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helix-py is a Python library for interacting with helix-db, a powerful graph-vector database written in Rust. It provides both a simple query interface and a PyTorch-like front-end for defining and executing custom graph queries and vector-based operations. This makes it well-suited for use cases such as similarity search, knowledge graph construction, and machine learning pipelines.

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Installation

uv add helix-py

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Client

To setup a simple Client to interface with a running helix instance:
Python
import helix

# Connect to a local helix instance
db = helix.Client(local=True, verbose=True)

# Note that the query name is case sensitive
db.query('add_user', {"name": "John", "age": 20})
The default port is 6969, but you can change it by passing in the port parameter. For cloud instances, you can pass in the api_endpoint parameter.

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Queries

helix-py allows users to define a PyTorch-like manner, similar to how you would define a neural network’s forward pass. You can use built-in queries in helix/client.py to get started with inserting and search vectors, or you can define your own queries for more complex workflows. Pytorch-like Query Given a HelixQL query like this:
query.hx
QUERY add_user(name: String, age: I64) =>
  usr <- AddV<User>({name: name, age: age})
  RETURN usr
You can define a matching Python class:
Python
from helix.client import Query
from helix.types import Payload

class add_user(Query):
    def __init__(self, name: str, age: int):
        super().__init__()
        self.name = name
        self.age = age

    def query(self) -> Payload:
        return [{ "name": self.name, "age": self.age }]

    def response(self, response):
        return response
        
db.query(add_user("John", 20))
Make sure that the Query.query method returns a list of objects.

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Instance

To setup a simple Instance that manages and automatically starts and stops a helix instance with respect to the lifetime of the script:
Python
from helix.instance import Instance
helix_instance = Instance("helixdb-cfg", 6969, verbose=True)

# Deploy & redeploy instance
helix_instance.deploy()

# Start instance
helix_instance.start()

# Stop instance
helix_instance.stop()

# Delete instance
helix_instance.delete()

# Instance status
print(helix_instance.status())
helixdb-cfg is the directory where the configuration files are stored.
and from there you can interact with the instance using Client. The instance will be automatically stopped when the script exits.

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Providers

Helix has LLM interfaces for popular LLM providers. Available providers:
  • OpenAIProvider
  • GeminiProvider
  • AnthropicProvider
Don’t forget to set the OPENAI_API_KEY, GEMINI_API_KEY, and ANTHROPIC_API_KEY environment variables depending on the provider you are using.
All providers expose two methods:
  • enable_mcps(name: str, url: str=...) -> bool to enable Helix MCP tools
  • generate(messages, response_model: BaseModel | None=None) -> str | BaseModel
The generate method supports messages in the 2 formats:
  • Free-form text: pass a string
  • Message lists: pass a list of dict or provider-specific Message models
It also supports structured outputs by passing a Pydantic model to get validated results. Example: 
from pydantic import BaseModel

# OpenAI
from helix.providers.openai_client import OpenAIProvider
openai_llm = OpenAIProvider(
    name="openai-llm",
    instructions="You are a helpful assistant.",
    model="gpt-5-nano",
    history=True
)
print(openai_llm.generate("Hello!"))

class Person(BaseModel):
    name: str
    age: int
    occupation: str

print(openai_llm.generate([{"role": "user", "content": "Who am I?"}], Person))
To enable MCP tools with a running Helix MCP server (see MCP Feature): 
openai_llm.enable_mcps("helix-mcp")         # uses default http://localhost:8000/mcp/
gemini_llm.enable_mcps("helix-mcp")         # uses default http://localhost:8000/mcp/
anthropic_llm.enable_mcps("helix-mcp", url="https://your-remote-mcp/...")
  • OpenAI GPT-5 family models support reasoning while other models use temperature.
  • Anthropic local streamable MCP is not supported; use a URL-based MCP.

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Embedders

Helix has embedder interfaces for popular embedding providers. Available embedders:
  • OpenAIEmbedder
  • GeminiEmbedder
  • VoyageAIEmbedder
Each embedder implements:
  • embed(text: str, **kwargs) returns a vector [F64]
  • embed_batch(texts: List[str], **kwargs) returns a list of vectors [F64]
Examples (see examples/llm_providers/providers.ipynb for more): 
from helix.embedding.openai_client import OpenAIEmbedder
openai_embedder = OpenAIEmbedder()  # requires OPENAI_API_KEY
vec = openai_embedder.embed("Hello world")
batch = openai_embedder.embed_batch(["a", "b", "c"])

from helix.embedding.gemini_client import GeminiEmbedder
gemini_embedder = GeminiEmbedder()
vec = gemini_embedder.embed("doc text", task_type="RETRIEVAL_DOCUMENT")

from helix.embedding.voyageai_client import VoyageAIEmbedder
voyage_embedder = VoyageAIEmbedder()
vec = voyage_embedder.embed("query text", input_type="query")

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Chunking

Helix uses Chonkie chunking methods to split text into manageable pieces for processing and embedding: 
from helix import Chunk

text = "Your long document text here..."
chunks = Chunk.token_chunk(text)

semantic_chunks = Chunk.semantic_chunk(text)

code_text = "def hello(): print('world')"
code_chunks = Chunk.code_chunk(code_text, language="python")

texts = ["Document 1...", "Document 2...", "Document 3..."]
batch_chunks = Chunk.sentence_chunk(texts)
You can find all the different chunking examples inside of Chunking Feature.

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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

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More Information

For more information, check out our examples!
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