Openspg Guide

# Docker deployment
git clone https://github.com/OpenSPG/openspg.git
cd openspg
docker-compose up -d

# Python SDK
pip install openspg

# Access KG Studio at http://localhost:8887

SPG Framework
├── Schema Layer (define types and relations)
│   ├── Entity types (Person, Paper, Concept)
│   ├── Properties (typed, constrained)
│   └── Relations (directed, typed edges)
├── Knowledge Layer (populate with data)
│   ├── Entity extraction (NER + linking)
│   ├── Relation extraction
│   └── Property filling
├── Reasoning Layer (infer new knowledge)
│   ├── Rule-based reasoning
│   ├── Statistical reasoning
│   └── LLM-augmented reasoning
└── Application Layer (query and use)
    ├── Graph queries (SPARQL-like)
    ├── Question answering
    └── Knowledge-augmented generation

from openspg import Schema, EntityType, RelationType

# Define a research knowledge graph schema
schema = Schema("research_kg")

# Entity types
paper = EntityType("Paper", properties={
    "title": "Text",
    "abstract": "Text",
    "year": "Integer",
    "venue": "Text",
    "doi": "Text",
    "citation_count": "Integer",
})

author = EntityType("Author", properties={
    "name": "Text",
    "affiliation": "Text",
    "h_index": "Integer",
})

concept = EntityType("Concept", properties={
    "name": "Text",
    "definition": "Text",
    "domain": "Text",
})

# Relations
schema.add_relation(RelationType(
    "authored_by", source=paper, target=author
))
schema.add_relation(RelationType(
    "cites", source=paper, target=paper
))
schema.add_relation(RelationType(
    "discusses", source=paper, target=concept
))
schema.add_relation(RelationType(
    "related_to", source=concept, target=concept
))

schema.deploy()

from openspg import KnowledgeBuilder

builder = KnowledgeBuilder(schema="research_kg")

# Add entities
builder.add_entity("Paper", {
    "title": "Attention Is All You Need",
    "year": 2017,
    "venue": "NeurIPS",
    "doi": "10.48550/arXiv.1706.03762",
})

# Automatic extraction from text
builder.extract_from_text(
    "Vaswani et al. proposed the Transformer architecture "
    "which uses self-attention mechanisms to replace "
    "recurrence. The model achieved state-of-the-art on "
    "WMT 2014 English-to-German translation.",
    entity_types=["Paper", "Author", "Concept"],
    relation_types=["authored_by", "discusses"],
)

# Batch import from structured data
builder.import_csv(
    "papers.csv",
    entity_type="Paper",
    column_mapping={"title": "title", "year": "year"},
)

builder.commit()

from openspg.kag import KAGPipeline

kag = KAGPipeline(
    knowledge_graph="research_kg",
    llm_provider="anthropic",
)

# Question answering over knowledge graph
answer = kag.ask(
    "What are the key papers on attention mechanisms "
    "and how are they related?"
)

print(answer.text)
for source in answer.sources:
    print(f"  [{source.type}] {source.name}: {source.evidence}")

# The KAG pipeline:
# 1. Parses question to identify relevant entities/relations
# 2. Queries knowledge graph for subgraph
# 3. Augments LLM context with structured knowledge
# 4. Generates grounded answer with provenance

from openspg import GraphQuery

gq = GraphQuery("research_kg")

# Find papers by concept
papers = gq.query("""
    MATCH (p:Paper)-[:discusses]->(c:Concept)
    WHERE c.name = 'self-attention'
    RETURN p.title, p.year, p.citation_count
    ORDER BY p.citation_count DESC
    LIMIT 10
""")

# Find co-author network
coauthors = gq.query("""
    MATCH (a1:Author)<-[:authored_by]-(p:Paper)
          -[:authored_by]->(a2:Author)
    WHERE a1.name = 'Ashish Vaswani'
    RETURN DISTINCT a2.name, COUNT(p) as papers
    ORDER BY papers DESC
""")

# Citation chain
chain = gq.query("""
    MATCH path = (p1:Paper)-[:cites*1..3]->(p2:Paper)
    WHERE p1.title CONTAINS 'GPT-4'
    RETURN path
    LIMIT 20
""")