Store raw text (do not contextualize) in chunk.text. Embed with contextualized text.

CHANGELOG.md

@@ -32,9 +32,14 @@
 - **BREAKING: Chunker Interface**: `DocumentChunker.chunk()` now returns `list[ChunkWithMetadata]` instead of `list[str]`
   - `ChunkWithMetadata` combines chunk text with `ChunkMetadata` (refs, labels, headings, page_numbers)
   - All chunker implementations updated: `DoclingLocalChunker`, `DoclingServeChunker`
-- **Page Image Generation**: `generate_page_images=True` is now the default for local docling converter
+- **Page Image Generation**: `generate_page_images=True` is now always enabled for local docling converter
   - Required for visual grounding features
-  - docling-serve already generates page images by default
+  - Removed `generate_page_images` config option (docling-serve already generates page images by default)
+- **Chunk Text Storage**: Chunks now store raw text without heading contextualization
+  - Section headings are prepended only at embedding time for better semantic search
+  - Stored chunk content stays clean without duplicate heading prefixes
+  - Headings remain available in `ChunkMetadata` for display and citations
+  - Local and serve chunkers now produce identical output
 - **QA Prompts**: Updated to use page numbers and section headings in citations when available
 
 ### Migration

haiku_rag_slim/haiku/rag/chunkers/docling_local.py

@@ -117,7 +117,9 @@ class DoclingLocalChunker(DocumentChunker):
         result: list[ChunkWithMetadata] = []
 
         for chunk in raw_chunks:
-            text = self.chunker.contextualize(chunk)
+            # Use raw chunk text - headings are stored separately in metadata
+            # and prepended at embedding time for better semantic search
+            text = chunk.text
 
             # Extract metadata from DocChunk.meta (cast to DocMeta for type safety)
             doc_item_refs: list[str] = []

haiku_rag_slim/haiku/rag/store/repositories/chunk.py

@@ -227,8 +227,16 @@ class ChunkRepository:
 
         chunks_with_metadata = await chunker.chunk(processed_document)
 
-        chunk_texts = [c.text for c in chunks_with_metadata]
+        # Build embedding texts with headings prepended for better semantic search
-        embeddings = await self.embedder.embed(chunk_texts)
+        # The stored content stays raw, but embeddings capture section context
+        embedding_texts = []
+        for c in chunks_with_metadata:
+            if c.metadata.headings:
+                embedding_text = "\n".join(c.metadata.headings) + "\n" + c.text
+            else:
+                embedding_text = c.text
+            embedding_texts.append(embedding_text)
+        embeddings = await self.embedder.embed(embedding_texts)
 
         # Prepare all chunk records for batch insertion
         chunk_records = []