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/base.py

@@ -0,0 +1,95 @@
+"""Base chunking strategy interface and shared utilities.
+
+Every strategy inherits from ChunkingStrategy and implements chunk().
+The base class provides token counting, chunk ID generation, and
+the standard Chunk construction path.
+"""
+
+from __future__ import annotations
+
+import hashlib
+import re
+from abc import ABC, abstractmethod
+
+import tiktoken
+
+from src.core.models import Chunk, DocumentTree, StrategyName
+
+# cl100k_base is the encoding used by text-embedding-3-small and gpt-4o-mini
+_encoder = tiktoken.get_encoding("cl100k_base")
+
+
+def count_tokens(text: str) -> int:
+    """Return the token count for a string."""
+    return len(_encoder.encode(text))
+
+
+def make_chunk_id(strategy: StrategyName, doc_name: str, index: int) -> str:
+    """Generate a deterministic chunk ID: {strategy}_{doc}_{index:06d}."""
+    safe_doc = re.sub(r"[^a-zA-Z0-9]", "_", doc_name)[:32]
+    return f"{strategy.value}_{safe_doc}_{index:06d}"
+
+
+def build_chunk(
+    *,
+    strategy: StrategyName,
+    doc_name: str,
+    index: int,
+    text: str,
+    parent_id: str | None = None,
+    enriched_content: str | None = None,
+) -> Chunk:
+    """Construct a Chunk with token/character counts pre-filled."""
+    return Chunk(
+        document_name=doc_name,
+        chunk_id=make_chunk_id(strategy, doc_name, index),
+        strategy_name=strategy,
+        chunk_index=index,
+        text=text,
+        token_count=count_tokens(text),
+        character_count=len(text),
+        parent_id=parent_id,
+        enriched_content=enriched_content,
+    )
+
+
+# ── Sentence splitting ────────────────────────────────────────────
+
+_SENTENCE_RE = re.compile(r"(?<=[.!?])\s+(?=[A-Z0-9])")
+
+
+def split_sentences(text: str) -> list[str]:
+    """Split text into sentences using a simple regex heuristic."""
+    sentences = _SENTENCE_RE.split(text.strip())
+    return [s.strip() for s in sentences if s.strip()]
+
+
+# ── Abstract base ─────────────────────────────────────────────────
+
+class ChunkingStrategy(ABC):
+    """Base class for all chunking strategies.
+
+    Subclasses implement chunk() which receives the full document
+    context and returns a list of Chunks conforming to the unified model.
+    """
+
+    name: StrategyName
+
+    @abstractmethod
+    def chunk(
+        self,
+        *,
+        doc_name: str,
+        tree: DocumentTree,
+        markdown: str,
+    ) -> list[Chunk]:
+        """Produce chunks from a parsed document.
+
+        Args:
+            doc_name:  Original filename (for metadata).
+            tree:      Hierarchical DocumentTree from the parser.
+            markdown:  Flat markdown rendering of the document.
+
+        Returns:
+            List of Chunk objects (unified model).
+        """