arangodb-schema-analyzer 0.4.0

Agentic schema analyzer for ArangoDB: conceptual model + conceptual-to-physical mapping for transpilers.

arangodb-schema-analyzer (v0.1)

Standalone Python library that analyzes an ArangoDB database's physical schema and produces:

• a conceptual schema (entities, relationships, properties)
• a conceptual→physical mapping suitable for transpilers (Cypher, SPARQL, future)
• metadata (confidence, timestamp, analyzed collection counts, detected patterns)

Install

From source (this repo):

python -m pip install -e .

Optional LLM provider extras:

python -m pip install -e ".[openai]"
python -m pip install -e ".[anthropic]"

OpenRouter is also supported and requires no extra SDK (uses stdlib urllib).

MCP (Model Context Protocol) — optional stdio server wrapping the v1 JSON tool contract:

python -m pip install -e ".[mcp]"
arangodb-schema-analyzer-mcp

If you don't install a provider SDK (or you don't provide an API key), analysis degrades gracefully to deterministic baseline inference.

Usage

from arango import ArangoClient

from schema_analyzer import AgenticSchemaAnalyzer

client = ArangoClient(hosts="http://localhost:8529")
db = client.db("mydb", username="root", password="openSesame")

analyzer = AgenticSchemaAnalyzer(
    llm_provider="openai",  # or "anthropic" or "openrouter"
    api_key=None,           # e.g. os.environ["OPENAI_API_KEY"]
    model="gpt-4o-mini",
    cache={"type": "filesystem", "directory": ".schema-analyzer-cache"},
)

analysis = analyzer.analyze_physical_schema(
    db,
    timeout_ms=60_000,
    sample_limit_per_collection=5,
)

print(analysis.metadata.confidence)

Tool usage (CLI)

This project can be called as a non-interactive tool (stdin JSON → stdout JSON) using the v1 contract under docs/tool-contract/v1/.

Install (editable):

python -m pip install -e .

Example (analyze) using the provided request example:

cat docs/tool-contract/v1/examples/request.analyze.json | arangodb-schema-analyzer --pretty

CLI options

arangodb-schema-analyzer [--request FILE] [--out FILE] [--pretty] [-v]

• --request FILE — path to request JSON (default: read from stdin)
• --out FILE — write response JSON to file (default: stdout)
• --pretty — pretty-print JSON output
• -v — enable verbose logging

Evaluation CLI

Run analysis quality benchmarks against domain packs:

arangodb-schema-analyzer eval \
  --provider openai \
  --model gpt-4o-mini \
  --report eval_report.json \
  --baseline eval_baseline.json

Options: --url, --user, --password, --database, --domains, --sample-limit, --timeout-ms, --scale, --no-cleanup.

Domains included: healthcare, financial_fraud_detection, insurance, intelligence, network_asset_management.

Public API

Exports:

• AgenticSchemaAnalyzer — main analyzer class
• ConceptualSchema — conceptual schema dataclass
• PhysicalMapping — physical mapping dataclass with AQL helpers
• generate_schema_docs(analysis) — Markdown documentation generator
• export_mapping(analysis, target) — transpiler export (v0.1: cypher)
• export_conceptual_model_as_owl_turtle(analysis) — OWL Turtle export
• register_provider(name, ...) — register custom LLM providers
• list_providers() — list registered LLM provider names

Configuration

Tunable defaults live in schema_analyzer/defaults.py. Key parameters:

Parameter	Default	Description
MAX_REPAIR_ATTEMPTS	2	LLM repair loop iterations
LLM_TEMPERATURE	0.0	Sampling temperature
DEFAULT_TIMEOUT_MS	60000	Analysis timeout (ms)
DEFAULT_REVIEW_THRESHOLD	0.6	Confidence threshold for review_required
DEFAULT_CACHE_TTL_SECONDS	86400	Cache TTL (seconds)

Notes

• Secrets: API keys are read from config/env; never persisted by this library.
• AQL fragments: helper methods return AQL text + bind variables; collection names are passed via bind parameters.
• Graceful degradation: without an LLM provider, the analyzer returns deterministic baseline inference with review_required=True.

Integration evaluation (Docker ArangoDB)

Bring up a local ArangoDB:

docker compose up -d

Run integration tests (opt-in):

export RUN_INTEGRATION=1
export ARANGO_URL=http://localhost:18529
export ARANGO_DB=schema_analyzer_it
export ARANGO_USER=root
export ARANGO_PASS=openSesame
pytest -q -m integration