Nextcloud MCP Server

"""Context expansion for search results. Provides utilities to expand matched chunks with surrounding context and position markers for better visualization and understanding of search results. """ import logging from dataclasses import dataclass 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: """Resolve file_id to file_path by querying Qdrant payload. Args: user_id: User ID who owns the file file_id: Numeric file ID chunk_start: Character offset where chunk starts chunk_end: Character offset where chunk ends Returns: File path string, or None if not found in Qdrant """ 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=file_id)), FieldCondition(key="doc_type", match=MatchValue(value="file")), FieldCondition( key="chunk_start_offset", match=MatchValue(value=chunk_start) ), FieldCondition( key="chunk_end_offset", match=MatchValue(value=chunk_end) ), ] ), limit=1, with_payload=["file_path"], with_vectors=False, ) if scroll_result[0]: point = scroll_result[0][0] file_path = point.payload.get("file_path") if file_path: logger.debug(f"Resolved file_id {file_id} to file_path {file_path}") return str(file_path) logger.warning( f"Could not find file_path in Qdrant for file_id {file_id}, " f"chunk [{chunk_start}:{chunk_end}]" ) return None except Exception as e: logger.error(f"Error querying Qdrant for file_path: {e}", exc_info=True) return None async def _get_deck_metadata_from_qdrant( user_id: str, card_id: int ) -> dict[str, int] | None: """Retrieve board_id and stack_id for a deck card from Qdrant payload. Args: user_id: User ID who owns the card card_id: Card ID Returns: Dictionary with board_id and stack_id, 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 any chunk of this card (we just need metadata) 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=card_id)), FieldCondition(key="doc_type", match=MatchValue(value="deck_card")), ] ), limit=1, with_payload=["board_id", "stack_id"], with_vectors=False, ) if scroll_result[0]: point = scroll_result[0][0] board_id = point.payload.get("board_id") stack_id = point.payload.get("stack_id") if board_id is not None and stack_id is not None: logger.debug( f"Retrieved deck metadata for card {card_id}: " f"board_id={board_id}, stack_id={stack_id}" ) return {"board_id": int(board_id), "stack_id": int(stack_id)} logger.debug( f"Could not find deck metadata in Qdrant for card {card_id} " f"(might be legacy data without board_id/stack_id)" ) return None except Exception as e: logger.debug(f"Error querying Qdrant for deck metadata: {e}") return None @dataclass class ChunkContext: """Expanded chunk with surrounding context and position markers. Attributes: chunk_text: The matched chunk text before_context: Text before the chunk (up to context_chars) after_context: Text after the chunk (up to context_chars) chunk_start_offset: Character position where chunk starts in document chunk_end_offset: Character position where chunk ends in document page_number: Page number for PDFs (None for other doc types) chunk_index: Zero-based chunk index (N in "chunk N of M") total_chunks: Total number of chunks in document marked_text: Full text with position markers around the chunk has_before_truncation: True if before_context was truncated has_after_truncation: True if after_context was truncated """ chunk_text: str before_context: str after_context: str chunk_start_offset: int chunk_end_offset: int page_number: int | None chunk_index: int total_chunks: int marked_text: str has_before_truncation: bool has_after_truncation: bool async def get_chunk_with_context( nc_client: NextcloudClient, user_id: str, doc_id: str | int, doc_type: str, chunk_start: int, chunk_end: int, page_number: int | None = None, chunk_index: int = 0, total_chunks: int = 1, context_chars: int = 300, ) -> ChunkContext | None: """Fetch chunk with surrounding context. First tries to retrieve the chunk from Qdrant (fast, cached). If that fails (e.g., legacy data with truncated excerpts), falls back to fetching and parsing the full document (slower, for PDFs especially). Args: nc_client: Authenticated Nextcloud client user_id: User ID who owns the document doc_id: Document ID (int for notes/files) doc_type: Type of document ("note", "file", etc.) chunk_start: Character offset where chunk starts chunk_end: Character offset where chunk ends page_number: Optional page number for PDFs chunk_index: Zero-based chunk index in document total_chunks: Total number of chunks in document context_chars: Number of characters to include before/after chunk Returns: 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): file_path = await _get_file_path_from_qdrant( user_id, doc_id, chunk_start, chunk_end ) if not file_path: logger.warning( f"Could not resolve file_id {doc_id} to file_path from Qdrant" ) return None resolved_doc_id = file_path logger.debug(f"Resolved file_id {doc_id} to file_path {file_path}") # Fetch full document text full_text = await _fetch_document_text( nc_client, resolved_doc_id, doc_type, user_id ) if full_text is None: logger.warning( f"Could not fetch document text for {doc_type} {doc_id}, " "skipping context expansion" ) return None # Validate offsets if chunk_start < 0 or chunk_end > len(full_text) or chunk_start >= chunk_end: logger.warning( f"Invalid chunk offsets for {doc_type} {doc_id}: " f"start={chunk_start}, end={chunk_end}, doc_len={len(full_text)}" ) return None # Extract chunk text chunk_text = full_text[chunk_start:chunk_end] # Calculate context boundaries context_start = max(0, chunk_start - context_chars) context_end = min(len(full_text), chunk_end + context_chars) # Extract context before_context = full_text[context_start:chunk_start] after_context = full_text[chunk_end:context_end] # Check for truncation has_before_truncation = context_start > 0 has_after_truncation = context_end < len(full_text) # Create marked text with position markers 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, ) async def _fetch_document_text( nc_client: NextcloudClient, doc_id: str | int, doc_type: str, user_id: str ) -> str | None: """Fetch full text content of a document. Args: nc_client: Authenticated Nextcloud client doc_id: Document ID (note ID or file path) doc_type: Type of document ("note", "file", etc.) Returns: Full document text, or None if document cannot be retrieved """ try: if doc_type == "note": # Fetch note by ID note = await nc_client.notes.get_note(note_id=int(doc_id)) # Reconstruct full content as indexed: title + "\n\n" + content # This ensures chunk offsets align with indexed content structure title = note.get("title", "") content = note.get("content", "") return f"{title}\n\n{content}" elif doc_type == "file": # Fetch file content via WebDAV try: file_path = str(doc_id) file_content, content_type = await nc_client.webdav.read_file(file_path) # Check if it's a PDF (by content type or file extension) is_pdf = ( content_type and "pdf" in content_type.lower() ) or file_path.lower().endswith(".pdf") if is_pdf: # Extract text from PDF using PyMuPDF # IMPORTANT: Use pymupdf4llm.to_markdown() to match indexing extraction # This ensures character offsets align between indexed chunks and retrieval import pymupdf import pymupdf4llm logger.debug(f"Extracting text from PDF: {file_path}") pdf_doc = pymupdf.open(stream=file_content, filetype="pdf") text_parts = [] # Extract each page as markdown (same as indexing) for page_num in range(pdf_doc.page_count): page_md = pymupdf4llm.to_markdown( pdf_doc, pages=[page_num], write_images=False, # Don't need images for context page_chunks=False, ) text_parts.append(page_md) pdf_doc.close() # Join pages (no separator - matches indexing) full_text = "".join(text_parts) logger.debug( f"Extracted {len(full_text)} characters from " f"{pdf_doc.page_count} pages in {file_path}" ) return full_text else: # Assume it's a text file, decode to string logger.debug(f"Decoding text file: {file_path}") return file_content.decode("utf-8", errors="replace") except Exception as e: logger.error( f"Error fetching file content for {doc_id}: {e}", exc_info=True ) return None elif doc_type == "news_item": # Fetch news item by ID from nextcloud_mcp_server.vector.html_processor import html_to_markdown item = await nc_client.news.get_item(int(doc_id)) # Reconstruct full content as indexed: title + source + URL + body # This ensures chunk offsets align with indexed content structure body_markdown = html_to_markdown(item.get("body", "")) item_title = item.get("title", "") item_url = item.get("url", "") feed_title = item.get("feedTitle", "") content_parts = [item_title] if feed_title: content_parts.append(f"Source: {feed_title}") if item_url: content_parts.append(f"URL: {item_url}") content_parts.append("") # Blank line content_parts.append(body_markdown) return "\n".join(content_parts) elif doc_type == "deck_card": # Fetch card from Deck API # Try to get board_id/stack_id from Qdrant metadata (O(1) lookup) # Otherwise fall back to iteration (legacy data) card = None deck_metadata = await _get_deck_metadata_from_qdrant(user_id, int(doc_id)) if deck_metadata: # Fast path: Direct lookup with known board_id/stack_id board_id = deck_metadata["board_id"] stack_id = deck_metadata["stack_id"] try: card = await nc_client.deck.get_card( board_id=board_id, stack_id=stack_id, card_id=int(doc_id) ) logger.debug( f"Retrieved deck card {doc_id} using metadata " f"(board_id={board_id}, stack_id={stack_id})" ) except Exception as e: logger.warning( f"Failed to fetch card with metadata (board_id={board_id}, " f"stack_id={stack_id}, card_id={doc_id}): {e}, falling back to iteration" ) # Fallback: Iterate through all boards/stacks (for legacy data or if fast path failed) if card is None: boards = await nc_client.deck.get_boards() card_found = False for board in boards: if card_found: break # Skip deleted boards (soft delete: deletedAt > 0) if board.deletedAt > 0: logger.debug( f"Skipping deleted board {board.id} while searching for card {doc_id}" ) continue stacks = await nc_client.deck.get_stacks(board.id) for stack in stacks: if card_found: break if stack.cards: for c in stack.cards: if c.id == int(doc_id): card = c card_found = True logger.debug( f"Found deck card {doc_id} in board {board.id}, " f"stack {stack.id} (fallback iteration)" ) break if not card_found: logger.warning(f"Deck card {doc_id} not found in any board/stack") return None # Type narrowing: card is set if we reach here assert card is not None # Reconstruct full content as indexed: title + "\n\n" + description # This ensures chunk offsets align with indexed content structure content_parts = [card.title] if card.description: content_parts.append(card.description) return "\n\n".join(content_parts) else: logger.warning(f"Unsupported doc_type for context expansion: {doc_type}") return None except Exception as e: logger.error(f"Error fetching document {doc_type} {doc_id}: {e}", exc_info=True) return None def _insert_position_markers( before_context: str, chunk_text: str, after_context: str, page_number: int | None, chunk_index: int, total_chunks: int, has_before_truncation: bool, has_after_truncation: bool, ) -> str: """Insert position markers around matched chunk. Creates markdown-formatted text with visual markers indicating chunk boundaries and metadata. Args: before_context: Text before chunk chunk_text: The matched chunk after_context: Text after chunk page_number: Optional page number chunk_index: Zero-based chunk index total_chunks: Total chunks in document has_before_truncation: Whether before_context is truncated has_after_truncation: Whether after_context is truncated Returns: Formatted text with position markers """ # Build position metadata position_parts = [] if page_number is not None: position_parts.append(f"Page {page_number}") position_parts.append(f"Chunk {chunk_index + 1} of {total_chunks}") position_metadata = ", ".join(position_parts) # Build marked text parts = [] # Add truncation indicator for before context if has_before_truncation: parts.append("**[...]**\n\n") # Add before context if present if before_context: parts.append(before_context) # Add chunk start marker parts.append(f"\n\n🔍 **MATCHED CHUNK START** ({position_metadata})\n\n") # Add chunk text parts.append(chunk_text) # Add chunk end marker parts.append("\n\n🔍 **MATCHED CHUNK END**\n\n") # Add after context if present if after_context: parts.append(after_context) # Add truncation indicator for after context if has_after_truncation: parts.append("\n\n**[...]**") return "".join(parts)