diff --git a/check_batch_results.py b/check_batch_results.py
new file mode 100644
index 0000000..e809aba
--- /dev/null
+++ b/check_batch_results.py
@@ -0,0 +1,72 @@
+"""Check batch upload results in Weaviate."""
+
+import sys
+from pathlib import Path
+
+# Fix Windows encoding
+if sys.platform == "win32":
+    import io
+    sys.stdout = io.TextIOWrapper(sys.stdout.buffer, encoding='utf-8')
+
+# Add parent directory to path
+sys.path.insert(0, str(Path(__file__).parent / "generations" / "library_rag"))
+
+import weaviate
+
+print("Connecting to Weaviate...")
+client = weaviate.connect_to_local(host="localhost", port=8080, grpc_port=50051)
+
+try:
+    chunk_collection = client.collections.get("Chunk_v2")
+
+    # Fetch recent chunks and look for the new documents
+    print("\n1. Fetching recent chunks (last 50)...")
+    all_chunks = chunk_collection.query.fetch_objects(limit=50)
+
+    # Group by work title
+    works = {}
+    for chunk in all_chunks.objects:
+        work_info = chunk.properties.get('work', {})
+        title = work_info.get('title', 'N/A')
+        author = work_info.get('author', 'N/A')
+        if title not in works:
+            works[title] = {'author': author, 'count': 0}
+        works[title]['count'] += 1
+
+    # Check for our test documents
+    cartesian_found = False
+    turing_found = False
+
+    print("\n2. Looking for test documents in recent chunks...")
+    for title, info in works.items():
+        if 'Cartesian' in title or 'artificial intelligence' in title.lower():
+            print(f"   ✓ Found: {title[:70]}")
+            print(f"     Author: {info['author']}")
+            print(f"     Chunks: {info['count']}")
+            cartesian_found = True
+        if 'Turing' in title or 'von Neumann' in title:
+            print(f"   ✓ Found: {title[:70]}")
+            print(f"     Author: {info['author']}")
+            print(f"     Chunks: {info['count']}")
+            turing_found = True
+
+    if not cartesian_found:
+        print("   ✗ Cartesian document not found in recent chunks")
+    if not turing_found:
+        print("   ✗ Turing document not found in recent chunks")
+
+    # Count all chunks
+    print("\n3. Total chunks in database:")
+    result = chunk_collection.aggregate.over_all()
+    print(f"   Total: {result.total_count}")
+
+    # List recent works (last 5)
+    print("\n4. Recent works (showing first 5 chunks by creation time):")
+    all_chunks = chunk_collection.query.fetch_objects(limit=5)
+    for i, chunk in enumerate(all_chunks.objects, 1):
+        work_title = chunk.properties.get('work', {}).get('title', 'N/A')
+        print(f"   {i}. {work_title[:60]}...")
+
+finally:
+    client.close()
+    print("\n✓ Done")
diff --git a/create_missing_works.py b/create_missing_works.py
new file mode 100644
index 0000000..edd55fd
--- /dev/null
+++ b/create_missing_works.py
@@ -0,0 +1,58 @@
+"""Create missing Work entries for test documents."""
+
+import json
+import sys
+from pathlib import Path
+
+# Fix Windows encoding
+if sys.platform == "win32":
+    import io
+    sys.stdout = io.TextIOWrapper(sys.stdout.buffer, encoding='utf-8')
+
+# Add parent directory to path
+sys.path.insert(0, str(Path(__file__).parent / "generations" / "library_rag"))
+
+from utils.weaviate_ingest import create_or_get_work, get_weaviate_client
+
+# Documents to create Works for
+documents = [
+    "A_Cartesian_critique_of_the_artificial_i",
+    "Alan_Turing_and_John_von_Neumann_Their_B"
+]
+
+output_dir = Path(__file__).parent / "generations" / "library_rag" / "output"
+
+print("Creating missing Work entries...\n")
+
+with get_weaviate_client() as client:
+    if client is None:
+        print("Error: Could not connect to Weaviate")
+        sys.exit(1)
+
+    for doc_name in documents:
+        print(f"Processing: {doc_name}")
+
+        # Load metadata from chunks JSON
+        chunks_file = output_dir / doc_name / f"{doc_name}_chunks.json"
+        if not chunks_file.exists():
+            print(f"  ✗ Chunks file not found")
+            continue
+
+        with open(chunks_file, 'r', encoding='utf-8') as f:
+            data = json.load(f)
+
+        metadata = data.get("metadata", {})
+        pages = data.get("pages", 0)
+
+        # Create or get Work
+        work_uuid = create_or_get_work(client, doc_name, metadata, pages)
+
+        if work_uuid:
+            print(f"  ✓ Work created/retrieved: {work_uuid[:8]}...")
+        else:
+            print(f"  ✗ Failed to create Work")
+
+        print()
+
+print("=" * 70)
+print("Done! Checking /documents page now should show the new works.")
diff --git a/generations/library_rag/utils/weaviate_ingest.py b/generations/library_rag/utils/weaviate_ingest.py
index 0a28707..9da6db5 100644
--- a/generations/library_rag/utils/weaviate_ingest.py
+++ b/generations/library_rag/utils/weaviate_ingest.py
@@ -559,6 +559,69 @@ def get_weaviate_client() -> Generator[Optional[WeaviateClient], None, None]:
             client.close()
 
 
+def create_or_get_work(
+    client: WeaviateClient,
+    doc_name: str,
+    metadata: Dict[str, Any],
+    pages: int = 0,
+) -> Optional[str]:
+    """Create or retrieve a Work entry for a document.
+
+    Creates a Work object representing the philosophical work/document.
+    If a Work with the same sourceId already exists, returns its UUID.
+
+    Args:
+        client: Active Weaviate client connection.
+        doc_name: Unique document identifier (sourceId).
+        metadata: Extracted metadata dict with keys: title, author, year, etc.
+        pages: Number of pages in the source document.
+
+    Returns:
+        UUID string of the Work object, or None if creation failed.
+    """
+    try:
+        work_collection: Collection[Any, Any] = client.collections.get("Work")
+    except Exception as e:
+        logger.warning(f"Collection Work non trouvée: {e}")
+        return None
+
+    title = metadata.get("title") or doc_name
+    author = metadata.get("author") or "Inconnu"
+    year = metadata.get("year", 0) if metadata.get("year") else 0
+
+    try:
+        # Check if Work already exists with this sourceId
+        existing = work_collection.query.fetch_objects(
+            filters=wvq.Filter.by_property("sourceId").equal(doc_name),
+            limit=1
+        )
+
+        if existing.objects:
+            work_uuid = str(existing.objects[0].uuid)
+            logger.info(f"Work déjà existant: {title} (UUID: {work_uuid[:8]}...)")
+            return work_uuid
+
+        # Create new Work
+        work_obj: Dict[str, Any] = {
+            "title": title,
+            "author": author,
+            "year": year,
+            "language": metadata.get("language", "en"),
+            "genre": metadata.get("genre", "philosophy"),
+            "sourceId": doc_name,
+            "pages": pages,
+        }
+
+        result = work_collection.data.insert(work_obj)
+        work_uuid = str(result)
+        logger.info(f"Work créé: {title} par {author} (UUID: {work_uuid[:8]}...)")
+        return work_uuid
+
+    except Exception as e:
+        logger.warning(f"Erreur création Work: {e}")
+        return None
+
+
 def ingest_document_metadata(
     client: WeaviateClient,
     doc_name: str,
@@ -832,6 +895,11 @@ def ingest_document(
                     inserted=[],
                 )
 
+            # Créer ou récupérer le Work (toujours, pour la page /documents)
+            work_uuid: Optional[str] = create_or_get_work(
+                client, doc_name, metadata, pages
+            )
+
             # Insérer les métadonnées du document (optionnel)
             doc_uuid: Optional[str] = None
             if ingest_document_collection:
diff --git a/reingest_batch_documents.py b/reingest_batch_documents.py
new file mode 100644
index 0000000..572227b
--- /dev/null
+++ b/reingest_batch_documents.py
@@ -0,0 +1,88 @@
+"""Reingest documents that failed to ingest due to collection name bug."""
+
+import json
+import sys
+from pathlib import Path
+
+# Fix Windows encoding
+if sys.platform == "win32":
+    import io
+    sys.stdout = io.TextIOWrapper(sys.stdout.buffer, encoding='utf-8')
+
+# Add parent directory to path
+sys.path.insert(0, str(Path(__file__).parent / "generations" / "library_rag"))
+
+from utils.weaviate_ingest import ingest_document
+
+# Documents to reingest
+documents = [
+    "A_Cartesian_critique_of_the_artificial_i",
+    "Alan_Turing_and_John_von_Neumann_Their_B"
+]
+
+output_dir = Path(__file__).parent / "generations" / "library_rag" / "output"
+
+print("🔄 Reingesting batch upload documents with fixed collection names...\n")
+
+for doc_name in documents:
+    print(f"📄 Processing: {doc_name}")
+
+    # Load chunks JSON
+    chunks_file = output_dir / doc_name / f"{doc_name}_chunks.json"
+    if not chunks_file.exists():
+        print(f"   ✗ Chunks file not found: {chunks_file}")
+        continue
+
+    with open(chunks_file, 'r', encoding='utf-8') as f:
+        data = json.load(f)
+
+    chunks = data.get("chunks", [])
+    metadata = data.get("metadata", {})
+    toc = data.get("toc", [])
+    pages = data.get("pages", 0)
+    language = metadata.get("language", "en")
+
+    if not chunks:
+        print(f"   ⚠️  No chunks found in file")
+        continue
+
+    print(f"   • Chunks: {len(chunks)}")
+    print(f"   • Title: {metadata.get('title', 'N/A')}")
+    print(f"   • Author: {metadata.get('author', 'N/A')}")
+    print(f"   • Language: {language}")
+
+    # Ingest to Weaviate
+    print(f"   🚀 Ingesting to Weaviate...")
+    result = ingest_document(
+        doc_name=doc_name,
+        chunks=chunks,
+        metadata=metadata,
+        language=language,
+        toc=toc,
+        pages=pages,
+        ingest_document_collection=True,
+        ingest_summary_collection=False,
+    )
+
+    if result["success"]:
+        print(f"   ✅ Success! Inserted {result['count']} chunks")
+    else:
+        print(f"   ✗ Failed: {result.get('error', 'Unknown error')}")
+
+    print()
+
+print("=" * 70)
+print("✓ Reingestion complete!")
+print()
+
+# Verify total count
+import weaviate
+print("🔍 Verifying total chunks in Weaviate...")
+client = weaviate.connect_to_local()
+try:
+    chunk_coll = client.collections.get("Chunk_v2")
+    total = chunk_coll.aggregate.over_all().total_count
+    print(f"   Total chunks: {total}")
+    print(f"   Expected: {5304 + 7 + 11} = 5,322")
+finally:
+    client.close()