langflow/docs/versioned_docs/version-1.10.0/Components/knowledge-base.mdx

---
title: Knowledge Base
slug: /knowledge-base
---

import Icon from "@site/src/components/icon";
import Tabs from '@theme/Tabs';
import TabItem from '@theme/TabItem';
import PartialParams from '@site/docs/_partial-hidden-params.mdx';
import PartialKbSummary from '@site/docs/_partial-kb-summary.mdx';
import PartialLegacy from '@site/docs/_partial-legacy.mdx';

<PartialKbSummary />

The **Knowledge Base** component reads from and writes to knowledge bases using a mode selector.

Select **Ingest** mode to embed and index data into a knowledge base, or **Retrieve** mode to search an existing knowledge base using semantic search.

The output for both modes is a [`Table`](/data-types#table) containing the results.

## Knowledge Base parameters

<PartialParams />

The following parameters are shared across both modes.

| Name | Display Name | Info |
|------|--------------|------|
| mode | Mode | Input parameter. Tab selector that switches the component between **Ingest** and **Retrieve** modes. |
| knowledge_base | Knowledge | Input parameter. Select the knowledge base to ingest data into or retrieve data from. |

<Tabs>
<TabItem value="ingest" label="Ingest mode">

| Name | Display Name | Info |
|------|--------------|------|
| input_df | Input | Input parameter. Table with all original columns (already chunked or processed). Accepts Message, Data, or DataFrame. |
| column_config | Column Configuration | Input parameter. Configure column behavior. Use the **Vectorize** flag to create embeddings for a column, and the **Identifier** flag to use a column as a unique identifier. |
| api_key | Embedding Provider API Key | Input parameter. Optional. Overrides the globally configured API key for the embedding provider. Leave blank to use the pre-configured key. |
| chunk_size | Chunk Size | Input parameter. Batch size for processing embeddings. Default: `1000`. |
| allow_duplicates | Allow Duplicates | Input parameter. If enabled, allows duplicate rows in the knowledge base. Default: Disabled (false). |
| metadata_json | Metadata | Input parameter. Optional JSON object of user metadata applied to every chunk in this run (for example, `{"tag": "invoice", "year": "2026"}`). This metadata is compatible with the **Metadata Filter** parameter in Retrieve mode. Malformed JSON is ignored with a warning. |

</TabItem>
<TabItem value="retrieve" label="Retrieve mode">

| Name | Display Name | Info |
|------|--------------|------|
| search_query | Search Query | Input parameter. Optional search query to filter knowledge base data using semantic similarity. If omitted, the top results are returned. |
| api_key | Embedding Provider API Key | Input parameter. Optional API key for the embedding provider to override a previously-provided key. The embedding provider and model are chosen when you create a knowledge base. |
| top_k | Top K Results | Input parameter. Number of search results to return. Default: `5`. |
| include_metadata | Include Metadata | Input parameter. Whether to include all metadata in the output. If enabled, each output row includes all metadata and content. If disabled, only the content is returned. Default: Enabled (true). |
| include_embeddings | Include Embeddings | Input parameter. Whether to include raw embedding vectors in the output. Only applicable when **Include Metadata** is enabled. Default: Disabled (false). |
| metadata_filter | Metadata Filter | Input parameter. Optional JSON object of key/value pairs to filter results by user metadata (for example, `{"tag": "invoice"}` or `{"tag": ["invoice", "audit"]}` for OR-of-values matching). Backends without native filtering apply the match client-side after retrieval. |

</TabItem>
</Tabs>

## Use the Knowledge Base component in a flow

<Tabs>
<TabItem value="ingest" label="Ingest mode">

After you create a knowledge base, you can use the **Knowledge Base** component in **Ingest** mode to populate it from a DataFrame in your flow.

1. Add a **Knowledge Base** component to your flow.

2. In the **Mode** tab, select **Ingest**.

3. In the **Knowledge** field, select the knowledge base you want to ingest into, or create a new one.

4. Connect a source component, such as a [**Read File** component](/read-file) or [**JSON Operations** component](/data-operations) to the **Input** handle to provide the DataFrame to embed.

5. In the **Column Configuration** table, configure each column:
   - Enable **Vectorize** for columns whose text should be embedded for semantic search.
   - Enable **Identifier** for columns that uniquely identify each row (used for duplicate detection).

6. Click <Icon name="Play" aria-hidden="true"/> **Run component** to embed and index the data into your knowledge base.

</TabItem>
<TabItem value="retrieve" label="Retrieve mode">

After you create and load data to a [knowledge base](/knowledge), you can use the **Knowledge Base** component in **Retrieve** mode to search it using semantic similarity.

1. Add a **Knowledge Base** component to your flow.

2. In the **Mode** tab, select **Retrieve**.

3. In the **Knowledge** field, select the knowledge base you want to search, such as the customer sales data knowledge base created in the previous steps.

4. To view the search results as chat messages, connect the **Results** output to a [**Chat Output** component](/chat-input-and-output).

5. In **Search Query**, enter a query that relates to your embedded data.

   For the customer sales data example, enter a product name like `laptop` or `wireless devices`.

6. Click <Icon name="Play" aria-hidden="true"/> **Run component** on the **Knowledge Base** component, and then open the **Playground** to view the output.

</TabItem>
</Tabs>

## See also

* [Manage vector data](/knowledge)