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

# Vector Stores

> Create, manage, and test vector stores used for storing and retrieving embeddings in retrieval‑augmented workflows.

## Overview

The **Vector Store Management** page allows administrators to register and manage vector stores that hold embeddings used for semantic search, retrieval, and grounding. Vector stores are typically used in **RAG (Retrieval‑Augmented Generation)** scenarios and can be attached to agents, prompts, and workflows.

Vector stores are configured centrally and referenced by ID during request execution.

***

## Vector Stores List

The main Vector Stores page displays all configured vector stores for the tenant.

### Primary Actions

* **Add Vector Store**\
  Opens the configuration dialog to register a new vector store.

***

### Vector Stores Table

Each row represents a configured vector store.

**Columns include:**

* **Vector Store ID**\
  Unique identifier of the vector store (provided by the underlying provider).
* **Name**\
  Human‑friendly name for easier identification.
* **Description**\
  Optional description of the store’s purpose.
* **Provider**\
  Provider backing the vector store.
* **Created At**\
  Creation timestamp.
* **Updated At**\
  Last update timestamp.
* **Actions**\
  Edit or delete the vector store.

***

## Vector Store Details

Selecting a Vector Store ID opens the detail view.

### Details Tab

The **Details** tab displays:

* **ID** – The provider‑assigned vector store ID
* **Name** – Display name
* **Description** – Optional description
* **Provider** – Provider backing the store
* **Metadata** – Arbitrary JSON metadata
* **Created** – Creation timestamp
* **Last Updated** – Most recent update timestamp

Select **Edit Vector Store** to modify editable fields.

***

### Test Vector Store

The **Test Vector Store** tab allows you to validate connectivity and availability of the configured vector store without impacting production traffic.

Use this test during:

* Initial setup
* Credential rotation
* Troubleshooting retrieval issues

***

## Add New Vector Store

Select **Add Vector Store** to register a new vector store.

### Required Fields

#### Provider

Select the provider that backs the vector store.

The selected provider determines which additional fields are required.

***

#### Vector Store ID

Enter the vector store identifier as defined by the provider.

This value is required to reference the existing vector store during retrieval operations.

***

### Optional Fields

#### Vector Store Name

A human‑friendly name to identify the vector store in the UI.

***

#### Description

Optional text describing what data the vector store contains or how it is used.

***

#### Existing Credentials

Optionally select previously stored credentials instead of entering new connection details.

This simplifies reuse across multiple vector stores.

***

#### Metadata

Optional JSON metadata associated with the vector store.

Metadata can be used for:

* Classification
* Internal annotations
* Integration context

***

### Provider‑Specific Configuration

Depending on the selected provider, additional fields may appear, such as:

* API key
* API base URL
* Custom connector endpoints

All sensitive values are stored securely and masked after saving.

***

### Create Vector Store

Select **Create** to save the vector store configuration.

Once created, the vector store becomes available for:

* Agents
* Search and retrieval workflows
* Prompt and tool invocation scenarios

***

## How Vector Stores Are Used

Vector stores are used to:

* Store embeddings for documents or records
* Perform semantic similarity search
* Ground model responses with contextual data
* Enable retrieval‑augmented generation (RAG)

Vector stores do not generate embeddings themselves—they store and retrieve them for use during requests.

***

## Best Practices

* Use clear naming to reflect stored data or use case
* Test vector stores immediately after creation
* Reuse credentials where possible
* Attach vector stores only to trusted workflows
* Review metadata usage for maintainability
* Periodically remove unused vector stores

***

## Common Use Cases

* Knowledge‑base retrieval
* Document search
* Contextual grounding for agents
* Internal or external semantic search
* Enterprise RAG implementations

***

## Related Sections

* **Agents** – Use vector stores for retrieval
* **Search Tools** – Combine search with vector retrieval
* **Prompts** – Include retrieved context in prompts
* **Models** – Generate embeddings and responses
* **Logs** – Debug vector‑backed requests