coordinode 0.5.0

Python client for CoordiNode — graph + vector + full-text database

coordinode

Python gRPC client for CoordiNode — the graph-native hybrid retrieval engine for AI and GraphRAG.

Installation

pip install coordinode

uv add coordinode

Requirements

• Python 3.11+
• Running CoordiNode instance (docker compose up -d or binary)

Quick Start

from coordinode import CoordinodeClient

# Synchronous client — context manager handles connection lifecycle
with CoordinodeClient("localhost:7080") as db:
    # Cypher query — returns List[Dict[str, Any]]
    result = db.cypher("RETURN 1 AS n")
    print(result)  # [{'n': 1}]

    # With parameters
    rows = db.cypher(
        "MATCH (n:Person {name: $name}) RETURN n.age AS age",
        params={"name": "Alice"},
    )

    # Create nodes
    db.cypher(
        "CREATE (n:Document {title: $title, embedding: $vec})",
        params={"title": "RAG intro", "vec": [0.1, 0.2, 0.3, 0.4]},
    )

    # Health check
    assert db.health()

Async Client

import asyncio
from coordinode import AsyncCoordinodeClient

async def main():
    async with AsyncCoordinodeClient("localhost:7080") as db:
        rows = await db.cypher("MATCH (n:Concept) RETURN n.name AS name LIMIT 5")
        for row in rows:
            print(row["name"])

asyncio.run(main())

Connection Options

# host:port string
client = CoordinodeClient("localhost:7080")

# Separate host and port
client = CoordinodeClient("localhost", port=7080)

# TLS
client = CoordinodeClient("db.example.com:7443", tls=True)

# Custom timeout (seconds)
client = CoordinodeClient("localhost:7080", timeout=60.0)

Type Mapping

CoordiNode properties map to Python types automatically:

Python type	CoordiNode type
int	int_value
float	float_value
str	string_value
bool	bool_value
bytes	bytes_value
list[float]	Vector (HNSW-indexable)
list[Any]	PropertyList
dict[str, Any]	PropertyMap
None	unset (null semantics)

Vector Search

# Store a node with a vector embedding
db.cypher(
    "CREATE (d:Doc {title: $title, embedding: $vec})",
    params={"title": "RAG intro", "vec": [0.1] * 384},
)

# Nearest-neighbour search (requires HNSW index — coming in v0.4)
results = db.vector_search(
    label="Doc",
    property="embedding",
    vector=[0.1] * 384,
    top_k=10,
    metric="cosine",  # "cosine" | "l2" | "dot" | "l1"
)
for r in results:
    print(r.node.id, r.distance)

Related Packages

Package	Description
langchain-coordinode	LangChain GraphStore + GraphCypherQAChain
llama-index-graph-stores-coordinode	LlamaIndex PropertyGraphStore

Links

• Source
• CoordiNode server
• Issues
• Changelog

Support

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License

Apache-2.0