Managing stores

Renamed in FlowX.AI 5.7.0 — the Content Sources tab is now called Stores. The underlying concept is unchanged: each store still represents a collection of ingested, indexed content.

Overview

Stores are the building blocks of a Knowledge Base. Each store represents a collection of related information that has been ingested, processed, and indexed for semantic search.

Store types

There are two types of stores:

Manual

Files uploaded directly through the Knowledge Base admin interface

Workflow

JSON content ingestion through workflows (planned for future release)

Viewing stores

Navigate to your Knowledge Base to see all stores. The Stores tab displays:

Store information

Each row shows:

Type

string

Manual: Files uploaded in the Knowledge Base page
Workflow: Content ingestion through workflow variables (planned for future release)

Store

string

The unique name of the store within the Knowledge Base

Status

string

Current processing state:

New: Store created, awaiting content upload
Processing: Content is being uploaded, chunked, and indexed
Ready: Content is fully processed and available for queries
Failed: An error occurred during processing, or content is partially degraded

Status badges update in real-time — you don’t need to refresh the page to see status changes.

Updated by

string

Username: For files uploaded in the Knowledge Base page
Workflow name: For content ingested through workflows (planned for future release)

Updated at

timestamp

Timestamp of the last upload or workflow operation execution

Uploading documents

Navigate to Knowledge Base

Open your Knowledge Base in FlowX Designer and select the Stores tab.

Open the upload dialog

Click the + button in the top-right of the Stores tab. A file picker opens.

Select file

Choose a PDF file from your computer and click Open. The Upload Store modal appears.

Set the store name

The Name field is pre-filled with the file name (without extension). Edit it if needed — the name must be unique within the Knowledge Base.

Review metadata (optional)

The Metadata section shows the metadata schema defined for this Knowledge Base. If no metadata is defined, a “No metadata defined” placeholder appears — click Go to Metadata Setup to configure a schema on the Metadata tab before uploading. See User-defined metadata for details.

Upload

Click Upload to start the ingestion process. The store appears in the list with status Processing, and transitions to Ready once chunking and indexing complete.

File requirements

Supported formats (current release):

PDF files only

File size:

Maximum file size: 20 MB (can be changed using environment variables)

Future releases will support:

Images
PowerPoint presentations
Word documents
Excel spreadsheets

Store naming

When uploading a document, you must provide a unique store name:

Store name rules:

Must be unique within the Knowledge Base
Default name is the file name without extension
Choose descriptive names for easy identification
Consider using versioning in names (e.g., “Product Guide v2.1”)

Duplicate stores

If you select a file with the same name as an existing store, you’ll see a warning:

A Store with this name already exists. We recommend updating the existing store instead.

Available options:

Append Content: Add the new document’s content to the existing store
Replace Content: Replace the existing content with the new document
Cancel: Choose a different name or file

Managing stores

Once content is uploaded and processed, you can perform the following operations:

Append content

Add new content to an existing store without removing the existing chunks.

Select store

Click on the store you want to update

Choose Append Content

Select the Append Content operation

Upload new file

Select a new PDF file with additional content

Confirm

The new content will be processed and added to the existing chunks

Use case: Adding a new chapter to existing documentation or supplementing information without losing the original content.

Replace content

Replace all existing content in a store with new content.

Select store

Click on the store you want to replace

Choose Replace Content

Select the Replace Content operation

Upload new file

Select a new PDF file that will replace the existing content

Confirm

All existing chunks will be deleted and new chunks will be created from the new content

Replace Content will permanently delete all existing chunks from the store. This operation cannot be undone.

Use case: Updating documentation to a new version where the old content is no longer relevant.

Delete store

Remove a store and all its associated chunks from the Knowledge Base.

Select store

Click on the store you want to delete

Choose Delete

Select the Delete Store operation

Confirm deletion

Confirm that you want to permanently delete the store

Deleting a store will:

Remove all chunks generated from that content
Permanently delete the content from the Knowledge Base
This operation cannot be undone

Store states

Stores progress through different states during their lifecycle. Status badges update in real-time — you don’t need to refresh the page to see transitions.

New

The initial state after content is uploaded to the Knowledge Base. What’s happening:

File has been transferred to the FlowX platform
Content is being validated and queued for processing

Duration: Usually a few seconds, depending on file size

Processing

Content is being chunked, embedded, and indexed in the vector database. What’s happening:

Content is being extracted from the document
Text is being split into semantically meaningful chunks
Chunks are being embedded and indexed in the vector database

Duration: Can take from seconds to minutes depending on content size

Ready

Store is fully processed and available for queries. What you can do:

Query the store in AI agents
View individual chunks
Append or replace content
Delete the store

Failed

An error occurred during processing, or the store is partially degraded. What to do:

Check the error message in the Store History modal
Verify the file format and content
Retry the operation

Error handling

When processing fails, the store will show a warning indicator.

Error states and recovery

Upload failed

Cause: File upload was interrupted or the file format is invalidSolution:

Verify the file format (must be PDF)
Check your network connection
Try uploading again

Chunking failed

Cause: Error occurred while processing the contentSolution:

Check the Store History for specific error details
Verify the file is not corrupted
Contact support if the issue persists

Retry behavior

When retrying a failed operation:

Any chunks already created will be deleted
The content will be processed entirely from the beginning
No duplicate chunks will be created

Warning indicators

When a store has a failed update:

Yellow warning icon appears on the store row
Tooltip message: “Last Store update failed. Check history.”
Dismiss trigger: The warning is automatically dismissed after a successful update

Viewing store history

Store History is a low-priority feature that may be available in future releases.

Store History will show all operations performed on a store:

Operation	Description
Upload Document	Creates store and inserts content
Append Content	Adds new content to existing chunks
Replace Content	Deletes existing content and inserts new content
Delete Store	Removes the store and all chunks

Each history entry will include:

Timestamp
User or workflow name
Operation type (Manual/Workflow/Test)
Operation performed
View option (for uploaded files or JSON payloads)

Working with chunks

Chunks are the individual pieces of content that AI agents query. To view and test chunks:

Navigate to Chunks tab

Click the Chunks tab in your Knowledge Base

Search chunks

Enter a query to search for relevant chunks

Filter results

Apply filters to refine the search results

Review chunks

Examine the returned chunks, their relevance scores, and metadata

Chunk information

Each chunk displays:

Relevance Score

percentage

How relevant the chunk is to your query (0-100%)

Store

string

The store that generated this chunk (clickable link)

Submitted Content

link

Link to the original document or JSON payload that created this chunk

Chunk Content

text

The actual text content of the chunk

Chunk Metadata

object

System metadata associated with the chunk:

source: manual_upload or from_workflow
path: Document filepath or JSON payload
chunk_id: UUID from the vector database
knowledge_base: Knowledge Base ID

User-defined metadata keys (if configured) are also included.

User-defined metadata

Available starting with FlowX.AI 5.6.0

You can define custom metadata keys on a Knowledge Base and assign values to stores. User-defined metadata enables filtering and scoping when searching chunks — for example, filtering by department, document version, or region.

Defining metadata keys

Metadata keys are managed on the Metadata tab of the Knowledge Base data source.

To add a key, click Add Metadata Key and fill in the following fields:

Field	Description
Name	Unique key name. Cannot be changed after creation.
Type	Data type: `String`, `Number`, `Boolean`, `Enum`, or `Date`. Cannot be changed after creation.
Enum values	Allowed values (only visible when Type is `Enum`). Values defined at creation cannot be removed.
Description	Optional free-text description of the key’s purpose.

Name and type are locked after a key is saved. Only description and enum values (additions only) can be updated later.

Assigning metadata values

When uploading or appending a store (manually or through the Update Knowledge Base workflow node), a Metadata section appears in the upload modal listing all defined keys for the Knowledge Base. Assign a value to each relevant key before uploading. The values are stored alongside the content and propagated to the vector database.

Filtering by metadata

When searching chunks (in the Chunks tab or through the Context Retrieval workflow node), you can add metadata filters using the query builder. The system metadata key source (values: manual_upload, from_workflow, test_operation) is always available as a filter option alongside any user-defined keys.

Enhanced in FlowX.AI 5.7.0 — the metadata filter UI now supports a full query builder with typed operators, AND/OR logic, and grouping. Previous releases supported only equality filters combined with AND.

Filters are expressed as conditions made of field / operator / value. Each condition uses operators appropriate to the metadata key’s type:

Type	Available operators
String	equals, not equals, contains, starts with, ends with, matches regex, in, not in, exists, is null
Number	equals, not equals, greater than, greater or equal, less than, less or equal, between, in, exists, is null
Boolean	is true, is false, exists
Date	equals, before, after, between, exists, is null
Enum	equals, not equals, in, not in, exists

Conditions are organized in groups. Within a group, conditions can be combined with and or or. Groups themselves are also combined with and or or, which lets you express non-trivial logic such as (region = "EU" AND tier IN ["gold", "platinum"]) OR priority >= 5. Use New Filter to add a condition and New Group to add a nested group.

Searching chunks

Use the Chunks tab to test how chunks will be retrieved by AI agents:

Store

string

Filter by a specific store. Default: all stores

Query Prompt

string

Enter a natural language question or search query

Search Type

select

Strategy used for retrieving context:

Hybrid (default) — combines semantic and keyword search
Semantic — vector similarity only
Keywords — lexical match only (hides Min. Relevance Score)

Max. Number of Results

number

Maximum number of chunks to return. Range: 1-10. Default: 4.

Min. Relevance Score

percentage

Only return chunks with relevance score above this threshold. Range: 0-100%. Hidden when Search Type is Keywords.

Use Re-rank

boolean

Reorders results to surface the best matches. Improves quality but takes slightly longer.

Metadata Filters

object

Refine results using the query builder (see Filtering by metadata)

Use chunk search to understand what information AI agents will receive for different queries. This helps you optimize your Knowledge Base content and structure.

Best practices

Content organization

Organize by topic or version:

Create separate stores for different topics
Use versioning in store names (e.g., “User Guide v1.0”, “User Guide v2.0”)
Group related documents in the same store using Append Content

Content updates

Choose the right operation:

Use Append Content when adding supplementary information
Use Replace Content when updating to a new version
Test queries after updates to verify the changes work as expected

Error prevention

Avoid common errors:

Ensure PDF files are not corrupted before uploading
Use unique, descriptive names for stores
Monitor the status of stores during processing
Check Store History when errors occur

Next steps

Using in Workflows

Learn how to query Knowledge Bases from workflows

Knowledge Base Overview

Understanding Knowledge Base capabilities

Getting Started

Concepts

FlowX.AI Designer

Building projects

Building blocks

Platform deep dive

API reference

Documentation Index

​Overview

​Store types

Manual

Workflow

​Viewing stores

​Store information

​Uploading documents

​File requirements

​Store naming

​Duplicate stores

​Managing stores

​Append content

​Replace content

​Delete store

​Store states

​New

​Processing

​Ready

​Failed

​Error handling

​Error states and recovery

​Warning indicators

​Viewing store history

​Working with chunks

​Chunk information

​User-defined metadata

​Defining metadata keys

​Assigning metadata values

​Filtering by metadata

​Searching chunks

​Best practices

​Content organization

​Content updates

​Error prevention

​Next steps

Using in Workflows

​Related resources

Knowledge Base Overview

Overview

Store types

Viewing stores

Store information

Uploading documents

File requirements

Store naming

Duplicate stores

Managing stores

Append content

Replace content

Delete store

Store states

New

Processing

Ready

Failed

Error handling

Error states and recovery

Warning indicators

Viewing store history

Working with chunks

Chunk information

User-defined metadata

Defining metadata keys

Assigning metadata values

Filtering by metadata

Searching chunks

Best practices

Content organization

Content updates

Error prevention

Next steps

Related resources