fix(chunking): require semantic boundary detection

Why:
- Semantic strategies were silently falling back to fixed-count grouping when boundary embeds were missing.

Changes:
- Fail hard without aligned unit embeddings; orchestrator supplies Boundary embeds; Farsi-aware sentence split with line/paragraph fallback.

Co-authored-by: Cursor <cursoragent@cursor.com>

src/chunking/base.py

@@ -55,13 +55,36 @@ def build_chunk(
 
 # ── Sentence splitting ────────────────────────────────────────────
 
-_SENTENCE_RE = re.compile(r"(?<=[.!?])\s+(?=[A-Z0-9])")
+# After . ! ? or Persian/fullwidth ؟ ！, split on following whitespace.
+# Does NOT require a Latin capital next (that broke Farsi documents).
+# Periods inside numbers (12.5) are safe because there is no whitespace after.
+_SENTENCE_RE = re.compile(r"(?<=[.!?؟！])\s+")
 
 
 def split_sentences(text: str) -> list[str]:
-    """Split text into sentences using a simple regex heuristic."""
+    """Split text into sentence-like units for Semantic Boundary Detection.
-    sentences = _SENTENCE_RE.split(text.strip())
+
-    return [s.strip() for s in sentences if s.strip()]
+    Primary: punctuation-based splits (English + Farsi terminators).
+    Fallback: if that yields a single unit, use non-empty lines, then
+    blank-line paragraphs — so table/list docs still get multiple units.
+    """
+    text = text.strip()
+    if not text:
+        return []
+
+    sentences = [s.strip() for s in _SENTENCE_RE.split(text) if s.strip()]
+    if len(sentences) > 1:
+        return sentences
+
+    lines = [ln.strip() for ln in text.splitlines() if ln.strip()]
+    if len(lines) > 1:
+        return lines
+
+    paragraphs = [p.strip() for p in re.split(r"\n\s*\n", text) if p.strip()]
+    if len(paragraphs) > 1:
+        return paragraphs
+
+    return sentences if sentences else [text]
 
 
 # ── Abstract base ─────────────────────────────────────────────────

src/chunking/service.py

@@ -4,7 +4,8 @@ Runs selected strategies on a document, embeds chunks, and stores
 them in Qdrant.  Per-strategy failure isolation (ADR 0003): if one
 strategy fails, the others' results are still committed.
 
-This replaces the stub in src/documents/service.py.
+ADR-0024: Boundary Embedding Model for semantic cuts; Corpus Embedding
+Model for finished-chunk vectors and collection scoping.
 """
 
 from __future__ import annotations
@@ -12,16 +13,19 @@ from __future__ import annotations
 import logging
 import time
 
-from src.chunking.base import ChunkingStrategy
+from src.chunking.base import ChunkingStrategy, split_sentences
 from src.chunking.embedding import embed_texts
+from src.chunking.embedding_models import EmbeddingModelSpec, get_semantic_threshold
 from src.chunking.strategies.recursive import RecursiveStrategy
 from src.chunking.strategies.fixed_size import FixedSizeStrategy
 from src.chunking.strategies.semantic import SemanticStrategy
 from src.chunking.strategies.contextual_retrieval import ContextualRetrievalStrategy
-from src.chunking.strategies.semantic_parent_child import SemanticParentChildStrategy
+from src.chunking.strategies.semantic_parent_child import (
+    SemanticParentChildStrategy,
+    split_paragraphs,
+)
 from src.core.exceptions import ChunkingError
 from src.core.models import (
-    Chunk,
     DocumentTree,
     StrategyName,
 )
@@ -30,6 +34,11 @@ from src.storage import sqlite as db
 
 logger = logging.getLogger(__name__)
 
+_BOUNDARY_STRATEGIES = {
+    StrategyName.SEMANTIC,
+    StrategyName.SEMANTIC_PARENT_CHILD,
+}
+
 # ── Strategy registry ─────────────────────────────────────────────
 
 _STRATEGIES: dict[StrategyName, ChunkingStrategy] = {
@@ -48,6 +57,73 @@ def _get_strategy(name: StrategyName) -> ChunkingStrategy:
     return s
 
 
+def _chunk_document(
+    strategy_name: StrategyName,
+    strategy: ChunkingStrategy,
+    *,
+    doc_name: str,
+    tree: DocumentTree,
+    markdown: str,
+    boundary_model: EmbeddingModelSpec,
+) -> list:
+    """Run strategy.chunk(), supplying Boundary embeds when needed."""
+    if strategy_name == StrategyName.SEMANTIC:
+        sentences = split_sentences(markdown)
+        if not sentences:
+            return []
+        threshold = get_semantic_threshold(boundary_model.id)
+        logger.info(
+            "Strategy semantic: embedding %d sentences for boundary detection "
+            "(%s, threshold=%.3f)",
+            len(sentences),
+            boundary_model.id,
+            threshold,
+        )
+        sentence_embeddings = embed_texts(
+            sentences,
+            model=boundary_model,
+            purpose="document",
+        )
+        return strategy.chunk(  # type: ignore[call-arg]
+            doc_name=doc_name,
+            tree=tree,
+            markdown=markdown,
+            sentence_embeddings=sentence_embeddings,
+            semantic_threshold=threshold,
+        )
+
+    if strategy_name == StrategyName.SEMANTIC_PARENT_CHILD:
+        paragraphs = split_paragraphs(markdown)
+        if not paragraphs:
+            return []
+        threshold = get_semantic_threshold(boundary_model.id)
+        logger.info(
+            "Strategy semantic_parent_child: embedding %d paragraphs for boundary detection "
+            "(%s, threshold=%.3f)",
+            len(paragraphs),
+            boundary_model.id,
+            threshold,
+        )
+        paragraph_embeddings = embed_texts(
+            paragraphs,
+            model=boundary_model,
+            purpose="document",
+        )
+        return strategy.chunk(  # type: ignore[call-arg]
+            doc_name=doc_name,
+            tree=tree,
+            markdown=markdown,
+            paragraph_embeddings=paragraph_embeddings,
+            semantic_threshold=threshold,
+        )
+
+    return strategy.chunk(
+        doc_name=doc_name,
+        tree=tree,
+        markdown=markdown,
+    )
+
+
 # ── Single-strategy runner ────────────────────────────────────────
 
 def _run_strategy(
@@ -56,6 +132,9 @@ def _run_strategy(
     doc_name: str,
     tree: DocumentTree,
     markdown: str,
+    *,
+    boundary_model: EmbeddingModelSpec,
+    corpus_model: EmbeddingModelSpec,
 ) -> int:
     """Run one strategy: chunk → embed → store in Qdrant.
 
@@ -64,16 +143,21 @@ def _run_strategy(
     """
     strategy = _get_strategy(strategy_name)
 
-    # Ensure Qdrant collection exists
+    qdr.ensure_collection(
-    qdr.ensure_collection(strategy_name)
+        strategy_name,
+        model_id=corpus_model.id,
+        dimension=corpus_model.dimension,
+    )
 
     t0 = time.time()
 
-    # Step 1: Chunk
+    chunks = _chunk_document(
-    chunks = strategy.chunk(
+        strategy_name,
+        strategy,
         doc_name=doc_name,
         tree=tree,
         markdown=markdown,
+        boundary_model=boundary_model,
     )
 
     if not chunks:
@@ -86,8 +170,6 @@ def _run_strategy(
         strategy_name.value, len(chunks), t_chunk,
     )
 
-    # Step 2: Embed
-    # For contextual strategy, embed enriched_content; for others, embed text
     texts_to_embed = []
     for chunk in chunks:
         if chunk.enriched_content:
@@ -96,16 +178,19 @@ def _run_strategy(
             texts_to_embed.append(chunk.text)
 
     t1 = time.time()
-    embeddings = embed_texts(texts_to_embed)
+    embeddings = embed_texts(
+        texts_to_embed,
+        model=corpus_model,
+        purpose="document",
+    )
     t_embed = time.time() - t1
     logger.info(
-        "Strategy %s: embedded %d texts in %.2fs",
+        "Strategy %s: embedded %d texts in %.2fs (corpus=%s)",
-        strategy_name.value, len(embeddings), t_embed,
+        strategy_name.value, len(embeddings), t_embed, corpus_model.id,
     )
 
-    # Step 3: Upsert to Qdrant
     t2 = time.time()
-    stored = qdr.upsert_chunks(chunks, embeddings)
+    stored = qdr.upsert_chunks(chunks, embeddings, model_id=corpus_model.id)
     t_store = time.time() - t2
     logger.info(
         "Strategy %s: stored %d vectors in %.2fs",
@@ -120,17 +205,39 @@ def _run_strategy(
 def run_strategies(
     doc_id: str,
     strategies: list[StrategyName],
+    *,
+    boundary_model_id: str | None = None,
+    corpus_model_id: str | None = None,
 ) -> tuple[list[dict], list[dict]]:
     """Run multiple strategies on a document with per-strategy failure isolation.
 
     Returns:
         (completed, failed) — lists of result dicts.
     """
+    from src.chunking.embedding import resolve_boundary_model, resolve_corpus_model
+
     doc = db.get_document(doc_id)
     if doc is None:
         raise ChunkingError(f"Document not found: {doc_id}")
 
-    # Parse the stored document tree (may be dict or JSON string)
+    corpus_model = resolve_corpus_model(corpus_model_id)
+    needs_boundary = any(s in _BOUNDARY_STRATEGIES for s in strategies)
+    boundary_model = (
+        resolve_boundary_model(boundary_model_id)
+        if needs_boundary
+        else corpus_model  # unused for non-semantic; keep a valid spec
+    )
+
+    logger.info(
+        "Processing doc %s corpus=%s (%s) boundary=%s (%s) needs_boundary=%s",
+        doc_id,
+        corpus_model.id,
+        corpus_model.provider.value,
+        boundary_model.id if needs_boundary else "—",
+        boundary_model.provider.value if needs_boundary else "—",
+        needs_boundary,
+    )
+
     tree_raw = doc["document_tree"]
     if isinstance(tree_raw, str):
         tree = DocumentTree.model_validate_json(tree_raw)
@@ -151,14 +258,21 @@ def run_strategies(
                 doc_name=doc_name,
                 tree=tree,
                 markdown=markdown,
+                boundary_model=boundary_model,
+                corpus_model=corpus_model,
             )
             elapsed = time.time() - t0
-            completed.append({
+            entry = {
                 "strategy": strategy_name,
                 "status": "completed",
                 "chunks_produced": chunks_produced,
                 "elapsed_seconds": round(elapsed, 2),
-            })
+                "corpus_embedding_model_id": corpus_model.id,
+                "embedding_model_id": corpus_model.id,  # legacy alias
+            }
+            if strategy_name in _BOUNDARY_STRATEGIES:
+                entry["boundary_embedding_model_id"] = boundary_model.id
+            completed.append(entry)
         except Exception as exc:
             logger.error(
                 "Strategy %s failed for doc %s: %s",
@@ -170,10 +284,16 @@ def run_strategies(
                 "error": str(exc),
             })
 
-    # Update chunk counts on the document
     counts = doc.get("chunk_counts", {})
     for result in completed:
         counts[result["strategy"].value] = result["chunks_produced"]
     db.update_chunk_counts(doc_id, counts)
 
+    if completed:
+        db.update_process_embedding_provenance(
+            doc_id,
+            corpus_model_id=corpus_model.id,
+            boundary_model_id=boundary_model.id if needs_boundary else None,
+        )
+
     return completed, failed

src/chunking/strategies/semantic.py

@@ -1,17 +1,15 @@
 """Semantic chunking strategy.
 
-Sentence-level granularity (ADR 0012):
+Sentence-level granularity (ADR 0012 / ADR 0020):
   1. Split markdown into sentences.
-  2. Embed each sentence via OpenAI.
+  2. Orchestrator embeds each sentence (Active Embedding Model).
   3. Compute cosine similarity between adjacent sentences.
-  4. When similarity drops below SEMANTIC_THRESHOLD, create a chunk boundary.
+  4. When similarity drops below the Active Embedding Model's semantic_threshold, create a chunk boundary.
   5. Enforce SEMANTIC_MIN_CHUNK_SIZE (minimum sentences per chunk).
   6. Boundary sentence stays with the previous chunk.
+  7. Orchestrator embeds finished chunks for Qdrant storage.
 
-Note: this strategy requires embeddings at chunk-time.  The chunk()
+Semantic Boundary Detection is required — no fixed-count fallback.
-method returns text chunks WITHOUT embeddings — the embedding step
-happens in the orchestration layer (service.py) which calls the
-embedding service after chunking.
 """
 
 from __future__ import annotations
@@ -20,6 +18,7 @@ import numpy as np
 
 from src.chunking.base import ChunkingStrategy, build_chunk, split_sentences
 from src.core.config import settings
+from src.core.exceptions import ChunkingError
 from src.core.models import Chunk, DocumentTree, StrategyName
 
 
@@ -85,30 +84,33 @@ class SemanticStrategy(ChunkingStrategy):
         tree: DocumentTree,
         markdown: str,
         sentence_embeddings: list[list[float]] | None = None,
+        semantic_threshold: float | None = None,
     ) -> list[Chunk]:
-        """Produce semantic chunks.
+        """Produce semantic chunks via Semantic Boundary Detection.
 
-        If sentence_embeddings is provided (from the orchestration layer),
+        Requires sentence_embeddings aligned 1:1 with split_sentences(markdown).
-        uses them for boundary detection.  Otherwise, falls back to
-        paragraph-level chunking (sentences without similarity-based splits).
         """
         sentences = split_sentences(markdown)
         if not sentences:
             return []
 
-        threshold = settings.semantic_threshold
+        if sentence_embeddings is None or len(sentence_embeddings) != len(sentences):
+            got = 0 if sentence_embeddings is None else len(sentence_embeddings)
+            raise ChunkingError(
+                f"semantic requires sentence embeddings for Semantic Boundary Detection "
+                f"(got {got}, need {len(sentences)}). Fixed-count fallback is disabled (ADR-0020)."
+            )
+
+        threshold = (
+            semantic_threshold
+            if semantic_threshold is not None
+            else settings.semantic_threshold
+        )
         min_size = settings.semantic_min_chunk_size
 
-        if sentence_embeddings and len(sentence_embeddings) == len(sentences):
+        chunk_texts = _group_sentences_into_chunks(
-            chunk_texts = _group_sentences_into_chunks(
+            sentences, sentence_embeddings, threshold, min_size
-                sentences, sentence_embeddings, threshold, min_size
+        )
-            )
-        else:
-            # Fallback: group sentences into fixed-size chunks
-            chunk_texts = []
-            for i in range(0, len(sentences), min_size):
-                group = sentences[i:i + min_size]
-                chunk_texts.append(" ".join(group))
 
         chunks: list[Chunk] = []
         for i, text in enumerate(chunk_texts):

src/chunking/strategies/semantic_parent_child.py

@@ -8,11 +8,13 @@ parent cluster is returned as context — giving the LLM richer
 information than a single paragraph.
 
 No headings or document structure needed — uses meaning instead.
+Semantic Boundary Detection is required — no fixed-count fallback (ADR-0020).
 """
 
 from __future__ import annotations
 
 import logging
+import re
 
 import numpy as np
 
@@ -20,9 +22,9 @@ from src.chunking.base import (
     ChunkingStrategy,
     build_chunk,
     make_chunk_id,
-    count_tokens,
 )
 from src.core.config import settings
+from src.core.exceptions import ChunkingError
 from src.core.models import Chunk, DocumentTree, StrategyName
 
 logger = logging.getLogger(__name__)
@@ -34,10 +36,8 @@ def _cosine_similarity(a: np.ndarray, b: np.ndarray) -> float:
     return float(dot / norm) if norm > 0 else 0.0
 
 
-def _split_paragraphs(text: str) -> list[str]:
+def split_paragraphs(text: str) -> list[str]:
     """Split markdown into paragraphs (double newline or single newline)."""
-    import re
-    # Split on double newlines first, then filter empties
     parts = re.split(r"\n\s*\n", text)
     paragraphs = [p.strip() for p in parts if p.strip()]
 
@@ -89,34 +89,35 @@ class SemanticParentChildStrategy(ChunkingStrategy):
         tree: DocumentTree,
         markdown: str,
         paragraph_embeddings: list[list[float]] | None = None,
+        semantic_threshold: float | None = None,
     ) -> list[Chunk]:
-        """Produce parent-child chunks via semantic clustering.
+        """Produce parent-child chunks via Semantic Boundary Detection.
 
-        If paragraph_embeddings is provided (from orchestration layer),
+        Requires paragraph_embeddings aligned 1:1 with split_paragraphs(markdown).
-        uses them for clustering.  Otherwise, groups paragraphs by
-        fixed count.
         """
-        paragraphs = _split_paragraphs(markdown)
+        paragraphs = split_paragraphs(markdown)
         if not paragraphs:
             return []
 
-        threshold = settings.semantic_threshold
+        if paragraph_embeddings is None or len(paragraph_embeddings) != len(paragraphs):
+            got = 0 if paragraph_embeddings is None else len(paragraph_embeddings)
+            raise ChunkingError(
+                f"semantic_parent_child requires paragraph embeddings for "
+                f"Semantic Boundary Detection (got {got}, need {len(paragraphs)}). "
+                f"Fixed-count fallback is disabled (ADR-0020)."
+            )
 
-        if paragraph_embeddings and len(paragraph_embeddings) == len(paragraphs):
+        threshold = (
-            clusters = _cluster_paragraphs(paragraphs, paragraph_embeddings, threshold)
+            semantic_threshold
-        else:
+            if semantic_threshold is not None
-            # Fallback: group every N paragraphs
+            else settings.semantic_threshold
-            group_size = max(3, settings.semantic_min_chunk_size)
+        )
-            clusters = []
+        clusters = _cluster_paragraphs(paragraphs, paragraph_embeddings, threshold)
-            for i in range(0, len(paragraphs), group_size):
-                clusters.append(list(range(i, min(i + group_size, len(paragraphs)))))
 
         chunks: list[Chunk] = []
         chunk_index = 0
 
         for cluster_indices in clusters:
-            # Parent = full cluster text
-            parent_text = "\n\n".join(paragraphs[i] for i in cluster_indices)
             parent_id = make_chunk_id(self.name, doc_name, chunk_index)
 
             # Each paragraph in the cluster is a child