ontosight 0.1.1

OntoSight: A flexible, AI-ready visualization engine for interactive knowledge graphs and hypergraphs.

OntoSight 🔍

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Interactive Visualization Engine for AI-Enhanced Knowledge Graphs & Hypergraphs

OntoSight is a lightweight yet powerful Python library designed to bridge the gap between static graph visualizations and dynamic AI-driven exploration. It allows developers to create highly interactive, searchable, and "chat-ready" visualizations for complex knowledge structures with just a few lines of code.

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🌟 Key Features

• Standard & Hypergraph Support: Seamlessly switch between traditional node-edge graphs and advanced hypergraphs.
• AI-Ready Callbacks: Flexible on_search and on_chat hooks to integrate with any LLM (GPT-4, Claude, Llama 3) or Vector Database (Milvus, Pinecone, Chroma).
• Interactive Exploration: Built-in detail panels, filtering, and real-time highlighting.
• Framework Agnostic: Works with any data source. Define your schema using Pydantic and let OntoSight handle the rest.
• Developer First: Python-native API with automatic web-server management and browser launching.

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📸 Visualization Previews

1. Core Architectures

OntoSight supports both traditional graphs and hypergraphs with a unified interface.

Graph	Hypergraph

2. Intelligent Search (Vector DB Ready)

Define custom search callbacks to highlight matching subgraphs via embedding-based retrieval.

Search in Graph	Search in Hypergraph

3. GraphRAG & AI Chat

Seamlessly connect your Graph to LLMs. Auto-highlight relevant entities while generating textual answers.

AI Chat (Graph)	AI Chat (Hypergraph)

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🚀 Quick Start

Installation

pip install ontosight

Basic Usage

Define your data structure using Pydantic models:

from pydantic import BaseModel
from ontosight import view_graph

class Entity(BaseModel):
    name: str
    type: str

class Relation(BaseModel):
    source: str
    target: str
    relation: str

# Your data
nodes = [Entity(name="Alice", type="Person"), Entity(name="Wonderland", type="Place")]
edges = [Relation(source="Alice", target="Wonderland", relation="visits")]

# Launch visualization
view_graph(
    node_list=nodes,
    edge_list=edges,
    node_schema=Entity,
    edge_schema=Relation,
    node_id_extractor=lambda n: n.name,
    node_ids_in_edge_extractor=lambda e: (e.source, e.target),
    edge_label_extractor=lambda e: e.relation
)

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🧠 AI Integration (The "Sight" in OntoSight)

OntoSight is built for the Age of AI. While it doesn't ship with a specific LLM, it provides the "plumbing" to make your graph interactive and intelligent.

Flexible Search (Vector DB Ready)

You can define a custom search callback to perform semantic search using embedding models.

def my_vector_search(query: str):
    # Logic to call your Vector DB (e.g., Milvus)
    # returns matching_nodes, matching_edges
    pass

view_graph(..., on_search=my_vector_search)

GraphRAG & Chat

Connect a Chat interface directly to your Graph. When a user asks a question, the LLM can provide a textual answer, and OntoSight will auto-highlight the relevant subgraph.

def my_chat_handler(question: str):
    # 1. Send question to LLM (e.g., GPT-4)
    # 2. Get relevant nodes/edges from your Retrieval logic
    return "Alice is in Wonderland.", (relevant_nodes, relevant_edges)

view_graph(..., on_chat=my_chat_handler)

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🛠 Advanced Features

• Schema-Driven Detail Panels: Automatically generates UI panels based on your Pydantic models.
• Hypergraph Modeling: Visualize relationships between multiple nodes simultaneously.

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📄 License

OntoSight is released under the Apache License 2.0.