LiteLLM Minor Fixes & Improvements (11/29/2024) (#6965)

* fix(factory.py): ensure tool call converts image url

Fixes https://github.com/BerriAI/litellm/issues/6953

* fix(transformation.py): support mp4 + pdf url's for vertex ai

Fixes https://github.com/BerriAI/litellm/issues/6936

* fix(http_handler.py): mask gemini api key in error logs

Fixes https://github.com/BerriAI/litellm/issues/6963

* docs(prometheus.md): update prometheus FAQs

* feat(auth_checks.py): ensure specific model access > wildcard model access

if wildcard model is in access group, but specific model is not - deny access

* fix(auth_checks.py): handle auth checks for team based model access groups

handles scenario where model access group used for wildcard models

* fix(internal_user_endpoints.py): support adding guardrails on `/user/update`

Fixes https://github.com/BerriAI/litellm/issues/6942

* fix(key_management_endpoints.py): fix prepare_metadata_fields helper

* fix: fix tests

* build(requirements.txt): bump openai dep version

fixes proxies argument

* test: fix tests

* fix(http_handler.py): fix error message masking

* fix(bedrock_guardrails.py): pass in prepped data

* test: fix test

* test: fix nvidia nim test

* fix(http_handler.py): return original response headers

* fix: revert maskedhttpstatuserror

* test: update tests

* test: cleanup test

* fix(key_management_endpoints.py): fix metadata field update logic

* fix(key_management_endpoints.py): maintain initial order of guardrails in key update

* fix(key_management_endpoints.py): handle prepare metadata

* fix: fix linting errors

* fix: fix linting errors

* fix: fix linting errors

* fix: fix key management errors

* fix(key_management_endpoints.py): update metadata

* test: update test

* refactor: add more debug statements

* test: skip flaky test

* test: fix test

* fix: fix test

* fix: fix update metadata logic

* fix: fix test

* ci(config.yml): change db url for e2e ui testing

enterprise/utils.py

@@ -2,7 +2,9 @@
 from typing import Optional, List
 from litellm._logging import verbose_logger
 from litellm.proxy.proxy_server import PrismaClient, HTTPException
+from litellm.llms.custom_httpx.http_handler import HTTPHandler
 import collections
+import httpx
 from datetime import datetime
 
 
@@ -114,7 +116,6 @@ async def ui_get_spend_by_tags(
 
 
 def _forecast_daily_cost(data: list):
-    import requests  # type: ignore
     from datetime import datetime, timedelta
 
     if len(data) == 0:
@@ -136,17 +137,17 @@ def _forecast_daily_cost(data: list):
 
     print("last entry date", last_entry_date)
 
-    # Assuming today_date is a datetime object
-    today_date = datetime.now()
-
     # Calculate the last day of the month
     last_day_of_todays_month = datetime(
         today_date.year, today_date.month % 12 + 1, 1
     ) - timedelta(days=1)
 
+    print("last day of todays month", last_day_of_todays_month)
     # Calculate the remaining days in the month
     remaining_days = (last_day_of_todays_month - last_entry_date).days
 
+    print("remaining days", remaining_days)
+
     current_spend_this_month = 0
     series = {}
     for entry in data:
@@ -176,13 +177,19 @@ def _forecast_daily_cost(data: list):
         "Content-Type": "application/json",
     }
 
-    response = requests.post(
-        url="https://trend-api-production.up.railway.app/forecast",
-        json=payload,
-        headers=headers,
-    )
-    # check the status code
-    response.raise_for_status()
+    client = HTTPHandler()
+
+    try:
+        response = client.post(
+            url="https://trend-api-production.up.railway.app/forecast",
+            json=payload,
+            headers=headers,
+        )
+    except httpx.HTTPStatusError as e:
+        raise HTTPException(
+            status_code=500,
+            detail={"error": f"Error getting forecast: {e.response.text}"},
+        )
 
     json_response = response.json()
     forecast_data = json_response["forecast"]
@@ -206,13 +213,3 @@ def _forecast_daily_cost(data: list):
         f"Predicted Spend for { today_month } 2024, ${total_predicted_spend}"
     )
     return {"response": response_data, "predicted_spend": predicted_spend}
-
-    # print(f"Date: {entry['date']}, Spend: {entry['spend']}, Response: {response.text}")
-
-
-# _forecast_daily_cost(
-#     [
-#         {"date": "2022-01-01", "spend": 100},
-
-#     ]
-# )