Migrate all repos into monorepo context folders

Bahn: aisupport, Analyse-O2C-C2S, awesome-bahn-mcp-servers, beam-mcp, Confluence_Bot, db-planet-mcp-server, O2C-Harness, project-audit, Projekt-KIQ-HP, teamlandkarte-mcp Dhive: Jury-Voting Privat: CV, NoteGraph (NOTE: NoteGraph needs complete redo after consolidation) Shared: AI-Orchestrator, OrgMyLife, power_skills_and_more Shared/references: symphony (read-only) Bahn repos remain available as independent remotes - this monorepo pulls them in via subtree, the originals are untouched.
2026-06-30 20:39:52 +02:00
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+# Capability: Embedding Prefetch & Batch Similarity
+
+## ADDED Requirements
+
+### Requirement: Bulk prefetch and deduplicate embedding inputs
+
+The server MUST collect all texts that require embeddings for a matching run, normalize them, deduplicate them, and prefetch their embeddings before computing similarity scores.
+
+#### Scenario: Matching run with repeated competences
+
+- Given a matching run where the required competences contain duplicates (e.g. "Python", "Python")
+- And a candidate list contains overlapping competences (e.g. multiple candidates list "Python")
+- When the similarity computation starts
+- Then the embedding for "Python" is requested at most once from the Azure embeddings API
+- And all uses of "Python" reuse the same embedding vector for similarity computation
+
+### Requirement: Compute similarity using prefetched vectors (no inner-loop embedding calls)
+
+The similarity computation MUST use prefetched embedding vectors and MUST NOT request embeddings from inside per-competence or per-candidate inner loops.
+
+#### Scenario: Per-skill similarity on large candidate set
+
+- Given required competences and a large candidate competence list
+- When `per_skill` similarity is computed
+- Then all embeddings are resolved prior to the nested loops
+- And the nested loops perform only cosine similarity calculations
+
+## MODIFIED Requirements
+
+### Requirement: Batch embedding API support in Azure client
+
+`AzureOpenAIClient.get_embeddings_batch(...)` MUST support true request-level batching using the Azure embeddings API (`input=[...]`) and MUST preserve input ordering.
+
+#### Scenario: Batch embedding input list
+
+- Given a list of N texts to embed
+- When `get_embeddings_batch(texts)` is called
+- Then the client makes as few Azure embedding requests as possible (subject to chunking)
+- And the returned list of embeddings aligns with `texts` order
+
+### Requirement: Run-scoped in-memory embedding cache
+
+The server MUST maintain a run-scoped in-memory cache to prevent repeated SQLite cache reads for identical texts during a single matching operation.
+
+#### Scenario: Frequent repeated cache hits within one run
+
+- Given cached embeddings are present on disk
+- And a matching run needs the same text embedding multiple times
+- When the similarity engine resolves embeddings
+- Then repeated lookups for the same text are served from an in-memory cache after the first lookup
+- And no additional SQLite reads are required for that text during the run
+
+## REMOVED Requirements
+
+### Requirement: Sequential batch behavior requirement
+
+The previous constraint that `get_embeddings_batch` must call the embeddings API sequentially per text is removed.
+
+#### Scenario: Legacy sequential constraint
+
+- Given earlier requirements constrained `get_embeddings_batch` to per-text calls
+- When the system is upgraded for performance
+- Then this constraint no longer applies
+- And request-level batching is allowed
+