matchmaking-v2

growqr-app/matchmaking-v2

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Author	SHA1	Message	Date
raulgupta	b1e9cdd182	Engine v2: embeddings sift + Opus curator + Postgres feed persistence - A1: fire the hard filter (Apify is precise); embedding vibe-ranker (embed.py) blends with the floor-free white-box (rank.py) to sift ~90 -> top 18 - A2: Opus curator (curate.py) reads the 18 -> honest <=count + Gemini-voiced report cards; graceful fallback to the white-box + templated cards - LLM via opencode.ai/zen gateway (llm.py): Opus chat + direct-OpenAI embeddings - Run LLM work off the event loop (asyncio.to_thread) so the Redis response publishes - C1: dedicated Postgres (app/db/) persists the per-user feed; get_scout_feed replays it so matches survive navigation/refresh - match contract + report-card fields (schema.py); skills.py; tests/	2026-06-19 15:51:16 +05:30
raulgupta	89ca9ad647	Initial commit: matchmaking-v2 on-demand multi-board job-search agent On-demand Scout service (replaces the nightly aggregator): - FastAPI agent-mesh service; card name "matchmaking-service" (HTTP /a2a/tasks + Redis-stream worker matching the sibling-service pattern) - run_search: parallel multi-board sweep (Naukri/blackfalcondata + Foundit + LinkedIn), city-filtered at the board, cheapest-per-result first - per-board adapters + normalizers -> ScoutJob with rich read-only details and offsite apply links; recall mode + MVQ guard - result cache for dev replay ($0); per-board cost knobs; retry-on-5xx - research/: engine design, board cost economics, India-actor shortlist, POC	2026-06-18 19:22:03 +05:30

Author

SHA1

Message

Date

raulgupta

b1e9cdd182

Engine v2: embeddings sift + Opus curator + Postgres feed persistence

- A1: fire the hard filter (Apify is precise); embedding vibe-ranker (embed.py)
  blends with the floor-free white-box (rank.py) to sift ~90 -> top 18
- A2: Opus curator (curate.py) reads the 18 -> honest <=count + Gemini-voiced
  report cards; graceful fallback to the white-box + templated cards
- LLM via opencode.ai/zen gateway (llm.py): Opus chat + direct-OpenAI embeddings
- Run LLM work off the event loop (asyncio.to_thread) so the Redis response publishes
- C1: dedicated Postgres (app/db/) persists the per-user feed; get_scout_feed
  replays it so matches survive navigation/refresh
- match contract + report-card fields (schema.py); skills.py; tests/

2026-06-19 15:51:16 +05:30

raulgupta

89ca9ad647

Initial commit: matchmaking-v2 on-demand multi-board job-search agent

On-demand Scout service (replaces the nightly aggregator):
- FastAPI agent-mesh service; card name "matchmaking-service" (HTTP /a2a/tasks
  + Redis-stream worker matching the sibling-service pattern)
- run_search: parallel multi-board sweep (Naukri/blackfalcondata + Foundit +
  LinkedIn), city-filtered at the board, cheapest-per-result first
- per-board adapters + normalizers -> ScoutJob with rich read-only details and
  offsite apply links; recall mode + MVQ guard
- result cache for dev replay ($0); per-board cost knobs; retry-on-5xx
- research/: engine design, board cost economics, India-actor shortlist, POC

2026-06-18 19:22:03 +05:30

2 Commits