diff --git a/comps/cores/proto/api_protocol.py b/comps/cores/proto/api_protocol.py
index ff3988b8e9..5068a81145 100644
--- a/comps/cores/proto/api_protocol.py
+++ b/comps/cores/proto/api_protocol.py
@@ -232,6 +232,7 @@ class RetrievalRequest(BaseModel):
 class RetrievalRequestArangoDB(RetrievalRequest):
     graph_name: str | None = None
     search_start: str | None = None  # "node", "edge", "chunk"
+    search_type: str | None = None  # "vector", "hybrid"
     num_centroids: int | None = None
     distance_strategy: str | None = None  #  # "COSINE", "EUCLIDEAN_DISTANCE"
     use_approx_search: bool | None = None
diff --git a/comps/dataprep/src/integrations/arangodb.py b/comps/dataprep/src/integrations/arangodb.py
index d75152c76a..07c44fe9f1 100644
--- a/comps/dataprep/src/integrations/arangodb.py
+++ b/comps/dataprep/src/integrations/arangodb.py
@@ -370,18 +370,20 @@ async def ingest_files(self, input: Union[DataprepRequest, ArangoDBDataprepReque
         chunk_overlap = input.chunk_overlap
         process_table = input.process_table
         table_strategy = input.table_strategy
-        graph_name = getattr(input, "graph_name", ARANGO_GRAPH_NAME)
-        insert_async = getattr(input, "insert_async", ARANGO_INSERT_ASYNC)
-        insert_batch_size = getattr(input, "insert_batch_size", ARANGO_BATCH_SIZE)
-        embed_nodes = getattr(input, "embed_nodes", EMBED_NODES)
-        embed_edges = getattr(input, "embed_edges", EMBED_EDGES)
-        embed_chunks = getattr(input, "embed_chunks", EMBED_CHUNKS)
-        allowed_node_types = getattr(input, "allowed_node_types", ALLOWED_NODE_TYPES)
-        allowed_edge_types = getattr(input, "allowed_edge_types", ALLOWED_EDGE_TYPES)
-        node_properties = getattr(input, "node_properties", NODE_PROPERTIES)
-        edge_properties = getattr(input, "edge_properties", EDGE_PROPERTIES)
-        text_capitalization_strategy = getattr(input, "text_capitalization_strategy", TEXT_CAPITALIZATION_STRATEGY)
-        include_chunks = getattr(input, "include_chunks", INCLUDE_CHUNKS)
+        graph_name = getattr(input, "graph_name", ARANGO_GRAPH_NAME) or ARANGO_GRAPH_NAME
+        insert_async = getattr(input, "insert_async", ARANGO_INSERT_ASYNC) or ARANGO_INSERT_ASYNC
+        insert_batch_size = getattr(input, "insert_batch_size", ARANGO_BATCH_SIZE) or ARANGO_BATCH_SIZE
+        embed_nodes = getattr(input, "embed_nodes", EMBED_NODES) or EMBED_NODES
+        embed_edges = getattr(input, "embed_edges", EMBED_EDGES) or EMBED_EDGES
+        embed_chunks = getattr(input, "embed_chunks", EMBED_CHUNKS) or EMBED_CHUNKS
+        allowed_node_types = getattr(input, "allowed_node_types", ALLOWED_NODE_TYPES) or ALLOWED_NODE_TYPES
+        allowed_edge_types = getattr(input, "allowed_edge_types", ALLOWED_EDGE_TYPES) or ALLOWED_EDGE_TYPES
+        node_properties = getattr(input, "node_properties", NODE_PROPERTIES) or NODE_PROPERTIES
+        edge_properties = getattr(input, "edge_properties", EDGE_PROPERTIES) or EDGE_PROPERTIES
+        text_capitalization_strategy = (
+            getattr(input, "text_capitalization_strategy", TEXT_CAPITALIZATION_STRATEGY) or TEXT_CAPITALIZATION_STRATEGY
+        )
+        include_chunks = getattr(input, "include_chunks", INCLUDE_CHUNKS) or INCLUDE_CHUNKS
 
         self._initialize_llm(
             allowed_node_types=allowed_node_types,
diff --git a/comps/dataprep/src/requirements.txt b/comps/dataprep/src/requirements.txt
index 9cf39a4ec5..03a3c4b382 100644
--- a/comps/dataprep/src/requirements.txt
+++ b/comps/dataprep/src/requirements.txt
@@ -16,7 +16,7 @@ huggingface_hub
 ipython
 json-repair
 langchain
-langchain-arangodb
+langchain-arangodb==0.0.6
 langchain-community
 langchain-elasticsearch
 langchain-experimental
diff --git a/comps/retrievers/src/README_arangodb.md b/comps/retrievers/src/README_arangodb.md
index 2a91a8548e..f5a95fc0f4 100644
--- a/comps/retrievers/src/README_arangodb.md
+++ b/comps/retrievers/src/README_arangodb.md
@@ -116,6 +116,7 @@ ArangoDB Vector configuration
 - `ARANGO_USE_APPROX_SEARCH`: If set to True, the microservice will use the approximate nearest neighbor search for as part of the retrieval step. Defaults to `False`, which means the microservice will use the exact search.
 - `ARANGO_NUM_CENTROIDS`: The number of centroids to use for the approximate nearest neighbor search. Defaults to `1`.
 - `ARANGO_SEARCH_START`: The starting point for the search. Defaults to `node`. Other option could be `"edge"`, or `"chunk"`.
+- `ARANGO_SEARCH_TYPE`: The type of search to use for the ArangoDB service. Defaults to `vector`. Other option could be `"hybrid"`, which combines Vector Search + Full Text Search via Reciprocal Rank Fusion (RRF).
 
 ArangoDB Traversal configuration
 
@@ -165,6 +166,7 @@ class RetrievalRequest(BaseModel): ...
 class RetrievalRequestArangoDB(RetrievalRequest):
     graph_name: str | None = None
     search_start: str | None = None  # "node", "edge", "chunk"
+    search_type: str | None = None  # "vector", "hybrid"
     num_centroids: int | None = None
     distance_strategy: str | None = None  #  # "COSINE", "EUCLIDEAN_DISTANCE"
     use_approx_search: bool | None = None
diff --git a/comps/retrievers/src/integrations/arangodb.py b/comps/retrievers/src/integrations/arangodb.py
index 9905d9134e..c68432f941 100644
--- a/comps/retrievers/src/integrations/arangodb.py
+++ b/comps/retrievers/src/integrations/arangodb.py
@@ -23,6 +23,7 @@
     ARANGO_NUM_CENTROIDS,
     ARANGO_PASSWORD,
     ARANGO_SEARCH_START,
+    ARANGO_SEARCH_TYPE,
     ARANGO_TRAVERSAL_ENABLED,
     ARANGO_TRAVERSAL_MAX_DEPTH,
     ARANGO_TRAVERSAL_MAX_RETURNED,
@@ -325,25 +326,34 @@ async def invoke(
         # Process Input #
         #################
 
-        query = getattr(input, "input", getattr(input, "text"))
+        input_dict = input.model_dump(exclude_none=True)
+        query = input_dict.get("input", input_dict.get("text"))
+
         if not query:
             if logflag:
-                logger.error("Query is empty.")
+                logger.error("Query is empty. Please provide a valid query.")
 
             return []
 
         embedding = input.embedding if isinstance(input.embedding, list) else None
-        graph_name = getattr(input, "graph_name", ARANGO_GRAPH_NAME)
-        search_start = getattr(input, "search_start", ARANGO_SEARCH_START)
-        enable_traversal = getattr(input, "enable_traversal", ARANGO_TRAVERSAL_ENABLED)
-        enable_summarizer = getattr(input, "enable_summarizer", SUMMARIZER_ENABLED)
-        distance_strategy = getattr(input, "distance_strategy", ARANGO_DISTANCE_STRATEGY)
-        use_approx_search = getattr(input, "use_approx_search", ARANGO_USE_APPROX_SEARCH)
-        num_centroids = getattr(input, "num_centroids", ARANGO_NUM_CENTROIDS)
-        traversal_max_depth = getattr(input, "traversal_max_depth", ARANGO_TRAVERSAL_MAX_DEPTH)
-        traversal_max_returned = getattr(input, "traversal_max_returned", ARANGO_TRAVERSAL_MAX_RETURNED)
-        traversal_score_threshold = getattr(input, "traversal_score_threshold", ARANGO_TRAVERSAL_SCORE_THRESHOLD)
-        traversal_query = getattr(input, "traversal_query", ARANGO_TRAVERSAL_QUERY)
+        graph_name = input_dict.get("graph_name", ARANGO_GRAPH_NAME)
+        search_start = input_dict.get("search_start", ARANGO_SEARCH_START)
+        search_type = input_dict.get("search_type", ARANGO_SEARCH_TYPE)
+        enable_traversal = input_dict.get("enable_traversal", ARANGO_TRAVERSAL_ENABLED)
+        enable_summarizer = input_dict.get("enable_summarizer", SUMMARIZER_ENABLED)
+        distance_strategy = input_dict.get("distance_strategy", ARANGO_DISTANCE_STRATEGY)
+        use_approx_search = input_dict.get("use_approx_search", ARANGO_USE_APPROX_SEARCH)
+        num_centroids = input_dict.get("num_centroids", ARANGO_NUM_CENTROIDS)
+        traversal_max_depth = input_dict.get("traversal_max_depth", ARANGO_TRAVERSAL_MAX_DEPTH)
+        traversal_max_returned = input_dict.get("traversal_max_returned", ARANGO_TRAVERSAL_MAX_RETURNED)
+        traversal_score_threshold = input_dict.get("traversal_score_threshold", ARANGO_TRAVERSAL_SCORE_THRESHOLD)
+        traversal_query = input_dict.get("traversal_query", ARANGO_TRAVERSAL_QUERY)
+
+        if not graph_name:
+            raise HTTPException(
+                status_code=400,
+                detail="Graph name is empty. Please provide a valid graph name.",
+            )
 
         if search_start == "node":
             collection_name = f"{graph_name}_ENTITY"
@@ -375,7 +385,12 @@ async def invoke(
 
         if not (v_col_exists or e_col_exists):
             if logflag:
-                collection_names = self.db.graph(graph_name).vertex_collections()
+                collection_names = set()
+                for e_d in self.db.graph(graph_name).edge_definitions():
+                    collection_names.add(e_d["edge_collection"])
+                    collection_names.update(e_d["from_vertex_collections"])
+                    collection_names.update(e_d["to_vertex_collections"])
+
                 m = f"Collection '{collection_name}' does not exist in graph '{graph_name}'. Collections: {collection_names}"
                 logger.error(m)
             return []
@@ -430,16 +445,23 @@ async def invoke(
         else:
             embeddings = HuggingFaceBgeEmbeddings(model_name=TEI_EMBED_MODEL)
 
-        vector_db = ArangoVector(
-            embedding=embeddings,
-            embedding_dimension=dimension,
-            database=self.db,
-            collection_name=collection_name,
-            embedding_field=ARANGO_EMBEDDING_FIELD,
-            text_field=ARANGO_TEXT_FIELD,
-            distance_strategy=distance_strategy,
-            num_centroids=num_centroids,
-        )
+        try:
+            vector_db = ArangoVector(
+                embedding=embeddings,
+                embedding_dimension=dimension,
+                database=self.db,
+                collection_name=collection_name,
+                embedding_field=ARANGO_EMBEDDING_FIELD,
+                text_field=ARANGO_TEXT_FIELD,
+                distance_strategy=distance_strategy,
+                num_centroids=num_centroids,
+                search_type=search_type,
+            )
+        except Exception as e:
+            if logflag:
+                logger.error(f"Error during ArangoVector initialization: {e}")
+
+            return []
 
         ######################
         # Compute Similarity #
diff --git a/comps/retrievers/src/integrations/config.py b/comps/retrievers/src/integrations/config.py
index 8514192611..85e16d42fb 100644
--- a/comps/retrievers/src/integrations/config.py
+++ b/comps/retrievers/src/integrations/config.py
@@ -205,6 +205,7 @@ def format_opensearch_conn_from_env():
 ARANGO_USE_APPROX_SEARCH = os.getenv("ARANGO_USE_APPROX_SEARCH", "false").lower() == "true"
 ARANGO_NUM_CENTROIDS = os.getenv("ARANGO_NUM_CENTROIDS", 1)
 ARANGO_SEARCH_START = os.getenv("ARANGO_SEARCH_START", "node")
+ARANGO_SEARCH_TYPE = os.getenv("ARANGO_SEARCH_TYPE", "vector")
 
 # ArangoDB Traversal configuration
 ARANGO_TRAVERSAL_ENABLED = os.getenv("ARANGO_TRAVERSAL_ENABLED", "false").lower() == "true"
diff --git a/comps/retrievers/src/requirements.txt b/comps/retrievers/src/requirements.txt
index a98da36023..b143c157c2 100644
--- a/comps/retrievers/src/requirements.txt
+++ b/comps/retrievers/src/requirements.txt
@@ -10,7 +10,7 @@ fastapi
 future
 graspologic
 haystack-ai==2.3.1
-langchain-arangodb
+langchain-arangodb==0.0.6
 langchain-elasticsearch
 langchain-mariadb
 langchain-openai
diff --git a/tests/dataprep/test_dataprep_arango.sh b/tests/dataprep/test_dataprep_arango.sh
index 86e8bdc6c9..b615dfae37 100644
--- a/tests/dataprep/test_dataprep_arango.sh
+++ b/tests/dataprep/test_dataprep_arango.sh
@@ -61,7 +61,7 @@ function start_service() {
     docker compose up ${service_name} -d > ${LOG_PATH}/start_services_with_compose.log
 
     # Debug time
-    sleep 1m
+    sleep 2m
 
     check_healthy "dataprep-arangodb" || exit 1
 }
diff --git a/tests/retrievers/test_retrievers_arango.sh b/tests/retrievers/test_retrievers_arango.sh
index 402be6af4b..15184ce7a8 100644
--- a/tests/retrievers/test_retrievers_arango.sh
+++ b/tests/retrievers/test_retrievers_arango.sh
@@ -16,7 +16,6 @@ export ARANGO_URL=${ARANGO_URL:-"http://${host_ip}:8529"}
 export ARANGO_USERNAME=${ARANGO_USERNAME:-"root"}
 export ARANGO_PASSWORD=${ARANGO_PASSWORD:-"test"}
 export ARANGO_DB_NAME=${ARANGO_DB_NAME:-"_system"}
-export ARANGO_COLLECTION_NAME=${ARANGO_COLLECTION_NAME:-"test"}
 
 function build_docker_images() {
     cd $WORKPATH
@@ -43,27 +42,34 @@ function validate_microservice() {
     source activate
     URL="http://${host_ip}:7000/v1/retrieval"
 
-     # Create ARANGO_COLLECTION_NAME
+    # Create vertex collection GRAPH_ENTITY
     curl -X POST --header 'accept: application/json' \
     --header 'Content-Type: application/json' \
-    --data '{"name": "'${ARANGO_COLLECTION_NAME}'", "type": 2, "waitForSync": true}' \
+    --data '{"name": "GRAPH_ENTITY", "type": 2, "waitForSync": true}' \
     "${ARANGO_URL}/_db/${ARANGO_DB_NAME}/_api/collection" \
     -u ${ARANGO_USERNAME}:${ARANGO_PASSWORD}
 
+    # Create graph GRAPH with GRAPH_ENTITY as vertex collection
+    curl -X POST --header 'accept: application/json' \
+    --header 'Content-Type: application/json' \
+    --data '{"name": "GRAPH", "edgeDefinitions": [], "orphanCollections": ["GRAPH_ENTITY"]}' \
+    "${ARANGO_URL}/_db/${ARANGO_DB_NAME}/_api/gharial" \
+    -u ${ARANGO_USERNAME}:${ARANGO_PASSWORD}
+
     # Insert data into arango: {text: "test", embedding: [0.1, 0.2, 0.3, 0.4, 0.5]}
     curl -X POST --header 'accept: application/json' \
     --header 'Content-Type: application/json' \
     --data '{"text": "test", "embedding": [0.1, 0.2, 0.3, 0.4, 0.5]}' \
-    "${ARANGO_URL}/_db/${ARANGO_DB_NAME}/_api/document/${ARANGO_COLLECTION_NAME}" \
+    "${ARANGO_URL}/_db/${ARANGO_DB_NAME}/_api/document/GRAPH_ENTITY" \
     -u ${ARANGO_USERNAME}:${ARANGO_PASSWORD}
 
     sleep 1m
 
     test_embedding="[0.1, 0.2, 0.3, 0.4, 0.5]"
-    HTTP_STATUS=$(curl -s -o /dev/null -w "%{http_code}" -X POST -d "{\"text\":\"test\",\"embedding\":${test_embedding}}" -H 'Content-Type: application/json' "$URL")
+    HTTP_STATUS=$(curl -s -o /dev/null -w "%{http_code}" -X POST -d "{\"input\":\"test\",\"embedding\":${test_embedding},\"graph_name\":\"GRAPH\"}" -H 'Content-Type: application/json' "$URL")
     if [ "$HTTP_STATUS" -eq 200 ]; then
         echo "[ retriever ] HTTP status is 200. Checking content..."
-        local CONTENT=$(curl -s -X POST -d "{\"text\":\"test\",\"embedding\":${test_embedding}}" -H 'Content-Type: application/json' "$URL" | tee ${LOG_PATH}/retriever.log)
+        local CONTENT=$(curl -s -X POST -d "{\"input\":\"test\",\"embedding\":${test_embedding},\"graph_name\":\"GRAPH\"}" -H 'Content-Type: application/json' "$URL" | tee ${LOG_PATH}/retriever.log)
 
         if echo "$CONTENT" | grep -q "retrieved_docs"; then
             echo "[ retriever ] Content is as expected."