diff --git a/application/vectorstore/qdrant.py b/application/vectorstore/qdrant.py
index 61a9d63d..4435378f 100644
--- a/application/vectorstore/qdrant.py
+++ b/application/vectorstore/qdrant.py
@@ -1,5 +1,7 @@
+import logging
 from application.vectorstore.base import BaseVectorStore
 from application.core.settings import settings
+from application.vectorstore.document_class import Document
 
 
 class QdrantStore(BaseVectorStore):
@@ -7,18 +9,22 @@ class QdrantStore(BaseVectorStore):
         from qdrant_client import models
         from langchain_community.vectorstores.qdrant import Qdrant
 
+        # Store the source_id for use in add_chunk
+        self._source_id = str(source_id).replace("application/indexes/", "").rstrip("/")
+        
         self._filter = models.Filter(
             must=[
                 models.FieldCondition(
                     key="metadata.source_id",
-                    match=models.MatchValue(value=source_id.replace("application/indexes/", "").rstrip("/")),
+                    match=models.MatchValue(value=self._source_id),
                 )
             ]
         )
 
+        embedding=self._get_embeddings(settings.EMBEDDINGS_NAME, embeddings_key)
         self._docsearch = Qdrant.construct_instance(
             ["TEXT_TO_OBTAIN_EMBEDDINGS_DIMENSION"],
-            embedding=self._get_embeddings(settings.EMBEDDINGS_NAME, embeddings_key),
+            embedding=embedding,
             collection_name=settings.QDRANT_COLLECTION_NAME,
             location=settings.QDRANT_LOCATION,
             url=settings.QDRANT_URL,
@@ -32,6 +38,32 @@ class QdrantStore(BaseVectorStore):
             path=settings.QDRANT_PATH,
             distance_func=settings.QDRANT_DISTANCE_FUNC,
         )
+        try:
+            collections = self._docsearch.client.get_collections()
+            collection_exists = settings.QDRANT_COLLECTION_NAME in [
+                collection.name for collection in collections.collections
+            ]
+            
+            if not collection_exists:
+                self._docsearch.client.recreate_collection(
+                    collection_name=settings.QDRANT_COLLECTION_NAME,
+                    vectors_config=models.VectorParams(size=embedding.client[1].word_embedding_dimension, distance=models.Distance.COSINE),
+                )
+            
+            # Ensure the required index exists for metadata.source_id
+            try:
+                self._docsearch.client.create_payload_index(
+                    collection_name=settings.QDRANT_COLLECTION_NAME,
+                    field_name="metadata.source_id",
+                    field_schema=models.PayloadSchemaType.KEYWORD,
+                )
+            except Exception as index_error:
+                # Index might already exist, which is fine
+                if "already exists" not in str(index_error).lower():
+                    logging.warning(f"Could not create index for metadata.source_id: {index_error}")
+                    
+        except Exception as e:
+            logging.warning(f"Could not check for collection: {e}")
 
     def search(self, *args, **kwargs):
         return self._docsearch.similarity_search(filter=self._filter, *args, **kwargs)
@@ -46,3 +78,60 @@ class QdrantStore(BaseVectorStore):
         return self._docsearch.client.delete(
             collection_name=settings.QDRANT_COLLECTION_NAME, points_selector=self._filter
         )
+
+    def get_chunks(self):
+        try:
+            from qdrant_client import models
+
+            chunks = []
+            offset = None
+            while True:
+                records, offset = self._docsearch.client.scroll(
+                    collection_name=settings.QDRANT_COLLECTION_NAME,
+                    scroll_filter=self._filter,
+                    limit=10,
+                    with_payload=True,
+                    with_vectors=False,
+                    offset=offset,
+                )
+                for record in records:
+                    doc_id = record.id
+                    text = record.payload.get("page_content")
+                    metadata = record.payload.get("metadata")
+                    chunks.append(
+                        {"doc_id": doc_id, "text": text, "metadata": metadata}
+                    )
+                if offset is None:
+                    break
+            return chunks
+        except Exception as e:
+            logging.error(f"Error getting chunks: {e}", exc_info=True)
+            return []
+
+    def add_chunk(self, text, metadata=None):
+        import uuid
+        metadata = metadata or {}
+        
+        # Create a copy to avoid modifying the original metadata
+        final_metadata = metadata.copy()
+        
+        # Ensure the source_id is in the metadata so the chunk can be found by filters
+        final_metadata["source_id"] = self._source_id
+        
+        doc = Document(page_content=text, metadata=final_metadata)
+        # Generate a unique ID for the document
+        doc_id = str(uuid.uuid4())
+        doc.id = doc_id
+        doc_ids = self._docsearch.add_documents([doc])
+        return doc_ids[0] if doc_ids else doc_id
+
+    def delete_chunk(self, chunk_id):
+        try:
+            self._docsearch.client.delete(
+                collection_name=settings.QDRANT_COLLECTION_NAME,
+                points_selector=[chunk_id],
+            )
+            return True
+        except Exception as e:
+            logging.error(f"Error deleting chunk: {e}", exc_info=True)
+            return False