> For the complete documentation index, see [llms.txt](https://docs.ebbot.ai/ebbot-docs/llms.txt). Markdown versions of documentation pages are available by appending `.md` to page URLs; this page is available as [Markdown](https://docs.ebbot.ai/ebbot-docs/developer-resources/ebbot-chat/conversation-data-object.md).
# Conversation Data Object
## Using the Data object
Note: the Conversation data object is to be used for chat only.
The data object is a powerful tool for managing and accessing information throughout a conversation in Ebbot. It allows you to store and retrieve data as well as access conversation and customer information, providing flexibility and customization for your chat agent.
This documentation will guide you through the structure of the data object and its various components.
### Overview
The data object consists of the following key components:
* `id`: Unique identifier for the current state in the conversation.
* `name`: Name of the current state.
* `parameters`: An object to pass data into custom components, not persisted across states.
* `db`: An object to store and retrieve data throughout the conversation.
* `conversation`: An object containing information about the conversation.
* `customer_info`: An object containing information about the customer.
* `chatId`: Unique identifier for the chat.
### Structure
The data object has the following structure:
```json
{
"id": "example_id",
"name": "state_example",
"parameters": {},
"db": {},
"conversation": {
"user_last_input": "Hello there 😀",
"browser": "chrome",
"os": "Mac OS",
"platform": "web",
"time_before_start": 0
},
"customer_info": {
"id": "example_customer_id",
"image": null,
"info": {
"invoice_id": "3453342",
"premium_customer": "true"
},
"first_name": "Inclined",
"last_name": "Kangaroo"
},
"chatId": "example_chat_id"
}
```
#### Parameters object
The `parameters` object is empty by default but can be used to pass data into custom components. The data stored in the `parameters` object will not be persisted during the conversation and is only valid for the triggered component.
#### DB Object
The `db` object allows you to store and retrieve data during the conversation. For example, you can store user inputs, variable button presses, and information about uploaded files.
#### Conversation object
The `conversation` object contains information about the ongoing conversation, such as the user's last input, the browser used, the operating system, and the platform.
#### Customer object
The `customer_info` object contains information about the customer, such as their unique ID, first and last name, and any additional information you'd like to store.
### Usage
You can access and manipulate the data object throughout the conversation to provide a personalized and dynamic user experience. Store data in the `db` object, pass temporary data through the `parameters` object, and access conversation and customer information to customize the chatbot's responses.
### File upload card example
When a user uploads a file using a File Upload Card, the information about the uploaded file is stored in the `db` object. Here is an example of how the `db` object would look like after a file has been uploaded:
```json
"db": {
"customer_uploaded_file": {
"messageId": "example_message_id",
"files": [{
"filename": "logo (1).png",
"size": 3385,
"mimetype": "image/png",
"label": "logo (1).png",
"key": "example_key",
"url": "https://example.storage.googleapis.com/example_path/logo_(1).png"
}],
"type": "file_list"
}
}
```
In this example, a user has uploaded a file named "logo (1).png". The file information is stored under the `customer_uploaded_file` key in the `db` object. You can access this information throughout the conversation to perform actions such as displaying the file, sending it to another API, or storing it in a database.
To access the uploaded file's URL, you can use the following syntax:
```python
file_url = data['db']['customer_uploaded_file']['files'][0]['url']
```
This will retrieve the URL of the uploaded file, which can be used for further processing or displaying to the user.
---
# Agent Instructions
This documentation is published with GitBook. GitBook is the documentation platform designed so that both humans and AI agents can read, navigate, and reason over technical content effectively. Learn more at gitbook.com.
## Querying This Documentation
If you need additional information that is not directly available in this page, you can query the documentation dynamically by asking a question.
Perform an HTTP GET request on the current page URL with the `ask` query parameter, and the optional `goal` query parameter:
```
GET https://docs.ebbot.ai/ebbot-docs/developer-resources/ebbot-chat/conversation-data-object.md?ask=&goal=
```
`ask` is the immediate question: it should be specific, self-contained, and written in natural language.
`goal` is optional and describes the broader end goal you are ultimately trying to accomplish on behalf of the user. GitBook uses it to tailor the answer towards what is most useful for that goal.
The response will contain a direct answer to the question and relevant excerpts and sources from the documentation.
Use this mechanism when the answer is not explicitly present in the current page, you need clarification or additional context, or you want to retrieve related documentation sections.