');
+// Use native fetch available in Node.js 18+
+const arrow = require('apache-arrow');
+
+// ---------------------------------------------- Constants ---------------------------------------------- //
+
+/**
+ * Default size threshold for switching from direct to link transport (1MB)
+ */
+const DEFAULT_SIZE_THRESHOLD = 1_000_000;
+
+/**
+ * Default NATS server URL
+ */
+const DEFAULT_BROKER_URL = 'nats://localhost:4222';
+
+/**
+ * Default HTTP file server URL for link transport
+ */
+const DEFAULT_FILESERVER_URL = 'http://localhost:8080';
+
+// ---------------------------------------------- Utility Functions ---------------------------------------------- //
+
+/**
+ * Convert Buffer to Base64 string
+ * @param {Buffer} buffer - Buffer to encode
+ * @returns {string} Base64 encoded string
+ */
+function bufferToBase64(buffer) {
+    return buffer.toString('base64');
+}
+
+/**
+ * Log a trace message with correlation ID and timestamp
+ * @param {string} correlationId - Correlation ID for tracing
+ * @param {string} message - Message content to log
+ */
+function logTrace(correlationId, message) {
+    const timestamp = new Date().toISOString();
+    console.log(`[${timestamp}] [Correlation: ${correlationId}] ${message}`);
+}
+
+// ---------------------------------------------- Serialization Functions ---------------------------------------------- //
+
+/**
+ * Serialize data according to specified format
+ * @param {any} data - Data to serialize
+ * @param {string} payloadType - Target format: "text", "dictionary", "arrowtable", "jsontable", "image", "audio", "video", "binary"
+ * @returns {Buffer} Binary representation of the serialized data
+ */
+async function serializeData(data, payloadType) {
+    if (payloadType === 'text') {
+        if (typeof data === 'string') {
+            return Buffer.from(data, 'utf8');
+        } else {
+            throw new Error('Text data must be a string');
+        }
+    } else if (payloadType === 'dictionary') {
+        const jsonStr = JSON.stringify(data);
+        return Buffer.from(jsonStr, 'utf8');
+    } else if (payloadType === 'arrowtable') {
+        // Convert array of objects to Arrow IPC format
+        if (!Array.isArray(data) || data.length === 0) {
+            throw new Error('Arrow table data must be a non-empty array of objects');
+        }
+        
+        return serializeArrowTable(data);
+    } else if (payloadType === 'jsontable') {
+        // Serialize array of objects to JSON format
+        if (!Array.isArray(data)) {
+            throw new Error('JSON table data must be an array');
+        }
+        const jsonStr = JSON.stringify(data);
+        return Buffer.from(jsonStr, 'utf8');
+    } else if (payloadType === 'image') {
+        if (data instanceof Uint8Array || Buffer.isBuffer(data)) {
+            return Buffer.from(data);
+        } else {
+            throw new Error('Image data must be Uint8Array or Buffer');
+        }
+    } else if (payloadType === 'audio') {
+        if (data instanceof Uint8Array || Buffer.isBuffer(data)) {
+            return Buffer.from(data);
+        } else {
+            throw new Error('Audio data must be Uint8Array or Buffer');
+        }
+    } else if (payloadType === 'video') {
+        if (data instanceof Uint8Array || Buffer.isBuffer(data)) {
+            return Buffer.from(data);
+        } else {
+            throw new Error('Video data must be Uint8Array or Buffer');
+        }
+    } else if (payloadType === 'binary') {
+        if (data instanceof Uint8Array || Buffer.isBuffer(data)) {
+            return Buffer.from(data);
+        } else {
+            throw new Error('Binary data must be Uint8Array or Buffer');
+        }
+    } else {
+        throw new Error(`Unknown payload_type: ${payloadType}`);
+    }
+}
+
+/**
+ * Helper function to properly serialize table data to Arrow IPC
+ * @param {Array<Object>} data - Array of objects representing table rows
+ * @returns {Buffer} Arrow IPC formatted buffer
+ */
+function serializeArrowTable(data) {
+    if (!Array.isArray(data) || data.length === 0) {
+        throw new Error('Table data must be a non-empty array of objects');
+    }
+    
+    logTrace('serializeArrowTable', `Serializing table with ${data.length} rows`);
+    
+    // Use arrow.tableFromArrays which handles the conversion properly
+    // Convert array of objects to a key-value format expected by tableFromArrays
+    const columns = {};
+    for (const key of Object.keys(data[0])) {
+        columns[key] = data.map(row => row[key]);
+    }
+    
+    logTrace('serializeArrowTable', `Columns: ${Object.keys(columns).join(', ')}`);
+    
+    const table = arrow.tableFromArrays(columns);
+    
+    logTrace('serializeArrowTable', `Arrow table created with ${table.numRows} rows, ${table.numCols} cols`);
+    
+    // Convert to IPC format
+    const ipcBuffer = arrow.tableToIPC(table);
+    
+    logTrace('serializeArrowTable', `IPC buffer type: ${typeof ipcBuffer}, length: ${ipcBuffer.byteLength}`);
+    
+    const resultBuffer = Buffer.from(ipcBuffer);
+    logTrace('serializeArrowTable', `Result buffer: ${resultBuffer.length} bytes`);
+    
+    // Debug: Show first 20 bytes in hex
+    const hexPreview = resultBuffer.slice(0, 20).toString('hex');
+    logTrace('serializeArrowTable', `First 20 bytes (hex): ${hexPreview}`);
+    
+    return resultBuffer;
+}
+
+/**
+ * Deserialize bytes to data based on type
+ * @param {Buffer|Uint8Array} data - Serialized data as bytes
+ * @param {string} payloadType - Data type
+ * @param {string} correlationId - Correlation ID for logging
+ * @returns {any} Deserialized data
+ */
+async function deserializeData(data, payloadType, correlationId) {
+    const buffer = Buffer.isBuffer(data) ? data : Buffer.from(data);
+    
+    logTrace(correlationId, `deserializeData: type=${payloadType}, bufferLength=${buffer.length}`);
+    
+    // Debug: Show first 20 bytes in hex for binary data
+    if (payloadType === 'arrowtable' || payloadType === 'jsontable' || payloadType === 'image' || payloadType === 'binary') {
+        const hexPreview = buffer.slice(0, 20).toString('hex');
+        logTrace(correlationId, `deserializeData: First 20 bytes (hex): ${hexPreview}`);
+    }
+    
+    if (payloadType === 'text') {
+        const result = buffer.toString('utf8');
+        logTrace(correlationId, `deserializeData: text result length=${result.length}`);
+        return result;
+    } else if (payloadType === 'dictionary') {
+        const jsonStr = buffer.toString('utf8');
+        const result = JSON.parse(jsonStr);
+        logTrace(correlationId, `deserializeData: dictionary keys=${Object.keys(result).join(', ')}`);
+        return result;
+    } else if (payloadType === 'arrowtable') {
+        logTrace(correlationId, `deserializeData: Attempting Arrow table deserialization`);
+        
+        // Debug: Check available arrow methods
+        logTrace(correlationId, `deserializeData: arrow.tableFromRawBytes exists: ${typeof arrow.tableFromRawBytes}`);
+        logTrace(correlationId, `deserializeData: arrow.tableFromIPC exists: ${typeof arrow.tableFromIPC}`);
+        
+        try {
+            // Try tableFromRawBytes first (older API)
+            if (typeof arrow.tableFromRawBytes === 'function') {
+                logTrace(correlationId, `deserializeData: Using tableFromRawBytes`);
+                const table = arrow.tableFromRawBytes(buffer);
+                logTrace(correlationId, `deserializeData: Arrow table - rows=${table.numRows}, cols=${table.numCols}`);
+                return table;
+            }
+        } catch (e) {
+            logTrace(correlationId, `deserializeData: tableFromRawBytes failed: ${e.message}`);
+        }
+        
+        try {
+            // Try tableFromIPC (newer API)
+            if (typeof arrow.tableFromIPC === 'function') {
+                logTrace(correlationId, `deserializeData: Using tableFromIPC`);
+                const table = arrow.tableFromIPC(buffer);
+                logTrace(correlationId, `deserializeData: Arrow table from IPC - rows=${table.numRows}, cols=${table.numCols}`);
+                return table;
+            }
+        } catch (e) {
+            logTrace(correlationId, `deserializeData: tableFromIPC failed: ${e.message}`);
+        }
+        
+        throw new Error(`Unable to deserialize Arrow table: neither tableFromRawBytes nor tableFromIPC worked`);
+    } else if (payloadType === 'jsontable') {
+        const jsonStr = buffer.toString('utf8');
+        const result = JSON.parse(jsonStr);
+        logTrace(correlationId, `deserializeData: jsontable result length=${Array.isArray(result) ? result.length : 'N/A'}`);
+        return result;
+    } else if (payloadType === 'image') {
+        logTrace(correlationId, `deserializeData: image buffer length=${buffer.length}`);
+        return buffer;
+    } else if (payloadType === 'audio') {
+        logTrace(correlationId, `deserializeData: audio buffer length=${buffer.length}`);
+        return buffer;
+    } else if (payloadType === 'video') {
+        logTrace(correlationId, `deserializeData: video buffer length=${buffer.length}`);
+        return buffer;
+    } else if (payloadType === 'binary') {
+        logTrace(correlationId, `deserializeData: binary buffer length=${buffer.length}`);
+        return buffer;
+    } else {
+        throw new Error(`Unknown payload_type: ${payloadType}`);
+    }
+}
+
+// ---------------------------------------------- File Server Handlers ---------------------------------------------- //
+
+/**
+ * Upload data to plik server in one-shot mode
+ * @param {string} fileServerUrl - Base URL of the plik server
+ * @param {string} dataname - Name of the file being uploaded
+ * @param {Buffer|Uint8Array} data - Raw byte data of the file content
+ * @returns {Promise<{status: number, uploadid: string, fileid: string, url: string}>}
+ */
+async function plikOneshotUpload(fileServerUrl, dataname, data) {
+    const buffer = Buffer.isBuffer(data) ? data : Buffer.from(data);
+    
+    // Get upload id
+    const urlGetUploadID = `${fileServerUrl}/upload`;
+    const headers = { 'Content-Type': 'application/json' };
+    const body = JSON.stringify({ OneShot: true });
+    
+    const httpResponse = await fetch(urlGetUploadID, {
+        method: 'POST',
+        headers,
+        body
+    });
+    
+    const responseJson = await httpResponse.json();
+    const uploadid = responseJson.id;
+    const uploadtoken = responseJson.uploadToken;
+
+    // Upload file
+    const urlUpload = `${fileServerUrl}/file/${uploadid}`;
+    const form = new FormData();
+    const blob = new Blob([buffer], { type: 'application/octet-stream' });
+    form.append('file', blob, dataname);
+    
+    const uploadHeaders = {
+        'X-UploadToken': uploadtoken
+    };
+
+    const uploadResponse = await fetch(urlUpload, {
+        method: 'POST',
+        headers: uploadHeaders,
+        body: form
+    });
+    
+    const uploadJson = await uploadResponse.json();
+    const fileid = uploadJson.id;
+
+    const url = `${fileServerUrl}/file/${uploadid}/${fileid}/${dataname}`;
+
+    return {
+        status: uploadResponse.status,
+        uploadid,
+        fileid,
+        url
+    };
+}
+
+/**
+ * Fetch data from URL with exponential backoff
+ * @param {string} url - URL to fetch from
+ * @param {number} maxRetries - Maximum number of retry attempts
+ * @param {number} baseDelay - Initial delay in milliseconds
+ * @param {number} maxDelay - Maximum delay in milliseconds
+ * @param {string} correlationId - Correlation ID for logging
+ * @returns {Promise<Uint8Array>} Fetched data as bytes
+ */
+async function fetchWithBackoff(url, maxRetries, baseDelay, maxDelay, correlationId) {
+    let delay = baseDelay;
+    
+    for (let attempt = 1; attempt <= maxRetries; attempt++) {
+        try {
+            const response = await fetch(url);
+            
+            if (response.status === 200) {
+                logTrace(correlationId, `Successfully fetched data from ${url} on attempt ${attempt}`);
+                const arrayBuffer = await response.arrayBuffer();
+                return new Uint8Array(arrayBuffer);
+            } else {
+                throw new Error(`Failed to fetch: ${response.status}`);
+            }
+        } catch (e) {
+            logTrace(correlationId, `Attempt ${attempt} failed: ${e.constructor.name} - ${e.message}`);
+            
+            if (attempt < maxRetries) {
+                await new Promise(resolve => setTimeout(resolve, delay));
+                delay = Math.min(delay * 2, maxDelay);
+            }
+        }
+    }
+    
+    throw new Error(`Failed to fetch data after ${maxRetries} attempts`);
+}
+
+// ---------------------------------------------- NATS Client ---------------------------------------------- //
+
+/**
+ * NATS client wrapper for connection management
+ */
+class NATSClient {
+    /**
+     * Create a new NATS client
+     * @param {string} url - NATS server URL
+     */
+    constructor(url) {
+        this.url = url;
+        this.connection = null;
+    }
+
+    /**
+     * Connect to NATS server
+     * @returns {Promise<NATS.Connection>}
+     */
+    async connect() {
+        this.connection = await nats.connect({ servers: this.url });
+        return this.connection;
+    }
+
+    /**
+     * Publish message to NATS subject
+     * @param {string} subject - NATS subject to publish to
+     * @param {string} message - Message to publish
+     * @param {string} correlationId - Correlation ID for logging
+     */
+    async publish(subject, message, correlationId) {
+        if (!this.connection) {
+            await this.connect();
+        }
+        await this.connection.publish(subject, message);
+        logTrace(correlationId, `Message published to ${subject}`);
+    }
+
+    /**
+     * Close the NATS connection
+     */
+    async close() {
+        if (this.connection) {
+            this.connection.close();
+        }
+    }
+}
+
+// ---------------------------------------------- Core Functions ---------------------------------------------- //
+
+/**
+ * Publish message to NATS
+ * @param {string|NATSClient|NATS.Connection} brokerUrlOrClient - NATS URL, client, or connection
+ * @param {string} subject - NATS subject to publish to
+ * @param {string} message - JSON message to publish
+ * @param {string} correlationId - Correlation ID for tracing
+ */
+async function publishMessage(brokerUrlOrClient, subject, message, correlationId) {
+    let conn;
+    
+    if (brokerUrlOrClient instanceof NATSClient) {
+        conn = brokerUrlOrClient;
+    } else if (brokerUrlOrClient && typeof brokerUrlOrClient.publish === 'function') {
+        // Create a wrapper for direct connection (duck-typing check for NATS connection)
+        conn = {
+            async publish(subj, msg) {
+                await brokerUrlOrClient.publish(subj, msg);
+            },
+            async close() {
+                await brokerUrlOrClient.close();
+            }
+        };
+    } else {
+        // String URL - create new client
+        const client = new NATSClient(brokerUrlOrClient);
+        conn = client;
+    }
+    
+    await conn.publish(subject, message, correlationId);
+    
+    if (conn instanceof NATSClient) {
+        await conn.close();
+    }
+}
+
+/**
+ * Build message envelope from payloads and metadata
+ * @param {string} subject - NATS subject
+ * @param {Array} payloads - Array of payload objects
+ * @param {Object} options - Envelope metadata options
+ * @returns {Object} Envelope object
+ */
+function buildEnvelope(subject, payloads, options) {
+    return {
+        correlation_id: options.correlation_id,
+        msg_id: options.msg_id,
+        timestamp: new Date().toISOString(),
+        send_to: subject,
+        msg_purpose: options.msg_purpose,
+        sender_name: options.sender_name,
+        sender_id: options.sender_id,
+        receiver_name: options.receiver_name,
+        receiver_id: options.receiver_id,
+        reply_to: options.reply_to,
+        reply_to_msg_id: options.reply_to_msg_id,
+        broker_url: options.broker_url,
+        metadata: options.metadata || {},
+        payloads: payloads
+    };
+}
+
+/**
+ * Build payload object from serialized data
+ * @param {string} dataname - Name of the payload
+ * @param {string} payloadType - Type of the payload
+ * @param {Buffer} payloadBytes - Serialized payload bytes
+ * @param {string} transport - Transport type ("direct" or "link")
+ * @param {string} data - Data (base64 for direct, URL for link)
+ * @returns {Object} Payload object
+ */
+function buildPayload(dataname, payloadType, payloadBytes, transport, data) {
+    // Determine encoding based on payload type (matching Julia implementation)
+    let encoding = 'base64';
+    if (payloadType === 'jsontable') {
+        encoding = 'json';
+    } else if (payloadType === 'arrowtable') {
+        encoding = 'arrow-ipc';
+    }
+    
+    return {
+        id: crypto.randomUUID(),
+        dataname,
+        payload_type: payloadType,
+        transport,
+        encoding,
+        size: payloadBytes.byteLength,
+        data,
+        metadata: transport === 'direct' ? { payload_bytes: payloadBytes.byteLength } : {}
+    };
+}
+
+/**
+ * Send data via NATS with automatic transport selection
+ * 
+ * This function intelligently routes data delivery based on payload size.
+ * If the serialized payload is smaller than size_threshold, it encodes the data as Base64
+ * and publishes directly over NATS. Otherwise, it uploads the data to a fileserver
+ * and publishes only the download URL over NATS.
+ * 
+ * @param {string} subject - NATS subject to publish the message to
+ * @param {Array} data - List of [dataname, data, type] tuples to send
+ *          - type: "text", "dictionary", "arrowtable", "jsontable", "image", "audio", "video", "binary"
+ * @param {Object} options - Optional configuration
+ * @param {string} [options.broker_url=DEFAULT_BROKER_URL] - URL of the NATS server
+ * @param {string} [options.fileserver_url=DEFAULT_FILESERVER_URL] - URL of the HTTP file server
+ * @param {Function} [options.fileserver_upload_handler=plikOneshotUpload] - Function to handle fileserver uploads
+ * @param {number} [options.size_threshold=DEFAULT_SIZE_THRESHOLD] - Threshold separating direct vs link transport
+ * @param {string} [options.correlation_id=crypto.randomUUID()] - Correlation ID for tracing
+ * @param {string} [options.msg_purpose="chat"] - Purpose of the message
+ * @param {string} [options.sender_name="NATSBridge"] - Name of the sender
+ * @param {string} [options.receiver_name=""] - Name of the receiver (empty means broadcast)
+ * @param {string} [options.receiver_id=""] - UUID of the receiver (empty means broadcast)
+ * @param {string} [options.reply_to=""] - Topic to reply to
+ * @param {string} [options.reply_to_msg_id=""] - Message ID this message is replying to
+ * @param {boolean} [options.is_publish=true] - Whether to automatically publish the message
+ * @param {NATSClient|NATS.Connection} [options.nats_connection=null] - Pre-existing NATS connection
+ * @param {string} [options.msg_id=crypto.randomUUID()] - Message ID
+ * @param {string} [options.sender_id=crypto.randomUUID()] - Sender ID
+ * @returns {Promise<[Object, string]>} Tuple of [env, env_json_str]
+ * 
+ * @example
+ * // Send a single payload
+ * const [env, envJsonStr] = await smartsend(
+ *     "/test",
+ *     [["dataname1", data1, "dictionary"]],
+ *     { broker_url: "nats://localhost:4222" }
+ * );
+ * 
+ * // Send multiple payloads
+ * const [env, envJsonStr] = await smartsend(
+ *     "/test",
+ *     [
+ *         ["dataname1", data1, "dictionary"],
+ *         ["dataname2", data2, "arrowtable"]
+ *     ],
+ *     { broker_url: "nats://localhost:4222" }
+ * );
+ * 
+ * // Send with pre-existing connection
+ * const client = await NATSBridge.NATSClient.connect("nats://localhost:4222");
+ * const [env, envJsonStr] = await smartsend(
+ *     "/test",
+ *     [["data", myData, "text"]],
+ *     { nats_connection: client }
+ * );
+ */
+async function smartsend(subject, data, options = {}) {
+    const {
+        broker_url = DEFAULT_BROKER_URL,
+        fileserver_url = DEFAULT_FILESERVER_URL,
+        fileserver_upload_handler = plikOneshotUpload,
+        size_threshold = DEFAULT_SIZE_THRESHOLD,
+        correlation_id = crypto.randomUUID(),
+        msg_purpose = 'chat',
+        sender_name = 'NATSBridge',
+        receiver_name = '',
+        receiver_id = '',
+        reply_to = '',
+        reply_to_msg_id = '',
+        is_publish = true,
+        nats_connection = null,
+        msg_id = crypto.randomUUID(),
+        sender_id = crypto.randomUUID()
+    } = options;
+
+    logTrace(correlation_id, `Starting smartsend for subject: ${subject}`);
+    logTrace(correlation_id, `smartsend: data array length=${data.length}`);
+    
+    // Debug: Log input data structure
+    for (let i = 0; i < data.length; i++) {
+        const [dataname, payloadData, payloadType] = data[i];
+        logTrace(correlation_id, `smartsend: payload[${i}] dataname=${dataname}, type=${payloadType}, data type=${typeof payloadData}, constructor=${payloadData?.constructor?.name}`);
+    }
+
+    // Process payloads
+    const payloads = [];
+    for (const [dataname, payloadData, payloadType] of data) {
+        logTrace(correlation_id, `smartsend: Processing payload '${dataname}' type=${payloadType}`);
+        logTrace(correlation_id, `smartsend: payloadData type=${typeof payloadData}, constructor=${payloadData?.constructor?.name}`);
+        
+        const payloadBytes = await serializeData(payloadData, payloadType);
+        const payloadSize = payloadBytes.byteLength;
+
+        logTrace(correlation_id, `Serialized payload '${dataname}' (type: ${payloadType}) size: ${payloadSize} bytes`);
+
+        // Debug: Show first 20 bytes of serialized data for table type
+        if (payloadType === 'table') {
+            const hexPreview = payloadBytes.slice(0, 20).toString('hex');
+            logTrace(correlation_id, `Serialized table data first 20 bytes (hex): ${hexPreview}`);
+        }
+
+        if (payloadSize < size_threshold) {
+            // Direct path
+            const payloadB64 = bufferToBase64(payloadBytes);
+            logTrace(correlation_id, `Using direct transport for ${payloadSize} bytes, base64 length=${payloadB64.length}`);
+
+            const payload = buildPayload(dataname, payloadType, payloadBytes, 'direct', payloadB64);
+            payloads.push(payload);
+        } else {
+            // Link path
+            logTrace(correlation_id, `Using link transport, uploading to fileserver`);
+
+            const response = await fileserver_upload_handler(fileserver_url, dataname, payloadBytes);
+
+            if (response.status !== 200) {
+                throw new Error(`Failed to upload data to fileserver: ${response.status}`);
+            }
+
+            logTrace(correlation_id, `Uploaded to URL: ${response.url}`);
+
+            const payload = buildPayload(dataname, payloadType, payloadBytes, 'link', response.url);
+            payloads.push(payload);
+        }
+    }
+
+    // Build envelope
+    const env = buildEnvelope(subject, payloads, {
+        correlation_id,
+        msg_id,
+        msg_purpose,
+        sender_name,
+        sender_id,
+        receiver_name,
+        receiver_id,
+        reply_to,
+        reply_to_msg_id,
+        broker_url
+    });
+
+    const env_json_str = JSON.stringify(env);
+
+    if (is_publish) {
+        if (nats_connection) {
+            await publishMessage(nats_connection, subject, env_json_str, correlation_id);
+        } else {
+            await publishMessage(broker_url, subject, env_json_str, correlation_id);
+        }
+    }
+
+    return [env, env_json_str];
+}
+
+/**
+ * Receive and process NATS message
+ * 
+ * This function processes incoming NATS messages, handling both direct transport
+ * (base64 decoded payloads) and link transport (URL-based payloads).
+ * It deserializes the data based on the transport type and returns the result.
+ * 
+ * @param {Object} msg - NATS message object with payload property
+ * @param {Object} options - Optional configuration
+ * @param {Function} [options.fileserver_download_handler=fetchWithBackoff] - Function to handle fileserver downloads
+ * @param {number} [options.max_retries=5] - Maximum retry attempts for fetching URL
+ * @param {number} [options.base_delay=100] - Initial delay for exponential backoff in ms
+ * @param {number} [options.max_delay=5000] - Maximum delay for exponential backoff in ms
+ * @returns {Promise<Object>} Envelope object with processed payloads
+ * 
+ * @example
+ * // Receive and process message
+ * const env = await smartreceive(msg, {
+ *     fileserver_download_handler: fetchWithBackoff,
+ *     max_retries: 5,
+ *     base_delay: 100,
+ *     max_delay: 5000
+ * });
+ * // env.payloads is an Array of [dataname, data, type] arrays
+ * for (const [dataname, data, type] of env.payloads) {
+ *     console.log(`${dataname}: ${data} (type: ${type})`);
+ * }
+ */
+async function smartreceive(msg, options = {}) {
+    const {
+        fileserver_download_handler = fetchWithBackoff,
+        max_retries = 5,
+        base_delay = 100,
+        max_delay = 5000
+    } = options;
+
+    // Debug: Log message object structure
+    logTrace('smartreceive', `smartreceive: msg object keys: ${Object.keys(msg).join(', ')}`);
+    logTrace('smartreceive', `smartreceive: msg.data type: ${typeof msg.data}, constructor: ${msg.data?.constructor?.name}`);
+    logTrace('smartreceive', `smartreceive: msg.payload type: ${typeof msg.payload}, constructor: ${msg.payload?.constructor?.name}`);
+    
+    // Parse the JSON envelope
+    // NATS.js v2.x uses msg.data instead of msg.payload
+    let payload;
+    if (msg.data !== undefined) {
+        payload = typeof msg.data === 'string' ? msg.data : Buffer.from(msg.data).toString('utf8');
+    } else if (msg.payload !== undefined) {
+        payload = typeof msg.payload === 'string' ? msg.payload : Buffer.from(msg.payload).toString('utf8');
+    } else {
+        throw new Error('Message has neither data nor payload property');
+    }
+    
+    logTrace('smartreceive', `smartreceive: raw payload length=${payload.length}`);
+    
+    // Debug: Show first 200 chars of payload
+    const payloadPreview = payload.substring(0, 200);
+    logTrace('smartreceive', `smartreceive: payload preview: ${payloadPreview}`);
+    
+    let envJsonObj;
+    try {
+        envJsonObj = JSON.parse(payload);
+    } catch (e) {
+        logTrace('smartreceive', `smartreceive: JSON parse failed: ${e.message}`);
+        throw e;
+    }
+    
+    logTrace(envJsonObj.correlation_id, 'Processing received message');
+    logTrace(envJsonObj.correlation_id, `smartreceive: envelope has ${envJsonObj.payloads.length} payloads`);
+
+    // Process all payloads in the envelope
+    const payloadsList = [];
+    const numPayloads = envJsonObj.payloads.length;
+
+    logTrace(envJsonObj.correlation_id, `smartreceive: Processing ${numPayloads} payloads`);
+
+    for (let i = 0; i < numPayloads; i++) {
+        const payloadObj = envJsonObj.payloads[i];
+        const transport = payloadObj.transport;
+        const dataname = payloadObj.dataname;
+        const payloadType = payloadObj.payload_type;
+
+        logTrace(envJsonObj.correlation_id, `smartreceive: Processing payload ${i + 1}/${numPayloads}: dataname=${dataname}, type=${payloadType}, transport=${transport}`);
+
+        if (transport === 'direct') {
+            logTrace(envJsonObj.correlation_id, `Direct transport - decoding payload '${dataname}'`);
+
+            // Extract base64 payload from the payload
+            const payloadB64 = payloadObj.data;
+            logTrace(envJsonObj.correlation_id, `Direct transport: base64 length=${payloadB64?.length}`);
+
+            // Decode Base64 payload
+            const payloadBytes = Buffer.from(payloadB64, 'base64');
+            logTrace(envJsonObj.correlation_id, `Direct transport: decoded bytes=${payloadBytes.length}`);
+
+            // Deserialize based on type
+            const dataType = payloadObj.payload_type;
+            const data = await deserializeData(payloadBytes, dataType, envJsonObj.correlation_id);
+            logTrace(envJsonObj.correlation_id, `Direct transport: deserialized data type=${typeof data}, constructor=${data?.constructor?.name}`);
+
+            payloadsList.push([dataname, data, dataType]);
+        } else if (transport === 'link') {
+            // Extract download URL from the payload
+            const url = payloadObj.data;
+            logTrace(envJsonObj.correlation_id, `Link transport - fetching '${dataname}' from URL: ${url}`);
+
+            // Fetch with exponential backoff using the download handler
+            const downloadedData = await fileserver_download_handler(
+                url,
+                max_retries,
+                base_delay,
+                max_delay,
+                envJsonObj.correlation_id
+            );
+
+            // Deserialize based on type
+            const dataType = payloadObj.payload_type;
+            const data = await deserializeData(downloadedData, dataType, envJsonObj.correlation_id);
+
+            payloadsList.push([dataname, data, dataType]);
+        } else {
+            throw new Error(`Unknown transport type for payload '${dataname}': ${transport}`);
+        }
+    }
+
+    logTrace(envJsonObj.correlation_id, `smartreceive: Successfully processed all ${payloadsList.length} payloads`);
+    envJsonObj.payloads = payloadsList;
+    return envJsonObj;
+}
+
+// ---------------------------------------------- Module Exports ---------------------------------------------- //
+
+const NATSBridge = {
+    /**
+     * NATS client class for connection management
+     */
+    NATSClient,
+
+    /**
+     * Send data via NATS with automatic transport selection
+     */
+    smartsend,
+
+    /**
+     * Receive and process NATS message
+     */
+    smartreceive,
+
+    /**
+     * Upload data to plik server in one-shot mode
+     */
+    plikOneshotUpload,
+
+    /**
+     * Fetch data from URL with exponential backoff
+     */
+    fetchWithBackoff,
+
+    /**
+     * Default constants
+     */
+    DEFAULT_SIZE_THRESHOLD,
+    DEFAULT_BROKER_URL,
+    DEFAULT_FILESERVER_URL
+};
+
+module.exports = NATSBridge;

src/natsbridge.py

@@ -0,0 +1,843 @@
+"""
+NATSBridge - Cross-Platform Bi-Directional Data Bridge
+Python Desktop Implementation
+
+This module provides functionality for sending and receiving data across network boundaries
+using NATS as the message bus, with support for both direct payload transport and
+URL-based transport for larger payloads.
+
+@package natsbridge
+"""
+
+import asyncio
+import base64
+import json
+import uuid
+from datetime import datetime
+from typing import Any, Callable, Dict, List, Tuple, Union
+import aiohttp
+
+try:
+    import pyarrow as arrow
+    import pyarrow.ipc as ipc
+    ARROW_AVAILABLE = True
+except ImportError:
+    ARROW_AVAILABLE = False
+
+try:
+    import nats
+    from nats.aio.client import Client as NATSClient
+    NATS_AVAILABLE = True
+except ImportError:
+    NATS_AVAILABLE = False
+
+# ---------------------------------------------- Constants ---------------------------------------------- #
+
+"""
+Default size threshold for switching from direct to link transport (1MB)
+"""
+DEFAULT_SIZE_THRESHOLD = 1_000_000
+
+"""
+Default NATS server URL
+"""
+DEFAULT_BROKER_URL = "nats://localhost:4222"
+
+"""
+Default HTTP file server URL for link transport
+"""
+DEFAULT_FILESERVER_URL = "http://localhost:8080"
+
+
+# ---------------------------------------------- Utility Functions ---------------------------------------------- #
+
+def log_trace(correlation_id: str, message: str) -> None:
+    """
+    Log a trace message with correlation ID and timestamp.
+    
+    Args:
+        correlation_id: Correlation ID for tracing
+        message: Message content to log
+    """
+    timestamp = datetime.utcnow().isoformat() + 'Z'
+    print(f"[{timestamp}] [Correlation: {correlation_id}] {message}")
+
+
+# ---------------------------------------------- Serialization Functions ---------------------------------------------- #
+
+def _serialize_data(data: Any, payload_type: str) -> bytes:
+    """
+    Serialize data according to specified format.
+    
+    Args:
+        data: Data to serialize (string for "text", JSON-serializable for "dictionary",
+              table-like for "arrowtable"/"jsontable", binary for "image", "audio", "video", "binary")
+        payload_type: Target format: "text", "dictionary", "arrowtable", "jsontable",
+                      "image", "audio", "video", "binary"
+    
+    Returns:
+        Binary representation of the serialized data
+    
+    Raises:
+        Error: If payload_type is not one of the supported types
+        Error: If payload_type is "image", "audio", or "video" but data is not bytes
+        Error: If payload_type is "arrowtable" but data is not a pandas DataFrame or pyarrow Table
+        Error: If payload_type is "jsontable" but data is not a list of dicts
+    """
+    if payload_type == 'text':
+        if isinstance(data, str):
+            return data.encode('utf-8')
+        else:
+            raise ValueError('Text data must be a string')
+    elif payload_type == 'dictionary':
+        json_str = json.dumps(data)
+        return json_str.encode('utf-8')
+    elif payload_type == 'arrowtable':
+        if not ARROW_AVAILABLE:
+            raise RuntimeError('pyarrow not available for arrowtable serialization')
+        
+        import io
+        buf = io.BytesIO()
+        
+        import pandas as pd
+        if isinstance(data, pd.DataFrame):
+            table = arrow.Table.from_pandas(data)
+            sink = ipc.new_file(buf, table.schema)
+            ipc.write_table(table, sink)
+            sink.close()
+            return buf.getvalue()
+        elif isinstance(data, arrow.Table):
+            sink = ipc.new_file(buf, data.schema)
+            ipc.write_table(data, sink)
+            sink.close()
+            return buf.getvalue()
+        else:
+            raise ValueError('Arrow table data must be a pandas DataFrame or pyarrow Table')
+    elif payload_type == 'jsontable':
+        # Serialize list of dicts to JSON format
+        if isinstance(data, list) and all(isinstance(row, dict) for row in data):
+            json_str = json.dumps(data)
+            return json_str.encode('utf-8')
+        else:
+            raise ValueError('JSON table data must be a list of dicts')
+    elif payload_type == 'image':
+        if isinstance(data, (bytes, bytearray)):
+            return bytes(data)
+        else:
+            raise ValueError('Image data must be bytes')
+    elif payload_type == 'audio':
+        if isinstance(data, (bytes, bytearray)):
+            return bytes(data)
+        else:
+            raise ValueError('Audio data must be bytes')
+    elif payload_type == 'video':
+        if isinstance(data, (bytes, bytearray)):
+            return bytes(data)
+        else:
+            raise ValueError('Video data must be bytes')
+    elif payload_type == 'binary':
+        if isinstance(data, (bytes, bytearray)):
+            return bytes(data)
+        else:
+            raise ValueError('Binary data must be bytes')
+    else:
+        raise ValueError(f'Unknown payload_type: {payload_type}')
+
+
+def _deserialize_data(data: bytes, payload_type: str, correlation_id: str) -> Any:
+    """
+    Deserialize bytes to data based on type.
+    
+    Args:
+        data: Serialized data as bytes
+        payload_type: Data type ("text", "dictionary", "arrowtable", "jsontable",
+                      "image", "audio", "video", "binary")
+        correlation_id: Correlation ID for logging
+    
+    Returns:
+        Deserialized data (String for "text", DataFrame for "arrowtable",
+        Vector{Dict} for "jsontable"/"dictionary", bytes for "image", "audio", "video", "binary")
+    
+    Raises:
+        Error: If payload_type is not one of the supported types
+    """
+    if payload_type == 'text':
+        return data.decode('utf-8')
+    elif payload_type == 'dictionary':
+        json_str = data.decode('utf-8')
+        return json.loads(json_str)
+    elif payload_type == 'arrowtable':
+        if not ARROW_AVAILABLE:
+            raise RuntimeError('pyarrow not available for arrowtable deserialization')
+        
+        import io
+        buf = io.BytesIO(data)
+        reader = ipc.open_file(buf)
+        return reader.read_all().to_pandas()
+    elif payload_type == 'jsontable':
+        # Deserialize JSON to list of dicts
+        json_str = data.decode('utf-8')
+        return json.loads(json_str)
+    elif payload_type == 'image':
+        return data
+    elif payload_type == 'audio':
+        return data
+    elif payload_type == 'video':
+        return data
+    elif payload_type == 'binary':
+        return data
+    else:
+        raise ValueError(f'Unknown payload_type: {payload_type}')
+
+
+# ---------------------------------------------- File Server Handlers ---------------------------------------------- #
+
+async def plik_oneshot_upload(
+    file_server_url: str,
+    dataname: str,
+    data: bytes
+) -> Dict[str, Any]:
+    """
+    Upload data to plik server in one-shot mode.
+    
+    This function uploads a raw byte array to a plik server in one-shot mode (no upload session).
+    It first creates a one-shot upload session by sending a POST request with {"OneShot": true},
+    retrieves an upload ID and token, then uploads the file data as multipart form data using the token.
+    
+    Args:
+        file_server_url: Base URL of the plik server (e.g., "http://localhost:8080")
+        dataname: Name of the file being uploaded
+        data: Raw byte data of the file content
+    
+    Returns:
+        Dict with keys:
+            - "status": HTTP server response status
+            - "uploadid": ID of the one-shot upload session
+            - "fileid": ID of the uploaded file within the session
+            - "url": Full URL to download the uploaded file
+    
+    Example:
+        >>> fileserver_url = "http://localhost:8080"
+        >>> dataname = "test.txt"
+        >>> data = b"hello world"
+        >>> result = await plik_oneshot_upload(file_server_url, dataname, data)
+        >>> result["status"], result["uploadid"], result["fileid"], result["url"]
+    """
+    async with aiohttp.ClientSession() as session:
+        # Get upload id
+        url_getUploadID = f"{file_server_url}/upload"
+        headers = {'Content-Type': 'application/json'}
+        body = json.dumps({"OneShot": True})
+        
+        async with session.post(url_getUploadID, headers=headers, data=body) as response:
+            response_json = await response.json()
+            uploadid = response_json['id']
+            uploadtoken = response_json['uploadToken']
+
+        # Upload file
+        url_upload = f"{file_server_url}/file/{uploadid}"
+        headers = {'X-UploadToken': uploadtoken}
+        
+        form = aiohttp.FormData()
+        form.add_field('file', data, filename=dataname, content_type='application/octet-stream')
+        
+        async with session.post(url_upload, headers=headers, data=form) as upload_response:
+            upload_json = await upload_response.json()
+            fileid = upload_json['id']
+
+    url = f"{file_server_url}/file/{uploadid}/{fileid}/{dataname}"
+
+    return {
+        'status': upload_response.status,
+        'uploadid': uploadid,
+        'fileid': fileid,
+        'url': url
+    }
+
+
+async def fetch_with_backoff(
+    url: str,
+    max_retries: int,
+    base_delay: int,
+    max_delay: int,
+    correlation_id: str
+) -> bytes:
+    """
+    Fetch data from URL with exponential backoff.
+    
+    This internal function retrieves data from a URL with retry logic using
+    exponential backoff to handle transient failures.
+    
+    Args:
+        url: URL to fetch from
+        max_retries: Maximum number of retry attempts
+        base_delay: Initial delay in milliseconds
+        max_delay: Maximum delay in milliseconds
+        correlation_id: Correlation ID for logging
+    
+    Returns:
+        Fetched data as bytes
+    
+    Raises:
+        Error: If all retry attempts fail
+    
+    Example:
+        >>> data = await fetch_with_backoff("http://example.com/file.zip", 5, 100, 5000, "correlation123")
+    """
+    delay = base_delay
+    
+    for attempt in range(1, max_retries + 1):
+        try:
+            async with aiohttp.ClientSession() as session:
+                async with session.get(url) as response:
+                    if response.status == 200:
+                        log_trace(correlation_id, f"Successfully fetched data from {url} on attempt {attempt}")
+                        return await response.read()
+                    else:
+                        raise Exception(f"Failed to fetch: {response.status}")
+        except Exception as e:
+            log_trace(correlation_id, f"Attempt {attempt} failed: {type(e).__name__}")
+            
+            if attempt < max_retries:
+                await asyncio.sleep(delay / 1000.0)
+                delay = min(delay * 2, max_delay)
+    
+    raise Exception(f"Failed to fetch data after {max_retries} attempts")
+
+
+# ---------------------------------------------- NATS Client ---------------------------------------------- #
+
+class NATSClient:
+    """NATS client wrapper for connection management."""
+    
+    def __init__(self, url: str = DEFAULT_BROKER_URL):
+        """
+        Create a new NATS client.
+        
+        Args:
+            url: NATS server URL
+        """
+        self.url = url
+        self._client: NATSClient = None
+    
+    async def connect(self) -> NATSClient:
+        """
+        Connect to NATS server.
+        
+        Returns:
+            NATS client instance
+        """
+        if NATS_AVAILABLE:
+            self._client = nats.connect(self.url)
+            await self._client
+        else:
+            raise RuntimeError('nats-py not available')
+        return self._client
+    
+    async def publish(self, subject: str, message: str, correlation_id: str = "") -> None:
+        """
+        Publish message to NATS subject.
+        
+        Args:
+            subject: NATS subject to publish to
+            message: Message to publish
+            correlation_id: Correlation ID for logging
+        """
+        if self._client:
+            await self._client.publish(subject, message)
+            if correlation_id:
+                log_trace(correlation_id, f"Message published to {subject}")
+    
+    async def close(self) -> None:
+        """Close the NATS connection."""
+        if self._client:
+            await self._client.drain()
+            await self._client.close()
+
+
+# ---------------------------------------------- Core Functions ---------------------------------------------- #
+
+def _build_envelope(
+    subject: str,
+    payloads: List[Dict[str, Any]],
+    options: Dict[str, Any]
+) -> Dict[str, Any]:
+    """
+    Build message envelope from payloads and metadata.
+    
+    Args:
+        subject: NATS subject
+        payloads: Array of payload objects
+        options: Envelope metadata options
+    
+    Returns:
+        Envelope object
+    """
+    return {
+        'correlation_id': options['correlation_id'],
+        'msg_id': options['msg_id'],
+        'timestamp': datetime.utcnow().isoformat() + 'Z',
+        'send_to': subject,
+        'msg_purpose': options['msg_purpose'],
+        'sender_name': options['sender_name'],
+        'sender_id': options['sender_id'],
+        'receiver_name': options['receiver_name'],
+        'receiver_id': options['receiver_id'],
+        'reply_to': options['reply_to'],
+        'reply_to_msg_id': options['reply_to_msg_id'],
+        'broker_url': options['broker_url'],
+        'metadata': options.get('metadata', {}),
+        'payloads': payloads
+    }
+
+
+def _build_payload(
+    dataname: str,
+    payload_type: str,
+    payload_bytes: bytes,
+    transport: str,
+    data: Union[str, bytes]
+) -> Dict[str, Any]:
+    """
+    Build payload object from serialized data.
+    
+    Args:
+        dataname: Name of the payload
+        payload_type: Type of the payload
+        payload_bytes: Serialized payload bytes
+        transport: Transport type ("direct" or "link")
+        data: Data (base64 for direct, URL for link)
+    
+    Returns:
+        Payload object
+    """
+    # Determine encoding based on payload type (matching Julia/JS implementation)
+    encoding = 'base64'
+    if payload_type == 'jsontable':
+        encoding = 'json'
+    elif payload_type == 'arrowtable':
+        encoding = 'arrow-ipc'
+    
+    return {
+        'id': str(uuid.uuid4()),
+        'dataname': dataname,
+        'payload_type': payload_type,
+        'transport': transport,
+        'encoding': encoding,
+        'size': len(payload_bytes),
+        'data': data,
+        'metadata': {'payload_bytes': len(payload_bytes)} if transport == 'direct' else {}
+    }
+
+
+async def publish_message(
+    broker_url_or_client: Union[str, NATSClient, Any],
+    subject: str,
+    message: str,
+    correlation_id: str
+) -> None:
+    """
+    Publish message to NATS.
+    
+    Args:
+        broker_url_or_client: NATS URL, client, or connection
+        subject: NATS subject to publish to
+        message: JSON message to publish
+        correlation_id: Correlation ID for tracing
+    """
+    if isinstance(broker_url_or_client, NATSClient):
+        client = broker_url_or_client
+    elif NATS_AVAILABLE and hasattr(broker_url_or_client, 'publish'):
+        # Direct NATS client connection
+        await broker_url_or_client.publish(subject, message)
+        log_trace(correlation_id, f"Message published to {subject}")
+        return
+    else:
+        # String URL - create new client
+        client = NATSClient(broker_url_or_client)
+        await client.connect()
+    
+    await client.publish(subject, message, correlation_id)
+    
+    if isinstance(broker_url_or_client, NATSClient):
+        await broker_url_or_client.close()
+    elif not (NATS_AVAILABLE and hasattr(broker_url_or_client, 'publish')):
+        await client.close()
+
+
+async def smartsend(
+    subject: str,
+    data: List[Tuple[str, Any, str]],
+    broker_url: str = DEFAULT_BROKER_URL,
+    fileserver_url: str = DEFAULT_FILESERVER_URL,
+    fileserver_upload_handler: Callable = plik_oneshot_upload,
+    size_threshold: int = DEFAULT_SIZE_THRESHOLD,
+    correlation_id: str = None,
+    msg_purpose: str = "chat",
+    sender_name: str = "NATSBridge",
+    receiver_name: str = "",
+    receiver_id: str = "",
+    reply_to: str = "",
+    reply_to_msg_id: str = "",
+    is_publish: bool = True,
+    nats_connection: Any = None,
+    msg_id: str = None,
+    sender_id: str = None
+) -> Tuple[Dict, str]:
+    """
+    Send data via NATS with automatic transport selection.
+    
+    This function intelligently routes data delivery based on payload size.
+    If the serialized payload is smaller than size_threshold, it encodes the data as Base64
+    and publishes directly over NATS. Otherwise, it uploads the data to a fileserver
+    and publishes only the download URL over NATS.
+    
+    Args:
+        subject: NATS subject to publish the message to
+        data: List of (dataname, data, type) tuples to send
+            - dataname: Name of the payload
+            - data: The actual data to send
+            - type: Payload type: "text", "dictionary", "arrowtable", "jsontable", "image", "audio", "video", "binary"
+        broker_url: URL of the NATS server
+        fileserver_url: URL of the HTTP file server for large payloads
+        fileserver_upload_handler: Function to handle fileserver uploads (must return Dict with "status",
+                                   "uploadid", "fileid", "url" keys)
+        size_threshold: Threshold in bytes separating direct vs link transport
+        correlation_id: Correlation ID for tracing (auto-generated UUID if not provided)
+        msg_purpose: Purpose of the message: "ACK", "NACK", "updateStatus", "shutdown", "chat", etc.
+        sender_name: Name of the sender
+        receiver_name: Name of the receiver (empty string means broadcast)
+        receiver_id: UUID of the receiver (empty string means broadcast)
+        reply_to: Topic to reply to (empty string if no reply expected)
+        reply_to_msg_id: Message ID this message is replying to
+        is_publish: Whether to automatically publish the message to NATS
+        nats_connection: Pre-existing NATS connection (if provided, uses this connection instead of
+                        creating a new one; saves connection establishment overhead)
+        msg_id: Message ID (auto-generated UUID if not provided)
+        sender_id: Sender ID (auto-generated UUID if not provided)
+    
+    Returns:
+        Tuple of (env, env_json_str) where:
+            - env: Dict containing all metadata and payloads
+            - env_json_str: JSON string for publishing to NATS
+    
+    Example:
+        >>> # Send a single payload (still wrapped in a list)
+        >>> data = {"key": "value"}
+        >>> env, env_json_str = await smartsend(
+        ...     "my.subject",
+        ...     [("dataname1", data, "dictionary")],
+        ...     broker_url="nats://localhost:4222"
+        ... )
+        >>>
+        >>> # Send multiple payloads with different types
+        >>> data1 = {"key1": "value1"}
+        >>> data2 = [1, 2, 3, 4, 5]
+        >>> env, env_json_str = await smartsend(
+        ...     "my.subject",
+        ...     [("dataname1", data1, "dictionary"), ("dataname2", data2, "arrowtable")]
+        ... )
+        >>>
+        >>> # Send a large array using fileserver upload
+        >>> data = list(range(10_000_000))  # ~80 MB
+        >>> env, env_json_str = await smartsend(
+        ...     "large.data",
+        ...     [("large_table", data, "arrowtable")]
+        ... )
+        >>>
+        >>> # Send jsontable (JSON format for human-readable tabular data)
+        >>> users = [{"id": 1, "name": "Alice"}, {"id": 2, "name": "Bob"}]
+        >>> env, env_json_str = await smartsend(
+        ...     "json.data",
+        ...     [("users", users, "jsontable")]
+        ... )
+        >>>
+        >>> # Mixed content (e.g., chat with text and image)
+        >>> env, env_json_str = await smartsend(
+        ...     "chat.subject",
+        ...     [
+        ...         ("message_text", "Hello!", "text"),
+        ...         ("user_image", image_data, "image"),
+        ...         ("audio_clip", audio_data, "audio")
+        ...     ]
+        ... )
+        >>>
+        >>> # Publish the JSON string directly using NATS request-reply pattern
+        >>> # reply = await nats.request(broker_url, subject, env_json_str, reply_to=reply_to_topic)
+    """
+    if correlation_id is None:
+        correlation_id = str(uuid.uuid4())
+    if msg_id is None:
+        msg_id = str(uuid.uuid4())
+    if sender_id is None:
+        sender_id = str(uuid.uuid4())
+    
+    log_trace(correlation_id, f"Starting smartsend for subject: {subject}")
+    
+    # Process payloads
+    payloads = []
+    for dataname, payload_data, payload_type in data:
+        payload_bytes = _serialize_data(payload_data, payload_type)
+        payload_size = len(payload_bytes)
+        
+        log_trace(correlation_id, f"Serialized payload '{dataname}' (type: {payload_type}) size: {payload_size} bytes")
+        
+        if payload_size < size_threshold:
+            # Direct path
+            payload_b64 = base64.b64encode(payload_bytes).decode('utf-8')
+            log_trace(correlation_id, f"Using direct transport for {payload_size} bytes")
+            
+            payload = _build_payload(dataname, payload_type, payload_bytes, 'direct', payload_b64)
+            payloads.append(payload)
+        else:
+            # Link path
+            log_trace(correlation_id, "Using link transport, uploading to fileserver")
+            
+            response = await fileserver_upload_handler(fileserver_url, dataname, payload_bytes)
+            
+            if response['status'] != 200:
+                raise Exception(f"Failed to upload data to fileserver: {response['status']}")
+            
+            log_trace(correlation_id, f"Uploaded to URL: {response['url']}")
+            
+            payload = _build_payload(dataname, payload_type, payload_bytes, 'link', response['url'])
+            payloads.append(payload)
+    
+    # Build envelope
+    env = _build_envelope(subject, payloads, {
+        'correlation_id': correlation_id,
+        'msg_id': msg_id,
+        'msg_purpose': msg_purpose,
+        'sender_name': sender_name,
+        'sender_id': sender_id,
+        'receiver_name': receiver_name,
+        'receiver_id': receiver_id,
+        'reply_to': reply_to,
+        'reply_to_msg_id': reply_to_msg_id,
+        'broker_url': broker_url
+    })
+    
+    env_json_str = json.dumps(env)
+    
+    if is_publish:
+        if nats_connection:
+            await publish_message(nats_connection, subject, env_json_str, correlation_id)
+        else:
+            await publish_message(broker_url, subject, env_json_str, correlation_id)
+    
+    return env, env_json_str
+
+
+async def smartreceive(
+    msg: Any,
+    fileserver_download_handler: Callable = fetch_with_backoff,
+    max_retries: int = 5,
+    base_delay: int = 100,
+    max_delay: int = 5000
+) -> Dict[str, Any]:
+    """
+    Receive and process NATS messages.
+    
+    This function processes incoming NATS messages, handling both direct transport
+    (base64 decoded payloads) and link transport (URL-based payloads).
+    It deserializes the data based on the transport type and returns the result.
+    
+    Args:
+        msg: NATS message to process
+        fileserver_download_handler: Function to handle downloading data from file server URLs
+        max_retries: Maximum retry attempts for fetching URL
+        base_delay: Initial delay for exponential backoff in ms
+        max_delay: Maximum delay for exponential backoff in ms
+    
+    Returns:
+        Dict with envelope metadata and payloads field containing List[Tuple[str, Any, str]]
+    
+    Example:
+        >>> # Receive and process message
+        >>> env = await smartreceive(msg, fileserver_download_handler=fetch_with_backoff)
+        >>> # env is a Dict with "payloads" key containing List[Tuple[str, Any, str]]
+        >>> # Access payloads: for dataname, data, type_ in env["payloads"]
+        >>> for dataname, data, type_ in env["payloads"]:
+        >>>     print(f"{dataname}: {data} (type: {type_})")
+    """
+    # Parse the JSON envelope
+    if isinstance(msg, dict):
+        # Already parsed
+        env_json_obj = msg
+    elif hasattr(msg, 'payload'):
+        # NATS message object
+        payload = msg.payload if isinstance(msg.payload, str) else msg.payload.decode('utf-8')
+        env_json_obj = json.loads(payload)
+    else:
+        # Assume it's already a JSON string or dict
+        env_json_obj = json.loads(msg) if isinstance(msg, str) else msg
+    
+    log_trace(env_json_obj['correlation_id'], "Processing received message")
+    
+    # Process all payloads in the envelope
+    payloads_list = []
+    num_payloads = len(env_json_obj['payloads'])
+    
+    for i in range(num_payloads):
+        payload_obj = env_json_obj['payloads'][i]
+        transport = payload_obj['transport']
+        dataname = payload_obj['dataname']
+        
+        if transport == 'direct':
+            log_trace(env_json_obj['correlation_id'], f"Direct transport - decoding payload '{dataname}'")
+            
+            # Extract base64 payload from the payload
+            payload_b64 = payload_obj['data']
+            
+            # Decode Base64 payload
+            payload_bytes = base64.b64decode(payload_b64)
+            
+            # Deserialize based on type
+            data_type = payload_obj['payload_type']
+            data = _deserialize_data(payload_bytes, data_type, env_json_obj['correlation_id'])
+            
+            payloads_list.append((dataname, data, data_type))
+        elif transport == 'link':
+            # Extract download URL from the payload
+            url = payload_obj['data']
+            log_trace(env_json_obj['correlation_id'], f"Link transport - fetching '{dataname}' from URL: {url}")
+            
+            # Fetch with exponential backoff using the download handler
+            downloaded_data = await fileserver_download_handler(
+                url,
+                max_retries,
+                base_delay,
+                max_delay,
+                env_json_obj['correlation_id']
+            )
+            
+            # Deserialize based on type
+            data_type = payload_obj['payload_type']
+            data = _deserialize_data(downloaded_data, data_type, env_json_obj['correlation_id'])
+            
+            payloads_list.append((dataname, data, data_type))
+        else:
+            raise Exception(f"Unknown transport type for payload '{dataname}': {transport}")
+    
+    env_json_obj['payloads'] = payloads_list
+    return env_json_obj
+
+
+# ---------------------------------------------- Module Exports ---------------------------------------------- #
+
+class NATSBridge:
+    """
+    Cross-platform NATS bridge implementation.
+    
+    This class provides a convenient interface for NATSBridge functionality,
+    encapsulating the main functions and providing a class-based API.
+    """
+    
+    DEFAULT_SIZE_THRESHOLD = DEFAULT_SIZE_THRESHOLD
+    DEFAULT_BROKER_URL = DEFAULT_BROKER_URL
+    DEFAULT_FILESERVER_URL = DEFAULT_FILESERVER_URL
+    
+    def __init__(self, broker_url: str = None, fileserver_url: str = None):
+        """
+        Initialize NATSBridge.
+        
+        Args:
+            broker_url: NATS server URL (defaults to DEFAULT_BROKER_URL)
+            fileserver_url: HTTP file server URL (defaults to DEFAULT_FILESERVER_URL)
+        """
+        self.broker_url = broker_url or self.DEFAULT_BROKER_URL
+        self.fileserver_url = fileserver_url or self.DEFAULT_FILESERVER_URL
+    
+    async def smartsend(
+        self,
+        subject: str,
+        data: List[Tuple[str, Any, str]],
+        **kwargs
+    ) -> Tuple[Dict, str]:
+        """
+        Send data via NATS.
+        
+        Args:
+            subject: NATS subject to publish to
+            data: List of (dataname, data, type) tuples
+            **kwargs: Additional options passed to smartsend
+        
+        Returns:
+            Tuple of (env, env_json_str)
+        """
+        kwargs['broker_url'] = kwargs.get('broker_url', self.broker_url)
+        kwargs['fileserver_url'] = kwargs.get('fileserver_url', self.fileserver_url)
+        return await smartsend(subject, data, **kwargs)
+    
+    async def smartreceive(
+        self,
+        msg: Any,
+        **kwargs
+    ) -> Dict[str, Any]:
+        """
+        Receive and process NATS message.
+        
+        Args:
+            msg: NATS message to process
+            **kwargs: Additional options passed to smartreceive
+        
+        Returns:
+            Dict with envelope metadata and payloads
+        """
+        return await smartreceive(msg, **kwargs)
+
+
+# Convenience functions for module-level usage
+def send(
+    subject: str,
+    data: List[Tuple[str, Any, str]],
+    **kwargs
+) -> Tuple[Dict, str]:
+    """
+    Convenience function for sending data.
+    
+    Args:
+        subject: NATS subject to publish to
+        data: List of (dataname, data, type) tuples
+        **kwargs: Additional options
+    
+    Returns:
+        Tuple of (env, env_json_str)
+    """
+    return asyncio.run(smartsend(subject, data, **kwargs))
+
+
+def receive(
+    msg: Any,
+    **kwargs
+) -> Dict[str, Any]:
+    """
+    Convenience function for receiving messages.
+    
+    Args:
+        msg: NATS message to process
+        **kwargs: Additional options
+    
+    Returns:
+        Dict with envelope metadata and payloads
+    """
+    return asyncio.run(smartreceive(msg, **kwargs))
+
+
+__all__ = [
+    'smartsend',
+    'smartreceive',
+    'plik_oneshot_upload',
+    'fetch_with_backoff',
+    'NATSBridge',
+    'send',
+    'receive',
+    'DEFAULT_SIZE_THRESHOLD',
+    'DEFAULT_BROKER_URL',
+    'DEFAULT_FILESERVER_URL',
+    'NATSClient',
+    '_serialize_data',
+    '_deserialize_data',
+    'log_trace',
+    'publish_message'
+]

src/natsbridge_mpy.py

@@ -0,0 +1,673 @@
+"""
+NATSBridge - Cross-Platform Bi-Directional Data Bridge
+MicroPython Implementation
+
+This module provides functionality for sending and receiving data across network boundaries
+using NATS as the message bus, with support for both direct payload transport and
+URL-based transport for larger payloads.
+
+Note: MicroPython has significant constraints compared to desktop implementations:
+- Limited memory (~256KB - 1MB)
+- No Arrow IPC support (memory constraints)
+- Synchronous API (no async/await)
+- Lower size threshold for direct transport
+"""
+
+import network
+import time
+import json
+import base64
+import uos
+import struct
+import random
+
+# ---------------------------------------------- Constants ---------------------------------------------- #
+
+"""
+Default size threshold for switching from direct to link transport (100KB for MicroPython)
+"""
+DEFAULT_SIZE_THRESHOLD = 100000
+
+"""
+Default NATS server URL
+"""
+DEFAULT_BROKER_URL = "nats://localhost:4222"
+
+"""
+Default HTTP file server URL for link transport
+"""
+DEFAULT_FILESERVER_URL = "http://localhost:8080"
+
+"""
+Hard limit for payload size in MicroPython (50KB)
+"""
+MAX_PAYLOAD_SIZE = 50000
+
+
+# ---------------------------------------------- Utility Functions ---------------------------------------------- #
+
+def log_trace(correlation_id, message):
+    """
+    Log a trace message with correlation ID and timestamp.
+    
+    Args:
+        correlation_id: Correlation ID for tracing
+        message: Message content to log
+    """
+    timestamp = time.strftime('%Y-%m-%dT%H:%M:%SZ', time.localtime())
+    print(f"[{timestamp}] [Correlation: {correlation_id}] {message}")
+
+
+def _generate_uuid():
+    """
+    Generate a simple UUID compatible with MicroPython.
+    
+    Returns:
+        UUID string
+    """
+    # Generate a simple UUID-like string
+    # Format: xxxxxxxx-xxxx-xxxx-xxxx-xxxxxxxxxxxx
+    hex_chars = '0123456789abcdef'
+    uuid_str = ''.join([random.choice(hex_chars) for _ in range(32)])
+    # Insert hyphens at proper positions
+    return f"{uuid_str[:8]}-{uuid_str[8:12]}-{uuid_str[12:16]}-{uuid_str[16:20]}-{uuid_str[20:]}"
+
+
+# ---------------------------------------------- Serialization Functions ---------------------------------------------- #
+
+def _serialize_data(data, payload_type):
+    """
+    Serialize data according to specified format.
+    
+    Args:
+        data: Data to serialize (string for "text", dict for "dictionary",
+              bytes for "image", "audio", "video", "binary")
+        payload_type: Target format: "text", "dictionary", "image", "audio", "video", "binary"
+    
+    Returns:
+        Binary representation of the serialized data
+    
+    Note:
+        MicroPython does not support "table" type due to memory constraints.
+    
+    Raises:
+        ValueError: If payload_type is not one of the supported types
+    """
+    if payload_type == 'text':
+        if isinstance(data, str):
+            return data.encode('utf-8')
+        else:
+            raise ValueError('Text data must be a string')
+    elif payload_type == 'dictionary':
+        json_str = json.dumps(data)
+        return json_str.encode('utf-8')
+    elif payload_type in ('image', 'audio', 'video', 'binary'):
+        if isinstance(data, (bytes, bytearray, memoryview)):
+            return bytes(data)
+        else:
+            raise ValueError(f'{payload_type} data must be bytes')
+    else:
+        raise ValueError(f'Unknown payload_type: {payload_type}')
+
+
+def _deserialize_data(data, payload_type):
+    """
+    Deserialize bytes to data based on type.
+    
+    Args:
+        data: Serialized data as bytes
+        payload_type: Data type ("text", "dictionary", "image", "audio", "video", "binary")
+    
+    Returns:
+        Deserialized data (String for "text", dict for "dictionary", bytes for others)
+    
+    Note:
+        MicroPython does not support "table" type due to memory constraints.
+    
+    Raises:
+        ValueError: If payload_type is not one of the supported types
+    """
+    if payload_type == 'text':
+        return data.decode('utf-8')
+    elif payload_type == 'dictionary':
+        json_str = data.decode('utf-8')
+        return json.loads(json_str)
+    elif payload_type in ('image', 'audio', 'video', 'binary'):
+        return data
+    else:
+        raise ValueError(f'Unknown payload_type: {payload_type}')
+
+
+# ---------------------------------------------- File Server Handlers ---------------------------------------------- #
+
+def _sync_fileserver_upload(file_server_url, dataname, data):
+    """
+    Synchronous file upload to HTTP server.
+    
+    Note:
+        This is a simplified implementation for MicroPython.
+        In practice, would use network.HTTP or similar.
+        Currently raises NotImplementedError as file upload is not fully supported.
+    
+    Args:
+        file_server_url: Base URL of the file server
+        dataname: Name of the file being uploaded
+        data: Raw byte data of the file content
+    
+    Returns:
+        Dict with keys: 'status', 'url'
+    
+    Raises:
+        NotImplementedError: File upload is not implemented in MicroPython
+    """
+    raise NotImplementedError("File upload not fully implemented in MicroPython. "
+                              "Use direct transport only for memory-constrained devices.")
+
+
+def _sync_fileserver_download(url, max_retries, base_delay, max_delay, correlation_id):
+    """
+    Synchronous file download with exponential backoff.
+    
+    Note:
+        This is a simplified implementation for MicroPython.
+        In practice, would use network.HTTP or similar.
+        Currently raises NotImplementedError as file download is not fully supported.
+    
+    Args:
+        url: URL to download from
+        max_retries: Maximum retry attempts
+        base_delay: Initial delay in ms
+        max_delay: Maximum delay in ms
+        correlation_id: Correlation ID for logging
+    
+    Returns:
+        Downloaded bytes
+    
+    Raises:
+        NotImplementedError: File download is not implemented in MicroPython
+    """
+    raise NotImplementedError("File download not fully implemented in MicroPython. "
+                              "Use direct transport only for memory-constrained devices.")
+
+
+# ---------------------------------------------- NATS Client ---------------------------------------------- #
+
+class NATSClient:
+    """
+    NATS client wrapper for MicroPython.
+    
+    Note:
+        This is a simplified implementation for MicroPython.
+        Full NATS client implementation would require additional network stack support.
+    """
+    
+    def __init__(self, url=DEFAULT_BROKER_URL):
+        """
+        Initialize NATS client.
+        
+        Args:
+            url: NATS server URL
+        """
+        self.url = url
+        self._connected = False
+    
+    def connect(self):
+        """
+        Connect to NATS server.
+        
+        Note:
+            This is a placeholder implementation.
+            Actual NATS client would require network stack support.
+        
+        Returns:
+            True if connected, False otherwise
+        """
+        # Placeholder - actual implementation would connect to NATS server
+        self._connected = True
+        return self._connected
+    
+    def publish(self, subject, message):
+        """
+        Publish message to NATS subject.
+        
+        Note:
+            This is a placeholder implementation.
+            Actual NATS client would require network stack support.
+        
+        Args:
+            subject: NATS subject to publish to
+            message: Message to publish
+        """
+        if not self._connected:
+            raise RuntimeError("Not connected to NATS server")
+        # Placeholder - actual implementation would publish to NATS
+        print(f"[NATS] Publish to {subject}: {message[:50]}...")
+    
+    def close(self):
+        """Close the NATS connection."""
+        self._connected = False
+
+
+# ---------------------------------------------- Core Functions ---------------------------------------------- #
+
+def _build_envelope(subject, payloads, options):
+    """
+    Build message envelope from payloads and metadata.
+    
+    Args:
+        subject: NATS subject
+        payloads: Array of payload objects
+        options: Envelope metadata options
+    
+    Returns:
+        Envelope dict
+    """
+    return {
+        'correlation_id': options['correlation_id'],
+        'msg_id': options['msg_id'],
+        'timestamp': time.strftime('%Y-%m-%dT%H:%M:%SZ', time.localtime()),
+        'send_to': subject,
+        'msg_purpose': options['msg_purpose'],
+        'sender_name': options['sender_name'],
+        'sender_id': options['sender_id'],
+        'receiver_name': options['receiver_name'],
+        'receiver_id': options['receiver_id'],
+        'reply_to': options['reply_to'],
+        'reply_to_msg_id': options['reply_to_msg_id'],
+        'broker_url': options['broker_url'],
+        'metadata': {},
+        'payloads': payloads
+    }
+
+
+def _build_payload(dataname, payload_type, payload_bytes, transport, data):
+    """
+    Build payload object from serialized data.
+    
+    Args:
+        dataname: Name of the payload
+        payload_type: Type of the payload
+        payload_bytes: Serialized payload bytes
+        transport: Transport type ("direct" or "link")
+        data: Data (base64 for direct, URL for link)
+    
+    Returns:
+        Payload dict
+    """
+    return {
+        'id': _generate_uuid(),
+        'dataname': dataname,
+        'payload_type': payload_type,
+        'transport': transport,
+        'encoding': 'base64' if transport == 'direct' else 'none',
+        'size': len(payload_bytes),
+        'data': data,
+        'metadata': {'payload_bytes': len(payload_bytes)} if transport == 'direct' else {}
+    }
+
+
+def _publish(subject, message, correlation_id):
+    """
+    Publish message to NATS.
+    
+    Note:
+        This is a simplified implementation for MicroPython.
+    
+    Args:
+        subject: NATS subject to publish to
+        message: JSON message to publish
+        correlation_id: Correlation ID for logging
+    """
+    log_trace(correlation_id, f"Publishing to {subject}")
+    # Placeholder - actual implementation would use NATSClient
+    # client = NATSClient()
+    # client.connect()
+    # client.publish(subject, message)
+    # client.close()
+
+
+def smartsend(subject, data, **kwargs):
+    """
+    Send data via NATS with automatic transport selection.
+    
+    This function intelligently routes data delivery based on payload size.
+    If the serialized payload is smaller than size_threshold, it encodes the data as Base64
+    and publishes directly over NATS. Otherwise, it uploads the data to a fileserver
+    and publishes only the download URL over NATS.
+    
+    Note:
+        MicroPython has memory constraints, so the default size_threshold is lower (100KB).
+        Table type is not supported due to memory constraints.
+    
+    Args:
+        subject: NATS subject to publish the message to
+        data: List of (dataname, data, type) tuples to send
+            - dataname: Name of the payload
+            - data: The actual data to send
+            - type: Payload type: "text", "dictionary", "image", "audio", "video", "binary"
+        broker_url: NATS server URL (default: DEFAULT_BROKER_URL)
+        fileserver_url: HTTP file server URL (default: DEFAULT_FILESERVER_URL)
+        fileserver_upload_handler: Function to handle fileserver uploads (default: _sync_fileserver_upload)
+        size_threshold: Threshold in bytes separating direct vs link transport (default: 100000)
+        correlation_id: Correlation ID for tracing (auto-generated if not provided)
+        msg_purpose: Purpose of the message (default: "chat")
+        sender_name: Name of the sender (default: "NATSBridge")
+        receiver_name: Name of the receiver (empty means broadcast)
+        receiver_id: UUID of the receiver (empty means broadcast)
+        reply_to: Topic to reply to (empty if no reply expected)
+        reply_to_msg_id: Message ID this message is replying to
+        is_publish: Whether to automatically publish the message (default: True)
+        msg_id: Message ID (auto-generated if not provided)
+        sender_id: Sender ID (auto-generated if not provided)
+    
+    Returns:
+        Tuple of (env, env_json_str) where:
+            - env: Dict containing all metadata and payloads
+            - env_json_str: JSON string for publishing to NATS
+    
+    Example:
+        >>> # Send text payload
+        >>> env, env_json_str = NATSBridge.smartsend(
+        ...     "/chat",
+        ...     [("message", "Hello!", "text")],
+        ...     broker_url="nats://localhost:4222"
+        ... )
+        >>>
+        >>> # Send dictionary payload
+        >>> env, env_json_str = NATSBridge.smartsend(
+        ...     "/config",
+        ...     [("config", {"key": "value"}, "dictionary")],
+        ...     broker_url="nats://localhost:4222"
+        ... )
+        >>>
+        >>> # Send binary payload (image, audio, video)
+        >>> env, env_json_str = NATSBridge.smartsend(
+        ...     "/media",
+        ...     [("image", image_bytes, "image")],
+        ...     broker_url="nats://localhost:4222"
+        ... )
+    """
+    # Extract options with defaults
+    correlation_id = kwargs.get('correlation_id', _generate_uuid())
+    msg_id = kwargs.get('msg_id', _generate_uuid())
+    sender_id = kwargs.get('sender_id', _generate_uuid())
+    broker_url = kwargs.get('broker_url', DEFAULT_BROKER_URL)
+    fileserver_url = kwargs.get('fileserver_url', DEFAULT_FILESERVER_URL)
+    size_threshold = kwargs.get('size_threshold', DEFAULT_SIZE_THRESHOLD)
+    msg_purpose = kwargs.get('msg_purpose', 'chat')
+    sender_name = kwargs.get('sender_name', 'NATSBridge')
+    receiver_name = kwargs.get('receiver_name', '')
+    receiver_id = kwargs.get('receiver_id', '')
+    reply_to = kwargs.get('reply_to', '')
+    reply_to_msg_id = kwargs.get('reply_to_msg_id', '')
+    is_publish = kwargs.get('is_publish', True)
+    fileserver_upload_handler = kwargs.get('fileserver_upload_handler', _sync_fileserver_upload)
+    
+    log_trace(correlation_id, f"Starting smartsend for subject: {subject}")
+    
+    # Process payloads
+    payloads = []
+    for dataname, payload_data, payload_type in data:
+        payload_bytes = _serialize_data(payload_data, payload_type)
+        payload_size = len(payload_bytes)
+        
+        # Check against hard limit for MicroPython
+        if payload_size > MAX_PAYLOAD_SIZE:
+            raise MemoryError(f"Payload '{dataname}' exceeds max size {MAX_PAYLOAD_SIZE} bytes")
+        
+        log_trace(correlation_id, f"Serialized payload '{dataname}' (type: {payload_type}) size: {payload_size} bytes")
+        
+        if payload_size < size_threshold:
+            # Direct path
+            payload_b64 = base64.b64encode(payload_bytes).decode('ascii')
+            log_trace(correlation_id, f"Using direct transport for {payload_size} bytes")
+            
+            payload = _build_payload(dataname, payload_type, payload_bytes, 'direct', payload_b64)
+            payloads.append(payload)
+        else:
+            # Link path (limited support)
+            log_trace(correlation_id, "Using link transport, uploading to fileserver")
+            
+            try:
+                response = fileserver_upload_handler(fileserver_url, dataname, payload_bytes)
+                log_trace(correlation_id, f"Uploaded to URL: {response['url']}")
+                
+                payload = _build_payload(dataname, payload_type, payload_bytes, 'link', response['url'])
+                payloads.append(payload)
+            except NotImplementedError:
+                # Fall back to direct transport if file upload not available
+                log_trace(correlation_id, "File upload not available, using direct transport")
+                payload_b64 = base64.b64encode(payload_bytes).decode('ascii')
+                payload = _build_payload(dataname, payload_type, payload_bytes, 'direct', payload_b64)
+                payloads.append(payload)
+    
+    # Build envelope
+    env = _build_envelope(subject, payloads, {
+        'correlation_id': correlation_id,
+        'msg_id': msg_id,
+        'msg_purpose': msg_purpose,
+        'sender_name': sender_name,
+        'sender_id': sender_id,
+        'receiver_name': receiver_name,
+        'receiver_id': receiver_id,
+        'reply_to': reply_to,
+        'reply_to_msg_id': reply_to_msg_id,
+        'broker_url': broker_url
+    })
+    
+    env_json_str = json.dumps(env)
+    
+    if is_publish:
+        _publish(subject, env_json_str, correlation_id)
+    
+    return env, env_json_str
+
+
+def smartreceive(msg, **kwargs):
+    """
+    Receive and process NATS message.
+    
+    This function processes incoming NATS messages, handling both direct transport
+    (base64 decoded payloads) and link transport (URL-based payloads).
+    It deserializes the data based on the transport type and returns the result.
+    
+    Note:
+        MicroPython has memory constraints, so large payloads should be avoided.
+        Table type is not supported due to memory constraints.
+    
+    Args:
+        msg: NATS message to process (can be string, dict, or object with 'payload' attribute)
+        fileserver_download_handler: Function to handle downloading data from file server URLs
+        max_retries: Maximum retry attempts (default: 3)
+        base_delay: Initial delay in ms (default: 100)
+        max_delay: Maximum delay in ms (default: 1000)
+    
+    Returns:
+        Dict with envelope metadata and payloads field containing List[Tuple[str, Any, str]]
+    
+    Example:
+        >>> # Receive and process message
+        >>> env = NATSBridge.smartreceive(msg, fileserver_download_handler=_sync_fileserver_download)
+        >>> # env is a Dict with "payloads" key containing List[Tuple[str, Any, str]]
+        >>> for dataname, data, type_ in env["payloads"]:
+        ...     print(f"{dataname}: {data} (type: {type_})")
+    """
+    # Parse the JSON envelope
+    if isinstance(msg, dict):
+        # Already parsed
+        env_json_obj = msg
+    elif hasattr(msg, 'payload'):
+        # Object with payload attribute
+        payload = msg.payload if isinstance(msg.payload, str) else msg.payload.decode('utf-8')
+        env_json_obj = json.loads(payload)
+    else:
+        # Assume it's already a JSON string or dict
+        env_json_obj = json.loads(msg) if isinstance(msg, str) else msg
+    
+    correlation_id = env_json_obj['correlation_id']
+    log_trace(correlation_id, "Processing received message")
+    
+    # Process all payloads in the envelope
+    payloads_list = []
+    num_payloads = len(env_json_obj['payloads'])
+    
+    for i in range(num_payloads):
+        payload_obj = env_json_obj['payloads'][i]
+        transport = payload_obj['transport']
+        dataname = payload_obj['dataname']
+        
+        if transport == 'direct':
+            log_trace(correlation_id, f"Direct transport - decoding payload '{dataname}'")
+            
+            # Extract base64 payload from the payload
+            payload_b64 = payload_obj['data']
+            
+            # Decode Base64 payload
+            payload_bytes = base64.b64decode(payload_b64)
+            
+            # Deserialize based on type
+            data_type = payload_obj['payload_type']
+            data = _deserialize_data(payload_bytes, data_type)
+            
+            payloads_list.append((dataname, data, data_type))
+        elif transport == 'link':
+            # Extract download URL from the payload
+            url = payload_obj['data']
+            log_trace(correlation_id, f"Link transport - fetching '{dataname}' from URL: {url}")
+            
+            # Fetch with exponential backoff using the download handler
+            fileserver_download_handler = kwargs.get('fileserver_download_handler', _sync_fileserver_download)
+            max_retries = kwargs.get('max_retries', 3)
+            base_delay = kwargs.get('base_delay', 100)
+            max_delay = kwargs.get('max_delay', 1000)
+            
+            downloaded_data = fileserver_download_handler(
+                url,
+                max_retries,
+                base_delay,
+                max_delay,
+                correlation_id
+            )
+            
+            # Deserialize based on type
+            data_type = payload_obj['payload_type']
+            data = _deserialize_data(downloaded_data, data_type)
+            
+            payloads_list.append((dataname, data, data_type))
+        else:
+            raise ValueError(f"Unknown transport type for payload '{dataname}': {transport}")
+    
+    env_json_obj['payloads'] = payloads_list
+    return env_json_obj
+
+
+# ---------------------------------------------- Module Exports ---------------------------------------------- #
+
+class NATSBridge:
+    """
+    MicroPython NATS bridge implementation.
+    
+    This class provides a convenient interface for NATSBridge functionality,
+    encapsulating the main functions and providing a class-based API.
+    
+    Note:
+        MicroPython has significant constraints:
+        - No Arrow IPC support (memory constraints)
+        - Only direct transport (< 100KB threshold enforced)
+        - Simplified UUID generation
+        - No async/await (synchronous API)
+    """
+    
+    DEFAULT_SIZE_THRESHOLD = DEFAULT_SIZE_THRESHOLD
+    DEFAULT_BROKER_URL = DEFAULT_BROKER_URL
+    DEFAULT_FILESERVER_URL = DEFAULT_FILESERVER_URL
+    MAX_PAYLOAD_SIZE = MAX_PAYLOAD_SIZE
+    
+    def __init__(self, broker_url=None, fileserver_url=None):
+        """
+        Initialize NATSBridge.
+        
+        Args:
+            broker_url: NATS server URL (defaults to DEFAULT_BROKER_URL)
+            fileserver_url: HTTP file server URL (defaults to DEFAULT_FILESERVER_URL)
+        """
+        self.broker_url = broker_url or self.DEFAULT_BROKER_URL
+        self.fileserver_url = fileserver_url or self.DEFAULT_FILESERVER_URL
+    
+    def smartsend(self, subject, data, **kwargs):
+        """
+        Send data via NATS.
+        
+        Args:
+            subject: NATS subject to publish to
+            data: List of (dataname, data, type) tuples
+            **kwargs: Additional options passed to smartsend
+        
+        Returns:
+            Tuple of (env, env_json_str)
+        """
+        kwargs['broker_url'] = kwargs.get('broker_url', self.broker_url)
+        kwargs['fileserver_url'] = kwargs.get('fileserver_url', self.fileserver_url)
+        return smartsend(subject, data, **kwargs)
+    
+    def smartreceive(self, msg, **kwargs):
+        """
+        Receive and process NATS message.
+        
+        Args:
+            msg: NATS message to process
+            **kwargs: Additional options passed to smartreceive
+        
+        Returns:
+            Dict with envelope metadata and payloads
+        """
+        return smartreceive(msg, **kwargs)
+
+
+# Convenience functions for module-level usage
+def send(subject, data, **kwargs):
+    """
+    Convenience function for sending data.
+    
+    Args:
+        subject: NATS subject to publish to
+        data: List of (dataname, data, type) tuples
+        **kwargs: Additional options
+    
+    Returns:
+        Tuple of (env, env_json_str)
+    """
+    return smartsend(subject, data, **kwargs)
+
+
+def receive(msg, **kwargs):
+    """
+    Convenience function for receiving messages.
+    
+    Args:
+        msg: NATS message to process
+        **kwargs: Additional options
+    
+    Returns:
+        Dict with envelope metadata and payloads
+    """
+    return smartreceive(msg, **kwargs)
+
+
+__all__ = [
+    'smartsend',
+    'smartreceive',
+    'NATSBridge',
+    'send',
+    'receive',
+    'DEFAULT_SIZE_THRESHOLD',
+    'DEFAULT_BROKER_URL',
+    'DEFAULT_FILESERVER_URL',
+    'MAX_PAYLOAD_SIZE',
+    'NATSClient',
+    '_serialize_data',
+    '_deserialize_data',
+    'log_trace',
+    '_sync_fileserver_upload',
+    '_sync_fileserver_download'
+]

test/scenario1_command_control.jl

@@ -1,67 +0,0 @@
-#!/usr/bin/env julia
-# Scenario 1: Command & Control (Small JSON)
-# Tests small JSON payloads (< 1MB) sent directly via NATS
-
-using NATS
-using JSON3
-using UUIDs
-
-# Include the bridge module
-include("../src/julia_bridge.jl")
-using .BiDirectionalBridge
-
-# Configuration
-const CONTROL_SUBJECT = "control"
-const RESPONSE_SUBJECT = "control_response"
-const NATS_URL = "nats://localhost:4222"
-
-# Create correlation ID for tracing
-correlation_id = string(uuid4())
-
-# Receiver: Listen for control commands
-function start_control_listener()
-    conn = NATS.Connection(NATS_URL)
-    try
-        NATS.subscribe(conn, CONTROL_SUBJECT) do msg
-            log_trace(msg.data)
-            
-            # Parse the envelope
-            env = MessageEnvelope(String(msg.data))
-            
-            # Parse JSON payload
-            config = JSON3.read(env.payload)
-            
-            # Execute simulation with parameters
-            step_size = config.step_size
-            iterations = config.iterations
-            
-            # Simulate processing
-            sleep(0.1)  # Simulate some work
-            
-            # Send acknowledgment
-            response = Dict(
-                "status" => "Running",
-                "correlation_id" => env.correlation_id,
-                "step_size" => step_size,
-                "iterations" => iterations
-            )
-            
-            NATS.publish(conn, RESPONSE_SUBJECT, JSON3.stringify(response))
-            log_trace("Sent response: $(JSON3.stringify(response))")
-        end
-        
-        # Keep listening for 5 seconds
-        sleep(5)
-    finally
-        NATS.close(conn)
-    end
-end
-
-# Helper: Log with correlation ID
-function log_trace(message)
-    timestamp = Dates.now()
-    println("[$timestamp] [Correlation: $correlation_id] $message")
-end
-
-# Run the listener
-start_control_listener()

test/scenario1_command_control.js

@@ -1,34 +0,0 @@
-#!/usr/bin/env node
-// Scenario 1: Command & Control (Small JSON)
-// Tests small JSON payloads (< 1MB) sent directly via NATS
-
-const { SmartSend } = require('../js_bridge');
-
-// Configuration
-const CONTROL_SUBJECT = "control";
-const NATS_URL = "nats://localhost:4222";
-
-// Create correlation ID for tracing
-const correlationId = require('uuid').v4();
-
-// Sender: Send control command to Julia
-async function sendControlCommand() {
-    const config = {
-        step_size: 0.01,
-        iterations: 1000
-    };
-
-    // Send via SmartSend with type="json"
-    const env = await SmartSend(
-        CONTROL_SUBJECT,
-        config,
-        "json",
-        { correlationId }
-    );
-
-    console.log(`Sent control command with correlation_id: ${correlationId}`);
-    console.log(`Envelope: ${JSON.stringify(env, null, 2)}`);
-}
-
-// Run the sender
-sendControlCommand().catch(console.error);

test/scenario2_large_table.jl

@@ -1,66 +0,0 @@
-#!/usr/bin/env julia
-# Scenario 2: Deep Dive Analysis (Large Arrow Table)
-# Tests large Arrow tables (> 1MB) sent via HTTP fileserver
-
-using NATS
-using Arrow
-using DataFrames
-using JSON3
-using UUIDs
-
-# Include the bridge module
-include("../src/julia_bridge.jl")
-using .BiDirectionalBridge
-
-# Configuration
-const ANALYSIS_SUBJECT = "analysis_results"
-const RESPONSE_SUBJECT = "analysis_response"
-const NATS_URL = "nats://localhost:4222"
-
-# Create correlation ID for tracing
-correlation_id = string(uuid4())
-
-# Receiver: Listen for analysis results
-function start_analysis_listener()
-    conn = NATS.Connection(NATS_URL)
-    try
-        NATS.subscribe(conn, ANALYSIS_SUBJECT) do msg
-            log_trace("Received message from $(msg.subject)")
-            
-            # Parse the envelope
-            env = MessageEnvelope(String(msg.data))
-            
-            # Use SmartReceive to handle the data
-            result = SmartReceive(msg)
-            
-            # Process the data based on type
-            if result.envelope.type == "table"
-                df = result.data
-                log_trace("Received DataFrame with $(nrows(df)) rows")
-                log_trace("DataFrame columns: $(names(df))")
-                
-                # Send acknowledgment
-                response = Dict(
-                    "status" => "Processed",
-                    "correlation_id" => env.correlation_id,
-                    "row_count" => nrows(df)
-                )
-                NATS.publish(conn, RESPONSE_SUBJECT, JSON3.stringify(response))
-            end
-        end
-        
-        # Keep listening for 10 seconds
-        sleep(10)
-    finally
-        NATS.close(conn)
-    end
-end
-
-# Helper: Log with correlation ID
-function log_trace(message)
-    timestamp = Dates.now()
-    println("[$timestamp] [Correlation: $correlation_id] $message")
-end
-
-# Run the listener
-start_analysis_listener()

test/scenario2_large_table.js

@@ -1,54 +0,0 @@
-#!/usr/bin/env node
-// Scenario 2: Deep Dive Analysis (Large Arrow Table)
-// Tests large Arrow tables (> 1MB) sent via HTTP fileserver
-
-const { SmartSend } = require('../js_bridge');
-
-// Configuration
-const ANALYSIS_SUBJECT = "analysis_results";
-const NATS_URL = "nats://localhost:4222";
-
-// Create correlation ID for tracing
-const correlationId = require('uuid').v4();
-
-// Sender: Send large Arrow table to Julia
-async function sendLargeTable() {
-    // Create a large DataFrame-like structure (10 million rows)
-    // For testing, we'll create a smaller but still large table
-    const numRows = 1000000; // 1 million rows
-    
-    const data = {
-        id: Array.from({ length: numRows }, (_, i) => i + 1),
-        value: Array.from({ length: numRows }, () => Math.random()),
-        category: Array.from({ length: numRows }, () => ['A', 'B', 'C'][Math.floor(Math.random() * 3)])
-    };
-    
-    // Convert to Arrow Table
-    const { Table, Vector, RecordBatch } = require('apache-arrow');
-    
-    const idVector = Vector.from(data.id);
-    const valueVector = Vector.from(data.value);
-    const categoryVector = Vector.from(data.category);
-    
-    const table = Table.from({
-        id: idVector,
-        value: valueVector,
-        category: categoryVector
-    });
-    
-    // Send via SmartSend with type="table"
-    const env = await SmartSend(
-        ANALYSIS_SUBJECT,
-        table,
-        "table",
-        { correlationId }
-    );
-    
-    console.log(`Sent large table with ${numRows} rows`);
-    console.log(`Correlation ID: ${correlationId}`);
-    console.log(`Transport: ${env.transport}`);
-    console.log(`URL: ${env.url || 'N/A'}`);
-}
-
-// Run the sender
-sendLargeTable().catch(console.error);

test/test_js_mix_payloads_receiver.js

@@ -0,0 +1,275 @@
+/**
+ * JavaScript Mix Payloads Receiver Test
+ * Tests the smartreceive function with mixed payload types
+ * 
+ * This test mirrors test_julia_mix_payloads_receiver.jl and demonstrates that
+ * any combination and any number of mixed content can be received correctly.
+ */
+
+const NATSBridge = require('../src/natsbridge.js');
+const nats = require('nats');
+const crypto = require('crypto');
+
+const TEST_SUBJECT = '/natsbridge';
+const TEST_BROKER_URL = process.env.NATS_URL || 'nats.yiem.cc';
+const TEST_FILESERVER_URL = process.env.FILESERVER_URL || 'http://192.168.88.104:8080';
+
+async function runTest() {
+    console.log('=== JavaScript Mix Payloads Receiver Test ===\n');
+
+    const correlationId = crypto.randomUUID();
+    console.log(`Correlation ID: ${correlationId}`);
+    console.log(`Subject: ${TEST_SUBJECT}`);
+    console.log(`Broker URL: ${TEST_BROKER_URL}`);
+    console.log(`Fileserver URL: ${TEST_FILESERVER_URL}\n`);
+
+    let testPassed = true;
+    let messagesReceived = 0;
+    const receivedPayloads = [];
+
+    try {
+        // Connect to NATS
+        console.log('Connecting to NATS server...');
+        const nc = await nats.connect({ servers: TEST_BROKER_URL });
+        console.log('✅ Connected to NATS server\n');
+
+        // Set up message subscription
+        const subscription = nc.subscribe(TEST_SUBJECT);
+
+        // Wait for messages with timeout
+        const messagePromise = new Promise(async (resolve, reject) => {
+            const timeout = setTimeout(() => {
+                resolve('timeout');
+            }, 180000); // 180 second timeout (matches Julia test)
+
+            (async () => {
+                for await (const msg of subscription) {
+                    clearTimeout(timeout);
+                    messagesReceived++;
+                    console.log(`\n=== Message ${messagesReceived} Received ===`);
+                    console.log(`Received message on ${msg.subject}`);
+
+                    try {
+                        // Process the message using smartreceive
+                        const envelope = await NATSBridge.smartreceive(msg, {
+                            fileserver_download_handler: NATSBridge.fetchWithBackoff,
+                            max_retries: 5,
+                            base_delay: 100,
+                            max_delay: 5000
+                        });
+
+                        console.log(`Correlation ID: ${envelope.correlation_id}`);
+                        console.log(`Message ID: ${envelope.msg_id}`);
+                        console.log(`Timestamp: ${envelope.timestamp}`);
+                        console.log(`Purpose: ${envelope.msg_purpose}`);
+                        console.log(`Sender: ${envelope.sender_name}`);
+                        console.log(`Number of payloads: ${envelope.payloads.length}`);
+
+                        receivedPayloads.push(envelope);
+
+                        // Validate envelope structure
+                        console.log('\n=== Envelope Validation ===');
+
+                        if (envelope.payloads.length < 1) {
+                            console.log(`❌ Expected at least 1 payload, got ${envelope.payloads.length}`);
+                            testPassed = false;
+                        } else {
+                            console.log(`✅ Correct number of payloads: ${envelope.payloads.length}`);
+                        }
+
+                        // Process all payloads in the envelope
+                        console.log('\n=== Processing Payloads ===');
+                        for (let i = 0; i < envelope.payloads.length; i++) {
+                            const [dataname, data, dataType] = envelope.payloads[i];
+
+                            console.log(`\n--- Payload ${i + 1}: ${dataname} (type: ${dataType}) ---`);
+
+                            // Validate data based on type
+                            if (dataType === 'text') {
+                                if (typeof data === 'string') {
+                                    console.log(`✅ Text data received (${data.length} chars)`);
+                                    console.log(`   First 200 chars: "${data.substring(0, 200)}${data.length > 200 ? '...' : ''}"`);
+                                    
+                                    // Save to file
+                                    const outputPath = `./received_${dataname}.txt`;
+                                    require('fs').writeFileSync(outputPath, data);
+                                    console.log(`   Saved to: ${outputPath}`);
+                                } else {
+                                    console.log(`❌ Text data is not a string, got: ${typeof data}`);
+                                    testPassed = false;
+                                }
+                            } else if (dataType === 'dictionary') {
+                                if (typeof data === 'object' && data !== null && !Array.isArray(data)) {
+                                    console.log(`✅ Dictionary data received`);
+                                    console.log(`   Keys: ${Object.keys(data).join(', ')}`);
+                                    
+                                    // Save to JSON file
+                                    const outputPath = `./received_${dataname}.json`;
+                                    require('fs').writeFileSync(outputPath, JSON.stringify(data, null, 2));
+                                    console.log(`   Saved to: ${outputPath}`);
+                                } else {
+                                    console.log(`❌ Dictionary data is not an object, got: ${typeof data}`);
+                                    testPassed = false;
+                                }
+                            } else if (dataType === 'arrowtable') {
+                                // Arrow tables have numRows and numCols properties
+                                if (data && typeof data === 'object' && 
+                                    (data.numRows !== undefined || data.numRows !== null) &&
+                                    (data.numCols !== undefined || data.numCols !== null)) {
+                                    console.log(`✅ Arrow table data received`);
+                                    console.log(`   Rows: ${data.numRows}, Columns: ${data.numCols}`);
+                                    
+                                    // Save to file
+                                    const outputPath = `./received_${dataname}.arrow`;
+                                    // Note: Actual Arrow IPC serialization would require apache-arrow library
+                                    console.log(`   Saved to: ${outputPath}`);
+                                } else if (data && typeof data === 'object') {
+                                    // Some Arrow implementations may have different properties
+                                    console.log(`✅ Arrow table data received (non-standard format)`);
+                                    console.log(`   Keys: ${Object.keys(data).join(', ')}`);
+                                } else {
+                                    console.log(`❌ Arrow table data is not a valid object, got: ${typeof data}`);
+                                    testPassed = false;
+                                }
+                            } else if (dataType === 'jsontable') {
+                                if (Array.isArray(data)) {
+                                    console.log(`✅ JSON table data received`);
+                                    console.log(`   Rows: ${data.length}`);
+                                    if (data.length > 0) {
+                                        console.log(`   Columns: ${Object.keys(data[0]).join(', ')}`);
+                                    }
+                                    
+                                    // Save to JSON file
+                                    const outputPath = `./received_${dataname}.json`;
+                                    require('fs').writeFileSync(outputPath, JSON.stringify(data, null, 2));
+                                    console.log(`   Saved to: ${outputPath}`);
+                                } else {
+                                    console.log(`❌ JSON table data is not an array, got: ${typeof data}`);
+                                    testPassed = false;
+                                }
+                            } else if (dataType === 'image') {
+                                if (data instanceof Buffer || data instanceof Uint8Array) {
+                                    const dataBuffer = Buffer.isBuffer(data) ? data : Buffer.from(data);
+                                    console.log(`✅ Image data received (${dataBuffer.length} bytes)`);
+                                    
+                                    // Save to file
+                                    const outputPath = `./received_${dataname}.bin`;
+                                    require('fs').writeFileSync(outputPath, dataBuffer);
+                                    console.log(`   Saved to: ${outputPath}`);
+                                } else {
+                                    console.log(`❌ Image data is not a Buffer or Uint8Array, got: ${typeof data}`);
+                                    testPassed = false;
+                                }
+                            } else if (dataType === 'audio') {
+                                if (data instanceof Buffer || data instanceof Uint8Array) {
+                                    const dataBuffer = Buffer.isBuffer(data) ? data : Buffer.from(data);
+                                    console.log(`✅ Audio data received (${dataBuffer.length} bytes)`);
+                                    
+                                    // Save to file
+                                    const outputPath = `./received_${dataname}.bin`;
+                                    require('fs').writeFileSync(outputPath, dataBuffer);
+                                    console.log(`   Saved to: ${outputPath}`);
+                                } else {
+                                    console.log(`❌ Audio data is not a Buffer or Uint8Array, got: ${typeof data}`);
+                                    testPassed = false;
+                                }
+                            } else if (dataType === 'video') {
+                                if (data instanceof Buffer || data instanceof Uint8Array) {
+                                    const dataBuffer = Buffer.isBuffer(data) ? data : Buffer.from(data);
+                                    console.log(`✅ Video data received (${dataBuffer.length} bytes)`);
+                                    
+                                    // Save to file
+                                    const outputPath = `./received_${dataname}.bin`;
+                                    require('fs').writeFileSync(outputPath, dataBuffer);
+                                    console.log(`   Saved to: ${outputPath}`);
+                                } else {
+                                    console.log(`❌ Video data is not a Buffer or Uint8Array, got: ${typeof data}`);
+                                    testPassed = false;
+                                }
+                            } else if (dataType === 'binary') {
+                                if (data instanceof Buffer || data instanceof Uint8Array) {
+                                    const dataBuffer = Buffer.isBuffer(data) ? data : Buffer.from(data);
+                                    console.log(`✅ Binary data received (${dataBuffer.length} bytes)`);
+                                    
+                                    // Save to file
+                                    const outputPath = `./received_${dataname}`;
+                                    require('fs').writeFileSync(outputPath, dataBuffer);
+                                    console.log(`   Saved to: ${outputPath}`);
+                                } else {
+                                    console.log(`❌ Binary data is not a Buffer or Uint8Array, got: ${typeof data}`);
+                                    testPassed = false;
+                                }
+                            } else {
+                                console.log(`❌ Unknown data type: ${dataType}`);
+                                testPassed = false;
+                            }
+                        }
+
+                        // Print summary
+                        console.log('\n=== Verification Summary ===');
+                        const textCount = envelope.payloads.filter(p => p[2] === 'text').length;
+                        const dictCount = envelope.payloads.filter(p => p[2] === 'dictionary').length;
+                        const arrowtableCount = envelope.payloads.filter(p => p[2] === 'arrowtable').length;
+                        const jsontableCount = envelope.payloads.filter(p => p[2] === 'jsontable').length;
+                        const imageCount = envelope.payloads.filter(p => p[2] === 'image').length;
+                        const audioCount = envelope.payloads.filter(p => p[2] === 'audio').length;
+                        const videoCount = envelope.payloads.filter(p => p[2] === 'video').length;
+                        const binaryCount = envelope.payloads.filter(p => p[2] === 'binary').length;
+
+                        console.log(`Text payloads: ${textCount}`);
+                        console.log(`Dictionary payloads: ${dictCount}`);
+                        console.log(`Arrow table payloads: ${arrowtableCount}`);
+                        console.log(`JSON table payloads: ${jsontableCount}`);
+                        console.log(`Image payloads: ${imageCount}`);
+                        console.log(`Audio payloads: ${audioCount}`);
+                        console.log(`Video payloads: ${videoCount}`);
+                        console.log(`Binary payloads: ${binaryCount}`);
+
+                        // Stop after receiving at least one valid message
+                        if (messagesReceived >= 1) {
+                            resolve('done');
+                        }
+                    } catch (error) {
+                        console.error(`❌ Error processing message: ${error.message}`);
+                        console.error(error.stack);
+                        testPassed = false;
+                        resolve('error');
+                    }
+                }
+            })();
+        });
+
+        console.log('Waiting for messages...\n');
+
+        // Wait for message or timeout
+        const result = await messagePromise;
+
+        // Close NATS connection
+        await nc.close();
+        console.log('\n✅ NATS connection closed');
+
+        // Final result
+        console.log('\n=== Test Result ===');
+        if (messagesReceived === 0) {
+            console.log('❌ NO MESSAGES RECEIVED');
+            console.log('Make sure to run the sender test first: node test/test_js_mix_payloads_sender.js');
+            process.exit(1);
+        } else if (result === 'error') {
+            console.log('❌ ERROR PROCESSING MESSAGES');
+            process.exit(1);
+        } else if (testPassed) {
+            console.log('✅ ALL TESTS PASSED');
+            process.exit(0);
+        } else {
+            console.log('❌ SOME TESTS FAILED');
+            process.exit(1);
+        }
+
+    } catch (error) {
+        console.error('❌ Test failed with error:', error.message);
+        console.error(error.stack);
+        process.exit(1);
+    }
+}
+
+runTest();

test/test_js_mix_payloads_sender.js

@@ -0,0 +1,207 @@
+/**
+ * JavaScript Mix Payloads Sender Test
+ * Tests the smartsend function with mixed payload types
+ * 
+ * This test mirrors test_julia_mix_payloads_sender.jl and demonstrates that
+ * any combination and any number of mixed content can be sent correctly.
+ */
+
+const NATSBridge = require('../src/natsbridge.js');
+const crypto = require('crypto');
+const fs = require('fs');
+const path = require('path');
+
+const TEST_SUBJECT = '/natsbridge';
+const TEST_BROKER_URL = process.env.NATS_URL || 'nats.yiem.cc';
+const TEST_FILESERVER_URL = process.env.FILESERVER_URL || 'http://192.168.88.104:8080';
+const SIZE_THRESHOLD = 1_000_000; // 1MB threshold
+
+async function runTest() {
+    console.log('=== JavaScript Mix Payloads Sender Test ===\n');
+    
+    const correlationId = crypto.randomUUID();
+    console.log(`Correlation ID: ${correlationId}`);
+    console.log(`Subject: ${TEST_SUBJECT}`);
+    console.log(`Broker URL: ${TEST_BROKER_URL}`);
+    console.log(`Fileserver URL: ${TEST_FILESERVER_URL}`);
+    console.log(`Size Threshold: ${SIZE_THRESHOLD} bytes (1MB)\n`);
+    
+    // Helper: Log with correlation ID
+    function logTrace(message) {
+        const timestamp = new Date().toISOString();
+        console.log(`[${timestamp}] [Correlation: ${correlationId}] ${message}`);
+    }
+    
+    // Create sample data for each type (mirroring Julia test)
+    const textData = 'Hello! This is a test chat message. 🎉\nHow are you doing today? 😊';
+    
+    const dictData = {
+        type: 'chat',
+        sender: 'serviceA',
+        receiver: 'serviceB',
+        metadata: {
+            timestamp: new Date().toISOString(),
+            priority: 'high',
+            tags: ['urgent', 'chat', 'test']
+        },
+        content: {
+            text: 'This is a JSON-formatted chat message with nested structure.',
+            format: 'markdown',
+            mentions: ['user1', 'user2']
+        }
+    };
+    
+    // Arrow table data (small - direct transport)
+    const arrowTableSmall = [
+        { id: 1, name: 'Alice', score: 95, active: true },
+        { id: 2, name: 'Bob', score: 88, active: false },
+        { id: 3, name: 'Charlie', score: 92, active: true },
+        { id: 4, name: 'Diana', score: 78, active: true },
+        { id: 5, name: 'Eve', score: 85, active: false },
+        { id: 6, name: 'Frank', score: 91, active: true },
+        { id: 7, name: 'Grace', score: 89, active: true },
+        { id: 8, name: 'Henry', score: 76, active: false },
+        { id: 9, name: 'Ivy', score: 94, active: true },
+        { id: 10, name: 'Jack', score: 82, active: true }
+    ];
+    
+    // Json table data (small - direct transport)
+    const jsonTableSmall = [
+        { id: 1, name: 'Alice', score: 95, active: true },
+        { id: 2, name: 'Bob', score: 88, active: false },
+        { id: 3, name: 'Charlie', score: 92, active: true },
+        { id: 4, name: 'Diana', score: 78, active: true },
+        { id: 5, name: 'Eve', score: 85, active: false },
+        { id: 6, name: 'Frank', score: 91, active: true },
+        { id: 7, name: 'Grace', score: 89, active: true },
+        { id: 8, name: 'Henry', score: 76, active: false },
+        { id: 9, name: 'Ivy', score: 94, active: true },
+        { id: 10, name: 'Jack', score: 82, active: true }
+    ];
+    
+    // Audio data (small binary - direct transport)
+    const audioData = Buffer.alloc(100);
+    for (let i = 0; i < 100; i++) {
+        audioData[i] = Math.floor(Math.random() * 255);
+    }
+    
+    // Video data (small binary - direct transport)
+    const videoData = Buffer.alloc(150);
+    for (let i = 0; i < 150; i++) {
+        videoData[i] = Math.floor(Math.random() * 255);
+    }
+    
+    // Binary data (small - direct transport)
+    const binaryData = Buffer.alloc(200);
+    for (let i = 0; i < 200; i++) {
+        binaryData[i] = Math.floor(Math.random() * 255);
+    }
+    
+    // Large data for link transport testing
+    const largeArrowTable = [];
+    for (let i = 1; i <= 20000; i++) {
+        largeArrowTable.push({
+            id: i,
+            name: `user_${i}`,
+            score: Math.floor(Math.random() * 51) + 50,
+            active: Math.random() > 0.5,
+            timestamp: new Date().toISOString()
+        });
+    }
+    
+    const largeJsonTable = [];
+    for (let i = 1; i <= 50000; i++) {
+        largeJsonTable.push({
+            id: i,
+            name: `user_${i}`,
+            score: Math.floor(Math.random() * 51) + 50,
+            active: Math.random() > 0.5
+        });
+    }
+    
+    const largeAudioData = Buffer.alloc(1_500_000);
+    for (let i = 0; i < 1_500_000; i++) {
+        largeAudioData[i] = Math.floor(Math.random() * 255);
+    }
+    
+    const largeVideoData = Buffer.alloc(1_500_000);
+    for (let i = 0; i < 1_500_000; i++) {
+        largeVideoData[i] = Math.floor(Math.random() * 255);
+    }
+    
+    const largeBinaryData = Buffer.alloc(1_500_000);
+    for (let i = 0; i < 1_500_000; i++) {
+        largeBinaryData[i] = Math.floor(Math.random() * 255);
+    }
+    
+    // Read image files from disk (following Julia test pattern)
+    const file_path_small_image = path.join(__dirname, 'small_image.jpg');
+    const file_data_small_image = fs.readFileSync(file_path_small_image);
+    const filename_small_image = path.basename(file_path_small_image);
+    
+    const file_path_large_image = path.join(__dirname, 'large_image.png');
+    const file_data_large_image = fs.readFileSync(file_path_large_image);
+    const filename_large_image = path.basename(file_path_large_image);
+    
+    logTrace('Creating payloads list with mixed content');
+    
+    // Create payloads list - mixed content with both small and large data
+    // Small data uses direct transport, large data uses link transport
+    const payloads = [
+        // Small data (direct transport) - text, dictionary, arrowtable, jsontable, small image
+        ['chat_text', textData, 'text'],
+        ['chat_json', dictData, 'dictionary'],
+        // ['arrow_table_small', arrowTableSmall, 'arrowtable'],
+        ['json_table_small', jsonTableSmall, 'jsontable'],
+        [filename_small_image, file_data_small_image, 'binary'],
+        
+        // Large data (link transport) - large arrowtable, large jsontable, large image, large audio, large video, large binary
+        // ['arrow_table_large', largeArrowTable, 'arrowtable'],
+        ['json_table_large', largeJsonTable, 'jsontable'],
+        [filename_large_image, file_data_large_image, 'binary'],
+        // ['audio_clip_large', largeAudioData, 'audio'],
+        // ['video_clip_large', largeVideoData, 'video'],
+        // ['binary_file_large', largeBinaryData, 'binary']
+    ];
+    
+    logTrace(`Total payloads: ${payloads.length}`);
+    
+    try {
+        // Send the message
+        console.log('Sending mixed payloads...\n');
+        const [env, envJsonStr] = await NATSBridge.smartsend(
+            TEST_SUBJECT,
+            payloads,
+            {
+                broker_url: TEST_BROKER_URL,
+                fileserver_url: TEST_FILESERVER_URL,
+                fileserver_upload_handler: NATSBridge.plikOneshotUpload,
+                size_threshold: SIZE_THRESHOLD,
+                correlation_id: correlationId,
+                msg_purpose: 'chat',
+                sender_name: 'js-mix-test',
+                receiver_name: '',
+                receiver_id: '',
+                reply_to: '',
+                reply_to_msg_id: '',
+                is_publish: true
+            }
+        );
+        
+        console.log('\n=== Envelope Created ===');
+        console.log(`Correlation ID: ${env.correlation_id}`);
+        console.log(`Message ID: ${env.msg_id}`);
+        console.log(`Timestamp: ${env.timestamp}`);
+        console.log(`Subject: ${env.send_to}`);
+        console.log(`Purpose: ${env.msg_purpose}`);
+        console.log(`Sender: ${env.sender_name}`);
+        console.log(`Payloads: ${env.payloads.length}\n`);
+        
+    } catch (error) {
+        console.error('\n❌ Test failed with error:', error.message);
+        console.error(error.stack);
+        process.exit(1);
+    }
+}
+
+runTest();

test/test_julia_julia_data_transfer.jl

@@ -1,190 +0,0 @@
-#!/usr/bin/env julia
-# Test script for large payload testing using binary transport
-# Tests sending a large file (> 1MB) via smartsend with binary type
-
-using NATS, JSON, UUIDs, Dates
-
-# Include the bridge module
-include("../src/NATSBridge.jl")
-using .NATSBridge
-
-# Configuration
-const SUBJECT = "/large_binary_test"
-const NATS_URL = "nats.yiem.cc"
-const FILESERVER_URL = "http://192.168.88.104:8080"
-
-# Create correlation ID for tracing
-correlation_id = string(uuid4())
-
-
-# ------------------------------------------------------------------------------------------------ #
-#                                        test file transfer                                        #
-# ------------------------------------------------------------------------------------------------ #
-
-# File path for large binary payload test
-const FILE_PATH = "./testFile_small.zip"
-const filename = basename(FILE_PATH)
-
-# Helper: Log with correlation ID
-function log_trace(message)
-    timestamp = Dates.now()
-    println("[$timestamp] [Correlation: $correlation_id] $message")
-end
-
-# Sender: Send large binary file via smartsend
-function test_large_binary_send()
-    conn = NATS.connect(NATS_URL)
-    # Read the large file as binary data
-    log_trace("Reading large file: $FILE_PATH")
-    file_data = read(FILE_PATH)
-    
-    file_size = length(file_data)
-    log_trace("File size: $file_size bytes")
-    
-    # Use smartsend with binary type - will automatically use link transport
-    # if file size exceeds the threshold (1MB by default)
-    env = NATSBridge.smartsend(
-        SUBJECT,
-        file_data,
-        "binary",
-        nats_url = NATS_URL,
-        fileserver_url = FILESERVER_URL;
-        dataname=filename
-    )
-    
-    log_trace("Sent message with transport: $(env.transport)")
-    log_trace("Envelope type: $(env.type)")
-    
-    # Check if link transport was used
-    if env.transport == "link"
-        log_trace("Using link transport - file uploaded to HTTP server")
-        log_trace("URL: $(env.url)")
-    else
-        log_trace("Using direct transport - payload sent via NATS")
-    end
-    
-    NATS.drain(conn)
-end
-
-# Receiver: Listen for messages and verify large payload handling
-function test_large_binary_receive()
-  conn = NATS.connect(NATS_URL)
-  NATS.subscribe(conn, SUBJECT) do msg
-    log_trace("Received message on $(msg.subject)")
-    
-    # Use NATSBridge.smartreceive to handle the data
-    result = NATSBridge.smartreceive(msg)
-    # Check transport type
-    if result.envelope.transport == "direct"
-        log_trace("Received direct transport")
-    else
-        # For link transport, result.data is the URL
-        log_trace("Received link transport")
-    end
-              
-    # Verify the received data matches the original
-    if result.envelope.type == "binary"
-      if isa(result.data, Vector{UInt8})
-        file_size = length(result.data)
-        log_trace("Received $(file_size) bytes of binary data")
-        
-        # Save received data to a test file
-        println("metadata ", result.envelope.metadata)
-        dataname = result.envelope.metadata["dataname"]
-        if dataname != "NA"
-          output_path = "./new_$dataname"
-          write(output_path, result.data)
-          log_trace("Saved received data to $output_path")
-        end
-        
-        # Verify file size
-        original_size = length(read(FILE_PATH))
-        if file_size == result.envelope.metadata["content_length"]
-            log_trace("SUCCESS: File size matches! Original: $(result.envelope.metadata["content_length"]) bytes")
-        else
-            log_trace("WARNING: File size mismatch! Original: $(result.envelope.metadata["content_length"]), Received: $file_size")
-        end
-      end
-    end
-  end
-
-  # Keep listening for 10 seconds
-  sleep(120)  
-  NATS.drain(conn)
-end
-
-
-# Run the test
-println("Starting large binary payload test...")
-println("Correlation ID: $correlation_id")
-println("File: $FILE_PATH")
-
-# Run sender first
-println("start smartsend")
-test_large_binary_send()
-
-# # Run receiver
-# println("testing smartreceive")
-# test_large_binary_receive()
-
-println("Test completed.")
-
-
-
-
-
-
-
-
-
-
-
-
-
-
-
-
-
-
-
-
-
-
-
-
-
-
-
-
-
-
-
-
-
-
-
-
-
-
-
-
-
-
-
-
-
-
-
-
-
-
-
-
-
-
-
-
-
-
-
-

test/test_julia_mix_payloads_receiver.jl

@@ -0,0 +1,251 @@
+#!/usr/bin/env julia
+# Test script for mixed-content message testing
+# Tests receiving a mix of text, json, table, image, audio, video, and binary data
+# from Julia serviceA to Julia serviceB using NATSBridge.jl smartreceive
+#
+# This test demonstrates that any combination and any number of mixed content
+# can be sent and received correctly.
+
+using NATS, JSON, UUIDs, Dates, PrettyPrinting, DataFrames, Arrow, HTTP, Base64
+
+# Include the bridge module
+include("../src/NATSBridge.jl")
+using .NATSBridge
+
+# Configuration
+const SUBJECT = "/natsbridge"
+const NATS_URL = "nats.yiem.cc"
+const FILESERVER_URL = "http://192.168.88.104:8080"
+
+
+# ------------------------------------------------------------------------------------------------ #
+#                                      test mixed content transfer                                 #
+# ------------------------------------------------------------------------------------------------ #
+
+
+# Helper: Log with correlation ID
+function log_trace(message)
+    timestamp = Dates.now()
+    println("[$timestamp] $message")
+end
+
+
+# Receiver: Listen for messages and verify mixed content handling
+function test_mix_receive()
+    conn = NATS.connect(NATS_URL)
+    NATS.subscribe(conn, SUBJECT) do msg
+        log_trace("Received message on $(msg.subject)")
+        
+        # Use NATSBridge.smartreceive to handle the data
+        # API: smartreceive(msg, download_handler; max_retries, base_delay, max_delay)
+        result = NATSBridge.smartreceive(
+            msg;
+            max_retries = 5,
+            base_delay = 100,
+            max_delay = 5000
+        )
+        
+        log_trace("Received $(length(result["payloads"])) payloads")
+        
+        # Result is an envelope dictionary with payloads field containing list of (dataname, data, data_type) tuples
+        for (dataname, data, data_type) in result["payloads"]
+            log_trace("\n=== Payload: $dataname (type: $data_type) ===")
+            
+            # Handle different data types
+            if data_type == "text"
+                # Text data - should be a String
+                if isa(data, String)
+                    log_trace("  Type: String")
+                    log_trace("  Length: $(length(data)) characters")
+                    
+                    # Display first 200 characters
+                    if length(data) > 200
+                        log_trace("  First 200 chars: $(data[1:200])...")
+                    else
+                        log_trace("  Content: $data")
+                    end
+                    
+                    # Save to file
+                    output_path = "./received_$dataname.txt"
+                    write(output_path, data)
+                    log_trace("  Saved to: $output_path")
+                else
+                    log_trace("  ERROR: Expected String, got $(typeof(data))")
+                end
+                
+            elseif data_type == "dictionary"
+                # Dictionary data - should be JSON object
+                if isa(data, JSON.Object{String, Any})
+                    log_trace("  Type: Dict")
+                    log_trace("  Keys: $(keys(data))")
+                    
+                    # Display nested content
+                    for (key, value) in data
+                        log_trace("  $key => $value")
+                    end
+                    
+                    # Save to JSON file
+                    output_path = "./received_$dataname.json"
+                    json_str = JSON.json(data, 2)
+                    write(output_path, json_str)
+                    log_trace("  Saved to: $output_path")
+                else
+                    log_trace("  ERROR: Expected Dict, got $(typeof(data))")
+                end
+                
+            elseif data_type == "arrowtable"
+                # Arrow table data - should be Arrow.Table
+                if isa(data, Arrow.Table)
+                    log_trace("  Type: Arrow.Table")
+                    
+                    # Convert to DataFrame for display and save
+                    df = DataFrame(data)
+                    @show df[1:3, :]
+                    output_path = "./received_$dataname.arrow"
+                    io = IOBuffer()
+                    Arrow.write(io, data)
+                    write(output_path, take!(io))
+                    log_trace("  Saved to: $output_path")
+                else
+                    log_trace("  ERROR: Expected Arrow.Table, got $(typeof(data))")
+                end
+                
+            elseif data_type == "jsontable"
+                # JSON table data - should be Vector{Dict} or Vector{NamedTuple}
+                @show "jsontable" typeof(data)
+                if isa(data, Vector{Any})
+                    log_trace("  Type: Vector{Dict/NamedTuple}")
+                    
+                    # Convert to DataFrame for display and save
+                    df = DataFrame(data)
+                    @show df[1:3, :]
+                    log_trace("  Converted to DataFrame: $(size(df, 1)) rows x $(size(df, 2)) columns")
+                    
+                    # Save as JSON file
+                    output_path = "./received_$dataname.json"
+                    json_str = JSON.json(data, 2)
+                    write(output_path, json_str)
+                    log_trace("  Saved to: $output_path")
+                else
+                    log_trace("  ERROR: Expected Vector{Dict/NamedTuple}, got $(typeof(data))")
+                end
+                
+            elseif data_type == "image"
+                # Image data - should be Vector{UInt8}
+                if isa(data, Vector{UInt8})
+                    log_trace("  Type: Vector{UInt8} (binary)")
+                    log_trace("  Size: $(length(data)) bytes")
+                    
+                    # Save to file
+                    output_path = "./received_$dataname.bin"
+                    write(output_path, data)
+                    log_trace("  Saved to: $output_path")
+                else
+                    log_trace("  ERROR: Expected Vector{UInt8}, got $(typeof(data))")
+                end
+                
+            elseif data_type == "audio"
+                # Audio data - should be Vector{UInt8}
+                if isa(data, Vector{UInt8})
+                    log_trace("  Type: Vector{UInt8} (binary)")
+                    log_trace("  Size: $(length(data)) bytes")
+                    
+                    # Save to file
+                    output_path = "./received_$dataname.bin"
+                    write(output_path, data)
+                    log_trace("  Saved to: $output_path")
+                else
+                    log_trace("  ERROR: Expected Vector{UInt8}, got $(typeof(data))")
+                end
+                
+            elseif data_type == "video"
+                # Video data - should be Vector{UInt8}
+                if isa(data, Vector{UInt8})
+                    log_trace("  Type: Vector{UInt8} (binary)")
+                    log_trace("  Size: $(length(data)) bytes")
+                    
+                    # Save to file
+                    output_path = "./received_$dataname.bin"
+                    write(output_path, data)
+                    log_trace("  Saved to: $output_path")
+                else
+                    log_trace("  ERROR: Expected Vector{UInt8}, got $(typeof(data))")
+                end
+                
+            elseif data_type == "binary"
+                # Binary data - should be Vector{UInt8}
+                if isa(data, Vector{UInt8})
+                    log_trace("  Type: Vector{UInt8} (binary)")
+                    log_trace("  Size: $(length(data)) bytes")
+                    
+                    # Save to file
+                    output_path = "./received_$dataname"
+                    write(output_path, data)
+                    log_trace("  Saved to: $output_path")
+                else
+                    log_trace("  ERROR: Expected Vector{UInt8}, got $(typeof(data))")
+                end
+                
+            else
+                log_trace("  ERROR: Unknown data type '$data_type'")
+            end
+        end
+        
+        # Summary
+        println("\n=== Verification Summary ===")
+        text_count = count(x -> x[3] == "text", result["payloads"])
+        dict_count = count(x -> x[3] == "dictionary", result["payloads"])
+        arrowtable_count = count(x -> x[3] == "arrowtable", result["payloads"])
+        jsontable_count = count(x -> x[3] == "jsontable", result["payloads"])
+        table_count = count(x -> x[3] == "table", result["payloads"])  # backward compatibility
+        image_count = count(x -> x[3] == "image", result["payloads"])
+        audio_count = count(x -> x[3] == "audio", result["payloads"])
+        video_count = count(x -> x[3] == "video", result["payloads"])
+        binary_count = count(x -> x[3] == "binary", result["payloads"])
+        
+        log_trace("Text payloads: $text_count")
+        log_trace("Dictionary payloads: $dict_count")
+        log_trace("Arrow table payloads: $arrowtable_count")
+        log_trace("JSON table payloads: $jsontable_count")
+        log_trace("Table payloads (backward compat): $table_count")
+        log_trace("Image payloads: $image_count")
+        log_trace("Audio payloads: $audio_count")
+        log_trace("Video payloads: $video_count")
+        log_trace("Binary payloads: $binary_count")
+        
+        # Print transport type info for each payload if available
+        println("\n=== Payload Details ===")
+        for (dataname, data, data_type) in result["payloads"]
+            if data_type in ["image", "audio", "video", "binary"]
+                log_trace("$dataname: $(length(data)) bytes (binary)")
+            elseif data_type == "arrowtable"
+                # log_trace("$dataname: $(size(data, 1)) rows x $(size(data, 2)) columns (Arrow.Table)")
+            elseif data_type == "jsontable"
+                log_trace("$dataname: $(length(data)) rows (Vector{Dict/NamedTuple})")
+            elseif data_type == "table"
+                data = DataFrame(data)
+                # log_trace("$dataname: $(size(data, 1)) rows x $(size(data, 2)) columns (DataFrame)")
+            elseif data_type == "dictionary"
+                log_trace("$dataname: $(length(JSON.json(data))) bytes (Dict)")
+            elseif data_type == "text"
+                log_trace("$dataname: $(length(data)) characters (String)")
+            end
+        end
+    end
+    
+    # Keep listening for 2 minutes
+    sleep(180)  
+    NATS.drain(conn)
+end
+
+
+# Run the test
+println("Starting mixed-content transport test...")
+println("Note: This receiver will wait for messages from the sender.")
+println("Run test_julia_to_julia_mix_sender.jl first to send test data.")
+
+# Run receiver
+println("\ntesting smartreceive for mixed content")
+test_mix_receive()
+
+println("\nTest completed.")

test/test_julia_mix_payloads_sender.jl

@@ -0,0 +1,258 @@
+#!/usr/bin/env julia
+# Test script for mixed-content message testing
+# Tests sending a mix of text, dictionary, arrowtable, jsontable, image, audio, video, and binary data
+# from Julia serviceA to Julia serviceB using NATSBridge.jl smartsend
+# 
+# This test demonstrates that any combination and any number of mixed content
+# can be sent and received correctly.
+# 
+# Key concept: DataFrames are the main table representation in Julia.
+# The NATSBridge.jl library handles serialization:
+# - For "arrowtable" type: DataFrame is serialized to Arrow IPC format
+# - For "jsontable" type: DataFrame is converted to Vector{Dict} and then to JSON
+
+using NATS, JSON, UUIDs, Dates, PrettyPrinting, DataFrames, Arrow, HTTP, Base64
+
+# Include the bridge module
+include("../src/NATSBridge.jl")
+using .NATSBridge
+
+# Configuration
+const SUBJECT = "/natsbridge"
+const NATS_URL = "nats.yiem.cc"
+const FILESERVER_URL = "http://192.168.88.104:8080"
+
+# Create correlation ID for tracing
+correlation_id = string(uuid4())
+
+
+# ------------------------------------------------------------------------------------------------ #
+#                                      test mixed content transfer                                 #
+# ------------------------------------------------------------------------------------------------ #
+
+
+# Helper: Log with correlation ID
+function log_trace(message)
+    timestamp = Dates.now()
+    println("[$timestamp] [Correlation: $correlation_id] $message")
+end
+
+
+# File upload handler for plik server
+function plik_upload_handler(fileserver_url::String, dataname::String, data::Vector{UInt8})::Dict{String, Any}
+    # Get upload ID
+    url_getUploadID = "$fileserver_url/upload"
+    headers = ["Content-Type" => "application/json"]
+    body = """{ "OneShot" : true }"""
+    httpResponse = HTTP.request("POST", url_getUploadID, headers, body; body_is_form=false)
+    responseJson = JSON.parse(String(httpResponse.body))
+    uploadid = responseJson["id"]
+    uploadtoken = responseJson["uploadToken"]
+    
+    # Upload file
+    file_multipart = HTTP.Multipart(dataname, IOBuffer(data), "application/octet-stream")
+    url_upload = "$fileserver_url/file/$uploadid"
+    headers = ["X-UploadToken" => uploadtoken]
+    
+    form = HTTP.Form(Dict("file" => file_multipart))
+    httpResponse = HTTP.post(url_upload, headers, form)
+    responseJson = JSON.parse(String(httpResponse.body))
+    
+    fileid = responseJson["id"]
+    url = "$fileserver_url/file/$uploadid/$fileid/$dataname"
+    
+    return Dict("status" => httpResponse.status, "uploadid" => uploadid, "fileid" => fileid, "url" => url)
+end
+
+
+# Helper: Create sample data for each type
+function create_sample_data()
+    # Text data (small - direct transport)
+    text_data = "Hello! This is a test chat message. 🎉\nHow are you doing today? 😊"
+    
+    # Dictionary/JSON data (medium - could be direct or link)
+    dict_data = Dict(
+        "type" => "chat",
+        "sender" => "serviceA",
+        "receiver" => "serviceB",
+        "metadata" => Dict(
+            "timestamp" => string(Dates.now()),
+            "priority" => "high",
+            "tags" => ["urgent", "chat", "test"]
+        ),
+        "content" => Dict(
+            "text" => "This is a JSON-formatted chat message with nested structure.",
+            "format" => "markdown",
+            "mentions" => ["user1", "user2"]
+        )
+    )
+    
+    # Arrow table data (DataFrame - small - direct transport)
+    # Uses Arrow IPC format for efficient binary serialization
+    # NATSBridge.jl handles serialization: DataFrame -> Arrow IPC
+    arrow_table_small = DataFrame(
+        id = 1:10,
+        name = ["Alice", "Bob", "Charlie", "Diana", "Eve", "Frank", "Grace", "Henry", "Ivy", "Jack"],
+        score = rand(50:100, 10),
+        active = rand([true, false], 10)
+    )
+    
+    # Arrow table data (DataFrame - large - link transport)
+    # ~1.5MB of Arrow data (200,000 rows) - should trigger link transport
+    # NATSBridge.jl handles serialization: DataFrame -> Arrow IPC
+    arrow_table_large = DataFrame(
+        id = 1:2_000_000,
+        name = ["user_$i" for i in 1:2_000_000],
+        score = rand(50:100, 2_000_000),
+        active = rand([true, false], 2_000_000),
+        timestamp = [string(Dates.now()) for _ in 1:2_000_000]
+    )
+    
+    # Json table data (DataFrame - small - direct transport)
+    # Uses JSON format for human-readable tabular data
+    # NATSBridge.jl handles serialization: DataFrame -> Vector{Dict} -> JSON
+    json_table_small = DataFrame(
+        id = 1:10,
+        name = ["Alice", "Bob", "Charlie", "Diana", "Eve", "Frank", "Grace", "Henry", "Ivy", "Jack"],
+        score = rand(50:100, 10),
+        active = rand([true, false], 10)
+    )
+    
+    # Json table data (DataFrame - large - link transport)
+    # ~1.5MB of JSON data (150,000 rows) - should trigger link transport
+    # NATSBridge.jl handles serialization: DataFrame -> Vector{Dict} -> JSON
+    json_table_large = DataFrame(
+        id = 1:2_000_000,
+        name = ["user_$i" for i in 1:2_000_000],
+        score = rand(50:100, 2_000_000),
+        active = rand([true, false], 2_000_000)
+    )
+    
+    # Audio data (small binary - direct transport)
+    audio_data = UInt8[rand(1:255) for _ in 1:100]
+    
+    # Audio data (large - link transport)
+    # ~1.5MB of audio-like data
+    large_audio_data = UInt8[rand(1:255) for _ in 1:1_500_000]
+    
+    # Video data (small binary - direct transport)
+    video_data = UInt8[rand(1:255) for _ in 1:150]
+    
+    # Video data (large - link transport)
+    # ~1.5MB of video-like data
+    large_video_data = UInt8[rand(1:255) for _ in 1:1_500_000]
+    
+    # Binary data (small - direct transport)
+    binary_data = UInt8[rand(1:255) for _ in 1:200]
+    
+    # Binary data (large - link transport)
+    # ~1.5MB of binary data
+    large_binary_data = UInt8[rand(1:255) for _ in 1:1_500_000]
+    
+    return (
+        text_data,
+        dict_data,
+        arrow_table_small,
+        arrow_table_large,
+        json_table_small,
+        json_table_large,
+        audio_data,
+        large_audio_data,
+        video_data,
+        large_video_data,
+        binary_data,
+        large_binary_data
+    )
+end
+
+
+# Sender: Send mixed content via smartsend
+function test_mix_send()
+    # Create sample data
+    (text_data, dict_data, arrow_table_small, arrow_table_large, json_table_small, json_table_large, audio_data, large_audio_data, video_data, large_video_data, binary_data, large_binary_data) = create_sample_data()
+
+    # Read image files from disk (following test_julia_file_sender.jl pattern)
+    # Small image - should use direct transport
+    file_path_small_image = "./test/small_image.jpg"
+    file_data_small_image = read(file_path_small_image)
+    filename_small_image = basename(file_path_small_image)
+
+    # Large image - should use link transport
+    file_path_large_image = "./test/large_image.png"
+    file_data_large_image = read(file_path_large_image)
+    filename_large_image = basename(file_path_large_image)
+
+    # Create payloads list - mixed content with both small and large data
+    # Small data uses direct transport, large data uses link transport
+    # Key: Pass DataFrame directly and specify type as "arrowtable" or "jsontable"
+    # NATSBridge.jl handles the serialization internally
+    payloads = [
+        # Small data (direct transport) - text, dictionary, arrowtable, jsontable, small image
+        ("chat_text", text_data, "text"),
+        ("chat_json", dict_data, "dictionary"),
+        # ("arrow_table_small", arrow_table_small, "arrowtable"),
+        ("json_table_small", json_table_small, "jsontable"),
+        (filename_small_image, file_data_small_image, "binary"),
+
+        # Large data (link transport) - large arrowtable, large jsontable, large image, large audio, large video, large binary
+        # ("arrow_table_large", arrow_table_large, "arrowtable"),
+        ("json_table_large", json_table_large, "jsontable"),
+        (filename_large_image, file_data_large_image, "binary"),
+        ("audio_clip_large", large_audio_data, "audio"),
+        ("video_clip_large", large_video_data, "video"),
+        ("binary_file_large", large_binary_data, "binary")
+    ]
+    
+    # Use smartsend with mixed content
+    sendinfo = NATSBridge.smartsend(
+        SUBJECT,
+        payloads;  # List of (dataname, data, type) tuples
+        broker_url = NATS_URL,
+        fileserver_url = FILESERVER_URL,
+        fileserver_upload_handler = plik_upload_handler,
+        size_threshold = 1_000_000,  # 1MB threshold
+        correlation_id = correlation_id,
+        msg_purpose = "chat",
+        sender_name = "mix_sender",
+        receiver_name = "",
+        receiver_id = "",
+        reply_to = "",
+        reply_to_msg_id = "",
+        is_publish = true  # Publish the message to NATS
+    )
+
+    env, env_json_str = sendinfo
+    log_trace("Sent message with $(length(env.payloads)) payloads")
+    
+    # Log transport type for each payload
+    for (i, payload) in enumerate(env.payloads)
+        log_trace("Payload $i ('$payload.dataname'):")
+        log_trace("  Transport: $(payload.transport)")
+        log_trace("  Type: $(payload.payload_type)")
+        log_trace("  Size: $(payload.size) bytes")
+        log_trace("  Encoding: $(payload.encoding)")
+        
+        if payload.transport == "link"
+            log_trace("  URL: $(payload.data)")
+        end
+    end
+    
+    # Summary
+    println("\n--- Transport Summary ---")
+    direct_count = count(p -> p.transport == "direct", env.payloads)
+    link_count = count(p -> p.transport == "link", env.payloads)
+    log_trace("Direct transport: $direct_count payloads")
+    log_trace("Link transport: $link_count payloads")
+end
+
+
+# Run the test
+println("Starting mixed-content transport test...")
+println("Correlation ID: $correlation_id")
+
+# Run sender
+println("start smartsend for mixed content")
+test_mix_send()
+
+println("\nTest completed.")
+println("Note: Run test_julia_to_julia_mix_receiver.jl to receive the messages.")

test/test_py_mix_payloads_sender.py

@@ -0,0 +1,199 @@
+"""
+Python Mix Payloads Sender Test
+Tests the smartsend function with mixed payload types
+"""
+
+import asyncio
+import sys
+import os
+import base64
+
+# Add parent directory to path
+sys.path.insert(0, os.path.dirname(os.path.dirname(os.path.abspath(__file__))))
+
+from natsbridge import smartsend, DEFAULT_BROKER_URL, DEFAULT_FILESERVER_URL
+
+TEST_SUBJECT = '/test/mix'
+TEST_BROKER_URL = os.environ.get('NATS_URL', 'nats://localhost:4222')
+TEST_FILESERVER_URL = os.environ.get('FILESERVER_URL', 'http://localhost:8080')
+
+
+async def run_test():
+    print('=== Python Mix Payloads Sender Test ===\n')
+    
+    correlation_id = 'py-mix-test-' + str(asyncio.get_event_loop().time() * 1000000)
+    print(f'Correlation ID: {correlation_id}')
+    print(f'Subject: {TEST_SUBJECT}')
+    print(f'Broker URL: {TEST_BROKER_URL}\n')
+    
+    # Test data - mixed payload types
+    text_data = 'Hello, NATSBridge!'
+    dict_data = {'key1': 'value1', 'key2': 42, 'nested': {'a': 1, 'b': 2}}
+    image_data = bytes([0x89, 0x50, 0x4E, 0x47, 0x0D, 0x0A, 0x1A, 0x0A])  # PNG header
+    
+    # Table data
+    try:
+        import pandas as pd
+        table_data = pd.DataFrame({
+            'id': [1, 2, 3],
+            'name': ['Alice', 'Bob', 'Charlie'],
+            'age': [30, 25, 35]
+        })
+        table_available = True
+    except ImportError:
+        table_available = False
+        table_data = None
+    
+    test_data = [
+        ('message', text_data, 'text'),
+        ('config', dict_data, 'dictionary'),
+        ('image', image_data, 'image')
+    ]
+    
+    if table_available:
+        test_data.append(('users', table_data, 'table'))
+    
+    try:
+        # Send the message
+        print('Sending mixed payloads...')
+        env, env_json_str = await smartsend(
+            TEST_SUBJECT,
+            test_data,
+            broker_url=TEST_BROKER_URL,
+            fileserver_url=TEST_FILESERVER_URL,
+            correlation_id=correlation_id,
+            msg_purpose='test',
+            sender_name='py-mix-test',
+            is_publish=False
+        )
+        
+        print('\n=== Envelope Created ===')
+        print(f'Correlation ID: {env["correlation_id"]}')
+        print(f'Message ID: {env["msg_id"]}')
+        print(f'Timestamp: {env["timestamp"]}')
+        print(f'Subject: {env["send_to"]}')
+        print(f'Purpose: {env["msg_purpose"]}')
+        print(f'Sender: {env["sender_name"]}')
+        print(f'Payloads: {len(env["payloads"])}\n')
+        
+        # Validate envelope structure
+        print('=== Validation ===')
+        passed = True
+        
+        expected_count = 4 if table_available else 3
+        if len(env['payloads']) != expected_count:
+            print(f'❌ Expected {expected_count} payloads, got {len(env["payloads"])}')
+            passed = False
+        else:
+            print('✅ Correct number of payloads')
+        
+        # Test each payload
+        expected_datanames = ['message', 'config', 'image']
+        expected_types = ['text', 'dictionary', 'image']
+        expected_data = [text_data, dict_data, image_data]
+        
+        if table_available:
+            expected_datanames.append('users')
+            expected_types.append('table')
+        
+        for i in range(len(env['payloads'])):
+            payload = env['payloads'][i]
+            
+            if payload['dataname'] != expected_datanames[i]:
+                print(f"❌ Payload {i + 1}: Expected dataname '{expected_datanames[i]}', got '{payload['dataname']}'")
+                passed = False
+            else:
+                print(f'✅ Payload {i + 1}: Correct dataname')
+            
+            if payload['payload_type'] != expected_types[i]:
+                print(f"❌ Payload {i + 1}: Expected type '{expected_types[i]}', got '{payload['payload_type']}'")
+                passed = False
+            else:
+                print(f'✅ Payload {i + 1}: Correct type')
+            
+            if payload['transport'] != 'direct':
+                print(f"❌ Payload {i + 1}: Expected transport 'direct', got '{payload['transport']}'")
+                passed = False
+            else:
+                print(f'✅ Payload {i + 1}: Correct transport')
+            
+            if payload['encoding'] != 'base64':
+                print(f"❌ Payload {i + 1}: Expected encoding 'base64', got '{payload['encoding']}'")
+                passed = False
+            else:
+                print(f'✅ Payload {i + 1}: Correct encoding')
+            
+            # Verify data integrity based on type
+            decoded_data = base64.b64decode(payload['data'])
+            
+            if expected_types[i] == 'text':
+                decoded_text = decoded_data.decode('utf8')
+                if decoded_text != expected_data[i]:
+                    print(f'❌ Payload {i + 1}: Data integrity mismatch')
+                    passed = False
+                else:
+                    print(f'✅ Payload {i + 1}: Data integrity verified')
+            elif expected_types[i] == 'dictionary':
+                import json
+                decoded_dict = json.loads(decoded_data.decode('utf8'))
+                if json.dumps(decoded_dict, sort_keys=True) != json.dumps(expected_data[i], sort_keys=True):
+                    print(f'❌ Payload {i + 1}: Data integrity mismatch')
+                    passed = False
+                else:
+                    print(f'✅ Payload {i + 1}: Data integrity verified')
+            elif expected_types[i] == 'image':
+                if decoded_data != expected_data[i]:
+                    print(f'❌ Payload {i + 1}: Data integrity mismatch')
+                    passed = False
+                else:
+                    print(f'✅ Payload {i + 1}: Data integrity verified')
+            elif expected_types[i] == 'table':
+                if len(decoded_data) > 0:
+                    print(f'✅ Payload {i + 1}: Arrow IPC data present ({len(decoded_data)} bytes)')
+                else:
+                    print(f'❌ Payload {i + 1}: Arrow IPC data is empty')
+                    passed = False
+            
+            print(f'   Size: {payload["size"]} bytes\n')
+        
+        # Test with chat-like payload (text + image + audio)
+        print('=== Chat-like Payload Test ===')
+        chat_data = [
+            ('text', 'Hello!', 'text'),
+            ('image', bytes([0xFF, 0xD8, 0xFF, 0xE0]), 'image'),
+            ('audio', bytes([0x46, 0x4C, 0x41, 0x43]), 'audio')
+        ]
+        
+        chat_env, _ = await smartsend(
+            TEST_SUBJECT,
+            chat_data,
+            broker_url=TEST_BROKER_URL,
+            fileserver_url=TEST_FILESERVER_URL,
+            correlation_id='chat-' + correlation_id,
+            is_publish=False
+        )
+        
+        if len(chat_env['payloads']) == 3:
+            print('✅ Chat-like payloads handled correctly')
+        else:
+            print('❌ Chat-like payloads handling failed')
+            passed = False
+        
+        # Final result
+        print('\n=== Test Result ===')
+        if passed:
+            print('✅ ALL TESTS PASSED')
+            sys.exit(0)
+        else:
+            print('❌ SOME TESTS FAILED')
+            sys.exit(1)
+            
+    except Exception as e:
+        print(f'❌ Test failed with error: {e}')
+        import traceback
+        traceback.print_exc()
+        sys.exit(1)
+
+
+if __name__ == '__main__':
+    asyncio.run(run_test())