diff --git a/backend/pyproject.toml b/backend/pyproject.toml
index 220a08a..5c9c88c 100644
--- a/backend/pyproject.toml
+++ b/backend/pyproject.toml
@@ -61,7 +61,8 @@ pre-commit = [
 # Additional optional libraries
 libs = [
     "weaviate-client>=4.17.0",
-    "docling>=2.55.0",
+    "docling>=2.60.0",
+    "docling-core>=2.60.0",
     "chromadb>=0.5.0",
 ]
 
diff --git a/backend/syft_space/components/dataset_types/chromadb_local/chromadb_type.py b/backend/syft_space/components/dataset_types/chromadb_local/chromadb_type.py
index b8e7ab5..a3ac480 100644
--- a/backend/syft_space/components/dataset_types/chromadb_local/chromadb_type.py
+++ b/backend/syft_space/components/dataset_types/chromadb_local/chromadb_type.py
@@ -3,13 +3,11 @@
 from __future__ import annotations
 
 import asyncio
-import hashlib
 import os
 import re
 import threading
 import uuid
 from functools import lru_cache
-from io import BytesIO
 from pathlib import Path as SyncPath
 from types import ModuleType
 from typing import Any
@@ -17,16 +15,20 @@
 from anyio import Path as AsyncPath
 from pydantic import BaseModel, Field, ValidationError, field_validator
 
+from syft_space.components.dataset_types.chunking import (
+    DocumentChunker,
+    build_image_urls,
+)
 from syft_space.components.dataset_types.interfaces import (
     FileIngestableDatasetType,
-    IngestFile,
+    IngestContext,
     IngestRequest,
+    SearchContext,
     SearchedDocument,
     SearchParameters,
     SearchResult,
 )
 from syft_space.components.shared.domain_types import (
-    Context,
     HealthcheckResponse,
     HealthcheckStatus,
 )
@@ -133,8 +135,7 @@ class LocalFSChromaDBDatasetType(FileIngestableDatasetType):
 
     NAME = "local_file"
 
-    # Class-level locks for thread-safe lazy initialization (shared resources)
-    _converter_lock = threading.Lock()
+    # Class-level lock for thread-safe lazy initialization
     _embedding_fn_lock = threading.Lock()
 
     def __init__(self, config: dict[str, Any]) -> None:
@@ -147,24 +148,12 @@ def __init__(self, config: dict[str, Any]) -> None:
         self.config = ChromaDBLocalConfiguration.model_validate(config)
 
         # Lazy initialization (instance-level)
-        self._converter = None
         self._embedding_fn = None
         self._client: chromadb.AsyncClientAPI | None = None  # noqa: F821
         self._client_lock = asyncio.Lock()  # Instance-level lock for client
 
-    @property
-    def converter(self):
-        """Lazily initialize DocumentConverter on first use.
-
-        Thread-safe via double-checked locking.
-        """
-        from docling.document_converter import DocumentConverter
-
-        if self._converter is None:
-            with self._converter_lock:
-                if self._converter is None:
-                    self._converter = DocumentConverter()
-        return self._converter
+        # Shared chunking pipeline
+        self._document_chunker = DocumentChunker()
 
     async def get_client(self) -> chromadb.AsyncClientAPI:  # noqa: F821
         """Get or create the ChromaDB async client.
@@ -284,46 +273,19 @@ def _generate_embeddings(self, texts: list[str]) -> list:
                     self._embedding_fn = ONNXMiniLM_L6_V2()
         return self._embedding_fn(texts)
 
-    def _parse_document(self, file: IngestFile) -> dict[str, Any]:
-        """Parse the document into a dictionary.
-
-        Args:
-            file: IngestFile to parse
+    async def ingest(self, ctx: IngestContext, request: IngestRequest) -> None:
+        """Ingest files into ChromaDB collection as chunks.
 
-        Returns:
-            Dictionary with parsed document content and metadata
-        """
-        from docling.document_converter import DocumentStream
-
-        # Convert the file to a document stream
-        stream = BytesIO(file.file_handle.read())
-
-        # If the file is a JSON or TXT file, read the content directly
-        if SyncPath(file.filename).suffix.lower() in [".json", ".txt"]:
-            content = stream.read().decode("utf-8")
-        else:
-            # Otherwise, convert the file to a document stream and export to markdown
-            document_stream = DocumentStream(name=file.filename, stream=stream)
-            conv_result = self.converter.convert(document_stream)
-            content = conv_result.document.export_to_markdown()
-
-        # Return the parsed document content and metadata
-        return {
-            "content": content,
-            "file_name": file.filename,
-            "file_type": file.content_type,
-            "file_size": file.file_size or 0,
-        }
-
-    async def ingest(self, ctx: Context, request: IngestRequest) -> None:
-        """Ingest files into ChromaDB collection.
+        Each file is parsed into multiple chunks via the shared DocumentChunker.
+        Each chunk is stored as a separate vector with metadata linking back
+        to the source document and page numbers.
 
         Args:
-            ctx: Request context with sender information
+            ctx: Ingest context with dataset identifier
             request: Ingest request with files to process
         """
         if not _chromadb_available():
-            raise ImportError("ChromaDB and docling are required for ingestion")
+            raise ImportError("ChromaDB is required for ingestion")
 
         client = await self.get_client()
 
@@ -338,45 +300,159 @@ async def ingest(self, ctx: Context, request: IngestRequest) -> None:
             if ext not in self.allowed_extensions():
                 raise ValueError(f"Unsupported file type: {ext}")
 
-            # Run CPU-bound docling parsing in executor to avoid blocking event loop
-            parsed_doc = await asyncio.to_thread(self._parse_document, file)
+            # Run CPU-bound parsing in executor to avoid blocking event loop
+            chunks = await asyncio.to_thread(
+                self._document_chunker.parse_document,
+                file,
+                self.collection_name,
+            )
+
+            await self._store_chunks(collection, chunks)
+
+    async def _store_chunks(self, collection, chunks: list[dict]) -> None:
+        """Embed and store chunks with neighbor references in ChromaDB.
+
+        Each chunk gets prev/next pointers so the search method can fetch
+        surrounding context in a single batch call.
+        """
+        if not chunks:
+            return
+
+        doc_id = chunks[0]["doc_id"]
+        chunk_ids = [f"{doc_id}_{i}" for i in range(len(chunks))]
+
+        for i, chunk in enumerate(chunks):
+            prev_chunk_id = chunk_ids[i - 1] if i > 0 else ""
+            next_chunk_id = chunk_ids[i + 1] if i < len(chunks) - 1 else ""
 
-            # Generate embeddings in executor (CPU-bound)
             embeddings = await asyncio.to_thread(
-                self._generate_embeddings, [parsed_doc["content"]]
+                self._generate_embeddings, [chunk["embedding_text"]]
             )
 
-            # Generate unique ID based on filename and content hash
-            doc_id = hashlib.sha256(
-                f"{file.filename}:{parsed_doc['content'][:100]}".encode()
-            ).hexdigest()[:32]
-
-            # Add to collection
             await collection.add(
-                ids=[doc_id],
-                documents=[parsed_doc["content"]],
+                ids=[chunk_ids[i]],
+                documents=[chunk["text"]],
                 embeddings=embeddings,
                 metadatas=[
                     {
-                        "file_name": parsed_doc["file_name"],
-                        "file_type": parsed_doc["file_type"] or "",
-                        "file_size": parsed_doc["file_size"],
+                        "doc_id": doc_id,
+                        "chunk_index": i,
+                        "prev_chunk_id": prev_chunk_id,
+                        "next_chunk_id": next_chunk_id,
+                        "file_name": chunk["file_name"],
+                        "file_type": chunk["file_type"] or "",
+                        "file_size": chunk["file_size"],
+                        "page_numbers": ",".join(map(str, chunk["page_numbers"])),
+                        "headings": " > ".join(chunk["headings"]),
+                        "picture_ids": ",".join(chunk["picture_ids"]),
                     }
                 ],
             )
 
+    def _process_query_results(
+        self,
+        results: dict,
+        dataset_id: str,
+        similarity_threshold: float,
+    ) -> tuple[list[SearchedDocument], set[str]]:
+        """Convert raw ChromaDB query results into SearchedDocuments.
+
+        Applies similarity threshold filtering and builds image URLs.
+
+        Returns:
+            Tuple of (matched documents, set of matched chunk IDs).
+        """
+        documents: list[SearchedDocument] = []
+        matched_ids: set[str] = set()
+
+        if not results["ids"] or not results["ids"][0]:
+            return documents, matched_ids
+
+        for i, doc_id in enumerate(results["ids"][0]):
+            distance = results["distances"][0][i] if results["distances"] else 0.0
+
+            # ChromaDB cosine distance: 0 = identical, 2 = opposite
+            similarity_score = 1.0 - (distance / 2.0)
+
+            if similarity_score <= similarity_threshold:
+                continue
+
+            matched_ids.add(doc_id)
+            content = results["documents"][0][i] if results["documents"] else ""
+            metadata = results["metadatas"][0][i] if results["metadatas"] else {}
+
+            # Add image URLs to metadata
+            metadata["image_urls"] = build_image_urls(
+                dataset_id,
+                metadata.get("doc_id", ""),
+                metadata.get("picture_ids", ""),
+            )
+
+            documents.append(
+                SearchedDocument(
+                    document_id=doc_id,
+                    content=content,
+                    metadata=metadata,
+                    similarity_score=similarity_score,
+                )
+            )
+
+        return documents, matched_ids
+
+    @staticmethod
+    async def _enrich_with_neighbor_context(
+        collection,
+        documents: list[SearchedDocument],
+        matched_ids: set[str],
+    ) -> None:
+        """Fetch prev/next chunk text and attach to each matched document.
+
+        Skips neighbors that are themselves already in the matched set
+        to avoid redundant content.
+        """
+        neighbor_ids: set[str] = set()
+        for doc in documents:
+            for key in ("prev_chunk_id", "next_chunk_id"):
+                nid = doc.metadata.get(key, "")
+                if nid and nid not in matched_ids:
+                    neighbor_ids.add(nid)
+
+        neighbor_map: dict[str, str] = {}
+        if neighbor_ids:
+            neighbors = await collection.get(
+                ids=list(neighbor_ids),
+                include=["documents"],
+            )
+            if neighbors["ids"]:
+                for nid, ndoc in zip(
+                    neighbors["ids"], neighbors["documents"] or [], strict=False
+                ):
+                    neighbor_map[nid] = ndoc or ""
+
+        for doc in documents:
+            doc.metadata["prev_context"] = neighbor_map.get(
+                doc.metadata.get("prev_chunk_id", ""), ""
+            )
+            doc.metadata["next_context"] = neighbor_map.get(
+                doc.metadata.get("next_chunk_id", ""), ""
+            )
+
     async def search(
-        self, ctx: Context, query: str, params: SearchParameters | None = None
+        self, ctx: SearchContext, query: str, params: SearchParameters | None = None
     ) -> SearchResult:
-        """Search the ChromaDB collection for similar documents.
+        """Search the ChromaDB collection for matching chunks.
+
+        Returns top-k matching chunks enriched with neighboring chunk text
+        for better RAG context. Image URLs use dataset_id to avoid leaking
+        internal collection names.
 
         Args:
-            ctx: Request context with sender information
+            ctx: Search context with dataset identifier
             query: Search query string
             params: Optional search parameters
 
         Returns:
-            SearchResult with matching documents
+            SearchResult with matching document chunks
         """
         if not _chromadb_available():
             raise ImportError("ChromaDB is required for search")
@@ -393,64 +469,36 @@ async def search(
         try:
             client = await self.get_client()
 
-            # Try to get collection
             try:
                 collection = await client.get_collection(name=self.collection_name)
             except Exception:
-                # Collection doesn't exist
                 return SearchResult(
                     documents=[],
                     metadata={"count": 0, "error": "Collection not found"},
                 )
 
-            # Generate query embedding in executor (CPU-bound)
             query_embedding = await asyncio.to_thread(
                 self._generate_embeddings, [query]
             )
 
-            # Query with embedding
             results = await collection.query(
                 query_embeddings=query_embedding,
                 n_results=params.limit,
                 include=["documents", "metadatas", "distances"],
             )
 
-            documents = []
-
-            # Process results (ChromaDB returns nested lists)
-            if results["ids"] and results["ids"][0]:
-                for i, doc_id in enumerate(results["ids"][0]):
-                    distance = (
-                        results["distances"][0][i] if results["distances"] else 0.0
-                    )
-
-                    # Convert distance to similarity score
-                    # ChromaDB cosine distance: 0 = identical, 2 = opposite
-                    # Similarity = 1 - (distance / 2)
-                    similarity_score = 1.0 - (distance / 2.0)
-
-                    # Apply similarity threshold filter
-                    if similarity_score < similarity_threshold:
-                        continue
-
-                    content = results["documents"][0][i] if results["documents"] else ""
-                    metadata = (
-                        results["metadatas"][0][i] if results["metadatas"] else {}
-                    )
+            documents, matched_ids = self._process_query_results(
+                results, ctx.dataset_id, similarity_threshold
+            )
 
-                    documents.append(
-                        SearchedDocument(
-                            document_id=doc_id,
-                            content=content,
-                            metadata=metadata,
-                            similarity_score=similarity_score,
-                        )
-                    )
+            if documents:
+                await self._enrich_with_neighbor_context(
+                    collection, documents, matched_ids
+                )
 
             return SearchResult(documents=documents, metadata={"count": len(documents)})
 
         except Exception as e:
-            # Handle connection errors
             return SearchResult(
                 documents=[],
                 metadata={"count": 0, "error": str(e)},
@@ -461,7 +509,7 @@ def enabled(cls) -> bool:
         """Check if this dataset type is enabled.
 
         Returns:
-            True if chromadb and docling are installed
+            True if chromadb is installed
         """
         return _chromadb_available()
 
@@ -512,3 +560,28 @@ async def healthcheck(self) -> HealthcheckResponse:
             status=HealthcheckStatus.UNHEALTHY,
             message="ChromaDB is unhealthy",
         )
+
+    async def delete(self, ctx: IngestContext) -> None:
+        """Delete the entire ChromaDB collection and its page images.
+
+        Since each dataset owns its own collection, deletion drops the
+        entire collection rather than filtering by dataset_id.
+
+        Args:
+            ctx: Ingest context with dataset identifier
+        """
+        if not _chromadb_available():
+            raise ImportError("ChromaDB is required for deletion")
+
+        client = await self.get_client()
+
+        try:
+            await client.delete_collection(name=self.collection_name)
+        except Exception as e:
+            raise ValueError(f"Error deleting collection: {str(e)}") from e
+
+        # Remove all page images for this collection
+        await asyncio.to_thread(
+            self._document_chunker.purge_page_images,
+            self.collection_name,
+        )
diff --git a/backend/syft_space/components/dataset_types/chunking.py b/backend/syft_space/components/dataset_types/chunking.py
new file mode 100644
index 0000000..9b35bde
--- /dev/null
+++ b/backend/syft_space/components/dataset_types/chunking.py
@@ -0,0 +1,228 @@
+"""Shared document chunking pipeline using Docling.
+
+Provides a unified parse → chunk → save images pipeline used by all
+dataset types (ChromaDB, Weaviate, etc.). Each dataset type handles
+its own DB-specific storage and retrieval.
+"""
+
+import shutil
+import threading
+from io import BytesIO
+from pathlib import Path
+from typing import Any
+from uuid import uuid4
+
+from syft_space.components.dataset_types.interfaces import IngestFile
+
+# Page images stored at ~/.syft-space/page_images/{collection_name}/{doc_id}/
+PAGE_IMAGES_BASE_DIR = Path.home() / ".syft-space" / "page_images"
+
+# API route prefix for serving document images
+IMAGE_ENDPOINT_PREFIX = "/api/v1/datasets"
+
+
+def build_image_urls(dataset_id: str, doc_id: str, picture_ids: str) -> list[str]:
+    """Build image endpoint URLs from chunk metadata.
+
+    Uses dataset_id (not collection_name) in URLs to avoid leaking
+    internal collection names in public-facing responses.
+
+    Args:
+        dataset_id: Dataset identifier (public-facing key)
+        doc_id: Hash-based document identifier
+        picture_ids: Comma-separated picture filenames (e.g. "ab12cd34ef56.png,...")
+
+    Returns:
+        List of URI paths like
+        ["/api/v1/datasets/{dataset_id}/images/{doc_id}/{uuid_hex}.png", ...]
+    """
+    urls: list[str] = []
+    if not doc_id or not dataset_id:
+        return urls
+    for pic in picture_ids.split(","):
+        if pic.strip():
+            urls.append(
+                f"{IMAGE_ENDPOINT_PREFIX}/{dataset_id}/images/{doc_id}/{pic.strip()}"
+            )
+    return urls
+
+
+class DocumentChunker:
+    """Shared Docling + HybridChunker pipeline for all dataset types.
+
+    Handles document conversion, tokenizer-aware chunking, and image
+    extraction. Converter and chunker are lazily initialized and shared
+    across instances via class-level locks.
+
+    Usage:
+        chunker = DocumentChunker()
+        chunks = chunker.parse_document(file)
+        # Each chunk is a dict with: text, embedding_text, doc_id,
+        # page_numbers, headings, picture_ids, file_name, file_type, file_size
+    """
+
+    _converter_lock = threading.Lock()
+    _chunker_lock = threading.Lock()
+
+    # Shared across all instances
+    _converter = None
+    _chunker = None
+
+    @property
+    def converter(self):
+        """Lazily initialize Docling DocumentConverter.
+
+        Configured with picture extraction so embedded figures can be
+        saved to disk separately. Page renders are not generated.
+        Thread-safe via double-checked locking.
+        """
+        from docling.datamodel.base_models import InputFormat
+        from docling.datamodel.pipeline_options import PdfPipelineOptions
+        from docling.document_converter import DocumentConverter, PdfFormatOption
+
+        if DocumentChunker._converter is None:
+            with self._converter_lock:
+                if DocumentChunker._converter is None:
+                    pipeline_options = PdfPipelineOptions(
+                        generate_picture_images=True,
+                        images_scale=2.0,
+                    )
+                    DocumentChunker._converter = DocumentConverter(
+                        format_options={
+                            InputFormat.PDF: PdfFormatOption(
+                                pipeline_options=pipeline_options
+                            )
+                        }
+                    )
+        return DocumentChunker._converter
+
+    @property
+    def chunker(self):
+        """Lazily initialize Docling HybridChunker.
+
+        Uses the default tokenizer (all-MiniLM-L6-v2) to ensure chunk
+        sizes align with the embedding model's context window.
+        Thread-safe via double-checked locking.
+        """
+        from docling_core.transforms.chunker import HybridChunker
+
+        if DocumentChunker._chunker is None:
+            with self._chunker_lock:
+                if DocumentChunker._chunker is None:
+                    DocumentChunker._chunker = HybridChunker()
+        return DocumentChunker._chunker
+
+    @staticmethod
+    def get_page_images_dir(collection_name: str, doc_id: str) -> Path:
+        """Get the directory for storing page images for a document."""
+        return PAGE_IMAGES_BASE_DIR / collection_name / doc_id
+
+    def parse_document(
+        self, file: IngestFile, collection_name: str
+    ) -> list[dict[str, Any]]:
+        """Parse document into chunks with metadata and save images to disk.
+
+        Uses Docling for PDFs and other rich formats (DOCX, XLSX, HTML, etc.)
+        with HybridChunker for tokenizer-aware chunking. Plain text/JSON files
+        are handled directly without Docling overhead.
+
+        Args:
+            file: IngestFile to parse
+            collection_name: Dataset collection name (used for image storage partitioning)
+
+        Returns:
+            List of chunk dicts, each with keys:
+                text: clean text for storage/display
+                embedding_text: heading-enriched text for embedding
+                doc_id: unique document identifier (UUID-based)
+                page_numbers: list[int] of referenced pages
+                headings: list[str] of section headings
+                picture_ids: list[str] of extracted picture filenames
+                file_name: original filename
+                file_type: MIME type
+                file_size: size in bytes
+        """
+        stream = BytesIO(file.file_handle.read())
+        ext = Path(file.filename).suffix.lower()
+        doc_id = uuid4().hex[:16]
+
+        # Simple text files - no Docling overhead needed
+        if ext in [".json", ".txt"]:
+            content = stream.read().decode("utf-8")
+            return [
+                {
+                    "text": content,
+                    "embedding_text": content,
+                    "doc_id": doc_id,
+                    "page_numbers": [],
+                    "headings": [],
+                    "picture_ids": [],
+                    "file_name": file.filename,
+                    "file_type": file.content_type,
+                    "file_size": file.file_size or 0,
+                }
+            ]
+
+        # All other formats via Docling
+        from docling.datamodel.base_models import DocumentStream
+
+        stream.seek(0)
+        source = DocumentStream(name=file.filename, stream=stream)
+        result = self.converter.convert(source)
+        doc = result.document
+        images_dir = self.get_page_images_dir(collection_name, doc_id)
+        images_dir.mkdir(parents=True, exist_ok=True)
+
+        # Save extracted pictures and map to pages
+        picture_page_map: dict[int, list[str]] = {}
+        for picture in doc.pictures:
+            pil_image = picture.get_image(doc)
+            if pil_image:
+                pic_filename = f"{uuid4().hex}.png"
+                pil_image.save(images_dir / pic_filename, "PNG")
+                if picture.prov:
+                    for prov in picture.prov:
+                        picture_page_map.setdefault(prov.page_no, []).append(
+                            pic_filename
+                        )
+
+        # Chunk the document
+        chunker = self.chunker
+        chunks = []
+        for chunk in chunker.chunk(doc):
+            # Extract page numbers from chunk provenance
+            page_numbers = sorted(
+                {
+                    prov.page_no
+                    for item in chunk.meta.doc_items
+                    for prov in (item.prov or [])
+                }
+            )
+
+            # Collect picture IDs for this chunk's pages
+            chunk_picture_ids: list[str] = []
+            for pn in page_numbers:
+                chunk_picture_ids.extend(picture_page_map.get(pn, []))
+            chunk_picture_ids = sorted(set(chunk_picture_ids))
+
+            chunks.append(
+                {
+                    "text": chunk.text,
+                    "embedding_text": chunker.contextualize(chunk),
+                    "doc_id": doc_id,
+                    "page_numbers": page_numbers,
+                    "headings": chunk.meta.headings or [],
+                    "picture_ids": chunk_picture_ids,
+                    "file_name": file.filename,
+                    "file_type": file.content_type,
+                    "file_size": file.file_size or 0,
+                }
+            )
+
+        return chunks
+
+    def purge_page_images(self, collection_name: str) -> None:
+        """Purge all page images for a collection."""
+        page_images_dir = PAGE_IMAGES_BASE_DIR / collection_name
+        if page_images_dir.exists():
+            shutil.rmtree(page_images_dir)
diff --git a/backend/syft_space/components/dataset_types/interfaces.py b/backend/syft_space/components/dataset_types/interfaces.py
index d18c4a8..a3470b6 100644
--- a/backend/syft_space/components/dataset_types/interfaces.py
+++ b/backend/syft_space/components/dataset_types/interfaces.py
@@ -3,12 +3,25 @@
 from io import BytesIO
 from tempfile import SpooledTemporaryFile
 from typing import Any, BinaryIO, Protocol
+from uuid import UUID
 
 from pydantic import BaseModel, Field
 
 from syft_space.components.shared.domain_types import Context, HealthcheckResponse
 
 
+class SearchContext(Context):
+    """Context for search requests."""
+
+    dataset_id: UUID = Field(..., description="Unique identifier for the dataset")
+
+
+class IngestContext(Context):
+    """Context for ingestion requests."""
+
+    dataset_id: UUID = Field(..., description="Unique identifier for the dataset")
+
+
 class SearchParameters(BaseModel):
     """Domain contract for search parameters."""
 
@@ -140,12 +153,12 @@ async def validate_configuration(cls, configuration: dict[str, Any]) -> None:
         ...
 
     async def search(
-        self, ctx: Context, query: str, params: SearchParameters | None = None
+        self, ctx: SearchContext, query: str, params: SearchParameters | None = None
     ) -> SearchResult:
         """Search the dataset for the given query.
 
         Args:
-            ctx: Request context with sender information
+            ctx: Search context with dataset identifier
             query: Search query string
             params: Optional search parameters
 
@@ -199,15 +212,23 @@ class IngestableDatasetType(BaseDatasetType):
     For file-based ingestion with watching, see FileIngestableDatasetType.
     """
 
-    async def ingest(self, ctx: Context, request: IngestRequest) -> None:
+    async def ingest(self, ctx: IngestContext, request: IngestRequest) -> None:
         """Ingest data into the dataset.
 
         Args:
-            ctx: Request context with sender information
+            ctx: Ingest context with dataset identifier
             request: Ingest request with files to add
         """
         ...
 
+    async def delete(self, ctx: IngestContext) -> None:
+        """Delete data from the dataset.
+
+        Args:
+            ctx: Ingest context with dataset identifier
+        """
+        ...
+
 
 class FileIngestableDatasetType(IngestableDatasetType):
     """Dataset type interface for file-based ingestion with watching.
diff --git a/backend/syft_space/components/dataset_types/weaviate_remote/weaviate_type.py b/backend/syft_space/components/dataset_types/weaviate_remote/weaviate_type.py
index 107337b..2508d91 100644
--- a/backend/syft_space/components/dataset_types/weaviate_remote/weaviate_type.py
+++ b/backend/syft_space/components/dataset_types/weaviate_remote/weaviate_type.py
@@ -7,12 +7,12 @@
 
 from syft_space.components.dataset_types.interfaces import (
     BaseDatasetType,
+    SearchContext,
     SearchedDocument,
     SearchParameters,
     SearchResult,
 )
 from syft_space.components.shared.domain_types import (
-    Context,
     HealthcheckResponse,
     HealthcheckStatus,
 )
@@ -122,12 +122,12 @@ def collection_name(self) -> str:
         return self.config.collection_name
 
     async def search(
-        self, ctx: Context, query: str, params: SearchParameters | None = None
+        self, ctx: SearchContext, query: str, params: SearchParameters | None = None
     ) -> SearchResult:
         """Search the dataset for the given query.
 
         Args:
-            ctx: Request context with sender information
+            ctx: Search context with dataset identifier
             query: Search query string
             params: Optional search parameters
 
@@ -215,7 +215,7 @@ def enabled(cls) -> bool:
         """Check if this dataset type is enabled.
 
         Returns:
-            True if weaviate and docling are installed
+            True if weaviate is installed
         """
         return enabled
 
diff --git a/backend/syft_space/components/datasets/entities.py b/backend/syft_space/components/datasets/entities.py
index 883aa57..6cff256 100644
--- a/backend/syft_space/components/datasets/entities.py
+++ b/backend/syft_space/components/datasets/entities.py
@@ -85,7 +85,10 @@ class Dataset(SQLModel, table=True):
     tenant: "Tenant" = Relationship(back_populates="datasets")
     endpoints: list["Endpoint"] = Relationship(
         back_populates="dataset",
-        sa_relationship_kwargs={"foreign_keys": "[Endpoint.dataset_id]"},
+        sa_relationship_kwargs={
+            "foreign_keys": "[Endpoint.dataset_id]",
+            "cascade": "all, delete",
+        },
     )
 
     class Config:
diff --git a/backend/syft_space/components/datasets/handlers.py b/backend/syft_space/components/datasets/handlers.py
index b7a0eb6..0525ca1 100644
--- a/backend/syft_space/components/datasets/handlers.py
+++ b/backend/syft_space/components/datasets/handlers.py
@@ -1,12 +1,19 @@
 """Dataset handlers for business logic."""
 
+import re
 from datetime import datetime, timezone
 from pathlib import Path
 from typing import Any
+from uuid import UUID
 
 from fastapi import HTTPException
 from loguru import logger
 
+from syft_space.components.dataset_types.chunking import PAGE_IMAGES_BASE_DIR
+from syft_space.components.dataset_types.interfaces import (
+    IngestableDatasetType,
+    IngestContext,
+)
 from syft_space.components.dataset_types.registry import DatasetTypeRegistry
 from syft_space.components.datasets.entities import (
     Dataset,
@@ -509,6 +516,25 @@ async def delete_dataset(self, name: str, tenant: Tenant) -> dict:
                 f"Dataset '{name}' was linked to provisioner, keeping provisioner running"
             )
 
+        # Clean up dataset type resources (collection, images) before deleting the record
+        try:
+            dataset_type_cls = self.registry.get_dataset_type(dataset.dtype)
+            if dataset.provisioner_state_id and issubclass(
+                dataset_type_cls, IngestableDatasetType
+            ):
+                dataset_type = dataset_type_cls(dataset.configuration)
+                ctx = IngestContext(
+                    sender="system@openmined.org",
+                    dataset_id=dataset.id,
+                )
+                await dataset_type.delete(ctx)
+        except KeyError:
+            logger.warning(
+                f"Dataset type '{dataset.dtype}' not registered, skipping resource cleanup"
+            )
+        except Exception as e:
+            logger.error(f"Failed to clean up resources for dataset '{name}': {e}")
+
         deleted = await self.repository.delete_by_name(name, tenant.id)
         if not deleted:
             raise HTTPException(status_code=404, detail=f"Dataset '{name}' not found")
@@ -893,3 +919,69 @@ def browse_directory(
             parent=parent,
             items=items,
         )
+
+    async def serve_image(
+        self, dataset_id: str, doc_id: str, filename: str, tenant: Tenant
+    ) -> Path:
+        """Validate and return the filesystem path for a document image.
+
+        Resolves dataset_id to collection_name internally so that collection
+        names are never exposed in public URLs.
+
+        Args:
+            dataset_id: Dataset ID
+            doc_id: Hash-based document identifier (16-char hex)
+            filename: Image filename (32-char hex UUID .png)
+            tenant: Tenant
+        Returns:
+            Resolved Path to the image file
+
+        Raises:
+            HTTPException 400: If any parameter format is invalid
+            HTTPException 404: If the dataset or image file does not exist
+        """
+        # Validate dataset_id format: UUID hex (32 chars or with dashes)
+        if not re.match(
+            r"^[a-f0-9]{8}-?[a-f0-9]{4}-?[a-f0-9]{4}-?[a-f0-9]{4}-?[a-f0-9]{12}$",
+            dataset_id,
+        ):
+            raise HTTPException(status_code=400, detail="Invalid dataset ID format")
+
+        # Validate doc_id format: 16-char lowercase hex
+        if not re.match(r"^[a-f0-9]{16}$", doc_id):
+            raise HTTPException(status_code=400, detail="Invalid document ID format")
+
+        # Validate filename format: {uuid_hex}.png (32-char lowercase hex)
+        if not re.match(r"^[a-f0-9]{32}\.png$", filename):
+            raise HTTPException(status_code=400, detail="Invalid filename format")
+
+        # Resolve dataset_id to collection_name via the dataset type
+        dataset = await self.repository.get_by_id(UUID(dataset_id), tenant.id)
+        if not dataset:
+            raise HTTPException(status_code=404, detail="Dataset not found")
+
+        try:
+            dataset_type_cls = self.registry.get_dataset_type(dataset.dtype)
+        except KeyError:
+            raise HTTPException(
+                status_code=400, detail=f"Dataset type '{dataset.dtype}' not registered"
+            ) from None
+
+        dataset_instance = dataset_type_cls(dataset.configuration)
+        collection_name = getattr(dataset_instance, "collection_name", None)
+        if not collection_name:
+            raise HTTPException(
+                status_code=404, detail="Dataset has no collection configured"
+            )
+
+        # Resolve path and prevent traversal
+        image_path = (
+            PAGE_IMAGES_BASE_DIR / collection_name / doc_id / filename
+        ).resolve()
+        if not image_path.is_relative_to(PAGE_IMAGES_BASE_DIR.resolve()):
+            raise HTTPException(status_code=400, detail="Invalid path")
+
+        if not image_path.exists():
+            raise HTTPException(status_code=404, detail="Image not found")
+
+        return image_path
diff --git a/backend/syft_space/components/datasets/repository.py b/backend/syft_space/components/datasets/repository.py
index 4033fea..8695275 100644
--- a/backend/syft_space/components/datasets/repository.py
+++ b/backend/syft_space/components/datasets/repository.py
@@ -116,8 +116,11 @@ async def delete_by_name(self, name: str, tenant_id: UUID) -> bool:
             True if deleted, False if not found
         """
         async with self.db.get_session() as session:
-            statement = select(Dataset).where(
-                Dataset.name == name, Dataset.tenant_id == tenant_id
+            statement = (
+                select(Dataset)
+                .where(Dataset.name == name, Dataset.tenant_id == tenant_id)
+                .options(selectinload(Dataset.endpoints))
+                .with_for_update()
             )
             result = await session.exec(statement)
             obj = result.first()
diff --git a/backend/syft_space/components/datasets/routes.py b/backend/syft_space/components/datasets/routes.py
index 82f63d4..354fc94 100644
--- a/backend/syft_space/components/datasets/routes.py
+++ b/backend/syft_space/components/datasets/routes.py
@@ -3,6 +3,7 @@
 from typing import TYPE_CHECKING, Any, Optional
 
 from fastapi import APIRouter, Body, Depends
+from fastapi.responses import FileResponse
 
 from syft_space.components.auth.public import public_route
 from syft_space.components.datasets.handlers import DatasetHandler
@@ -107,6 +108,35 @@ async def browse_directory(
         """
         return handler.browse_directory(path, show_hidden)
 
+    # ============== Image Serving Endpoint ==============
+
+    @public_route
+    @router.get("/{dataset_id}/images/{doc_id}/{filename}")
+    async def serve_image(
+        dataset_id: str,
+        doc_id: str,
+        filename: str,
+        handler: DatasetHandler = Depends(get_handler),
+        tenant: Tenant = Depends(get_tenant_dependency),
+    ) -> FileResponse:
+        """Serve a document image (page render or extracted picture).
+
+        Images are saved to disk during ingestion and served via this endpoint.
+        Search results return URIs pointing here so clients can fetch images.
+        Uses dataset_id (not collection_name) in the URL to avoid leaking
+        internal collection names.
+
+        Args:
+            dataset_id: Dataset UUID (resolved to collection_name internally)
+            doc_id: Hash-based document identifier (16-char hex)
+            filename: Image filename (32-char hex UUID, e.g., a1b2c3d4e5f67890abcdef1234567890.png)
+
+        Returns:
+            PNG image file
+        """
+        image_path = await handler.serve_image(dataset_id, doc_id, filename, tenant)
+        return FileResponse(image_path, media_type="image/png")
+
     # ============== Dataset CRUD Endpoints ==============
 
     @router.post("/", response_model=DatasetResponse, status_code=201)
diff --git a/backend/syft_space/components/endpoints/handlers.py b/backend/syft_space/components/endpoints/handlers.py
index 54c5c75..fc08b57 100644
--- a/backend/syft_space/components/endpoints/handlers.py
+++ b/backend/syft_space/components/endpoints/handlers.py
@@ -6,7 +6,10 @@
 from fastapi import HTTPException
 from loguru import logger
 
-from syft_space.components.dataset_types.interfaces import SearchParameters
+from syft_space.components.dataset_types.interfaces import (
+    SearchContext,
+    SearchParameters,
+)
 from syft_space.components.dataset_types.registry import DatasetTypeRegistry
 from syft_space.components.datasets.repository import DatasetRepository
 from syft_space.components.endpoints.entities import Endpoint, ResponseType
@@ -34,7 +37,11 @@
 from syft_space.components.marketplaces.entities import Marketplace
 from syft_space.components.marketplaces.repository import MarketplaceRepository
 from syft_space.components.marketplaces.utils import ensure_valid_accounting_credentials
-from syft_space.components.model_types.interfaces import ChatMessage, ChatParameters
+from syft_space.components.model_types.interfaces import (
+    ChatContext,
+    ChatMessage,
+    ChatParameters,
+)
 from syft_space.components.model_types.registry import ModelTypeRegistry
 from syft_space.components.models.repository import ModelRepository
 from syft_space.components.policies.repository import PolicyRepository
@@ -43,7 +50,6 @@
     PolicyViolationError,
 )
 from syft_space.components.policy_types.registry import PolicyTypeRegistry
-from syft_space.components.shared.domain_types import Context
 from syft_space.components.shared.syfthub_client import SyftHubClient, SyftHubError
 from syft_space.components.tenants.entities import Tenant
 
@@ -390,7 +396,7 @@ async def _search_dataset(
             query = user_messages[-1].content if user_messages else ""
 
         # Search with verified sender email
-        ctx = Context(sender=request.sender_email)
+        ctx = SearchContext(sender=request.sender_email, dataset_id=dataset.id)
         search_params = SearchParameters(
             similarity_threshold=request.similarity_threshold,
             limit=request.limit,
@@ -481,7 +487,7 @@ async def _chat_with_model(
             messages.insert(0, context_message)
 
         # Chat with verified sender email
-        ctx = Context(sender=request.sender_email)
+        ctx = ChatContext(sender=request.sender_email, model_id=model.id)
         chat_params = ChatParameters(
             temperature=request.temperature,
             max_tokens=request.max_tokens,
diff --git a/backend/syft_space/components/ingestion/manager.py b/backend/syft_space/components/ingestion/manager.py
index b3aa463..2b56233 100644
--- a/backend/syft_space/components/ingestion/manager.py
+++ b/backend/syft_space/components/ingestion/manager.py
@@ -20,7 +20,11 @@
 from watchdog.events import FileSystemEvent, FileSystemEventHandler
 from watchdog.observers import Observer
 
-from syft_space.components.dataset_types.interfaces import IngestFile, IngestRequest
+from syft_space.components.dataset_types.interfaces import (
+    IngestContext,
+    IngestFile,
+    IngestRequest,
+)
 from syft_space.components.dataset_types.registry import DatasetTypeRegistry
 from syft_space.components.datasets.entities import Dataset
 from syft_space.components.ingestion.entities import IngestionJob, IngestionJobStatus
@@ -28,7 +32,6 @@
 from syft_space.components.ingestion.events import FileEvent, FileEventType
 from syft_space.components.ingestion.repository import IngestionJobRepository
 from syft_space.components.ingestion.utils import rglob_visible
-from syft_space.components.shared.domain_types import Context
 from syft_space.components.shared.lifecycle import LifecycleService
 
 if TYPE_CHECKING:
@@ -507,7 +510,7 @@ async def _process_single_job(self, job: IngestionJob) -> None:
             ingest_request = IngestRequest(files=[ingest_file])
 
             # Create context (use system context for background ingestion)
-            ctx = Context(sender="system@openmined.org")
+            ctx = IngestContext(sender="system@openmined.org", dataset_id=dataset.id)
 
             # Call ingest (native async)
             await dataset_type.ingest(ctx, ingest_request)
diff --git a/backend/syft_space/components/model_types/interfaces.py b/backend/syft_space/components/model_types/interfaces.py
index 18016df..e3c3841 100644
--- a/backend/syft_space/components/model_types/interfaces.py
+++ b/backend/syft_space/components/model_types/interfaces.py
@@ -7,6 +7,12 @@
 from syft_space.components.shared.domain_types import Context, HealthcheckResponse
 
 
+class ChatContext(Context):
+    """Context for chat requests."""
+
+    model_id: str = Field(..., description="Unique identifier for the model")
+
+
 class ChatMessage(BaseModel):
     """Domain model for a chat message."""
 
@@ -121,14 +127,14 @@ def configuration_schema(cls) -> dict[str, Any]:
 
     async def chat(
         self,
-        ctx: Context,
+        ctx: ChatContext,
         messages: list[ChatMessage],
         params: ChatParameters | None = None,
     ) -> ChatResult:
         """Chat with the model.
 
         Args:
-            ctx: Request context with sender information
+            ctx: Chat context with model identifier
             messages: List of chat messages
             params: Optional chat parameters
 
diff --git a/backend/syft_space/components/model_types/openai/openai_type.py b/backend/syft_space/components/model_types/openai/openai_type.py
index 6aac7c4..241f22b 100644
--- a/backend/syft_space/components/model_types/openai/openai_type.py
+++ b/backend/syft_space/components/model_types/openai/openai_type.py
@@ -4,6 +4,7 @@
 
 from syft_space.components.model_types.interfaces import (
     BaseModelType,
+    ChatContext,
     ChatMessage,
     ChatMessageResult,
     ChatParameters,
@@ -11,7 +12,6 @@
     TokenUsage,
 )
 from syft_space.components.shared.domain_types import (
-    Context,
     HealthcheckResponse,
     HealthcheckStatus,
 )
@@ -110,14 +110,14 @@ def configuration_schema(cls) -> dict[str, Any]:
 
     async def chat(
         self,
-        ctx: Context,
+        ctx: ChatContext,
         messages: list[ChatMessage],
         params: ChatParameters | None = None,
     ) -> ChatResult:
         """Chat with the OpenAI model.
 
         Args:
-            ctx: Request context with sender information
+            ctx: Chat context with model identifier
             messages: List of chat messages
             params: Optional chat parameters
 
diff --git a/backend/syft_space/components/models/entities.py b/backend/syft_space/components/models/entities.py
index a4ecca5..06e1e55 100644
--- a/backend/syft_space/components/models/entities.py
+++ b/backend/syft_space/components/models/entities.py
@@ -43,7 +43,10 @@ class Model(SQLModel, table=True):
     tenant: "Tenant" = Relationship(back_populates="models")
     endpoints: list["Endpoint"] = Relationship(
         back_populates="model",
-        sa_relationship_kwargs={"foreign_keys": "[Endpoint.model_id]"},
+        sa_relationship_kwargs={
+            "foreign_keys": "[Endpoint.model_id]",
+            "cascade": "all, delete",
+        },
     )
 
     class Config:
diff --git a/backend/uv.lock b/backend/uv.lock
index 5b32b1d..d50d795 100644
--- a/backend/uv.lock
+++ b/backend/uv.lock
@@ -6,14 +6,18 @@ resolution-markers = [
     "python_full_version == '3.12.*' and sys_platform == 'darwin'",
     "python_full_version >= '3.13' and platform_machine == 'aarch64' and sys_platform == 'linux'",
     "python_full_version == '3.12.*' and platform_machine == 'aarch64' and sys_platform == 'linux'",
-    "(python_full_version >= '3.13' and platform_machine != 'aarch64' and sys_platform == 'linux') or (python_full_version >= '3.13' and sys_platform != 'darwin' and sys_platform != 'linux')",
-    "(python_full_version == '3.12.*' and platform_machine != 'aarch64' and sys_platform == 'linux') or (python_full_version == '3.12.*' and sys_platform != 'darwin' and sys_platform != 'linux')",
+    "python_full_version >= '3.13' and sys_platform == 'win32'",
+    "(python_full_version >= '3.13' and platform_machine != 'aarch64' and sys_platform == 'linux') or (python_full_version >= '3.13' and sys_platform != 'darwin' and sys_platform != 'linux' and sys_platform != 'win32')",
+    "python_full_version == '3.12.*' and sys_platform == 'win32'",
+    "(python_full_version == '3.12.*' and platform_machine != 'aarch64' and sys_platform == 'linux') or (python_full_version == '3.12.*' and sys_platform != 'darwin' and sys_platform != 'linux' and sys_platform != 'win32')",
     "python_full_version == '3.11.*' and sys_platform == 'darwin'",
     "python_full_version == '3.11.*' and platform_machine == 'aarch64' and sys_platform == 'linux'",
-    "(python_full_version == '3.11.*' and platform_machine != 'aarch64' and sys_platform == 'linux') or (python_full_version == '3.11.*' and sys_platform != 'darwin' and sys_platform != 'linux')",
+    "python_full_version == '3.11.*' and sys_platform == 'win32'",
+    "(python_full_version == '3.11.*' and platform_machine != 'aarch64' and sys_platform == 'linux') or (python_full_version == '3.11.*' and sys_platform != 'darwin' and sys_platform != 'linux' and sys_platform != 'win32')",
     "python_full_version < '3.11' and sys_platform == 'darwin'",
     "python_full_version < '3.11' and platform_machine == 'aarch64' and sys_platform == 'linux'",
-    "(python_full_version < '3.11' and platform_machine != 'aarch64' and sys_platform == 'linux') or (python_full_version < '3.11' and sys_platform != 'darwin' and sys_platform != 'linux')",
+    "python_full_version < '3.11' and sys_platform == 'win32'",
+    "(python_full_version < '3.11' and platform_machine != 'aarch64' and sys_platform == 'linux') or (python_full_version < '3.11' and sys_platform != 'darwin' and sys_platform != 'linux' and sys_platform != 'win32')",
 ]
 
 [[package]]
@@ -468,7 +472,8 @@ dependencies = [
     { name = "mmh3" },
     { name = "numpy", version = "2.2.6", source = { registry = "https://pypi.org/simple" }, marker = "python_full_version < '3.11'" },
     { name = "numpy", version = "2.4.1", source = { registry = "https://pypi.org/simple" }, marker = "python_full_version >= '3.11'" },
-    { name = "onnxruntime" },
+    { name = "onnxruntime", version = "1.20.1", source = { registry = "https://pypi.org/simple" }, marker = "(platform_machine != 'aarch64' and sys_platform == 'linux') or (sys_platform != 'darwin' and sys_platform != 'linux')" },
+    { name = "onnxruntime", version = "1.23.2", source = { registry = "https://pypi.org/simple" }, marker = "(platform_machine == 'aarch64' and sys_platform == 'linux') or sys_platform == 'darwin'" },
     { name = "opentelemetry-api" },
     { name = "opentelemetry-exporter-otlp-proto-grpc" },
     { name = "opentelemetry-sdk" },
@@ -705,6 +710,40 @@ wheels = [
     { url = "https://files.pythonhosted.org/packages/bc/58/6b3d24e6b9bc474a2dcdee65dfd1f008867015408a271562e4b690561a4d/cryptography-46.0.5-pp311-pypy311_pp73-win_amd64.whl", hash = "sha256:8456928655f856c6e1533ff59d5be76578a7157224dbd9ce6872f25055ab9ab7", size = 3407605, upload-time = "2026-02-10T19:18:29.233Z" },
 ]
 
+[[package]]
+name = "cuda-bindings"
+version = "12.9.4"
+source = { registry = "https://pypi.org/simple" }
+dependencies = [
+    { name = "cuda-pathfinder", marker = "(platform_machine != 'aarch64' and sys_platform == 'linux') or (sys_platform != 'darwin' and sys_platform != 'linux' and sys_platform != 'win32')" },
+]
+wheels = [
+    { url = "https://files.pythonhosted.org/packages/7a/d8/b546104b8da3f562c1ff8ab36d130c8fe1dd6a045ced80b4f6ad74f7d4e1/cuda_bindings-12.9.4-cp310-cp310-manylinux_2_24_x86_64.manylinux_2_28_x86_64.whl", hash = "sha256:4d3c842c2a4303b2a580fe955018e31aea30278be19795ae05226235268032e5", size = 12148218, upload-time = "2025-10-21T14:51:28.855Z" },
+    { url = "https://files.pythonhosted.org/packages/45/e7/b47792cc2d01c7e1d37c32402182524774dadd2d26339bd224e0e913832e/cuda_bindings-12.9.4-cp311-cp311-manylinux_2_24_x86_64.manylinux_2_28_x86_64.whl", hash = "sha256:c912a3d9e6b6651853eed8eed96d6800d69c08e94052c292fec3f282c5a817c9", size = 12210593, upload-time = "2025-10-21T14:51:36.574Z" },
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@@ -4192,7 +4306,8 @@ source = { registry = "https://pypi.org/simple" }
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     "python_full_version < '3.11' and sys_platform == 'darwin'",
     "python_full_version < '3.11' and platform_machine == 'aarch64' and sys_platform == 'linux'",
-    "(python_full_version < '3.11' and platform_machine != 'aarch64' and sys_platform == 'linux') or (python_full_version < '3.11' and sys_platform != 'darwin' and sys_platform != 'linux')",
+    "python_full_version < '3.11' and sys_platform == 'win32'",
+    "(python_full_version < '3.11' and platform_machine != 'aarch64' and sys_platform == 'linux') or (python_full_version < '3.11' and sys_platform != 'darwin' and sys_platform != 'linux' and sys_platform != 'win32')",
 ]
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@@ -4248,84 +4363,87 @@ wheels = [
 
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+    "python_full_version >= '3.13' and sys_platform == 'win32'",
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+    "python_full_version == '3.12.*' and sys_platform == 'win32'",
+    "(python_full_version == '3.12.*' and platform_machine != 'aarch64' and sys_platform == 'linux') or (python_full_version == '3.12.*' and sys_platform != 'darwin' and sys_platform != 'linux' and sys_platform != 'win32')",
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+    "python_full_version == '3.11.*' and sys_platform == 'win32'",
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@@ -4586,6 +4704,7 @@ all = [
     { name = "bandit", extra = ["toml"] },
     { name = "chromadb" },
     { name = "docling" },
+    { name = "docling-core" },
     { name = "httpx" },
     { name = "mypy" },
     { name = "pre-commit" },
@@ -4603,6 +4722,7 @@ dev = [
     { name = "bandit", extra = ["toml"] },
     { name = "chromadb" },
     { name = "docling" },
+    { name = "docling-core" },
     { name = "httpx" },
     { name = "mypy" },
     { name = "pre-commit" },
@@ -4616,6 +4736,7 @@ dev = [
 libs = [
     { name = "chromadb" },
     { name = "docling" },
+    { name = "docling-core" },
     { name = "weaviate-client" },
 ]
 lint = [
@@ -4640,7 +4761,8 @@ requires-dist = [
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-    { name = "docling", marker = "extra == 'libs'", specifier = ">=2.55.0" },
+    { name = "docling", marker = "extra == 'libs'", specifier = ">=2.60.0" },
+    { name = "docling-core", marker = "extra == 'libs'", specifier = ">=2.60.0" },
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 [[package]]
 name = "torch"
-version = "2.9.1"
+version = "2.10.0"
 source = { registry = "https://pypi.org/simple" }
 dependencies = [
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     { name = "fsspec" },
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