(feat) /daily/metrics

litellm/integrations/clickhouse.py

@@ -27,6 +27,126 @@ import litellm, uuid
 from litellm._logging import print_verbose, verbose_logger
 
 
+def create_client():
+    try:
+        import clickhouse_connect
+
+        port = os.getenv("CLICKHOUSE_PORT")
+        clickhouse_host = os.getenv("CLICKHOUSE_HOST")
+        if clickhouse_host is not None:
+            verbose_logger.debug("setting up clickhouse")
+            if port is not None and isinstance(port, str):
+                port = int(port)
+
+            client = clickhouse_connect.get_client(
+                host=os.getenv("CLICKHOUSE_HOST"),
+                port=port,
+                username=os.getenv("CLICKHOUSE_USERNAME"),
+                password=os.getenv("CLICKHOUSE_PASSWORD"),
+            )
+            return client
+        else:
+            raise Exception("Clickhouse: Clickhouse host not set")
+    except Exception as e:
+        raise ValueError(f"Clickhouse: {e}")
+
+
+def build_daily_metrics():
+    click_house_client = create_client()
+
+    # get daily spend
+    daily_spend = click_house_client.query_df(
+        """
+        SELECT sumMerge(DailySpend) as daily_spend, day FROM daily_aggregated_spend GROUP BY day
+        """
+    )
+
+    # get daily spend per model
+
+    daily_spend_per_model = click_house_client.query_df(
+        """
+        SELECT sumMerge(DailySpend) as daily_spend, day, model FROM daily_aggregated_spend_per_model GROUP BY day, model
+        """
+    )
+    new_df = daily_spend_per_model.to_dict(orient="records")
+    import pandas as pd
+
+    df = pd.DataFrame(new_df)
+    # Group by 'day' and create a dictionary for each group
+    result_dict = {}
+    for day, group in df.groupby("day"):
+        models = group["model"].tolist()
+        spend = group["daily_spend"].tolist()
+        spend_per_model = {model: spend for model, spend in zip(models, spend)}
+        result_dict[day] = spend_per_model
+
+    # Display the resulting dictionary
+
+    daily_spend_per_api_key = click_house_client.query_df(
+        """
+            SELECT
+                daily_spend,
+                day,
+                api_key
+            FROM (
+                SELECT
+                    sumMerge(DailySpend) as daily_spend,
+                    day,
+                    api_key,
+                    RANK() OVER (PARTITION BY day ORDER BY sumMerge(DailySpend) DESC) as spend_rank
+                FROM
+                    daily_aggregated_spend_per_api_key
+                GROUP BY
+                    day,
+                    api_key
+            ) AS ranked_api_keys
+            WHERE
+                spend_rank <= 5
+                AND day IS NOT NULL
+            ORDER BY
+                day,
+                daily_spend DESC
+        """
+    )
+    new_df = daily_spend_per_api_key.to_dict(orient="records")
+    import pandas as pd
+
+    df = pd.DataFrame(new_df)
+    # Group by 'day' and create a dictionary for each group
+    api_key_result_dict = {}
+    for day, group in df.groupby("day"):
+        api_keys = group["api_key"].tolist()
+        spend = group["daily_spend"].tolist()
+        spend_per_api_key = {api_key: spend for api_key, spend in zip(api_keys, spend)}
+        api_key_result_dict[day] = spend_per_api_key
+
+    # Display the resulting dictionary
+
+    # for each day in daily spend, look up the day in result_dict and api_key_result_dict
+    # Assuming daily_spend DataFrame has 'day' column
+    result = []
+    for index, row in daily_spend.iterrows():
+        day = row["day"]
+        data_day = row.to_dict()
+
+        # Look up in result_dict
+        if day in result_dict:
+            spend_per_model = result_dict[day]
+            # Assuming there is a column named 'model' in daily_spend
+            data_day["spend_per_model"] = spend_per_model  # Assign 0 if model not found
+
+        # Look up in api_key_result_dict
+        if day in api_key_result_dict:
+            spend_per_api_key = api_key_result_dict[day]
+            # Assuming there is a column named 'api_key' in daily_spend
+            data_day["spend_per_api_key"] = spend_per_api_key
+
+        result.append(data_day)
+
+    # print("FINAL daily metric", result)
+    return result
+
+
 def _create_clickhouse_material_views(client=None, table_names=[]):
     # Create Materialized Views if they don't exist
     # Materialized Views send new inserted rows to the aggregate tables