n8n-nodes-openai-vectorstore

!n8n.io - Workflow Automation
!npm
!License

This is an n8n community node to manage OpenAI Vector Stores for semantic search and RAG (Retrieval-Augmented Generation) applications.

n8n is a fair-code licensed workflow automation platform.

Features

Full implementation of OpenAI Vector Stores API:

$3

| Operation | Description |
|-----------|-------------|
| Create | Create a new vector store with custom chunking strategy |
| Get | Retrieve vector store details |
| Get Many | List all vector stores with pagination |
| Update | Modify name, description, expiration policies |
| Delete | Remove a vector store |
| Search | Semantic search with filters, ranking, and score threshold |

$3

| Operation | Description |
|-----------|-------------|
| Create | Add a file with custom attributes for filtering |
| Get | Retrieve file information |
| Get Content | Get parsed content of a file |
| Get Many | List files with status filter |
| Update | Update file attributes/metadata |
| Delete | Remove file from vector store |

$3

| Operation | Description |
|-----------|-------------|
| Create | Add multiple files in batch |
| Get | Check batch status |
| Cancel | Cancel batch operation |
| List Files | List files in a batch |

Installation

$3

1. Go to Settings > Community Nodes
2. Select Install
3. Enter n8n-nodes-openai-vectorstore
4. Click Install

$3

``bash


In your n8n installation directory


npm install n8n-nodes-openai-vectorstore
`

Credentials

You need an OpenAI API key:

1. Go to OpenAI API Keys
2. Create a new API key
3. In n8n, create new credentials for "OpenAI Vector Store API"
4. Enter your API key

Usage Examples

$3

`json
{
"resource": "vectorStore",
"operation": "create",
"name": "Knowledge Base",
"additionalFields": {
"description": "Support documentation",
"expiresAfterDays": 30
}
}
`

$3

`json
{
"resource": "vectorStore",
"operation": "search",
"vectorStoreId": "vs_abc123",
"query": "How to reset password?",
"searchOptions": {
"max_num_results": 5,
"score_threshold": 0.7,
"rewrite_query": true
}
}
`

$3

`json
{
"resource": "vectorStoreFile",
"operation": "create",
"vectorStoreId": "vs_abc123",
"fileId": "file-xyz789",
"additionalFields": {
"attributes": {
"category": "support",
"language": "en"
}
}
}
`

$3

`json
{
"searchOptions": {
"filters": {
"type": "eq",
"key": "category",
"value": "support"
}
}
}
`

Use Cases

- RAG Chatbots: Provide relevant context to AI assistants
- Support Systems: Search documentation with natural language
- Knowledge Management: Build enterprise knowledge bases
- Document Analysis: Extract information from large document sets

Example Workflow

`
[Webhook] → [Vector Store: Search] → [OpenAI: Chat] → [Respond]
``

1. Receive user question
2. Search for relevant documents
3. Generate response with context
4. Send answer

Resources

- OpenAI Vector Stores API
- n8n Community Nodes Docs
- Report Issues

License

MIT