llama-index-graph-stores-rushdb

A LlamaIndex property graph store on RushDB: build a PropertyGraphIndex from your documents and keep the knowledge graph in RushDB, self-hosted or on RushDB Cloud.

• Real labels, no schema to declare. Every extracted entity is stored under its own label (PERSON, COMPANY, …) and every relation as a typed relationship. Nothing is collapsed into a generic Entity node, and no table or label has to be created first.
• Vectors in the same database. Entity and chunk embeddings go to RushDB vector indexes, so VectorContextRetriever searches and traverses in one place, with metadata filters.
• A schema derived from the data. get_schema_str() returns what is actually stored: labels with counts, typed properties with value ranges, and relationship patterns such as (PERSON)-[:WORKS_AT]->(COMPANY). That is what text-to-Cypher prompts are given.

Install

pip install llama-index-graph-stores-rushdb

Requires Python 3.10+, llama-index-core 0.13 or 0.14, and RushDB 3.x: a self-hosted engine 3.0.10 or later, or RushDB Cloud.

Connect

Start a local engine (other ways to run it):

docker run -p 3000:3000 -e RUSHDB_TOKEN=change-me rushdb/rushdb:3 --languages sql,cypher

Then point the store at it, or at RushDB Cloud, through the environment:

# Self-hosted engine
export RUSHDB_ENGINE_URL=http://127.0.0.1:3000 RUSHDB_ENGINE_TOKEN=change-me
# and RUSHDB_SCOPE=<tenant> on a multitenant engine

# or RushDB Cloud
export RUSHDB_API_KEY=...

or pass a client yourself:

from rushdb import RushDB
from llama_index.graph_stores.rushdb import RushDBPropertyGraphStore

graph_store = RushDBPropertyGraphStore(
    RushDB("change-me", direct=True, engine_url="http://127.0.0.1:3000")
)

Quick start

from llama_index.core import PropertyGraphIndex, SimpleDirectoryReader
from llama_index.core.indices.property_graph import SchemaLLMPathExtractor
from llama_index.embeddings.openai import OpenAIEmbedding
from llama_index.llms.openai import OpenAI
from llama_index.graph_stores.rushdb import RushDBPropertyGraphStore

graph_store = RushDBPropertyGraphStore()  # RUSHDB_* from the environment
llm = OpenAI(model="gpt-4o-mini")

index = PropertyGraphIndex.from_documents(
    SimpleDirectoryReader("./data").load_data(),
    property_graph_store=graph_store,
    kg_extractors=[
        SchemaLLMPathExtractor(
            llm=llm,
            possible_entities=["PERSON", "COMPANY", "PRODUCT"],
            possible_relations=["WORKS_AT", "FOUNDED", "MAKES"],
        )
    ],
    embed_model=OpenAIEmbedding(model_name="text-embedding-3-small"),
)

print(index.as_query_engine(llm=llm).query("Who founded the companies that make our products?"))

examples/quickstart.py is the same flow as a script.

How the graph is stored

Every record the store writes carries _kg_kind (entity or chunk), so the graph can share a RushDB project with other data. Nodes are matched by id: upserting a node updates it in place, and a node that first appeared only as a relation endpoint (label entity) takes its real label when the node itself arrives.

Retrievers

from llama_index.core.indices.property_graph import VectorContextRetriever

retriever = index.as_retriever(
    sub_retrievers=[VectorContextRetriever(graph_store, embed_model=embed_model, path_depth=2)]
)

vector_query supports metadata filters with ==, !=, >, >=, <, <=, in, nin, contains (an element of a list), text_match (substring) and is_empty, combined with and, or and not.

Notes

• A node has one label. Upserting a node under a different label relabels it.
• A relation is created once per (source, type, target); upserting it again keeps the first properties, as the Neo4j store does.
• Objects in properties, and lists of objects, are stored as JSON text (RushDB would otherwise ingest a nested object as a related record). ISO 8601 date strings are stored as datetimes.
• Deleting a node removes its relationships; its neighbours stay.
• persist() does nothing: the graph is already in RushDB.

Development

uv sync
scripts/start-engines.sh   # two local engines in Docker: single-tenant and multitenant
export RUSHDB_TEST_ENGINE_URL=http://127.0.0.1:3917 RUSHDB_TEST_ENGINE_TOKEN=test-token
export RUSHDB_TEST_MT_ENGINE_URL=http://127.0.0.1:3918 RUSHDB_TEST_MT_ENGINE_TOKEN=test-token
uv run pytest
uv run ruff check . && uv run ruff format --check . && uv run mypy llama_index

The integration tests compare the store with LlamaIndex's in-memory SimplePropertyGraphStore where the two define the same behaviour, and run a PropertyGraphIndex end to end with a deterministic extractor. CI runs them on both self-hosted engines, on Python 3.10 and 3.13 and on the lowest supported dependency versions. Set RUSHDB_TEST_API_KEY to a RushDB Cloud project used only for testing to run them there too: the tests delete the store's records in it.

License

Apache-2.0