Merge branch 'BerriAI:main' into ollama-image-handling

.gitignore

@@ -1,5 +1,6 @@
 .venv
 .env
+litellm/proxy/myenv/*
 litellm_uuid.txt
 __pycache__/
 *.pyc
@@ -52,3 +53,6 @@ litellm/proxy/_new_secret_config.yaml
 litellm/proxy/_new_secret_config.yaml
 litellm/proxy/_super_secret_config.yaml
 litellm/proxy/_super_secret_config.yaml
+litellm/proxy/myenv/bin/activate
+litellm/proxy/myenv/bin/Activate.ps1
+myenv/*

.pre-commit-config.yaml

@@ -16,11 +16,11 @@ repos:
         name: Check if files match
         entry: python3 ci_cd/check_files_match.py
         language: system
--   repo: local
-    hooks:
-    -   id: mypy
-        name: mypy
-        entry: python3 -m mypy --ignore-missing-imports
-        language: system
-        types: [python]
-        files: ^litellm/
+# -   repo: local
+#     hooks:
+#     -   id: mypy
+#         name: mypy
+#         entry: python3 -m mypy --ignore-missing-imports
+#         language: system
+#         types: [python]
+#         files: ^litellm/

deploy/azure_resource_manager/azure_marketplace/createUiDefinition.json

@@ -0,0 +1,15 @@
+{
+    "$schema": "https://schema.management.azure.com/schemas/0.1.2-preview/CreateUIDefinition.MultiVm.json#",
+    "handler": "Microsoft.Azure.CreateUIDef",
+    "version": "0.1.2-preview",
+    "parameters": {
+        "config": {
+            "isWizard": false,
+            "basics": { }
+        },
+        "basics": [ ],
+        "steps": [ ],
+        "outputs": { },
+        "resourceTypes": [ ]
+    }
+}

deploy/azure_resource_manager/azure_marketplace/mainTemplate.json

@@ -0,0 +1,63 @@
+{
+    "$schema": "https://schema.management.azure.com/schemas/2019-04-01/deploymentTemplate.json#",
+    "contentVersion": "1.0.0.0",
+    "parameters": {
+      "imageName": {
+        "type": "string",
+        "defaultValue": "ghcr.io/berriai/litellm:main-latest"
+      },
+      "containerName": {
+        "type": "string",
+        "defaultValue": "litellm-container"
+      },
+      "dnsLabelName": {
+        "type": "string",
+        "defaultValue": "litellm"
+      },
+      "portNumber": {
+        "type": "int",
+        "defaultValue": 4000
+      }
+    },
+    "resources": [
+      {
+        "type": "Microsoft.ContainerInstance/containerGroups",
+        "apiVersion": "2021-03-01",
+        "name": "[parameters('containerName')]",
+        "location": "[resourceGroup().location]",
+        "properties": {
+          "containers": [
+            {
+              "name": "[parameters('containerName')]",
+              "properties": {
+                "image": "[parameters('imageName')]",
+                "resources": {
+                  "requests": {
+                    "cpu": 1,
+                    "memoryInGB": 2
+                  }
+                },
+                "ports": [
+                  {
+                    "port": "[parameters('portNumber')]"
+                  }
+                ]
+              }
+            }
+          ],
+          "osType": "Linux",
+          "restartPolicy": "Always",
+          "ipAddress": {
+            "type": "Public",
+            "ports": [
+              {
+                "protocol": "tcp",
+                "port": "[parameters('portNumber')]"
+              }
+            ],
+            "dnsNameLabel": "[parameters('dnsLabelName')]"
+          }
+        }
+      }
+    ]
+  }

deploy/azure_resource_manager/main.bicep

@@ -0,0 +1,42 @@
+param imageName string = 'ghcr.io/berriai/litellm:main-latest'
+param containerName string = 'litellm-container'
+param dnsLabelName string = 'litellm'
+param portNumber int = 4000
+
+resource containerGroupName 'Microsoft.ContainerInstance/containerGroups@2021-03-01' = {
+  name: containerName
+  location: resourceGroup().location
+  properties: {
+    containers: [
+      {
+        name: containerName
+        properties: {
+          image: imageName
+          resources: {
+            requests: {
+              cpu: 1
+              memoryInGB: 2
+            }
+          }
+          ports: [
+            {
+              port: portNumber
+            }
+          ]
+        }
+      }
+    ]
+    osType: 'Linux'
+    restartPolicy: 'Always'
+    ipAddress: {
+      type: 'Public'
+      ports: [
+        {
+          protocol: 'tcp'
+          port: portNumber
+        }
+      ]
+      dnsNameLabel: dnsLabelName
+    }
+  }
+}

docs/my-website/docs/completion/input.md

@@ -83,6 +83,7 @@ def completion(
     top_p: Optional[float] = None,
     n: Optional[int] = None,
     stream: Optional[bool] = None,
+    stream_options: Optional[dict] = None,
     stop=None,
     max_tokens: Optional[int] = None,
     presence_penalty: Optional[float] = None,
@@ -139,6 +140,10 @@ def completion(
 
 - `stream`: *boolean or null (optional)* - If set to true, it sends partial message deltas. Tokens will be sent as they become available, with the stream terminated by a [DONE] message.
 
+- `stream_options` *dict or null (optional)* - Options for streaming response. Only set this when you set `stream: true`
+
+    - `include_usage` *boolean (optional)* - If set, an additional chunk will be streamed before the data: [DONE] message. The usage field on this chunk shows the token usage statistics for the entire request, and the choices field will always be an empty array. All other chunks will also include a usage field, but with a null value. 
+
 - `stop`: *string/ array/ null (optional)* - Up to 4 sequences where the API will stop generating further tokens.
 
 - `max_tokens`: *integer (optional)* - The maximum number of tokens to generate in the chat completion.

enterprise/utils.py

@@ -291,7 +291,7 @@ def _create_clickhouse_aggregate_tables(client=None, table_names=[]):
 
 
 def _forecast_daily_cost(data: list):
-    import requests
+    import requests  # type: ignore
     from datetime import datetime, timedelta
 
     if len(data) == 0:

litellm/_redis.py

@@ -10,8 +10,8 @@
 # s/o [@Frank Colson](https://www.linkedin.com/in/frank-colson-422b9b183/) for this redis implementation
 import os
 import inspect
-import redis, litellm
-import redis.asyncio as async_redis
+import redis, litellm  # type: ignore
+import redis.asyncio as async_redis  # type: ignore
 from typing import List, Optional
 
 

litellm/budget_manager.py

@@ -10,7 +10,7 @@
 import os, json, time
 import litellm
 from litellm.utils import ModelResponse
-import requests, threading
+import requests, threading  # type: ignore
 from typing import Optional, Union, Literal
 
 

litellm/integrations/aispend.py

@@ -1,7 +1,6 @@
 #### What this does ####
 #    On success + failure, log events to aispend.io
 import dotenv, os
-import requests
 
 dotenv.load_dotenv()  # Loading env variables using dotenv
 import traceback

litellm/integrations/athina.py

@@ -4,18 +4,30 @@ import datetime
 class AthinaLogger:
     def __init__(self):
         import os
+
         self.athina_api_key = os.getenv("ATHINA_API_KEY")
         self.headers = {
             "athina-api-key": self.athina_api_key,
-            "Content-Type": "application/json"
+            "Content-Type": "application/json",
         }
         self.athina_logging_url = "https://log.athina.ai/api/v1/log/inference"
-        self.additional_keys = ["environment", "prompt_slug", "customer_id", "customer_user_id", "session_id", "external_reference_id", "context", "expected_response", "user_query"]
+        self.additional_keys = [
+            "environment",
+            "prompt_slug",
+            "customer_id",
+            "customer_user_id",
+            "session_id",
+            "external_reference_id",
+            "context",
+            "expected_response",
+            "user_query",
+        ]
 
     def log_event(self, kwargs, response_obj, start_time, end_time, print_verbose):
-        import requests
+        import requests  # type: ignore
         import json
         import traceback
+
         try:
             response_json = response_obj.model_dump() if response_obj else {}
             data = {
@@ -23,32 +35,51 @@ class AthinaLogger:
                 "request": kwargs,
                 "response": response_json,
                 "prompt_tokens": response_json.get("usage", {}).get("prompt_tokens"),
-                "completion_tokens": response_json.get("usage", {}).get("completion_tokens"),
+                "completion_tokens": response_json.get("usage", {}).get(
+                    "completion_tokens"
+                ),
                 "total_tokens": response_json.get("usage", {}).get("total_tokens"),
             }
-            
-            if type(end_time) == datetime.datetime and type(start_time) == datetime.datetime:
-                data["response_time"] = int((end_time - start_time).total_seconds() * 1000)
+
+            if (
+                type(end_time) == datetime.datetime
+                and type(start_time) == datetime.datetime
+            ):
+                data["response_time"] = int(
+                    (end_time - start_time).total_seconds() * 1000
+                )
 
             if "messages" in kwargs:
                 data["prompt"] = kwargs.get("messages", None)
 
             # Directly add tools or functions if present
             optional_params = kwargs.get("optional_params", {})
-            data.update((k, v) for k, v in optional_params.items() if k in ["tools", "functions"])
+            data.update(
+                (k, v)
+                for k, v in optional_params.items()
+                if k in ["tools", "functions"]
+            )
 
             # Add additional metadata keys
-            metadata = kwargs.get("litellm_params", {}).get("metadata", {})        
+            metadata = kwargs.get("litellm_params", {}).get("metadata", {})
             if metadata:
                 for key in self.additional_keys:
                     if key in metadata:
                         data[key] = metadata[key]
 
-            response = requests.post(self.athina_logging_url, headers=self.headers, data=json.dumps(data, default=str))
+            response = requests.post(
+                self.athina_logging_url,
+                headers=self.headers,
+                data=json.dumps(data, default=str),
+            )
             if response.status_code != 200:
-                print_verbose(f"Athina Logger Error - {response.text}, {response.status_code}")
+                print_verbose(
+                    f"Athina Logger Error - {response.text}, {response.status_code}"
+                )
             else:
                 print_verbose(f"Athina Logger Succeeded - {response.text}")
         except Exception as e:
-            print_verbose(f"Athina Logger Error - {e}, Stack trace: {traceback.format_exc()}")
-            pass
+            print_verbose(
+                f"Athina Logger Error - {e}, Stack trace: {traceback.format_exc()}"
+            )
+            pass

litellm/integrations/berrispend.py

@@ -1,7 +1,7 @@
 #### What this does ####
 #    On success + failure, log events to aispend.io
 import dotenv, os
-import requests
+import requests  # type: ignore
 
 dotenv.load_dotenv()  # Loading env variables using dotenv
 import traceback

litellm/integrations/clickhouse.py

@@ -3,7 +3,6 @@
 #### What this does ####
 #    On success, logs events to Promptlayer
 import dotenv, os
-import requests
 
 from litellm.proxy._types import UserAPIKeyAuth
 from litellm.caching import DualCache

litellm/integrations/custom_logger.py

@@ -1,7 +1,6 @@
 #### What this does ####
 #    On success, logs events to Promptlayer
 import dotenv, os
-import requests
 
 from litellm.proxy._types import UserAPIKeyAuth
 from litellm.caching import DualCache

litellm/integrations/datadog.py

@@ -2,7 +2,7 @@
 #    On success + failure, log events to Supabase
 
 import dotenv, os
-import requests
+import requests  # type: ignore
 
 dotenv.load_dotenv()  # Loading env variables using dotenv
 import traceback

litellm/integrations/dynamodb.py

@@ -2,7 +2,7 @@
 #    On success + failure, log events to Supabase
 
 import dotenv, os
-import requests
+import requests  # type: ignore
 
 dotenv.load_dotenv()  # Loading env variables using dotenv
 import traceback

litellm/integrations/greenscale.py

@@ -1,15 +1,17 @@
-import requests
+import requests  # type: ignore
 import json
 import traceback
 from datetime import datetime, timezone
 
+
 class GreenscaleLogger:
     def __init__(self):
         import os
+
         self.greenscale_api_key = os.getenv("GREENSCALE_API_KEY")
         self.headers = {
             "api-key": self.greenscale_api_key,
-            "Content-Type": "application/json"
+            "Content-Type": "application/json",
         }
         self.greenscale_logging_url = os.getenv("GREENSCALE_ENDPOINT")
 
@@ -19,33 +21,48 @@ class GreenscaleLogger:
             data = {
                 "modelId": kwargs.get("model"),
                 "inputTokenCount": response_json.get("usage", {}).get("prompt_tokens"),
-                "outputTokenCount": response_json.get("usage", {}).get("completion_tokens"),
+                "outputTokenCount": response_json.get("usage", {}).get(
+                    "completion_tokens"
+                ),
             }
-            data["timestamp"] = datetime.now(timezone.utc).strftime('%Y-%m-%dT%H:%M:%SZ')
-            
-            if type(end_time) == datetime and type(start_time) == datetime:
-                data["invocationLatency"] = int((end_time - start_time).total_seconds() * 1000)
+            data["timestamp"] = datetime.now(timezone.utc).strftime(
+                "%Y-%m-%dT%H:%M:%SZ"
+            )
 
+            if type(end_time) == datetime and type(start_time) == datetime:
+                data["invocationLatency"] = int(
+                    (end_time - start_time).total_seconds() * 1000
+                )
 
             # Add additional metadata keys to tags
             tags = []
             metadata = kwargs.get("litellm_params", {}).get("metadata", {})
             for key, value in metadata.items():
-                if key.startswith("greenscale"): 
+                if key.startswith("greenscale"):
                     if key == "greenscale_project":
                         data["project"] = value
                     elif key == "greenscale_application":
                         data["application"] = value
                     else:
-                        tags.append({"key": key.replace("greenscale_", ""), "value": str(value)})
-            
+                        tags.append(
+                            {"key": key.replace("greenscale_", ""), "value": str(value)}
+                        )
+
             data["tags"] = tags
 
-            response = requests.post(self.greenscale_logging_url, headers=self.headers, data=json.dumps(data, default=str))
+            response = requests.post(
+                self.greenscale_logging_url,
+                headers=self.headers,
+                data=json.dumps(data, default=str),
+            )
             if response.status_code != 200:
-                print_verbose(f"Greenscale Logger Error - {response.text}, {response.status_code}")
+                print_verbose(
+                    f"Greenscale Logger Error - {response.text}, {response.status_code}"
+                )
             else:
                 print_verbose(f"Greenscale Logger Succeeded - {response.text}")
         except Exception as e:
-            print_verbose(f"Greenscale Logger Error - {e}, Stack trace: {traceback.format_exc()}")
-            pass
+            print_verbose(
+                f"Greenscale Logger Error - {e}, Stack trace: {traceback.format_exc()}"
+            )
+            pass

litellm/integrations/helicone.py

@@ -1,7 +1,7 @@
 #### What this does ####
 #    On success, logs events to Helicone
 import dotenv, os
-import requests
+import requests  # type: ignore
 import litellm
 
 dotenv.load_dotenv()  # Loading env variables using dotenv

litellm/integrations/langsmith.py

@@ -1,15 +1,14 @@
 #### What this does ####
 #    On success, logs events to Langsmith
-import dotenv, os
-import requests
-import requests
+import dotenv, os  # type: ignore
+import requests  # type: ignore
 from datetime import datetime
 
 dotenv.load_dotenv()  # Loading env variables using dotenv
 import traceback
 import asyncio
 import types
-from pydantic import BaseModel
+from pydantic import BaseModel  # type: ignore
 
 
 def is_serializable(value):
@@ -79,8 +78,6 @@ class LangsmithLogger:
                 except:
                     response_obj = response_obj.dict()  # type: ignore
 
-            print(f"response_obj: {response_obj}")
-
             data = {
                 "name": run_name,
                 "run_type": "llm",  # this should always be llm, since litellm always logs llm calls. Langsmith allow us to log "chain"
@@ -90,7 +87,6 @@ class LangsmithLogger:
                 "start_time": start_time,
                 "end_time": end_time,
             }
-            print(f"data: {data}")
 
             response = requests.post(
                 "https://api.smith.langchain.com/runs",

litellm/integrations/openmeter.py

@@ -2,7 +2,6 @@
 ## On Success events log cost to OpenMeter - https://github.com/BerriAI/litellm/issues/1268
 
 import dotenv, os, json
-import requests
 import litellm
 
 dotenv.load_dotenv()  # Loading env variables using dotenv
@@ -60,7 +59,7 @@ class OpenMeterLogger(CustomLogger):
                 "total_tokens": response_obj["usage"].get("total_tokens"),
             }
 
-        subject = kwargs.get("user", None),  # end-user passed in via 'user' param
+        subject = (kwargs.get("user", None),)  # end-user passed in via 'user' param
         if not subject:
             raise Exception("OpenMeter: user is required")
 

litellm/integrations/prometheus.py

@@ -3,7 +3,7 @@
 #    On success, log events to Prometheus
 
 import dotenv, os
-import requests
+import requests  # type: ignore
 
 dotenv.load_dotenv()  # Loading env variables using dotenv
 import traceback
@@ -19,7 +19,6 @@ class PrometheusLogger:
         **kwargs,
     ):
         try:
-            print(f"in init prometheus metrics")
             from prometheus_client import Counter
 
             self.litellm_llm_api_failed_requests_metric = Counter(

litellm/integrations/prometheus_services.py

@@ -4,7 +4,7 @@
 
 
 import dotenv, os
-import requests
+import requests  # type: ignore
 
 dotenv.load_dotenv()  # Loading env variables using dotenv
 import traceback
@@ -183,7 +183,6 @@ class PrometheusServicesLogger:
                     )
 
     async def async_service_failure_hook(self, payload: ServiceLoggerPayload):
-        print(f"received error payload: {payload.error}")
         if self.mock_testing:
             self.mock_testing_failure_calls += 1
 

litellm/integrations/prompt_layer.py

@@ -1,12 +1,13 @@
 #### What this does ####
 #    On success, logs events to Promptlayer
 import dotenv, os
-import requests
+import requests  # type: ignore
 from pydantic import BaseModel
 
 dotenv.load_dotenv()  # Loading env variables using dotenv
 import traceback
 
+
 class PromptLayerLogger:
     # Class variables or attributes
     def __init__(self):
@@ -32,7 +33,11 @@ class PromptLayerLogger:
                     tags = kwargs["litellm_params"]["metadata"]["pl_tags"]
 
                 # Remove "pl_tags" from metadata
-                metadata = {k:v for k, v in kwargs["litellm_params"]["metadata"].items() if k != "pl_tags"}
+                metadata = {
+                    k: v
+                    for k, v in kwargs["litellm_params"]["metadata"].items()
+                    if k != "pl_tags"
+                }
 
             print_verbose(
                 f"Prompt Layer Logging - Enters logging function for model kwargs: {new_kwargs}\n, response: {response_obj}"

litellm/integrations/s3.py

@@ -2,7 +2,6 @@
 #    On success + failure, log events to Supabase
 
 import dotenv, os
-import requests
 
 dotenv.load_dotenv()  # Loading env variables using dotenv
 import traceback

litellm/integrations/supabase.py

@@ -2,7 +2,7 @@
 #    On success + failure, log events to Supabase
 
 import dotenv, os
-import requests
+import requests  # type: ignore
 
 dotenv.load_dotenv()  # Loading env variables using dotenv
 import traceback

litellm/llms/ai21.py

@@ -1,8 +1,8 @@
 import os, types, traceback
 import json
 from enum import Enum
-import requests
-import time, httpx
+import requests  # type: ignore
+import time, httpx  # type: ignore
 from typing import Callable, Optional
 from litellm.utils import ModelResponse, Choices, Message
 import litellm

litellm/llms/aleph_alpha.py

@@ -1,12 +1,12 @@
 import os, types
 import json
 from enum import Enum
-import requests
+import requests  # type: ignore
 import time
 from typing import Callable, Optional
 import litellm
 from litellm.utils import ModelResponse, Choices, Message, Usage
-import httpx
+import httpx  # type: ignore
 
 
 class AlephAlphaError(Exception):

litellm/llms/anthropic.py

@@ -1,7 +1,7 @@
 import os, types
 import json
 from enum import Enum
-import requests, copy
+import requests, copy  # type: ignore
 import time
 from typing import Callable, Optional, List
 from litellm.utils import ModelResponse, Usage, map_finish_reason, CustomStreamWrapper
@@ -9,7 +9,7 @@ import litellm
 from .prompt_templates.factory import prompt_factory, custom_prompt
 from litellm.llms.custom_httpx.http_handler import AsyncHTTPHandler
 from .base import BaseLLM
-import httpx
+import httpx  # type: ignore
 
 
 class AnthropicConstants(Enum):

litellm/llms/azure.py

@@ -1,4 +1,4 @@
-from typing import Optional, Union, Any
+from typing import Optional, Union, Any, Literal
 import types, requests
 from .base import BaseLLM
 from litellm.utils import (
@@ -12,7 +12,7 @@ from litellm.utils import (
 from typing import Callable, Optional, BinaryIO
 from litellm import OpenAIConfig
 import litellm, json
-import httpx
+import httpx  # type: ignore
 from .custom_httpx.azure_dall_e_2 import CustomHTTPTransport, AsyncCustomHTTPTransport
 from openai import AzureOpenAI, AsyncAzureOpenAI
 import uuid
@@ -952,6 +952,81 @@ class AzureChatCompletion(BaseLLM):
             )
             raise e
 
+    def get_headers(
+        self,
+        model: Optional[str],
+        api_key: str,
+        api_base: str,
+        api_version: str,
+        timeout: float,
+        mode: str,
+        messages: Optional[list] = None,
+        input: Optional[list] = None,
+        prompt: Optional[str] = None,
+    ) -> dict:
+        client_session = litellm.client_session or httpx.Client(
+            transport=CustomHTTPTransport(),  # handle dall-e-2 calls
+        )
+        if "gateway.ai.cloudflare.com" in api_base:
+            ## build base url - assume api base includes resource name
+            if not api_base.endswith("/"):
+                api_base += "/"
+            api_base += f"{model}"
+            client = AzureOpenAI(
+                base_url=api_base,
+                api_version=api_version,
+                api_key=api_key,
+                timeout=timeout,
+                http_client=client_session,
+            )
+            model = None
+            # cloudflare ai gateway, needs model=None
+        else:
+            client = AzureOpenAI(
+                api_version=api_version,
+                azure_endpoint=api_base,
+                api_key=api_key,
+                timeout=timeout,
+                http_client=client_session,
+            )
+
+            # only run this check if it's not cloudflare ai gateway
+            if model is None and mode != "image_generation":
+                raise Exception("model is not set")
+
+        completion = None
+
+        if messages is None:
+            messages = [{"role": "user", "content": "Hey"}]
+        try:
+            completion = client.chat.completions.with_raw_response.create(
+                model=model,  # type: ignore
+                messages=messages,  # type: ignore
+            )
+        except Exception as e:
+            raise e
+        response = {}
+
+        if completion is None or not hasattr(completion, "headers"):
+            raise Exception("invalid completion response")
+
+        if (
+            completion.headers.get("x-ratelimit-remaining-requests", None) is not None
+        ):  # not provided for dall-e requests
+            response["x-ratelimit-remaining-requests"] = completion.headers[
+                "x-ratelimit-remaining-requests"
+            ]
+
+        if completion.headers.get("x-ratelimit-remaining-tokens", None) is not None:
+            response["x-ratelimit-remaining-tokens"] = completion.headers[
+                "x-ratelimit-remaining-tokens"
+            ]
+
+        if completion.headers.get("x-ms-region", None) is not None:
+            response["x-ms-region"] = completion.headers["x-ms-region"]
+
+        return response
+
     async def ahealth_check(
         self,
         model: Optional[str],
@@ -963,7 +1038,7 @@ class AzureChatCompletion(BaseLLM):
         messages: Optional[list] = None,
         input: Optional[list] = None,
         prompt: Optional[str] = None,
-    ):
+    ) -> dict:
         client_session = litellm.aclient_session or httpx.AsyncClient(
             transport=AsyncCustomHTTPTransport(),  # handle dall-e-2 calls
         )
@@ -1040,4 +1115,8 @@ class AzureChatCompletion(BaseLLM):
             response["x-ratelimit-remaining-tokens"] = completion.headers[
                 "x-ratelimit-remaining-tokens"
             ]
+
+        if completion.headers.get("x-ms-region", None) is not None:
+            response["x-ms-region"] = completion.headers["x-ms-region"]
+
         return response

litellm/llms/azure_text.py

@@ -1,5 +1,5 @@
 from typing import Optional, Union, Any
-import types, requests
+import types, requests  # type: ignore
 from .base import BaseLLM
 from litellm.utils import (
     ModelResponse,

litellm/llms/baseten.py

@@ -1,7 +1,7 @@
 import os
 import json
 from enum import Enum
-import requests
+import requests  # type: ignore
 import time
 from typing import Callable
 from litellm.utils import ModelResponse, Usage

litellm/llms/bedrock.py

@@ -163,10 +163,9 @@ class AmazonAnthropicClaude3Config:
             "stop",
             "temperature",
             "top_p",
-            "extra_headers"
+            "extra_headers",
         ]
 
-
     def map_openai_params(self, non_default_params: dict, optional_params: dict):
         for param, value in non_default_params.items():
             if param == "max_tokens":
@@ -534,10 +533,12 @@ class AmazonStabilityConfig:
 
 def add_custom_header(headers):
     """Closure to capture the headers and add them."""
+
     def callback(request, **kwargs):
         """Actual callback function that Boto3 will call."""
         for header_name, header_value in headers.items():
             request.headers.add_header(header_name, header_value)
+
     return callback
 
 
@@ -672,7 +673,9 @@ def init_bedrock_client(
             config=config,
         )
     if extra_headers:
-        client.meta.events.register('before-sign.bedrock-runtime.*', add_custom_header(extra_headers))
+        client.meta.events.register(
+            "before-sign.bedrock-runtime.*", add_custom_header(extra_headers)
+        )
 
     return client
 
@@ -1224,7 +1227,7 @@ def _embedding_func_single(
             "input_type", "search_document"
         )  # aws bedrock example default - https://us-east-1.console.aws.amazon.com/bedrock/home?region=us-east-1#/providers?model=cohere.embed-english-v3
         data = {"texts": [input], **inference_params}  # type: ignore
-    body = json.dumps(data).encode("utf-8")
+    body = json.dumps(data).encode("utf-8")  # type: ignore
     ## LOGGING
     request_str = f"""
     response = client.invoke_model(
@@ -1416,7 +1419,7 @@ def image_generation(
     ## LOGGING
     request_str = f"""
     response = client.invoke_model(
-        body={body},
+        body={body}, # type: ignore
         modelId={modelId},
         accept="application/json",
         contentType="application/json",

litellm/llms/cloudflare.py

@@ -1,11 +1,11 @@
 import os, types
 import json
 from enum import Enum
-import requests
+import requests  # type: ignore
 import time
 from typing import Callable, Optional
 import litellm
-import httpx
+import httpx  # type: ignore
 from litellm.utils import ModelResponse, Usage
 from .prompt_templates.factory import prompt_factory, custom_prompt
 

litellm/llms/cohere.py

@@ -1,12 +1,12 @@
 import os, types
 import json
 from enum import Enum
-import requests
+import requests  # type: ignore
 import time, traceback
 from typing import Callable, Optional
 from litellm.utils import ModelResponse, Choices, Message, Usage
 import litellm
-import httpx
+import httpx  # type: ignore
 
 
 class CohereError(Exception):

litellm/llms/cohere_chat.py

@@ -1,12 +1,12 @@
 import os, types
 import json
 from enum import Enum
-import requests
+import requests  # type: ignore
 import time, traceback
 from typing import Callable, Optional
 from litellm.utils import ModelResponse, Choices, Message, Usage
 import litellm
-import httpx
+import httpx  # type: ignore
 from .prompt_templates.factory import cohere_message_pt
 
 

litellm/llms/maritalk.py

@@ -1,7 +1,7 @@
 import os, types
 import json
 from enum import Enum
-import requests
+import requests  # type: ignore
 import time, traceback
 from typing import Callable, Optional, List
 from litellm.utils import ModelResponse, Choices, Message, Usage

litellm/llms/nlp_cloud.py

@@ -1,7 +1,7 @@
 import os, types
 import json
 from enum import Enum
-import requests
+import requests  # type: ignore
 import time
 from typing import Callable, Optional
 import litellm

litellm/llms/ollama.py

@@ -1,10 +1,10 @@
 from itertools import chain
-import requests, types, time
+import requests, types, time  # type: ignore
 import json, uuid
 import traceback
 from typing import Optional
 import litellm
-import httpx, aiohttp, asyncio
+import httpx, aiohttp, asyncio  # type: ignore
 from .prompt_templates.factory import prompt_factory, custom_prompt
 
 
@@ -245,7 +245,10 @@ def get_ollama_response(
             tool_calls=[
                 {
                     "id": f"call_{str(uuid.uuid4())}",
-                    "function": {"name": function_call["name"], "arguments": json.dumps(function_call["arguments"])},
+                    "function": {
+                        "name": function_call["name"],
+                        "arguments": json.dumps(function_call["arguments"]),
+                    },
                     "type": "function",
                 }
             ],
@@ -257,7 +260,9 @@ def get_ollama_response(
     model_response["created"] = int(time.time())
     model_response["model"] = "ollama/" + model
     prompt_tokens = response_json.get("prompt_eval_count", len(encoding.encode(prompt, disallowed_special=())))  # type: ignore
-    completion_tokens = response_json.get("eval_count", len(response_json.get("message",dict()).get("content", "")))
+    completion_tokens = response_json.get(
+        "eval_count", len(response_json.get("message", dict()).get("content", ""))
+    )
     model_response["usage"] = litellm.Usage(
         prompt_tokens=prompt_tokens,
         completion_tokens=completion_tokens,
@@ -298,7 +303,10 @@ def ollama_completion_stream(url, data, logging_obj):
                     tool_calls=[
                         {
                             "id": f"call_{str(uuid.uuid4())}",
-                            "function": {"name": function_call["name"], "arguments": json.dumps(function_call["arguments"])},
+                            "function": {
+                                "name": function_call["name"],
+                                "arguments": json.dumps(function_call["arguments"]),
+                            },
                             "type": "function",
                         }
                     ],
@@ -339,9 +347,10 @@ async def ollama_async_streaming(url, data, model_response, encoding, logging_ob
                 first_chunk_content = first_chunk.choices[0].delta.content or ""
                 response_content = first_chunk_content + "".join(
                     [
-                    chunk.choices[0].delta.content
-                    async for chunk in streamwrapper
-                    if chunk.choices[0].delta.content]
+                        chunk.choices[0].delta.content
+                        async for chunk in streamwrapper
+                        if chunk.choices[0].delta.content
+                    ]
                 )
                 function_call = json.loads(response_content)
                 delta = litellm.utils.Delta(
@@ -349,7 +358,10 @@ async def ollama_async_streaming(url, data, model_response, encoding, logging_ob
                     tool_calls=[
                         {
                             "id": f"call_{str(uuid.uuid4())}",
-                            "function": {"name": function_call["name"], "arguments": json.dumps(function_call["arguments"])},
+                            "function": {
+                                "name": function_call["name"],
+                                "arguments": json.dumps(function_call["arguments"]),
+                            },
                             "type": "function",
                         }
                     ],
@@ -398,7 +410,10 @@ async def ollama_acompletion(url, data, model_response, encoding, logging_obj):
                     tool_calls=[
                         {
                             "id": f"call_{str(uuid.uuid4())}",
-                            "function": {"name": function_call["name"], "arguments": json.dumps(function_call["arguments"])},
+                            "function": {
+                                "name": function_call["name"],
+                                "arguments": json.dumps(function_call["arguments"]),
+                            },
                             "type": "function",
                         }
                     ],
@@ -412,7 +427,10 @@ async def ollama_acompletion(url, data, model_response, encoding, logging_obj):
             model_response["created"] = int(time.time())
             model_response["model"] = "ollama/" + data["model"]
             prompt_tokens = response_json.get("prompt_eval_count", len(encoding.encode(data["prompt"], disallowed_special=())))  # type: ignore
-            completion_tokens = response_json.get("eval_count", len(response_json.get("message",dict()).get("content", "")))
+            completion_tokens = response_json.get(
+                "eval_count",
+                len(response_json.get("message", dict()).get("content", "")),
+            )
             model_response["usage"] = litellm.Usage(
                 prompt_tokens=prompt_tokens,
                 completion_tokens=completion_tokens,
@@ -500,6 +518,7 @@ async def ollama_aembeddings(
     }
     return model_response
 
+
 def ollama_embeddings(
     api_base: str,
     model: str,
@@ -517,5 +536,6 @@ def ollama_embeddings(
             optional_params,
             logging_obj,
             model_response,
-            encoding)
+            encoding,
         )
+    )

litellm/llms/oobabooga.py

@@ -1,7 +1,7 @@
 import os
 import json
 from enum import Enum
-import requests
+import requests  # type: ignore
 import time
 from typing import Callable, Optional
 from litellm.utils import ModelResponse, Usage

litellm/llms/openai.py

@@ -22,7 +22,6 @@ from litellm.utils import (
     TextCompletionResponse,
 )
 from typing import Callable, Optional
-import aiohttp, requests
 import litellm
 from .prompt_templates.factory import prompt_factory, custom_prompt
 from openai import OpenAI, AsyncOpenAI
@@ -531,6 +530,7 @@ class OpenAIChatCompletion(BaseLLM):
             model=model,
             custom_llm_provider="openai",
             logging_obj=logging_obj,
+            stream_options=data.get("stream_options", None),
         )
         return streamwrapper
 
@@ -580,6 +580,7 @@ class OpenAIChatCompletion(BaseLLM):
                 model=model,
                 custom_llm_provider="openai",
                 logging_obj=logging_obj,
+                stream_options=data.get("stream_options", None),
             )
             return streamwrapper
         except (

litellm/llms/petals.py

@@ -1,7 +1,7 @@
 import os, types
 import json
 from enum import Enum
-import requests
+import requests  # type: ignore
 import time
 from typing import Callable, Optional
 import litellm

litellm/llms/prompt_templates/factory.py

@@ -981,7 +981,7 @@ def anthropic_messages_pt(messages: list):
     # add role=tool support to allow function call result/error submission
     user_message_types = {"user", "tool", "function"}
     # reformat messages to ensure user/assistant are alternating, if there's either 2 consecutive 'user' messages or 2 consecutive 'assistant' message, merge them.
-    new_messages = []
+    new_messages: list = []
     msg_i = 0
     tool_use_param = False
     while msg_i < len(messages):

litellm/llms/replicate.py

@@ -1,11 +1,11 @@
 import os, types
 import json
-import requests
+import requests  # type: ignore
 import time
 from typing import Callable, Optional
 from litellm.utils import ModelResponse, Usage
 import litellm
-import httpx
+import httpx  # type: ignore
 from .prompt_templates.factory import prompt_factory, custom_prompt
 
 

litellm/llms/sagemaker.py

@@ -1,14 +1,14 @@
 import os, types, traceback
 from enum import Enum
 import json
-import requests
+import requests  # type: ignore
 import time
 from typing import Callable, Optional, Any
 import litellm
 from litellm.utils import ModelResponse, EmbeddingResponse, get_secret, Usage
 import sys
 from copy import deepcopy
-import httpx
+import httpx  # type: ignore
 from .prompt_templates.factory import prompt_factory, custom_prompt
 
 
@@ -295,7 +295,7 @@ def completion(
                 EndpointName={model},
                 InferenceComponentName={model_id},
                 ContentType="application/json",
-                Body={data},
+                Body={data}, # type: ignore
                 CustomAttributes="accept_eula=true",
             )
             """  # type: ignore
@@ -321,7 +321,7 @@ def completion(
             response = client.invoke_endpoint(
                 EndpointName={model},
                 ContentType="application/json",
-                Body={data},
+                Body={data}, # type: ignore
                 CustomAttributes="accept_eula=true",
             )
             """  # type: ignore
@@ -688,7 +688,7 @@ def embedding(
     response = client.invoke_endpoint(
         EndpointName={model},
         ContentType="application/json",
-        Body={data},
+        Body={data}, # type: ignore
         CustomAttributes="accept_eula=true",
     )"""  # type: ignore
     logging_obj.pre_call(

litellm/llms/together_ai.py

@@ -6,11 +6,11 @@ Reference: https://docs.together.ai/docs/openai-api-compatibility
 import os, types
 import json
 from enum import Enum
-import requests
+import requests  # type: ignore
 import time
 from typing import Callable, Optional
 import litellm
-import httpx
+import httpx  # type: ignore
 from litellm.utils import ModelResponse, Usage
 from .prompt_templates.factory import prompt_factory, custom_prompt
 

litellm/llms/vertex_ai.py

@@ -1,12 +1,12 @@
 import os, types
 import json
 from enum import Enum
-import requests
+import requests  # type: ignore
 import time
 from typing import Callable, Optional, Union, List
 from litellm.utils import ModelResponse, Usage, CustomStreamWrapper, map_finish_reason
 import litellm, uuid
-import httpx, inspect
+import httpx, inspect  # type: ignore
 
 
 class VertexAIError(Exception):

litellm/llms/vertex_ai_anthropic.py

@@ -3,7 +3,7 @@
 import os, types
 import json
 from enum import Enum
-import requests, copy
+import requests, copy  # type: ignore
 import time, uuid
 from typing import Callable, Optional, List
 from litellm.utils import ModelResponse, Usage, map_finish_reason, CustomStreamWrapper
@@ -17,7 +17,7 @@ from .prompt_templates.factory import (
     extract_between_tags,
     parse_xml_params,
 )
-import httpx
+import httpx  # type: ignore
 
 
 class VertexAIError(Exception):

litellm/llms/vllm.py

@@ -1,8 +1,8 @@
 import os
 import json
 from enum import Enum
-import requests
-import time, httpx
+import requests  # type: ignore
+import time, httpx  # type: ignore
 from typing import Callable, Any
 from litellm.utils import ModelResponse, Usage
 from .prompt_templates.factory import prompt_factory, custom_prompt

litellm/llms/watsonx.py

@@ -3,8 +3,8 @@ import json, types, time  # noqa: E401
 from contextlib import contextmanager
 from typing import Callable, Dict, Optional, Any, Union, List
 
-import httpx
-import requests
+import httpx  # type: ignore
+import requests  # type: ignore
 import litellm
 from litellm.utils import ModelResponse, get_secret, Usage
 

litellm/main.py

@@ -187,6 +187,7 @@ async def acompletion(
     top_p: Optional[float] = None,
     n: Optional[int] = None,
     stream: Optional[bool] = None,
+    stream_options: Optional[dict] = None,
     stop=None,
     max_tokens: Optional[int] = None,
     presence_penalty: Optional[float] = None,
@@ -206,6 +207,7 @@ async def acompletion(
     api_version: Optional[str] = None,
     api_key: Optional[str] = None,
     model_list: Optional[list] = None,  # pass in a list of api_base,keys, etc.
+    extra_headers: Optional[dict] = None,
     # Optional liteLLM function params
     **kwargs,
 ):
@@ -223,6 +225,7 @@ async def acompletion(
         top_p (float, optional): The top-p parameter for nucleus sampling (default is 1.0).
         n (int, optional): The number of completions to generate (default is 1).
         stream (bool, optional): If True, return a streaming response (default is False).
+        stream_options (dict, optional): A dictionary containing options for the streaming response. Only use this if stream is True.
         stop(string/list, optional): - Up to 4 sequences where the LLM API will stop generating further tokens.
         max_tokens (integer, optional): The maximum number of tokens in the generated completion (default is infinity).
         presence_penalty (float, optional): It is used to penalize new tokens based on their existence in the text so far.
@@ -260,6 +263,7 @@ async def acompletion(
         "top_p": top_p,
         "n": n,
         "stream": stream,
+        "stream_options": stream_options,
         "stop": stop,
         "max_tokens": max_tokens,
         "presence_penalty": presence_penalty,
@@ -457,6 +461,7 @@ def completion(
     top_p: Optional[float] = None,
     n: Optional[int] = None,
     stream: Optional[bool] = None,
+    stream_options: Optional[dict] = None,
     stop=None,
     max_tokens: Optional[int] = None,
     presence_penalty: Optional[float] = None,
@@ -496,6 +501,7 @@ def completion(
         top_p (float, optional): The top-p parameter for nucleus sampling (default is 1.0).
         n (int, optional): The number of completions to generate (default is 1).
         stream (bool, optional): If True, return a streaming response (default is False).
+        stream_options (dict, optional): A dictionary containing options for the streaming response. Only set this when you set stream: true.
         stop(string/list, optional): - Up to 4 sequences where the LLM API will stop generating further tokens.
         max_tokens (integer, optional): The maximum number of tokens in the generated completion (default is infinity).
         presence_penalty (float, optional): It is used to penalize new tokens based on their existence in the text so far.
@@ -573,6 +579,7 @@ def completion(
         "top_p",
         "n",
         "stream",
+        "stream_options",
         "stop",
         "max_tokens",
         "presence_penalty",
@@ -648,6 +655,8 @@ def completion(
         "base_model",
         "stream_timeout",
         "supports_system_message",
+        "region_name",
+        "allowed_model_region",
     ]
     default_params = openai_params + litellm_params
     non_default_params = {
@@ -783,6 +792,7 @@ def completion(
             top_p=top_p,
             n=n,
             stream=stream,
+            stream_options=stream_options,
             stop=stop,
             max_tokens=max_tokens,
             presence_penalty=presence_penalty,
@@ -2716,6 +2726,8 @@ def embedding(
         "ttl",
         "cache",
         "no-log",
+        "region_name",
+        "allowed_model_region",
     ]
     default_params = openai_params + litellm_params
     non_default_params = {
@@ -3589,6 +3601,8 @@ def image_generation(
             "caching_groups",
             "ttl",
             "cache",
+            "region_name",
+            "allowed_model_region",
         ]
         default_params = openai_params + litellm_params
         non_default_params = {

litellm/proxy/_experimental/out/_next/static/chunks/app/layout-3cc8192d7f94ae35.js

@@ -1 +0,0 @@
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litellm/proxy/_experimental/out/_next/static/chunks/app/layout-94ae1345f5d85446.js

@@ -0,0 +1 @@
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litellm/proxy/_experimental/out/_next/static/chunks/main-app-9b4fb13a7db53edf.js

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 1:null

litellm/proxy/_types.py

@@ -458,6 +458,27 @@ class UpdateUserRequest(GenerateRequestBase):
         return values
 
 
+class NewEndUserRequest(LiteLLMBase):
+    user_id: str
+    alias: Optional[str] = None  # human-friendly alias
+    blocked: bool = False  # allow/disallow requests for this end-user
+    max_budget: Optional[float] = None
+    budget_id: Optional[str] = None  # give either a budget_id or max_budget
+    allowed_model_region: Optional[Literal["eu"]] = (
+        None  # require all user requests to use models in this specific region
+    )
+    default_model: Optional[str] = (
+        None  # if no equivalent model in allowed region - default all requests to this model
+    )
+
+    @root_validator(pre=True)
+    def check_user_info(cls, values):
+        if values.get("max_budget") is not None and values.get("budget_id") is not None:
+            raise ValueError("Set either 'max_budget' or 'budget_id', not both.")
+
+        return values
+
+
 class Member(LiteLLMBase):
     role: Literal["admin", "user"]
     user_id: Optional[str] = None
@@ -494,6 +515,8 @@ class NewTeamRequest(TeamBase):
 
 class GlobalEndUsersSpend(LiteLLMBase):
     api_key: Optional[str] = None
+    startTime: Optional[datetime] = None
+    endTime: Optional[datetime] = None
 
 
 class TeamMemberAddRequest(LiteLLMBase):
@@ -836,6 +859,7 @@ class UserAPIKeyAuth(
 
     api_key: Optional[str] = None
     user_role: Optional[Literal["proxy_admin", "app_owner", "app_user"]] = None
+    allowed_model_region: Optional[Literal["eu"]] = None
 
     @root_validator(pre=True)
     def check_api_key(cls, values):
@@ -881,6 +905,8 @@ class LiteLLM_EndUserTable(LiteLLMBase):
     blocked: bool
     alias: Optional[str] = None
     spend: float = 0.0
+    allowed_model_region: Optional[Literal["eu"]] = None
+    default_model: Optional[str] = None
     litellm_budget_table: Optional[LiteLLM_BudgetTable] = None
 
     @root_validator(pre=True)

litellm/proxy/auth/auth_checks.py

@@ -208,7 +208,9 @@ async def get_end_user_object(
         return None
 
     # check if in cache
-    cached_user_obj = user_api_key_cache.async_get_cache(key=end_user_id)
+    cached_user_obj = user_api_key_cache.async_get_cache(
+        key="end_user_id:{}".format(end_user_id)
+    )
     if cached_user_obj is not None:
         if isinstance(cached_user_obj, dict):
             return LiteLLM_EndUserTable(**cached_user_obj)
@@ -223,7 +225,14 @@ async def get_end_user_object(
         if response is None:
             raise Exception
 
-        return LiteLLM_EndUserTable(**response.dict())
+        # save the end-user object to cache
+        await user_api_key_cache.async_set_cache(
+            key="end_user_id:{}".format(end_user_id), value=response
+        )
+
+        _response = LiteLLM_EndUserTable(**response.dict())
+
+        return _response
     except Exception as e:  # if end-user not in db
         return None
 

litellm/proxy/proxy_cli.py

@@ -252,7 +252,7 @@ def run_server(
     if model and "ollama" in model and api_base is None:
         run_ollama_serve()
     if test_async is True:
-        import requests, concurrent, time
+        import requests, concurrent, time  # type: ignore
 
         api_base = f"http://{host}:{port}"
 
@@ -418,7 +418,7 @@ def run_server(
             read from there and save it to os.env['DATABASE_URL']
             """
             try:
-                import yaml, asyncio
+                import yaml, asyncio  # type: ignore
             except:
                 raise ImportError(
                     "yaml needs to be imported. Run - `pip install 'litellm[proxy]'`"

litellm/proxy/proxy_server.py

@@ -30,7 +30,7 @@ sys.path.insert(
 try:
     import fastapi
     import backoff
-    import yaml
+    import yaml  # type: ignore
     import orjson
     import logging
     from apscheduler.schedulers.asyncio import AsyncIOScheduler
@@ -231,6 +231,11 @@ class SpecialModelNames(enum.Enum):
     all_team_models = "all-team-models"
 
 
+class CommonProxyErrors(enum.Enum):
+    db_not_connected_error = "DB not connected"
+    no_llm_router = "No models configured on proxy"
+
+
 @app.exception_handler(ProxyException)
 async def openai_exception_handler(request: Request, exc: ProxyException):
     # NOTE: DO NOT MODIFY THIS, its crucial to map to Openai exceptions
@@ -467,10 +472,6 @@ async def user_api_key_auth(
                         prisma_client=prisma_client,
                         user_api_key_cache=user_api_key_cache,
                     )
-                    # save the end-user object to cache
-                    await user_api_key_cache.async_set_cache(
-                        key=end_user_id, value=end_user_object
-                    )
 
                 global_proxy_spend = None
                 if litellm.max_budget > 0:  # user set proxy max budget
@@ -952,13 +953,16 @@ async def user_api_key_auth(
 
             _end_user_object = None
             if "user" in request_data:
-                _id = "end_user_id:{}".format(request_data["user"])
-                _end_user_object = await user_api_key_cache.async_get_cache(key=_id)
-                if _end_user_object is not None:
-                    _end_user_object = LiteLLM_EndUserTable(**_end_user_object)
+                _end_user_object = await get_end_user_object(
+                    end_user_id=request_data["user"],
+                    prisma_client=prisma_client,
+                    user_api_key_cache=user_api_key_cache,
+                )
 
             global_proxy_spend = None
-            if litellm.max_budget > 0:  # user set proxy max budget
+            if (
+                litellm.max_budget > 0 and prisma_client is not None
+            ):  # user set proxy max budget
                 # check cache
                 global_proxy_spend = await user_api_key_cache.async_get_cache(
                     key="{}:spend".format(litellm_proxy_admin_name)
@@ -1011,6 +1015,12 @@ async def user_api_key_auth(
             )
             valid_token_dict = _get_pydantic_json_dict(valid_token)
             valid_token_dict.pop("token", None)
+
+            if _end_user_object is not None:
+                valid_token_dict["allowed_model_region"] = (
+                    _end_user_object.allowed_model_region
+                )
+
             """
             asyncio create task to update the user api key cache with the user db table as well
 
@@ -1035,10 +1045,7 @@ async def user_api_key_auth(
                         # check if user can access this route
                         query_params = request.query_params
                         key = query_params.get("key")
-                        if (
-                            key is not None
-                            and prisma_client.hash_token(token=key) != api_key
-                        ):
+                        if key is not None and hash_token(token=key) != api_key:
                             raise HTTPException(
                                 status_code=status.HTTP_403_FORBIDDEN,
                                 detail="user not allowed to access this key's info",
@@ -1091,6 +1098,7 @@ async def user_api_key_auth(
         # sso/login, ui/login, /key functions and /user functions
         # this will never be allowed to call /chat/completions
         token_team = getattr(valid_token, "team_id", None)
+
         if token_team is not None and token_team == "litellm-dashboard":
             # this token is only used for managing the ui
             allowed_routes = [
@@ -3612,6 +3620,10 @@ async def chat_completion(
                     **data,
                 }  # add the team-specific configs to the completion call
 
+        ### END-USER SPECIFIC PARAMS ###
+        if user_api_key_dict.allowed_model_region is not None:
+            data["allowed_model_region"] = user_api_key_dict.allowed_model_region
+
         global user_temperature, user_request_timeout, user_max_tokens, user_api_base
         # override with user settings, these are params passed via cli
         if user_temperature:
@@ -3719,6 +3731,7 @@ async def chat_completion(
                 "x-litellm-model-id": model_id,
                 "x-litellm-cache-key": cache_key,
                 "x-litellm-model-api-base": api_base,
+                "x-litellm-version": version,
             }
             selected_data_generator = select_data_generator(
                 response=response,
@@ -3734,6 +3747,7 @@ async def chat_completion(
         fastapi_response.headers["x-litellm-model-id"] = model_id
         fastapi_response.headers["x-litellm-cache-key"] = cache_key
         fastapi_response.headers["x-litellm-model-api-base"] = api_base
+        fastapi_response.headers["x-litellm-version"] = version
 
         ### CALL HOOKS ### - modify outgoing data
         response = await proxy_logging_obj.post_call_success_hook(
@@ -3890,14 +3904,10 @@ async def completion(
                 },
             )
 
-        if hasattr(response, "_hidden_params"):
-            model_id = response._hidden_params.get("model_id", None) or ""
-            original_response = (
-                response._hidden_params.get("original_response", None) or ""
-            )
-        else:
-            model_id = ""
-            original_response = ""
+        hidden_params = getattr(response, "_hidden_params", {}) or {}
+        model_id = hidden_params.get("model_id", None) or ""
+        cache_key = hidden_params.get("cache_key", None) or ""
+        api_base = hidden_params.get("api_base", None) or ""
 
         verbose_proxy_logger.debug("final response: %s", response)
         if (
@@ -3905,6 +3915,9 @@ async def completion(
         ):  # use generate_responses to stream responses
             custom_headers = {
                 "x-litellm-model-id": model_id,
+                "x-litellm-cache-key": cache_key,
+                "x-litellm-model-api-base": api_base,
+                "x-litellm-version": version,
             }
             selected_data_generator = select_data_generator(
                 response=response,
@@ -3919,6 +3932,10 @@ async def completion(
             )
 
         fastapi_response.headers["x-litellm-model-id"] = model_id
+        fastapi_response.headers["x-litellm-cache-key"] = cache_key
+        fastapi_response.headers["x-litellm-model-api-base"] = api_base
+        fastapi_response.headers["x-litellm-version"] = version
+
         return response
     except Exception as e:
         data["litellm_status"] = "fail"  # used for alerting
@@ -3958,6 +3975,7 @@ async def completion(
 )  # azure compatible endpoint
 async def embeddings(
     request: Request,
+    fastapi_response: Response,
     model: Optional[str] = None,
     user_api_key_dict: UserAPIKeyAuth = Depends(user_api_key_auth),
 ):
@@ -4104,6 +4122,17 @@ async def embeddings(
         ### ALERTING ###
         data["litellm_status"] = "success"  # used for alerting
 
+        ### RESPONSE HEADERS ###
+        hidden_params = getattr(response, "_hidden_params", {}) or {}
+        model_id = hidden_params.get("model_id", None) or ""
+        cache_key = hidden_params.get("cache_key", None) or ""
+        api_base = hidden_params.get("api_base", None) or ""
+
+        fastapi_response.headers["x-litellm-model-id"] = model_id
+        fastapi_response.headers["x-litellm-cache-key"] = cache_key
+        fastapi_response.headers["x-litellm-model-api-base"] = api_base
+        fastapi_response.headers["x-litellm-version"] = version
+
         return response
     except Exception as e:
         data["litellm_status"] = "fail"  # used for alerting
@@ -4142,6 +4171,7 @@ async def embeddings(
 )
 async def image_generation(
     request: Request,
+    fastapi_response: Response,
     user_api_key_dict: UserAPIKeyAuth = Depends(user_api_key_auth),
 ):
     global proxy_logging_obj
@@ -4261,6 +4291,17 @@ async def image_generation(
         ### ALERTING ###
         data["litellm_status"] = "success"  # used for alerting
 
+        ### RESPONSE HEADERS ###
+        hidden_params = getattr(response, "_hidden_params", {}) or {}
+        model_id = hidden_params.get("model_id", None) or ""
+        cache_key = hidden_params.get("cache_key", None) or ""
+        api_base = hidden_params.get("api_base", None) or ""
+
+        fastapi_response.headers["x-litellm-model-id"] = model_id
+        fastapi_response.headers["x-litellm-cache-key"] = cache_key
+        fastapi_response.headers["x-litellm-model-api-base"] = api_base
+        fastapi_response.headers["x-litellm-version"] = version
+
         return response
     except Exception as e:
         data["litellm_status"] = "fail"  # used for alerting
@@ -4297,6 +4338,7 @@ async def image_generation(
 )
 async def audio_transcriptions(
     request: Request,
+    fastapi_response: Response,
     file: UploadFile = File(...),
     user_api_key_dict: UserAPIKeyAuth = Depends(user_api_key_auth),
 ):
@@ -4441,6 +4483,18 @@ async def audio_transcriptions(
 
         ### ALERTING ###
         data["litellm_status"] = "success"  # used for alerting
+
+        ### RESPONSE HEADERS ###
+        hidden_params = getattr(response, "_hidden_params", {}) or {}
+        model_id = hidden_params.get("model_id", None) or ""
+        cache_key = hidden_params.get("cache_key", None) or ""
+        api_base = hidden_params.get("api_base", None) or ""
+
+        fastapi_response.headers["x-litellm-model-id"] = model_id
+        fastapi_response.headers["x-litellm-cache-key"] = cache_key
+        fastapi_response.headers["x-litellm-model-api-base"] = api_base
+        fastapi_response.headers["x-litellm-version"] = version
+
         return response
     except Exception as e:
         data["litellm_status"] = "fail"  # used for alerting
@@ -4480,6 +4534,7 @@ async def audio_transcriptions(
 )
 async def moderations(
     request: Request,
+    fastapi_response: Response,
     user_api_key_dict: UserAPIKeyAuth = Depends(user_api_key_auth),
 ):
     """
@@ -4604,6 +4659,17 @@ async def moderations(
         ### ALERTING ###
         data["litellm_status"] = "success"  # used for alerting
 
+        ### RESPONSE HEADERS ###
+        hidden_params = getattr(response, "_hidden_params", {}) or {}
+        model_id = hidden_params.get("model_id", None) or ""
+        cache_key = hidden_params.get("cache_key", None) or ""
+        api_base = hidden_params.get("api_base", None) or ""
+
+        fastapi_response.headers["x-litellm-model-id"] = model_id
+        fastapi_response.headers["x-litellm-cache-key"] = cache_key
+        fastapi_response.headers["x-litellm-model-api-base"] = api_base
+        fastapi_response.headers["x-litellm-version"] = version
+
         return response
     except Exception as e:
         data["litellm_status"] = "fail"  # used for alerting
@@ -5809,35 +5875,38 @@ async def global_spend_end_users(data: Optional[GlobalEndUsersSpend] = None):
     if prisma_client is None:
         raise HTTPException(status_code=500, detail={"error": "No db connected"})
 
-    if data is None:
-        sql_query = f"""SELECT * FROM "Last30dTopEndUsersSpend";"""
+    """
+    Gets the top 100 end-users for a given api key
+    """
+    startTime = None
+    endTime = None
+    selected_api_key = None
+    if data is not None:
+        startTime = data.startTime
+        endTime = data.endTime
+        selected_api_key = data.api_key
 
-        response = await prisma_client.db.query_raw(query=sql_query)
-    else:
-        """
-        Gets the top 100 end-users for a given api key
-        """
-        current_date = datetime.now()
-        past_date = current_date - timedelta(days=30)
-        response = await prisma_client.db.litellm_spendlogs.group_by(  # type: ignore
-            by=["end_user"],
-            where={
-                "AND": [{"startTime": {"gte": past_date}}, {"api_key": data.api_key}]  # type: ignore
-            },
-            sum={"spend": True},
-            order={"_sum": {"spend": "desc"}},  # type: ignore
-            take=100,
-            count=True,
-        )
-        if response is not None and isinstance(response, list):
-            new_response = []
-            for r in response:
-                new_r = r
-                new_r["total_spend"] = r["_sum"]["spend"]
-                new_r["total_count"] = r["_count"]["_all"]
-                new_r.pop("_sum")
-                new_r.pop("_count")
-                new_response.append(new_r)
+    startTime = startTime or datetime.now() - timedelta(days=30)
+    endTime = endTime or datetime.now()
+
+    sql_query = """
+SELECT end_user, COUNT(*) AS total_count, SUM(spend) AS total_spend
+FROM "LiteLLM_SpendLogs"
+WHERE "startTime" >= $1::timestamp
+  AND "startTime" < $2::timestamp
+  AND (
+    CASE
+      WHEN $3::TEXT IS NULL THEN TRUE
+      ELSE api_key = $3
+    END
+  )
+GROUP BY end_user
+ORDER BY total_spend DESC
+LIMIT 100
+    """
+    response = await prisma_client.db.query_raw(
+        sql_query, startTime, endTime, selected_api_key
+    )
 
     return response
 
@@ -5883,7 +5952,7 @@ async def global_predict_spend_logs(request: Request):
     return _forecast_daily_cost(data)
 
 
-#### USER MANAGEMENT ####
+#### INTERNAL USER MANAGEMENT ####
 @router.post(
     "/user/new",
     tags=["user management"],
@@ -6376,6 +6445,43 @@ async def user_get_requests():
         )
 
 
+@router.get(
+    "/user/get_users",
+    tags=["user management"],
+    dependencies=[Depends(user_api_key_auth)],
+)
+async def get_users(
+    role: str = fastapi.Query(
+        default=None,
+        description="Either 'proxy_admin', 'proxy_viewer', 'app_owner', 'app_user'",
+    )
+):
+    """
+    [BETA] This could change without notice. Give feedback - https://github.com/BerriAI/litellm/issues
+
+    Get all users who are a specific `user_role`.
+
+    Used by the UI to populate the user lists.
+
+    Currently - admin-only endpoint.
+    """
+    global prisma_client
+
+    if prisma_client is None:
+        raise HTTPException(
+            status_code=500,
+            detail={"error": f"No db connected. prisma client={prisma_client}"},
+        )
+    all_users = await prisma_client.get_data(
+        table_name="user", query_type="find_all", key_val={"user_role": role}
+    )
+
+    return all_users
+
+
+#### END-USER MANAGEMENT ####
+
+
 @router.post(
     "/end_user/block",
     tags=["End User Management"],
@@ -6466,38 +6572,140 @@ async def unblock_user(data: BlockUsers):
     return {"blocked_users": litellm.blocked_user_list}
 
 
-@router.get(
-    "/user/get_users",
-    tags=["user management"],
+@router.post(
+    "/end_user/new",
+    tags=["End User Management"],
     dependencies=[Depends(user_api_key_auth)],
 )
-async def get_users(
-    role: str = fastapi.Query(
-        default=None,
-        description="Either 'proxy_admin', 'proxy_viewer', 'app_owner', 'app_user'",
-    )
+async def new_end_user(
+    data: NewEndUserRequest,
+    user_api_key_dict: UserAPIKeyAuth = Depends(user_api_key_auth),
 ):
     """
-    [BETA] This could change without notice. Give feedback - https://github.com/BerriAI/litellm/issues
+    [TODO] Needs to be implemented.
 
-    Get all users who are a specific `user_role`.
+    Allow creating a new end-user 
 
-    Used by the UI to populate the user lists.
+    - Allow specifying allowed regions 
+    - Allow specifying default model
 
-    Currently - admin-only endpoint.
+    Example curl:
+    ```
+    curl --location 'http://0.0.0.0:4000/end_user/new' \
+        --header 'Authorization: Bearer sk-1234' \
+        --header 'Content-Type: application/json' \
+        --data '{
+            "end_user_id" : "ishaan-jaff-3", <- specific customer
+            
+            "allowed_region": "eu" <- set region for models        
+
+                    + 
+
+            "default_model": "azure/gpt-3.5-turbo-eu" <- all calls from this user, use this model? 
+
+        }'
+
+        # return end-user object
+    ```
     """
-    global prisma_client
+    global prisma_client, llm_router
+    """
+    Validation:
+        - check if default model exists 
+        - create budget object if not already created
+    
+    - Add user to end user table 
 
+    Return 
+    - end-user object
+    - currently allowed models 
+    """
     if prisma_client is None:
         raise HTTPException(
             status_code=500,
-            detail={"error": f"No db connected. prisma client={prisma_client}"},
+            detail={"error": CommonProxyErrors.db_not_connected_error.value},
         )
-    all_users = await prisma_client.get_data(
-        table_name="user", query_type="find_all", key_val={"user_role": role}
+
+    ## VALIDATION ##
+    if data.default_model is not None:
+        if llm_router is None:
+            raise HTTPException(
+                status_code=422, detail={"error": CommonProxyErrors.no_llm_router.value}
+            )
+        elif data.default_model not in llm_router.get_model_names():
+            raise HTTPException(
+                status_code=422,
+                detail={
+                    "error": "Default Model not on proxy. Configure via `/model/new` or config.yaml. Default_model={}, proxy_model_names={}".format(
+                        data.default_model, set(llm_router.get_model_names())
+                    )
+                },
+            )
+
+    new_end_user_obj: Dict = {}
+
+    ## CREATE BUDGET ## if set
+    if data.max_budget is not None:
+        budget_record = await prisma_client.db.litellm_budgettable.create(
+            data={
+                "max_budget": data.max_budget,
+                "created_by": user_api_key_dict.user_id or litellm_proxy_admin_name,  # type: ignore
+                "updated_by": user_api_key_dict.user_id or litellm_proxy_admin_name,
+            }
+        )
+
+        new_end_user_obj["budget_id"] = budget_record.budget_id
+    elif data.budget_id is not None:
+        new_end_user_obj["budget_id"] = data.budget_id
+
+    _user_data = data.dict(exclude_none=True)
+
+    for k, v in _user_data.items():
+        if k != "max_budget" and k != "budget_id":
+            new_end_user_obj[k] = v
+
+    ## WRITE TO DB ##
+    end_user_record = await prisma_client.db.litellm_endusertable.create(
+        data=new_end_user_obj  # type: ignore
     )
 
-    return all_users
+    return end_user_record
+
+
+@router.post(
+    "/end_user/info",
+    tags=["End User Management"],
+    dependencies=[Depends(user_api_key_auth)],
+)
+async def end_user_info():
+    """
+    [TODO] Needs to be implemented.
+    """
+    pass
+
+
+@router.post(
+    "/end_user/update",
+    tags=["End User Management"],
+    dependencies=[Depends(user_api_key_auth)],
+)
+async def update_end_user():
+    """
+    [TODO] Needs to be implemented.
+    """
+    pass
+
+
+@router.post(
+    "/end_user/delete",
+    tags=["End User Management"],
+    dependencies=[Depends(user_api_key_auth)],
+)
+async def delete_end_user():
+    """
+    [TODO] Needs to be implemented.
+    """
+    pass
 
 
 #### TEAM MANAGEMENT ####

litellm/proxy/schema.prisma

@@ -150,6 +150,8 @@ model LiteLLM_EndUserTable {
   user_id String @id
   alias    String? // admin-facing alias
   spend      Float    @default(0.0)
+  allowed_model_region String? // require all user requests to use models in this specific region
+  default_model String? // use along with 'allowed_model_region'. if no available model in region, default to this model.
   budget_id String?
   litellm_budget_table LiteLLM_BudgetTable?   @relation(fields: [budget_id], references: [budget_id])
   blocked Boolean @default(false)

litellm/proxy/utils.py

@@ -526,7 +526,7 @@ class PrismaClient:
             finally:
                 os.chdir(original_dir)
             # Now you can import the Prisma Client
-            from prisma import Prisma  # type: ignore
+            from prisma import Prisma
 
         self.db = Prisma()  # Client to connect to Prisma db
 
@@ -1689,12 +1689,12 @@ def get_instance_fn(value: str, config_file_path: Optional[str] = None) -> Any:
             module_file_path = os.path.join(directory, *module_name.split("."))
             module_file_path += ".py"
 
-            spec = importlib.util.spec_from_file_location(module_name, module_file_path)
+            spec = importlib.util.spec_from_file_location(module_name, module_file_path)  # type: ignore
             if spec is None:
                 raise ImportError(
                     f"Could not find a module specification for {module_file_path}"
                 )
-            module = importlib.util.module_from_spec(spec)
+            module = importlib.util.module_from_spec(spec)  # type: ignore
             spec.loader.exec_module(module)  # type: ignore
         else:
             # Dynamically import the module

litellm/router.py

@@ -32,6 +32,7 @@ from litellm.utils import (
     CustomStreamWrapper,
     get_utc_datetime,
     calculate_max_parallel_requests,
+    _is_region_eu,
 )
 import copy
 from litellm._logging import verbose_router_logger
@@ -1999,7 +2000,11 @@ class Router:
             # user can pass vars directly or they can pas os.environ/AZURE_API_KEY, in which case we will read the env
             # we do this here because we init clients for Azure, OpenAI and we need to set the right key
             api_key = litellm_params.get("api_key") or default_api_key
-            if api_key and api_key.startswith("os.environ/"):
+            if (
+                api_key
+                and isinstance(api_key, str)
+                and api_key.startswith("os.environ/")
+            ):
                 api_key_env_name = api_key.replace("os.environ/", "")
                 api_key = litellm.get_secret(api_key_env_name)
                 litellm_params["api_key"] = api_key
@@ -2023,6 +2028,7 @@ class Router:
             if (
                 is_azure_ai_studio_model == True
                 and api_base is not None
+                and isinstance(api_base, str)
                 and not api_base.endswith("/v1/")
             ):
                 # check if it ends with a trailing slash
@@ -2103,13 +2109,14 @@ class Router:
                 organization = litellm.get_secret(organization_env_name)
                 litellm_params["organization"] = organization
 
-            if "azure" in model_name:
-                if api_base is None:
+            if "azure" in model_name and isinstance(api_key, str):
+                if api_base is None or not isinstance(api_base, str):
                     raise ValueError(
                         f"api_base is required for Azure OpenAI. Set it on your config. Model - {model}"
                     )
                 if api_version is None:
                     api_version = "2023-07-01-preview"
+
                 if "gateway.ai.cloudflare.com" in api_base:
                     if not api_base.endswith("/"):
                         api_base += "/"
@@ -2532,7 +2539,7 @@ class Router:
             self.default_deployment = deployment.to_json(exclude_none=True)
 
         # Azure GPT-Vision Enhancements, users can pass os.environ/
-        data_sources = deployment.litellm_params.get("dataSources", [])
+        data_sources = deployment.litellm_params.get("dataSources", []) or []
 
         for data_source in data_sources:
             params = data_source.get("parameters", {})
@@ -2549,6 +2556,22 @@ class Router:
         # init OpenAI, Azure clients
         self.set_client(model=deployment.to_json(exclude_none=True))
 
+        # set region (if azure model)
+        try:
+            if "azure" in deployment.litellm_params.model:
+                region = litellm.utils.get_model_region(
+                    litellm_params=deployment.litellm_params, mode=None
+                )
+
+                deployment.litellm_params.region_name = region
+        except Exception as e:
+            verbose_router_logger.error(
+                "Unable to get the region for azure model - {}, {}".format(
+                    deployment.litellm_params.model, str(e)
+                )
+            )
+            pass  # [NON-BLOCKING]
+
         return deployment
 
     def add_deployment(self, deployment: Deployment) -> Optional[Deployment]:
@@ -2820,14 +2843,17 @@ class Router:
         model: str,
         healthy_deployments: List,
         messages: List[Dict[str, str]],
+        allowed_model_region: Optional[Literal["eu"]] = None,
     ):
         """
         Filter out model in model group, if:
 
         - model context window < message length
         - filter models above rpm limits
+        - if region given, filter out models not in that region / unknown region
         - [TODO] function call and model doesn't support function calling
         """
+
         verbose_router_logger.debug(
             f"Starting Pre-call checks for deployments in model={model}"
         )
@@ -2878,9 +2904,9 @@ class Router:
             except Exception as e:
                 verbose_router_logger.debug("An error occurs - {}".format(str(e)))
 
-            ## RPM CHECK ##
             _litellm_params = deployment.get("litellm_params", {})
             model_id = deployment.get("model_info", {}).get("id", "")
+            ## RPM CHECK ##
             ### get local router cache ###
             current_request_cache_local = (
                 self.cache.get_cache(key=model_id, local_only=True) or 0
@@ -2908,6 +2934,28 @@ class Router:
                         _rate_limit_error = True
                         continue
 
+            ## REGION CHECK ##
+            if allowed_model_region is not None:
+                if _litellm_params.get("region_name") is not None and isinstance(
+                    _litellm_params["region_name"], str
+                ):
+                    # check if in allowed_model_region
+                    if (
+                        _is_region_eu(model_region=_litellm_params["region_name"])
+                        == False
+                    ):
+                        invalid_model_indices.append(idx)
+                        continue
+                else:
+                    verbose_router_logger.debug(
+                        "Filtering out model - {}, as model_region=None, and allowed_model_region={}".format(
+                            model_id, allowed_model_region
+                        )
+                    )
+                    # filter out since region unknown, and user wants to filter for specific region
+                    invalid_model_indices.append(idx)
+                    continue
+
         if len(invalid_model_indices) == len(_returned_deployments):
             """
             - no healthy deployments available b/c context window checks or rate limit error
@@ -3047,10 +3095,31 @@ class Router:
 
         # filter pre-call checks
         if self.enable_pre_call_checks and messages is not None:
-            healthy_deployments = self._pre_call_checks(
-                model=model, healthy_deployments=healthy_deployments, messages=messages
+            _allowed_model_region = (
+                request_kwargs.get("allowed_model_region")
+                if request_kwargs is not None
+                else None
             )
 
+            if _allowed_model_region == "eu":
+                healthy_deployments = self._pre_call_checks(
+                    model=model,
+                    healthy_deployments=healthy_deployments,
+                    messages=messages,
+                    allowed_model_region=_allowed_model_region,
+                )
+            else:
+                verbose_router_logger.debug(
+                    "Ignoring given 'allowed_model_region'={}. Only 'eu' is allowed".format(
+                        _allowed_model_region
+                    )
+                )
+                healthy_deployments = self._pre_call_checks(
+                    model=model,
+                    healthy_deployments=healthy_deployments,
+                    messages=messages,
+                )
+
         if len(healthy_deployments) == 0:
             raise ValueError(
                 f"{RouterErrors.no_deployments_available.value}, passed model={model}"

litellm/router_strategy/least_busy.py

@@ -6,7 +6,7 @@
 #   - use litellm.success + failure callbacks to log when a request completed
 #   - in get_available_deployment, for a given model group name -> pick based on traffic
 
-import dotenv, os, requests, random
+import dotenv, os, requests, random  # type: ignore
 from typing import Optional
 
 dotenv.load_dotenv()  # Loading env variables using dotenv

litellm/router_strategy/lowest_cost.py

@@ -1,7 +1,7 @@
 #### What this does ####
 #   picks based on response time (for streaming, this is time to first token)
 from pydantic import BaseModel, Extra, Field, root_validator
-import dotenv, os, requests, random
+import dotenv, os, requests, random  # type: ignore
 from typing import Optional, Union, List, Dict
 from datetime import datetime, timedelta
 import random

litellm/router_strategy/lowest_latency.py

@@ -1,7 +1,7 @@
 #### What this does ####
 #   picks based on response time (for streaming, this is time to first token)
-from pydantic import BaseModel, Extra, Field, root_validator
-import dotenv, os, requests, random
+from pydantic import BaseModel, Extra, Field, root_validator  # type: ignore
+import dotenv, os, requests, random  # type: ignore
 from typing import Optional, Union, List, Dict
 from datetime import datetime, timedelta
 import random

litellm/tests/test_acompletion.py

@@ -1,5 +1,6 @@
 import pytest
 from litellm import acompletion
+from litellm import completion
 
 
 def test_acompletion_params():
@@ -7,17 +8,29 @@ def test_acompletion_params():
     from litellm.types.completion import CompletionRequest
 
     acompletion_params_odict = inspect.signature(acompletion).parameters
-    acompletion_params = {name: param.annotation for name, param in acompletion_params_odict.items()}
-    completion_params = {field_name: field_type for field_name, field_type in CompletionRequest.__annotations__.items()}
+    completion_params_dict = inspect.signature(completion).parameters
 
-    # remove kwargs
-    acompletion_params.pop("kwargs", None)
+    acompletion_params = {
+        name: param.annotation for name, param in acompletion_params_odict.items()
+    }
+    completion_params = {
+        name: param.annotation for name, param in completion_params_dict.items()
+    }
 
     keys_acompletion = set(acompletion_params.keys())
     keys_completion = set(completion_params.keys())
 
+    print(keys_acompletion)
+    print("\n\n\n")
+    print(keys_completion)
+
+    print("diff=", keys_completion - keys_acompletion)
+
     # Assert that the parameters are the same
     if keys_acompletion != keys_completion:
-        pytest.fail("The parameters of the acompletion function and the CompletionRequest class are not the same.")
+        pytest.fail(
+            "The parameters of the litellm.acompletion function and litellm.completion are not the same."
+        )
+
 
 # test_acompletion_params()

litellm/tests/test_completion_cost.py

@@ -231,14 +231,17 @@ def test_cost_bedrock_pricing():
     assert cost == predicted_cost
 
 
-@pytest.mark.skip(reason="AWS disabled our access")
 def test_cost_bedrock_pricing_actual_calls():
     litellm.set_verbose = True
     model = "anthropic.claude-instant-v1"
     messages = [{"role": "user", "content": "Hey, how's it going?"}]
-    response = litellm.completion(model=model, messages=messages)
-    assert response._hidden_params["region_name"] is not None
+    response = litellm.completion(
+        model=model, messages=messages, mock_response="hello cool one"
+    )
+
+    print("response", response)
     cost = litellm.completion_cost(
+        model="bedrock/anthropic.claude-instant-v1",
         completion_response=response,
         messages=[{"role": "user", "content": "Hey, how's it going?"}],
     )

litellm/tests/test_model_alias_map.py

@@ -16,7 +16,7 @@ litellm.set_verbose = True
 model_alias_map = {"good-model": "anyscale/meta-llama/Llama-2-7b-chat-hf"}
 
 
-def test_model_alias_map():
+def test_model_alias_map(caplog):
     try:
         litellm.model_alias_map = model_alias_map
         response = completion(
@@ -27,9 +27,15 @@ def test_model_alias_map():
             max_tokens=10,
         )
         print(response.model)
+
+        captured_logs = [rec.levelname for rec in caplog.records]
+
+        for log in captured_logs:
+            assert "ERROR" not in log
+
         assert "Llama-2-7b-chat-hf" in response.model
     except Exception as e:
         pytest.fail(f"Error occurred: {e}")
 
 
-test_model_alias_map()
+# test_model_alias_map()

litellm/tests/test_streaming.py

@@ -1501,6 +1501,37 @@ def test_openai_chat_completion_complete_response_call():
 
 
 # test_openai_chat_completion_complete_response_call()
+def test_openai_stream_options_call():
+    litellm.set_verbose = False
+    response = litellm.completion(
+        model="gpt-3.5-turbo",
+        messages=[{"role": "system", "content": "say GM - we're going to make it "}],
+        stream=True,
+        stream_options={"include_usage": True},
+        max_tokens=10,
+    )
+    usage = None
+    chunks = []
+    for chunk in response:
+        print("chunk: ", chunk)
+        chunks.append(chunk)
+
+    last_chunk = chunks[-1]
+    print("last chunk: ", last_chunk)
+
+    """
+    Assert that:
+    - Last Chunk includes Usage
+    - All chunks prior to last chunk have usage=None
+    """
+
+    assert last_chunk.usage is not None
+    assert last_chunk.usage.total_tokens > 0
+    assert last_chunk.usage.prompt_tokens > 0
+    assert last_chunk.usage.completion_tokens > 0
+
+    # assert all non last chunks have usage=None
+    assert all(chunk.usage is None for chunk in chunks[:-1])
 
 
 def test_openai_text_completion_call():

litellm/types/router.py

@@ -123,6 +123,8 @@ class GenericLiteLLMParams(BaseModel):
     )
     max_retries: Optional[int] = None
     organization: Optional[str] = None  # for openai orgs
+    ## UNIFIED PROJECT/REGION ##
+    region_name: Optional[str] = None
     ## VERTEX AI ##
     vertex_project: Optional[str] = None
     vertex_location: Optional[str] = None
@@ -150,6 +152,8 @@ class GenericLiteLLMParams(BaseModel):
             None  # timeout when making stream=True calls, if str, pass in as os.environ/
         ),
         organization: Optional[str] = None,  # for openai orgs
+        ## UNIFIED PROJECT/REGION ##
+        region_name: Optional[str] = None,
         ## VERTEX AI ##
         vertex_project: Optional[str] = None,
         vertex_location: Optional[str] = None,

litellm/utils.py

@@ -14,7 +14,7 @@ import subprocess, os
 from os.path import abspath, join, dirname
 import litellm, openai
 import itertools
-import random, uuid, requests
+import random, uuid, requests  # type: ignore
 from functools import wraps
 import datetime, time
 import tiktoken
@@ -36,7 +36,7 @@ import litellm._service_logger  # for storing API inputs, outputs, and metadata
 
 try:
     # this works in python 3.8
-    import pkg_resources
+    import pkg_resources  # type: ignore
 
     filename = pkg_resources.resource_filename(__name__, "llms/tokenizers")
 # try:
@@ -612,6 +612,7 @@ class ModelResponse(OpenAIObject):
         system_fingerprint=None,
         usage=None,
         stream=None,
+        stream_options=None,
         response_ms=None,
         hidden_params=None,
         **params,
@@ -658,6 +659,12 @@ class ModelResponse(OpenAIObject):
             usage = usage
         elif stream is None or stream == False:
             usage = Usage()
+        elif (
+            stream == True
+            and stream_options is not None
+            and stream_options.get("include_usage") == True
+        ):
+            usage = Usage()
         if hidden_params:
             self._hidden_params = hidden_params
 
@@ -4161,8 +4168,30 @@ def cost_per_token(
                 model_with_provider_and_region in model_cost_ref
             ):  # use region based pricing, if it's available
                 model_with_provider = model_with_provider_and_region
-    if model_with_provider in model_cost_ref:
+
+    model_without_prefix = model
+    model_parts = model.split("/")
+    if len(model_parts) > 1:
+        model_without_prefix = model_parts[1]
+    else:
+        model_without_prefix = model
+    """
+    Code block that formats model to lookup in litellm.model_cost
+    Option1. model = "bedrock/ap-northeast-1/anthropic.claude-instant-v1". This is the most accurate since it is region based. Should always be option 1
+    Option2. model = "openai/gpt-4"       - model = provider/model
+    Option3. model = "anthropic.claude-3" - model = model
+    """
+    if (
+        model_with_provider in model_cost_ref
+    ):  # Option 2. use model with provider, model = "openai/gpt-4"
         model = model_with_provider
+    elif model in model_cost_ref:  # Option 1. use model passed, model="gpt-4"
+        model = model
+    elif (
+        model_without_prefix in model_cost_ref
+    ):  # Option 3. if user passed model="bedrock/anthropic.claude-3", use model="anthropic.claude-3"
+        model = model_without_prefix
+
     # see this https://learn.microsoft.com/en-us/azure/ai-services/openai/concepts/models
     print_verbose(f"Looking up model={model} in model_cost_map")
     if model in model_cost_ref:
@@ -4817,6 +4846,7 @@ def get_optional_params(
     top_p=None,
     n=None,
     stream=False,
+    stream_options=None,
     stop=None,
     max_tokens=None,
     presence_penalty=None,
@@ -4886,6 +4916,7 @@ def get_optional_params(
         "top_p": None,
         "n": None,
         "stream": None,
+        "stream_options": None,
         "stop": None,
         "max_tokens": None,
         "presence_penalty": None,
@@ -5757,6 +5788,8 @@ def get_optional_params(
             optional_params["n"] = n
         if stream is not None:
             optional_params["stream"] = stream
+        if stream_options is not None:
+            optional_params["stream_options"] = stream_options
         if stop is not None:
             optional_params["stop"] = stop
         if max_tokens is not None:
@@ -5844,6 +5877,40 @@ def calculate_max_parallel_requests(
     return None
 
 
+def _is_region_eu(model_region: str) -> bool:
+    EU_Regions = ["europe", "sweden", "switzerland", "france", "uk"]
+    for region in EU_Regions:
+        if "europe" in model_region.lower():
+            return True
+    return False
+
+
+def get_model_region(
+    litellm_params: LiteLLM_Params, mode: Optional[str]
+) -> Optional[str]:
+    """
+    Pass the litellm params for an azure model, and get back the region
+    """
+    if (
+        "azure" in litellm_params.model
+        and isinstance(litellm_params.api_key, str)
+        and isinstance(litellm_params.api_base, str)
+    ):
+        _model = litellm_params.model.replace("azure/", "")
+        response: dict = litellm.AzureChatCompletion().get_headers(
+            model=_model,
+            api_key=litellm_params.api_key,
+            api_base=litellm_params.api_base,
+            api_version=litellm_params.api_version or "2023-07-01-preview",
+            timeout=10,
+            mode=mode or "chat",
+        )
+
+        region: Optional[str] = response.get("x-ms-region", None)
+        return region
+    return None
+
+
 def get_api_base(model: str, optional_params: dict) -> Optional[str]:
     """
     Returns the api base used for calling the model.
@@ -5878,6 +5945,8 @@ def get_api_base(model: str, optional_params: dict) -> Optional[str]:
     if _optional_params.api_base is not None:
         return _optional_params.api_base
 
+    if litellm.model_alias_map and model in litellm.model_alias_map:
+        model = litellm.model_alias_map[model]
     try:
         model, custom_llm_provider, dynamic_api_key, dynamic_api_base = (
             get_llm_provider(
@@ -6027,6 +6096,7 @@ def get_supported_openai_params(model: str, custom_llm_provider: str):
             "top_p",
             "n",
             "stream",
+            "stream_options",
             "stop",
             "max_tokens",
             "presence_penalty",
@@ -7732,11 +7802,11 @@ def _calculate_retry_after(
             try:
                 retry_after = int(retry_header)
             except Exception:
-                retry_date_tuple = email.utils.parsedate_tz(retry_header)
+                retry_date_tuple = email.utils.parsedate_tz(retry_header)  # type: ignore
                 if retry_date_tuple is None:
                     retry_after = -1
                 else:
-                    retry_date = email.utils.mktime_tz(retry_date_tuple)
+                    retry_date = email.utils.mktime_tz(retry_date_tuple)  # type: ignore
                     retry_after = int(retry_date - time.time())
         else:
             retry_after = -1
@@ -9423,7 +9493,9 @@ def get_secret(
         else:
             secret = os.environ.get(secret_name)
             try:
-                secret_value_as_bool = ast.literal_eval(secret) if secret is not None else None
+                secret_value_as_bool = (
+                    ast.literal_eval(secret) if secret is not None else None
+                )
                 if isinstance(secret_value_as_bool, bool):
                     return secret_value_as_bool
                 else:
@@ -9442,7 +9514,12 @@ def get_secret(
 # replicate/anthropic/cohere
 class CustomStreamWrapper:
     def __init__(
-        self, completion_stream, model, custom_llm_provider=None, logging_obj=None
+        self,
+        completion_stream,
+        model,
+        custom_llm_provider=None,
+        logging_obj=None,
+        stream_options=None,
     ):
         self.model = model
         self.custom_llm_provider = custom_llm_provider
@@ -9468,6 +9545,7 @@ class CustomStreamWrapper:
         self.response_id = None
         self.logging_loop = None
         self.rules = Rules()
+        self.stream_options = stream_options
 
     def __iter__(self):
         return self
@@ -9908,6 +9986,7 @@ class CustomStreamWrapper:
             is_finished = False
             finish_reason = None
             logprobs = None
+            usage = None
             original_chunk = None  # this is used for function/tool calling
             if len(str_line.choices) > 0:
                 if (
@@ -9942,12 +10021,15 @@ class CustomStreamWrapper:
                 else:
                     logprobs = None
 
+            usage = getattr(str_line, "usage", None)
+
             return {
                 "text": text,
                 "is_finished": is_finished,
                 "finish_reason": finish_reason,
                 "logprobs": logprobs,
                 "original_chunk": str_line,
+                "usage": usage,
             }
         except Exception as e:
             traceback.print_exc()
@@ -10250,7 +10332,9 @@ class CustomStreamWrapper:
             raise e
 
     def model_response_creator(self):
-        model_response = ModelResponse(stream=True, model=self.model)
+        model_response = ModelResponse(
+            stream=True, model=self.model, stream_options=self.stream_options
+        )
         if self.response_id is not None:
             model_response.id = self.response_id
         else:
@@ -10570,6 +10654,12 @@ class CustomStreamWrapper:
                 if response_obj["logprobs"] is not None:
                     model_response.choices[0].logprobs = response_obj["logprobs"]
 
+                if (
+                    self.stream_options is not None
+                    and self.stream_options["include_usage"] == True
+                ):
+                    model_response.usage = response_obj["usage"]
+
             model_response.model = self.model
             print_verbose(
                 f"model_response finish reason 3: {self.received_finish_reason}; response_obj={response_obj}"
@@ -10657,6 +10747,11 @@ class CustomStreamWrapper:
                         except Exception as e:
                             model_response.choices[0].delta = Delta()
                 else:
+                    if (
+                        self.stream_options is not None
+                        and self.stream_options["include_usage"] == True
+                    ):
+                        return model_response
                     return
             print_verbose(
                 f"model_response.choices[0].delta: {model_response.choices[0].delta}; completion_obj: {completion_obj}"

proxy_server_config.yaml

@@ -1,4 +1,9 @@
 model_list:
+  - model_name: gpt-3.5-turbo
+    litellm_params:
+      model: azure/gpt-35-turbo
+      api_base: https://my-endpoint-europe-berri-992.openai.azure.com/
+      api_key: os.environ/AZURE_EUROPE_API_KEY
   - model_name: gpt-3.5-turbo
     litellm_params:
       model: azure/chatgpt-v-2

pyproject.toml

@@ -1,6 +1,6 @@
 [tool.poetry]
 name = "litellm"
-version = "1.36.3"
+version = "1.36.4"
 description = "Library to easily interface with LLM API providers"
 authors = ["BerriAI"]
 license = "MIT"
@@ -80,7 +80,7 @@ requires = ["poetry-core", "wheel"]
 build-backend = "poetry.core.masonry.api"
 
 [tool.commitizen]
-version = "1.36.3"
+version = "1.36.4"
 version_files = [
     "pyproject.toml:^version"
 ]

schema.prisma

@@ -150,6 +150,8 @@ model LiteLLM_EndUserTable {
   user_id String @id
   alias    String? // admin-facing alias
   spend      Float    @default(0.0)
+  allowed_model_region String? // require all user requests to use models in this specific region
+  default_model String? // use along with 'allowed_model_region'. if no available model in region, default to this model.
   budget_id String?
   litellm_budget_table LiteLLM_BudgetTable?   @relation(fields: [budget_id], references: [budget_id])
   blocked Boolean @default(false)

tests/test_end_users.py

@@ -0,0 +1,173 @@
+# What is this?
+## Unit tests for the /end_users/* endpoints
+import pytest
+import asyncio
+import aiohttp
+import time
+import uuid
+from openai import AsyncOpenAI
+from typing import Optional
+
+"""
+- `/end_user/new` 
+- `/end_user/info` 
+"""
+
+
+async def chat_completion_with_headers(session, key, model="gpt-4"):
+    url = "http://0.0.0.0:4000/chat/completions"
+    headers = {
+        "Authorization": f"Bearer {key}",
+        "Content-Type": "application/json",
+    }
+    data = {
+        "model": model,
+        "messages": [
+            {"role": "system", "content": "You are a helpful assistant."},
+            {"role": "user", "content": "Hello!"},
+        ],
+    }
+
+    async with session.post(url, headers=headers, json=data) as response:
+        status = response.status
+        response_text = await response.text()
+
+        print(response_text)
+        print()
+
+        if status != 200:
+            raise Exception(f"Request did not return a 200 status code: {status}")
+
+        response_header_check(
+            response
+        )  # calling the function to check response headers
+
+        raw_headers = response.raw_headers
+        raw_headers_json = {}
+
+        for (
+            item
+        ) in (
+            response.raw_headers
+        ):  # ((b'date', b'Fri, 19 Apr 2024 21:17:29 GMT'), (), )
+            raw_headers_json[item[0].decode("utf-8")] = item[1].decode("utf-8")
+
+        return raw_headers_json
+
+
+async def generate_key(
+    session,
+    i,
+    budget=None,
+    budget_duration=None,
+    models=["azure-models", "gpt-4", "dall-e-3"],
+    max_parallel_requests: Optional[int] = None,
+    user_id: Optional[str] = None,
+    team_id: Optional[str] = None,
+    calling_key="sk-1234",
+):
+    url = "http://0.0.0.0:4000/key/generate"
+    headers = {
+        "Authorization": f"Bearer {calling_key}",
+        "Content-Type": "application/json",
+    }
+    data = {
+        "models": models,
+        "aliases": {"mistral-7b": "gpt-3.5-turbo"},
+        "duration": None,
+        "max_budget": budget,
+        "budget_duration": budget_duration,
+        "max_parallel_requests": max_parallel_requests,
+        "user_id": user_id,
+        "team_id": team_id,
+    }
+
+    print(f"data: {data}")
+
+    async with session.post(url, headers=headers, json=data) as response:
+        status = response.status
+        response_text = await response.text()
+
+        print(f"Response {i} (Status code: {status}):")
+        print(response_text)
+        print()
+
+        if status != 200:
+            raise Exception(f"Request {i} did not return a 200 status code: {status}")
+
+        return await response.json()
+
+
+async def new_end_user(
+    session, i, user_id=str(uuid.uuid4()), model_region=None, default_model=None
+):
+    url = "http://0.0.0.0:4000/end_user/new"
+    headers = {"Authorization": "Bearer sk-1234", "Content-Type": "application/json"}
+    data = {
+        "user_id": user_id,
+        "allowed_model_region": model_region,
+        "default_model": default_model,
+    }
+
+    async with session.post(url, headers=headers, json=data) as response:
+        status = response.status
+        response_text = await response.text()
+
+        print(f"Response {i} (Status code: {status}):")
+        print(response_text)
+        print()
+
+        if status != 200:
+            raise Exception(f"Request {i} did not return a 200 status code: {status}")
+
+        return await response.json()
+
+
+@pytest.mark.asyncio
+async def test_end_user_new():
+    """
+    Make 20 parallel calls to /user/new. Assert all worked.
+    """
+    async with aiohttp.ClientSession() as session:
+        tasks = [new_end_user(session, i, str(uuid.uuid4())) for i in range(1, 11)]
+        await asyncio.gather(*tasks)
+
+
+@pytest.mark.asyncio
+async def test_end_user_specific_region():
+    """
+    - Specify region user can make calls in
+    - Make a generic call
+    - assert returned api base is for model in region
+
+    Repeat 3 times
+    """
+    key: str = ""
+    ## CREATE USER ##
+    async with aiohttp.ClientSession() as session:
+        end_user_obj = await new_end_user(
+            session=session,
+            i=0,
+            user_id=str(uuid.uuid4()),
+            model_region="eu",
+        )
+
+        ## MAKE CALL ##
+        key_gen = await generate_key(session=session, i=0, models=["gpt-3.5-turbo"])
+
+        key = key_gen["key"]
+
+    for _ in range(3):
+        client = AsyncOpenAI(api_key=key, base_url="http://0.0.0.0:4000")
+
+        print("SENDING USER PARAM - {}".format(end_user_obj["user_id"]))
+        result = await client.chat.completions.with_raw_response.create(
+            model="gpt-3.5-turbo",
+            messages=[{"role": "user", "content": "Hey!"}],
+            user=end_user_obj["user_id"],
+        )
+
+        assert (
+            result.headers.get("x-litellm-model-api-base")
+            == "https://my-endpoint-europe-berri-992.openai.azure.com/"
+        )

ui/litellm-dashboard/out/_next/static/K8KXTbmuI2ArWjjdMi2iq/_buildManifest.js

@@ -0,0 +1 @@
+self.__BUILD_MANIFEST={__rewrites:{afterFiles:[],beforeFiles:[],fallback:[]},"/_error":["static/chunks/pages/_error-d6107f1aac0c574c.js"],sortedPages:["/_app","/_error"]},self.__BUILD_MANIFEST_CB&&self.__BUILD_MANIFEST_CB();

ui/litellm-dashboard/out/_next/static/K8KXTbmuI2ArWjjdMi2iq/_ssgManifest.js

@@ -0,0 +1 @@
+self.__SSG_MANIFEST=new Set([]);self.__SSG_MANIFEST_CB&&self.__SSG_MANIFEST_CB()

ui/litellm-dashboard/out/_next/static/chunks/app/layout-94ae1345f5d85446.js

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ui/litellm-dashboard/out/_next/static/chunks/main-app-9b4fb13a7db53edf.js

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ui/litellm-dashboard/out/index.html

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ui/litellm-dashboard/out/index.txt

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

@@ -189,6 +189,7 @@ const CreateKeyPage = () => {
               userRole={userRole}
               token={token}
               accessToken={accessToken}
+              keys={keys}
             />
           )}
         </div>

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

@@ -786,7 +786,9 @@ export const adminTopKeysCall = async (accessToken: String) => {
 
 export const adminTopEndUsersCall = async (
   accessToken: String,
-  keyToken: String | null
+  keyToken: String | null,
+  startTime: String | undefined,
+  endTime: String | undefined
 ) => {
   try {
     let url = proxyBaseUrl
@@ -795,8 +797,11 @@ export const adminTopEndUsersCall = async (
 
     let body = "";
     if (keyToken) {
-      body = JSON.stringify({ api_key: keyToken });
+      body = JSON.stringify({ api_key: keyToken, startTime: startTime, endTime: endTime });
+    } else {
+      body = JSON.stringify({ startTime: startTime, endTime: endTime });
     }
+    
     //message.info("Making top end users request");
 
     // Define requestOptions with body as an optional property
@@ -815,9 +820,7 @@ export const adminTopEndUsersCall = async (
       },
     };
 
-    if (keyToken) {
-      requestOptions.body = JSON.stringify({ api_key: keyToken });
-    }
+    requestOptions.body = body;
 
     const response = await fetch(url, requestOptions);
     if (!response.ok) {

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

@@ -341,7 +341,7 @@ const Settings: React.FC<SettingsPageProps> = ({
   return (
     <div className="w-full mx-4">
       <Grid numItems={1} className="gap-2 p-8 w-full mt-2">
-      <Callout title="Presidio PII / Guardrails Coming Soon" color="sky">
+      <Callout title="[UI] Presidio PII + Guardrails Coming Soon. https://docs.litellm.ai/docs/proxy/pii_masking" color="sky">
 
 </Callout>
         <TabGroup>
@@ -353,9 +353,6 @@ const Settings: React.FC<SettingsPageProps> = ({
           <TabPanel>
           
           <Card >
-            <Text>
-              Presidio + Guardrails coming soon
-            </Text>
             <Table>
               <TableHead>
                 <TableRow>