Phase 2.2: Embedding Integration

Full HTML reader

Read the full artifact

Extracted abstract or opening context

# Phase 2.2: Embedding Integration **Week:** 2 **Duration:** 1.5 days **Status:** ✅ Complete **Dependencies:** None (can run parallel with 2.1) Integrate IRCP's `SentenceTransformerICP` model with DLM's newly created `BaseEmbeddingProvider` interface, creating a unified, production-ready embedding system with caching and batch processing. **DLM has:** - ✅ `dlm/engine/embedder.py` (61KB) - Generic embedding, no IRCP - ✅ `dlm/engine/ircp_embedder.py` (9KB) - Exists but basic - ✅ `dlm/response/embedding_provider.py` (NEW) - Abstract `BaseEmbeddingProvider` - ✅ `dlm/response/utils.py` (NEW) - `EmbeddingCache`, batch processing **IRCP has:** - ✅ `ircp/models/sentence_transformer_icp.py` - Full IRCP model - `SentenceTransformerICP` class - `IRCPCustomHeads` (coordinates, patterns, confidence) - `InverseAttentionMechanism` - `IRCPMeasurePreservingTransform` 1. **Move IRCP model** → `dlm/core/embeddings.py` 2. **Extend BaseEmbeddingProvider** - Inherit caching and batching 3. **Keep IRCP theory** → `dlm/core/ircp/` subdirectory 4. **Replace dlm/engine/ircp_embedder.py** - Use new unified version 5. **Maintain backward compatibility** - Old imports still work

Promotion decision

What has to happen next

Keep as idea/proposal unless evidence and implementation anchors exist.

Why this is not always a full paper yet

Corpus pages are public-safe readers for discovered workspace artifacts. They are not automatically final papers. A corpus item becomes a polished paper only after the editable source, evidence checkpoints, references, figures, render path, and release status are attached through the paper schema.