forked from diegoecab/oci-deal-accelerator
Diagram generation: ref-arch-driven procedure + spec validator + KB enrichment
The diagram path now follows a documented standard procedure (lookup the closest Oracle Architecture Center reference → confirm components → author absolute_layout → spec validator → render → visually verify) and ships persistent guardrails so layout regressions can't recur. Persistent procedure changes (apply to all users, all sessions): - tools/diagram_spec_validator.py — geometry checks (CONTAINER_TOO_THIN, CONTAINER_PADDING_VIOLATION, LABEL_OVERFLOW_PARENT) run BEFORE either renderer (drawio + PPTX). Catches the subnet-collapse / label-overflow bugs that the post-render drawio validator missed. - tools/oci_diagram_gen.py + tools/oci_pptx_diagram_gen.py — call the spec validator before emitting any output. Adds mysql / mysql_heatwave type aliases. - tools/archcenter_pattern_lookup.py — scores against cached page descriptions (not just the 1-line summary), supports --queries for multi-fragment composition, and applies synonym expansion via kb/architecture-center/synonyms.yaml so "LB HA cross AD" matches "load balancer high availability availability domain". - kb/architecture-center/synonyms.yaml — canonical synonym table (load balancer, autonomous database, data guard, …) used by the lookup scorer. KB enrichment: - tools/archcenter_description_fetcher.py + 121 cached _description.md under kb/diagram/assets/archcenter-refs/<slug>/. Removes the runtime dependency on docs.oracle.com when authoring specs and feeds the pattern-lookup scorer. - 110+ cached .drawio / .svg / .png references for offline reuse, plus the OCI Toolkit v24.2 import (kb/diagram/assets/oci-toolkit-drawio). Documentation: - docs/skill/output-formats.md — new "Standard diagram-generation procedure (MANDATORY)" + geometry rules + the new validator entry. - SKILL.md option 2 — references the mandatory procedure. - README.md — describes the spec validator, archcenter_pattern_lookup and description fetcher, and updates the KB-health table. Tooling that backs the procedure (cumulative across recent sessions): tools/archcenter_case_runner.py, archcenter_batch_driver.py, archcenter_zip_downloader.py, drawio_visual_validator.py, drawio_fidelity_eval.py, harvest_drawio_icon.py, import_oci_library.py, oci_pptx_diagram_gen.py, oci_pptx_render.py, refresh_pptx_icon_index.py. Co-Authored-By: Claude Opus 4.7 (1M context) <noreply@anthropic.com>
This commit is contained in:
root
96
scripts/analyze_mcp_outputs.py
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96
scripts/analyze_mcp_outputs.py
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#!/usr/bin/env python3
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"""Download MCP-generated artifacts and compare them against local analysis."""
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from __future__ import annotations
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import argparse
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import json
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import sys
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import urllib.request
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from pathlib import Path
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PROJECT_ROOT = Path(__file__).resolve().parent.parent
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SCRIPT_DIR = Path(__file__).resolve().parent
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if str(SCRIPT_DIR) not in sys.path:
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sys.path.insert(0, str(SCRIPT_DIR))
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from random_eval_cases import analyze_drawio, analyze_pptx, analyze_xlsx
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def download(url: str, path: Path):
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path.parent.mkdir(parents=True, exist_ok=True)
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urllib.request.urlretrieve(url, path)
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def main():
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parser = argparse.ArgumentParser(description="Analyze MCP artifacts against local baseline.")
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parser.add_argument("--out-dir", required=True)
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parser.add_argument("--local-analysis", required=True)
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parser.add_argument("--deck-url", required=True)
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parser.add_argument("--diagram-url", required=True)
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parser.add_argument("--bizcase-url", required=True)
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parser.add_argument("--bom-url", required=True)
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parser.add_argument("--appca-url", required=True)
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args = parser.parse_args()
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out_dir = PROJECT_ROOT / args.out_dir
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out_dir.mkdir(parents=True, exist_ok=True)
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files = {
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"deck": ("mcp-deck.pptx", args.deck_url, analyze_pptx),
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"diagram": ("mcp-diagram.drawio", args.diagram_url, analyze_drawio),
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"business_case": ("mcp-bizcase.pptx", args.bizcase_url, analyze_pptx),
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"bom": ("mcp-bom.xlsx", args.bom_url, analyze_xlsx),
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"bom_appca": ("mcp-bom-appca.xlsx", args.appca_url, analyze_xlsx),
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}
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analysis = {}
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for key, (filename, url, analyzer) in files.items():
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path = out_dir / filename
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download(url, path)
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analysis[key] = analyzer(path)
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local = json.loads((PROJECT_ROOT / args.local_analysis).read_text(encoding="utf-8"))
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local_analysis = local["local_analysis"]
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comparison = {
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"deck": {
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"local_slides": local_analysis["deck"]["slide_count"],
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"mcp_slides": analysis["deck"]["slide_count"],
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"local_blank_slides": local_analysis["deck"]["blank_slides"],
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"mcp_blank_slides": analysis["deck"]["blank_slides"],
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},
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"business_case": {
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"local_slides": local_analysis["business_case"]["slide_count"],
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"mcp_slides": analysis["business_case"]["slide_count"],
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"local_blank_slides": local_analysis["business_case"]["blank_slides"],
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"mcp_blank_slides": analysis["business_case"]["blank_slides"],
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},
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"bom": {
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"local_empty_sheets": local_analysis["bom"]["empty_sheets"],
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"mcp_empty_sheets": analysis["bom"]["empty_sheets"],
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},
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"bom_appca": {
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"local_empty_sheets": local_analysis["bom_appca"]["empty_sheets"],
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"mcp_empty_sheets": analysis["bom_appca"]["empty_sheets"],
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},
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"diagram": {
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"local_bytes": local_analysis["diagram"]["bytes"],
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"mcp_bytes": analysis["diagram"]["bytes"],
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"local_cell_values": local_analysis["diagram"]["cell_values"],
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"mcp_cell_values": analysis["diagram"]["cell_values"],
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},
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}
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output = {
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"mcp_analysis": analysis,
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"comparison": comparison,
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}
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output_path = out_dir / "mcp-analysis.json"
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output_path.write_text(json.dumps(output, indent=2), encoding="utf-8")
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print(output_path.relative_to(PROJECT_ROOT))
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if __name__ == "__main__":
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main()
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438
scripts/random_eval_cases.py
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438
scripts/random_eval_cases.py
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#!/usr/bin/env python3
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"""
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Generate reproducible random evaluation cases plus local artifacts.
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This script exists to stress the skill with sparse-but-valid prompts:
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- Creates a Spanish discovery prompt
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- Derives minimal local specs/payloads for deck, diagram, business case, and BOM
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- Renders local artifacts (deck, drawio, business case, BOM, AppCA BOM, PDF)
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- Produces lightweight artifact quality metrics for later MCP comparison
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"""
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from __future__ import annotations
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import argparse
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import json
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import random
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import re
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import sys
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from pathlib import Path
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from zipfile import ZipFile
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import yaml
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from openpyxl import load_workbook
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from pptx import Presentation
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PROJECT_ROOT = Path(__file__).resolve().parent.parent
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TOOLS_DIR = PROJECT_ROOT / "tools"
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if str(TOOLS_DIR) not in sys.path:
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sys.path.insert(0, str(TOOLS_DIR))
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from oci_bizcase_gen import BusinessCaseDeckGenerator
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from oci_bom_gen import OCIBomGenerator
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from oci_deck_gen import OCIDeckGenerator, _enrich_partial_proposal_spec
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from oci_diagram_gen import OCIDiagramGenerator
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from oci_pdf_gen import OCIPDFGenerator
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INDUSTRIES = [
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("banca", "banking regulator", "PCI-DSS"),
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("retail", "consumer peak events", "PCI-DSS"),
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("salud", "patient data controls", "HIPAA-aligned controls"),
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("manufactura", "plant downtime sensitivity", "ISO 27001"),
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("logística", "cross-border operations", "SOC 2"),
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]
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TEMPLATES = [
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{
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"id": "adbs",
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"workload": "e-commerce platform",
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"current": "dos bases Oracle 19c on-prem y VMs para web",
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"target": "ADB-S para transaccional, compute flexible para app y Object Storage para backups",
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"services": [
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{"name": "ADB-S", "sku": "B95701", "kind": "database"},
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{"name": "ADB Storage", "sku": "B95706", "kind": "database_storage"},
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{"name": "ADB Backup", "sku": "B95754a", "kind": "database_storage"},
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{"name": "Block Volume", "sku": "B91961", "kind": "storage"},
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{"name": "FastConnect 1 Gbps", "sku": "B88325", "kind": "network"},
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],
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},
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{
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"id": "exacs",
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"workload": "core banking database platform",
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"current": "Exadata X8M on-prem con base crítica y reporting separado",
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"target": "ExaCS X11M BYOL para base crítica, DR cross-region y Object Storage para respaldo",
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"services": [
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{"name": "Exadata Base System", "sku": "B90777", "kind": "database"},
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{"name": "Exadata DB Server X11M", "sku": "B110627", "kind": "database"},
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{"name": "Exadata Storage Server X11M", "sku": "B110629", "kind": "database"},
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{"name": "Exadata Dedicated ECPU BYOL", "sku": "B110632", "kind": "database"},
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],
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},
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{
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"id": "analytics",
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"workload": "analytics and data science stack",
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"current": "ETL batch en Hadoop heredado y notebooks aislados",
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"target": "Big Data Service + Data Science + Data Flow con Object Storage como data lake",
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"services": [
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{"name": "Big Data Service Standard", "sku": "B91128", "kind": "analytics"},
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{"name": "Data Science Notebook Estimate", "sku": "EST-DS-NOTEBOOK", "kind": "analytics"},
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{"name": "Data Science Model Estimate", "sku": "EST-DS-MODEL", "kind": "analytics"},
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{"name": "Data Flow Spark Estimate", "sku": "EST-DF-SPARK", "kind": "analytics"},
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{"name": "Block Volume", "sku": "B91961", "kind": "storage"},
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],
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},
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]
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PRIMARY_REGIONS = ["us-ashburn-1", "us-phoenix-1", "eu-frankfurt-1", "sa-saopaulo-1"]
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DR_REGIONS = ["us-phoenix-1", "us-sanjose-1", "eu-amsterdam-1", "sa-vinhedo-1"]
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def slugify(text: str) -> str:
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return re.sub(r"[^a-z0-9]+", "-", text.lower()).strip("-")
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def build_case(seed: int) -> dict:
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rng = random.Random(seed)
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industry, urgency, compliance = rng.choice(INDUSTRIES)
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template = rng.choice(TEMPLATES)
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customer = f"{rng.choice(['Nova', 'Andes', 'Pacific', 'Vector', 'Apex'])} {rng.choice(['Digital', 'Holdings', 'Retail', 'Bank', 'Health'])}"
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primary_region = rng.choice(PRIMARY_REGIONS)
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dr_region = rng.choice([r for r in DR_REGIONS if r != primary_region])
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timeline_weeks = rng.choice([12, 14, 16, 20, 24])
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peak = rng.choice(["2x", "3x", "4x"])
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budget = rng.choice(["ajustado", "moderado", "sujeto a aprobación trimestral"])
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team = rng.choice(["2 DBAs y 1 sysadmin", "1 DBA senior y equipo de aplicaciones", "equipo pequeño sin experiencia OCI"])
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quantities = {}
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for service in template["services"]:
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sku = service["sku"]
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if sku in {"B95701", "B110632"}:
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quantities[sku] = rng.choice([16, 24, 32, 64])
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elif sku in {"B95706", "B95754a", "B91961"}:
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quantities[sku] = rng.choice([512, 1024, 2048, 4096])
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elif sku in {"B90777", "B88325"}:
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quantities[sku] = 1
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elif sku in {"B110627"}:
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quantities[sku] = 2
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elif sku in {"B110629"}:
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quantities[sku] = 3
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else:
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quantities[sku] = rng.choice([2, 4, 8, 16])
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prompt = (
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f"Cliente: {customer}. Industria: {industry}. Driver principal: reducir costo y mejorar DR.\n"
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f"Estado actual: {template['current']}. Objetivo: {template['target']}.\n"
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f"Región primaria: {primary_region}. Región DR: {dr_region}. Ventana objetivo: {timeline_weeks} semanas.\n"
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f"Compliance: {compliance}. Sensibilidad de presupuesto: {budget}. Equipo actual: {team}.\n"
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f"Picos esperados: {peak}. Contexto adicional: {urgency}."
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)
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customer_id = slugify(customer)
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summary_current = [
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template["current"],
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f"Primary region target: {primary_region}",
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f"DR region target: {dr_region}",
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f"Compliance baseline: {compliance}",
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f"Peak growth assumption: {peak}",
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]
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summary_target = (
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f"{template['target']}. Primary region {primary_region}; DR region {dr_region}. "
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f"Timeline target {timeline_weeks} weeks."
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)
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minimal_proposal_spec = {
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"metadata": {
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"customer": customer,
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"project": template["workload"].title(),
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"subtitle": f"OCI proposal for {template['workload']}",
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},
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"summary": {
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"why": "Modernize the platform while protecting business continuity and commercial efficiency.",
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"current_state": summary_current,
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"target_state": summary_target,
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"timeline": f"{timeline_weeks} weeks",
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},
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}
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diagram_services = []
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for idx, service in enumerate(template["services"][:3], 1):
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svc_type = "database" if service["kind"] in {"database", "database_storage"} else "compute"
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if service["kind"] == "network":
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svc_type = "fastconnect"
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elif service["kind"] == "storage":
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svc_type = "object_storage"
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diagram_services.append({
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"id": f"svc{idx}",
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"label": service["name"],
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"type": svc_type,
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})
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diagram_spec = {
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"title": f"{customer} — {template['workload']}",
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"external": [
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{"id": "users", "label": "Enterprise\nUsers", "icon": "user", "x": 30, "y": 260, "w": 80, "h": 80},
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],
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"tenancy": {
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"label": f"OCI Tenancy — {customer}",
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"regions": [
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{
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"id": "primary",
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"label": f"Region — {primary_region} (Primary)",
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"primary": True,
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"vcns": [
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{
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"id": "vcn1",
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"label": "Application VCN",
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"subnets": [
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{"id": "subnet1", "label": "Application / Data Subnet", "services": diagram_services},
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],
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}
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],
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},
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{
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"id": "dr",
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"label": f"Region — {dr_region} (DR)",
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"primary": False,
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"vcns": [
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{
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"id": "vcn2",
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"label": "DR VCN",
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"subnets": [
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{
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"id": "subnet2",
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"label": "Standby Subnet",
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"services": [
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{
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"id": "drsvc1",
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"label": "Standby / DR",
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"type": "database",
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}
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],
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},
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],
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}
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],
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},
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],
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},
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"connections": [
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{"from": "users", "to": "svc1", "type": "standard", "label": "Private access"},
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{"from": "svc1", "to": "drsvc1", "type": "data", "label": "Replication"},
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],
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}
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business_case_spec = {
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"customer_name": customer,
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"executive_summary": prompt.replace("\n", " "),
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}
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services_payload = [
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{"sku": service["sku"], "quantity": quantities[service["sku"]]}
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for service in template["services"]
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]
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bom_spec = {
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"bom": {
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"customer_name": customer,
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"project_name": template["workload"].title(),
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"prepared_by": "Codex Evaluation Harness",
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"currency": "USD",
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"line_items": [
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{"sku": service["sku"], "qty": quantities[service["sku"]], "months": 12, "discount": 0.0}
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for service in template["services"]
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],
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"notes": [
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f"Generated from seed {seed}",
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"Commercial validation required before quoting",
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],
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}
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}
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return {
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"seed": seed,
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"customer": customer,
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"customer_id": customer_id,
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"prompt": prompt,
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"proposal_spec": minimal_proposal_spec,
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"diagram_spec": diagram_spec,
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"business_case_spec": business_case_spec,
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"bom_spec": bom_spec,
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"mcp_payloads": {
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"deck": {
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"customer_id": customer_id,
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"preview": False,
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"tier": "standard",
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"spec": minimal_proposal_spec,
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},
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"diagram": {
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"customer_id": customer_id,
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"preview": False,
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"spec": diagram_spec,
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},
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"business_case": {
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"customer_id": customer_id,
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"preview": False,
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"discovery_notes": prompt,
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},
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"bom": {
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"customer_id": customer_id,
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"preview": False,
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"currency": "USD",
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"discount_pct": 0.0,
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"services": services_payload,
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},
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"bom_appca": {
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"customer_id": customer_id,
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"preview": False,
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"currency": "USD",
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"discount_pct": 0.0,
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"services": services_payload,
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},
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},
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}
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def save_yaml(path: Path, payload: dict):
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path.write_text(yaml.safe_dump(payload, sort_keys=False), encoding="utf-8")
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def analyze_pptx(path: Path) -> dict:
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prs = Presentation(path)
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slides = []
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blank = 0
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for slide in prs.slides:
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texts = []
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tables = 0
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for shape in slide.shapes:
|
||||
if hasattr(shape, "text") and shape.text.strip():
|
||||
texts.append(shape.text.strip())
|
||||
if getattr(shape, "has_table", False):
|
||||
tables += 1
|
||||
for row in shape.table.rows:
|
||||
for cell in row.cells:
|
||||
if cell.text.strip():
|
||||
texts.append(cell.text.strip())
|
||||
if len(texts) <= 1 and tables == 0:
|
||||
blank += 1
|
||||
slides.append({"text_items": len(texts), "tables": tables, "sample": texts[:6]})
|
||||
return {"slide_count": len(prs.slides), "blank_slides": blank, "slides": slides}
|
||||
|
||||
|
||||
def analyze_xlsx(path: Path) -> dict:
|
||||
wb = load_workbook(path, data_only=False)
|
||||
sheets = []
|
||||
empty = 0
|
||||
for ws in wb.worksheets:
|
||||
nonempty = 0
|
||||
for row in ws.iter_rows():
|
||||
for cell in row:
|
||||
if cell.value not in (None, ""):
|
||||
nonempty += 1
|
||||
if nonempty == 0:
|
||||
empty += 1
|
||||
sheets.append({"name": ws.title, "rows": ws.max_row, "cols": ws.max_column, "nonempty_cells": nonempty})
|
||||
return {"sheet_count": len(wb.worksheets), "empty_sheets": empty, "sheets": sheets}
|
||||
|
||||
|
||||
def analyze_drawio(path: Path) -> dict:
|
||||
text = path.read_text(encoding="utf-8")
|
||||
service_labels = len(re.findall(r'value="[^"]+"', text))
|
||||
return {"bytes": path.stat().st_size, "cell_values": service_labels}
|
||||
|
||||
|
||||
def analyze_pdf(path: Path) -> dict:
|
||||
raw = path.read_bytes()
|
||||
page_count = raw.count(b"/Type /Page")
|
||||
snippets = re.findall(rb"[A-Za-z][A-Za-z0-9 ,.:()/-]{8,}", raw)
|
||||
decoded = []
|
||||
for snippet in snippets[:20]:
|
||||
try:
|
||||
decoded.append(snippet.decode("utf-8"))
|
||||
except UnicodeDecodeError:
|
||||
decoded.append(snippet.decode("latin-1", errors="ignore"))
|
||||
return {"bytes": path.stat().st_size, "page_count_estimate": page_count, "snippets": decoded[:10]}
|
||||
|
||||
|
||||
def render_local(case: dict, out_dir: Path) -> dict:
|
||||
local_dir = out_dir / "local"
|
||||
local_dir.mkdir(parents=True, exist_ok=True)
|
||||
|
||||
proposal_spec = case["proposal_spec"]
|
||||
proposal_enriched = _enrich_partial_proposal_spec(proposal_spec)
|
||||
|
||||
save_yaml(local_dir / "proposal-minimal.yaml", proposal_spec)
|
||||
save_yaml(local_dir / "proposal-enriched.yaml", proposal_enriched)
|
||||
save_yaml(local_dir / "diagram.yaml", case["diagram_spec"])
|
||||
save_yaml(local_dir / "business-case-minimal.yaml", case["business_case_spec"])
|
||||
save_yaml(local_dir / "bom.yaml", case["bom_spec"])
|
||||
|
||||
deck_path = local_dir / f"{case['customer_id']}-deck.pptx"
|
||||
diagram_path = local_dir / f"{case['customer_id']}.drawio"
|
||||
bizcase_path = local_dir / f"{case['customer_id']}-bizcase.pptx"
|
||||
bom_path = local_dir / f"{case['customer_id']}-bom.xlsx"
|
||||
appca_path = local_dir / f"{case['customer_id']}-bom-appca.xlsx"
|
||||
pdf_path = local_dir / f"{case['customer_id']}.pdf"
|
||||
|
||||
OCIDeckGenerator.from_spec(proposal_spec).save(str(deck_path))
|
||||
OCIDiagramGenerator.from_spec(case["diagram_spec"]).save(str(diagram_path))
|
||||
BusinessCaseDeckGenerator.from_spec(case["business_case_spec"]).save(str(bizcase_path))
|
||||
bom = OCIBomGenerator.from_spec(case["bom_spec"])
|
||||
bom.save(str(bom_path))
|
||||
bom.save(str(appca_path), appca=True)
|
||||
OCIPDFGenerator.from_spec(proposal_enriched, diagram_path=str(diagram_path)).save(str(pdf_path))
|
||||
|
||||
analysis = {
|
||||
"deck": analyze_pptx(deck_path),
|
||||
"diagram": analyze_drawio(diagram_path),
|
||||
"business_case": analyze_pptx(bizcase_path),
|
||||
"bom": analyze_xlsx(bom_path),
|
||||
"bom_appca": analyze_xlsx(appca_path),
|
||||
"pdf": analyze_pdf(pdf_path),
|
||||
}
|
||||
return analysis
|
||||
|
||||
|
||||
def main():
|
||||
parser = argparse.ArgumentParser(description="Generate random local evaluation cases for OCI Deal Accelerator.")
|
||||
parser.add_argument("--iterations", type=int, default=10, help="How many cases to generate.")
|
||||
parser.add_argument("--start-seed", type=int, default=1, help="Starting seed number.")
|
||||
parser.add_argument("--output-dir", default="tmp/evals", help="Directory for generated cases.")
|
||||
args = parser.parse_args()
|
||||
|
||||
base_dir = PROJECT_ROOT / args.output_dir
|
||||
base_dir.mkdir(parents=True, exist_ok=True)
|
||||
|
||||
manifest = []
|
||||
for seed in range(args.start_seed, args.start_seed + args.iterations):
|
||||
case = build_case(seed)
|
||||
case_dir = base_dir / f"iter-{seed:02d}"
|
||||
case_dir.mkdir(parents=True, exist_ok=True)
|
||||
(case_dir / "prompt.txt").write_text(case["prompt"], encoding="utf-8")
|
||||
(case_dir / "mcp-payloads.json").write_text(json.dumps(case["mcp_payloads"], indent=2), encoding="utf-8")
|
||||
local_analysis = render_local(case, case_dir)
|
||||
summary = {
|
||||
"seed": seed,
|
||||
"customer": case["customer"],
|
||||
"customer_id": case["customer_id"],
|
||||
"prompt": case["prompt"],
|
||||
"local_analysis": local_analysis,
|
||||
}
|
||||
(case_dir / "local-analysis.json").write_text(json.dumps(summary, indent=2), encoding="utf-8")
|
||||
manifest.append({
|
||||
"seed": seed,
|
||||
"customer": case["customer"],
|
||||
"customer_id": case["customer_id"],
|
||||
"prompt_path": str((case_dir / "prompt.txt").relative_to(PROJECT_ROOT)),
|
||||
"mcp_payloads_path": str((case_dir / "mcp-payloads.json").relative_to(PROJECT_ROOT)),
|
||||
"local_analysis_path": str((case_dir / "local-analysis.json").relative_to(PROJECT_ROOT)),
|
||||
})
|
||||
|
||||
manifest_path = base_dir / "manifest.json"
|
||||
manifest_path.write_text(json.dumps(manifest, indent=2), encoding="utf-8")
|
||||
print(manifest_path.relative_to(PROJECT_ROOT))
|
||||
|
||||
|
||||
if __name__ == "__main__":
|
||||
main()
|
||||
Reference in New Issue
Block a user