diff --git a/src/backend/base/langflow/components/vectorstores/astradb_graph.py b/src/backend/base/langflow/components/vectorstores/astradb_graph.py
index d8471dd4f..3e5b398d2 100644
--- a/src/backend/base/langflow/components/vectorstores/astradb_graph.py
+++ b/src/backend/base/langflow/components/vectorstores/astradb_graph.py
@@ -6,11 +6,12 @@ from loguru import logger
 
 from langflow.base.vectorstores.model import LCVectorStoreComponent, check_cached_vector_store
 from langflow.helpers import docs_to_data
-from langflow.inputs import DictInput, FloatInput
-from langflow.io import (
+from langflow.inputs import (
     BoolInput,
     DataInput,
+    DictInput,
     DropdownInput,
+    FloatInput,
     HandleInput,
     IntInput,
     MultilineInput,
@@ -71,11 +72,10 @@ class AstraDBGraphVectorStoreComponent(LCVectorStoreComponent):
             advanced=True,
         ),
         HandleInput(
-            name="embedding",
+            name="embedding_model",
             display_name="Embedding Model",
             input_types=["Embeddings"],
-            info="Embedding model.",
-            required=True,
+            info="Allows an embedding model configuration.",
         ),
         DropdownInput(
             name="metric",
@@ -156,8 +156,14 @@ class AstraDBGraphVectorStoreComponent(LCVectorStoreComponent):
             name="search_type",
             display_name="Search Type",
             info="Search type to use",
-            options=["Similarity", "Similarity with score threshold", "MMR (Max Marginal Relevance)"],
-            value="Similarity",
+            options=[
+                "Similarity",
+                "Similarity with score threshold",
+                "MMR (Max Marginal Relevance)",
+                "Graph Traversal",
+                "MMR (Max Marginal Relevance) Graph Traversal",
+            ],
+            value="MMR (Max Marginal Relevance) Graph Traversal",
             advanced=True,
         ),
         FloatInput(
@@ -199,8 +205,10 @@ class AstraDBGraphVectorStoreComponent(LCVectorStoreComponent):
             raise ValueError(msg) from e
 
         try:
+            logger.debug(f"Initializing Graph Vector Store {self.collection_name}")
+
             vector_store = AstraDBGraphVectorStore(
-                embedding=self.embedding,
+                embedding=self.embedding_model,
                 collection_name=self.collection_name,
                 metadata_incoming_links_key=self.metadata_incoming_links_key or "incoming_links",
                 token=self.token,
@@ -216,7 +224,7 @@ class AstraDBGraphVectorStoreComponent(LCVectorStoreComponent):
                 pre_delete_collection=self.pre_delete_collection,
                 metadata_indexing_include=[s for s in self.metadata_indexing_include if s] or None,
                 metadata_indexing_exclude=[s for s in self.metadata_indexing_exclude if s] or None,
-                collection_indexing_policy=orjson.dumps(self.collection_indexing_policy)
+                collection_indexing_policy=orjson.loads(self.collection_indexing_policy.encode("utf-8"))
                 if self.collection_indexing_policy
                 else None,
             )
@@ -224,6 +232,7 @@ class AstraDBGraphVectorStoreComponent(LCVectorStoreComponent):
             msg = f"Error initializing AstraDBGraphVectorStore: {e}"
             raise ValueError(msg) from e
 
+        logger.debug(f"Vector Store initialized: {vector_store.astra_env.collection_name}")
         self._add_documents_to_vector_store(vector_store)
 
         return vector_store
@@ -248,11 +257,19 @@ class AstraDBGraphVectorStoreComponent(LCVectorStoreComponent):
             logger.debug("No documents to add to the Vector Store.")
 
     def _map_search_type(self) -> str:
-        if self.search_type == "Similarity with score threshold":
-            return "similarity_score_threshold"
-        if self.search_type == "MMR (Max Marginal Relevance)":
-            return "mmr"
-        return "similarity"
+        match self.search_type:
+            case "Similarity":
+                return "similarity"
+            case "Similarity with score threshold":
+                return "similarity_score_threshold"
+            case "MMR (Max Marginal Relevance)":
+                return "mmr"
+            case "Graph Traversal":
+                return "traversal"
+            case "MMR (Max Marginal Relevance) Graph Traversal":
+                return "mmr_traversal"
+            case _:
+                return "similarity"
 
     def _build_search_args(self):
         args = {
@@ -270,6 +287,7 @@ class AstraDBGraphVectorStoreComponent(LCVectorStoreComponent):
         if not vector_store:
             vector_store = self.build_vector_store()
 
+        logger.debug("Searching for documents in AstraDBGraphVectorStore.")
         logger.debug(f"Search input: {self.search_input}")
         logger.debug(f"Search type: {self.search_type}")
         logger.debug(f"Number of results: {self.number_of_results}")
@@ -280,6 +298,14 @@ class AstraDBGraphVectorStoreComponent(LCVectorStoreComponent):
                 search_args = self._build_search_args()
 
                 docs = vector_store.search(query=self.search_input, search_type=search_type, **search_args)
+
+                # Drop links from the metadata. At this point the links don't add any value for building the
+                # context and haven't been restored to json which causes the conversion to fail.
+                logger.debug("Removing links from metadata.")
+                for doc in docs:
+                    if "links" in doc.metadata:
+                        doc.metadata.pop("links")
+
             except Exception as e:
                 msg = f"Error performing search in AstraDBGraphVectorStore: {e}"
                 raise ValueError(msg) from e
@@ -287,7 +313,9 @@ class AstraDBGraphVectorStoreComponent(LCVectorStoreComponent):
             logger.debug(f"Retrieved documents: {len(docs)}")
 
             data = docs_to_data(docs)
+
             logger.debug(f"Converted documents to data: {len(data)}")
+
             self.status = data
             return data
         logger.debug("No search input provided. Skipping search.")