feat: Add context expansion to semantic search with chunk overlap removal

Implements optional context expansion for semantic search results that
fetches adjacent chunks (N-1 and N+1) from Qdrant to provide before/after
context. Removes configurable chunk overlap (default 200 chars) to avoid
duplicate text appearing in both context and excerpt.

Key changes:
- Add include_context and context_chars parameters to nc_semantic_search
  and nc_semantic_search_answer tools
- Implement Qdrant cache fast path for chunk retrieval (avoids re-fetching
  and re-parsing documents, especially important for PDFs)
- Add _get_chunk_by_index_from_qdrant() to fetch adjacent chunks
- Remove chunk overlap from before_context (last N chars) and after_context
  (first N chars) to prevent duplicate text
- Fetch context in parallel with anyio.Semaphore (max 20 concurrent)
- Pass through page_number from SearchResult to SemanticSearchResult
- Remove document-level deduplication (keep chunk-level dedup from algorithm)

Context expansion is opt-in via include_context=true parameter. When enabled:
- Populates has_context_expansion, marked_text, before_context, after_context
- Adds truncation flags when context exceeds context_chars limit
- Falls back to document fetch for legacy data with truncated excerpts

Related: nextcloud_mcp_server/search/context.py:87-382,
         nextcloud_mcp_server/server/semantic.py:161-255

nextcloud_mcp_server/auth/templates/vector_viz.html

@@ -122,7 +122,8 @@
                         <a :href="getNextcloudUrl(result)" target="_blank" style="font-weight: 500; color: #0066cc; text-decoration: none;">
                             <span x-text="result.title"></span>
                         </a>
-                        <div style="font-size: 14px; color: #666; margin-top: 4px;" x-text="result.excerpt"></div>
+                        <div style="font-size: 14px; color: #666; margin-top: 4px;"
+                             x-text="result.excerpt.length > 200 ? result.excerpt.substring(0, 200) + '...' : result.excerpt"></div>
                         <div style="font-size: 12px; color: #999; margin-top: 4px;">
                             Raw Score: <span x-text="result.original_score.toFixed(3)"></span>
                             (<span x-text="(result.score * 100).toFixed(0)"></span>% relative) |

nextcloud_mcp_server/document_processors/pymupdf.py

@@ -8,12 +8,12 @@ from typing import Any, Optional
 
 import pymupdf
 import pymupdf.layout
-import pymupdf4llm
 
 from .base import DocumentProcessor, ProcessingResult, ProcessorError
 
 # Activate layout analysis for better text extraction
 pymupdf.layout.activate()
+import pymupdf4llm  # noqa
 
 logger = logging.getLogger(__name__)
 

nextcloud_mcp_server/search/context.py

@@ -12,6 +12,141 @@ from nextcloud_mcp_server.client import NextcloudClient
 logger = logging.getLogger(__name__)
 
 
+async def _get_chunk_from_qdrant(
+    user_id: str, doc_id: int, doc_type: str, chunk_start: int, chunk_end: int
+) -> str | None:
+    """Retrieve full chunk text from Qdrant payload.
+
+    This avoids re-fetching and re-parsing documents by using the cached
+    chunk content already stored in Qdrant.
+
+    Args:
+        user_id: User ID who owns the document
+        doc_id: Document ID
+        doc_type: Document type (e.g., "note", "file")
+        chunk_start: Character offset where chunk starts
+        chunk_end: Character offset where chunk ends
+
+    Returns:
+        Full chunk text from Qdrant excerpt field, or None if not found
+    """
+    try:
+        from qdrant_client.models import FieldCondition, Filter, MatchValue
+
+        from nextcloud_mcp_server.config import get_settings
+        from nextcloud_mcp_server.vector.qdrant_client import get_qdrant_client
+
+        qdrant_client = await get_qdrant_client()
+        settings = get_settings()
+
+        # Query for the specific chunk
+        scroll_result = await qdrant_client.scroll(
+            collection_name=settings.get_collection_name(),
+            scroll_filter=Filter(
+                must=[
+                    FieldCondition(key="user_id", match=MatchValue(value=user_id)),
+                    FieldCondition(key="doc_id", match=MatchValue(value=doc_id)),
+                    FieldCondition(key="doc_type", match=MatchValue(value=doc_type)),
+                    FieldCondition(
+                        key="chunk_start_offset", match=MatchValue(value=chunk_start)
+                    ),
+                    FieldCondition(
+                        key="chunk_end_offset", match=MatchValue(value=chunk_end)
+                    ),
+                ]
+            ),
+            limit=1,
+            with_payload=["excerpt"],
+            with_vectors=False,
+        )
+
+        if scroll_result[0]:
+            point = scroll_result[0][0]
+            excerpt = point.payload.get("excerpt")
+            if excerpt:
+                logger.debug(
+                    f"Retrieved chunk from Qdrant for {doc_type} {doc_id}: "
+                    f"{len(excerpt)} chars"
+                )
+                return str(excerpt)
+
+        logger.debug(
+            f"Chunk not found in Qdrant for {doc_type} {doc_id}, "
+            f"chunk [{chunk_start}:{chunk_end}]. Will fall back to document fetch."
+        )
+        return None
+
+    except Exception as e:
+        logger.error(
+            f"Error querying Qdrant for chunk: {e}. Falling back to document fetch.",
+            exc_info=True,
+        )
+        return None
+
+
+async def _get_chunk_by_index_from_qdrant(
+    user_id: str, doc_id: int, doc_type: str, chunk_index: int
+) -> str | None:
+    """Retrieve chunk text by chunk_index from Qdrant payload.
+
+    Used to fetch adjacent chunks for context expansion.
+
+    Args:
+        user_id: User ID who owns the document
+        doc_id: Document ID
+        doc_type: Document type (e.g., "note", "file")
+        chunk_index: Zero-based chunk index in document
+
+    Returns:
+        Full chunk text from Qdrant excerpt field, or None if not found
+    """
+    try:
+        from qdrant_client.models import FieldCondition, Filter, MatchValue
+
+        from nextcloud_mcp_server.config import get_settings
+        from nextcloud_mcp_server.vector.qdrant_client import get_qdrant_client
+
+        qdrant_client = await get_qdrant_client()
+        settings = get_settings()
+
+        # Query for chunk by index
+        scroll_result = await qdrant_client.scroll(
+            collection_name=settings.get_collection_name(),
+            scroll_filter=Filter(
+                must=[
+                    FieldCondition(key="user_id", match=MatchValue(value=user_id)),
+                    FieldCondition(key="doc_id", match=MatchValue(value=doc_id)),
+                    FieldCondition(key="doc_type", match=MatchValue(value=doc_type)),
+                    FieldCondition(
+                        key="chunk_index", match=MatchValue(value=chunk_index)
+                    ),
+                ]
+            ),
+            limit=1,
+            with_payload=["excerpt"],
+            with_vectors=False,
+        )
+
+        if scroll_result[0]:
+            point = scroll_result[0][0]
+            excerpt = point.payload.get("excerpt")
+            if excerpt:
+                logger.debug(
+                    f"Retrieved adjacent chunk {chunk_index} from Qdrant for "
+                    f"{doc_type} {doc_id}: {len(excerpt)} chars"
+                )
+                return str(excerpt)
+
+        return None
+
+    except Exception as e:
+        logger.debug(
+            f"Could not retrieve adjacent chunk {chunk_index} for "
+            f"{doc_type} {doc_id}: {e}"
+        )
+        return None
+
+
 async def _get_file_path_from_qdrant(
     user_id: str, file_id: int, chunk_start: int, chunk_end: int
 ) -> str | None:
@@ -117,11 +252,11 @@ async def get_chunk_with_context(
     total_chunks: int = 1,
     context_chars: int = 300,
 ) -> ChunkContext | None:
-    """Fetch chunk with surrounding context from original document.
+    """Fetch chunk with surrounding context.
 
-    Retrieves the full document text and expands the matched chunk to include
+    First tries to retrieve the chunk from Qdrant (fast, cached). If that fails
-    surrounding context for better understanding. Inserts position markers
+    (e.g., legacy data with truncated excerpts), falls back to fetching and
-    around the chunk for visualization.
+    parsing the full document (slower, for PDFs especially).
 
     Args:
         nc_client: Authenticated Nextcloud client
@@ -139,6 +274,111 @@ async def get_chunk_with_context(
         ChunkContext with expanded context and markers, or None if document
         cannot be retrieved
     """
+    # Convert doc_id to int for Qdrant query
+    doc_id_int = (
+        int(doc_id)
+        if isinstance(doc_id, str) and doc_id.isdigit()
+        else (doc_id if isinstance(doc_id, int) else None)
+    )
+
+    # Try to get chunk from Qdrant first (fast path)
+    if doc_id_int is not None:
+        chunk_text = await _get_chunk_from_qdrant(
+            user_id, doc_id_int, doc_type, chunk_start, chunk_end
+        )
+        if chunk_text:
+            logger.info(
+                f"Retrieved chunk from Qdrant cache for {doc_type} {doc_id} "
+                f"(avoids document re-fetch/re-parse)"
+            )
+
+            # Fetch adjacent chunks for context expansion
+            # Get chunk overlap from config to remove duplicate text
+            from nextcloud_mcp_server.config import get_settings
+
+            settings = get_settings()
+            chunk_overlap = settings.document_chunk_overlap
+
+            before_context = ""
+            after_context = ""
+            has_before_truncation = False
+            has_after_truncation = False
+
+            # Fetch previous chunk if not first chunk
+            if chunk_index > 0:
+                before_chunk = await _get_chunk_by_index_from_qdrant(
+                    user_id, doc_id_int, doc_type, chunk_index - 1
+                )
+                if before_chunk:
+                    # Remove overlap: the last chunk_overlap chars of previous chunk
+                    # overlap with the first chunk_overlap chars of current chunk
+                    before_context = (
+                        before_chunk[:-chunk_overlap]
+                        if len(before_chunk) > chunk_overlap
+                        else ""
+                    )
+                    # Truncate if requested context_chars < remaining length
+                    if before_context and len(before_context) > context_chars:
+                        before_context = before_context[-context_chars:]
+                        has_before_truncation = True
+                else:
+                    # Could not fetch previous chunk, but we're not at start
+                    has_before_truncation = True
+
+            # Fetch next chunk if not last chunk
+            if chunk_index < total_chunks - 1:
+                after_chunk = await _get_chunk_by_index_from_qdrant(
+                    user_id, doc_id_int, doc_type, chunk_index + 1
+                )
+                if after_chunk:
+                    # Remove overlap: the first chunk_overlap chars of next chunk
+                    # overlap with the last chunk_overlap chars of current chunk
+                    after_context = (
+                        after_chunk[chunk_overlap:]
+                        if len(after_chunk) > chunk_overlap
+                        else ""
+                    )
+                    # Truncate if requested context_chars < remaining length
+                    if after_context and len(after_context) > context_chars:
+                        after_context = after_context[:context_chars]
+                        has_after_truncation = True
+                else:
+                    # Could not fetch next chunk, but we're not at end
+                    has_after_truncation = True
+
+            marked_text = _insert_position_markers(
+                before_context=before_context,
+                chunk_text=chunk_text,
+                after_context=after_context,
+                page_number=page_number,
+                chunk_index=chunk_index,
+                total_chunks=total_chunks,
+                has_before_truncation=has_before_truncation,
+                has_after_truncation=has_after_truncation,
+            )
+            return ChunkContext(
+                chunk_text=chunk_text,
+                before_context=before_context,
+                after_context=after_context,
+                chunk_start_offset=chunk_start,
+                chunk_end_offset=chunk_end,
+                page_number=page_number,
+                chunk_index=chunk_index,
+                total_chunks=total_chunks,
+                marked_text=marked_text,
+                has_before_truncation=has_before_truncation,
+                has_after_truncation=has_after_truncation,
+            )
+
+    # Fallback: Fetch full document and extract chunk with context
+    # This path is taken for:
+    # 1. Legacy data with truncated excerpts in Qdrant
+    # 2. Failed Qdrant queries
+    logger.info(
+        f"Falling back to document fetch for {doc_type} {doc_id} "
+        f"(Qdrant cache miss, possibly legacy data)"
+    )
+
     # For files, retrieve file_path from Qdrant payload
     resolved_doc_id = doc_id
     if doc_type == "file" and isinstance(doc_id, int):

nextcloud_mcp_server/server/semantic.py

@@ -26,6 +26,7 @@ from nextcloud_mcp_server.observability.metrics import (
     instrument_tool,
 )
 from nextcloud_mcp_server.search.bm25_hybrid import BM25HybridSearchAlgorithm
+from nextcloud_mcp_server.search.context import get_chunk_with_context
 
 logger = logging.getLogger(__name__)
 
@@ -43,6 +44,8 @@ def configure_semantic_tools(mcp: FastMCP):
         doc_types: list[str] | None = None,
         score_threshold: float = 0.0,
         fusion: str = "rrf",
+        include_context: bool = False,
+        context_chars: int = 300,
     ) -> SemanticSearchResponse:
         """
         Search Nextcloud content using BM25 hybrid search with cross-app support.
@@ -66,6 +69,8 @@ def configure_semantic_tools(mcp: FastMCP):
             fusion: Fusion algorithm: "rrf" (Reciprocal Rank Fusion, default) or "dbsf" (Distribution-Based Score Fusion)
                    RRF: Good general-purpose fusion using reciprocal ranks
                    DBSF: Uses distribution-based normalization, may better balance different score ranges
+            include_context: Whether to expand results with surrounding context (default: False)
+            context_chars: Number of characters to include before/after matched chunk (default: 300)
 
         Returns:
             SemanticSearchResponse with matching documents ranked by fusion scores
@@ -128,18 +133,16 @@ def configure_semantic_tools(mcp: FastMCP):
                 # Sort combined results by score
                 all_results.sort(key=lambda r: r.score, reverse=True)
 
-            # Deduplicate results (hybrid search may return same doc from dense + sparse)
+            # Note: BM25HybridSearchAlgorithm already deduplicates at chunk level
-            # Qdrant already filters by user_id for multi-tenant isolation
+            # (doc_id, doc_type, chunk_start, chunk_end), which allows multiple
-            # Sampling tool will verify access when fetching full content
+            # chunks from the same document while preventing duplicate chunks.
-            seen = set()
+            # No additional deduplication needed here - multiple chunks per document
-            unique_results = []
+            # are valuable for RAG contexts.
-            for result in all_results:
+            # Qdrant already filters by user_id for multi-tenant isolation.
-                key = (result.id, result.doc_type)
+            # Sampling tool will verify access when fetching full content.
-                if key not in seen:
+            search_results = all_results[
-                    seen.add(key)
+                :limit
-                    unique_results.append(result)
+            ]  # Final limit after chunk-level dedup in algorithm
-
-            search_results = unique_results[:limit]  # Final limit after deduplication
 
             # Convert SearchResult objects to SemanticSearchResult for response
             results = []
@@ -160,9 +163,99 @@ def configure_semantic_tools(mcp: FastMCP):
                         else 1,
                         chunk_start_offset=r.chunk_start_offset,
                         chunk_end_offset=r.chunk_end_offset,
+                        page_number=r.page_number,
                     )
                 )
 
+            # Expand results with surrounding context if requested
+            if include_context and results:
+                logger.info(
+                    f"Expanding {len(results)} results with context "
+                    f"(context_chars={context_chars})"
+                )
+
+                # Fetch context for all results in parallel
+                # Limit concurrent requests to prevent connection pool exhaustion
+                max_concurrent = 20
+                semaphore = anyio.Semaphore(max_concurrent)
+                expanded_results = [None] * len(results)
+
+                async def fetch_context(index: int, result: SemanticSearchResult):
+                    """Fetch context for a single result (parallel with semaphore)."""
+                    async with semaphore:
+                        # Only expand if we have valid chunk offsets
+                        if (
+                            result.chunk_start_offset is None
+                            or result.chunk_end_offset is None
+                        ):
+                            # Keep result as-is without context expansion
+                            expanded_results[index] = result
+                            return
+
+                        try:
+                            chunk_context = await get_chunk_with_context(
+                                nc_client=client,
+                                user_id=username,
+                                doc_id=result.id,
+                                doc_type=result.doc_type,
+                                chunk_start=result.chunk_start_offset,
+                                chunk_end=result.chunk_end_offset,
+                                page_number=result.page_number,
+                                chunk_index=result.chunk_index,
+                                total_chunks=result.total_chunks,
+                                context_chars=context_chars,
+                            )
+
+                            if chunk_context:
+                                # Create new result with context fields populated
+                                expanded_results[index] = SemanticSearchResult(
+                                    id=result.id,
+                                    doc_type=result.doc_type,
+                                    title=result.title,
+                                    category=result.category,
+                                    excerpt=result.excerpt,
+                                    score=result.score,
+                                    chunk_index=result.chunk_index,
+                                    total_chunks=result.total_chunks,
+                                    chunk_start_offset=result.chunk_start_offset,
+                                    chunk_end_offset=result.chunk_end_offset,
+                                    page_number=result.page_number,
+                                    # Context expansion fields
+                                    has_context_expansion=True,
+                                    marked_text=chunk_context.marked_text,
+                                    before_context=chunk_context.before_context,
+                                    after_context=chunk_context.after_context,
+                                    has_before_truncation=chunk_context.has_before_truncation,
+                                    has_after_truncation=chunk_context.has_after_truncation,
+                                )
+                                logger.debug(
+                                    f"Expanded context for {result.doc_type} {result.id}"
+                                )
+                            else:
+                                # Context expansion failed, keep original result
+                                expanded_results[index] = result
+                                logger.debug(
+                                    f"Failed to expand context for {result.doc_type} {result.id}, "
+                                    "keeping original result"
+                                )
+                        except Exception as e:
+                            # Context expansion failed, keep original result
+                            expanded_results[index] = result
+                            logger.warning(
+                                f"Error expanding context for {result.doc_type} {result.id}: {e}"
+                            )
+
+                # Run all context fetches in parallel using anyio task group
+                async with anyio.create_task_group() as tg:
+                    for idx, result in enumerate(results):
+                        tg.start_soon(fetch_context, idx, result)
+
+                # Replace results with expanded versions
+                results = [r for r in expanded_results if r is not None]
+                logger.info(
+                    f"Context expansion completed: {len(results)} results with context"
+                )
+
             logger.info(f"Returning {len(results)} results from BM25 hybrid search")
 
             return SemanticSearchResponse(
@@ -202,6 +295,8 @@ def configure_semantic_tools(mcp: FastMCP):
         score_threshold: float = 0.7,
         max_answer_tokens: int = 500,
         fusion: str = "rrf",
+        include_context: bool = False,
+        context_chars: int = 300,
     ) -> SamplingSearchResponse:
         """
         Semantic search with LLM-generated answer using MCP sampling.
@@ -227,6 +322,8 @@ def configure_semantic_tools(mcp: FastMCP):
             score_threshold: Minimum similarity score 0-1 (default: 0.7)
             max_answer_tokens: Maximum tokens for generated answer (default: 500)
             fusion: Fusion algorithm: "rrf" (Reciprocal Rank Fusion, default) or "dbsf" (Distribution-Based Score Fusion)
+            include_context: Whether to expand results with surrounding context (default: False)
+            context_chars: Number of characters to include before/after matched chunk (default: 300)
 
         Returns:
             SamplingSearchResponse containing:
@@ -267,6 +364,8 @@ def configure_semantic_tools(mcp: FastMCP):
             limit=limit,
             score_threshold=score_threshold,
             fusion=fusion,
+            include_context=include_context,
+            context_chars=context_chars,
         )
 
         # 2. Handle no results case - don't waste a sampling call

nextcloud_mcp_server/vector/processor.py

@@ -391,7 +391,7 @@ async def _index_document(
                     "doc_type": doc_task.doc_type,
                     "is_placeholder": False,  # Real indexed document (not placeholder)
                     "title": title,
-                    "excerpt": chunk.text[:200],
+                    "excerpt": chunk.text,  # Full chunk text (up to chunk_size, default 2048 chars)
                     "indexed_at": indexed_at,
                     "modified_at": doc_task.modified_at,
                     "etag": etag,