# Creating workflows **Building AI Workflows with CogniBlocks** At the heart of **CogniBlocks**, individual **Cognitive Units** (Blocks) serve as the fundamental components for constructing AI-driven workflows. The platform includes a library of **predefined modules**, but users can also **create, modify, and share custom Blocks** to build scalable AI solutions. By designing Blocks with **modularity** in mind, they can be **reused across multiple workflows**, enabling a seamless **drag-and-drop** development experience. CogniBlocks also integrates **AI-assisted coding** to streamline modifications directly within the platform, ensuring efficiency without requiring external tools. Below, we explore the core components of Blocks, how they function, and how users can **create or modify** them. *** ### **Understanding Block Components** Each **CogniBlock** consists of different elements depending on its function. There are three primary types of Blocks: #### **1️⃣ Parameter Blocks** * Define **input values** for workflows. * No input nodes—values are manually set via the UI. * Examples: **File Input, Text, Number, List, Boolean.** #### **2️⃣ Processing Blocks** * Contain the **core logic** of AI operations. * Accept multiple inputs and generate processed outputs. * Examples: **Train Model, Edge Detection, Data Transformation.** #### **3️⃣ Visualization Blocks** * Render graphical representations of outputs. * Display results in real time based on defined logic. * Examples: **Graph Renderer, Image Viewer, Data Plotter.** Each **Block** is saved in a directory that mirrors its name inside: 📂 **workspace/modules/blocks/{block-name}** *** ### **Structure of a Block** Each Block is composed of multiple files, depending on its type. Below is an overview of the key components: #### **Parameter Block Components** * **specs.json** – Defines the Block’s configuration and parameters.\ 📂 *workspace/modules/blocks/{block-name}/specs.json* #### **Processing Block Components** A **Processing Block** includes five essential files: 📄 **computations.py** * Contains the **compute() function**, which processes inputs and generates outputs. * Includes a **test() function** to validate computations. 📄 **Dockerfile** * Defines the environment setup for execution. * Always includes: `COPY computations.py ..` 📄 **requirements.txt** * Lists required dependencies for execution. 📄 **run\_test.py** * Builds a test container and runs validation scripts. 📄 **specs.json** * Stores the **Block’s metadata and structure**. #### **Visualization Block Components** **Visualization Blocks** extend **Processing Blocks** by rendering results dynamically. These Blocks include: * `computations.py` (with HTML-based visualization logic) * `Dockerfile` * `requirements.txt` * `run_test.py` * `specs.json` *** ### **Creating New Blocks** To develop a **custom Block**, use the **prebuilt templates** available in the **Component Library**. #### **Steps to Create a New Block:** 1️⃣ Drag a **template Block** (e.g., "New Python") into the workspace.\ 2️⃣ Modify its logic within the **Block Editor**.\ 3️⃣ Save changes and compile the Block. *** ### **Modifying Blocks with the Code Editor** To edit an existing Block: * Click the **\ icon** on the Block header to open the **Code Editor Sidebar**. * Modify the **compute() function** within **computations.py**. * Save changes and recompile the Block. *** ### **Block Templates** #### **Processing Block Template** ```python pythonCopyEditimport ... def compute(input_1, input_2, ...): """ Processes input data and returns structured output. """ # Custom logic here return {'output_1': result_1, 'output_2': result_2} def test(): print("Executing test case") # Test implementation ``` #### **Visualization Block Template** ```python pythonCopyEditimport uuid import json import ... def compute(input_data): """ Generates an interactive visualization from input data. """ html_template = """ ... """ unique_id = str(uuid.uuid4()) output_file = f"visual_{unique_id}.html" formatted_data = json.dumps(input_data) html_code = html_template.replace("$DATA_PLACEHOLDER", formatted_data) with open(output_file, "w") as file: file.write(html_code) return {"html": output_file} def test(): print("Testing visualization Block") ``` 🔹 **Note:** The `compute()` function always returns a **dictionary** where **keys match output node names** in `specs.json`. *** ### **Compiling & Saving Blocks** Once modifications are complete, recompile the Block using: 📷 *(compile-run-block-editor.png)* * **Compile Block Files** – Saves all changes. * **Compile & Run Block** – Saves changes and executes validation tests. 💡 **Next Steps:** Dive deeper into the **CogniBlocks Editor Guide** for advanced customization techniques. 🚀 **Now you're ready to build and integrate your own CogniBlocks components!**