grafeo-llamaindex 0.2.0

LlamaIndex PropertyGraphStore backed by GrafeoDB embedded graph database

grafeo-llamaindex

LlamaIndex PropertyGraphStore backed by GrafeoDB, an embedded graph database with native vector search.

Build knowledge graphs from documents, query them with GQL, and run vector similarity search, all in a single .db file. No servers, no infrastructure.

Install

uv add grafeo-llamaindex

Quickstart

from llama_index.core import PropertyGraphIndex, SimpleDirectoryReader
from grafeo_llamaindex import GrafeoPropertyGraphStore

documents = SimpleDirectoryReader("./data").load_data()

graph_store = GrafeoPropertyGraphStore(db_path="./knowledge_graph.db")

index = PropertyGraphIndex.from_documents(
    documents,
    property_graph_store=graph_store,
    embed_kg_nodes=True,
)

retriever = index.as_retriever(include_text=True)
nodes = retriever.retrieve("What are the key relationships?")

Features

• Full PropertyGraphStore: all 8 abstract methods implemented (get, get_triplets, get_rel_map, upsert_nodes, upsert_relations, delete, structured_query, vector_query)
• Structured + vector queries: supports_structured_queries = True and supports_vector_queries = True in a single store
• Embedded database: no Docker, no cloud, no external services. Just uv add grafeo
• Single-file persistence: the entire knowledge graph lives in one .db file
• Native HNSW vector search: embeddings stored alongside graph nodes, no separate vector DB needed
• Multi-language queries: GQL, Cypher, Gremlin, GraphQL, SPARQL and SQL/PGQ all supported
• Built-in graph algorithms: PageRank, Louvain, shortest paths, centrality and 30+ more via graph_store.client.algorithms

API Reference

GrafeoPropertyGraphStore

from grafeo_llamaindex import GrafeoPropertyGraphStore

store = GrafeoPropertyGraphStore(
    db_path=None,                # str | None - path for persistent storage, None for in-memory
    embedding_dimensions=1536,   # int - vector dimensions for HNSW index
    embedding_metric="cosine",   # str - "cosine", "euclidean", "dot_product", or "manhattan"
    dedup_threshold=None,        # float | None - cosine similarity threshold for entity dedup
)

Properties:

• store.client: access the underlying grafeo.GrafeoDB instance for direct queries and algorithms
• store.supports_structured_queries: True
• store.supports_vector_queries: True

Methods (PropertyGraphStore interface):

Method	Description
upsert_nodes(nodes)	Insert or update EntityNode / ChunkNode objects
upsert_relations(relations)	Insert edges between existing nodes
get(properties, ids)	Retrieve nodes by ID or property filter
get_triplets(entity_names, relation_names, ids)	Get (source, relation, target) triplets
get_rel_map(graph_nodes, depth, ignore_rels)	BFS traversal from seed nodes
delete(entity_names, relation_names, ids)	Remove nodes and/or edges
structured_query(query)	Execute raw GQL/Cypher (or Gremlin with g. prefix)
vector_query(query)	HNSW similarity search over node embeddings
get_schema() / get_schema_str()	Inspect graph labels, edge types, and properties
persist(path)	Save in-memory database to disk
close()	Close the database connection

Persistence

The entire knowledge graph lives in a single .db file. Pass db_path to store data on disk, or omit it for in-memory use.

from grafeo_llamaindex import GrafeoPropertyGraphStore

# Create and populate
store = GrafeoPropertyGraphStore(db_path="./my_graph.db")
# ... upsert nodes and relations ...
store.close()

# Reopen later with the same path
store = GrafeoPropertyGraphStore(db_path="./my_graph.db")
print(store.node_count, store.edge_count)  # data is still there

You can also save an in-memory store to disk:

store = GrafeoPropertyGraphStore()  # in-memory
# ... populate ...
store.persist("./snapshot.db")

Deduplication

When dedup_threshold is set, upsert_nodes checks whether an incoming EntityNode's embedding is similar enough to an existing node (same label) to merge them instead of creating a duplicate.

store = GrafeoPropertyGraphStore(
    dedup_threshold=0.95,  # cosine similarity threshold
    embedding_dimensions=1536,
)

Key behavior:

• Threshold semantics: if cosine_similarity(new, existing) >= dedup_threshold, the new node merges into the existing one (properties are overwritten, the original created_at timestamp is preserved).
• Label-scoped: dedup only compares nodes with the same label. A "Person" and a "Company" with identical embeddings are never merged.
• ChunkNode excluded: ChunkNode objects are never deduplicated, only EntityNode.
• Requires embedding: nodes without an embedding are never deduplicated.
• Runtime toggle: you can set store.dedup_threshold = 0.9 at any time and it takes effect on the next upsert_nodes call.

Relation Upsert Behavior

upsert_relations silently skips relations whose source_id or target_id does not match any existing node (by name or LlamaIndex ID). A UserWarning is emitted for each skipped relation, so you can catch these with Python's warnings module if needed.

Comparison

	Neo4j	FalkorDB	Grafeo
Requires server	Yes	Yes	No (embedded)
Vector search	Plugin (5.x+)	Limited	Native HNSW
Graph algorithms	GDS plugin ($)	Built-in	Built-in (30+)
Query languages	Cypher	Cypher	GQL, Cypher, Gremlin, GraphQL, SPARQL, SQL/PGQ
Deployment	Docker/Cloud	Docker/Cloud	uv add grafeo
Persistence	Server-managed	Server-managed	Single .db file

Examples

See the examples/ directory:

• mock_embedding_demo.py: full demo with hand-crafted embeddings, no API key required
• basic_graph_rag.py: build a Property Graph Index from documents and query it (requires OpenAI API key)
• hybrid_retrieval.py: structured queries + vector search + PageRank, all in one script

Development

uv sync                  # install deps
uv run pytest -v         # run tests
uv run ruff check .      # lint
uv run ruff format .     # format
uv run ty check          # type check

License

Apache-2.0