# Adding an AI/ML Source
Source: https://docs.squared.ai/activation/add-ai-source

How to connect and configure a hosted AI/ML model source in AI Squared.

You can connect your hosted AI/ML model endpoints to AI Squared in just a few steps. This allows your models to power real-time insights across business applications.

***

## Step 1: Select Your AI/ML Source

1. Navigate to **Sources** → **AI/ML Sources** in the sidebar.
2. Click **“Add Source”**.
3. Select the AI/ML source connector from the list.

> 📸 *Add screenshot of “Add AI/ML Source” UI*

***

## Step 2: Define and Connect the Endpoint

Fill in the required connection details:

* **Endpoint Name** – A descriptive name for easy identification.
* **Endpoint URL** – The hosted URL of your AI/ML model.
* **Authentication Method** – Choose between `OAuth`, `API Key`, etc.
* **Authorization Header** – Format of the header (if applicable).
* **Secret Key** – For secure access.
* **Request Format** – Define the input structure (e.g., JSON).
* **Response Format** – Define how the model returns predictions.

> 📸 *Add screenshot of endpoint configuration UI*

***

## Step 3: Test the Source

Before saving, click **“Test Connection”** to verify that the endpoint is reachable and properly configured.

> ⚠️ If the test fails, check for errors in the endpoint URL, headers, or authentication values.

> 📸 *Add screenshot of test results with success/failure examples*

***

## Step 4: Save the Source

Once the test passes:

* Provide a name and optional description.
* Click **“Save”** to finalize setup.
* Your model source will now appear under **AI/ML Sources**.

> 📸 *Add screenshot showing saved model in the source list*

***

## Step 5: Define Input Schema

The **Input Schema** tells AI Squared how to format data before sending it to the model.

Each input field requires:

* **Name** – Matches the key in your model’s input payload.
* **Type** – `String`, `Integer`, `Float`, or `Boolean`.
* **Value Type** – `Dynamic` (from data/apps) or `Static` (fixed value).

> 📸 *Add screenshot of input schema editor*

***

## Step 6: Define Output Schema

The **Output Schema** tells AI Squared how to interpret the model's response.

Each output field requires:

* **Field Name** – The key returned by the model.
* **Type** – Define the type: `String`, `Integer`, `Float`, `Boolean`.

This ensures downstream systems or visualizations can consume the output consistently.

> 📸 *Add screenshot of output schema editor*

***

## ✅ You’re Done!

You’ve successfully added and configured your hosted AI/ML model as a source in AI Squared. Your model can now be used in **Data Apps**, **Chatbots**, and other workflow automations.


# Anthropic Model
Source: https://docs.squared.ai/activation/ai-ml-sources/anthropic-model



## Connect AI Squared to Anthropic Model

This guide will help you configure the Anthropic Model Connector in AI Squared to access your Anthropic Model Endpoint.

### Prerequisites

Before proceeding, ensure you have the necessary API key from Anthropic.

## Step-by-Step Guide to Connect to an Anthropic Model Endpoint

## Step 1: Navigate to Anthropic Console

Start by logging into your Anthropic Console.

1. Sign in to your Anthropic account at [Anthropic](https://console.anthropic.com/dashboard).
   <Frame>
     <img src="https://res.cloudinary.com/dspflukeu/image/upload/v1742405724/Multiwoven/connectors/Antropic-model/Dashboard_xr5wie.png" />
   </Frame>

## Step 2: Locate API keys

Once you're in the Anthropic, you'll find the necessary configuration details:

1. **API Key:**

   * Click on "API keys" to view your API keys.
   * If you haven't created an API Key before, click on "Create API key" to generate a new one. Make sure to copy the API Key as they are shown only once.

   <Frame>
     <img src="https://res.cloudinary.com/dspflukeu/image/upload/v1742405724/Multiwoven/connectors/Antropic-model/API_keys_q4zhke.png" />
   </Frame>

## Step 3: Configure Anthropic Model Connector in Your Application

Now that you have gathered all the necessary details enter the following information:

* **API Key:** Your Anthropic API key.

## Sample Request and Response

<AccordionGroup>
  <Accordion title="Stream disabled" icon="key">
    **Request:**

    ```json
    {
      "model": "claude-3-7-sonnet-20250219",
      "max_tokens": 256,
      "messages": [{"role": "user", "content": "Hi."}],
      "stream": false
    }
    ```

    **Response:**

    ```json
    {
      "id": "msg_0123ABC",
      "type": "message",
      "role": "assistant",
      "model": "claude-3-7-sonnet-20250219",
      "content": [
        {
          "type": "text",
          "text": "Hello there! How can I assist you today? Whether you have a question, need some information, or just want to chat, I'm here to help. What's on your mind?"
        }
      ],
      "stop_reason": "end_turn",
      "stop_sequence": null,
      "usage": {
        "input_tokens": 10,
        "cache_creation_input_tokens": 0,
        "cache_read_input_tokens": 0,
        "output_tokens": 41
      }
    }
    ```
  </Accordion>
</AccordionGroup>

<AccordionGroup>
  <Accordion title="Stream enabled" icon="key">
    **Request:**

    ```json
    {
      "model": "claude-3-7-sonnet-20250219",
      "max_tokens": 1024,
      "messages": [{"role": "user", "content": "Hi"}],
      "stream": true
    }
    ```

    **Response:**

    ```json
    {
      "type": "content_block_delta",
      "index": 0,
      "delta": {
        "type": "text_delta",
        "text": "Hello!"
      }
    }
    ```
  </Accordion>
</AccordionGroup>


# Google Vertex Model
Source: https://docs.squared.ai/activation/ai-ml-sources/google_vertex-model



## Connect AI Squared to Google Vertex Model

This guide will help you configure the Google Vertex Model Connector in AI Squared to access your Google Vertex Model Endpoint.

### Prerequisites

Before proceeding, ensure you have the necessary project id, endpoint id, region, and credential json from Google Vertex.

## Step-by-Step Guide to Connect to an Google Vertex Model Endpoint

## Step 1: Navigate to Google Cloud Console

Start by logging into your Google Cloud Console.

1. Sign in to your google cloud account at [Google Cloud Console](https://console.cloud.google.com/).

## Step 2: Enable Vertex API

* If you don't have a project, create one.
* Enable the [Vertex API for your project](https://console.cloud.google.com/apis/library/aiplatform.googleapis.com).

## Step 3: Locate Google Vertex Configuration Details

1. **Project ID, Endpoint ID, and Region:**

   * In the search bar search and select "Vertex AI".
   * Choose "Online prediction" from the menu on the left hand side.
   * Select the region where your endpoint is and select your endpoint. Note down the Region that is shown.
   * Click on "SAMPLE REQUEST" and note down the Endpoint ID and Project ID
     <Frame>
       <img src="https://res.cloudinary.com/dspflukeu/image/upload/v1725470985/Multiwoven/connectors/google_vertex-model/Details_hd4uhu.jpg" />
     </Frame>

2. **JSON Key File:**

   * In the search bar search and select "APIs & Services".
   * Choose "Credentials" from the menu on the left hand side.
   * In the "Credentials" section, you can create or select your service account.
   * After selecting your service account goto the "KEYS" tab and click "ADD KEY". For Key type select JSON.
     <Frame>
       <img src="https://res.cloudinary.com/dspflukeu/image/upload/v1725470985/Multiwoven/connectors/google_vertex-model/Add_Key_qi9ogq.jpg" />
     </Frame>

## Step 3: Configure Google Vertex Model Connector in Your Application

Now that you have gathered all the necessary details enter the following information:

* **Project ID:** Your Google Vertex Project ID.
* **Endpoint ID:** Your Google Vertex Region ID.
* **Region:** The Endpoint region where your Google Vertex resources are located.
* **JSON Key File:** The JSON key file containing the authentication credentials for your service account.


# HTTP Model Source Connector
Source: https://docs.squared.ai/activation/ai-ml-sources/http-model-endpoint

Guide on how to configure the HTTP Model Connector on the AI Squared platform

## Connect AI Squared to HTTP Model

This guide will help you configure the HTTP Model Connector in AI Squared to access your HTTP Model Endpoint.

### Prerequisites

Before starting, ensure you have the URL of your HTTP Model and any required headers for authentication or request configuration.

## Step-by-Step Guide to Connect to an HTTP Model Endpoint

## Step 1: Log in to AI Squared

Sign in to your AI Squared account and navigate to the **Source** section.

## Step 2: Add a New HTTP Model Source Connector

From AI/ML Sources in Sources click **Add Source** and select **HTTP Model** from the list of available source types.

<Frame>
  <img src="https://res.cloudinary.com/dspflukeu/image/upload/v1731535400/Multiwoven/connectors/HTTP-model/http_model_source_lz03gb.png" alt="Configure HTTP Destination" />
</Frame>

## Step 3: Configure HTTP Connection Details

Enter the following information to set up your HTTP connection:

<Frame>
  <img src="https://res.cloudinary.com/dspflukeu/image/upload/v1731595872/Multiwoven/connectors/HTTP-model/HTTP_Model_Source_Connection_Page_h5rwe3.png" alt="Configure HTTP Destination" />
</Frame>

* **URL**: The URL where your model resides.
* **Headers**: Any required headers as key-value pairs, such as authentication tokens or content types.
* **Timeout**: The maximum time, in seconds, to wait for a response from the server before the request is canceled

## Step 4: Test the Connection

Use the **Test Connection** feature to ensure that AI Squared can connect to your HTTP Model endpoint. If the test is successful, you’ll receive a confirmation message. If not, review your connection details.

<Frame>
  <img src="https://res.cloudinary.com/dspflukeu/image/upload/v1731595872/Multiwoven/connectors/HTTP-model/HTTP_Model_Source_Connection_Success_clnbnf.png" alt="Configure HTTP Destination" />
</Frame>

## Step 5: Save the Connector Settings

Once the connection test is successful, save the connector settings to establish the destination.


# Open AI Model
Source: https://docs.squared.ai/activation/ai-ml-sources/open_ai-model



## Connect AI Squared to Open AI Model

This guide will help you configure the Open AI Model Connector in AI Squared to access your Open AI Model Endpoint.

### Prerequisites

Before proceeding, ensure you have the necessary API key from Open AI.

## Step-by-Step Guide to Connect to an Open AI Model Endpoint

## Step 1: Navigate to Open AI Console

Start by logging into your Open AI Console.

1. Sign in to your Open AI account at [Open AI](https://platform.openai.com/docs/overview).

## Step 2: Locate Developer Access

Once you're in the Open AI, you'll find the necessary configuration details:

1. **API Key:**

   * Click the gear icon on the top right corner.
     <Frame>
       <img src="https://res.cloudinary.com/dspflukeu/image/upload/v1742430767/Multiwoven/connectors/Open_ai/Setting_hutqpy.png" />
     </Frame>
   * Click on "API keys" to view your API keys.
   * If you haven't created an API Key before, click on "Create new secret key" to generate a new one. Make sure to copy the API Key as they are shown only once.
     <Frame>
       <img src="https://res.cloudinary.com/dspflukeu/image/upload/v1742430766/Multiwoven/connectors/Open_ai/Open_ai_API_keys_oae2fn.png" />
     </Frame>

## Step 3: Configure Open AI Model Connector in Your Application

Now that you have gathered all the necessary details enter the following information:

* **API Key:** Your Open ai API key.


# WatsonX.AI Model
Source: https://docs.squared.ai/activation/ai-ml-sources/watsonx_ai-model



## Connect AI Squared to WatsonX.AI Model

This guide will help you configure the WatsonX.AI Model Connector in AI Squared to access your WatsonX.AI Model Endpoint.

### Prerequisites

Before proceeding, ensure you have the necessary API key, region, and deployment id from WatsonX.AI.

## Step-by-Step Guide to Connect to an WatsonX.AI Model Endpoint

## Step 1: Navigate to WatsonX.AI Console

Start by logging into your WatsonX.AI Console.

1. Sign in to your IBM WatsonX account at [WatsonX.AI](https://dataplatform.cloud.ibm.com/wx/home?context=wx).

## Step 2: Locate Developer Access

Once you're in the WatsonX.AI, you'll find the necessary configuration details:

1. **API Key:**

* Scroll down to Developer access.
  <Frame>
    <img src="https://res.cloudinary.com/dspflukeu/image/upload/v1742348073/Multiwoven/connectors/WatsonX_AI/Discover_g59hes.png" />
  </Frame>
* Click on "Manage IBM Cloud API keys" to view your API keys.
* If you haven't created an API Key before, click on "Create API key" to generate a new one. Make sure to copy the API Key as they are shown only once.
  <Frame>
    <img src="https://res.cloudinary.com/dspflukeu/image/upload/v1742348072/Multiwoven/connectors/WatsonX_AI/Create_API_Key_qupq4r.png" />
  </Frame>

2. **Region**

* The IBM Cloud region can be selected from the top right corner of the WatsonX.AI Console. Choose the region where your WatsonX.AI resources is located and note down the region.
  <Frame>
    <img src="https://res.cloudinary.com/dspflukeu/image/upload/v1742400772/Multiwoven/connectors/WatsonX_AI/Region_mlxbpz.png" />
  </Frame>

3. **Deployment Id**

* Scroll down to Deployment spaces and click on your deployment space.
  <Frame>
    <img src="https://res.cloudinary.com/dspflukeu/image/upload/v1742392179/Multiwoven/connectors/WatsonX_AI/Deployment_ojvyuk.png" />
  </Frame>
* In your selected deployment space select your online deployed model
  <Frame>
    <img src="https://res.cloudinary.com/dspflukeu/image/upload/v1742398916/Multiwoven/connectors/WatsonX_AI/Deployment_Space_oszqu6.png" />
  </Frame>
* On the right-hand side, under "About this deployment", the Deployment ID will appear under "Deployment Details".
  <Frame>
    <img src="https://res.cloudinary.com/dspflukeu/image/upload/v1742392179/Multiwoven/connectors/WatsonX_AI/Deployment_ID_ij3k50.png" />
  </Frame>

## Step 3: Configure WatsonX.AI Model Connector in Your Application

Now that you have gathered all the necessary details enter the following information:

* **API Key:** Your IBM Cloud API key.
* **Region:** The IBM Cloud region where your WatsonX.AI resources are located.
* **Deployment ID:** The WatsonX.AI online deployment id


# Connect Source
Source: https://docs.squared.ai/activation/ai-modelling/connect-source

Learn how to connect and configure an AI/ML model as a source for use within the AI Squared platform.

Connecting an AI/ML source is the first step in activating AI within your business workflows. AI Squared allows you to seamlessly integrate your deployed model endpoints—from providers like SageMaker, Vertex AI, Databricks, or custom HTTP APIs.

This guide walks you through connecting a new model source.

***

## Step 1: Select an AI/ML Source

1. Navigate to **AI Activation → AI Modeling → Connect Source**
2. Click on **Add Source**
3. Choose your desired connector from the list:
   * AWS SageMaker
   * Google Vertex AI
   * Databricks Model
   * OpenAI Model Endpoint
   * HTTP Model Source (Generic)

📸 *Placeholder for: Screenshot of “Add Source” screen*

***

## Step 2: Enter Endpoint Details

Each connector requires some basic configuration for successful integration.

### Required Fields

* **Endpoint Name** – A meaningful name for this model source
* **Endpoint URL** – The endpoint where the model is hosted
* **Authentication Method** – e.g., OAuth, API Key, Bearer Token
* **Auth Header / Secret Key** – If applicable
* **Request Format** – Structure expected by the model (e.g., JSON payload)
* **Response Format** – Format returned by the model (e.g., structured JSON with keys)

📸 *Placeholder for: Screenshot of endpoint input form*

***

## Step 3: Test Connection

Click **Test Connection** to validate that the model endpoint is reachable and returns a valid response.

* Ensure all fields are correct
* The system will validate the endpoint and return a success or error message

📸 *Placeholder for: Screenshot of test success/failure*

***

## Step 4: Define Input Schema

The input schema specifies the fields your model expects during inference.

| Field     | Description                                |
| --------- | ------------------------------------------ |
| **Name**  | Key name expected by the model             |
| **Type**  | Data type: String, Integer, Float, Boolean |
| **Value** | Static or dynamic input value              |

📸 *Placeholder for: Input schema editor screenshot*

***

## Step 5: Define Output Schema

The output schema ensures consistent mapping of the model’s response.

| Field          | Description                                |
| -------------- | ------------------------------------------ |
| **Field Name** | Key name from the model response           |
| **Type**       | Data type: String, Integer, Float, Boolean |

📸 *Placeholder for: Output schema editor screenshot*

***

## Step 6: Save the Source

Click **Save** once configuration is complete. Your model source will now appear in the **AI Modeling** tab and can be used in downstream workflows such as Data Apps or visualizations.

📸 *Placeholder for: Final save and confirmation screen*

***

Need help? Head over to our [Support & FAQs](/support) section for troubleshooting tips or reach out via the in-app help widget.


# Input Schema
Source: https://docs.squared.ai/activation/ai-modelling/input-schema

Define and configure the input schema to structure the data your model receives.

The **Input Schema** defines the structure of the data passed to your AI/ML model during inference. This ensures that inputs sent from your business applications or workflows match the format expected by your model endpoint.

AI Squared provides a no-code interface to configure input fields, set value types, and ensure compatibility with model requirements.

***

## Why Input Schema Matters

* Ensures data integrity before reaching the model
* Maps business inputs to model parameters
* Prevents inference failures due to malformed payloads
* Enables dynamic or static parameter configuration

***

## Defining Input Fields

Each input field includes the following:

| Field          | Description                                                           |
| -------------- | --------------------------------------------------------------------- |
| **Name**       | The key name expected in your model’s request payload                 |
| **Type**       | The data type: `String`, `Integer`, `Float`, or `Boolean`             |
| **Value Type** | `Dynamic` (changes with each query/request) or `Static` (fixed value) |

📸 *Placeholder for: Screenshot of input schema editor*

***

## Static vs. Dynamic Values

* **Static**: Hardcoded values used for all model requests. Example: `country: "US"`
* **Dynamic**: Values sourced from the business application or runtime context. Example: `user_id` passed from Salesforce record

📘 *Tip: Use harvesting (covered later) to auto-fetch dynamic values from frontend apps like CRMs.*

***

## Example Input Schema

```json
{
  "customer_id": "12345",
  "email": "user@example.com",
  "plan_type": "premium",
  "language": "en"
}
```

In this example:

customer\_id and email may be dynamic

plan\_type could be static

Each key must align with your model's expected input structure

## Next Steps

Once your input schema is defined, you can:

Add optional Preprocessing logic to transform or clean inputs

Move forward with configuring your Output Schema


# Introduction
Source: https://docs.squared.ai/activation/ai-modelling/introduction



AI Activation in AI Squared refers to the process of operationalizing your AI models—bringing model outputs directly into business tools where decisions are made.

This capability allows teams to go beyond experimentation and deploy context-aware AI insights across real business workflows, such as CRMs, service platforms, or internal tools.

***

## What AI Activation Enables

With AI Activation, you can:

* **Connect any AI model** from cloud providers (e.g., SageMaker, Vertex, OpenAI) or your own endpoints
* **Define input & output schemas** to standardize how models consume and return data
* **Visualize model results** using low-code Data Apps
* **Embed insights directly** inside enterprise applications like Salesforce, ServiceNow, or custom web apps
* **Capture user feedback** to evaluate relevance and improve model performance over time

***

## Core Concepts

| Concept         | Description                                                                                              |
| --------------- | -------------------------------------------------------------------------------------------------------- |
| **AI Modeling** | Configure how input is passed to the model and how output is interpreted.                                |
| **Data Apps**   | Visual components used to surface model predictions directly within business tools.                      |
| **Feedback**    | Capture user responses (e.g., thumbs up/down, star ratings) to monitor model quality and iterate faster. |

***

## What's Next

Start by configuring your [AI Model](./ai-modeling), then move on to building and embedding [Data Apps](./data-apps) into your business environment.


# Overview
Source: https://docs.squared.ai/activation/ai-modelling/modelling-overview

Understand what AI Modeling means inside AI Squared and how to configure your models for activation.

# AI Modeling

AI Modeling in AI Squared allows you to connect, configure, and prepare your hosted AI/ML models for use inside business applications. This process ensures that AI outputs are both reliable and context-aware—ready for consumption by business users within CRMs, ERPs, and custom interfaces.

<img className="block" src="https://res.cloudinary.com/dspflukeu/image/upload/f_auto,q_auto/v1/DevRel/models" alt="Hero Light" />

## Why AI Modeling Matters

Simply connecting a model isn't enough—each model expects specific inputs and returns outputs in a particular format. AI Modeling provides a no-code interface to:

* Define input and output schemas
* Format and validate requests before they're sent
* Clean and transform responses before embedding
* Map model insights directly into business apps

## Key Benefits

* **Standardization**: Ensure data passed to and from models adheres to consistent formats.
* **Configurability**: Customize model payloads, headers, and transformations without writing code.
* **Reusability**: Use one model across multiple Data Apps with different UI contexts.
* **Feedback-Ready**: Configure outputs to support user feedback mechanisms like thumbs-up/down, scale ratings, and more.

## What You Can Do in This Section

* Connect to an AI/ML model source (like OpenAI, SageMaker, or Vertex AI)
* Define input and output fields
* Add optional pre-processing and post-processing logic
* Test your model’s behavior with sample payloads
* Finalize your model for embedding into business workflows

AI Modeling is the foundation for building **Data Apps**—which surface model results in enterprise applications and enable user feedback.

> Ready to configure your first model? Jump into [Connecting a Model Source](./connect-source) or learn how to [define your input schema](./input-schema).


# Output Schema
Source: https://docs.squared.ai/activation/ai-modelling/output-schema

Define how to handle and structure your AI/ML model's responses.

The **Output Schema** defines the structure of the response returned by your AI/ML model. This ensures that predictions or insights received from the model are properly formatted, mapped, and usable within downstream components like Data Apps, feedback mechanisms, or automation triggers.

AI Squared allows you to specify each expected field and its data type so the platform can interpret and surface the response correctly.

***

## Why Output Schema Matters

* Standardizes how model results are parsed and displayed
* Enables seamless integration into Data Apps or embedded tools
* Ensures feedback mechanisms are correctly tied to model responses
* Supports chaining outputs to downstream actions

***

## Defining Output Fields

Each field you expect from the model response must be described in the schema:

| Field          | Description                                           |
| -------------- | ----------------------------------------------------- |
| **Field Name** | The key name returned in the model’s response payload |
| **Type**       | Data type: `String`, `Integer`, `Float`, `Boolean`    |

📸 *Placeholder for: Screenshot of output schema configuration UI*

***

## Example Output Payload

```json
{
  "churn_risk_score": 0.92,
  "prediction_label": "High Risk",
  "confidence": 0.88
}
```

Your output schema should include:

churn\_risk\_score → Float

prediction\_label → String

confidence → Float

This structure ensures consistent formatting across visualizations and workflows.

## Tips for Defining Output Fields

Make sure field names exactly match the keys returned by the model.

Use descriptive names that make the output easy to understand in UI or downstream logic.

Choose the right type — AI Squared uses this for formatting (e.g., number rounding, boolean flags, etc.).

## What's Next

You’ve now connected your source, defined inputs, optionally transformed them, and configured the expected output. Next, you can:

* Test Your Model with sample payloads

* Embed the output into Data Apps

* Set up Feedback Capture


# Preprocessing
Source: https://docs.squared.ai/activation/ai-modelling/preprocessing

Configure transformations on input data before sending it to your AI/ML model.

**Preprocessing** allows you to transform or enrich the input data before it is sent to your AI/ML model endpoint. This is useful when your source data requires formatting, restructuring, or enhancement to match the model's expected input.

With AI Squared, preprocessing is fully configurable through a no-code interface or optional custom logic for more advanced cases.

***

## When to Use Preprocessing

* Format inputs to match the model schema (e.g., convert a date to ISO format)
* Add additional metadata required by the model
* Clean raw input (e.g., remove special characters from text)
* Combine or derive fields (e.g., full name = first + last)

***

## How Preprocessing Works

Each input field can be passed through one or more transformations before being sent to the model. These transformations are applied in the order defined in the UI.

> ⚠️ Preprocessing does not modify your original data — it only adjusts the payload sent to the model for that request.

***

## Common Use Cases

| Example Use Case              | Transformation                |
| ----------------------------- | ----------------------------- |
| Format `created_at` timestamp | Convert to ISO 8601           |
| Combine first and last name   | Join with space               |
| Normalize text input          | Lowercase, remove punctuation |
| Apply static fallback         | Use default if no value found |

📸 *Placeholder for: Screenshot of preprocessing config screen*

***

## Dynamic Input + Preprocessing

Preprocessing is often used alongside **Dynamic Input Values** to shape data pulled from apps like Salesforce, ServiceNow, or custom web tools.

📘 Example:\
If you're harvesting a value like `deal_amount` from a CRM, you might want to round it or convert it into another currency before sending it to the model.

***

## Optional Scripting (Advanced)

In upcoming versions, advanced users may have the option to inject lightweight transformation scripts for more customized logic. Contact support to learn more about enabling this feature.

***

## What’s Next

Now that your inputs are prepared, it’s time to define how your model’s **responses** are structured.

👉 Proceed to [Output Schema](./output-schema) to configure your response handling.


# Create a Data App
Source: https://docs.squared.ai/activation/data-apps/create-data-app

Step-by-step guide to building and configuring a Data App in AI Squared.

A **Data App** allows you to visualize and embed AI model predictions into business applications. This guide walks through the setup steps to publish your first Data App using a connected AI/ML model.

***

## Step 1: Select a Model

1. Navigate to **Data Apps** from the sidebar.
2. Click **Create New Data App**.
3. Select the AI model you want to connect from the dropdown list.
   * Only models with input and output schemas defined will appear here.

***

## Step 2: Choose Display Type

Choose how the AI output will be displayed:

* **Table**: For listing multiple rows of output
* **Bar Chart** / **Pie Chart**: For aggregate or category-based insights
* **Text Card**: For single prediction or summary output

Each display type supports basic customization (e.g., column order, labels, units).

***

## Step 3: Customize Appearance

You can optionally style the Data App to match your brand:

* Modify font styles, background colors, and borders
* Add custom labels or tooltips
* Choose dark/light mode compatibility

> 📌 Custom CSS is not supported; visual changes are made through the built-in configuration options.

***

## Step 4: Configure Feedback (Optional)

Enable in-app feedback collection for business users interacting with the app:

* **Thumbs Up / Down**
* **Rating Scale (1–5, configurable)**
* **Text Comments**
* **Predefined Options (Multi-select)**

Feedback will be collected and visible under **Reports > Data Apps Reports**.

***

## Step 5: Save & Preview

1. Click **Save** to create the Data App.
2. Use the **Preview** mode to validate how the results and layout look.
3. If needed, go back to edit layout or display type.

***

## Next Steps

* 👉 [Embed in Business Apps](../embed-in-business-apps): Learn how to add the Data App to CRMs or other tools.
* 👉 [Feedback & Ratings](../feedback-and-ratings): Set up capture options and monitor usage.


# Embed in Business Apps
Source: https://docs.squared.ai/activation/data-apps/embed

Learn how to embed Data Apps into tools like CRMs, support platforms, or internal web apps.

Once your Data App is configured and saved, you can embed it within internal or third-party business tools where your users work—such as CRMs, support platforms, or internal dashboards.

AI Squared supports multiple embedding options for flexibility across environments.

***

## Option 1: Embed via IFrame

1. Go to **Data Apps**.
2. Select the Data App you want to embed.
3. Click on **Embed Options** > **IFrame**.
4. Copy the generated `<iframe>` snippet.
5. Paste this into your target application (e.g., internal dashboard, web app).

> Note: Ensure the host application supports iframe embedding and cross-origin requests.

***

## Option 2: Embed using Browser Extension

1. Install the AI Squared browser extension (Chrome/Edge).
2. Navigate to the target business app (e.g., Salesforce).
3. Use the extension to “pin” a Data App to a specific screen.
   * Example: Pin a churn score Data App on a Salesforce account details page.
4. Configure visibility rules if needed (e.g., user role, page section).

This option does not require modifying the application code.

***

## Best Practices

* Embed Data Apps near where decisions happen—sales records, support tickets, lead workflows.
* Keep layout minimal for seamless user experience.
* Use feedback capture where helpful for continual model improvement.

***

## Next Steps

* 👉 [Feedback & Ratings](../feedback-and-ratings): Set up qualitative or quantitative feedback mechanisms.
* 👉 [Monitor Usage](../data-apps-reports): Track adoption and model performance.


# Feedback
Source: https://docs.squared.ai/activation/data-apps/feedback

Learn how to collect user feedback on AI insights delivered via Data Apps.

AI Squared allows you to capture direct feedback from business users who interact with AI model outputs embedded through Data Apps. This feedback is essential for evaluating model relevance, accuracy, and user confidence—fueling continuous improvement.

***

## Types of Feedback Supported

### 1. Thumbs Up / Thumbs Down

A binary feedback option to help users indicate whether the insight was useful.

* ✅ Thumbs Up — Insight was helpful
* ❌ Thumbs Down — Insight was not helpful

***

### 2. Rating (1–5 Scale)

Provides a more granular option for rating insight usefulness.

* Configure number of stars (3 to 5)
* Users select one rating per insight interaction

***

### 3. Text-Based Feedback

Capture open-ended qualitative feedback from users.

* Use for additional context when feedback is negative
* Example: “Prediction didn’t match actual customer churn status.”

***

### 4. Multiple Choice

Provide users with a predefined set of reasons for their rating.

* Example for thumbs down:
  * ❌ Not relevant
  * ❌ Incomplete data
  * ❌ Low confidence prediction

***

## How to Enable Feedback

1. Go to your **Data App** > **Edit**.
2. Scroll to the **Feedback Settings** section.
3. Toggle ON any of the following:
   * Thumbs
   * Star Ratings
   * Text Input
   * Multi-Select Options
4. Save the Data App.

Feedback will now appear alongside model outputs when embedded in business apps.

***

## Viewing Collected Feedback

Navigate to:

**Reports > Data Apps Reports** → Select a Data App

There, you’ll find:

* Feedback submission counts
* % positive feedback
* Breakdown by feedback type
* Most common comments or reasons selected

***

## Best Practices

* Keep feedback simple and non-intrusive
* Use feedback data to validate models
* Combine with usage metrics to gauge adoption quality

***

## Next Steps

* 👉 [Monitor Usage](../data-apps-reports): Analyze how your AI models are performing based on user activity and feedback.


# Overview
Source: https://docs.squared.ai/activation/data-apps/overview

Understand what Data Apps are and how they help bring AI into business workflows.

# What Are Data Apps?

**Data Apps** in AI Squared are lightweight, embeddable interfaces that bring AI/ML outputs directly to the point of business decision-making. These apps allow business users to interact with AI model outputs in context—within CRMs, support tools, or web apps—without needing to switch platforms or understand the underlying AI infrastructure.

Data Apps bridge the last mile between ML models and real business outcomes.

***

## Key Benefits

* ⚡ **Instant Access to AI**: Serve AI insights where work happens (e.g., Salesforce, ServiceNow, internal portals)
* 🧠 **Contextualized Results**: Results are customized for the business context and the specific user
* 🛠 **No-Code Setup**: Configure and publish Data Apps with zero front-end or back-end development
* 📊 **Feedback Loop**: Collect structured user feedback to improve AI performance and relevance over time

***

## Where Can You Use Data Apps?

* Sales & CRM platforms (e.g., Salesforce, Hubspot)
* Support & ITSM platforms (e.g., Zendesk, ServiceNow)
* Marketing tools (e.g., Klaviyo, Iterable)
* Internal dashboards or custom web apps

***

## What’s Next?

* 👉 [Create a Data App](../create-a-data-app): Build your first app from a connected model
* 👉 [Embed in Business Apps](../embed-in-business-apps): Learn how to deploy your Data App across tools
* 👉 [Configure Feedback](../feedback-and-ratings): Capture real-time user input
* 👉 [Analyze Reports](../reports-and-analytics): Review app usage and AI effectiveness

***


# Data Apps Reports
Source: https://docs.squared.ai/activation/data-apps/reports

Understand how to monitor and analyze user engagement and model effectiveness with Data Apps.

After embedding a Data App into your business application, AI Squared provides a reporting dashboard to help you track model usage, feedback, and performance over time.

These reports help teams understand how users interact with AI insights and identify opportunities to refine use cases.

***

## Accessing Reports

1. Navigate to **Reports** from the main sidebar.
2. Select the **Data Apps Reports** tab.
3. Choose the Data App you want to analyze.

***

## Key Metrics Tracked

### 1. **Sessions Rendered**

Tracks the number of sessions where model outputs were displayed to users.

### 2. **User Feedback Rate**

% of sessions where users submitted feedback (thumbs, ratings, etc.).

### 3. **Positive Feedback Rate**

% of total feedback that was marked as positive.

### 4. **Top Feedback Tags**

Most common reasons provided by users (e.g., “Not relevant,” “Incomplete”).

### 5. **Most Active Users**

List of users who frequently interact with the Data App.

***

## Using Reports to Improve AI Performance

* **Low positive feedback?** → Revisit model logic, prompt formatting, or context.
* **Low engagement?** → Ensure placement within the business app is visible and accessible.
* **Inconsistent feedback?** → Collect additional context using text or multi-select feedback options.

***

## Exporting Reports

* Use the **Export** button in the top-right of the Data App Reports view.
* Reports are exported in `.csv` format for deeper analysis or integration into your BI stack.

***

## Best Practices

* Regularly review feedback to guide model improvements.
* Correlate usage with business KPIs for value attribution.
* Enable feedback on new Data Apps by default to gather early signals.

***


# Overview
Source: https://docs.squared.ai/deployment-and-security/auth/overview





# Cloud (Managed by AI Squared)
Source: https://docs.squared.ai/deployment-and-security/cloud

Learn how to access and use AI Squared's fully managed cloud deployment.

The cloud-hosted version of AI Squared offers a fully managed environment, ideal for teams that want fast onboarding, minimal infrastructure overhead, and secure access to all platform capabilities.

***

## Accessing the Platform

To access the managed cloud environment:

1. Visit [app.squared.ai](https://app.squared.ai) to log in to your workspace.
2. If you don’t have an account yet, go to [squared.ai](https://squared.ai) and submit the **Contact Us** form. Our team will provision your workspace and guide you through onboarding.

***

## What’s Included

When deployed in the cloud, AI Squared provides:

* A dedicated workspace per team or business unit
* Preconfigured connectors for supported data sources and AI/ML model endpoints
* Secure role-based access control
* Managed infrastructure, updates, and scaling

***

## Use Cases

* Scaling across departments without IT dependencies
* Centralized AI insights delivery into enterprise tools

***

## Next Steps

Once your workspace is provisioned and you're logged in:

* Set up your **data sources** and **AI/ML model endpoints**
* Build **data models** and configure **syncs**
* Create and deploy **data apps** into business applications

Refer to the [Getting Started](/getting-started/introduction) section for first-time user guidance.


# Overview
Source: https://docs.squared.ai/deployment-and-security/data-security-infra/overview





# Overview
Source: https://docs.squared.ai/deployment-and-security/overview



AI Squared is built to be enterprise-ready, with flexible deployment options and strong security foundations to meet your organization’s IT, compliance, and operational requirements.

This section provides an overview of how you can deploy AI Squared, how we handle access control, and what security measures are in place.

***

## Deployment Options

AI Squared offers three main deployment models to support different enterprise needs:

* **Cloud (Managed by AI Squared)**\
  Fully managed SaaS experience hosted by AI Squared. Ideal for teams looking for fast setup and scalability without infrastructure overhead.

* **Deploy Locally**\
  Install and run AI Squared locally on your enterprise infrastructure. This allows tighter control while leveraging the full platform capabilities.

* **Self-Hosted (On-Premise)**\
  For highly regulated environments, AI Squared can be deployed entirely within your own data center or private cloud with no external dependencies.

→ Explore deployment modes in detail under the **Deployment** section.

***

## Authentication & Access Control

The platform supports **Role-Based Access Control (RBAC)** and integrates with enterprise identity providers (e.g., Okta, Azure AD) via SSO.

→ Learn more in the **Authentication & Access Control** section.

***

## Data Security & Infrastructure

We follow industry best practices for data security, including:

* Data encryption in transit and at rest
* Secure key management and audit logging
* Isolated tenant environments in the managed cloud

→ Review our **Security & Infrastructure** details.

***

## Compliance & Certifications

AI Squared maintains security controls aligned with industry standards. We are SOC 2 Type II compliant, with ongoing security reviews and controls in place.

→ View our **Compliance & Certifications** for more details.

***

Need help deciding which deployment option fits your needs best? Reach out to our support team.


# SOC 2 Type II
Source: https://docs.squared.ai/deployment-and-security/security-and-compliance/overview



<Frame>
  <img src="https://res.cloudinary.com/dspflukeu/image/upload/v1727312424/SOC_2_Type_2_Certification_Announcement_-_Blog_Banner_zmeurr.png" />
</Frame>

At AI Squared, we are dedicated to safeguarding your data and privacy. We adhere to industry best practices to ensure the security and protection of your information.

We are SOC 2 Type II certified, demonstrating that we meet stringent standards for information security. This certification confirms that we have implemented robust policies and procedures to ensure the security, availability, processing integrity, and confidentiality of user data. You can trust that your data is safeguarded by the highest levels of security.

## Data Security

We encrypt data at rest and in transit for all our customers. Using Azure's Key Vault, we securely manage encryption keys in accordance with industry best practices. Additionally, customer data is securely isolated from that of other customers, ensuring that your information remains protected and segregated at all times.

## Infrastructure Security

We use Azure AKS to host our application, ensuring robust security through tools like Azure Key Vault, Azure Defender, and Azure Policy. We implement Role-Based Access Control (RBAC) to restrict access to customer data, ensuring that only authorized personnel have access. Your information is safeguarded by stringent security protocols, including limited access to our staff, and is protected by industry-leading infrastructure security measures.

## Reporting a Vulnerability

If you discover a security issue in this project, please report it by sending an email to [security@squared.ai](mailto:security@squared.ai).

We will respond to your report as soon as possible and will work with you to address the issue. We take security issues seriously and appreciate your help in making Multiwoven safe for everyone.


# Azure AKS (Kubernetes)
Source: https://docs.squared.ai/deployment-and-security/setup/aks



## Deploying Multiwoven on Azure Kubernetes Service (AKS)

This guide will walk you through setting up Multiwoven on AKS. We'll cover configuring and deploying an AKS cluster after which, you can refer to the Helm Charts section of our guide to install Multiwoven into it.

**Prerequisites**

* An active Azure subscription
* Basic knowledge of Kuberenetes and Helm

**Note:** AKS clusters are not free. Please refer to [https://azure.microsoft.com/en-us/pricing/details/kubernetes-service/#pricing](https://azure.microsoft.com/en-us/pricing/details/kubernetes-service/#pricing) for current pricing information.

**1. AKS Cluster Deployment:**

1. **Select a Resource Group for your deployment:**
   * Navigate to your Azure subscription and select a Resource Group or, if necessary, start by creating a new Resource Group.
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     <img src="https://res.cloudinary.com/dspflukeu/image/upload/v1715290055/Screenshot_2024-05-09_at_5.26.32_PM_mvrv2n.png" />
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     <img src="https://res.cloudinary.com/dspflukeu/image/upload/v1715290055/Screenshot_2024-05-09_at_5.26.41_PM_walsv7.png" />
   </Frame>
2. **Initiate AKS Deployment**
   * Select the **Create +** button at the top of the overview section of your Resource Group which will take you to the Azure Marketplace.
   * In the Azure Marketplace, type **aks** into the search field at the top. Select **Azure Kuberenetes Service (AKS)** and create.
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     <img src="https://res.cloudinary.com/dspflukeu/image/upload/v1715286916/Screenshot_2024-05-07_at_12.04.46_PM_vrtry3.png" />
   </Frame>
3. **Configure your AKS Cluster**
   * **Basics**
     * For **Cluster Preset Configuration**, we recommend **Dev/Test** for Development deployments.
     * For **Resource Group**, select your Resource Group.
     * For **AKS Pricing Tier**, we recommend **Standard**.
     * For **Kubernetes version**, we recommend sticking with the current **default**.
     * For **Authentication and Authorization**, we recommend **Local accounts with Kubernetes RBAC** for simplicity.
     <Frame>
       <img src="https://res.cloudinary.com/dspflukeu/image/upload/v1715286917/Screenshot_2024-05-07_at_12.06.03_PM_xp7soo.png" />
     </Frame>
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       <img src="https://res.cloudinary.com/dspflukeu/image/upload/v1715286917/Screenshot_2024-05-07_at_12.06.23_PM_lflhwv.png" />
     </Frame>
     <Frame>
       <img src="https://res.cloudinary.com/dspflukeu/image/upload/v1715286917/Screenshot_2024-05-07_at_12.06.31_PM_xal5nh.png" />
     </Frame>
   * **Node Pools**
     * Leave defaults
     <Frame>
       <img src="https://res.cloudinary.com/dspflukeu/image/upload/v1715286917/Screenshot_2024-05-07_at_12.07.23_PM_ynj6cu.png" />
     </Frame>
     <Frame>
       <img src="https://res.cloudinary.com/dspflukeu/image/upload/v1715286917/Screenshot_2024-05-07_at_12.07.29_PM_arveg8.png" />
     </Frame>
   * **Networking**
     * For **Network Configuration**, we recommend the **Azure CNI** network configuration for simplicity.
     * For **Network Policy**, we recommend **Azure**.
     <Frame>
       <img src="https://res.cloudinary.com/dspflukeu/image/upload/v1715286917/Screenshot_2024-05-07_at_12.07.57_PM_v3thlf.png" />
     </Frame>
     <Frame>
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     </Frame>
   * **Integrations**
     * Leave defaults
     <Frame>
       <img src="https://res.cloudinary.com/dspflukeu/image/upload/v1715286916/Screenshot_2024-05-07_at_12.09.36_PM_juypye.png" />
     </Frame>
   * **Monitoring**
     * Leave defaults, however, to reduce costs, you can uncheck **Managed Prometheus** which will automatically uncheck **Managed Grafana**.
     <Frame>
       <img src="https://res.cloudinary.com/dspflukeu/image/upload/v1715286917/Screenshot_2024-05-07_at_12.10.44_PM_epn32u.png" />
     </Frame>
     <Frame>
       <img src="https://res.cloudinary.com/dspflukeu/image/upload/v1715286916/Screenshot_2024-05-07_at_12.10.57_PM_edxypj.png" />
     </Frame>
   * **Advanced**
     * Leave defaults
     <Frame>
       <img src="https://res.cloudinary.com/dspflukeu/image/upload/v1715286916/Screenshot_2024-05-07_at_12.11.19_PM_i2smpg.png" />
     </Frame>
   * **Tags**
     * Add tags if necessary, otherwise, leave defaults.
     <Frame>
       <img src="https://res.cloudinary.com/dspflukeu/image/upload/v1715289232/Screenshot_2024-05-09_at_5.13.26_PM_su7yyx.png" />
     </Frame>
   * **Review + Create**
     * If there are validation errors that arise during the review, like a missed mandatory field, address the errors and create. If there are no validation errors, proceed to create.
     * Wait for your deployment to complete before proceeding.
4. **Connecting to your AKS Cluster**
   * In the **Overview** section of your AKS cluster, there is a **Connect** button at the top. Choose whichever method suits you best and follow the on-screen instructions. Make sure to run at least one of the test commands to verify that your kubectl commands are being run against your new AKS cluster.
   <Frame>
     <img src="https://res.cloudinary.com/dspflukeu/image/upload/v1715289389/Screenshot_2024-05-09_at_5.14.58_PM_enzily.png" />
   </Frame>
   <Frame>
     <img src="https://res.cloudinary.com/dspflukeu/image/upload/v1715289389/Screenshot_2024-05-09_at_5.15.39_PM_fbhv86.png" />
   </Frame>
5. **Deploying Multiwoven**
   * Please refer to the **Helm Charts** section of our guide to proceed with your installation of Multiwoven!\
     [Helm Chart Deployment Guide](https://docs.squared.ai/open-source/guides/setup/helm)


# Azure VMs
Source: https://docs.squared.ai/deployment-and-security/setup/avm



## Deploying Multiwoven on Azure VMs

This guide will walk you through setting up Multiwoven on an Azure VM. We'll cover launching the VM, installing Docker, running Multiwoven with its dependencies, and finally, accessing the Multiwoven UI.

**Prerequisites:**

* An Azure account with an active VM (Ubuntu recommended).
* Basic knowledge of Docker, Azure, and command-line tools.
* Docker Compose installed on your local machine.

**Note:** This guide uses environment variables for sensitive information. Replace the placeholders with your own values before proceeding.

**1. Azure VM Setup:**

1. **Launch an Azure VM:** Choose an Ubuntu VM with suitable specifications for your workload.

   **Network Security Group Configuration:**

   * Open port 22 (SSH) for inbound traffic from your IP address.
   * Open port 8000 (Multiwoven UI) for inbound traffic from your IP address (optional).

   **SSH Key Pair:** Create a new key pair or use an existing one to connect to your VM.

2. **Connect to your VM:** Use SSH to connect to your Azure VM.

   **Example:**

   ```
   ssh -i /path/to/your-key-pair.pem azureuser@<your_vm_public_ip>
   ```

   Replace `/path/to/your-key-pair.pem` with the path to your key pair file and `<your_vm_public_ip>` with your VM's public IP address.

3. **Update and upgrade:** Run `sudo apt update && sudo apt upgrade -y` to ensure your system is up-to-date.

**2. Docker and Docker Compose Installation:**

1. **Install Docker:** Follow the official Docker installation instructions for Ubuntu: [https://docs.docker.com/engine/install/](https://docs.docker.com/engine/install/)

2. **Install Docker Compose:** Download the latest version from the Docker Compose releases page and place it in a suitable directory (e.g., `/usr/local/bin/docker-compose`). Make the file executable: `sudo chmod +x /usr/local/bin/docker-compose`.

3. **Start and enable Docker:** Run `sudo systemctl start docker` and `sudo systemctl enable docker` to start Docker and configure it to start automatically on boot.

**3. Download Multiwoven `docker-compose.yml` file and Configure Environment:**

1. **Download the file:**

```
curl -LO https://multiwoven-deployments.s3.amazonaws.com/docker/docker-compose/docker-compose.yaml
```

2. **Download the `.env` file:**

```
curl -LO https://multiwoven-deployments.s3.amazonaws.com/docker/docker-compose/.env.production
```

3. Rename the file .env.production to .env and update the environment variables if required.

```bash
mv .env.production .env
```

4. \*\*Configure `.env`, This file holds environment variables for various services. Replace the placeholders with your own values, including:
   * `DB_PASSWORD` and `DB_USERNAME` for your PostgreSQL database
   * `REDIS_PASSWORD` for your Redis server
   * (Optional) Additional environment variables specific to your Multiwoven configuration

**Example `.env` file:**

```
DB_PASSWORD=your_db_password
DB_USERNAME=your_db_username
REDIS_PASSWORD=your_redis_password
# Modify your Multiwoven-specific environment variables here
```

**4. Run Multiwoven with Docker Compose:**

1. **Start Multiwoven:** Navigate to the `multiwoven` directory and run `docker-compose up -d`. This will start all Multiwoven services in the background, including the Multiwoven UI.

**5. Accessing Multiwoven UI:**

Open your web browser and navigate to `http://<your_vm_public_ip>:8000` (replace `<your_vm_public_ip>` with your VM's public IP address). You should now see the Multiwoven UI.

**6. Stopping Multiwoven:**

To stop Multiwoven, navigate to the `multiwoven` directory and run.

```bash
docker-compose down
```

**7. Upgrading Multiwoven:**
When a new version of Multiwoven is released, you can upgrade the Multiwoven using the following command.

```bash
docker-compose pull && docker-compose up -d
```

<Tip> Make sure to run the above command from the same directory where the `docker-compose.yml` file is present.</Tip>

**Additional Notes:**

<Tip>**Note**: the frontend and backend services run on port 8000 and 3000, respectively. Make sure you update the **VITE\_API\_HOST** environment variable in the **.env** file to the desired backend service URL running on port 3000. </Tip>

* Depending on your network security group configuration, you might need to open port 8000 (Multiwoven UI) for inbound traffic.
* For production deployments, consider using a reverse proxy (e.g., Nginx) and a domain name with SSL/TLS certificates for secure access to the Multiwoven UI.


# Docker
Source: https://docs.squared.ai/deployment-and-security/setup/docker-compose

Deploying Multiwoven using Docker

Below steps will guide you through deploying Multiwoven on a server using Docker Compose. We require PostgreSQL database to store meta data for Multiwoven. We will use Docker Compose to deploy Multiwoven and PostgreSQL.

**Important Note:** TLS is mandatory for deployment. To successfully deploy the Platform via docker-compose, you must have access to a DNS record and obtain a valid TLS certificate from a Certificate Authority. You can acquire a free TLS certificate using tools like CertBot, Let's Encrypt, or other ACME-based solutions. If using a reverse proxy (e.g., Nginx or Traefik), consider integrating an automated certificate management tool such as letsencrypt-nginx-proxy-companion or Traefik's built-in Let's Encrypt support.

<Tip>Note: If you are setting up Multiwoven on your local machine, you can skip this section and refer to [Local Setup](/guides/setup/docker-compose-dev) section.</Tip>

## Prerequisites

* [Docker](https://docs.docker.com/get-docker/)
* [Docker Compose](https://docs.docker.com/compose/install/)

<Info> All our Docker images are available in x86\_64 architecture, make sure your server supports x86\_64 architecture.</Info>

## Deployment options

Multiwoven can be deployed using two different options for PostgreSQL database.

<Tabs>
  <Tab title="In-built PostgreSQL">
    1. Create a new directory for Multiwoven and navigate to it.

    ```bash
    mkdir multiwoven
    cd multiwoven
    ```

    2. Download the production `docker-compose.yml` file from the following link.

    ```bash
    curl -LO https://multiwoven-deployments.s3.amazonaws.com/docker/docker-compose/docker-compose.yaml
    ```

    3. Download the `.env.production` file from the following link.

    ```bash
    curl -LO https://multiwoven-deployments.s3.amazonaws.com/docker/docker-compose/.env.production
    ```

    4. Rename the file .env.production to .env and update the environment variables if required.

    ```bash
    mv .env.production .env
    ```

    5. Start the Multiwoven using the following command.

    ```bash
    docker-compose up -d
    ```

    6. Stopping Multiwoven

    To stop the Multiwoven, use the following command.

    ```bash
    docker-compose down
    ```

    7. Upgrading Multiwoven

    When a new version of Multiwoven is released, you can upgrade the Multiwoven using the following command.

    ```bash
    docker-compose pull && docker-compose up -d
    ```

    <Tip> Make sure to run the above command from the same directory where the `docker-compose.yml` file is present.</Tip>
  </Tab>

  <Tab title="Cloud PostgreSQL">
    1. Create a new directory for Multiwoven and navigate to it.

       ```bash
       mkdir multiwoven
       cd multiwoven
       ```

       2. Download the production `docker-compose.yml` file from the following link.

       ```bash
       curl -LO https://multiwoven-deployments.s3.amazonaws.com/docker/docker-compose/docker-compose-cloud-postgres.yaml
       ```

       3. Rename the file .env.production to .env and update the **PostgreSQL** environment variables.

       `DB_HOST` - Database Host

       `DB_USERNAME` - Database Username

       `DB_PASSWORD` - Database Password

       The default port for PostgreSQL is 5432. If you are using a different port, update the `DB_PORT` environment variable.

       ```bash
       mv .env.production .env
       ```

       4. Start the Multiwoven using the following command.

       ```bash
       docker-compose up -d
       ```
  </Tab>
</Tabs>

## Accessing Multiwoven

Once the Multiwoven is up and running, you can access it using the following URL and port.

Multiwoven Server URL:

```http
http://<server-ip>:3000
```

Multiwoven UI Service:

```http
http://<server-ip>:8000
```

<Info>If you are using a custom domain you can update the `API_HOST` and `UI_HOST` environment variable in the `.env` file.</Info>

### Important considerations

* Make sure to update the environment variables in the `.env` file before starting the Multiwoven.

* Make sure to take regular **backups** of the PostgreSQL database.

To restore the backup, you can use the following command.

```bash
cat dump.sql | docker exec -i --user postgres <postgres-container-name> psql -U postgres
```

* If you are using a custom domain, make sure to update the `API_HOST` and `UI_HOST` environment variables in the `.env` file.


# Docker
Source: https://docs.squared.ai/deployment-and-security/setup/docker-compose-dev



<Warning>**WARNING** The following guide is intended for developers to set-up Multiwoven locally. If you are a user, please refer to the [Self-Hosted](/guides/setup/docker-compose) guide.</Warning>

## Prerequisites

* [Docker](https://docs.docker.com/get-docker/)
* [Docker Compose](https://docs.docker.com/compose/install/)

<Tip>**Note**: if you are using Mac or Windows, you will need to install [Docker Desktop](https://www.docker.com/products/docker-desktop) instead of just docker. Docker Desktop includes both docker and docker-compose.</Tip>

Verify that you have the correct versions installed:

```bash
docker --version
docker-compose --version
```

## Installation

1. Clone the repository

```bash
git clone git@github.com:Multiwoven/multiwoven.git
```

2. Navigate to the `multiwoven` directory

```bash
cd multiwoven
```

3. Initialize .env file

```bash
cp .env.example .env
```

<Tip>**Note**: Refer to the [Environment Variables](/guides/setup/environment-variables) section for details on the ENV variables used in the Docker environment.</Tip>

4. Build docker images

```bash
docker-compose build
```

<Tip>Note: The default build architecture is for **x86\_64**. If you are using **arm64** architecture, you will need to run the below command to build the images for arm64.</Tip>

```bash
TARGETARCH=arm64 docker-compose
```

5. Start the containers

```bash
docker-compose up
```

6. Stop the containers

```bash
docker-compose down
```

## Usage

Once the containers are running, you can access the `Multiwoven UI` at [http://localhost:8000](http://localhost:8000).

The `multiwoven API` is available at [http://localhost:3000/api/v1](http://localhost:3000/api/v1).

## Running Tests

1. Running the complete test suite on the multiwoven server

```bash
docker-compose exec multiwoven-server bundle exec rspec
```

## Troubleshooting

To cleanup all images and containers, run the following commands:

```bash
docker rmi -f $(docker images -q)
docker rm -f $(docker ps -a -q)
```

prune all unused images, containers, networks and volumes

<Warning>**Danger:** This will remove all unused images, containers, networks and volumes.</Warning>

```bash
docker system prune -a
```

Please open a new issue at [https://github.com/Multiwoven/multiwoven/issues](https://github.com/Multiwoven/multiwoven/issues) if you run into any issues or join our [Slack]() to chat with us.


# Digital Ocean Droplets
Source: https://docs.squared.ai/deployment-and-security/setup/dod

Coming soon...



# Digital Ocean Kubernetes
Source: https://docs.squared.ai/deployment-and-security/setup/dok

Coming soon...



# AWS EC2
Source: https://docs.squared.ai/deployment-and-security/setup/ec2



## Deploying Multiwoven on AWS EC2 Using Docker Compose

This guide walks you through setting up Multiwoven, on an AWS EC2 instance using Docker Compose. We'll cover launching the instance, installing Docker, running Multiwoven with its dependencies, and finally, accessing the Multiwoven UI.

**Important Note:** At present, TLS is required. This means that to successfully deploy the Platform via docker-compose, you will need access to a DNS record set as well as the ability to obtain a valid TLS certificate from a Certificate Authority. You can obtain a free TLS certificates via tools like CertBot, Amazon Certificate Manager (if using an AWS Application Load Balancer to front an EC2 instance), letsencrypt-nginx-proxy-companion (if you add an nginx proxy to the docker-compose file to front the other services), etc.

**Prerequisites:**

* An active AWS account
* Basic knowledge of AWS and Docker
* A private repository access key (please contact your AIS point of contact if you have not received one)

**Notes:**

* This guide uses environment variables for sensitive information. Replace the placeholders with your own values before proceeding.
* This guide uses an Application Load Balancer (ALB) to front the EC2 instance for ease of enabling secure TLS communication with the backend using an Amazon Certificate Manager (ACM) TLS certificate. These certificates are free of charge and ACM automatically rotates them every 90 days. While the ACM certificate is free, the ALB is not. You can refer to the following document for current ALB pricing: [ALB Pricing Page](https://aws.amazon.com/elasticloadbalancing/pricing/?nc=sn\&loc=3).

**1. Obtain TLS Certificate (Requires access to DNS Record Set)**

**1.1** In the AWS Management Console, navigate to Amazon Certificate Manager and request a new certificate.

<Frame>
  <img src="https://res.cloudinary.com/dspflukeu/image/upload/v1718661486/Screenshot_2024-06-17_at_5.54.16_PM_tffjih.png" />
</Frame>

1.2 Unless your organization has created a Private CA (Certificate Authority), we recommend requesting a public certificate.

1.3 Request a single ACM certificate that can verify all three of your chosen subdomains for this deployment. DNS validation is recommended for automatic rotation of your certificate but this method requires access to your domain's DNS record set.

<Frame>
  <img src="https://res.cloudinary.com/dspflukeu/image/upload/v1718661706/Screenshot_2024-06-17_at_6.01.25_PM_egtqer.png" />
</Frame>

1.4 Once you have added your selected sub-domains, scroll down and click **Request**.

5. Once your request has been made, you will be taken to a page that will describe your certificate request and its current status. Scroll down a bit and you will see a section labeled **Domains** with 3 subdomains and 1 CNAME validation record for each. These records need to be added to your DNS record set. Please refer to your organization's internal documentation or the documentation of your DNS service for further instruction on how to add DNS records to your domain's record set. <br />

   <Frame>
     <img src="https://res.cloudinary.com/dspflukeu/image/upload/v1718663532/Screenshot_2024-06-17_at_6.29.24_PM_qoauh2.png" />
   </Frame>

   **Note:** For automatic certificate rotation, you need to leave these records
   in your record set. If they are removed, automatic rotation will fail.

6. Once your ACM certificate has been issued, note the ARN of your certificate and proceed.

**2. Create and Configure Application Load Balancer and Target Groups**

1. In the AWS Management Console, navigate to the EC2 Dashboard and select **Load Balancers**.

   {" "}

   <Frame>
     <img src="https://res.cloudinary.com/dspflukeu/image/upload/v1718663854/Screenshot_2024-06-17_at_6.37.03_PM_lorrnq.png" />
   </Frame>

2. On the next screen select **Create** under **Application Load Balancer**.

   {" "}

   <Frame>
     <img src="https://res.cloudinary.com/dspflukeu/image/upload/v1718665389/Screenshot_2024-06-17_at_7.02.31_PM_qjjo3i.png" />
   </Frame>

3. Under **Basic configuration** name your load balancer. If you are deploying this application within a private network, select **Internal**. Otherwise, select **Internet-facing**. Consult with your internal Networking team if you are unsure as this setting can not be changed post-deployment and you will need to create an entirely new load balancer to correct this.

   {" "}

   <Frame>
     <img src="https://res.cloudinary.com/dspflukeu/image/upload/v1718665609/Screenshot_2024-06-17_at_7.06.16_PM_xfeq5r.png" />
   </Frame>

4. Under **Network mapping**, select a VPC and write it down somewhere for later use. Also, select 2 subnets (2 are **required** for an Application Load Balancer) and write them down too for later use.<br />

   **Note:** If you are using the **internal** configuration, select only **private** subnets. If you are using the **internet-facing** configuration, you must select **public** subnets and they must have routes to an **Internet Gateway**.

   <Frame>
     <img src="https://res.cloudinary.com/dspflukeu/image/upload/v1718665808/Screenshot_2024-06-17_at_7.09.18_PM_gqd6pb.png" />
   </Frame>

5. Under **Security groups**, select the link to **create a new security group** and a new tab will open.

   <Frame>
     <img src="https://res.cloudinary.com/dspflukeu/image/upload/v1718666010/Screenshot_2024-06-17_at_7.12.56_PM_f809y7.png" />
   </Frame>

6. Under **Basic details**, name your security group and provide a description. Be sure to pick the same VPC that you selected for your load balancer configuration.

   <Frame>
     <img src="https://res.cloudinary.com/dspflukeu/image/upload/v1718666207/Screenshot_2024-06-17_at_7.16.18_PM_ssg81d.png" />
   </Frame>

7. Under **Inbound rules**, create rules for HTTP and HTTPS and set the source for both rules to **Anywhere**. This will expose inbound ports 80 and 443 on the load balancer. Leave the default **Outbound rules** allowing for all outbound traffic for simplicity. Scroll down and select **Create security group**.

   <Frame>
     <img src="https://res.cloudinary.com/dspflukeu/image/upload/v1718666442/Screenshot_2024-06-17_at_7.20.01_PM_meylpq.png" />
   </Frame>

8. Once the security group has been created, close the security group tab and return to the load balancer tab. On the load balancer tab, in the **Security groups** section, hit the refresh icon and select your newly created security group. If the VPC's **default security group** gets appended automatically, be sure to remove it before proceeding.

   <Frame>
     <img src="https://res.cloudinary.com/dspflukeu/image/upload/v1718667183/Screenshot_2024-06-17_at_7.32.24_PM_bdmsf3.png" />
   </Frame>

9. Under **Listeners and routing** in the card for **Listener HTTP:80**, select **Create target group**. A new tab will open.

   <Frame>
     <img src="https://res.cloudinary.com/dspflukeu/image/upload/v1718666826/Screenshot_2024-06-17_at_7.26.35_PM_sc62nw.png" />
   </Frame>

10. Under **Basic configuration**, select **Instances**.

    <Frame>
      <img src="https://res.cloudinary.com/dspflukeu/image/upload/v1718666904/Screenshot_2024-06-17_at_7.27.42_PM_ne7euy.png" />
    </Frame>

11. Scroll down and name your target group. This first one will be for the Platform's web app so you should name it accordingly. Leave the protocol set to HTTP **but** change the port value to 8000. Also, make sure that the pre-selected VPC matches the VPC that you selected for the load balancer. Scroll down and click **Next**.

    <Frame>
      <img src="https://res.cloudinary.com/dspflukeu/image/upload/v1718667095/Screenshot_2024-06-17_at_7.30.56_PM_wna7en.png" />
    </Frame>

12. Leave all defaults on the next screen, scroll down and select **Create target group**. Repeat this process 2 more times, once for the **Platform API** on **port 3000** and again for **Temporal UI** on **port 8080**. You should now have 3 target groups.

    <Frame>
      <img src="https://res.cloudinary.com/dspflukeu/image/upload/v1718667613/Screenshot_2024-06-17_at_7.38.59_PM_pqvtbv.png" />
    </Frame>

13. Navigate back to the load balancer configuration screen and hit the refresh button in the card for **Listener HTTP:80**. Now, in the target group dropdown, you should see your 3 new target groups. For now, select any one of them. There will be some further configuration needed after the creation of the load balancer.

    <Frame>
      <img src="https://res.cloudinary.com/dspflukeu/image/upload/v1718667785/Screenshot_2024-06-17_at_7.41.49_PM_u9jecz.png" />
    </Frame>

14. Now, click **Add listener**.

    <Frame>
      <img src="https://res.cloudinary.com/dspflukeu/image/upload/v1718667845/Screenshot_2024-06-17_at_7.43.30_PM_vtjpyk.png" />
    </Frame>

15. Change the protocol to HTTPS and in the target group dropdown, again, select any one of the target groups that you previously created.

    <Frame>
      <img src="https://res.cloudinary.com/dspflukeu/image/upload/v1718668686/Screenshot_2024-06-17_at_7.45.24_PM_m77rvm.png" />
    </Frame>

16. Scroll down to the **Secure listener settings**. Under **Default SSL/TLS server certificate**, select **From ACM** and in the **Select a certificate** dropdown, select the certificate that you created in Step 1. In the dropdown, your certificate will only show the first subdomain that you listed when you created the certificate request. This is expected behavior.

    **Note:** If you do not see your certificate in the dropdown list, the most likely issues are:<br />
    (1) your certificate has not yet been successfully issued. Navigate back to ACM and verify that your certificate has a status of **Issued**.
    (2) you created your certificate in a different region and will need to either recreate your load balancer in the same region as your certificate OR recreate your certificate in the region in which you are creating your load balancer.

    <Frame>
      <img src="https://res.cloudinary.com/dspflukeu/image/upload/v1718668686/Screenshot_2024-06-17_at_7.57.37_PM_jeyltt.png" />
    </Frame>

17. Scroll down to the bottom of the page and click **Create load balancer**. Load balancers take a while to create, approximately 10 minutes or more. However, while the load balancer is creating, copy the DNS name of the load balancer and create CNAME records in your DNS record set, pointing all 3 of your chosen subdomains to the DNS name of the load balancer. Until you complete this step, the deployment will not work as expected. You can proceed with the final steps of the deployment but you need to create those CNAME records.

    <Frame>
      <img src="https://res.cloudinary.com/dspflukeu/image/upload/v1718669401/Screenshot_2024-06-17_at_8.08.00_PM_lscyfu.png" />
    </Frame>

18. At the bottom of the details page for your load balancer, you will see the section **Listeners and rules**. Click on the listener labeled **HTTP:80**.

    <Frame>
      <img src="https://res.cloudinary.com/dspflukeu/image/upload/v1718669552/Screenshot_2024-06-17_at_8.12.05_PM_hyybin.png" />
    </Frame>

19. Check the box next to the **Default** rule and click the **Actions** dropdown.

    <Frame>
      <img src="https://res.cloudinary.com/dspflukeu/image/upload/v1718669716/Screenshot_2024-06-17_at_8.14.41_PM_xnv4fc.png" />
    </Frame>

20. Scroll down to **Routing actions** and select **Redirect to URL**. Leave **URI parts** selected. In the **Protocol** dropdown, select **HTTPS** and set the port value to **443**. This configuration step will automatically redirect all insecure requests to the load balancer on port 80 (HTTP) to port 443 (HTTPS). Scroll to the bottom and click **Save**.

    <Frame>
      <img src="https://res.cloudinary.com/dspflukeu/image/upload/v1718670073/Screenshot_2024-06-17_at_8.20.53_PM_sapmoj.png" />
    </Frame>

21. Return to the load balancer's configuration page (screenshot in step 18) and scroll back down to the *Listeners and rules* section. This time, click the listener labled **HTTPS:443**.

    <Frame>
      <img src="https://res.cloudinary.com/dspflukeu/image/upload/v1718684557/Screenshot_2024-06-18_at_12.22.10_AM_pbjtuo.png" />
    </Frame>

22. Click **Add rule**.

    <Frame>
      <img src="https://res.cloudinary.com/dspflukeu/image/upload/v1718732781/Screenshot_2024-06-18_at_1.45.19_PM_egsfx2.png" />
    </Frame>

23. On the next page, you can optionally add a name to this new rule. Click **Next**.

24. On the next page, click **Add condition**. In the **Add condition** pop-up, select **Host header** from the dropdown. For the host header, put the subdomain that you selected for the Platform web app and click **Confirm** and then click **Next**.

    <Frame>
      <img src="https://res.cloudinary.com/dspflukeu/image/upload/v1718734838/Screenshot_2024-06-18_at_2.11.36_PM_cwazra.png" />
    </Frame>

    <Frame>
      <img src="https://res.cloudinary.com/dspflukeu/image/upload/v1718736912/Screenshot_2024-06-18_at_2.54.32_PM_o7ylel.png" />
    </Frame>

25. One the next page, under **Actions**. Leave the **Routing actions** set to **Forward to target groups**. From the **Target group** dropdown, select the target group that you created for the web app. Click **Next**.

    <Frame>
      <img src="https://res.cloudinary.com/dspflukeu/image/upload/v1718737171/Screenshot_2024-06-18_at_2.58.50_PM_rcmuao.png" />
    </Frame>

26. On the next page, you can set the **Priority** to 1 and click **Next**.

    <Frame>
      <img src="https://res.cloudinary.com/dspflukeu/image/upload/v1718737279/Screenshot_2024-06-18_at_3.00.49_PM_kovsvw.png" />
    </Frame>

27. On the next page, click **Create**.

28. Repeat steps 24 - 27 for the **api** (priority 2) and **temporal ui** (priority 3).

29. Optionally, you can also edit the default rule so that it **Returns a fixed response**. The default **Response code** of 503 is fine.

    <Frame>
      <img src="https://res.cloudinary.com/dspflukeu/image/upload/v1718737699/Screenshot_2024-06-18_at_3.07.52_PM_hlt91e.png" />
    </Frame>

**3. Launch EC2 Instance**

1. Navigate to the EC2 Dashboard and click **Launch Instance**.

   <Frame>
     <img src="https://res.cloudinary.com/dspflukeu/image/upload/v1718738785/Screenshot_2024-06-18_at_3.25.56_PM_o1ffon.png" />
   </Frame>

2. Name your instance and select **Ubuntu 22.04 or later** with **64-bit** architecture.

   <Frame>
     <img src="https://res.cloudinary.com/dspflukeu/image/upload/v1718739054/Screenshot_2024-06-18_at_3.29.02_PM_ormuxu.png" />
   </Frame>

3. For instance type, we recommend **t3.large**. You can find EC2 on-demand pricing here: [EC2 Instance On-Demand Pricing](https://aws.amazon.com/ec2/pricing/on-demand). Also, create a **key pair** or select a pre-existing one as you will need it to SSH into the instance later.

   <Frame>
     <img src="https://res.cloudinary.com/dspflukeu/image/upload/v1718739395/Screenshot_2024-06-18_at_3.36.09_PM_ohv7jn.png" />
   </Frame>

4. Under **Network settings**, click **Edit**.

   <Frame>
     <img src="https://res.cloudinary.com/dspflukeu/image/upload/v1718890642/Screenshot_2024-06-18_at_3.38.21_PM_pp1sxo.png" />
   </Frame>

5. First, verify that the listed **VPC** is the same one that you selected for the load balancer. Also, verify that the pre-selected subnet is one of the two that you selected earlier for the load balancer as well. If either is incorrect, make the necessary changes. If you are using **private subnets** because your load balancer is **internal**, you do not need to auto-assign a public IP. However, if you chose **internet-facing**, you may need to associate a public IP address with your instance so you can SSH into it from your local machine.

   <Frame>
     <img src="https://res.cloudinary.com/dspflukeu/image/upload/v1718739981/Screenshot_2024-06-18_at_3.45.06_PM_sbiike.png" />
   </Frame>

6. Under **Firewall (security groups)**, we recommend that you name the security group but this is optional. After naming the security security group, click the button \*Add security group rule\*\* 3 times to create 3 additional rules.

   <Frame>
     <img src="https://res.cloudinary.com/dspflukeu/image/upload/v1718740294/Screenshot_2024-06-18_at_3.50.03_PM_hywm9g.png" />
   </Frame>

7. In the first new rule (rule 2), set the port to **3000**. Click the **Source** input box and scroll down until you see the security group that you previously created for the load balancer. Doing this will firewall inbound traffic to port 3000 on the EC2 instance, only allowing inbound traffic from the load balancer that you created earlier. Do the same for rules 3 and 4, using ports 8000 and 8080 respectively.

   <Frame>
     <img src="https://res.cloudinary.com/dspflukeu/image/upload/v1718740803/Screenshot_2024-06-18_at_3.57.10_PM_gvvpig.png" />
   </Frame>

   <Frame>
     <img src="https://res.cloudinary.com/dspflukeu/image/upload/v1718740802/Screenshot_2024-06-18_at_3.58.37_PM_gyxneg.png" />
   </Frame>

8. Scroll to the bottom of the screen and click on **Advanced Details**.

   <Frame>
     <img src="https://res.cloudinary.com/dspflukeu/image/upload/v1718745225/Screenshot_2024-06-18_at_5.12.35_PM_cioo3f.png" />
   </Frame>

9. In the **User data** box, paste the following to automate the installation of **Docker** and **docker-compose**.

```
Content-Type: multipart/mixed; boundary="//"
MIME-Version: 1.0

--//
Content-Type: text/cloud-config; charset="us-ascii"
MIME-Version: 1.0
Content-Transfer-Encoding: 7bit
Content-Disposition: attachment; filename="cloud-config.txt"

#cloud-config
cloud_final_modules:
- [scripts-user, always]

--//
Content-Type: text/x-shellscript; charset="us-ascii"
MIME-Version: 1.0
Content-Transfer-Encoding: 7bit
Content-Disposition: attachment; filename="userdata.txt"
#!/bin/bash
sudo mkdir ais
cd ais
# install docker
sudo apt-get update
yes Y | sudo apt-get install apt-transport-https ca-certificates curl software-properties-common
sudo curl -fsSL https://download.docker.com/linux/ubuntu/gpg | sudo apt-key add -
echo | sudo add-apt-repository "deb [arch=amd64] https://download.docker.com/linux/ubuntu $(lsb_release -cs) stable"
sudo apt-get update
yes Y | sudo apt-get install docker-ce
sudo systemctl status docker --no-pager && echo "Docker status checked"
# install docker-compose
sudo apt-get install -y jq
VERSION=$(curl -s https://api.github.com/repos/docker/compose/releases/latest | jq -r .tag_name)
sudo curl -L "https://github.com/docker/compose/releases/download/${VERSION}/docker-compose-$(uname -s)-$(uname -m)" -o /usr/local/bin/docker-compose
sudo chmod +x /usr/local/bin/docker-compose
docker-compose --version
sudo systemctl enable docker
```

<Frame>
  <img src="https://res.cloudinary.com/dspflukeu/image/upload/v1718745225/Screenshot_2024-06-18_at_5.13.02_PM_gd4lfi.png" />
</Frame>

10. In the right-hand panel, click **Launch instance**.

    <Frame>
      <img src="https://res.cloudinary.com/dspflukeu/image/upload/v1718745564/Screenshot_2024-06-18_at_5.15.36_PM_zaw3m6.png" />
    </Frame>

**4. Register EC2 Instance in Target Groups**

1. Navigate back to the EC2 Dashboard and in the left panel, scroll down to **Target groups**.

   <Frame>
     <img src="https://res.cloudinary.com/dspflukeu/image/upload/v1718745704/Screenshot_2024-06-18_at_5.21.20_PM_icj8mi.png" />
   </Frame>

2. Click on the name of the first listed target group.

   <Frame>
     <img src="https://res.cloudinary.com/dspflukeu/image/upload/v1718745784/Screenshot_2024-06-18_at_5.22.46_PM_vn4pwm.png" />
   </Frame>

3. Under **Registered targets**, click **Register targets**.

   <Frame>
     <img src="https://res.cloudinary.com/dspflukeu/image/upload/v1718745869/Screenshot_2024-06-18_at_5.23.40_PM_ubfog9.png" />
   </Frame>

4. Under **Available instances**, you should see the instance that you just created. Check the tick-box next to the instance and click **Include as pending below**. Once the instance shows in **Review targets**, click **Register pending targets**.

   <Frame>
     <img src="https://res.cloudinary.com/dspflukeu/image/upload/v1718746192/Screenshot_2024-06-18_at_5.26.56_PM_sdzm0e.png" />
   </Frame>

   <Frame>
     <img src="https://res.cloudinary.com/dspflukeu/image/upload/v1718746130/Screenshot_2024-06-18_at_5.27.54_PM_ojsle5.png" />
   </Frame>

5. **Repeat steps 2 - 4 for the remaining 2 target groups.**

**5. Deploy AIS Platform**

1. SSH into the EC2 instance that you created earlier. For assistance, you can navigate to your EC2 instance in the EC2 dashboard and click the **Connect** button. In the **Connect to instance** screen, click on **SSH client** and follow the instructions on the screen.

   <Frame>
     <img src="https://res.cloudinary.com/dspflukeu/image/upload/v1718746962/Screenshot_2024-06-18_at_5.39.06_PM_h1ourx.png" />
   </Frame>

2. Verify that **Docker** and **docker-compose** were successfully installed by running the following commands

```
sudo docker --version
sudo docker-compose --version
```

You should see something similar to

<Frame>
  <img src="https://res.cloudinary.com/dspflukeu/image/upload/v1718746612/Screenshot_2024-06-18_at_5.34.45_PM_uppsh1.png" />
</Frame>

3. Change directory to the **ais** directory and download the AIS Platform docker-compose file and the corresponding .env file.

```
cd \ais
sudo curl -LO https://multiwoven-deployments.s3.amazonaws.com/docker/docker-compose/docker-compose.yaml
sudo curl -LO https://multiwoven-deployments.s3.amazonaws.com/docker/docker-compose/.env.production && sudo mv /ais/.env.production /ais/.env
```

Verify the downloads

```
ls -a
```

You should see the following

<Frame>
  <img src="https://res.cloudinary.com/dspflukeu/image/upload/v1718747493/Screenshot_2024-06-18_at_5.50.35_PM_gk3n7e.png" />
</Frame>

4. You will need to edit both files a little before deploying. First open the .env file.

```
sudo nano .env
```

**There are 3 required changes.**<br /><br />
**(1)** Set the variable **VITE\_API\_HOST** so the UI knows to send requests to your **API subdomain**.<br /><br />
**(2)** If not present already, add a variable **Track** and set its value to **no**.<br /><br />
**(3)** If not present already, add a variable **ALLOWED\_HOST**. The value for this is dependent on how you selected your subdomains earlier. This variable only allows for a single step down in subdomain so if, for instance, you selected ***app.mydomain.com***, ***api.mydomain.com*** and ***temporal.mydomain.com*** you would set the value to **.mydomain.com**.

If you selected ***app.c1.mydomain.com***, ***api.c1.mydomain.com*** and ***temporal.c1.mydomain.com*** you would set the value to **.c1.mydomain.com**.<br /><br />
For simplicity, the remaining defaults are fine.

<Frame>
  <img src="https://res.cloudinary.com/dspflukeu/image/upload/v1718748317/Screenshot_2024-06-18_at_5.54.59_PM_upnaov.png" />
</Frame>

<Frame>
  <img src="https://res.cloudinary.com/dspflukeu/image/upload/v1720563829/Screenshot_2024-07-09_at_6.22.27_PM_q4prkv.png" />
</Frame>

Commands to save and exit **nano**.<br />
**Mac users:**

```
  - to save your changes: Control + S
  - to exit: Control + X
```

**Windows users:**

```
  - to save your changes: Ctrl + O
  - to exit: Ctrl + X
```

5. Next, open the **docker-compose** file.

```
sudo nano docker-compose.yaml
```

The only changes that you should make here are to the AIS Platform image repositories. After opening the docker-compose file, scroll down to the Multiwoven Services and append **-ee** to the end of each repostiory and change the tag for each to **edge**.
Before changes

<Frame>
  <img src="https://res.cloudinary.com/dspflukeu/image/upload/v1718750766/Screenshot_2024-06-18_at_6.44.34_PM_ewwwn4.png" />
</Frame>

After changes

<Frame>
  <img src="https://res.cloudinary.com/dspflukeu/image/upload/v1718751265/Screenshot_2024-06-18_at_6.53.55_PM_hahs8c.png" />
</Frame>

6. Deploy the AIS Platform. This step requires a private repository access key that you should have received from your AIS point of contact. If you do not have one, please reach out to AIS.

```
DOCKERHUB_USERNAME="multiwoven"
DOCKERHUB_PASSWORD="YOUR_PRIVATE_ACCESS_TOKEN"
sudo docker login --username $DOCKERHUB_USERNAME --password $DOCKERHUB_PASSWORD
sudo docker-compose up -d
```

You can use the following command to ensure that none of the containers have exited

```
sudo docker ps -a
```

7. Return to your browser and navigate back to the EC2 dashboard. In the left panel, scroll back down to **Target groups**. Click through each target group and verify that each has the registered instance showing as **healthy**. This may take a minute or two after starting the containers.

8. Once all target groups are showing your instance as healthy, you can navigate to your browser and enter the subdomain that you selected for the AIS Platform web app to get started!


# AWS ECS
Source: https://docs.squared.ai/deployment-and-security/setup/ecs

Coming soon...



# AWS EKS (Kubernetes)
Source: https://docs.squared.ai/deployment-and-security/setup/eks

Coming soon...



# Environment Variables
Source: https://docs.squared.ai/deployment-and-security/setup/environment-variables



Multiwoven uses the following environment variables for both the client and server:

<Note>
  If you have any questions about these variables, please contact us at{" "}
  <a href="mailto:hello@multiwoven.com">Hello Multiwoven</a> or join our{" "}
  <a href="https://multiwoven.slack.com">Slack Community</a>.
</Note>

## Required Variables

`RAILS_ENV` - Rails Environment (development, test, production)

`UI_HOST` - Hostname for UI service. Default is **localhost:8000**

`API_HOST` - Hostname for API service. Default to **localhost:3000**

`DB_HOST` - Database Host

`DB_USERNAME` - Database Username

`DB_PASSWORD` - Database Password

`ALLOWED_HOST` - Frontend host that can connect to API. Prevents against DNS rebinding and other Host header attacks. Default values is localhost.

`JWT_SECRET` - secret key used to sign generated token

`USER_EMAIL_VERIFICATION` - Skip user email verification after signup.When set to true, ensure SMTP credentials are configured correctly so that verification emails can be sent to users.

## SMTP Configuration

`SMTP_HOST` - This variable represents the host name of the SMTP server that the application will connect to for sending emails. The default configuration for SMTP\_HOST is set to `multiwoven.com`, indicating the server host.

`SMTP_ADDRESS` - This environment variable specifies the server address where the SMTP service is hosted, critical for establishing a connection with the email server. Depending on the service provider, this address will vary. Here are examples of SMTP server addresses for some popular email providers:

* Gmail: smtp.gmail.com - This is the server address for Google's Gmail service, allowing applications to send emails through Gmail's SMTP server.
* Outlook: smtp-mail.outlook.com - This address is used for Microsoft's Outlook email service, enabling applications to send emails through Outlook's SMTP server.
* Yahoo Mail: smtp.mail.yahoo.com - This address is used for Yahoo's SMTP server when configuring applications to send emails via Yahoo Mail.
* AWS SES: *.*.amazonaws.com - This address format is used for AWS SES (Simple Email Service) SMTP servers when configuring applications to send emails via AWS SES. The specific region address should be used as shown in [here](https://docs.aws.amazon.com/general/latest/gr/ses.html)
* Custom SMTP Server: mail.yourdomain.com - For custom SMTP servers, typically hosted by organizations or specialized email service providers, the SMTP address is specific to the domain or provider hosting the service.

  `SMTP_PORT` - This indicates the port number on which the SMTP server listens. The default configuration for SMTP\_PORT is set to 587, which is commonly used for SMTP with TLS/SSL.

`SMTP_USERNAME` - This environment variable specifies the username required to authenticate with the SMTP server. This username could be an email address or a specific account identifier, depending on the requirements of the SMTP service provider being used (such as Gmail, Outlook, etc.). The username is essential for logging into the SMTP server to send emails. It is kept as an environment variable to maintain security and flexibility, allowing changes without code modification.

`SMTP_PASSWORD` - Similar to the username, this environment variable holds the password associated with the SMTP\_USERNAME for authentication purposes. The password is critical for verifying the user's identity to the SMTP server, enabling the secure sending of emails. It is defined as an environment variable to ensure that sensitive credentials are not hard-coded into the application's source code, thereby protecting against unauthorized access and making it easy to update credentials securely.

`SMTP_SENDER_EMAIL` - This variable specifies the email address that appears as the sender in the emails sent by the application.

`BRAND_NAME` - This variable is used to customize the 'From' name in the emails sent from the application, allowing a personalized touch. It is set to **BRAND NAME**, which appears alongside the sender email address in outgoing emails.

## Sync Configuration

`SYNC_EXTRACTOR_BATCH_SIZE` - Sync Extractor Batch Size

`SYNC_LOADER_BATCH_SIZE` - Sync Loader Batch Size

`SYNC_EXTRACTOR_THREAD_POOL_SIZE` - Sync Extractor Thread Pool Size

`SYNC_LOADER_THREAD_POOL_SIZE` - Sync Loader Thread Pool Size

## Temporal Configuration

`TEMPORAL_VERSION` - Temporal Version

`TEMPORAL_UI_VERSION` - Temporal UI Version

`TEMPORAL_HOST` - Temporal Host

`TEMPORAL_PORT` - Temporal Port

`TEMPORAL_ROOT_CERT` - Temporal Root Certificate

`TEMPORAL_CLIENT_KEY` - Temporal Client Key

`TEMPORAL_CLIENT_CHAIN` - Temporal Client Chain

`TEMPORAL_POSTGRESQL_VERSION` - Temporal Postgres Version

`TEMPORAL_POSTGRES_PASSWORD` - Temporal Postgres Password

`TEMPORAL_POSTGRES_USER` - Temporal Postgres User

`TEMPORAL_POSTGRES_DEFAULT_PORT` - Temporal Postgres Default Port

`TEMPORAL_NAMESPACE` - Temporal Namespace

`TEMPORAL_TASK_QUEUE` - Temporal Task Queue

`TEMPORAL_ACTIVITY_THREAD_POOL_SIZE` - Temporal Activity Thread Pool Size

`TEMPORAL_WORKFLOW_THREAD_POOL_SIZE` - Temporal Workflow Thread Pool Size

## Community Edition Configuration

`VITE_API_HOST` - Hostname of API server

`VITE_APPSIGNAL_PUSH_API_KEY` - AppSignal API key

`VITE_BRAND_NAME` - Community Brand Name

`VITE_LOGO_URL` - URL of Brand Logo

`VITE_BRAND_COLOR` - Community Theme Color

`VITE_BRAND_HOVER_COLOR` - Community Theme Color On Hover

`VITE_FAV_ICON_URL` - URL of Brand Favicon

## Deployment Variables

`APP_ENV` - Deployment environment. Default: community.

`APP_REVISION` - Latest github commit sha. Used to identify revision of deployments.

## AWS Variables

`AWS_ACCESS_KEY_ID` - AWS Access Key Id. Used to assume role in S3 connector.

`AWS_SECRET_ACCESS_KEY` - AWS Secret Access Key. Used to assume role in S3 connector.

## Optional Variables

`APPSIGNAL_PUSH_API_KEY` - API Key for AppSignal integration.

`TRACK` - Track usage events.

`NEW_RELIC_KEY` - New Relic Key

`RAILS_LOG_LEVEL` - Rails log level. Default: info.


# Google Cloud Compute Engine
Source: https://docs.squared.ai/deployment-and-security/setup/gce



## Deploying Multiwoven on Google Cloud Platform using Docker Compose

This guide walks you through setting up Multiwoven, on a Google Cloud Platform (GCP) Compute Engine instance using Docker Compose. We'll cover launching the instance, installing Docker, running Multiwoven with its dependencies, and accessing the Multiwoven UI.

**Prerequisites:**

* A Google Cloud Platform account with an active project and billing enabled.
* Basic knowledge of GCP, Docker, and command-line tools.
* Docker Compose installed on your local machine.

**Note:** This guide uses environment variables for sensitive information. Replace the placeholders with your own values before proceeding.

**1. Create a GCP Compute Engine Instance:**

1. **Open the GCP Console:** [https://console.cloud.google.com](https://console.cloud.google.com)
2. **Navigate to Compute Engine:** Go to the "Compute Engine" section and click on "VM Instances."
3. **Create a new instance:** Choose an appropriate machine type based on your workload requirements. Ubuntu is a popular choice.
4. **Configure your instance:**
   * Select a suitable boot disk size and operating system image (Ubuntu recommended).
   * Enable SSH access with a strong password or SSH key.
   * Configure firewall rules to allow inbound traffic on port 22 (SSH) and potentially port 8000 (Multiwoven UI, optional).
5. **Create the instance:** Review your configuration and click "Create" to launch the instance.

**2. Connect to your Instance:**

1. **Get the external IP address:** Once the instance is running, find its external IP address in the GCP Console.
2. **Connect via SSH:** Use your preferred SSH client to connect to the instance:

```
ssh -i your_key_pair.pem user@<external_ip_address>
```

**3. Install Docker and Docker Compose:**

1. **Update and upgrade:** Run `sudo apt update && sudo apt upgrade -y` to ensure your system is up-to-date.
2. **Install Docker:** Follow the official Docker installation instructions for Ubuntu: [https://docs.docker.com/engine/install/](https://docs.docker.com/engine/install/)
3. **Install Docker Compose:** Download the latest version from the Docker Compose releases page and place it in a suitable directory (e.g., `/usr/local/bin/docker-compose`). Make the file executable: `sudo chmod +x /usr/local/bin/docker-compose`.
4. **Start and enable Docker:** Run `sudo systemctl start docker` and `sudo systemctl enable docker` to start Docker and configure it to start automatically on boot.

**4. Download Multiwoven `docker-compose.yml` file and Configure Environment:**

1. **Download the file:** 

```
curl -LO https://multiwoven-deployments.s3.amazonaws.com/docker/docker-compose/docker-compose.yaml
```

2. **Download the `.env` file:**  

```
curl -LO https://multiwoven-deployments.s3.amazonaws.com/docker/docker-compose/.env
```

3. **Create and Configure `.env` File:** Rename `multiwoven/.env.example` to `.env`. This file holds environment variables for various services. Replace the placeholders with your own values, including:
     \* `DB_PASSWORD` and `DB_USERNAME` for your PostgreSQL database
     \* `REDIS_PASSWORD` for your Redis server
     \* (Optional) Additional environment variables specific to your Multiwoven configuration

**Example `.env` file:**

```
DB_PASSWORD=your_db_password
DB_USERNAME=your_db_username
REDIS_PASSWORD=your_redis_password
# Modify your Multiwoven-specific environment variables here
```

**5. Run Multiwoven with Docker Compose:**

**Start Multiwoven:** Navigate to the `multiwoven` directory and run.

```bash
docker-compose up -d
```

**6. Accessing Multiwoven UI:**

Open your web browser and navigate to `http://<external_ip_address>:8000` (replace `<external_ip_address>` with your instance's IP address). You should now see the Multiwoven UI.

**7. Stopping Multiwoven:**

To stop Multiwoven, navigate to the `multiwoven` directory and run.

```bash
docker-compose down
```

**8. Upgrading Multiwoven:**
When a new version of Multiwoven is released, you can upgrade the Multiwoven using the following command.

```bash
docker-compose pull && docker-compose up -d
```

<Tip> Make sure to run the above command from the same directory where the `docker-compose.yml` file is present.</Tip>

**Additional Notes:**

<Tip>**Note**: the frontend and backend services run on port 8000 and 3000, respectively. Make sure you update the **VITE\_API\_HOST** environment variable in the **.env** file to the desired backend service URL running on port 3000. </Tip>

* Depending on your firewall configuration, you might need to open port 8000 for inbound traffic.
* For production deployments, consider using a managed load balancer and a Cloud SQL database instance for better performance and scalability.


# Google Cloud GKE (Kubernetes)
Source: https://docs.squared.ai/deployment-and-security/setup/gke

Coming soon...



# Helm Charts	
Source: https://docs.squared.ai/deployment-and-security/setup/helm



## Description:

This helm chart is designed to deploy AI Squared's Platform 2.0 into a Kubernetes cluster.

Platform 2.0 is cloud-agnostic and can be deployed successfully into any Kubernetes cluster, including clusters deployed via Azure Kubernetes Service, Elastic Kubernetes Service, Microk8s, etc.

Along with the platform containers, there are also a couple of additional support resources added to simplify and further automate the installation process. These include: the **nginx-ingress resources** to expose the platform to end-users and **cert-manager** to automate the
creation and renewal of TLS certificates.

### Coming Soon!

We have a couple of useful features that are still in development that will further promote high availability, scalability and visibility into the platform pods!

These features include **horizontal-pod autoscaling** based on pod CPU and memory utilization as well as in-cluster instances of both **Prometheus** and **Grafana**.

## Prerequisites:

* Access to a DNS record set
* Kubernetes cluster
* [Install Kubernetes 1.16+](https://kubernetes.io/docs/tasks/tools/)
* [Install Helm 3.1.0+](https://kubernetes.io/docs/tasks/tools/)
* Temporal Namespace (optional)

## Overview of the Deployment Process

1. Install kubectl and helm on your local machine
2. Select required subdomains
3. Deploy the Cert-Manager Helm chart
4. Deploy the Multiwoven Helm Chart
5. Deploy additional (required) Nginx Ingress resources
6. Obtain the public IP address associated with your Nginx Ingress Controller
7. Create A records in your DNS record set that resolve to the public IP address of your Nginx Ingress Controller.
8. Wait for cert-manager to issue an invalid staging certificate to your K8s cluster
9. Switch letsencrypt-staging to letsencrypt-prod and upgrade Multiwoven again, this time obtaining a valid TLS certificate

## Installing Multiwoven via Helm

Below is a shell script that can be used to deploy Multiwoven and its dependencies.

### Chart Dependencies

#### Cert-Manager

Cert-Manager is used to automatically request, implement and rotate TLS certificates for your deployment. Enabling TLS is required.

#### Nginx-Ingress

Nginx-Ingress resources are added to provide the Multiwoven Ingress Controller with a external IP address.

### Install Multiwoven

#### Environment Variables:

##### Generic

1. <b>tls-admin-email-address</b> -> the email address that will receive email notifications about pending automatic TLS certificate rotations

2. <b>api-host</b> -> api.your\_domain (ex. api.multiwoven.com)

3. <b>ui-host</b> -> app.your\_domain (ex. app.multiwoven.com)

##### Temporal - Please read the [Notes](#notes) section below

4. <b>temporal-ui-host</b> -> temporal.your\_domain (ex. temporal.multiwoven.com)

5. <b>temporalHost</b> -> your Temporal Cloud host name (ex. my.personal.tmprl.cloud)

6. <b>temporalNamespace</b> -> your Temporal Namespace, verify within your Temporal Cloud account (ex. my.personal)

#### Notes:

* Deploying with the default In-cluster Temporal (<b>recommended for Development workloads</b>):
  1. Only temporal-ui-host is required. You should leave multiwovenConfig.temporalHost, temporal.enabled and multiwovenConfig.temporalNamespace commented out. You should also leave the temporal-cloud secret commented out as well.
* Deploying with Temporal Cloud (<b>HIGHLY recommended for Production workloads</b>):
  1. comment out or remove the flag setting multiwovenConfig.temporalUiHost
  2. Uncomment the flags setting multiwovenConfig.temporalHost, temporal.enabled and multiwovenConfig.temporalNamespace. Also uncomment the temporal-cloud secret.
  3. Before running this script, you need to make sure that your Temporal Namespace authentication certificate key and pem files are in the same directory as the script. We recommend renaming these files to temporal.key and temporal.pem for simplicity.
* Notice that for tlsCertIssuer, the value letsencrypt-staging is present. When the intial installation is done and cert-manager has successfully issued an invalid certificate for your 3 subdomains, you will switch this value to letsencrypt-prod to obtain a valid certificate. It is very important that you follow the steps written out here as LetsEncrypt's production server only allows 5 attempts per week to obtain a valid certificate. This switch should be done LAST after you have verified that everything is already working as expected.

```
#### Pull and deploy the cert-manager Helm chart
cd charts/multiwoven
echo "installing cert-manager"
helm repo add jetstack https://charts.jetstack.io --force-update
helm repo update
helm install cert-manager jetstack/cert-manager --namespace cert-manager --create-namespace --version v1.14.5 --set installCRDs=true

#### Pull and deploy the Multiwoven Helm chart
echo "installing Multiwoven"
helm repo add multiwoven https://multiwoven.github.io/helm-charts
helm upgrade -i multiwoven multiwoven/multiwoven \
  --set multiwovenConfig.tlsAdminEmail=<tls-admin-email-address> \
  --set multiwovenConfig.tlsCertIssuer=letsencrypt-staging \
  --set multiwovenConfig.apiHost=<api-host> \
  --set multiwovenConfig.uiHost=<ui-host> \
  --set multiwovenWorker.multiwovenWorker.args={./app/temporal/cli/worker} \
  --set multiwovenConfig.temporalUiHost=<temporal-ui-host>
  # --set temporal.enabled=false \
  # --set multiwovenConfig.temporalHost=<temporal-host> \
  # --set multiwovenConfig.temporalNamespace=<temporal-namespace>

# kubectl create secret generic temporal-cloud -n multiwoven \
#     --from-file=temporal-root-cert=./temporal.pem \
#     --from-file=temporal-client-key=./temporal.key

# Install additional required Nginx ingress resources
echo "installing ingress-nginx resources"
kubectl apply -f https://raw.githubusercontent.com/kubernetes/ingress-nginx/main/deploy/static/provider/cloud/deploy.yaml
```

#### Post Installation Steps

1. Run the following command to find the external IP address of your Ingress Controller. Note that it may take a minute or two for this value to become available post installation.

```
kubectl get svc -n ingress-nginx
```

<Frame>
  <img src="https://res.cloudinary.com/dspflukeu/image/upload/v1715374296/Screenshot_2024-05-10_at_4.45.06_PM_k5bh0d.png" />
</Frame>

2. Once you have this IP address, go to your DNS record set and use that IP address to create three A records, one for each subdomain. Below are a list of Cloud Service Provider DNS tools but please refer to the documentation of your specific provider if not listed below.

* [Adding a new record in Azure DNS Zones](https://learn.microsoft.com/en-us/azure/dns/dns-operations-recordsets-portal)
* [Adding a new record in AWS Route 53](https://docs.aws.amazon.com/Route53/latest/DeveloperGuide/resource-record-sets-creating.html)
* [Adding a new record in GCP Cloud DNS](https://cloud.google.com/dns/docs/records)

3. Run the following command, repeatedly, until an invalid LetsEncrypt staging certificate has been issued for your Ingress Controller.

```
kubectl describe certificate -n multiwoven mw-tls-cert
```

When the certificate has been issued, you will see the following output from the command above.

<Frame>
  <img src="https://res.cloudinary.com/dspflukeu/image/upload/v1715374727/Screenshot_2024-05-10_at_4.41.12_PM_b3mjhs.png" />
</Frame>

We also encourage you to further verify by navigating to your subdomain, app.your\_domain, and check the certificate received by the browser. You should see something similar to the image below:

<Frame>
  <img src="https://res.cloudinary.com/dspflukeu/image/upload/v1715374727/Screenshot_2024-05-10_at_4.43.02_PM_twq1gs.png" />
</Frame>

Once the invalid certificate has been successfully issued, you are ready for the final steps.

4. Edit the shell script above by changing the tlsCertIssuer value from <b>letsencrypt-staging</b> to <b>letsencrypt-prod</b> and run the script again. Do not worry when you see Installation Failed for cert-manager, you are seeing this because it was installed on the intial run.

5. Repeat Post Installation Step 3 until a valid certificate has been issued. Once issued, your deployment is complete and you can navigate to app.your\_domain to get started using Mutliwoven!

Happy Helming!

## Helm Chart Environment Values

### Multiwoven Helm Configuration

#### General Configuration

* **kubernetesClusterDomain**: The domain used within the Kubernetes cluster.
  * Default: `cluster.local`
* **kubernetesNamespace**: The Kubernetes namespace for deployment.
  * Default: `multiwoven`

#### Multiwoven Configuration

| Parameter                                         | Description                                                 | Default                                       |
| ------------------------------------------------- | ----------------------------------------------------------- | --------------------------------------------- |
| `multiwovenConfig.apiHost`                        | Hostname for the API service.                               | `api.multiwoven.com`                          |
| `multiwovenConfig.appEnv`                         | Deployment environment.                                     | `community`                                   |
| `multiwovenConfig.appRevision`                    | Latest github commit sha, identifies revision of deployment | \`\`                                          |
| `multiwovenConfig.appsignalPushApiKey`            | AppSignal API key.                                          | `yourkey`                                     |
| `multiwovenConfig.awsAccessKeyId`                 | AWS Access Key Id. Used to assume role in S3 connector.     | \`\`                                          |
| `multiwovenConfig.awsSecretAccessKey`             | AWS Secret Access Key. Used to assume role in S3 connector. | \`\`                                          |
| `multiwovenConfig.dbHost`                         | Hostname for the PostgreSQL database service.               | `multiwoven-postgresql`                       |
| `multiwovenConfig.dbPassword`                     | Password for the database user.                             | `password`                                    |
| `multiwovenConfig.dbPort`                         | Port on which the database service is running.              | `5432`                                        |
| `multiwovenConfig.dbUsername`                     | Username for the database.                                  | `multiwoven`                                  |
| `multiwovenConfig.grpcEnableForkSupport`          | GRPC\_ENABLE\_FORK\_SUPPORT env variable.                   | `1`                                           |
| `multiwovenConfig.newRelicKey`                    | New Relic License Key.                                      | `yourkey`                                     |
| `multiwovenConfig.railsEnv`                       | Rails environment setting.                                  | `development`                                 |
| `multiwovenConfig.railsLogLevel`                  | Rails log level.                                            | `info`                                        |
| `multiwovenConfig.smtpAddress`                    | SMTP server address.                                        | `smtp.yourdomain.com`                         |
| `multiwovenConfig.smtpBrandName`                  | SMTP brand name used in From email.                         | `Multiwoven`                                  |
| `multiwovenConfig.smtpHost`                       | SMTP server host.                                           | `yourdomain.com`                              |
| `multiwovenConfig.smtpPassword`                   | SMTP server password.                                       | `yourpassword`                                |
| `multiwovenConfig.smtpPort`                       | SMTP server port.                                           | `587`                                         |
| `multiwovenConfig.smtpUsername`                   | SMTP server username.                                       | `yourusername`                                |
| `multiwovenConfig.smtpSenderEmail`                | SMTP sender email.                                          | `admin@yourdomain.com`                        |
| `multiwovenConfig.snowflakeDriverPath`            | Path to the Snowflake ODBC driver.                          | `/usr/lib/snowflake/odbc/lib/libSnowflake.so` |
| `multiwovenConfig.syncExtractorBatchSize`         | Batch size for the sync extractor.                          | `1000`                                        |
| `multiwovenConfig.syncExtractorThreadPoolSize`    | Thread pool size for the sync extractor.                    | `10`                                          |
| `multiwovenConfig.syncLoaderBatchSize`            | Batch size for the sync loader.                             | `1000`                                        |
| `multiwovenConfig.syncLoaderThreadPoolSize`       | Thread pool size for the sync loader.                       | `10`                                          |
| `multiwovenConfig.temporalActivityThreadPoolSize` | Thread pool size for Temporal activities.                   | `20`                                          |
| `multiwovenConfig.temporalClientChain`            | Path to Temporal client chain certificate.                  | `/certs/temporal.chain.pem`                   |
| `multiwovenConfig.temporalClientKey`              | Path to Temporal client key.                                | `/certs/temporal.key`                         |
| `multiwovenConfig.temporalHost`                   | Hostname for Temporal service.                              | `multiwoven-temporal`                         |
| `multiwovenConfig.temporalNamespace`              | Namespace for Temporal service.                             | `multiwoven-dev`                              |
| `multiwovenConfig.temporalPort`                   | Port for Temporal service.                                  | `7233`                                        |
| `multiwovenConfig.temporalPostgresDefaultPort`    | Default port for Temporal's PostgreSQL database.            | `5432`                                        |
| `multiwovenConfig.temporalPostgresPassword`       | Password for Temporal's PostgreSQL database.                | `password`                                    |
| `multiwovenConfig.temporalPostgresUser`           | Username for Temporal's PostgreSQL database.                | `multiwoven`                                  |
| `multiwovenConfig.temporalPostgresqlVersion`      | PostgreSQL version for Temporal.                            | `13`                                          |
| `multiwovenConfig.temporalRootCert`               | Path to Temporal root certificate.                          | `/certs/temporal.pem`                         |
| `multiwovenConfig.temporalTaskQueue`              | Task queue for Temporal workflows.                          | `sync-dev`                                    |
| `multiwovenConfig.temporalUiVersion`              | Version of Temporal UI.                                     | `2.23.2`                                      |
| `multiwovenConfig.temporalVersion`                | Version of Temporal service.                                | `1.22.4`                                      |
| `multiwovenConfig.temporalWorkflowThreadPoolSize` | Thread pool size for Temporal workflows.                    | `10`                                          |
| `multiwovenConfig.uiHost`                         | UI host for the application interface.                      | `app.multiwoven.com`                          |
| `multiwovenConfig.viteApiHost`                    | API host for the web application.                           | `api.multiwoven.com`                          |
| `multiwovenConfig.viteAppsignalPushApiKey`        | AppSignal API key.                                          | `yourkey`                                     |
| `multiwovenConfig.viteBrandName`                  | Community Brand Name.                                       | `Multiwoven`                                  |
| `multiwovenConfig.viteLogoUrl`                    | URL of Brand Logo.                                          |                                               |
| `multiwovenConfig.viteBrandColor`                 | Community Theme Color.                                      |                                               |
| `multiwovenConfig.viteBrandHoverColor`            | Community Theme Color On Hover.                             |                                               |
| `multiwovenConfig.viteFavIconUrl`                 | URL of Brand Favicon.                                       |                                               |
| 'multiwovenConfig.workerHost\`                    | Worker host for the worker service.                         | 'worker.multiwoven.com'                       |

### Multiwoven PostgreSQL Configuration

| Parameter                                        | Description                                        | Default     |
| ------------------------------------------------ | -------------------------------------------------- | ----------- |
| `multiwovenPostgresql.enabled`                   | Whether or not to deploy PostgreSQL.               | `true`      |
| `multiwovenPostgresql.image.repository`          | Docker image repository for PostgreSQL.            | `postgres`  |
| `multiwovenPostgresql.image.tag`                 | Docker image tag for PostgreSQL.                   | `13`        |
| `multiwovenPostgresql.resources.limits.cpu`      | CPU resource limits for PostgreSQL pod.            | `1`         |
| `multiwovenPostgresql.resources.limits.memory`   | Memory resource limits for PostgreSQL pod.         | `2Gi`       |
| `multiwovenPostgresql.resources.requests.cpu`    | CPU resource requests for PostgreSQL pod.          | `500m`      |
| `multiwovenPostgresql.resources.requests.memory` | Memory resource requests for PostgreSQL pod.       | `1Gi`       |
| `multiwovenPostgresql.ports.name`                | Port name for PostgreSQL service.                  | `postgres`  |
| `multiwovenPostgresql.ports.port`                | Port number for PostgreSQL service.                | `5432`      |
| `multiwovenPostgresql.ports.targetPort`          | Target port for PostgreSQL service within the pod. | `5432`      |
| `multiwovenPostgresql.replicas`                  | Number of PostgreSQL pod replicas.                 | `1`         |
| `multiwovenPostgresql.type`                      | Service type for PostgreSQL.                       | `ClusterIP` |

### Multiwoven Server Configuration

| Parameter                                    | Description                                               | Default                        |
| -------------------------------------------- | --------------------------------------------------------- | ------------------------------ |
| `multiwovenServer.image.repository`          | Docker image repository for Multiwoven server.            | `multiwoven/multiwoven-server` |
| `multiwovenServer.image.tag`                 | Docker image tag for Multiwoven server.                   | `latest`                       |
| `multiwovenServer.resources.limits.cpu`      | CPU resource limits for Multiwoven server pod.            | `2`                            |
| `multiwovenServer.resources.limits.memory`   | Memory resource limits for Multiwoven server pod.         | `2Gi`                          |
| `multiwovenServer.resources.requests.cpu`    | CPU resource requests for Multiwoven server pod.          | `1`                            |
| `multiwovenServer.resources.requests.memory` | Memory resource requests for Multiwoven server pod.       | `1Gi`                          |
| `multiwovenServer.ports.name`                | Port name for Multiwoven server service.                  | `3000`                         |
| `multiwovenServer.ports.port`                | Port number for Multiwoven server service.                | `3000`                         |
| `multiwovenServer.ports.targetPort`          | Target port for Multiwoven server service within the pod. | `3000`                         |
| `multiwovenServer.replicas`                  | Number of Multiwoven server pod replicas.                 | `1`                            |
| `multiwovenServer.type`                      | Service type for Multiwoven server.                       | `ClusterIP`                    |

### Multiwoven Worker Configuration

| Parameter                                    | Description                                               | Default                        |
| -------------------------------------------- | --------------------------------------------------------- | ------------------------------ |
| `multiwovenWorker.args`                      | Command arguments for the Multiwoven worker.              | See value                      |
| `multiwovenWorker.healthPort`                | The port in which the health check endpoint is exposed.   | `4567`                         |
| `multiwovenWorker.image.repository`          | Docker image repository for Multiwoven worker.            | `multiwoven/multiwoven-server` |
| `multiwovenWorker.image.tag`                 | Docker image tag for Multiwoven worker.                   | `latest`                       |
| `multiwovenWorker.resources.limits.cpu`      | CPU resource limits for Multiwoven worker pod.            | `1`                            |
| `multiwovenWorker.resources.limits.memory`   | Memory resource limits for Multiwoven worker pod.         | `1Gi`                          |
| `multiwovenWorker.resources.requests.cpu`    | CPU resource requests for Multiwoven worker pod.          | `500m`                         |
| `multiwovenWorker.resources.requests.memory` | Memory resource requests for Multiwoven worker pod.       | `512Mi`                        |
| `multiwovenWorker.ports.name`                | Port name for Multiwoven worker service.                  | `4567`                         |
| `multiwovenWorker.ports.port`                | Port number for Multiwoven worker service.                | `4567`                         |
| `multiwovenWorker.ports.targetPort`          | Target port for Multiwoven worker service within the pod. | `4567`                         |
| `multiwovenWorker.replicas`                  | Number of Multiwoven worker pod replicas.                 | `1`                            |
| `multiwovenWorker.type`                      | Service type for Multiwoven worker.                       | `ClusterIP`                    |

### Persistent Volume Claim (PVC)

| Parameter            | Description                       | Default |
| -------------------- | --------------------------------- | ------- |
| `pvc.storageRequest` | Storage request size for the PVC. | `100Mi` |

### Temporal Configuration

| Parameter                                     | Description                                                | Default                 |
| --------------------------------------------- | ---------------------------------------------------------- | ----------------------- |
| `temporal.enabled`                            | Whether or not to deploy Temporal and Temporal UI service. | `true`                  |
| `temporal.ports.name`                         | Port name for Temporal service.                            | `7233`                  |
| `temporal.ports.port`                         | Port number for Temporal service.                          | `7233`                  |
| `temporal.ports.targetPort`                   | Target port for Temporal service within the pod.           | `7233`                  |
| `temporal.replicas`                           | Number of Temporal service pod replicas.                   | `1`                     |
| `temporal.temporal.env.db`                    | Database type for Temporal.                                | `postgresql`            |
| `temporal.temporal.image.repository`          | Docker image repository for Temporal.                      | `temporalio/auto-setup` |
| `temporal.temporal.image.tag`                 | Docker image tag for Temporal.                             | `1.22.4`                |
| `temporal.temporal.resources.limits.cpu`      | CPU resource limits for Temporal pod.                      | `1`                     |
| `temporal.temporal.resources.limits.memory`   | Memory resource limits for Temporal pod.                   | `2Gi`                   |
| `temporal.temporal.resources.requests.cpu`    | CPU resource requests for Temporal pod.                    | `500m`                  |
| `temporal.temporal.resources.requests.memory` | Memory resource requests for Temporal pod.                 | `1Gi`                   |
| `temporal.type`                               | Service type for Temporal.                                 | `ClusterIP`             |

### Temporal UI Configuration

| Parameter                                            | Description                                                     | Default                    |
| ---------------------------------------------------- | --------------------------------------------------------------- | -------------------------- |
| `temporalUi.ports.name`                              | Port name for Temporal UI service.                              | `8080`                     |
| `temporalUi.ports.port`                              | Port number for Temporal UI service.                            | `8080`                     |
| `temporalUi.ports.targetPort`                        | Target port for Temporal UI service within the pod.             | `8080`                     |
| `temporalUi.replicas`                                | Number of Temporal UI service pod replicas.                     | `1`                        |
| `temporalUi.temporalUi.env.temporalAddress`          | Temporal service address for UI.                                | `multiwoven-temporal:7233` |
| `temporalUi.temporalUi.env.temporalAuthCallbackUrl`  | Authentication/authorization callback URL.                      |                            |
| `temporalUi.temporalUi.env.temporalAuthClientId`     | Authentication/authorization client ID.                         |                            |
| `temporalUi.temporalUi.env.temporalAuthClientSecret` | Authentication/authorization client secret.                     |                            |
| `temporalUi.temporalUi.env.temporalAuthEnabled`      | Enable or disable authentication/authorization for Temporal UI. | `false`                    |
| `temporalUi.temporalUi.env.temporalAuthProviderUrl`  | Authentication/authorization OIDC provider URL.                 |                            |
| `temporalUi.temporalUi.env.temporalCorsOrigins`      | Allowed CORS origins for Temporal UI.                           | `http://localhost:3000`    |
| `temporalUi.temporalUi.env.temporalUiPort`           | Port for Temporal UI service.                                   | `8080`                     |
| `temporalUi.temporalUi.image.repository`             | Docker image repository for Temporal UI.                        | `temporalio/ui`            |
| `temporalUi.temporalUi.image.tag`                    | Docker image tag for Temporal UI.                               | `2.22.3`                   |
| `temporalUi.type`                                    | Service type for Temporal UI.                                   | `ClusterIP`                |


# Heroku
Source: https://docs.squared.ai/deployment-and-security/setup/heroku

Coming soon...



# OpenShift
Source: https://docs.squared.ai/deployment-and-security/setup/openshift

Coming soon...



# Billing & Account
Source: https://docs.squared.ai/faqs/billing-and-account





# Data & AI Integration
Source: https://docs.squared.ai/faqs/data-and-ai-integration



This section addresses frequently asked questions when connecting data sources, setting up AI/ML model endpoints, or troubleshooting integration issues within AI Squared.

***

## Data Source Integration

### Why is my data source connection failing?

* Verify that the connection credentials (e.g., host, port, username, password) are correct.
* Ensure that the network/firewall rules allow connections to AI Squared’s IPs (for on-prem data).
* Check if the database is online and reachable.

### What formats are supported for ingesting data?

* AI Squared supports connections to major databases like Snowflake, BigQuery, PostgreSQL, Oracle, and more.
* Files such as CSV, Excel, and JSON can be ingested via SFTP or cloud storage (e.g., S3).

***

## AI/ML Model Integration

### How do I connect my hosted model?

* Use the [Add AI/ML Source](/activation/ai-modelling/connect-source) guide to define your model endpoint.
* Provide input/output schema details so the platform can handle data mapping effectively.

### What types of model endpoints are supported?

* REST-based hosted models with JSON payloads
* Hosted services like AWS SageMaker, Vertex AI, and custom HTTP endpoints

***

## Sync & Schema Issues

### Why is my sync failing?

* Confirm that your data model and sync mapping are valid
* Check that input types in your model schema match your data source fields
* Review logs for any missing fields or payload mismatches

### How can I test if my connection is working?

* Use the “Test Connection” button when setting up a new source or sync.
* If testing fails, examine error messages and retry with updated configs.

***


# Data Apps
Source: https://docs.squared.ai/faqs/data-apps





# Deployment & Security
Source: https://docs.squared.ai/faqs/deployment-and-security





# Feedback
Source: https://docs.squared.ai/faqs/feedback





# Model Configuration
Source: https://docs.squared.ai/faqs/model-config



This section answers frequently asked questions about configuring AI models within AI Squared—including how to define schemas, preprocess inputs, and ensure model compatibility.

***

## Input & Output Schema

### What is the input schema used for?

The input schema defines the structure of data sent to your model. Each key in the schema maps to a variable used by the model endpoint.

* Ensure all required fields are covered
* Match data types (string, integer, float, boolean) exactly
* Use dynamic/static value tagging depending on your use case

### What is the output schema used for?

The output schema maps the model’s prediction response to fields that can be used in visualizations and downstream applications.

* Identify key-value pairs in the model response
* Use flat structures (nested objects not supported currently)
* Label predictions clearly for user-facing display

***

## Preprocessing & Formatting

### How do I clean or transform inputs before sending them to the model?

Use AI Squared's built-in **Preprocessing Rules**, which allow no-code logic to:

* Format strings or numbers (e.g., round decimals)
* Apply conditional logic (e.g., replace nulls)
* Normalize or scale inputs

Preprocessors are configurable per input field.

***

## Updating a Model Source

### Can I update a connected model after initial setup?

Yes, you can:

* Edit endpoint details (URL, auth, headers)
* Modify input/output schemas
* Add or update preprocessing rules

Changes will reflect in associated Data Apps using the model.

***

## Debugging Model Issues

### How can I test if my model responds correctly?

1. Navigate to the AI Modeling section and click on **Test Model**
2. Provide sample input data based on your schema
3. Review the response payload

Common issues include:

* Auth failures (invalid API keys or tokens)
* Incorrect input field names or types
* Mismatched response format from expected schema

***


# Introduction to AI Squared
Source: https://docs.squared.ai/getting-started/introduction

Learn what AI Squared is, who it's for, and how it helps businesses integrate AI seamlessly into their workflows.

![title](https://mintlify.s3.us-west-1.amazonaws.com/multiwoven-74/images/data-ai-source-intro.png)

## What is AI Squared?

AI Squared powers your business applications with AI, turns feedback into decisions, and delivers better customer experiences.

## Who is AI Squared for?

AI Squared is for data science teams and AI/Analytics teams at large enterprises who are:

* Unable to scale AI beyond the pilot stage
* Unable to showcase immediate business impact

## How AI Squared Works

### Data & AI Integration

* Connect your data sources and AI sources effortlessly.
* Bring Your Own Model (BYOM) or integrate any LLM (Open AI, Anthropic, Llama) into your workflows.

### Deliver Actionable Insights

* Embed insights directly into your business applications, right when and where they’re needed by your business users.
* Use Retrieval-Augmented Generation (RAG) to provide contextual data to existing LLMs and deliver relevant insights to users.

### Leverage Business Context

* Capture key business information from tools like CRMs, ERPs, and other business applications to ensure AI models respond to the right context and deliver the right insights.

### Enable Feedback Mechanisms

* Collect user feedback using formats like Thumb Up/Down, Star Ratings, and more and analyze it to improve model performance.
* Gain insights into model effectiveness, user engagement, and areas for improvement by assessing feedback reports.

### Optimize for Maximum Impact

* Evaluate model performance and use data-driven decisions to make informed decisions.
* Continuously iterate use-cases and AI experiments that your teams rely on by using feedback, data, and reporting.


# Navigating AI Squared
Source: https://docs.squared.ai/getting-started/navigation

Explore the AI Squared dashboard, including reports, sources, destinations, models, syncs, and data apps.

Once you log in to the platform, you will land on the **Reports** section by default, providing a performance overview of AI and data models.

## Reports

### Sync Reports

View real-time sync status on **data movement** from your data sources to destinations, such as:

* Databases
* Data warehouses
* Business applications

### Data App Reports

Monitor the **real-time usage of AI models** deployed within business applications, tracking adoption and performance.

## Sources

### Data Source

These include:

* Data warehouses
* Databases
* Other structured/unstructured data sources

### AI/ML Source

Endpoints for AI/ML models, such as:

* Large Language Models (LLMs)
* Custom AI/ML models

## Destinations

Business applications where AI insights or transformed data are delivered, including:

* **CRMs**
* **Support systems**
* **Marketing automation tools**
* **Other databases**

## Models

Data models define and organize the data you want to query from a source.

## Syncs

Syncs define how query results from a model should be **mapped and transferred** to a destination.

## Data Apps

**Data Apps** enable you to:

* Integrate AI/ML insights directly into business applications.
* Connect to AI/ML sources.
* Build visualizations for business users to consume insights effortlessly.


# Setting Up Your Account
Source: https://docs.squared.ai/getting-started/setup

Learn how to create an account, manage workspaces, and define user roles and permissions in AI Squared.

## Creating an Account & Logging In

You can sign up with your email ID at the following URL: [app.squared.ai](https://app.squared.ai).

![title](https://mintlify.s3.us-west-1.amazonaws.com/multiwoven-74/images/get-started/login.png)

## User Roles & Permissions

### Accessing Your Workspace

Once you log in to the platform, navigate to **Workspace** under **Settings** to create your workspace, add team members, and define access rights.

### Adding Team Members and Defining Permissions

To add team members in the selected workspace:

1. Click on the **Profile** section.
2. Select the option to **Add Team Members**.
3. Assign appropriate roles and permissions.

## How to Create Multiple Workspaces

If you are collaborating with multiple teams across the organization and need to organize access to data, you can create multiple workspaces.

### Steps to Create a New Workspace:

1. Click on the **drop-down menu** in the left corner to switch between workspaces or create a new one.
2. Select **Manage Workspaces**.
3. Click **Create New Workspace** and fill in the required details.

Once created, you can manage access rights and team collaboration across different workspaces.


# Workspace Management
Source: https://docs.squared.ai/getting-started/workspace-management/overview

Learn how to create a new workspace, manage settings and workspace users.

## Introduction

Workspaces enable the governance of data & AI activation. Each workspace within an organization's account will have self-contained data sources, data & AI models, syncs and business application destinations.

### Key workspace concepts

* Organization: An AI Squared account that is a container for a set of workspaces.

* Workspace: Represents a set of users and resources. One or more workspaces are contained within an organization.

* User: An individual within a workspace, with a specific Role. A user can be a part of one or more workspaces.

* Role: Defined by a set of rules that govern a user’s access to resources within a workspace

* Resources: Product features within the workspace that enable the activation of data and AI. These include data sources, destinations, models, syncs, and more.

### Workspace settings

You can access Workspace settings from within Settings on the left navigation menu. The workspace’s name and description can be edited at any time for clarity.

<img src="https://res.cloudinary.com/dsyfasxld/image/upload/v1718360388/workspace_settings_yb4ag0.jpg" />

### Inviting users to a workspace

You can view the list of active users on the Members tab, within Settings. Users can be invited or deleted from this screen.

<img src="https://res.cloudinary.com/dsyfasxld/image/upload/v1718360624/Members_Tab_gpuvor.png" />

To invite a user, enter their email ID and choose their role. The invited user will receive an email invite (with a link that will expire after 30 days).

<img src="https://res.cloudinary.com/dsyfasxld/image/upload/v1718360738/User_Invite_xwfajv.png" />

The invite to a user can be cancelled or resent from this screen.

<img src="https://res.cloudinary.com/dsyfasxld/image/upload/v1718360959/Cancel_Resend_invite_khuh2t.png" />

### Role-based access control (RBAC)

Governance within workspaces is enabled by user Role-based access control (RBAC).

* **Admins** have unrestricted access to all resources in the Organization’s account and all its workspaces. Admins can also create workspaces and manage the workspace itself, including inviting users and setting user roles.

* **Members** belong to a single workspace, with access to all its resources. Members are typically part of a team or purpose that a workspace has been specifically set up for.

* **Viewers** have read-only access to core resources within a workspace. Viewers can’t manage the workspace itself or add users.

### Creating a new workspace

To create a workspace, use the drop-down on the left navigation panel that shows your current active workspace, click Manage Workspaces.

<img src="https://res.cloudinary.com/dsyfasxld/image/upload/v1718361367/manage_workspace_selection_c2ybrp.png" />

Choose Create New Workspace.

<img src="https://res.cloudinary.com/dsyfasxld/image/upload/v1718361604/select_workspace_olhlwz.png" />

<img src="https://res.cloudinary.com/dsyfasxld/image/upload/v1718361523/create_new_workspace_wzjz1q.png" />

### Moving between workspaces

Your active workspace is visible on the left tab. The drop-down will allow you to view workspaces that you have access to, move between workspaces or create a workspace.

<img src="https://res.cloudinary.com/dsyfasxld/image/upload/v1718361751/moving_between_workspaces_aogs0l.png" />


# Adding a Data Source
Source: https://docs.squared.ai/guides/add-data-source

How to connect and configure a business data source in AI Squared.

AI Squared allows you to integrate data from databases, warehouses, and storage systems to power downstream AI insights and business workflows.

Follow the steps below to connect your first data source.

***

## Step 1: Navigate to Data Sources

1. Go to **Sources** → **Data Sources** in the left sidebar.
2. Click **“Add Source”**.
   ![title](https://mintlify.s3.us-west-1.amazonaws.com/multiwoven-74/images/add-data-source/01.png)
3. Select your connector from the available list (e.g., Snowflake, BigQuery, PostgreSQL, etc.).

![title](https://mintlify.s3.us-west-1.amazonaws.com/multiwoven-74/images/add-data-source/02.png)

***

## Step 2: Provide Connection Details

Each data source requires standard connection credentials. These typically include:

* **Source Name** – A descriptive label for your reference.
* **Host / Server URL** – Address of the database or data warehouse.
* **Port Number** – Default or custom port for the connection.
* **Database Name** – The name of the DB you want to access.
* **Authentication Type** – Options like password-based, token, or OAuth.
* **Username & Password / Token** – Credentials for access.
* **Schema (if applicable)** – Filter down to the relevant DB schema.

![title](https://mintlify.s3.us-west-1.amazonaws.com/multiwoven-74/images/add-data-source/03.png)

***

## Step 3: Test the Connection

Click **“Test Connection”** to validate that your source credentials are correct and the system can access the data.

> ⚠️ Common issues include invalid credentials, incorrect hostnames, or firewall rules blocking access.

![title](https://mintlify.s3.us-west-1.amazonaws.com/multiwoven-74/images/add-data-source/04.png)

***

## Step 4: Save the Source

After successful testing:

* Click **Finish** to finalize the connection.
* The source will now appear under **Data Sources** in your account.

![title](https://mintlify.s3.us-west-1.amazonaws.com/multiwoven-74/images/add-data-source/05.png)

***

## Step 5: Next Steps — Use the Source

Once added, your data source can be used to:

* Create **Data Models** (via SQL editor, dbt, or table selector)
* Build **Syncs** to move transformed data into downstream destinations
* Enable AI apps to reference live or transformed business data

> 📘 Refer to the [Data Modeling](../data-activation/data-modelling) section to begin querying your connected source.

***

## ✅ You're All Set!

Your data source is now ready for activation. Use it to power AI pipelines, syncs, and application-level insights.


# Introduction
Source: https://docs.squared.ai/guides/core-concepts



The core concepts of data movement in AI Squared are the foundation of your data journey. They include Sources, Destinations, Models, and Syncs. Understanding these concepts is crucial to building a robust data pipeline.

<img className="block" src="https://res.cloudinary.com/dspflukeu/image/upload/v1714756028/AIS/Core_Concepts_v4o7rp.png" alt="Hero Light" />

## Sources: The Foundation of Data

### Overview

Sources are the starting points of your data journey. It's where all your data is stored and where AI Squared pulls data from.

<img className="block" src="https://res.cloudinary.com/dspflukeu/image/upload/v1714756029/AIS/Sources_xrjsvz.png" alt="Hero Light" />

These can be:

* **Data Warehouses**: For example, `Snowflake` `Google BigQuery` and `Amazon Redshift`
* **Databases and Files**: Including traditional databases, `CSV files`, `SFTP`

### Adding a Source

To integrate a source with AI Squared, navigate to the Sources overview page and select 'Add source'.

## Destinations: Where Data Finds Value

### Overview

'Destinations' in AI Squared are business tools where you want to send your data stored in sources.

<img className="block" src="https://res.cloudinary.com/dspflukeu/image/upload/v1714756029/AIS/Destinations_p2du4o.png" alt="Hero Light" />

These can be:

* **CRM Systems**: Like Salesforce, HubSpot, etc.
* **Advertising Platforms**: Such as Google Ads, Facebook Ads, etc.
* **Marketing Tools**: Braze and Klaviyo, for example

### Integrating a Destination

Add a destination by going to the Destinations page and clicking 'Add destination'.

## Models: Shaping Your Data

### Overview

'Models' in AI Squared determine the data you wish to sync from a source to a destination. They are the building blocks of your data pipeline.

<img className="block" src="https://res.cloudinary.com/dspflukeu/image/upload/v1714756030/AIS/Models_dyihll.png" alt="Hero Light" />

They can be defined through:

* **SQL Editor**: For customized queries
* **Visual Table Selector**: For intuitive interface
* **Existing dbt Models or Looker Looks**: Leveraging pre-built models

### Importance of a Unique Primary Key

Every model must have a unique primary key to ensure each data entry is distinct, crucial for data tracking and updating.

## Syncs: Customizing Data Flow

### Overview

'Syncs' in AI Squared helps you move data from sources to destinations. They help you in mapping the data from your models to the destination.

<img className="block" src="https://res.cloudinary.com/dspflukeu/image/upload/v1714756030/AIS/Syncs_dncrnv.png" alt="Hero Light" />

There are two types of syncs:

* **Full Refresh Sync**: All data is synced from the source to the destination.
* **Incremental Sync**: Only the new or updated data is synced.


# Overview
Source: https://docs.squared.ai/guides/data-modelling/models/overview



## Introduction

**Models** are designed to define and organize data, simplifying the process of querying from various sources.

<img className="block" src="https://res.cloudinary.com/dspflukeu/image/upload/v1714756030/AIS/Models_dyihll.png" alt="Hero Light" />

This guide outlines the process of creating a model, from selecting a data source to defining the model using various methods such as SQL queries, table selectors, or dbt models.

## Understanding the Model Creation Process

Creating a model in AI Squared involves a series of steps designed to streamline the organization of your data for efficient querying. This overview will guide you through each step of the process.

### Step 1: Navigate to the Models Section

To start defining a model:

1. Access the AI Squared dashboard.
2. Look for the `Define` menu on the sidebar and click on the `Models` section.

### Step 2: Add a New Model

Once you log in to the AI Squared platform, you can access the Models section to create, manage, and monitor your models.

1. Click on the `Add Model` button to initiate the model creation process.

2. Select SQL Query, Table Selector, or dbt Model as the method to define your model.

### Step 3: Select a Data Source

1. Choose from the list of existing connected data warehouse sources. This source will be the foundation for your model.

<img className="block" src="https://res.cloudinary.com/dspflukeu/image/upload/v1708066638/Multiwoven/Docs/models/image_6_krkxp5.png" alt="Hero Light" />

### Step 4: Select a Modeling Method

Based on your requirements, select one of the following modeling methods:

1. **SQL Query**: Define your model directly using an SQL query.
2. **Table Selector**: For a straightforward, visual approach to model building.
3. **dbt Model**: Ideal for advanced data transformation, leveraging the dbt framework.

<img className="block" src="https://res.cloudinary.com/dspflukeu/image/upload/v1708066637/Multiwoven/Docs/models/image_7_bhyi24.png" alt="Hero Light" />

### Step 5: Define Your Model

If you selected the SQL Query method:

1. Write your SQL query in the provided field.
2. Use the `Run Query` option to preview the results and ensure accuracy.

<img className="block" src="https://res.cloudinary.com/dspflukeu/image/upload/v1708066459/Multiwoven/Docs/models/image_8_sy7n0f.png" alt="Hero Light" />

### Step 6: Finalize Your Model

Complete the model setup by:

1. Adding a name and a brief description for your model. This helps in identifying and managing your models within AI Squared.

<img className="block" src="https://res.cloudinary.com/dspflukeu/image/upload/v1708066496/Multiwoven/Docs/models/image_9_vkgq1a.png" alt="Hero Light" />

#### Unique Primary Key Requirement

* **Essential Configuration**:
  Every model in AI Squared must be configured with a unique primary key.
  This primary key is pivotal for uniquely identifying each record in your dataset.

* **Configuring the Primary Key**:

  * During the final step of model creation, select a column that holds unique values from your dataset.

  <Tip>Ensuring the uniqueness of this primary key is crucial for the integrity and accuracy of data synchronization.</Tip>

* **Importance of a Unique Key**:
  * A unique primary key is essential for effectively managing data synchronization.
  * It enables the system to track and sync only the new or updated data to the designated destinations, ensuring data consistency and reducing redundancy.

After completing these steps, your model will be set up and ready to use.


# SQL Editor
Source: https://docs.squared.ai/guides/data-modelling/models/sql

SQL Editor for Data Modeling in AI Squared

## Overview

AI Squared's SQL Editor allows you to define and manage your data models directly through SQL queries. This powerful tool supports native SQL commands compatible with your data warehouse, enabling you to seamlessly model your data.

## Creating a Model with the SQL Editor

### Starting with a Query

Begin by writing a SQL query to define your model. For instance, if using a typical eCommerce dataset, you might start with a query like:

```sql
SELECT * FROM sales_data.customers
```

### Previewing Your Data

Click the `Preview` button to review the first 100 rows of your data. This step ensures the query fetches the expected data. After verifying, proceed by clicking `Continue`.

<Tip>**Important Note:**  The model cannot be saved if the query is incorrect or yields no results.</Tip>

### Configuring Model Details

Finalize your model by:

* Naming the model descriptively.
* Choosing a column as the Primary Key.

### Completing the Setup

Finish your model setup by clicking the `Finish` button.

## Unique Primary Key Requirement

Every model requires a unique primary key. If no unique column exists, consider:

* Removing duplicate rows.
* Creating a composite column for the primary key.

## Handling Duplicate Data

To filter duplicates, use a `GROUP BY` clause in your SQL query. For instance:

```sql
SELECT *
FROM customer_data
GROUP BY unique_identifier_column
```

## Composite Primary Keys

In scenarios where a unique primary key is not available, construct a composite key. Example:

```sql
SELECT customer_id, 
       email, 
       purchase_date,
       MD5(CONCAT(customer_id, '-', email)) AS composite_key
FROM sales_data
```

## Saving a Model Without Current Results

To save a model expected to have future data:

```sql
UNION ALL
SELECT NULL, NULL, NULL
```

Add this to your query to include a dummy row, ensuring the model can be saved.

## Excluding Rows with Null Values

To exclude rows with null values:

```sql
SELECT *
FROM your_dataset
WHERE important_column1 IS NOT NULL AND important_column2 IS NOT NULL
```

Replace `important_column1`, `important_column2`, etc., with your relevant column names.


# Table Selector
Source: https://docs.squared.ai/guides/data-modelling/models/table-visualization



<Note>Watch out for this space, **Visual Table Selector** coming soon!</Note>


# Incremental - Cursor Field
Source: https://docs.squared.ai/guides/data-modelling/sync-modes/cursor-incremental

Incremental Cursor Field sync transfers only new or updated data, minimizing data transfer using a cursor field.

### Overview

Default Incremental Sync fetches all records from the source system and transfers only the new or updated ones to the destination. However, to optimize data transfer and reduce the number of duplicate fetches from the source, we implemented Incremental Sync with Cursor Field for those sources that support cursor fields

#### Cursor Field

A Cursor Field must be clearly defined within the dataset schema. It is identified based on its suitability for comparison and tracking changes over time.

* It serves as a marker to identify modified or added records since the previous sync.
* It facilitates efficient data retrieval by enabling the source to resume from where it left off during the last sync.

Note: Currently, only date fields are supported as Cursor Fields.

####

#### Sync Run 1

During the first sync run with the cursor field 'UpdatedAt', suppose we have the following data:
cursor field UpdatedAt value is 2024-04-20 10:00:00

| Name             | Plan | Updated At          |
| ---------------- | ---- | ------------------- |
| Charles Beaumont | free | 2024-04-20 10:00:00 |
| Eleanor Villiers | free | 2024-04-20 11:00:00 |

During this sync run, both Charles Beaumont's and Eleanor Villiers' records would meet the criteria since they both have an 'UpdatedAt' timestamp equal to '2024-04-20 10:00:00' or later. So, during the first sync run, both records would indeed be considered and fetched.

##### Query

```sql
SELECT * FROM source_table
WHERE updated_at >= '2024-04-20 10:00:00';
```

#### Sync Run 2

Now cursor field UpdatedAt value is 2024-04-20 11:00:00
Suppose after some time, there are further updates in the source data:

| Name             | Plan | Updated At          |
| ---------------- | ---- | ------------------- |
| Charles Beaumont | free | 2024-04-20 10:00:00 |
| Eleanor Villiers | paid | 2024-04-21 10:00:00 |

During the second sync run with the same cursor field, only the records for Eleanor Villiers with 'Updated At' timestamp after the last sync would be fetched, ensuring minimal data transfer.

##### Query

```sql
SELECT * FROM source_table
WHERE updated_at >= '2024-04-20 11:00:00';
```

#### Sync Run 3

If there are additional updates in the source data:
Now cursor field UpdatedAt value is 2024-04-21 10:00:00

| Name             | Plan | Updated At          |
| ---------------- | ---- | ------------------- |
| Charles Beaumont | paid | 2024-04-22 08:00:00 |
| Eleanor Villiers | pro  | 2024-04-22 09:00:00 |

During the third sync run with the same cursor field, only the records for Charles Beaumont and Eleanor Villiers with 'Updated At' timestamp after the last sync would be fetched, continuing the process of minimal data transfer.

##### Query

```sql
SELECT * FROM source_table
WHERE updated_at >= '2024-04-21 10:00:00 ';
```

### Handling Ambiguity and Inclusive Cursors

When syncing data incrementally, we ensure at least one delivery. Limited cursor field granularity may cause sources to resend previously sent data. For example, if a cursor only tracks dates, distinguishing new from old data on the same day becomes unclear.

#### Scenario

Imagine sales transactions with a cursor field `transaction_date`. If we sync on April 1st and later sync on the same day, distinguishing new transactions becomes ambiguous. To mitigate this, we guarantee at least one delivery, allowing sources to resend data as needed.

### Known Limitations

Modifications to underlying records without updating the cursor field may result in updated records not being picked up by the Incremental sync as expected.

Edit or remove of cursor field can mess up tracking data changes, causing issues and data loss. So Don't change or remove the cursor field to keep sync smooth and reliable.


# Full Refresh
Source: https://docs.squared.ai/guides/data-modelling/sync-modes/full-refresh

Full Refresh syncs replace existing data with new data.

### Overview

The Full Refresh mode in AI Squared is a straightforward method used to sync data to a destination. It retrieves all available information from the source, regardless of whether it has been synced before. This mode is ideal for scenarios where you want to completely replace existing data in the destination with fresh data from the source.

In the Full Refresh mode, new syncs will replace all existing data in the destination table with the new data from the source. This ensures that the destination contains the most up-to-date information available from the source.

### Example Behavior

Consider the following scenario where we have a database table named `Users` in the destination:

#### Before Sync

| **id** | **name** | **email**                                     |
| ------ | -------- | --------------------------------------------- |
| 1      | Alice    | [alice@example.com](mailto:alice@example.com) |
| 2      | Bob      | [bob@example.com](mailto:bob@example.com)     |

#### New Data in Source

| **id** | **name** | **email**                                     |
| ------ | -------- | --------------------------------------------- |
| 1      | Alice    | [alice@example.com](mailto:alice@example.com) |
| 3      | Carol    | [carol@example.com](mailto:carol@example.com) |
| 4      | Dave     | [dave@example.com](mailto:dave@example.com)   |

#### After Sync

| **id** | **name** | **email**                                     |
| ------ | -------- | --------------------------------------------- |
| 1      | Alice    | [alice@example.com](mailto:alice@example.com) |
| 3      | Carol    | [carol@example.com](mailto:carol@example.com) |
| 4      | Dave     | [dave@example.com](mailto:dave@example.com)   |

In this example, notice how the previous user "Bob" is no longer present in the destination after the sync, and new users "Carol" and "Dave" have been added.


# Incremental
Source: https://docs.squared.ai/guides/data-modelling/sync-modes/incremental

Incremental sync only transfer new or updated data, minimizing data transfer

### Overview

Incremental syncing involves transferring only new or updated data, thus avoiding duplication of previously replicated data. This is achieved through deduplication using a unique primary key specified in the model. For initial syncs, it functions like a full refresh since all data is considered new.

### Example

### Initial State

Suppose the following records already exist in our source:

| Name             | Plan     | Updated At |
| ---------------- | -------- | ---------- |
| Charles Beaumont | freemium | 6789       |
| Eleanor Villiers | freemium | 6790       |

### First sync

In this sync, the delta contains an updated record for Charles:

| Name             | Plan     | Updated At |
| ---------------- | -------- | ---------- |
| Charles Beaumont | freemium | 6791       |

After this incremental sync, the data in the warehouse would now be:

| Name             | Plan     | Updated At |
| ---------------- | -------- | ---------- |
| Charles Beaumont | freemium | 6791       |
| Eleanor Villiers | freemium | 6790       |

### Second sync

Let's assume in the next delta both customers have upgraded to a paid plan:

| Name             | Plan | Updated At |
| ---------------- | ---- | ---------- |
| Charles Beaumont | paid | 6795       |
| Eleanor Villiers | paid | 6795       |

The final data at the destination after this update will be:

| Name             | Plan | Updated At |
| ---------------- | ---- | ---------- |
| Charles Beaumont | paid | 6795       |
| Eleanor Villiers | paid | 6795       |


# Overview
Source: https://docs.squared.ai/guides/data-modelling/syncs/overview



### Introduction

Syncs help in determining how the data appears in your destination. They are used to map the data from the source to the destination.

<img className="block" src="https://res.cloudinary.com/dspflukeu/image/upload/v1714756030/AIS/Syncs_dncrnv.png" alt="Hero Light" />

In order to create a sync, you need to have a source and a destination. The source is the data that you want to sync and the destination is where you want to sync the data to.

### Types of Syncs

There are two types of syncs:

1. **Full Refresh Syncs**: This sync type replaces all the data in the destination with the data from the source. This is useful when you want to replace all the data in the destination with the data from the source.

2. **Incremental Syncs**: This sync type only syncs the data that has changed since the last sync. This is useful when you want to sync only the data that has changed since the last sync.

### Important Concepts

1. **Streams**: Streams in AI Squared are referred to the destination APIs that you want to sync the data to. For example, If you want to Sync the data to Salesforce, then `Account`, `Contact`, `Opportunity` are the streams.


# Facebook Custom Audiences
Source: https://docs.squared.ai/guides/destinations/retl-destinations/adtech/facebook-ads



## Connect AI Squared to Facebook Custom Audiences

This guide will walk you through configuring the Facebook Custom Audiences Connector in AI Squared to manage your custom audiences effectively.

### Prerequisites

Before you begin, make sure you have the following:

1. **Get your [System User Token](https://developers.facebook.com/docs/marketing-api/system-users/overview) from Facebook Business Manager account:**

   * Log in to your Facebook Business Manager account.
   * Go to Business Settings > Users > System Users.
   * Click "Add" to create a new system user if needed.
   * After creating the system user, access its details.
   * Generate a system user token by clicking "Generate New Token."
   * Copy the token for later use in the authentication process.

2. **Access to a Facebook Business Manager account:**

   * If you don't have an account, create one at [business.facebook.com](https://business.facebook.com/) by following the sign-up instructions.

3. **Custom Audiences:**

   * Log in to your Facebook Business Manager account.
   * Navigate to the Audiences section under Business Tools.
   * Create new custom audiences or access existing ones.

### Steps

### Authentication

Authentication is supported via two methods: System user token and Log in with Facebook account.

1. **System User Token:**

   * **[access\_token](https://developers.facebook.com/docs/marketing-api/system-users/create-retrieve-update)**: Obtain a system user token from your Facebook Business Manager account in step 1 of the prerequisites.
   * **[ad\_account\_id](https://www.facebook.com/business/help/1492627900875762)**: Paste the system user token into the provided authentication field in AI Squared.
   * **[audience\_id](https://developers.facebook.com/docs/marketing-api/reference/custom-audience/)**: Obtain the audience ID from step 3 of the prerequisites.

2. **Log in with Facebook Account**

   *Coming soon*

### Supported Sync Modes

| Mode             | Supported (Yes/No/Coming soon) |
| ---------------- | ------------------------------ |
| Incremental sync | YES                            |
| Full refresh     | Coming soon                    |


# Google Ads
Source: https://docs.squared.ai/guides/destinations/retl-destinations/adtech/google-ads





# Amplitude
Source: https://docs.squared.ai/guides/destinations/retl-destinations/analytics/amplitude





# Databricks
Source: https://docs.squared.ai/guides/destinations/retl-destinations/analytics/databricks_lakehouse



## Connect AI Squared to Databricks

This guide will help you configure the Databricks Connector in AI Squared to access and use your Databricks data.

### Prerequisites

Before proceeding, ensure you have the necessary Host URL and API Token from Databricks.

## Step-by-Step Guide to Connect to Databricks

## Step 1: Navigate to Databricks

Start by logging into your Databricks account and navigating to the Databricks workspace.

1. Sign in to your Databricks account at [Databricks Login](https://accounts.cloud.databricks.com/login).
2. Once logged in, you will be directed to the Databricks workspace dashboard.

## Step 2: Locate Databricks Host URL and API Token

Once you're logged into Databricks, you'll find the necessary configuration details:

1. **Host URL:**
   * The Host URL is the first part of the URL when you log into your Databricks. It will look something like `https://<your-instance>.databricks.com`.

2. **API Token:**
   * Click on your user icon in the upper right corner and select "Settings" from the dropdown menu.
   * In the Settings page, navigate to the "Developer" tab.
   * Here, you can create a new Access Token by clicking on Manage then "Generate New Token." Give it a name and set the expiration duration.
   * Once the token is generated, copy it as it will be required for configuring the connector. **Note:** This token will only be shown once, so make sure to store it securely.

<Frame>
  <img src="https://res.cloudinary.com/dspflukeu/image/upload/v1709669164/05_p6ikgb.jpg" />
</Frame>

## Step 3: Test the Databricks Connection

After configuring the connector in your application:

1. Save the configuration settings.
2. Test the connection to Databricks from the AI Squared platform to ensure a connection is made.

By following these steps, you’ve successfully set up a Databricks destination connector in AI Squared. You can now efficiently transfer data to your Databricks endpoint for storage or further distribution within AI Squared.

### Supported sync modes

| Mode             | Supported (Yes/No/Coming soon) |
| ---------------- | ------------------------------ |
| Incremental sync | YES                            |
| Full refresh     | Coming soon                    |

Follow these steps to configure and test your Databricks connector successfully.


# Google Analytics
Source: https://docs.squared.ai/guides/destinations/retl-destinations/analytics/google-analytics





# Mixpanel
Source: https://docs.squared.ai/guides/destinations/retl-destinations/analytics/mixpanel





# HubSpot
Source: https://docs.squared.ai/guides/destinations/retl-destinations/crm/hubspot

HubSpot is a customer platform with all the software, integrations, and resources you need to connect your marketing, sales, content management, and customer service. HubSpot's connected platform enables you to grow your business faster by focusing on what matters most: your customers.

## Hubspot Connector Configuration and Credential Retrieval Guide

### Prerequisite Requirements

Before initiating the Hubspot connector setup, ensure you have an created an Hubspot developer account.
This setup requires us to create an private app in Hubspot with [superuser admin access](https://knowledge.hubspot.com/user-management/hubspot-user-permissions-guide#super-admin).

<Tip>
  [Hubspot Developer
  Signup](https://app.hubspot.com/signup-v2/developers/step/join-hubspot?hubs_signup-url=developers.hubspot.com/get-started\&hubs_signup-cta=developers-getstarted-app&_ga=2.53325096.1868562849.1588606909-500942594.1573763828).
</Tip>

### Destination Setup

As mentioned earlier, this setup requires us to create an [private app](https://developers.hubspot.com/docs/api/private-apps) in Hubspot with superuser admin access.

HubSpot private applications facilitate interaction with your HubSpot account's data through the platform's APIs. Granular control over individual app permissions allows you to specify the data each app can access or modify. This process generates a unique access token for each app, ensuring secure authentication.

<Accordion title="Create a Private App" icon="lock">
  For AI Squared Open Source, we hubspot Private App Access Token for api authentication.

  <Steps>
    <Step title="Locate the Private Apps Section">
      Within your HubSpot account, access the settings menu from the main navigation bar. Navigate through the left sidebar menu to Integrations > Private Apps.

      <Frame>
        <img src="https://res.cloudinary.com/dspflukeu/image/upload/v1709115020/Multiwoven/connectors/hubspot/private-app-section.png" />
      </Frame>
    </Step>

    <Step title="Initiate App Creation">
      Click the Create Private App button.

      <Frame>
        <img src="https://res.cloudinary.com/dspflukeu/image/upload/v1709115178/Multiwoven/connectors/hubspot/create-app.png" />
      </Frame>
    </Step>

    <Step title="Define App Information">
      On the Basic Info tab, configure your app's details:

      * Name: Assign a descriptive name for your app.
      * Logo: Upload a square image to visually represent your app (optional).
      * Description: Provide a brief explanation of your app's functionality.
    </Step>

    <Step title="Specify Access Permissions">
      Navigate to the Scopes tab and select the desired access level (Write) for each data element your app requires. Utilize the search bar to locate specific scopes.

      <Frame>
        <img src="https://res.cloudinary.com/dspflukeu/image/upload/v1709115239/Multiwoven/connectors/hubspot/scope.png" />
      </Frame>
    </Step>

    <Step title="Finalize Creation">
      After configuration, click Create app in the top right corner.
    </Step>

    <Step title="Review Access Token">
      A dialog box will display your app's unique access token. Click Continue creating to proceed.

      <Frame>
        <img src="https://res.cloudinary.com/dspflukeu/image/upload/v1709115355/Multiwoven/connectors/hubspot/api-key.png" />
      </Frame>
    </Step>

    <Step title="Utilize the App">
      Once created, you can leverage the access token to setup hubspot in AI Squared destination section.
    </Step>
  </Steps>
</Accordion>


# Microsoft Dynamics
Source: https://docs.squared.ai/guides/destinations/retl-destinations/crm/microsoft_dynamics



## Connect AI Squared to Microsoft Dynamics

This guide will help you configure the Microsoft Dynamics Connector in AI Squared to access and transfer data to your Dynamics CRM.

### Prerequisites

Before proceeding, ensure you have the necessary instance URL, tenant ID, application ID, and client secret from Azure Portal.

## Step-by-Step Guide to Connect to Microsoft Dynamics

## Step 1: Navigate to Azure Portal to create App Registration

Start by logging into your Azure Portal.

<Frame>
  <img src="https://res.cloudinary.com/dspflukeu/image/upload/v1738204195/Multiwoven/connectors/Microsoft%20Dynamics/Portal_Home_bcool0.png" />
</Frame>

1. Navigate to the Azure Portal and go to [App Registration](https://portal.azure.com/#home).
2. Create a new registration
3. Name the app registration and select single or multi-tenant, depending on the needs
4. You can disregard the Redirect URI for now
5. From the Overview screen, make note of the Application ID and the Tenant ID
6. Under Manage on the left panel, select API Permissions
7. Scroll down and select Dynamics CRM
8. Check all available permissions and click Add Permissions
9. Under Manage on the left panel, select Certificates and secrets
10. Create a new client secret and make note of the Client Secret ID and the Client Secret Value

<Frame>
  <img src="https://res.cloudinary.com/dspflukeu/image/upload/v1738204173/Multiwoven/connectors/Microsoft%20Dynamics/AppRegistrations_Home_ceo2es.png" />
</Frame>

## Step 2: Add App Registration as Application User for Dynamics 365

When App Registration is created:

1. Navigate to the Application Users screen in Power Platform
2. At the top of the screen, select New App User
3. When the New App User blade opens, click Add an app
4. Find the name of your new App Registration and select Add
5. Select the appropriate Business Unit
6. Select appropriate Security Roles for your app, depending on its access needs
7. Click Create

## Step 3: Configure Microsoft Dynamics Connector in Your Application

Now that you have gathered all the necessary details, enter the following information in your application:

* **Instance URL:** The URL of your Microsoft Dynamics instance (e.g., https\://**instance\_url**.crm.dynamics.com).
* **Application ID:** The unique identifier for your registered Azure AD application.
* **tenant ID:** The unique identifier of your Azure AD directory (tenant) where the Dynamics instance is hosted.
* **Client Secret:** The corresponding Secret Access Key.

## Step 4: Test the Microsoft Dynamics Connection

After configuring the connector in your application:

1. Save the configuration settings.
2. Test the connection to Microsoft Dynamics from your application to ensure everything is set up correctly.

By following these steps, you’ve successfully set up an Microsoft Dynamics destination connector in AI Squared. You can now efficiently transfer data to your Microsoft Dynamics endpoint for storage or further distribution within AI Squared.

### Supported sync modes

| Mode             | Supported (Yes/No/Coming soon) |
| ---------------- | ------------------------------ |
| Incremental sync | YES                            |
| Full refresh     | Coming soon                    |


# Salesforce
Source: https://docs.squared.ai/guides/destinations/retl-destinations/crm/salesforce



## Salesforce Connector Configuration and Credential Retrieval Guide

### Prerequisite Requirements

Before initiating the Salesforce connector setup, ensure you have an appropriate Salesforce edition. This setup requires either the Enterprise edition of Salesforce, the Professional Edition with an API access add-on, or the Developer edition. For further information on API access within Salesforce, please consult the [Salesforce documentation](https://developer.salesforce.com/docs/).

<Tip>
  If you need a Developer edition of Salesforce, you can register at [Salesforce
  Developer Signup](https://developer.salesforce.com/signup).
</Tip>

### Destination Setup

<AccordionGroup>
  <Accordion title="Create a Connected App" icon="key">
    For AI Squared Open Source, certain OAuth credentials are necessary for authentication. These credentials include:

    * Access Token
    * Refresh Token
    * Instance URL
    * Client ID
    * Client Secret

    <Steps>
      <Step title="Login">
        Start by logging into your Salesforce account with admin rights. Look for a Setup option in the menu at the top-right corner of the screen and click on it.

        <Frame>
          <img src="https://res.cloudinary.com/dspflukeu/image/upload/v1707482972/Multiwoven/connectors/salesforce-crm/setup.png" />
        </Frame>
      </Step>

      <Step title="App Manager">
        On the left side of the screen, you'll see a menu. Click on Apps, then App Manager.

        <Frame>
          <img src="https://res.cloudinary.com/dspflukeu/image/upload/v1707484672/Multiwoven/connectors/salesforce-crm/app-manager.png" />
        </Frame>
      </Step>

      <Step title="New Connected App">
        Find a button that says New Connected App at the top right and click it.
      </Step>

      <Step title="Fill the details">
        You'll be taken to a page to set up your new app.
        Here, you need to fill in some basic info: the name you want for your app, its API name (a technical identifier), and your email address.

        <Frame>
          <img src="https://res.cloudinary.com/dspflukeu/image/upload/v1707485030/Multiwoven/connectors/salesforce-crm/details.png" />
        </Frame>
      </Step>

      <Step title="Enable OAuth Settings">
        Now, look for a section named API (Enable OAuth Settings) and check the box for Enable OAuth Settings.
        There’s a box for a Callback URL; type in [https://login.salesforce.com/](https://login.salesforce.com/) there.
        You also need to pick some permissions from a list called Selected OAuth Scopes. Choose these: Access and manage your data (api), Perform requests on your behalf at any time (refresh\_token, offline\_access), Provide access to your data via the Web (web), and then add them to your app settings.

        <Frame>
          <img src="https://res.cloudinary.com/dspflukeu/image/upload/v1707486682/Multiwoven/connectors/salesforce-crm/enable-oauth.png" />
        </Frame>
      </Step>

      <Step title="Save">
        Click Save to keep your new app's settings.
      </Step>

      <Step title="Apps > App Manager">
        Go back to where all your apps are listed (under Apps > App Manager), find the app you just created, and click Manage next to it.

        <Frame>
          <img src="https://res.cloudinary.com/dspflukeu/image/upload/v1707487232/Multiwoven/connectors/salesforce-crm/my-app.png" />
        </Frame>
      </Step>

      <Step title="OAuth policies">
        On the next screen, click Edit.
        There’s an option for OAuth policies; under Permitted Users, choose All users may self-authorize. Save your changes.

        <Frame>
          <img src="https://res.cloudinary.com/dspflukeu/image/upload/v1707487471/Multiwoven/connectors/salesforce-crm/self-authorize.png" />
        </Frame>
      </Step>

      <Step title="View App">
        Head back to your app list, find your new app again, and this time click View.

        <Frame>
          <img src="https://res.cloudinary.com/dspflukeu/image/upload/v1707487890/Multiwoven/connectors/salesforce-crm/view.png" />
        </Frame>
      </Step>

      <Step title="Save Permissions">
        Once more, go to the API (Enable OAuth Settings) section. Click on Manage Consumer Details.
        You need to write down two things: the Consumer Key and Consumer Secret.
        These are important because you'll use them to connect Salesforce.

        <Frame>
          <img src="https://res.cloudinary.com/dspflukeu/image/upload/v1707488140/Multiwoven/connectors/salesforce-crm/credentials.png" />
        </Frame>
      </Step>
    </Steps>
  </Accordion>

  <Accordion title="Obtain OAuth Credentials" icon="key">
    <Steps>
      <Step title="Getting the Code">
        First, open Salesforce in your preferred web browser.
        To get the code, open a new tab and type in a special web address (URL).
        You'll need to change **CONSUMER\_KEY** to the Consumer Key you noted earlier.
        Also, replace **INSTANCE\_URL** with your specific Salesforce instance name (for example, ours is multiwoven-dev in [https://multiwoven-dev.develop.lightning.force.com/](https://multiwoven-dev.develop.lightning.force.com/)).

        ```
          https://INSTANCE_URL.salesforce.com/services/oauth2/authorize?response_type=code&client_id=CONSUMER_KEY&redirect_uri=https://login.salesforce.com/
        ```

        If you see any alerts asking for permission, go ahead and allow them.
        After that, the browser will take you to a new webpage.
        Pay attention to this new web address because it contains the code you need.
        Save the code available in the new URL as shown in the below example.

        ```
          https://login.salesforce.com/services/oauth2/success?code=aPrx0jWjRo8KRXs42SX1Q7A5ckVpD9lSAvxdKnJUApCpikQQZf.YFm4bHNDUlgiG_PHwWQ%3D%3Dclient_id = "3MVG9pRzvMkjMb6kugcl2xWhaCVwiZPwg17wZSM42kf6HqY4jmw6ocKKoYYLz4ztHqM1vWxMbZB6sxQQU"
        ```
      </Step>

      <Step title="Getting the Access Token and Refresh Token">
        Now, you'll use a tool called curl to ask for more keys, known as tokens.
        You'll type a command into your computer that includes the special code you just got.
        Remember to replace **CODE** with your code, and also replace **CONSUMER\_KEY** and **CONSUMER\_SECRET** with the details you saved from when you set up the app in Salesforce.

        ```
        curl -X POST https://INSTANCE_URL.salesforce.com/services/oauth2/token?code=CODE&grant_type=authorization_code&client_id=CONSUMER_KEY&client_secret=CONSUMER_SECRET&redirect_uri=https://login.salesforce.com/
        ```

        After you send this command, you'll get back a response that includes your access\_token and refresh\_token.
        These tokens are what you'll use to securely access Salesforce data.

        ```
          {
            "access_token": "access_token",
            "refresh_token": "refresh_token",
            "signature": "signature",
            "scope": "scopes",
            "id_token": "id_token",
            "instance_url": "https://multiwoven-dev.develop.my.salesforce.com",
            "id": "id",
            "token_type": "Bearer",
            "issued_at": "1707415379555",
            "api_instance_url": "https://api.salesforce.com"
        }
        ```

        This way, you’re essentially getting the necessary permissions and access to work with Salesforce data in more detail.
      </Step>
    </Steps>
  </Accordion>
</AccordionGroup>

<Accordion title="Supported Sync" icon="arrows-rotate" defaultOpen="true">
  | Mode             | Supported (Yes/No/Coming soon) |
  | ---------------- | ------------------------------ |
  | Incremental sync | Yes                            |
  | Full refresh     | Coming soon                    |
</Accordion>

<Accordion title="Supported Streams">
  | Stream                                                                                                                             | Supported (Yes/No/Coming soon) |
  | ---------------------------------------------------------------------------------------------------------------------------------- | ------------------------------ |
  | [Account](https://developer.salesforce.com/docs/atlas.en-us.object_reference.meta/object_reference/sforce_api_objects_account.htm) | Yes                            |
</Accordion>


# Zoho
Source: https://docs.squared.ai/guides/destinations/retl-destinations/crm/zoho





# null
Source: https://docs.squared.ai/guides/destinations/retl-destinations/customer-support/intercom





# Zendesk
Source: https://docs.squared.ai/guides/destinations/retl-destinations/customer-support/zendesk

Zendesk is a customer service software and support ticketing system. Zendesk's connected platform enables you to improve customer relationships by providing seamless support and comprehensive customer insights.

## Zendesk Connector Configuration and Credential Retrieval Guide

### Prerequisite Requirements

Before initiating the Zendesk connector setup, ensure you have an active Zendesk account with admin privileges. This setup requires you to use your Zendesk username and password for authentication.

<Tip>
  [Zendesk Developer Signup](https://www.zendesk.com/signup)
</Tip>

### Destination Setup

As mentioned earlier, this setup requires your Zendesk username and password with admin access for authentication.

<Accordion title="Configure Zendesk Credentials" icon="key">
  For Multiwoven Open Source, we use Zendesk username and password for authentication.

  <Steps>
    <Step title="Access the Admin Console">
      Log into your Zendesk Developer account and navigate to the Admin Center by clicking on the gear icon in the sidebar.

      <Frame>
        <img src="https://res.cloudinary.com/dspflukeu/image/upload/v1716392386/Multiwoven/connectors/zendesk/zendesk-admin-console_nlu5ci.png" alt="Zendesk Admin Console" />
      </Frame>
    </Step>

    <Step title="Enable Password Access">
      Within the Admin Center, go to Channels > API. Ensure that the Password access is enabled by toggling the switch.

      <Frame>
        <img src="https://res.cloudinary.com/dspflukeu/image/upload/v1716392385/Multiwoven/connectors/zendesk/zendesk-auth-enablement_uuqkxg.png" alt="Zendesk Auth Enablement" />
      </Frame>
    </Step>

    <Step title="Utilize the Credentials">
      Ensure you have your Zendesk username and password. The username is typically your email address associated with the Zendesk account. Once you have your credentials, you can use the username and password to set up Zendesk in the Multiwoven destination section.
    </Step>
  </Steps>
</Accordion>


# MariaDB
Source: https://docs.squared.ai/guides/destinations/retl-destinations/database/maria_db



## Connect AI Squared to MariaDB

This guide will help you configure the MariaDB Connector in AI Squared to access and transfer data to your MariaDB database.

### Prerequisites

Before proceeding, ensure you have the necessary host, port, username, password, and database name from your MariaDB server.

## Step-by-Step Guide to Connect to MariaDB

## Step 1: Navigate to MariaDB Console

Start by logging into your MariaDB Management Console and navigating to the MariaDB service.

1. Sign in to your MariaDB account on your local server or through the MariaDB Enterprise interface.
2. In the MariaDB console, select the service you want to connect to.

## Step 2: Locate MariaDB Configuration Details

Once you're in the MariaDB console, you'll find the necessary configuration details:

1. **Host and Port:**
   * For local servers, the host is typically `localhost` and the default port is `3306`.
   * For remote servers, check your server settings or consult with your database administrator to get the correct host and port.
   * Note down the host and port as they will be used to connect to your MariaDB service.

2. **Username and Password:**
   * In the MariaDB console, you can find or create a user with the necessary permissions to access the database.
   * Note down the username and password as they are required for the connection.

3. **Database Name:**
   * List the available databases using the command `SHOW DATABASES;` in the MariaDB console.
   * Choose the database you want to connect to and note down its name.

## Step 3: Configure MariaDB Connector in Your Application

Now that you have gathered all the necessary details, enter the following information in your application:

* **Host:** The host of your MariaDB service.
* **Port:** The port number of your MariaDB service.
* **Username:** Your MariaDB service username.
* **Password:** The corresponding password for the username.
* **Database:** The name of the database you want to connect to.

## Step 4: Test the MariaDB Connection

After configuring the connector in your application:

1. Save the configuration settings.
2. Test the connection to MariaDB from your application to ensure everything is set up correctly.

By following these steps, you’ve successfully set up an MariaDB destination connector in AI Squared. You can now efficiently transfer data to your MariaDB endpoint for storage or further distribution within AI Squared.

### Supported sync modes

| Mode             | Supported (Yes/No/Coming soon) |
| ---------------- | ------------------------------ |
| Incremental sync | YES                            |
| Full refresh     | Coming soon                    |

This guide will help you seamlessly connect your AI Squared application to MariaDB, enabling you to leverage your database's full potential.


# MicrosoftSQL
Source: https://docs.squared.ai/guides/destinations/retl-destinations/database/ms_sql

Microsoft SQL Server (Structured Query Language) is a proprietary relational database management system developed by Microsoft. As a database server, it is a software product with the primary function of storing and retrieving data as requested by other software applications—which may run either on the same computer or on another computer across a network.

## Setting MS SQL Connector in AI Squared

To integrate Microsoft SQL with AI Squared, you need to establish a destination connector.
This connector will enable AI Squared to load data from various sources efficiently.
Below are the steps to set up the MS SQL Destination connector in AI Squared:

### Step 1: Access AI Squared

* Log in to your AI Squared account.
* Navigate to the `destinations` section where you can manage your destinations.

### Step 2: Create a New destination Connector

* Click on the `Add destination` button.
* Select `Microsoft SQL` from the list of available destination types.

### Step 3: Configure Connection Settings

You'll need to provide the following details to establish a connection between AI Squared and your MicrosoftSQL Database:

`Host` The hostname or IP address of the server where your MicrosoftSQL database is hosted.

`Port` The port number on which your MicrosoftSQL server is listening (default is 1433).

`Database` The name of the database you want to connect to.

`Schema` The schema within your MicrosoftSQL database you wish to access. The default schema for Microsoft SQL Server is dbo.

`Username` The username used to access the database.

`Password` The password associated with the username.

Enter these details in the respective fields on the connector configuration page and press continue.

### Step 4: Test the Connection

* Once you've entered the necessary information. The next step is automated **Test Connection** feature to ensure that AI Squared can successfully connect to your MicrosoftSQL database.
* If the test is successful, you'll receive a confirmation message. If not, double-check your entered details for any errors.

### Step 5: Finalize the destination Connector Setup

* Save the connector settings to establish the destination connection.

## Notes

* The Azure SQL Database firewall is a security feature that protects customer data by blocking access to the SQL Database server by default. To allow access, users can configure firewall rules to specify which IP addresses are permitted to access the database. [https://learn.microsoft.com/en-us/azure/azure-sql/database/firewall-configure?view=azuresql](https://learn.microsoft.com/en-us/azure/azure-sql/database/firewall-configure?view=azuresql)

* Your credentials must be able to: Add/update/delete rows in your sync's table.

* Get the connection information you need to connect to the database in Azure SQL Database. You'll need the fully qualified server name or host name, database name, and login information for the connection setup.
  * Sign in to the Azure portal.
  * Navigate to the SQL Databases or SQL Managed Instances page.
  * On the Overview page, review the fully qualified server name next to Server name for the database in Azure SQL Database or the fully qualified server name (or IP address) next to Host for an Azure SQL Managed Instance or SQL Server on Azure VM. To copy the server name or host name, hover over it and select the Copy icon.
  * More info at [https://learn.microsoft.com/en-us/azure/azure-sql/database/connect-query-content-reference-guide?view=azuresql](https://learn.microsoft.com/en-us/azure/azure-sql/database/connect-query-content-reference-guide?view=azuresql)


# Oracle
Source: https://docs.squared.ai/guides/destinations/retl-destinations/database/oracle



## Connect AI Squared to Oracle

This guide will help you configure the Oracle Connector in AI Squared to access and transfer data to your Oracle database.

### Prerequisites

Before proceeding, ensure you have the necessary host, port, SID or service name, username, and password from your Oracle database.

## Step-by-Step Guide to Connect to Oracle database

### Step 1: Locate Oracle database Configuration Details

In your Oracle database, you'll need to find the necessary configuration details:

1. **Host and Port:**
   * For local servers, the host is typically `localhost` and the default port is `1521`.
   * For remote servers, check your server settings or consult with your database administrator to get the correct host and port.
   * Note down the host and port as they will be used to connect to your Oracle database.

2. **SID or Service Name:**
   * To find your SID or Service name:
     1. **Using SQL\*Plus or SQL Developer:**
        * Connect to your Oracle database using SQL\*Plus or SQL Developer.
        * Execute the following query:
          ```sql
          select instance from v$thread
          ```
          or
          ```sql
          SELECT sys_context('userenv', 'service_name') AS service_name FROM dual;
          ```
        * The result will display the SID or service name of your Oracle database.
     2. **Checking the TNSNAMES.ORA File:**
        * Locate and open the `tnsnames.ora` file on your system. This file is usually found in the `ORACLE_HOME/network/admin` directory.
        * Look for the entry corresponding to your database connection. The `SERVICE_NAME` or `SID` will be listed within this entry.
     * Note down the SID or service name as it will be used to connect to your Oracle database.

3. **Username and Password:**
   * In the Oracle, you can find or create a user with the necessary permissions to access the database.
   * Note down the username and password as it will be used to connect to your Oracle database.

### Step 2: Configure Oracle Connector in Your Application

Now that you have gathered all the necessary details, enter the following information in your application:

* **Host:** The host of your Oracle database.
* **Port:** The port number of your Oracle database.
* **SID:** The SID or service name you want to connect to.
* **Username:** Your Oracle username.
* **Password:** The corresponding password for the username.

### Step 3: Test the Oracle Database Connection

After configuring the connector in your application:

1. Save the configuration settings.
2. Test the connection to Oracle database from your application to ensure everything is set up correctly.

By following these steps, you’ve successfully set up an Oracle database destination connector in AI Squared. You can now efficiently transfer data to your Oracle database for storage or further distribution within AI Squared.

## Supported sync modes

| Mode             | Supported (Yes/No/Coming soon) |
| ---------------- | ------------------------------ |
| Incremental sync | YES                            |
| Full refresh     | Coming soon                    |

This guide will help you seamlessly connect your AI Squared application to Oracle Database, enabling you to leverage your database's full potential.


# PostgreSQL
Source: https://docs.squared.ai/guides/destinations/retl-destinations/database/postgresql

PostgreSQL popularly known as Postgres, is a powerful, open-source object-relational database system that uses and extends the SQL language combined with many features that safely store and scale data workloads.

## Setting Up a destination Connector in AI Squared

To integrate PostgreSQL with AI Squared, you need to establish a destination connector.
This connector will enable AI Squared to extract data from your PostgreSQL database efficiently.
Below are the steps to set up the destination connector in AI Squared:

### Step 1: Access AI Squared

* Log in to your AI Squared account.
* Navigate to the `destinations` section where you can manage your data destinations.

### Step 2: Create a New destination Connector

* Click on the `Add destination` button.
* Select `PostgreSQL` from the list of available destination types.

### Step 3: Configure Connection Settings

You'll need to provide the following details to establish a connection between AI Squared and your PostgreSQL database:

`Host` The hostname or IP address of the server where your PostgreSQL database is hosted.

`Port` The port number on which your PostgreSQL server is listening (default is 5432).

`Database` The name of the database you want to connect to.

`Schema` The schema within your PostgreSQL database you wish to access.

`Username` The username used to access the database.

`Password` The password associated with the username.

Enter these details in the respective fields on the connector configuration page and press continue.

### Step 4: Test the Connection

* Once you've entered the necessary information. The next step is automated **Test Connection** feature to ensure that AI Squared can successfully connect to your PostgreSQL database.
* If the test is successful, you'll receive a confirmation message. If not, double-check your entered details for any errors.

### Step 5: Finalize the destination Connector Setup

* Save the connector settings to establish the destination connection.

### Conclusion

By following these steps, you've successfully set up a PostgreSQL destination connector in AI Squared.


# null
Source: https://docs.squared.ai/guides/destinations/retl-destinations/e-commerce/facebook-product-catalog





# null
Source: https://docs.squared.ai/guides/destinations/retl-destinations/e-commerce/shopify





# Amazon S3
Source: https://docs.squared.ai/guides/destinations/retl-destinations/file-storage/amazon_s3



## Connect AI Squared to Amazon S3

This guide will help you configure the Amazon S3 Connector in AI Squared to access and transfer data to your S3 bucket.

### Prerequisites

Before proceeding, ensure you have the necessary personal access key, secret access key, region, bucket name, and file path from your S3 account.

## Step-by-Step Guide to Connect to Amazon S3

## Step 1: Navigate to AWS Console

Start by logging into your AWS Management Console.

1. Sign in to your AWS account at [AWS Management Console](https://aws.amazon.com/console/).

## Step 2: Locate AWS Configuration Details

Once you're in the AWS console, you'll find the necessary configuration details:

1. **Access Key and Secret Access Key:**

   * Click on your username at the top right corner of the AWS Management Console.
   * Choose "Security Credentials" from the dropdown menu.
   * In the "Access keys" section, you can create or view your access keys.
   * If you haven't created an access key pair before, click on "Create access key" to generate a new one. Make sure to copy the Access Key ID and Secret Access Key as they are shown only once.
     <Frame>
       <img src="https://res.cloudinary.com/dspflukeu/image/upload/v1725025888/Multiwoven/connectors/aws_sagemaker-model/Create_access_keys_sh1tmz.jpg" />
     </Frame>

2. **Region:**
   * The AWS region can be selected from the top right corner of the AWS Management Console. Choose the region where your AWS Sagemaker resources is located and note down the region.
     <Frame>
       <img src="https://res.cloudinary.com/dspflukeu/image/upload/v1736442701/Multiwoven/connectors/amazon_S3/AmazonS3_Region_xpszth.png" />
     </Frame>

3. **Bucket Name:**
   * The S3 Bucket name can be found by selecting "General purpose buckets" on the left hand corner of the S3 Console. From there select the bucket you want to use and note down its name.
     <Frame>
       <img src="https://res.cloudinary.com/dspflukeu/image/upload/v1736442700/Multiwoven/connectors/amazon_S3/AmazonS3_Bucket_msmuow.png" />
     </Frame>

4. **File Path**
   * After select your S3 bucket you can create a folder where you want your file to be stored or use an exist one.
   <Frame>
     <img src="https://res.cloudinary.com/dspflukeu/image/upload/v1736442700/Multiwoven/connectors/amazon_S3/AmazonS3_File_Path_djofzv.png" />
   </Frame>

## Step 3: Configure Amazon S3 Connector in Your Application

Now that you have gathered all the necessary details, enter the following information in your application:

* **Personal Access Key:** Your AWS IAM user's Access Key ID.
* **Secret Access Key:** The corresponding Secret Access Key.
* **Region:** The AWS region where your Sagemaker resources are located.
* **Bucket Name:** The Amazon S3 Bucket you want to access.
* **File Path:** The Path to the directory where files will be written.
* **File Name:** The Name of the file to be written.

## Step 4: Test the Amazon S3 Connection

After configuring the connector in your application:

1. Save the configuration settings.
2. Test the connection to Amazon S3 from your application to ensure everything is set up correctly.

By following these steps, you’ve successfully set up an Amazon S3 destination connector in AI Squared. You can now efficiently transfer data to your Amazon S3 endpoint for storage or further distribution within AI Squared.

### Supported sync modes

| Mode             | Supported (Yes/No/Coming soon) |
| ---------------- | ------------------------------ |
| Incremental sync | YES                            |
| Full refresh     | Coming soon                    |

This guide will help you seamlessly connect your AI Squared application to MariaDB, enabling you to leverage your database's full potential.


# SFTP
Source: https://docs.squared.ai/guides/destinations/retl-destinations/file-storage/sftp

Learn how to set up a SFTP destination connector in AI Squared to efficiently transfer data to your SFTP server.

## Introduction

The Secure File Transfer Protocol (SFTP) is a secure method for transferring files between systems. Integrating SFTP with AI Squared allows you to efficiently transfer data to your SFTP server for storage or further distribution. This guide outlines the steps to set up an SFTP destination connector in AI Squared.

### Step 1: Access AI Squared

1. Log in to your AI Squared account.
2. Navigate to the **Destinations** section to manage your data destinations.

### Step 2: Create a New Destination Connector

1. Click on the **Add Destination** button.
2. Select **SFTP** from the list of available destination types.

### Step 3: Configure Connection Settings

Provide the following details to establish a connection between AI Squared and your SFTP server:

* **Host**: The hostname or IP address of the SFTP server.
* **Port**: The port number used for SFTP connections (default is 22).
* **Username**: Your username for accessing the SFTP server.
* **Password**: The password associated with the username.
* **Destination Path**: The directory path on the SFTP server where you want to store the files.
* **Filename**: The name of the file to be uploaded to the SFTP server, appended with the current timestamp.

Enter these details in the respective fields on the connector configuration page and press **Finish**.

### Step 4: Test the Connection

1. After entering the necessary information, use the automated **Test Connection** feature to ensure AI Squared can successfully connect to your SFTP server.
2. If the test is successful, you'll receive a confirmation message. If not, double-check your entered details for any errors.

### Step 5: Finalize the Destination Connector Setup

1. After a successful connection test, save the connector settings to establish the destination connection.

## Conclusion

By following these steps, you've successfully set up an SFTP destination connector in AI Squared. You can now efficiently transfer data to your SFTP server for storage or further distribution within AI Squared.


# HTTP
Source: https://docs.squared.ai/guides/destinations/retl-destinations/http/http

Learn how to set up a HTTP destination connector in AI Squared to efficiently transfer data to your HTTP destination.

## Introduction

The Hyper Text Transfer Protocol (HTTP) connector is a method of transerring data over the internet to specific url endpoints. Integrating the HTTP Destination connector with AI Squared allows you to efficiently transfer your data to HTTP endpoints of your choosing. This guide outlines how to setup your HTTP destination connector in AI Squared.

### Destination Setup

<AccordionGroup>
  <Accordion title="Create an HTTP destination" icon="key" defaultOpen="true">
    <Steps>
      <Step title="Access AI Squared">
        Log in to your AI Squared account and navigate to the **Destinations** section to manage your data destinations.

        <Frame>
          <img src="https://res.cloudinary.com/dspflukeu/image/upload/v1716396869/http_dest_1.png" />
        </Frame>
      </Step>

      <Step title="Create a New Destination Connector">
        Click on the **Add Destination** button. Select **HTTP** from the list of available destination types.

        <Frame>
          <img src="https://res.cloudinary.com/dspflukeu/image/upload/v1716396869/http_dest_2.png" />
        </Frame>
      </Step>

      <Step title="Configure Connection Settings">
        Provide the following details to establish a connection between AI Squared and your HTTP endpoint:

        * **Destination Url**: The HTTP address of where you are sending your data.
        * **Headers**: A list of key value pairs of your choosing. This can include any headers that are required to send along with your HTTP request.

        Enter these details in the respective fields on the connector configuration page and press **Finish**.

        <Frame>
          <img src="https://res.cloudinary.com/dspflukeu/image/upload/v1716396869/http_dest_3.png" />
        </Frame>
      </Step>

      <Step title="Test the Connection">
        After entering the necessary information, use the automated **Test Connection** feature to ensure AI Squared can successfully connect to your HTTP endpoint. If the test is successful, you'll receive a confirmation message. If not, double-check your entered details for any errors.
      </Step>

      <Step title="Finalize the Destination Connector Setup">
        After a successful connection test, save the connector settings to establish the destination connection.

        By following these steps, you've successfully set up an HTTP destination connector in AI Squared. You can now efficiently transfer data to your HTTP endpoint for storage or further distribution within AI Squared.
      </Step>
    </Steps>
  </Accordion>
</AccordionGroup>

<Accordion title="Supported Sync" icon="arrows-rotate" defaultOpen="true">
  | Mode             | Supported (Yes/No/Coming soon) |
  | ---------------- | ------------------------------ |
  | Incremental sync | Yes                            |
  | Full refresh     | No                             |
</Accordion>


# Braze
Source: https://docs.squared.ai/guides/destinations/retl-destinations/marketing-automation/braze





# CleverTap
Source: https://docs.squared.ai/guides/destinations/retl-destinations/marketing-automation/clevertap





# Iterable
Source: https://docs.squared.ai/guides/destinations/retl-destinations/marketing-automation/iterable



## Connect AI Squared to Iterable

This guide will help you configure the Iterable Connector in AI Squared to access and use your Iterable data.

### Prerequisites

Before proceeding, ensure you have the necessary API Key from Iterable.

## Step-by-Step Guide to Connect to Iterable

## Step 1: Navigate to Iterable

Start by logging into your Iterable account and navigating to the Iterable service.

1. Sign in to your Iterable account at [Iterable Login](https://www.iterable.com/login/).
2. Once logged in, you will be directed to the Iterable dashboard.

## Step 2: Locate Iterable API Key

Once you're logged into Iterable, you'll find the necessary configuration details:

1. **API Key:**
   * Click on "Integrations" and select "API Keys" from the dropdown menu.

<Frame>
  <img src="https://res.cloudinary.com/dspflukeu/image/upload/v1710242447/Multiwoven/connectors/iterable/iterable_api_key.png" />
</Frame>

* Here, you can create a new API key or use an existing one. Click on "+ New API key" if needed, and give it a name.
* Once the API key is created, copy it as it will be required for configuring the connector.

## Step 3: Test the Iterable Connection

After configuring the connector in your application:

1. Save the configuration settings.
2. Test the connection to Iterable from the AI Squared platform to ensure a connection is made.

By following these steps, you’ve successfully set up an Iterable destination connector in AI Squared. You can now efficiently transfer data to your Iterable endpoint for storage or further distribution within AI Squared.

### Supported sync modes

| Mode             | Supported (Yes/No/Coming soon) |
| ---------------- | ------------------------------ |
| Incremental sync | YES                            |
| Full refresh     | Coming soon                    |

Follow these steps to configure and test your Iterable connector successfully.


# Klaviyo
Source: https://docs.squared.ai/guides/destinations/retl-destinations/marketing-automation/klaviyo



# Destination/Klaviyo

### Overview

Enhance Your ECommerce Email Marketing Campaigns Using Warehouse Data in Klaviyo

### Setup

1. Create a [Klaviyo account](https://www.klaviyo.com/)
2. Generate a[ Private API Key](https://help.klaviyo.com/hc/en-us/articles/115005062267-How-to-Manage-Your-Account-s-API-Keys#your-private-api-keys3) and Ensure All Relevant Scopes are Included for the Streams You Wish to Replicate.

### Supported sync modes

| Mode             | Supported (Yes/No/Coming soon) |
| ---------------- | ------------------------------ |
| Incremental sync | Yes                            |
| Full refresh     | Coming soon                    |

### Supported streams

| Stream                                                                             | Supported (Yes/No/Coming soon) |
| ---------------------------------------------------------------------------------- | ------------------------------ |
| [Profiles](https://developers.klaviyo.com/en/v2023-02-22/reference/get_profiles)   | Yes                            |
| [Campaigns](https://developers.klaviyo.com/en/v2023-06-15/reference/get_campaigns) | Coming soon                    |
| [Events](https://developers.klaviyo.com/en/reference/get_events)                   | Coming soon                    |
| [Lists](https://developers.klaviyo.com/en/reference/get_lists)                     | Coming soon                    |


# null
Source: https://docs.squared.ai/guides/destinations/retl-destinations/marketing-automation/mailchimp



## Setting Up the Mailchimp Connector in AI Squared

To integrate Mailchimp with AI Squared, you need to establish a destination connector. This connector will allow AI Squared to sync data efficiently from various sources to Mailchimp.

***

## Step 1: Access AI Squared

1. Log in to your **AI Squared** account.
2. Navigate to the **Destinations** section to manage your destination connectors.

## Step 2: Create a New Destination Connector

<Frame>
  <img src="https://res.cloudinary.com/dspflukeu/image/upload/f_auto,q_auto/v1/DevRel/Mailchimp/zabdi90se75ehy0w1vhu" />
</Frame>

1. Click on the **Add Destination** button.
2. Select **Mailchimp** from the list of available destination types.

## Step 3: Configure Connection Settings

To establish a connection between AI Squared and Mailchimp, provide the following details:

<Frame>
  <img src="https://res.cloudinary.com/dspflukeu/image/upload/f_auto,q_auto/v1/DevRel/Mailchimp/eyt4nbbzjwdnomq72qpf" />
</Frame>

1. **API Key**
   * Used to authenticate your Mailchimp account.
   * Generate this key in your Mailchimp account under `Account > Extras > API Keys`.

2. **List ID**
   * The unique identifier for the specific audience (mailing list) you want to target in Mailchimp.
   * Find your Audience ID in Mailchimp by navigating to `Audience > Manage Audience > Settings > Audience name and defaults`.

3. **Email Template ID**
   * The unique ID of the email template you want to use for campaigns or automated emails in Mailchimp.
   * Locate or create templates in the **Templates** section of Mailchimp. The ID is retrievable via the Mailchimp API or from the template’s settings.

Enter these parameters in their respective fields on the connector configuration page and press **Continue** to proceed.

## Step 4: Test the Connection

<Frame>
  <img src="https://res.cloudinary.com/dspflukeu/image/upload/f_auto,q_auto/v1/DevRel/Mailchimp/qzf8qecchcr3vdtiskgu" />
</Frame>

1. Use the **Test Connection** feature to ensure AI Squared can successfully connect to your Mailchimp account.
2. If the test is successful, you’ll receive confirmation.
3. If unsuccessful, recheck the entered information.

## Step 5: Finalize the Destination Connector Setup

1. Save the connector settings to establish the Mailchimp destination connection.

<Frame>
  <img src="https://res.cloudinary.com/dspflukeu/image/upload/f_auto,q_auto/v1/DevRel/Mailchimp/gn1jbkrh7h6gsgldh3ct" />
</Frame>

***

## Setting Up a Model for Mailchimp

To sync data to Mailchimp, you first need to prepare your data by creating a model based on the source data. Here's how:

1. **Review Your Source Data**
   Identify the key fields you need from the source (e.g., email, first name, last name, and tags).

2. **Create the Model**
   Select the necessary fields from your source.
   Map these fields to match Mailchimp’s required parameters, such as `email`, `merge_fields.FNAME` (first name), and `tags.0`.

3. **Save and Validate**
   Ensure the model is structured properly and contains clean, valid data.

4. **Sync the Model**
   Use the model as the basis for setting up your sync to Mailchimp.
   Map fields from the model to the corresponding Mailchimp parameters during sync configuration.

This step ensures your data is well-structured and ready to integrate with Mailchimp seamlessly.

***

## Configuring the Mapping for Mailchimp

When creating a sync for the Mailchimp destination connector, the following parameters can be mapped to enhance data synchronization and segmentation capabilities:

### Core Parameters

1. `email`\
   **Description**: The email address of the subscriber.\
   **Purpose**: Required to uniquely identify and add/update contacts in a Mailchimp audience.

2. `status`\
   **Description**: The subscription status of the contact.\
   **Purpose**: Maintains accurate subscription data for compliance and segmentation.\
   **Options**:
   * `subscribed` – Actively subscribed to the mailing list.
   * `unsubscribed` – Opted out of the list.
   * `cleaned` – Undeliverable address.
   * `pending` – Awaiting confirmation (e.g., double opt-in).

### Personalization Parameters

1. `first_name`\
   **Description**: The first name of the contact.\
   **Purpose**: Used for personalization in email campaigns.

2. `last_name`
   **Description**: The last name of the contact.\
   **Purpose**: Complements personalization for formal messaging.

3. `merge_fields.FNAME`\
   **Description**: Merge field for the first name of the contact.\
   **Purpose**: Enables advanced personalization in email templates (e.g., "Hello, |FNAME|!").

4. `merge_fields.LNAME`\
   **Description**: Merge field for the last name of the contact.\
   **Purpose**: Adds dynamic content based on the last name.

### Segmentation Parameters

1. `tags.0`\
   **Description**: A tag assigned to the contact.\
   **Purpose**: Enables grouping and segmentation within the Mailchimp audience.

2. `vip`\
   **Description**: Marks the contact as a VIP (true or false).\
   **Purpose**: Identifies high-priority contacts for specialized campaigns.

3. `language`\
   **Description**: The preferred language of the contact using an ISO 639-1 code (e.g., `en` for English, `fr` for French).\
   **Purpose**: Supports localization and tailored communication for multilingual audiences.

### Compliance and Tracking Parameters

1. `ip_opt`\
   **Description**: The IP address from which the contact opted into the list.\
   **Purpose**: Ensures regulatory compliance and tracks opt-in origins.

2. `ip_signup`\
   **Description**: The IP address where the contact originally signed up.\
   **Purpose**: Tracks the geographical location of the signup for analytics and compliance.

3. `timestamp_opt`\
   **Description**: The timestamp when the contact opted into the list (ISO 8601 format).\
   **Purpose**: Provides a record for regulatory compliance and automation triggers.

4. `timestamp_signup`\
   **Description**: The timestamp when the contact signed up (ISO 8601 format).\
   **Purpose**: Tracks the signup date for lifecycle and engagement analysis.

***


# Stripe
Source: https://docs.squared.ai/guides/destinations/retl-destinations/payment/stripe



## Overview

Integrating customer data with subscription metrics from Stripe provides valuable insights into the actions that most frequently convert free accounts into paying ones.
It also helps identify accounts that may be at risk of churning due to low activity levels.
By recognizing these trends, you can proactively engage at-risk customers to prevent churn and enhance customer retention.

## Stripe Connector Configuration and Credential Retrieval Guide

### Prerequisite Requirements

To authenticate the Stripe connector using AI Squared, you'll need a Stripe API key.
While you can use an existing key, it's better to create a new restricted key specifically for AI Squared.
Make sure to grant it write privileges only. Additionally, it's advisable to enable write privileges for all possible permissions and tailor the specific data you wish to synchronize within AI Squared.

### Set up Stripe

<AccordionGroup>
  <Accordion title="Create API Key" icon="stripe" defaultOpen="true">
    <Steps>
      <Step title="Sign In">
        Sign into your Stripe account.
      </Step>

      <Step title="Developers">
        Click 'Developers' on the top navigation bar.

        <Frame>
          <img src="https://res.cloudinary.com/dspflukeu/image/upload/v1713863933/Multiwoven/connectors/stripe/developers_kyj50a.png" />
        </Frame>
      </Step>

      <Step title="API keys">
        At the top-left, click 'API keys'.
      </Step>

      <Step title="Restricted key">
        Select '+ Create restricted key'.
      </Step>

      <Step title="Naming and permission">
        Name your key, and ensure 'Write' is selected for all permissions.

        <Frame>
          <img src="https://res.cloudinary.com/dspflukeu/image/upload/v1713863934/Multiwoven/connectors/stripe/naming_z6njmb.png" />
        </Frame>
      </Step>

      <Step title="Create key">
        Click 'Create key'. You may need to verify by entering a code sent to your email.
      </Step>
    </Steps>
  </Accordion>
</AccordionGroup>

<Accordion title="Supported Sync" icon="arrows-rotate" defaultOpen="true">
  | Mode             | Supported (Yes/No/Coming soon) |
  | ---------------- | ------------------------------ |
  | Incremental sync | Yes                            |
  | Full refresh     | Coming soon                    |
</Accordion>

<Accordion title="Supported Streams" defaultOpen="true">
  | Stream   | Supported (Yes/No/Coming soon) |
  | -------- | ------------------------------ |
  | Customer | Yes                            |
  | Product  | Yes                            |
</Accordion>


# Airtable
Source: https://docs.squared.ai/guides/destinations/retl-destinations/productivity-tools/airtable



# Destination/Airtable

### Overview

Airtable combines the simplicity of a spreadsheet with the complexity of a database. This cloud-based platform enables users to organize work, manage projects, and automate workflows in a customizable and collaborative environment.

### Prerequisite Requirements

Ensure you have created an Airtable account before you begin. Sign up [here](https://airtable.com/signup) if you haven't already.

### Setup

1. **Generate a Personal Access Token**

   Start by generating a personal access token. Follow the guide [here](https://airtable.com/developers/web/guides/personal-access-tokens) for instructions.

<Frame>
  <img src="https://res.cloudinary.com/dspflukeu/image/upload/v1710242447/Multiwoven/connectors/airtable/create_token_vjkaye.png" />
</Frame>

2. **Grant Required Scopes**

   Assign the following scopes to your token for the necessary permissions:

   * `data.records:read`
   * `data.records:write`
   * `schema.bases:read`
   * `schema.bases:write`

   <Frame>
     <img src="https://res.cloudinary.com/dspflukeu/image/upload/v1710242455/Multiwoven/connectors/airtable/token_scope_lxw0ps.png" />
   </Frame>


# Google Sheets - Service Account
Source: https://docs.squared.ai/guides/destinations/retl-destinations/productivity-tools/google-sheets

Google Sheets serves as an effective reverse ETL destination, enabling real-time data synchronization from data warehouses to a collaborative, user-friendly spreadsheet environment. It democratizes data access, allowing stakeholders to analyze, share, and act on insights without specialized skills. The platform supports automation and customization, enhancing decision-making and operational efficiency. Google Sheets transforms complex data into actionable intelligence, fostering a data-driven culture across organizations.

<Warning>
  Google Sheets is equipped with specific data capacity constraints, which, when exceeded, can lead to synchronization issues. Here's a concise overview of these limitations:

  * **Cell Limit**: A Google Sheets document is capped at `10 million` cells, which can be spread across one or multiple sheets. Once this limit is reached, no additional data can be added, either in the form of new rows or columns.
  * **Character Limit per Cell**: Each cell in Google Sheets can contain up to `50,000` characters. It's crucial to consider this when syncing data that includes fields with lengthy text.
  * **Column Limit**: A single worksheet within Google Sheets is limited to `18,278` columns.
  * **Worksheet Limit**: There is a cap of `200` worksheets within a single Google Sheets spreadsheet.

  Given these restrictions, Google Sheets is recommended primarily for smaller, non-critical data engagements.
  It may not be the optimal choice for handling expansive data operations due to its potential for sync failures upon reaching these imposed limits.
</Warning>

## Connector Configuration and Credential Retrieval Guide

### Prerequisite Requirements

Before initiating the Google Sheet connector setup, ensure you have an created or access an [Google cloud account](https://console.cloud.google.com).

### Destination Setup

<Accordion title="Set up the Service Account Key" icon="key">
  <Steps>
    <Step title="Create a Service Account">
      * Navigate to the [Service Accounts](https://console.cloud.google.com/projectselector2/iam-admin/serviceaccounts) page in your Google Cloud console.
        <Frame>
          <img src="https://res.cloudinary.com/dspflukeu/image/upload/v1710246065/Multiwoven/connectors/google-sheets-service-account/service-account.png" />
        </Frame>
      * Choose an existing project or create a new one.
        <Frame>
          <img src="https://res.cloudinary.com/dspflukeu/image/upload/v1710246116/Multiwoven/connectors/google-sheets-service-account/service-account-form.png" />
        </Frame>
      * Click + Create service account, enter its name and description, then click Create and Continue.
      * Assign appropriate permissions, recommending the Editor role, then click Continue.
    </Step>

    <Step title="Generate a Key">
      * Access the [API Console > Credentials](https://console.cloud.google.com/apis/credentials) page, select your service account's email.
        <Frame>
          <img src="https://res.cloudinary.com/dspflukeu/image/upload/v1710246147/Multiwoven/connectors/google-sheets-service-account/credentials.png" />
        </Frame>
      * In the Keys tab, click + Add key and select Create new key.
      * Choose JSON as the Key type to download your authentication JSON key file. Click Continue.
        <Frame>
          <img src="https://res.cloudinary.com/dspflukeu/image/upload/v1710246195/Multiwoven/connectors/google-sheets-service-account/create-credentials.png" />
        </Frame>
    </Step>

    <Step title="Enable the Google Sheets API">
      * Navigate to the [API Console > Library](https://console.cloud.google.com/apis/library) page.
        <Frame>
          <img src="https://res.cloudinary.com/dspflukeu/image/upload/v1710246418/Multiwoven/connectors/google-sheets-service-account/api-library.png" />
        </Frame>
      * Verify that the correct project is selected at the top.
      * Find and select the Google Sheets API.
      * Click ENABLE.
        <Frame>
          <img src="https://res.cloudinary.com/dspflukeu/image/upload/v1710246457/Multiwoven/connectors/google-sheets-service-account/update-google-sheets-api.png" />
        </Frame>
    </Step>

    <Step title="Spreadsheet Access">
      * If your spreadsheet is link-accessible, no extra steps are needed.
      * If not, [grant your service account](https://support.google.com/a/answer/60781?hl=en\&sjid=11618327295115173982-AP) access to your spreadsheet.
    </Step>

    <Step title="Output Schema">
      * Each worksheet becomes a separate source-connector stream in AI Squared.
      * Data is coerced to string format; nested structures need further processing for analysis.
      * AI Squared replicates text via Grid Sheets only; charts and images aren't supported.
    </Step>
  </Steps>
</Accordion>


# Microsoft Excel
Source: https://docs.squared.ai/guides/destinations/retl-destinations/productivity-tools/microsoft-excel



## Connect AI Squared to Microsoft Excel

This guide will help you configure the Iterable Connector in AI Squared to access and use your Iterable data.

### Prerequisites

Before proceeding, ensure you have the necessary Access Token from Microsoft Graph.

## Step-by-Step Guide to Connect to Microsoft Excel

## Step 1: Navigate to Microsoft Graph Explorer

Start by logging into Microsoft Graph Explorer using your Microsoft account and consent to the required permissions.

1. Sign into Microsoft Graph Explorer at [developer.microsoft.com](https://developer.microsoft.com/en-us/graph/graph-explorer).
2. Once logged in, consent to the following under each category:
   * **Excel:**
     * worksheets in a workbook
     * used range in worksheet
   * **OneDrive:**
     * list items in my drive
   * **User:**
     * me
3. Once the following is consented to click Access token and copy the token

## Step 2: Navigate to Microsoft Excel

Once you're logged into Microsoft Excel do the following:

1. **Create a new workbook:**
   * Create a new workbook in excel to have the data stored.
   * Once you have create the workbook open it and navigate to the sheet of you choosing.
   * In the sheet of your choosing make a table with the required headers you want to transfer data to.

<Frame>
  <img src="https://res.cloudinary.com/dspflukeu/image/upload/v1723599643/Multiwoven/connectors/microsoft-excel/Workbook_setup_withfd.jpg" />
</Frame>

## Step 3: Configure Microsoft Excel Connector in Your Application

Now that you have gathered all the necessary details, enter the following information in your application:

* **Token:** The access token from Microsoft Graph Explorer.

## Step 4: Test the Microsoft Excel Connection

After configuring the connector in your application:

1. Save the configuration settings.
2. Test the connection to Microsoft Excel from the AI Squared platform to ensure a connection is made.

By following these steps, you’ve successfully set up an Microsoft Excel destination connector in AI Squared. You can now efficiently transfer data to your Microsoft Excel workbook for storage or further distribution within AI Squared.

### Supported sync modes

| Mode             | Supported (Yes/No/Coming soon) |
| ---------------- | ------------------------------ |
| Incremental sync | YES                            |
| Full refresh     | Coming soon                    |

Follow these steps to configure and test your Iterable connector successfully.


# Salesforce Consumer Goods Cloud
Source: https://docs.squared.ai/guides/destinations/retl-destinations/retail/salesforce-consumer-goods-cloud



## Overview

Salesforce Consumer Goods Cloud is a specialized CRM platform designed to help companies in the consumer goods industry manage their operations more efficiently.
It provides tools to optimize route-to-market strategies, increase sales performance, and enhance field execution.
This cloud-based solution leverages Salesforce's robust capabilities to deliver data-driven insights, streamline inventory and order management, and foster closer relationships with retailers and customers.

### Key Features:

* **Retail Execution**: Manage store visits, ensure product availability, and optimize shelf placement.
* **Sales Planning and Operations**: Create and manage sales plans that align with company goals.
* **Trade Promotion Management**: Plan, execute, and analyze promotional activities to maximize ROI.
* **Field Team Management**: Enable field reps with tools and data to improve productivity and effectiveness.

## Connector Configuration and Credential Retrieval Guide

### Prerequisite Requirements

When setting up an integration between Salesforce Consumer Goods Cloud and Multiwoven, certain credentials are required to authenticate and establish a secure connection. Below is a brief description of each credential needed:

* **Username**: The Salesforce username used to log in.
* **Password**: The password associated with the Salesforce username.
* **Host**: The URL of your Salesforce instance (e.g., [https://login.salesforce.com](https://login.salesforce.com)).
* **Security Token**: An additional security key that is appended to your password for API access from untrusted networks.
* **Client ID** and **Client Secret**: These are part of the OAuth credentials required for authenticating an application with Salesforce.
  They are obtained when you set up a new "Connected App" in Salesforce for integrating with external applications. You may refer our [Salesforce CRM docs](https://docs.multiwoven.com/destinations/crm/salesforce#destination-setup) for further details.

### Setting Up Security Token in Salesforce

<AccordionGroup>
  <Accordion title="Steps to Retrieve or Reset a Salesforce Security Token" icon="salesforce" defaultOpen="true">
    <Steps>
      <Step title="Sign In">
        Log in to your Salesforce account.
      </Step>

      <Step title="Settings">
        Navigate to Settings or My Settings by first clicking on My Profile and then clicking **Settings** under the Personal Information section.

        <Frame>
          <img src="https://res.cloudinary.com/dspflukeu/image/upload/v1713892144/Multiwoven/connectors/salesforce-consumer-goods-cloud/settings.png" />
        </Frame>
      </Step>

      <Step title="Quick Find">
        Once inside the Settings page click on the Quick Find box and type "Reset My Security Token" to quickly navigate to the option.

        <Frame>
          <img src="https://res.cloudinary.com/dspflukeu/image/upload/v1713892144/Multiwoven/connectors/salesforce-consumer-goods-cloud/reset.png" />
        </Frame>
      </Step>

      <Step title="Reset My Security Token">
        Click on Reset My Security Token under the Personal section. Salesforce will send the new security token to the email address associated with your account.
        If you do not see the option to reset the security token, it may be because your organization uses Single Sign-On (SSO) or has IP restrictions that negate the need for a token.

        <Frame>
          <img src="https://res.cloudinary.com/dspflukeu/image/upload/v1713892144/Multiwoven/connectors/salesforce-consumer-goods-cloud/security-token.png" />
        </Frame>
      </Step>
    </Steps>
  </Accordion>
</AccordionGroup>

<Accordion title="Supported Sync" icon="arrows-rotate" defaultOpen="true">
  | Mode             | Supported (Yes/No/Coming soon) |
  | ---------------- | ------------------------------ |
  | Incremental sync | Yes                            |
  | Full refresh     | Coming soon                    |
</Accordion>

<Accordion title="Supported Streams" defaultOpen="true">
  | Stream      | Supported (Yes/No/Coming soon) |
  | ----------- | ------------------------------ |
  | Account     | Yes                            |
  | User        | Yes                            |
  | Visit       | Yes                            |
  | RetailStore | Yes                            |
  | RecordType  | Yes                            |
</Accordion>


# null
Source: https://docs.squared.ai/guides/destinations/retl-destinations/team-collaboration/microsoft-teams





# Slack
Source: https://docs.squared.ai/guides/destinations/retl-destinations/team-collaboration/slack



## Usecase

<CardGroup cols={2}>
  <Card title="Sales and Support Alerts" icon="bell">
    Notify sales or customer support teams about significant customer events,
    like contract renewals or support tickets, directly in Slack.
  </Card>

  <Card title="Collaborative Data Analysis" icon="magnifying-glass-chart">
    Share real-time insights and reports in Slack channels to foster
    collaborative analysis and decision-making among teams. This is particularly
    useful for remote and distributed teams
  </Card>

  <Card title="Operational Efficiency" icon="triangle-exclamation">
    Integrate Slack with operational systems to streamline operations. For
    instance, sending real-time alerts about system downtimes, performance
    bottlenecks, or successful deployments to relevant engineering or operations
    Slack channels.
  </Card>

  <Card title="Event-Driven Marketing" icon="bullseye">
    Trigger marketing actions based on customer behavior. For example, if a
    customer action indicates high engagement, a notification can be sent to the
    marketing team to follow up with personalized content or offers.
  </Card>
</CardGroup>

## Slack Connector Configuration and Credential Retrieval Guide

### Prerequisite Requirements

To access Slack through AI Squared, you must authenticate using an API Token. This authentication can be obtained through a Slack App. However, if you already possess one, it remains valid for use with this integration.
Given that AI Squared operates as a reverse ETL platform, requiring write access to perform its functions, we recommend creating a restricted API key that permits write access specifically for AI Squared's use.
This strategy enables you to maintain control over the extent of actions AI Squared can execute within your Slack environment, ensuring security and compliance with your data governance policies.

<Tip>Link to view your [Slack Apps](https://api.slack.com/apps).</Tip>

### Destination Setup

<AccordionGroup>
  <Accordion title="Create Bot App" icon="robot">
    To facilitate the integration of your Slack destination connector with AI Squared, please follow the detailed steps below:

    <Steps>
      <Step title="Create New App">
        Initiate the process by selecting the "Create New App" option.

        <Frame>
          <img src="https://res.cloudinary.com/dspflukeu/image/upload/v1707307305/Multiwoven/connectors/slack/create-app.png" />
        </Frame>
      </Step>

      <Step title="From scratch">
        You will be required to create a Bot app from the ground up. To do this, select the "from scratch" option.
      </Step>

      <Step title="App Name & Workspace">
        Proceed by entering your desired App Name and selecting a workspace where the app will be deployed.

        <Frame>
          <img src="https://res.cloudinary.com/dspflukeu/image/upload/v1707307572/Multiwoven/connectors/slack/scratch.png" />
        </Frame>
      </Step>

      <Step title="Add features and functionality">
        Navigate to the **Add features and functionality** menu and select **Bots** to add this capability to your app.

        <Frame>
          <img src="https://res.cloudinary.com/dspflukeu/image/upload/v1707308671/Multiwoven/connectors/slack/bots.png" />
        </Frame>
      </Step>

      <Step title="OAuth & Permissions">
        Within the menu on the side labeled as **Features** column, locate and click on **OAuth & Permissions**.

        <Frame>
          <img src="https://res.cloudinary.com/dspflukeu/image/upload/v1707308830/Multiwoven/connectors/slack/oauth.png" />
        </Frame>
      </Step>

      <Step title="Add scope">
        In the "OAuth & Permissions" section, add the scope **chat:write** to define the permissions for your app.

        <Frame>
          <img src="https://res.cloudinary.com/dspflukeu/image/upload/v1707310851/Multiwoven/connectors/slack/write.png" />
        </Frame>
      </Step>

      <Step title="Install Bot">
        To finalize the Bot installation, click on "Install to workspace" found in the "OAuth Tokens for Your Workspace" section.

        <Frame>
          <img src="https://res.cloudinary.com/dspflukeu/image/upload/v1707311271/Multiwoven/connectors/slack/install.png" />
        </Frame>
      </Step>

      <Step title="Save Permissions">
        Upon successful installation, a Bot User OAuth Token will be generated.
        It is crucial to copy this token as it is required for the configuration of the Slack destination connector within AI Squared.

        <Frame>
          <img src="https://res.cloudinary.com/dspflukeu/image/upload/v1707311787/Multiwoven/connectors/slack/token.png" />
        </Frame>
      </Step>
    </Steps>
  </Accordion>

  <Accordion title="Obtain Channel ID" icon="key">
    <Steps>
      <Step title="View Channel Details">
        Additionally, acquiring the Channel ID is essential for configuring the Slack destination.
        This ID can be retrieved by right-clicking on the channel intended for message dispatch through the newly created bot.
        From the context menu, select **View channel details**

        <Frame>
          <img src="https://res.cloudinary.com/dspflukeu/image/upload/v1707312009/Multiwoven/connectors/slack/channel-selection.png" />
        </Frame>
      </Step>

      <Step title="Copy Channel ID">
        Locate and copy the Channel ID, which is displayed at the lower left corner of the tab.

        <Frame>
          <img src="https://res.cloudinary.com/dspflukeu/image/upload/v1707312154/Multiwoven/connectors/slack/channel-id.png" />
        </Frame>
      </Step>
    </Steps>
  </Accordion>
</AccordionGroup>


# S3
Source: https://docs.squared.ai/guides/sources/data-sources/amazon_s3



## Connect AI Squared to S3

This page describes how to add AWS S3 as a source.

AI Squared lets you pull data from CSV and Parquet files stored in an Amazon S3 bucket and push them to downstream destinations. To get started, you need an S3 bucket and AWS credentials.

## Connector Configuration and Credentials Guide

### Prerequisites

Before proceeding, ensure you have the necessary information based on how you plan to authenticate to AWS. The two types of authentication we support are:

* IAM User with access id and secret access key.
* IAM Role with ARN configured with an external ID so that AI Square can connect to your S3 bucket.

Additional info you will need regardless of authentication type will be:

* Region
* Bucket name
* The type of file we are working with (CSV or Parquet)
* Path to the CSV or Parquet files

### Setting Up AWS Requirements

<AccordionGroup>
  <Accordion title="Steps to Retrieve or Create an IAM Role User credentials">
    <Steps>
      <Step title="Sign In">
        Log in to your AWS account at [AWS Management Console](https://aws.amazon.com/console/).
      </Step>

      <Step title="Users">
        Navigate to the the **Users**. This can be found in the left navigation under "Access Management" -> "Users".

        <Frame>
          <img src="https://res.cloudinary.com/dspflukeu/image/upload/v1720193401/aws_users_view.png" />
        </Frame>
      </Step>

      <Step title="Access/Secret Key">
        Once inside the Users page, Select the User you would like to authenticate with. If there are no users to select, create one and make sure to give it the required permissions to read from S3 buckets. If you haven't created an access key pair before, click on "Create access key" to generate a new one. Make sure to copy the Secret Access Key as they are shown only once.
        After selecting the user, go to **Security Credentials** tab and in it you should be able to see the Access keys for that user.

        <Frame>
          <img src="https://res.cloudinary.com/dspflukeu/image/upload/v1720193401/aws_users_access_key.png" />
        </Frame>
      </Step>
    </Steps>
  </Accordion>

  <Accordion title="Steps to Retrieve or Create an IAM Role ARN">
    <Steps>
      <Step title="Sign In">
        Log in to your AWS account at [AWS Management Console](https://aws.amazon.com/console/).
      </Step>

      <Step title="External ID">
        The ARN is going to need an external ID which will be required during the configuration of the S3 source connector. The external ID will allow us to reach out to you S3 buckets and read data from it.
        You can generate an external Id using this [GUID generator tool](https://guidgenerator.com/). [Learn more about AWS STS external ID](https://docs.aws.amazon.com/IAM/latest/UserGuide/id_roles_create_for-user_externalid.html).
      </Step>

      <Step title="Roles">
        Navigate to the the **Roles**. This can be found in the left navigation under "Access Management" -> "Roles".

        <Frame>
          <img src="https://res.cloudinary.com/dspflukeu/image/upload/v1720193401/aws_roles_view.png" />
        </Frame>
      </Step>

      <Step title="Create or Select an existing role">
        Select an existing role to edit or create a new one by clicking on "Create Role".
      </Step>

      <Step title="ARN Premissions Policy">
        The "Permissions Policy" should look something like this:

        ```
        {
            "Version": "2012-10-17",
            "Statement": [
               {
                     "Sid": "VisualEditor0",
                     "Effect": "Allow",
                     "Action": [
                        "s3:GetObject",
                        "s3:GetObjectVersion",
                        "s3:ListBucket"
                     ],
                     "Resource": [
                        "arn:aws:s3:::{your-bucket-name}",
                        "arn:aws:s3:::{your-bucket-name}/*"
                     ]
               }
            ]
         }
        ```
      </Step>

      <Step title="ARN Trust Relationship">
        The "Trust Relationship" should look something like this:

        ```
        {
            "Version": "2012-10-17",
            "Statement": [
               {
                     "Sid": "Statement1",
                     "Effect": "Allow",
                     "Principal": {
                        "AWS": "{iam-user-principal-arn}"
                     },
                     "Action": "sts:AssumeRole",
                     "Condition": {
                        "StringEquals": {
                           "sts:ExternalId": "{generated-external-id}"
                        }
                     }
               }
            ]
         }
        ```
      </Step>
    </Steps>
  </Accordion>
</AccordionGroup>

### Step 2: Locate AWS S3 Configuration Details

Now you should be in the AWS and have found your credentials. Now we will navigate to the S3 service to find the necessary configuration details:

1. **IAM User Access Key and Secret Access Key or IAM Role ARN and External ID:**
   * This has been gathered from the previous step.

2. **Bucket:**
   * Once inside of the AWS S3 console you should be able to see the list of buckets available, if not go ahead and create a bucket by clicking on the "Create bucket" button.

3. **Region:**
   * In the same list showing the buckets, there's a region assotiated with it.

4. **Path:**
   * The path where the file you wish to read from. This field is optional and can be left blank.

5. **File type:**
   * The files within the path that was selected should help determine the file type.

### Step 3: Configure S3 Connector in Your Application

Now that you have gathered all the necessary details enter the following information:

* **Region:** The AWS region where your S3 bucket resources are located.
* **Access Key ID:** Your AWS IAM user's Access Key ID.
* **Secret Access Key:** The corresponding Secret Access Key.
* **Bucket:** The name of the bucket you want to use.
* **Path:** The path directory where the files are located at.
* **File type:** The type of file (csv, parquet).

### Step 4: Test the S3 Connection

After configuring the connector in your application:

1. Save the configuration settings.
2. Test the connection to S3 from your application to ensure everything is set up correctly.
3. Run a test query or check the connection status to verify successful connectivity.

Your S3 connector is now configured and ready to query data from your S3 data catalog.

## Building a Model Query

The S3 source connector is powered by [DuckDB S3 api support](https://duckdb.org/docs/extensions/httpfs/s3api.html). This allows us to use
SQL queries to describe and/or fetch data from an S3 bucket, for example:

```
SELECT * FROM 's3://my-bucket/path/to/file/file.parquet';
```

From the example, we can notice some details that are required in order to perform the query:

* **FROM command: `'s3://my-bucket/path/to/file/file.parquet'`** You need to provide a value in the same format as the example.
* **Bucket: `my-bucket`** In that format you will need to provide the bucket name. The bucket name needs to be
  the same one provided when configuring the S3 source connector.
* **Path: `/path/to/file`** In that format you will need to provide the path to the file. The path needs to be
  the same one provided when configuring the S3 source connector.
* **File name and type: `file.parquet`** In that format you will need to provide the file name and type at the end of the path. The file type needs to be
  the same one provided when configuring the S3 source connector.

## Supported sync modes

| Mode             | Supported (Yes/No/Coming soon) |
| ---------------- | ------------------------------ |
| Incremental sync | YES                            |
| Full refresh     | YES                            |


# AWS Athena
Source: https://docs.squared.ai/guides/sources/data-sources/aws_athena



## Connect AI Squared to AWS Athena

This guide will help you configure the AWS Athena Connector in AI Squared to access and use your AWS Athena data.

### Prerequisites

Before proceeding, ensure you have the necessary access key, secret access key, region, workgroup, catalog, and output location from AWS Athena.

## Step-by-Step Guide to Connect to AWS Athena

## Step 1: Navigate to AWS Athena Console

Start by logging into your AWS Management Console and navigating to the AWS Athena service.

1. Sign in to your AWS account at [AWS Management Console](https://aws.amazon.com/console/).
2. In the AWS services search bar, type "Athena" and select it from the dropdown.

## Step 2: Locate AWS Athena Configuration Details

Once you're in the AWS Athena console, you'll find the necessary configuration details:

1. **Access Key and Secret Access Key:**
   * Click on your username at the top right corner of the AWS Management Console.
   * Choose "Security Credentials" from the dropdown menu.
   * In the "Access keys" section, you can create or view your access keys.
   * If you haven't created an access key pair before, click on "Create access key" to generate a new one. Make sure to copy the Access Key ID and Secret Access Key as they are shown only once.

2. **Region:**
   * The AWS region can be selected from the top right corner of the AWS Management Console. Choose the region where your AWS Athena resources are located or where you want to perform queries.

3. **Workgroup:**
   * In the AWS Athena console, navigate to the "Workgroups" section in the left sidebar.
   * Here, you can view the existing workgroups or create a new one if needed. Note down the name of the workgroup you want to use.

4. **Catalog and Database:**
   * Go to the "Data Source" section in the in the left sidebar.
   * Select the catalog that contains the databases and tables you want to query. Note down the name of the catalog and database.

5. **Output Location:**
   * In the AWS Athena console, click on "Settings".
   * Under "Query result location," you can see the default output location for query results. You can also set a custom output location if needed. Note down the output location URL.

## Step 3: Configure AWS Athena Connector in Your Application

Now that you have gathered all the necessary details enter the following information:

* **Access Key ID:** Your AWS IAM user's Access Key ID.
* **Secret Access Key:** The corresponding Secret Access Key.
* **Region:** The AWS region where your Athena resources are located.
* **Workgroup:** The name of the workgroup you want to use.
* **Catalog:** The name of the catalog containing your data.
* **Schema:** The name of the database containing your data.
* **Output Location:** The URL of the output location for query results.

## Step 4: Test the AWS Athena Connection

After configuring the connector in your application:

1. Save the configuration settings.
2. Test the connection to AWS Athena from your application to ensure everything is set up correctly.
3. Run a test query or check the connection status to verify successful connectivity.

Your AWS Athena connector is now configured and ready to query data from your AWS Athena data catalog.

### Supported sync modes

| Mode             | Supported (Yes/No/Coming soon) |
| ---------------- | ------------------------------ |
| Incremental sync | YES                            |
| Full refresh     | Coming soon                    |


# AWS Sagemaker Model
Source: https://docs.squared.ai/guides/sources/data-sources/aws_sagemaker-model



## Connect AI Squared to AWS Sagemaker Model

This guide will help you configure the AWS Sagemaker Model Connector in AI Squared to access your AWS Sagemaker Model Endpoint.

### Prerequisites

Before proceeding, ensure you have the necessary access key, secret access key, and region from AWS.

## Step-by-Step Guide to Connect to an AWS Sagemaker Model Endpoint

## Step 1: Navigate to AWS Console

Start by logging into your AWS Management Console.

1. Sign in to your AWS account at [AWS Management Console](https://aws.amazon.com/console/).

## Step 2: Locate AWS Configuration Details

Once you're in the AWS console, you'll find the necessary configuration details:

1. **Access Key and Secret Access Key:**

   * Click on your username at the top right corner of the AWS Management Console.
   * Choose "Security Credentials" from the dropdown menu.
   * In the "Access keys" section, you can create or view your access keys.
   * If you haven't created an access key pair before, click on "Create access key" to generate a new one. Make sure to copy the Access Key ID and Secret Access Key as they are shown only once.
     <Frame>
       <img src="https://res.cloudinary.com/dspflukeu/image/upload/v1725025888/Multiwoven/connectors/aws_sagemaker-model/Create_access_keys_sh1tmz.jpg" />
     </Frame>

2. **Region:**
   * The AWS region can be selected from the top right corner of the AWS Management Console. Choose the region where your AWS Sagemaker resources is located and note down the region.
     <Frame>
       <img src="https://res.cloudinary.com/dspflukeu/image/upload/v1725025964/Multiwoven/connectors/aws_sagemaker-model/region_nonhav.jpg" />
     </Frame>

## Step 3: Configure AWS Sagemaker Model Connector in Your Application

Now that you have gathered all the necessary details enter the following information:

* **Access Key ID:** Your AWS IAM user's Access Key ID.
* **Secret Access Key:** The corresponding Secret Access Key.
* **Region:** The AWS region where your Sagemaker resources are located.


# Google Big Query
Source: https://docs.squared.ai/guides/sources/data-sources/bquery



## Connect AI Squared to BigQuery

This guide will help you configure the BigQuery Connector in AI Squared to access and use your BigQuery data.

### Prerequisites

Before you begin, you'll need to:

1. **Enable BigQuery API and Locate Dataset(s):**

   * Log in to the [Google Developers Console](https://console.cloud.google.com/apis/dashboard).
   * If you don't have a project, create one.
   * Enable the [BigQuery API for your project](https://console.cloud.google.com/flows/enableapi?apiid=bigquery&_ga=2.71379221.724057513.1673650275-1611021579.1664923822&_gac=1.213641504.1673650813.EAIaIQobChMIt9GagtPF_AIVkgB9Ch331QRREAAYASAAEgJfrfD_BwE).
   * Copy your Project ID.
   * Find the Project ID and Dataset ID of your BigQuery datasets. You can find this by querying the `INFORMATION_SCHEMA.SCHEMATA` view or by visiting the Google Cloud web console.

2. **Create a Service Account:**

   * Follow the instructions in our [Google Cloud Provider (GCP) documentation](https://cloud.google.com/iam/docs/service-accounts-create) to create a service account.

3. **Grant Access:**

   * In the Google Cloud web console, navigate to the [IAM](https://console.cloud.google.com/iam-admin/iam?supportedpurview=project,folder,organizationId) & Admin section and select IAM.
   * Find your service account and click on edit.
   * Go to the "Assign Roles" tab and click "Add another role".
   * Search and select the "BigQuery User" and "BigQuery Data Viewer" roles.
   * Click "Save".

4. **Download JSON Key File:**

   * In the Google Cloud web console, navigate to the [IAM](https://console.cloud.google.com/iam-admin/iam?supportedpurview=project,folder,organizationId) & Admin section and select IAM.
   * Find your service account and click on it.
   * Go to the "Keys" tab and click "Add Key".
   * Select "Create new key" and choose JSON format.
   * Click "Download".

### Steps

### Authentication

Authentication is supported via the following:

* **Dataset ID and JSON Key File**

  * **[Dataset ID](https://cloud.google.com/bigquery/docs/datasets):** The ID of the dataset within Google BigQuery that you want to access. This can be found in Step 1.
  * **[JSON Key File](https://cloud.google.com/iam/docs/keys-create-delete):** The JSON key file containing the authentication credentials for your service account.

### Supported sync modes

| Mode             | Supported (Yes/No/Coming soon) |
| ---------------- | ------------------------------ |
| Incremental sync | YES                            |
| Full refresh     | Coming soon                    |


# ClickHouse
Source: https://docs.squared.ai/guides/sources/data-sources/clickhouse



## Connect AI Squared to ClickHouse

This guide will help you configure the ClickHouse Connector in AI Squared to access and use your ClickHouse data.

### Prerequisites

Before proceeding, ensure you have the necessary URL, username, and password from ClickHouse.

## Step-by-Step Guide to Connect to ClickHouse

## Step 1: Navigate to ClickHouse Console

Start by logging into your ClickHouse Management Console and navigating to the ClickHouse service.

1. Sign in to your ClickHouse account at [ClickHouse](https://clickhouse.com/).
2. In the ClickHouse console, select the service you want to connect to.

## Step 2: Locate ClickHouse Configuration Details

Once you're in the ClickHouse console, you'll find the necessary configuration details:

1. **HTTP Interface URL:**
   * Click on the "Connect" button in your ClickHouse service.
   * In "Connect with" select HTTPS.
   * Find the HTTP interface URL, which typically looks like `http://<your-clickhouse-url>:8443`. Note down this URL as it will be used to connect to your ClickHouse service.

2. **Username and Password:**
   * Click on the "Connect" button in your ClickHouse service.
   * Here, you will see the credentials needed to connect, including the username and password.
   * Note down the username and password as they are required for the HTTP connection.

## Step 3: Configure ClickHouse Connector in Your Application

Now that you have gathered all the necessary details, enter the following information in your application:

* **HTTP Interface URL:** The URL of your ClickHouse service HTTP interface.
* **Username:** Your ClickHouse service username.
* **Password:** The corresponding password for the username.

## Step 4: Test the ClickHouse Connection

After configuring the connector in your application:

1. Save the configuration settings.
2. Test the connection to ClickHouse from your application to ensure everything is set up correctly.
3. Run a test query or check the connection status to verify successful connectivity.

Your ClickHouse connector is now configured and ready to query data from your ClickHouse service.

### Supported sync modes

| Mode             | Supported (Yes/No/Coming soon) |
| ---------------- | ------------------------------ |
| Incremental sync | YES                            |
| Full refresh     | Coming soon                    |


# Databricks
Source: https://docs.squared.ai/guides/sources/data-sources/databricks



### Overview

AI Squared enables you to transfer data from Databricks to various destinations by using Open Database Connectivity (ODBC). This guide explains how to obtain your Databricks cluster's ODBC URL and connect to AI Squared using your credentials. Follow the instructions to efficiently link your Databricks data with downstream platforms.

### Setup

<Steps>
  <Step title="Open workspace">
    In your Databricks account, navigate to the "Workspaces" page, choose the desired workspace, and click Open workspace

    <Frame>
      <img src="https://res.cloudinary.com/dspflukeu/image/upload/v1709668680/01-select_workspace_hsovls.jpg" />
    </Frame>
  </Step>

  <Step title="Go to warehouse">
    In your workspace, go the SQL warehouses and click on the relevant warehouse

    <Frame>
      <img src="https://res.cloudinary.com/dspflukeu/image/upload/v1709669032/02-select-warehouse_kzonnt.jpg" />
    </Frame>
  </Step>

  <Step title="Get connection details">
    Go to the Connection details section.This tab shows your cluster's Server Hostname, Port, and HTTP Path, essential for connecting to AI Squared

    <Frame>
      <img src="https://res.cloudinary.com/dspflukeu/image/upload/v1709669111/03_yoeixj.jpg" />
    </Frame>
  </Step>

  <Step title="Create personal token">
    Then click on the create a personal token link to generate the personal access token

    <Frame>
      <img src="https://res.cloudinary.com/dspflukeu/image/upload/v1709669164/05_p6ikgb.jpg" />
    </Frame>
  </Step>
</Steps>

### Configuration

| Field               | Description                                                                                                                                  |
| ------------------- | -------------------------------------------------------------------------------------------------------------------------------------------- |
| **Server Hostname** | Visit the Databricks web console, locate your cluster, click for Advanced options, and go to the JDBC/ODBC tab to find your server hostname. |
| **Port**            | The default port is 443, although it might vary.                                                                                             |
| **HTTP Path**       | For the HTTP Path, repeat the steps for Server Hostname and Port.                                                                            |
| **Catalog**         | Database  catalog                                                                                                                            |
| **Schema**          | The initial schema to use when connecting.                                                                                                   |


# Databricks Model
Source: https://docs.squared.ai/guides/sources/data-sources/databricks-model



### Overview

AI Squared enables you to transfer data from a Databricks Model to various destinations or data apps. This guide explains how to obtain your Databricks Model URL and connect to AI Squared using your credentials.

### Setup

<Steps>
  <Step title="Get connection details">
    Go to the Serving tab in Databricks, select the endpoint you want to
    configure, and locate the Databricks host and endpoint as shown below.

    <Frame>
      <img src="https://res.cloudinary.com/dspflukeu/image/upload/v1724264572/Multiwoven/connectors/DataBricks/endpoint_rt3tea.png" />
    </Frame>
  </Step>

  <Step title="Create personal token">
    Generate a personal access token by following the steps in the [Databricks
    documentation](https://docs.databricks.com/en/dev-tools/auth/pat.html).

    <Frame>
      <img src="https://res.cloudinary.com/dspflukeu/image/upload/v1709669164/05_p6ikgb.jpg" />
    </Frame>
  </Step>
</Steps>

### Configuration

| Field                | Description                                    |
| -------------------- | ---------------------------------------------- |
| **databricks\_host** | The databricks-instance url                    |
| **token**            | Bearer token to connect with Databricks Model. |
| **endpoint**         | Name of the serving endpoint                   |


# MariaDB
Source: https://docs.squared.ai/guides/sources/data-sources/maria_db



## Connect AI Squared to MariaDB

This guide will help you configure the MariaDB Connector in AI Squared to access and use your MariaDB data.

### Prerequisites

Before proceeding, ensure you have the necessary host, port, username, password, and database name from your MariaDB server.

## Step-by-Step Guide to Connect to MariaDB

## Step 1: Navigate to MariaDB Console

Start by logging into your MariaDB Management Console and navigating to the MariaDB service.

1. Sign in to your MariaDB account on your local server or through the MariaDB Enterprise interface.
2. In the MariaDB console, select the service you want to connect to.

## Step 2: Locate MariaDB Configuration Details

Once you're in the MariaDB console, you'll find the necessary configuration details:

1. **Host and Port:**
   * For local servers, the host is typically `localhost` and the default port is `3306`.
   * For remote servers, check your server settings or consult with your database administrator to get the correct host and port.
   * Note down the host and port as they will be used to connect to your MariaDB service.

2. **Username and Password:**
   * In the MariaDB console, you can find or create a user with the necessary permissions to access the database.
   * Note down the username and password as they are required for the connection.

3. **Database Name:**
   * List the available databases using the command `SHOW DATABASES;` in the MariaDB console.
   * Choose the database you want to connect to and note down its name.

## Step 3: Configure MariaDB Connector in Your Application

Now that you have gathered all the necessary details, enter the following information in your application:

* **Host:** The host of your MariaDB service.
* **Port:** The port number of your MariaDB service.
* **Username:** Your MariaDB service username.
* **Password:** The corresponding password for the username.
* **Database:** The name of the database you want to connect to.

## Step 4: Test the MariaDB Connection

After configuring the connector in your application:

1. Save the configuration settings.
2. Test the connection to MariaDB from your application to ensure everything is set up correctly.
3. Run a test query or check the connection status to verify successful connectivity.

Your MariaDB connector is now configured and ready to query data from your MariaDB service.

### Supported sync modes

| Mode             | Supported (Yes/No/Coming soon) |
| ---------------- | ------------------------------ |
| Incremental sync | YES                            |
| Full refresh     | Coming soon                    |

This guide will help you seamlessly connect your AI Squared application to MariaDB, enabling you to leverage your database's full potential.


# Oracle
Source: https://docs.squared.ai/guides/sources/data-sources/oracle



## Connect AI Squared to Oracle

This guide will help you configure the Oracle Connector in AI Squared to access and transfer data to your Oracle database.

### Prerequisites

Before proceeding, ensure you have the necessary host, port, SID or service name, username, and password from your Oracle database.

## Step-by-Step Guide to Connect to Oracle database

### Step 1: Locate Oracle database Configuration Details

In your Oracle database, you'll need to find the necessary configuration details:

1. **Host and Port:**
   * For local servers, the host is typically `localhost` and the default port is `1521`.
   * For remote servers, check your server settings or consult with your database administrator to get the correct host and port.
   * Note down the host and port as they will be used to connect to your Oracle database.

2. **SID or Service Name:**
   * To find your SID or Service name:
     1. **Using SQL\*Plus or SQL Developer:**
        * Connect to your Oracle database using SQL\*Plus or SQL Developer.
        * Execute the following query:
          ```sql
          select instance from v$thread
          ```
          or
          ```sql
          SELECT sys_context('userenv', 'service_name') AS service_name FROM dual;
          ```
        * The result will display the SID or service name of your Oracle database.
     2. **Checking the TNSNAMES.ORA File:**
        * Locate and open the `tnsnames.ora` file on your system. This file is usually found in the `ORACLE_HOME/network/admin` directory.
        * Look for the entry corresponding to your database connection. The `SERVICE_NAME` or `SID` will be listed within this entry.
     * Note down the SID or service name as it will be used to connect to your Oracle database.

3. **Username and Password:**
   * In the Oracle, you can find or create a user with the necessary permissions to access the database.
   * Note down the username and password as it will be used to connect to your Oracle database.

### Step 2: Configure Oracle Connector in Your Application

Now that you have gathered all the necessary details, enter the following information in your application:

* **Host:** The host of your Oracle database.
* **Port:** The port number of your Oracle database.
* **SID:** The SID or service name you want to connect to.
* **Username:** Your Oracle username.
* **Password:** The corresponding password for the username.

### Step 3: Test the Oracle Database Connection

After configuring the connector in your application:

1. Save the configuration settings.
2. Test the connection to Oracle database from your application to ensure everything is set up correctly.
3. Run a test query or check the connection status to verify successful connectivity.

Your Oracle connector is now configured and ready to query data from your Oracle database.

## Supported sync modes

| Mode             | Supported (Yes/No/Coming soon) |
| ---------------- | ------------------------------ |
| Incremental sync | YES                            |
| Full refresh     | Coming soon                    |

This guide will help you seamlessly connect your AI Squared application to Oracle Database, enabling you to leverage your database's full potential.


# PostgreSQL
Source: https://docs.squared.ai/guides/sources/data-sources/postgresql

PostgreSQL popularly known as Postgres, is a powerful, open-source object-relational database system that uses and extends the SQL language combined with many features that safely store and scale data workloads.

## Setting Up a Source Connector in AI Squared

To integrate PostgreSQL with AI Squared, you need to establish a source connector.
This connector will enable AI Squared to extract data from your PostgreSQL database efficiently.
Below are the steps to set up the source connector in AI Squared:

### Step 1: Access AI Squared

* Log in to your AI Squared account.
* Navigate to the `Sources` section where you can manage your data sources.

### Step 2: Create a New Source Connector

* Click on the `Add Source` button.
* Select `PostgreSQL` from the list of available source types.

### Step 3: Configure Connection Settings

You'll need to provide the following details to establish a connection between AI Squared and your PostgreSQL database:

`Host` The hostname or IP address of the server where your PostgreSQL database is hosted.

`Port` The port number on which your PostgreSQL server is listening (default is 5432).

`Database` The name of the database you want to connect to.

`Schema` The schema within your PostgreSQL database you wish to access.

`Username` The username used to access the database.

`Password` The password associated with the username.

Enter these details in the respective fields on the connector configuration page and press continue.

### Step 4: Test the Connection

* Once you've entered the necessary information. The next step is automated **Test Connection** feature to ensure that AI Squared can successfully connect to your PostgreSQL database.
* If the test is successful, you'll receive a confirmation message. If not, double-check your entered details for any errors.

### Step 5: Finalize the Source Connector Setup

* Save the connector settings to establish the source connection.

### Conclusion

By following these steps, you've successfully set up a PostgreSQL source connector in AI Squared.


# Amazon Redshift
Source: https://docs.squared.ai/guides/sources/data-sources/redshift



## Overview

Amazon Redshift connector is built on top of JDBC and is based on the [Redshift JDBC driver](https://docs.aws.amazon.com/redshift/latest/mgmt/configure-jdbc-connection.html). It allows you to connect to your Redshift data warehouse and extract data for further processing and analysis.

## Prerequisites

Before proceeding, ensure you have the necessary Redshift credentials available, including the endpoint (host), port, database name, user, and password. You might also need appropriate permissions to create connections and execute queries within your Redshift cluster.

## Step-by-Step Guide to Connect Amazon Redshift

### Step 1: Navigate to the Sources Section

Begin by accessing your AI Squared dashboard. From there:

1. Click on the Setup menu found on the sidebar.
2. Select the `Sources` section to proceed.

### Step 2: Add Redshift as a New Source

Within the Sources section:

1. Find and click on the `Add Source` button.
2. From the list of data warehouse options, select **Amazon Redshift**.

### Step 3: Enter Redshift Credentials

You will be prompted to enter the credentials for your Redshift cluster. This includes:

**`Endpoint (Host)`** The URL of your Redshift cluster endpoint.

**`Port`** The port number used by your Redshift cluster (default is 5439).

**`Database Name`** The name of the database you wish to connect.

**`User`** Your Redshift username.

**`Password`** Your Redshift password.

<Warning>Make sure to enter these details accurately to ensure a successful connection.</Warning>

### Step 4: Test the Connection

Before finalizing the connection:

Click on the `Test Connection` button. This step verifies that AI Squared can successfully connect to your Redshift cluster with the provided credentials.

### Step 5: Finalize Your Redshift Source Connection

After a successful connection test:

1. Assign a name and a brief description to your Redshift source. This helps in identifying and managing your source within AI Squared.
2. Click `Save` to complete the setup process.

### Step 6: Configure Redshift User Permissions

<Note>It is recommended to create a dedicated user with read-only access to the tables you want to query. Ensure that the new user has the necessary permissions to access the required tables and views.</Note>

```sql
CREATE USER aisquared PASSWORD 'password';
GRANT USAGE ON SCHEMA public TO aisquared;
GRANT SELECT ON ALL TABLES IN SCHEMA public TO aisquared;
```

Your Amazon Redshift data warehouse is now connected to AI Squared. You can now start creating models and running queries on your Redshift data.


# Salesforce Consumer Goods Cloud
Source: https://docs.squared.ai/guides/sources/data-sources/salesforce-consumer-goods-cloud



## Overview

Salesforce Consumer Goods Cloud is a specialized CRM platform designed to help companies in the consumer goods industry manage their operations more efficiently.
It provides tools to optimize route-to-market strategies, increase sales performance, and enhance field execution.
This cloud-based solution leverages Salesforce's robust capabilities to deliver data-driven insights, streamline inventory and order management, and foster closer relationships with retailers and customers.

### Key Features:

* **Retail Execution**: Manage store visits, ensure product availability, and optimize shelf placement.
* **Sales Planning and Operations**: Create and manage sales plans that align with company goals.
* **Trade Promotion Management**: Plan, execute, and analyze promotional activities to maximize ROI.
* **Field Team Management**: Enable field reps with tools and data to improve productivity and effectiveness.

## Connector Configuration and Credential Retrieval Guide

### Prerequisite Requirements

When setting up an integration between Salesforce Consumer Goods Cloud and Multiwoven, certain credentials are required to authenticate and establish a secure connection. Below is a brief description of each credential needed:

* **Username**: The Salesforce username used to log in.
* **Password**: The password associated with the Salesforce username.
* **Host**: The URL of your Salesforce instance (e.g., [https://login.salesforce.com](https://login.salesforce.com)).
* **Security Token**: An additional security key that is appended to your password for API access from untrusted networks.
* **Client ID** and **Client Secret**: These are part of the OAuth credentials required for authenticating an application with Salesforce.
  They are obtained when you set up a new "Connected App" in Salesforce for integrating with external applications. You may refer our [Salesforce CRM docs](https://docs.multiwoven.com/destinations/crm/salesforce#destination-setup) for further details.

### Setting Up Security Token in Salesforce

<AccordionGroup>
  <Accordion title="Steps to Retrieve or Reset a Salesforce Security Token" icon="salesforce" defaultOpen="true">
    <Steps>
      <Step title="Sign In">
        Log in to your Salesforce account.
      </Step>

      <Step title="Settings">
        Navigate to Settings or My Settings by first clicking on My Profile and then clicking **Settings** under the Personal Information section.

        <Frame>
          <img src="https://res.cloudinary.com/dspflukeu/image/upload/v1713892144/Multiwoven/connectors/salesforce-consumer-goods-cloud/settings.png" />
        </Frame>
      </Step>

      <Step title="Quick Find">
        Once inside the Settings page click on the Quick Find box and type "Reset My Security Token" to quickly navigate to the option.

        <Frame>
          <img src="https://res.cloudinary.com/dspflukeu/image/upload/v1713892144/Multiwoven/connectors/salesforce-consumer-goods-cloud/reset.png" />
        </Frame>
      </Step>

      <Step title="Reset My Security Token">
        Click on Reset My Security Token under the Personal section. Salesforce will send the new security token to the email address associated with your account.
        If you do not see the option to reset the security token, it may be because your organization uses Single Sign-On (SSO) or has IP restrictions that negate the need for a token.

        <Frame>
          <img src="https://res.cloudinary.com/dspflukeu/image/upload/v1713892144/Multiwoven/connectors/salesforce-consumer-goods-cloud/security-token.png" />
        </Frame>
      </Step>
    </Steps>
  </Accordion>
</AccordionGroup>

<Accordion title="Supported Sync" icon="arrows-rotate" defaultOpen="true">
  | Mode             | Supported (Yes/No/Coming soon) |
  | ---------------- | ------------------------------ |
  | Incremental sync | Yes                            |
  | Full refresh     | Coming soon                    |
</Accordion>

<Accordion title="Supported Streams" defaultOpen="true">
  | Stream      | Supported (Yes/No/Coming soon) |
  | ----------- | ------------------------------ |
  | Account     | Yes                            |
  | User        | Yes                            |
  | Visit       | Yes                            |
  | RetailStore | Yes                            |
  | RecordType  | Yes                            |
</Accordion>


# SFTP
Source: https://docs.squared.ai/guides/sources/data-sources/sftp



## Connect AI Squared to SFTP

The Secure File Transfer Protocol (SFTP) is a secure method for transferring files between systems. This guide will help you configure the SFTP Connector with AI Squared allows you to access your data.

### Prerequisites

Before proceeding, ensure you have the hostname/ip address, port, username, password, file path, and file name from your SFTP Server.

## Step-by-Step Guide to Connect to a SFTP Server Endpoint

### Step 1: Navigate to your SFTP Server

1. Log in to your SFTP Server.
2. Select your SFTP instances.

### Step 2: Locate SFTP Configuration Details

Once you're in your select instance of your SFTP Server, you'll find the necessary configuration details:

#### 1. User section

* **Host**: The hostname or IP address of the SFTP server.
* **Port**: The port number used for SFTP connections (default is 22).
* **Username**: Your username for accessing the SFTP server.
* **Password**: The password associated with the username.

<Frame>
  <img src="https://res.cloudinary.com/dspflukeu/image/upload/v1735878893/Multiwoven/connectors/SFTP-Source/SFTP_credentials_ngkpu0.png" />
</Frame>

#### 2. File Manager section

* **File Path**: The directory path on the SFTP server where your file is stored.
* **File Name**: The name of the file to be read.

<Frame>
  <img src="https://res.cloudinary.com/dspflukeu/image/upload/v1735879781/Multiwoven/connectors/SFTP-Source/SFTP_File_vnb0am.png" />
</Frame>

### Step 3: Configure and Test the SFTP Connection

Now that you have gathered all the necessary details, enter the necessary details for the connector in your application:

1. Save the configuration settings.
2. Test the connection to SFTP from your application to ensure everything is set up correctly.
3. Run a test query or check the connection status to verify successful connectivity.

Your SFTP connector is now configured and ready to query data from your SFTP service.

### Supported sync modes

| Mode             | Supported (Yes/No/Coming soon) |
| ---------------- | ------------------------------ |
| Incremental sync | YES                            |
| Full refresh     | Coming soon                    |


# Snowflake
Source: https://docs.squared.ai/guides/sources/data-sources/snowflake



# Source/Snowflake

### Overview

This Snowflake source connector is built on top of the ODBC and is configured to rely on the Snowflake ODBC driver as described in Snowflake [documentation](https://docs.snowflake.com/en/developer-guide/odbc/odbc).

### Setup

#### Authentication

Authentication is supported via two methods: username/password and OAuth 2.0.

1. Login and Password

| Field                                                                                                 | Description                                                                                                                                                                                            |
| ----------------------------------------------------------------------------------------------------- | ------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------ |
| [Host](https://docs.snowflake.com/en/user-guide/admin-account-identifier.html)                        | The host domain of the Snowflake instance. Must include the account, region, cloud environment, and end with snowflakecomputing.com. Example: accountname.us-east-2.aws.snowflakecomputing.com         |
| [Warehouse](https://docs.snowflake.com/en/user-guide/warehouses-overview.html#overview-of-warehouses) | The Snowflake warehouse to be used for processing queries.                                                                                                                                             |
| [Database](https://docs.snowflake.com/en/sql-reference/ddl-database.html#database-schema-share-ddl)   | The specific database in Snowflake to connect to.                                                                                                                                                      |
| [Schema](https://docs.snowflake.com/en/sql-reference/ddl-database.html#database-schema-share-ddl)     | The schema within the database you want to access.                                                                                                                                                     |
| Username                                                                                              | The username associated with your account                                                                                                                                                              |
| Password                                                                                              | The password associated with the username.                                                                                                                                                             |
| [JDBC URL Params](https://docs.snowflake.com/en/user-guide/jdbc-parameters.html)                      | (Optional) Additional properties to pass to the JDBC URL string when connecting to the database formatted as key=value pairs separated by the symbol &. Example: key1=value1\&key2=value2\&key3=value3 |

2. Oauth 2.0

   Coming soon

### Supported sync modes

| Mode             | Supported (Yes/No/Coming soon) |
| ---------------- | ------------------------------ |
| Incremental sync | YES                            |
| Full refresh     | Coming soon                    |


# WatsonX.Data
Source: https://docs.squared.ai/guides/sources/data-sources/watsonx_data



## Connect AI Squared to WatsonX.Data

This guide will help you configure the WatsonX.Data Connector in AI Squared to access your WatsxonX.Data databases.

### Prerequisites

Before proceeding, ensure you have the following details:
API key, region, Instance Id (CRN), Engine Id, Database, and Schema.

## Step-by-Step Guide to Connect to a WatsonX.Data Database Engine.

## Step 1: Navigate to WatsonX.Data Console

Start by logging into your [WatsonX Console](https://dataplatform.cloud.ibm.com/wx/home?context=wx).

## Step 2: Locate Developer Access

Once you're in WatsonX, you'll need find the necessary configuration details
by following these steps in order:

### **API Key:**

1. Scroll down to Developer access.

<Frame>
  <img src="https://res.cloudinary.com/dspflukeu/image/upload/v1743647604/Multiwoven/connectors/WatsonX_Data/Developer_Access_pcrfvl.png" />
</Frame>

2. Click on `Manage IBM Cloud API keys` to view your API keys.
3. If you haven't created an API key before, click on `Create API key` to generate a new one. Make sure to copy the API Key as they are shown only once.

<Frame>
  <img src="https://res.cloudinary.com/dspflukeu/image/upload/v1743648360/Multiwoven/connectors/WatsonX_Data/Create_Key_mhnxfm.png" />
</Frame>

### Region

4. The IBM Cloud region can be selected from the top right corner of the WatsonX Console.
   Choose the region where your WatsonX.Data resources are located and note down the region.

## Instance Id

5. Open the `Navigation Menu`, select `Administration`, `Services`, and finally `Service instances`.

<Frame>
  <img src="https://res.cloudinary.com/dspflukeu/image/upload/v1743632852/Multiwoven/connectors/WatsonX_Data/Navigation_Menu_kvecrn.png" />
</Frame>

6. From the `Service instances` table, select your WatsonX.Data instance.

<Frame>
  <img src="https://res.cloudinary.com/dspflukeu/image/upload/v1743632852/Multiwoven/connectors/WatsonX_Data/Services_Instances_frhyzd.png" />
</Frame>

7. Scroll down to Deployment details, and write down the CRN, that's your Instance Id.

<Frame>
  <img src="https://res.cloudinary.com/dspflukeu/image/upload/v1743632851/Multiwoven/connectors/WatsonX_Data/Deployment_Details_l8vdgx.png" />
</Frame>

### Engine ID

8. Scroll back up and click `Open web console`.

<Frame>
  <img src="https://res.cloudinary.com/dspflukeu/image/upload/v1743632852/Multiwoven/connectors/WatsonX_Data/Watsonx.Data_Manage_ewukot.png" />
</Frame>

9. Open the Global Menu, select `Infrastructure manager`.

10. Select the Presto engine you are building the connector for to show the Engine details.

<Frame>
  <img src="https://res.cloudinary.com/dspflukeu/image/upload/v1743632851/Multiwoven/connectors/WatsonX_Data/Infrastructure_Manager_hnniyt.png" />
</Frame>

11. Write down the Engine ID.

<Frame>
  <img src="https://res.cloudinary.com/dspflukeu/image/upload/v1743632852/Multiwoven/connectors/WatsonX_Data/Engine_Details_auru98.png" />
</Frame>

### Database

11. On the same screen as the previous step, your database is one of the Associated catalogs in the Presto engine.

### Schema

12. Open the Global Menu, select `Data manager`, and expand your associated catalog to show the available schemas.

<Frame>
  <img src="https://res.cloudinary.com/dspflukeu/image/upload/v1743632851/Multiwoven/connectors/WatsonX_Data/Data_Manager_errsmu.png" />
</Frame>

## Step 3: Configure WatsonX.Data Source Connector in Your Application

Now that you have gathered all the necessary details enter the following information:

* **API Key:** Your IBM Cloud API key.
* **Region:** The IBM Cloud region where your WatsonX.Data resources are located.
* **Instance Id:** The instance ID, or CRN (Clourd Resource Name) for your WatsonX.Data deployment.
* **Engine Id:** The Engine Id of the Presto engine.
* **Database:** The catalog associated to your Presto engine.
* **Schema:** The schema you want to connect to.


# null
Source: https://docs.squared.ai/home/welcome



export function openSearch() {
  document.getElementById('search-bar-entry').click();
}

<div className="relative w-full flex items-center justify-center" style={{ height: '31.25rem', backgroundColor: '#1F1F33', overflow: 'hidden' }}>
  <div style={{ flex: 'none' }}>
    <img className="pointer-events-none" src="https://mintlify.s3.us-west-1.amazonaws.com/multiwoven-74/images/aisquared_banner.png" />
  </div>

  <div style={{ position: 'absolute', textAlign: 'center' }}>
    <div
      style={{
      color: 'white',
      fontWeight: '400',
      fontSize: '48px',
      margin: '0',
    }}
    >
      Bring AI To Where Work Happens
    </div>

    <p
      style={{
      color: 'white',
      fontWeight: '400',
      fontSize: '20px',
      opacity: '0.7',
    }}
    >
      What can we help you build?
    </p>

    <button
      type="button"
      className="mx-auto w-full flex items-center text-sm leading-6 shadow-sm text-gray-400 bg-white gap-2 ring-1 ring-gray-400/20 focus:outline-primary"
      id="home-search-entry"
      style={{
      maxWidth: '24rem',
      borderRadius: '4px',
      marginTop: '3rem',
      paddingLeft: '0.75rem',
      paddingRight: '0.75rem',
      paddingTop: '0.75rem',
      paddingBottom: '0.75rem',
    }}
      onClick={openSearch}
    >
      <svg
        className="h-4 w-4 ml-1.5 mr-3 flex-none bg-gray-500 hover:bg-gray-600 dark:bg-white/50 dark:hover:bg-white/70"
        style={{
        maskImage:
          'url("https://mintlify.b-cdn.net/v6.5.1/solid/magnifying-glass.svg")',
        maskRepeat: 'no-repeat',
        maskPosition: 'center center',
      }}
      />

      Start a chat with us...
    </button>
  </div>
</div>

<div
  style={{marginTop: '6rem', marginBottom: '8rem', maxWidth: '70rem', marginLeft: 'auto',
  marginRight: 'auto', paddingLeft: '1.25rem',
  paddingRight: '1.25rem' }}
>
  <div
    style={{
  textAlign: 'center',
  fontSize: '24px',
  fontWeight: '600',
  marginBottom: '3rem',
}}
  >
    <h1 className="text-black dark:text-white">
      Choose a topic below or simply{' '}
      <a href="https://app.squared.ai" className="text-primary underline" style={{textUnderlineOffset: "5px"}}>get started</a>
    </h1>
  </div>

  <CardGroup cols={3}>
    <Card title="Getting Started" icon="book-open" href="/getting-started">
      Onboarding, setup, and key concepts to get started with AI Squared.
    </Card>

    <Card title="AI Activation" icon="brain" href="/activation/ai-modelling/introduction">
      Connect to AI/ML models, databases, and business data sources.
    </Card>

    <Card title="Data Movement" icon="database" iconType="solid" href="/guides/core-concepts">
      Add AI-powered insights, chatbots, and automation into business apps.
    </Card>

    <Card title="Deployment & Security" icon="link-simple" href="/deployment-and-security/overview">
      Deployment options, security best practices, and compliance.
    </Card>

    <Card title="Support & FAQs" icon="question" href="/faqs/overview">
      Troubleshooting, common issues, and frequently asked questions.
    </Card>

    <Card title="Product Updates" icon="party-horn" href="/release-notes">
      Latest features, enhancements, and release notes.
    </Card>
  </CardGroup>
</div>


# Commit Message Guidelines
Source: https://docs.squared.ai/open-source/community-support/commit-message-guidelines



Multiwoven follows the following format for all commit messages.

Format: `<type>([<edition>]) : <subject>`

## Example

```
feat(CE): add source/snowflake connector
^--^  ^--^  ^------------^
|     |     |
|     |     +-> Summary in present tense.
|     |
|     +-------> Edition: CE for Community Edition or EE for Enterprise Edition.
|
+-------------> Type: chore, docs, feat, fix, refactor, style, or test.
```

Supported Types:

* `feat`: (new feature for the user, not a new feature for build script)
* `fix`: (bug fix for the user, not a fix to a build script)
* `docs`: (changes to the documentation)
* `style`: (formatting, missing semi colons, etc; no production code change)
* `refactor`: (refactoring production code, eg. renaming a variable)
* `test`: (adding missing tests, refactoring tests; no production code change)
* `chore`: (updating grunt tasks etc; no production code change)

Sample messages:

* feat(CE): add source/snowflake connector
* feat(EE): add google sso

References:

* [https://gist.github.com/joshbuchea/6f47e86d2510bce28f8e7f42ae84c716](https://gist.github.com/joshbuchea/6f47e86d2510bce28f8e7f42ae84c716)
* [https://www.conventionalcommits.org/](https://www.conventionalcommits.org/)
* [https://seesparkbox.com/foundry/semantic\_commit\_messages](https://seesparkbox.com/foundry/semantic_commit_messages)
* [http://karma-runner.github.io/1.0/dev/git-commit-msg.html](http://karma-runner.github.io/1.0/dev/git-commit-msg.html)


# Contributor Code of Conduct
Source: https://docs.squared.ai/open-source/community-support/contribution

Contributor Covenant Code of Conduct

## Our Pledge

In the interest of fostering an open and welcoming environment, we as contributors and maintainers pledge to make participation in our project and our community a harassment-free experience for everyone, regardless of age, body size, disability, ethnicity, gender identity and expression, level of experience, nationality, personal appearance, race, religion, or sexual identity and orientation.

## Our Standards

Examples of behavior that contributes to creating a positive environment include:

* Using welcoming and inclusive language
* Being respectful of differing viewpoints and experiences
* Gracefully accepting constructive criticism
* Focusing on what is best for the community
* Showing empathy towards other community members

Examples of unacceptable behavior by participants include:

* The use of sexualized language or imagery and unwelcome sexual attention or advances
* Trolling, insulting/derogatory comments, and personal or political attacks
* Public or private harassment
* Publishing others' private information, such as a physical or electronic address, without explicit permission
* Other conduct which could reasonably be considered inappropriate in a professional setting

## Our Responsibilities

Project maintainers are responsible for clarifying the standards of acceptable behavior and are expected to take appropriate and fair corrective action in response to any instances of unacceptable behavior.

Maintainers have the right and responsibility to remove, edit, or reject comments, commits, code, wiki edits, issues, and other contributions that are not aligned to this Code of Conduct, or to ban temporarily or permanently any contributor for other behaviors that they deem inappropriate, threatening, offensive, or harmful.

## Scope

This Code of Conduct applies both within project spaces and in public spaces when an individual is representing the project or its community.

## Enforcement

Instances of abusive, harassing, or otherwise unacceptable behavior may be reported by contacting the project team at \[your email]. All complaints will be reviewed and investigated and will result in a response that is deemed necessary and appropriate to the circumstances.

## Attribution

This Code of Conduct is adapted from the [Contributor Covenant](https://www.contributor-covenant.org/) , version 1.4, available at [https://www.contributor-covenant.org/version/1/4/code-of-conduct.html]()

For answers to common questions about this code of conduct, see [https://www.contributor-covenant.org/faq]()


# Overview
Source: https://docs.squared.ai/open-source/community-support/overview



<img className="block" src="https://res.cloudinary.com/dspflukeu/image/upload/v1715100646/AIS/Community_Support_-_multiwoven_dtp6dr.png" alt="Hero Light" />

The aim of our community to provide anyone with the assistance they need, connect them with fellow users, and encourage them to contribute to the growth of the Multiwoven ecosystem.

## Getting Help from the Community

How to get help from the community?

* Join our Slack channel and ask your question in the relevant channel.

* Share as much information as possible about your issue, including screenshots, error messages, and steps to reproduce the issue.

* If you’re reporting a bug, please include the steps to reproduce the issue, the expected behavior, and the actual behavior.

### Github Issues

If you find a bug or have a feature request, please open an issue on GitHub.

To open an issue for a specific repository, go to the repository and click on the `Issues` tab. Then click on the `New Issue` button.

**Multiwoven server** issues can be reported [here](https://github.com/Multiwoven/multiwoven-server/issues).

**Multiwoven frontend** issues can be reported [here](https://github.com/Multiwoven/multiwoven-ui/issues).

**Multiwoven integration** issues can be reported [here](https://github.com/Multiwoven/multiwoven-integrations/issues).

### Contributing to Multiwoven

We welcome contributions to the Multiwoven ecosystem. Please read our [contributing guidelines](https://github.com/Multiwoven/multiwoven/blob/main/CONTRIBUTING.md) to get started.

We're always looking for ways to improve our documentation. If you find any mistakes or have suggestions for improvement, please [open an issue](https://github.com/Multiwoven/multiwoven/issues/new) on GitHub.


# Release Process
Source: https://docs.squared.ai/open-source/community-support/release-process



The release process at Multiwoven is fully automated through GitHub Actions.

<AccordionGroup>
  <Accordion title="Automation Stages" icon="github" defaultOpen="true">
    Here's an overview of our automation stages, each facilitated by specific GitHub Actions:

    <Steps>
      <Step title="Weekly Release Workflow">
        * **Action**: [Release Workflow](https://github.com/Multiwoven/multiwoven/actions/workflows/release.yaml)
        * **Description**: Every Tuesday, a new release is automatically generated with a minor version tag (e.g., v0.4.0) following semantic versioning rules. This process also creates a pull request (PR) for release notes that summarize the changes in the new version.
        * **Additional Triggers**: The same workflow can be manually triggered to create a patch version (e.g., v0.4.1 for quick fixes) or a major version (e.g., v1.0.0 for significant architectural changes). This is done using the workflow dispatch feature in GitHub Actions.

        <Frame>
          <img src="https://res.cloudinary.com/dspflukeu/image/upload/v1714027592/Multiwoven/Docs/release-process/manual_kyjtne.png" />
        </Frame>
      </Step>

      <Step title="Automated Release Notes on Merge">
        * **Action**: [Create Release Note on Merge](https://github.com/Multiwoven/multiwoven/actions/workflows/create-release-notes.yaml)
        * **Description**: When the release notes PR is merged, it triggers the creation of a new release with detailed [release notes](https://github.com/Multiwoven/multiwoven/releases/tag/v0.4.0) on GitHub.
      </Step>

      <Step title="Docker Image Releases">
        * **Description**: Docker images need to be manually released based on the newly created tags from the GitHub Actions.
        * **Actions**:
          * [Build and push Multiwoven server docker image to Docker Hub](https://github.com/Multiwoven/multiwoven/actions/workflows/server-docker-hub-push-tags.yaml): This action handles the server-side Docker image push to docker hub with tag as latest and the new release tag i.e **v0.4.0**

            <Frame>
              <img src="https://res.cloudinary.com/dspflukeu/image/upload/v1714027592/Multiwoven/Docs/release-process/docker-server_ujdnap.png" />
            </Frame>
          * [Build and push Multiwoven UI docker image to Docker Hub](https://github.com/Multiwoven/multiwoven/actions/workflows/ui-docker-hub-push-tags.yaml): This action handles the user interface Docker image  to docker hub with tag as latest and the new release tag i.e **v0.4.0**

            <Frame>
              <img src="https://res.cloudinary.com/dspflukeu/image/upload/v1714027593/Multiwoven/Docs/release-process/docker-ui_sjo8nv.png" />
            </Frame>
      </Step>
    </Steps>
  </Accordion>
</AccordionGroup>


# Slack Code of Conduct
Source: https://docs.squared.ai/open-source/community-support/slack-conduct



## Introduction

At Multiwoven, we firmly believe that diversity and inclusion are the bedrock of a vibrant and effective community. We are committed to creating an environment that embraces a wide array of backgrounds and perspectives, and we want to clearly communicate our position on this.

## Our Commitment

We aim to foster a community that is safe, supportive, and friendly for all members, regardless of their experience, gender identity and expression, sexual orientation, disability, personal appearance, body size, race, ethnicity, age, religion, nationality, or any other defining characteristics.

## Scope

These guidelines apply to all forms of behavior and communication within our community spaces, both online and offline, including one-on-one interactions. This extends to any behavior that could impact the safety and well-being of community members, regardless of where it occurs.

## Expected Behaviors

* **Be Welcoming:**  Create an environment that is inviting and open to all.
* **Be Kind:**  Treat others with respect, understanding, and compassion.
* **Support Each Other:**  Actively look out for the well-being of fellow community members.

## Multiwoven Slack Etiquette Guidelines

To maintain a respectful, organized, and efficient communication environment within the Multiwoven community, we ask all members to adhere to the following etiquette guidelines on Slack:

## Etiquette Rules

1. **Be Respectful to Everyone:**  Treat all community members with kindness and respect. A positive attitude fosters a collaborative and friendly environment.
2. **Mark Resolved Questions:**  If your query is resolved, please indicate it by adding a ✅ reaction or a reply. This helps in identifying resolved issues and assists others with similar questions.
3. **Avoid Reposting Questions:**  If your question remains unanswered after 24 hours, review it for clarity and revise if necessary. If you still require assistance, you may tag @navaneeth for further attention.
4. **Public Posts Over Direct Messages:**  Please ask questions in public channels rather than through direct messages, unless you have explicit permission. Sharing questions and answers publicly benefits the entire community.
5. **Minimize Use of Tags:**  Our community is active and responsive. Please refrain from over-tagging members. Reserve tagging for urgent matters to respect everyone's time and attention.
6. **Use Threads for Detailed Discussions:**  To keep the main channel tidy, please use threads for ongoing discussions. This helps in keeping conversations organized and the main channel uncluttered.

## Conclusion

Following these etiquette guidelines will help ensure that our Slack workspace remains a supportive, efficient, and welcoming space for all members of the Multiwoven community. Your cooperation is greatly appreciated!


# Architecture Overview
Source: https://docs.squared.ai/open-source/guides/architecture/introduction



Multiwoven is structured into two primary components: the server and the connectors.

The server delivers all the essential horizontal services needed for configuring and executing data movement tasks, such as the[ User Interface](https://github.com/Multiwoven/multiwoven-ui), [API](https://github.com/Multiwoven/multiwoven-server), Job Scheduling, etc., and is organised as a collection of microservices.

Connectors are developed within the [multiwoven-integrations](https://github.com/Multiwoven/multiwoven-integrations) Ruby gem, which pushes and pulls data to and from various sources and destinations. These connectors are constructed following the [Multiwoven Protocol](https://docs.multiwoven.com/guides/architecture/multiwoven-protocol), which outlines the interface for transferring data between a source and a destination.

<Frame>
  <img src="https://res.cloudinary.com/dspflukeu/image/upload/v1706791257/dev%20docs%20assets/Screenshot_2024-02-01_at_5.50.40_PM_qj6ikq.png" />
</Frame>

1. [Multiwoven-UI](https://github.com/Multiwoven/multiwoven-ui) - User interface to interact with [ multiwoven-server](https://github.com/Multiwoven/multiwoven-server).
2. [Multiwoven-Server](https://github.com/Multiwoven/multiwoven-server) - Multiwoven’s control plane. All operations in Multiwoven such as creating sources, destinations, connections, managing configurations, etc., are configured and invoked from the server.
3. Database: Stores all connector/sync information.
4. [Temporal ](https://temporal.io/)- Orchestrates the the sync workflows.
5. Multiwoven-Workers - The worker connects to a source connector, pulls the data, and writes it to a destination. The workers' code resides in the [ multiwoven-server](https://github.com/Multiwoven/multiwoven-server) repo.


# Multiwoven Protocol
Source: https://docs.squared.ai/open-source/guides/architecture/multiwoven-protocol



### Introduction

Multiwoven [protocol](https://github.com/Multiwoven/multiwoven-integrations/blob/main/lib/multiwoven/integrations/protocol/protocol.rb#L4) defines a set of interfaces for building connectors. Connectors can be implemented independent of our server application, this protocol allows developers to create connectors without requiring in-depth knowledge of our core platform.

### Concepts

**[Source](https://github.com/Multiwoven/multiwoven-integrations/blob/6462867b1a2698b4c30ae5abcdf3219a207a28d9/lib/multiwoven/integrations/protocol/protocol.rb#L66)** - A source in business data storage typically refers to data warehouses like Snowflake, AWS Redshift and Google BigQuery, as well as databases.

**[Destination](https://github.com/Multiwoven/multiwoven-integrations/blob/6462867b1a2698b4c30ae5abcdf3219a207a28d9/lib/multiwoven/integrations/protocol/protocol.rb#L66)** - A destination is a tool or third party service where source data is sent and utilised, often by end-users. It includes CRM systems, ad platforms, marketing automation, and support tools.

**[Stream](https://github.com/Multiwoven/multiwoven-integrations/blob/6462867b1a2698b4c30ae5abcdf3219a207a28d9/lib/multiwoven/integrations/protocol/protocol.rb#L105)** - A Stream defines the structure and metadata of a resource, such as a database table, REST API resource, or data stream, outlining how users can interact with it using query or request.

***Fields***

| Field                                   | Description                                                                                                                       |
| --------------------------------------- | --------------------------------------------------------------------------------------------------------------------------------- |
| `name`                                  | A string representing the name of the stream.                                                                                     |
| `action` (optional)                     | Defines the action associated with the stream, e.g., "create", "update", or "delete".                                             |
| `json_schema`                           | A hash representing the JSON schema of the stream.                                                                                |
| `supported_sync_modes` (optional)       | An array of supported synchronization modes for the stream.                                                                       |
| `source_defined_cursor` (optional)      | A boolean indicating whether the source has defined a cursor for the stream.                                                      |
| `default_cursor_field` (optional)       | An array of strings representing the default cursor field(s) for the stream.                                                      |
| `source_defined_primary_key` (optional) | An array of arrays of strings representing the source-defined primary key(s) for the stream.                                      |
| `namespace` (optional)                  | A string representing the namespace of the stream.                                                                                |
| `url` (optional)                        | A string representing the URL of the API stream.                                                                                  |
| `request_method` (optional)             | A string representing the request method (e.g., "GET", "POST") for the API stream.                                                |
| `batch_support`                         | A boolean indicating whether the stream supports batching.                                                                        |
| `batch_size`                            | An integer representing the batch size for the stream.                                                                            |
| `request_rate_limit`                    | An integer value, specifying the maximum number of requests that can be made to the user data API within a given time limit unit. |
| `request_rate_limit_unit`               | A string value indicating the unit of time for the rate limit.                                                                    |
| `request_rate_concurrency`              | An integer value which limits the number of concurrent requests.                                                                  |

**[Catalog](https://github.com/Multiwoven/multiwoven-integrations/blob/6462867b1a2698b4c30ae5abcdf3219a207a28d9/lib/multiwoven/integrations/protocol/protocol.rb#L123)** - A Catalog is a collection of Streams detailing the data within a data store represented by a Source/Destination eg: Catalog = Schema, Streams = List\[Tables]
***Fields***

| Field                      | Description                                                                                                                                                                                                                         |
| -------------------------- | ----------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------- |
| `streams`                  | An array of Streams detailing the data within the data store. This encapsulates various data streams available for synchronization or processing, each potentially with its own schema, sync modes, and other configurations.       |
| `request_rate_limit`       | An integer value, specifying the maximum number of requests that can be made to the user data API within a given time limit unit. This serves to prevent overloading the system by limiting the rate at which requests can be made. |
| `request_rate_limit_unit`  | A string value indicating the unit of time for the rate limit, such as "minute" or "second". This defines the time window in which the `request_rate_limit` applies.                                                                |
| `request_rate_concurrency` | An integer value which limits the number of concurrent requests that can be made. This is used to control the load on the system by restricting how many requests can be processed at the same time.                                |
| `schema_mode `             | A string value that identifies the schema handling mode for the connector. Supported values include **static, dynamic, and schemaless**. This parameter is crucial for determining how the connector handles data schema.           |

<Note>
  {" "}

  Rate limit specified in catalog will applied to stream if there is no stream specific
  rate limit is defined.{" "}
</Note>

**[Model](https://github.com/Multiwoven/multiwoven-integrations/blob/6462867b1a2698b4c30ae5abcdf3219a207a28d9/lib/multiwoven/integrations/protocol/protocol.rb#L86)** - Models specify the data to be extracted from a source
***Fields***

* `name` (optional): A string representing the name of the model.
* `query`: A string representing the query used to extract data from the source.
* `query_type`: A type representing the type of query used by the model.
* `primary_key`: A string representing the primary key of the model.

**[Sync](https://github.com/Multiwoven/multiwoven-integrations/blob/6462867b1a2698b4c30ae5abcdf3219a207a28d9/lib/multiwoven/integrations/protocol/protocol.rb#L134)** - A Sync sets the rules for data transfer from a chosen source to a destination

***Fields***

* `source`: The source connector from which data is transferred.
* `destination`: The destination connector where data is transferred.
* `model`: The model specifying the data to be transferred.
* `stream`: The stream defining the structure and metadata of the data to be transferred.
* `sync_mode`: The synchronization mode determining how data is transferred.
* `cursor_field` (optional): The field used as a cursor for incremental data transfer.
* `destination_sync_mode`: The synchronization mode at the destination.

### Interfaces

The output of each method in the interface is encapsulated in an [MultiwovenMessage](https://github.com/Multiwoven/multiwoven-integrations/blob/main/lib/multiwoven/integrations/protocol/protocol.rb#L170), serving as an envelope for the message's return value. These are omitted in interface explanations for sake of simplicity.

#### Common

1. `connector_spec() -> ConnectorSpecification`

Description - [connector\_spec](https://github.com/Multiwoven/multiwoven-integrations/blob/main/lib/multiwoven/integrations/core/base_connector.rb#L10) returns information about how the connector can be configured

Input - `None`

Output - [ConnectorSpecification](https://github.com/Multiwoven/multiwoven-integrations/blob/6462867b1a2698b4c30ae5abcdf3219a207a28d9/lib/multiwoven/integrations/protocol/protocol.rb#L49)
-One of the main pieces of information the specification shares is what information is needed to configure an Actor.

* **`documentation_url`**:\
  URL providing information about the connector.

* **`stream_type`**:\
  The type of stream supported by the connector. Possible values include:

  * `static`: The connector catalog is static.
  * `dynamic`: The connector catalog is dynamic, which can be either schemaless or with a schema.
  * `user_defined`: The connector catalog is defined by the user.

* **`connector_query_type`**:\
  The type of query supported by the connector. Possible values include:

  * `raw_sql`: The connector is SQL-based.
  * `soql`: Specifically for Salesforce.
  * `ai_ml`: Specific for AI model source connectors.

* **`connection_specification`**:\
  The properties required to connect to the source or destination.

* **`sync_mode`**:\
  The synchronization modes supported by the connector.

2. `meta_data() -> Hash`

Description - [meta\_data](https://github.com/Multiwoven/multiwoven-integrations/blob/main/lib/multiwoven/integrations/core/base_connector.rb#L17) returns information about how the connector can be shown in the multiwoven ui eg: icon, labels etc.

Input - `None`

Output - `Hash`. Sample hash can be found [here](https://github.com/Multiwoven/multiwoven-integrations/blob/main/lib/multiwoven/integrations/source/bigquery/config/meta.json)

3. `check_connection(connection_config) -> ConnectionStatus`

Description: The [check\_connection](https://github.com/Multiwoven/multiwoven-integrations/blob/main/lib/multiwoven/integrations/core/base_connector.rb#L21) method verifies if a given configuration allows successful connection and access to necessary resources for a source/destination, such as confirming Snowflake database connectivity with provided credentials. It returns a success response if successful or a failure response with an error message in case of issues like incorrect passwords

Input - `Hash`

Output - [ConnectionStatus](https://github.com/Multiwoven/multiwoven-integrations/blob/main/lib/multiwoven/integrations/protocol/protocol.rb#L37)

4. `discover(connection_config) -> Catalog`

Description: The [discover](https://github.com/Multiwoven/multiwoven-integrations/blob/main/lib/multiwoven/integrations/core/base_connector.rb#L26) method identifies and outlines the data structure in a source/destination. Eg: Given a valid configuration for a Snowflake source, the discover method returns a list of accessible tables, formatted as streams.

Input - `Hash`

Output - [Catalog](https://github.com/Multiwoven/multiwoven-integrations/blob/main/lib/multiwoven/integrations/protocol/protocol.rb#L121)

#### Source

[Source](https://github.com/Multiwoven/multiwoven-integrations/blob/main/lib/multiwoven/integrations/core/source_connector.rb) implements the following interface methods including the common methods.

```
connector_spec() -> ConnectorSpecification
meta_data() -> Hash
check_connection(connection_config) -> ConnectionStatus
discover(connection_config)  -> Catalog
read(SyncConfig) ->Array[RecordMessage]
```

1. `read(SyncConfig) ->Array[RecordMessage]`

Description -The [read](https://github.com/Multiwoven/multiwoven-integrations/blob/main/lib/multiwoven/integrations/core/source_connector.rb#L6) method extracts data from a data store and outputs it as RecordMessages.

Input - [SyncConfig](https://github.com/Multiwoven/multiwoven-integrations/blob/main/lib/multiwoven/integrations/protocol/protocol.rb#L132)

Output - List\[[RecordMessage](https://github.com/Multiwoven/multiwoven-integrations/blob/main/lib/multiwoven/integrations/protocol/protocol.rb#L93)]

#### Destination

[Destination](https://github.com/Multiwoven/multiwoven-integrations/blob/main/lib/multiwoven/integrations/core/destination_connector.rb) implements the following interface methods including the common methods.

```
connector_spec() -> ConnectorSpecification
meta_data() -> Hash
check_connection(connection_config) -> ConnectionStatus
discover(connection_config)  -> Catalog
write(SyncConfig,Array[records]) -> TrackingMessage
```

1. `write(SyncConfig,Array[records]) -> TrackingMessage`

Description -The [write](https://github.com/Multiwoven/multiwoven-integrations/blob/main/lib/multiwoven/integrations/core/destination_connector.rb#L6C11-L6C40) method loads data to destinations.

Input - `Array[Record]`

Output - [TrackingMessage](https://github.com/Multiwoven/multiwoven-integrations/blob/main/lib/multiwoven/integrations/protocol/protocol.rb#L157)

Note: Complete multiwoven protocol models can be found [here](https://github.com/Multiwoven/multiwoven-integrations/blob/main/lib/multiwoven/integrations/protocol/protocol.rb)

### Acknowledgements

We've been significantly influenced by the [Airbyte protocol](https://github.com/airbytehq/airbyte-protocol), and their design choices greatly accelerated our project's development.


# Sync States
Source: https://docs.squared.ai/open-source/guides/architecture/sync-states



# Overview

This document details the states and transitions of sync operations, organizing the sync process into specific statuses and run states. These categories are vital for managing data flow during sync operations, ensuring successful and efficient execution.

## Sync Status Definitions

Each sync run operation can be in one of the following states, which represent the sync run's current status:

| State        | Description                                                                                       |
| ------------ | ------------------------------------------------------------------------------------------------- |
| **Healthy**  | A state indicating the successful completion of a recent sync run operation without any issues.   |
| **Disabled** | Indicates that the sync operation has been manually turned off and will not run until re-enabled. |
| **Pending**  | Assigned immediately after a sync is set up, signaling that no sync runs have been initiated yet. |
| **Failed**   | Denotes a sync operation that encountered an error, preventing successful completion.             |

> **Note:** Ensure that sync configurations are regularly reviewed to prevent prolonged periods in the Disabled or Failed states.

### Sync State Transitions

The following describes the allowed transitions between the sync states:

* **Pending ➔ Healthy**: Occurs when a sync run completes successfully.
* **Pending ➔ Failed**: Triggered if a sync run fails or is aborted.
* **Failed ➔ Healthy**: A successful sync run after a previously failed attempt.
* **Any state ➔ Disabled**: Reflects the manual disabling or enabling of the sync operation.

## Sync Run Status Definitions

| Status             | Description                                                                                                                                                                                                                                                                                    |
| ------------------ | ---------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------- |
| **Started**        | Indicates that the sync operation has begun. This status serves as the initial state of a new sync run operation after being triggered.                                                                                                                                                        |
| **Querying**       | The sync is currently querying a source with its associated model to retrieve the latest data. This involves moving data to a temporary table called "SyncRecord".                                                                                                                             |
| **Queued**         | Indicates the sync is scheduled for execution, following the successful transfer of source data to the "SyncRecord" table. This marks the completion of the preparation phase, with the sync now ready to transmit data to the destination as per system scheduling and resource availability. |
| **In Progress**    | The sync is actively transferring data from the "SyncRecord" table to the destination. This phase marks the actual update or insertion of data into the destination database, reflecting the final step of the sync process.                                                                   |
| **Success**        | The sync run is completed successfully without any issues.                                                                                                                                                                                                                                     |
| **Paused**         | Indicates a temporary interruption occurred while transferring data from the "SyncRecord" table to the destination. The sync is paused but designed to automatically resume in a subsequent run, ensuring continuity of the sync process.                                                      |
| **Aborted/Failed** | The sync has encountered an error that prevents it from completing successfully.                                                                                                                                                                                                               |

### Sync Run State Transitions

The following describes the allowed transitions between the sync run states:

* **Started ➔ Querying**: Transition post-initiation as data retrieval begins.
* **Querying ➔ Queued**: After staging data in the "SyncRecord" table, indicating readiness for transmission.
* **Queued ➔ In Progress**: Commences as the sync operation begins writing data to the destination, based on availability of system resources.
* **In Progress ➔ Success**: Marks the successful completion of data transmission.
* **In Progress ➔ Paused**: Triggered by a temporary interruption in the sync process.
* **Paused ➔ In Progress**: Signifies the resumption of a sync operation post-interruption.
* **In Progress ➔ Aborted/Failed**: Initiated when an error prevents the successful completion of the sync operation.


# Technical Stack
Source: https://docs.squared.ai/open-source/guides/architecture/technical-stack



### Frameworks

* **Ruby on Rails**

* **Typescript**

* **ReactJS**

## Database & Workers

* **PostgreSQL**

* **Temporal**

* **Redis**

## Deployment

* **Docker**

* **Kubernetes**

* **Helm**

## Monitoring

* **Prometheus**

* **Grafana**

## CI/CD

* **Github Actions**

## Testing

* **RSpec**

* **Cypress**


# 2024 releases
Source: https://docs.squared.ai/release-notes/2024



<CardGroup cols={3}>
  <Card title="December 2024" icon="book-open" href="/release-notes/December_2024">
    Version: v0.36.0 to v0.38.0
  </Card>

  <Card title="November 2024" icon="book-open" href="/release-notes/November_2024">
    Version: v0.31.0 to v0.35.0
  </Card>

  <Card title="October 2024" icon="book-open" href="/release-notes/October_2024">
    Version: v0.25.0 to v0.30.0
  </Card>

  <Card title="September 2024" icon="book-open" href="/release-notes/September_2024">
    Version: v0.23.0 to v0.24.0
  </Card>

  <Card title="August 2024" icon="book-open" href="/release-notes/August_2024">
    Version: v0.20.0 to v0.22.0
  </Card>

  <Card title="July 2024" icon="book-open" href="/release-notes/July_2024">
    Version: v0.14.0 to v0.19.0
  </Card>

  <Card title="June 2024" icon="book-open" href="/release-notes/June_2024">
    Version: v0.12.0 to v0.13.0
  </Card>

  <Card title="May 2024" icon="book-open" href="/release-notes/May_2024">
    Version: v0.5.0 to v0.8.0
  </Card>
</CardGroup>


# 2025 releases
Source: https://docs.squared.ai/release-notes/2025



<CardGroup cols={3}>
  <Card title="January 2025" icon="book-open" href="/release-notes/January_2025">
    Version: v0.39.0 to v0.45.0
  </Card>

  <Card title="February 2025" icon="book-open" href="/release-notes/Feb-2025">
    Version: v0.46.0 to v0.48.0
  </Card>
</CardGroup>


# August 2024 releases
Source: https://docs.squared.ai/release-notes/August_2024

Release updates for the month of August

## 🚀 **New Features**

### 🔄 **Enable/Disable Sync**

We’ve introduced the ability to enable or disable a sync. When a sync is disabled, it won’t execute according to its schedule, allowing you to effectively pause it without the need to delete it. This feature provides greater control and flexibility in managing your sync operations.

### 🧠 **Source: Databricks AI Model Connector**

Multiwoven now integrates seamlessly with [Databricks AI models](https://docs.squared.ai/guides/data-integration/sources/databricks-model) in the source connectors. This connection allows users to activate AI models directly through Multiwoven, enhancing your data processing and analytical capabilities with cutting-edge AI tools.

### 📊 **Destination: Microsoft Excel**

You can now use [Microsoft Excel](https://docs.squared.ai/guides/data-integration/destinations/productivity-tools/microsoft-excel) as a destination connector. Deliver your modeled data directly to Excel sheets for in-depth analysis or reporting. This addition simplifies workflows for those who rely on Excel for their data presentation and analysis needs.

### ✅ **Triggering Test Sync**

Before running a full sync, users can now initiate a test sync to verify that everything is functioning as expected. This feature ensures that potential issues are caught early, saving time and resources.

### 🏷️ **Sync Run Type**

Sync types are now clearly labeled as either "General" or "Test" in the Syncs Tab. This enhancement provides clearer context for each sync operation, making it easier to distinguish between different sync runs.

### 🛢️ **Oracle DB as a Destination Connector**

[Oracle DB](https://docs.squared.ai/guides/data-integration/destinations/database/oracle) is now available as a destination connector. Users can navigate to **Add Destination**, select **Oracle**, and input the necessary database details to route data directly to Oracle databases.

### 🗄️ **Oracle DB as a Source Connector**

[Oracle DB](https://docs.squared.ai/guides/data-integration/sources/oracle) has also been added as a source connector. Users can pull data from Oracle databases by navigating to **Add Source**, selecting **Oracle**, and entering the database details.

***

## 🔧 **Improvements**

### **Memory Bloat Issue in Sync**

Resolved an issue where memory bloat was affecting sync performance over time, ensuring more stable and efficient sync operations.

### **Discover and Table URL Fix**

Fixed issues with discovering and accessing table URLs, enhancing the reliability and accuracy of data retrieval processes.

### **Disable to Fields**

Added the option to disable fields where necessary, giving users more customization options to fit their specific needs.

### **Query Source Response Update**

Updated the query source response mechanism, improving data handling and accuracy in data query operations.

### **OCI8 Version Fix**

Resolved issues related to the OCI8 version, ensuring better compatibility and smoother database interactions.

### **User Read Permission Update**

Updated user read permissions to enhance security and provide more granular control over data access.

### **Connector Name Update**

Updated connector names across the platform to ensure better clarity and consistency, making it easier to manage and understand your integrations.

### **Account Verification Route Removal**

Streamlined the user signup process by removing the account verification route, reducing friction for new users.

### **Connector Creation Process**

Refined the connector creation process, making it more intuitive and user-friendly, thus reducing the learning curve for new users.

### **README Update**

The README file has been updated to reflect the latest changes and enhancements, providing more accurate and helpful guidance.

### **Request/Response Logs Added**

We’ve added request/response logs for multiple connectors, including Klaviyo, HTTP, Airtable, Slack, MariaDB, Google Sheets, Iterable, Zendesk, HubSpot, Stripe, and Salesforce CRM, improving debugging and traceability.

### **Logger Issue in Sync**

Addressed a logging issue within sync operations, ensuring that logs are accurate and provide valuable insights.

### **Main Layout Protected**

Wrapped the main layout with a protector, enhancing security and stability across the platform.

### **User Email Verification**

Implemented email verification during signup using Devise, increasing account security and ensuring that only verified users have access.

### **Databricks Datawarehouse Connector Name Update**

Renamed the Databricks connection to "Databricks Datawarehouse" for improved clarity and better alignment with user expectations.

### **Version Upgrade to 0.9.1**

The platform has been upgraded to version `0.9.1`, incorporating all the above features and improvements, ensuring a more robust and feature-rich experience.

### **Error Message Refactoring**

Refactored error messages to align with agreed-upon standards, resulting in clearer and more consistent communication across the platform.


# December 2024 releases
Source: https://docs.squared.ai/release-notes/December_2024

Release updates for the month of December

# 🚀 Features and Improvements

## **Features**

### **Audit Logs UI**

Streamline the monitoring of user activities with a new, intuitive interface for audit logs.

### **Custom Visual Components**

Create tailored visual elements for unique data representation and insights.

### **Dynamic Query Data Models**

Enhance query flexibility with support for dynamic data models.

### **Stream Support in HTTP Model**

Enable efficient data streaming directly in HTTP models.

### **Pagination for Connectors, Models, and Sync Pages**

Improve navigation and usability with added pagination support.

### **Multiple Choice Feedback**

Collect more detailed user feedback with multiple-choice options.

### **Rendering Type Filter for Data Apps**

Filter data apps effectively with the new rendering type filter.

### **Improved User Login**

Fixes for invited user logins and prevention of duplicate invitations for already verified users.

### **Context-Aware Titles**

Titles dynamically change based on the current route for better navigation.

## **Improvements**

### **Bug Fixes**

* Fixed audit log filter badge calculation.
* Corrected timestamp formatting in utilities.
* Limited file size for custom visual components to 2MB.
* Resolved BigQuery test sync failures.
* Improved UI for audit log views.
* Addressed sidebar design inconsistencies with Figma.
* Ensured correct settings tab highlights.
* Adjusted visual component height for tables and custom visual types.
* Fixed issues with HTTP request method retrieval.

### **Enhancements**

* Added support for exporting audit logs without filters.
* Updated query type handling during model fetching.
* Improved exception handling in resource builder.
* Introduced catalog and schedule sync resources.
* Refined action names across multiple controllers for consistency.
* Reordered deployment steps, removing unnecessary commands.

### **Resource Links and Controllers**

* Added resource links to:
  * Audit Logs
  * Catalogs
  * Connectors
  * Models
  * Syncs
  * Schedule Syncs
  * Enterprise components (Users, Profiles, Feedbacks, Data Apps)
* Updated audit logs for comprehensive coverage across controllers.

### **UI and Usability**

* Improved design consistency in audit logs and data apps.
* Updated export features for audit logs.

***


# February 2025 Releases
Source: https://docs.squared.ai/release-notes/Feb-2025

Release updates for the month of February

## 🚀 Features

* **PG vector as source changes**\
  Made changes to the PostgreSQL connector to support PG Vector.

## 🐛 Bug Fixes

* **Vulnerable integration gem versions update**\
  Upgraded Server Gems to the new versions, fixing vulnerabilities found in previous versions of the Gems.

## ⚙️ Miscellaneous Tasks

* **Sync alert bug fixes**\
  Fixed certain issues in the Sync Alert mailers.


# January 2025 Releases
Source: https://docs.squared.ai/release-notes/January_2025

Release updates for the month of January

## 🚀 Features

* **Added Empty State for Feedback Overview Table**\
  Introduces a default view when no feedback data is available, ensuring clearer guidance and intuitive messaging for end users.

* **Custom Visual Component for Writing Data to Destination Connectors**\
  Simplifies the process of sending or mapping data to various destination connectors within the platform’s interface.

* **Azure Blob Storage Integration**\
  Adds support for storing and retrieving data from Azure Blob, expanding available cloud storage options.

* **Update Workflows to Deploy Solid Worker**\
  Automates deployment of a dedicated worker process, improving back-end task management and system scalability.

* **Chatbot Visual Type**\
  Adds a dedicated visualization type designed for chatbot creation and management, enabling more intuitive configuration of conversational experiences.

* **Trigger Sync Alerts / Sync Alerts**\
  Implements a notification system to inform teams about the success or failure of data synchronization events in real time.

* **Runner Script Enhancements for Chatbot**\
  Improves the runner script’s capability to handle chatbot logic, ensuring smoother automated operations.

* **Add S3 Destination Connector**\
  Enables direct export of transformed or collected data to Amazon S3, broadening deployment possibilities for cloud-based workflows.

* **Add SFTP Source Connector**\
  Permits data ingestion from SFTP servers, streamlining workflows where secure file transfers are a primary data source.

## 🐛 Bug Fixes

* **Handle Chatbot Response When Streaming Is Off**\
  Resolves an issue causing chatbot responses to fail when streaming mode was disabled, improving overall reliability.

* **Sync Alert Issues**\
  Fixes various edge cases where alerts either triggered incorrectly or failed to trigger for certain data sync events.

* **UI Enhancements and Fixes**\
  Addresses multiple interface inconsistencies, refining the user experience for navigation and data presentation.

* **Validation for “Continue” CTA During Chatbot Creation**\
  Ensures that all mandatory fields are properly completed before users can progress through chatbot setup.

* **Refetch Data Model After Update**\
  Corrects a scenario where updated data models were not automatically reloaded, preventing stale information in certain views.

* **OpenAI Connector Failure Handling**\
  Improves error handling and retry mechanisms for OpenAI-related requests, reducing the impact of transient network issues.

* **Stream Fetch Fix for Salesforce**\
  Patches a problem causing occasional timeouts or failed data streams when retrieving records from Salesforce.

* **Radio Button Inconsistencies**\
  Unifies radio button behavior across the platform’s interface, preventing unexpected selection or styling errors.

* **Keep Reports Link Highlight**\
  Ensures the “Reports” link remains visibly highlighted in the navigation menu, maintaining consistent visual cues.

## ⚙️ Miscellaneous Tasks

* **Add Default Request and Response in Connection Configuration for OpenAI**\
  Provides pre-populated request/response templates for OpenAI connectors, simplifying initial setup for users.

* **Add Alert Policy to Roles**\
  Integrates alert policies into user role management, allowing fine-grained control over who can create or modify data alerts.


# July 2024 releases
Source: https://docs.squared.ai/release-notes/July_2024

Release updates for the month of July

## ✨ **New Features**

### 🔍 **Search Filter in Table Selector**

The table selector method now includes a powerful search filter. This feature enhances your workflow by allowing you to swiftly locate and select the exact tables you need, even in large datasets. It’s all about saving time and boosting productivity.

### 🏠 **Databricks Lakehouse Destination**

We're excited to introduce Databricks Lakehouse as a new destination connector. Seamlessly integrate your data pipelines with Databricks Lakehouse, harnessing its advanced analytics capabilities for data processing and AI-driven insights. This feature empowers your data strategies with greater flexibility and power.

### 📅 **Manual Sync Schedule Controller**

Take control of your data syncs with the new Manual Sync Schedule controller. This feature gives you the freedom to define when and how often syncs occur, ensuring they align perfectly with your business needs while optimizing resource usage.

### 🛢️ **MariaDB Destination Connector**

MariaDB is now available as a destination connector! You can now channel your processed data directly into MariaDB databases, enabling robust data storage and processing workflows. This integration is perfect for users operating in MariaDB environments.

### 🎛️ **Table Selector and Layout Enhancements**

We’ve made significant improvements to the table selector and layout. The interface is now more intuitive, making it easier than ever to navigate and manage your tables, especially in complex data scenarios.

### 🔄 **Catalog Refresh**

Introducing on-demand catalog refresh! Keep your data sources up-to-date with a simple refresh, ensuring you always have the latest data structure available. Say goodbye to outdated data and hello to consistency and accuracy.

### 🛡️ **S3 Connector ARN Support for Authentication**

Enhance your security with ARN (Amazon Resource Name) support for Amazon S3 connectors. This update provides a more secure and scalable approach to managing access to your S3 resources, particularly beneficial for large-scale environments.

### 📊 **Integration Changes for Sync Record Log**

We’ve optimized the integration logic for sync record logs. These changes ensure more reliable logging, making it easier to track sync operations and diagnose issues effectively.

### 🗄️ **Server Changes for Log Storage in Sync Record Table**

Logs are now stored directly in the sync record table, centralizing your data and improving log accessibility. This update ensures that all relevant sync information is easily retrievable for analysis.

### ✅ **Select Row Support in Data Table**

Interact with your data tables like never before! We've added row selection support, allowing for targeted actions such as editing or deleting entries directly from the table interface.

### 🛢️ **MariaDB Source Connector**

The MariaDB source connector is here! Pull data directly from MariaDB databases into Multiwoven for seamless integration into your data workflows.

### 🛠️ **Sync Records Error Log**

A detailed error log feature has been added to sync records, providing granular visibility into issues that occur during sync operations. Troubleshooting just got a whole lot easier!

### 🛠️ **Model Query Type - Table Selector**

The table selector is now available as a model query type, offering enhanced flexibility in defining queries and working with your data models.

### 🔄 **Force Catalog Refresh**

Set the refresh flag to true, and the catalog will be forcefully refreshed. This ensures you're always working with the latest data, reducing the chances of outdated information impacting your operations.

## 🔧 **Improvements**

* **Manual Sync Delete API Call**: Enhanced the API call for deleting manual syncs for smoother operations.
* **Server Error Handling**: Improved error handling to better display server errors when data fetches return empty results.
* **Heartbeat Timeout in Extractor**: Introduced new actions to handle heartbeat timeouts in extractors for improved reliability.
* **Sync Run Type Column**: Added a `sync_run_type` column in sync logs for better tracking and operational clarity.
* **Refactor Discover Stream**: Refined the discover stream process, leading to better efficiency and reliability.
* **DuckDB HTTPFS Extension**: Introduced server installation steps for the DuckDB `httpfs` extension.
* **Temporal Initialization**: Temporal processes are now initialized in all registered namespaces, improving system stability.
* **Password Reset Email**: Updated the reset password email template and validation for a smoother user experience.
* **Organization Model Changes**: Applied structural changes to the organization model, enhancing functionality.
* **Log Response Validation**: Added validation to log response bodies, improving error detection.
* **Missing DuckDB Dependencies**: Resolved missing dependencies for DuckDB, ensuring smoother operations.
* **STS Client Initialization**: Removed unnecessary credential parameters from STS client initialization, boosting security.
* **Main Layout Error Handling**: Added error screens for the main layout to improve user experience when data is missing or errors occur.
* **Server Gem Updates**: Upgraded server gems to the latest versions, enhancing performance and security.
* **AppSignal Logging**: Enhanced AppSignal logging by including app request and response logs for better monitoring.
* **Sync Records Table**: Added a dedicated table for sync records to improve data management and retrieval.
* **AWS S3 Connector**: Improved handling of S3 credentials and added support for STS credentials in AWS S3 connectors.
* **Sync Interval Dropdown Fix**: Fixed an issue where the sync interval dropdown text was hidden on smaller screens.
* **Form Data Processing**: Added a pre-check process for form data before checking connections, improving validation and accuracy.
* **S3 Connector ARN Support**: Updated the gem to support ARN-based authentication for S3 connectors, enhancing security.
* **Role Descriptions**: Updated role descriptions for clearer understanding and easier management.
* **JWT Secret Configuration**: JWT secret is now configurable from environment variables, boosting security practices.
* **MariaDB README Update**: Updated the README file to include the latest information on MariaDB connectors.
* **Logout Authorization**: Streamlined the logout process by skipping unnecessary authorization checks.
* **Sync Record JSON Error**: Added a JSON error field in sync records to enhance error tracking and debugging.
* **MariaDB DockerFile Update**: Added `mariadb-dev` to the DockerFile to better support MariaDB integrations.
* **Signup Error Response**: Improved the clarity and detail of signup error responses.
* **Role Policies Update**: Refined role policies for enhanced access control and security.
* **Pundit Policy Enhancements**: Applied Pundit policies at the role permission level, ensuring robust authorization management.


# June 2024 releases
Source: https://docs.squared.ai/release-notes/June_2024

Release updates for the month of June

# 🚀 New Features

* **Iterable Destination Connector**\
  Integrate with Iterable, allowing seamless data flow to this popular marketing automation platform.

* **Workspace Settings and useQueryWrapper**\
  New enhancements to workspace settings and the introduction of `useQueryWrapper` for improved data handling.

* **Amazon S3 Source Connector**\
  Added support for Amazon S3 as a source connector, enabling data ingestion directly from your S3 buckets.

# 🛠️ Improvements

* **GitHub URL Issues**\
  Addressed inconsistencies with GitHub URLs in the application.

* **Change GitHub PAT to SSH Private Key**\
  Updated authentication method from GitHub PAT to SSH Private Key for enhanced security.

* **UI Maintainability and Workspace ID on Page Refresh**\
  Improved UI maintainability and ensured that the workspace ID persists after page refresh.

* **CE Sync Commit for Multiple Commits**\
  Fixed the issue where CE sync commits were not functioning correctly for multiple commits.

* **Add Role in User Info API Response**\
  Enhanced the user info API to include role details in the response.

* **Sync Write Update Action for Destination**\
  Synchronized the write update action across various destinations for consistency.

* **Fix Sync Name Validation Error**\
  Resolved validation errors in sync names due to contract issues.

* **Update Commit Message Regex**\
  Updated the regular expression for commit messages to follow git conventions.

* **Update Insert and Update Actions**\
  Renamed `insert` and `update` actions to `destination_insert` and `destination_update` for clarity.

* **Comment Contract Valid Rule in Update Sync Action**\
  Adjusted the contract validation rule in the update sync action to prevent failures.

* **Fix for Primary Key in `destination_update`**\
  Resolved the issue where `destination_update` was not correctly picking up the primary key.

* **Add Limit and Offset Query Validator**\
  Introduced validation for limit and offset queries to improve API reliability.

* **Ignore RBAC for Get Workspaces API**\
  Modified the API to bypass Role-Based Access Control (RBAC) for fetching workspaces.

* **Heartbeat Timeout Update for Loader**\
  Updated the heartbeat timeout for the loader to ensure smoother operations.

* **Add Strong Migration Gem**\
  Integrated the Strong Migration gem to help with safe database migrations.

<Note>Stay tuned for more exciting updates in the upcoming releases!</Note>


# May 2024 releases
Source: https://docs.squared.ai/release-notes/May_2024

Release updates for the month of May

# 🚀 New Features

* **Role and Resource Migration**\
  Introduced migration capabilities for roles and resources, enhancing data management and security.

* **Zendesk Destination Connector**\
  Added support for Zendesk as a destination connector, enabling seamless integration with Zendesk for data flow.

* **Athena Connector**\
  Integrated the Athena Connector, allowing users to connect to and query Athena directly from the platform.

* **Support for Temporal Cloud**\
  Enabled support for Temporal Cloud, facilitating advanced workflow orchestration in the cloud.

* **Workspace APIs for CE**\
  Added Workspace APIs for the Community Edition, expanding workspace management capabilities.

* **HTTP Destination Connector**\
  Introduced the HTTP Destination Connector, allowing data to be sent to any HTTP endpoint.

* **Separate Routes for Main Application**\
  Organized and separated routes for the main application, improving modularity and maintainability.

* **Compression Support for SFTP**\
  Added compression support for SFTP, enabling faster and more efficient data transfers.

* **Password Field Toggle**\
  Introduced a toggle to view or hide password field values, enhancing user experience and security.

* **Dynamic UI Schema Generation**\
  Added dynamic generation of UI schemas, streamlining the user interface customization process.

* **Health Check Endpoint for Worker**\
  Added a health check endpoint for worker services, ensuring better monitoring and reliability.

* **Skip Rows in Sync Runs Table**\
  Implemented functionality to skip rows in the sync runs table, providing more control over data synchronization.

* **Cron Expression as Schedule Type**\
  Added support for using cron expressions as a schedule type, offering more flexibility in task scheduling.

* **SQL Autocomplete**\
  Introduced SQL autocomplete functionality, improving query writing efficiency.

# 🛠️ Improvements

* **Text Update in Finalize Source Form**\
  Changed and improved the text in the Finalize Source Form for clarity.

* **Rate Limiter Spec Failure**\
  Fixed a failure issue in the rate limiter specifications, ensuring better performance and stability.

* **Check for Null Record Data**\
  Added a condition to check if record data is null, preventing errors during data processing.

* **Cursor Field Mandatory Check**\
  Ensured that the cursor field is mandatory, improving data integrity during synchronization.

* **Docker Build for ARM64 Release**\
  Fixed the Docker build process for ARM64 releases, ensuring compatibility across architectures.

* **UI Auto Deploy**\
  Improved the UI auto-deployment process for more efficient updates.

* **Cursor Query for SOQL**\
  Added support for cursor queries in SOQL, enhancing Salesforce data operations.

* **Skip Cursor Query for Empty Cursor Field**\
  Implemented a check to skip cursor queries when the cursor field is empty, avoiding unnecessary processing.

* **Updated Integration Gem Version**\
  Updated the integration gem to version 0.1.67, including support for Athena source, Zendesk, and HTTP destinations.

* **Removed Stale User Management APIs**\
  Deleted outdated user management APIs and made changes to role ID handling for better security.

* **Color and Logo Theme Update**\
  Changed colors and logos to align with the new theme, providing a refreshed UI appearance.

* **Refactored Modeling Method Screen**\
  Refactored the modeling method screen for better usability and code maintainability.

* **Removed Hardcoded UI Schema**\
  Removed hardcoded UI schema elements, making the UI more dynamic and adaptable.

* **Heartbeat Timeout for Loader**\
  Updated the heartbeat timeout for the loader, improving the reliability of the loading process.

* **Integration Gem to 1.63**\
  Bumped the integration gem version to 1.63, including various improvements and bug fixes.

* **Core Chakra Config Update**\
  Updated the core Chakra UI configuration to support new branding requirements.

* **Branding Support in Config**\
  Modified the configuration to support custom branding, allowing for more personalized user experiences.

* **Strong Migration Gem Addition**\
  Integrated the Strong Migration gem to ensure safer and more efficient database migrations.

<Note>Stay tuned for more exciting updates in future releases!</Note>


# November 2024 releases
Source: https://docs.squared.ai/release-notes/November_2024

Release updates for the month of November

# 🚀 New Features

### **Add HTTP Model Source Connector**

Enables seamless integration with HTTP-based model sources, allowing users to fetch and manage data directly from APIs with greater flexibility.

### **Paginate and Delete Data App**

Introduces functionality to paginate data within apps and delete them as needed, improving data app lifecycle management.

### **Data App Report Export**

Enables exporting comprehensive reports from data apps, making it easier to share insights with stakeholders.

### **Fetch JSON Schema from Model**

Adds support to fetch the JSON schema for models, aiding in better structure and schema validation.

### **Custom Preview of Data Apps**

Offers a customizable preview experience for data apps, allowing users to tailor the visualization to their needs.

### **Bar Chart Visual Type**

Introduces bar charts as a new visual type, complete with a color picker for enhanced customization.

### **Support Multiple Data in a Single Chart**

Allows users to combine multiple datasets into a single chart, providing a consolidated view of insights.

### **Mailchimp Destination Connector**

Adds a connector for Mailchimp, enabling direct data integration with email marketing campaigns.

### **Session Management During Rendering**

Improves session handling for rendering data apps, ensuring smoother and more secure experiences.

### **Update iFrame URL for Multiple Components**

Supports multiple visual components within a single iFrame, streamlining complex data app designs.

***

# 🔧 Improvements

### **Error Handling Enhancements**

Improved logging for duplicated primary keys and other edge cases to ensure smoother operations.

### **Borderless iFrame Rendering**

Removed borders from iFrame elements for a cleaner, more modern design.

### **Audit Logging Across Controllers**

Audit logs are now available for sync, report, user, role, and feedback controllers to improve traceability and compliance.

### **Improved Session Management**

Fixed session management bugs to enhance user experience during data app rendering.

### **Responsive Data App Rendering**

Improved rendering for smaller elements to ensure better usability on various screen sizes.

### **Improved Token Expiry**

Increased token expiry duration for extended session stability.

***

# ⚙️ Miscellaneous Updates

* Added icons for HTTP Model for better visual representation.
* Refactored code to remove hardcoded elements and improve maintainability.
* Updated dependencies to resolve build and compatibility issues.
* Enhanced feedback submission with component-specific IDs for more precise data collection.

***


# October 2024 releases
Source: https://docs.squared.ai/release-notes/October_2024

Release updates for the month of October

# 🚀 New Features

* **Data Apps Configurations and Rendering**\
  Provides robust configurations and rendering capabilities for data apps, enhancing customization.

* **Scale and Text Input Feedback Methods**\
  Introduces new feedback options with scale and text inputs to capture user insights effectively.

* **Support for Multiple Visual Components**\
  Expands visualization options by supporting multiple visual components, enriching data presentation.

* **Audit Log Filter**\
  Adds a filter feature in the Audit Log, simplifying the process of finding specific entries.

***

# 🛠 Improvements

* **Disable Mixpanel Tracking**\
  Disabled Mixpanel tracking for enhanced data privacy and user control.

* **Data App Runner Script URL Fix**\
  Resolved an issue with the UI host URL in the data app runner script for smoother operation.

* **Text Input Bugs**\
  Fixed bugs affecting text input functionality, improving stability and responsiveness.

* **Dynamic Variables in Naming and Filters**\
  Adjusted naming conventions and filters to rely exclusively on dynamic variables, increasing flexibility and reducing redundancy.

* **Sort Data Apps List in Descending Order**\
  The data apps list is now sorted in descending order by default for easier access to recent entries.

* **Data App Response Enhancements**\
  Updated responses for data app creation and update APIs, improving clarity and usability.

***

> For further details on any feature or update, check the detailed documentation or contact our support team. We’re here to help make your experience seamless!

***


# September 2024 releases
Source: https://docs.squared.ai/release-notes/September_2024

Release updates for the month of September

# 🚀 New Features

* **AI/ML Sources**\
  Introduces support for a range of AI/ML sources, broadening model integration capabilities.

* **Added AI/ML Models Support**\
  Comprehensive support for integrating and managing AI and ML models across various workflows.

* **Data App Update API**\
  This API endpoint allows users to update existing data apps without needing to recreate them from scratch. By enabling seamless updates with the latest configurations and features, users can Save time, Improve accuracy and Ensure consistency

* **Donut Chart Component**
  The donut chart component enhances data visualization by providing a clear, concise way to represent proportions or percentages within a dataset.

* **Google Vertex Model Source Connector**\
  Enables connection to Google Vertex AI, expanding options for model sourcing and integration.

***

# 🛠️ Improvements

* **Verify User After Signup**\
  A new verification step ensures all users are authenticated right after signing up, enhancing security.

* **Enable and Disable Sync via UI**\
  Users can now control sync processes directly from the UI, giving flexibility to manage syncs as needed.

* **Disable Catalog Validation for Data Models**\
  Catalog validation is now disabled for non-AI data models, improving compatibility and accuracy.

* **Model Query Preview API Error Handling**\
  Added try-catch blocks to the model query preview API call, providing better error management and debugging.

* **Fixed Sync Mapping for Model Column Values**\
  Corrected an issue in sync mapping to ensure accurate model column value assignments.

* **Test Connection Text**\
  Fixed display issues with the "Test Connection" text, making it clearer and more user-friendly.

* **Enable Catalog Validation Only for AI Models**\
  Ensures that catalog validation is applied exclusively to AI models, maintaining model integrity.

* **Disable Catalog Validation for Data Models**\
  Disables catalog validation for non-AI data models to improve compatibility.

* **AIML Source Schema Components**\
  Refined AI/ML source schema components, enhancing performance and readability in configurations.

* **Setup Charting Library and Tailwind CSS**\
  Tailwind CSS integration and charting library setup provide better styling and data visualization tools.

* **Add Model Name in Data App Response**\
  Model names are now included in data app responses, offering better clarity for users.

* **Add Connector Icon in Data App Response**\
  Connector icons are displayed within data app responses, making it easier to identify connections visually.

* **Add Catalog Presence Validation for Models**\
  Ensures that a catalog is present and validated for all applicable models.

* **Validate Catalog for Query Source**\
  Introduces validation for query source catalogs, enhancing data accuracy.

* **Add Filtering Scope to Connectors**\
  Allows for targeted filtering within connectors, simplifying the search for relevant connections.

* **Common Elements for Sign Up & Sign In**\
  Moved shared components for sign-up and sign-in into separate views to improve code organization.

* **Updated Sync Records UX**\
  Enhanced the user experience for sync records, providing a more intuitive interface.

* **Setup Models Renamed to Define Setup**\
  Updated terminology from "setup models" to "define setup" for clearer, more precise language.

***

> For further details on any feature or update, check the detailed documentation or contact our support team. We’re here to help make your experience seamless!

***