feat(chunking): add base strategy interface, embedding service, and orchestration

Why:
- Need abstract base class for all chunking strategies
- Need OpenAI embedding service (text-embedding-3-small)
- Need orchestration to run chunk → embed → store pipeline

Changes:
- Base: ChunkingStrategy ABC, token counting, chunk ID generation, sentence splitting
- Embedding: batch embedding with 2048 text limit per call
- Service: strategy registry, single/multi-strategy runners with per-strategy failure isolation

src/chunking/embedding.py

@@ -0,0 +1,68 @@
+"""OpenAI embedding service using text-embedding-3-small.
+
+All strategies share the same embedding model (fixed, not configurable)
+to ensure fair comparison.  Batch support up to 2048 texts per call.
+"""
+
+from __future__ import annotations
+
+import logging
+
+from openai import OpenAI
+
+from src.core.config import settings
+from src.core.dependencies import get_openai_client
+from src.core.exceptions import EmbeddingError
+
+logger = logging.getLogger(__name__)
+
+# OpenAI batch limit for text-embedding-3-small
+_BATCH_SIZE = 2048
+
+
+def embed_texts(texts: list[str]) -> list[list[float]]:
+    """Embed a list of texts and return their vectors.
+
+    For the contextual_structure strategy, these are the enriched texts
+    (not raw content) — this is by design (ADR 0011).
+
+    Args:
+        texts: List of strings to embed.
+
+    Returns:
+        List of embedding vectors (same order as input).
+
+    Raises:
+        EmbeddingError: If the OpenAI API call fails.
+    """
+    if not texts:
+        return []
+
+    client = get_openai_client()
+    all_embeddings: list[list[float]] = []
+
+    try:
+        for start in range(0, len(texts), _BATCH_SIZE):
+            batch = texts[start:start + _BATCH_SIZE]
+            response = client.embeddings.create(
+                model=settings.embedding_model,
+                input=batch,
+            )
+            # Sort by index to guarantee order matches input
+            sorted_data = sorted(response.data, key=lambda x: x.index)
+            all_embeddings.extend([item.embedding for item in sorted_data])
+
+            logger.debug(
+                "Embedded batch %d-%d (%d texts)",
+                start, start + len(batch), len(batch),
+            )
+
+        return all_embeddings
+    except Exception as exc:
+        raise EmbeddingError(f"Embedding failed: {exc}") from exc
+
+
+def embed_single(text: str) -> list[float]:
+    """Embed a single text (convenience wrapper)."""
+    results = embed_texts([text])
+    return results[0]