The Enterprise Knowledge Graph
Engineered for Zero Operational Drag

-- LineagIQ Recursive Downstream Lineage Traversal
WITH RECURSIVE downstream_traverse AS (
    -- Anchor: Immediate downstream dependencies of target node
    SELECT source_id, target_id, type, 1 as depth
    FROM delta_edges_view
    WHERE source_id = 'postgres.raw.raw_customers'
      AND type != 'BELONGS_TO'

    UNION ALL

    -- Recursive Step: Multi-hop graph expansion up to max_depth
    SELECT e.source_id, e.target_id, e.type, d.depth + 1
    FROM delta_edges_view e
    JOIN downstream_traverse d ON e.source_id = d.target_id
    WHERE d.depth < 5 AND e.type != 'BELONGS_TO'
)
SELECT DISTINCT source_id, target_id, type, depth
FROM downstream_traverse;

from openai import OpenAI
from control_plane.src.agent_tools import (
    get_dataset_blast_radius,
    get_lineage_time_travel_diff
)

# 1. Retrieve Graph Context & Synthesized GraphRAG Prompt (configured via DATA_PATH)
blast_radius_prompt = get_dataset_blast_radius(
    node_id="postgres.raw.raw_customers",
    max_depth=5
)

# 2. Dispatch Context to LLM of your choice (OpenAI, Gemini, or local Ollama)
client = OpenAI()
response = client.chat.completions.create(
    model="gpt-4o",
    messages=[
        {"role": "system", "content": "You are LineagIQ AI Agent, an expert enterprise data architect."},
        {"role": "user", "content": blast_radius_prompt}
    ]
)

print(response.choices[0].message.content)

# 1. Generate 3-Version Time-Travel Demo Dataset with Public Image
docker run --rm \
  -v $(pwd)/data:/data \
  ghcr.io/timor-dataworks/lineagiq-collection-agent:latest \
  --multiversion-demo

# 2. Launch Serverless Control Plane & Web Visualizer (Port 8000)
docker run -d --name lineagiq-control-plane \
  -p 8000:8000 \
  -e DATA_PATH=/data \
  -v $(pwd)/data:/data \
  ghcr.io/timor-dataworks/lineagiq-control-plane:latest

# Or launch entire stack via Docker Compose:
docker compose up -d control_plane