Metadata-Version: 2.4
Name: graflo
Version: 1.7.17
Summary: A framework for transforming tabular (CSV, SQL) and hierarchical data (JSON, XML) into property graphs and ingesting them into graph databases (ArangoDB, Neo4j, TigerGraph, FalkorDB, Memgraph, NebulaGraph). Features automatic PostgreSQL schema inference.
Project-URL: Homepage, https://github.com/growgraph/graflo
Author-email: Alexander Belikov <alexander@growgraph.dev>
License-File: LICENSE
Classifier: Programming Language :: Python :: 3
Classifier: Programming Language :: Python :: 3.11
Classifier: Programming Language :: Python :: 3.12
Classifier: Programming Language :: Python :: Implementation :: CPython
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Description-Content-Type: text/markdown

# GraFlo — Graph Schema & Transformation Language (GSTL) <img src="https://raw.githubusercontent.com/growgraph/graflo/main/docs/assets/favicon.ico" alt="graflo logo" style="height: 32px; width:32px;"/>


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**GraFlo** is a manifest-driven toolkit for **labeled property graphs (LPGs)**: describe vertices, edges, and ingestion (`GraphManifest` — YAML or Python), then project and load into a target graph database.

### What you get

- **One pipeline, several graph databases** — The same manifest targets ArangoDB, Neo4j, TigerGraph, FalkorDB, Memgraph, or NebulaGraph; `DatabaseProfile` and DB-aware types absorb naming, defaults, and indexing differences.
- **Explicit identities** — Vertex identity fields and indexes back upserts so reloads merge on keys instead of blindly duplicating nodes.
- **Reusable ingestion** — `Resource` actor pipelines (including **vertex_router** / **edge_router** steps) bind to files, SQL, SPARQL/RDF, APIs, or in-memory batches via `Bindings` and the `DataSourceRegistry`.

### What’s in the manifest

- **`schema`** — `Schema`: metadata, **`core_schema`** (vertices, edges, typed **`properties`**, identities), and **`db_profile`** (`DatabaseProfile`: target flavor, storage names, secondary indexes, TigerGraph `default_property_values`, …).
- **`ingestion_model`** — `IngestionModel`: named **`resources`** (actor sequences: *descend*, *transform*, *vertex*, *edge*, …) and a registry of reusable **`transforms`**.
- **`bindings`** — Connectors (e.g. `FileConnector`, `TableConnector`, `SparqlConnector`) plus **`resource_connector`** wiring. Optional **`connector_connection`** maps connectors to **`conn_proxy`** labels so YAML stays secret-free; a runtime **`ConnectionProvider`** supplies credentials.

### Runtime path

1. **Source instance** — Batches from a `DataSourceType` adapter (`FileDataSource`, `SQLDataSource`, `SparqlEndpointDataSource`, `APIDataSource`, …).
2. **Resource (actors)** — Maps records to graph elements against the logical schema (validated during `IngestionModel.finish_init` / pipeline execution).
3. **`GraphContainer`** — Intermediate, database-agnostic vertex/edge batches.
4. **DB-aware projection** — `Schema.resolve_db_aware()` plus `VertexConfigDBAware` / `EdgeConfigDBAware` for the active `DBType`.
5. **Graph DB** — `DBWriter` + `ConnectionManager` and the backend-specific `Connection` implementation.

| Piece | Role | Code |
|-------|------|------|
| **Logical graph schema** | Manifest `schema`: vertex/edge definitions, identities, typed **properties**, DB profile. Constrains pipeline output and projection; not a separate queue between steps. | `Schema`, `VertexConfig`, `EdgeConfig` (under `core_schema`). |
| **Source instance** | Concrete input: file, SQL table, SPARQL endpoint, API payload, in-memory rows. | `AbstractDataSource` + `DataSourceType`. |
| **Resource** | Ordered actors; resources are looked up by name when sources are registered. | `Resource` in `IngestionModel`. |
| **Covariant graph** (`GraphContainer`) | Batches of vertices/edges before load. | `GraphContainer`. |
| **DB-aware projection** | Physical names, defaults, indexes for the target. | `Schema.resolve_db_aware()`, `VertexConfigDBAware`, `EdgeConfigDBAware`. |
| **Graph DB** | Target LPG; each `DBType` has its own connector, orchestrated the same way. | `ConnectionManager`, `DBWriter`, per-backend `Connection`. |

### Supported source types (`DataSourceType`)

| DataSourceType | Connector | DataSource | Schema inference |
|---|---|---|---|
| `FILE` — CSV / JSON / JSONL / Parquet | `FileConnector` | `FileDataSource` | manual |
| `SQL` — relational tables (docs focus on PostgreSQL; other engines via SQLAlchemy where supported) | `TableConnector` | `SQLDataSource` | automatic for PostgreSQL-style 3NF (PK/FK heuristics) |
| `SPARQL` — RDF files (`.ttl`, `.rdf`, `.n3`) | `SparqlConnector` | `RdfFileDataSource` | automatic (OWL/RDFS ontology) |
| `SPARQL` — SPARQL endpoints (Fuseki, …) | `SparqlConnector` | `SparqlEndpointDataSource` | automatic (OWL/RDFS ontology) |
| `API` — REST APIs | — | `APIDataSource` | manual |
| `IN_MEMORY` — list / DataFrame | — | `InMemoryDataSource` | manual |

### Supported targets

The graph engines listed in **What you get** are the supported **output** `DBType` values in `graflo.onto`. Each backend uses its own `Connection` implementation under the shared `ConnectionManager` / `DBWriter` / `GraphEngine` flow.

## More capabilities

- **SPARQL & RDF** — Endpoints and RDF files (`.ttl`, `.rdf`, `.n3`, …); optional OWL/RDFS schema inference (`rdflib`, `SPARQLWrapper` in the default install).
- **Schema inference** — From PostgreSQL-style 3NF layouts (PK/FK heuristics) or from OWL/RDFS (`owl:Class` → vertices, `owl:ObjectProperty` → edges, `owl:DatatypeProperty` → vertex fields).
- **Schema migrations** — Plan and apply guarded schema deltas (`migrate_schema` console script → `graflo.cli.migrate_schema`; library in `graflo.migrate`; see docs).
- **Typed `properties`** — Optional field types (`INT`, `FLOAT`, `STRING`, `DATETIME`, `BOOL`) on vertices and edges.
- **Batching & concurrency** — Configurable batch sizes, worker counts, and DB write concurrency on `IngestionParams` / `DBWriter`.
- **`GraphEngine`** — High-level orchestration for infer, define schema, and ingest (`define_and_ingest`, …); `Caster` stays available for lower-level control.

## Documentation
Full documentation is available at: [growgraph.github.io/graflo](https://growgraph.github.io/graflo)

## Installation

```bash
pip install graflo
```

Optional extras (see `pyproject.toml` → `[project.optional-dependencies]`):

- `dev` — pytest, ty, pre-commit
- `docs` — MkDocs stack for building the documentation site
- `plot` — `pygraphviz` for the `plot_manifest` CLI (install system Graphviz first)

```bash
pip install "graflo[dev]"
pip install "graflo[dev,docs,plot]"
```

## Usage Examples

### Simple ingest

```python
from suthing import FileHandle

from graflo import Bindings, GraphManifest
from graflo.db.connection.onto import ArangoConfig

manifest = GraphManifest.from_config(FileHandle.load("schema.yaml"))
manifest.finish_init()
schema = manifest.require_schema()
ingestion_model = manifest.require_ingestion_model()

# Option 1: Load config from docker/arango/.env (recommended)
conn_conf = ArangoConfig.from_docker_env()

# Option 2: Load from environment variables
# Set: ARANGO_URI, ARANGO_USERNAME, ARANGO_PASSWORD, ARANGO_DATABASE
conn_conf = ArangoConfig.from_env()

# Option 3: Load with custom prefix (for multiple configs)
# Set: USER_ARANGO_URI, USER_ARANGO_USERNAME, USER_ARANGO_PASSWORD, USER_ARANGO_DATABASE
user_conn_conf = ArangoConfig.from_env(prefix="USER")

# Option 4: Create config directly
# conn_conf = ArangoConfig(
#     uri="http://localhost:8535",
#     username="root",
#     password="123",
#     database="mygraph",  # For ArangoDB, 'database' maps to schema/graph
# )
# Note: If 'database' (or 'schema_name' for TigerGraph) is not set,
# Caster will automatically use Schema.metadata.name as fallback

from graflo.architecture.contract.bindings import FileConnector
import pathlib

# Create Bindings with file connectors
bindings = Bindings()
work_connector = FileConnector(regex="\Sjson$", sub_path=pathlib.Path("./data"))
bindings.add_connector(
    work_connector,
)
bindings.bind_resource("work", work_connector)

# Or initialize via connectors + resource_connector
# bindings = Bindings(
#     connectors=[
#         FileConnector(
#             name="work_files",
#             regex="^work\\.json$",
#             sub_path=pathlib.Path("./data"),
#         )
#     ],
#     resource_connector=[{"resource": "work", "connector": "work_files"}],
#     # Optional: for SQL/SPARQL connectors, name a proxy; register secrets via ConnectionProvider.
#     # connector_connection=[{"connector": "work_files", "conn_proxy": "files_readonly"}],
# )

from graflo.hq.caster import IngestionParams
from graflo.hq import GraphEngine

# Option 1: Use GraphEngine for schema definition and ingestion (recommended)
engine = GraphEngine()
ingestion_params = IngestionParams(
    clear_data=False,
    # max_items=1000,  # Optional: limit number of items to process
    # batch_size=10000,  # Optional: customize batch size
)

ingest_manifest = manifest.model_copy(update={"bindings": bindings})
ingest_manifest.finish_init()

engine.define_and_ingest(
    manifest=ingest_manifest,
    target_db_config=conn_conf,  # Target database config
    ingestion_params=ingestion_params,
    recreate_schema=False,  # Set to True to drop and redefine schema (script halts if schema exists)
)

# Option 2: Use Caster directly (schema must be defined separately)
# from graflo.hq import GraphEngine
# engine = GraphEngine()
# engine.define_schema(manifest=manifest, target_db_config=conn_conf, recreate_schema=False)
# 
# caster = Caster(schema=schema, ingestion_model=ingestion_model)
# caster.ingest(
#     target_db_config=conn_conf,
#     bindings=bindings,
#     ingestion_params=ingestion_params,
# )
```

### PostgreSQL Schema Inference

```python
from graflo.hq import GraphEngine
from graflo.db.connection.onto import PostgresConfig, ArangoConfig
from graflo import Caster
from graflo.onto import DBType

# Connect to PostgreSQL
postgres_config = PostgresConfig.from_docker_env()  # or PostgresConfig.from_env()

# Create GraphEngine and infer schema from PostgreSQL 3NF database
# Connection is automatically managed inside infer_schema()
engine = GraphEngine(target_db_flavor=DBType.ARANGO)
manifest = engine.infer_manifest(
    postgres_config,
    schema_name="public",  # PostgreSQL schema name
)
schema = manifest.require_schema()
ingestion_model = manifest.require_ingestion_model()

# Define schema in target database (optional, can also use define_and_ingest)
target_config = ArangoConfig.from_docker_env()
engine.define_schema(
    manifest=manifest,
    target_db_config=target_config,
    recreate_schema=False,
)

# Use the inferred schema with Caster for ingestion
caster = Caster(schema=schema, ingestion_model=ingestion_model)
# ... continue with ingestion
```

### RDF / SPARQL Ingestion

```python
from pathlib import Path
from graflo.hq import GraphEngine
from graflo.db.connection.onto import ArangoConfig
from graflo.architecture.manifest import GraphManifest

engine = GraphEngine()

# Infer schema from an OWL/RDFS ontology file
ontology = Path("ontology.ttl")
schema, ingestion_model = engine.infer_schema_from_rdf(source=ontology)

# Create source bindings (reads a local .ttl file per rdf:Class)
bindings = engine.create_bindings_from_rdf(source=ontology)

# Or point at a SPARQL endpoint instead:
# from graflo.db.connection.onto import SparqlEndpointConfig
# sparql_cfg = SparqlEndpointConfig(uri="http://localhost:3030", dataset="mydata")
# bindings = engine.create_bindings_from_rdf(
#     source=ontology,
#     endpoint_url=sparql_cfg.query_endpoint,
# )

target = ArangoConfig.from_docker_env()
engine.define_and_ingest(
    manifest=GraphManifest(
        graph_schema=schema,
        ingestion_model=ingestion_model,
        bindings=bindings,
    ),
    target_db_config=target,
)
```

## Development

To install requirements

```shell
git clone git@github.com:growgraph/graflo.git && cd graflo
uv sync --extra dev
```

### Tests

#### Test databases

**Quick Start:** To start all test databases at once, use the convenience scripts from the [docker folder](./docker):

```shell
cd docker
./start-all.sh    # Start all services
./stop-all.sh      # Stop all services
./cleanup-all.sh   # Remove containers and volumes
```

**Individual Services:** To start individual databases, navigate to each database folder and run:

Spin up Arango from [arango docker folder](./docker/arango) by

```shell
docker-compose --env-file .env up arango
```

Neo4j from [neo4j docker folder](./docker/neo4j) by

```shell
docker-compose --env-file .env up neo4j
```

TigerGraph from [tigergraph docker folder](./docker/tigergraph) by

```shell
docker-compose --env-file .env up tigergraph
```

FalkorDB from [falkordb docker folder](./docker/falkordb) by

```shell
docker-compose --env-file .env up falkordb
```

Memgraph from [memgraph docker folder](./docker/memgraph) by

```shell
docker-compose --env-file .env up memgraph
```

NebulaGraph from [nebula docker folder](./docker/nebula) by

```shell
docker-compose --env-file .env up
```

and Apache Fuseki from [fuseki docker folder](./docker/fuseki) by

```shell
docker-compose --env-file .env up fuseki
```

To run unit tests

```shell
uv run pytest test
```

> **Note**: Tests require external database containers (ArangoDB, Neo4j, TigerGraph, FalkorDB, Memgraph, NebulaGraph, Fuseki) to be running. CI builds intentionally skip test execution. Tests must be run locally with the required database images started (see [Test databases](#test-databases) section above). NebulaGraph tests are gated behind `pytest --run-nebula`.

## Requirements

- Python 3.11+ (Python 3.11 and 3.12 are officially supported)
- python-arango
- nebula3-python>=3.8.3 (NebulaGraph v3.x support)
- nebula5-python>=5.2.1 (NebulaGraph v5.x support)
- sqlalchemy>=2.0.0 (for PostgreSQL and SQL data sources)
- rdflib>=7.0.0 + SPARQLWrapper>=2.0.0 (included in the default install)

## Contributing

Contributions are welcome! Please feel free to submit a Pull Request.