> ## Documentation Index
> Fetch the complete documentation index at: https://docs.flowx.ai/llms.txt
> Use this file to discover all available pages before exploring further.

# Knowledge Base integration

> Learn how to create and manage FlowX Knowledge Bases to provide AI agents with contextual information from static documents and dynamic data feeds.

## Overview

The FlowX Knowledge Base integration enables you to ingest static documents into contextual Knowledge Bases that AI agents can query when preparing responses. This integration provides a centralized repository for information that can be semantically searched and retrieved by AI agents during workflow execution.

<Info>
  Knowledge Bases use vector embeddings and semantic search to find the most relevant information for AI agent queries.
</Info>

## Key features

* **Centralized content management**: Upload documents into organized Knowledge Bases
* **Multiple stores**: Manage content by splitting it across different stores for better organization
* **Semantic search**: AI agents can find relevant information using natural language queries
* **Multiple search modes**: Choose between hybrid, semantic, or keyword search algorithms
* **Testing capabilities**: Test queries and operations in isolation before adding them to workflows
* **Content versioning**: Append, replace, or delete stores with full traceability
* **Relevance scoring**: Understand which content chunks are most relevant for agent responses
* **Audit trail**: Track all store operations with user and timestamp information
* **Version tracking**: Knowledge Base entries are tied to app versions for consistency

## How it works

```mermaid theme={"system"}
graph LR
    A[Upload Documents] -->|Ingest| B[Knowledge Base]
    B -->|Chunk & Index| D[Vector Database]
    E[Custom Agent Node] -->|Query| D
    D -->|Return Chunks| E
    E -->|Generate Response| F[AI Service]
```

### High-level workflow

1. **Create Knowledge Base**: Add a new Knowledge Base data source in Integration Designer
2. **Ingest content**: Upload documents into stores
3. **Automatic chunking**: Content is automatically split into chunks and indexed for semantic search
4. **Query in workflows**: AI agents query the Knowledge Base to find relevant information
5. **Monitor usage**: Track which chunks are used and their relevance scores in console logs

## Main capabilities

### Content ingestion

<CardGroup cols={2}>
  <Card title="Manual upload" icon="upload">
    Upload PDF documents directly from the Knowledge Base admin interface
  </Card>
</CardGroup>

### Content management

<Steps>
  <Step title="Organize by stores">
    Manage content by organizing it into separate stores for better control and traceability
  </Step>

  <Step title="Append or replace">
    Update existing stores by appending new information or replacing it entirely
  </Step>

  <Step title="Delete when needed">
    Remove stores and all associated chunks when they're no longer needed
  </Step>
</Steps>

### AI agent integration

AI agents can use Knowledge Bases with the ReAct (Reasoning and Acting) model to:

* Find relevant information based on user queries
* Understand which stores provided the information
* See relevance scores for retrieved chunks
* Make informed decisions based on contextual knowledge

## Stores

Stores represent individual collections of information within a Knowledge Base. Each store can be:

* **Manual**: Files uploaded through the Knowledge Base admin interface
* **Workflow**: Content ingestion through workflow operations. The Update Knowledge Base node accepts a file path, so workflows can ingest content uploaded earlier in the run (5.8.0+)

<Frame>
  ![Knowledge Base Stores](https://s3.eu-west-1.amazonaws.com/docx.flowx.ai/5.6/kb_stores.png)
</Frame>

### Store lifecycle

```mermaid theme={"system"}
stateDiagram-v2
    [*] --> New: Create
    New --> Processing: Upload/Ingest
    Processing --> Ready: Complete
    Processing --> Failed: Error
    Ready --> Processing: Update
    Ready --> Failed: Partial failure
    Ready --> MarkedForRemoval: Delete
    Failed --> Processing: Retry
    MarkedForRemoval --> [*]
```

Stores progress through the following states:

| State          | Description                                                       |
| -------------- | ----------------------------------------------------------------- |
| **New**        | Store created, awaiting content                                   |
| **Processing** | Content is being uploaded, chunked, and indexed                   |
| **Ready**      | Content is fully processed and available for queries              |
| **Failed**     | Processing encountered an error, or content is partially degraded |
| **Deleting**   | Store is scheduled for deletion                                   |

### Audit trail

All store operations are tracked in the audit trail with full traceability:

| Field              | Description                                                                                              |
| ------------------ | -------------------------------------------------------------------------------------------------------- |
| **Operation type** | `ADD_CONTENT_SOURCE`, `REPLACE_CONTENT_SOURCE`, `DELETE_CONTENT_SOURCE`, `ADD_CONTENT`, `REMOVE_CONTENT` |
| **Triggered by**   | How the operation was initiated: Manual, API, Workflow, or Test                                          |
| **User**           | The user who performed the operation (for manual operations)                                             |
| **Status**         | Final operation state: Completed or Failed                                                               |
| **Timestamp**      | When the operation occurred                                                                              |

<Tip>
  Use the audit trail to track who made changes to Knowledge Base content and when, which is useful for compliance and troubleshooting.
</Tip>

## Search modes

Knowledge Bases support three search algorithms that determine how queries are matched against stored content:

| Search Mode  | Description                                                                     | Best For                                                          |
| ------------ | ------------------------------------------------------------------------------- | ----------------------------------------------------------------- |
| **Hybrid**   | Combines semantic and keyword search for balanced results                       | General-purpose queries where both meaning and exact terms matter |
| **Semantic** | Uses vector embeddings to match by meaning, not exact words                     | Natural language questions, conceptual queries                    |
| **Keyword**  | Term-based matching that also finds closely related words, not just exact terms | Precise lookups, technical terms, codes, identifiers              |

<Tip>
  Start with **Hybrid** search mode for most use cases. Switch to Semantic for conversational queries or Keyword for exact term lookups.
</Tip>

***

## Chunks and search

Chunks are small snippets of content that are indexed in the vector database for semantic search. When an AI agent queries a Knowledge Base, the most relevant chunks are returned based on:

* **Content similarity**: Semantic meaning of the query vs. chunk content
* **Relevance score**: Percentage indicating how relevant the chunk is (0-100%)
* **Metadata filters**: Optional filters built from typed operators and AND/OR logic — see [Filtering by metadata](./managing-content#filtering-by-metadata)

<Tip>
  You can test different query parameters to see which chunks are returned and their relevance scores before using the Knowledge Base in production workflows.
</Tip>

## Use cases

### Product documentation assistant

Create a Knowledge Base with product documentation and allow AI agents to answer customer questions based on the latest documentation.

### Policy compliance

Upload company policies and compliance documents. AI agents can reference these when processing requests to ensure compliance.

### Dynamic knowledge updates

Upload updated documents to Knowledge Bases to keep AI agent knowledge current. Content can be managed manually through the admin interface or programmatically through workflows and APIs.

### Multi-source information synthesis

Organize information across multiple stores and let AI agents synthesize information from different sources to provide comprehensive answers.

## Limitations

<Warning>
  **Current limitations:**

  * AI agents can only use one Knowledge Base per Custom Agent node
  * Documents received from integrations cannot be directly ingested (Document Plugin links are treated as strings)
  * Store renaming is not available
</Warning>

***

## Creating a knowledge base

### Prerequisites

Before you begin, ensure you have:

* Access to FlowX Designer with appropriate permissions
* A project where you want to add the Knowledge Base
* Content ready to ingest (PDF documents or JSON data)

### Setup steps

To add a new Knowledge Base data source:

<Steps>
  <Step title="Navigate to Integration Designer">
    Go to **FlowX Designer** → **Workspaces** → **Your workspace** → **Projects** → **Your project** → **Integrations** → **Data Sources**
  </Step>

  <Step title="Create new data source">
    Click **Add New Data Source** and select **FlowX Knowledge Base** as the data source type
  </Step>

  <Step title="Configure basic settings">
    Fill in the required fields with the following information
  </Step>
</Steps>

<Frame>
  ![Add Knowledge Base](https://s3.eu-west-1.amazonaws.com/docx.flowx.ai/5.x/add_kb.png)
</Frame>

### Configuration fields

#### Name

The unique identifier for your Knowledge Base within the project.

<ParamField path="Name" type="string" required>
  **Validation rules:**

  * **Required**: Field cannot be empty
  * **Uniqueness**: Must be unique within the project
  * **Special characters**: Only letters, numbers, and the following characters are allowed: `[]`, `()`, `.`, `_`, `-`
  * **Length**: Minimum 3 characters, maximum 50 characters
</ParamField>

#### Description

An optional description explaining the purpose and contents of the Knowledge Base.

<ParamField path="Description" type="string">
  Provide additional context about what information this Knowledge Base contains and how it should be used.
</ParamField>

### Example configuration

Here's an example of a well-configured Knowledge Base:

```yaml theme={"system"}
Name: Product Documentation KB
Description: Contains all product documentation, user guides, and FAQ documents for the customer support AI agent
```

### After creation

Once you've created a Knowledge Base, you'll see three tabs:

<CardGroup cols={3}>
  <Card title="Stores" icon="folder">
    View and manage all stores ingested into the Knowledge Base
  </Card>

  <Card title="Chunks" icon="cube">
    Search and view the individual chunks created from your content
  </Card>

  <Card title="Metadata" icon="tags">
    Define custom metadata keys used to filter and scope retrieval
  </Card>
</CardGroup>

<Frame>
  ![Knowledge Base Dashboard](https://s3.eu-west-1.amazonaws.com/docx.flowx.ai/5.x/kb_dashboard.png)
</Frame>

***

## Next steps

<CardGroup cols={3}>
  <Card title="Managing Content" href="./managing-content" icon="folder">
    Upload documents and manage stores
  </Card>

  <Card title="Using in Workflows" href="./using-knowledge-base-in-workflows" icon="workflow">
    Query Knowledge Bases from workflows
  </Card>

  <Card title="Testing Operations" href="./testing-knowledge-base" icon="flask">
    Test queries and operations before production use
  </Card>
</CardGroup>

## Related resources

<Card title="Integration Designer" href="../../integration-designer/designer-overview" icon="link">
  Integration Designer and data sources
</Card>

<Card title="Custom Agent Nodes" href="../../ai-agents/custom-agent-node" icon="link">
  Using AI agents in workflows
</Card>

<Card title="Integrations Overview" href="../../integrations-overview" icon="link">
  Integration ecosystem overview
</Card>