diff --git a/apps/slackbot/ai_fcts.py b/apps/slackbot/ai_fcts.py
index 7226f02..28d7c25 100644
--- a/apps/slackbot/ai_fcts.py
+++ b/apps/slackbot/ai_fcts.py
@@ -31,7 +31,7 @@
     client=QdrantClient(url=qdrant_uri, port=6333),
     collection_name=qdrant_collection_name,
     content_payload_key="content",
-    metadata_payload_key=None,
+    metadata_payload_key="metadata",
     distance_strategy="Cosine",
     embeddings=InfinityEmbeddings(model=infinity_model, infinity_api_url=infinity_api_url)
 )
@@ -80,7 +80,7 @@
 def format_docs(docs):
     context = ""
     for index, doc in enumerate(docs):
-        context += f"Document Rank {index + 1}: {doc.page_content}\n\n"
+        context += f"Document Rank {index + 1}. Source: https://www.notion.so/{doc.metadata['page_id']}. Content: {doc.page_content}\n\n"
     return context
 
 # create function to invoke the retrievalQA
diff --git a/apps/vectordb_sync/vectordb_sync_fcts.py b/apps/vectordb_sync/vectordb_sync_fcts.py
index 3997ffb..a81c147 100644
--- a/apps/vectordb_sync/vectordb_sync_fcts.py
+++ b/apps/vectordb_sync/vectordb_sync_fcts.py
@@ -113,7 +113,7 @@ def notion_to_qdrant(id_to_process) -> None:
         
         points_to_update = [PointStruct(id=str(uuid.uuid4()), 
                                         vector=chunk_embedding,
-                                        payload={"content": chunk, "page_id": id_to_process, "type": "notion"}) for chunk_embedding, chunk in zip(chunks_embedded, chunks)]
+                                        payload={"content": chunk, "metadata":{"page_id": id_to_process, "type": "notion"}}) for chunk_embedding, chunk in zip(chunks_embedded, chunks)]
         
         
 
@@ -179,7 +179,7 @@ def web_to_qdrant(id_to_process):
         # Insert the preprocessed chunk into Qdrant
         points_to_update = [PointStruct(id=str(uuid.uuid4()),
                                         vector=chunk_embedding,
-                                        payload={"content": chunk, "page_id": id_to_process, "type": "web"}) for
+                                        payload={"content": chunk, "metadata":{"page_id": id_to_process, "type": "web"}}) for
                             chunk_embedding, chunk in zip(chunks_embedded, chunks)]
         
         qdrant_client.upsert(
@@ -258,7 +258,7 @@ def slack_to_qdrant(id_to_process):
         
         points_to_update = [PointStruct(id=str(uuid.uuid4()), 
                                         vector=chunk_embedding,
-                                        payload={"content": chunk, "page_id": id_to_process, "type": "slack"}) for chunk_embedding, chunk in zip(chunks_embedded, chunks)]
+                                        payload={"content": chunk, "metadata":{"page_id": id_to_process, "type": "slack"}}) for chunk_embedding, chunk in zip(chunks_embedded, chunks)]