n8n-nodes-minimemory

An n8n community node for embedded vector database operations - 100% serverless.

Perform vector similarity search directly within n8n without any external server. Perfect for:

• RAG (Retrieval Augmented Generation)
• Semantic search
• Recommendations
• Deduplication

Features

• No server required - Everything runs locally within n8n
• Similarity search - Find the k most similar vectors
• Hybrid search - Combine vector + keyword (BM25) search
• Multiple metrics - Cosine, Euclidean, Dot Product
• Metadata support - Associate additional information with each vector
• Metadata filtering - MongoDB-style query operators
• Persistence - Save/load from JSON files
• Bulk insert - Insert multiple vectors from previous nodes

Installation

From n8n UI (Recommended)

1. Go to Settings > Community Nodes
2. Search for n8n-nodes-minimemory
3. Click Install

From npm

cd ~/.n8n/nodes
npm install n8n-nodes-minimemory

Then restart n8n.

Operations

Create Database

Create a new vector database in memory.

Parameter	Description	Example
Database Name	Unique name for the DB	my_vectors
Dimensions	Number of dimensions	384 (MiniLM), 1536 (OpenAI)
Distance Metric	Similarity metric	cosine, euclidean, dot
Index Type	Index type	flat (exact), hnsw (fast)

Insert Vector

Insert a single vector with ID and optional metadata.

Parameter	Description
Vector ID	Unique identifier
Vector	Array of numbers [0.1, 0.2, ...]
Metadata	JSON object {"title": "Doc 1"}

Insert Many

Bulk insert vectors from input items.

Parameter	Description	Default
ID Field	Field containing the ID	id
Vector Field	Field containing the vector	embedding
Metadata Fields	Fields for metadata (empty = all)

Example input:

[
  {"id": "doc1", "embedding": [0.1, 0.2, ...], "title": "Document 1"},
  {"id": "doc2", "embedding": [0.3, 0.4, ...], "title": "Document 2"}
]

Search

Search for the k nearest neighbors using vector, keyword, or hybrid search.

Parameter	Description	Default
Search Mode	vector, keyword, or hybrid	vector
Query Vector	Vector for similarity search	(required for vector/hybrid)
Keywords	Text for BM25 keyword search	(required for keyword/hybrid)
Text Fields	Metadata fields to search	content,text,title,description
Number of Results (K)	Number of results	10
Include Vectors	Include vectors in result	false
Minimum Similarity	Filter by minimum similarity	0
Use Metadata Filter	Enable metadata filtering	false
Metadata Filter	JSON filter (see below)	{}
Hybrid Alpha	Vector(1) vs Keyword(0) balance	0.5
Fusion Method	rrf or weighted	rrf
BM25 K1	Term saturation parameter	1.2
BM25 B	Length normalization	0.75

Output:

{
  "success": true,
  "searchMode": "hybrid",
  "filterApplied": true,
  "results": [
    {"id": "doc1", "score": 0.032, "vectorSimilarity": 0.9, "keywordScore": 12.4, "metadata": {...}},
    {"id": "doc2", "score": 0.028, "vectorSimilarity": 0.8, "keywordScore": 8.2, "metadata": {...}}
  ]
}

Metadata Filtering (New in v0.2.0)

Filter search results by metadata fields using MongoDB-style operators.

Basic Filters

// Exact match
{"category": "tech"}

// Multiple conditions (implicit AND)
{"category": "tech", "author": "John"}

Comparison Operators

Operator	Description	Example
$eq	Equal (implicit)	{"status": "active"}
$ne	Not equal	{"status": {"$ne": "deleted"}}
$gt	Greater than	{"score": {"$gt": 0.5}}
$gte	Greater or equal	{"price": {"$gte": 100}}
$lt	Less than	{"age": {"$lt": 30}}
$lte	Less or equal	{"count": {"$lte": 10}}

Array Operators

Operator	Description	Example
$in	In array	{"category": {"$in": ["tech", "science"]}}
$nin	Not in array	{"type": {"$nin": ["spam", "ad"]}}

String Operators

Operator	Description	Example
$contains	Contains (case-insensitive)	{"title": {"$contains": "AI"}}
$startsWith	Starts with	{"name": {"$startsWith": "Dr."}}
$endsWith	Ends with	{"file": {"$endsWith": ".pdf"}}

Other Operators

Operator	Description	Example
$exists	Field exists	{"email": {"$exists": true}}

Logical Operators

// AND - all conditions must match
{
  "$and": [
    {"category": "tech"},
    {"score": {"$gt": 0.8}}
  ]
}

// OR - any condition matches
{
  "$or": [
    {"type": "article"},
    {"type": "blog"}
  ]
}

Nested Fields

Access nested object fields using dot notation:

{"user.profile.country": "US"}

Filter Examples

Find tech articles with high score:

{
  "$and": [
    {"category": "tech"},
    {"score": {"$gte": 0.8}}
  ]
}

Find documents from specific authors:

{"author": {"$in": ["Alice", "Bob", "Charlie"]}}

Find recent documents with keyword:

{
  "timestamp": {"$gt": "2024-01-01"},
  "title": {"$contains": "machine learning"}
}

Get

Get a vector by its ID.

Delete Vector

Delete a vector by its ID.

Save to File

Save the database to a JSON file.

Load from File

Load a database from a JSON file.

Get Info

Get database information and statistics.

List Databases

List all databases in memory.

Clear Database

Remove all vectors from the database.

Delete Database

Remove a database from memory.

Example Workflow

Basic RAG with OpenAI

[Trigger]
    |
[OpenAI Embeddings] -> [Minimemory: Insert Many]
    |
[Query Input]
    |
[OpenAI Embeddings] -> [Minimemory: Search] -> [OpenAI Chat]
    |
[Response]

Step by step:

1. Create DB (run once):

   • Operation: Create Database
   • Database Name: docs
   • Dimensions: 1536 (OpenAI)
   • Distance: cosine

2. Index documents:

   • Connect node that generates embeddings
   • Operation: Insert Many
   • ID Field: id
   • Vector Field: embedding

3. Search:

   • Connect query with embedding
   • Operation: Search
   • Query Vector: {{ $json.embedding }}
   • K: 5

Persistence

Databases live in memory while n8n is running. To persist:

[Startup Trigger] -> [Minimemory: Load from File]
                           |
                    (DB available for use)
                           |
[Before shutdown] -> [Minimemory: Save to File]

Embedding Compatibility

Model	Dimensions	Notes
all-MiniLM-L6-v2	384	Fast, good for short text
all-mpnet-base-v2	768	Better quality
text-embedding-ada-002	1536	OpenAI legacy
text-embedding-3-small	1536	OpenAI new
text-embedding-3-large	3072	OpenAI high quality
embed-english-v3.0	1024	Cohere

Performance

• Flat Index: O(n) - Exact, ideal for < 10,000 vectors
• HNSW Index: O(log n) - Approximate, for large datasets

Troubleshooting

"Database not found"

Make sure to run Create Database or Load from File first.

"Dimension mismatch"

The inserted/searched vector has a different number of dimensions than the DB.

Node doesn't appear

1. Verify npm run build completed without errors
2. Check the link with npm ls -g --link
3. Restart n8n

Support

For issues and feature requests, please visit:
https://github.com/MauricioPerera/n8n-nodes-minimemory/issues

License

MIT