hyperxdb 0.1.0

Python SDK for HyperX - the knowledge layer for AI

HyperX Python SDK

The official Python SDK for HyperX - the knowledge layer for AI that outgrows vector search.

HyperX is a hypergraph database designed for AI/ML applications. Unlike vector databases that only find similar items, HyperX enables multi-hop reasoning across complex relationships - the kind of inference that RAG applications actually need.

Installation

pip install hyperxdb

Requirements: Python 3.10+

Quick Start

from hyperx import HyperX

# Initialize the client
db = HyperX(api_key="hx_sk_live_abc123...")

# Create entities (nodes in the hypergraph)
react = db.entities.create(name="React", entity_type="framework")
hooks = db.entities.create(name="Hooks", entity_type="concept")

# Create hyperedges (n-ary relationships)
edge = db.hyperedges.create(
    description="React provides Hooks for state management",
    members=[
        {"entity_id": react.id, "role": "subject"},
        {"entity_id": hooks.id, "role": "object"},
    ]
)

# Find multi-hop paths (the hero feature!)
paths = db.paths.find(
    from_entity="e:useState",
    to_entity="e:redux",
    max_hops=4
)

for path in paths:
    print(f"Path cost: {path.cost}, Hops: {len(path.hyperedges)}")

Async Support

For async/await patterns, use AsyncHyperX:

from hyperx import AsyncHyperX

async def main():
    async with AsyncHyperX(api_key="hx_sk_live_abc123...") as db:
        entity = await db.entities.create(name="React", entity_type="concept")
        paths = await db.paths.find(from_entity="e:...", to_entity="e:...")

API Reference

Client Initialization

from hyperx import HyperX, AsyncHyperX

# Sync client
db = HyperX(
    api_key="hx_sk_live_abc123...",           # Required: your API key
    base_url="https://api...",     # Optional: custom API URL
    timeout=30.0,                  # Optional: request timeout in seconds
)

# Async client (same parameters)
db = AsyncHyperX(api_key="hx_sk_live_abc123...")

# Both support context managers
with HyperX(api_key="hx_sk_live_abc123...") as db:
    ...

async with AsyncHyperX(api_key="hx_sk_live_abc123...") as db:
    ...

Entities

Entities are nodes in the hypergraph - the "things" in your knowledge base.

# Create an entity
entity = db.entities.create(
    name="React Hooks",           # Required: human-readable name
    entity_type="concept",        # Required: type classification
    attributes={"version": "18"}, # Optional: key-value attributes
    embedding=[0.1, 0.2, ...],    # Optional: vector embedding
)

# Get an entity by ID
entity = db.entities.get("e:uuid...")

# Update an entity
entity = db.entities.update(
    "e:uuid...",
    name="New Name",
    attributes={"updated": True}
)

# List entities with pagination
entities = db.entities.list(limit=100, offset=0)

# Delete an entity
db.entities.delete("e:uuid...")

Hyperedges

Hyperedges are n-ary relationships connecting multiple entities with semantic roles.

from hyperx import MemberInput

# Create a hyperedge with dict syntax
edge = db.hyperedges.create(
    description="React provides Hooks for state management",
    members=[
        {"entity_id": "e:react", "role": "subject"},
        {"entity_id": "e:hooks", "role": "object"},
        {"entity_id": "e:state", "role": "context"},
    ],
    attributes={"source": "documentation"},
)

# Or use the MemberInput helper
edge = db.hyperedges.create(
    description="React provides Hooks",
    members=[
        MemberInput("e:react", "subject"),
        MemberInput("e:hooks", "object"),
    ]
)

# Get a hyperedge
edge = db.hyperedges.get("h:uuid...")

# List hyperedges
edges = db.hyperedges.list(limit=100, offset=0)

# Update a hyperedge
edge = db.hyperedges.update(
    "h:uuid...",
    description="Updated description",
)

# Delete a hyperedge
db.hyperedges.delete("h:uuid...")

Paths (Hero Feature)

The paths API enables multi-hop reasoning - finding how concepts connect through chains of relationships. This is what sets HyperX apart from vector databases.

# Find paths between two entities
paths = db.paths.find(
    from_entity="e:useState",     # Starting entity
    to_entity="e:redux",          # Target entity
    max_hops=4,                   # Maximum hyperedge hops (default: 4)
    intersection_size=1,          # Min bridge entities between edges (default: 1)
    k_paths=3,                    # Number of paths to return (default: 3)
)

# Each path contains:
for path in paths:
    print(f"Hyperedges: {path.hyperedges}")  # List of hyperedge IDs
    print(f"Bridges: {path.bridges}")         # Bridge entities between edges
    print(f"Cost: {path.cost}")               # Path cost (lower = better)

Why this matters: Vector search finds "React is similar to Vue". Path finding discovers "useState connects to Redux through React's state management pattern, which inspired Redux's design." That's the difference between similarity and understanding.

Search

HyperX supports hybrid search combining vector similarity and text matching.

# Hybrid search (recommended)
results = db.search("react state management", limit=10)

# Access results
for entity in results.entities:
    print(entity.name)
for edge in results.hyperedges:
    print(edge.description)

# Vector-only search
results = db.search.vector(embedding=[0.1, 0.2, ...], limit=10)

# Text-only search (BM25)
results = db.search.text("react hooks tutorial", limit=10)

Error Handling

The SDK provides typed exceptions for different error cases:

from hyperx import (
    HyperXError,          # Base exception
    AuthenticationError,  # Invalid or missing API key
    NotFoundError,        # Resource not found
    ValidationError,      # Request validation failed
    RateLimitError,       # Rate limit exceeded
    ServerError,          # Server error (5xx)
)

try:
    entity = db.entities.get("e:nonexistent")
except NotFoundError:
    print("Entity not found")
except AuthenticationError:
    print("Invalid API key")
except HyperXError as e:
    print(f"HyperX error: {e.message}")

Models

The SDK uses Pydantic models for type safety:

from hyperx import Entity, Hyperedge, HyperedgeMember, PathResult, SearchResult

# Entity fields
entity.id           # str: "e:uuid..."
entity.name         # str
entity.entity_type  # str
entity.attributes   # dict[str, Any]
entity.confidence   # float
entity.created_at   # datetime
entity.updated_at   # datetime

# Hyperedge fields
edge.id             # str: "h:uuid..."
edge.description    # str
edge.members        # list[HyperedgeMember]
edge.attributes     # dict[str, Any]
edge.confidence     # float
edge.created_at     # datetime
edge.updated_at     # datetime

# HyperedgeMember fields
member.entity_id    # str
member.role         # str

# PathResult fields
path.hyperedges     # list[str]: ordered hyperedge IDs
path.bridges        # list[list[str]]: bridge entities
path.cost           # float: path cost

# SearchResult fields
results.entities    # list[Entity]
results.hyperedges  # list[Hyperedge]

Examples

Building a Knowledge Graph

from hyperx import HyperX

db = HyperX(api_key="hx_sk_live_abc123...")

# Create entities for a tech knowledge graph
python = db.entities.create(name="Python", entity_type="language")
django = db.entities.create(name="Django", entity_type="framework")
flask = db.entities.create(name="Flask", entity_type="framework")
web = db.entities.create(name="Web Development", entity_type="concept")

# Create relationships
db.hyperedges.create(
    description="Django is built with Python",
    members=[
        {"entity_id": django.id, "role": "subject"},
        {"entity_id": python.id, "role": "language"},
    ]
)

db.hyperedges.create(
    description="Flask is a Python microframework",
    members=[
        {"entity_id": flask.id, "role": "subject"},
        {"entity_id": python.id, "role": "language"},
    ]
)

db.hyperedges.create(
    description="Django enables web development",
    members=[
        {"entity_id": django.id, "role": "tool"},
        {"entity_id": web.id, "role": "domain"},
    ]
)

Multi-Hop Reasoning for RAG

# When your LLM asks "How does Flask relate to web development?"
paths = db.paths.find(
    from_entity=flask.id,
    to_entity=web.id,
    max_hops=3
)

# Build context from the path
context = []
for path in paths:
    for edge_id in path.hyperedges:
        edge = db.hyperedges.get(edge_id)
        context.append(edge.description)

# Result: "Flask is a Python microframework" -> "Django is built with Python"
#         -> "Django enables web development"
# Your LLM now understands the indirect connection!

Async Batch Operations

import asyncio
from hyperx import AsyncHyperX

async def create_entities(db: AsyncHyperX, names: list[str]):
    tasks = [
        db.entities.create(name=name, entity_type="concept")
        for name in names
    ]
    return await asyncio.gather(*tasks)

async def main():
    async with AsyncHyperX(api_key="hx_sk_live_abc123...") as db:
        entities = await create_entities(db, ["React", "Vue", "Angular"])
        print(f"Created {len(entities)} entities")

asyncio.run(main())

Development

# Install dev dependencies
pip install hyperx[dev]

# Run tests
pytest

# Type checking
mypy src/hyperx

# Linting
ruff check src/hyperx

License

MIT License - see LICENSE for details.

Links

• Website: hyperxdb.dev
• Documentation: hyperxdb.dev/docs
• GitHub: github.com/hyperxdb/hyperx-python
• Issues: github.com/hyperxdb/hyperx-python/issues