Postgres Vector Store Tool (n8n)

AI agent tool for Postgres/pgvector with flexible vector search capabilities. Unlike the built-in Vector Store Tool, this node gives you full control over your queries through Custom SQL mode.

Why This Tool?

The built-in n8n Vector Store Tool is limited to basic similarity search. This tool solves that by offering Custom SQL mode - write any query you need while still getting vector embeddings from connected embedding nodes.

Helps With

- Standard vector similarity search - quick setup with configurable table/columns
- Complex search queries - JOINs, filters, aggregations with vector operations
- Access control patterns - implement row-level security, user-scoped searches
- Multi-table retrieval - search across related tables in one query
- Custom ranking - combine vector similarity with other scoring factors
- Hybrid search - mix full-text search with vector similarity

Features

$3

Write any SQL query with $1 placeholder for the embedding vector:

``sql
SELECT content, metadata, 1 - (embedding <=> $1) AS similarity
FROM documents
WHERE user_id = 'user123'
ORDER BY embedding <=> $1
LIMIT 10
`

$3

Quick setup for standard similarity search:
- Configurable table and column names
- Adjustable result limit (Top K)
- Optional metadata inclusion

$3

Enable in Options to get detailed logging for troubleshooting:
- SQL queries being executed
- Embedding vector dimensions
- Query execution timing
- Connection details
- Error stack traces

All debug output goes to n8n logs - essential for tracing issues in production.

Installation

$3

`bash
npm install n8n-nodes-postgres-vector-store-tool
`

$3

1. Clone or download this repository
2. Build:
`bash
npm install
npm run build
`
3. Link to n8n:
`bash
npm link
cd ~/.n8n/custom
npm link n8n-nodes-postgres-vector-store-tool
`

Usage

$3

- PostgreSQL with pgvector extension
- Standard Postgres credentials in n8n
- Embedding node connected (OpenAI, Cohere, etc.)

$3

1. Add the node to your AI Agent workflow
2. Connect an embedding node to the input
3. Configure Postgres credentials
4. Choose your mode:
- Regular Retrieval: Set table name, adjust options
- Custom SQL: Write your query with $1 for embeddings

$3

Default column names (customizable in Options):
- Vector: embedding
- Content: text
- Metadata: metadata

$3

Results return in text field for direct AI agent consumption:

`json
[
{"text": "Document content here", "metadata": {...}},
{"text": "Another document", "metadata": {...}}
]
``

Testing

This node works as an AI Agent tool. To test:
1. Create a workflow with Chat Trigger → AI Agent
2. Connect this node to the Agent's tools
3. Connect an embedding node
4. Chat with the agent and ask questions that trigger vector search

Note: "Execute Step" button doesn't work for AI Tool nodes in n8n - this is a platform limitation, not a bug.

License

MIT