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Author	SHA1	Message	Date
alvis	08d08ad7b0	feat(clustering): LLM-enrichment before embedding (port from taskpile #129 ) Ported from taskpile experiments/clustering_eval (prompt v1, qwen2.5:1.5b). The experiment showed ARI 0.22→0.77 and AUROC 0.76→0.91 on synthetic tasks when embedding LLM-expanded descriptions instead of raw titles. - Expand each task title via LiteLLM tip-generator before embedding - Prefix with "clustering: " (nomic-embed-text task instruction prefix) - Cache expansions in-memory by content hash within a compute cycle - Falls back to raw title if enrichment fails; no change to fallback behaviour Fixes #129 Co-Authored-By: Claude Sonnet 4.6 <noreply@anthropic.com>	2026-05-12 14:20:48 +00:00
alvis	1ca2351488	fix(clustering): route embeddings through LiteLLM instead of Ollama directly The old code called Ollama's /api/embeddings one task at a time, which caused silent fallback to project-based grouping when host.docker.internal:11434 was unreachable from the ml-serving container. - Switch to LiteLLM /embeddings (model alias "embedder") as primary path - Batch all task contents in one request instead of N serial calls - Fall back to Ollama /api/embed (updated to current API) when LITELLM_URL is absent - Update tests to mock _embed_batch instead of the removed _embed Fixes #123 Co-Authored-By: Claude Sonnet 4.6 <noreply@anthropic.com>	2026-05-12 13:42:53 +00:00
alvis	26fc67776f	feat(agents): semantic task clustering + focus-area inferred preferred_areas (#97 , #113 ) - New ml/agents/clustering.py: embed task content via nomic-embed-text (Ollama), greedy cosine clustering (threshold 0.72, max 6 clusters), graceful fallback to project-id grouping when Ollama is unreachable - focus_area v2.0.0: compute() uses semantic clusters as focus areas; adds preferred_areas InferredParam inferred from top-2 projects by task_completion count - 135 tests, all passing Co-Authored-By: Claude Sonnet 4.6 <noreply@anthropic.com>	2026-05-06 06:54:46 +00:00

Author

SHA1

Message

Date

alvis

08d08ad7b0

feat(clustering): LLM-enrichment before embedding (port from taskpile #129 )

Ported from taskpile experiments/clustering_eval (prompt v1, qwen2.5:1.5b).
The experiment showed ARI 0.22→0.77 and AUROC 0.76→0.91 on synthetic tasks
when embedding LLM-expanded descriptions instead of raw titles.

- Expand each task title via LiteLLM tip-generator before embedding
- Prefix with "clustering: " (nomic-embed-text task instruction prefix)
- Cache expansions in-memory by content hash within a compute cycle
- Falls back to raw title if enrichment fails; no change to fallback behaviour

Fixes #129

Co-Authored-By: Claude Sonnet 4.6 <noreply@anthropic.com>

2026-05-12 14:20:48 +00:00

alvis

1ca2351488

fix(clustering): route embeddings through LiteLLM instead of Ollama directly

The old code called Ollama's /api/embeddings one task at a time, which caused
silent fallback to project-based grouping when host.docker.internal:11434 was
unreachable from the ml-serving container.

- Switch to LiteLLM /embeddings (model alias "embedder") as primary path
- Batch all task contents in one request instead of N serial calls
- Fall back to Ollama /api/embed (updated to current API) when LITELLM_URL is absent
- Update tests to mock _embed_batch instead of the removed _embed

Fixes #123

Co-Authored-By: Claude Sonnet 4.6 <noreply@anthropic.com>

2026-05-12 13:42:53 +00:00

alvis

26fc67776f

feat(agents): semantic task clustering + focus-area inferred preferred_areas (#97 , #113 )

- New ml/agents/clustering.py: embed task content via nomic-embed-text
  (Ollama), greedy cosine clustering (threshold 0.72, max 6 clusters),
  graceful fallback to project-id grouping when Ollama is unreachable
- focus_area v2.0.0: compute() uses semantic clusters as focus areas;
  adds preferred_areas InferredParam inferred from top-2 projects by
  task_completion count
- 135 tests, all passing

Co-Authored-By: Claude Sonnet 4.6 <noreply@anthropic.com>

2026-05-06 06:54:46 +00:00

3 Commits