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- Python 86.8%
- HTML 6.1%
- JavaScript 5.4%
- CSS 1%
- Shell 0.7%
Matt
dfab388769
Add Netsyms AddressDatabase2025 (159M US+CA addresses) as tier-2
in the geocode chain: address_book → netsyms → photon.
- lib/netsyms.py: SQLite lookup module (lazy, read-only, thread-safe)
- lib/netsyms_api.py: Flask blueprints for /api/netsyms/* and /api/geocode
- lib/netsyms_test.py: 7 test cases (street, free-text, zipcode, health)
- lib/nav_tools.py: new geocode() with consistent {name,lat,lon,source,raw}
- lib/api.py: register netsyms_bp and geocode_bp
Co-Authored-By: Claude Opus 4.6 <noreply@anthropic.com>
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| config | ||
| 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.