n8n-nodes-openai-vectorstore

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:

🗃️ Vector Store

| 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 |

📄 Vector Store Files

| 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 |

📦 File Batches

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

Installation

Community Nodes (Recommended)

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

Manual Installation

# 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

Create a Vector Store

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

Semantic Search

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

Add File with Attributes

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

Search with Filters

{
  "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