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Matt
1a9dfc8f8d
Phase A: Trail Entry Point Index - Extract highway endpoints from idaho-latest.osm.pbf using osmium + ogr2ogr - Store 740,430 entry points in /mnt/nav/navi.db (SQLite with spatial index) - Entry points by class: service (271k), footway (152k), residential (146k), track (111k), path (26k), unclassified (16k), tertiary (9k), secondary (4k), primary (4k), bridleway (15) Phase B: Pathfinder → Valhalla Stitching (router.py) - OffrouteRouter orchestrates wilderness pathfinding + Valhalla on-network routing - Queries entry points within 50km (expanding to 100km if needed) - MCP pathfinder routes to nearest reachable entry point - Calls Valhalla pedestrian/bicycle/auto costing for on-network segment - Returns GeoJSON FeatureCollection with wilderness + network + combined segments Phase C: Flask Endpoint - POST /api/offroute with start/end coordinates, mode, boundary_mode - Returns GeoJSON route with per-segment metadata and turn-by-turn maneuvers Validated: 42.35,-114.30 → Twin Falls downtown - Wilderness: 0.5km, 9min | Network: 36km, 413min | Total: ~421min - 21 turn-by-turn instructions, segments connect at entry point Co-Authored-By: Claude Opus 4.5 <noreply@anthropic.com> |
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| lib | ||
| scripts | ||
| static | ||
| templates | ||
| .gitignore | ||
| api.py | ||
| config.yaml | ||
| enricher.py | ||
| migrate_paths.py | ||
| PROJECT-BIBLE.md | ||
| README.md | ||
| recon.py | ||
| requirements.txt | ||
| run-pipeline-now.sh | ||
| sweep_gated.sh | ||
RECON -- Knowledge Extraction Pipeline
Extracts structured knowledge from PDFs and web content into a Qdrant vector database for RAG retrieval by Aurora.
Quick Start
# Activate
cd /opt/recon && source venv/bin/activate
# Scan library for new PDFs
recon scan
# Queue and process
recon queue
recon extract
recon enrich
recon embed
# Or run full pipeline
recon run
# Ingest a web page
recon ingest-url "https://example.com/article" --category "Category" --process
# Crawl an entire docs site
recon crawl "https://docs.example.com" --include /docs/ --category "Category" --process
# Upload a PDF
recon upload --file /path/to/document.pdf --category "Category"
# Search
recon search "water purification methods"
# Check status
recon status
recon failures
Dashboard
Services
| Service | Location | Purpose |
|---|---|---|
| RECON Dashboard | recon:8420 | Pipeline management + API |
| Qdrant | cortex:6333 | Vector database |
| TEI | cortex:8090 | Embeddings (1,711/sec) |
| Ollama | cortex:11434 | Chat + fallback embeddings |
| OpenWebUI | cortex:8080 (ai.echo6.co) | Aurora chat with RAG |
| File Server | recon:8888 (files.echo6.co) | PDF downloads |
Key Paths
| Path | Contents |
|---|---|
| /opt/recon/ | Application code |
| /opt/recon/data/concepts/ | Gemini extractions (CRITICAL -- back these up) |
| /opt/recon/data/text/ | Extracted text |
| /opt/recon/data/recon.db | SQLite status DB |
| /mnt/library/ | PDF library (NFS from pi-nas) |
Backups
Automated every 6 hours to Contabo VPS via /opt/recon/scripts/backup.sh.
Concept JSONs are the most valuable data ($130+ of Gemini API work).
Qdrant is NOT backed up -- rebuilt from JSONs in ~10 minutes via recon rebuild.
Monitoring
# Pipeline status
recon status
# Tail logs
tail -f /opt/recon/logs/recon.log
# Pipeline run log
tail -f /opt/recon/pipeline.log
# Validate consistency
recon validate --deep
Full Documentation
See PROJECT-BIBLE.md for complete system documentation.