Create Genie API wrapper

pyproject.toml

@@ -6,7 +6,7 @@ authors = [
     { name="Prithvi Kannan", email="[email protected]" },
 ]
 readme = "README.md"
-requires-python = ">=3.9"
+requires-python = ">=3.8"
 dependencies = [
   "typing_extensions",
   "pydantic"

src/databricks_ai_bridge/genie.py

@@ -0,0 +1,115 @@
+import time
+from datetime import datetime
+from typing import Union
+
+import pandas as pd
+from databricks.sdk import WorkspaceClient
+
+
+def _parse_query_result(resp) -> Union[str, pd.DataFrame]:
+    columns = resp["manifest"]["schema"]["columns"]
+    header = [str(col["name"]) for col in columns]
+    rows = []
+    output = resp["result"]
+    if not output:
+        return "EMPTY"
+
+    for item in resp["result"]["data_typed_array"]:
+        row = []
+        for column, value in zip(columns, item["values"]):
+            type_name = column["type_name"]
+            str_value = value.get("str", None)
+            if str_value is None:
+                row.append(None)
+                continue
+
+            if type_name in ["INT", "LONG", "SHORT", "BYTE"]:
+                row.append(int(str_value))
+            elif type_name in ["FLOAT", "DOUBLE", "DECIMAL"]:
+                row.append(float(str_value))
+            elif type_name == "BOOLEAN":
+                row.append(str_value.lower() == "true")
+            elif type_name == "DATE":
+                row.append(datetime.strptime(str_value[:10], "%Y-%m-%d").date())
+            elif type_name == "TIMESTAMP":
+                row.append(datetime.strptime(str_value[:10], "%Y-%m-%d").date())
+            elif type_name == "BINARY":
+                row.append(bytes(str_value, "utf-8"))
+            else:
+                row.append(str_value)
+
+        rows.append(row)
+
+    query_result = pd.DataFrame(rows, columns=header).to_string()
+    return query_result
+
+
+class Genie:
+    def __init__(self, space_id):
+        self.space_id = space_id
+        workspace_client = WorkspaceClient()
+        self.genie = workspace_client.genie
+        self.headers = {
+            "Accept": "application/json",
+            "Content-Type": "application/json",
+        }
+
+    def start_conversation(self, content):
+        resp = self.genie._api.do(
+            "POST",
+            f"/api/2.0/genie/spaces/{self.space_id}/start-conversation",
+            body={"content": content},
+            headers=self.headers,
+        )
+        return resp
+
+    def create_message(self, conversation_id, content):
+        resp = self.genie._api.do(
+            "POST",
+            f"/api/2.0/genie/spaces/{self.space_id}/conversations/{conversation_id}/messages",
+            body={"content": content},
+            headers=self.headers,
+        )
+        return resp
+
+    def poll_for_result(self, conversation_id, message_id):
+        def poll_result():
+            while True:
+                resp = self.genie._api.do(
+                    "GET",
+                    f"/api/2.0/genie/spaces/{self.space_id}/conversations/{conversation_id}/messages/{message_id}",
+                    headers=self.headers,
+                )
+                if resp["status"] == "EXECUTING_QUERY":
+                    sql = next(r for r in resp["attachments"] if "query" in r)["query"]["query"]
+                    # print(f"SQL: {sql}")
+                    return poll_query_results()
+                elif resp["status"] == "COMPLETED":
+                    return next(r for r in resp["attachments"] if "text" in r)["text"]["content"]
+                else:
+                    # print(f"Waiting...: {resp['status']}")
+                    time.sleep(5)
+
+        def poll_query_results():
+            while True:
+                resp = self.genie._api.do(
+                    "GET",
+                    f"/api/2.0/genie/spaces/{self.space_id}/conversations/{conversation_id}/messages/{message_id}/query-result",
+                    headers=self.headers,
+                )["statement_response"]
+                state = resp["status"]["state"]
+                if state == "SUCCEEDED":
+                    return _parse_query_result(resp)
+                elif state == "RUNNING" or state == "PENDING":
+                    # print(f"Waiting for query result...")
+                    time.sleep(5)
+                else:
+                    # print(f"No query result: {resp['state']}")
+                    return None
+
+        return poll_result()
+
+    def ask_question(self, question):
+        resp = self.start_conversation(question)
+        # TODO (prithvi): return the query and the result
+        return self.poll_for_result(resp["conversation_id"], resp["message_id"])

src/databricks_ai_bridge/version.py

@@ -0,0 +1 @@
+VERSION = "0.0.1"

tests/databricks_ai_bridge/test_genie.py

@@ -0,0 +1,141 @@
+from datetime import datetime
+from unittest.mock import patch
+
+import pandas as pd
+import pytest
+
+from databricks_ai_bridge.genie import Genie, _parse_query_result
+
+
+@pytest.fixture
+def mock_workspace_client():
+    with patch("databricks_ai_bridge.genie.WorkspaceClient") as MockWorkspaceClient:
+        mock_client = MockWorkspaceClient.return_value
+        yield mock_client
+
+
+@pytest.fixture
+def genie(mock_workspace_client):
+    return Genie(space_id="test_space_id")
+
+
+def test_start_conversation(genie, mock_workspace_client):
+    mock_workspace_client.genie._api.do.return_value = {"conversation_id": "123"}
+    response = genie.start_conversation("Hello")
+    assert response == {"conversation_id": "123"}
+    mock_workspace_client.genie._api.do.assert_called_once_with(
+        "POST",
+        "/api/2.0/genie/spaces/test_space_id/start-conversation",
+        body={"content": "Hello"},
+        headers=genie.headers,
+    )
+
+
+def test_create_message(genie, mock_workspace_client):
+    mock_workspace_client.genie._api.do.return_value = {"message_id": "456"}
+    response = genie.create_message("123", "Hello again")
+    assert response == {"message_id": "456"}
+    mock_workspace_client.genie._api.do.assert_called_once_with(
+        "POST",
+        "/api/2.0/genie/spaces/test_space_id/conversations/123/messages",
+        body={"content": "Hello again"},
+        headers=genie.headers,
+    )
+
+
+def test_poll_for_result_completed(genie, mock_workspace_client):
+    mock_workspace_client.genie._api.do.side_effect = [
+        {"status": "COMPLETED", "attachments": [{"text": {"content": "Result"}}]},
+    ]
+    result = genie.poll_for_result("123", "456")
+    assert result == "Result"
+
+
+def test_poll_for_result_executing_query(genie, mock_workspace_client):
+    mock_workspace_client.genie._api.do.side_effect = [
+        {"status": "EXECUTING_QUERY", "attachments": [{"query": {"query": "SELECT *"}}]},
+        {
+            "statement_response": {
+                "status": {"state": "SUCCEEDED"},
+                "manifest": {"schema": {"columns": []}},
+                "result": {
+                    "data_typed_array": [],
+                },
+            }
+        },
+    ]
+    result = genie.poll_for_result("123", "456")
+    assert result == pd.DataFrame().to_string()
+
+
+def test_ask_question(genie, mock_workspace_client):
+    mock_workspace_client.genie._api.do.side_effect = [
+        {"conversation_id": "123", "message_id": "456"},
+        {"status": "COMPLETED", "attachments": [{"text": {"content": "Answer"}}]},
+    ]
+    result = genie.ask_question("What is the meaning of life?")
+    assert result == "Answer"
+
+
+def test_parse_query_result_empty():
+    resp = {"manifest": {"schema": {"columns": []}}, "result": None}
+    result = _parse_query_result(resp)
+    assert result == "EMPTY"
+
+
+def test_parse_query_result_with_data():
+    resp = {
+        "manifest": {
+            "schema": {
+                "columns": [
+                    {"name": "id", "type_name": "INT"},
+                    {"name": "name", "type_name": "STRING"},
+                    {"name": "created_at", "type_name": "TIMESTAMP"},
+                ]
+            }
+        },
+        "result": {
+            "data_typed_array": [
+                {"values": [{"str": "1"}, {"str": "Alice"}, {"str": "2023-10-01T00:00:00Z"}]},
+                {"values": [{"str": "2"}, {"str": "Bob"}, {"str": "2023-10-02T00:00:00Z"}]},
+            ]
+        },
+    }
+    result = _parse_query_result(resp)
+    expected_df = pd.DataFrame(
+        {
+            "id": [1, 2],
+            "name": ["Alice", "Bob"],
+            "created_at": [datetime(2023, 10, 1).date(), datetime(2023, 10, 2).date()],
+        }
+    )
+    assert result == expected_df.to_string()
+
+
+def test_parse_query_result_with_null_values():
+    resp = {
+        "manifest": {
+            "schema": {
+                "columns": [
+                    {"name": "id", "type_name": "INT"},
+                    {"name": "name", "type_name": "STRING"},
+                    {"name": "created_at", "type_name": "TIMESTAMP"},
+                ]
+            }
+        },
+        "result": {
+            "data_typed_array": [
+                {"values": [{"str": "1"}, {"str": None}, {"str": "2023-10-01T00:00:00Z"}]},
+                {"values": [{"str": "2"}, {"str": "Bob"}, {"str": None}]},
+            ]
+        },
+    }
+    result = _parse_query_result(resp)
+    expected_df = pd.DataFrame(
+        {
+            "id": [1, 2],
+            "name": [None, "Bob"],
+            "created_at": [datetime(2023, 10, 1).date(), None],
+        }
+    )
+    assert result == expected_df.to_string()