@arcaelas/rag


MCP server with Ollama + Vectra for semantic memory and RAG operations.

Build intelligent AI agents with persistent semantic memory - Store, search, and retrieve knowledge using vector embeddings, powered by local Ollama models and Vectra vector database.

Features

- 🧠 Semantic memory with vector embeddings
- 🔍 Semantic search using similarity
- 📦 Bulk import/export via JSONL
- 🚀 Local-first with Ollama and Vectra
- 🔧 Zero configuration with sensible defaults

Prerequisites

- Node.js >= 18
- Ollama running locally
- An embedding model installed (e.g., ollama pull nomic-embed-text)

Installation

$3

Add to your ~/.claude.json:

``json
{
"mcpServers": {
"rag": {
"command": "npx",
"args": ["-y", "@arcaelas/rag"],
"env": {
"OLLAMA_HOSTNAME": "http://localhost:11434",
"OLLAMA_MODEL_NAME": "nomic-embed-text"
}
}
}
}
`

$3

`bash
npm install -g @arcaelas/rag

Or with yarn


yarn global add @arcaelas/rag
`

Then in ~/.claude.json:

`json
{
"mcpServers": {
"rag": {
"command": "rag",
"args": [],
"env": {
"OLLAMA_HOSTNAME": "http://localhost:11434",
"OLLAMA_MODEL_NAME": "nomic-embed-text"
}
}
}
}
`

Environment Variables

| Variable | Default | Description |
|----------|---------|-------------|
| OLLAMA_HOSTNAME | http://localhost:11434 | Ollama server URL |
| OLLAMA_MODEL_NAME | nomic-embed-text | Embedding model name |

Available Tools

$3

Save knowledge to semantic memory database.

`typescript
await save("TypeScript is a typed superset of JavaScript", {
category: "programming",
importance: "high"
})
`

$3

Search knowledge base using semantic similarity.

`typescript
await search("typed javascript", 5)
`

$3

List stored memories with pagination.

`typescript
await list(0, 10)
`

$3

Retrieve specific memory by ID.

`typescript
await get("uuid-here")
`

$3

Add tag to a memory.

`typescript
await tag("uuid-here", "important")
`

$3

Permanently delete a memory.

`typescript
await destroy("uuid-here")
`

$3

Bulk import memories from JSONL file.

`typescript
await upload("/path/to/memories.jsonl")
// Returns: { filename, done: 299, error: [{ line: 297, error: "..." }] }
`

$3

Export memories to JSONL file.

`typescript
await download(0, 100, "/path/to/export.jsonl")
// Returns: { filename, offset, limit, count: 303 }
`

JSONL Format

Each line in the JSONL file should be:

`json
{"context": "Your knowledge content here"}
`

Optional fields:

`json
{
"context": "Content here",
"metadata": {
"category": "programming",
"importance": "high",
"project": "my-project"
},
"tags": "tag1,tag2,tag3"
}
`

Data Storage

Vector database is stored in:
- npx/global install: ~/.cache/@arcaelas/rag/data/
- Local install: /data/

Collection name: arcaelas_mcp_rag_collection

Development

`bash


Clone repository

git clone https://github.com/arcaelas/rag.git
cd rag

Install dependencies

yarn install

Build

yarn build

Run locally

yarn start

Watch mode

yarn dev
`

Contributing

Contributions are welcome! Please read our contributing guidelines before submitting PRs.

1. Fork the repository
2. Create your feature branch (git checkout -b feature/amazing-feature)
3. Commit your changes (git commit -m 'feat: add amazing feature')
4. Push to the branch (git push origin feature/amazing-feature`)
5. Open a Pull Request

Security

See SECURITY.md for security policies and reporting vulnerabilities.

Changelog

See CHANGELOG.md for release history.

License

MIT © Miguel Guevara (Arcaela)

Links

- npm Package
- GitHub Repository
- Issues & Bug Reports
- Model Context Protocol
- Ollama

Support

- 📧 Email: arcaela.reyes@gmail.com
- 🐛 Issues: GitHub Issues
- 💬 Discussions: GitHub Discussions