Merge branch 'main' into litellm_dynamic_tpm_limits

docs/my-website/docs/providers/anthropic.md

@@ -2,9 +2,9 @@ import Tabs from '@theme/Tabs';
 import TabItem from '@theme/TabItem';
 
 # Anthropic
-LiteLLM supports
+LiteLLM supports all anthropic models.
 
-- `claude-3.5`
+- `claude-3.5` (`claude-3-5-sonnet-20240620`)
 - `claude-3` (`claude-3-haiku-20240307`, `claude-3-opus-20240229`, `claude-3-sonnet-20240229`)
 - `claude-2`
 - `claude-2.1`

litellm/integrations/slack_alerting.py

@@ -24,6 +24,7 @@ import litellm.types
 from litellm._logging import verbose_logger, verbose_proxy_logger
 from litellm.caching import DualCache
 from litellm.integrations.custom_logger import CustomLogger
+from litellm.litellm_core_utils.litellm_logging import Logging
 from litellm.llms.custom_httpx.http_handler import AsyncHTTPHandler
 from litellm.proxy._types import AlertType, CallInfo, UserAPIKeyAuth, WebhookEvent
 from litellm.types.router import LiteLLM_Params
@@ -229,7 +230,7 @@ class SlackAlerting(CustomLogger):
             "db_exceptions",
         ]
 
-    def _add_langfuse_trace_id_to_alert(
+    async def _add_langfuse_trace_id_to_alert(
         self,
         request_data: Optional[dict] = None,
     ) -> Optional[str]:
@@ -242,21 +243,19 @@ class SlackAlerting(CustomLogger):
         -> litellm_call_id
         """
         # do nothing for now
-        if request_data is not None:
+        if (
-            trace_id = None
+            request_data is not None
-            if (
+            and request_data.get("litellm_logging_obj", None) is not None
-                request_data.get("metadata", {}).get("existing_trace_id", None)
+        ):
-                is not None
+            trace_id: Optional[str] = None
-            ):
+            litellm_logging_obj: Logging = request_data["litellm_logging_obj"]
-                trace_id = request_data["metadata"]["existing_trace_id"]
+
-            elif request_data.get("metadata", {}).get("trace_id", None) is not None:
+            for _ in range(3):
-                trace_id = request_data["metadata"]["trace_id"]
+                trace_id = litellm_logging_obj._get_trace_id(service_name="langfuse")
-            elif request_data.get("litellm_logging_obj", None) is not None and hasattr(
+                if trace_id is not None:
-                request_data["litellm_logging_obj"], "model_call_details"
+                    break
-            ):
+                await asyncio.sleep(3)  # wait 3s before retrying for trace id
-                trace_id = request_data["litellm_logging_obj"].model_call_details[
+
-                    "litellm_call_id"
-                ]
             if litellm.litellm_core_utils.litellm_logging.langFuseLogger is not None:
                 base_url = (
                     litellm.litellm_core_utils.litellm_logging.langFuseLogger.Langfuse.base_url
@@ -645,7 +644,7 @@ class SlackAlerting(CustomLogger):
                 )
 
                 if "langfuse" in litellm.success_callback:
-                    langfuse_url = self._add_langfuse_trace_id_to_alert(
+                    langfuse_url = await self._add_langfuse_trace_id_to_alert(
                         request_data=request_data,
                     )
 

litellm/litellm_core_utils/litellm_logging.py

@@ -10,7 +10,7 @@ import sys
 import time
 import traceback
 import uuid
-from typing import Any, Callable, Dict, List, Optional
+from typing import Any, Callable, Dict, List, Literal, Optional
 
 import litellm
 from litellm import (
@@ -19,7 +19,8 @@ from litellm import (
     turn_off_message_logging,
     verbose_logger,
 )
-from litellm.caching import DualCache, S3Cache
+
+from litellm.caching import InMemoryCache, S3Cache, DualCache
 from litellm.integrations.custom_logger import CustomLogger
 from litellm.litellm_core_utils.redact_messages import (
     redact_message_input_output_from_logging,
@@ -111,7 +112,25 @@ additional_details: Optional[Dict[str, str]] = {}
 local_cache: Optional[Dict[str, str]] = {}
 last_fetched_at = None
 last_fetched_at_keys = None
+
+
 ####
+class ServiceTraceIDCache:
+    def __init__(self) -> None:
+        self.cache = InMemoryCache()
+
+    def get_cache(self, litellm_call_id: str, service_name: str) -> Optional[str]:
+        key_name = "{}:{}".format(service_name, litellm_call_id)
+        response = self.cache.get_cache(key=key_name)
+        return response
+
+    def set_cache(self, litellm_call_id: str, service_name: str, trace_id: str) -> None:
+        key_name = "{}:{}".format(service_name, litellm_call_id)
+        self.cache.set_cache(key=key_name, value=trace_id)
+        return None
+
+
+in_memory_trace_id_cache = ServiceTraceIDCache()
 
 
 class Logging:
@@ -155,7 +174,7 @@ class Logging:
                     new_messages.append({"role": "user", "content": m})
                 messages = new_messages
         self.model = model
-        self.messages = messages
+        self.messages = copy.deepcopy(messages)
         self.stream = stream
         self.start_time = start_time  # log the call start time
         self.call_type = call_type
@@ -245,10 +264,17 @@ class Logging:
             if headers is None:
                 headers = {}
             data = additional_args.get("complete_input_dict", {})
-            api_base = additional_args.get("api_base", "")
+            api_base = str(additional_args.get("api_base", ""))
-            self.model_call_details["litellm_params"]["api_base"] = str(
+            if "key=" in api_base:
-                api_base
+                # Find the position of "key=" in the string
-            )  # used for alerting
+                key_index = api_base.find("key=") + 4
+                # Mask the last 5 characters after "key="
+                masked_api_base = (
+                    api_base[:key_index] + "*" * 5 + api_base[key_index + 5 :]
+                )
+            else:
+                masked_api_base = api_base
+            self.model_call_details["litellm_params"]["api_base"] = masked_api_base
             masked_headers = {
                 k: (
                     (v[:-44] + "*" * 44)
@@ -821,7 +847,7 @@ class Logging:
                                 langfuse_secret=self.langfuse_secret,
                                 langfuse_host=self.langfuse_host,
                             )
-                        langFuseLogger.log_event(
+                        _response = langFuseLogger.log_event(
                             kwargs=kwargs,
                             response_obj=result,
                             start_time=start_time,
@@ -829,6 +855,14 @@ class Logging:
                             user_id=kwargs.get("user", None),
                             print_verbose=print_verbose,
                         )
+                        if _response is not None and isinstance(_response, dict):
+                            _trace_id = _response.get("trace_id", None)
+                            if _trace_id is not None:
+                                in_memory_trace_id_cache.set_cache(
+                                    litellm_call_id=self.litellm_call_id,
+                                    service_name="langfuse",
+                                    trace_id=_trace_id,
+                                )
                     if callback == "datadog":
                         global dataDogLogger
                         verbose_logger.debug("reaches datadog for success logging!")
@@ -1607,7 +1641,7 @@ class Logging:
                                 langfuse_secret=self.langfuse_secret,
                                 langfuse_host=self.langfuse_host,
                             )
-                        langFuseLogger.log_event(
+                        _response = langFuseLogger.log_event(
                             start_time=start_time,
                             end_time=end_time,
                             response_obj=None,
@@ -1617,6 +1651,14 @@ class Logging:
                             level="ERROR",
                             kwargs=self.model_call_details,
                         )
+                        if _response is not None and isinstance(_response, dict):
+                            _trace_id = _response.get("trace_id", None)
+                            if _trace_id is not None:
+                                in_memory_trace_id_cache.set_cache(
+                                    litellm_call_id=self.litellm_call_id,
+                                    service_name="langfuse",
+                                    trace_id=_trace_id,
+                                )
                     if callback == "traceloop":
                         traceloopLogger.log_event(
                             start_time=start_time,
@@ -1721,6 +1763,24 @@ class Logging:
                     )
                 )
 
+    def _get_trace_id(self, service_name: Literal["langfuse"]) -> Optional[str]:
+        """
+        For the given service (e.g. langfuse), return the trace_id actually logged.
+
+        Used for constructing the url in slack alerting.
+
+        Returns:
+            - str: The logged trace id
+            - None: If trace id not yet emitted.
+        """
+        trace_id: Optional[str] = None
+        if service_name == "langfuse":
+            trace_id = in_memory_trace_id_cache.get_cache(
+                litellm_call_id=self.litellm_call_id, service_name=service_name
+            )
+
+        return trace_id
+
 
 def set_callbacks(callback_list, function_id=None):
     """

litellm/proxy/_experimental/out/_next/static/chunks/app/onboarding/page-664c7288e11fff5a.js

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litellm/proxy/_experimental/out/model_hub.txt

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litellm/proxy/_experimental/out/onboarding.txt

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 1:null

litellm/proxy/_types.py

@@ -188,6 +188,9 @@ class LiteLLMRoutes(enum.Enum):
         # audio transcription
         "/audio/transcriptions",
         "/v1/audio/transcriptions",
+        # audio Speech
+        "/audio/speech",
+        "/v1/audio/speech",
         # moderations
         "/moderations",
         "/v1/moderations",

litellm/proxy/proxy_server.py

@@ -165,9 +165,10 @@ from litellm.proxy.secret_managers.aws_secret_manager import (
     load_aws_secret_manager,
 )
 from litellm.proxy.secret_managers.google_kms import load_google_kms
-from litellm.proxy.spend_reporting_endpoints.spend_management_endpoints import (
+from litellm.proxy.spend_tracking.spend_management_endpoints import (
     router as spend_management_router,
 )
+from litellm.proxy.spend_tracking.spend_tracking_utils import get_logging_payload
 from litellm.proxy.utils import (
     DBClient,
     PrismaClient,
@@ -180,7 +181,6 @@ from litellm.proxy.utils import (
     encrypt_value,
     get_error_message_str,
     get_instance_fn,
-    get_logging_payload,
     hash_token,
     html_form,
     missing_keys_html_form,
@@ -7509,6 +7509,12 @@ async def login(request: Request):
             litellm_dashboard_ui += "/ui/"
         import jwt
 
+        if litellm_master_key_hash is None:
+            raise HTTPException(
+                status_code=500,
+                detail={"error": "No master key set, please set LITELLM_MASTER_KEY"},
+            )
+
         jwt_token = jwt.encode(
             {
                 "user_id": user_id,
@@ -7518,11 +7524,13 @@ async def login(request: Request):
                 "login_method": "username_password",
                 "premium_user": premium_user,
             },
-            "secret",
+            litellm_master_key_hash,
             algorithm="HS256",
         )
-        litellm_dashboard_ui += "?userID=" + user_id + "&token=" + jwt_token
+        litellm_dashboard_ui += "?userID=" + user_id
-        return RedirectResponse(url=litellm_dashboard_ui, status_code=303)
+        redirect_response = RedirectResponse(url=litellm_dashboard_ui, status_code=303)
+        redirect_response.set_cookie(key="token", value=jwt_token)
+        return redirect_response
     elif _user_row is not None:
         """
         When sharing invite links
@@ -7571,6 +7579,14 @@ async def login(request: Request):
                 litellm_dashboard_ui += "/ui/"
             import jwt
 
+            if litellm_master_key_hash is None:
+                raise HTTPException(
+                    status_code=500,
+                    detail={
+                        "error": "No master key set, please set LITELLM_MASTER_KEY"
+                    },
+                )
+
             jwt_token = jwt.encode(
                 {
                     "user_id": user_id,
@@ -7580,11 +7596,15 @@ async def login(request: Request):
                     "login_method": "username_password",
                     "premium_user": premium_user,
                 },
-                "secret",
+                litellm_master_key_hash,
                 algorithm="HS256",
             )
-            litellm_dashboard_ui += "?userID=" + user_id + "&token=" + jwt_token
+            litellm_dashboard_ui += "?userID=" + user_id
-            return RedirectResponse(url=litellm_dashboard_ui, status_code=303)
+            redirect_response = RedirectResponse(
+                url=litellm_dashboard_ui, status_code=303
+            )
+            redirect_response.set_cookie(key="token", value=jwt_token)
+            return redirect_response
         else:
             raise ProxyException(
                 message=f"Invalid credentials used to access UI. Passed in username: {username}, passed in password: {password}.\nNot valid credentials for {username}",
@@ -7695,6 +7715,12 @@ async def onboarding(invite_link: str):
         litellm_dashboard_ui += "/ui/onboarding"
     import jwt
 
+    if litellm_master_key_hash is None:
+        raise HTTPException(
+            status_code=500,
+            detail={"error": "No master key set, please set LITELLM_MASTER_KEY"},
+        )
+
     jwt_token = jwt.encode(
         {
             "user_id": user_obj.user_id,
@@ -7704,7 +7730,7 @@ async def onboarding(invite_link: str):
             "login_method": "username_password",
             "premium_user": premium_user,
         },
-        "secret",
+        litellm_master_key_hash,
         algorithm="HS256",
     )
 
@@ -8115,6 +8141,12 @@ async def auth_callback(request: Request):
 
     import jwt
 
+    if litellm_master_key_hash is None:
+        raise HTTPException(
+            status_code=500,
+            detail={"error": "No master key set, please set LITELLM_MASTER_KEY"},
+        )
+
     jwt_token = jwt.encode(
         {
             "user_id": user_id,
@@ -8124,11 +8156,13 @@ async def auth_callback(request: Request):
             "login_method": "sso",
             "premium_user": premium_user,
         },
-        "secret",
+        litellm_master_key_hash,
         algorithm="HS256",
     )
-    litellm_dashboard_ui += "?userID=" + user_id + "&token=" + jwt_token
+    litellm_dashboard_ui += "?userID=" + user_id
-    return RedirectResponse(url=litellm_dashboard_ui)
+    redirect_response = RedirectResponse(url=litellm_dashboard_ui, status_code=303)
+    redirect_response.set_cookie(key="token", value=jwt_token)
+    return redirect_response
 
 
 #### INVITATION MANAGEMENT ####

litellm/proxy/spend_tracking/spend_management_endpoints.py

@@ -1,13 +1,14 @@
 #### SPEND MANAGEMENT #####
-from typing import Optional, List
+from datetime import datetime, timedelta, timezone
+from typing import List, Optional
+
+import fastapi
+from fastapi import APIRouter, Depends, Header, HTTPException, Request, status
+
 import litellm
 from litellm._logging import verbose_proxy_logger
-from datetime import datetime, timedelta, timezone
-from litellm.proxy.auth.user_api_key_auth import user_api_key_auth
-import fastapi
-from fastapi import Depends, Request, APIRouter, Header, status
-from fastapi import HTTPException
 from litellm.proxy._types import *
+from litellm.proxy.auth.user_api_key_auth import user_api_key_auth
 
 router = APIRouter()
 
@@ -227,7 +228,7 @@ async def get_global_activity(
     start_date_obj = datetime.strptime(start_date, "%Y-%m-%d")
     end_date_obj = datetime.strptime(end_date, "%Y-%m-%d")
 
-    from litellm.proxy.proxy_server import prisma_client, llm_router
+    from litellm.proxy.proxy_server import llm_router, prisma_client
 
     try:
         if prisma_client is None:
@@ -355,7 +356,7 @@ async def get_global_activity_model(
     start_date_obj = datetime.strptime(start_date, "%Y-%m-%d")
     end_date_obj = datetime.strptime(end_date, "%Y-%m-%d")
 
-    from litellm.proxy.proxy_server import prisma_client, llm_router, premium_user
+    from litellm.proxy.proxy_server import llm_router, premium_user, prisma_client
 
     try:
         if prisma_client is None:
@@ -500,7 +501,7 @@ async def get_global_activity_exceptions_per_deployment(
     start_date_obj = datetime.strptime(start_date, "%Y-%m-%d")
     end_date_obj = datetime.strptime(end_date, "%Y-%m-%d")
 
-    from litellm.proxy.proxy_server import prisma_client, llm_router, premium_user
+    from litellm.proxy.proxy_server import llm_router, premium_user, prisma_client
 
     try:
         if prisma_client is None:
@@ -634,7 +635,7 @@ async def get_global_activity_exceptions(
     start_date_obj = datetime.strptime(start_date, "%Y-%m-%d")
     end_date_obj = datetime.strptime(end_date, "%Y-%m-%d")
 
-    from litellm.proxy.proxy_server import prisma_client, llm_router
+    from litellm.proxy.proxy_server import llm_router, prisma_client
 
     try:
         if prisma_client is None:
@@ -739,7 +740,7 @@ async def get_global_spend_provider(
     start_date_obj = datetime.strptime(start_date, "%Y-%m-%d")
     end_date_obj = datetime.strptime(end_date, "%Y-%m-%d")
 
-    from litellm.proxy.proxy_server import prisma_client, llm_router
+    from litellm.proxy.proxy_server import llm_router, prisma_client
 
     try:
         if prisma_client is None:
@@ -1091,7 +1092,6 @@ async def global_view_spend_tags(
     """
 
     from enterprise.utils import ui_get_spend_by_tags
-
     from litellm.proxy.proxy_server import prisma_client
 
     try:

litellm/proxy/spend_tracking/spend_tracking_utils.py

@@ -0,0 +1,125 @@
+import json
+import traceback
+from typing import Optional
+
+import litellm
+from litellm._logging import verbose_proxy_logger
+from litellm.proxy._types import SpendLogsMetadata, SpendLogsPayload
+from litellm.proxy.utils import hash_token
+
+
+def get_logging_payload(
+    kwargs, response_obj, start_time, end_time, end_user_id: Optional[str]
+) -> SpendLogsPayload:
+    from pydantic import Json
+
+    from litellm.proxy._types import LiteLLM_SpendLogs
+
+    verbose_proxy_logger.debug(
+        f"SpendTable: get_logging_payload - kwargs: {kwargs}\n\n"
+    )
+
+    if kwargs is None:
+        kwargs = {}
+    # standardize this function to be used across, s3, dynamoDB, langfuse logging
+    litellm_params = kwargs.get("litellm_params", {})
+    metadata = (
+        litellm_params.get("metadata", {}) or {}
+    )  # if litellm_params['metadata'] == None
+    completion_start_time = kwargs.get("completion_start_time", end_time)
+    call_type = kwargs.get("call_type")
+    cache_hit = kwargs.get("cache_hit", False)
+    usage = response_obj["usage"]
+    if type(usage) == litellm.Usage:
+        usage = dict(usage)
+    id = response_obj.get("id", kwargs.get("litellm_call_id"))
+    api_key = metadata.get("user_api_key", "")
+    if api_key is not None and isinstance(api_key, str) and api_key.startswith("sk-"):
+        # hash the api_key
+        api_key = hash_token(api_key)
+
+    _model_id = metadata.get("model_info", {}).get("id", "")
+    _model_group = metadata.get("model_group", "")
+
+    request_tags = (
+        json.dumps(metadata.get("tags", []))
+        if isinstance(metadata.get("tags", []), list)
+        else "[]"
+    )
+
+    # clean up litellm metadata
+    clean_metadata = SpendLogsMetadata(
+        user_api_key=None,
+        user_api_key_alias=None,
+        user_api_key_team_id=None,
+        user_api_key_user_id=None,
+        user_api_key_team_alias=None,
+        spend_logs_metadata=None,
+    )
+    if isinstance(metadata, dict):
+        verbose_proxy_logger.debug(
+            "getting payload for SpendLogs, available keys in metadata: "
+            + str(list(metadata.keys()))
+        )
+
+        # Filter the metadata dictionary to include only the specified keys
+        clean_metadata = SpendLogsMetadata(
+            **{  # type: ignore
+                key: metadata[key]
+                for key in SpendLogsMetadata.__annotations__.keys()
+                if key in metadata
+            }
+        )
+
+    if litellm.cache is not None:
+        cache_key = litellm.cache.get_cache_key(**kwargs)
+    else:
+        cache_key = "Cache OFF"
+    if cache_hit is True:
+        import time
+
+        id = f"{id}_cache_hit{time.time()}"  # SpendLogs does not allow duplicate request_id
+
+    try:
+        payload: SpendLogsPayload = SpendLogsPayload(
+            request_id=str(id),
+            call_type=call_type or "",
+            api_key=str(api_key),
+            cache_hit=str(cache_hit),
+            startTime=start_time,
+            endTime=end_time,
+            completionStartTime=completion_start_time,
+            model=kwargs.get("model", "") or "",
+            user=kwargs.get("litellm_params", {})
+            .get("metadata", {})
+            .get("user_api_key_user_id", "")
+            or "",
+            team_id=kwargs.get("litellm_params", {})
+            .get("metadata", {})
+            .get("user_api_key_team_id", "")
+            or "",
+            metadata=json.dumps(clean_metadata),
+            cache_key=cache_key,
+            spend=kwargs.get("response_cost", 0),
+            total_tokens=usage.get("total_tokens", 0),
+            prompt_tokens=usage.get("prompt_tokens", 0),
+            completion_tokens=usage.get("completion_tokens", 0),
+            request_tags=request_tags,
+            end_user=end_user_id or "",
+            api_base=litellm_params.get("api_base", ""),
+            model_group=_model_group,
+            model_id=_model_id,
+        )
+
+        verbose_proxy_logger.debug(
+            "SpendTable: created payload - payload: %s\n\n", payload
+        )
+
+        return payload
+    except Exception as e:
+        verbose_proxy_logger.error(
+            "Error creating spendlogs object - {}\n{}".format(
+                str(e), traceback.format_exc()
+            )
+        )
+        raise e

litellm/proxy/utils.py

@@ -473,7 +473,7 @@ class ProxyLogging:
         alerting_metadata = {}
         if request_data is not None:
 
-            _url = self.slack_alerting_instance._add_langfuse_trace_id_to_alert(
+            _url = await self.slack_alerting_instance._add_langfuse_trace_id_to_alert(
                 request_data=request_data
             )
 
@@ -2040,121 +2040,6 @@ def hash_token(token: str):
     return hashed_token
 
 
-def get_logging_payload(
-    kwargs, response_obj, start_time, end_time, end_user_id: Optional[str]
-) -> SpendLogsPayload:
-    from pydantic import Json
-
-    from litellm.proxy._types import LiteLLM_SpendLogs
-
-    verbose_proxy_logger.debug(
-        f"SpendTable: get_logging_payload - kwargs: {kwargs}\n\n"
-    )
-
-    if kwargs is None:
-        kwargs = {}
-    # standardize this function to be used across, s3, dynamoDB, langfuse logging
-    litellm_params = kwargs.get("litellm_params", {})
-    metadata = (
-        litellm_params.get("metadata", {}) or {}
-    )  # if litellm_params['metadata'] == None
-    completion_start_time = kwargs.get("completion_start_time", end_time)
-    call_type = kwargs.get("call_type")
-    cache_hit = kwargs.get("cache_hit", False)
-    usage = response_obj["usage"]
-    if type(usage) == litellm.Usage:
-        usage = dict(usage)
-    id = response_obj.get("id", kwargs.get("litellm_call_id"))
-    api_key = metadata.get("user_api_key", "")
-    if api_key is not None and isinstance(api_key, str) and api_key.startswith("sk-"):
-        # hash the api_key
-        api_key = hash_token(api_key)
-
-    _model_id = metadata.get("model_info", {}).get("id", "")
-    _model_group = metadata.get("model_group", "")
-
-    # clean up litellm metadata
-    clean_metadata = SpendLogsMetadata(
-        user_api_key=None,
-        user_api_key_alias=None,
-        user_api_key_team_id=None,
-        user_api_key_user_id=None,
-        user_api_key_team_alias=None,
-        spend_logs_metadata=None,
-    )
-    if isinstance(metadata, dict):
-        verbose_proxy_logger.debug(
-            "getting payload for SpendLogs, available keys in metadata: "
-            + str(list(metadata.keys()))
-        )
-
-        # Filter the metadata dictionary to include only the specified keys
-        clean_metadata = SpendLogsMetadata(
-            **{  # type: ignore
-                key: metadata[key]
-                for key in SpendLogsMetadata.__annotations__.keys()
-                if key in metadata
-            }
-        )
-
-    if litellm.cache is not None:
-        cache_key = litellm.cache.get_cache_key(**kwargs)
-    else:
-        cache_key = "Cache OFF"
-    if cache_hit is True:
-        import time
-
-        id = f"{id}_cache_hit{time.time()}"  # SpendLogs does not allow duplicate request_id
-
-    try:
-        payload: SpendLogsPayload = SpendLogsPayload(
-            request_id=str(id),
-            call_type=call_type or "",
-            api_key=str(api_key),
-            cache_hit=str(cache_hit),
-            startTime=start_time,
-            endTime=end_time,
-            completionStartTime=completion_start_time,
-            model=kwargs.get("model", "") or "",
-            user=kwargs.get("litellm_params", {})
-            .get("metadata", {})
-            .get("user_api_key_user_id", "")
-            or "",
-            team_id=kwargs.get("litellm_params", {})
-            .get("metadata", {})
-            .get("user_api_key_team_id", "")
-            or "",
-            metadata=json.dumps(clean_metadata),
-            cache_key=cache_key,
-            spend=kwargs.get("response_cost", 0),
-            total_tokens=usage.get("total_tokens", 0),
-            prompt_tokens=usage.get("prompt_tokens", 0),
-            completion_tokens=usage.get("completion_tokens", 0),
-            request_tags=(
-                json.dumps(metadata.get("tags", []))
-                if isinstance(metadata.get("tags", []), dict)
-                else "[]"
-            ),
-            end_user=end_user_id or "",
-            api_base=litellm_params.get("api_base", ""),
-            model_group=_model_group,
-            model_id=_model_id,
-        )
-
-        verbose_proxy_logger.debug(
-            "SpendTable: created payload - payload: %s\n\n", payload
-        )
-
-        return payload
-    except Exception as e:
-        verbose_proxy_logger.error(
-            "Error creating spendlogs object - {}\n{}".format(
-                str(e), traceback.format_exc()
-            )
-        )
-        raise e
-
-
 def _extract_from_regex(duration: str) -> Tuple[int, str]:
     match = re.match(r"(\d+)(mo|[smhd]?)", duration)
 

litellm/router.py

@@ -403,7 +403,11 @@ class Router:
         else:
             litellm.failure_callback = [self.deployment_callback_on_failure]
         print(  # noqa
-            f"Intialized router with Routing strategy: {self.routing_strategy}\n\nRouting fallbacks: {self.fallbacks}\n\nRouting context window fallbacks: {self.context_window_fallbacks}\n\nRouter Redis Caching={self.cache.redis_cache}"
+            f"Intialized router with Routing strategy: {self.routing_strategy}\n\n"
+            f"Routing fallbacks: {self.fallbacks}\n\n"
+            f"Routing content fallbacks: {self.content_policy_fallbacks}\n\n"
+            f"Routing context window fallbacks: {self.context_window_fallbacks}\n\n"
+            f"Router Redis Caching={self.cache.redis_cache}\n"
         )  # noqa
         self.routing_strategy_args = routing_strategy_args
         self.retry_policy: Optional[RetryPolicy] = retry_policy

litellm/tests/test_alangfuse.py

@@ -4,6 +4,7 @@ import json
 import logging
 import os
 import sys
+from typing import Any
 from unittest.mock import MagicMock, patch
 
 logging.basicConfig(level=logging.DEBUG)
@@ -24,11 +25,21 @@ import pytest
 def langfuse_client():
     import langfuse
 
-    langfuse_client = langfuse.Langfuse(
+    _langfuse_cache_key = (
-        public_key=os.environ["LANGFUSE_PUBLIC_KEY"],
+        f"{os.environ['LANGFUSE_PUBLIC_KEY']}-{os.environ['LANGFUSE_SECRET_KEY']}"
-        secret_key=os.environ["LANGFUSE_SECRET_KEY"],
-        host=None,
     )
+    # use a in memory langfuse client for testing, RAM util on ci/cd gets too high when we init many langfuse clients
+    if _langfuse_cache_key in litellm.in_memory_llm_clients_cache:
+        langfuse_client = litellm.in_memory_llm_clients_cache[_langfuse_cache_key]
+    else:
+        langfuse_client = langfuse.Langfuse(
+            public_key=os.environ["LANGFUSE_PUBLIC_KEY"],
+            secret_key=os.environ["LANGFUSE_SECRET_KEY"],
+            host=None,
+        )
+        litellm.in_memory_llm_clients_cache[_langfuse_cache_key] = langfuse_client
+
+        print("NEW LANGFUSE CLIENT")
 
     with patch(
         "langfuse.Langfuse", MagicMock(return_value=langfuse_client)

litellm/tests/test_alerting.py

@@ -1,33 +1,37 @@
 # What is this?
 ## Tests slack alerting on proxy logging object
 
-import sys, json, uuid, random, httpx
+import asyncio
+import io
+import json
 import os
-import io, asyncio
+import random
+import sys
+import time
+import uuid
 from datetime import datetime, timedelta
 from typing import Optional
 
+import httpx
+
 # import logging
 # logging.basicConfig(level=logging.DEBUG)
 sys.path.insert(0, os.path.abspath("../.."))
-from litellm.proxy.utils import ProxyLogging
-from litellm.caching import DualCache, RedisCache
-import litellm
-import pytest
 import asyncio
-from unittest.mock import patch, MagicMock
-from litellm.utils import get_api_base
-from litellm.caching import DualCache
-from litellm.integrations.slack_alerting import SlackAlerting, DeploymentMetrics
-import unittest.mock
-from unittest.mock import AsyncMock
-import pytest
-from litellm.router import AlertingConfig, Router
-from litellm.proxy._types import CallInfo
-from openai import APIError
-from litellm.router import AlertingConfig
-import litellm
 import os
+import unittest.mock
+from unittest.mock import AsyncMock, MagicMock, patch
+
+import pytest
+from openai import APIError
+
+import litellm
+from litellm.caching import DualCache, RedisCache
+from litellm.integrations.slack_alerting import DeploymentMetrics, SlackAlerting
+from litellm.proxy._types import CallInfo
+from litellm.proxy.utils import ProxyLogging
+from litellm.router import AlertingConfig, Router
+from litellm.utils import get_api_base
 
 
 @pytest.mark.parametrize(
@@ -123,8 +127,8 @@ def test_init():
     print("passed testing slack alerting init")
 
 
-from unittest.mock import patch, AsyncMock
 from datetime import datetime, timedelta
+from unittest.mock import AsyncMock, patch
 
 
 @pytest.fixture
@@ -805,3 +809,53 @@ async def test_alerting():
         pass
     finally:
         await asyncio.sleep(3)
+
+
+@pytest.mark.asyncio
+async def test_langfuse_trace_id():
+    """
+    - Unit test for `_add_langfuse_trace_id_to_alert` function in slack_alerting.py
+    """
+    from litellm.litellm_core_utils.litellm_logging import Logging
+
+    litellm.success_callback = ["langfuse"]
+
+    litellm_logging_obj = Logging(
+        model="gpt-3.5-turbo",
+        messages=[{"role": "user", "content": "hi"}],
+        stream=False,
+        call_type="acompletion",
+        litellm_call_id="1234",
+        start_time=datetime.now(),
+        function_id="1234",
+    )
+
+    litellm.completion(
+        model="gpt-3.5-turbo",
+        messages=[{"role": "user", "content": "Hey how's it going?"}],
+        mock_response="Hey!",
+        litellm_logging_obj=litellm_logging_obj,
+    )
+
+    await asyncio.sleep(3)
+
+    assert litellm_logging_obj._get_trace_id(service_name="langfuse") is not None
+
+    slack_alerting = SlackAlerting(
+        alerting_threshold=32,
+        alerting=["slack"],
+        alert_types=["llm_exceptions"],
+        internal_usage_cache=DualCache(),
+    )
+
+    trace_url = await slack_alerting._add_langfuse_trace_id_to_alert(
+        request_data={"litellm_logging_obj": litellm_logging_obj}
+    )
+
+    assert trace_url is not None
+
+    returned_trace_id = int(trace_url.split("/")[-1])
+
+    assert returned_trace_id == int(
+        litellm_logging_obj._get_trace_id(service_name="langfuse")
+    )

litellm/tests/test_amazing_langfuse.py

@@ -1,869 +0,0 @@
-import asyncio
-import copy
-import json
-import logging
-import os
-import sys
-from unittest.mock import MagicMock, patch
-
-logging.basicConfig(level=logging.DEBUG)
-sys.path.insert(0, os.path.abspath("../.."))
-
-import litellm
-from litellm import completion
-
-litellm.num_retries = 3
-litellm.success_callback = ["langfuse"]
-os.environ["LANGFUSE_DEBUG"] = "True"
-import time
-
-import pytest
-
-
-@pytest.fixture
-def langfuse_client():
-    import langfuse
-
-    langfuse_client = langfuse.Langfuse(
-        public_key=os.environ["LANGFUSE_PUBLIC_KEY"],
-        secret_key=os.environ["LANGFUSE_SECRET_KEY"],
-        host=None,
-    )
-
-    with patch(
-        "langfuse.Langfuse", MagicMock(return_value=langfuse_client)
-    ) as mock_langfuse_client:
-        yield mock_langfuse_client()
-
-
-def search_logs(log_file_path, num_good_logs=1):
-    """
-    Searches the given log file for logs containing the "/api/public" string.
-
-    Parameters:
-    - log_file_path (str): The path to the log file to be searched.
-
-    Returns:
-    - None
-
-    Raises:
-    - Exception: If there are any bad logs found in the log file.
-    """
-    import re
-
-    print("\n searching logs")
-    bad_logs = []
-    good_logs = []
-    all_logs = []
-    try:
-        with open(log_file_path, "r") as log_file:
-            lines = log_file.readlines()
-            print(f"searching logslines: {lines}")
-            for line in lines:
-                all_logs.append(line.strip())
-                if "/api/public" in line:
-                    print("Found log with /api/public:")
-                    print(line.strip())
-                    print("\n\n")
-                    match = re.search(
-                        r'"POST /api/public/ingestion HTTP/1.1" (\d+) (\d+)',
-                        line,
-                    )
-                    if match:
-                        status_code = int(match.group(1))
-                        print("STATUS CODE", status_code)
-                        if (
-                            status_code != 200
-                            and status_code != 201
-                            and status_code != 207
-                        ):
-                            print("got a BAD log")
-                            bad_logs.append(line.strip())
-                        else:
-                            good_logs.append(line.strip())
-        print("\nBad Logs")
-        print(bad_logs)
-        if len(bad_logs) > 0:
-            raise Exception(f"bad logs, Bad logs = {bad_logs}")
-        assert (
-            len(good_logs) == num_good_logs
-        ), f"Did not get expected number of good logs, expected {num_good_logs}, got {len(good_logs)}. All logs \n {all_logs}"
-        print("\nGood Logs")
-        print(good_logs)
-        if len(good_logs) <= 0:
-            raise Exception(
-                f"There were no Good Logs from Langfuse. No logs with /api/public status 200. \nAll logs:{all_logs}"
-            )
-
-    except Exception as e:
-        raise e
-
-
-def pre_langfuse_setup():
-    """
-    Set up the logging for the 'pre_langfuse_setup' function.
-    """
-    # sends logs to langfuse.log
-    import logging
-
-    # Configure the logging to write to a file
-    logging.basicConfig(filename="langfuse.log", level=logging.DEBUG)
-    logger = logging.getLogger()
-
-    # Add a FileHandler to the logger
-    file_handler = logging.FileHandler("langfuse.log", mode="w")
-    file_handler.setLevel(logging.DEBUG)
-    logger.addHandler(file_handler)
-    return
-
-
-def test_langfuse_logging_async():
-    # this tests time added to make langfuse logging calls, vs just acompletion calls
-    try:
-        pre_langfuse_setup()
-        litellm.set_verbose = True
-
-        # Make 5 calls with an empty success_callback
-        litellm.success_callback = []
-        start_time_empty_callback = asyncio.run(make_async_calls())
-        print("done with no callback test")
-
-        print("starting langfuse test")
-        # Make 5 calls with success_callback set to "langfuse"
-        litellm.success_callback = ["langfuse"]
-        start_time_langfuse = asyncio.run(make_async_calls())
-        print("done with langfuse test")
-
-        # Compare the time for both scenarios
-        print(f"Time taken with success_callback='langfuse': {start_time_langfuse}")
-        print(f"Time taken with empty success_callback: {start_time_empty_callback}")
-
-        # assert the diff is not more than 1 second - this was 5 seconds before the fix
-        assert abs(start_time_langfuse - start_time_empty_callback) < 1
-
-    except litellm.Timeout as e:
-        pass
-    except Exception as e:
-        pytest.fail(f"An exception occurred - {e}")
-
-
-async def make_async_calls(metadata=None, **completion_kwargs):
-    tasks = []
-    for _ in range(5):
-        tasks.append(create_async_task())
-
-    # Measure the start time before running the tasks
-    start_time = asyncio.get_event_loop().time()
-
-    # Wait for all tasks to complete
-    responses = await asyncio.gather(*tasks)
-
-    # Print the responses when tasks return
-    for idx, response in enumerate(responses):
-        print(f"Response from Task {idx + 1}: {response}")
-
-    # Calculate the total time taken
-    total_time = asyncio.get_event_loop().time() - start_time
-
-    return total_time
-
-
-def create_async_task(**completion_kwargs):
-    """
-    Creates an async task for the litellm.acompletion function.
-    This is just the task, but it is not run here.
-    To run the task it must be awaited or used in other asyncio coroutine execution functions like asyncio.gather.
-    Any kwargs passed to this function will be passed to the litellm.acompletion function.
-    By default a standard set of arguments are used for the litellm.acompletion function.
-    """
-    completion_args = {
-        "model": "azure/chatgpt-v-2",
-        "api_version": "2024-02-01",
-        "messages": [{"role": "user", "content": "This is a test"}],
-        "max_tokens": 5,
-        "temperature": 0.7,
-        "timeout": 5,
-        "user": "langfuse_latency_test_user",
-        "mock_response": "It's simple to use and easy to get started",
-    }
-    completion_args.update(completion_kwargs)
-    return asyncio.create_task(litellm.acompletion(**completion_args))
-
-
-@pytest.mark.asyncio
-@pytest.mark.parametrize("stream", [False, True])
-async def test_langfuse_logging_without_request_response(stream, langfuse_client):
-    try:
-        import uuid
-
-        _unique_trace_name = f"litellm-test-{str(uuid.uuid4())}"
-        litellm.set_verbose = True
-        litellm.turn_off_message_logging = True
-        litellm.success_callback = ["langfuse"]
-        response = await create_async_task(
-            model="gpt-3.5-turbo",
-            stream=stream,
-            metadata={"trace_id": _unique_trace_name},
-        )
-        print(response)
-        if stream:
-            async for chunk in response:
-                print(chunk)
-
-        langfuse_client.flush()
-        await asyncio.sleep(2)
-
-        # get trace with _unique_trace_name
-        trace = langfuse_client.get_generations(trace_id=_unique_trace_name)
-
-        print("trace_from_langfuse", trace)
-
-        _trace_data = trace.data
-
-        assert _trace_data[0].input == {
-            "messages": [{"content": "redacted-by-litellm", "role": "user"}]
-        }
-        assert _trace_data[0].output == {
-            "role": "assistant",
-            "content": "redacted-by-litellm",
-        }
-
-    except Exception as e:
-        pytest.fail(f"An exception occurred - {e}")
-
-
-@pytest.mark.asyncio
-async def test_langfuse_masked_input_output(langfuse_client):
-    """
-    Test that creates a trace with masked input and output
-    """
-    import uuid
-
-    for mask_value in [True, False]:
-        _unique_trace_name = f"litellm-test-{str(uuid.uuid4())}"
-        litellm.set_verbose = True
-        litellm.success_callback = ["langfuse"]
-        response = await create_async_task(
-            model="gpt-3.5-turbo",
-            messages=[{"role": "user", "content": "This is a test"}],
-            metadata={
-                "trace_id": _unique_trace_name,
-                "mask_input": mask_value,
-                "mask_output": mask_value,
-            },
-            mock_response="This is a test response",
-        )
-        print(response)
-        expected_input = (
-            "redacted-by-litellm"
-            if mask_value
-            else {"messages": [{"content": "This is a test", "role": "user"}]}
-        )
-        expected_output = (
-            "redacted-by-litellm"
-            if mask_value
-            else {"content": "This is a test response", "role": "assistant"}
-        )
-        langfuse_client.flush()
-        await asyncio.sleep(2)
-
-        # get trace with _unique_trace_name
-        trace = langfuse_client.get_trace(id=_unique_trace_name)
-        generations = list(
-            reversed(langfuse_client.get_generations(trace_id=_unique_trace_name).data)
-        )
-
-        assert trace.input == expected_input
-        assert trace.output == expected_output
-        assert generations[0].input == expected_input
-        assert generations[0].output == expected_output
-
-
-@pytest.mark.asyncio
-async def test_alangfuse_logging_metadata(langfuse_client):
-    """
-    Test that creates multiple traces, with a varying number of generations and sets various metadata fields
-    Confirms that no metadata that is standard within Langfuse is duplicated in the respective trace or generation metadata
-    For trace continuation certain metadata of the trace is overriden with metadata from the last generation based on the update_trace_keys field
-    Version is set for both the trace and the generation
-    Release is just set for the trace
-    Tags is just set for the trace
-    """
-    import uuid
-
-    litellm.set_verbose = True
-    litellm.success_callback = ["langfuse"]
-
-    trace_identifiers = {}
-    expected_filtered_metadata_keys = {
-        "trace_name",
-        "trace_id",
-        "existing_trace_id",
-        "trace_user_id",
-        "session_id",
-        "tags",
-        "generation_name",
-        "generation_id",
-        "prompt",
-    }
-    trace_metadata = {
-        "trace_actual_metadata_key": "trace_actual_metadata_value"
-    }  # Allows for setting the metadata on the trace
-    run_id = str(uuid.uuid4())
-    session_id = f"litellm-test-session-{run_id}"
-    trace_common_metadata = {
-        "session_id": session_id,
-        "tags": ["litellm-test-tag1", "litellm-test-tag2"],
-        "update_trace_keys": [
-            "output",
-            "trace_metadata",
-        ],  # Overwrite the following fields in the trace with the last generation's output and the trace_user_id
-        "trace_metadata": trace_metadata,
-        "gen_metadata_key": "gen_metadata_value",  # Metadata key that should not be filtered in the generation
-        "trace_release": "litellm-test-release",
-        "version": "litellm-test-version",
-    }
-    for trace_num in range(1, 3):  # Two traces
-        metadata = copy.deepcopy(trace_common_metadata)
-        trace_id = f"litellm-test-trace{trace_num}-{run_id}"
-        metadata["trace_id"] = trace_id
-        metadata["trace_name"] = trace_id
-        trace_identifiers[trace_id] = []
-        print(f"Trace: {trace_id}")
-        for generation_num in range(
-            1, trace_num + 1
-        ):  # Each trace has a number of generations equal to its trace number
-            metadata["trace_user_id"] = f"litellm-test-user{generation_num}-{run_id}"
-            generation_id = (
-                f"litellm-test-trace{trace_num}-generation-{generation_num}-{run_id}"
-            )
-            metadata["generation_id"] = generation_id
-            metadata["generation_name"] = generation_id
-            metadata["trace_metadata"][
-                "generation_id"
-            ] = generation_id  # Update to test if trace_metadata is overwritten by update trace keys
-            trace_identifiers[trace_id].append(generation_id)
-            print(f"Generation: {generation_id}")
-            response = await create_async_task(
-                model="gpt-3.5-turbo",
-                mock_response=f"{session_id}:{trace_id}:{generation_id}",
-                messages=[
-                    {
-                        "role": "user",
-                        "content": f"{session_id}:{trace_id}:{generation_id}",
-                    }
-                ],
-                max_tokens=100,
-                temperature=0.2,
-                metadata=copy.deepcopy(
-                    metadata
-                ),  # Every generation needs its own metadata, langfuse is not async/thread safe without it
-            )
-            print(response)
-            metadata["existing_trace_id"] = trace_id
-
-    langfuse_client.flush()
-    await asyncio.sleep(10)
-
-    # Tests the metadata filtering and the override of the output to be the last generation
-    for trace_id, generation_ids in trace_identifiers.items():
-        trace = langfuse_client.get_trace(id=trace_id)
-        assert trace.id == trace_id
-        assert trace.session_id == session_id
-        assert trace.metadata != trace_metadata
-        generations = list(
-            reversed(langfuse_client.get_generations(trace_id=trace_id).data)
-        )
-        assert len(generations) == len(generation_ids)
-        assert (
-            trace.input == generations[0].input
-        )  # Should be set by the first generation
-        assert (
-            trace.output == generations[-1].output
-        )  # Should be overwritten by the last generation according to update_trace_keys
-        assert (
-            trace.metadata != generations[-1].metadata
-        )  # Should be overwritten by the last generation according to update_trace_keys
-        assert trace.metadata["generation_id"] == generations[-1].id
-        assert set(trace.tags).issuperset(trace_common_metadata["tags"])
-        print("trace_from_langfuse", trace)
-        for generation_id, generation in zip(generation_ids, generations):
-            assert generation.id == generation_id
-            assert generation.trace_id == trace_id
-            print(
-                "common keys in trace",
-                set(generation.metadata.keys()).intersection(
-                    expected_filtered_metadata_keys
-                ),
-            )
-
-            assert set(generation.metadata.keys()).isdisjoint(
-                expected_filtered_metadata_keys
-            )
-            print("generation_from_langfuse", generation)
-
-
-@pytest.mark.skip(reason="beta test - checking langfuse output")
-def test_langfuse_logging():
-    try:
-        pre_langfuse_setup()
-        litellm.set_verbose = True
-        response = completion(
-            model="claude-instant-1.2",
-            messages=[{"role": "user", "content": "Hi 👋 - i'm claude"}],
-            max_tokens=10,
-            temperature=0.2,
-        )
-        print(response)
-        # time.sleep(5)
-        # # check langfuse.log to see if there was a failed response
-        # search_logs("langfuse.log")
-
-    except litellm.Timeout as e:
-        pass
-    except Exception as e:
-        pytest.fail(f"An exception occurred - {e}")
-
-
-# test_langfuse_logging()
-
-
-@pytest.mark.skip(reason="beta test - checking langfuse output")
-def test_langfuse_logging_stream():
-    try:
-        litellm.set_verbose = True
-        response = completion(
-            model="gpt-3.5-turbo",
-            messages=[
-                {
-                    "role": "user",
-                    "content": "this is a streaming test for llama2 + langfuse",
-                }
-            ],
-            max_tokens=20,
-            temperature=0.2,
-            stream=True,
-        )
-        print(response)
-        for chunk in response:
-            pass
-            # print(chunk)
-    except litellm.Timeout as e:
-        pass
-    except Exception as e:
-        print(e)
-
-
-# test_langfuse_logging_stream()
-
-
-@pytest.mark.skip(reason="beta test - checking langfuse output")
-def test_langfuse_logging_custom_generation_name():
-    try:
-        litellm.set_verbose = True
-        response = completion(
-            model="gpt-3.5-turbo",
-            messages=[{"role": "user", "content": "Hi 👋 - i'm claude"}],
-            max_tokens=10,
-            metadata={
-                "langfuse/foo": "bar",
-                "langsmith/fizz": "buzz",
-                "prompt_hash": "asdf98u0j9131123",
-                "generation_name": "ishaan-test-generation",
-                "generation_id": "gen-id22",
-                "trace_id": "trace-id22",
-                "trace_user_id": "user-id2",
-            },
-        )
-        print(response)
-    except litellm.Timeout as e:
-        pass
-    except Exception as e:
-        pytest.fail(f"An exception occurred - {e}")
-        print(e)
-
-
-# test_langfuse_logging_custom_generation_name()
-
-
-@pytest.mark.skip(reason="beta test - checking langfuse output")
-def test_langfuse_logging_embedding():
-    try:
-        litellm.set_verbose = True
-        litellm.success_callback = ["langfuse"]
-        response = litellm.embedding(
-            model="text-embedding-ada-002",
-            input=["gm", "ishaan"],
-        )
-        print(response)
-    except litellm.Timeout as e:
-        pass
-    except Exception as e:
-        pytest.fail(f"An exception occurred - {e}")
-        print(e)
-
-
-@pytest.mark.skip(reason="beta test - checking langfuse output")
-def test_langfuse_logging_function_calling():
-    litellm.set_verbose = True
-    function1 = [
-        {
-            "name": "get_current_weather",
-            "description": "Get the current weather in a given location",
-            "parameters": {
-                "type": "object",
-                "properties": {
-                    "location": {
-                        "type": "string",
-                        "description": "The city and state, e.g. San Francisco, CA",
-                    },
-                    "unit": {"type": "string", "enum": ["celsius", "fahrenheit"]},
-                },
-                "required": ["location"],
-            },
-        }
-    ]
-    try:
-        response = completion(
-            model="gpt-3.5-turbo",
-            messages=[{"role": "user", "content": "what's the weather in boston"}],
-            temperature=0.1,
-            functions=function1,
-        )
-        print(response)
-    except litellm.Timeout as e:
-        pass
-    except Exception as e:
-        print(e)
-
-
-# test_langfuse_logging_function_calling()
-
-
-@pytest.mark.skip(reason="Need to address this on main")
-def test_aaalangfuse_existing_trace_id():
-    """
-    When existing trace id is passed, don't set trace params -> prevents overwriting the trace
-
-    Pass 1 logging object with a trace
-
-    Pass 2nd logging object with the trace id
-
-    Assert no changes to the trace
-    """
-    # Test - if the logs were sent to the correct team on langfuse
-    import datetime
-
-    import litellm
-    from litellm.integrations.langfuse import LangFuseLogger
-
-    langfuse_Logger = LangFuseLogger(
-        langfuse_public_key=os.getenv("LANGFUSE_PROJECT2_PUBLIC"),
-        langfuse_secret=os.getenv("LANGFUSE_PROJECT2_SECRET"),
-    )
-    litellm.success_callback = ["langfuse"]
-
-    # langfuse_args = {'kwargs': { 'start_time':  'end_time': datetime.datetime(2024, 5, 1, 7, 31, 29, 903685), 'user_id': None, 'print_verbose': <function print_verbose at 0x109d1f420>, 'level': 'DEFAULT', 'status_message': None}
-    response_obj = litellm.ModelResponse(
-        id="chatcmpl-9K5HUAbVRqFrMZKXL0WoC295xhguY",
-        choices=[
-            litellm.Choices(
-                finish_reason="stop",
-                index=0,
-                message=litellm.Message(
-                    content="I'm sorry, I am an AI assistant and do not have real-time information. I recommend checking a reliable weather website or app for the most up-to-date weather information in Boston.",
-                    role="assistant",
-                ),
-            )
-        ],
-        created=1714573888,
-        model="gpt-3.5-turbo-0125",
-        object="chat.completion",
-        system_fingerprint="fp_3b956da36b",
-        usage=litellm.Usage(completion_tokens=37, prompt_tokens=14, total_tokens=51),
-    )
-
-    ### NEW TRACE ###
-    message = [{"role": "user", "content": "what's the weather in boston"}]
-    langfuse_args = {
-        "response_obj": response_obj,
-        "kwargs": {
-            "model": "gpt-3.5-turbo",
-            "litellm_params": {
-                "acompletion": False,
-                "api_key": None,
-                "force_timeout": 600,
-                "logger_fn": None,
-                "verbose": False,
-                "custom_llm_provider": "openai",
-                "api_base": "https://api.openai.com/v1/",
-                "litellm_call_id": None,
-                "model_alias_map": {},
-                "completion_call_id": None,
-                "metadata": None,
-                "model_info": None,
-                "proxy_server_request": None,
-                "preset_cache_key": None,
-                "no-log": False,
-                "stream_response": {},
-            },
-            "messages": message,
-            "optional_params": {"temperature": 0.1, "extra_body": {}},
-            "start_time": "2024-05-01 07:31:27.986164",
-            "stream": False,
-            "user": None,
-            "call_type": "completion",
-            "litellm_call_id": None,
-            "completion_start_time": "2024-05-01 07:31:29.903685",
-            "temperature": 0.1,
-            "extra_body": {},
-            "input": [{"role": "user", "content": "what's the weather in boston"}],
-            "api_key": "my-api-key",
-            "additional_args": {
-                "complete_input_dict": {
-                    "model": "gpt-3.5-turbo",
-                    "messages": [
-                        {"role": "user", "content": "what's the weather in boston"}
-                    ],
-                    "temperature": 0.1,
-                    "extra_body": {},
-                }
-            },
-            "log_event_type": "successful_api_call",
-            "end_time": "2024-05-01 07:31:29.903685",
-            "cache_hit": None,
-            "response_cost": 6.25e-05,
-        },
-        "start_time": datetime.datetime(2024, 5, 1, 7, 31, 27, 986164),
-        "end_time": datetime.datetime(2024, 5, 1, 7, 31, 29, 903685),
-        "user_id": None,
-        "print_verbose": litellm.print_verbose,
-        "level": "DEFAULT",
-        "status_message": None,
-    }
-
-    langfuse_response_object = langfuse_Logger.log_event(**langfuse_args)
-
-    import langfuse
-
-    langfuse_client = langfuse.Langfuse(
-        public_key=os.getenv("LANGFUSE_PROJECT2_PUBLIC"),
-        secret_key=os.getenv("LANGFUSE_PROJECT2_SECRET"),
-    )
-
-    trace_id = langfuse_response_object["trace_id"]
-
-    assert trace_id is not None
-
-    langfuse_client.flush()
-
-    time.sleep(2)
-
-    print(langfuse_client.get_trace(id=trace_id))
-
-    initial_langfuse_trace = langfuse_client.get_trace(id=trace_id)
-
-    ### EXISTING TRACE ###
-
-    new_metadata = {"existing_trace_id": trace_id}
-    new_messages = [{"role": "user", "content": "What do you know?"}]
-    new_response_obj = litellm.ModelResponse(
-        id="chatcmpl-9K5HUAbVRqFrMZKXL0WoC295xhguY",
-        choices=[
-            litellm.Choices(
-                finish_reason="stop",
-                index=0,
-                message=litellm.Message(
-                    content="What do I know?",
-                    role="assistant",
-                ),
-            )
-        ],
-        created=1714573888,
-        model="gpt-3.5-turbo-0125",
-        object="chat.completion",
-        system_fingerprint="fp_3b956da36b",
-        usage=litellm.Usage(completion_tokens=37, prompt_tokens=14, total_tokens=51),
-    )
-    langfuse_args = {
-        "response_obj": new_response_obj,
-        "kwargs": {
-            "model": "gpt-3.5-turbo",
-            "litellm_params": {
-                "acompletion": False,
-                "api_key": None,
-                "force_timeout": 600,
-                "logger_fn": None,
-                "verbose": False,
-                "custom_llm_provider": "openai",
-                "api_base": "https://api.openai.com/v1/",
-                "litellm_call_id": "508113a1-c6f1-48ce-a3e1-01c6cce9330e",
-                "model_alias_map": {},
-                "completion_call_id": None,
-                "metadata": new_metadata,
-                "model_info": None,
-                "proxy_server_request": None,
-                "preset_cache_key": None,
-                "no-log": False,
-                "stream_response": {},
-            },
-            "messages": new_messages,
-            "optional_params": {"temperature": 0.1, "extra_body": {}},
-            "start_time": "2024-05-01 07:31:27.986164",
-            "stream": False,
-            "user": None,
-            "call_type": "completion",
-            "litellm_call_id": "508113a1-c6f1-48ce-a3e1-01c6cce9330e",
-            "completion_start_time": "2024-05-01 07:31:29.903685",
-            "temperature": 0.1,
-            "extra_body": {},
-            "input": [{"role": "user", "content": "what's the weather in boston"}],
-            "api_key": "my-api-key",
-            "additional_args": {
-                "complete_input_dict": {
-                    "model": "gpt-3.5-turbo",
-                    "messages": [
-                        {"role": "user", "content": "what's the weather in boston"}
-                    ],
-                    "temperature": 0.1,
-                    "extra_body": {},
-                }
-            },
-            "log_event_type": "successful_api_call",
-            "end_time": "2024-05-01 07:31:29.903685",
-            "cache_hit": None,
-            "response_cost": 6.25e-05,
-        },
-        "start_time": datetime.datetime(2024, 5, 1, 7, 31, 27, 986164),
-        "end_time": datetime.datetime(2024, 5, 1, 7, 31, 29, 903685),
-        "user_id": None,
-        "print_verbose": litellm.print_verbose,
-        "level": "DEFAULT",
-        "status_message": None,
-    }
-
-    langfuse_response_object = langfuse_Logger.log_event(**langfuse_args)
-
-    new_trace_id = langfuse_response_object["trace_id"]
-
-    assert new_trace_id == trace_id
-
-    langfuse_client.flush()
-
-    time.sleep(2)
-
-    print(langfuse_client.get_trace(id=trace_id))
-
-    new_langfuse_trace = langfuse_client.get_trace(id=trace_id)
-
-    initial_langfuse_trace_dict = dict(initial_langfuse_trace)
-    initial_langfuse_trace_dict.pop("updatedAt")
-    initial_langfuse_trace_dict.pop("timestamp")
-
-    new_langfuse_trace_dict = dict(new_langfuse_trace)
-    new_langfuse_trace_dict.pop("updatedAt")
-    new_langfuse_trace_dict.pop("timestamp")
-
-    assert initial_langfuse_trace_dict == new_langfuse_trace_dict
-
-
-@pytest.mark.skipif(
-    condition=not os.environ.get("OPENAI_API_KEY", False),
-    reason="Authentication missing for openai",
-)
-def test_langfuse_logging_tool_calling():
-    litellm.set_verbose = True
-
-    def get_current_weather(location, unit="fahrenheit"):
-        """Get the current weather in a given location"""
-        if "tokyo" in location.lower():
-            return json.dumps(
-                {"location": "Tokyo", "temperature": "10", "unit": "celsius"}
-            )
-        elif "san francisco" in location.lower():
-            return json.dumps(
-                {"location": "San Francisco", "temperature": "72", "unit": "fahrenheit"}
-            )
-        elif "paris" in location.lower():
-            return json.dumps(
-                {"location": "Paris", "temperature": "22", "unit": "celsius"}
-            )
-        else:
-            return json.dumps({"location": location, "temperature": "unknown"})
-
-    messages = [
-        {
-            "role": "user",
-            "content": "What's the weather like in San Francisco, Tokyo, and Paris?",
-        }
-    ]
-    tools = [
-        {
-            "type": "function",
-            "function": {
-                "name": "get_current_weather",
-                "description": "Get the current weather in a given location",
-                "parameters": {
-                    "type": "object",
-                    "properties": {
-                        "location": {
-                            "type": "string",
-                            "description": "The city and state, e.g. San Francisco, CA",
-                        },
-                        "unit": {"type": "string", "enum": ["celsius", "fahrenheit"]},
-                    },
-                    "required": ["location"],
-                },
-            },
-        }
-    ]
-
-    response = litellm.completion(
-        model="gpt-3.5-turbo-1106",
-        messages=messages,
-        tools=tools,
-        tool_choice="auto",  # auto is default, but we'll be explicit
-    )
-    print("\nLLM Response1:\n", response)
-    response_message = response.choices[0].message
-    tool_calls = response.choices[0].message.tool_calls
-
-
-# test_langfuse_logging_tool_calling()
-
-
-def get_langfuse_prompt(name: str):
-    import langfuse
-    from langfuse import Langfuse
-
-    try:
-        langfuse = Langfuse(
-            public_key=os.environ["LANGFUSE_DEV_PUBLIC_KEY"],
-            secret_key=os.environ["LANGFUSE_DEV_SK_KEY"],
-            host=os.environ["LANGFUSE_HOST"],
-        )
-
-        # Get current production version of a text prompt
-        prompt = langfuse.get_prompt(name=name)
-        return prompt
-    except Exception as e:
-        raise Exception(f"Error getting prompt: {e}")
-
-
-@pytest.mark.asyncio
-@pytest.mark.skip(
-    reason="local only test, use this to verify if we can send request to litellm proxy server"
-)
-async def test_make_request():
-    response = await litellm.acompletion(
-        model="openai/llama3",
-        api_key="sk-1234",
-        base_url="http://localhost:4000",
-        messages=[{"role": "user", "content": "Hi 👋 - i'm claude"}],
-        extra_body={
-            "metadata": {
-                "tags": ["openai"],
-                "prompt": get_langfuse_prompt("test-chat"),
-            }
-        },
-    )

litellm/tests/test_blocked_user_list.py

@@ -2,9 +2,14 @@
 ## This tests the blocked user pre call hook for the proxy server
 
 
-import sys, os, asyncio, time, random
+import asyncio
-from datetime import datetime
+import os
+import random
+import sys
+import time
 import traceback
+from datetime import datetime
+
 from dotenv import load_dotenv
 from fastapi import Request
 
@@ -14,57 +19,53 @@ import os
 sys.path.insert(
     0, os.path.abspath("../..")
 )  # Adds the parent directory to the system path
+import asyncio
+import logging
+
 import pytest
+
 import litellm
+from litellm import Router, mock_completion
+from litellm._logging import verbose_proxy_logger
+from litellm.caching import DualCache
+from litellm.proxy._types import UserAPIKeyAuth
 from litellm.proxy.enterprise.enterprise_hooks.blocked_user_list import (
     _ENTERPRISE_BlockedUserList,
 )
-from litellm import Router, mock_completion
+from litellm.proxy.management_endpoints.internal_user_endpoints import (
-from litellm.proxy.utils import ProxyLogging, hash_token
+    new_user,
-from litellm.proxy._types import UserAPIKeyAuth
+    user_info,
-from litellm.caching import DualCache
+    user_update,
-from litellm.proxy.utils import PrismaClient, ProxyLogging, hash_token
-
-import pytest, logging, asyncio
-import litellm, asyncio
-from litellm.proxy.proxy_server import (
-    user_api_key_auth,
-    block_user,
 )
 from litellm.proxy.management_endpoints.key_management_endpoints import (
     delete_key_fn,
-    info_key_fn,
-    update_key_fn,
     generate_key_fn,
     generate_key_helper_fn,
+    info_key_fn,
+    update_key_fn,
 )
-from litellm.proxy.management_endpoints.internal_user_endpoints import (
+from litellm.proxy.proxy_server import block_user, user_api_key_auth
-    new_user,
+from litellm.proxy.spend_tracking.spend_management_endpoints import (
-    user_update,
-    user_info,
-)
-from litellm.proxy.spend_reporting_endpoints.spend_management_endpoints import (
-    spend_user_fn,
     spend_key_fn,
+    spend_user_fn,
     view_spend_logs,
 )
 from litellm.proxy.utils import PrismaClient, ProxyLogging, hash_token
-from litellm._logging import verbose_proxy_logger
 
 verbose_proxy_logger.setLevel(level=logging.DEBUG)
 
+from starlette.datastructures import URL
+
+from litellm.caching import DualCache
 from litellm.proxy._types import (
-    NewUserRequest,
-    GenerateKeyRequest,
-    DynamoDBArgs,
-    KeyRequest,
-    UpdateKeyRequest,
-    GenerateKeyRequest,
     BlockUsers,
+    DynamoDBArgs,
+    GenerateKeyRequest,
+    KeyRequest,
+    NewUserRequest,
+    UpdateKeyRequest,
 )
 from litellm.proxy.utils import DBClient
-from starlette.datastructures import URL
-from litellm.caching import DualCache
 
 proxy_logging_obj = ProxyLogging(user_api_key_cache=DualCache())
 

litellm/tests/test_exceptions.py

@@ -670,7 +670,7 @@ def test_litellm_predibase_exception():
 # print(f"accuracy_score: {accuracy_score}")
 
 
-@pytest.mark.parametrize("provider", ["predibase", "vertex_ai_beta"])
+@pytest.mark.parametrize("provider", ["predibase", "vertex_ai_beta", "anthropic"])
 def test_exception_mapping(provider):
     """
     For predibase, run through a set of mock exceptions
@@ -712,3 +712,27 @@ def test_exception_mapping(provider):
         )
 
     pass
+
+
+def test_anthropic_tool_calling_exception():
+    """
+    Related - https://github.com/BerriAI/litellm/issues/4348
+    """
+    tools = [
+        {
+            "type": "function",
+            "function": {
+                "name": "get_current_weather",
+                "description": "Get the current weather in a given location",
+                "parameters": {},
+            },
+        }
+    ]
+    try:
+        litellm.completion(
+            model="claude-3-5-sonnet-20240620",
+            messages=[{"role": "user", "content": "Hey, how's it going?"}],
+            tools=tools,
+        )
+    except litellm.BadRequestError:
+        pass

litellm/tests/test_key_generate_prisma.py

@@ -75,7 +75,7 @@ from litellm.proxy.proxy_server import (
     new_end_user,
     user_api_key_auth,
 )
-from litellm.proxy.spend_reporting_endpoints.spend_management_endpoints import (
+from litellm.proxy.spend_tracking.spend_management_endpoints import (
     spend_key_fn,
     spend_user_fn,
     view_spend_logs,

litellm/tests/test_proxy_routes.py

@@ -0,0 +1,52 @@
+import os
+import sys
+
+from dotenv import load_dotenv
+
+load_dotenv()
+import io
+import os
+
+# this file is to test litellm/proxy
+
+sys.path.insert(
+    0, os.path.abspath("../..")
+)  # Adds the parent directory to the system path
+import asyncio
+import logging
+
+import pytest
+
+import litellm
+from litellm.proxy._types import LiteLLMRoutes
+from litellm.proxy.proxy_server import router
+
+# Configure logging
+logging.basicConfig(
+    level=logging.DEBUG,  # Set the desired logging level
+    format="%(asctime)s - %(levelname)s - %(message)s",
+)
+
+
+def test_routes_on_litellm_proxy():
+    """
+    Goal of this test: Test that we have all the critical OpenAI Routes on the Proxy server Fast API router
+
+
+    this prevents accidentelly deleting /threads, or /batches etc
+    """
+    _all_routes = []
+    for route in router.routes:
+
+        _path_as_str = str(route.path)
+        if ":path" in _path_as_str:
+            # remove the :path
+            _path_as_str = _path_as_str.replace(":path", "")
+        _all_routes.append(_path_as_str)
+
+    print("ALL ROUTES on LiteLLM Proxy:", _all_routes)
+    print("\n\n")
+    print("ALL OPENAI ROUTES:", LiteLLMRoutes.openai_routes.value)
+
+    for route in LiteLLMRoutes.openai_routes.value:
+        assert route in _all_routes

litellm/tests/test_spend_logs.py

@@ -1,26 +1,32 @@
-import sys, os
+import os
-import traceback, uuid
+import sys
+import traceback
+import uuid
+
 from dotenv import load_dotenv
 from fastapi import Request
 from fastapi.routing import APIRoute
 
 load_dotenv()
-import os, io, time
+import io
+import os
+import time
 
 # this file is to test litellm/proxy
 
 sys.path.insert(
     0, os.path.abspath("../..")
 )  # Adds the parent directory to the system path
-import pytest, logging, asyncio
+import asyncio
-import litellm, asyncio
-import json
 import datetime
-from litellm.proxy.utils import (
+import json
-    get_logging_payload,
+import logging
-    SpendLogsPayload,
+
-    SpendLogsMetadata,
+import pytest
-)  # noqa: E402
+
+import litellm
+from litellm.proxy.spend_tracking.spend_tracking_utils import get_logging_payload
+from litellm.proxy.utils import SpendLogsMetadata, SpendLogsPayload  # noqa: E402
 
 
 def test_spend_logs_payload():
@@ -53,6 +59,7 @@ def test_spend_logs_payload():
                 "model_alias_map": {},
                 "completion_call_id": None,
                 "metadata": {
+                    "tags": ["model-anthropic-claude-v2.1", "app-ishaan-prod"],
                     "user_api_key": "88dc28d0f030c55ed4ab77ed8faf098196cb1c05df778539800c9f1243fe6b4b",
                     "user_api_key_alias": None,
                     "user_api_end_user_max_budget": None,
@@ -193,3 +200,8 @@ def test_spend_logs_payload():
     assert isinstance(payload["metadata"], str)
     payload["metadata"] = json.loads(payload["metadata"])
     assert set(payload["metadata"].keys()) == set(expected_metadata_keys)
+
+    # This is crucial - used in PROD, it should pass, related issue: https://github.com/BerriAI/litellm/issues/4334
+    assert (
+        payload["request_tags"] == '["model-anthropic-claude-v2.1", "app-ishaan-prod"]'
+    )

litellm/tests/test_tpm_rpm_routing_v2.py

@@ -394,7 +394,7 @@ async def test_router_completion_streaming():
                 "model": "azure/gpt-turbo",
                 "api_key": "os.environ/AZURE_FRANCE_API_KEY",
                 "api_base": "https://openai-france-1234.openai.azure.com",
-                "rpm": 1440,
+                "tpm": 1440,
                 "mock_response": "Hello world",
             },
             "model_info": {"id": 1},
@@ -405,7 +405,7 @@ async def test_router_completion_streaming():
                 "model": "azure/gpt-35-turbo",
                 "api_key": "os.environ/AZURE_EUROPE_API_KEY",
                 "api_base": "https://my-endpoint-europe-berri-992.openai.azure.com",
-                "rpm": 6,
+                "tpm": 6,
                 "mock_response": "Hello world",
             },
             "model_info": {"id": 2},

litellm/tests/test_update_spend.py

@@ -2,9 +2,14 @@
 ## This tests the batch update spend logic on the proxy server
 
 
-import sys, os, asyncio, time, random
+import asyncio
-from datetime import datetime
+import os
+import random
+import sys
+import time
 import traceback
+from datetime import datetime
+
 from dotenv import load_dotenv
 from fastapi import Request
 
@@ -14,54 +19,50 @@ import os
 sys.path.insert(
     0, os.path.abspath("../..")
 )  # Adds the parent directory to the system path
+import asyncio
+import logging
+
 import pytest
+
 import litellm
 from litellm import Router, mock_completion
-from litellm.proxy.utils import ProxyLogging
+from litellm._logging import verbose_proxy_logger
-from litellm.proxy._types import UserAPIKeyAuth
 from litellm.caching import DualCache
-from litellm.proxy.utils import PrismaClient, ProxyLogging, hash_token
+from litellm.proxy._types import UserAPIKeyAuth
-
-import pytest, logging, asyncio
-import litellm, asyncio
-from litellm.proxy.proxy_server import (
-    user_api_key_auth,
-    block_user,
-)
-from litellm.proxy.spend_reporting_endpoints.spend_management_endpoints import (
-    spend_user_fn,
-    spend_key_fn,
-    view_spend_logs,
-)
 from litellm.proxy.management_endpoints.internal_user_endpoints import (
     new_user,
-    user_update,
     user_info,
+    user_update,
 )
 from litellm.proxy.management_endpoints.key_management_endpoints import (
     delete_key_fn,
-    info_key_fn,
-    update_key_fn,
     generate_key_fn,
     generate_key_helper_fn,
+    info_key_fn,
+    update_key_fn,
+)
+from litellm.proxy.proxy_server import block_user, user_api_key_auth
+from litellm.proxy.spend_tracking.spend_management_endpoints import (
+    spend_key_fn,
+    spend_user_fn,
+    view_spend_logs,
 )
 from litellm.proxy.utils import PrismaClient, ProxyLogging, hash_token, update_spend
-from litellm._logging import verbose_proxy_logger
 
 verbose_proxy_logger.setLevel(level=logging.DEBUG)
 
+from starlette.datastructures import URL
+
+from litellm.caching import DualCache
 from litellm.proxy._types import (
-    NewUserRequest,
-    GenerateKeyRequest,
-    DynamoDBArgs,
-    KeyRequest,
-    UpdateKeyRequest,
-    GenerateKeyRequest,
     BlockUsers,
+    DynamoDBArgs,
+    GenerateKeyRequest,
+    KeyRequest,
+    NewUserRequest,
+    UpdateKeyRequest,
 )
 from litellm.proxy.utils import DBClient
-from starlette.datastructures import URL
-from litellm.caching import DualCache
 
 proxy_logging_obj = ProxyLogging(user_api_key_cache=DualCache())
 

litellm/tests/test_utils.py

@@ -1,5 +1,6 @@
 import copy
 import sys
+import time
 from datetime import datetime
 from unittest import mock
 
@@ -548,3 +549,63 @@ def test_get_llm_provider_ft_models():
 
     model, custom_llm_provider, _, _ = get_llm_provider(model="ft:gpt-4o-2024-05-13")
     assert custom_llm_provider == "openai"
+
+
+@pytest.mark.parametrize("langfuse_trace_id", [None, "my-unique-trace-id"])
+@pytest.mark.parametrize(
+    "langfuse_existing_trace_id", [None, "my-unique-existing-trace-id"]
+)
+def test_logging_trace_id(langfuse_trace_id, langfuse_existing_trace_id):
+    """
+    - Unit test for `_get_trace_id` function in Logging obj
+    """
+    from litellm.litellm_core_utils.litellm_logging import Logging
+
+    litellm.success_callback = ["langfuse"]
+    litellm_call_id = "my-unique-call-id"
+    litellm_logging_obj = Logging(
+        model="gpt-3.5-turbo",
+        messages=[{"role": "user", "content": "hi"}],
+        stream=False,
+        call_type="acompletion",
+        litellm_call_id=litellm_call_id,
+        start_time=datetime.now(),
+        function_id="1234",
+    )
+
+    metadata = {}
+
+    if langfuse_trace_id is not None:
+        metadata["trace_id"] = langfuse_trace_id
+    if langfuse_existing_trace_id is not None:
+        metadata["existing_trace_id"] = langfuse_existing_trace_id
+
+    litellm.completion(
+        model="gpt-3.5-turbo",
+        messages=[{"role": "user", "content": "Hey how's it going?"}],
+        mock_response="Hey!",
+        litellm_logging_obj=litellm_logging_obj,
+        metadata=metadata,
+    )
+
+    time.sleep(3)
+    assert litellm_logging_obj._get_trace_id(service_name="langfuse") is not None
+
+    ## if existing_trace_id exists
+    if langfuse_existing_trace_id is not None:
+        assert (
+            litellm_logging_obj._get_trace_id(service_name="langfuse")
+            == langfuse_existing_trace_id
+        )
+    ## if trace_id exists
+    elif langfuse_trace_id is not None:
+        assert (
+            litellm_logging_obj._get_trace_id(service_name="langfuse")
+            == langfuse_trace_id
+        )
+    ## if existing_trace_id exists
+    else:
+        assert (
+            litellm_logging_obj._get_trace_id(service_name="langfuse")
+            == litellm_call_id
+        )

litellm/utils.py

@@ -5927,21 +5927,28 @@ def exception_type(
                 if "prompt is too long" in error_str or "prompt: length" in error_str:
                     exception_mapping_worked = True
                     raise ContextWindowExceededError(
-                        message=error_str,
+                        message="AnthropicError - {}".format(error_str),
                         model=model,
                         llm_provider="anthropic",
                     )
                 if "Invalid API Key" in error_str:
                     exception_mapping_worked = True
                     raise AuthenticationError(
-                        message=error_str,
+                        message="AnthropicError - {}".format(error_str),
                         model=model,
                         llm_provider="anthropic",
                     )
                 if "content filtering policy" in error_str:
                     exception_mapping_worked = True
                     raise ContentPolicyViolationError(
-                        message=error_str,
+                        message="AnthropicError - {}".format(error_str),
+                        model=model,
+                        llm_provider="anthropic",
+                    )
+                if "Client error '400 Bad Request'" in error_str:
+                    exception_mapping_worked = True
+                    raise BadRequestError(
+                        message="AnthropicError - {}".format(error_str),
                         model=model,
                         llm_provider="anthropic",
                     )
@@ -5953,7 +5960,6 @@ def exception_type(
                             message=f"AnthropicException - {error_str}",
                             llm_provider="anthropic",
                             model=model,
-                            response=original_exception.response,
                         )
                     elif (
                         original_exception.status_code == 400
@@ -5964,7 +5970,13 @@ def exception_type(
                             message=f"AnthropicException - {error_str}",
                             model=model,
                             llm_provider="anthropic",
-                            response=original_exception.response,
+                        )
+                    elif original_exception.status_code == 404:
+                        exception_mapping_worked = True
+                        raise NotFoundError(
+                            message=f"AnthropicException - {error_str}",
+                            model=model,
+                            llm_provider="anthropic",
                         )
                     elif original_exception.status_code == 408:
                         exception_mapping_worked = True
@@ -5979,16 +5991,20 @@ def exception_type(
                             message=f"AnthropicException - {error_str}",
                             llm_provider="anthropic",
                             model=model,
-                            response=original_exception.response,
                         )
                     elif original_exception.status_code == 500:
                         exception_mapping_worked = True
-                        raise APIError(
+                        raise litellm.InternalServerError(
-                            status_code=500,
+                            message=f"AnthropicException - {error_str}. Handle with `litellm.InternalServerError`.",
-                            message=f"AnthropicException - {error_str}. Handle with `litellm.APIError`.",
+                            llm_provider="anthropic",
+                            model=model,
+                        )
+                    elif original_exception.status_code == 503:
+                        exception_mapping_worked = True
+                        raise litellm.ServiceUnavailableError(
+                            message=f"AnthropicException - {error_str}. Handle with `litellm.ServiceUnavailableError`.",
                             llm_provider="anthropic",
                             model=model,
-                            request=original_exception.request,
                         )
             elif custom_llm_provider == "replicate":
                 if "Incorrect authentication token" in error_str:

pyproject.toml

@@ -1,6 +1,6 @@
 [tool.poetry]
 name = "litellm"
-version = "1.40.21"
+version = "1.40.24"
 description = "Library to easily interface with LLM API providers"
 authors = ["BerriAI"]
 license = "MIT"
@@ -90,7 +90,7 @@ requires = ["poetry-core", "wheel"]
 build-backend = "poetry.core.masonry.api"
 
 [tool.commitizen]
-version = "1.40.21"
+version = "1.40.24"
 version_files = [
     "pyproject.toml:^version"
 ]

ui/litellm-dashboard/out/_next/static/chunks/app/onboarding/page-664c7288e11fff5a.js

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ui/litellm-dashboard/out/index.txt

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ui/litellm-dashboard/out/model_hub.txt

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ui/litellm-dashboard/out/onboarding.txt

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ui/litellm-dashboard/src/app/onboarding/page.tsx

@@ -20,10 +20,19 @@ import {
 } from "@/components/networking";
 import { jwtDecode } from "jwt-decode";
 import { Form, Button as Button2, message } from "antd";
+
+function getCookie(name: string) {
+  console.log("COOKIES", document.cookie)
+  const cookieValue = document.cookie
+      .split('; ')
+      .find(row => row.startsWith(name + '='));
+  return cookieValue ? cookieValue.split('=')[1] : null;
+}
+
 export default function Onboarding() {
   const [form] = Form.useForm();
   const searchParams = useSearchParams();
-  const token = searchParams.get("token");
+  const token = getCookie('token');
   const inviteID = searchParams.get("id");
   const [accessToken, setAccessToken] = useState<string | null>(null);
   const [defaultUserEmail, setDefaultUserEmail] = useState<string>("");

ui/litellm-dashboard/src/app/page.tsx

@@ -19,6 +19,15 @@ import CacheDashboard from "@/components/cache_dashboard";
 import { jwtDecode } from "jwt-decode";
 import { Typography } from "antd";
 
+function getCookie(name: string) {
+  console.log("COOKIES", document.cookie)
+  const cookieValue = document.cookie
+      .split('; ')
+      .find(row => row.startsWith(name + '='));
+  return cookieValue ? cookieValue.split('=')[1] : null;
+}
+
+
 function formatUserRole(userRole: string) {
   if (!userRole) {
     return "Undefined Role";
@@ -68,7 +77,7 @@ const CreateKeyPage = () => {
   const searchParams = useSearchParams();
   const [modelData, setModelData] = useState<any>({ data: [] });
   const userID = searchParams.get("userID");
-  const token = searchParams.get("token");
+  const token = getCookie('token');
 
   const [page, setPage] = useState("api-keys");
   const [accessToken, setAccessToken] = useState<string | null>(null);

ui/litellm-dashboard/src/components/user_dashboard.tsx

@@ -24,6 +24,14 @@ type UserSpendData = {
   max_budget?: number | null;
 };
 
+function getCookie(name: string) {
+  console.log("COOKIES", document.cookie)
+  const cookieValue = document.cookie
+      .split('; ')
+      .find(row => row.startsWith(name + '='));
+  return cookieValue ? cookieValue.split('=')[1] : null;
+}
+
 interface UserDashboardProps {
   userID: string | null;
   userRole: string | null;
@@ -66,7 +74,8 @@ const UserDashboard: React.FC<UserDashboardProps> = ({
   const viewSpend = searchParams.get("viewSpend");
   const router = useRouter();
 
-  const token = searchParams.get("token");
+  const token = getCookie('token');
+
   const [accessToken, setAccessToken] = useState<string | null>(null);
   const [teamSpend, setTeamSpend] = useState<number | null>(null);
   const [userModels, setUserModels] = useState<string[]>([]);