gandalf-csr 0.3.2

Fast path finding in large knowledge graphs

GANDALF

Graph Analysis Navigator for Discovery And Link Finding

Features

• Compressed Sparse Row (CSR) graph representation for memory efficiency
• Bidirectional search for optimal performance
• O(1) property lookups via hash indexing
• Predicate filtering to reduce path explosion
• Batch property enrichment for fast results
• Diagnostic tools to understand path counts

Installation

Recommended: Use a virtual environment

Some transitive dependencies (e.g., stringcase, pytest-logging) require modern pip/setuptools to build correctly. Using a virtual environment ensures you have updated tools.

# Create and activate a virtual environment
python -m venv venv
source venv/bin/activate  # On Windows: venv\Scripts\activate

# Upgrade pip and setuptools (important for building dependencies)
pip install --upgrade pip setuptools wheel

# Install the package
pip install -e .

Alternative: Direct install (may fail on some systems)

If you have a recent pip/setuptools already, you can try:

pip install -e .

Quick Start

Unzipping a full translator kgx

• tar -xvf translator_kg.tar.zst This will output a nodes.jsonl and edges.jsonl file

Build a graph from JSONL

from gandalf import build_graph_from_jsonl

# Build with ontology filtering
graph = build_graph_from_jsonl(
    edges_path="data/raw/edges.jsonl",
    nodes_path="data/raw/nodes.jsonl",
    excluded_predicates={'biolink:subclass_of'}
)

# Save for fast loading
graph.save_mmap("data/processed/graph_filtered")

Query paths

from gandalf import CSRGraph, find_paths

# Load graph (takes ~1-2 seconds)
graph = CSRGraph.load_mmap("data/processed/graph")

# Find paths
paths = find_paths(
    graph,
    start_id="CHEBI:45783",
    end_id="MONDO:0004979"
)

print(f"Found {len(paths)} paths")

Filter by predicates

from gandalf import find_paths_filtered

# Only mechanistic relationships
paths = find_paths_filtered(
    graph,
    start_id="CHEBI:45783",
    end_id="MONDO:0004979",
    allowed_predicates={
        'biolink:treats',
        'biolink:affects',
        'biolink:has_metabolite'
    }
)

Architecture

The package uses a three-stage pipeline:

1. Topology Search (fast) - Find all paths using indices only
2. Filtering (medium) - Apply business logic on necessary node or edge properties
3. Enrichment (batch) - Load all properties for final paths only

This separation allows filtering millions of paths before expensive property lookups.

Configuration

The server is configured via environment variables:

Variable	Default	Description
GANDALF_GRAPH_PATH	../12_17_2025/gandalf_mmap	Path to the mmap graph directory
GANDALF_GRAPH_FORMAT	auto	Graph format (auto or mmap)
GANDALF_LOG_LEVEL	INFO	Logging level (DEBUG, INFO, WARNING, ERROR)
GANDALF_LOG_FORMAT	text	Log format (text for human-readable, json for structured)
GANDALF_CORS_ORIGINS	*	Comma-separated list of allowed CORS origins
GANDALF_MAX_REQUEST_SIZE_MB	10	Maximum request body size in MB
GANDALF_RATE_LIMIT	100	Maximum requests per minute per client IP (0 to disable)

Docker

# Build the image
docker build -t gandalf .

# Run with a graph volume
docker run -p 6429:6429 \
  -v /path/to/graph:/data/graph \
  -e GANDALF_GRAPH_PATH=/data/graph \
  gandalf

Health Check

curl http://localhost:6429/health
# {"status": "ok", "graph_loaded": true, "node_count": 38456123, "edge_count": 127843456}

Releases

Run this on the mmap folder:

• tar -czvf gandalf_mmap_<date>.tar.gz gandalf_mmap