fraq 0.2.3

Fractal Query Data Library — model data as infinite, self-similar fractal structures

fraq — Fractal Query Data Library

Model data as infinite, self-similar fractal structures in hyperspace. Each zoom level reveals procedurally generated detail — data exists only virtually and materialises on demand via lazy evaluation.

Core Idea

Imagine a schema that is itself an infinite fractal. You can zoom into any region and receive an unbounded amount of deterministic data in any format you choose (JSON, CSV, YAML, binary). The data doesn't live in memory or on disk — it is generated on the fly from the fractal coordinates and a deterministic seed.

root (depth 0)
 ├── zoom (1,0,0) → child A (depth 1)
 │    ├── zoom (1,0,0) → grandchild AA (depth 2) …
 │    └── zoom (0,1,0) → grandchild AB (depth 2) …
 └── zoom (0,0,1) → child B (depth 1)
      └── … infinite …

Key Properties

Property	Description
Deterministic	Same path → same data, always
Lazy	Nodes materialise only when accessed
Unbounded	No depth limit — zoom forever
Format-agnostic	JSON, CSV, YAML, binary, msgpack-lite, or register your own
Source-agnostic	File, HTTP, SQL, Sensor, Hybrid — same query interface
Schema-exportable	NLP2CMD, OpenAPI, GraphQL, AsyncAPI, gRPC, JSON Schema
Zero storage	Data exists only as computation

Quick Start

pip install -e ".[dev]"

# CLI
fraq explore --dims 3 --depth 5 --format json
fraq stream  --dims 2 --count 20 --format csv
fraq schema  --fields "name:str,value:float,flag:bool" --depth 2

# Docker
docker compose run test          # run full test suite
docker compose run cli explore --depth 5

Python API

from fraq import FraqNode, FraqSchema, FraqCursor, FormatRegistry, query

# 1. One-liner query
data = query(depth=2, fields=["temp:float", "id:str"], format="csv", limit=10)

# 2. Fluent query builder
from fraq import FraqQuery, FraqExecutor

q = (
    FraqQuery()
    .zoom(5, direction=(0.1, 0.2, 0.7))
    .select("temperature:float", "sensor_id:str", "active:bool")
    .where("temperature", "gt", 0.5)
    .output("json")
    .take(20)
)
result = FraqExecutor(dims=3).execute(q)

# 3. Source adapters — same query, different sources
from fraq import FileAdapter, SQLAdapter, SensorAdapter, HybridAdapter

# Disk
adapter = FileAdapter()
root = adapter.load_root("gradient_root.json")

# SQL
adapter = SQLAdapter(table="sensors")
root = adapter.load_root("", rows=[{"id": 1, "x": 0.0, "y": 0.0, "z": 0.0}])

# Sensors (infinite stream, zero storage)
adapter = SensorAdapter(base_temp=23.5)
for reading in adapter.stream(depth=3, count=100):
    print(reading)  # {'temperature': 24.1, 'humidity': 58.3, ...}

# Hybrid (merge multiple sources)
hybrid = HybridAdapter()
hybrid.add(FileAdapter(), "local_backup.json")
hybrid.add(SensorAdapter(), "")
merged = hybrid.load_root()

# 4. Async streaming (FastAPI SSE, Kafka, NATS)
from fraq.streaming import async_stream
async for record in async_stream(count=1000, interval=0.1):
    yield f"data: {json.dumps(record)}\n\n"

NLP2CMD Integration

Export FraqSchema to NLP2CMD's SchemaRegistry for natural language → command transformation:

from fraq import FraqNode, FraqSchema, to_nlp2cmd_schema, to_nlp2cmd_actions

schema = FraqSchema(root=FraqNode(position=(0.0, 0.0, 0.0)))
schema.add_field("temperature", "float")
schema.add_field("sensor_id", "str")

# Command schema → command_schemas/fraq_sensor.json
nlp2cmd_schema = to_nlp2cmd_schema(schema, command_name="fraq_sensor")

# Action registry entries
actions = to_nlp2cmd_actions(schema)
# → fraq_zoom, fraq_query, fraq_stream, fraq_save

# NLP2CMD can now transform:
#   "Show active sensors" → fraq query --fields "temperature:float,sensor_id:str" --depth 2

Schema Export Formats

from fraq import to_openapi, to_graphql, to_asyncapi, to_proto, to_json_schema

to_openapi(schema)     # OpenAPI 3.0 — /zoom, /query, /stream endpoints
to_graphql(schema)     # GraphQL type + Query definitions
to_asyncapi(schema)    # AsyncAPI 3.0 — Kafka/WebSocket channels
to_proto(schema)       # gRPC .proto with FraqService (Zoom + Stream RPCs)
to_json_schema(schema) # JSON Schema for validation

Generators

Generator	Output	Use Case
HashGenerator	float in configurable range	General-purpose pseudo-random
FibonacciGenerator	float based on depth	Mathematical sequences
PerlinGenerator	Smooth float noise	Organic sensor streams
SensorStreamGenerator	dict with IoT fields	Embedded / edge simulation

Data Source Adapters

Adapter	Source	Key Feature
FileAdapter	JSON/YAML/CSV on disk	Save/load roundtrip
HTTPAdapter	REST APIs	Fallback to deterministic roots
SQLAdapter	PostgreSQL/SQLite	Row mapping, SQL function generation
SensorAdapter	IoT streams (RPi/ESP32)	Infinite deterministic readings
HybridAdapter	Multiple sources merged	Mean position, XOR seeds

text2fraq — Natural Language to Fractal Query

Convert natural language to fractal queries using LLM (LiteLLM) or rule-based fallback.

Setup (Ollama with small models)

# Install Ollama: https://ollama.com
ollama pull qwen2.5:3b      # Fast, instruction-tuned
ollama pull llama3.2:3b     # Balanced, multilingual
ollama pull phi3:3.8b       # Strong reasoning

# Install with AI extras
pip install -e ".[ai]"

# Copy and edit config
cp .env.example .env

Usage

from fraq import text2fraq, Text2Fraq, Text2FraqSimple

# One-liner (auto-fallback to rule-based if LLM unavailable)
result = text2fraq("Show 20 temperature readings in CSV")

# With specific model
from fraq.text2fraq import Text2FraqConfig
t2f = Text2Fraq(Text2FraqConfig(model="qwen2.5:3b"))
parsed = t2f.parse("Get deep analysis with depth 5")

# Rule-based (deterministic, no LLM needed)
parser = Text2FraqSimple()
query = parser.parse("Stream sensor data as JSON")

Environment Variables (.env)

Variable	Default	Description
LITELLM_PROVIDER	ollama	LLM provider
LITELLM_MODEL	qwen2.5:3b	Model name
LITELLM_BASE_URL	http://localhost:11434	API endpoint
LITELLM_TEMPERATURE	0.1	Generation temperature
TEXT2FRAQ_DEFAULT_FORMAT	json	Default output format
TEXT2FRAQ_DEFAULT_DIMS	3	Default fractal dimensions
TEXT2FRAQ_DEFAULT_DEPTH	3	Default query depth

Application Integrations

See examples/app_integrations.py for templates:

• FastAPI — REST API with /query, /stream (SSE), /zoom/{depth}
• Streamlit — Interactive dashboard with sliders and charts
• Flask — Blueprints with NL endpoints
• WebSocket — Real-time streaming server
• Kafka — Producer/consumer with aiokafka
• gRPC — High-performance RPC service
• Celery — Background task processing
• Jupyter — Interactive exploration widgets

Testing

pytest -v --cov=fraq    # 132 tests, 96% coverage

Project Structure

fraq/
├── fraq/
│   ├── __init__.py          # public API
│   ├── core.py              # FraqNode, FraqSchema, FraqCursor
│   ├── formats.py           # FormatRegistry + 6 built-in serialisers
│   ├── generators.py        # Hash, Fibonacci, Perlin, SensorStream
│   ├── query.py             # FraqQuery, FraqExecutor, FraqFilter
│   ├── adapters.py          # File, HTTP, SQL, Sensor, Hybrid adapters
│   ├── schema_export.py     # NLP2CMD, OpenAPI, GraphQL, AsyncAPI, Proto, JSON Schema
│   ├── streaming.py         # AsyncFraqStream, async_query, async_stream
│   └── cli.py               # CLI entry point
├── tests/                   # test suite
├── examples/
│   ├── query_examples.py    # All data sources (disk, HTTP, SQL, sensor, hybrid)
│   ├── nlp2cmd_integration.py  # NLP2CMD schema workflow
│   ├── applications.py      # IoT, ERP, AI/ML, DevOps, Finance, Legal
│   ├── app_integrations.py  # FastAPI, Flask, Streamlit, Kafka, gRPC, Celery
│   ├── text2fraq_examples.py # LiteLLM + Ollama + small local model examples
│   └── async_streaming.py   # FastAPI SSE, Kafka patterns
├── .env.example             # LiteLLM and text2fraq configuration template
├── Dockerfile
├── docker-compose.yml
├── pyproject.toml
└── README.md

License

Apache License 2.0 - see LICENSE for details.

Author

Created by Tom Sapletta - [e-mail]