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

> ## Agent Instructions
> Choose User guide for browser tasks, Developers for CLI, SDK and external agents, and Administration for organization management.
> Confirm identity and organization/project context before operations. Documentation access does not grant ThinkPlatform permissions.
> Review side effects, verify changes with a read, and do not blindly retry an operation with an uncertain outcome.
> Use documented UI steps for the web app and documented public CLI or SDK interfaces for automation. Follow each page’s prerequisites and authentication requirements.

# Work with tables and vector stores

> Manage structured records and prepare documents for vector indexing.

**Tables** holds structured records and views. **Vector** provides stores for document indexing when the required services are configured.

## Before you begin

Select the intended organization and project. Reading, creating, editing, and deleting resources can require different permissions. Vector stores require the configured search and storage services.

## Work with a table

1. Open **DB → Tables**, choose **New table**, and enter `reading_list` as **Table name**. Set the first column to `title` with type **Text**, then use **Add column** to add `owner` with type **Text**. Create the table.
2. In the empty row, enter `Ocean report` and `Avery`, then save the row. Add a second row, `Energy brief` and `Morgan`, and save it.
3. Search for `Ocean report` and check that the first row displays its owner. Clear the search to see both rows. Before using **Delete selected**, confirm the selected records; deletion changes the data.

For programmatic operations, see [CLI database tables](https://docs.thinkplatform.ai/cli/guides/database-tables.md).

## Prepare a vector store

1. Prepare a small text file named `sample.txt` containing `The blue turbine is inspected on Tuesday.` Use only data approved for the project.
2. Open **DB → Vector**, choose **Create vector store**, enter `inspection-example` as **Store name**, and use the managed Vector Store folder. Create the store, select it, and use **Upload files** to upload `sample.txt`.
3. Check the file list and indexing status. Upload and indexing are separate, and indexing can take time or fail. To test retrieval, an authorized agent can be configured with this store under **Vector DB** and asked `When is the blue turbine inspected?`; inspect its answer and supporting result against the source sentence. The store view itself does not provide a query control, and this test requires an agent with access and a working search service. If those prerequisites are unavailable, indexing status alone cannot confirm retrieval.

Deleting a store can delete search assets and, depending on the source choice, managed source files. Read the confirmation carefully. Deleting documents also removes them from the search index during a subsequent indexer run.

## Troubleshoot

Check the selected project, your permissions, and the displayed error. If documents appear in the file list but are not searchable, inspect indexing status and consult your administrator about the search service.


This documentation is built and hosted on [Mintlify](https://mintlify.com), a developer documentation platform.