diff --git a/.semversioner/next-release/patch-20240904161252783119.json b/.semversioner/next-release/patch-20240904161252783119.json
new file mode 100644
index 0000000000..1bde7b3e8c
--- /dev/null
+++ b/.semversioner/next-release/patch-20240904161252783119.json
@@ -0,0 +1,4 @@
+{
+  "type": "patch",
+  "description": "add querying from azure blob storage"
+}
diff --git a/graphrag/query/cli.py b/graphrag/query/cli.py
index 3ac76f81c8..872a4d0993 100644
--- a/graphrag/query/cli.py
+++ b/graphrag/query/cli.py
@@ -9,8 +9,14 @@
 
 import pandas as pd
 
-from graphrag.config import load_config, resolve_path
+from graphrag.config import (
+    GraphRagConfig,
+    load_config,
+    resolve_path,
+)
+from graphrag.index.create_pipeline_config import create_pipeline_config
 from graphrag.index.progress import PrintProgressReporter
+from graphrag.utils.storage import _create_storage, _load_table_from_storage
 
 from . import api
 
@@ -36,17 +42,21 @@ def run_global_search(
     if data_dir:
         config.storage.base_dir = str(resolve_path(data_dir, root))
 
-    data_path = Path(config.storage.base_dir).resolve()
-
-    final_nodes: pd.DataFrame = pd.read_parquet(
-        data_path / "create_final_nodes.parquet"
-    )
-    final_entities: pd.DataFrame = pd.read_parquet(
-        data_path / "create_final_entities.parquet"
-    )
-    final_community_reports: pd.DataFrame = pd.read_parquet(
-        data_path / "create_final_community_reports.parquet"
+    dataframe_dict = _resolve_parquet_files(
+        root_dir=root_dir,
+        config=config,
+        parquet_list=[
+            "create_final_nodes.parquet",
+            "create_final_entities.parquet",
+            "create_final_community_reports.parquet",
+        ],
+        optional_list=[],
     )
+    final_nodes: pd.DataFrame = dataframe_dict["create_final_nodes"]
+    final_entities: pd.DataFrame = dataframe_dict["create_final_entities"]
+    final_community_reports: pd.DataFrame = dataframe_dict[
+        "create_final_community_reports"
+    ]
 
     # call the Query API
     if streaming:
@@ -112,23 +122,26 @@ def run_local_search(
     if data_dir:
         config.storage.base_dir = str(resolve_path(data_dir, root))
 
-    data_path = Path(config.storage.base_dir).resolve()
-
-    final_nodes = pd.read_parquet(data_path / "create_final_nodes.parquet")
-    final_community_reports = pd.read_parquet(
-        data_path / "create_final_community_reports.parquet"
-    )
-    final_text_units = pd.read_parquet(data_path / "create_final_text_units.parquet")
-    final_relationships = pd.read_parquet(
-        data_path / "create_final_relationships.parquet"
-    )
-    final_entities = pd.read_parquet(data_path / "create_final_entities.parquet")
-    final_covariates_path = data_path / "create_final_covariates.parquet"
-    final_covariates = (
-        pd.read_parquet(final_covariates_path)
-        if final_covariates_path.exists()
-        else None
+    dataframe_dict = _resolve_parquet_files(
+        root_dir=root_dir,
+        config=config,
+        parquet_list=[
+            "create_final_nodes.parquet",
+            "create_final_community_reports.parquet",
+            "create_final_text_units.parquet",
+            "create_final_relationships.parquet",
+            "create_final_entities.parquet",
+        ],
+        optional_list=["create_final_covariates.parquet"],
     )
+    final_nodes: pd.DataFrame = dataframe_dict["create_final_nodes"]
+    final_community_reports: pd.DataFrame = dataframe_dict[
+        "create_final_community_reports"
+    ]
+    final_text_units: pd.DataFrame = dataframe_dict["create_final_text_units"]
+    final_relationships: pd.DataFrame = dataframe_dict["create_final_relationships"]
+    final_entities: pd.DataFrame = dataframe_dict["create_final_entities"]
+    final_covariates: pd.DataFrame | None = dataframe_dict["create_final_covariates"]
 
     # call the Query API
     if streaming:
@@ -179,3 +192,35 @@ async def run_streaming_search():
     # NOTE: we return the response and context data here purely as a complete demonstration of the API.
     # External users should use the API directly to get the response and context data.
     return response, context_data
+
+
+def _resolve_parquet_files(
+    root_dir: str,
+    config: GraphRagConfig,
+    parquet_list: list[str],
+    optional_list: list[str],
+) -> dict[str, pd.DataFrame]:
+    """Read parquet files to a dataframe dict."""
+    dataframe_dict = {}
+    pipeline_config = create_pipeline_config(config)
+    storage_obj = _create_storage(root_dir=root_dir, config=pipeline_config.storage)
+    for parquet_file in parquet_list:
+        df_key = parquet_file.split(".")[0]
+        df_value = asyncio.run(
+            _load_table_from_storage(name=parquet_file, storage=storage_obj)
+        )
+        dataframe_dict[df_key] = df_value
+
+    # for optional parquet files, set the dict entry to None instead of erroring out if it does not exist
+    for optional_file in optional_list:
+        file_exists = asyncio.run(storage_obj.has(optional_file))
+        df_key = optional_file.split(".")[0]
+        if file_exists:
+            df_value = asyncio.run(
+                _load_table_from_storage(name=optional_file, storage=storage_obj)
+            )
+            dataframe_dict[df_key] = df_value
+        else:
+            dataframe_dict[df_key] = None
+
+    return dataframe_dict