> ## Documentation Index
> Fetch the complete documentation index at: https://docs.nekt.com/llms.txt
> Use this file to discover all available pages before exploring further.
# Treble AI as a data source
> Bring data from Treble AI to Nekt.
Treble AI is a conversational AI platform that helps businesses automate customer interactions and manage conversations at scale. It provides tools for creating and managing automated conversations, tracking customer interactions, and analyzing conversation data.
## Configuring Treble AI as a Source
In the [Sources](https://app.nekt.ai/sources) tab, click on the "Add source" button located on the top right of your screen. Then, select the Treble AI option from the list of connectors.
Click **Next** and you'll be prompted to add your access.
### 1. Add account access
You'll need to provide the following credentials to connect to Treble AI:
* **API key**: The token to authenticate against the API service. Check [this resource](https://help.treble.ai/en/api-reference/intro/api-key#how-to-obtain-an-api-key%3F) for more info on how to generate the token.
Additionally, you can configure:
* **Lookback window**: The number of days to look back when fetching session history. This is important to ensure new replies are fetched after the session creation date.
* **Start date**: The start date from which you want to start syncing records.
Once this is done, click **Next**.
### 2. Select streams
Choose which data streams you want to sync - you can select all streams or pick specific ones that matter most to you.
> Tip: The stream can be found more easily by typing its name.
Select the streams and click **Next**.
### 3. Configure data streams
Customize how you want your data to appear in your catalog. Select a name for each table (which will contain the fetched data) and the type of sync.
* **Table name**: we suggest a name, but feel free to customize it. You have the option to add a **prefix** and make this process faster!
* **Sync Type**: you can choose between INCREMENTAL and FULL\_TABLE.
* Incremental: every time the extraction happens, we'll get only the new data - which is good if, for example, you want to keep every record ever fetched.
* Full table: every time the extraction happens, we'll get the current state of the data - which is good if, for example, you don't want to have deleted data in your catalog.
Once you are done configuring, click **Next**.
### 4. Configure data source
Describe your data source for easy identification within your organization, not exceeding 140 characters.
To define your [Trigger](https://docs.nekt.com/runs/scheduling-and-triggers), consider how often you want data to be extracted from this source. This decision usually depends on how frequently you need the new table data updated (every day, once a week, or only at specific times).
Optionally, you can determine when to execute a [full sync](https://docs.nekt.com/get-started/core-concepts/types-of-sync#additional-full-sync). This will complement the incremental data extractions, ensuring that your data is completely synchronized with your source every once in a while.
Once you are ready, click **Next** to finalize the setup.
### 5. Check your new source
You can view your new source on the [Sources](https://app.nekt.ai/sources) page. If needed, manually trigger the source extraction by clicking on the arrow button. Once executed, your data will appear in your Catalog.
For you to be able to see it on your [Catalog](https://app.nekt.ai/catalog), you need at least one successful source run.
# Streams and Fields
Below you'll find all available data streams from Treble AI and their corresponding fields:
Stream for managing conversation flows and polls.
**Key Fields:**
* `id` - The conversation's system ID (Primary Key)
* `name` - The conversation's name
Stream for tracking individual conversation sessions.
**Key Fields:**
* `id` - The conversation's unique identifier (Primary Key)
* `poll_id` - The poll's unique identifier
* `created_at` - When the conversation was created
* `finished_at` - When the conversation was completed
**User Information:**
* `user` - Information about the conversation participant:
* `country_code` - User's country calling code
* `cellphone` - User's phone number
Stream for detailed message history within sessions.
**Key Fields:**
* `_id` - The unique identifier for the message (auto-generated by the connector)
* `session_id` - The conversation's session identifier
* `type` - The type of message
* `timestamp` - The timestamp of the message
**Message Details:**
* `message` - Detailed message information:
* `type` - The message type
* `sender` - Who sent the message
* `text` - The message text content
* `file_url` - URL to any attached file
* `created_at` - Timestamp when message was created
* `delivered_at` - Timestamp when message was delivered
* `read_at` - Timestamp when message was read
* `extra` - Additional message metadata
## Data Model
The following diagram illustrates the relationships between the core data streams in Treble AI. The arrows indicate the join keys that link the different entities.
```mermaid theme={null}
graph TD;
subgraph "Core Entities"
Conversations("Conversations");
Sessions("Sessions");
SessionHistory("Session History");
end
Sessions -- "poll_id" --> Conversations;
SessionHistory -- "session_id" --> Sessions;
```
## Skills for agents
Treble AI connector documentation as plain markdown, for use in AI agent contexts.