Rene Fichtmueller
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0a63307505
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feat: Phase 6 — FAQ + troubleshooting knowledge base embeddings
19 curated FAQ entries covering form factors, fiber types, reach,
compatibility, WDM, power, and emerging tech (CPO, LPO, 400ZR).
10 troubleshooting guides with symptom/cause/solution format.
All 6 Qdrant collections now populated:
- product_embeddings: 89 transceivers
- datasheet_chunks: 40 chunks (OCR pipeline)
- faq_embeddings: 19 FAQ entries
- troubleshooting_embeddings: 10 guides
- news_embeddings: 33 articles
- manual_chunks: 0 (pending manual ingestion)
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2026-03-28 00:24:50 +13:00 |
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Rene Fichtmueller
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122ca8444d
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feat: Phase 5 — OCR pipeline + document/news search
Docling-powered OCR pipeline: PDF → markdown → chunks → Ollama embed → Qdrant.
News embedding seeder for news_embeddings collection.
Document and news semantic search API endpoints.
- embeddings/ocr-pipeline.ts: Docling convert → chunk → embed pipeline
- embeddings/seed-news.ts: Batch embed news_articles into Qdrant
- routes/documents.ts: POST /api/documents/process, GET /api/documents
- routes/search.ts: GET /search/documents, GET /search/news endpoints
- sql/005-documents.sql: Add chunks_count, processed_at to documents table
- Ollama + nomic-embed-text installed on Erik (CPU mode)
- 89 products + 40 datasheet chunks + 33 news articles in Qdrant
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2026-03-28 00:22:01 +13:00 |
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Rene Fichtmueller
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0260d0b365
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feat: Phase 4 — Vector embeddings + semantic search
Ollama nomic-embed-text (768 dim) → Qdrant vector search pipeline.
Embeds all 89 transceivers with rich text representation and payload
filters (form_factor, speed_gbps, fiber_type, wdm_type).
- embeddings/client.ts: Ollama embed + Qdrant upsert/search
- embeddings/seed-products.ts: Batch seeder for product_embeddings
- routes/search.ts: GET /api/search, /search/products, /search/stats
- 6 Qdrant collections: products, datasheets, FAQs, manuals, troubleshooting, news
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2026-03-28 00:05:29 +13:00 |
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Rene Fichtmueller
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a6f7968393
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feat: Phase 3 — Norton-Bass Hype Cycle Engine
Implements the full Norton-Bass Multigenerational Diffusion Model for
transceiver technology lifecycle forecasting.
Math: Bass diffusion F(t) + logistic adoption S(t) = L / (1 + e^(-k(t-t0)))
Parameters: p (innovation ~0.03), q (imitation ~0.3-0.5), m (market potential)
Phase Classification Engine (composite score):
30% Port shipment share + 20% ASP decline rate + 15% Standards maturity
+ 15% Interop validation + 10% Vendor trajectory + 10% Media sentiment
11 technologies tracked: 1G → 10G → 25G → 40G → 100G → 400G → 800G → 1.6T
+ CPO, LPO, 400ZR Coherent
5-year adoption forecast per technology
API: GET /api/hype-cycle (all) + GET /api/hype-cycle/:tech (detail)
Live: https://transceiver-db.context-x.org/api/hype-cycle
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2026-03-27 23:35:57 +13:00 |
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Rene Fichtmueller
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b43bdd3060
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feat: TIP Phase 0+1 — monorepo, DB schema, API, scraper engine
Phase 0 - Foundation:
- Restructure into npm workspace monorepo (packages/core, api, scraper)
- PostgreSQL 17 + TimescaleDB schema (15 tables incl. hypertables)
- Docker Compose for local dev (PostgreSQL on 5433 + Qdrant)
- Express 5 API on port 3200 with 6 routes
- Seed script to migrate 159 transceivers + 42 standards from npm package
- Erik server setup script + PM2 ecosystem config
Phase 1 - Scraper Engine:
- Crawlee + Playwright framework with pg-boss scheduler
- FS.com scraper (PlaywrightCrawler, anti-bot workaround)
- Optcore.net scraper (WP REST API enumeration + PlaywrightCrawler)
- Uses /wp-json/wp/v2/product to get 2000+ product URLs
- Playwright renders individual product pages for price extraction
- Cisco TMG Matrix scraper (compatibility data)
- News RSS aggregator (optics.org, SPIE, Network World, Nature Photonics)
- Keyword relevance scoring for transceiver/fiber topics
- xml2js with malformed XML sanitization
- SHA-256 content hashing for change detection (skip unchanged records)
- pg-boss v10 with explicit queue creation before scheduling
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2026-03-27 16:27:31 +13:00 |
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