vetgraph 0.1.1

Transform unstructured text into NetworkX knowledge graphs using LLMs

VetGraph

Transform unstructured text into NetworkX knowledge graphs using LLMs

VetGraph is a provider-agnostic Python library that leverages Large Language Models to extract entities and relationships from unstructured text and construct knowledge graphs using NetworkX.

Features

• Provider-Agnostic — Works with OpenAI, Anthropic (Claude), Google Gemini, Ollama, and Azure OpenAI
• NetworkX Integration — Builds standard NetworkX directed graphs for powerful graph analysis
• Dual Visualization — Static plots with Matplotlib and interactive HTML visualizations with Pyvis
• Type Safety — Pydantic models with Instructor for robust structured outputs
• Flexible Extraction — Schema-constrained relationship types and multiple export formats

Installation

# Install with OpenAI support
pip install vetgraph[openai]

# Install with Anthropic support
pip install vetgraph[anthropic]

# Install with Gemini support
pip install vetgraph[gemini]

# Install with all providers
pip install vetgraph[all-providers]

# Basic installation (bring your own LLM client)
pip install vetgraph

Quick Start

OpenAI

from vetgraph import VetGraph

vg = VetGraph.from_openai(api_key="sk-...")

text = """
Albert Einstein was a theoretical physicist who developed the theory of relativity.
He was born in Germany and later moved to the United States. Einstein worked at
Princeton University and won the Nobel Prize in Physics in 1921.
"""

result = vg.add_text(text)
print(f"Extracted {result.get_triple_count()} triples")

vg.visualize("einstein_graph.html")

Anthropic Claude

from vetgraph import VetGraph

vg = VetGraph.from_anthropic(
    api_key="sk-ant-...",
    model="claude-3-5-sonnet-20241022"
)

result = vg.add_text("Marie Curie discovered radium and polonium.")

Google Gemini

from vetgraph import VetGraph

vg = VetGraph.from_gemini(
    api_key="...",
    model="gemini-2.5-flash"
)

result = vg.add_text("The Eiffel Tower is located in Paris, France.")

Ollama (Local, Free)

from vetgraph import VetGraph

vg = VetGraph.from_ollama(model="llama3.2")
result = vg.add_text("Python was created by Guido van Rossum.")

Usage

Schema-Constrained Extraction

Restrict extracted relationships to a predefined set of types:

from vetgraph import VetGraph

vg = VetGraph.from_openai(api_key="sk-...")

schema = ["works_for", "located_in", "developed_by", "invented"]

result = vg.add_text(
    "Steve Jobs co-founded Apple in Cupertino.",
    schema=schema
)

Graph Analysis with NetworkX

import networkx as nx

graph = vg.get_graph()

centrality = nx.degree_centrality(graph)
print("Most central nodes:",
      sorted(centrality.items(), key=lambda x: x[1], reverse=True)[:5])

if nx.has_path(graph, "Einstein", "Princeton"):
    path = nx.shortest_path(graph, "Einstein", "Princeton")
    print("Path:", " -> ".join(path))

Graph Statistics

stats = vg.get_statistics()
print(f"Nodes: {stats['nodes']}")
print(f"Edges: {stats['edges']}")
print(f"Density: {stats['density']:.3f}")
print(f"Connected: {stats['is_connected']}")
print(f"Unique relations: {stats['unique_relations']}")

Visualization

# Interactive HTML visualization (default)
vg.visualize("graph.html")

# Pyvis with custom options
net = vg.visualize_interactive(
    output_file="custom_graph.html",
    height="800px",
    width="100%",
    notebook=False
)

# Matplotlib static plot
vg.visualize_matplotlib(
    figsize=(15, 10),
    node_color="lightcoral",
    node_size=4000,
    save_path="graph.png"
)

Saving and Loading Graphs

# Export in various formats
vg.save_graph("my_graph.json", format="json")           # Node-link JSON
vg.save_graph("my_graph.graphml", format="graphml")     # GraphML (XML)
vg.save_graph("my_graph.gexf", format="gexf")           # GEXF (for Gephi)
vg.save_graph("my_graph.edgelist", format="edgelist")   # Edge list

# Load a previously saved graph
vg.load_graph("my_graph.json", format="json")

Batch Processing

texts = [
    "Leonardo da Vinci painted the Mona Lisa.",
    "The Mona Lisa is displayed in the Louvre Museum.",
    "The Louvre Museum is located in Paris."
]

for text in texts:
    vg.add_text(text)

print(f"Total nodes: {vg.graph.number_of_nodes()}")
print(f"Total edges: {vg.graph.number_of_edges()}")

Custom Provider Configuration

from vetgraph import VetGraph, create_openai_client

client = create_openai_client(
    api_key="sk-...",
    base_url="https://custom-endpoint.com/v1"
)

vg = VetGraph(client=client, model="gpt-4o-mini")

Azure OpenAI

vg = VetGraph.from_azure_openai(
    api_key="your-azure-key",
    azure_endpoint="https://your-resource.openai.azure.com/",
    model="gpt-4-deployment-name",
    api_version="2024-02-01"
)

API Reference

Factory Methods

Method	Description
VetGraph.from_openai(api_key, model="gpt-4o-mini")	Initialize with OpenAI
VetGraph.from_anthropic(api_key, model="claude-3-5-sonnet-20241022")	Initialize with Anthropic
VetGraph.from_gemini(api_key, model="gemini-2.5-flash")	Initialize with Google Gemini
VetGraph.from_ollama(model="llama3.2")	Initialize with Ollama
VetGraph.from_azure_openai(...)	Initialize with Azure OpenAI

Core Methods

Method	Description
add_text(text, schema=None, temperature=0.3)	Extract triples from text and add to graph
get_graph()	Return the underlying NetworkX DiGraph
clear_graph()	Remove all nodes and edges
get_statistics()	Return graph statistics as a dictionary

Visualization Methods

Method	Description
visualize(output_path="graph.html")	Create an interactive HTML visualization
visualize_interactive(...)	Create a Pyvis visualization with full options
visualize_matplotlib(...)	Create a static Matplotlib visualization

Persistence Methods

Method	Description
save_graph(output_path, format="json")	Export graph to file
load_graph(input_path, format="json")	Load graph from file

Supported Models

OpenAI

• gpt-4o-mini (default, recommended)
• gpt-4o
• gpt-4-turbo

Anthropic

• claude-3-5-sonnet-20241022 (default, recommended)
• claude-3-5-haiku-20241022
• claude-3-opus-20240229

Google Gemini

• gemini-2.5-flash (default, recommended)
• gemini-2.5-pro
• gemini-2.0-flash

Ollama

• llama3.2 (default)
• llama3.1
• mistral
• mixtral
• Any other locally installed Ollama model

Dependencies

• instructor — Structured outputs for LLMs
• NetworkX — Graph construction and analysis
• Pyvis — Interactive graph visualizations
• Pydantic — Data validation and type safety

Contributing

Contributions are welcome. Please submit a pull request or open an issue on GitHub.

License

MIT License. See the LICENSE file for details.

Contact

• Author: Vishesh Raju
• Email: [e-mail]
• GitHub: github.com/Vishesh0-7/vetgraph