Merge branch 'main' into litellm_return-response_headers

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

@@ -22,6 +22,7 @@ Anthropic API fails requests when `max_tokens` are not passed. Due to this litel
 import os
 
 os.environ["ANTHROPIC_API_KEY"] = "your-api-key"
+# os.environ["ANTHROPIC_API_BASE"] = "" # [OPTIONAL] or 'ANTHROPIC_BASE_URL'
 ```
 
 ## Usage

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

@@ -1,3 +1,6 @@
+import Tabs from '@theme/Tabs';
+import TabItem from '@theme/TabItem';
+
 # Mistral AI API
 https://docs.mistral.ai/api/
 
@@ -41,9 +44,106 @@ for chunk in response:
 ```
 
 
+
+## Usage with LiteLLM Proxy 
+
+### 1. Set Mistral Models on config.yaml
+
+```yaml
+model_list:
+  - model_name: mistral-small-latest
+    litellm_params:
+      model: mistral/mistral-small-latest
+      api_key: "os.environ/MISTRAL_API_KEY" # ensure you have `MISTRAL_API_KEY` in your .env
+```
+
+### 2. Start Proxy 
+
+```
+litellm --config config.yaml
+```
+
+### 3. Test it
+
+
+<Tabs>
+<TabItem value="Curl" label="Curl Request">
+
+```shell
+curl --location 'http://0.0.0.0:4000/chat/completions' \
+--header 'Content-Type: application/json' \
+--data ' {
+      "model": "mistral-small-latest",
+      "messages": [
+        {
+          "role": "user",
+          "content": "what llm are you"
+        }
+      ]
+    }
+'
+```
+</TabItem>
+<TabItem value="openai" label="OpenAI v1.0.0+">
+
+```python
+import openai
+client = openai.OpenAI(
+    api_key="anything",
+    base_url="http://0.0.0.0:4000"
+)
+
+response = client.chat.completions.create(model="mistral-small-latest", messages = [
+    {
+        "role": "user",
+        "content": "this is a test request, write a short poem"
+    }
+])
+
+print(response)
+
+```
+</TabItem>
+<TabItem value="langchain" label="Langchain">
+
+```python
+from langchain.chat_models import ChatOpenAI
+from langchain.prompts.chat import (
+    ChatPromptTemplate,
+    HumanMessagePromptTemplate,
+    SystemMessagePromptTemplate,
+)
+from langchain.schema import HumanMessage, SystemMessage
+
+chat = ChatOpenAI(
+    openai_api_base="http://0.0.0.0:4000", # set openai_api_base to the LiteLLM Proxy
+    model = "mistral-small-latest",
+    temperature=0.1
+)
+
+messages = [
+    SystemMessage(
+        content="You are a helpful assistant that im using to make a test request to."
+    ),
+    HumanMessage(
+        content="test from litellm. tell me why it's amazing in 1 sentence"
+    ),
+]
+response = chat(messages)
+
+print(response)
+```
+</TabItem>
+</Tabs>
+
 ## Supported Models
+
+:::info
 All models listed here https://docs.mistral.ai/platform/endpoints are supported. We actively maintain the list of models, pricing, token window, etc. [here](https://github.com/BerriAI/litellm/blob/main/model_prices_and_context_window.json).
 
+:::
+
+
 | Model Name     | Function Call                                                |
 |----------------|--------------------------------------------------------------|
 | Mistral Small  | `completion(model="mistral/mistral-small-latest", messages)` |
@@ -53,6 +153,10 @@ All models listed here https://docs.mistral.ai/platform/endpoints are supported.
 | Mixtral 8x7B   | `completion(model="mistral/open-mixtral-8x7b", messages)`    |
 | Mixtral 8x22B  | `completion(model="mistral/open-mixtral-8x22b", messages)`   |
 | Codestral      | `completion(model="mistral/codestral-latest", messages)`     |
+| Mistral NeMo      | `completion(model="mistral/open-mistral-nemo", messages)`     |
+| Mistral NeMo 2407      | `completion(model="mistral/open-mistral-nemo-2407", messages)`     |
+| Codestral Mamba      | `completion(model="mistral/open-codestral-mamba", messages)`     |
+| Codestral Mamba    | `completion(model="mistral/codestral-mamba-latest"", messages)`     |
 
 ## Function Calling 
 

litellm/llms/openai.py

@@ -831,6 +831,7 @@ class OpenAIChatCompletion(BaseLLM):
         client=None,
         organization: Optional[str] = None,
         custom_llm_provider: Optional[str] = None,
+        drop_params: Optional[bool] = None,
     ):
         super().completion()
         exception_mapping_worked = False
@@ -886,6 +887,7 @@ class OpenAIChatCompletion(BaseLLM):
                                 client=client,
                                 max_retries=max_retries,
                                 organization=organization,
+                                drop_params=drop_params,
                             )
                         else:
                             return self.acompletion(
@@ -899,6 +901,7 @@ class OpenAIChatCompletion(BaseLLM):
                                 client=client,
                                 max_retries=max_retries,
                                 organization=organization,
+                                drop_params=drop_params,
                             )
                     elif optional_params.get("stream", False):
                         return self.streaming(
@@ -962,6 +965,33 @@ class OpenAIChatCompletion(BaseLLM):
                             model_response_object=model_response,
                             _response_headers=headers,
                         )
+                except openai.UnprocessableEntityError as e:
+                    ## check if body contains unprocessable params - related issue https://github.com/BerriAI/litellm/issues/4800
+                    if litellm.drop_params is True or drop_params is True:
+                        if e.body is not None and e.body.get("detail"):  # type: ignore
+                            detail = e.body.get("detail")  # type: ignore
+                            invalid_params: List[str] = []
+                            if (
+                                isinstance(detail, List)
+                                and len(detail) > 0
+                                and isinstance(detail[0], dict)
+                            ):
+                                for error_dict in detail:
+                                    if (
+                                        error_dict.get("loc")
+                                        and isinstance(error_dict.get("loc"), list)
+                                        and len(error_dict.get("loc")) == 2
+                                    ):
+                                        invalid_params.append(error_dict["loc"][1])
+
+                        new_data = {}
+                        for k, v in optional_params.items():
+                            if k not in invalid_params:
+                                new_data[k] = v
+                        optional_params = new_data
+                    else:
+                        raise e
+                    # e.message
                 except Exception as e:
                     if print_verbose is not None:
                         print_verbose(f"openai.py: Received openai error - {str(e)}")
@@ -1019,30 +1049,36 @@ class OpenAIChatCompletion(BaseLLM):
         client=None,
         max_retries=None,
         headers=None,
+        drop_params: Optional[bool] = None,
     ):
         response = None
-        try:
+        for _ in range(
-            openai_aclient = self._get_openai_client(
+            2
-                is_async=True,
+        ):  # if call fails due to alternating messages, retry with reformatted message
-                api_key=api_key,
+            try:
-                api_base=api_base,
+                openai_aclient = self._get_openai_client(
-                timeout=timeout,
+                    is_async=True,
-                max_retries=max_retries,
+                    api_key=api_key,
-                organization=organization,
+                    api_base=api_base,
-                client=client,
+                    timeout=timeout,
-            )
+                    max_retries=max_retries,
+                    organization=organization,
+                    client=client,
+                )
 
-            ## LOGGING
+                ## LOGGING
-            logging_obj.pre_call(
+                logging_obj.pre_call(
-                input=data["messages"],
+                    input=data["messages"],
-                api_key=openai_aclient.api_key,
+                    api_key=openai_aclient.api_key,
-                additional_args={
+                    additional_args={
-                    "headers": {"Authorization": f"Bearer {openai_aclient.api_key}"},
+                        "headers": {
-                    "api_base": openai_aclient._base_url._uri_reference,
+                            "Authorization": f"Bearer {openai_aclient.api_key}"
-                    "acompletion": True,
+                        },
-                    "complete_input_dict": data,
+                        "api_base": openai_aclient._base_url._uri_reference,
-                },
+                        "acompletion": True,
-            )
+                        "complete_input_dict": data,
+                    },
+                )
 
             headers, response = await self.make_openai_chat_completion_request(
                 openai_aclient=openai_aclient, data=data, timeout=timeout
@@ -1063,6 +1099,51 @@ class OpenAIChatCompletion(BaseLLM):
             )
         except Exception as e:
             raise e
+                headers, response = await self.make_openai_chat_completion_request(
+                    openai_aclient=openai_aclient, data=data, timeout=timeout
+                )
+                stringified_response = response.model_dump()
+                logging_obj.post_call(
+                    input=data["messages"],
+                    api_key=api_key,
+                    original_response=stringified_response,
+                    additional_args={"complete_input_dict": data},
+                )
+                logging_obj.model_call_details["response_headers"] = headers
+                return convert_to_model_response_object(
+                    response_object=stringified_response,
+                    model_response_object=model_response,
+                    hidden_params={"headers": headers},
+                )
+            except openai.UnprocessableEntityError as e:
+                ## check if body contains unprocessable params - related issue https://github.com/BerriAI/litellm/issues/4800
+                if litellm.drop_params is True or drop_params is True:
+                    if e.body is not None and e.body.get("detail"):  # type: ignore
+                        detail = e.body.get("detail")  # type: ignore
+                        invalid_params: List[str] = []
+                        if (
+                            isinstance(detail, List)
+                            and len(detail) > 0
+                            and isinstance(detail[0], dict)
+                        ):
+                            for error_dict in detail:
+                                if (
+                                    error_dict.get("loc")
+                                    and isinstance(error_dict.get("loc"), list)
+                                    and len(error_dict.get("loc")) == 2
+                                ):
+                                    invalid_params.append(error_dict["loc"][1])
+
+                    new_data = {}
+                    for k, v in data.items():
+                        if k not in invalid_params:
+                            new_data[k] = v
+                    data = new_data
+                else:
+                    raise e
+                # e.message
+            except Exception as e:
+                raise e
 
     def streaming(
         self,
@@ -1126,29 +1207,31 @@ class OpenAIChatCompletion(BaseLLM):
         client=None,
         max_retries=None,
         headers=None,
+        drop_params: Optional[bool] = None,
     ):
         response = None
-        try:
+        for _ in range(2):
-            openai_aclient = self._get_openai_client(
+            try:
-                is_async=True,
+                openai_aclient = self._get_openai_client(
-                api_key=api_key,
+                    is_async=True,
-                api_base=api_base,
+                    api_key=api_key,
-                timeout=timeout,
+                    api_base=api_base,
-                max_retries=max_retries,
+                    timeout=timeout,
-                organization=organization,
+                    max_retries=max_retries,
-                client=client,
+                    organization=organization,
-            )
+                    client=client,
-            ## LOGGING
+                )
-            logging_obj.pre_call(
+                ## LOGGING
-                input=data["messages"],
+                logging_obj.pre_call(
-                api_key=api_key,
+                    input=data["messages"],
-                additional_args={
+                    api_key=api_key,
-                    "headers": headers,
+                    additional_args={
-                    "api_base": api_base,
+                        "headers": headers,
-                    "acompletion": True,
+                        "api_base": api_base,
-                    "complete_input_dict": data,
+                        "acompletion": True,
-                },
+                        "complete_input_dict": data,
-            )
+                    },
+                )
 
             headers, response = await self.make_openai_chat_completion_request(
                 openai_aclient=openai_aclient, data=data, timeout=timeout
@@ -1170,14 +1253,63 @@ class OpenAIChatCompletion(BaseLLM):
                 raise OpenAIError(
                     status_code=500,
                     message=f"{str(e)}\n\nOriginal Response: {response.text}",
+
+                headers, response = await self.make_openai_chat_completion_request(
+                    openai_aclient=openai_aclient, data=data, timeout=timeout
+
                 )
-            else:
+                logging_obj.model_call_details["response_headers"] = headers
-                if type(e).__name__ == "ReadTimeout":
+                streamwrapper = CustomStreamWrapper(
-                    raise OpenAIError(status_code=408, message=f"{type(e).__name__}")
+                    completion_stream=response,
-                elif hasattr(e, "status_code"):
+                    model=model,
-                    raise OpenAIError(status_code=e.status_code, message=str(e))
+                    custom_llm_provider="openai",
+                    logging_obj=logging_obj,
+                    stream_options=data.get("stream_options", None),
+                )
+                return streamwrapper
+            except openai.UnprocessableEntityError as e:
+                ## check if body contains unprocessable params - related issue https://github.com/BerriAI/litellm/issues/4800
+                if litellm.drop_params is True or drop_params is True:
+                    if e.body is not None and e.body.get("detail"):  # type: ignore
+                        detail = e.body.get("detail")  # type: ignore
+                        invalid_params: List[str] = []
+                        if (
+                            isinstance(detail, List)
+                            and len(detail) > 0
+                            and isinstance(detail[0], dict)
+                        ):
+                            for error_dict in detail:
+                                if (
+                                    error_dict.get("loc")
+                                    and isinstance(error_dict.get("loc"), list)
+                                    and len(error_dict.get("loc")) == 2
+                                ):
+                                    invalid_params.append(error_dict["loc"][1])
+
+                    new_data = {}
+                    for k, v in data.items():
+                        if k not in invalid_params:
+                            new_data[k] = v
+                    data = new_data
                 else:
-                    raise OpenAIError(status_code=500, message=f"{str(e)}")
+                    raise e
+            except (
+                Exception
+            ) as e:  # need to exception handle here. async exceptions don't get caught in sync functions.
+                if response is not None and hasattr(response, "text"):
+                    raise OpenAIError(
+                        status_code=500,
+                        message=f"{str(e)}\n\nOriginal Response: {response.text}",
+                    )
+                else:
+                    if type(e).__name__ == "ReadTimeout":
+                        raise OpenAIError(
+                            status_code=408, message=f"{type(e).__name__}"
+                        )
+                    elif hasattr(e, "status_code"):
+                        raise OpenAIError(status_code=e.status_code, message=str(e))
+                    else:
+                        raise OpenAIError(status_code=500, message=f"{str(e)}")
 
     # Embedding
     async def make_openai_embedding_request(

litellm/main.py

@@ -1176,6 +1176,7 @@ def completion(
                     client=client,  # pass AsyncOpenAI, OpenAI client
                     organization=organization,
                     custom_llm_provider=custom_llm_provider,
+                    drop_params=non_default_params.get("drop_params"),
                 )
             except Exception as e:
                 ## LOGGING - log the original exception returned
@@ -1487,6 +1488,7 @@ def completion(
                     api_base
                     or litellm.api_base
                     or get_secret("ANTHROPIC_API_BASE")
+                    or get_secret("ANTHROPIC_BASE_URL")
                     or "https://api.anthropic.com/v1/complete"
                 )
                 response = anthropic_text_completions.completion(
@@ -1512,6 +1514,7 @@ def completion(
                     api_base
                     or litellm.api_base
                     or get_secret("ANTHROPIC_API_BASE")
+                    or get_secret("ANTHROPIC_BASE_URL")
                     or "https://api.anthropic.com/v1/messages"
                 )
                 response = anthropic_chat_completions.completion(

litellm/model_prices_and_context_window_backup.json

@@ -929,6 +929,46 @@
         "litellm_provider": "mistral",
         "mode": "chat"
     },
+    "mistral/open-mistral-nemo": {
+        "max_tokens": 128000,
+        "max_input_tokens": 128000,
+        "max_output_tokens": 128000,
+        "input_cost_per_token":  0.0000003,
+        "output_cost_per_token": 0.0000003,
+        "litellm_provider": "mistral",
+        "mode": "chat",
+        "source": "https://mistral.ai/technology/"
+    },
+    "mistral/open-mistral-nemo-2407": {
+        "max_tokens": 128000,
+        "max_input_tokens": 128000,
+        "max_output_tokens": 128000,
+        "input_cost_per_token":  0.0000003,
+        "output_cost_per_token": 0.0000003,
+        "litellm_provider": "mistral",
+        "mode": "chat",
+        "source": "https://mistral.ai/technology/"
+    },
+    "mistral/open-codestral-mamba": {
+        "max_tokens": 256000,
+        "max_input_tokens": 256000,
+        "max_output_tokens": 256000,
+        "input_cost_per_token": 0.00000025,
+        "output_cost_per_token": 0.00000025,
+        "litellm_provider": "mistral",
+        "mode": "chat",
+        "source": "https://mistral.ai/technology/"
+    },
+    "mistral/codestral-mamba-latest": {
+        "max_tokens": 256000,
+        "max_input_tokens": 256000,
+        "max_output_tokens": 256000,
+        "input_cost_per_token": 0.00000025,
+        "output_cost_per_token": 0.00000025,
+        "litellm_provider": "mistral",
+        "mode": "chat",
+        "source": "https://mistral.ai/technology/"
+    },
     "mistral/mistral-embed": {
         "max_tokens": 8192,
         "max_input_tokens": 8192,

litellm/proxy/_experimental/out/index.html

@@ -1 +1 @@
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litellm/proxy/_experimental/out/index.txt

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litellm/proxy/_experimental/out/model_hub.txt

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litellm/proxy/_experimental/out/onboarding.txt

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litellm/proxy/_new_secret_config.yaml

@@ -1,13 +1,9 @@
 model_list:
-  - model_name: bad-azure-model
+  - model_name: azure-mistral
     litellm_params:
-      model: azure/chatgpt-v-2
+      model: azure_ai/mistral
-      azure_ad_token: ""
+      api_key: os.environ/AZURE_AI_MISTRAL_API_KEY
-      api_base: os.environ/AZURE_API_BASE
+      api_base: os.environ/AZURE_AI_MISTRAL_API_BASE
-
-  - model_name: good-openai-model
-    litellm_params:
-      model: gpt-3.5-turbo
 
 litellm_settings:
-  fallbacks: [{"bad-azure-model": ["good-openai-model"]}]
+  drop_params: true

litellm/proxy/_types.py

@@ -886,6 +886,7 @@ class LiteLLM_TeamTable(TeamBase):
     budget_duration: Optional[str] = None
     budget_reset_at: Optional[datetime] = None
     model_id: Optional[int] = None
+    last_refreshed_at: Optional[float] = None
 
     model_config = ConfigDict(protected_namespaces=())
 
@@ -1238,6 +1239,9 @@ class LiteLLM_VerificationTokenView(LiteLLM_VerificationToken):
     end_user_rpm_limit: Optional[int] = None
     end_user_max_budget: Optional[float] = None
 
+    # Time stamps
+    last_refreshed_at: Optional[float] = None  # last time joint view was pulled from db
+
 
 class UserAPIKeyAuth(
     LiteLLM_VerificationTokenView

litellm/proxy/auth/auth_checks.py

@@ -59,7 +59,7 @@ def common_checks(
     6. [OPTIONAL] If 'litellm.max_budget' is set (>0), is proxy under budget
     """
     _model = request_body.get("model", None)
-    if team_object is not None and team_object.blocked == True:
+    if team_object is not None and team_object.blocked is True:
         raise Exception(
             f"Team={team_object.team_id} is blocked. Update via `/team/unblock` if your admin."
         )
@@ -349,6 +349,15 @@ async def get_user_object(
         )
 
 
+async def _cache_team_object(
+    team_id: str,
+    team_table: LiteLLM_TeamTable,
+    user_api_key_cache: DualCache,
+):
+    key = "team_id:{}".format(team_id)
+    await user_api_key_cache.async_set_cache(key=key, value=team_table)
+
+
 @log_to_opentelemetry
 async def get_team_object(
     team_id: str,
@@ -386,7 +395,9 @@ async def get_team_object(
 
         _response = LiteLLM_TeamTable(**response.dict())
         # save the team object to cache
-        await user_api_key_cache.async_set_cache(key=key, value=_response)
+        await _cache_team_object(
+            team_id=team_id, team_table=_response, user_api_key_cache=user_api_key_cache
+        )
 
         return _response
     except Exception as e:

litellm/proxy/auth/user_api_key_auth.py

@@ -453,6 +453,32 @@ async def user_api_key_auth(
 
             return valid_token
 
+        if (
+            valid_token is not None
+            and isinstance(valid_token, UserAPIKeyAuth)
+            and valid_token.team_id is not None
+            and user_api_key_cache.get_cache(
+                key="team_id:{}".format(valid_token.team_id)
+            )
+            is not None
+        ):
+            ## UPDATE TEAM VALUES BASED ON CACHED TEAM OBJECT - allows `/team/update` values to work for cached token
+            team_obj: LiteLLM_TeamTable = user_api_key_cache.get_cache(
+                key="team_id:{}".format(valid_token.team_id)
+            )
+
+            if (
+                team_obj.last_refreshed_at is not None
+                and valid_token.last_refreshed_at is not None
+                and team_obj.last_refreshed_at > valid_token.last_refreshed_at
+            ):
+                team_obj_dict = team_obj.__dict__
+
+                for k, v in team_obj_dict.items():
+                    field_name = f"team_{k}"
+                    if field_name in valid_token.__fields__:
+                        setattr(valid_token, field_name, v)
+
         try:
             is_master_key_valid = secrets.compare_digest(api_key, master_key)  # type: ignore
         except Exception as e:
@@ -504,7 +530,6 @@ async def user_api_key_auth(
             raise Exception("No connected db.")
 
         ## check for cache hit (In-Memory Cache)
-        original_api_key = api_key  # (Patch: For DynamoDB Backwards Compatibility)
         _user_role = None
         if api_key.startswith("sk-"):
             api_key = hash_token(token=api_key)
@@ -521,10 +546,13 @@ async def user_api_key_auth(
                     parent_otel_span=parent_otel_span,
                     proxy_logging_obj=proxy_logging_obj,
                 )
+
                 if _valid_token is not None:
+                    ## update cached token
                     valid_token = UserAPIKeyAuth(
                         **_valid_token.model_dump(exclude_none=True)
                     )
+
             verbose_proxy_logger.debug("Token from db: %s", valid_token)
         elif valid_token is not None and isinstance(valid_token, UserAPIKeyAuth):
             verbose_proxy_logger.debug("API Key Cache Hit!")

litellm/proxy/management_endpoints/team_endpoints.py

@@ -328,11 +328,13 @@ async def update_team(
     }'
     ```
     """
+    from litellm.proxy.auth.auth_checks import _cache_team_object
     from litellm.proxy.proxy_server import (
         _duration_in_seconds,
         create_audit_log_for_update,
         litellm_proxy_admin_name,
         prisma_client,
+        user_api_key_cache,
     )
 
     if prisma_client is None:
@@ -361,11 +363,22 @@ async def update_team(
         # set the budget_reset_at in DB
         updated_kv["budget_reset_at"] = reset_at
 
-    team_row = await prisma_client.update_data(
+    team_row: Optional[
-        update_key_values=updated_kv,
+        LiteLLM_TeamTable
-        data=updated_kv,
+    ] = await prisma_client.db.litellm_teamtable.update(
-        table_name="team",
+        where={"team_id": data.team_id}, data=updated_kv  # type: ignore
-        team_id=data.team_id,
+    )
+
+    if team_row is None or team_row.team_id is None:
+        raise HTTPException(
+            status_code=400,
+            detail={"error": "Team doesn't exist. Got={}".format(team_row)},
+        )
+
+    await _cache_team_object(
+        team_id=team_row.team_id,
+        team_table=team_row,
+        user_api_key_cache=user_api_key_cache,
     )
 
     # Enterprise Feature - Audit Logging. Enable with litellm.store_audit_logs = True
@@ -392,7 +405,7 @@ async def update_team(
             )
         )
 
-    return team_row
+    return {"team_id": team_row.team_id, "data": team_row}
 
 
 @router.post(

litellm/proxy/utils.py

@@ -1331,7 +1331,9 @@ class PrismaClient:
                             response["team_models"] = []
                         if response["team_blocked"] is None:
                             response["team_blocked"] = False
-                        response = LiteLLM_VerificationTokenView(**response)
+                        response = LiteLLM_VerificationTokenView(
+                            **response, last_refreshed_at=time.time()
+                        )
                         # for prisma we need to cast the expires time to str
                         if response.expires is not None and isinstance(
                             response.expires, datetime

litellm/router.py

@@ -417,14 +417,15 @@ class Router:
             litellm.failure_callback.append(self.deployment_callback_on_failure)
         else:
             litellm.failure_callback = [self.deployment_callback_on_failure]
-        print(  # noqa
+        verbose_router_logger.debug(
             f"Intialized router with Routing strategy: {self.routing_strategy}\n\n"
             f"Routing enable_pre_call_checks: {self.enable_pre_call_checks}\n\n"
             f"Routing fallbacks: {self.fallbacks}\n\n"
             f"Routing content fallbacks: {self.content_policy_fallbacks}\n\n"
             f"Routing context window fallbacks: {self.context_window_fallbacks}\n\n"
             f"Router Redis Caching={self.cache.redis_cache}\n"
-        )  # noqa
+        )
+
         self.routing_strategy_args = routing_strategy_args
         self.retry_policy: Optional[RetryPolicy] = retry_policy
         self.model_group_retry_policy: Optional[Dict[str, RetryPolicy]] = (

litellm/tests/test_completion.py

@@ -142,6 +142,36 @@ def test_completion_azure_ai_command_r():
         pytest.fail(f"Error occurred: {e}")
 
 
+@pytest.mark.parametrize("sync_mode", [True, False])
+@pytest.mark.asyncio
+async def test_completion_azure_ai_mistral_invalid_params(sync_mode):
+    try:
+        import os
+
+        litellm.set_verbose = True
+
+        os.environ["AZURE_AI_API_BASE"] = os.getenv("AZURE_MISTRAL_API_BASE", "")
+        os.environ["AZURE_AI_API_KEY"] = os.getenv("AZURE_MISTRAL_API_KEY", "")
+
+        data = {
+            "model": "azure_ai/mistral",
+            "messages": [{"role": "user", "content": "What is the meaning of life?"}],
+            "frequency_penalty": 0.1,
+            "presence_penalty": 0.1,
+            "drop_params": True,
+        }
+        if sync_mode:
+            response: litellm.ModelResponse = completion(**data)  # type: ignore
+        else:
+            response: litellm.ModelResponse = await litellm.acompletion(**data)  # type: ignore
+
+        assert "azure_ai" in response.model
+    except litellm.Timeout as e:
+        pass
+    except Exception as e:
+        pytest.fail(f"Error occurred: {e}")
+
+
 def test_completion_azure_command_r():
     try:
         litellm.set_verbose = True

litellm/tests/test_streaming.py

@@ -2881,6 +2881,40 @@ def test_azure_streaming_and_function_calling():
         raise e
 
 
+@pytest.mark.parametrize("sync_mode", [True, False])
+@pytest.mark.asyncio
+async def test_completion_azure_ai_mistral_invalid_params(sync_mode):
+    try:
+        import os
+
+        litellm.set_verbose = True
+
+        os.environ["AZURE_AI_API_BASE"] = os.getenv("AZURE_MISTRAL_API_BASE", "")
+        os.environ["AZURE_AI_API_KEY"] = os.getenv("AZURE_MISTRAL_API_KEY", "")
+
+        data = {
+            "model": "azure_ai/mistral",
+            "messages": [{"role": "user", "content": "What is the meaning of life?"}],
+            "frequency_penalty": 0.1,
+            "presence_penalty": 0.1,
+            "drop_params": True,
+            "stream": True,
+        }
+        if sync_mode:
+            response: litellm.ModelResponse = completion(**data)  # type: ignore
+            for chunk in response:
+                print(chunk)
+        else:
+            response: litellm.ModelResponse = await litellm.acompletion(**data)  # type: ignore
+
+            async for chunk in response:
+                print(chunk)
+    except litellm.Timeout as e:
+        pass
+    except Exception as e:
+        pytest.fail(f"Error occurred: {e}")
+
+
 @pytest.mark.asyncio
 async def test_azure_astreaming_and_function_calling():
     import uuid

litellm/tests/test_user_api_key_auth.py

@@ -44,3 +44,48 @@ def test_check_valid_ip(
     request = Request(client_ip)
 
     assert _check_valid_ip(allowed_ips, request) == expected_result  # type: ignore
+
+
+@pytest.mark.asyncio
+async def test_check_blocked_team():
+    """
+    cached valid_token obj has team_blocked = true
+
+    cached team obj has team_blocked = false
+
+    assert team is not blocked
+    """
+    import asyncio
+    import time
+
+    from fastapi import Request
+    from starlette.datastructures import URL
+
+    from litellm.proxy._types import LiteLLM_TeamTable, UserAPIKeyAuth
+    from litellm.proxy.auth.user_api_key_auth import user_api_key_auth
+    from litellm.proxy.proxy_server import hash_token, user_api_key_cache
+
+    _team_id = "1234"
+    user_key = "sk-12345678"
+
+    valid_token = UserAPIKeyAuth(
+        team_id=_team_id,
+        team_blocked=True,
+        token=hash_token(user_key),
+        last_refreshed_at=time.time(),
+    )
+    await asyncio.sleep(1)
+    team_obj = LiteLLM_TeamTable(
+        team_id=_team_id, blocked=False, last_refreshed_at=time.time()
+    )
+    user_api_key_cache.set_cache(key=hash_token(user_key), value=valid_token)
+    user_api_key_cache.set_cache(key="team_id:{}".format(_team_id), value=team_obj)
+
+    setattr(litellm.proxy.proxy_server, "user_api_key_cache", user_api_key_cache)
+    setattr(litellm.proxy.proxy_server, "master_key", "sk-1234")
+    setattr(litellm.proxy.proxy_server, "prisma_client", "hello-world")
+
+    request = Request(scope={"type": "http"})
+    request._url = URL(url="/chat/completions")
+
+    await user_api_key_auth(request=request, api_key="Bearer " + user_key)

model_prices_and_context_window.json

@@ -929,6 +929,46 @@
         "litellm_provider": "mistral",
         "mode": "chat"
     },
+    "mistral/open-mistral-nemo": {
+        "max_tokens": 128000,
+        "max_input_tokens": 128000,
+        "max_output_tokens": 128000,
+        "input_cost_per_token":  0.0000003,
+        "output_cost_per_token": 0.0000003,
+        "litellm_provider": "mistral",
+        "mode": "chat",
+        "source": "https://mistral.ai/technology/"
+    },
+    "mistral/open-mistral-nemo-2407": {
+        "max_tokens": 128000,
+        "max_input_tokens": 128000,
+        "max_output_tokens": 128000,
+        "input_cost_per_token":  0.0000003,
+        "output_cost_per_token": 0.0000003,
+        "litellm_provider": "mistral",
+        "mode": "chat",
+        "source": "https://mistral.ai/technology/"
+    },
+    "mistral/open-codestral-mamba": {
+        "max_tokens": 256000,
+        "max_input_tokens": 256000,
+        "max_output_tokens": 256000,
+        "input_cost_per_token": 0.00000025,
+        "output_cost_per_token": 0.00000025,
+        "litellm_provider": "mistral",
+        "mode": "chat",
+        "source": "https://mistral.ai/technology/"
+    },
+    "mistral/codestral-mamba-latest": {
+        "max_tokens": 256000,
+        "max_input_tokens": 256000,
+        "max_output_tokens": 256000,
+        "input_cost_per_token": 0.00000025,
+        "output_cost_per_token": 0.00000025,
+        "litellm_provider": "mistral",
+        "mode": "chat",
+        "source": "https://mistral.ai/technology/"
+    },
     "mistral/mistral-embed": {
         "max_tokens": 8192,
         "max_input_tokens": 8192,

pyproject.toml

@@ -1,6 +1,6 @@
 [tool.poetry]
 name = "litellm"
-version = "1.41.25"
+version = "1.41.26"
 description = "Library to easily interface with LLM API providers"
 authors = ["BerriAI"]
 license = "MIT"
@@ -91,7 +91,7 @@ requires = ["poetry-core", "wheel"]
 build-backend = "poetry.core.masonry.api"
 
 [tool.commitizen]
-version = "1.41.25"
+version = "1.41.26"
 version_files = [
     "pyproject.toml:^version"
 ]

ui/litellm-dashboard/out/index.html

@@ -1 +1 @@
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ui/litellm-dashboard/out/index.txt

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ui/litellm-dashboard/out/model_hub.txt

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ui/litellm-dashboard/out/onboarding.txt

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