Tables are an essential feature in the explorer, functioning as a local database within your chatbot’s environment. They provide structured storage for various types of data that can be accessed and manipulated as required within your workflows.

They are particularly useful for storing information that needs to be persisted across multiple sessions, such as user profiles, transaction histories, and other types of data that need to be retrieved and updated over time.

Note

You can create up to 10 tables with 20 columns and 10,000 rows in the Free Plan. Get in touch if you want to increase the limits. However, the 20 columns per table is a hard limit that can’t be increased.

Table row capacity filling up quicker than you expected? Check your table’s row factor.

What are row factors?

Creating a Table

To add a new table, go to the explorer menu and select Add Table. You can then give your table a name and add fields to it. Each field has a name and a type. The type determines the kind of data that can be stored in the field. For example, a field with the type String can store any text value, while a field with the type Number can only store numeric values.

Making Fields Searchable

You can make a field searchable by checking the Searchable checkbox. This will allow you to search for records based on the values in the field. For example, if you have a field called Name and you make it searchable, you can search for records based on the name of the person. This is available for fields of type String, Object, Array and custom schemas.

Types of Fields (Columns)

There are several types of fields you can add to a table. Each field type has a specific purpose and can only store certain types of data. These are the field types you can choose from: String, Number, Boolean, Date, Object, Array and custom schemas.

Warning

The number of columns has a hard limit of 20 columns per table.

Filtering Records

Table filters are a powerful feature that allows you to refine and display only the records that meet certain criteria. Filters can be based on specific conditions, such as date ranges, text matches, and numerical comparisons.

Logical Operators

AND: Use the AND operator when you want all conditions to be true for a record to be displayed. For example, filtering by createdAt AND status will only show records that match both conditions.
OR: Use the OR operator to display records that match any one of multiple conditions. For example, filtering by createdAt OR updatedAt will show records that match either one of the conditions.

Types of Rules

Rules define the individual conditions applied to each column of data.

is equal to: Shows records where the field is equal to the specified value.
is not equal to: Shows records where the field is not equal to the specified value.
less than: Shows records where the field is less than the specified value.
less than or equal to: Shows records where the field is less than or equal to the specified value.
greater than: Shows records where the field is greater than the specified value.
greater than or equal to: Shows records where the field is greater than or equal to the specified value.
in: Shows records where the field matches any of the specified values.
not in: Shows records where the field does not match any of the specified values.
is null: Shows records where the field is empty.
is not null: Shows records where the field is not empty.

Rule Groups

Group: A group is a collection of rules combined by a logical operator (AND/OR). You can nest groups to create complex filters.

Creating a Filter

Click on the Filter button to expand the filter menu.
Select a logical operator (AND/OR) from the dropdown.
Choose a field to filter by (e.g., createdAt).
Select a rule (e.g., is equal to).
Enter the required value or select from a calendar if it’s a date field.
To add another rule within the same logical condition, click the + Rule button.
To create a nested group of conditions, click the + Group button.
Apply the filter by clicking the Apply filter button.

Manually Editing Filters

Click on Edit Manually to view and edit the filter in JSON format. You can then copy this JSON filter and use it in your table operations via code.

Operations on Tables - via the Interface

Add records to a table

Once you’ve created a table, you can add records to it. To do this, click on the table in the explorer menu. You’ll then see a list of all the records in the table. Click on the Add Record button to add a new record. You can then enter the values for each field in the record.

Update records in a table

Double click on a a table cell or press click it and press Enter to see an input that allows you to change the cell value. Once you are done, press Enter again or click away to save the new value.

Delete records from a table

Right Click on the Record and select Delete Record to delete a record from a table.

Operations on Tables - via Cards

Check out the Table Cards documentation to learn more about operations with cards.

Operations on Tables - via Botpress Client

You can also interact with tables using the Botpress Client. Here are some of the functions you can use to interact with tables:

listTables

To list all the tables in the bot, use the listTables function. This function will return an array of all the tables in the bot.

try {
  const tables = await client.listTables({})
  console.log('Tables:', tables)
} catch (err) {
  console.error('Error fetching tables', err)
}

Create Table Rows

Execute the following query to create one or multiple rows. The response will include the inserted rows, along with any encountered errors or warnings during the operation (e.g., unrecognized fields)

const { rows, errors, warnings } = await client.createTableRows({
  table: 'Data1Table',
  rows: [
    {
      name: 'test'
    }
  ]
})

Find & Filter Table Rows

To find and filter rows in a table, use the findTableRows function. You can specify the limit (number of records to return) and the offset (number of rows to start from).

const { rows, limit, offset, count } = await client.findTableRows({
  table: 'Data1Table',
  limit: 50,
  offset: 0,
  filter: {},
  orderBy: 'row_id',
  orderDirection: 'asc'
})

Utilize filters to conduct more refined queries for specific rows, employing a MongoDB-like syntax. The query builder aids in understanding available operations and constructing the filter.

const { rows, limit, offset, count } = await client.findTableRows({
table: 'Data1Table',
filter: {
  // Retrieve rows where date is greater than a specific date
  updatedAt: { gte: '2024-01-01' },
  // Or more complex conditions, such as name starting with 'a' or 'b', or 'isActive' being true
  $or: [{ name: { $regex: '^a' } }, { name: { $regex: '^b' } }, { isActive: true }]
}
})

The “search” property allows for a partial text search across the table. The query returns multiple results, each accompanied by a similarity score to indicate relevance.

const { rows, limit, offset, count } = await client.findTableRows({
  table: 'Data1Table',
  search: 'John Doe'
})

Projection & Aggregation

Employ the group property for projections and aggregations. The following example demonstrates returning a single row with requested data.

const { rows } = await client.findTableRows({
  table: 'Data1Table',
  group: {
    id: 'count',
    name: ['unique', 'count'], // will return a list of unique values & the total count
    updatedAt: ['min', 'max'] // Retrieves the earliest and latest date
  }
})

// Output variables are named after the field, suffixed with the operation
const oldestChange = rows[0].updatedAtMin
const mostRecentChange = rows[0].updatedAtMax
const count = rows[0]['nameCount']
const uniqueValues = rows[0]['nameUnique']

Column	Type	Key	Count	Sum	Avg	Max	Min	Unique
id	number	✔️	✔️	✔️	✔️	✔️	✔️	✔️
createdAt	date	✔️				✔️	✔️	✔️
updatedAt	date	✔️				✔️	✔️	✔️
name	string	✔️				✔️	✔️	✔️

Key: Projections are grouped by keys (ex: date)
Count: Returns the total number of non-null values
sum: Returns the sum of all values
Avg: Returns the average of all values
Max: Returns the maximum value
Min: Returns the minimum value
Unique: Returns a list of unique values

Providing one or multiple keys yields more specific results.

const { rows } = await client.findTableRows({
  table: 'Data1Table',
  group: {
    someField: 'key', // Either provide a single operation
    someNumber: ['avg', 'sum'] // Or multiple operations
  }
})

// Returns multiple rows for each unique combination of keys
const result = rows.map((row) => {
  const { someFieldKey, someNumberAvg, someNumberSum } = row
})

Update Table Rows

This operation updates rows based solely on their numeric IDs.

const { rows, errors, warnings } = await client.updateTableRows({
  table: 'Data1Table',
  rows: [
    {
      id: 1,
      name: 'test'
    }
  ]
})

Delete Table Rows

Rows can be deleted from a table in three distinct manners, each mutually exclusive.

By specifying row IDs
By applying a filter
By removing all rows

const { deletedRows } = await client.deleteTableRows({
  table: 'Data1Table',
  // Specify a list of IDs
  ids: [1, 2, 3],
  // Or use a filter (caution: all rows matching the filter will be deleted. It's advisable to first test with the findTableRows query)
  filter: { name: 'John Doe' },
  // Deletes every row within this table, irreversible action
  deleteAllRows: true
})

Upsert Table Rows

The upsert operation is a combination of insert or update, allowing you to insert new rows or update existing ones. The operation is based on the row’s keyColumn.

keyColumn: The column used to identify the row. This column must be unique. Failure occurs if more than one row is found for the same key.

const { rows, inserted, updated, errors, warnings } = await client.upsertTableRows({
  table: 'Data1Table',
  keyColumn: 'email',
  rows: [
    {
      email: '[email protected]', // will update this row, or create a new one if not found
      name: 'test'
    }
  ]
})

Create Table

To create a table, simply provide a name and a schema. The naming convention follows the same rules as those applied within the studio. Initially, the schema can be empty and populated or modified later. The schema accepts either an example object, from which it will infer the structure, or a directly provided JSON schema.

const { table } = await client.createTable({
  name: 'Data1Table',
  schema: {
    name: 'test'
  }
})

Example using a JSON schema. A field marked as ‘searchable’ will be indexed, enhancing its availability for search queries. Note, ‘index’ is a system-managed, read-only attribute.

const { table } = await client.createTable({
  name: 'Data1Table',
  schema: {
    type: 'object',
    'x-zui': {},
    properties: {
      name: {
        type: 'string',
        'x-zui': {
          index: 0,
          searchable: true
        },
        nullable: true
      }
    },
    additionalProperties: true
  }
})

Update Table

You can update a table’s name, schema, or both. Providing only one of these attributes is also permissible. Should you alter the type of an existing column, be advised that all its entries will default to NULL.

Adding a new field

const { table } = await client.updateTable({
  table: 'Data1Table',
  schema: {
    myNewField: 'abc', // Direct value assignment
    // Or specify with a JSON schema
    myNewField: { type: 'string' }
  }
})

Removing an existing field:

const { table } = await client.updateTable({
  table: 'Data1Table',
  schema: {
    myOldField: null // Field removal
  }
})

Renaming the table

A table name must end with “Table”.

await client.updateTable({ table: 'Data1Table', name: 'myNewTable' })

Rename Table Column

To rename a table column, specify both the current and new names. The column’s data type and attributes will remain unchanged.

const { table } = await client.renameTableColumn({
  table: 'Data1Table',
  name: 'My Old Column Name',
  newName: 'New Column Name'
})

Get Table

To retrieve a table’s schema, use the getTable function. This will return the table definition, along with the total number of rows, number of stale rows and number of indexing rows.

const { table, indexing, rows, stale } = await client.getTable({ table: 'Data1Table' })

Delete Table

To delete a table, use the deleteTable function. This will permanently remove the table and all its records.

const { table } = await client.deleteTable({ table: 'Data1Table' })

Operations on Tables - via Code

createRecord

To create a new record in a table, use the createRecord function. You can specify the values for each field in the record. This function can be particularly useful when you need to store new information, such as a new user’s details or a newly completed transaction.

// Create a record in the table
await Data1Table.createRecord({ Age: 25, Name: 'Tom', Occupation: 'Student' })

createRecords

To create multiple records in a table, use the createRecords function. You can specify the values for each field in the record. Useful for creating multiple records at once.

// Creating two records in the table
await Data1Table.createRecords([
  { Name: 'Tom', Age: 25, Occupation: 'Student', 'Is Friendly': true },
  { Name: 'Didier', Age: 54, Occupation: 'Pilot', 'Is Friendly': true },
])

deleteRecord

To delete a record in a table, use the deleteRecord function. You need to specify the record ID of the record you want to delete.

// Delete a record in the table at record ID 7
await Data1Table.deleteRecord(7)

deleteRecords

To delete multiple records in a table, use the deleteRecords function. You need to specify the record IDs of the records you want to delete in an array.

 // Delete records in the table at record IDs 7, 8, 9
await Data1Table.deleteRecords([7, 8, 9])

findRecords

To find records in a table, use the findRecords function. You can add filters, just query everything or search using semantic search (like a Google Search!).

Basic usage and pagination

To retrieve data from a table in Botpress, you can use the findRecords function. This function allows you to specify the limit (number of records to return) and the offset (number of rows to start from).

For instance, if you want the first 10 records, you can set the limit to 10 and the offset to 0. To fetch the next 10 records, you can set the limit to 10 and the offset to 10.

To determine if there are more records available, you can check the length of the array returned. If the length is greater than 0, it indicates the presence of additional records.

Here’s an example:

const limit = 10
const startRow = 0

workflow.records = await DataTable1.findRecords({
  limit,
  offset: startRow,
})

// Checking the second page
const secondPage = await Data1Table.findRecords({
  limit,
  filter: AI`everything`,
  offset: startRow + limit,
})

workflow.hasNextPage = secondPage.length > 0 // This flag can be consumed in your bot flow to handle pagination

You can apply a similar approach to determine if there are previous pages as well.

Searching fields with natural language

To perform a traditional search, use the search parameter when calling the DataTable1.findRecords() function in Botpress. Here is an example:

workflow.records = await DataTable1.findRecords({ search: 'red velvet chairs' })

You can pass variables like event.preview to the search parameter to search for user input or any other variable. This allows you to preprocess the user’s input before performing the search.

Filtering results

You can filter results using filters. Similar to MongoDB, this code provides methods to filter results. The filtering mechanism can be divided into two main categories: Primitive and Logical.

Don’t want to deal with code? Skip to the next section for AI generated filters.

Primitive Filter

Primitive filters are easy and simple to use, allowing you to match the exact values you are looking for. Here are some of the primitives you can use:

$eq: Matches values that are equal to a specified value.
```
{ "name": { "$eq": "John" } }
```
$gt: Matches values that are greater than a specified value.
```
{ "age": { "$gt": 20 } }
```
$gte: Matches values that are greater than or equal to a specified value.
```
{ "age": { "$gte": 20 } }
```
$lt: Matches values that are less than a specified value.
```
{ "age": { "$lt": 20 } }
```
$lte: Matches values that are less than or equal to a specified value.
```
{ "age": { "$lte": 20 } }
```
$ne: Matches all values that are not equal to a specified value.
```
{ "name": { "$ne": "John" } }
```
$in: Matches any of the values specified in an array.
```
{ "name": { "$in": ["Alice", "Bob", "John"] } }
```
$nin: Matches none of the values specified in an array.
```
{ "name": { "$nin": ["Alice", "Bob", "John"] } }
```
$exists: Matches documents that have the specified field.
```
{ "name": { "$exists": true } }
```
$mod: Performs modulo operation on the value of field and matches documents where field % divisor equals the specified remainder.
```
{ "qty": { "$mod": [4, 0] } }
```
$size: Matches any document where an array field contains a specified number of elements.
```
{ "tags": { "$size": 3 } }
```
$regex: Provides regular expression capabilities for pattern matching strings in queries. It uses a placeholder to prevent SQL injections.
```
{ "name": { "$regex": "[a-z]" } }
```

$options: Modifies the $regex operator to enable options. Currently, there are two options, ‘i’ for case insensitive and ‘c’ for case sensitive.

{ "name": { "$regex": "[a-z]", "$options": 'i' } } // case insensitive

{ "name": { "$regex": "[a-z]", "$options": 'c' } } // case sensitive

Logical Filter

Logical operators allow you to connect more filtering clauses.

$and: Joins query clauses with a logical AND. Returns all documents that match the conditions of both clauses.
```
{ "$and": [{ "price": { "$ne": 1.99 } }, { "price": { "$exists": true } }] }
```
$or: Joins query clauses with a logical OR. Returns all documents that match the conditions of either clause.
```
{ "$or": [{ "price": { "$eq": 1.99 } }, { "price": { "$exists": false } }] }
```
$not: Inverts the effect of a query expression and returns documents that do not match the query expression.
```
{ "$not": { "price": { "$eq": 1.99 } } }
```

Please note that these filters and operators can be used together to create complex queries to exactly fit your needs. You can also nest filters like so :

{
   "$and": [
      { "price": { "$lte": 19.99 }},
      { "$or":  [
         {"category": { "$eq": "Shoes" }},
         {"category": { "$eq": "Hats"}}
        ]
      }
   ]
 }

If you have a column of type object, you can also use operators on nested paths:

{ 
  "customer.name": { "$regex": "[a-z]" },
  "customer.age": { "$gte": 18 }
}

Using AI to generate filters

Thankfully, Botpress comes with artificial intelligence (AI) capabilities when it comes to filtering records. This means you can provide a human-like query, and the AI will intelligently decipher it and create the right filters for your query.

This can significantly simplify the construction of complex queries and reduce the likelihood of human errors in the process.

const leads = await LeadsTable.findRecords({
  filter: AI`works either at ${workflow.company} OR is not in IT`,
  limit: 10,
  offset: 0,
})

The query is written inside the backticks (`) that come after `filter: AI`. The AI expects a query string, and that’s why backticks are used.

Re-using filters (selectors)

Selectors are filters that you can re-use. They don’t have to be written again and again.

For example, you might use the query selector “People with age greater than 18” to find all users who are above 18.

Create a query selector in the table and give it a name. (e.g. adults)
Add query as People with age greater than 18
use below code to find records and save it in a variable called adults

// Find records in the table
const data = await Data1Table.findRecords({ selectorName: 'adults' })
workflow.adults = data

Difference between search and AI filters

A standard Search in Botpress is employed to identify specific strings like a user’s name. It gives precision-based results by matching specific sets of characters.

On the other hand, an AI Filter broadens the scope and is used primarily for filtering results rather than explicitly searching them. It helps you track down users based on certain criteria, such as age above 25.

Although the AI Filter is not primarily designed for exact string hunting, it’s flexible enough to allow it. For instance, you could use it to filter results for Canada as a country.

getRecord

You can use the getRecord function to retrieve a specific record from a table based on a unique identifier. For example, you might use the getRecord function to retrieve a user’s profile information based on their unique user ID.

// 1 is the record ID and User is the variable name
const data = await Data1Table.getRecord(1)
workflow.User = data

updateRecord

You can use the updateRecord function to modify a specific record in a table. This will be useful when you need to update a specific field in the record. For example, you might use this function to update a user’s profile information or to update the status of a transaction.

// Update at record ID 8's Age to 45
await Data1Table.updateRecord(8, { Age: 45 })

upsertRecords

The upsertRecords function allows you to update or create records in a table in a single operation. It is useful for any situation where you need to update or insert data into a table without having to worry about whether the rows already exist.

Example:

// Update the name and address of the records with IDs 1 and 2.
await CustomersTable.upsertRecords('id', [
  { id: 1, name: 'John Doe', address: '123 Main Street' },
  { id: 2, name: 'Jane Doe', address: '456 Elm Street' },
])

This code will upsert the records into the CustomersTable. If the records with IDs 1 and 2 already exist in the table, the function will update the existing rows with the new name and address values. If the records with IDs 1 and 2 don’t already exist in the table, the function will insert new rows into the table with the new name and address values.

Computed Columns

Columns can be marked as “Computed” which allows the bot builder to observe changes in tables and generate computed values. This includes leveraging AI, as well as running code. This can be useful for a wide variety of use cases such as CRM enrichment, sentiment analysis, classification.

To use computed columns, simply create a column and toggle the Computed field as shown below.

Selecting the basis of computation

To compute columns, you must select what information you will base your computed columns on. To do so, click the Dependencies field and select all column names that you will use.

In the above example, we selected a column called number.

AI-based computed columns

You can perform AI text generation tasks by filling the prompt and including rows like this {{propertyName}}.

Come up with the title for a story like the three little pigs for the number {{number}}

In the above example, for the number 3, you might get “The Three Clever Squirrels” as a value in the cell.

Code-based computed columns

To run arbitrary code, select the Execute code tab. Then, click Edit Code.

You will see a window with a code editor.

Simply add your code above. The proper syntax for accessing columns you’ve identified as dependencies is row[propertyName], and what is displayed as the cell value is what is returned by this function. You can call asynchronous functions within the function, and have the axios library available to perform HTTP requests.

For instance, the following code will return the fibonacci number of the row value for the column number.

function fibonacci(n) {
  if (n <= 1) {
    return n
  }
  return fibonacci(n - 1) + fibonacci(n - 2)
}

if (!row.number) {
  return 0;
}

return fibonacci(row.number)

There is currently an execution timeout of 2 minutes these.

Updating computed values

To ensure computed values are always kept up to date, we recommend Auto-compute be toggled on.

If you do not have Auto-compute enabled, a grey icon will appear next to stale table cells.

To update the stale data, simply re-activate Auto-compute.

Importing and Exporting

Import a Table

You can import a table from a CSV file. Click on the Import Table button above the table and select the CSV file you want to import. Botpress will automatically detect the column names and types from the CSV file and create the table structure for you.

Export a Table

Click on the Export Table button to export the table. This will download the table as a CSV file to your computer.

Reminder

Don’t edit the CSV file directly to avoid data loss and import errors. Instead, make the changes in Botpress Studio and then export the updated table again.

Note

When you export a bot the tables are exported as well so there is no need to export them separately unless you want to use them in another bot.

Get Started

Guides

Reference

​Creating a Table

​Making Fields Searchable

​Types of Fields (Columns)

​Filtering Records

​Logical Operators

​Types of Rules

​Rule Groups

​Creating a Filter

​Manually Editing Filters

​Operations on Tables - via the Interface

​Add records to a table

​Update records in a table

​Delete records from a table

​Operations on Tables - via Cards

​Operations on Tables - via Botpress Client

​listTables

​Create Table Rows

​Find & Filter Table Rows

​Projection & Aggregation

​Update Table Rows

​Delete Table Rows

​Upsert Table Rows

​Create Table

​Update Table

​Adding a new field

​Removing an existing field:

​Renaming the table

​Rename Table Column

​Get Table

​Delete Table

​Operations on Tables - via Code

​createRecord

​createRecords

​deleteRecord

​deleteRecords

​findRecords

​Basic usage and pagination

​Searching fields with natural language

​Filtering results

​Primitive Filter

​Logical Filter

​Using AI to generate filters

​Re-using filters (selectors)

​Difference between search and AI filters

​getRecord

​updateRecord

​upsertRecords

​Computed Columns

​Selecting the basis of computation

​AI-based computed columns

​Code-based computed columns

​Updating computed values

​Importing and Exporting

​Import a Table

​Export a Table

Creating a Table

Making Fields Searchable

Types of Fields (Columns)

Filtering Records

Logical Operators

Types of Rules

Rule Groups

Creating a Filter

Manually Editing Filters

Operations on Tables - via the Interface

Add records to a table

Update records in a table

Delete records from a table

Operations on Tables - via Cards

Operations on Tables - via Botpress Client

listTables

Create Table Rows

Find & Filter Table Rows

Projection & Aggregation

Update Table Rows

Delete Table Rows

Upsert Table Rows

Create Table

Update Table

Adding a new field

Removing an existing field:

Renaming the table

Rename Table Column

Get Table

Delete Table

Operations on Tables - via Code

createRecord

createRecords

deleteRecord

deleteRecords

findRecords

Basic usage and pagination

Searching fields with natural language

Filtering results

Primitive Filter

Logical Filter

Using AI to generate filters

Re-using filters (selectors)

Difference between search and AI filters

getRecord

updateRecord

upsertRecords

Computed Columns

Selecting the basis of computation

AI-based computed columns

Code-based computed columns

Updating computed values

Importing and Exporting

Import a Table

Export a Table