diff --git a/ANALYSE_MCP_TOOLS.md b/ANALYSE_MCP_TOOLS.md
index e02c715..1a976fc 100644
--- a/ANALYSE_MCP_TOOLS.md
+++ b/ANALYSE_MCP_TOOLS.md
@@ -37,7 +37,7 @@ ping()
 
 #### 2. `parse_pdf(pdf_path)`
 **Type**: Ingestion PDF
-**Collection cible**: Work, Chunk_v2, Summary_v2
+**Collection cible**: Work, Chunk, Summary
 
 **Paramètres**:
 - `pdf_path` (str): Chemin local ou URL vers le PDF
@@ -87,7 +87,7 @@ ping()
 
 #### 3. `search_chunks(query, limit, min_similarity, author_filter, work_filter, language_filter)`
 **Type**: Recherche sémantique
-**Collection**: Chunk_v2 (5,372 chunks)
+**Collection**: Chunk (5,372 chunks)
 
 **Paramètres**:
 - `query` (str): Requête en langage naturel
@@ -126,7 +126,7 @@ ping()
 
 #### 4. `search_summaries(query, limit, min_level, max_level)`
 **Type**: Recherche sémantique (haut niveau)
-**Collection**: Summary_v2 (114 résumés)
+**Collection**: Summary (114 résumés)
 
 **Paramètres**:
 - `query` (str): Requête en langage naturel
@@ -161,7 +161,7 @@ ping()
 
 #### 5. `get_document(source_id, include_chunks, include_toc)`
 **Type**: Récupération document
-**Collections**: Work, Chunk_v2
+**Collections**: Work, Chunk
 
 **Paramètres**:
 - `source_id` (str): Identifiant du document
@@ -226,7 +226,7 @@ ping()
 
 #### 7. `get_chunks_by_document(source_id, limit, offset)`
 **Type**: Récupération chunks
-**Collection**: Chunk_v2
+**Collection**: Chunk
 
 **Paramètres**:
 - `source_id` (str): Identifiant du document
@@ -260,7 +260,7 @@ ping()
 
 #### 8. `filter_by_author(author, limit)`
 **Type**: Filtrage par auteur
-**Collections**: Work, Chunk_v2
+**Collections**: Work, Chunk
 
 **Paramètres**:
 - `author` (str): Nom de l'auteur
@@ -294,7 +294,7 @@ ping()
 
 #### 9. `delete_document(source_id, confirm)`
 **Type**: Suppression
-**Collections**: Work, Chunk_v2, Summary_v2
+**Collections**: Work, Chunk, Summary
 
 **Paramètres**:
 - `source_id` (str): Identifiant du document
@@ -605,7 +605,7 @@ ping()
 | Catégorie | Nombre | Collections utilisées | GPU Embedder |
 |-----------|--------|----------------------|--------------|
 | **Système** | 1 | - | - |
-| **Library RAG** | 8 | Work, Chunk_v2, Summary_v2 | ✅ |
+| **Library RAG** | 8 | Work, Chunk, Summary | ✅ |
 | **Memory** | 9 | Thought, Message, Conversation | ✅ |
 | **TOTAL** | **18** | **6 collections** | **5 vectorisées** |
 
@@ -719,8 +719,8 @@ messages = search_messages("transformers architecture")
 
 RAG (3):
 - Work (no vectorizer) - 19 œuvres
-- Chunk_v2 (GPU embedder) - 5,372 chunks
-- Summary_v2 (GPU embedder) - 114 résumés
+- Chunk (GPU embedder) - 5,372 chunks
+- Summary (GPU embedder) - 114 résumés
 
 Memory (3):
 - Conversation (GPU embedder) - 12 conversations
diff --git a/README.md b/README.md
index 59f2c9a..645de43 100644
--- a/README.md
+++ b/README.md
@@ -18,8 +18,8 @@ Library RAG combine deux systèmes de recherche sémantique distincts:
 ```
 📦 Library Philosophique (3 collections)
 ├─ Work           → Métadonnées des œuvres philosophiques
-├─ Chunk_v2       → 5355 passages de texte (1024-dim vectors)
-└─ Summary_v2     → Résumés hiérarchiques des documents
+├─ Chunk       → 5355 passages de texte (1024-dim vectors)
+└─ Summary     → Résumés hiérarchiques des documents
 
 🧠 Memory Ikario (2 collections)
 ├─ Thought        → 104 pensées (réflexions, insights)
@@ -249,7 +249,7 @@ curl http://localhost:5000/search?q=Turing
 curl http://localhost:8080/v1/meta
 
 # Vérifier nombre de chunks
-python -c "import weaviate; c=weaviate.connect_to_local(); print(c.collections.get('Chunk_v2').aggregate.over_all()); c.close()"
+python -c "import weaviate; c=weaviate.connect_to_local(); print(c.collections.get('Chunk').aggregate.over_all()); c.close()"
 ```
 
 ## 📊 Métriques de Performance
@@ -407,7 +407,7 @@ docker compose restart
 **Solution**:
 ```bash
 # Vérifier nombre de chunks dans Weaviate
-python -c "import weaviate; c=weaviate.connect_to_local(); print(f'Chunks: {c.collections.get(\"Chunk_v2\").aggregate.over_all().total_count}'); c.close()"
+python -c "import weaviate; c=weaviate.connect_to_local(); print(f'Chunks: {c.collections.get(\"Chunk\").aggregate.over_all().total_count}'); c.close()"
 
 # Réinjecter les données si nécessaire
 python schema.py --recreate-chunk
diff --git a/generations/library_rag/flask_app.py b/generations/library_rag/flask_app.py
index f7bd433..b5fc475 100644
--- a/generations/library_rag/flask_app.py
+++ b/generations/library_rag/flask_app.py
@@ -193,7 +193,7 @@ def get_collection_stats() -> Optional[CollectionStats]:
             stats: CollectionStats = {}
 
             # Chunk stats (renamed from Passage)
-            passages = client.collections.get("Chunk_v2")
+            passages = client.collections.get("Chunk")
             passage_count = passages.aggregate.over_all(total_count=True)
             stats["passages"] = passage_count.total_count or 0
 
@@ -248,7 +248,7 @@ def get_all_passages(
             if client is None:
                 return []
 
-            chunks = client.collections.get("Chunk_v2")
+            chunks = client.collections.get("Chunk")
 
             result = chunks.query.fetch_objects(
                 limit=limit,
@@ -293,7 +293,7 @@ def simple_search(
             if client is None:
                 return []
 
-            chunks = client.collections.get("Chunk_v2")
+            chunks = client.collections.get("Chunk")
 
             # Build filters using top-level properties (workAuthor, workTitle)
             filters: Optional[Any] = None
@@ -377,7 +377,7 @@ def hierarchical_search(
             # STAGE 1: Search Summary collection for relevant sections
             # ═══════════════════════════════════════════════════════════════
 
-            summary_collection = client.collections.get("Summary_v2")
+            summary_collection = client.collections.get("Summary")
 
             # Generate query vector with GPU embedder (Phase 5: manual vectorization)
             embedder = get_gpu_embedder()
@@ -423,7 +423,7 @@ def hierarchical_search(
                     "similarity": round((1 - summary_obj.metadata.distance) * 100, 1) if summary_obj.metadata and summary_obj.metadata.distance else 0,
                 })
 
-            # Post-filter sections by author/work (Summary_v2 has workTitle property)
+            # Post-filter sections by author/work (Summary has workTitle property)
             if author_filter or work_filter:
                 print(f"[HIERARCHICAL] Post-filtering {len(sections_data)} sections by work='{work_filter}'")
 
@@ -485,7 +485,7 @@ def hierarchical_search(
             # For each section, search chunks using the section's summary text
             # This groups chunks under their relevant sections
 
-            chunk_collection = client.collections.get("Chunk_v2")
+            chunk_collection = client.collections.get("Chunk")
 
             # Build base filters (author/work only)
             base_filters: Optional[Any] = None
@@ -650,9 +650,9 @@ def summary_only_search(
             if client is None:
                 return []
 
-            summaries = client.collections.get("Summary_v2")
+            summaries = client.collections.get("Summary")
 
-            # Build Work map for metadata lookup (Summary_v2 has workTitle, not document)
+            # Build Work map for metadata lookup (Summary has workTitle, not document)
             work_collection = client.collections.get("Work")
             work_map = {}
             for work in work_collection.iterator(include_vector=False):
@@ -1043,7 +1043,7 @@ def rag_search(
                 print("[RAG Search] Weaviate client unavailable")
                 return []
 
-            chunks = client.collections.get("Chunk_v2")
+            chunks = client.collections.get("Chunk")
 
             # Build work filter if selected_works is provided
             work_filter: Optional[Any] = None
@@ -1536,8 +1536,8 @@ def api_get_works() -> Union[Response, tuple[Response, int]]:
                     "message": "Cannot connect to Weaviate database"
                 }), 500
 
-            # Query Chunk_v2 collection to get all unique works with counts
-            chunks = client.collections.get("Chunk_v2")
+            # Query Chunk collection to get all unique works with counts
+            chunks = client.collections.get("Chunk")
 
             # Fetch all chunks to aggregate by work
             # In v2: work is NOT a nested object, use workTitle and workAuthor properties
@@ -3421,7 +3421,7 @@ def documents() -> str:
             # Get all Works (now with sourceId added in Phase 1 of migration)
             try:
                 work_collection = client.collections.get("Work")
-                chunk_collection = client.collections.get("Chunk_v2")
+                chunk_collection = client.collections.get("Chunk")
 
                 # Build documents from Work collection
                 for work in work_collection.iterator(include_vector=False):
@@ -3461,7 +3461,7 @@ def documents() -> str:
 
             # Count summaries (if collection exists)
             try:
-                summary_collection = client.collections.get("Summary_v2")
+                summary_collection = client.collections.get("Summary")
                 for summary in summary_collection.iterator(include_vector=False):
                     work_title = summary.properties.get("workTitle")
 
diff --git a/generations/library_rag/migrate_rename_collections.py b/generations/library_rag/migrate_rename_collections.py
new file mode 100644
index 0000000..b07778e
--- /dev/null
+++ b/generations/library_rag/migrate_rename_collections.py
@@ -0,0 +1,337 @@
+#!/usr/bin/env python3
+"""
+Rename collections: Chunk_v2 -> Chunk, Summary_v2 -> Summary
+
+Weaviate doesn't support renaming collections directly, so this script:
+1. Creates new collections (Chunk, Summary) with identical schema
+2. Copies all objects with their vectors (batch insert)
+3. Validates the migration (count check)
+4. Optionally deletes old collections (--cleanup flag)
+
+Usage:
+    python migrate_rename_collections.py --dry-run    # Preview without changes
+    python migrate_rename_collections.py              # Execute migration
+    python migrate_rename_collections.py --cleanup    # Delete old collections after validation
+"""
+
+import weaviate
+import weaviate.classes as wvc
+from weaviate.classes.config import Configure, Property, DataType, VectorDistances
+from weaviate.classes.query import Filter
+import sys
+import argparse
+from typing import Any
+import time
+
+MIGRATIONS = [
+    ("Chunk_v2", "Chunk"),
+    ("Summary_v2", "Summary"),
+]
+
+BATCH_SIZE = 100
+
+
+def get_collection_count(client: weaviate.WeaviateClient, name: str) -> int:
+    """Get the number of objects in a collection."""
+    try:
+        coll = client.collections.get(name)
+        return coll.aggregate.over_all().total_count
+    except Exception:
+        return 0
+
+
+def collection_exists(client: weaviate.WeaviateClient, name: str) -> bool:
+    """Check if a collection exists."""
+    try:
+        client.collections.get(name)
+        return True
+    except Exception:
+        return False
+
+
+def create_chunk_collection(client: weaviate.WeaviateClient) -> None:
+    """Create the new Chunk collection with schema from Chunk_v2."""
+    print("  Creating Chunk collection...")
+
+    client.collections.create(
+        name="Chunk",
+        description="Document chunks with manual GPU vectorization (BAAI/bge-m3, 1024-dim)",
+        vectorizer_config=Configure.Vectorizer.none(),
+        vector_index_config=Configure.VectorIndex.hnsw(
+            distance_metric=VectorDistances.COSINE,
+            ef_construction=128,
+            max_connections=32,
+            quantizer=Configure.VectorIndex.Quantizer.rq(),
+        ),
+        properties=[
+            Property(name="text", data_type=DataType.TEXT, description="Chunk text content"),
+            Property(name="workTitle", data_type=DataType.TEXT, description="Work title"),
+            Property(name="workAuthor", data_type=DataType.TEXT, description="Work author"),
+            Property(name="sectionPath", data_type=DataType.TEXT, description="Section path"),
+            Property(name="sectionLevel", data_type=DataType.INT, description="Section level"),
+            Property(name="chapterTitle", data_type=DataType.TEXT, description="Chapter title"),
+            Property(name="canonicalReference", data_type=DataType.TEXT, description="Canonical reference"),
+            Property(name="unitType", data_type=DataType.TEXT, description="Unit type"),
+            Property(name="keywords", data_type=DataType.TEXT_ARRAY, description="Keywords"),
+            Property(name="language", data_type=DataType.TEXT, description="Language code"),
+            Property(name="year", data_type=DataType.INT, description="Publication year"),
+            Property(name="orderIndex", data_type=DataType.INT, description="Order index"),
+            Property(name="summary", data_type=DataType.TEXT, description="Chunk summary"),
+            Property(name="document", data_type=DataType.TEXT, description="Document reference"),
+        ],
+    )
+    print("  [OK] Chunk collection created")
+
+
+def create_summary_collection(client: weaviate.WeaviateClient) -> None:
+    """Create the new Summary collection with schema from Summary_v2."""
+    print("  Creating Summary collection...")
+
+    client.collections.create(
+        name="Summary",
+        description="Section summaries (v2 - sans Document)",
+        vectorizer_config=Configure.Vectorizer.none(),
+        vector_index_config=Configure.VectorIndex.hnsw(
+            distance_metric=VectorDistances.COSINE,
+            ef_construction=128,
+            max_connections=32,
+            quantizer=Configure.VectorIndex.Quantizer.rq(),
+        ),
+        properties=[
+            Property(name="text", data_type=DataType.TEXT, description="Summary text (vectorized)"),
+            Property(name="concepts", data_type=DataType.TEXT_ARRAY, description="Key concepts"),
+            Property(name="workTitle", data_type=DataType.TEXT, description="Work title"),
+            Property(name="sectionPath", data_type=DataType.TEXT, description="Section path"),
+            Property(name="title", data_type=DataType.TEXT, description="Section title"),
+            Property(name="level", data_type=DataType.INT, description="Hierarchy level"),
+            Property(name="chunksCount", data_type=DataType.INT, description="Chunks count"),
+            Property(name="language", data_type=DataType.TEXT, description="Language code"),
+            Property(name="workAuthor", data_type=DataType.TEXT, description="Work author"),
+            Property(name="year", data_type=DataType.NUMBER, description="Publication year"),
+        ],
+    )
+    print("  [OK] Summary collection created")
+
+
+def clean_properties(props: dict[str, Any], collection_name: str) -> dict[str, Any]:
+    """Clean properties to ensure correct types."""
+    cleaned = dict(props)
+
+    # Integer fields that may have been stored as float
+    int_fields = ["sectionLevel", "year", "orderIndex", "level", "chunksCount"]
+
+    for field in int_fields:
+        if field in cleaned and cleaned[field] is not None:
+            try:
+                cleaned[field] = int(cleaned[field])
+            except (ValueError, TypeError):
+                pass
+
+    return cleaned
+
+
+def migrate_objects(
+    client: weaviate.WeaviateClient,
+    source_name: str,
+    target_name: str,
+    dry_run: bool = False
+) -> int:
+    """Copy all objects from source to target collection with vectors."""
+    source = client.collections.get(source_name)
+    target = client.collections.get(target_name)
+
+    total = source.aggregate.over_all().total_count
+    print(f"  Migrating {total} objects from {source_name} -> {target_name}")
+
+    if dry_run:
+        print(f"  [DRY-RUN] Would migrate {total} objects")
+        return total
+
+    migrated = 0
+    errors = 0
+    batch_objects: list[dict[str, Any]] = []
+
+    for obj in source.iterator(include_vector=True):
+        # Get vector (handle both dict and direct vector)
+        vector = obj.vector
+        if isinstance(vector, dict):
+            vector = vector.get("default", list(vector.values())[0] if vector else None)
+
+        # Clean properties to ensure correct types
+        cleaned_props = clean_properties(obj.properties, target_name)
+
+        batch_objects.append({
+            "uuid": obj.uuid,
+            "properties": cleaned_props,
+            "vector": vector,
+        })
+
+        if len(batch_objects) >= BATCH_SIZE:
+            # Insert batch
+            with target.batch.dynamic() as batch:
+                for item in batch_objects:
+                    batch.add_object(
+                        uuid=item["uuid"],
+                        properties=item["properties"],
+                        vector=item["vector"],
+                    )
+            migrated += len(batch_objects)
+            print(f"    Progress: {migrated}/{total} ({100*migrated//total}%)", end='\r')
+            batch_objects = []
+
+    # Insert remaining objects
+    if batch_objects:
+        with target.batch.dynamic() as batch:
+            for item in batch_objects:
+                batch.add_object(
+                    uuid=item["uuid"],
+                    properties=item["properties"],
+                    vector=item["vector"],
+                )
+        migrated += len(batch_objects)
+
+    print(f"    Progress: {migrated}/{total} (100%)      ")
+    print(f"  [OK] Migrated {migrated} objects")
+
+    return migrated
+
+
+def validate_migration(
+    client: weaviate.WeaviateClient,
+    source_name: str,
+    target_name: str
+) -> bool:
+    """Validate that source and target have same object count."""
+    source_count = get_collection_count(client, source_name)
+    target_count = get_collection_count(client, target_name)
+
+    if source_count == target_count:
+        print(f"  [OK] Validation passed: {source_name}={source_count}, {target_name}={target_count}")
+        return True
+    else:
+        print(f"  [ERROR] Validation FAILED: {source_name}={source_count}, {target_name}={target_count}")
+        return False
+
+
+def cleanup_old_collections(client: weaviate.WeaviateClient, dry_run: bool = False) -> None:
+    """Delete old collections after successful migration."""
+    print("\n" + "="*70)
+    print("CLEANUP: Deleting old collections")
+    print("="*70)
+
+    for source_name, target_name in MIGRATIONS:
+        if not collection_exists(client, source_name):
+            print(f"  {source_name}: Already deleted")
+            continue
+
+        if not collection_exists(client, target_name):
+            print(f"  [ERROR] Cannot delete {source_name}: {target_name} doesn't exist!")
+            continue
+
+        # Validate before deleting
+        if not validate_migration(client, source_name, target_name):
+            print(f"  [ERROR] Skipping {source_name} deletion: validation failed")
+            continue
+
+        if dry_run:
+            print(f"  [DRY-RUN] Would delete {source_name}")
+        else:
+            client.collections.delete(source_name)
+            print(f"  [OK] Deleted {source_name}")
+
+
+def main() -> None:
+    parser = argparse.ArgumentParser(description="Rename Weaviate collections")
+    parser.add_argument("--dry-run", action="store_true", help="Preview without making changes")
+    parser.add_argument("--cleanup", action="store_true", help="Delete old collections after validation")
+    args = parser.parse_args()
+
+    print("="*70)
+    print("WEAVIATE COLLECTION RENAME: Chunk_v2 -> Chunk, Summary_v2 -> Summary")
+    print("="*70)
+    print(f"Mode: {'DRY-RUN' if args.dry_run else 'LIVE'}")
+    print(f"Cleanup: {'YES' if args.cleanup else 'NO'}")
+    print()
+
+    client = weaviate.connect_to_local()
+
+    try:
+        # Show current state
+        print("Current collections:")
+        for source_name, target_name in MIGRATIONS:
+            source_count = get_collection_count(client, source_name)
+            target_exists = collection_exists(client, target_name)
+            target_count = get_collection_count(client, target_name) if target_exists else 0
+            print(f"  {source_name}: {source_count} objects")
+            print(f"  {target_name}: {'exists (' + str(target_count) + ' objects)' if target_exists else 'does not exist'}")
+        print()
+
+        if args.cleanup:
+            cleanup_old_collections(client, dry_run=args.dry_run)
+            return
+
+        # Migration
+        for source_name, target_name in MIGRATIONS:
+            print("="*70)
+            print(f"MIGRATING: {source_name} -> {target_name}")
+            print("="*70)
+
+            # Check source exists
+            if not collection_exists(client, source_name):
+                print(f"  [ERROR] Source collection {source_name} does not exist!")
+                continue
+
+            # Check if target already exists
+            if collection_exists(client, target_name):
+                target_count = get_collection_count(client, target_name)
+                if target_count > 0:
+                    print(f"  Target {target_name} already exists with {target_count} objects")
+                    print(f"  Skipping (already migrated)")
+                    continue
+                else:
+                    print(f"  Target {target_name} exists but empty, will populate")
+            else:
+                # Create target collection
+                if not args.dry_run:
+                    if target_name == "Chunk":
+                        create_chunk_collection(client)
+                    elif target_name == "Summary":
+                        create_summary_collection(client)
+                else:
+                    print(f"  [DRY-RUN] Would create {target_name} collection")
+
+            # Migrate objects
+            if not args.dry_run:
+                migrate_objects(client, source_name, target_name, dry_run=False)
+            else:
+                migrate_objects(client, source_name, target_name, dry_run=True)
+
+            # Validate
+            if not args.dry_run:
+                validate_migration(client, source_name, target_name)
+
+            print()
+
+        # Final status
+        print("="*70)
+        print("MIGRATION COMPLETE")
+        print("="*70)
+        print("\nFinal state:")
+        for source_name, target_name in MIGRATIONS:
+            source_count = get_collection_count(client, source_name)
+            target_count = get_collection_count(client, target_name)
+            print(f"  {source_name}: {source_count} objects")
+            print(f"  {target_name}: {target_count} objects")
+
+        if not args.dry_run:
+            print("\nNext steps:")
+            print("  1. Update code: replace 'Chunk_v2' -> 'Chunk', 'Summary_v2' -> 'Summary'")
+            print("  2. Test the application")
+            print("  3. Run: python migrate_rename_collections.py --cleanup")
+
+    finally:
+        client.close()
+
+
+if __name__ == "__main__":
+    main()
diff --git a/generations/library_rag/schema.py b/generations/library_rag/schema.py
index 10502f9..d65defc 100644
--- a/generations/library_rag/schema.py
+++ b/generations/library_rag/schema.py
@@ -9,8 +9,8 @@ Schema Architecture:
     querying. The hierarchy is::
 
         Work (metadata only)
-          ├── Chunk_v2 (vectorized text fragments)
-          └── Summary_v2 (vectorized chapter summaries)
+          ├── Chunk (vectorized text fragments)
+          └── Summary (vectorized chapter summaries)
 
 Collections:
     **Work** (no vectorization):
@@ -18,21 +18,21 @@ Collections:
         Stores canonical metadata: title, author, year, language, genre.
         Not vectorized - used only for metadata and relationships.
 
-    **Chunk_v2** (manual GPU vectorization):
+    **Chunk** (manual GPU vectorization):
         Text fragments optimized for semantic search (200-800 chars).
         Vectorized with Python GPU embedder (BAAI/bge-m3, 1024-dim).
         Vectorized fields: text, keywords.
         Non-vectorized fields: workTitle, workAuthor, sectionPath, chapterTitle, unitType, orderIndex.
         Includes nested Work reference for denormalized access.
 
-    **Summary_v2** (manual GPU vectorization):
+    **Summary** (manual GPU vectorization):
         LLM-generated chapter/section summaries for high-level search.
         Vectorized with Python GPU embedder (BAAI/bge-m3, 1024-dim).
         Vectorized fields: text, concepts.
         Includes nested Work reference for denormalized access.
 
 Vectorization Strategy:
-    - Only Chunk_v2.text, Chunk_v2.keywords, Summary_v2.text, and Summary_v2.concepts are vectorized
+    - Only Chunk.text, Chunk.keywords, Summary.text, and Summary.concepts are vectorized
     - Manual vectorization with Python GPU embedder (BAAI/bge-m3, 1024-dim, RTX 4070)
     - Metadata fields use skip_vectorization=True for filtering only
     - Work collection has no vectorizer (metadata only)
@@ -56,8 +56,8 @@ Nested Objects:
     denormalized data access. This allows single-query retrieval of chunk
     data with its Work metadata without joins::
 
-        Chunk_v2.work = {title, author}
-        Summary_v2.work = {title, author}
+        Chunk.work = {title, author}
+        Summary.work = {title, author}
 
 Usage:
     From command line::
diff --git a/generations/library_rag/utils/weaviate_ingest.py b/generations/library_rag/utils/weaviate_ingest.py
index 71f5e9e..d679ae1 100644
--- a/generations/library_rag/utils/weaviate_ingest.py
+++ b/generations/library_rag/utils/weaviate_ingest.py
@@ -190,8 +190,8 @@ class DeleteResult(TypedDict, total=False):
     Attributes:
         success: Whether deletion succeeded.
         error: Error message if deletion failed.
-        deleted_chunks: Number of chunks deleted from Chunk_v2 collection.
-        deleted_summaries: Number of summaries deleted from Summary_v2 collection.
+        deleted_chunks: Number of chunks deleted from Chunk collection.
+        deleted_summaries: Number of summaries deleted from Summary collection.
 
     Example:
         >>> result = delete_document_chunks("platon_republique")
@@ -725,7 +725,7 @@ def ingest_summaries(
         Recursively processes nested TOC entries (children).
     """
     try:
-        summary_collection: Collection[Any, Any] = client.collections.get("Summary_v2")
+        summary_collection: Collection[Any, Any] = client.collections.get("Summary")
     except Exception as e:
         logger.warning(f"Collection Summary non trouvée: {e}")
         return 0
@@ -833,9 +833,9 @@ def ingest_document(
 ) -> IngestResult:
     """Ingest document chunks into Weaviate with nested objects.
 
-    Main ingestion function that inserts chunks into the Chunk_v2 collection
+    Main ingestion function that inserts chunks into the Chunk collection
     with nested Work references. Optionally also creates entries in the
-    Summary_v2 collection.
+    Summary collection.
 
     This function uses batch insertion for optimal performance and
     constructs proper nested objects for filtering capabilities.
@@ -856,7 +856,7 @@ def ingest_document(
         toc: Optional table of contents for Summary collection.
         hierarchy: Optional complete document hierarchy structure.
         pages: Number of pages in source document. Defaults to 0.
-        ingest_summary_collection: If True, also insert into Summary_v2
+        ingest_summary_collection: If True, also insert into Summary
             collection (requires toc). Defaults to False.
 
     Returns:
@@ -911,7 +911,7 @@ def ingest_document(
 
             # Récupérer la collection Chunk
             try:
-                chunk_collection: Collection[Any, Any] = client.collections.get("Chunk_v2")
+                chunk_collection: Collection[Any, Any] = client.collections.get("Chunk")
             except Exception as e:
                 return IngestResult(
                     success=False,
@@ -983,14 +983,14 @@ def ingest_document(
                     "keywords": chunk.get("concepts", chunk.get("keywords", [])),
                     "language": language,
                     "orderIndex": idx,
-                    # Use flat fields instead of nested objects for Chunk_v2 schema
+                    # Use flat fields instead of nested objects for Chunk schema
                     "workTitle": title,
                     "workAuthor": author,
                     "year": metadata.get("year", 0) if metadata.get("year") else 0,
-                    # Note: document reference fields not used in current Chunk_v2 schema
+                    # Note: document reference fields not used in current Chunk schema
                 }
 
-                # Note: Nested objects validation skipped for Chunk_v2 flat schema
+                # Note: Nested objects validation skipped for Chunk flat schema
                 # validate_chunk_nested_objects(chunk_obj, idx, doc_name)
 
                 objects_to_insert.append(chunk_obj)
@@ -1130,7 +1130,7 @@ def delete_document_chunks(doc_name: str) -> DeleteResult:
 
             # Supprimer les chunks (filtrer sur document.sourceId nested)
             try:
-                chunk_collection: Collection[Any, Any] = client.collections.get("Chunk_v2")
+                chunk_collection: Collection[Any, Any] = client.collections.get("Chunk")
                 result = chunk_collection.data.delete_many(
                     where=wvq.Filter.by_property("document.sourceId").equal(doc_name)
                 )
@@ -1140,7 +1140,7 @@ def delete_document_chunks(doc_name: str) -> DeleteResult:
 
             # Supprimer les summaries (filtrer sur document.sourceId nested)
             try:
-                summary_collection: Collection[Any, Any] = client.collections.get("Summary_v2")
+                summary_collection: Collection[Any, Any] = client.collections.get("Summary")
                 result = summary_collection.data.delete_many(
                     where=wvq.Filter.by_property("document.sourceId").equal(doc_name)
                 )