helix-py

Helix-DB | Homepage | Documentation | PyPi

Helix-py is a python library for interacting with helix-db a graph-vector database written in rust. This library will make it easy to quickly setup a rag agent with your documents and favorite model.

Features

Queries

helix-py using a pytorch like front-end to creating queries. Like you would define a neural network forward pass, you can do the same thing for a helix-db query. We provide some default queries in helix/client.py to get started with inserting and search vectors, but you can also define you're own queries if you plan on doing more complex things. For example, for this hql query

QUERY addUser(name: String, age: I64) =>
  usr <- AddV<User>({name: name, nge: age})
  RETURN usr

you would write

class addUser(Query):
    def __init__(self, user: Tuple[str, int]):
        super().__init__()
        self.user = user

    def query(self) -> List[Any]:
        return [{ "name": self.user[0], "age": self.user[1] }]

    def response(self, response):
        return response

for your python script. Make sure that the Query.query method returns a list of objects.

Loader

The loader (helix/loader.py) currently supports .parquet, .fvecs, and .csv data. Simply pass in the path to your file or files and the columns you want to process and the loader does the rest for you and is easy to integrate with your queries

Installation

Install helix-py

pip install helix-py

See getting started for more information on installing helix-db

Install the Helix CLI

curl -sSL "https://install.helix-db.com" | bash
helix install
helix init
helix deploy

Documentation

Proper docs are coming soon. See examples/tutorial.py for now.

import helix
from helix.client import hnswload, hnswsearch

db = helix.Client(local=True)
data = helix.Loader("path/to/data", cols=["vecs"])
ids = db.query(hnswload(data)) # build hnsw index

my_query = [0.32, ..., -1.321]
nearest = db.query(hnswsearch(my_query)) # query hnsw index

LLM Install for Demo

For the demo in examples/rag_demo/ you can also install Ollama here to get up and running with a local model.

Just run this after installing ollama. (We used llama3.1:8b, but you can just use whatever you want ofcourse.)

ollama serve
ollama pull llama3.1:8b

Now you're good to go! See examples/ for how to use helix-py. See helixdb-queries/queries.hx for the queries installed with helix deploy --local. You can add your own here and write corresponding Query classes in your python script.

Getting Started With MCP With Helix

Helix's custom mcp server backend is built into the db and the mcp_server.py server can be used to interface with that. To get started with this, you can for example use uv:

uv init project
cp mcp_server.py project
cd project
uv venv && source .venv/bin/activate
uv add helix-py "mcp[cli]"

then for claude-desktop for example add this to ~/Library/Application Support/Claude/claude_desktop_config.json adjusting paths of course

{
  "mcpServers": {
    "helix-mcp": {
      "command": "uv",
      "args": [
        "--directory",
        "/Users/user/helix-py/proect",
        "run",
        "mcp_server.py"
      ]
    }
  }
}

License

helix-py is licensed under the GNU General Public License v3.0 (GPL-3.0).