[Feat-Proxy] Slack Alerting - allow using os.environ/ vars for alert to webhook url (#5726)

* allow using os.environ for slack urls

* use env vars for webhook urls

* fix types for get_secret

* fix linting

* fix linting

* fix linting

* linting fixes

* linting fix

* docs alerting slack

* fix get data

docs/my-website/docs/proxy/alerting.md

@@ -1,4 +1,6 @@
 import Image from '@theme/IdealImage';
+import Tabs from '@theme/Tabs';
+import TabItem from '@theme/TabItem';
 
 # 🚨 Alerting / Webhooks
 
@@ -149,6 +151,10 @@ spend_reports -> go to slack channel #llm-spend-reports
 
 Set `alert_to_webhook_url` on your config.yaml
 
+<Tabs>
+
+<TabItem label="1 channel per alert" value="1">
+
 ```yaml
 model_list:
   - model_name: gpt-4
@@ -177,6 +183,44 @@ general_settings:
 litellm_settings:
   success_callback: ["langfuse"]
 ```
+</TabItem>
+
+<TabItem label="multiple channels per alert" value="2">
+
+Provide multiple slack channels for a given alert type
+
+```yaml
+model_list:
+  - model_name: gpt-4
+    litellm_params:
+      model: openai/fake
+      api_key: fake-key
+      api_base: https://exampleopenaiendpoint-production.up.railway.app/
+
+general_settings: 
+  master_key: sk-1234
+  alerting: ["slack"]
+  alerting_threshold: 0.0001 # (Seconds) set an artifically low threshold for testing alerting
+  alert_to_webhook_url: {
+    "llm_exceptions": ["os.environ/SLACK_WEBHOOK_URL", "os.environ/SLACK_WEBHOOK_URL_2"],
+    "llm_too_slow": ["https://webhook.site/7843a980-a494-4967-80fb-d502dbc16886", "https://webhook.site/28cfb179-f4fb-4408-8129-729ff55cf213"],
+    "llm_requests_hanging": ["os.environ/SLACK_WEBHOOK_URL_5", "os.environ/SLACK_WEBHOOK_URL_6"],
+    "budget_alerts": ["os.environ/SLACK_WEBHOOK_URL_7", "os.environ/SLACK_WEBHOOK_URL_8"],
+    "db_exceptions": ["os.environ/SLACK_WEBHOOK_URL_9", "os.environ/SLACK_WEBHOOK_URL_10"],
+    "daily_reports": ["os.environ/SLACK_WEBHOOK_URL_11", "os.environ/SLACK_WEBHOOK_URL_12"],
+    "spend_reports": ["os.environ/SLACK_WEBHOOK_URL_13", "os.environ/SLACK_WEBHOOK_URL_14"],
+    "cooldown_deployment": ["os.environ/SLACK_WEBHOOK_URL_15", "os.environ/SLACK_WEBHOOK_URL_16"],
+    "new_model_added": ["os.environ/SLACK_WEBHOOK_URL_17", "os.environ/SLACK_WEBHOOK_URL_18"],
+    "outage_alerts": ["os.environ/SLACK_WEBHOOK_URL_19", "os.environ/SLACK_WEBHOOK_URL_20"],
+  }
+
+litellm_settings:
+  success_callback: ["langfuse"]
+```
+
+</TabItem>
+
+</Tabs>
 
 Test it - send a valid llm request - expect to see a `llm_too_slow` alert in it's own slack channel
 
@@ -193,36 +237,6 @@ curl -i http://localhost:4000/v1/chat/completions \
 ```
 
 
-### Provide multiple slack channels for a given alert type
-
-Just add it like this - `alert_type: [<hook_url_channel_1>, <hook_url_channel_2>]`. 
-
-1. Setup config.yaml
-
-```yaml
-general_settings: 
-  master_key: sk-1234
-  alerting: ["slack"]
-  alert_to_webhook_url: {
-    "spend_reports": ["https://webhook.site/7843a980-a494-4967-80fb-d502dbc16886", "https://webhook.site/28cfb179-f4fb-4408-8129-729ff55cf213"]
-  }
-```
-
-2. Start proxy 
-
-```bash
-litellm --config /path/to/config.yaml
-```
-
-3. Test it! 
-
-```bash
-curl -X GET 'http://0.0.0.0:4000/health/services?service=slack' \
--H 'Authorization: Bearer sk-1234'
-```
-
-In case of error, check server logs for the error message!
-
 ### Using MS Teams Webhooks
 
 MS Teams provides a slack compatible webhook url that you can use for alerting

docs/my-website/docs/proxy/call_hooks.md

@@ -57,7 +57,7 @@ class MyCustomHandler(CustomLogger): # https://docs.litellm.ai/docs/observabilit
         self,
         data: dict,
         user_api_key_dict: UserAPIKeyAuth,
-        call_type: Literal["completion", "embeddings", "image_generation"],
+        call_type: Literal["completion", "embeddings", "image_generation", "moderation", "audio_transcription"],
     ):
         pass
 

docs/my-website/docs/proxy/guardrails/custom_guardrail.md

@@ -84,7 +84,7 @@ class myCustomGuardrail(CustomGuardrail):
         self,
         data: dict,
         user_api_key_dict: UserAPIKeyAuth,
-        call_type: Literal["completion", "embeddings", "image_generation"],
+        call_type: Literal["completion", "embeddings", "image_generation", "moderation", "audio_transcription"],
     ):
         """
         Runs in parallel to LLM API call

enterprise/enterprise_hooks/aporia_ai.py

@@ -174,7 +174,13 @@ class AporiaGuardrail(CustomGuardrail):
         self,
         data: dict,
         user_api_key_dict: UserAPIKeyAuth,
-        call_type: Literal["completion", "embeddings", "image_generation"],
+        call_type: Literal[
+            "completion",
+            "embeddings",
+            "image_generation",
+            "moderation",
+            "audio_transcription",
+        ],
     ):
         from litellm.proxy.common_utils.callback_utils import (
             add_guardrail_to_applied_guardrails_header,

enterprise/enterprise_hooks/google_text_moderation.py

@@ -97,7 +97,13 @@ class _ENTERPRISE_GoogleTextModeration(CustomLogger):
         self,
         data: dict,
         user_api_key_dict: UserAPIKeyAuth,
-        call_type: Literal["completion", "embeddings", "image_generation"],
+        call_type: Literal[
+            "completion",
+            "embeddings",
+            "image_generation",
+            "moderation",
+            "audio_transcription",
+        ],
     ):
         """
         - Calls Google's Text Moderation API

enterprise/enterprise_hooks/llama_guard.py

@@ -100,7 +100,13 @@ class _ENTERPRISE_LlamaGuard(CustomLogger):
         self,
         data: dict,
         user_api_key_dict: UserAPIKeyAuth,
-        call_type: Literal["completion", "embeddings", "image_generation"],
+        call_type: Literal[
+            "completion",
+            "embeddings",
+            "image_generation",
+            "moderation",
+            "audio_transcription",
+        ],
     ):
         """
         - Calls the Llama Guard Endpoint

enterprise/enterprise_hooks/llm_guard.py

@@ -127,7 +127,13 @@ class _ENTERPRISE_LLMGuard(CustomLogger):
         self,
         data: dict,
         user_api_key_dict: UserAPIKeyAuth,
-        call_type: Literal["completion", "embeddings", "image_generation"],
+        call_type: Literal[
+            "completion",
+            "embeddings",
+            "image_generation",
+            "moderation",
+            "audio_transcription",
+        ],
     ):
         """
         - Calls the LLM Guard Endpoint

enterprise/enterprise_hooks/openai_moderation.py

@@ -43,7 +43,13 @@ class _ENTERPRISE_OpenAI_Moderation(CustomLogger):
         self,
         data: dict,
         user_api_key_dict: UserAPIKeyAuth,
-        call_type: Literal["completion", "embeddings", "image_generation"],
+        call_type: Literal[
+            "completion",
+            "embeddings",
+            "image_generation",
+            "moderation",
+            "audio_transcription",
+        ],
     ):
         if "messages" in data and isinstance(data["messages"], list):
             text = ""

litellm/integrations/SlackAlerting/slack_alerting.py

@@ -41,6 +41,7 @@ from litellm.types.router import LiteLLM_Params
 from ..email_templates.templates import *
 from .batching_handler import send_to_webhook, squash_payloads
 from .types import *
+from .utils import process_slack_alerting_variables
 
 
 class SlackAlerting(CustomBatchLogger):
@@ -70,7 +71,7 @@ class SlackAlerting(CustomBatchLogger):
             "outage_alerts",
         ],
         alert_to_webhook_url: Optional[
-            Dict
+            Dict[AlertType, Union[List[str], str]]
         ] = None,  # if user wants to separate alerts to diff channels
         alerting_args={},
         default_webhook_url: Optional[str] = None,
@@ -85,7 +86,9 @@ class SlackAlerting(CustomBatchLogger):
         self.async_http_handler = get_async_httpx_client(
             llm_provider=httpxSpecialProvider.LoggingCallback
         )
-        self.alert_to_webhook_url = alert_to_webhook_url
+        self.alert_to_webhook_url = process_slack_alerting_variables(
+            alert_to_webhook_url=alert_to_webhook_url
+        )
         self.is_running = False
         self.alerting_args = SlackAlertingArgs(**alerting_args)
         self.default_webhook_url = default_webhook_url
@@ -97,7 +100,7 @@ class SlackAlerting(CustomBatchLogger):
         alerting: Optional[List] = None,
         alerting_threshold: Optional[float] = None,
         alert_types: Optional[List] = None,
-        alert_to_webhook_url: Optional[Dict] = None,
+        alert_to_webhook_url: Optional[Dict[AlertType, Union[List[str], str]]] = None,
         alerting_args: Optional[Dict] = None,
         llm_router: Optional[litellm.Router] = None,
     ):
@@ -113,9 +116,17 @@ class SlackAlerting(CustomBatchLogger):
         if alert_to_webhook_url is not None:
             # update the dict
             if self.alert_to_webhook_url is None:
-                self.alert_to_webhook_url = alert_to_webhook_url
+                self.alert_to_webhook_url = process_slack_alerting_variables(
+                    alert_to_webhook_url=alert_to_webhook_url
+                )
             else:
-                self.alert_to_webhook_url.update(alert_to_webhook_url)
+                _new_values = (
+                    process_slack_alerting_variables(
+                        alert_to_webhook_url=alert_to_webhook_url
+                    )
+                    or {}
+                )
+                self.alert_to_webhook_url.update(_new_values)
         if llm_router is not None:
             self.llm_router = llm_router
 

litellm/integrations/SlackAlerting/utils.py

@@ -0,0 +1,51 @@
+"""
+Utils used for slack alerting
+"""
+
+from typing import Dict, List, Optional, Union
+
+import litellm
+from litellm.proxy._types import AlertType
+from litellm.secret_managers.main import get_secret
+
+
+def process_slack_alerting_variables(
+    alert_to_webhook_url: Optional[Dict[AlertType, Union[List[str], str]]]
+) -> Optional[Dict[AlertType, Union[List[str], str]]]:
+    """
+    process alert_to_webhook_url
+    - check if any urls are set as os.environ/SLACK_WEBHOOK_URL_1 read env var and set the correct value
+    """
+    if alert_to_webhook_url is None:
+        return None
+
+    for alert_type, webhook_urls in alert_to_webhook_url.items():
+        if isinstance(webhook_urls, list):
+            _webhook_values: List[str] = []
+            for webhook_url in webhook_urls:
+                if "os.environ/" in webhook_url:
+                    _env_value = get_secret(secret_name=webhook_url)
+                    if not isinstance(_env_value, str):
+                        raise ValueError(
+                            f"Invalid webhook url value for: {webhook_url}. Got type={type(_env_value)}"
+                        )
+                    _webhook_values.append(_env_value)
+                else:
+                    _webhook_values.append(webhook_url)
+
+            alert_to_webhook_url[alert_type] = _webhook_values
+        else:
+            _webhook_value_str: str = webhook_urls
+            if "os.environ/" in webhook_urls:
+                _env_value = get_secret(secret_name=webhook_urls)
+                if not isinstance(_env_value, str):
+                    raise ValueError(
+                        f"Invalid webhook url value for: {webhook_urls}. Got type={type(_env_value)}"
+                    )
+                _webhook_value_str = _env_value
+            else:
+                _webhook_value_str = webhook_urls
+
+            alert_to_webhook_url[alert_type] = _webhook_value_str
+
+    return alert_to_webhook_url

litellm/integrations/custom_guardrail.py

@@ -17,6 +17,11 @@ class CustomGuardrail(CustomLogger):
         self.event_hook: Optional[GuardrailEventHooks] = event_hook
         super().__init__(**kwargs)
 
+        # older v1 implementation - not used, just kept for backward compatibility
+        self.moderation_check: Literal["pre_call", "in_parallel"] = kwargs.get(
+            "moderation_check", "pre_call"
+        )
+
     def should_run_guardrail(self, data, event_type: GuardrailEventHooks) -> bool:
         metadata = data.get("metadata") or {}
         requested_guardrails = metadata.get("guardrails") or []

litellm/integrations/custom_logger.py

@@ -151,7 +151,13 @@ class CustomLogger:  # https://docs.litellm.ai/docs/observability/custom_callbac
         self,
         data: dict,
         user_api_key_dict: UserAPIKeyAuth,
-        call_type: Literal["completion", "embeddings", "image_generation"],
+        call_type: Literal[
+            "completion",
+            "embeddings",
+            "image_generation",
+            "moderation",
+            "audio_transcription",
+        ],
     ):
         pass
 

litellm/proxy/custom_callbacks1.py

@@ -52,7 +52,13 @@ class MyCustomHandler(
         self,
         data: dict,
         user_api_key_dict: UserAPIKeyAuth,
-        call_type: Literal["completion", "embeddings", "image_generation"],
+        call_type: Literal[
+            "completion",
+            "embeddings",
+            "image_generation",
+            "moderation",
+            "audio_transcription",
+        ],
     ):
         pass
 

litellm/proxy/custom_guardrail.py

@@ -61,7 +61,13 @@ class myCustomGuardrail(CustomGuardrail):
         self,
         data: dict,
         user_api_key_dict: UserAPIKeyAuth,
-        call_type: Literal["completion", "embeddings", "image_generation"],
+        call_type: Literal[
+            "completion",
+            "embeddings",
+            "image_generation",
+            "moderation",
+            "audio_transcription",
+        ],
     ):
         """
         Runs in parallel to LLM API call

litellm/proxy/example_config_yaml/custom_guardrail.py

@@ -61,7 +61,13 @@ class myCustomGuardrail(CustomGuardrail):
         self,
         data: dict,
         user_api_key_dict: UserAPIKeyAuth,
-        call_type: Literal["completion", "embeddings", "image_generation"],
+        call_type: Literal[
+            "completion",
+            "embeddings",
+            "image_generation",
+            "moderation",
+            "audio_transcription",
+        ],
     ):
         """
         Runs in parallel to LLM API call

litellm/proxy/guardrails/guardrail_hooks/aporia_ai.py

@@ -177,7 +177,13 @@ class AporiaGuardrail(CustomGuardrail):
         self,
         data: dict,
         user_api_key_dict: UserAPIKeyAuth,
-        call_type: Literal["completion", "embeddings", "image_generation"],
+        call_type: Literal[
+            "completion",
+            "embeddings",
+            "image_generation",
+            "moderation",
+            "audio_transcription",
+        ],
     ):
         from litellm.proxy.common_utils.callback_utils import (
             add_guardrail_to_applied_guardrails_header,

litellm/proxy/guardrails/guardrail_hooks/bedrock_guardrails.py

@@ -218,7 +218,7 @@ class BedrockGuardrail(CustomGuardrail, BaseAWSLLM):
         response = await self.async_handler.post(
             url=prepared_request.url,
             json=request_data,  # type: ignore
-            headers=prepared_request.headers,
+            headers=dict(prepared_request.headers),
         )
         verbose_proxy_logger.debug("Bedrock AI response: %s", response.text)
         if response.status_code == 200:
@@ -243,7 +243,13 @@ class BedrockGuardrail(CustomGuardrail, BaseAWSLLM):
         self,
         data: dict,
         user_api_key_dict: UserAPIKeyAuth,
-        call_type: Literal["completion", "embeddings", "image_generation"],
+        call_type: Literal[
+            "completion",
+            "embeddings",
+            "image_generation",
+            "moderation",
+            "audio_transcription",
+        ],
     ):
         from litellm.proxy.common_utils.callback_utils import (
             add_guardrail_to_applied_guardrails_header,

litellm/proxy/guardrails/guardrail_hooks/custom_guardrail.py

@@ -61,7 +61,13 @@ class myCustomGuardrail(CustomGuardrail):
         self,
         data: dict,
         user_api_key_dict: UserAPIKeyAuth,
-        call_type: Literal["completion", "embeddings", "image_generation"],
+        call_type: Literal[
+            "completion",
+            "embeddings",
+            "image_generation",
+            "moderation",
+            "audio_transcription",
+        ],
     ):
         """
         Runs in parallel to LLM API call

litellm/proxy/guardrails/guardrail_hooks/lakera_ai.py

@@ -143,6 +143,7 @@ class lakeraAI_Moderation(CustomGuardrail):
         ):
             return
         text = ""
+        _json_data: str = ""
         if "messages" in data and isinstance(data["messages"], list):
             prompt_injection_obj: Optional[GuardrailItem] = (
                 litellm.guardrail_name_config_map.get("prompt_injection")
@@ -320,7 +321,13 @@ class lakeraAI_Moderation(CustomGuardrail):
         self,
         data: dict,
         user_api_key_dict: UserAPIKeyAuth,
-        call_type: Literal["completion", "embeddings", "image_generation"],
+        call_type: Literal[
+            "completion",
+            "embeddings",
+            "image_generation",
+            "moderation",
+            "audio_transcription",
+        ],
     ):
         if self.event_hook is None:
             if self.moderation_check == "pre_call":

litellm/proxy/health_endpoints/_health_endpoints.py

@@ -174,6 +174,7 @@ async def health_services_endpoint(
                             not in proxy_logging_obj.slack_alerting_instance.alert_types
                         ):
                             continue
+
                         test_message = "default test message"
                         if alert_type == "llm_exceptions":
                             test_message = f"LLM Exception test alert"
@@ -189,6 +190,8 @@ async def health_services_endpoint(
                             test_message = f"Outage Alert Exception test alert"
                         elif alert_type == "daily_reports":
                             test_message = f"Daily Reports test alert"
+                        else:
+                            test_message = f"Budget Alert test alert"
 
                         await proxy_logging_obj.alerting_handler(
                             message=test_message, level="Low", alert_type=alert_type
@@ -354,7 +357,7 @@ async def health_endpoint(
 db_health_cache = {"status": "unknown", "last_updated": datetime.now()}
 
 
-def _db_health_readiness_check():
+async def _db_health_readiness_check():
     from litellm.proxy.proxy_server import prisma_client
 
     global db_health_cache
@@ -365,7 +368,12 @@ def _db_health_readiness_check():
     time_diff = datetime.now() - db_health_cache["last_updated"]
     if db_health_cache["status"] != "unknown" and time_diff < timedelta(minutes=2):
         return db_health_cache
-    prisma_client.health_check()
+
+    if prisma_client is None:
+        db_health_cache = {"status": "disconnected", "last_updated": datetime.now()}
+        return db_health_cache
+
+    await prisma_client.health_check()
     db_health_cache = {"status": "connected", "last_updated": datetime.now()}
     return db_health_cache
 
@@ -478,7 +486,7 @@ async def health_readiness():
 
         # check DB
         if prisma_client is not None:  # if db passed in, check if it's connected
-            db_health_status = _db_health_readiness_check()
+            db_health_status = await _db_health_readiness_check()
             return {
                 "status": "healthy",
                 "db": "connected",

litellm/proxy/hooks/prompt_injection_detection.py

@@ -7,18 +7,22 @@
 ## Reject a call if it contains a prompt injection attack.
 
 
-from typing import Optional, Literal
-import litellm
-from litellm.caching import DualCache
-from litellm.proxy._types import UserAPIKeyAuth, LiteLLMPromptInjectionParams
-from litellm.integrations.custom_logger import CustomLogger
-from litellm._logging import verbose_proxy_logger
-from litellm.utils import get_formatted_prompt
-from litellm.llms.prompt_templates.factory import prompt_injection_detection_default_pt
-from fastapi import HTTPException
-import json, traceback, re
+import json
+import re
+import traceback
 from difflib import SequenceMatcher
-from typing import List
+from typing import List, Literal, Optional
+
+from fastapi import HTTPException
+from typing_extensions import overload
+
+import litellm
+from litellm._logging import verbose_proxy_logger
+from litellm.caching import DualCache
+from litellm.integrations.custom_logger import CustomLogger
+from litellm.llms.prompt_templates.factory import prompt_injection_detection_default_pt
+from litellm.proxy._types import LiteLLMPromptInjectionParams, UserAPIKeyAuth
+from litellm.utils import get_formatted_prompt
 
 
 class _OPTIONAL_PromptInjectionDetection(CustomLogger):
@@ -201,7 +205,7 @@ class _OPTIONAL_PromptInjectionDetection(CustomLogger):
                 and self.prompt_injection_params is not None
                 and self.prompt_injection_params.reject_as_response
             ):
-                return e.detail["error"]
+                return e.detail.get("error")
             raise e
         except Exception as e:
             verbose_proxy_logger.error(
@@ -211,18 +215,24 @@ class _OPTIONAL_PromptInjectionDetection(CustomLogger):
             )
             verbose_proxy_logger.debug(traceback.format_exc())
 
-    async def async_moderation_hook(
+    async def async_moderation_hook(  # type: ignore
         self,
         data: dict,
         user_api_key_dict: UserAPIKeyAuth,
-        call_type: Literal["completion", "embeddings", "image_generation"],
-    ):
+        call_type: Literal[
+            "completion",
+            "embeddings",
+            "image_generation",
+            "moderation",
+            "audio_transcription",
+        ],
+    ) -> Optional[bool]:
         self.print_verbose(
             f"IN ASYNC MODERATION HOOK - self.prompt_injection_params = {self.prompt_injection_params}"
         )
 
         if self.prompt_injection_params is None:
-            return
+            return None
 
         formatted_prompt = get_formatted_prompt(data=data, call_type=call_type)  # type: ignore
         is_prompt_attack = False

litellm/proxy/proxy_config.yaml

@@ -20,9 +20,20 @@ model_list:
       api_key: fake-key
       api_base: https://exampleopenaiendpoint-production.up.railway.app
 
+
 general_settings: 
+ master_key: sk-1234 
+ alerting: ["slack"]
+ alerting_threshold: 0.0001 # (Seconds) set an artifically low threshold for testing alerting
+ alert_to_webhook_url: {
+    "llm_too_slow": [
+      "os.environ/SLACK_WEBHOOK_URL",
+      "os.environ/SLACK_WEBHOOK_URL_2",
+    ],
+  }
   key_management_system: "azure_key_vault"
 
+
 litellm_settings:
   success_callback: ["prometheus"]
 

litellm/proxy/utils.py

@@ -93,6 +93,7 @@ def safe_deep_copy(data):
         return data
 
     # Step 1: Remove the litellm_parent_otel_span
+    litellm_parent_otel_span = None
     if isinstance(data, dict):
         # remove litellm_parent_otel_span since this is not picklable
         if "metadata" in data and "litellm_parent_otel_span" in data["metadata"]:
@@ -519,13 +520,7 @@ class ProxyLogging:
         self,
         message: str,
         level: Literal["Low", "Medium", "High"],
-        alert_type: Literal[
-            "llm_exceptions",
-            "llm_too_slow",
-            "llm_requests_hanging",
-            "budget_alerts",
-            "db_exceptions",
-        ],
+        alert_type: AlertType,
         request_data: Optional[dict] = None,
     ):
         """
@@ -1302,6 +1297,7 @@ class PrismaClient:
                 table_name is not None and table_name == "key"
             ):
                 # check if plain text or hash
+                hashed_token = None
                 if token is not None:
                     if isinstance(token, str):
                         hashed_token = token
@@ -1712,7 +1708,7 @@ class PrismaClient:
                     updated_table_row = self.db.litellm_config.upsert(
                         where={"param_name": k},
                         data={
-                            "create": {"param_name": k, "param_value": updated_data},
+                            "create": {"param_name": k, "param_value": updated_data},  # type: ignore
                             "update": {"param_value": updated_data},
                         },
                     )
@@ -2265,11 +2261,15 @@ class DBClient:
         """
         For closing connection on server shutdown
         """
-        return await self.db.disconnect()
+        if self.db is not None:
+            return await self.db.disconnect()  # type: ignore
+        return asyncio.sleep(0)  # Return a dummy coroutine if self.db is None
 
 
 ### CUSTOM FILE ###
 def get_instance_fn(value: str, config_file_path: Optional[str] = None) -> Any:
+    module_name = value
+    instance_name = None
     try:
         print_verbose(f"value: {value}")
         # Split the path by dots to separate module from instance
@@ -2363,6 +2363,15 @@ async def send_email(receiver_email, subject, html):
         "sending email from %s to %s", sender_email, receiver_email
     )
 
+    if smtp_host is None:
+        raise ValueError("Trying to use SMTP, but SMTP_HOST is not set")
+
+    if smtp_username is None:
+        raise ValueError("Trying to use SMTP, but SMTP_USERNAME is not set")
+
+    if smtp_password is None:
+        raise ValueError("Trying to use SMTP, but SMTP_PASSWORD is not set")
+
     # Attach the body to the email
     email_message.attach(MIMEText(html, "html"))
 
@@ -2555,6 +2564,7 @@ async def update_spend(
     spend_logs: list,
     """
     n_retry_times = 3
+    i = None
     ### UPDATE USER TABLE ###
     if len(prisma_client.user_list_transactons.keys()) > 0:
         for i in range(n_retry_times + 1):
@@ -2930,6 +2940,8 @@ async def update_spend(
                     )
                 break
             except httpx.ReadTimeout:
+                if i is None:
+                    i = 0
                 if i >= n_retry_times:  # If we've reached the maximum number of retries
                     raise  # Re-raise the last exception
                 # Optionally, sleep for a bit before retrying
@@ -3044,10 +3056,11 @@ def get_error_message_str(e: Exception) -> str:
         elif isinstance(e.detail, dict):
             error_message = json.dumps(e.detail)
         elif hasattr(e, "message"):
-            if isinstance(e.message, "str"):
-                error_message = e.message
-            elif isinstance(e.message, dict):
-                error_message = json.dumps(e.message)
+            _error = getattr(e, "message", None)
+            if isinstance(_error, str):
+                error_message = _error
+            elif isinstance(_error, dict):
+                error_message = json.dumps(_error)
         else:
             error_message = str(e)
     else: