> For the complete documentation index, see [llms.txt](https://docs.snackprompt.com/llms.txt). Markdown versions of documentation pages are available by appending `.md` to page URLs; this page is available as [Markdown](https://docs.snackprompt.com/bring-your-data-into-ai/get-started/ingesting-data-into-the-knowledge-base.md).

# Ingesting Data into the Knowledge Base

In this tutorial, you'll learn how to send data to the SnackPrompt AI Engine Knowledge Base.

### What is an Elemental?

An **elemental** is the basic unit of data in SnackPrompt. It can be:

* A **table** with columns and items
* A **document** with sections
* A **file** with structured content

When you send an elemental, the API:

1. Processes the content
2. Generates embeddings (vector representations)
3. Stores in the Knowledge Base
4. Makes the data searchable

### How to create a simple Knowledge Base

#### Step 1: Set up your Knowledge Base

The first step is to ensure your data is accessible to the AI. In Snackprompt, you can transform any element into a data source:

1. Create a **Table**, **Document**, or **Prompt**.
2. Populate it with your data.
3. Enable the **Knowledge Base** toggle in the settings menu.

For a detailed step-by-step on this process, see our guide: [Creating your first Knowledge Base](https://snack-prompt.gitbook.io/snack-prompt-docs/getting-started/create-a-simple-knowledge-base-using-our-elements).

### What Happens During Ingestion?

When you send an elemental, the API executes the following steps:

```
1. Receives the elemental_id
       ↓
2. Fetches complete data from SnackPrompt platform
       ↓
3. Processes the content (parsing)
       ↓
4. Splits into chunks (smaller pieces)
       ↓
5. Generates embeddings
       ↓
6. Stores in our Vector Database
       ↓
7. Data available for search!
```

### Stored Metadata

During ingestion, the following metadata is stored with each chunk:

| Field                | Description                                   |
| -------------------- | --------------------------------------------- |
| `tenant_id`          | Tenant ID (for isolation)                     |
| `user_id`            | User ID                                       |
| `snack_elemental_id` | Source elemental ID                           |
| `snack_column_id`    | Column ID (if applicable)                     |
| `snack_item_id`      | Specific item ID                              |
| `source`             | Source type (`elemental`, `document`, `file`) |
| `type_name`          | Elemental type (Table, Document, etc.)        |
| `category_name`      | Elemental category                            |
| `original_text`      | Original chunk content                        |
| `tag_ids`            | Associated tag IDs                            |
| `tag_names`          | Associated tag names                          |

### Tag Inheritance

Tags are inherited in cascade:

```
Document (document tags)
    └── Column (column tags)
            └── Item (item tags)
```

The final item will have the **merge** of all tags (no duplicates).

### Next Steps

Now that your data is in the Knowledge Base:

1. [Semantic Search](/bring-your-data-into-ai/get-started/semantic-search.md) - Learn how to search your data
2. [Chat with your Data](/bring-your-data-into-ai/get-started/chat-with-your-data-rag.md) - Chat with your data using AI
3. [Filter by Tags](/bring-your-data-into-ai/how-to/how-to-filter-by-tags.md) - Use tags to filter results

***

**Estimated time:** 10 minutes ✅