> ## 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, agents, and context-grounded fine-tuning.

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

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 available options
  </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