Merge branch 'main' into litellm_vertex_completion_httpx

.circleci/config.yml

@@ -202,6 +202,7 @@ jobs:
               -e REDIS_PORT=$REDIS_PORT \
               -e AZURE_FRANCE_API_KEY=$AZURE_FRANCE_API_KEY \
               -e AZURE_EUROPE_API_KEY=$AZURE_EUROPE_API_KEY \
+              -e MISTRAL_API_KEY=$MISTRAL_API_KEY \
               -e AWS_ACCESS_KEY_ID=$AWS_ACCESS_KEY_ID \
               -e AWS_SECRET_ACCESS_KEY=$AWS_SECRET_ACCESS_KEY \
               -e AWS_REGION_NAME=$AWS_REGION_NAME \

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

@@ -1,10 +1,13 @@
 # Clarifai
 Anthropic, OpenAI, Mistral, Llama and Gemini LLMs are Supported on Clarifai. 
 
+:::warning 
+
+Streaming is not yet supported on using clarifai and litellm. Tracking support here: https://github.com/BerriAI/litellm/issues/4162
+
+:::
+
 ## Pre-Requisites
-
-`pip install clarifai`
-
 `pip install litellm`
 
 ## Required Environment Variables
@@ -12,6 +15,7 @@ To obtain your Clarifai Personal access token follow this [link](https://docs.cl
 
 ```python
 os.environ["CLARIFAI_API_KEY"] = "YOUR_CLARIFAI_PAT"  # CLARIFAI_PAT
+
 ```
 
 ## Usage
@@ -68,7 +72,7 @@ Example  Usage - Note: liteLLM supports all models deployed on Clarifai
 | clarifai/meta.Llama-2.codeLlama-70b-Python   | `completion('clarifai/meta.Llama-2.codeLlama-70b-Python', messages)`| 
 | clarifai/meta.Llama-2.codeLlama-70b-Instruct | `completion('clarifai/meta.Llama-2.codeLlama-70b-Instruct', messages)` |   
 
-## Mistal LLMs
+## Mistral LLMs
 | Model Name                                  | Function Call                                                         |
 |---------------------------------------------|------------------------------------------------------------------------|
 | clarifai/mistralai.completion.mixtral-8x22B            | `completion('clarifai/mistralai.completion.mixtral-8x22B', messages)`               |

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

@@ -558,6 +558,29 @@ All models listed [here](https://github.com/BerriAI/litellm/blob/57f37f743886a02
 | text-embedding-preview-0409 | `embedding(model="vertex_ai/text-embedding-preview-0409", input)` |
 | text-multilingual-embedding-preview-0409 | `embedding(model="vertex_ai/text-multilingual-embedding-preview-0409", input)` | 
 
+### Advanced Use `task_type` and `title` (Vertex Specific Params)
+
+👉 `task_type` and `title` are vertex specific params
+
+LiteLLM Supported Vertex Specific Params
+
+```python
+auto_truncate: Optional[bool] = None
+task_type: Optional[Literal["RETRIEVAL_QUERY","RETRIEVAL_DOCUMENT", "SEMANTIC_SIMILARITY", "CLASSIFICATION", "CLUSTERING", "QUESTION_ANSWERING", "FACT_VERIFICATION"]] = None
+title: Optional[str] = None # The title of the document to be embedded. (only valid with task_type=RETRIEVAL_DOCUMENT).
+```
+
+**Example Usage with LiteLLM**
+```python
+response = litellm.embedding(
+    model="vertex_ai/text-embedding-004",
+    input=["good morning from litellm", "gm"]
+    task_type = "RETRIEVAL_DOCUMENT",
+    dimensions=1,
+    auto_truncate=True,
+)
+```
+
 ## Image Generation Models
 
 Usage 

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

@@ -1,5 +1,6 @@
 import Tabs from '@theme/Tabs';
 import TabItem from '@theme/TabItem';
+import Image from '@theme/IdealImage';
 
 # 🐳 Docker, Deploying LiteLLM Proxy
 
@@ -537,7 +538,9 @@ ghcr.io/berriai/litellm-database:main-latest --config your_config.yaml
 
 ## Advanced Deployment Settings
 
-### Customization of the server root path
+### 1. Customization of the server root path (custom Proxy base url)
+
+💥 Use this when you want to serve LiteLLM on a custom base url path like `https://localhost:4000/api/v1` 
 
 :::info
 
@@ -548,9 +551,29 @@ In a Kubernetes deployment, it's possible to utilize a shared DNS to host multip
 Customize the root path to eliminate the need for employing multiple DNS configurations during deployment.
 
 👉 Set `SERVER_ROOT_PATH` in your .env and this will be set as your server root path
+```
+export SERVER_ROOT_PATH="/api/v1"
+```
 
+**Step 1. Run Proxy with `SERVER_ROOT_PATH` set in your env **
 
-### Setting SSL Certification 
+```shell
+docker run --name litellm-proxy \
+-e DATABASE_URL=postgresql://<user>:<password>@<host>:<port>/<dbname> \
+-e SERVER_ROOT_PATH="/api/v1" \
+-p 4000:4000 \
+ghcr.io/berriai/litellm-database:main-latest --config your_config.yaml
+```
+
+After running the proxy you can access it on `http://0.0.0.0:4000/api/v1/` (since we set `SERVER_ROOT_PATH="/api/v1"`)
+
+**Step 2. Verify Running on correct path**
+
+<Image img={require('../../img/custom_root_path.png')} />
+
+**That's it**, that's all you need to run the proxy on a custom root path
+
+### 2. Setting SSL Certification 
 
 Use this, If you need to set ssl certificates for your on prem litellm proxy
 

litellm/__init__.py

@@ -766,8 +766,8 @@ from .llms.gemini import GeminiConfig
 from .llms.nlp_cloud import NLPCloudConfig
 from .llms.aleph_alpha import AlephAlphaConfig
 from .llms.petals import PetalsConfig
-from .llms.vertex_ai import VertexAIConfig
 from .llms.vertex_httpx import VertexGeminiConfig
+from .llms.vertex_ai import VertexAIConfig, VertexAITextEmbeddingConfig
 from .llms.vertex_ai_anthropic import VertexAIAnthropicConfig
 from .llms.sagemaker import SagemakerConfig
 from .llms.ollama import OllamaConfig
@@ -789,7 +789,9 @@ from .llms.openai import (
     OpenAIConfig,
     OpenAITextCompletionConfig,
     MistralConfig,
+    MistralEmbeddingConfig,
     DeepInfraConfig,
+    AzureAIStudioConfig,
 )
 from .llms.azure import (
     AzureOpenAIConfig,

litellm/llms/azure.py

@@ -36,6 +36,9 @@ from ..types.llms.openai import (
     AsyncAssistantStreamManager,
     AssistantStreamManager,
 )
+from litellm.caching import DualCache
+
+azure_ad_cache = DualCache()
 
 
 class AzureOpenAIError(Exception):
@@ -309,9 +312,10 @@ def select_azure_base_url_or_endpoint(azure_client_params: dict):
 
 def get_azure_ad_token_from_oidc(azure_ad_token: str):
     azure_client_id = os.getenv("AZURE_CLIENT_ID", None)
-    azure_tenant = os.getenv("AZURE_TENANT_ID", None)
+    azure_tenant_id = os.getenv("AZURE_TENANT_ID", None)
+    azure_authority_host = os.getenv("AZURE_AUTHORITY_HOST", "https://login.microsoftonline.com")
 
-    if azure_client_id is None or azure_tenant is None:
+    if azure_client_id is None or azure_tenant_id is None:
         raise AzureOpenAIError(
             status_code=422,
             message="AZURE_CLIENT_ID and AZURE_TENANT_ID must be set",
@@ -325,8 +329,19 @@ def get_azure_ad_token_from_oidc(azure_ad_token: str):
             message="OIDC token could not be retrieved from secret manager.",
         )
 
+    azure_ad_token_cache_key = json.dumps({
+        "azure_client_id": azure_client_id,
+        "azure_tenant_id": azure_tenant_id,
+        "azure_authority_host": azure_authority_host,
+        "oidc_token": oidc_token,
+    })
+
+    azure_ad_token_access_token = azure_ad_cache.get_cache(azure_ad_token_cache_key)
+    if azure_ad_token_access_token is not None:
+        return azure_ad_token_access_token
+
     req_token = httpx.post(
-        f"https://login.microsoftonline.com/{azure_tenant}/oauth2/v2.0/token",
+        f"{azure_authority_host}/{azure_tenant_id}/oauth2/v2.0/token",
         data={
             "client_id": azure_client_id,
             "grant_type": "client_credentials",
@@ -342,12 +357,23 @@ def get_azure_ad_token_from_oidc(azure_ad_token: str):
             message=req_token.text,
         )
 
-    possible_azure_ad_token = req_token.json().get("access_token", None)
+    azure_ad_token_json = req_token.json()
+    azure_ad_token_access_token = azure_ad_token_json.get("access_token", None)
+    azure_ad_token_expires_in = azure_ad_token_json.get("expires_in", None)
 
-    if possible_azure_ad_token is None:
+    if azure_ad_token_access_token is None:
-        raise AzureOpenAIError(status_code=422, message="Azure AD Token not returned")
+        raise AzureOpenAIError(
+            status_code=422, message="Azure AD Token access_token not returned"
+        )
 
-    return possible_azure_ad_token
+    if azure_ad_token_expires_in is None:
+        raise AzureOpenAIError(
+            status_code=422, message="Azure AD Token expires_in not returned"
+        )
+
+    azure_ad_cache.set_cache(key=azure_ad_token_cache_key, value=azure_ad_token_access_token, ttl=azure_ad_token_expires_in)
+
+    return azure_ad_token_access_token
 
 
 class AzureChatCompletion(BaseLLM):

litellm/llms/bedrock_httpx.py

@@ -53,7 +53,9 @@ from litellm.types.llms.openai import (
     ChatCompletionToolCallFunctionChunk,
     ChatCompletionDeltaChunk,
 )
+from litellm.caching import DualCache
 
+iam_cache = DualCache()
 
 class AmazonCohereChatConfig:
     """
@@ -325,38 +327,53 @@ class BedrockLLM(BaseLLM):
         ) = params_to_check
 
         ### CHECK STS ###
-        if (
+        if aws_web_identity_token is not None and aws_role_name is not None and aws_session_name is not None:
-            aws_web_identity_token is not None
+            iam_creds_cache_key = json.dumps({
-            and aws_role_name is not None
+                "aws_web_identity_token": aws_web_identity_token,
-            and aws_session_name is not None
+                "aws_role_name": aws_role_name,
-        ):
+                "aws_session_name": aws_session_name,
-            oidc_token = get_secret(aws_web_identity_token)
+                "aws_region_name": aws_region_name,
+            })
 
-            if oidc_token is None:
+            iam_creds_dict = iam_cache.get_cache(iam_creds_cache_key)
-                raise BedrockError(
+            if iam_creds_dict is None:
-                    message="OIDC token could not be retrieved from secret manager.",
+                oidc_token = get_secret(aws_web_identity_token)
-                    status_code=401,
+
+                if oidc_token is None:
+                    raise BedrockError(
+                        message="OIDC token could not be retrieved from secret manager.",
+                        status_code=401,
+                    )
+
+                sts_client = boto3.client(
+                    "sts",
+                    region_name=aws_region_name,
+                    endpoint_url=f"https://sts.{aws_region_name}.amazonaws.com"
                 )
 
-            sts_client = boto3.client("sts")
+                # https://docs.aws.amazon.com/STS/latest/APIReference/API_AssumeRoleWithWebIdentity.html
+                # https://boto3.amazonaws.com/v1/documentation/api/latest/reference/services/sts/client/assume_role_with_web_identity.html
+                sts_response = sts_client.assume_role_with_web_identity(
+                    RoleArn=aws_role_name,
+                    RoleSessionName=aws_session_name,
+                    WebIdentityToken=oidc_token,
+                    DurationSeconds=3600,
+                )
 
-            # https://docs.aws.amazon.com/STS/latest/APIReference/API_AssumeRoleWithWebIdentity.html
+                iam_creds_dict = {
-            # https://boto3.amazonaws.com/v1/documentation/api/latest/reference/services/sts/client/assume_role_with_web_identity.html
+                    "aws_access_key_id": sts_response["Credentials"]["AccessKeyId"],
-            sts_response = sts_client.assume_role_with_web_identity(
+                    "aws_secret_access_key": sts_response["Credentials"]["SecretAccessKey"],
-                RoleArn=aws_role_name,
+                    "aws_session_token": sts_response["Credentials"]["SessionToken"],
-                RoleSessionName=aws_session_name,
+                    "region_name": aws_region_name,
-                WebIdentityToken=oidc_token,
+                }
-                DurationSeconds=3600,
-            )
 
-            session = boto3.Session(
+                iam_cache.set_cache(key=iam_creds_cache_key, value=json.dumps(iam_creds_dict), ttl=3600 - 60)
-                aws_access_key_id=sts_response["Credentials"]["AccessKeyId"],
-                aws_secret_access_key=sts_response["Credentials"]["SecretAccessKey"],
-                aws_session_token=sts_response["Credentials"]["SessionToken"],
-                region_name=aws_region_name,
-            )
 
-            return session.get_credentials()
+            session = boto3.Session(**iam_creds_dict)
+
+            iam_creds = session.get_credentials()
+
+            return iam_creds
         elif aws_role_name is not None and aws_session_name is not None:
             sts_client = boto3.client(
                 "sts",
@@ -1416,38 +1433,53 @@ class BedrockConverseLLM(BaseLLM):
         ) = params_to_check
 
         ### CHECK STS ###
-        if (
+        if aws_web_identity_token is not None and aws_role_name is not None and aws_session_name is not None:
-            aws_web_identity_token is not None
+            iam_creds_cache_key = json.dumps({
-            and aws_role_name is not None
+                "aws_web_identity_token": aws_web_identity_token,
-            and aws_session_name is not None
+                "aws_role_name": aws_role_name,
-        ):
+                "aws_session_name": aws_session_name,
-            oidc_token = get_secret(aws_web_identity_token)
+                "aws_region_name": aws_region_name,
+            })
 
-            if oidc_token is None:
+            iam_creds_dict = iam_cache.get_cache(iam_creds_cache_key)
-                raise BedrockError(
+            if iam_creds_dict is None:
-                    message="OIDC token could not be retrieved from secret manager.",
+                oidc_token = get_secret(aws_web_identity_token)
-                    status_code=401,
+
+                if oidc_token is None:
+                    raise BedrockError(
+                        message="OIDC token could not be retrieved from secret manager.",
+                        status_code=401,
+                    )
+
+                sts_client = boto3.client(
+                    "sts",
+                    region_name=aws_region_name,
+                    endpoint_url=f"https://sts.{aws_region_name}.amazonaws.com"
                 )
 
-            sts_client = boto3.client("sts")
+                # https://docs.aws.amazon.com/STS/latest/APIReference/API_AssumeRoleWithWebIdentity.html
+                # https://boto3.amazonaws.com/v1/documentation/api/latest/reference/services/sts/client/assume_role_with_web_identity.html
+                sts_response = sts_client.assume_role_with_web_identity(
+                    RoleArn=aws_role_name,
+                    RoleSessionName=aws_session_name,
+                    WebIdentityToken=oidc_token,
+                    DurationSeconds=3600,
+                )
 
-            # https://docs.aws.amazon.com/STS/latest/APIReference/API_AssumeRoleWithWebIdentity.html
+                iam_creds_dict = {
-            # https://boto3.amazonaws.com/v1/documentation/api/latest/reference/services/sts/client/assume_role_with_web_identity.html
+                    "aws_access_key_id": sts_response["Credentials"]["AccessKeyId"],
-            sts_response = sts_client.assume_role_with_web_identity(
+                    "aws_secret_access_key": sts_response["Credentials"]["SecretAccessKey"],
-                RoleArn=aws_role_name,
+                    "aws_session_token": sts_response["Credentials"]["SessionToken"],
-                RoleSessionName=aws_session_name,
+                    "region_name": aws_region_name,
-                WebIdentityToken=oidc_token,
+                }
-                DurationSeconds=3600,
-            )
 
-            session = boto3.Session(
+                iam_cache.set_cache(key=iam_creds_cache_key, value=json.dumps(iam_creds_dict), ttl=3600 - 60)
-                aws_access_key_id=sts_response["Credentials"]["AccessKeyId"],
-                aws_secret_access_key=sts_response["Credentials"]["SecretAccessKey"],
-                aws_session_token=sts_response["Credentials"]["SessionToken"],
-                region_name=aws_region_name,
-            )
 
-            return session.get_credentials()
+            session = boto3.Session(**iam_creds_dict)
+
+            iam_creds = session.get_credentials()
+
+            return iam_creds
         elif aws_role_name is not None and aws_session_name is not None:
             sts_client = boto3.client(
                 "sts",

litellm/llms/clarifai.py

@@ -139,6 +139,7 @@ def process_response(
 
 def convert_model_to_url(model: str, api_base: str):
     user_id, app_id, model_id = model.split(".")
+    model_id = model_id.lower()
     return f"{api_base}/users/{user_id}/apps/{app_id}/models/{model_id}/outputs"
 
 
@@ -171,19 +172,55 @@ async def async_completion(
 
     async_handler = AsyncHTTPHandler(timeout=httpx.Timeout(timeout=600.0, connect=5.0))
     response = await async_handler.post(
-        api_base, headers=headers, data=json.dumps(data)
+        url=model, headers=headers, data=json.dumps(data)
     )
 
-    return process_response(
+    logging_obj.post_call(
-        model=model,
+        input=prompt,
-        prompt=prompt,
-        response=response,
-        model_response=model_response,
         api_key=api_key,
-        data=data,
+        original_response=response.text,
-        encoding=encoding,
+        additional_args={"complete_input_dict": data},
-        logging_obj=logging_obj,
     )
+    ## RESPONSE OBJECT
+    try:
+        completion_response = response.json()
+    except Exception:
+        raise ClarifaiError(
+            message=response.text, status_code=response.status_code, url=model
+        )
+    # print(completion_response)
+    try:
+        choices_list = []
+        for idx, item in enumerate(completion_response["outputs"]):
+            if len(item["data"]["text"]["raw"]) > 0:
+                message_obj = Message(content=item["data"]["text"]["raw"])
+            else:
+                message_obj = Message(content=None)
+            choice_obj = Choices(
+                finish_reason="stop",
+                index=idx + 1,  # check
+                message=message_obj,
+            )
+            choices_list.append(choice_obj)
+        model_response["choices"] = choices_list
+
+    except Exception as e:
+        raise ClarifaiError(
+            message=traceback.format_exc(), status_code=response.status_code, url=model
+        )
+
+    # Calculate Usage
+    prompt_tokens = len(encoding.encode(prompt))
+    completion_tokens = len(
+        encoding.encode(model_response["choices"][0]["message"].get("content"))
+    )
+    model_response["model"] = model
+    model_response["usage"] = Usage(
+        prompt_tokens=prompt_tokens,
+        completion_tokens=completion_tokens,
+        total_tokens=prompt_tokens + completion_tokens,
+    )
+    return model_response
 
 
 def completion(
@@ -241,7 +278,7 @@ def completion(
         additional_args={
             "complete_input_dict": data,
             "headers": headers,
-            "api_base": api_base,
+            "api_base": model,
         },
     )
     if acompletion == True:

litellm/llms/openai.py

@@ -28,6 +28,7 @@ from .prompt_templates.factory import prompt_factory, custom_prompt
 from openai import OpenAI, AsyncOpenAI
 from ..types.llms.openai import *
 import openai
+from litellm.types.utils import ProviderField
 
 
 class OpenAIError(Exception):
@@ -164,6 +165,68 @@ class MistralConfig:
         return optional_params
 
 
+class MistralEmbeddingConfig:
+    """
+    Reference: https://docs.mistral.ai/api/#operation/createEmbedding
+    """
+
+    def __init__(
+        self,
+    ) -> None:
+        locals_ = locals().copy()
+        for key, value in locals_.items():
+            if key != "self" and value is not None:
+                setattr(self.__class__, key, value)
+
+    @classmethod
+    def get_config(cls):
+        return {
+            k: v
+            for k, v in cls.__dict__.items()
+            if not k.startswith("__")
+            and not isinstance(
+                v,
+                (
+                    types.FunctionType,
+                    types.BuiltinFunctionType,
+                    classmethod,
+                    staticmethod,
+                ),
+            )
+            and v is not None
+        }
+
+    def get_supported_openai_params(self):
+        return [
+            "encoding_format",
+        ]
+
+    def map_openai_params(self, non_default_params: dict, optional_params: dict):
+        for param, value in non_default_params.items():
+            if param == "encoding_format":
+                optional_params["encoding_format"] = value
+        return optional_params
+
+
+class AzureAIStudioConfig:
+    def get_required_params(self) -> List[ProviderField]:
+        """For a given provider, return it's required fields with a description"""
+        return [
+            ProviderField(
+                field_name="api_key",
+                field_type="string",
+                field_description="Your Azure AI Studio API Key.",
+                field_value="zEJ...",
+            ),
+            ProviderField(
+                field_name="api_base",
+                field_type="string",
+                field_description="Your Azure AI Studio API Base.",
+                field_value="https://Mistral-serverless.",
+            ),
+        ]
+
+
 class DeepInfraConfig:
     """
     Reference: https://deepinfra.com/docs/advanced/openai_api

litellm/llms/vertex_ai.py

@@ -4,6 +4,7 @@ from enum import Enum
 import requests  # type: ignore
 import time
 from typing import Callable, Optional, Union, List, Literal, Any
+from pydantic import BaseModel
 from litellm.utils import ModelResponse, Usage, CustomStreamWrapper, map_finish_reason
 import litellm, uuid
 import httpx, inspect  # type: ignore
@@ -1298,6 +1299,95 @@ async def async_streaming(
     return streamwrapper
 
 
+class VertexAITextEmbeddingConfig(BaseModel):
+    """
+    Reference: https://cloud.google.com/vertex-ai/generative-ai/docs/model-reference/text-embeddings-api#TextEmbeddingInput
+
+    Args:
+        auto_truncate: Optional(bool) If True, will truncate input text to fit within the model's max input length.
+        task_type: Optional(str) The type of task to be performed. The default is "RETRIEVAL_QUERY".
+        title: Optional(str) The title of the document to be embedded. (only valid with task_type=RETRIEVAL_DOCUMENT).
+    """
+
+    auto_truncate: Optional[bool] = None
+    task_type: Optional[
+        Literal[
+            "RETRIEVAL_QUERY",
+            "RETRIEVAL_DOCUMENT",
+            "SEMANTIC_SIMILARITY",
+            "CLASSIFICATION",
+            "CLUSTERING",
+            "QUESTION_ANSWERING",
+            "FACT_VERIFICATION",
+        ]
+    ] = None
+    title: Optional[str] = None
+
+    def __init__(
+        self,
+        auto_truncate: Optional[bool] = None,
+        task_type: Optional[
+            Literal[
+                "RETRIEVAL_QUERY",
+                "RETRIEVAL_DOCUMENT",
+                "SEMANTIC_SIMILARITY",
+                "CLASSIFICATION",
+                "CLUSTERING",
+                "QUESTION_ANSWERING",
+                "FACT_VERIFICATION",
+            ]
+        ] = None,
+        title: Optional[str] = None,
+    ) -> None:
+        locals_ = locals()
+        for key, value in locals_.items():
+            if key != "self" and value is not None:
+                setattr(self.__class__, key, value)
+
+    @classmethod
+    def get_config(cls):
+        return {
+            k: v
+            for k, v in cls.__dict__.items()
+            if not k.startswith("__")
+            and not isinstance(
+                v,
+                (
+                    types.FunctionType,
+                    types.BuiltinFunctionType,
+                    classmethod,
+                    staticmethod,
+                ),
+            )
+            and v is not None
+        }
+
+    def get_supported_openai_params(self):
+        return [
+            "dimensions",
+        ]
+
+    def map_openai_params(self, non_default_params: dict, optional_params: dict):
+        for param, value in non_default_params.items():
+            if param == "dimensions":
+                optional_params["output_dimensionality"] = value
+        return optional_params
+
+    def get_mapped_special_auth_params(self) -> dict:
+        """
+        Common auth params across bedrock/vertex_ai/azure/watsonx
+        """
+        return {"project": "vertex_project", "region_name": "vertex_location"}
+
+    def map_special_auth_params(self, non_default_params: dict, optional_params: dict):
+        mapped_params = self.get_mapped_special_auth_params()
+
+        for param, value in non_default_params.items():
+            if param in mapped_params:
+                optional_params[mapped_params[param]] = value
+        return optional_params
+
+
 def embedding(
     model: str,
     input: Union[list, str],
@@ -1321,7 +1411,7 @@ def embedding(
             message="vertexai import failed please run `pip install google-cloud-aiplatform`",
         )
 
-    from vertexai.language_models import TextEmbeddingModel
+    from vertexai.language_models import TextEmbeddingModel, TextEmbeddingInput
     import google.auth  # type: ignore
 
     ## Load credentials with the correct quota project ref: https://github.com/googleapis/python-aiplatform/issues/2557#issuecomment-1709284744
@@ -1352,6 +1442,16 @@ def embedding(
     if isinstance(input, str):
         input = [input]
 
+    if optional_params is not None and isinstance(optional_params, dict):
+        if optional_params.get("task_type") or optional_params.get("title"):
+            # if user passed task_type or title, cast to TextEmbeddingInput
+            _task_type = optional_params.pop("task_type", None)
+            _title = optional_params.pop("title", None)
+            input = [
+                TextEmbeddingInput(text=x, task_type=_task_type, title=_title)
+                for x in input
+            ]
+
     try:
         llm_model = TextEmbeddingModel.from_pretrained(model)
     except Exception as e:
@@ -1368,7 +1468,8 @@ def embedding(
             encoding=encoding,
         )
 
-    request_str = f"""embeddings = llm_model.get_embeddings({input})"""
+    _input_dict = {"texts": input, **optional_params}
+    request_str = f"""embeddings = llm_model.get_embeddings({_input_dict})"""
     ## LOGGING PRE-CALL
     logging_obj.pre_call(
         input=input,
@@ -1380,7 +1481,7 @@ def embedding(
     )
 
     try:
-        embeddings = llm_model.get_embeddings(input)
+        embeddings = llm_model.get_embeddings(**_input_dict)
     except Exception as e:
         raise VertexAIError(status_code=500, message=str(e))
 
@@ -1388,6 +1489,7 @@ def embedding(
     logging_obj.post_call(input=input, api_key=None, original_response=embeddings)
     ## Populate OpenAI compliant dictionary
     embedding_response = []
+    input_tokens: int = 0
     for idx, embedding in enumerate(embeddings):
         embedding_response.append(
             {
@@ -1396,14 +1498,10 @@ def embedding(
                 "embedding": embedding.values,
             }
         )
+        input_tokens += embedding.statistics.token_count
     model_response["object"] = "list"
     model_response["data"] = embedding_response
     model_response["model"] = model
-    input_tokens = 0
-
-    input_str = "".join(input)
-
-    input_tokens += len(encoding.encode(input_str))
 
     usage = Usage(
         prompt_tokens=input_tokens, completion_tokens=0, total_tokens=input_tokens
@@ -1425,7 +1523,8 @@ async def async_embedding(
     """
     Async embedding implementation
     """
-    request_str = f"""embeddings = llm_model.get_embeddings({input})"""
+    _input_dict = {"texts": input, **optional_params}
+    request_str = f"""embeddings = llm_model.get_embeddings({_input_dict})"""
     ## LOGGING PRE-CALL
     logging_obj.pre_call(
         input=input,
@@ -1437,7 +1536,7 @@ async def async_embedding(
     )
 
     try:
-        embeddings = await client.get_embeddings_async(input)
+        embeddings = await client.get_embeddings_async(**_input_dict)
     except Exception as e:
         raise VertexAIError(status_code=500, message=str(e))
 
@@ -1445,6 +1544,7 @@ async def async_embedding(
     logging_obj.post_call(input=input, api_key=None, original_response=embeddings)
     ## Populate OpenAI compliant dictionary
     embedding_response = []
+    input_tokens: int = 0
     for idx, embedding in enumerate(embeddings):
         embedding_response.append(
             {
@@ -1453,18 +1553,13 @@ async def async_embedding(
                 "embedding": embedding.values,
             }
         )
+        input_tokens += embedding.statistics.token_count
+
     model_response["object"] = "list"
     model_response["data"] = embedding_response
     model_response["model"] = model
-    input_tokens = 0
-
-    input_str = "".join(input)
-
-    input_tokens += len(encoding.encode(input_str))
-
     usage = Usage(
         prompt_tokens=input_tokens, completion_tokens=0, total_tokens=input_tokens
     )
     model_response.usage = usage
-
     return model_response

litellm/main.py

@@ -336,6 +336,7 @@ async def acompletion(
             or custom_llm_provider == "predibase"
             or custom_llm_provider == "bedrock"
             or custom_llm_provider == "databricks"
+            or custom_llm_provider == "clarifai"
             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)

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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/_types.py

@@ -224,6 +224,7 @@ class LiteLLMRoutes(enum.Enum):
         "/key/delete",
         "/global/spend/logs",
         "/global/predict/spend/logs",
+        "/sso/get/logout_url",
     ]
 
     management_routes: List = [  # key

litellm/proxy/litellm_pre_call_utils.py

@@ -79,10 +79,6 @@ async def add_litellm_data_to_request(
                     data["cache"][k] = v
 
     verbose_proxy_logger.debug("receiving data: %s", data)
-    # users can pass in 'user' param to /chat/completions. Don't override it
-    if data.get("user", None) is None and user_api_key_dict.user_id is not None:
-        # if users are using user_api_key_auth, set `user` in `data`
-        data["user"] = user_api_key_dict.user_id
 
     if "metadata" not in data:
         data["metadata"] = {}

litellm/proxy/proxy_config.yaml

@@ -14,10 +14,9 @@ model_list:
     litellm_params:
       model: openai/*
       api_key: os.environ/OPENAI_API_KEY
-  - model_name: my-triton-model
+  - model_name: mistral-embed
     litellm_params:
-      model: triton/any"
+      model: mistral/mistral-embed
-      api_base: https://exampleopenaiendpoint-production.up.railway.app/triton/embeddings
 
 general_settings:
   master_key: sk-1234

litellm/proxy/proxy_server.py

@@ -879,6 +879,7 @@ async def user_api_key_auth(
 
         ## 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)
         valid_token: Optional[UserAPIKeyAuth] = user_api_key_cache.get_cache(  # type: ignore
@@ -1512,7 +1513,7 @@ async def user_api_key_auth(
                 ):
                     return UserAPIKeyAuth(
                         api_key=api_key,
-                        user_role="app_owner",
+                        user_role=_user_role,
                         parent_otel_span=parent_otel_span,
                         **valid_token_dict,
                     )
@@ -8857,7 +8858,7 @@ async def new_user(data: NewUserRequest):
     - organization_id: Optional[str] - specify the org a user belongs to.
     - user_email: Optional[str] - Specify a user email.
     - send_invite_email: Optional[bool] - Specify if an invite email should be sent.
-    - user_role: Optional[str] - Specify a user role - "admin", "app_owner", "app_user"
+    - user_role: Optional[str] - Specify a user role - "proxy_admin", "proxy_admin_viewer", "internal_user", "internal_user_viewer", "team", "customer". Info about each role here: `https://github.com/BerriAI/litellm/litellm/proxy/_types.py#L20`
     - max_budget: Optional[float] - Specify max budget for a given user.
     - models: Optional[list] - Model_name's a user is allowed to call. (if empty, key is allowed to call all models)
     - tpm_limit: Optional[int] - Specify tpm limit for a given user (Tokens per minute)
@@ -14737,6 +14738,22 @@ async def cache_flushall():
         )
 
 
+@router.get(
+    "/get/litellm_model_cost_map",
+    include_in_schema=False,
+    dependencies=[Depends(user_api_key_auth)],
+)
+async def get_litellm_model_cost_map():
+    try:
+        _model_cost_map = litellm.model_cost
+        return _model_cost_map
+    except Exception as e:
+        raise HTTPException(
+            status_code=500,
+            detail=f"Internal Server Error ({str(e)})",
+        )
+
+
 @router.get("/", dependencies=[Depends(user_api_key_auth)])
 async def home(request: Request):
     return "LiteLLM: RUNNING"

litellm/router.py

@@ -3618,6 +3618,7 @@ class Router:
                 except Exception:
                     model_info = None
                 # get llm provider
+                model, llm_provider = "", ""
                 try:
                     model, llm_provider, _, _ = litellm.get_llm_provider(
                         model=litellm_params.model,

litellm/tests/test_amazing_vertex_completion.py

@@ -905,10 +905,17 @@ def test_vertexai_embedding_embedding_latest():
     try:
         load_vertex_ai_credentials()
         litellm.set_verbose = True
+
         response = embedding(
             model="vertex_ai/text-embedding-004",
-            input=["good morning from litellm", "this is another item"],
+            input=["hi"],
+            dimensions=1,
+            auto_truncate=True,
+            task_type="RETRIEVAL_QUERY",
         )
+
+        assert len(response.data[0]["embedding"]) == 1
+        assert response.usage.prompt_tokens > 0
         print(f"response:", response)
     except litellm.RateLimitError as e:
         pass

litellm/tests/test_bedrock_completion.py

@@ -220,13 +220,13 @@ def test_completion_bedrock_claude_sts_oidc_auth():
     aws_web_identity_token = "oidc/circleci_v2/"
     aws_region_name = os.environ["AWS_REGION_NAME"]
     # aws_role_name = os.environ["AWS_TEMP_ROLE_NAME"]
-    # TODO: This is using David's IAM role, we should use Litellm's IAM role eventually
+    # TODO: This is using ai.moda's IAM role, we should use LiteLLM's IAM role eventually
     aws_role_name = "arn:aws:iam::335785316107:role/litellm-github-unit-tests-circleci"
 
     try:
         litellm.set_verbose = True
 
-        response = completion(
+        response_1 = completion(
             model="bedrock/anthropic.claude-3-haiku-20240307-v1:0",
             messages=messages,
             max_tokens=10,
@@ -236,8 +236,40 @@ def test_completion_bedrock_claude_sts_oidc_auth():
             aws_role_name=aws_role_name,
             aws_session_name="my-test-session",
         )
-        # Add any assertions here to check the response
+        print(response_1)
-        print(response)
+        assert len(response_1.choices) > 0
+        assert len(response_1.choices[0].message.content) > 0
+
+        # This second call is to verify that the cache isn't breaking anything
+        response_2 = completion(
+            model="bedrock/anthropic.claude-3-haiku-20240307-v1:0",
+            messages=messages,
+            max_tokens=5,
+            temperature=0.2,
+            aws_region_name=aws_region_name,
+            aws_web_identity_token=aws_web_identity_token,
+            aws_role_name=aws_role_name,
+            aws_session_name="my-test-session",
+        )
+        print(response_2)
+        assert len(response_2.choices) > 0
+        assert len(response_2.choices[0].message.content) > 0
+
+        # This third call is to verify that the cache isn't used for a different region
+        response_3 = completion(
+            model="bedrock/anthropic.claude-3-haiku-20240307-v1:0",
+            messages=messages,
+            max_tokens=6,
+            temperature=0.3,
+            aws_region_name="us-east-1",
+            aws_web_identity_token=aws_web_identity_token,
+            aws_role_name=aws_role_name,
+            aws_session_name="my-test-session",
+        )
+        print(response_3)
+        assert len(response_3.choices) > 0
+        assert len(response_3.choices[0].message.content) > 0
+
     except RateLimitError:
         pass
     except Exception as e:
@@ -255,7 +287,7 @@ def test_completion_bedrock_httpx_command_r_sts_oidc_auth():
     aws_web_identity_token = "oidc/circleci_v2/"
     aws_region_name = os.environ["AWS_REGION_NAME"]
     # aws_role_name = os.environ["AWS_TEMP_ROLE_NAME"]
-    # TODO: This is using David's IAM role, we should use Litellm's IAM role eventually
+    # TODO: This is using ai.moda's IAM role, we should use LiteLLM's IAM role eventually
     aws_role_name = "arn:aws:iam::335785316107:role/litellm-github-unit-tests-circleci"
 
     try:

litellm/tests/test_streaming.py

@@ -557,7 +557,13 @@ async def test_completion_predibase_streaming(sync_mode):
         print(f"complete_response: {complete_response}")
     except litellm.Timeout as e:
         pass
+    except litellm.InternalServerError as e:
+        pass
     except Exception as e:
+        print("ERROR class", e.__class__)
+        print("ERROR message", e)
+        print("ERROR traceback", traceback.format_exc())
+
         pytest.fail(f"Error occurred: {e}")
 
 

litellm/utils.py

@@ -4903,6 +4903,18 @@ def get_optional_params_embeddings(
         )
         final_params = {**optional_params, **kwargs}
         return final_params
+    if custom_llm_provider == "vertex_ai":
+        supported_params = get_supported_openai_params(
+            model=model,
+            custom_llm_provider="vertex_ai",
+            request_type="embeddings",
+        )
+        _check_valid_arg(supported_params=supported_params)
+        optional_params = litellm.VertexAITextEmbeddingConfig().map_openai_params(
+            non_default_params=non_default_params, optional_params={}
+        )
+        final_params = {**optional_params, **kwargs}
+        return final_params
     if custom_llm_provider == "vertex_ai":
         if len(non_default_params.keys()) > 0:
             if litellm.drop_params is True:  # drop the unsupported non-default values
@@ -4936,7 +4948,18 @@ def get_optional_params_embeddings(
                 message=f"Setting user/encoding format is not supported by {custom_llm_provider}. To drop it from the call, set `litellm.drop_params = True`.",
             )
         return {**non_default_params, **kwargs}
-
+    if custom_llm_provider == "mistral":
+        supported_params = get_supported_openai_params(
+            model=model,
+            custom_llm_provider="mistral",
+            request_type="embeddings",
+        )
+        _check_valid_arg(supported_params=supported_params)
+        optional_params = litellm.MistralEmbeddingConfig().map_openai_params(
+            non_default_params=non_default_params, optional_params={}
+        )
+        final_params = {**optional_params, **kwargs}
+        return final_params
     if (
         custom_llm_provider != "openai"
         and custom_llm_provider != "azure"
@@ -6355,7 +6378,10 @@ def get_supported_openai_params(
             "max_retries",
         ]
     elif custom_llm_provider == "mistral":
-        return litellm.MistralConfig().get_supported_openai_params()
+        if request_type == "chat_completion":
+            return litellm.MistralConfig().get_supported_openai_params()
+        elif request_type == "embeddings":
+            return litellm.MistralEmbeddingConfig().get_supported_openai_params()
     elif custom_llm_provider == "replicate":
         return [
             "stream",
@@ -6397,7 +6423,10 @@ def get_supported_openai_params(
     elif custom_llm_provider == "palm" or custom_llm_provider == "gemini":
         return ["temperature", "top_p", "stream", "n", "stop", "max_tokens"]
     elif custom_llm_provider == "vertex_ai":
-        return litellm.VertexAIConfig().get_supported_openai_params()
+        if request_type == "chat_completion":
+            return litellm.VertexAIConfig().get_supported_openai_params()
+        elif request_type == "embeddings":
+            return litellm.VertexAITextEmbeddingConfig().get_supported_openai_params()
     elif custom_llm_provider == "sagemaker":
         return ["stream", "temperature", "max_tokens", "top_p", "stop", "n"]
     elif custom_llm_provider == "aleph_alpha":
@@ -7207,6 +7236,9 @@ def get_provider_fields(custom_llm_provider: str) -> List[ProviderField]:
     elif custom_llm_provider == "ollama":
         return litellm.OllamaConfig().get_required_params()
 
+    elif custom_llm_provider == "azure_ai":
+        return litellm.AzureAIStudioConfig().get_required_params()
+
     else:
         return []
 
@@ -10081,6 +10113,14 @@ def get_secret(
                 return oidc_token
             else:
                 raise ValueError("Github OIDC provider failed")
+        elif oidc_provider == "azure":
+            # https://azure.github.io/azure-workload-identity/docs/quick-start.html
+            azure_federated_token_file = os.getenv("AZURE_FEDERATED_TOKEN_FILE")
+            if azure_federated_token_file is None:
+                raise ValueError("AZURE_FEDERATED_TOKEN_FILE not found in environment")
+            with open(azure_federated_token_file, "r") as f:
+                oidc_token = f.read()
+                return oidc_token
         else:
             raise ValueError("Unsupported OIDC provider")
 

proxy_server_config.yaml

@@ -85,6 +85,9 @@ model_list:
     litellm_params:
       model: openai/*
       api_key: os.environ/OPENAI_API_KEY
+  - model_name: mistral-embed
+    litellm_params:
+      model: mistral/mistral-embed
   - model_name: gpt-instruct # [PROD TEST] - tests if `/health` automatically infers this to be a text completion model
     litellm_params:
       model: text-completion-openai/gpt-3.5-turbo-instruct

pyproject.toml

@@ -1,6 +1,6 @@
 [tool.poetry]
 name = "litellm"
-version = "1.40.9"
+version = "1.40.10"
 description = "Library to easily interface with LLM API providers"
 authors = ["BerriAI"]
 license = "MIT"
@@ -85,7 +85,7 @@ requires = ["poetry-core", "wheel"]
 build-backend = "poetry.core.masonry.api"
 
 [tool.commitizen]
-version = "1.40.9"
+version = "1.40.10"
 version_files = [
     "pyproject.toml:^version"
 ]

tests/test_openai_endpoints.py

@@ -22,6 +22,7 @@ async def generate_key(
         "text-embedding-ada-002",
         "dall-e-2",
         "fake-openai-endpoint-2",
+        "mistral-embed",
     ],
 ):
     url = "http://0.0.0.0:4000/key/generate"
@@ -197,14 +198,14 @@ async def completion(session, key):
         return response
 
 
-async def embeddings(session, key):
+async def embeddings(session, key, model="text-embedding-ada-002"):
     url = "http://0.0.0.0:4000/embeddings"
     headers = {
         "Authorization": f"Bearer {key}",
         "Content-Type": "application/json",
     }
     data = {
-        "model": "text-embedding-ada-002",
+        "model": model,
         "input": ["hello world"],
     }
 
@@ -408,6 +409,9 @@ async def test_embeddings():
         key_2 = key_gen["key"]
         await embeddings(session=session, key=key_2)
 
+        # embedding request with non OpenAI model
+        await embeddings(session=session, key=key, model="mistral-embed")
+
 
 @pytest.mark.asyncio
 async def test_image_generation():

ui/litellm-dashboard/out/_next/static/chunks/app/layout-9bbef188642a56c0.js

@@ -1 +1 @@
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+(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_12bbc4', '__Inter_Fallback_12bbc4'",fontStyle:"normal"},className:"__className_12bbc4"}}},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/webpack-496e007a0280178b.js

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ui/litellm-dashboard/out/index.txt

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ui/litellm-dashboard/out/model_hub.txt

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ui/litellm-dashboard/out/onboarding.txt

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

@@ -139,6 +139,7 @@ interface ProviderSettings {
 enum Providers {
   OpenAI = "OpenAI",
   Azure = "Azure",
+  Azure_AI_Studio = "Azure AI Studio",
   Anthropic = "Anthropic",
   Google_AI_Studio = "Google AI Studio",
   Bedrock = "Amazon Bedrock",
@@ -151,6 +152,7 @@ enum Providers {
 const provider_map: Record<string, string> = {
   OpenAI: "openai",
   Azure: "azure",
+  Azure_AI_Studio: "azure_ai",
   Anthropic: "anthropic",
   Google_AI_Studio: "gemini",
   Bedrock: "bedrock",
@@ -158,6 +160,7 @@ const provider_map: Record<string, string> = {
   Vertex_AI: "vertex_ai",
   Databricks: "databricks",
   Ollama: "ollama",
+
 };
 
 const retry_policy_map: Record<string, string> = {
@@ -1245,6 +1248,10 @@ const ModelDashboard: React.FC<ModelDashboardProps> = ({
       return "claude-3-opus";
     } else if (selectedProvider == Providers.Google_AI_Studio) {
       return "gemini-pro";
+    } else if (selectedProvider == Providers.Azure_AI_Studio) {
+      return "azure_ai/command-r-plus";
+    } else if (selectedProvider == Providers.Azure) {
+      return "azure/my-deployment";
     } else {
       return "gpt-3.5-turbo";
     }

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

@@ -14,11 +14,12 @@ export interface Model {
 
 export const modelCostMap = async () => {
   try {
+    const url = proxyBaseUrl ? `${proxyBaseUrl}/get/litellm_model_cost_map` : `/get/litellm_model_cost_map`;
     const response = await fetch(
-      "https://raw.githubusercontent.com/BerriAI/litellm/main/model_prices_and_context_window.json"
+      url
     );
     const jsonData = await response.json();
-    console.log(`received data: ${jsonData}`);
+    console.log(`received litellm model cost data: ${jsonData}`);
     return jsonData;
   } catch (error) {
     console.error("Failed to get model cost map:", error);
@@ -2405,7 +2406,6 @@ export const getProxyBaseUrlAndLogoutUrl = async (
 
     if (!response.ok) {
       const errorData = await response.text();
-      message.error(errorData, 10);
       throw new Error("Network response was not ok");
     }