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Initial commit: Conflict Checker

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Cross-discipline design-contradiction checker for construction drawing
sets. Standalone tool broken out from Iron_Bid; a pipeline stage may
later fold back into Iron_Bid.

Pipeline: PDF->images -> per-sheet assertion extraction -> deterministic
clustering by location -> per-cluster reasoning -> report.
Includes CLI (cli/run_check.py) and web UI (backend/main.py).

Co-Authored-By: Claude Opus 4.8 <noreply@anthropic.com>
This commit is contained in:
woogi
2026-07-03 00:22:02 +00:00
co-authored by Claude Opus 4.8
commit 1d248a8808
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backend/config.py
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"""
config.py - Environment-driven configuration for the Conflict Checker.
All knobs come from backend/.env (see .env.example). Paths are derived from this
file's location so the app runs regardless of the current working directory.
"""
import os
from dotenv import load_dotenv
load_dotenv(os.path.join(os.path.dirname(os.path.abspath(__file__)), ".env"))
_BASE_DIR = os.path.dirname(os.path.abspath(__file__))
# -- AI Backend (OpenRouter) ----------------------------------------
# One multimodal model does both extraction (Stage 1) and conflict
# reasoning (Stage 3). Override MODEL per-stage if you ever split them.
AI_BASE_URL = os.getenv("AI_BASE_URL", "https://openrouter.ai/api/v1")
AI_API_KEY = os.getenv("AI_API_KEY", "")
MODEL = os.getenv("MODEL", "google/gemini-2.5-pro")
# Text model when text stages run on OpenRouter (non-hybrid). Falls back to
# MODEL when unset.
TEXT_MODEL = os.getenv("TEXT_MODEL", "") or MODEL
# -- Hybrid (local text LLM) ----------------------------------------
# Optional OpenAI-compatible local endpoint (e.g. a vLLM box) for the text-only
# QAQC stages. Vision stages ALWAYS use OpenRouter. The user picks hybrid per
# job in the UI; these say WHERE local is + the toggle's default state. Empty
# LOCAL_BASE_URL = hybrid disabled (falls back to OpenRouter).
LOCAL_BASE_URL = os.getenv("LOCAL_BASE_URL", "")
LOCAL_API_KEY = os.getenv("LOCAL_API_KEY", "local")
LOCAL_TEXT_MODEL = os.getenv("LOCAL_TEXT_MODEL", "")
HYBRID_DEFAULT = os.getenv("HYBRID_DEFAULT", "false").strip().lower() in ("1", "true", "yes")
# -- Pipeline -------------------------------------------------------
PDF_DPI = int(os.getenv("PDF_DPI", "100"))
MAX_PAGES = int(os.getenv("MAX_PAGES", "60"))
MAX_DIMENSION = int(os.getenv("MAX_DIMENSION", "2400")) # px cap on the long edge
LLM_TIMEOUT = int(os.getenv("LLM_TIMEOUT", "180")) # seconds per call
EXTRACT_MAX_TOKENS = int(os.getenv("EXTRACT_MAX_TOKENS", "16384"))
REASON_MAX_TOKENS = int(os.getenv("REASON_MAX_TOKENS", "4096"))
# -- QAQC stage knobs (Stages 0-1, 3, 6-11) -------------------------
# Token caps per stage. Most are single whole-set calls, so they need more
# headroom than a per-cluster reason call.
JURISDICTION_MAX_TOKENS = int(os.getenv("JURISDICTION_MAX_TOKENS", "4096"))
SHEET_INDEX_MAX_TOKENS = int(os.getenv("SHEET_INDEX_MAX_TOKENS", "16384"))
NORMALIZE_MAX_TOKENS = int(os.getenv("NORMALIZE_MAX_TOKENS", "16384"))
QAQC_MAX_TOKENS = int(os.getenv("QAQC_MAX_TOKENS", "16384"))
CODE_MAX_TOKENS = int(os.getenv("CODE_MAX_TOKENS", "16384"))
CONSTRUCT_MAX_TOKENS = int(os.getenv("CONSTRUCT_MAX_TOKENS", "16384"))
VALIDATE_MAX_TOKENS = int(os.getenv("VALIDATE_MAX_TOKENS", "16384"))
RISK_MAX_TOKENS = int(os.getenv("RISK_MAX_TOKENS", "16384"))
RFI_MAX_TOKENS = int(os.getenv("RFI_MAX_TOKENS", "16384"))
# Stage 4 clustering engine: "llm" (semantic, fuzzy matches + catches more) or
# "deterministic" (clusterer.py, free/reproducible). Swap via env to A/B.
CLUSTERER = os.getenv("CLUSTERER", "llm").strip().lower()
CLUSTER_MAX_TOKENS = int(os.getenv("CLUSTER_MAX_TOKENS", "16384"))
CLUSTER_MAX = int(os.getenv("CLUSTER_MAX", "120"))
# Disk-backed LLM response cache for the testing loop. When on, identical calls
# (same model/prompt/images/params) replay the saved response at zero API cost,
# so re-running a set only pays for stages whose input actually changed. Off by
# default so production never serves stale results. Clear: rm -rf the dir.
LLM_CACHE = os.getenv("LLM_CACHE", "false").strip().lower() in ("1", "true", "yes")
LLM_CACHE_DIR = os.getenv("LLM_CACHE_DIR", os.path.join(_BASE_DIR, ".llm_cache"))
# Parallelism (ThreadPoolExecutor workers)
EXTRACT_CONCURRENCY = int(os.getenv("EXTRACT_CONCURRENCY", "4"))
REASON_CONCURRENCY = int(os.getenv("REASON_CONCURRENCY", "4"))
# Batched stages (normalization, per-sheet code/constructability) reuse this.
NORMALIZE_CONCURRENCY = int(os.getenv("NORMALIZE_CONCURRENCY", "4"))
CODE_CONCURRENCY = int(os.getenv("CODE_CONCURRENCY", "4"))
# Max assertions per code-review call. Code review batches sheets to keep each
# call small (avoids the truncation that zeroed out a whole-set call).
CODE_BATCH_SIZE = int(os.getenv("CODE_BATCH_SIZE", "80"))
CONSTRUCT_CONCURRENCY = int(os.getenv("CONSTRUCT_CONCURRENCY", "4"))
# Assertions per batch for the normalization stage.
NORMALIZE_BATCH_SIZE = int(os.getenv("NORMALIZE_BATCH_SIZE", "60"))
# -- App ------------------------------------------------------------
UPLOAD_DIR = os.path.join(_BASE_DIR, "uploads")
OUTPUT_DIR = os.path.join(_BASE_DIR, "outputs")
APP_TITLE = "Conflict Checker"
APP_VERSION = "0.1.0"
# Public base URL used to build the "view results" link in notification
# emails. Set to whatever address users reach this server on (e.g. the
# Tailscale/LAN URL) so the link in the email actually resolves.
APP_BASE_URL = os.getenv("APP_BASE_URL", "http://localhost:8099")
# -- Email / SMTP (optional notification on completion) -------------
# If unset, the app still works; it just logs "SMTP not configured" and
# skips the email. Mirrors IronBid's graceful behavior.
SMTP_HOST = os.getenv("SMTP_HOST", "")
SMTP_PORT = int(os.getenv("SMTP_PORT", "587"))
SMTP_USER = os.getenv("SMTP_USER", "")
SMTP_PASSWORD = os.getenv("SMTP_PASSWORD", "")
SMTP_FROM = os.getenv("SMTP_FROM", "")
SMTP_USE_TLS = os.getenv("SMTP_USE_TLS", "true").lower() == "true"
SMTP_USE_SSL = os.getenv("SMTP_USE_SSL", "false").lower() == "true"