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

# Vector Stores

> Create semantic indexes from your documents to power retrieval and agents.

Vector stores are semantic indexes built from your documents. They allow language models to retrieve contextually relevant information, powering workflows like agents and assistants.

Each chunk of your documents is converted into a vector embedding, making content searchable by semantic similarity rather than keywords.

## Create a vector store

<Steps>
  <Step>
    Navigate to **Data Engine > Storage**, then select the **Vector Stores** tab.
  </Step>

  <Step>
    Click **Create Vector Store**.
  </Step>

  <Step>
    Enter the vector store details:

    * **Name** (required, 100 characters max)
    * **Description** (optional, 500 characters max)
    * **Embedding model** – select from the available models, each shown with its country of origin

    Choose the embedding model before creating the store. It is fixed once the store exists and cannot be changed afterward without re-embedding your files. For each model's dimensions, input limits, language support, and country of origin, see [Embedding models](/flow/components/models/embedding-models).
  </Step>

  <Step>
    Click **Create Vector Store**.
  </Step>
</Steps>

The new store appears in the vector stores list with its name, file count, total size, and date modified.

## Vector store details

Click any vector store to open its details view, which displays:

* **ID**
* **Model** – the embedding model used
* **Size**
* **File Count**
* **Date Created**
* **Last Modified**
* **Description** – editable after creation
* Files currently embedded in the store
* Option to add more files

## Add files to a vector store

<Steps>
  <Step>
    Open the vector store details view.
  </Step>

  <Step>
    Click **Add Files**.
  </Step>

  <Step>
    In the modal, use **Select Files** to choose from existing files in File Storage, or switch to **Upload Files** to upload new files directly.
  </Step>

  <Step>
    Optionally expand **Advanced Embedding Options** to configure chunking behavior.
  </Step>

  <Step>
    Click **Add Files** to confirm.
  </Step>
</Steps>

Supported file types: `.pdf`, `.docx`, `.ppt`, `.md`, `.json`

Upload limits: 20 files at a time, maximum 150MB per file.

<Note>
  `.jsonl` and `.parquet` files cannot be added to a vector store.
</Note>

### Advanced Embedding Options

Expand this section to configure how content is segmented before embedding:

* **Chunk Size (Tokens)** — Maximum token count per chunk. Default: 800.
* **Chunk Overlap (Tokens)** — Token overlap between adjacent chunks to reduce information loss at boundaries. Default: 400.

## File processing

Once you click **Add Files**, SeekrFlow handles the rest automatically:

1. **Ingesting** — Converts compatible files to Markdown if needed.

   <Info>
     **Ingestion mode**

     Files added through the UI are always ingested using speed-optimized mode. To use accuracy-optimized ingestion, use the SDK instead.
   </Info>
2. **Chunking** — Breaks content into segments based on your settings.
3. **Embedding** — Transforms each chunk into a vector.
4. **Indexing** — Stores vectors into the semantic index.

When processing completes, files appear in the details view with their name, processing status, and size. If a file fails to process, an error is shown with guidance for resolution.

## File status

| Status | Description |
| - | - |
| Queued | File is waiting to be processed |
| Ingesting | File is being converted to Markdown |
| Chunking | Content is being segmented |
| Embedding | Chunks are being converted to vectors |
| Complete | File is indexed and ready for retrieval |

## Manage vector stores

From the details view you can add or remove files, monitor file processing status, and rebuild the store when content changes.

## Next steps

* [AI-Ready Data](/flow/app/ai-ready-data) – Generate context-grounded training data using a vector store
* [Create an agent](/flow/app/create-an-agent) – Attach a vector store to an agent for retrieval-augmented memory


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