Merge branch 'main' into litellm_region_based_routing

.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
+# -   repo: local
-    hooks:
+#     hooks:
-    -   id: mypy
+#     -   id: mypy
-        name: mypy
+#         name: mypy
-        entry: python3 -m mypy --ignore-missing-imports
+#         entry: python3 -m mypy --ignore-missing-imports
-        language: system
+#         language: system
-        types: [python]
+#         types: [python]
-        files: ^litellm/
+#         files: ^litellm/

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, litellm  # type: ignore
-import redis.asyncio as async_redis
+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 = {
@@ -23,19 +35,30 @@ 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:
+            if (
-                data["response_time"] = int((end_time - start_time).total_seconds() * 1000)
+                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", {})
@@ -44,11 +67,19 @@ class AthinaLogger:
                     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()}")
+            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")
 
@@ -19,13 +21,18 @@ 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')
+            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["invocationLatency"] = int(
-
+                    (end_time - start_time).total_seconds() * 1000
+                )
 
             # Add additional metadata keys to tags
             tags = []
@@ -37,15 +44,25 @@ class GreenscaleLogger:
                     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()}")
+            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 dotenv, os  # type: ignore
-import requests
+import requests  # type: ignore
-import requests
 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 requests  # type: ignore
-import time, httpx
+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

@@ -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

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
 
 
@@ -220,7 +220,10 @@ 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",
                 }
             ],
@@ -232,7 +235,9 @@ 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,
@@ -273,7 +278,10 @@ 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",
                         }
                     ],
@@ -316,7 +324,8 @@ async def ollama_async_streaming(url, data, model_response, encoding, logging_ob
                     [
                         chunk.choices[0].delta.content
                         async for chunk in streamwrapper
-                    if chunk.choices[0].delta.content]
+                        if chunk.choices[0].delta.content
+                    ]
                 )
                 function_call = json.loads(response_content)
                 delta = litellm.utils.Delta(
@@ -324,7 +333,10 @@ 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",
                         }
                     ],
@@ -373,7 +385,10 @@ 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",
                         }
                     ],
@@ -387,7 +402,10 @@ 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,
@@ -475,6 +493,7 @@ async def ollama_aembeddings(
     }
     return model_response
 
+
 def ollama_embeddings(
     api_base: str,
     model: str,
@@ -492,5 +511,6 @@ 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

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

@@ -487,7 +487,7 @@ def format_prompt_togetherai(messages, prompt_format, chat_template):
 
 def ibm_granite_pt(messages: list):
     """
-    IBM's Granite models uses the template:
+    IBM's Granite chat models uses the template:
     <|system|> {system_message} <|user|> {user_message} <|assistant|> {assistant_message}
 
     See: https://www.ibm.com/docs/en/watsonx-as-a-service?topic=solutions-supported-foundation-models
@@ -503,12 +503,13 @@ def ibm_granite_pt(messages: list):
                 "pre_message": "<|user|>\n",
                 "post_message": "\n",
             },
-            "assistant": {
+            'assistant': {
-                "pre_message": "<|assistant|>\n",
+                'pre_message': '<|assistant|>\n',
-                "post_message": "\n",
+                'post_message': '\n',
             },
         },
-    ).strip()
+        final_prompt_value='<|assistant|>\n',
+    )
 
 
 ### ANTHROPIC ###
@@ -981,7 +982,7 @@ 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):
@@ -1524,24 +1525,9 @@ def prompt_factory(
             return mistral_instruct_pt(messages=messages)
         elif "meta-llama/llama-3" in model and "instruct" in model:
             # https://llama.meta.com/docs/model-cards-and-prompt-formats/meta-llama-3/
-            return custom_prompt(
+            return hf_chat_template(
-                role_dict={
+                model="meta-llama/Meta-Llama-3-8B-Instruct",
-                    "system": {
-                        "pre_message": "<|start_header_id|>system<|end_header_id|>\n",
-                        "post_message": "<|eot_id|>",
-                    },
-                    "user": {
-                        "pre_message": "<|start_header_id|>user<|end_header_id|>\n",
-                        "post_message": "<|eot_id|>",
-                    },
-                    "assistant": {
-                        "pre_message": "<|start_header_id|>assistant<|end_header_id|>\n",
-                        "post_message": "<|eot_id|>",
-                    },
-                },
                 messages=messages,
-                initial_prompt_value="<|begin_of_text|>",
-                final_prompt_value="<|start_header_id|>assistant<|end_header_id|>\n",
             )
     try:
         if "meta-llama/llama-2" in model and "chat" in model:

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 requests  # type: ignore
-import time, httpx
+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

@@ -1,12 +1,13 @@
 from enum import Enum
 import json, types, time  # noqa: E401
-from contextlib import contextmanager
+from contextlib import asynccontextmanager, contextmanager
-from typing import Callable, Dict, Optional, Any, Union, List
+from typing import AsyncGenerator, Callable, Dict, Generator, Optional, Any, Union, List
 
-import httpx
+import httpx  # type: ignore
-import requests
+import requests  # type: ignore
 import litellm
-from litellm.utils import ModelResponse, get_secret, Usage
+from litellm.utils import Logging, ModelResponse, Usage, get_secret
+from litellm.llms.custom_httpx.http_handler import AsyncHTTPHandler
 
 from .base import BaseLLM
 from .prompt_templates import factory as ptf
@@ -192,7 +193,7 @@ class WatsonXAIEndpoint(str, Enum):
 
 class IBMWatsonXAI(BaseLLM):
     """
-    Class to interface with IBM Watsonx.ai API for text generation and embeddings.
+    Class to interface with IBM watsonx.ai API for text generation and embeddings.
 
     Reference: https://cloud.ibm.com/apidocs/watsonx-ai
     """
@@ -343,7 +344,7 @@ class IBMWatsonXAI(BaseLLM):
             )
         if token is None and api_key is not None:
             # generate the auth token
-            if print_verbose:
+            if print_verbose is not None:
                 print_verbose("Generating IAM token for Watsonx.ai")
             token = self.generate_iam_token(api_key)
         elif token is None and api_key is None:
@@ -377,8 +378,9 @@ class IBMWatsonXAI(BaseLLM):
         model_response: ModelResponse,
         print_verbose: Callable,
         encoding,
-        logging_obj,
+        logging_obj: Logging,
-        optional_params: dict,
+        optional_params: Optional[dict] = None,
+        acompletion: bool = None,
         litellm_params: Optional[dict] = None,
         logger_fn=None,
         timeout: Optional[float] = None,
@@ -402,12 +404,14 @@ class IBMWatsonXAI(BaseLLM):
             model, messages, provider, custom_prompt_dict
         )
         
-        def process_text_request(request_params: dict) -> ModelResponse:
+        manage_response = self._make_response_manager(async_=(acompletion is True), logging_obj=logging_obj)
-            with self._manage_response(
-                request_params, logging_obj=logging_obj, input=prompt, timeout=timeout
-            ) as resp:
-                json_resp = resp.json()
 
+        def process_text_gen_response(json_resp: dict) -> ModelResponse:
+            if "results" not in json_resp:
+                raise WatsonXAIError(
+                    status_code=500,
+                    message=f"Error: Invalid response from Watsonx.ai API: {json_resp}",
+                )
             generated_text = json_resp["results"][0]["generated_text"]
             prompt_tokens = json_resp["results"][0]["input_token_count"]
             completion_tokens = json_resp["results"][0]["generated_token_count"]
@@ -426,25 +430,52 @@ class IBMWatsonXAI(BaseLLM):
             )
             return model_response
 
-        def process_stream_request(
+        def handle_text_request(request_params: dict) -> ModelResponse:
+            with manage_response(
+                request_params, input=prompt, timeout=timeout,
+            ) as resp:
+                json_resp = resp.json()
+
+            return process_text_gen_response(json_resp)
+
+        async def handle_text_request_async(request_params: dict) -> ModelResponse:
+            async with manage_response(
+                request_params, input=prompt, timeout=timeout,
+            ) as resp:
+                json_resp = resp.json()
+            return process_text_gen_response(json_resp)
+
+        def handle_stream_request(
             request_params: dict,
         ) -> litellm.CustomStreamWrapper:
             # stream the response - generated chunks will be handled
             # by litellm.utils.CustomStreamWrapper.handle_watsonx_stream
-            with self._manage_response(
+            with manage_response(
-                request_params,
+                request_params, stream=True, input=prompt, timeout=timeout,
-                logging_obj=logging_obj,
-                stream=True,
-                input=prompt,
-                timeout=timeout,
             ) as resp:
-                response = litellm.CustomStreamWrapper(
+                streamwrapper = litellm.CustomStreamWrapper(
                     resp.iter_lines(),
                     model=model,
                     custom_llm_provider="watsonx",
                     logging_obj=logging_obj,
                 )
-            return response
+            return streamwrapper
+
+        async def handle_stream_request_async(
+            request_params: dict,
+        ) -> litellm.CustomStreamWrapper:
+            # stream the response - generated chunks will be handled
+            # by litellm.utils.CustomStreamWrapper.handle_watsonx_stream
+            async with manage_response(
+                request_params, stream=True, input=prompt, timeout=timeout,
+            ) as resp:
+                streamwrapper = litellm.CustomStreamWrapper(
+                    resp.aiter_lines(),
+                    model=model,
+                    custom_llm_provider="watsonx",
+                    logging_obj=logging_obj,
+                )
+            return streamwrapper
 
         try:
             ## Get the response from the model
@@ -455,10 +486,18 @@ class IBMWatsonXAI(BaseLLM):
                 optional_params=optional_params,
                 print_verbose=print_verbose,
             )
-            if stream:
+            if stream and acompletion:
-                return process_stream_request(req_params)
+                # stream and async text generation
+                return handle_stream_request_async(req_params)
+            elif stream:
+                # streaming text generation
+                return handle_stream_request(req_params)
+            elif acompletion:
+                # async text generation
+                return handle_text_request_async(req_params)
             else:
-                return process_text_request(req_params)
+                # regular text generation
+                return handle_text_request(req_params)
         except WatsonXAIError as e:
             raise e
         except Exception as e:
@@ -473,6 +512,7 @@ class IBMWatsonXAI(BaseLLM):
         model_response=None,
         optional_params=None,
         encoding=None,
+        aembedding=None,
     ):
         """
         Send a text embedding request to the IBM Watsonx.ai API.
@@ -507,9 +547,6 @@ class IBMWatsonXAI(BaseLLM):
         }
         request_params = dict(version=api_params["api_version"])
         url = api_params["url"].rstrip("/") + WatsonXAIEndpoint.EMBEDDINGS
-        # request = httpx.Request(
-        #     "POST", url, headers=headers, json=payload, params=request_params
-        # )
         req_params = {
             "method": "POST",
             "url": url,
@@ -517,11 +554,9 @@ class IBMWatsonXAI(BaseLLM):
             "json": payload,
             "params": request_params,
         }
-        with self._manage_response(
+        manage_response = self._make_response_manager(async_=(aembedding is True), logging_obj=logging_obj)
-            req_params, logging_obj=logging_obj, input=input
-        ) as resp:
-            json_resp = resp.json()
         
+        def process_embedding_response(json_resp: dict) -> ModelResponse:
             results = json_resp.get("results", [])
             embedding_response = []
             for idx, result in enumerate(results):
@@ -537,6 +572,30 @@ class IBMWatsonXAI(BaseLLM):
             )
             return model_response
         
+        def handle_embedding_request(request_params: dict) -> ModelResponse:
+            with manage_response(
+                request_params, input=input
+            ) as resp:
+                json_resp = resp.json()
+            return process_embedding_response(json_resp)
+        
+        async def handle_embedding_request_async(request_params: dict) -> ModelResponse:
+            async with manage_response(
+                request_params, input=input
+            ) as resp:
+                json_resp = resp.json()
+            return process_embedding_response(json_resp)
+        
+        try:
+            if aembedding:
+                return handle_embedding_request_async(req_params)
+            else:
+                return handle_embedding_request(req_params)
+        except WatsonXAIError as e:
+            raise e
+        except Exception as e:
+            raise WatsonXAIError(status_code=500, message=str(e))
+
     def generate_iam_token(self, api_key=None, **params):
         headers = {}
         headers["Content-Type"] = "application/x-www-form-urlencoded"
@@ -558,17 +617,33 @@ class IBMWatsonXAI(BaseLLM):
         self.token = iam_access_token
         return iam_access_token
     
-    @contextmanager
+    def _make_response_manager(
-    def _manage_response(
             self,
+            async_: bool,
+            logging_obj: Logging  
+        ) -> Callable[..., Generator[Union[requests.Response, httpx.Response], None, None]]:
+        """
+        Returns a context manager that manages the response from the request.
+        if async_ is True, returns an async context manager, otherwise returns a regular context manager.
+
+        Usage:
+        ```python
+        manage_response = self._make_response_manager(async_=True, logging_obj=logging_obj)
+        async with manage_response(request_params) as resp:
+            ...
+        # or
+        manage_response = self._make_response_manager(async_=False, logging_obj=logging_obj)
+        with manage_response(request_params) as resp:
+            ...
+        ```
+        """
+
+        def pre_call(
             request_params: dict,
-        logging_obj: Any,
-        stream: bool = False,
             input:Optional[Any]=None,
-        timeout: Optional[float] = None,
         ):
             request_str = (
-            f"response = {request_params['method']}(\n"
+                f"response = {'await ' if async_ else ''}{request_params['method']}(\n"
                 f"\turl={request_params['url']},\n"
                 f"\tjson={request_params['json']},\n"
                 f")"
@@ -581,29 +656,76 @@ class IBMWatsonXAI(BaseLLM):
                     "request_str": request_str,
                 },
             )
-        if timeout:
+
-            request_params["timeout"] = timeout
+        def post_call(resp, request_params):
-        try:
-            if stream:
-                resp = requests.request(
-                    **request_params,
-                    stream=True,
-                )
-                resp.raise_for_status()
-                yield resp
-            else:
-                resp = requests.request(**request_params)
-                resp.raise_for_status()
-                yield resp
-        except Exception as e:
-            raise WatsonXAIError(status_code=500, message=str(e))
-        if not stream:
             logging_obj.post_call(
                 input=input,
                 api_key=request_params["headers"].get("Authorization"),
                 original_response=json.dumps(resp.json()),
                 additional_args={
                     "status_code": resp.status_code,
-                    "complete_input_dict": request_params["json"],
+                    "complete_input_dict": request_params.get("data", request_params.get("json")),
                 },
             )
+        
+        @contextmanager
+        def _manage_response(
+            request_params: dict,
+            stream: bool = False,
+            input: Optional[Any] = None,
+            timeout: float = None,
+        ) -> Generator[requests.Response, None, None]:
+            """
+            Returns a context manager that yields the response from the request.
+            """
+            pre_call(request_params, input)
+            if timeout:
+                request_params["timeout"] = timeout
+            if stream:
+                request_params["stream"] = stream
+            try:
+                resp = requests.request(**request_params)
+                resp.raise_for_status()
+                yield resp
+            except Exception as e:
+                raise WatsonXAIError(status_code=500, message=str(e))
+            if not stream:
+                post_call(resp, request_params)
+            
+        
+        @asynccontextmanager
+        async def _manage_response_async(
+            request_params: dict,
+            stream: bool = False,
+            input: Optional[Any] = None,
+            timeout: float = None,
+        ) -> AsyncGenerator[httpx.Response, None]:
+            pre_call(request_params, input)
+            if timeout:
+                request_params["timeout"] = timeout
+            if stream:
+                request_params["stream"] = stream
+            try:
+                # async with AsyncHTTPHandler(timeout=timeout) as client:
+                self.async_handler = AsyncHTTPHandler(
+                    timeout=httpx.Timeout(timeout=request_params.pop("timeout", 600.0), connect=5.0),
+                )
+                # async_handler.client.verify = False
+                if "json" in request_params:
+                    request_params['data'] = json.dumps(request_params.pop("json", {}))
+                method = request_params.pop("method")
+                if method.upper() == "POST":
+                    resp = await self.async_handler.post(**request_params)
+                else:
+                    resp = await self.async_handler.get(**request_params)
+                yield resp
+                # await async_handler.close()
+            except Exception as e:
+                raise WatsonXAIError(status_code=500, message=str(e))
+            if not stream:
+                post_call(resp, request_params)
+
+        if async_:
+            return _manage_response_async
+        else:
+            return _manage_response

litellm/main.py

@@ -73,6 +73,7 @@ from .llms.azure_text import AzureTextCompletion
 from .llms.anthropic import AnthropicChatCompletion
 from .llms.anthropic_text import AnthropicTextCompletion
 from .llms.huggingface_restapi import Huggingface
+from .llms.watsonx import IBMWatsonXAI
 from .llms.prompt_templates.factory import (
     prompt_factory,
     custom_prompt,
@@ -109,6 +110,7 @@ anthropic_text_completions = AnthropicTextCompletion()
 azure_chat_completions = AzureChatCompletion()
 azure_text_completions = AzureTextCompletion()
 huggingface = Huggingface()
+watsonxai = IBMWatsonXAI()
 ####### COMPLETION ENDPOINTS ################
 
 
@@ -313,6 +315,7 @@ async def acompletion(
             or custom_llm_provider == "gemini"
             or custom_llm_provider == "sagemaker"
             or custom_llm_provider == "anthropic"
+            or custom_llm_provider == "watsonx"
             or custom_llm_provider in litellm.openai_compatible_providers
         ):  # currently implemented aiohttp calls for just azure, openai, hf, ollama, vertex ai soon all.
             init_response = await loop.run_in_executor(None, func_with_context)
@@ -1908,7 +1911,7 @@ def completion(
             response = response
         elif custom_llm_provider == "watsonx":
             custom_prompt_dict = custom_prompt_dict or litellm.custom_prompt_dict
-            response = watsonx.IBMWatsonXAI().completion(
+            response = watsonxai.completion(
                 model=model,
                 messages=messages,
                 custom_prompt_dict=custom_prompt_dict,
@@ -1919,7 +1922,8 @@ def completion(
                 logger_fn=logger_fn,
                 encoding=encoding,
                 logging_obj=logging,
-                timeout=timeout,  # type: ignore
+                acompletion=acompletion,
+                timeout=timeout,
             )
             if (
                 "stream" in optional_params
@@ -2572,6 +2576,7 @@ async def aembedding(*args, **kwargs):
             or custom_llm_provider == "fireworks_ai"
             or custom_llm_provider == "ollama"
             or custom_llm_provider == "vertex_ai"
+            or custom_llm_provider == "watsonx"
         ):  # currently implemented aiohttp calls for just azure and openai, soon all.
             # Await normally
             init_response = await loop.run_in_executor(None, func_with_context)
@@ -3029,13 +3034,14 @@ def embedding(
                 aembedding=aembedding,
             )
         elif custom_llm_provider == "watsonx":
-            response = watsonx.IBMWatsonXAI().embedding(
+            response = watsonxai.embedding(
                 model=model,
                 input=input,
                 encoding=encoding,
                 logging_obj=logging,
                 optional_params=optional_params,
                 model_response=EmbeddingResponse(),
+                aembedding=aembedding,
             )
         else:
             args = locals()

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

@@ -1 +1 @@
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+(self.webpackChunk_N_E=self.webpackChunk_N_E||[]).push([[744],{32028:function(e,n,t){Promise.resolve().then(t.t.bind(t,47690,23)),Promise.resolve().then(t.t.bind(t,48955,23)),Promise.resolve().then(t.t.bind(t,5613,23)),Promise.resolve().then(t.t.bind(t,11902,23)),Promise.resolve().then(t.t.bind(t,31778,23)),Promise.resolve().then(t.t.bind(t,77831,23))}},function(e){var n=function(n){return e(e.s=n)};e.O(0,[971,69],function(){return n(35317),n(32028)}),_N_E=e.O()}]);

litellm/proxy/_experimental/out/_next/static/chunks/webpack-5b257e1ab47d4b4a.js

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+0:["K8KXTbmuI2ArWjjdMi2iq",[[["",{"children":["__PAGE__",{}]},"$undefined","$undefined",true],["",{"children":["__PAGE__",{},["$L1",["$","$L2",null,{"propsForComponent":{"params":{}},"Component":"$3","isStaticGeneration":true}],null]]},[null,["$","html",null,{"lang":"en","children":["$","body",null,{"className":"__className_c23dc8","children":["$","$L4",null,{"parallelRouterKey":"children","segmentPath":["children"],"loading":"$undefined","loadingStyles":"$undefined","loadingScripts":"$undefined","hasLoading":false,"error":"$undefined","errorStyles":"$undefined","errorScripts":"$undefined","template":["$","$L5",null,{}],"templateStyles":"$undefined","templateScripts":"$undefined","notFound":[["$","title",null,{"children":"404: This page could not be found."}],["$","div",null,{"style":{"fontFamily":"system-ui,\"Segoe UI\",Roboto,Helvetica,Arial,sans-serif,\"Apple Color Emoji\",\"Segoe UI Emoji\"","height":"100vh","textAlign":"center","display":"flex","flexDirection":"column","alignItems":"center","justifyContent":"center"},"children":["$","div",null,{"children":[["$","style",null,{"dangerouslySetInnerHTML":{"__html":"body{color:#000;background:#fff;margin:0}.next-error-h1{border-right:1px solid rgba(0,0,0,.3)}@media (prefers-color-scheme:dark){body{color:#fff;background:#000}.next-error-h1{border-right:1px solid rgba(255,255,255,.3)}}"}}],["$","h1",null,{"className":"next-error-h1","style":{"display":"inline-block","margin":"0 20px 0 0","padding":"0 23px 0 0","fontSize":24,"fontWeight":500,"verticalAlign":"top","lineHeight":"49px"},"children":"404"}],["$","div",null,{"style":{"display":"inline-block"},"children":["$","h2",null,{"style":{"fontSize":14,"fontWeight":400,"lineHeight":"49px","margin":0},"children":"This page could not be found."}]}]]}]}]],"notFoundStyles":[],"styles":null}]}]}],null]],[[["$","link","0",{"rel":"stylesheet","href":"/ui/_next/static/css/a1602eb39f799143.css","precedence":"next","crossOrigin":""}]],"$L6"]]]]
 6:[["$","meta","0",{"name":"viewport","content":"width=device-width, initial-scale=1"}],["$","meta","1",{"charSet":"utf-8"}],["$","title","2",{"children":"LiteLLM Dashboard"}],["$","meta","3",{"name":"description","content":"LiteLLM Proxy Admin UI"}],["$","link","4",{"rel":"icon","href":"/ui/favicon.ico","type":"image/x-icon","sizes":"16x16"}],["$","meta","5",{"name":"next-size-adjust"}]]
 1:null

litellm/proxy/_types.py

@@ -515,6 +515,8 @@ class NewTeamRequest(TeamBase):
 
 class GlobalEndUsersSpend(LiteLLMBase):
     api_key: Optional[str] = None
+    startTime: Optional[datetime] = None
+    endTime: Optional[datetime] = None
 
 
 class TeamMemberAddRequest(LiteLLMBase):

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
@@ -3731,6 +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,
@@ -3746,6 +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(
@@ -3902,14 +3904,10 @@ async def completion(
                 },
             )
 
-        if hasattr(response, "_hidden_params"):
+        hidden_params = getattr(response, "_hidden_params", {}) or {}
-            model_id = response._hidden_params.get("model_id", None) or ""
+        model_id = hidden_params.get("model_id", None) or ""
-            original_response = (
+        cache_key = hidden_params.get("cache_key", None) or ""
-                response._hidden_params.get("original_response", None) or ""
+        api_base = hidden_params.get("api_base", None) or ""
-            )
-        else:
-            model_id = ""
-            original_response = ""
 
         verbose_proxy_logger.debug("final response: %s", response)
         if (
@@ -3917,6 +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,
@@ -3931,6 +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
@@ -3970,6 +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),
 ):
@@ -4116,6 +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
@@ -4154,6 +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
@@ -4273,6 +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
@@ -4309,6 +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),
 ):
@@ -4453,6 +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
@@ -4492,6 +4534,7 @@ async def audio_transcriptions(
 )
 async def moderations(
     request: Request,
+    fastapi_response: Response,
     user_api_key_dict: UserAPIKeyAuth = Depends(user_api_key_auth),
 ):
     """
@@ -4616,6 +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
@@ -5821,35 +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";"""
-
-        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()
+    startTime = None
-        past_date = current_date - timedelta(days=30)
+    endTime = None
-        response = await prisma_client.db.litellm_spendlogs.group_by(  # type: ignore
+    selected_api_key = None
-            by=["end_user"],
+    if data is not None:
-            where={
+        startTime = data.startTime
-                "AND": [{"startTime": {"gte": past_date}}, {"api_key": data.api_key}]  # type: ignore
+        endTime = data.endTime
-            },
+        selected_api_key = data.api_key
-            sum={"spend": True},
+
-            order={"_sum": {"spend": "desc"}},  # type: ignore
+    startTime = startTime or datetime.now() - timedelta(days=30)
-            take=100,
+    endTime = endTime or datetime.now()
-            count=True,
+
+    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
     )
-        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)
 
     return response
 

litellm/proxy/utils.py

@@ -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_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
+from pydantic import BaseModel, Extra, Field, root_validator  # type: ignore
-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/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)
+    response = litellm.completion(
-    assert response._hidden_params["region_name"] is not None
+        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/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:
@@ -4161,8 +4161,30 @@ 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:
@@ -7766,11 +7788,11 @@ 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
@@ -10545,6 +10567,18 @@ class CustomStreamWrapper:
             elif self.custom_llm_provider == "watsonx":
                 response_obj = self.handle_watsonx_stream(chunk)
                 completion_obj["content"] = response_obj["text"]
+                print_verbose(f"completion obj content: {completion_obj['content']}")
+                if getattr(model_response, "usage", None) is None:
+                    model_response.usage = Usage()
+                if response_obj.get("prompt_tokens") is not None:
+                    prompt_token_count = getattr(model_response.usage, "prompt_tokens", 0)
+                    model_response.usage.prompt_tokens = (prompt_token_count+response_obj["prompt_tokens"])
+                if response_obj.get("completion_tokens") is not None:
+                    model_response.usage.completion_tokens = response_obj["completion_tokens"]
+                model_response.usage.total_tokens = (
+                    getattr(model_response.usage, "prompt_tokens", 0)
+                    + getattr(model_response.usage, "completion_tokens", 0)
+                )
                 if response_obj["is_finished"]:
                     self.received_finish_reason = response_obj["finish_reason"]
             elif self.custom_llm_provider == "text-completion-openai":
@@ -10949,6 +10983,7 @@ class CustomStreamWrapper:
                 or self.custom_llm_provider == "sagemaker"
                 or self.custom_llm_provider == "gemini"
                 or self.custom_llm_provider == "cached_response"
+                or self.custom_llm_provider == "watsonx"
                 or self.custom_llm_provider in litellm.openai_compatible_endpoints
             ):
                 async for chunk in self.completion_stream:

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"
 ]

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

@@ -0,0 +1 @@
+(self.webpackChunk_N_E=self.webpackChunk_N_E||[]).push([[185],{87421:function(n,e,t){Promise.resolve().then(t.t.bind(t,99646,23)),Promise.resolve().then(t.t.bind(t,63385,23))},63385:function(){},99646:function(n){n.exports={style:{fontFamily:"'__Inter_c23dc8', '__Inter_Fallback_c23dc8'",fontStyle:"normal"},className:"__className_c23dc8"}}},function(n){n.O(0,[971,69,744],function(){return n(n.s=87421)}),_N_E=n.O()}]);

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/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 = body;
-      requestOptions.body = JSON.stringify({ api_key: keyToken });
-    }
 
     const response = await fetch(url, requestOptions);
     if (!response.ok) {

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

@@ -3,13 +3,14 @@ import { BarChart, BarList, Card, Title, Table, TableHead, TableHeaderCell, Tabl
 import React, { useState, useEffect } from "react";
 
 import ViewUserSpend from "./view_user_spend";
-import { Grid, Col, Text, LineChart, TabPanel, TabPanels, TabGroup, TabList, Tab, Select, SelectItem } from "@tremor/react";
+import { Grid, Col, Text, LineChart, TabPanel, TabPanels, TabGroup, TabList, Tab, Select, SelectItem, DateRangePicker, DateRangePickerValue } from "@tremor/react";
 import {
   userSpendLogsCall,
   keyInfoCall,
   adminSpendLogsCall,
   adminTopKeysCall,
   adminTopModelsCall,
+  adminTopEndUsersCall,
   teamSpendLogsCall,
   tagsSpendLogsCall,
   modelMetricsCall,
@@ -23,6 +24,7 @@ interface UsagePageProps {
   token: string | null;
   userRole: string | null;
   userID: string | null;
+  keys: any[] | null;
 }
 
 type CustomTooltipTypeBar = {
@@ -95,47 +97,14 @@ function getTopKeys(data: Array<{ [key: string]: unknown }>): any[] {
 }
 type DataDict = { [key: string]: unknown };
 type UserData = { user_id: string; spend: number };
-function getTopUsers(data: Array<DataDict>): UserData[] {
-  const userSpend: { [key: string]: number } = {};
 
-  data.forEach((dict) => {
-    const payload: DataDict = dict["users"] as DataDict;
-    Object.entries(payload).forEach(([user_id, value]) => {
-      if (
-        user_id === "" ||
-        user_id === undefined ||
-        user_id === null ||
-        user_id == "None"
-      ) {
-        return;
-      }
-
-      if (!userSpend[user_id]) {
-        userSpend[user_id] = 0;
-      }
-      userSpend[user_id] += value as number;
-    });
-  });
-
-  const spendUsers: UserData[] = Object.entries(userSpend).map(
-    ([user_id, spend]) => ({
-      user_id,
-      spend,
-    })
-  );
-
-  spendUsers.sort((a, b) => b.spend - a.spend);
-
-  const topKeys = spendUsers.slice(0, 5);
-  console.log(`topKeys: ${Object.values(topKeys[0])}`);
-  return topKeys;
-}
 
 const UsagePage: React.FC<UsagePageProps> = ({
   accessToken,
   token,
   userRole,
   userID,
+  keys,
 }) => {
   const currentDate = new Date();
   const [keySpendData, setKeySpendData] = useState<any[]>([]);
@@ -146,6 +115,11 @@ const UsagePage: React.FC<UsagePageProps> = ({
   const [topTagsData, setTopTagsData] = useState<any[]>([]);
   const [uniqueTeamIds, setUniqueTeamIds] = useState<any[]>([]);
   const [totalSpendPerTeam, setTotalSpendPerTeam] = useState<any[]>([]);
+  const [selectedKeyID, setSelectedKeyID] = useState<string | null>("");
+  const [dateValue, setDateValue] = useState<DateRangePickerValue>({
+    from: new Date(Date.now() - 7 * 24 * 60 * 60 * 1000), 
+    to: new Date(),
+  });
 
   const firstDay = new Date(
     currentDate.getFullYear(),
@@ -161,6 +135,26 @@ const UsagePage: React.FC<UsagePageProps> = ({
   let startTime = formatDate(firstDay);
   let endTime = formatDate(lastDay);
 
+  console.log("keys in usage", keys);
+
+  const updateEndUserData = async (startTime:  Date | undefined, endTime:  Date | undefined, uiSelectedKey: string | null) => {
+    if (!startTime || !endTime || !accessToken) {
+      return;
+    }
+
+    console.log("uiSelectedKey", uiSelectedKey);
+
+    let newTopUserData = await adminTopEndUsersCall(
+      accessToken,
+      uiSelectedKey,
+      startTime.toISOString(),
+      endTime.toISOString()
+    )
+    console.log("End user data updated successfully", newTopUserData);
+    setTopUsers(newTopUserData);
+  
+  }
+
   function formatDate(date: Date) {
     const year = date.getFullYear();
     let month = date.getMonth() + 1; // JS month index starts from 0
@@ -227,6 +221,12 @@ const UsagePage: React.FC<UsagePageProps> = ({
             const top_tags = await tagsSpendLogsCall(accessToken);
             setTopTagsData(top_tags.top_10_tags);
 
+            // get spend per end-user
+            let spend_user_call = await adminTopEndUsersCall(accessToken, null, undefined, undefined);
+            setTopUsers(spend_user_call);
+
+            console.log("spend/user result", spend_user_call);
+
           } else if (userRole == "App Owner") {
             await userSpendLogsCall(
               accessToken,
@@ -258,7 +258,6 @@ const UsagePage: React.FC<UsagePageProps> = ({
                   spend: k["spend"],
                 }));
                 setTopKeys(filtered_keys);
-                setTopUsers(getTopUsers(response));
                 setKeySpendData(response);
               }
             });
@@ -286,6 +285,7 @@ const UsagePage: React.FC<UsagePageProps> = ({
         <TabList className="mt-2">
           <Tab>All Up</Tab>
           <Tab>Team Based Usage</Tab>
+          <Tab>End User Usage</Tab>
            <Tab>Tag Based Usage</Tab>
         </TabList>
         <TabPanels>
@@ -323,22 +323,6 @@ const UsagePage: React.FC<UsagePageProps> = ({
                   />
                 </Card>
               </Col>
-              <Col numColSpan={1}>
-                <Card>
-                  <Title>Top Users</Title>
-                  <BarChart
-                    className="mt-4 h-40"
-                    data={topUsers}
-                    index="user_id"
-                    categories={["spend"]}
-                    colors={["blue"]}
-                    yAxisWidth={200}
-                    layout="vertical"
-                    showXAxis={false}
-                    showLegend={false}
-                  />
-                </Card>
-              </Col>
               <Col numColSpan={1}>
               <Card>
                   <Title>Top Models</Title>
@@ -354,6 +338,10 @@ const UsagePage: React.FC<UsagePageProps> = ({
                     showLegend={false}
                   />
                 </Card>
+               
+              </Col>
+              <Col numColSpan={1}>
+                
               </Col>
             </Grid>
             </TabPanel>
@@ -385,6 +373,88 @@ const UsagePage: React.FC<UsagePageProps> = ({
               <Col numColSpan={2}>
               </Col>
             </Grid>
+            </TabPanel>
+            <TabPanel>
+            <p className="mb-2 text-gray-500 italic text-[12px]">End-Users of your LLM API calls. Tracked when a `user` param is passed in your LLM calls <a className="text-blue-500" href="https://docs.litellm.ai/docs/proxy/users" target="_blank">docs here</a></p>
+              <Grid numItems={2}>
+                <Col>
+                <Text>Select Time Range</Text>
+       
+              <DateRangePicker 
+                  enableSelect={true} 
+                  value={dateValue} 
+                  onValueChange={(value) => {
+                    setDateValue(value);
+                    updateEndUserData(value.from, value.to, null); // Call updateModelMetrics with the new date range
+                  }}
+                />
+                         </Col>
+                         <Col>
+                  <Text>Select Key</Text>
+                  <Select defaultValue="all-keys">
+                  <SelectItem
+                    key="all-keys"
+                    value="all-keys"
+                    onClick={() => {
+                      updateEndUserData(dateValue.from, dateValue.to, null);
+                    }}
+                  >
+                    All Keys
+                  </SelectItem>
+                    {keys?.map((key: any, index: number) => {
+                      if (
+                        key &&
+                        key["key_alias"] !== null &&
+                        key["key_alias"].length > 0
+                      ) {
+                        return (
+                          
+                          <SelectItem
+                            key={index}
+                            value={String(index)}
+                            onClick={() => {
+                              updateEndUserData(dateValue.from, dateValue.to, key["token"]);
+                            }}
+                          >
+                            {key["key_alias"]}
+                          </SelectItem>
+                        );
+                      }
+                      return null; // Add this line to handle the case when the condition is not met
+                    })}
+                  </Select>
+                  </Col>
+
+              </Grid>
+            
+                
+                
+              <Card className="mt-4">
+
+
+             
+              <Table className="max-h-[70vh] min-h-[500px]">
+                  <TableHead>
+                    <TableRow>
+                      <TableHeaderCell>End User</TableHeaderCell>
+                      <TableHeaderCell>Spend</TableHeaderCell>
+                      <TableHeaderCell>Total Events</TableHeaderCell>
+                    </TableRow>
+                  </TableHead>
+
+                  <TableBody>
+                    {topUsers?.map((user: any, index: number) => (
+                      <TableRow key={index}>
+                        <TableCell>{user.end_user}</TableCell>
+                        <TableCell>{user.total_spend?.toFixed(4)}</TableCell>
+                        <TableCell>{user.total_count}</TableCell>
+                      </TableRow>
+                    ))}
+                  </TableBody>
+                </Table>
+
+              </Card>
+
             </TabPanel>
             <TabPanel>
             <Grid numItems={2} className="gap-2 h-[75vh] w-full mb-4">

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

@@ -24,7 +24,7 @@ import {
   Icon,
   TextInput,
 } from "@tremor/react";
-import { userInfoCall, adminTopEndUsersCall } from "./networking";
+import { userInfoCall } from "./networking";
 import { Badge, BadgeDelta, Button } from "@tremor/react";
 import RequestAccess from "./request_model_access";
 import CreateUser from "./create_user_button";
@@ -83,22 +83,7 @@ const ViewUserDashboard: React.FC<ViewUserDashboardProps> = ({
       fetchData();
     }
 
-    const fetchEndUserSpend = async () => {
+
-      try {
-        const topEndUsers = await adminTopEndUsersCall(accessToken, null);
-        console.log("user data response:", topEndUsers);
-        setEndUsers(topEndUsers);
-      } catch (error) {
-        console.error("There was an error fetching the model data", error);
-      }
-    };
-    if (
-      userRole &&
-      (userRole == "Admin" || userRole == "Admin Viewer") &&
-      !endUsers
-    ) {
-      fetchEndUserSpend();
-    }
   }, [accessToken, token, userRole, userID, currentPage]);
 
   if (!userData) {
@@ -109,16 +94,6 @@ const ViewUserDashboard: React.FC<ViewUserDashboardProps> = ({
     return <div>Loading...</div>;
   }
 
-  const onKeyClick = async (keyToken: String) => {
-    try {
-      const topEndUsers = await adminTopEndUsersCall(accessToken, keyToken);
-      console.log("user data response:", topEndUsers);
-      setEndUsers(topEndUsers);
-    } catch (error) {
-      console.error("There was an error fetching the model data", error);
-    }
-  };
-
   function renderPagination() {
     if (!userData) return null;
 
@@ -157,14 +132,11 @@ const ViewUserDashboard: React.FC<ViewUserDashboardProps> = ({
         <CreateUser userID={userID} accessToken={accessToken} teams={teams}/>
         <Card className="w-full mx-auto flex-auto overflow-y-auto max-h-[80vh] mb-4">
         <div className="mb-4 mt-1">
-        <Text><b>Key Owners: </b> Users on LiteLLM that created API Keys. Automatically tracked by LiteLLM</Text>
+        <Text>These are Users on LiteLLM that created API Keys. Automatically tracked by LiteLLM</Text>
-        <Text className="mt-1"><b>End Users: </b>End Users of your LLM API calls. Tracked When a `user` param is passed in your LLM calls</Text>
+       
         </div>
           <TabGroup>
-            <TabList variant="line" defaultValue="1">
+
-              <Tab value="1">Key Owners</Tab>
-              <Tab value="2">End-Users</Tab>
-            </TabList>
             <TabPanels>
               <TabPanel>
                 
@@ -190,7 +162,7 @@ const ViewUserDashboard: React.FC<ViewUserDashboardProps> = ({
                             ? user.models
                             : "All Models"}
                         </TableCell>
-                        <TableCell>{user.spend ? user.spend : 0}</TableCell>
+                        <TableCell>{user.spend ? user.spend?.toFixed(2) : 0}</TableCell>
                         <TableCell>
                           {user.max_budget ? user.max_budget : "Unlimited"}
                         </TableCell>
@@ -220,29 +192,10 @@ const ViewUserDashboard: React.FC<ViewUserDashboardProps> = ({
                 <div className="flex items-center">
                   <div className="flex-1"></div>
                   <div className="flex-1 flex justify-between items-center">
-                    <Text className="w-1/4 mr-2 text-right">Key</Text>
+   
-                    <Select defaultValue="1" className="w-3/4">
-                      {keys?.map((key: any, index: number) => {
-                        if (
-                          key &&
-                          key["key_alias"] !== null &&
-                          key["key_alias"].length > 0
-                        ) {
-                          return (
-                            <SelectItem
-                              key={index}
-                              value={String(index)}
-                              onClick={() => onKeyClick(key["token"])}
-                            >
-                              {key["key_alias"]}
-                            </SelectItem>
-                          );
-                        }
-                      })}
-                    </Select>
                   </div>
                 </div>
-                <Table className="max-h-[70vh] min-h-[500px]">
+                {/* <Table className="max-h-[70vh] min-h-[500px]">
                   <TableHead>
                     <TableRow>
                       <TableHeaderCell>End User</TableHeaderCell>
@@ -260,7 +213,7 @@ const ViewUserDashboard: React.FC<ViewUserDashboardProps> = ({
                       </TableRow>
                     ))}
                   </TableBody>
-                </Table>
+                </Table> */}
               </TabPanel>
             </TabPanels>
           </TabGroup>