bump: version 1.53.1 → 1.53.2

LiteLLM Minor Fixes & Improvements (11/29/2024) (#6965 )
* fix(factory.py): ensure tool call converts image url Fixes https://github.com/BerriAI/litellm/issues/6953 * fix(transformation.py): support mp4 + pdf url's for vertex ai Fixes https://github.com/BerriAI/litellm/issues/6936 * fix(http_handler.py): mask gemini api key in error logs Fixes https://github.com/BerriAI/litellm/issues/6963 * docs(prometheus.md): update prometheus FAQs * feat(auth_checks.py): ensure specific model access > wildcard model access if wildcard model is in access group, but specific model is not - deny access * fix(auth_checks.py): handle auth checks for team based model access groups handles scenario where model access group used for wildcard models * fix(internal_user_endpoints.py): support adding guardrails on `/user/update` Fixes https://github.com/BerriAI/litellm/issues/6942 * fix(key_management_endpoints.py): fix prepare_metadata_fields helper * fix: fix tests * build(requirements.txt): bump openai dep version fixes proxies argument * test: fix tests * fix(http_handler.py): fix error message masking * fix(bedrock_guardrails.py): pass in prepped data * test: fix test * test: fix nvidia nim test * fix(http_handler.py): return original response headers * fix: revert maskedhttpstatuserror * test: update tests * test: cleanup test * fix(key_management_endpoints.py): fix metadata field update logic * fix(key_management_endpoints.py): maintain initial order of guardrails in key update * fix(key_management_endpoints.py): handle prepare metadata * fix: fix linting errors * fix: fix linting errors * fix: fix linting errors * fix: fix key management errors * fix(key_management_endpoints.py): update metadata * test: update test * refactor: add more debug statements * test: skip flaky test * test: fix test * fix: fix test * fix: fix update metadata logic * fix: fix test * ci(config.yml): change db url for e2e ui testing
2024-12-01 06:55:33 -08:00 · 2024-12-01 05:24:11 -08:00 · 2024-11-29 02:02:54 -08:00 · 2024-11-27 19:34:51 -08:00 · 2024-11-27 18:55:06 -08:00 · 2024-11-27 18:40:33 -08:00
127 changed files with 5751 additions and 2822 deletions
--- a/.circleci/config.yml
+++ b/.circleci/config.yml
@ -807,11 +807,12 @@ jobs:
            curl https://raw.githubusercontent.com/helm/helm/main/scripts/get-helm-3 | bash
      - run: python -c "from litellm import *" || (echo '🚨 import failed, this means you introduced unprotected imports! 🚨'; exit 1)
      - run: ruff check ./litellm
-      - run: python ./tests/documentation_tests/test_general_setting_keys.py
+      # - run: python ./tests/documentation_tests/test_general_setting_keys.py
      - run: python ./tests/code_coverage_tests/router_code_coverage.py
      - run: python ./tests/code_coverage_tests/test_router_strategy_async.py
      - run: python ./tests/code_coverage_tests/litellm_logging_code_coverage.py
      - run: python ./tests/documentation_tests/test_env_keys.py
      - run: python ./tests/documentation_tests/test_router_settings.py
      - run: python ./tests/documentation_tests/test_api_docs.py
      - run: python ./tests/code_coverage_tests/ensure_async_clients_test.py
      - run: helm lint ./deploy/charts/litellm-helm
@ -1407,7 +1408,7 @@ jobs:
          command: |
            docker run -d \
              -p 4000:4000 \
-              -e DATABASE_URL=$PROXY_DATABASE_URL \
+              -e DATABASE_URL=$PROXY_DATABASE_URL_2 \
              -e LITELLM_MASTER_KEY="sk-1234" \
              -e OPENAI_API_KEY=$OPENAI_API_KEY \
              -e UI_USERNAME="admin" \
--- a/docs/my-website/docs/moderation.md
+++ b/docs/my-website/docs/moderation.md
@ -0,0 +1,135 @@
 import Tabs from '@theme/Tabs';
 import TabItem from '@theme/TabItem';
 # Moderation
 ### Usage
 <Tabs>
 <TabItem value="python" label="LiteLLM Python SDK">
 ```python
 from litellm import moderation
 response = moderation(
    input="hello from litellm",
    model="text-moderation-stable"
 )
 ```
 </TabItem>
 <TabItem value="proxy" label="LiteLLM Proxy Server">
 For `/moderations` endpoint, there is **no need to specify `model` in the request or on the litellm config.yaml**
 Start litellm proxy server 
 ```
 litellm
 ```
 <Tabs>
 <TabItem value="python" label="OpenAI Python SDK">
 ```python
 from openai import OpenAI
 # set base_url to your proxy server
 # set api_key to send to proxy server
 client = OpenAI(api_key="<proxy-api-key>", base_url="http://0.0.0.0:4000")
 response = client.moderations.create(
    input="hello from litellm",
    model="text-moderation-stable" # optional, defaults to `omni-moderation-latest`
 )
 print(response)
 ```
 </TabItem>
 <TabItem value="curl" label="Curl Request">
 ```shell
 curl --location 'http://0.0.0.0:4000/moderations' \
    --header 'Content-Type: application/json' \
    --header 'Authorization: Bearer sk-1234' \
    --data '{"input": "Sample text goes here", "model": "text-moderation-stable"}'
 ```
 </TabItem>
 </Tabs>
 </TabItem>
 </Tabs>
 ## Input Params
 LiteLLM accepts and translates the [OpenAI Moderation params](https://platform.openai.com/docs/api-reference/moderations) across all supported providers.
 ### Required Fields
 - `input`: *string or array* - Input (or inputs) to classify. Can be a single string, an array of strings, or an array of multi-modal input objects similar to other models.
  - If string: A string of text to classify for moderation
  - If array of strings: An array of strings to classify for moderation
  - If array of objects: An array of multi-modal inputs to the moderation model, where each object can be:
    - An object describing an image to classify with:
      - `type`: *string, required* - Always `image_url`
      - `image_url`: *object, required* - Contains either an image URL or a data URL for a base64 encoded image
    - An object describing text to classify with:
      - `type`: *string, required* - Always `text`
      - `text`: *string, required* - A string of text to classify
 ### Optional Fields
 - `model`: *string (optional)* - The moderation model to use. Defaults to `omni-moderation-latest`.
 ## Output Format
 Here's the exact json output and type you can expect from all moderation calls:
 [**LiteLLM follows OpenAI's output format**](https://platform.openai.com/docs/api-reference/moderations/object)
 ```python
 {
  "id": "modr-AB8CjOTu2jiq12hp1AQPfeqFWaORR",
  "model": "text-moderation-007",
  "results": [
    {
      "flagged": true,
      "categories": {
        "sexual": false,
        "hate": false,
        "harassment": true,
        "self-harm": false,
        "sexual/minors": false,
        "hate/threatening": false,
        "violence/graphic": false,
        "self-harm/intent": false,
        "self-harm/instructions": false,
        "harassment/threatening": true,
        "violence": true
      },
      "category_scores": {
        "sexual": 0.000011726012417057063,
        "hate": 0.22706663608551025,
        "harassment": 0.5215635299682617,
        "self-harm": 2.227119921371923e-6,
        "sexual/minors": 7.107352217872176e-8,
        "hate/threatening": 0.023547329008579254,
        "violence/graphic": 0.00003391829886822961,
        "self-harm/intent": 1.646940972932498e-6,
        "self-harm/instructions": 1.1198755256458526e-9,
        "harassment/threatening": 0.5694745779037476,
        "violence": 0.9971134662628174
      }
    }
  ]
 }
 ```
 ## **Supported Providers**
 | Provider    |
 |-------------|
 | OpenAI      |  
--- a/docs/my-website/docs/observability/argilla.md
+++ b/docs/my-website/docs/observability/argilla.md
@ -4,24 +4,63 @@ import TabItem from '@theme/TabItem';
 # Argilla 
-Argilla is a tool for annotating datasets. 
+Argilla is a collaborative annotation tool for AI engineers and domain experts who need to build high-quality datasets for their projects.
 ## Getting Started
-## Usage 
+To log the data to Argilla, first you need to deploy the Argilla server. If you have not deployed the Argilla server, please follow the instructions [here](https://docs.argilla.io/latest/getting_started/quickstart/).
 Next, you will need to configure and create the Argilla dataset.
 ```python
 import argilla as rg
 client = rg.Argilla(api_url="<api_url>", api_key="<api_key>")
 settings = rg.Settings(
    guidelines="These are some guidelines.",
    fields=[
        rg.ChatField(
            name="user_input",
        ),
        rg.TextField(
            name="llm_output",
        ),
    ],
    questions=[
        rg.RatingQuestion(
            name="rating",
            values=[1, 2, 3, 4, 5, 6, 7],
        ),
    ],
 )
 dataset = rg.Dataset(
    name="my_first_dataset",
    settings=settings,
 )
 dataset.create()
 ```
 For further configuration, please refer to the [Argilla documentation](https://docs.argilla.io/latest/how_to_guides/dataset/).
 ## Usage
 <Tabs>
 <Tab value="sdk" label="SDK">
 ```python
-from litellm import completion
+import os
 import litellm
-import os 
+from litellm import completion
 # add env vars
 os.environ["ARGILLA_API_KEY"]="argilla.apikey"
 os.environ["ARGILLA_BASE_URL"]="http://localhost:6900"
-os.environ["ARGILLA_DATASET_NAME"]="my_second_dataset"   
+os.environ["ARGILLA_DATASET_NAME"]="my_first_dataset"   
 os.environ["OPENAI_API_KEY"]="sk-proj-..."
 litellm.callbacks = ["argilla"]
--- a/docs/my-website/docs/pass_through/vertex_ai.md
+++ b/docs/my-website/docs/pass_through/vertex_ai.md
@ -69,6 +69,44 @@ generateContent();
 </Tabs>
 ## Quick Start
 Let's call the Vertex AI [`/generateContent` endpoint](https://cloud.google.com/vertex-ai/generative-ai/docs/model-reference/inference)
 1. Add Vertex AI Credentials to your environment 
 ```bash
 export DEFAULT_VERTEXAI_PROJECT="" # "adroit-crow-413218"
 export DEFAULT_VERTEXAI_LOCATION="" # "us-central1"
 export DEFAULT_GOOGLE_APPLICATION_CREDENTIALS="" # "/Users/Downloads/adroit-crow-413218-a956eef1a2a8.json"
 ```
 2. Start LiteLLM Proxy 
 ```bash
 litellm
 # RUNNING on http://0.0.0.0:4000
 ```
 3. Test it! 
 Let's call the Google AI Studio token counting endpoint
 ```bash
 curl http://localhost:4000/vertex-ai/publishers/google/models/gemini-1.0-pro:generateContent \
  -H "Content-Type: application/json" \
  -H "Authorization: Bearer sk-1234" \
  -d '{
    "contents":[{
      "role": "user",
      "parts":[{"text": "How are you doing today?"}]
    }]
  }'
 ```
 ## Supported API Endpoints
 - Gemini API
@ -87,206 +125,12 @@ LiteLLM Proxy Server supports two methods of authentication to Vertex AI:
 2. Set Vertex AI credentials on proxy server
 ## Quick Start Usage 
 <Tabs>
 <TabItem value="without_default_config" label="Pass Vertex Credetials client side to proxy server">
 #### 1. Start litellm proxy
 ```shell
 litellm --config /path/to/config.yaml
 ```
 #### 2. Test it 
 ```python
 import vertexai
 from vertexai.preview.generative_models import GenerativeModel
 LITE_LLM_ENDPOINT = "http://localhost:4000"
 vertexai.init(
    project="<your-vertex_ai-project-id>", # enter your project id
    location="<your-vertex_ai-location>", # enter your region
    api_endpoint=f"{LITE_LLM_ENDPOINT}/vertex_ai", # route on litellm
    api_transport="rest",
 )
 model = GenerativeModel(model_name="gemini-1.0-pro")
 model.generate_content("hi")
 ```
 </TabItem>
 <TabItem value="with_default_config" label="Set Vertex AI Credentials on Proxy Server">
 #### 1. Set `default_vertex_config` on your `config.yaml`
 Add the following credentials to your litellm config.yaml to use the Vertex AI endpoints.
 ```yaml
 default_vertex_config:
  vertex_project: "adroit-crow-413218"
  vertex_location: "us-central1"
  vertex_credentials: "/Users/ishaanjaffer/Downloads/adroit-crow-413218-a956eef1a2a8.json" # Add path to service account.json
 ```
 #### 2. Start litellm proxy
 ```shell
 litellm --config /path/to/config.yaml
 ```
 #### 3. Test it 
 ```python
 import vertexai
 from google.auth.credentials import Credentials
 from vertexai.generative_models import GenerativeModel
 LITELLM_PROXY_API_KEY = "sk-1234"
 LITELLM_PROXY_BASE = "http://0.0.0.0:4000/vertex_ai"
 import datetime
 class CredentialsWrapper(Credentials):
    def __init__(self, token=None):
        super().__init__()
        self.token = token
        self.expiry = None  # or set to a future date if needed
    def refresh(self, request):
        pass
    def apply(self, headers, token=None):
        headers["Authorization"] = f"Bearer {self.token}"
    @property
    def expired(self):
        return False  # Always consider the token as non-expired
    @property
    def valid(self):
        return True  # Always consider the credentials as valid
 credentials = CredentialsWrapper(token=LITELLM_PROXY_API_KEY)
 vertexai.init(
    project="adroit-crow-413218",
    location="us-central1",
    api_endpoint=LITELLM_PROXY_BASE,
    credentials=credentials,
    api_transport="rest",
 )
 model = GenerativeModel("gemini-1.5-flash-001")
 response = model.generate_content(
    "What's a good name for a flower shop that specializes in selling bouquets of dried flowers?"
 )
 print(response.text)
 ```
 </TabItem>
 </Tabs>
 ## Usage Examples
 ### Gemini API (Generate Content)
 <Tabs>
 <TabItem value="client_side" label="Vertex Python SDK (client side vertex credentials)">
 ```python
 import vertexai
 from vertexai.generative_models import GenerativeModel
 LITELLM_PROXY_API_KEY = "sk-1234"
 LITELLM_PROXY_BASE = "http://0.0.0.0:4000/vertex_ai"
 vertexai.init(
    project="adroit-crow-413218",
    location="us-central1",
    api_endpoint=LITELLM_PROXY_BASE,
    api_transport="rest",
 )
 model = GenerativeModel("gemini-1.5-flash-001")
 response = model.generate_content(
    "What's a good name for a flower shop that specializes in selling bouquets of dried flowers?"
 )
 print(response.text)
 ```
 </TabItem>
 <TabItem value="py" label="Vertex Python SDK (litellm virtual keys client side)">
 ```python
 import vertexai
 from google.auth.credentials import Credentials
 from vertexai.generative_models import GenerativeModel
 LITELLM_PROXY_API_KEY = "sk-1234"
 LITELLM_PROXY_BASE = "http://0.0.0.0:4000/vertex_ai"
 import datetime
 class CredentialsWrapper(Credentials):
    def __init__(self, token=None):
        super().__init__()
        self.token = token
        self.expiry = None  # or set to a future date if needed
    def refresh(self, request):
        pass
    def apply(self, headers, token=None):
        headers["Authorization"] = f"Bearer {self.token}"
    @property
    def expired(self):
        return False  # Always consider the token as non-expired
    @property
    def valid(self):
        return True  # Always consider the credentials as valid
 credentials = CredentialsWrapper(token=LITELLM_PROXY_API_KEY)
 vertexai.init(
    project="adroit-crow-413218",
    location="us-central1",
    api_endpoint=LITELLM_PROXY_BASE,
    credentials=credentials,
    api_transport="rest",
 )
 model = GenerativeModel("gemini-1.5-flash-001")
 response = model.generate_content(
    "What's a good name for a flower shop that specializes in selling bouquets of dried flowers?"
 )
 print(response.text)
 ```
 </TabItem>
 <TabItem value="Curl" label="Curl">
 ```shell
 curl http://localhost:4000/vertex_ai/publishers/google/models/gemini-1.5-flash-001:generateContent \
@ -295,114 +139,10 @@ curl http://localhost:4000/vertex_ai/publishers/google/models/gemini-1.5-flash-0
  -d '{"contents":[{"role": "user", "parts":[{"text": "hi"}]}]}'
 ```
 </TabItem>
 </Tabs>
 ### Embeddings API
 <Tabs>
 <TabItem value="client_side" label="Vertex Python SDK (client side vertex credentials)">
 ```python
 from typing import List, Optional
 from vertexai.language_models import TextEmbeddingInput, TextEmbeddingModel
 import vertexai
 from vertexai.generative_models import GenerativeModel
 LITELLM_PROXY_API_KEY = "sk-1234"
 LITELLM_PROXY_BASE = "http://0.0.0.0:4000/vertex_ai"
 import datetime
 vertexai.init(
    project="adroit-crow-413218",
    location="us-central1",
    api_endpoint=LITELLM_PROXY_BASE,
    api_transport="rest",
 )
 def embed_text(
    texts: List[str] = ["banana muffins? ", "banana bread? banana muffins?"],
    task: str = "RETRIEVAL_DOCUMENT",
    model_name: str = "text-embedding-004",
    dimensionality: Optional[int] = 256,
 ) -> List[List[float]]:
    """Embeds texts with a pre-trained, foundational model."""
    model = TextEmbeddingModel.from_pretrained(model_name)
    inputs = [TextEmbeddingInput(text, task) for text in texts]
    kwargs = dict(output_dimensionality=dimensionality) if dimensionality else {}
    embeddings = model.get_embeddings(inputs, **kwargs)
    return [embedding.values for embedding in embeddings]
 ```
 </TabItem>
 <TabItem value="py" label="Vertex Python SDK (litellm virtual keys client side)">
 ```python
 from typing import List, Optional
 from vertexai.language_models import TextEmbeddingInput, TextEmbeddingModel
 import vertexai
 from google.auth.credentials import Credentials
 from vertexai.generative_models import GenerativeModel
 LITELLM_PROXY_API_KEY = "sk-1234"
 LITELLM_PROXY_BASE = "http://0.0.0.0:4000/vertex_ai"
 import datetime
 class CredentialsWrapper(Credentials):
    def __init__(self, token=None):
        super().__init__()
        self.token = token
        self.expiry = None  # or set to a future date if needed
    def refresh(self, request):
        pass
    def apply(self, headers, token=None):
        headers["Authorization"] = f"Bearer {self.token}"
    @property
    def expired(self):
        return False  # Always consider the token as non-expired
    @property
    def valid(self):
        return True  # Always consider the credentials as valid
 credentials = CredentialsWrapper(token=LITELLM_PROXY_API_KEY)
 vertexai.init(
    project="adroit-crow-413218",
    location="us-central1",
    api_endpoint=LITELLM_PROXY_BASE,
    credentials=credentials,
    api_transport="rest",
 )
 def embed_text(
    texts: List[str] = ["banana muffins? ", "banana bread? banana muffins?"],
    task: str = "RETRIEVAL_DOCUMENT",
    model_name: str = "text-embedding-004",
    dimensionality: Optional[int] = 256,
 ) -> List[List[float]]:
    """Embeds texts with a pre-trained, foundational model."""
    model = TextEmbeddingModel.from_pretrained(model_name)
    inputs = [TextEmbeddingInput(text, task) for text in texts]
    kwargs = dict(output_dimensionality=dimensionality) if dimensionality else {}
    embeddings = model.get_embeddings(inputs, **kwargs)
    return [embedding.values for embedding in embeddings]
 ```
 </TabItem>
 <TabItem value="curl" label="Curl">
 ```shell
 curl http://localhost:4000/vertex_ai/publishers/google/models/textembedding-gecko@001:predict \
@ -411,133 +151,9 @@ curl http://localhost:4000/vertex_ai/publishers/google/models/textembedding-geck
  -d '{"instances":[{"content": "gm"}]}'
 ```
 </TabItem>
 </Tabs>
 ### Imagen API
 <Tabs>
 <TabItem value="client_side" label="Vertex Python SDK (client side vertex credentials)">
 ```python
 from typing import List, Optional
 from vertexai.preview.vision_models import ImageGenerationModel
 import vertexai
 from google.auth.credentials import Credentials
 LITELLM_PROXY_API_KEY = "sk-1234"
 LITELLM_PROXY_BASE = "http://0.0.0.0:4000/vertex_ai"
 import datetime
 vertexai.init(
    project="adroit-crow-413218",
    location="us-central1",
    api_endpoint=LITELLM_PROXY_BASE,
    api_transport="rest",
 )
 model = ImageGenerationModel.from_pretrained("imagen-3.0-generate-001")
 images = model.generate_images(
    prompt=prompt,
    # Optional parameters
    number_of_images=1,
    language="en",
    # You can't use a seed value and watermark at the same time.
    # add_watermark=False,
    # seed=100,
    aspect_ratio="1:1",
    safety_filter_level="block_some",
    person_generation="allow_adult",
 )
 images[0].save(location=output_file, include_generation_parameters=False)
 # Optional. View the generated image in a notebook.
 # images[0].show()
 print(f"Created output image using {len(images[0]._image_bytes)} bytes")
 ```
 </TabItem>
 <TabItem value="py" label="Vertex Python SDK (litellm virtual keys client side)">
 ```python
 from typing import List, Optional
 from vertexai.preview.vision_models import ImageGenerationModel
 import vertexai
 from google.auth.credentials import Credentials
 LITELLM_PROXY_API_KEY = "sk-1234"
 LITELLM_PROXY_BASE = "http://0.0.0.0:4000/vertex_ai"
 import datetime
 class CredentialsWrapper(Credentials):
    def __init__(self, token=None):
        super().__init__()
        self.token = token
        self.expiry = None  # or set to a future date if needed
    def refresh(self, request):
        pass
    def apply(self, headers, token=None):
        headers["Authorization"] = f"Bearer {self.token}"
    @property
    def expired(self):
        return False  # Always consider the token as non-expired
    @property
    def valid(self):
        return True  # Always consider the credentials as valid
 credentials = CredentialsWrapper(token=LITELLM_PROXY_API_KEY)
 vertexai.init(
    project="adroit-crow-413218",
    location="us-central1",
    api_endpoint=LITELLM_PROXY_BASE,
    credentials=credentials,
    api_transport="rest",
 )
 model = ImageGenerationModel.from_pretrained("imagen-3.0-generate-001")
 images = model.generate_images(
    prompt=prompt,
    # Optional parameters
    number_of_images=1,
    language="en",
    # You can't use a seed value and watermark at the same time.
    # add_watermark=False,
    # seed=100,
    aspect_ratio="1:1",
    safety_filter_level="block_some",
    person_generation="allow_adult",
 )
 images[0].save(location=output_file, include_generation_parameters=False)
 # Optional. View the generated image in a notebook.
 # images[0].show()
 print(f"Created output image using {len(images[0]._image_bytes)} bytes")
 ```
 </TabItem>
 <TabItem value="curl" label="Curl">
 ```shell
 curl http://localhost:4000/vertex_ai/publishers/google/models/imagen-3.0-generate-001:predict \
  -H "Content-Type: application/json" \
@ -545,252 +161,19 @@ curl http://localhost:4000/vertex_ai/publishers/google/models/imagen-3.0-generat
  -d '{"instances":[{"prompt": "make an otter"}], "parameters": {"sampleCount": 1}}'
 ```
 </TabItem>
 </Tabs>
 ### Count Tokens API
 <Tabs>
 <TabItem value="client_side" label="Vertex Python SDK (client side vertex credentials)">
 ```python
 from typing import List, Optional
 from vertexai.generative_models import GenerativeModel
 import vertexai
 LITELLM_PROXY_API_KEY = "sk-1234"
 LITELLM_PROXY_BASE = "http://0.0.0.0:4000/vertex_ai"
 import datetime
 vertexai.init(
    project="adroit-crow-413218",
    location="us-central1",
    api_endpoint=LITELLM_PROXY_BASE,
    api_transport="rest",
 )
 model = GenerativeModel("gemini-1.5-flash-001")
 prompt = "Why is the sky blue?"
 # Prompt tokens count
 response = model.count_tokens(prompt)
 print(f"Prompt Token Count: {response.total_tokens}")
 print(f"Prompt Character Count: {response.total_billable_characters}")
 # Send text to Gemini
 response = model.generate_content(prompt)
 # Response tokens count
 usage_metadata = response.usage_metadata
 print(f"Prompt Token Count: {usage_metadata.prompt_token_count}")
 print(f"Candidates Token Count: {usage_metadata.candidates_token_count}")
 print(f"Total Token Count: {usage_metadata.total_token_count}")
 ```
 </TabItem>
 <TabItem value="py" label="Vertex Python SDK (litellm virtual keys client side)">
 ```python
 from typing import List, Optional
 from vertexai.generative_models import GenerativeModel
 import vertexai
 from google.auth.credentials import Credentials
 LITELLM_PROXY_API_KEY = "sk-1234"
 LITELLM_PROXY_BASE = "http://0.0.0.0:4000/vertex_ai"
 import datetime
 class CredentialsWrapper(Credentials):
    def __init__(self, token=None):
        super().__init__()
        self.token = token
        self.expiry = None  # or set to a future date if needed
    def refresh(self, request):
        pass
    def apply(self, headers, token=None):
        headers["Authorization"] = f"Bearer {self.token}"
    @property
    def expired(self):
        return False  # Always consider the token as non-expired
    @property
    def valid(self):
        return True  # Always consider the credentials as valid
 credentials = CredentialsWrapper(token=LITELLM_PROXY_API_KEY)
 vertexai.init(
    project="adroit-crow-413218",
    location="us-central1",
    api_endpoint=LITELLM_PROXY_BASE,
    credentials=credentials,
    api_transport="rest",
 )
 model = GenerativeModel("gemini-1.5-flash-001")
 prompt = "Why is the sky blue?"
 # Prompt tokens count
 response = model.count_tokens(prompt)
 print(f"Prompt Token Count: {response.total_tokens}")
 print(f"Prompt Character Count: {response.total_billable_characters}")
 # Send text to Gemini
 response = model.generate_content(prompt)
 # Response tokens count
 usage_metadata = response.usage_metadata
 print(f"Prompt Token Count: {usage_metadata.prompt_token_count}")
 print(f"Candidates Token Count: {usage_metadata.candidates_token_count}")
 print(f"Total Token Count: {usage_metadata.total_token_count}")
 ```
 </TabItem>
 <TabItem value="curl" label="Curl">
 ```shell
 curl http://localhost:4000/vertex_ai/publishers/google/models/gemini-1.5-flash-001:countTokens \
  -H "Content-Type: application/json" \
  -H "x-litellm-api-key: Bearer sk-1234" \
  -d '{"contents":[{"role": "user", "parts":[{"text": "hi"}]}]}'
 ```
 </TabItem>
 </Tabs>
 ### Tuning API 
 Create Fine Tuning Job
 <Tabs>
 <TabItem value="client_side" label="Vertex Python SDK (client side vertex credentials)">
 ```python
 from typing import List, Optional
 from vertexai.preview.tuning import sft
 import vertexai
 LITELLM_PROXY_API_KEY = "sk-1234"
 LITELLM_PROXY_BASE = "http://0.0.0.0:4000/vertex_ai"
 vertexai.init(
    project="adroit-crow-413218",
    location="us-central1",
    api_endpoint=LITELLM_PROXY_BASE,
    api_transport="rest",
 )
 # TODO(developer): Update project
 vertexai.init(project=PROJECT_ID, location="us-central1")
 sft_tuning_job = sft.train(
    source_model="gemini-1.0-pro-002",
    train_dataset="gs://cloud-samples-data/ai-platform/generative_ai/sft_train_data.jsonl",
 )
 # Polling for job completion
 while not sft_tuning_job.has_ended:
    time.sleep(60)
    sft_tuning_job.refresh()
 print(sft_tuning_job.tuned_model_name)
 print(sft_tuning_job.tuned_model_endpoint_name)
 print(sft_tuning_job.experiment)
 ```
 </TabItem>
 <TabItem value="py" label="Vertex Python SDK (litellm virtual keys client side)">
 ```python
 from typing import List, Optional
 from vertexai.preview.tuning import sft
 import vertexai
 from google.auth.credentials import Credentials
 LITELLM_PROXY_API_KEY = "sk-1234"
 LITELLM_PROXY_BASE = "http://0.0.0.0:4000/vertex_ai"
 import datetime
 class CredentialsWrapper(Credentials):
    def __init__(self, token=None):
        super().__init__()
        self.token = token
        self.expiry = None  # or set to a future date if needed
    def refresh(self, request):
        pass
    def apply(self, headers, token=None):
        headers["Authorization"] = f"Bearer {self.token}"
    @property
    def expired(self):
        return False  # Always consider the token as non-expired
    @property
    def valid(self):
        return True  # Always consider the credentials as valid
 credentials = CredentialsWrapper(token=LITELLM_PROXY_API_KEY)
 vertexai.init(
    project="adroit-crow-413218",
    location="us-central1",
    api_endpoint=LITELLM_PROXY_BASE,
    credentials=credentials,
    api_transport="rest",
 )
 # TODO(developer): Update project
 vertexai.init(project=PROJECT_ID, location="us-central1")
 sft_tuning_job = sft.train(
    source_model="gemini-1.0-pro-002",
    train_dataset="gs://cloud-samples-data/ai-platform/generative_ai/sft_train_data.jsonl",
 )
 # Polling for job completion
 while not sft_tuning_job.has_ended:
    time.sleep(60)
    sft_tuning_job.refresh()
 print(sft_tuning_job.tuned_model_name)
 print(sft_tuning_job.tuned_model_endpoint_name)
 print(sft_tuning_job.experiment)
 ```
 </TabItem>
 <TabItem value="curl" label="Curl">
 ```shell
 curl http://localhost:4000/vertex_ai/tuningJobs \
@ -804,118 +187,6 @@ curl http://localhost:4000/vertex_ai/tuningJobs \
 }'
 ```
 </TabItem>
 </Tabs>
 ### Context Caching
 Use Vertex AI Context Caching
 [**Relevant VertexAI Docs**](https://cloud.google.com/vertex-ai/generative-ai/docs/context-cache/context-cache-overview)
 <Tabs>
 <TabItem value="proxy" label="LiteLLM PROXY">
 1. Add model to config.yaml
 ```yaml
 model_list:
  # used for /chat/completions, /completions, /embeddings endpoints
  - model_name: gemini-1.5-pro-001
    litellm_params:
      model: vertex_ai/gemini-1.5-pro-001
      vertex_project: "project-id"
      vertex_location: "us-central1"
      vertex_credentials: "adroit-crow-413218-a956eef1a2a8.json" # Add path to service account.json
 # used for the /cachedContent and vertexAI native endpoints
 default_vertex_config:
  vertex_project: "adroit-crow-413218"
  vertex_location: "us-central1"
  vertex_credentials: "adroit-crow-413218-a956eef1a2a8.json" # Add path to service account.json
 ```
 2. Start Proxy 
 ```
 $ litellm --config /path/to/config.yaml
 ```
 3. Make Request!
 We make the request in two steps:
 - Create a cachedContents object
 - Use the cachedContents object in your /chat/completions 
 **Create a cachedContents object**
 First, create a cachedContents object by calling the Vertex `cachedContents` endpoint. The LiteLLM proxy forwards the `/cachedContents` request to the VertexAI API.
 ```python
 import httpx
 # Set Litellm proxy variables
 LITELLM_BASE_URL = "http://0.0.0.0:4000"
 LITELLM_PROXY_API_KEY = "sk-1234"
 httpx_client = httpx.Client(timeout=30)
 print("Creating cached content")
 create_cache = httpx_client.post(
    url=f"{LITELLM_BASE_URL}/vertex_ai/cachedContents",
    headers={"x-litellm-api-key": f"Bearer {LITELLM_PROXY_API_KEY}"},
    json={
        "model": "gemini-1.5-pro-001",
        "contents": [
            {
                "role": "user",
                "parts": [{
                    "text": "This is sample text to demonstrate explicit caching." * 4000
                }]
            }
        ],
    }
 )
 print("Response from create_cache:", create_cache)
 create_cache_response = create_cache.json()
 print("JSON from create_cache:", create_cache_response)
 cached_content_name = create_cache_response["name"]
 ```
 **Use the cachedContents object in your /chat/completions request to VertexAI**
 ```python
 import openai
 # Set Litellm proxy variables
 LITELLM_BASE_URL = "http://0.0.0.0:4000"
 LITELLM_PROXY_API_KEY = "sk-1234"
 client = openai.OpenAI(api_key=LITELLM_PROXY_API_KEY, base_url=LITELLM_BASE_URL)
 response = client.chat.completions.create(
    model="gemini-1.5-pro-001",
    max_tokens=8192,
    messages=[
        {
            "role": "user",
            "content": "What is the sample text about?",
        },
    ],
    temperature=0.7,
    extra_body={"cached_content": cached_content_name},  # Use the cached content
 )
 print("Response from proxy:", response)
 ```
 </TabItem>
 </Tabs>
 ## Advanced
 Pre-requisites
@ -930,6 +201,11 @@ Use this, to avoid giving developers the raw Anthropic API key, but still lettin
 ```bash
 export DATABASE_URL=""
 export LITELLM_MASTER_KEY=""
 # vertex ai credentials
 export DEFAULT_VERTEXAI_PROJECT="" # "adroit-crow-413218"
 export DEFAULT_VERTEXAI_LOCATION="" # "us-central1"
 export DEFAULT_GOOGLE_APPLICATION_CREDENTIALS="" # "/Users/Downloads/adroit-crow-413218-a956eef1a2a8.json"
 ```
 ```bash
--- a/docs/my-website/docs/proxy/config_management.md
+++ b/docs/my-website/docs/proxy/config_management.md
@ -0,0 +1,59 @@
 # File Management
 ## `include` external YAML files in a config.yaml 
 You can use `include` to include external YAML files in a config.yaml. 
 **Quick Start Usage:**
 To include a config file, use `include` with either a single file or a list of files. 
 Contents of `parent_config.yaml`:
 ```yaml
 include:
  - model_config.yaml # 👈 Key change, will include the contents of model_config.yaml
 litellm_settings:
  callbacks: ["prometheus"] 
 ```
 Contents of `model_config.yaml`:
 ```yaml
 model_list:
  - model_name: gpt-4o
    litellm_params:
      model: openai/gpt-4o
      api_base: https://exampleopenaiendpoint-production.up.railway.app/
  - model_name: fake-anthropic-endpoint
    litellm_params:
      model: anthropic/fake
      api_base: https://exampleanthropicendpoint-production.up.railway.app/
 ```
 Start proxy server 
 This will start the proxy server with config `parent_config.yaml`. Since the `include` directive is used, the server will also include the contents of `model_config.yaml`.
 ```
 litellm --config parent_config.yaml --detailed_debug
 ```
 ## Examples using `include`
 Include a single file:
 ```yaml
 include:
  - model_config.yaml
 ```
 Include multiple files:
 ```yaml
 include:
  - model_config.yaml
  - another_config.yaml
 ```
--- a/docs/my-website/docs/proxy/config_settings.md
+++ b/docs/my-website/docs/proxy/config_settings.md
@ -0,0 +1,507 @@
 # All settings
 ```yaml
 environment_variables: {}
 model_list:
  - model_name: string
    litellm_params: {}
    model_info:
      id: string
      mode: embedding
      input_cost_per_token: 0
      output_cost_per_token: 0
      max_tokens: 2048
      base_model: gpt-4-1106-preview
      additionalProp1: {}
 litellm_settings:
  # Logging/Callback settings
  success_callback: ["langfuse"]  # list of success callbacks
  failure_callback: ["sentry"]  # list of failure callbacks
  callbacks: ["otel"]  # list of callbacks - runs on success and failure
  service_callbacks: ["datadog", "prometheus"]  # logs redis, postgres failures on datadog, prometheus
  turn_off_message_logging: boolean  # prevent the messages and responses from being logged to on your callbacks, but request metadata will still be logged.
  redact_user_api_key_info: boolean  # Redact information about the user api key (hashed token, user_id, team id, etc.), from logs. Currently supported for Langfuse, OpenTelemetry, Logfire, ArizeAI logging.
  langfuse_default_tags: ["cache_hit", "cache_key", "proxy_base_url", "user_api_key_alias", "user_api_key_user_id", "user_api_key_user_email", "user_api_key_team_alias", "semantic-similarity", "proxy_base_url"] # default tags for Langfuse Logging
  # Networking settings
  request_timeout: 10 # (int) llm requesttimeout in seconds. Raise Timeout error if call takes longer than 10s. Sets litellm.request_timeout 
  force_ipv4: boolean # If true, litellm will force ipv4 for all LLM requests. Some users have seen httpx ConnectionError when using ipv6 + Anthropic API
  set_verbose: boolean # sets litellm.set_verbose=True to view verbose debug logs. DO NOT LEAVE THIS ON IN PRODUCTION
  json_logs: boolean # if true, logs will be in json format
  # Fallbacks, reliability
  default_fallbacks: ["claude-opus"] # set default_fallbacks, in case a specific model group is misconfigured / bad.
  content_policy_fallbacks: [{"gpt-3.5-turbo-small": ["claude-opus"]}] # fallbacks for ContentPolicyErrors
  context_window_fallbacks: [{"gpt-3.5-turbo-small": ["gpt-3.5-turbo-large", "claude-opus"]}] # fallbacks for ContextWindowExceededErrors
  # Caching settings
  cache: true 
  cache_params:        # set cache params for redis
    type: redis        # type of cache to initialize
    # Optional - Redis Settings
    host: "localhost"  # The host address for the Redis cache. Required if type is "redis".
    port: 6379  # The port number for the Redis cache. Required if type is "redis".
    password: "your_password"  # The password for the Redis cache. Required if type is "redis".
    namespace: "litellm.caching.caching" # namespace for redis cache
    # Optional - Redis Cluster Settings
    redis_startup_nodes: [{"host": "127.0.0.1", "port": "7001"}] 
    # Optional - Redis Sentinel Settings
    service_name: "mymaster"
    sentinel_nodes: [["localhost", 26379]]
    # Optional - Qdrant Semantic Cache Settings
    qdrant_semantic_cache_embedding_model: openai-embedding # the model should be defined on the model_list
    qdrant_collection_name: test_collection
    qdrant_quantization_config: binary
    similarity_threshold: 0.8   # similarity threshold for semantic cache
    # Optional - S3 Cache Settings
    s3_bucket_name: cache-bucket-litellm   # AWS Bucket Name for S3
    s3_region_name: us-west-2              # AWS Region Name for S3
    s3_aws_access_key_id: os.environ/AWS_ACCESS_KEY_ID  # us os.environ/<variable name> to pass environment variables. This is AWS Access Key ID for S3
    s3_aws_secret_access_key: os.environ/AWS_SECRET_ACCESS_KEY  # AWS Secret Access Key for S3
    s3_endpoint_url: https://s3.amazonaws.com  # [OPTIONAL] S3 endpoint URL, if you want to use Backblaze/cloudflare s3 bucket
    # Common Cache settings
    # Optional - Supported call types for caching
    supported_call_types: ["acompletion", "atext_completion", "aembedding", "atranscription"]
                          # /chat/completions, /completions, /embeddings, /audio/transcriptions
    mode: default_off # if default_off, you need to opt in to caching on a per call basis
    ttl: 600 # ttl for caching
 callback_settings:
  otel:
    message_logging: boolean  # OTEL logging callback specific settings
 general_settings:
  completion_model: string
  disable_spend_logs: boolean  # turn off writing each transaction to the db
  disable_master_key_return: boolean  # turn off returning master key on UI (checked on '/user/info' endpoint)
  disable_retry_on_max_parallel_request_limit_error: boolean  # turn off retries when max parallel request limit is reached
  disable_reset_budget: boolean  # turn off reset budget scheduled task
  disable_adding_master_key_hash_to_db: boolean  # turn off storing master key hash in db, for spend tracking
  enable_jwt_auth: boolean  # allow proxy admin to auth in via jwt tokens with 'litellm_proxy_admin' in claims
  enforce_user_param: boolean  # requires all openai endpoint requests to have a 'user' param
  allowed_routes: ["route1", "route2"]  # list of allowed proxy API routes - a user can access. (currently JWT-Auth only)
  key_management_system: google_kms  # either google_kms or azure_kms
  master_key: string
  # Database Settings
  database_url: string
  database_connection_pool_limit: 0  # default 100
  database_connection_timeout: 0  # default 60s
  allow_requests_on_db_unavailable: boolean  # if true, will allow requests that can not connect to the DB to verify Virtual Key to still work 
  custom_auth: string
  max_parallel_requests: 0  # the max parallel requests allowed per deployment 
  global_max_parallel_requests: 0  # the max parallel requests allowed on the proxy all up 
  infer_model_from_keys: true
  background_health_checks: true
  health_check_interval: 300
  alerting: ["slack", "email"]
  alerting_threshold: 0
  use_client_credentials_pass_through_routes: boolean  # use client credentials for all pass through routes like "/vertex-ai", /bedrock/. When this is True Virtual Key auth will not be applied on these endpoints
 ```
 ### litellm_settings - Reference
 | Name | Type | Description |
 |------|------|-------------|
 | success_callback | array of strings | List of success callbacks. [Doc Proxy logging callbacks](logging), [Doc Metrics](prometheus) |
 | failure_callback | array of strings | List of failure callbacks [Doc Proxy logging callbacks](logging), [Doc Metrics](prometheus) |
 | callbacks | array of strings | List of callbacks - runs on success and failure [Doc Proxy logging callbacks](logging), [Doc Metrics](prometheus) |
 | service_callbacks | array of strings | System health monitoring - Logs redis, postgres failures on specified services (e.g. datadog, prometheus) [Doc Metrics](prometheus) |
 | turn_off_message_logging | boolean | If true, prevents messages and responses from being logged to callbacks, but request metadata will still be logged [Proxy Logging](logging) |
 | modify_params | boolean | If true, allows modifying the parameters of the request before it is sent to the LLM provider |
 | enable_preview_features | boolean | If true, enables preview features - e.g. Azure O1 Models with streaming support.|
 | redact_user_api_key_info | boolean | If true, redacts information about the user api key from logs [Proxy Logging](logging#redacting-userapikeyinfo) |
 | langfuse_default_tags | array of strings | Default tags for Langfuse Logging. Use this if you want to control which LiteLLM-specific fields are logged as tags by the LiteLLM proxy. By default LiteLLM Proxy logs no LiteLLM-specific fields as tags. [Further docs](./logging#litellm-specific-tags-on-langfuse---cache_hit-cache_key) |
 | set_verbose | boolean | If true, sets litellm.set_verbose=True to view verbose debug logs. DO NOT LEAVE THIS ON IN PRODUCTION |
 | json_logs | boolean | If true, logs will be in json format. If you need to store the logs as JSON, just set the `litellm.json_logs = True`. We currently just log the raw POST request from litellm as a JSON [Further docs](./debugging) |
 | default_fallbacks | array of strings | List of fallback models to use if a specific model group is misconfigured / bad. [Further docs](./reliability#default-fallbacks) |
 | request_timeout | integer | The timeout for requests in seconds. If not set, the default value is `6000 seconds`. [For reference OpenAI Python SDK defaults to `600 seconds`.](https://github.com/openai/openai-python/blob/main/src/openai/_constants.py) |
 | force_ipv4 | boolean | If true, litellm will force ipv4 for all LLM requests. Some users have seen httpx ConnectionError when using ipv6 + Anthropic API |
 | content_policy_fallbacks | array of objects | Fallbacks to use when a ContentPolicyViolationError is encountered. [Further docs](./reliability#content-policy-fallbacks) |
 | context_window_fallbacks | array of objects | Fallbacks to use when a ContextWindowExceededError is encountered. [Further docs](./reliability#context-window-fallbacks) |
 | cache | boolean | If true, enables caching. [Further docs](./caching) |
 | cache_params | object | Parameters for the cache. [Further docs](./caching) |
 | cache_params.type | string | The type of cache to initialize. Can be one of ["local", "redis", "redis-semantic", "s3", "disk", "qdrant-semantic"]. Defaults to "redis". [Furher docs](./caching) |
 | cache_params.host | string | The host address for the Redis cache. Required if type is "redis". |
 | cache_params.port | integer | The port number for the Redis cache. Required if type is "redis". |
 | cache_params.password | string | The password for the Redis cache. Required if type is "redis". |
 | cache_params.namespace | string | The namespace for the Redis cache. |
 | cache_params.redis_startup_nodes | array of objects | Redis Cluster Settings. [Further docs](./caching) |
 | cache_params.service_name | string | Redis Sentinel Settings. [Further docs](./caching) |
 | cache_params.sentinel_nodes | array of arrays | Redis Sentinel Settings. [Further docs](./caching) |
 | cache_params.ttl | integer | The time (in seconds) to store entries in cache. |
 | cache_params.qdrant_semantic_cache_embedding_model | string | The embedding model to use for qdrant semantic cache. |
 | cache_params.qdrant_collection_name | string | The name of the collection to use for qdrant semantic cache. |
 | cache_params.qdrant_quantization_config | string | The quantization configuration for the qdrant semantic cache. |
 | cache_params.similarity_threshold | float | The similarity threshold for the semantic cache. |
 | cache_params.s3_bucket_name | string | The name of the S3 bucket to use for the semantic cache. |
 | cache_params.s3_region_name | string | The region name for the S3 bucket. |
 | cache_params.s3_aws_access_key_id | string | The AWS access key ID for the S3 bucket. |
 | cache_params.s3_aws_secret_access_key | string | The AWS secret access key for the S3 bucket. |
 | cache_params.s3_endpoint_url | string | Optional - The endpoint URL for the S3 bucket. |
 | cache_params.supported_call_types | array of strings | The types of calls to cache. [Further docs](./caching) |
 | cache_params.mode | string | The mode of the cache. [Further docs](./caching) |
 | disable_end_user_cost_tracking | boolean | If true, turns off end user cost tracking on prometheus metrics + litellm spend logs table on proxy. |
 | key_generation_settings | object | Restricts who can generate keys. [Further docs](./virtual_keys.md#restricting-key-generation) |
 ### general_settings - Reference
 | Name | Type | Description |
 |------|------|-------------|
 | completion_model | string | The default model to use for completions when `model` is not specified in the request |
 | disable_spend_logs | boolean | If true, turns off writing each transaction to the database |
 | disable_master_key_return | boolean | If true, turns off returning master key on UI. (checked on '/user/info' endpoint) |
 | disable_retry_on_max_parallel_request_limit_error | boolean | If true, turns off retries when max parallel request limit is reached |
 | disable_reset_budget | boolean | If true, turns off reset budget scheduled task |
 | disable_adding_master_key_hash_to_db | boolean | If true, turns off storing master key hash in db |
 | enable_jwt_auth | boolean | allow proxy admin to auth in via jwt tokens with 'litellm_proxy_admin' in claims. [Doc on JWT Tokens](token_auth) |
 | enforce_user_param | boolean | If true, requires all OpenAI endpoint requests to have a 'user' param. [Doc on call hooks](call_hooks)|
 | allowed_routes | array of strings | List of allowed proxy API routes a user can access [Doc on controlling allowed routes](enterprise#control-available-public-private-routes)|
 | key_management_system | string | Specifies the key management system. [Doc Secret Managers](../secret) |
 | master_key | string | The master key for the proxy [Set up Virtual Keys](virtual_keys) |
 | database_url | string | The URL for the database connection [Set up Virtual Keys](virtual_keys) |
 | database_connection_pool_limit | integer | The limit for database connection pool [Setting DB Connection Pool limit](#configure-db-pool-limits--connection-timeouts) |
 | database_connection_timeout | integer | The timeout for database connections in seconds [Setting DB Connection Pool limit, timeout](#configure-db-pool-limits--connection-timeouts) |
 | allow_requests_on_db_unavailable | boolean | If true, allows requests to succeed even if DB is unreachable. **Only use this if running LiteLLM in your VPC** This will allow requests to work even when LiteLLM cannot connect to the DB to verify a Virtual Key |
 | custom_auth | string | Write your own custom authentication logic [Doc Custom Auth](virtual_keys#custom-auth) |
 | max_parallel_requests | integer | The max parallel requests allowed per deployment |
 | global_max_parallel_requests | integer | The max parallel requests allowed on the proxy overall |
 | infer_model_from_keys | boolean | If true, infers the model from the provided keys |
 | background_health_checks | boolean | If true, enables background health checks. [Doc on health checks](health) |
 | health_check_interval | integer | The interval for health checks in seconds [Doc on health checks](health) |
 | alerting | array of strings | List of alerting methods [Doc on Slack Alerting](alerting) |
 | alerting_threshold | integer | The threshold for triggering alerts [Doc on Slack Alerting](alerting) |
 | use_client_credentials_pass_through_routes | boolean | If true, uses client credentials for all pass-through routes. [Doc on pass through routes](pass_through) |
 | health_check_details | boolean | If false, hides health check details (e.g. remaining rate limit). [Doc on health checks](health) |
 | public_routes | List[str] | (Enterprise Feature) Control list of public routes |
 | alert_types | List[str] | Control list of alert types to send to slack (Doc on alert types)[./alerting.md] |
 | enforced_params | List[str] | (Enterprise Feature) List of params that must be included in all requests to the proxy |
 | enable_oauth2_auth | boolean | (Enterprise Feature) If true, enables oauth2.0 authentication |
 | use_x_forwarded_for | str | If true, uses the X-Forwarded-For header to get the client IP address |
 | service_account_settings | List[Dict[str, Any]] | Set `service_account_settings` if you want to create settings that only apply to service account keys (Doc on service accounts)[./service_accounts.md] | 
 | image_generation_model | str | The default model to use for image generation - ignores model set in request |
 | store_model_in_db | boolean | If true, allows `/model/new` endpoint to store model information in db. Endpoint disabled by default. [Doc on `/model/new` endpoint](./model_management.md#create-a-new-model) |
 | max_request_size_mb | int | The maximum size for requests in MB. Requests above this size will be rejected. |
 | max_response_size_mb | int | The maximum size for responses in MB. LLM Responses above this size will not be sent. |
 | proxy_budget_rescheduler_min_time | int | The minimum time (in seconds) to wait before checking db for budget resets. **Default is 597 seconds** |
 | proxy_budget_rescheduler_max_time | int | The maximum time (in seconds) to wait before checking db for budget resets. **Default is 605 seconds** |
 | proxy_batch_write_at | int | Time (in seconds) to wait before batch writing spend logs to the db. **Default is 10 seconds** |
 | alerting_args | dict | Args for Slack Alerting [Doc on Slack Alerting](./alerting.md) |
 | custom_key_generate | str | Custom function for key generation [Doc on custom key generation](./virtual_keys.md#custom--key-generate) |
 | allowed_ips | List[str] | List of IPs allowed to access the proxy. If not set, all IPs are allowed. |
 | embedding_model | str | The default model to use for embeddings - ignores model set in request |
 | default_team_disabled | boolean | If true, users cannot create 'personal' keys (keys with no team_id). |
 | alert_to_webhook_url | Dict[str] | [Specify a webhook url for each alert type.](./alerting.md#set-specific-slack-channels-per-alert-type) |
 | key_management_settings | List[Dict[str, Any]] | Settings for key management system (e.g. AWS KMS, Azure Key Vault) [Doc on key management](../secret.md) |
 | allow_user_auth | boolean | (Deprecated) old approach for user authentication. |
 | user_api_key_cache_ttl | int | The time (in seconds) to cache user api keys in memory. |
 | disable_prisma_schema_update | boolean | If true, turns off automatic schema updates to DB |
 | litellm_key_header_name | str | If set, allows passing LiteLLM keys as a custom header. [Doc on custom headers](./virtual_keys.md#custom-headers) |
 | moderation_model | str | The default model to use for moderation. |
 | custom_sso | str | Path to a python file that implements custom SSO logic. [Doc on custom SSO](./custom_sso.md) |
 | allow_client_side_credentials | boolean | If true, allows passing client side credentials to the proxy. (Useful when testing finetuning models) [Doc on client side credentials](./virtual_keys.md#client-side-credentials) |
 | admin_only_routes | List[str] | (Enterprise Feature) List of routes that are only accessible to admin users. [Doc on admin only routes](./enterprise#control-available-public-private-routes) |
 | use_azure_key_vault | boolean | If true, load keys from azure key vault | 
 | use_google_kms | boolean | If true, load keys from google kms |
 | spend_report_frequency | str | Specify how often you want a Spend Report to be sent (e.g. "1d", "2d", "30d") [More on this](./alerting.md#spend-report-frequency) |
 | ui_access_mode | Literal["admin_only"] | If set, restricts access to the UI to admin users only. [Docs](./ui.md#restrict-ui-access) |
 | litellm_jwtauth | Dict[str, Any] | Settings for JWT authentication. [Docs](./token_auth.md) |
 | litellm_license | str | The license key for the proxy. [Docs](../enterprise.md#how-does-deployment-with-enterprise-license-work) |
 | oauth2_config_mappings | Dict[str, str] | Define the OAuth2 config mappings | 
 | pass_through_endpoints | List[Dict[str, Any]] | Define the pass through endpoints. [Docs](./pass_through) |
 | enable_oauth2_proxy_auth | boolean | (Enterprise Feature) If true, enables oauth2.0 authentication |
 | forward_openai_org_id | boolean | If true, forwards the OpenAI Organization ID to the backend LLM call (if it's OpenAI). |
 | forward_client_headers_to_llm_api | boolean | If true, forwards the client headers (any `x-` headers) to the backend LLM call |
 ### router_settings - Reference
 :::info
 Most values can also be set via `litellm_settings`. If you see overlapping values, settings on `router_settings` will override those on `litellm_settings`.
 :::
 ```yaml
 router_settings:
  routing_strategy: usage-based-routing-v2 # Literal["simple-shuffle", "least-busy", "usage-based-routing","latency-based-routing"], default="simple-shuffle"
  redis_host: <your-redis-host>           # string
  redis_password: <your-redis-password>   # string
  redis_port: <your-redis-port>           # string
  enable_pre_call_check: true             # bool - Before call is made check if a call is within model context window 
  allowed_fails: 3 # cooldown model if it fails > 1 call in a minute. 
  cooldown_time: 30 # (in seconds) how long to cooldown model if fails/min > allowed_fails
  disable_cooldowns: True                  # bool - Disable cooldowns for all models 
  enable_tag_filtering: True                # bool - Use tag based routing for requests
  retry_policy: {                          # Dict[str, int]: retry policy for different types of exceptions
    "AuthenticationErrorRetries": 3,
    "TimeoutErrorRetries": 3,
    "RateLimitErrorRetries": 3,
    "ContentPolicyViolationErrorRetries": 4,
    "InternalServerErrorRetries": 4
  }
  allowed_fails_policy: {
    "BadRequestErrorAllowedFails": 1000, # Allow 1000 BadRequestErrors before cooling down a deployment
    "AuthenticationErrorAllowedFails": 10, # int 
    "TimeoutErrorAllowedFails": 12, # int 
    "RateLimitErrorAllowedFails": 10000, # int 
    "ContentPolicyViolationErrorAllowedFails": 15, # int 
    "InternalServerErrorAllowedFails": 20, # int 
  }
  content_policy_fallbacks=[{"claude-2": ["my-fallback-model"]}] # List[Dict[str, List[str]]]: Fallback model for content policy violations
  fallbacks=[{"claude-2": ["my-fallback-model"]}] # List[Dict[str, List[str]]]: Fallback model for all errors
 ```
 | Name | Type | Description |
 |------|------|-------------|
 | routing_strategy | string | The strategy used for routing requests. Options: "simple-shuffle", "least-busy", "usage-based-routing", "latency-based-routing". Default is "simple-shuffle". [More information here](../routing) |
 | redis_host | string | The host address for the Redis server. **Only set this if you have multiple instances of LiteLLM Proxy and want current tpm/rpm tracking to be shared across them** |
 | redis_password | string | The password for the Redis server. **Only set this if you have multiple instances of LiteLLM Proxy and want current tpm/rpm tracking to be shared across them** |
 | redis_port | string | The port number for the Redis server. **Only set this if you have multiple instances of LiteLLM Proxy and want current tpm/rpm tracking to be shared across them**|
 | enable_pre_call_check | boolean | If true, checks if a call is within the model's context window before making the call. [More information here](reliability) |
 | content_policy_fallbacks | array of objects | Specifies fallback models for content policy violations. [More information here](reliability) |
 | fallbacks | array of objects | Specifies fallback models for all types of errors. [More information here](reliability) |
 | enable_tag_filtering | boolean | If true, uses tag based routing for requests [Tag Based Routing](tag_routing) |
 | cooldown_time | integer | The duration (in seconds) to cooldown a model if it exceeds the allowed failures. |
 | disable_cooldowns | boolean | If true, disables cooldowns for all models. [More information here](reliability) |
 | retry_policy | object | Specifies the number of retries for different types of exceptions. [More information here](reliability) |
 | allowed_fails | integer | The number of failures allowed before cooling down a model. [More information here](reliability) |
 | allowed_fails_policy | object | Specifies the number of allowed failures for different error types before cooling down a deployment. [More information here](reliability) |
 | default_max_parallel_requests | Optional[int] | The default maximum number of parallel requests for a deployment. |
 | default_priority | (Optional[int]) | The default priority for a request. Only for '.scheduler_acompletion()'. Default is None. | 
 | polling_interval | (Optional[float]) | frequency of polling queue. Only for '.scheduler_acompletion()'. Default is 3ms. |
 | max_fallbacks | Optional[int] | The maximum number of fallbacks to try before exiting the call. Defaults to 5. |
 | default_litellm_params | Optional[dict] | The default litellm parameters to add to all requests (e.g. `temperature`, `max_tokens`). |
 | timeout | Optional[float] | The default timeout for a request. |
 | debug_level | Literal["DEBUG", "INFO"] | The debug level for the logging library in the router. Defaults to "INFO". |
 | client_ttl | int | Time-to-live for cached clients in seconds. Defaults to 3600. |
 | cache_kwargs | dict | Additional keyword arguments for the cache initialization. |
 | routing_strategy_args | dict | Additional keyword arguments for the routing strategy - e.g. lowest latency routing default ttl |
 | model_group_alias | dict | Model group alias mapping. E.g. `{"claude-3-haiku": "claude-3-haiku-20240229"}` |
 | num_retries | int | Number of retries for a request. Defaults to 3. |
 | default_fallbacks | Optional[List[str]] | Fallbacks to try if no model group-specific fallbacks are defined. |
 | caching_groups | Optional[List[tuple]] | List of model groups for caching across model groups. Defaults to None. - e.g. caching_groups=[("openai-gpt-3.5-turbo", "azure-gpt-3.5-turbo")]|
 | alerting_config | AlertingConfig | [SDK-only arg] Slack alerting configuration. Defaults to None. [Further Docs](../routing.md#alerting-) |
 | assistants_config | AssistantsConfig | Set on proxy via `assistant_settings`. [Further docs](../assistants.md) |
 | set_verbose | boolean | [DEPRECATED PARAM - see debug docs](./debugging.md) If true, sets the logging level to verbose. |
 | retry_after | int | Time to wait before retrying a request in seconds. Defaults to 0. If `x-retry-after` is received from LLM API, this value is overridden. |
 | provider_budget_config | ProviderBudgetConfig | Provider budget configuration. Use this to set llm_provider budget limits. example $100/day to OpenAI, $100/day to Azure, etc. Defaults to None. [Further Docs](./provider_budget_routing.md) |
 | enable_pre_call_checks | boolean | If true, checks if a call is within the model's context window before making the call. [More information here](reliability) |
 | model_group_retry_policy | Dict[str, RetryPolicy] | [SDK-only arg] Set retry policy for model groups. |
 | context_window_fallbacks | List[Dict[str, List[str]]] | Fallback models for context window violations. |
 | redis_url | str | URL for Redis server. **Known performance issue with Redis URL.** |
 | cache_responses | boolean | Flag to enable caching LLM Responses, if cache set under `router_settings`. If true, caches responses. Defaults to False. |
 | router_general_settings | RouterGeneralSettings | [SDK-Only] Router general settings - contains optimizations like 'async_only_mode'. [Docs](../routing.md#router-general-settings) |
 ### environment variables - Reference
 | Name | Description |
 |------|-------------|
 | ACTIONS_ID_TOKEN_REQUEST_TOKEN | Token for requesting ID in GitHub Actions
 | ACTIONS_ID_TOKEN_REQUEST_URL | URL for requesting ID token in GitHub Actions
 | AISPEND_ACCOUNT_ID | Account ID for AI Spend
 | AISPEND_API_KEY | API Key for AI Spend
 | ALLOWED_EMAIL_DOMAINS | List of email domains allowed for access
 | ARIZE_API_KEY | API key for Arize platform integration
 | ARIZE_SPACE_KEY | Space key for Arize platform
 | ARGILLA_BATCH_SIZE | Batch size for Argilla logging
 | ARGILLA_API_KEY | API key for Argilla platform
 | ARGILLA_SAMPLING_RATE | Sampling rate for Argilla logging
 | ARGILLA_DATASET_NAME | Dataset name for Argilla logging
 | ARGILLA_BASE_URL | Base URL for Argilla service
 | ATHINA_API_KEY | API key for Athina service
 | AUTH_STRATEGY | Strategy used for authentication (e.g., OAuth, API key)
 | AWS_ACCESS_KEY_ID | Access Key ID for AWS services
 | AWS_PROFILE_NAME | AWS CLI profile name to be used
 | AWS_REGION_NAME | Default AWS region for service interactions
 | AWS_ROLE_NAME | Role name for AWS IAM usage
 | AWS_SECRET_ACCESS_KEY | Secret Access Key for AWS services
 | AWS_SESSION_NAME | Name for AWS session
 | AWS_WEB_IDENTITY_TOKEN | Web identity token for AWS
 | AZURE_API_VERSION | Version of the Azure API being used
 | AZURE_AUTHORITY_HOST | Azure authority host URL
 | AZURE_CLIENT_ID | Client ID for Azure services
 | AZURE_CLIENT_SECRET | Client secret for Azure services
 | AZURE_FEDERATED_TOKEN_FILE | File path to Azure federated token
 | AZURE_KEY_VAULT_URI | URI for Azure Key Vault
 | AZURE_TENANT_ID | Tenant ID for Azure Active Directory
 | BERRISPEND_ACCOUNT_ID | Account ID for BerriSpend service
 | BRAINTRUST_API_KEY | API key for Braintrust integration
 | CIRCLE_OIDC_TOKEN | OpenID Connect token for CircleCI
 | CIRCLE_OIDC_TOKEN_V2 | Version 2 of the OpenID Connect token for CircleCI
 | CONFIG_FILE_PATH | File path for configuration file
 | CUSTOM_TIKTOKEN_CACHE_DIR | Custom directory for Tiktoken cache
 | DATABASE_HOST | Hostname for the database server
 | DATABASE_NAME | Name of the database
 | DATABASE_PASSWORD | Password for the database user
 | DATABASE_PORT | Port number for database connection
 | DATABASE_SCHEMA | Schema name used in the database
 | DATABASE_URL | Connection URL for the database
 | DATABASE_USER | Username for database connection
 | DATABASE_USERNAME | Alias for database user
 | DATABRICKS_API_BASE | Base URL for Databricks API
 | DD_BASE_URL | Base URL for Datadog integration
 | DATADOG_BASE_URL | (Alternative to DD_BASE_URL) Base URL for Datadog integration
 | _DATADOG_BASE_URL | (Alternative to DD_BASE_URL) Base URL for Datadog integration
 | DD_API_KEY | API key for Datadog integration
 | DD_SITE | Site URL for Datadog (e.g., datadoghq.com)
 | DD_SOURCE | Source identifier for Datadog logs
 | DD_ENV | Environment identifier for Datadog logs. Only supported for `datadog_llm_observability` callback
 | DD_SERVICE | Service identifier for Datadog logs. Defaults to "litellm-server"
 | DD_VERSION | Version identifier for Datadog logs. Defaults to "unknown"
 | DEBUG_OTEL | Enable debug mode for OpenTelemetry
 | DIRECT_URL | Direct URL for service endpoint
 | DISABLE_ADMIN_UI | Toggle to disable the admin UI
 | DISABLE_SCHEMA_UPDATE | Toggle to disable schema updates
 | DOCS_DESCRIPTION | Description text for documentation pages
 | DOCS_FILTERED | Flag indicating filtered documentation
 | DOCS_TITLE | Title of the documentation pages
 | DOCS_URL | The path to the Swagger API documentation. **By default this is "/"**
 | EMAIL_SUPPORT_CONTACT | Support contact email address
 | GCS_BUCKET_NAME | Name of the Google Cloud Storage bucket
 | GCS_PATH_SERVICE_ACCOUNT | Path to the Google Cloud service account JSON file
 | GCS_FLUSH_INTERVAL | Flush interval for GCS logging (in seconds). Specify how often you want a log to be sent to GCS. **Default is 20 seconds**
 | GCS_BATCH_SIZE | Batch size for GCS logging. Specify after how many logs you want to flush to GCS. If `BATCH_SIZE` is set to 10, logs are flushed every 10 logs. **Default is 2048**
 | GENERIC_AUTHORIZATION_ENDPOINT | Authorization endpoint for generic OAuth providers
 | GENERIC_CLIENT_ID | Client ID for generic OAuth providers
 | GENERIC_CLIENT_SECRET | Client secret for generic OAuth providers
 | GENERIC_CLIENT_STATE | State parameter for generic client authentication
 | GENERIC_INCLUDE_CLIENT_ID | Include client ID in requests for OAuth
 | GENERIC_SCOPE | Scope settings for generic OAuth providers
 | GENERIC_TOKEN_ENDPOINT | Token endpoint for generic OAuth providers
 | GENERIC_USER_DISPLAY_NAME_ATTRIBUTE | Attribute for user's display name in generic auth
 | GENERIC_USER_EMAIL_ATTRIBUTE | Attribute for user's email in generic auth
 | GENERIC_USER_FIRST_NAME_ATTRIBUTE | Attribute for user's first name in generic auth
 | GENERIC_USER_ID_ATTRIBUTE | Attribute for user ID in generic auth
 | GENERIC_USER_LAST_NAME_ATTRIBUTE | Attribute for user's last name in generic auth
 | GENERIC_USER_PROVIDER_ATTRIBUTE | Attribute specifying the user's provider
 | GENERIC_USER_ROLE_ATTRIBUTE | Attribute specifying the user's role
 | GENERIC_USERINFO_ENDPOINT | Endpoint to fetch user information in generic OAuth
 | GALILEO_BASE_URL | Base URL for Galileo platform
 | GALILEO_PASSWORD | Password for Galileo authentication
 | GALILEO_PROJECT_ID | Project ID for Galileo usage
 | GALILEO_USERNAME | Username for Galileo authentication
 | GREENSCALE_API_KEY | API key for Greenscale service
 | GREENSCALE_ENDPOINT | Endpoint URL for Greenscale service
 | GOOGLE_APPLICATION_CREDENTIALS | Path to Google Cloud credentials JSON file
 | GOOGLE_CLIENT_ID | Client ID for Google OAuth
 | GOOGLE_CLIENT_SECRET | Client secret for Google OAuth
 | GOOGLE_KMS_RESOURCE_NAME | Name of the resource in Google KMS
 | HF_API_BASE | Base URL for Hugging Face API
 | HELICONE_API_KEY | API key for Helicone service
 | HUGGINGFACE_API_BASE | Base URL for Hugging Face API
 | IAM_TOKEN_DB_AUTH | IAM token for database authentication
 | JSON_LOGS | Enable JSON formatted logging
 | JWT_AUDIENCE | Expected audience for JWT tokens
 | JWT_PUBLIC_KEY_URL | URL to fetch public key for JWT verification
 | LAGO_API_BASE | Base URL for Lago API
 | LAGO_API_CHARGE_BY | Parameter to determine charge basis in Lago
 | LAGO_API_EVENT_CODE | Event code for Lago API events
 | LAGO_API_KEY | API key for accessing Lago services
 | LANGFUSE_DEBUG | Toggle debug mode for Langfuse
 | LANGFUSE_FLUSH_INTERVAL | Interval for flushing Langfuse logs
 | LANGFUSE_HOST | Host URL for Langfuse service
 | LANGFUSE_PUBLIC_KEY | Public key for Langfuse authentication
 | LANGFUSE_RELEASE | Release version of Langfuse integration
 | LANGFUSE_SECRET_KEY | Secret key for Langfuse authentication
 | LANGSMITH_API_KEY | API key for Langsmith platform
 | LANGSMITH_BASE_URL | Base URL for Langsmith service
 | LANGSMITH_BATCH_SIZE | Batch size for operations in Langsmith
 | LANGSMITH_DEFAULT_RUN_NAME | Default name for Langsmith run
 | LANGSMITH_PROJECT | Project name for Langsmith integration
 | LANGSMITH_SAMPLING_RATE | Sampling rate for Langsmith logging
 | LANGTRACE_API_KEY | API key for Langtrace service
 | LITERAL_API_KEY | API key for Literal integration
 | LITERAL_API_URL | API URL for Literal service
 | LITERAL_BATCH_SIZE | Batch size for Literal operations
 | LITELLM_DONT_SHOW_FEEDBACK_BOX | Flag to hide feedback box in LiteLLM UI
 | LITELLM_DROP_PARAMS | Parameters to drop in LiteLLM requests
 | LITELLM_EMAIL | Email associated with LiteLLM account
 | LITELLM_GLOBAL_MAX_PARALLEL_REQUEST_RETRIES | Maximum retries for parallel requests in LiteLLM
 | LITELLM_GLOBAL_MAX_PARALLEL_REQUEST_RETRY_TIMEOUT | Timeout for retries of parallel requests in LiteLLM
 | LITELLM_HOSTED_UI | URL of the hosted UI for LiteLLM
 | LITELLM_LICENSE | License key for LiteLLM usage
 | LITELLM_LOCAL_MODEL_COST_MAP | Local configuration for model cost mapping in LiteLLM
 | LITELLM_LOG | Enable detailed logging for LiteLLM
 | LITELLM_MODE | Operating mode for LiteLLM (e.g., production, development)
 | LITELLM_SALT_KEY | Salt key for encryption in LiteLLM
 | LITELLM_SECRET_AWS_KMS_LITELLM_LICENSE | AWS KMS encrypted license for LiteLLM
 | LITELLM_TOKEN | Access token for LiteLLM integration
 | LOGFIRE_TOKEN | Token for Logfire logging service
 | MICROSOFT_CLIENT_ID | Client ID for Microsoft services
 | MICROSOFT_CLIENT_SECRET | Client secret for Microsoft services
 | MICROSOFT_TENANT | Tenant ID for Microsoft Azure
 | NO_DOCS | Flag to disable documentation generation
 | NO_PROXY | List of addresses to bypass proxy
 | OAUTH_TOKEN_INFO_ENDPOINT | Endpoint for OAuth token info retrieval
 | OPENAI_API_BASE | Base URL for OpenAI API
 | OPENAI_API_KEY | API key for OpenAI services
 | OPENAI_ORGANIZATION | Organization identifier for OpenAI
 | OPENID_BASE_URL | Base URL for OpenID Connect services
 | OPENID_CLIENT_ID | Client ID for OpenID Connect authentication
 | OPENID_CLIENT_SECRET | Client secret for OpenID Connect authentication
 | OPENMETER_API_ENDPOINT | API endpoint for OpenMeter integration
 | OPENMETER_API_KEY | API key for OpenMeter services
 | OPENMETER_EVENT_TYPE | Type of events sent to OpenMeter
 | OTEL_ENDPOINT | OpenTelemetry endpoint for traces
 | OTEL_ENVIRONMENT_NAME | Environment name for OpenTelemetry
 | OTEL_EXPORTER | Exporter type for OpenTelemetry
 | OTEL_HEADERS | Headers for OpenTelemetry requests
 | OTEL_SERVICE_NAME | Service name identifier for OpenTelemetry
 | OTEL_TRACER_NAME | Tracer name for OpenTelemetry tracing
 | PREDIBASE_API_BASE | Base URL for Predibase API
 | PRESIDIO_ANALYZER_API_BASE | Base URL for Presidio Analyzer service
 | PRESIDIO_ANONYMIZER_API_BASE | Base URL for Presidio Anonymizer service
 | PROMETHEUS_URL | URL for Prometheus service
 | PROMPTLAYER_API_KEY | API key for PromptLayer integration
 | PROXY_ADMIN_ID | Admin identifier for proxy server
 | PROXY_BASE_URL | Base URL for proxy service
 | PROXY_LOGOUT_URL | URL for logging out of the proxy service
 | PROXY_MASTER_KEY | Master key for proxy authentication
 | QDRANT_API_BASE | Base URL for Qdrant API
 | QDRANT_API_KEY | API key for Qdrant service
 | QDRANT_URL | Connection URL for Qdrant database
 | REDIS_HOST | Hostname for Redis server
 | REDIS_PASSWORD | Password for Redis service
 | REDIS_PORT | Port number for Redis server
 | REDOC_URL | The path to the Redoc Fast API documentation. **By default this is "/redoc"**
 | SERVER_ROOT_PATH | Root path for the server application
 | SET_VERBOSE | Flag to enable verbose logging
 | SLACK_DAILY_REPORT_FREQUENCY | Frequency of daily Slack reports (e.g., daily, weekly)
 | SLACK_WEBHOOK_URL | Webhook URL for Slack integration
 | SMTP_HOST | Hostname for the SMTP server
 | SMTP_PASSWORD | Password for SMTP authentication
 | SMTP_PORT | Port number for SMTP server
 | SMTP_SENDER_EMAIL | Email address used as the sender in SMTP transactions
 | SMTP_SENDER_LOGO | Logo used in emails sent via SMTP
 | SMTP_TLS | Flag to enable or disable TLS for SMTP connections
 | SMTP_USERNAME | Username for SMTP authentication
 | SPEND_LOGS_URL | URL for retrieving spend logs
 | SSL_CERTIFICATE | Path to the SSL certificate file
 | SSL_VERIFY | Flag to enable or disable SSL certificate verification
 | SUPABASE_KEY | API key for Supabase service
 | SUPABASE_URL | Base URL for Supabase instance
 | TEST_EMAIL_ADDRESS | Email address used for testing purposes
 | UI_LOGO_PATH | Path to the logo image used in the UI
 | UI_PASSWORD | Password for accessing the UI
 | UI_USERNAME | Username for accessing the UI
 | UPSTREAM_LANGFUSE_DEBUG | Flag to enable debugging for upstream Langfuse
 | UPSTREAM_LANGFUSE_HOST | Host URL for upstream Langfuse service
 | UPSTREAM_LANGFUSE_PUBLIC_KEY | Public key for upstream Langfuse authentication
 | UPSTREAM_LANGFUSE_RELEASE | Release version identifier for upstream Langfuse
 | UPSTREAM_LANGFUSE_SECRET_KEY | Secret key for upstream Langfuse authentication
 | USE_AWS_KMS | Flag to enable AWS Key Management Service for encryption
 | WEBHOOK_URL | URL for receiving webhooks from external services
--- a/docs/my-website/docs/proxy/configs.md
+++ b/docs/my-website/docs/proxy/configs.md
@ -2,7 +2,7 @@ import Image from '@theme/IdealImage';
 import Tabs from '@theme/Tabs';
 import TabItem from '@theme/TabItem';
-# Proxy Config.yaml
+# Overview
 Set model list, `api_base`, `api_key`, `temperature` & proxy server settings (`master-key`) on the config.yaml. 
 | Param Name           | Description                                                   |
@ -357,77 +357,6 @@ curl --location 'http://0.0.0.0:4000/v1/model/info' \
 --data ''
 ```
 ### Provider specific wildcard routing 
 **Proxy all models from a provider**
 Use this if you want to **proxy all models from a specific provider without defining them on the config.yaml**
 **Step 1** - define provider specific routing on config.yaml
 ```yaml
 model_list:
  # provider specific wildcard routing
  - model_name: "anthropic/*"
    litellm_params:
      model: "anthropic/*"
      api_key: os.environ/ANTHROPIC_API_KEY
  - model_name: "groq/*"
    litellm_params:
      model: "groq/*"
      api_key: os.environ/GROQ_API_KEY
  - model_name: "fo::*:static::*" # all requests matching this pattern will be routed to this deployment, example: model="fo::hi::static::hi" will be routed to deployment: "openai/fo::*:static::*"
    litellm_params:
      model: "openai/fo::*:static::*"
      api_key: os.environ/OPENAI_API_KEY
 ```
 Step 2 - Run litellm proxy 
 ```shell
 $ litellm --config /path/to/config.yaml
 ```
 Step 3 Test it 
 Test with `anthropic/` - all models with `anthropic/` prefix will get routed to `anthropic/*`
 ```shell
 curl http://localhost:4000/v1/chat/completions \
  -H "Content-Type: application/json" \
  -H "Authorization: Bearer sk-1234" \
  -d '{
    "model": "anthropic/claude-3-sonnet-20240229",
    "messages": [
      {"role": "user", "content": "Hello, Claude!"}
    ]
  }'
 ```
 Test with `groq/` - all models with `groq/` prefix will get routed to `groq/*`
 ```shell
 curl http://localhost:4000/v1/chat/completions \
  -H "Content-Type: application/json" \
  -H "Authorization: Bearer sk-1234" \
  -d '{
    "model": "groq/llama3-8b-8192",
    "messages": [
      {"role": "user", "content": "Hello, Claude!"}
    ]
  }'
 ```
 Test with `fo::*::static::*` - all requests matching this pattern will be routed to `openai/fo::*:static::*`
 ```shell
 curl http://localhost:4000/v1/chat/completions \
  -H "Content-Type: application/json" \
  -H "Authorization: Bearer sk-1234" \
  -d '{
    "model": "fo::hi::static::hi",
    "messages": [
      {"role": "user", "content": "Hello, Claude!"}
    ]
  }'
 ```
 ### Load Balancing 
 :::info
@ -597,481 +526,6 @@ general_settings:
  database_connection_timeout: 60 # sets a 60s timeout for any connection call to the db 
 ```
 ## **All settings**
 ```yaml
 environment_variables: {}
 model_list:
  - model_name: string
    litellm_params: {}
    model_info:
      id: string
      mode: embedding
      input_cost_per_token: 0
      output_cost_per_token: 0
      max_tokens: 2048
      base_model: gpt-4-1106-preview
      additionalProp1: {}
 litellm_settings:
  # Logging/Callback settings
  success_callback: ["langfuse"]  # list of success callbacks
  failure_callback: ["sentry"]  # list of failure callbacks
  callbacks: ["otel"]  # list of callbacks - runs on success and failure
  service_callbacks: ["datadog", "prometheus"]  # logs redis, postgres failures on datadog, prometheus
  turn_off_message_logging: boolean  # prevent the messages and responses from being logged to on your callbacks, but request metadata will still be logged.
  redact_user_api_key_info: boolean  # Redact information about the user api key (hashed token, user_id, team id, etc.), from logs. Currently supported for Langfuse, OpenTelemetry, Logfire, ArizeAI logging.
  langfuse_default_tags: ["cache_hit", "cache_key", "proxy_base_url", "user_api_key_alias", "user_api_key_user_id", "user_api_key_user_email", "user_api_key_team_alias", "semantic-similarity", "proxy_base_url"] # default tags for Langfuse Logging
  # Networking settings
  request_timeout: 10 # (int) llm requesttimeout in seconds. Raise Timeout error if call takes longer than 10s. Sets litellm.request_timeout 
  force_ipv4: boolean # If true, litellm will force ipv4 for all LLM requests. Some users have seen httpx ConnectionError when using ipv6 + Anthropic API
  set_verbose: boolean # sets litellm.set_verbose=True to view verbose debug logs. DO NOT LEAVE THIS ON IN PRODUCTION
  json_logs: boolean # if true, logs will be in json format
  # Fallbacks, reliability
  default_fallbacks: ["claude-opus"] # set default_fallbacks, in case a specific model group is misconfigured / bad.
  content_policy_fallbacks: [{"gpt-3.5-turbo-small": ["claude-opus"]}] # fallbacks for ContentPolicyErrors
  context_window_fallbacks: [{"gpt-3.5-turbo-small": ["gpt-3.5-turbo-large", "claude-opus"]}] # fallbacks for ContextWindowExceededErrors
  # Caching settings
  cache: true 
  cache_params:        # set cache params for redis
    type: redis        # type of cache to initialize
    # Optional - Redis Settings
    host: "localhost"  # The host address for the Redis cache. Required if type is "redis".
    port: 6379  # The port number for the Redis cache. Required if type is "redis".
    password: "your_password"  # The password for the Redis cache. Required if type is "redis".
    namespace: "litellm.caching.caching" # namespace for redis cache
    # Optional - Redis Cluster Settings
    redis_startup_nodes: [{"host": "127.0.0.1", "port": "7001"}] 
    # Optional - Redis Sentinel Settings
    service_name: "mymaster"
    sentinel_nodes: [["localhost", 26379]]
    # Optional - Qdrant Semantic Cache Settings
    qdrant_semantic_cache_embedding_model: openai-embedding # the model should be defined on the model_list
    qdrant_collection_name: test_collection
    qdrant_quantization_config: binary
    similarity_threshold: 0.8   # similarity threshold for semantic cache
    # Optional - S3 Cache Settings
    s3_bucket_name: cache-bucket-litellm   # AWS Bucket Name for S3
    s3_region_name: us-west-2              # AWS Region Name for S3
    s3_aws_access_key_id: os.environ/AWS_ACCESS_KEY_ID  # us os.environ/<variable name> to pass environment variables. This is AWS Access Key ID for S3
    s3_aws_secret_access_key: os.environ/AWS_SECRET_ACCESS_KEY  # AWS Secret Access Key for S3
    s3_endpoint_url: https://s3.amazonaws.com  # [OPTIONAL] S3 endpoint URL, if you want to use Backblaze/cloudflare s3 bucket
    # Common Cache settings
    # Optional - Supported call types for caching
    supported_call_types: ["acompletion", "atext_completion", "aembedding", "atranscription"]
                          # /chat/completions, /completions, /embeddings, /audio/transcriptions
    mode: default_off # if default_off, you need to opt in to caching on a per call basis
    ttl: 600 # ttl for caching
 callback_settings:
  otel:
    message_logging: boolean  # OTEL logging callback specific settings
 general_settings:
  completion_model: string
  disable_spend_logs: boolean  # turn off writing each transaction to the db
  disable_master_key_return: boolean  # turn off returning master key on UI (checked on '/user/info' endpoint)
  disable_retry_on_max_parallel_request_limit_error: boolean  # turn off retries when max parallel request limit is reached
  disable_reset_budget: boolean  # turn off reset budget scheduled task
  disable_adding_master_key_hash_to_db: boolean  # turn off storing master key hash in db, for spend tracking
  enable_jwt_auth: boolean  # allow proxy admin to auth in via jwt tokens with 'litellm_proxy_admin' in claims
  enforce_user_param: boolean  # requires all openai endpoint requests to have a 'user' param
  allowed_routes: ["route1", "route2"]  # list of allowed proxy API routes - a user can access. (currently JWT-Auth only)
  key_management_system: google_kms  # either google_kms or azure_kms
  master_key: string
  # Database Settings
  database_url: string
  database_connection_pool_limit: 0  # default 100
  database_connection_timeout: 0  # default 60s
  allow_requests_on_db_unavailable: boolean  # if true, will allow requests that can not connect to the DB to verify Virtual Key to still work 
  custom_auth: string
  max_parallel_requests: 0  # the max parallel requests allowed per deployment 
  global_max_parallel_requests: 0  # the max parallel requests allowed on the proxy all up 
  infer_model_from_keys: true
  background_health_checks: true
  health_check_interval: 300
  alerting: ["slack", "email"]
  alerting_threshold: 0
  use_client_credentials_pass_through_routes: boolean  # use client credentials for all pass through routes like "/vertex-ai", /bedrock/. When this is True Virtual Key auth will not be applied on these endpoints
 ```
 ### litellm_settings - Reference
 | Name | Type | Description |
 |------|------|-------------|
 | success_callback | array of strings | List of success callbacks. [Doc Proxy logging callbacks](logging), [Doc Metrics](prometheus) |
 | failure_callback | array of strings | List of failure callbacks [Doc Proxy logging callbacks](logging), [Doc Metrics](prometheus) |
 | callbacks | array of strings | List of callbacks - runs on success and failure [Doc Proxy logging callbacks](logging), [Doc Metrics](prometheus) |
 | service_callbacks | array of strings | System health monitoring - Logs redis, postgres failures on specified services (e.g. datadog, prometheus) [Doc Metrics](prometheus) |
 | turn_off_message_logging | boolean | If true, prevents messages and responses from being logged to callbacks, but request metadata will still be logged [Proxy Logging](logging) |
 | modify_params | boolean | If true, allows modifying the parameters of the request before it is sent to the LLM provider |
 | enable_preview_features | boolean | If true, enables preview features - e.g. Azure O1 Models with streaming support.|
 | redact_user_api_key_info | boolean | If true, redacts information about the user api key from logs [Proxy Logging](logging#redacting-userapikeyinfo) |
 | langfuse_default_tags | array of strings | Default tags for Langfuse Logging. Use this if you want to control which LiteLLM-specific fields are logged as tags by the LiteLLM proxy. By default LiteLLM Proxy logs no LiteLLM-specific fields as tags. [Further docs](./logging#litellm-specific-tags-on-langfuse---cache_hit-cache_key) |
 | set_verbose | boolean | If true, sets litellm.set_verbose=True to view verbose debug logs. DO NOT LEAVE THIS ON IN PRODUCTION |
 | json_logs | boolean | If true, logs will be in json format. If you need to store the logs as JSON, just set the `litellm.json_logs = True`. We currently just log the raw POST request from litellm as a JSON [Further docs](./debugging) |
 | default_fallbacks | array of strings | List of fallback models to use if a specific model group is misconfigured / bad. [Further docs](./reliability#default-fallbacks) |
 | request_timeout | integer | The timeout for requests in seconds. If not set, the default value is `6000 seconds`. [For reference OpenAI Python SDK defaults to `600 seconds`.](https://github.com/openai/openai-python/blob/main/src/openai/_constants.py) |
 | force_ipv4 | boolean | If true, litellm will force ipv4 for all LLM requests. Some users have seen httpx ConnectionError when using ipv6 + Anthropic API |
 | content_policy_fallbacks | array of objects | Fallbacks to use when a ContentPolicyViolationError is encountered. [Further docs](./reliability#content-policy-fallbacks) |
 | context_window_fallbacks | array of objects | Fallbacks to use when a ContextWindowExceededError is encountered. [Further docs](./reliability#context-window-fallbacks) |
 | cache | boolean | If true, enables caching. [Further docs](./caching) |
 | cache_params | object | Parameters for the cache. [Further docs](./caching) |
 | cache_params.type | string | The type of cache to initialize. Can be one of ["local", "redis", "redis-semantic", "s3", "disk", "qdrant-semantic"]. Defaults to "redis". [Furher docs](./caching) |
 | cache_params.host | string | The host address for the Redis cache. Required if type is "redis". |
 | cache_params.port | integer | The port number for the Redis cache. Required if type is "redis". |
 | cache_params.password | string | The password for the Redis cache. Required if type is "redis". |
 | cache_params.namespace | string | The namespace for the Redis cache. |
 | cache_params.redis_startup_nodes | array of objects | Redis Cluster Settings. [Further docs](./caching) |
 | cache_params.service_name | string | Redis Sentinel Settings. [Further docs](./caching) |
 | cache_params.sentinel_nodes | array of arrays | Redis Sentinel Settings. [Further docs](./caching) |
 | cache_params.ttl | integer | The time (in seconds) to store entries in cache. |
 | cache_params.qdrant_semantic_cache_embedding_model | string | The embedding model to use for qdrant semantic cache. |
 | cache_params.qdrant_collection_name | string | The name of the collection to use for qdrant semantic cache. |
 | cache_params.qdrant_quantization_config | string | The quantization configuration for the qdrant semantic cache. |
 | cache_params.similarity_threshold | float | The similarity threshold for the semantic cache. |
 | cache_params.s3_bucket_name | string | The name of the S3 bucket to use for the semantic cache. |
 | cache_params.s3_region_name | string | The region name for the S3 bucket. |
 | cache_params.s3_aws_access_key_id | string | The AWS access key ID for the S3 bucket. |
 | cache_params.s3_aws_secret_access_key | string | The AWS secret access key for the S3 bucket. |
 | cache_params.s3_endpoint_url | string | Optional - The endpoint URL for the S3 bucket. |
 | cache_params.supported_call_types | array of strings | The types of calls to cache. [Further docs](./caching) |
 | cache_params.mode | string | The mode of the cache. [Further docs](./caching) |
 | disable_end_user_cost_tracking | boolean | If true, turns off end user cost tracking on prometheus metrics + litellm spend logs table on proxy. |
 | key_generation_settings | object | Restricts who can generate keys. [Further docs](./virtual_keys.md#restricting-key-generation) |
 ### general_settings - Reference
 | Name | Type | Description |
 |------|------|-------------|
 | completion_model | string | The default model to use for completions when `model` is not specified in the request |
 | disable_spend_logs | boolean | If true, turns off writing each transaction to the database |
 | disable_master_key_return | boolean | If true, turns off returning master key on UI. (checked on '/user/info' endpoint) |
 | disable_retry_on_max_parallel_request_limit_error | boolean | If true, turns off retries when max parallel request limit is reached |
 | disable_reset_budget | boolean | If true, turns off reset budget scheduled task |
 | disable_adding_master_key_hash_to_db | boolean | If true, turns off storing master key hash in db |
 | enable_jwt_auth | boolean | allow proxy admin to auth in via jwt tokens with 'litellm_proxy_admin' in claims. [Doc on JWT Tokens](token_auth) |
 | enforce_user_param | boolean | If true, requires all OpenAI endpoint requests to have a 'user' param. [Doc on call hooks](call_hooks)|
 | allowed_routes | array of strings | List of allowed proxy API routes a user can access [Doc on controlling allowed routes](enterprise#control-available-public-private-routes)|
 | key_management_system | string | Specifies the key management system. [Doc Secret Managers](../secret) |
 | master_key | string | The master key for the proxy [Set up Virtual Keys](virtual_keys) |
 | database_url | string | The URL for the database connection [Set up Virtual Keys](virtual_keys) |
 | database_connection_pool_limit | integer | The limit for database connection pool [Setting DB Connection Pool limit](#configure-db-pool-limits--connection-timeouts) |
 | database_connection_timeout | integer | The timeout for database connections in seconds [Setting DB Connection Pool limit, timeout](#configure-db-pool-limits--connection-timeouts) |
 | allow_requests_on_db_unavailable | boolean | If true, allows requests to succeed even if DB is unreachable. **Only use this if running LiteLLM in your VPC** This will allow requests to work even when LiteLLM cannot connect to the DB to verify a Virtual Key |
 | custom_auth | string | Write your own custom authentication logic [Doc Custom Auth](virtual_keys#custom-auth) |
 | max_parallel_requests | integer | The max parallel requests allowed per deployment |
 | global_max_parallel_requests | integer | The max parallel requests allowed on the proxy overall |
 | infer_model_from_keys | boolean | If true, infers the model from the provided keys |
 | background_health_checks | boolean | If true, enables background health checks. [Doc on health checks](health) |
 | health_check_interval | integer | The interval for health checks in seconds [Doc on health checks](health) |
 | alerting | array of strings | List of alerting methods [Doc on Slack Alerting](alerting) |
 | alerting_threshold | integer | The threshold for triggering alerts [Doc on Slack Alerting](alerting) |
 | use_client_credentials_pass_through_routes | boolean | If true, uses client credentials for all pass-through routes. [Doc on pass through routes](pass_through) |
 | health_check_details | boolean | If false, hides health check details (e.g. remaining rate limit). [Doc on health checks](health) |
 | public_routes | List[str] | (Enterprise Feature) Control list of public routes |
 | alert_types | List[str] | Control list of alert types to send to slack (Doc on alert types)[./alerting.md] |
 | enforced_params | List[str] | (Enterprise Feature) List of params that must be included in all requests to the proxy |
 | enable_oauth2_auth | boolean | (Enterprise Feature) If true, enables oauth2.0 authentication |
 | use_x_forwarded_for | str | If true, uses the X-Forwarded-For header to get the client IP address |
 | service_account_settings | List[Dict[str, Any]] | Set `service_account_settings` if you want to create settings that only apply to service account keys (Doc on service accounts)[./service_accounts.md] | 
 | image_generation_model | str | The default model to use for image generation - ignores model set in request |
 | store_model_in_db | boolean | If true, allows `/model/new` endpoint to store model information in db. Endpoint disabled by default. [Doc on `/model/new` endpoint](./model_management.md#create-a-new-model) |
 | max_request_size_mb | int | The maximum size for requests in MB. Requests above this size will be rejected. |
 | max_response_size_mb | int | The maximum size for responses in MB. LLM Responses above this size will not be sent. |
 | proxy_budget_rescheduler_min_time | int | The minimum time (in seconds) to wait before checking db for budget resets. **Default is 597 seconds** |
 | proxy_budget_rescheduler_max_time | int | The maximum time (in seconds) to wait before checking db for budget resets. **Default is 605 seconds** |
 | proxy_batch_write_at | int | Time (in seconds) to wait before batch writing spend logs to the db. **Default is 10 seconds** |
 | alerting_args | dict | Args for Slack Alerting [Doc on Slack Alerting](./alerting.md) |
 | custom_key_generate | str | Custom function for key generation [Doc on custom key generation](./virtual_keys.md#custom--key-generate) |
 | allowed_ips | List[str] | List of IPs allowed to access the proxy. If not set, all IPs are allowed. |
 | embedding_model | str | The default model to use for embeddings - ignores model set in request |
 | default_team_disabled | boolean | If true, users cannot create 'personal' keys (keys with no team_id). |
 | alert_to_webhook_url | Dict[str] | [Specify a webhook url for each alert type.](./alerting.md#set-specific-slack-channels-per-alert-type) |
 | key_management_settings | List[Dict[str, Any]] | Settings for key management system (e.g. AWS KMS, Azure Key Vault) [Doc on key management](../secret.md) |
 | allow_user_auth | boolean | (Deprecated) old approach for user authentication. |
 | user_api_key_cache_ttl | int | The time (in seconds) to cache user api keys in memory. |
 | disable_prisma_schema_update | boolean | If true, turns off automatic schema updates to DB |
 | litellm_key_header_name | str | If set, allows passing LiteLLM keys as a custom header. [Doc on custom headers](./virtual_keys.md#custom-headers) |
 | moderation_model | str | The default model to use for moderation. |
 | custom_sso | str | Path to a python file that implements custom SSO logic. [Doc on custom SSO](./custom_sso.md) |
 | allow_client_side_credentials | boolean | If true, allows passing client side credentials to the proxy. (Useful when testing finetuning models) [Doc on client side credentials](./virtual_keys.md#client-side-credentials) |
 | admin_only_routes | List[str] | (Enterprise Feature) List of routes that are only accessible to admin users. [Doc on admin only routes](./enterprise#control-available-public-private-routes) |
 | use_azure_key_vault | boolean | If true, load keys from azure key vault | 
 | use_google_kms | boolean | If true, load keys from google kms |
 | spend_report_frequency | str | Specify how often you want a Spend Report to be sent (e.g. "1d", "2d", "30d") [More on this](./alerting.md#spend-report-frequency) |
 | ui_access_mode | Literal["admin_only"] | If set, restricts access to the UI to admin users only. [Docs](./ui.md#restrict-ui-access) |
 | litellm_jwtauth | Dict[str, Any] | Settings for JWT authentication. [Docs](./token_auth.md) |
 | litellm_license | str | The license key for the proxy. [Docs](../enterprise.md#how-does-deployment-with-enterprise-license-work) |
 | oauth2_config_mappings | Dict[str, str] | Define the OAuth2 config mappings | 
 | pass_through_endpoints | List[Dict[str, Any]] | Define the pass through endpoints. [Docs](./pass_through) |
 | enable_oauth2_proxy_auth | boolean | (Enterprise Feature) If true, enables oauth2.0 authentication |
 | forward_openai_org_id | boolean | If true, forwards the OpenAI Organization ID to the backend LLM call (if it's OpenAI). |
 | forward_client_headers_to_llm_api | boolean | If true, forwards the client headers (any `x-` headers) to the backend LLM call |
 ### router_settings - Reference
 ```yaml
 router_settings:
  routing_strategy: usage-based-routing-v2 # Literal["simple-shuffle", "least-busy", "usage-based-routing","latency-based-routing"], default="simple-shuffle"
  redis_host: <your-redis-host>           # string
  redis_password: <your-redis-password>   # string
  redis_port: <your-redis-port>           # string
  enable_pre_call_check: true             # bool - Before call is made check if a call is within model context window 
  allowed_fails: 3 # cooldown model if it fails > 1 call in a minute. 
  cooldown_time: 30 # (in seconds) how long to cooldown model if fails/min > allowed_fails
  disable_cooldowns: True                  # bool - Disable cooldowns for all models 
  enable_tag_filtering: True                # bool - Use tag based routing for requests
  retry_policy: {                          # Dict[str, int]: retry policy for different types of exceptions
    "AuthenticationErrorRetries": 3,
    "TimeoutErrorRetries": 3,
    "RateLimitErrorRetries": 3,
    "ContentPolicyViolationErrorRetries": 4,
    "InternalServerErrorRetries": 4
  }
  allowed_fails_policy: {
    "BadRequestErrorAllowedFails": 1000, # Allow 1000 BadRequestErrors before cooling down a deployment
    "AuthenticationErrorAllowedFails": 10, # int 
    "TimeoutErrorAllowedFails": 12, # int 
    "RateLimitErrorAllowedFails": 10000, # int 
    "ContentPolicyViolationErrorAllowedFails": 15, # int 
    "InternalServerErrorAllowedFails": 20, # int 
  }
  content_policy_fallbacks=[{"claude-2": ["my-fallback-model"]}] # List[Dict[str, List[str]]]: Fallback model for content policy violations
  fallbacks=[{"claude-2": ["my-fallback-model"]}] # List[Dict[str, List[str]]]: Fallback model for all errors
 ```
 | Name | Type | Description |
 |------|------|-------------|
 | routing_strategy | string | The strategy used for routing requests. Options: "simple-shuffle", "least-busy", "usage-based-routing", "latency-based-routing". Default is "simple-shuffle". [More information here](../routing) |
 | redis_host | string | The host address for the Redis server. **Only set this if you have multiple instances of LiteLLM Proxy and want current tpm/rpm tracking to be shared across them** |
 | redis_password | string | The password for the Redis server. **Only set this if you have multiple instances of LiteLLM Proxy and want current tpm/rpm tracking to be shared across them** |
 | redis_port | string | The port number for the Redis server. **Only set this if you have multiple instances of LiteLLM Proxy and want current tpm/rpm tracking to be shared across them**|
 | enable_pre_call_check | boolean | If true, checks if a call is within the model's context window before making the call. [More information here](reliability) |
 | content_policy_fallbacks | array of objects | Specifies fallback models for content policy violations. [More information here](reliability) |
 | fallbacks | array of objects | Specifies fallback models for all types of errors. [More information here](reliability) |
 | enable_tag_filtering | boolean | If true, uses tag based routing for requests [Tag Based Routing](tag_routing) |
 | cooldown_time | integer | The duration (in seconds) to cooldown a model if it exceeds the allowed failures. |
 | disable_cooldowns | boolean | If true, disables cooldowns for all models. [More information here](reliability) |
 | retry_policy | object | Specifies the number of retries for different types of exceptions. [More information here](reliability) |
 | allowed_fails | integer | The number of failures allowed before cooling down a model. [More information here](reliability) |
 | allowed_fails_policy | object | Specifies the number of allowed failures for different error types before cooling down a deployment. [More information here](reliability) |
 ### environment variables - Reference
 | Name | Description |
 |------|-------------|
 | ACTIONS_ID_TOKEN_REQUEST_TOKEN | Token for requesting ID in GitHub Actions
 | ACTIONS_ID_TOKEN_REQUEST_URL | URL for requesting ID token in GitHub Actions
 | AISPEND_ACCOUNT_ID | Account ID for AI Spend
 | AISPEND_API_KEY | API Key for AI Spend
 | ALLOWED_EMAIL_DOMAINS | List of email domains allowed for access
 | ARIZE_API_KEY | API key for Arize platform integration
 | ARIZE_SPACE_KEY | Space key for Arize platform
 | ARGILLA_BATCH_SIZE | Batch size for Argilla logging
 | ARGILLA_API_KEY | API key for Argilla platform
 | ARGILLA_SAMPLING_RATE | Sampling rate for Argilla logging
 | ARGILLA_DATASET_NAME | Dataset name for Argilla logging
 | ARGILLA_BASE_URL | Base URL for Argilla service
 | ATHINA_API_KEY | API key for Athina service
 | AUTH_STRATEGY | Strategy used for authentication (e.g., OAuth, API key)
 | AWS_ACCESS_KEY_ID | Access Key ID for AWS services
 | AWS_PROFILE_NAME | AWS CLI profile name to be used
 | AWS_REGION_NAME | Default AWS region for service interactions
 | AWS_ROLE_NAME | Role name for AWS IAM usage
 | AWS_SECRET_ACCESS_KEY | Secret Access Key for AWS services
 | AWS_SESSION_NAME | Name for AWS session
 | AWS_WEB_IDENTITY_TOKEN | Web identity token for AWS
 | AZURE_API_VERSION | Version of the Azure API being used
 | AZURE_AUTHORITY_HOST | Azure authority host URL
 | AZURE_CLIENT_ID | Client ID for Azure services
 | AZURE_CLIENT_SECRET | Client secret for Azure services
 | AZURE_FEDERATED_TOKEN_FILE | File path to Azure federated token
 | AZURE_KEY_VAULT_URI | URI for Azure Key Vault
 | AZURE_TENANT_ID | Tenant ID for Azure Active Directory
 | BERRISPEND_ACCOUNT_ID | Account ID for BerriSpend service
 | BRAINTRUST_API_KEY | API key for Braintrust integration
 | CIRCLE_OIDC_TOKEN | OpenID Connect token for CircleCI
 | CIRCLE_OIDC_TOKEN_V2 | Version 2 of the OpenID Connect token for CircleCI
 | CONFIG_FILE_PATH | File path for configuration file
 | CUSTOM_TIKTOKEN_CACHE_DIR | Custom directory for Tiktoken cache
 | DATABASE_HOST | Hostname for the database server
 | DATABASE_NAME | Name of the database
 | DATABASE_PASSWORD | Password for the database user
 | DATABASE_PORT | Port number for database connection
 | DATABASE_SCHEMA | Schema name used in the database
 | DATABASE_URL | Connection URL for the database
 | DATABASE_USER | Username for database connection
 | DATABASE_USERNAME | Alias for database user
 | DATABRICKS_API_BASE | Base URL for Databricks API
 | DD_BASE_URL | Base URL for Datadog integration
 | DATADOG_BASE_URL | (Alternative to DD_BASE_URL) Base URL for Datadog integration
 | _DATADOG_BASE_URL | (Alternative to DD_BASE_URL) Base URL for Datadog integration
 | DD_API_KEY | API key for Datadog integration
 | DD_SITE | Site URL for Datadog (e.g., datadoghq.com)
 | DD_SOURCE | Source identifier for Datadog logs
 | DD_ENV | Environment identifier for Datadog logs. Only supported for `datadog_llm_observability` callback
 | DEBUG_OTEL | Enable debug mode for OpenTelemetry
 | DIRECT_URL | Direct URL for service endpoint
 | DISABLE_ADMIN_UI | Toggle to disable the admin UI
 | DISABLE_SCHEMA_UPDATE | Toggle to disable schema updates
 | DOCS_DESCRIPTION | Description text for documentation pages
 | DOCS_FILTERED | Flag indicating filtered documentation
 | DOCS_TITLE | Title of the documentation pages
 | DOCS_URL | The path to the Swagger API documentation. **By default this is "/"**
 | EMAIL_SUPPORT_CONTACT | Support contact email address
 | GCS_BUCKET_NAME | Name of the Google Cloud Storage bucket
 | GCS_PATH_SERVICE_ACCOUNT | Path to the Google Cloud service account JSON file
 | GCS_FLUSH_INTERVAL | Flush interval for GCS logging (in seconds). Specify how often you want a log to be sent to GCS. **Default is 20 seconds**
 | GCS_BATCH_SIZE | Batch size for GCS logging. Specify after how many logs you want to flush to GCS. If `BATCH_SIZE` is set to 10, logs are flushed every 10 logs. **Default is 2048**
 | GENERIC_AUTHORIZATION_ENDPOINT | Authorization endpoint for generic OAuth providers
 | GENERIC_CLIENT_ID | Client ID for generic OAuth providers
 | GENERIC_CLIENT_SECRET | Client secret for generic OAuth providers
 | GENERIC_CLIENT_STATE | State parameter for generic client authentication
 | GENERIC_INCLUDE_CLIENT_ID | Include client ID in requests for OAuth
 | GENERIC_SCOPE | Scope settings for generic OAuth providers
 | GENERIC_TOKEN_ENDPOINT | Token endpoint for generic OAuth providers
 | GENERIC_USER_DISPLAY_NAME_ATTRIBUTE | Attribute for user's display name in generic auth
 | GENERIC_USER_EMAIL_ATTRIBUTE | Attribute for user's email in generic auth
 | GENERIC_USER_FIRST_NAME_ATTRIBUTE | Attribute for user's first name in generic auth
 | GENERIC_USER_ID_ATTRIBUTE | Attribute for user ID in generic auth
 | GENERIC_USER_LAST_NAME_ATTRIBUTE | Attribute for user's last name in generic auth
 | GENERIC_USER_PROVIDER_ATTRIBUTE | Attribute specifying the user's provider
 | GENERIC_USER_ROLE_ATTRIBUTE | Attribute specifying the user's role
 | GENERIC_USERINFO_ENDPOINT | Endpoint to fetch user information in generic OAuth
 | GALILEO_BASE_URL | Base URL for Galileo platform
 | GALILEO_PASSWORD | Password for Galileo authentication
 | GALILEO_PROJECT_ID | Project ID for Galileo usage
 | GALILEO_USERNAME | Username for Galileo authentication
 | GREENSCALE_API_KEY | API key for Greenscale service
 | GREENSCALE_ENDPOINT | Endpoint URL for Greenscale service
 | GOOGLE_APPLICATION_CREDENTIALS | Path to Google Cloud credentials JSON file
 | GOOGLE_CLIENT_ID | Client ID for Google OAuth
 | GOOGLE_CLIENT_SECRET | Client secret for Google OAuth
 | GOOGLE_KMS_RESOURCE_NAME | Name of the resource in Google KMS
 | HF_API_BASE | Base URL for Hugging Face API
 | HELICONE_API_KEY | API key for Helicone service
 | HUGGINGFACE_API_BASE | Base URL for Hugging Face API
 | IAM_TOKEN_DB_AUTH | IAM token for database authentication
 | JSON_LOGS | Enable JSON formatted logging
 | JWT_AUDIENCE | Expected audience for JWT tokens
 | JWT_PUBLIC_KEY_URL | URL to fetch public key for JWT verification
 | LAGO_API_BASE | Base URL for Lago API
 | LAGO_API_CHARGE_BY | Parameter to determine charge basis in Lago
 | LAGO_API_EVENT_CODE | Event code for Lago API events
 | LAGO_API_KEY | API key for accessing Lago services
 | LANGFUSE_DEBUG | Toggle debug mode for Langfuse
 | LANGFUSE_FLUSH_INTERVAL | Interval for flushing Langfuse logs
 | LANGFUSE_HOST | Host URL for Langfuse service
 | LANGFUSE_PUBLIC_KEY | Public key for Langfuse authentication
 | LANGFUSE_RELEASE | Release version of Langfuse integration
 | LANGFUSE_SECRET_KEY | Secret key for Langfuse authentication
 | LANGSMITH_API_KEY | API key for Langsmith platform
 | LANGSMITH_BASE_URL | Base URL for Langsmith service
 | LANGSMITH_BATCH_SIZE | Batch size for operations in Langsmith
 | LANGSMITH_DEFAULT_RUN_NAME | Default name for Langsmith run
 | LANGSMITH_PROJECT | Project name for Langsmith integration
 | LANGSMITH_SAMPLING_RATE | Sampling rate for Langsmith logging
 | LANGTRACE_API_KEY | API key for Langtrace service
 | LITERAL_API_KEY | API key for Literal integration
 | LITERAL_API_URL | API URL for Literal service
 | LITERAL_BATCH_SIZE | Batch size for Literal operations
 | LITELLM_DONT_SHOW_FEEDBACK_BOX | Flag to hide feedback box in LiteLLM UI
 | LITELLM_DROP_PARAMS | Parameters to drop in LiteLLM requests
 | LITELLM_EMAIL | Email associated with LiteLLM account
 | LITELLM_GLOBAL_MAX_PARALLEL_REQUEST_RETRIES | Maximum retries for parallel requests in LiteLLM
 | LITELLM_GLOBAL_MAX_PARALLEL_REQUEST_RETRY_TIMEOUT | Timeout for retries of parallel requests in LiteLLM
 | LITELLM_HOSTED_UI | URL of the hosted UI for LiteLLM
 | LITELLM_LICENSE | License key for LiteLLM usage
 | LITELLM_LOCAL_MODEL_COST_MAP | Local configuration for model cost mapping in LiteLLM
 | LITELLM_LOG | Enable detailed logging for LiteLLM
 | LITELLM_MODE | Operating mode for LiteLLM (e.g., production, development)
 | LITELLM_SALT_KEY | Salt key for encryption in LiteLLM
 | LITELLM_SECRET_AWS_KMS_LITELLM_LICENSE | AWS KMS encrypted license for LiteLLM
 | LITELLM_TOKEN | Access token for LiteLLM integration
 | LOGFIRE_TOKEN | Token for Logfire logging service
 | MICROSOFT_CLIENT_ID | Client ID for Microsoft services
 | MICROSOFT_CLIENT_SECRET | Client secret for Microsoft services
 | MICROSOFT_TENANT | Tenant ID for Microsoft Azure
 | NO_DOCS | Flag to disable documentation generation
 | NO_PROXY | List of addresses to bypass proxy
 | OAUTH_TOKEN_INFO_ENDPOINT | Endpoint for OAuth token info retrieval
 | OPENAI_API_BASE | Base URL for OpenAI API
 | OPENAI_API_KEY | API key for OpenAI services
 | OPENAI_ORGANIZATION | Organization identifier for OpenAI
 | OPENID_BASE_URL | Base URL for OpenID Connect services
 | OPENID_CLIENT_ID | Client ID for OpenID Connect authentication
 | OPENID_CLIENT_SECRET | Client secret for OpenID Connect authentication
 | OPENMETER_API_ENDPOINT | API endpoint for OpenMeter integration
 | OPENMETER_API_KEY | API key for OpenMeter services
 | OPENMETER_EVENT_TYPE | Type of events sent to OpenMeter
 | OTEL_ENDPOINT | OpenTelemetry endpoint for traces
 | OTEL_ENVIRONMENT_NAME | Environment name for OpenTelemetry
 | OTEL_EXPORTER | Exporter type for OpenTelemetry
 | OTEL_HEADERS | Headers for OpenTelemetry requests
 | OTEL_SERVICE_NAME | Service name identifier for OpenTelemetry
 | OTEL_TRACER_NAME | Tracer name for OpenTelemetry tracing
 | PREDIBASE_API_BASE | Base URL for Predibase API
 | PRESIDIO_ANALYZER_API_BASE | Base URL for Presidio Analyzer service
 | PRESIDIO_ANONYMIZER_API_BASE | Base URL for Presidio Anonymizer service
 | PROMETHEUS_URL | URL for Prometheus service
 | PROMPTLAYER_API_KEY | API key for PromptLayer integration
 | PROXY_ADMIN_ID | Admin identifier for proxy server
 | PROXY_BASE_URL | Base URL for proxy service
 | PROXY_LOGOUT_URL | URL for logging out of the proxy service
 | PROXY_MASTER_KEY | Master key for proxy authentication
 | QDRANT_API_BASE | Base URL for Qdrant API
 | QDRANT_API_KEY | API key for Qdrant service
 | QDRANT_URL | Connection URL for Qdrant database
 | REDIS_HOST | Hostname for Redis server
 | REDIS_PASSWORD | Password for Redis service
 | REDIS_PORT | Port number for Redis server
 | REDOC_URL | The path to the Redoc Fast API documentation. **By default this is "/redoc"**
 | SERVER_ROOT_PATH | Root path for the server application
 | SET_VERBOSE | Flag to enable verbose logging
 | SLACK_DAILY_REPORT_FREQUENCY | Frequency of daily Slack reports (e.g., daily, weekly)
 | SLACK_WEBHOOK_URL | Webhook URL for Slack integration
 | SMTP_HOST | Hostname for the SMTP server
 | SMTP_PASSWORD | Password for SMTP authentication
 | SMTP_PORT | Port number for SMTP server
 | SMTP_SENDER_EMAIL | Email address used as the sender in SMTP transactions
 | SMTP_SENDER_LOGO | Logo used in emails sent via SMTP
 | SMTP_TLS | Flag to enable or disable TLS for SMTP connections
 | SMTP_USERNAME | Username for SMTP authentication
 | SPEND_LOGS_URL | URL for retrieving spend logs
 | SSL_CERTIFICATE | Path to the SSL certificate file
 | SSL_VERIFY | Flag to enable or disable SSL certificate verification
 | SUPABASE_KEY | API key for Supabase service
 | SUPABASE_URL | Base URL for Supabase instance
 | TEST_EMAIL_ADDRESS | Email address used for testing purposes
 | UI_LOGO_PATH | Path to the logo image used in the UI
 | UI_PASSWORD | Password for accessing the UI
 | UI_USERNAME | Username for accessing the UI
 | UPSTREAM_LANGFUSE_DEBUG | Flag to enable debugging for upstream Langfuse
 | UPSTREAM_LANGFUSE_HOST | Host URL for upstream Langfuse service
 | UPSTREAM_LANGFUSE_PUBLIC_KEY | Public key for upstream Langfuse authentication
 | UPSTREAM_LANGFUSE_RELEASE | Release version identifier for upstream Langfuse
 | UPSTREAM_LANGFUSE_SECRET_KEY | Secret key for upstream Langfuse authentication
 | USE_AWS_KMS | Flag to enable AWS Key Management Service for encryption
 | WEBHOOK_URL | URL for receiving webhooks from external services
 ## Extras
--- a/docs/my-website/docs/proxy/db_info.md
+++ b/docs/my-website/docs/proxy/db_info.md
@ -50,18 +50,22 @@ You can see the full DB Schema [here](https://github.com/BerriAI/litellm/blob/ma
 | LiteLLM_ErrorLogs | Captures failed requests and errors. Stores exception details and request information. Helps with debugging and monitoring. | **Medium - on errors only** |
 | LiteLLM_AuditLog | Tracks changes to system configuration. Records who made changes and what was modified. Maintains history of updates to teams, users, and models. | **Off by default**, **High - when enabled** |
-## How to Disable `LiteLLM_SpendLogs`
+## Disable `LiteLLM_SpendLogs` & `LiteLLM_ErrorLogs`
-You can disable spend_logs by setting `disable_spend_logs` to `True` on the `general_settings` section of your proxy_config.yaml file.
+You can disable spend_logs and error_logs by setting `disable_spend_logs` and `disable_error_logs` to `True` on the `general_settings` section of your proxy_config.yaml file.
 ```yaml
 general_settings:
-  disable_spend_logs: True
+  disable_spend_logs: True   # Disable writing spend logs to DB
  disable_error_logs: True   # Disable writing error logs to DB
 ```
 ### What is the impact of disabling these logs?
-### What is the impact of disabling `LiteLLM_SpendLogs`?
+When disabling spend logs (`disable_spend_logs: True`):
 - You **will not** be able to view Usage on the LiteLLM UI
 - You **will** continue seeing cost metrics on s3, Prometheus, Langfuse (any other Logging integration you are using)
 When disabling error logs (`disable_error_logs: True`):
 - You **will not** be able to view Errors on the LiteLLM UI
 - You **will** continue seeing error logs in your application logs and any other logging integrations you are using
--- a/docs/my-website/docs/proxy/prod.md
+++ b/docs/my-website/docs/proxy/prod.md
@ -23,6 +23,7 @@ general_settings:
  # OPTIONAL Best Practices
  disable_spend_logs: True # turn off writing each transaction to the db. We recommend doing this is you don't need to see Usage on the LiteLLM UI and are tracking metrics via Prometheus
  disable_error_logs: True # turn off writing LLM Exceptions to DB
  allow_requests_on_db_unavailable: True # Only USE when running LiteLLM on your VPC. Allow requests to still be processed even if the DB is unavailable. We recommend doing this if you're running LiteLLM on VPC that cannot be accessed from the public internet.
 litellm_settings:
@ -102,17 +103,22 @@ general_settings:
  allow_requests_on_db_unavailable: True
 ```
-## 6. Disable spend_logs if you're not using the LiteLLM UI
+## 6. Disable spend_logs & error_logs if not using the LiteLLM UI
-By default LiteLLM will write every request to the `LiteLLM_SpendLogs` table. This is used for viewing Usage on the LiteLLM UI. 
+By default, LiteLLM writes several types of logs to the database:
 - Every LLM API request to the `LiteLLM_SpendLogs` table
 - LLM Exceptions to the `LiteLLM_LogsErrors` table
-If you're not viewing Usage on the LiteLLM UI (most users use Prometheus when this is disabled), you can disable spend_logs by setting `disable_spend_logs` to `True`.
+If you're not viewing these logs on the LiteLLM UI (most users use Prometheus for monitoring), you can disable them by setting the following flags to `True`:
 ```yaml
 general_settings:
-  disable_spend_logs: True
+  disable_spend_logs: True    # Disable writing spend logs to DB
  disable_error_logs: True    # Disable writing error logs to DB
 ```
 [More information about what the Database is used for here](db_info)
 ## 7. Use Helm PreSync Hook for Database Migrations [BETA]
 To ensure only one service manages database migrations, use our [Helm PreSync hook for Database Migrations](https://github.com/BerriAI/litellm/blob/main/deploy/charts/litellm-helm/templates/migrations-job.yaml). This ensures migrations are handled during `helm upgrade` or `helm install`, while LiteLLM pods explicitly disable migrations.
--- a/docs/my-website/docs/proxy/prometheus.md
+++ b/docs/my-website/docs/proxy/prometheus.md
@ -192,3 +192,13 @@ Here is a screenshot of the metrics you can monitor with the LiteLLM Grafana Das
 |----------------------|--------------------------------------|
 | `litellm_llm_api_failed_requests_metric`             | **deprecated** use `litellm_proxy_failed_requests_metric` |
 | `litellm_requests_metric`             | **deprecated** use `litellm_proxy_total_requests_metric` |
 ## FAQ 
 ### What are `_created` vs. `_total` metrics?
 - `_created` metrics are metrics that are created when the proxy starts
 - `_total` metrics are metrics that are incremented for each request
 You should consume the `_total` metrics for your counting purposes
--- a/docs/my-website/docs/router_architecture.md
+++ b/docs/my-website/docs/router_architecture.md
@ -0,0 +1,24 @@
 import Image from '@theme/IdealImage';
 import Tabs from '@theme/Tabs';
 import TabItem from '@theme/TabItem';
 # Router Architecture (Fallbacks / Retries)
 ## High Level architecture
 <Image img={require('../img/router_architecture.png')} style={{ width: '100%', maxWidth: '4000px' }} />
 ### Request Flow 
 1. **User Sends Request**: The process begins when a user sends a request to the LiteLLM Router endpoint. All unified endpoints (`.completion`, `.embeddings`, etc) are supported by LiteLLM Router.
 2. **function_with_fallbacks**: The initial request is sent to the `function_with_fallbacks` function. This function wraps the initial request in a try-except block, to handle any exceptions - doing fallbacks if needed. This request is then sent to the `function_with_retries` function.
 3. **function_with_retries**: The `function_with_retries` function wraps the request in a try-except block and passes the initial request to a base litellm unified function (`litellm.completion`, `litellm.embeddings`, etc) to handle LLM API calling. `function_with_retries` handles any exceptions - doing retries on the model group if needed (i.e. if the request fails, it will retry on an available model within the model group). 
 4. **litellm.completion**: The `litellm.completion` function is a base function that handles the LLM API calling. It is used by `function_with_retries` to make the actual request to the LLM API.
 ## Legend 
 **model_group**: A group of LLM API deployments that share the same `model_name`, are part of the same `model_group`, and can be load balanced across.
--- a/docs/my-website/docs/routing.md
+++ b/docs/my-website/docs/routing.md
@ -1891,3 +1891,22 @@ router = Router(
    debug_level="DEBUG"  # defaults to INFO
 )
 ```
 ## Router General Settings
 ### Usage 
 ```python
 router = Router(model_list=..., router_general_settings=RouterGeneralSettings(async_only_mode=True))
 ```
 ### Spec 
 ```python
 class RouterGeneralSettings(BaseModel):
    async_only_mode: bool = Field(
        default=False
    )  # this will only initialize async clients. Good for memory utils
    pass_through_all_models: bool = Field(
        default=False
    )  # if passed a model not llm_router model list, pass through the request to litellm.acompletion/embedding
 ```
--- a/docs/my-website/docs/text_completion.md
+++ b/docs/my-website/docs/text_completion.md
@ -0,0 +1,174 @@
 import Tabs from '@theme/Tabs';
 import TabItem from '@theme/TabItem';
 # Text Completion
 ### Usage
 <Tabs>
 <TabItem value="python" label="LiteLLM Python SDK">
 ```python
 from litellm import text_completion
 response = text_completion(
    model="gpt-3.5-turbo-instruct",
    prompt="Say this is a test",
    max_tokens=7
 )
 ```
 </TabItem>
 <TabItem value="proxy" label="LiteLLM Proxy Server">
 1. Define models on config.yaml
 ```yaml
 model_list:
  - model_name: gpt-3.5-turbo-instruct
    litellm_params:
      model: text-completion-openai/gpt-3.5-turbo-instruct # The `text-completion-openai/` prefix will call openai.completions.create
      api_key: os.environ/OPENAI_API_KEY
  - model_name: text-davinci-003
    litellm_params:
      model: text-completion-openai/text-davinci-003
      api_key: os.environ/OPENAI_API_KEY
 ```
 2. Start litellm proxy server 
 ```
 litellm --config config.yaml
 ```
 <Tabs>
 <TabItem value="python" label="OpenAI Python SDK">
 ```python
 from openai import OpenAI
 # set base_url to your proxy server
 # set api_key to send to proxy server
 client = OpenAI(api_key="<proxy-api-key>", base_url="http://0.0.0.0:4000")
 response = client.completions.create(
    model="gpt-3.5-turbo-instruct",
    prompt="Say this is a test",
    max_tokens=7
 )
 print(response)
 ```
 </TabItem>
 <TabItem value="curl" label="Curl Request">
 ```shell
 curl --location 'http://0.0.0.0:4000/completions' \
    --header 'Content-Type: application/json' \
    --header 'Authorization: Bearer sk-1234' \
    --data '{
        "model": "gpt-3.5-turbo-instruct",
        "prompt": "Say this is a test",
        "max_tokens": 7
    }'
 ```
 </TabItem>
 </Tabs>
 </TabItem>
 </Tabs>
 ## Input Params
 LiteLLM accepts and translates the [OpenAI Text Completion params](https://platform.openai.com/docs/api-reference/completions) across all supported providers.
 ### Required Fields
 - `model`: *string* - ID of the model to use
 - `prompt`: *string or array* - The prompt(s) to generate completions for
 ### Optional Fields
 - `best_of`: *integer* - Generates best_of completions server-side and returns the "best" one
 - `echo`: *boolean* - Echo back the prompt in addition to the completion.
 - `frequency_penalty`: *number* - Number between -2.0 and 2.0. Positive values penalize new tokens based on their existing frequency.
 - `logit_bias`: *map* - Modify the likelihood of specified tokens appearing in the completion
 - `logprobs`: *integer* - Include the log probabilities on the logprobs most likely tokens. Max value of 5
 - `max_tokens`: *integer* - The maximum number of tokens to generate.
 - `n`: *integer* - How many completions to generate for each prompt.
 - `presence_penalty`: *number* - Number between -2.0 and 2.0. Positive values penalize new tokens based on whether they appear in the text so far.
 - `seed`: *integer* - If specified, system will attempt to make deterministic samples
 - `stop`: *string or array* - Up to 4 sequences where the API will stop generating tokens
 - `stream`: *boolean* - Whether to stream back partial progress. Defaults to false
 - `suffix`: *string* - The suffix that comes after a completion of inserted text
 - `temperature`: *number* - What sampling temperature to use, between 0 and 2. 
 - `top_p`: *number* - An alternative to sampling with temperature, called nucleus sampling. 
 - `user`: *string* - A unique identifier representing your end-user
 ## Output Format
 Here's the exact JSON output format you can expect from completion calls:
 [**Follows OpenAI's output format**](https://platform.openai.com/docs/api-reference/completions/object)
 <Tabs>
 <TabItem value="non-streaming" label="Non-Streaming Response">
 ```python
 {
  "id": "cmpl-uqkvlQyYK7bGYrRHQ0eXlWi7",
  "object": "text_completion",
  "created": 1589478378,
  "model": "gpt-3.5-turbo-instruct",
  "system_fingerprint": "fp_44709d6fcb",
  "choices": [
    {
      "text": "\n\nThis is indeed a test",
      "index": 0,
      "logprobs": null,
      "finish_reason": "length"
    }
  ],
  "usage": {
    "prompt_tokens": 5,
    "completion_tokens": 7,
    "total_tokens": 12
  }
 }
 ```
 </TabItem>
 <TabItem value="streaming" label="Streaming Response">
 ```python
 {
  "id": "cmpl-7iA7iJjj8V2zOkCGvWF2hAkDWBQZe",
  "object": "text_completion",
  "created": 1690759702,
  "choices": [
    {
      "text": "This",
      "index": 0,
      "logprobs": null,
      "finish_reason": null
    }
  ],
  "model": "gpt-3.5-turbo-instruct"
  "system_fingerprint": "fp_44709d6fcb",
 }
 ```
 </TabItem>
 </Tabs>
 ## **Supported Providers**
 | Provider    | Link to Usage      |
 |-------------|--------------------|
 | OpenAI      |   [Usage](../docs/providers/text_completion_openai)                 | 
 | Azure OpenAI|   [Usage](../docs/providers/azure)                 |  
--- a/docs/my-website/docs/wildcard_routing.md
+++ b/docs/my-website/docs/wildcard_routing.md
@ -0,0 +1,140 @@
 import Tabs from '@theme/Tabs';
 import TabItem from '@theme/TabItem';
 # Provider specific Wildcard routing 
 **Proxy all models from a provider**
 Use this if you want to **proxy all models from a specific provider without defining them on the config.yaml**
 ## Step 1. Define provider specific routing 
 <Tabs>
 <TabItem value="sdk" label="SDK">
 ```python
 from litellm import Router
 router = Router(
    model_list=[
        {
            "model_name": "anthropic/*",
            "litellm_params": {
                "model": "anthropic/*",
                "api_key": os.environ["ANTHROPIC_API_KEY"]
            }
        }, 
        {
            "model_name": "groq/*",
            "litellm_params": {
                "model": "groq/*",
                "api_key": os.environ["GROQ_API_KEY"]
            }
        }, 
        {
            "model_name": "fo::*:static::*", # all requests matching this pattern will be routed to this deployment, example: model="fo::hi::static::hi" will be routed to deployment: "openai/fo::*:static::*"
            "litellm_params": {
                "model": "openai/fo::*:static::*",
                "api_key": os.environ["OPENAI_API_KEY"]
            }
        }
    ]
 )
 ```
 </TabItem>
 <TabItem value="proxy" label="PROXY">
 **Step 1** - define provider specific routing on config.yaml
 ```yaml
 model_list:
  # provider specific wildcard routing
  - model_name: "anthropic/*"
    litellm_params:
      model: "anthropic/*"
      api_key: os.environ/ANTHROPIC_API_KEY
  - model_name: "groq/*"
    litellm_params:
      model: "groq/*"
      api_key: os.environ/GROQ_API_KEY
  - model_name: "fo::*:static::*" # all requests matching this pattern will be routed to this deployment, example: model="fo::hi::static::hi" will be routed to deployment: "openai/fo::*:static::*"
    litellm_params:
      model: "openai/fo::*:static::*"
      api_key: os.environ/OPENAI_API_KEY
 ```
 </TabItem>
 </Tabs>
 ## [PROXY-Only] Step 2 - Run litellm proxy 
 ```shell
 $ litellm --config /path/to/config.yaml
 ```
 ## Step 3 - Test it 
 <Tabs>  
 <TabItem value="sdk" label="SDK">
 ```python
 from litellm import Router
 router = Router(model_list=...)
 # Test with `anthropic/` - all models with `anthropic/` prefix will get routed to `anthropic/*`
 resp = completion(model="anthropic/claude-3-sonnet-20240229", messages=[{"role": "user", "content": "Hello, Claude!"}])
 print(resp)
 # Test with `groq/` - all models with `groq/` prefix will get routed to `groq/*`
 resp = completion(model="groq/llama3-8b-8192", messages=[{"role": "user", "content": "Hello, Groq!"}])
 print(resp)
 # Test with `fo::*::static::*` - all requests matching this pattern will be routed to `openai/fo::*:static::*`
 resp = completion(model="fo::hi::static::hi", messages=[{"role": "user", "content": "Hello, Claude!"}])
 print(resp)
 ```
 </TabItem>
 <TabItem value="proxy" label="PROXY">
 Test with `anthropic/` - all models with `anthropic/` prefix will get routed to `anthropic/*`
 ```bash
 curl http://localhost:4000/v1/chat/completions \
  -H "Content-Type: application/json" \
  -H "Authorization: Bearer sk-1234" \
  -d '{
    "model": "anthropic/claude-3-sonnet-20240229",
    "messages": [
      {"role": "user", "content": "Hello, Claude!"}
    ]
  }'
 ```
 Test with `groq/` - all models with `groq/` prefix will get routed to `groq/*`
 ```shell
 curl http://localhost:4000/v1/chat/completions \
  -H "Content-Type: application/json" \
  -H "Authorization: Bearer sk-1234" \
  -d '{
    "model": "groq/llama3-8b-8192",
    "messages": [
      {"role": "user", "content": "Hello, Claude!"}
    ]
  }'
 ```
 Test with `fo::*::static::*` - all requests matching this pattern will be routed to `openai/fo::*:static::*`
 ```shell
 curl http://localhost:4000/v1/chat/completions \
  -H "Content-Type: application/json" \
  -H "Authorization: Bearer sk-1234" \
  -d '{
    "model": "fo::hi::static::hi",
    "messages": [
      {"role": "user", "content": "Hello, Claude!"}
    ]
  }'
 ```
 </TabItem>
 </Tabs>
--- a/docs/my-website/img/router_architecture.png
+++ b/docs/my-website/img/router_architecture.png
--- a/docs/my-website/sidebars.js
+++ b/docs/my-website/sidebars.js
@ -29,13 +29,17 @@ const sidebars = {
      },
      items: [
        "proxy/docker_quick_start", 
        {
          "type": "category", 
          "label": "Config.yaml",
          "items": ["proxy/configs", "proxy/config_management", "proxy/config_settings"]
        },
        {
          type: "category",
          label: "Setup & Deployment",
          items: [
            "proxy/deploy", 
            "proxy/prod", 
            "proxy/configs", 
            "proxy/cli",
            "proxy/model_management",
            "proxy/health",
@ -47,7 +51,7 @@ const sidebars = {
        {
          type: "category",
          label: "Architecture",
-          items: ["proxy/architecture", "proxy/db_info"],
+          items: ["proxy/architecture", "proxy/db_info", "router_architecture"],
        }, 
        {
          type: "link",
@ -242,6 +246,7 @@ const sidebars = {
            "completion/usage",
          ],
        },
        "text_completion",
        "embedding/supported_embedding",
        "image_generation",
        {
@ -257,6 +262,7 @@ const sidebars = {
        "batches",
        "realtime",
        "fine_tuning",
        "moderation",
        {
          type: "link",
          label: "Use LiteLLM Proxy with Vertex, Bedrock SDK",
@ -273,7 +279,7 @@ const sidebars = {
        description: "Learn how to load balance, route, and set fallbacks for your LLM requests",
        slug: "/routing-load-balancing",
      },
-      items: ["routing", "scheduler", "proxy/load_balancing", "proxy/reliability", "proxy/tag_routing", "proxy/provider_budget_routing", "proxy/team_based_routing", "proxy/customer_routing"],
+      items: ["routing", "scheduler", "proxy/load_balancing", "proxy/reliability", "proxy/tag_routing", "proxy/provider_budget_routing", "proxy/team_based_routing", "proxy/customer_routing", "wildcard_routing"],
    },
    {
      type: "category",
--- a/enterprise/utils.py
+++ b/enterprise/utils.py
@ -2,7 +2,9 @@
 from typing import Optional, List
 from litellm._logging import verbose_logger
 from litellm.proxy.proxy_server import PrismaClient, HTTPException
 from litellm.llms.custom_httpx.http_handler import HTTPHandler
 import collections
 import httpx
 from datetime import datetime
@ -114,7 +116,6 @@ async def ui_get_spend_by_tags(
 def _forecast_daily_cost(data: list):
    import requests  # type: ignore
    from datetime import datetime, timedelta
    if len(data) == 0:
@ -136,17 +137,17 @@ def _forecast_daily_cost(data: list):
    print("last entry date", last_entry_date)
    # Assuming today_date is a datetime object
    today_date = datetime.now()
    # Calculate the last day of the month
    last_day_of_todays_month = datetime(
        today_date.year, today_date.month % 12 + 1, 1
    ) - timedelta(days=1)
    print("last day of todays month", last_day_of_todays_month)
    # Calculate the remaining days in the month
    remaining_days = (last_day_of_todays_month - last_entry_date).days
    print("remaining days", remaining_days)
    current_spend_this_month = 0
    series = {}
    for entry in data:
@ -176,13 +177,19 @@ def _forecast_daily_cost(data: list):
        "Content-Type": "application/json",
    }
-    response = requests.post(
+    client = HTTPHandler()
-        url="https://trend-api-production.up.railway.app/forecast",
+
-        json=payload,
+    try:
-        headers=headers,
+        response = client.post(
-    )
+            url="https://trend-api-production.up.railway.app/forecast",
-    # check the status code
+            json=payload,
-    response.raise_for_status()
+            headers=headers,
        )
    except httpx.HTTPStatusError as e:
        raise HTTPException(
            status_code=500,
            detail={"error": f"Error getting forecast: {e.response.text}"},
        )
    json_response = response.json()
    forecast_data = json_response["forecast"]
@ -206,13 +213,3 @@ def _forecast_daily_cost(data: list):
        f"Predicted Spend for { today_month } 2024, ${total_predicted_spend}"
    )
    return {"response": response_data, "predicted_spend": predicted_spend}
    # print(f"Date: {entry['date']}, Spend: {entry['spend']}, Response: {response.text}")
 # _forecast_daily_cost(
 #     [
 #         {"date": "2022-01-01", "spend": 100},
 #     ]
 # )
--- a/litellm/init.py
+++ b/litellm/init.py
@ -68,6 +68,7 @@ callbacks: List[Union[Callable, _custom_logger_compatible_callbacks_literal]] =
 langfuse_default_tags: Optional[List[str]] = None
 langsmith_batch_size: Optional[int] = None
 argilla_batch_size: Optional[int] = None
 datadog_use_v1: Optional[bool] = False  # if you want to use v1 datadog logged payload
 argilla_transformation_object: Optional[Dict[str, Any]] = None
 _async_input_callback: List[Callable] = (
    []
--- a/litellm/_redis.py
+++ b/litellm/_redis.py
@ -313,12 +313,13 @@ def get_redis_async_client(**env_overrides) -> async_redis.Redis:
 def get_redis_connection_pool(**env_overrides):
    redis_kwargs = _get_redis_client_logic(**env_overrides)
    verbose_logger.debug("get_redis_connection_pool: redis_kwargs", redis_kwargs)
    if "url" in redis_kwargs and redis_kwargs["url"] is not None:
        return async_redis.BlockingConnectionPool.from_url(
            timeout=5, url=redis_kwargs["url"]
        )
    connection_class = async_redis.Connection
-    if "ssl" in redis_kwargs and redis_kwargs["ssl"] is not None:
+    if "ssl" in redis_kwargs:
        connection_class = async_redis.SSLConnection
        redis_kwargs.pop("ssl", None)
        redis_kwargs["connection_class"] = connection_class
--- a/litellm/integrations/datadog/datadog.py
+++ b/litellm/integrations/datadog/datadog.py
@ -32,9 +32,11 @@ from litellm.llms.custom_httpx.http_handler import (
    get_async_httpx_client,
    httpxSpecialProvider,
 )
 from litellm.proxy._types import UserAPIKeyAuth
 from litellm.types.integrations.datadog import *
 from litellm.types.services import ServiceLoggerPayload
 from litellm.types.utils import StandardLoggingPayload
 from .types import DD_ERRORS, DatadogPayload, DataDogStatus
 from .utils import make_json_serializable
 DD_MAX_BATCH_SIZE = 1000  # max number of logs DD API can accept
@ -106,20 +108,20 @@ class DataDogLogger(CustomBatchLogger):
            verbose_logger.debug(
                "Datadog: Logging - Enters logging function for model %s", kwargs
            )
-            dd_payload = self.create_datadog_logging_payload(
+            await self._log_async_event(kwargs, response_obj, start_time, end_time)
                kwargs=kwargs,
                response_obj=response_obj,
                start_time=start_time,
                end_time=end_time,
            )
-            self.log_queue.append(dd_payload)
+        except Exception as e:
            verbose_logger.exception(
                f"Datadog Layer Error - {str(e)}\n{traceback.format_exc()}"
            )
            pass
    async def async_log_failure_event(self, kwargs, response_obj, start_time, end_time):
        try:
            verbose_logger.debug(
-                f"Datadog, event added to queue. Will flush in {self.flush_interval} seconds..."
+                "Datadog: Logging - Enters logging function for model %s", kwargs
            )
-
+            await self._log_async_event(kwargs, response_obj, start_time, end_time)
            if len(self.log_queue) >= self.batch_size:
                await self.async_send_batch()
        except Exception as e:
            verbose_logger.exception(
@ -181,12 +183,20 @@ class DataDogLogger(CustomBatchLogger):
            verbose_logger.debug(
                "Datadog: Logging - Enters logging function for model %s", kwargs
            )
-            dd_payload = self.create_datadog_logging_payload(
+            if litellm.datadog_use_v1 is True:
-                kwargs=kwargs,
+                dd_payload = self._create_v0_logging_payload(
-                response_obj=response_obj,
+                    kwargs=kwargs,
-                start_time=start_time,
+                    response_obj=response_obj,
-                end_time=end_time,
+                    start_time=start_time,
-            )
+                    end_time=end_time,
                )
            else:
                dd_payload = self.create_datadog_logging_payload(
                    kwargs=kwargs,
                    response_obj=response_obj,
                    start_time=start_time,
                    end_time=end_time,
                )
            response = self.sync_client.post(
                url=self.intake_url,
@ -215,6 +225,22 @@ class DataDogLogger(CustomBatchLogger):
            pass
        pass
    async def _log_async_event(self, kwargs, response_obj, start_time, end_time):
        dd_payload = self.create_datadog_logging_payload(
            kwargs=kwargs,
            response_obj=response_obj,
            start_time=start_time,
            end_time=end_time,
        )
        self.log_queue.append(dd_payload)
        verbose_logger.debug(
            f"Datadog, event added to queue. Will flush in {self.flush_interval} seconds..."
        )
        if len(self.log_queue) >= self.batch_size:
            await self.async_send_batch()
    def create_datadog_logging_payload(
        self,
        kwargs: Union[dict, Any],
@ -236,73 +262,29 @@ class DataDogLogger(CustomBatchLogger):
        """
        import json
-        litellm_params = kwargs.get("litellm_params", {})
+        standard_logging_object: Optional[StandardLoggingPayload] = kwargs.get(
-        metadata = (
+            "standard_logging_object", None
-            litellm_params.get("metadata", {}) or {}
+        )
-        )  # if litellm_params['metadata'] == None
+        if standard_logging_object is None:
-        messages = kwargs.get("messages")
+            raise ValueError("standard_logging_object not found in kwargs")
        optional_params = kwargs.get("optional_params", {})
        call_type = kwargs.get("call_type", "litellm.completion")
        cache_hit = kwargs.get("cache_hit", False)
        usage = response_obj["usage"]
        id = response_obj.get("id", str(uuid.uuid4()))
        usage = dict(usage)
        try:
            response_time = (end_time - start_time).total_seconds() * 1000
        except Exception:
            response_time = None
-        try:
+        status = DataDogStatus.INFO
-            response_obj = dict(response_obj)
+        if standard_logging_object.get("status") == "failure":
-        except Exception:
+            status = DataDogStatus.ERROR
            response_obj = response_obj
        # Clean Metadata before logging - never log raw metadata
        # the raw metadata can contain circular references which leads to infinite recursion
        # we clean out all extra litellm metadata params before logging
        clean_metadata = {}
        if isinstance(metadata, dict):
            for key, value in metadata.items():
                # clean litellm metadata before logging
                if key in [
                    "endpoint",
                    "caching_groups",
                    "previous_models",
                ]:
                    continue
                else:
                    clean_metadata[key] = value
        # Build the initial payload
-        payload = {
+        make_json_serializable(standard_logging_object)
-            "id": id,
+        json_payload = json.dumps(standard_logging_object)
            "call_type": call_type,
            "cache_hit": cache_hit,
            "start_time": start_time,
            "end_time": end_time,
            "response_time": response_time,
            "model": kwargs.get("model", ""),
            "user": kwargs.get("user", ""),
            "model_parameters": optional_params,
            "spend": kwargs.get("response_cost", 0),
            "messages": messages,
            "response": response_obj,
            "usage": usage,
            "metadata": clean_metadata,
        }
        make_json_serializable(payload)
        json_payload = json.dumps(payload)
        verbose_logger.debug("Datadog: Logger - Logging payload = %s", json_payload)
        dd_payload = DatadogPayload(
-            ddsource=os.getenv("DD_SOURCE", "litellm"),
+            ddsource=self._get_datadog_source(),
-            ddtags="",
+            ddtags=self._get_datadog_tags(),
-            hostname="",
+            hostname=self._get_datadog_hostname(),
            message=json_payload,
-            service="litellm-server",
+            service=self._get_datadog_service(),
-            status=DataDogStatus.INFO,
+            status=status,
        )
        return dd_payload
@ -382,3 +364,140 @@ class DataDogLogger(CustomBatchLogger):
        No user has asked for this so far, this might be spammy on datatdog. If need arises we can implement this
        """
        return
    async def async_post_call_failure_hook(
        self,
        request_data: dict,
        original_exception: Exception,
        user_api_key_dict: UserAPIKeyAuth,
    ):
        """
        Handles Proxy Errors (not-related to LLM API), ex: Authentication Errors
        """
        import json
        _exception_payload = DatadogProxyFailureHookJsonMessage(
            exception=str(original_exception),
            error_class=str(original_exception.__class__.__name__),
            status_code=getattr(original_exception, "status_code", None),
            traceback=traceback.format_exc(),
            user_api_key_dict=user_api_key_dict.model_dump(),
        )
        json_payload = json.dumps(_exception_payload)
        verbose_logger.debug("Datadog: Logger - Logging payload = %s", json_payload)
        dd_payload = DatadogPayload(
            ddsource=self._get_datadog_source(),
            ddtags=self._get_datadog_tags(),
            hostname=self._get_datadog_hostname(),
            message=json_payload,
            service=self._get_datadog_service(),
            status=DataDogStatus.ERROR,
        )
        self.log_queue.append(dd_payload)
    def _create_v0_logging_payload(
        self,
        kwargs: Union[dict, Any],
        response_obj: Any,
        start_time: datetime.datetime,
        end_time: datetime.datetime,
    ) -> DatadogPayload:
        """
        Note: This is our V1 Version of DataDog Logging Payload
        (Not Recommended) If you want this to get logged set `litellm.datadog_use_v1 = True`
        """
        import json
        litellm_params = kwargs.get("litellm_params", {})
        metadata = (
            litellm_params.get("metadata", {}) or {}
        )  # if litellm_params['metadata'] == None
        messages = kwargs.get("messages")
        optional_params = kwargs.get("optional_params", {})
        call_type = kwargs.get("call_type", "litellm.completion")
        cache_hit = kwargs.get("cache_hit", False)
        usage = response_obj["usage"]
        id = response_obj.get("id", str(uuid.uuid4()))
        usage = dict(usage)
        try:
            response_time = (end_time - start_time).total_seconds() * 1000
        except Exception:
            response_time = None
        try:
            response_obj = dict(response_obj)
        except Exception:
            response_obj = response_obj
        # Clean Metadata before logging - never log raw metadata
        # the raw metadata can contain circular references which leads to infinite recursion
        # we clean out all extra litellm metadata params before logging
        clean_metadata = {}
        if isinstance(metadata, dict):
            for key, value in metadata.items():
                # clean litellm metadata before logging
                if key in [
                    "endpoint",
                    "caching_groups",
                    "previous_models",
                ]:
                    continue
                else:
                    clean_metadata[key] = value
        # Build the initial payload
        payload = {
            "id": id,
            "call_type": call_type,
            "cache_hit": cache_hit,
            "start_time": start_time,
            "end_time": end_time,
            "response_time": response_time,
            "model": kwargs.get("model", ""),
            "user": kwargs.get("user", ""),
            "model_parameters": optional_params,
            "spend": kwargs.get("response_cost", 0),
            "messages": messages,
            "response": response_obj,
            "usage": usage,
            "metadata": clean_metadata,
        }
        make_json_serializable(payload)
        json_payload = json.dumps(payload)
        verbose_logger.debug("Datadog: Logger - Logging payload = %s", json_payload)
        dd_payload = DatadogPayload(
            ddsource=self._get_datadog_source(),
            ddtags=self._get_datadog_tags(),
            hostname=self._get_datadog_hostname(),
            message=json_payload,
            service=self._get_datadog_service(),
            status=DataDogStatus.INFO,
        )
        return dd_payload
    @staticmethod
    def _get_datadog_tags():
        return f"env:{os.getenv('DD_ENV', 'unknown')},service:{os.getenv('DD_SERVICE', 'litellm')},version:{os.getenv('DD_VERSION', 'unknown')}"
    @staticmethod
    def _get_datadog_source():
        return os.getenv("DD_SOURCE", "litellm")
    @staticmethod
    def _get_datadog_service():
        return os.getenv("DD_SERVICE", "litellm-server")
    @staticmethod
    def _get_datadog_hostname():
        return ""
    @staticmethod
    def _get_datadog_env():
        return os.getenv("DD_ENV", "unknown")
--- a/litellm/llms/bedrock/chat/converse_transformation.py
+++ b/litellm/llms/bedrock/chat/converse_transformation.py
@ -458,7 +458,7 @@ class AmazonConverseConfig:
        """
        Abbreviations of regions AWS Bedrock supports for cross region inference
        """
-        return ["us", "eu"]
+        return ["us", "eu", "apac"]
    def _get_base_model(self, model: str) -> str:
        """
--- a/litellm/llms/custom_httpx/http_handler.py
+++ b/litellm/llms/custom_httpx/http_handler.py
@ -28,6 +28,62 @@ headers = {
 _DEFAULT_TIMEOUT = httpx.Timeout(timeout=5.0, connect=5.0)
 _DEFAULT_TTL_FOR_HTTPX_CLIENTS = 3600  # 1 hour, re-use the same httpx client for 1 hour
 import re
 def mask_sensitive_info(error_message):
    # Find the start of the key parameter
    if isinstance(error_message, str):
        key_index = error_message.find("key=")
    else:
        return error_message
    # If key is found
    if key_index != -1:
        # Find the end of the key parameter (next & or end of string)
        next_param = error_message.find("&", key_index)
        if next_param == -1:
            # If no more parameters, mask until the end of the string
            masked_message = error_message[: key_index + 4] + "[REDACTED_API_KEY]"
        else:
            # Replace the key with redacted value, keeping other parameters
            masked_message = (
                error_message[: key_index + 4]
                + "[REDACTED_API_KEY]"
                + error_message[next_param:]
            )
        return masked_message
    return error_message
 class MaskedHTTPStatusError(httpx.HTTPStatusError):
    def __init__(
        self, original_error, message: Optional[str] = None, text: Optional[str] = None
    ):
        # Create a new error with the masked URL
        masked_url = mask_sensitive_info(str(original_error.request.url))
        # Create a new error that looks like the original, but with a masked URL
        super().__init__(
            message=original_error.message,
            request=httpx.Request(
                method=original_error.request.method,
                url=masked_url,
                headers=original_error.request.headers,
                content=original_error.request.content,
            ),
            response=httpx.Response(
                status_code=original_error.response.status_code,
                content=original_error.response.content,
                headers=original_error.response.headers,
            ),
        )
        self.message = message
        self.text = text
 class AsyncHTTPHandler:
    def __init__(
@ -155,13 +211,16 @@ class AsyncHTTPHandler:
                headers=headers,
            )
        except httpx.HTTPStatusError as e:
-            setattr(e, "status_code", e.response.status_code)
+
            if stream is True:
                setattr(e, "message", await e.response.aread())
                setattr(e, "text", await e.response.aread())
            else:
-                setattr(e, "message", e.response.text)
+                setattr(e, "message", mask_sensitive_info(e.response.text))
-                setattr(e, "text", e.response.text)
+                setattr(e, "text", mask_sensitive_info(e.response.text))
            setattr(e, "status_code", e.response.status_code)
            raise e
        except Exception as e:
            raise e
@ -399,11 +458,17 @@ class HTTPHandler:
                llm_provider="litellm-httpx-handler",
            )
        except httpx.HTTPStatusError as e:
-            setattr(e, "status_code", e.response.status_code)
+
            if stream is True:
-                setattr(e, "message", e.response.read())
+                setattr(e, "message", mask_sensitive_info(e.response.read()))
                setattr(e, "text", mask_sensitive_info(e.response.read()))
            else:
-                setattr(e, "message", e.response.text)
+                error_text = mask_sensitive_info(e.response.text)
                setattr(e, "message", error_text)
                setattr(e, "text", error_text)
            setattr(e, "status_code", e.response.status_code)
            raise e
        except Exception as e:
            raise e
--- a/litellm/llms/prompt_templates/factory.py
+++ b/litellm/llms/prompt_templates/factory.py
@ -33,6 +33,7 @@ from litellm.types.llms.openai import (
    ChatCompletionAssistantToolCall,
    ChatCompletionFunctionMessage,
    ChatCompletionImageObject,
    ChatCompletionImageUrlObject,
    ChatCompletionTextObject,
    ChatCompletionToolCallFunctionChunk,
    ChatCompletionToolMessage,
@ -681,6 +682,27 @@ def construct_tool_use_system_prompt(
    return tool_use_system_prompt
 def convert_generic_image_chunk_to_openai_image_obj(
    image_chunk: GenericImageParsingChunk,
 ) -> str:
    """
    Convert a generic image chunk to an OpenAI image object.
    Input:
    GenericImageParsingChunk(
        type="base64",
        media_type="image/jpeg",
        data="...",
    )
    Return:
    "data:image/jpeg;base64,{base64_image}"
    """
    return "data:{};{},{}".format(
        image_chunk["media_type"], image_chunk["type"], image_chunk["data"]
    )
 def convert_to_anthropic_image_obj(openai_image_url: str) -> GenericImageParsingChunk:
    """
    Input:
@ -706,6 +728,7 @@ def convert_to_anthropic_image_obj(openai_image_url: str) -> GenericImageParsing
            data=base64_data,
        )
    except Exception as e:
        traceback.print_exc()
        if "Error: Unable to fetch image from URL" in str(e):
            raise e
        raise Exception(
@ -1136,15 +1159,44 @@ def convert_to_anthropic_tool_result(
        ]
    }
    """
-    content_str: str = ""
+    anthropic_content: Union[
        str,
        List[Union[AnthropicMessagesToolResultContent, AnthropicMessagesImageParam]],
    ] = ""
    if isinstance(message["content"], str):
-        content_str = message["content"]
+        anthropic_content = message["content"]
    elif isinstance(message["content"], List):
        content_list = message["content"]
        anthropic_content_list: List[
            Union[AnthropicMessagesToolResultContent, AnthropicMessagesImageParam]
        ] = []
        for content in content_list:
            if content["type"] == "text":
-                content_str += content["text"]
+                anthropic_content_list.append(
                    AnthropicMessagesToolResultContent(
                        type="text",
                        text=content["text"],
                    )
                )
            elif content["type"] == "image_url":
                if isinstance(content["image_url"], str):
                    image_chunk = convert_to_anthropic_image_obj(content["image_url"])
                else:
                    image_chunk = convert_to_anthropic_image_obj(
                        content["image_url"]["url"]
                    )
                anthropic_content_list.append(
                    AnthropicMessagesImageParam(
                        type="image",
                        source=AnthropicContentParamSource(
                            type="base64",
                            media_type=image_chunk["media_type"],
                            data=image_chunk["data"],
                        ),
                    )
                )
        anthropic_content = anthropic_content_list
    anthropic_tool_result: Optional[AnthropicMessagesToolResultParam] = None
    ## PROMPT CACHING CHECK ##
    cache_control = message.get("cache_control", None)
@ -1155,14 +1207,14 @@ def convert_to_anthropic_tool_result(
        # We can't determine from openai message format whether it's a successful or
        # error call result so default to the successful result template
        anthropic_tool_result = AnthropicMessagesToolResultParam(
-            type="tool_result", tool_use_id=tool_call_id, content=content_str
+            type="tool_result", tool_use_id=tool_call_id, content=anthropic_content
        )
    if message["role"] == "function":
        function_message: ChatCompletionFunctionMessage = message
        tool_call_id = function_message.get("tool_call_id") or str(uuid.uuid4())
        anthropic_tool_result = AnthropicMessagesToolResultParam(
-            type="tool_result", tool_use_id=tool_call_id, content=content_str
+            type="tool_result", tool_use_id=tool_call_id, content=anthropic_content
        )
    if anthropic_tool_result is None:
--- a/litellm/llms/vertex_ai_and_google_ai_studio/gemini/transformation.py
+++ b/litellm/llms/vertex_ai_and_google_ai_studio/gemini/transformation.py
@ -107,6 +107,10 @@ def _get_image_mime_type_from_url(url: str) -> Optional[str]:
        return "image/png"
    elif url.endswith(".webp"):
        return "image/webp"
    elif url.endswith(".mp4"):
        return "video/mp4"
    elif url.endswith(".pdf"):
        return "application/pdf"
    return None
@ -294,7 +298,12 @@ def _transform_request_body(
    optional_params = {k: v for k, v in optional_params.items() if k not in remove_keys}
    try:
-        content = _gemini_convert_messages_with_history(messages=messages)
+        if custom_llm_provider == "gemini":
            content = litellm.GoogleAIStudioGeminiConfig._transform_messages(
                messages=messages
            )
        else:
            content = litellm.VertexGeminiConfig._transform_messages(messages=messages)
        tools: Optional[Tools] = optional_params.pop("tools", None)
        tool_choice: Optional[ToolConfig] = optional_params.pop("tool_choice", None)
        safety_settings: Optional[List[SafetSettingsConfig]] = optional_params.pop(
--- a/litellm/llms/vertex_ai_and_google_ai_studio/gemini/vertex_and_google_ai_studio_gemini.py
+++ b/litellm/llms/vertex_ai_and_google_ai_studio/gemini/vertex_and_google_ai_studio_gemini.py
@ -35,7 +35,12 @@ from litellm.llms.custom_httpx.http_handler import (
    HTTPHandler,
    get_async_httpx_client,
 )
 from litellm.llms.prompt_templates.factory import (
    convert_generic_image_chunk_to_openai_image_obj,
    convert_to_anthropic_image_obj,
 )
 from litellm.types.llms.openai import (
    AllMessageValues,
    ChatCompletionResponseMessage,
    ChatCompletionToolCallChunk,
    ChatCompletionToolCallFunctionChunk,
@ -78,6 +83,8 @@ from ..common_utils import (
 )
 from ..vertex_llm_base import VertexBase
 from .transformation import (
    _gemini_convert_messages_with_history,
    _process_gemini_image,
    async_transform_request_body,
    set_headers,
    sync_transform_request_body,
@ -912,6 +919,10 @@ class VertexGeminiConfig:
        return model_response
    @staticmethod
    def _transform_messages(messages: List[AllMessageValues]) -> List[ContentType]:
        return _gemini_convert_messages_with_history(messages=messages)
 class GoogleAIStudioGeminiConfig(
    VertexGeminiConfig
@ -1015,6 +1026,32 @@ class GoogleAIStudioGeminiConfig(
            model, non_default_params, optional_params, drop_params
        )
    @staticmethod
    def _transform_messages(messages: List[AllMessageValues]) -> List[ContentType]:
        """
        Google AI Studio Gemini does not support image urls in messages.
        """
        for message in messages:
            _message_content = message.get("content")
            if _message_content is not None and isinstance(_message_content, list):
                _parts: List[PartType] = []
                for element in _message_content:
                    if element.get("type") == "image_url":
                        img_element = element
                        _image_url: Optional[str] = None
                        if isinstance(img_element.get("image_url"), dict):
                            _image_url = img_element["image_url"].get("url")  # type: ignore
                        else:
                            _image_url = img_element.get("image_url")  # type: ignore
                        if _image_url and "https://" in _image_url:
                            image_obj = convert_to_anthropic_image_obj(_image_url)
                            img_element["image_url"] = (  # type: ignore
                                convert_generic_image_chunk_to_openai_image_obj(
                                    image_obj
                                )
                            )
        return _gemini_convert_messages_with_history(messages=messages)
 async def make_call(
    client: Optional[AsyncHTTPHandler],
--- a/litellm/model_prices_and_context_window_backup.json
+++ b/litellm/model_prices_and_context_window_backup.json
@ -2032,7 +2032,6 @@
        "tool_use_system_prompt_tokens": 264,
        "supports_assistant_prefill": true,
        "supports_prompt_caching": true,
        "supports_pdf_input": true,
        "supports_response_schema": true
    },
    "claude-3-opus-20240229": {
@ -2098,6 +2097,7 @@
        "supports_vision": true,
        "tool_use_system_prompt_tokens": 159,
        "supports_assistant_prefill": true,
        "supports_pdf_input": true,
        "supports_prompt_caching": true,
        "supports_response_schema": true
    },
@ -3383,6 +3383,8 @@
        "supports_vision": true,
        "supports_response_schema": true,
        "supports_prompt_caching": true,
        "tpm": 4000000,
        "rpm": 2000,
        "source": "https://ai.google.dev/pricing"
    },
    "gemini/gemini-1.5-flash-001": {
@ -3406,6 +3408,8 @@
        "supports_vision": true,
        "supports_response_schema": true,
        "supports_prompt_caching": true,
        "tpm": 4000000,
        "rpm": 2000,
        "source": "https://ai.google.dev/pricing"
    },
    "gemini/gemini-1.5-flash": {
@ -3428,6 +3432,8 @@
        "supports_function_calling": true,
        "supports_vision": true,
        "supports_response_schema": true, 
        "tpm": 4000000,
        "rpm": 2000,
        "source": "https://ai.google.dev/pricing"
    },
    "gemini/gemini-1.5-flash-latest": {
@ -3450,6 +3456,32 @@
        "supports_function_calling": true,
        "supports_vision": true,
        "supports_response_schema": true,
        "tpm": 4000000,
        "rpm": 2000,
        "source": "https://ai.google.dev/pricing"
    },
    "gemini/gemini-1.5-flash-8b": {
        "max_tokens": 8192,
        "max_input_tokens": 1048576,
        "max_output_tokens": 8192,
        "max_images_per_prompt": 3000,
        "max_videos_per_prompt": 10,
        "max_video_length": 1,
        "max_audio_length_hours": 8.4,
        "max_audio_per_prompt": 1,
        "max_pdf_size_mb": 30, 
        "input_cost_per_token": 0,
        "input_cost_per_token_above_128k_tokens": 0,
        "output_cost_per_token": 0,
        "output_cost_per_token_above_128k_tokens": 0,
        "litellm_provider": "gemini",
        "mode": "chat",
        "supports_system_messages": true,
        "supports_function_calling": true,
        "supports_vision": true,
        "supports_response_schema": true,
        "tpm": 4000000,
        "rpm": 4000,
        "source": "https://ai.google.dev/pricing"
    },
    "gemini/gemini-1.5-flash-8b-exp-0924": {
@ -3472,6 +3504,8 @@
        "supports_function_calling": true,
        "supports_vision": true,
        "supports_response_schema": true,
        "tpm": 4000000,
        "rpm": 4000,
        "source": "https://ai.google.dev/pricing"
    },
    "gemini/gemini-exp-1114": {
@ -3494,7 +3528,12 @@
        "supports_function_calling": true,
        "supports_vision": true,
        "supports_response_schema": true,
-        "source": "https://ai.google.dev/pricing"
+        "tpm": 4000000,
        "rpm": 1000,
        "source": "https://ai.google.dev/pricing",
        "metadata": {
            "notes": "Rate limits not documented for gemini-exp-1114. Assuming same as gemini-1.5-pro."
        }
    },
    "gemini/gemini-1.5-flash-exp-0827": {
        "max_tokens": 8192,
@ -3516,6 +3555,8 @@
        "supports_function_calling": true,
        "supports_vision": true,
        "supports_response_schema": true,
        "tpm": 4000000,
        "rpm": 2000,
        "source": "https://ai.google.dev/pricing"
    },
    "gemini/gemini-1.5-flash-8b-exp-0827": {
@ -3537,6 +3578,9 @@
        "supports_system_messages": true,
        "supports_function_calling": true,
        "supports_vision": true,
        "supports_response_schema": true,
        "tpm": 4000000,
        "rpm": 4000,
        "source": "https://ai.google.dev/pricing"
    },
    "gemini/gemini-pro": {
@ -3550,7 +3594,10 @@
        "litellm_provider": "gemini",
        "mode": "chat",
        "supports_function_calling": true,
-        "source": "https://cloud.google.com/vertex-ai/generative-ai/docs/learn/models#foundation_models"
+        "rpd": 30000,
        "tpm": 120000,
        "rpm": 360,
        "source": "https://ai.google.dev/gemini-api/docs/models/gemini"
    },
    "gemini/gemini-1.5-pro": {
        "max_tokens": 8192,
@ -3567,6 +3614,8 @@
        "supports_vision": true,
        "supports_tool_choice": true, 
        "supports_response_schema": true, 
        "tpm": 4000000,
        "rpm": 1000,
        "source": "https://ai.google.dev/pricing"
    },
    "gemini/gemini-1.5-pro-002": {
@ -3585,6 +3634,8 @@
        "supports_tool_choice": true, 
        "supports_response_schema": true, 
        "supports_prompt_caching": true,
        "tpm": 4000000,
        "rpm": 1000,
        "source": "https://ai.google.dev/pricing"
    },
    "gemini/gemini-1.5-pro-001": {
@ -3603,6 +3654,8 @@
        "supports_tool_choice": true, 
        "supports_response_schema": true, 
        "supports_prompt_caching": true,
        "tpm": 4000000,
        "rpm": 1000,
        "source": "https://ai.google.dev/pricing"
    },
    "gemini/gemini-1.5-pro-exp-0801": {
@ -3620,6 +3673,8 @@
        "supports_vision": true,
        "supports_tool_choice": true,
        "supports_response_schema": true,
        "tpm": 4000000,
        "rpm": 1000,
        "source": "https://ai.google.dev/pricing"
    },
    "gemini/gemini-1.5-pro-exp-0827": {
@ -3637,6 +3692,8 @@
        "supports_vision": true,
        "supports_tool_choice": true,
        "supports_response_schema": true,
        "tpm": 4000000,
        "rpm": 1000,
        "source": "https://ai.google.dev/pricing"
    },
    "gemini/gemini-1.5-pro-latest": {
@ -3654,6 +3711,8 @@
        "supports_vision": true,
        "supports_tool_choice": true, 
        "supports_response_schema": true, 
        "tpm": 4000000,
        "rpm": 1000,
        "source": "https://ai.google.dev/pricing"
    },
    "gemini/gemini-pro-vision": {
@ -3668,6 +3727,9 @@
        "mode": "chat",
        "supports_function_calling": true,
        "supports_vision": true,
        "rpd": 30000,
        "tpm": 120000,
        "rpm": 360,
        "source": "https://cloud.google.com/vertex-ai/generative-ai/docs/learn/models#foundation_models"
    },
    "gemini/gemini-gemma-2-27b-it": {
--- a/litellm/proxy/_experimental/out/_next/static/chunks/131-3d2257b0ff5aadb2.js
+++ b/litellm/proxy/_experimental/out/_next/static/chunks/131-3d2257b0ff5aadb2.js
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--- a/litellm/proxy/_experimental/out/_next/static/pDx3dChtj-paUmJExuV6u/_buildManifest.js
+++ b/litellm/proxy/_experimental/out/_next/static/pDx3dChtj-paUmJExuV6u/_buildManifest.js
--- a/litellm/proxy/_experimental/out/_next/static/pDx3dChtj-paUmJExuV6u/_ssgManifest.js
+++ b/litellm/proxy/_experimental/out/_next/static/pDx3dChtj-paUmJExuV6u/_ssgManifest.js
--- a/litellm/proxy/_experimental/out/index.html
+++ b/litellm/proxy/_experimental/out/index.html
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--- a/litellm/proxy/_experimental/out/model_hub.txt
+++ b/litellm/proxy/_experimental/out/model_hub.txt
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--- a/litellm/proxy/_experimental/out/onboarding.txt
+++ b/litellm/proxy/_experimental/out/onboarding.txt
@ -1,7 +1,7 @@
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--- a/litellm/proxy/_new_secret_config.yaml
+++ b/litellm/proxy/_new_secret_config.yaml
@ -11,4 +11,44 @@ model_list:
      model: vertex_ai/claude-3-5-sonnet-v2
      vertex_ai_project: "adroit-crow-413218"
      vertex_ai_location: "us-east5"  
  - model_name: openai-gpt-4o-realtime-audio
    litellm_params:
      model: openai/gpt-4o-realtime-preview-2024-10-01
      api_key: os.environ/OPENAI_API_KEY
  - model_name: openai/*
    litellm_params:
      model: openai/*
      api_key: os.environ/OPENAI_API_KEY
  - model_name: openai/*
    litellm_params:
      model: openai/*
      api_key: os.environ/OPENAI_API_KEY
    model_info:
      access_groups: ["public-openai-models"] 
  - model_name: openai/gpt-4o
    litellm_params:
      model: openai/gpt-4o
      api_key: os.environ/OPENAI_API_KEY
    model_info:
      access_groups: ["private-openai-models"] 
 router_settings:
  routing_strategy: usage-based-routing-v2
  #redis_url: "os.environ/REDIS_URL"
  redis_host: "os.environ/REDIS_HOST"
  redis_port: "os.environ/REDIS_PORT"
 litellm_settings:
  cache: true
  cache_params:
    type: redis
    host: "os.environ/REDIS_HOST"
    port: "os.environ/REDIS_PORT"
    namespace: "litellm.caching"
    ttl: 600
 #   key_generation_settings:
 #     team_key_generation:
 #       allowed_team_member_roles: ["admin"]
 #       required_params: ["tags"] # require team admins to set tags for cost-tracking when generating a team key
 #     personal_key_generation: # maps to 'Default Team' on UI 
 #       allowed_user_roles: ["proxy_admin"]
--- a/litellm/proxy/_types.py
+++ b/litellm/proxy/_types.py
@ -1982,7 +1982,6 @@ class MemberAddRequest(LiteLLMBase):
            # Replace member_data with the single Member object
            data["member"] = member
        # Call the superclass __init__ method to initialize the object
        traceback.print_stack()
        super().__init__(**data)
@ -2184,3 +2183,11 @@ PassThroughEndpointLoggingResultValues = Union[
 class PassThroughEndpointLoggingTypedDict(TypedDict):
    result: Optional[PassThroughEndpointLoggingResultValues]
    kwargs: dict
 LiteLLM_ManagementEndpoint_MetadataFields = [
    "model_rpm_limit",
    "model_tpm_limit",
    "guardrails",
    "tags",
 ]
--- a/litellm/proxy/auth/auth_checks.py
+++ b/litellm/proxy/auth/auth_checks.py
@ -60,6 +60,7 @@ def common_checks(  # noqa: PLR0915
    global_proxy_spend: Optional[float],
    general_settings: dict,
    route: str,
    llm_router: Optional[litellm.Router],
 ) -> bool:
    """
    Common checks across jwt + key-based auth.
@ -97,7 +98,12 @@ def common_checks(  # noqa: PLR0915
            # this means the team has access to all models on the proxy
            pass
        # check if the team model is an access_group
-        elif model_in_access_group(_model, team_object.models) is True:
+        elif (
            model_in_access_group(
                model=_model, team_models=team_object.models, llm_router=llm_router
            )
            is True
        ):
            pass
        elif _model and "*" in _model:
            pass
@ -373,36 +379,33 @@ async def get_end_user_object(
        return None
-def model_in_access_group(model: str, team_models: Optional[List[str]]) -> bool:
+def model_in_access_group(
    model: str, team_models: Optional[List[str]], llm_router: Optional[litellm.Router]
 ) -> bool:
    from collections import defaultdict
    from litellm.proxy.proxy_server import llm_router
    if team_models is None:
        return True
    if model in team_models:
        return True
-    access_groups = defaultdict(list)
+    access_groups: dict[str, list[str]] = defaultdict(list)
    if llm_router:
-        access_groups = llm_router.get_model_access_groups()
+        access_groups = llm_router.get_model_access_groups(model_name=model)
    models_in_current_access_groups = []
    if len(access_groups) > 0:  # check if token contains any model access groups
        for idx, m in enumerate(
            team_models
        ):  # loop token models, if any of them are an access group add the access group
            if m in access_groups:
-                # if it is an access group we need to remove it from valid_token.models
+                return True
                models_in_group = access_groups[m]
                models_in_current_access_groups.extend(models_in_group)
    # Filter out models that are access_groups
    filtered_models = [m for m in team_models if m not in access_groups]
    filtered_models += models_in_current_access_groups
    if model in filtered_models:
        return True
    return False
@ -586,26 +589,63 @@ async def _get_team_db_check(team_id: str, prisma_client: PrismaClient):
    )
-async def get_team_object(
+async def _get_team_object_from_db(team_id: str, prisma_client: PrismaClient):
-    team_id: str,
+    return await prisma_client.db.litellm_teamtable.find_unique(
-    prisma_client: Optional[PrismaClient],
+        where={"team_id": team_id}
-    user_api_key_cache: DualCache,
+    )
    parent_otel_span: Optional[Span] = None,
    proxy_logging_obj: Optional[ProxyLogging] = None,
    check_cache_only: Optional[bool] = None,
 ) -> LiteLLM_TeamTableCachedObj:
    """
    - Check if team id in proxy Team Table
    - if valid, return LiteLLM_TeamTable object with defined limits
    - if not, then raise an error
    """
    if prisma_client is None:
        raise Exception(
            "No DB Connected. See - https://docs.litellm.ai/docs/proxy/virtual_keys"
        )
-    # check if in cache
+
-    key = "team_id:{}".format(team_id)
+async def _get_team_object_from_user_api_key_cache(
    team_id: str,
    prisma_client: PrismaClient,
    user_api_key_cache: DualCache,
    last_db_access_time: LimitedSizeOrderedDict,
    db_cache_expiry: int,
    proxy_logging_obj: Optional[ProxyLogging],
    key: str,
 ) -> LiteLLM_TeamTableCachedObj:
    db_access_time_key = key
    should_check_db = _should_check_db(
        key=db_access_time_key,
        last_db_access_time=last_db_access_time,
        db_cache_expiry=db_cache_expiry,
    )
    if should_check_db:
        response = await _get_team_db_check(
            team_id=team_id, prisma_client=prisma_client
        )
    else:
        response = None
    if response is None:
        raise Exception
    _response = LiteLLM_TeamTableCachedObj(**response.dict())
    # save the team object to cache
    await _cache_team_object(
        team_id=team_id,
        team_table=_response,
        user_api_key_cache=user_api_key_cache,
        proxy_logging_obj=proxy_logging_obj,
    )
    # save to db access time
    # save to db access time
    _update_last_db_access_time(
        key=db_access_time_key,
        value=_response,
        last_db_access_time=last_db_access_time,
    )
    return _response
 async def _get_team_object_from_cache(
    key: str,
    proxy_logging_obj: Optional[ProxyLogging],
    user_api_key_cache: DualCache,
    parent_otel_span: Optional[Span],
 ) -> Optional[LiteLLM_TeamTableCachedObj]:
    cached_team_obj: Optional[LiteLLM_TeamTableCachedObj] = None
    ## CHECK REDIS CACHE ##
@ -613,6 +653,7 @@ async def get_team_object(
        proxy_logging_obj is not None
        and proxy_logging_obj.internal_usage_cache.dual_cache
    ):
        cached_team_obj = (
            await proxy_logging_obj.internal_usage_cache.dual_cache.async_get_cache(
                key=key, parent_otel_span=parent_otel_span
@ -628,47 +669,58 @@ async def get_team_object(
        elif isinstance(cached_team_obj, LiteLLM_TeamTableCachedObj):
            return cached_team_obj
-    if check_cache_only:
+    return None
 async def get_team_object(
    team_id: str,
    prisma_client: Optional[PrismaClient],
    user_api_key_cache: DualCache,
    parent_otel_span: Optional[Span] = None,
    proxy_logging_obj: Optional[ProxyLogging] = None,
    check_cache_only: Optional[bool] = None,
    check_db_only: Optional[bool] = None,
 ) -> LiteLLM_TeamTableCachedObj:
    """
    - Check if team id in proxy Team Table
    - if valid, return LiteLLM_TeamTable object with defined limits
    - if not, then raise an error
    """
    if prisma_client is None:
        raise Exception(
-            f"Team doesn't exist in cache + check_cache_only=True. Team={team_id}."
+            "No DB Connected. See - https://docs.litellm.ai/docs/proxy/virtual_keys"
        )
    # check if in cache
    key = "team_id:{}".format(team_id)
    if not check_db_only:
        cached_team_obj = await _get_team_object_from_cache(
            key=key,
            proxy_logging_obj=proxy_logging_obj,
            user_api_key_cache=user_api_key_cache,
            parent_otel_span=parent_otel_span,
        )
        if cached_team_obj is not None:
            return cached_team_obj
        if check_cache_only:
            raise Exception(
                f"Team doesn't exist in cache + check_cache_only=True. Team={team_id}."
            )
    # else, check db
    try:
-        db_access_time_key = "team_id:{}".format(team_id)
+        return await _get_team_object_from_user_api_key_cache(
        should_check_db = _should_check_db(
            key=db_access_time_key,
            last_db_access_time=last_db_access_time,
            db_cache_expiry=db_cache_expiry,
        )
        if should_check_db:
            response = await _get_team_db_check(
                team_id=team_id, prisma_client=prisma_client
            )
        else:
            response = None
        if response is None:
            raise Exception
        _response = LiteLLM_TeamTableCachedObj(**response.dict())
        # save the team object to cache
        await _cache_team_object(
            team_id=team_id,
-            team_table=_response,
+            prisma_client=prisma_client,
            user_api_key_cache=user_api_key_cache,
            proxy_logging_obj=proxy_logging_obj,
        )
        # save to db access time
        # save to db access time
        _update_last_db_access_time(
            key=db_access_time_key,
            value=_response,
            last_db_access_time=last_db_access_time,
            db_cache_expiry=db_cache_expiry,
            key=key,
        )
        return _response
    except Exception:
        raise Exception(
            f"Team doesn't exist in db. Team={team_id}. Create team via `/team/new` call."
@ -825,7 +877,10 @@ async def get_org_object(
 async def can_key_call_model(
-    model: str, llm_model_list: Optional[list], valid_token: UserAPIKeyAuth
+    model: str,
    llm_model_list: Optional[list],
    valid_token: UserAPIKeyAuth,
    llm_router: Optional[litellm.Router],
 ) -> Literal[True]:
    """
    Checks if token can call a given model
@ -845,35 +900,29 @@ async def can_key_call_model(
    )
    from collections import defaultdict
    from litellm.proxy.proxy_server import llm_router
    access_groups = defaultdict(list)
    if llm_router:
-        access_groups = llm_router.get_model_access_groups()
+        access_groups = llm_router.get_model_access_groups(model_name=model)
-    models_in_current_access_groups = []
+    if (
-    if len(access_groups) > 0:  # check if token contains any model access groups
+        len(access_groups) > 0 and llm_router is not None
    ):  # check if token contains any model access groups
        for idx, m in enumerate(
            valid_token.models
        ):  # loop token models, if any of them are an access group add the access group
            if m in access_groups:
-                # if it is an access group we need to remove it from valid_token.models
+                return True
                models_in_group = access_groups[m]
                models_in_current_access_groups.extend(models_in_group)
    # Filter out models that are access_groups
    filtered_models = [m for m in valid_token.models if m not in access_groups]
    filtered_models += models_in_current_access_groups
    verbose_proxy_logger.debug(f"model: {model}; allowed_models: {filtered_models}")
    all_model_access: bool = False
    if (
-        len(filtered_models) == 0
+        len(filtered_models) == 0 and len(valid_token.models) == 0
-        or "*" in filtered_models
+    ) or "*" in filtered_models:
        or "openai/*" in filtered_models
    ):
        all_model_access = True
    if model is not None and model not in filtered_models and all_model_access is False:
--- a/litellm/proxy/auth/user_api_key_auth.py
+++ b/litellm/proxy/auth/user_api_key_auth.py
@ -28,6 +28,8 @@ from fastapi import (
    Request,
    Response,
    UploadFile,
    WebSocket,
    WebSocketDisconnect,
    status,
 )
 from fastapi.middleware.cors import CORSMiddleware
@ -195,6 +197,52 @@ def _is_allowed_route(
        )
 async def user_api_key_auth_websocket(websocket: WebSocket):
    # Accept the WebSocket connection
    request = Request(scope={"type": "http"})
    request._url = websocket.url
    query_params = websocket.query_params
    model = query_params.get("model")
    async def return_body():
        return_string = f'{{"model": "{model}"}}'
        # return string as bytes
        return return_string.encode()
    request.body = return_body  # type: ignore
    # Extract the Authorization header
    authorization = websocket.headers.get("authorization")
    # If no Authorization header, try the api-key header
    if not authorization:
        api_key = websocket.headers.get("api-key")
        if not api_key:
            await websocket.close(code=status.WS_1008_POLICY_VIOLATION)
            raise HTTPException(status_code=403, detail="No API key provided")
    else:
        # Extract the API key from the Bearer token
        if not authorization.startswith("Bearer "):
            await websocket.close(code=status.WS_1008_POLICY_VIOLATION)
            raise HTTPException(
                status_code=403, detail="Invalid Authorization header format"
            )
        api_key = authorization[len("Bearer ") :].strip()
    # Call user_api_key_auth with the extracted API key
    # Note: You'll need to modify this to work with WebSocket context if needed
    try:
        return await user_api_key_auth(request=request, api_key=f"Bearer {api_key}")
    except Exception as e:
        verbose_proxy_logger.exception(e)
        await websocket.close(code=status.WS_1008_POLICY_VIOLATION)
        raise HTTPException(status_code=403, detail=str(e))
 async def user_api_key_auth(  # noqa: PLR0915
    request: Request,
    api_key: str = fastapi.Security(api_key_header),
@ -211,6 +259,7 @@ async def user_api_key_auth(  # noqa: PLR0915
        jwt_handler,
        litellm_proxy_admin_name,
        llm_model_list,
        llm_router,
        master_key,
        open_telemetry_logger,
        prisma_client,
@ -494,6 +543,7 @@ async def user_api_key_auth(  # noqa: PLR0915
                    general_settings=general_settings,
                    global_proxy_spend=global_proxy_spend,
                    route=route,
                    llm_router=llm_router,
                )
                # return UserAPIKeyAuth object
@ -857,6 +907,7 @@ async def user_api_key_auth(  # noqa: PLR0915
                        model=model,
                        llm_model_list=llm_model_list,
                        valid_token=valid_token,
                        llm_router=llm_router,
                    )
                if fallback_models is not None:
@ -865,6 +916,7 @@ async def user_api_key_auth(  # noqa: PLR0915
                            model=m,
                            llm_model_list=llm_model_list,
                            valid_token=valid_token,
                            llm_router=llm_router,
                        )
            # Check 2. If user_id for this token is in budget - done in common_checks()
@ -1125,6 +1177,7 @@ async def user_api_key_auth(  # noqa: PLR0915
                general_settings=general_settings,
                global_proxy_spend=global_proxy_spend,
                route=route,
                llm_router=llm_router,
            )
            # Token passed all checks
            if valid_token is None:
@ -1197,13 +1250,15 @@ async def user_api_key_auth(  # noqa: PLR0915
            extra={"requester_ip": requester_ip},
        )
-        # Log this exception to OTEL
+        # Log this exception to OTEL, Datadog etc
-        if open_telemetry_logger is not None:
+        asyncio.create_task(
-            await open_telemetry_logger.async_post_call_failure_hook(  # type: ignore
+            proxy_logging_obj.async_log_proxy_authentication_errors(
                original_exception=e,
-                request_data={},
+                request=request,
-                user_api_key_dict=UserAPIKeyAuth(parent_otel_span=parent_otel_span),
+                parent_otel_span=parent_otel_span,
                api_key=api_key,
            )
        )
        if isinstance(e, litellm.BudgetExceededError):
            raise ProxyException(
--- a/litellm/proxy/common_utils/http_parsing_utils.py
+++ b/litellm/proxy/common_utils/http_parsing_utils.py
@ -1,6 +1,6 @@
 import ast
 import json
-from typing import List, Optional
+from typing import Dict, List, Optional
 from fastapi import Request, UploadFile, status
@ -8,31 +8,43 @@ from litellm._logging import verbose_proxy_logger
 from litellm.types.router import Deployment
-async def _read_request_body(request: Optional[Request]) -> dict:
+async def _read_request_body(request: Optional[Request]) -> Dict:
    """
-    Asynchronous function to read the request body and parse it as JSON or literal data.
+    Safely read the request body and parse it as JSON.
    Parameters:
    - request: The request object to read the body from
    Returns:
-    - dict: Parsed request data as a dictionary
+    - dict: Parsed request data as a dictionary or an empty dictionary if parsing fails
    """
    try:
        request_data: dict = {}
        if request is None:
-            return request_data
+            return {}
        # Read the request body
        body = await request.body()
-        if body == b"" or body is None:
+        # Return empty dict if body is empty or None
-            return request_data
+        if not body:
            return {}
        # Decode the body to a string
        body_str = body.decode()
-        try:
+
-            request_data = ast.literal_eval(body_str)
+        # Attempt JSON parsing (safe for untrusted input)
-        except Exception:
+        return json.loads(body_str)
-            request_data = json.loads(body_str)
+
-        return request_data
+    except json.JSONDecodeError:
-    except Exception:
+        # Log detailed information for debugging
        verbose_proxy_logger.exception("Invalid JSON payload received.")
        return {}
    except Exception as e:
        # Catch unexpected errors to avoid crashes
        verbose_proxy_logger.exception(
            "Unexpected error reading request body - {}".format(e)
        )
        return {}
--- a/litellm/proxy/guardrails/guardrail_hooks/bedrock_guardrails.py
+++ b/litellm/proxy/guardrails/guardrail_hooks/bedrock_guardrails.py
@ -214,10 +214,10 @@ class BedrockGuardrail(CustomGuardrail, BaseAWSLLM):
            prepared_request.url,
            prepared_request.headers,
        )
-        _json_data = json.dumps(request_data)  # type: ignore
+
        response = await self.async_handler.post(
            url=prepared_request.url,
-            json=request_data,  # type: ignore
+            data=prepared_request.body,  # type: ignore
            headers=prepared_request.headers,  # type: ignore
        )
        verbose_proxy_logger.debug("Bedrock AI response: %s", response.text)
--- a/litellm/proxy/hooks/proxy_failure_handler.py
+++ b/litellm/proxy/hooks/proxy_failure_handler.py
@ -0,0 +1,87 @@
 """
 Runs when LLM Exceptions occur on LiteLLM Proxy
 """
 import copy
 import json
 import uuid
 import litellm
 from litellm.proxy._types import LiteLLM_ErrorLogs
 async def _PROXY_failure_handler(
    kwargs,  # kwargs to completion
    completion_response: litellm.ModelResponse,  # response from completion
    start_time=None,
    end_time=None,  # start/end time for completion
 ):
    """
    Async Failure Handler - runs when LLM Exceptions occur on LiteLLM Proxy.
    This function logs the errors to the Prisma DB
    Can be disabled by setting the following on proxy_config.yaml:
    ```yaml
    general_settings:
      disable_error_logs: True
    ```
    """
    from litellm._logging import verbose_proxy_logger
    from litellm.proxy.proxy_server import general_settings, prisma_client
    if general_settings.get("disable_error_logs") is True:
        return
    if prisma_client is not None:
        verbose_proxy_logger.debug(
            "inside _PROXY_failure_handler kwargs=", extra=kwargs
        )
        _exception = kwargs.get("exception")
        _exception_type = _exception.__class__.__name__
        _model = kwargs.get("model", None)
        _optional_params = kwargs.get("optional_params", {})
        _optional_params = copy.deepcopy(_optional_params)
        for k, v in _optional_params.items():
            v = str(v)
            v = v[:100]
        _status_code = "500"
        try:
            _status_code = str(_exception.status_code)
        except Exception:
            # Don't let this fail logging the exception to the dB
            pass
        _litellm_params = kwargs.get("litellm_params", {}) or {}
        _metadata = _litellm_params.get("metadata", {}) or {}
        _model_id = _metadata.get("model_info", {}).get("id", "")
        _model_group = _metadata.get("model_group", "")
        api_base = litellm.get_api_base(model=_model, optional_params=_litellm_params)
        _exception_string = str(_exception)
        error_log = LiteLLM_ErrorLogs(
            request_id=str(uuid.uuid4()),
            model_group=_model_group,
            model_id=_model_id,
            litellm_model_name=kwargs.get("model"),
            request_kwargs=_optional_params,
            api_base=api_base,
            exception_type=_exception_type,
            status_code=_status_code,
            exception_string=_exception_string,
            startTime=kwargs.get("start_time"),
            endTime=kwargs.get("end_time"),
        )
        error_log_dict = error_log.model_dump()
        error_log_dict["request_kwargs"] = json.dumps(error_log_dict["request_kwargs"])
        await prisma_client.db.litellm_errorlogs.create(
            data=error_log_dict  # type: ignore
        )
    pass
--- a/litellm/proxy/litellm_pre_call_utils.py
+++ b/litellm/proxy/litellm_pre_call_utils.py
@ -288,12 +288,12 @@ class LiteLLMProxyRequestSetup:
        ## KEY-LEVEL SPEND LOGS / TAGS
        if "tags" in key_metadata and key_metadata["tags"] is not None:
-            if "tags" in data[_metadata_variable_name] and isinstance(
+            data[_metadata_variable_name]["tags"] = (
-                data[_metadata_variable_name]["tags"], list
+                LiteLLMProxyRequestSetup._merge_tags(
-            ):
+                    request_tags=data[_metadata_variable_name].get("tags"),
-                data[_metadata_variable_name]["tags"].extend(key_metadata["tags"])
+                    tags_to_add=key_metadata["tags"],
-            else:
+                )
-                data[_metadata_variable_name]["tags"] = key_metadata["tags"]
+            )
        if "spend_logs_metadata" in key_metadata and isinstance(
            key_metadata["spend_logs_metadata"], dict
        ):
@ -319,6 +319,30 @@ class LiteLLMProxyRequestSetup:
            data["disable_fallbacks"] = key_metadata["disable_fallbacks"]
        return data
    @staticmethod
    def _merge_tags(request_tags: Optional[list], tags_to_add: Optional[list]) -> list:
        """
        Helper function to merge two lists of tags, ensuring no duplicates.
        Args:
            request_tags (Optional[list]): List of tags from the original request
            tags_to_add (Optional[list]): List of tags to add
        Returns:
            list: Combined list of unique tags
        """
        final_tags = []
        if request_tags and isinstance(request_tags, list):
            final_tags.extend(request_tags)
        if tags_to_add and isinstance(tags_to_add, list):
            for tag in tags_to_add:
                if tag not in final_tags:
                    final_tags.append(tag)
        return final_tags
 async def add_litellm_data_to_request(  # noqa: PLR0915
    data: dict,
@ -442,12 +466,10 @@ async def add_litellm_data_to_request(  # noqa: PLR0915
    ## TEAM-LEVEL SPEND LOGS/TAGS
    team_metadata = user_api_key_dict.team_metadata or {}
    if "tags" in team_metadata and team_metadata["tags"] is not None:
-        if "tags" in data[_metadata_variable_name] and isinstance(
+        data[_metadata_variable_name]["tags"] = LiteLLMProxyRequestSetup._merge_tags(
-            data[_metadata_variable_name]["tags"], list
+            request_tags=data[_metadata_variable_name].get("tags"),
-        ):
+            tags_to_add=team_metadata["tags"],
-            data[_metadata_variable_name]["tags"].extend(team_metadata["tags"])
+        )
        else:
            data[_metadata_variable_name]["tags"] = team_metadata["tags"]
    if "spend_logs_metadata" in team_metadata and isinstance(
        team_metadata["spend_logs_metadata"], dict
    ):
--- a/litellm/proxy/management_endpoints/internal_user_endpoints.py
+++ b/litellm/proxy/management_endpoints/internal_user_endpoints.py
@ -32,6 +32,7 @@ from litellm.proxy.auth.user_api_key_auth import user_api_key_auth
 from litellm.proxy.management_endpoints.key_management_endpoints import (
    duration_in_seconds,
    generate_key_helper_fn,
    prepare_metadata_fields,
 )
 from litellm.proxy.management_helpers.utils import (
    add_new_member,
@ -42,7 +43,7 @@ from litellm.proxy.utils import handle_exception_on_proxy
 router = APIRouter()
-def _update_internal_user_params(data_json: dict, data: NewUserRequest) -> dict:
+def _update_internal_new_user_params(data_json: dict, data: NewUserRequest) -> dict:
    if "user_id" in data_json and data_json["user_id"] is None:
        data_json["user_id"] = str(uuid.uuid4())
    auto_create_key = data_json.pop("auto_create_key", True)
@ -145,7 +146,7 @@ async def new_user(
    from litellm.proxy.proxy_server import general_settings, proxy_logging_obj
    data_json = data.json()  # type: ignore
-    data_json = _update_internal_user_params(data_json, data)
+    data_json = _update_internal_new_user_params(data_json, data)
    response = await generate_key_helper_fn(request_type="user", **data_json)
    # Admin UI Logic
@ -438,6 +439,52 @@ async def user_info(  # noqa: PLR0915
        raise handle_exception_on_proxy(e)
 def _update_internal_user_params(data_json: dict, data: UpdateUserRequest) -> dict:
    non_default_values = {}
    for k, v in data_json.items():
        if (
            v is not None
            and v
            not in (
                [],
                {},
                0,
            )
            and k not in LiteLLM_ManagementEndpoint_MetadataFields
        ):  # models default to [], spend defaults to 0, we should not reset these values
            non_default_values[k] = v
    is_internal_user = False
    if data.user_role == LitellmUserRoles.INTERNAL_USER:
        is_internal_user = True
    if "budget_duration" in non_default_values:
        duration_s = duration_in_seconds(duration=non_default_values["budget_duration"])
        user_reset_at = datetime.now(timezone.utc) + timedelta(seconds=duration_s)
        non_default_values["budget_reset_at"] = user_reset_at
    if "max_budget" not in non_default_values:
        if (
            is_internal_user and litellm.max_internal_user_budget is not None
        ):  # applies internal user limits, if user role updated
            non_default_values["max_budget"] = litellm.max_internal_user_budget
    if (
        "budget_duration" not in non_default_values
    ):  # applies internal user limits, if user role updated
        if is_internal_user and litellm.internal_user_budget_duration is not None:
            non_default_values["budget_duration"] = (
                litellm.internal_user_budget_duration
            )
            duration_s = duration_in_seconds(
                duration=non_default_values["budget_duration"]
            )
            user_reset_at = datetime.now(timezone.utc) + timedelta(seconds=duration_s)
            non_default_values["budget_reset_at"] = user_reset_at
    return non_default_values
@router.post(
    "/user/update",
    tags=["Internal User management"],
@ -459,7 +506,8 @@ async def user_update(
        "user_id": "test-litellm-user-4",
        "user_role": "proxy_admin_viewer"
    }'
-
+    ```
    Parameters:
        - user_id: Optional[str] - Specify a user id. If not set, a unique id will be generated.
        - user_email: Optional[str] - Specify a user email.
@ -491,7 +539,7 @@ async def user_update(
        - duration: Optional[str] - [NOT IMPLEMENTED].
        - key_alias: Optional[str] - [NOT IMPLEMENTED].
-    ```
+    
    """
    from litellm.proxy.proxy_server import prisma_client
@ -502,46 +550,21 @@ async def user_update(
            raise Exception("Not connected to DB!")
        # get non default values for key
-        non_default_values = {}
+        non_default_values = _update_internal_user_params(
-        for k, v in data_json.items():
+            data_json=data_json, data=data
-            if v is not None and v not in (
+        )
                [],
                {},
                0,
            ):  # models default to [], spend defaults to 0, we should not reset these values
                non_default_values[k] = v
-        is_internal_user = False
+        existing_user_row = await prisma_client.get_data(
-        if data.user_role == LitellmUserRoles.INTERNAL_USER:
+            user_id=data.user_id, table_name="user", query_type="find_unique"
-            is_internal_user = True
+        )
-        if "budget_duration" in non_default_values:
+        existing_metadata = existing_user_row.metadata if existing_user_row else {}
            duration_s = duration_in_seconds(
                duration=non_default_values["budget_duration"]
            )
            user_reset_at = datetime.now(timezone.utc) + timedelta(seconds=duration_s)
            non_default_values["budget_reset_at"] = user_reset_at
-        if "max_budget" not in non_default_values:
+        non_default_values = prepare_metadata_fields(
-            if (
+            data=data,
-                is_internal_user and litellm.max_internal_user_budget is not None
+            non_default_values=non_default_values,
-            ):  # applies internal user limits, if user role updated
+            existing_metadata=existing_metadata or {},
-                non_default_values["max_budget"] = litellm.max_internal_user_budget
+        )
        if (
            "budget_duration" not in non_default_values
        ):  # applies internal user limits, if user role updated
            if is_internal_user and litellm.internal_user_budget_duration is not None:
                non_default_values["budget_duration"] = (
                    litellm.internal_user_budget_duration
                )
                duration_s = duration_in_seconds(
                    duration=non_default_values["budget_duration"]
                )
                user_reset_at = datetime.now(timezone.utc) + timedelta(
                    seconds=duration_s
                )
                non_default_values["budget_reset_at"] = user_reset_at
        ## ADD USER, IF NEW ##
        verbose_proxy_logger.debug("/user/update: Received data = %s", data)
--- a/litellm/proxy/management_endpoints/key_management_endpoints.py
+++ b/litellm/proxy/management_endpoints/key_management_endpoints.py
@ -17,7 +17,7 @@ import secrets
 import traceback
 import uuid
 from datetime import datetime, timedelta, timezone
-from typing import List, Optional, Tuple
+from typing import List, Optional, Tuple, cast
 import fastapi
 from fastapi import APIRouter, Depends, Header, HTTPException, Query, Request, status
@ -29,6 +29,7 @@ from litellm.proxy.auth.auth_checks import (
    _cache_key_object,
    _delete_cache_key_object,
    get_key_object,
    get_team_object,
 )
 from litellm.proxy.auth.user_api_key_auth import user_api_key_auth
 from litellm.proxy.hooks.key_management_event_hooks import KeyManagementEventHooks
@ -46,7 +47,19 @@ def _is_team_key(data: GenerateKeyRequest):
    return data.team_id is not None
 def _get_user_in_team(
    team_table: LiteLLM_TeamTableCachedObj, user_id: Optional[str]
 ) -> Optional[Member]:
    if user_id is None:
        return None
    for member in team_table.members_with_roles:
        if member.user_id is not None and member.user_id == user_id:
            return member
    return None
 def _team_key_generation_team_member_check(
    team_table: LiteLLM_TeamTableCachedObj,
    user_api_key_dict: UserAPIKeyAuth,
    team_key_generation: Optional[TeamUIKeyGenerationConfig],
 ):
@ -56,17 +69,19 @@ def _team_key_generation_team_member_check(
    ):
        return True
-    if user_api_key_dict.team_member is None:
+    user_in_team = _get_user_in_team(
        team_table=team_table, user_id=user_api_key_dict.user_id
    )
    if user_in_team is None:
        raise HTTPException(
            status_code=400,
-            detail=f"User not assigned to team. Got team_member={user_api_key_dict.team_member}",
+            detail=f"User={user_api_key_dict.user_id} not assigned to team={team_table.team_id}",
        )
-    team_member_role = user_api_key_dict.team_member.role
+    if user_in_team.role not in team_key_generation["allowed_team_member_roles"]:
    if team_member_role not in team_key_generation["allowed_team_member_roles"]:
        raise HTTPException(
            status_code=400,
-            detail=f"Team member role {team_member_role} not in allowed_team_member_roles={litellm.key_generation_settings['team_key_generation']['allowed_team_member_roles']}",  # type: ignore
+            detail=f"Team member role {user_in_team.role} not in allowed_team_member_roles={team_key_generation['allowed_team_member_roles']}",
        )
    return True
@ -88,7 +103,9 @@ def _key_generation_required_param_check(
 def _team_key_generation_check(
-    user_api_key_dict: UserAPIKeyAuth, data: GenerateKeyRequest
+    team_table: LiteLLM_TeamTableCachedObj,
    user_api_key_dict: UserAPIKeyAuth,
    data: GenerateKeyRequest,
 ):
    if (
        litellm.key_generation_settings is None
@ -99,7 +116,8 @@ def _team_key_generation_check(
    _team_key_generation = litellm.key_generation_settings["team_key_generation"]  # type: ignore
    _team_key_generation_team_member_check(
-        user_api_key_dict,
+        team_table=team_table,
        user_api_key_dict=user_api_key_dict,
        team_key_generation=_team_key_generation,
    )
    _key_generation_required_param_check(
@ -155,7 +173,9 @@ def _personal_key_generation_check(
 def key_generation_check(
-    user_api_key_dict: UserAPIKeyAuth, data: GenerateKeyRequest
+    team_table: Optional[LiteLLM_TeamTableCachedObj],
    user_api_key_dict: UserAPIKeyAuth,
    data: GenerateKeyRequest,
 ) -> bool:
    """
    Check if admin has restricted key creation to certain roles for teams or individuals
@ -170,8 +190,15 @@ def key_generation_check(
    is_team_key = _is_team_key(data=data)
    if is_team_key:
        if team_table is None:
            raise HTTPException(
                status_code=400,
                detail=f"Unable to find team object in database. Team ID: {data.team_id}",
            )
        return _team_key_generation_check(
-            user_api_key_dict=user_api_key_dict, data=data
+            team_table=team_table,
            user_api_key_dict=user_api_key_dict,
            data=data,
        )
    else:
        return _personal_key_generation_check(
@ -254,6 +281,7 @@ async def generate_key_fn(  # noqa: PLR0915
            litellm_proxy_admin_name,
            prisma_client,
            proxy_logging_obj,
            user_api_key_cache,
            user_custom_key_generate,
        )
@ -271,7 +299,20 @@ async def generate_key_fn(  # noqa: PLR0915
                    status_code=status.HTTP_403_FORBIDDEN, detail=message
                )
        elif litellm.key_generation_settings is not None:
-            key_generation_check(user_api_key_dict=user_api_key_dict, data=data)
+            if data.team_id is None:
                team_table: Optional[LiteLLM_TeamTableCachedObj] = None
            else:
                team_table = await get_team_object(
                    team_id=data.team_id,
                    prisma_client=prisma_client,
                    user_api_key_cache=user_api_key_cache,
                    parent_otel_span=user_api_key_dict.parent_otel_span,
                )
            key_generation_check(
                team_table=team_table,
                user_api_key_dict=user_api_key_dict,
                data=data,
            )
        # check if user set default key/generate params on config.yaml
        if litellm.default_key_generate_params is not None:
            for elem in data:
@ -353,7 +394,8 @@ async def generate_key_fn(  # noqa: PLR0915
                }
            )
            _budget_id = getattr(_budget, "budget_id", None)
-        data_json = data.json()  # type: ignore
+        data_json = data.model_dump(exclude_unset=True, exclude_none=True)  # type: ignore
        # if we get max_budget passed to /key/generate, then use it as key_max_budget. Since generate_key_helper_fn is used to make new users
        if "max_budget" in data_json:
            data_json["key_max_budget"] = data_json.pop("max_budget", None)
@ -379,6 +421,11 @@ async def generate_key_fn(  # noqa: PLR0915
            data_json.pop("tags")
        await _enforce_unique_key_alias(
            key_alias=data_json.get("key_alias", None),
            prisma_client=prisma_client,
        )
        response = await generate_key_helper_fn(
            request_type="key", **data_json, table_name="key"
        )
@ -406,12 +453,52 @@ async def generate_key_fn(  # noqa: PLR0915
        raise handle_exception_on_proxy(e)
 def prepare_metadata_fields(
    data: BaseModel, non_default_values: dict, existing_metadata: dict
 ) -> dict:
    """
    Check LiteLLM_ManagementEndpoint_MetadataFields (proxy/_types.py) for fields that are allowed to be updated
    """
    if "metadata" not in non_default_values:  # allow user to set metadata to none
        non_default_values["metadata"] = existing_metadata.copy()
    casted_metadata = cast(dict, non_default_values["metadata"])
    data_json = data.model_dump(exclude_unset=True, exclude_none=True)
    try:
        for k, v in data_json.items():
            if k == "model_tpm_limit" or k == "model_rpm_limit":
                if k not in casted_metadata or casted_metadata[k] is None:
                    casted_metadata[k] = {}
                casted_metadata[k].update(v)
            if k == "tags" or k == "guardrails":
                if k not in casted_metadata or casted_metadata[k] is None:
                    casted_metadata[k] = []
                seen = set(casted_metadata[k])
                casted_metadata[k].extend(
                    x for x in v if x not in seen and not seen.add(x)  # type: ignore
                )  # prevent duplicates from being added + maintain initial order
    except Exception as e:
        verbose_proxy_logger.exception(
            "litellm.proxy.proxy_server.prepare_metadata_fields(): Exception occured - {}".format(
                str(e)
            )
        )
    non_default_values["metadata"] = casted_metadata
    return non_default_values
 def prepare_key_update_data(
    data: Union[UpdateKeyRequest, RegenerateKeyRequest], existing_key_row
 ):
    data_json: dict = data.model_dump(exclude_unset=True)
    data_json.pop("key", None)
-    _metadata_fields = ["model_rpm_limit", "model_tpm_limit", "guardrails"]
+    _metadata_fields = ["model_rpm_limit", "model_tpm_limit", "guardrails", "tags"]
    non_default_values = {}
    for k, v in data_json.items():
        if k in _metadata_fields:
@ -435,24 +522,13 @@ def prepare_key_update_data(
            duration_s = duration_in_seconds(duration=budget_duration)
            key_reset_at = datetime.now(timezone.utc) + timedelta(seconds=duration_s)
            non_default_values["budget_reset_at"] = key_reset_at
            non_default_values["budget_duration"] = budget_duration
    _metadata = existing_key_row.metadata or {}
-    if data.model_tpm_limit:
+    non_default_values = prepare_metadata_fields(
-        if "model_tpm_limit" not in _metadata:
+        data=data, non_default_values=non_default_values, existing_metadata=_metadata
-            _metadata["model_tpm_limit"] = {}
+    )
        _metadata["model_tpm_limit"].update(data.model_tpm_limit)
        non_default_values["metadata"] = _metadata
    if data.model_rpm_limit:
        if "model_rpm_limit" not in _metadata:
            _metadata["model_rpm_limit"] = {}
        _metadata["model_rpm_limit"].update(data.model_rpm_limit)
        non_default_values["metadata"] = _metadata
    if data.guardrails:
        _metadata["guardrails"] = data.guardrails
        non_default_values["metadata"] = _metadata
    return non_default_values
@ -544,6 +620,12 @@ async def update_key_fn(
            data=data, existing_key_row=existing_key_row
        )
        await _enforce_unique_key_alias(
            key_alias=non_default_values.get("key_alias", None),
            prisma_client=prisma_client,
            existing_key_token=existing_key_row.token,
        )
        response = await prisma_client.update_data(
            token=key, data={**non_default_values, "token": key}
        )
@ -871,11 +953,11 @@ async def generate_key_helper_fn(  # noqa: PLR0915
    request_type: Literal[
        "user", "key"
    ],  # identifies if this request is from /user/new or /key/generate
-    duration: Optional[str],
+    duration: Optional[str] = None,
-    models: list,
+    models: list = [],
-    aliases: dict,
+    aliases: dict = {},
-    config: dict,
+    config: dict = {},
-    spend: float,
+    spend: float = 0.0,
    key_max_budget: Optional[float] = None,  # key_max_budget is used to Budget Per key
    key_budget_duration: Optional[str] = None,
    budget_id: Optional[float] = None,  # budget id <-> LiteLLM_BudgetTable
@ -904,8 +986,8 @@ async def generate_key_helper_fn(  # noqa: PLR0915
    allowed_cache_controls: Optional[list] = [],
    permissions: Optional[dict] = {},
    model_max_budget: Optional[dict] = {},
-    model_rpm_limit: Optional[dict] = {},
+    model_rpm_limit: Optional[dict] = None,
-    model_tpm_limit: Optional[dict] = {},
+    model_tpm_limit: Optional[dict] = None,
    guardrails: Optional[list] = None,
    teams: Optional[list] = None,
    organization_id: Optional[str] = None,
@ -1842,3 +1924,38 @@ async def test_key_logging(
            status="healthy",
            details=f"No logger exceptions triggered, system is healthy. Manually check if logs were sent to {logging_callbacks} ",
        )
 async def _enforce_unique_key_alias(
    key_alias: Optional[str],
    prisma_client: Any,
    existing_key_token: Optional[str] = None,
 ) -> None:
    """
    Helper to enforce unique key aliases across all keys.
    Args:
        key_alias (Optional[str]): The key alias to check
        prisma_client (Any): Prisma client instance
        existing_key_token (Optional[str]): ID of existing key being updated, to exclude from uniqueness check
            (The Admin UI passes key_alias, in all Edit key requests. So we need to be sure that if we find a key with the same alias, it's not the same key we're updating)
    Raises:
        ProxyException: If key alias already exists on a different key
    """
    if key_alias is not None and prisma_client is not None:
        where_clause: dict[str, Any] = {"key_alias": key_alias}
        if existing_key_token:
            # Exclude the current key from the uniqueness check
            where_clause["NOT"] = {"token": existing_key_token}
        existing_key = await prisma_client.db.litellm_verificationtoken.find_first(
            where=where_clause
        )
        if existing_key is not None:
            raise ProxyException(
                message=f"Key with alias '{key_alias}' already exists. Unique key aliases across all keys are required.",
                type=ProxyErrorTypes.bad_request_error,
                param="key_alias",
                code=status.HTTP_400_BAD_REQUEST,
            )
--- a/litellm/proxy/management_endpoints/team_endpoints.py
+++ b/litellm/proxy/management_endpoints/team_endpoints.py
@ -547,6 +547,7 @@ async def team_member_add(
        parent_otel_span=None,
        proxy_logging_obj=proxy_logging_obj,
        check_cache_only=False,
        check_db_only=True,
    )
    if existing_team_row is None:
        raise HTTPException(
@ -1366,6 +1367,7 @@ async def list_team(
            """.format(
                team.team_id, team.model_dump(), str(e)
            )
-            raise HTTPException(status_code=400, detail={"error": team_exception})
+            verbose_proxy_logger.exception(team_exception)
            continue
    return returned_responses
--- a/litellm/proxy/model_config.yaml
+++ b/litellm/proxy/model_config.yaml
@ -0,0 +1,10 @@
 model_list:
  - model_name: gpt-4o
    litellm_params:
      model: openai/gpt-4o
      api_base: https://exampleopenaiendpoint-production.up.railway.app/
  - model_name: fake-anthropic-endpoint
    litellm_params:
      model: anthropic/fake
      api_base: https://exampleanthropicendpoint-production.up.railway.app/
--- a/litellm/proxy/pass_through_endpoints/llm_passthrough_endpoints.py
+++ b/litellm/proxy/pass_through_endpoints/llm_passthrough_endpoints.py
@ -54,12 +54,19 @@ def create_request_copy(request: Request):
    }
-@router.api_route("/gemini/{endpoint:path}", methods=["GET", "POST", "PUT", "DELETE"])
+@router.api_route(
    "/gemini/{endpoint:path}",
    methods=["GET", "POST", "PUT", "DELETE", "PATCH"],
    tags=["Google AI Studio Pass-through", "pass-through"],
 )
 async def gemini_proxy_route(
    endpoint: str,
    request: Request,
    fastapi_response: Response,
 ):
    """
    [Docs](https://docs.litellm.ai/docs/pass_through/google_ai_studio)
    """
    ## CHECK FOR LITELLM API KEY IN THE QUERY PARAMS - ?..key=LITELLM_API_KEY
    google_ai_studio_api_key = request.query_params.get("key") or request.headers.get(
        "x-goog-api-key"
@ -113,13 +120,20 @@ async def gemini_proxy_route(
    return received_value
-@router.api_route("/cohere/{endpoint:path}", methods=["GET", "POST", "PUT", "DELETE"])
+@router.api_route(
    "/cohere/{endpoint:path}",
    methods=["GET", "POST", "PUT", "DELETE", "PATCH"],
    tags=["Cohere Pass-through", "pass-through"],
 )
 async def cohere_proxy_route(
    endpoint: str,
    request: Request,
    fastapi_response: Response,
    user_api_key_dict: UserAPIKeyAuth = Depends(user_api_key_auth),
 ):
    """
    [Docs](https://docs.litellm.ai/docs/pass_through/cohere)
    """
    base_target_url = "https://api.cohere.com"
    encoded_endpoint = httpx.URL(endpoint).path
@ -156,7 +170,9 @@ async def cohere_proxy_route(
@router.api_route(
-    "/anthropic/{endpoint:path}", methods=["GET", "POST", "PUT", "DELETE"]
+    "/anthropic/{endpoint:path}",
    methods=["GET", "POST", "PUT", "DELETE", "PATCH"],
    tags=["Anthropic Pass-through", "pass-through"],
 )
 async def anthropic_proxy_route(
    endpoint: str,
@ -164,6 +180,9 @@ async def anthropic_proxy_route(
    fastapi_response: Response,
    user_api_key_dict: UserAPIKeyAuth = Depends(user_api_key_auth),
 ):
    """
    [Docs](https://docs.litellm.ai/docs/anthropic_completion)
    """
    base_target_url = "https://api.anthropic.com"
    encoded_endpoint = httpx.URL(endpoint).path
@ -203,13 +222,20 @@ async def anthropic_proxy_route(
    return received_value
-@router.api_route("/bedrock/{endpoint:path}", methods=["GET", "POST", "PUT", "DELETE"])
+@router.api_route(
    "/bedrock/{endpoint:path}",
    methods=["GET", "POST", "PUT", "DELETE", "PATCH"],
    tags=["Bedrock Pass-through", "pass-through"],
 )
 async def bedrock_proxy_route(
    endpoint: str,
    request: Request,
    fastapi_response: Response,
    user_api_key_dict: UserAPIKeyAuth = Depends(user_api_key_auth),
 ):
    """
    [Docs](https://docs.litellm.ai/docs/pass_through/bedrock)
    """
    create_request_copy(request)
    try:
@ -277,13 +303,22 @@ async def bedrock_proxy_route(
    return received_value
-@router.api_route("/azure/{endpoint:path}", methods=["GET", "POST", "PUT", "DELETE"])
+@router.api_route(
    "/azure/{endpoint:path}",
    methods=["GET", "POST", "PUT", "DELETE", "PATCH"],
    tags=["Azure Pass-through", "pass-through"],
 )
 async def azure_proxy_route(
    endpoint: str,
    request: Request,
    fastapi_response: Response,
    user_api_key_dict: UserAPIKeyAuth = Depends(user_api_key_auth),
 ):
    """
    Call any azure endpoint using the proxy.
    Just use `{PROXY_BASE_URL}/azure/{endpoint:path}`
    """
    base_target_url = get_secret_str(secret_name="AZURE_API_BASE")
    if base_target_url is None:
        raise Exception(
--- a/litellm/proxy/pass_through_endpoints/pass_through_endpoints.py
+++ b/litellm/proxy/pass_through_endpoints/pass_through_endpoints.py
@ -529,16 +529,18 @@ async def pass_through_request(  # noqa: PLR0915
        response_body: Optional[dict] = get_response_body(response)
        passthrough_logging_payload["response_body"] = response_body
        end_time = datetime.now()
-        await pass_through_endpoint_logging.pass_through_async_success_handler(
+        asyncio.create_task(
-            httpx_response=response,
+            pass_through_endpoint_logging.pass_through_async_success_handler(
-            response_body=response_body,
+                httpx_response=response,
-            url_route=str(url),
+                response_body=response_body,
-            result="",
+                url_route=str(url),
-            start_time=start_time,
+                result="",
-            end_time=end_time,
+                start_time=start_time,
-            logging_obj=logging_obj,
+                end_time=end_time,
-            cache_hit=False,
+                logging_obj=logging_obj,
-            **kwargs,
+                cache_hit=False,
                **kwargs,
            )
        )
        return Response(
@ -607,6 +609,11 @@ def _init_kwargs_for_pass_through_endpoint(
 def _update_metadata_with_tags_in_header(request: Request, metadata: dict) -> dict:
    """
    If tags are in the request headers, add them to the metadata
    Used for google and vertex JS SDKs
    """
    _tags = request.headers.get("tags")
    if _tags:
        metadata["tags"] = _tags.split(",")
--- a/litellm/proxy/pass_through_endpoints/streaming_handler.py
+++ b/litellm/proxy/pass_through_endpoints/streaming_handler.py
@ -58,15 +58,17 @@ class PassThroughStreamingHandler:
            # After all chunks are processed, handle post-processing
            end_time = datetime.now()
-            await PassThroughStreamingHandler._route_streaming_logging_to_handler(
+            asyncio.create_task(
-                litellm_logging_obj=litellm_logging_obj,
+                PassThroughStreamingHandler._route_streaming_logging_to_handler(
-                passthrough_success_handler_obj=passthrough_success_handler_obj,
+                    litellm_logging_obj=litellm_logging_obj,
-                url_route=url_route,
+                    passthrough_success_handler_obj=passthrough_success_handler_obj,
-                request_body=request_body or {},
+                    url_route=url_route,
-                endpoint_type=endpoint_type,
+                    request_body=request_body or {},
-                start_time=start_time,
+                    endpoint_type=endpoint_type,
-                raw_bytes=raw_bytes,
+                    start_time=start_time,
-                end_time=end_time,
+                    raw_bytes=raw_bytes,
                    end_time=end_time,
                )
            )
        except Exception as e:
            verbose_proxy_logger.error(f"Error in chunk_processor: {str(e)}")
@ -108,9 +110,9 @@ class PassThroughStreamingHandler:
                all_chunks=all_chunks,
                end_time=end_time,
            )
-            standard_logging_response_object = anthropic_passthrough_logging_handler_result[
+            standard_logging_response_object = (
-                "result"
+                anthropic_passthrough_logging_handler_result["result"]
-            ]
+            )
            kwargs = anthropic_passthrough_logging_handler_result["kwargs"]
        elif endpoint_type == EndpointType.VERTEX_AI:
            vertex_passthrough_logging_handler_result = (
@ -125,9 +127,9 @@ class PassThroughStreamingHandler:
                    end_time=end_time,
                )
            )
-            standard_logging_response_object = vertex_passthrough_logging_handler_result[
+            standard_logging_response_object = (
-                "result"
+                vertex_passthrough_logging_handler_result["result"]
-            ]
+            )
            kwargs = vertex_passthrough_logging_handler_result["kwargs"]
        if standard_logging_response_object is None:
@ -168,4 +170,4 @@ class PassThroughStreamingHandler:
        # Split by newlines and filter out empty lines
        lines = [line.strip() for line in combined_str.split("\n") if line.strip()]
-        return lines
+        return lines
--- a/litellm/proxy/pass_through_endpoints/success_handler.py
+++ b/litellm/proxy/pass_through_endpoints/success_handler.py
@ -18,6 +18,7 @@ from litellm.llms.vertex_ai_and_google_ai_studio.gemini.vertex_and_google_ai_stu
 from litellm.proxy._types import PassThroughEndpointLoggingResultValues
 from litellm.proxy.auth.user_api_key_auth import user_api_key_auth
 from litellm.types.utils import StandardPassThroughResponseObject
 from litellm.utils import executor as thread_pool_executor
 from .llm_provider_handlers.anthropic_passthrough_logging_handler import (
    AnthropicPassthroughLoggingHandler,
@ -93,15 +94,16 @@ class PassThroughEndpointLogging:
            standard_logging_response_object = StandardPassThroughResponseObject(
                response=httpx_response.text
            )
-        threading.Thread(
+        thread_pool_executor.submit(
-            target=logging_obj.success_handler,
+            logging_obj.success_handler,
            args=(
                standard_logging_response_object,
                start_time,
                end_time,
                cache_hit,
            ),
-        ).start()
+        )
        await logging_obj.async_success_handler(
            result=(
                json.dumps(result)
--- a/litellm/proxy/proxy_config.yaml
+++ b/litellm/proxy/proxy_config.yaml
@ -1,24 +1,5 @@
-model_list:
+include:
-  - model_name: gpt-4o
+  - model_config.yaml
    litellm_params:
      model: openai/gpt-4o
      api_base: https://exampleopenaiendpoint-production.up.railway.app/
  - model_name: fake-anthropic-endpoint
    litellm_params:
      model: anthropic/fake
      api_base: https://exampleanthropicendpoint-production.up.railway.app/
 router_settings:
  provider_budget_config: 
    openai: 
      budget_limit: 0.3 # float of $ value budget for time period
      time_period: 1d # can be 1d, 2d, 30d 
    anthropic:
      budget_limit: 5
      time_period: 1d
  redis_host: os.environ/REDIS_HOST
  redis_port: os.environ/REDIS_PORT
  redis_password: os.environ/REDIS_PASSWORD
 litellm_settings:
-  callbacks: ["prometheus"]
+  callbacks: ["datadog"] 
--- a/litellm/proxy/proxy_server.py
+++ b/litellm/proxy/proxy_server.py
@ -134,7 +134,10 @@ from litellm.proxy.auth.model_checks import (
    get_key_models,
    get_team_models,
 )
-from litellm.proxy.auth.user_api_key_auth import user_api_key_auth
+from litellm.proxy.auth.user_api_key_auth import (
    user_api_key_auth,
    user_api_key_auth_websocket,
 )
 ## Import All Misc routes here ##
 from litellm.proxy.caching_routes import router as caching_router
@ -173,6 +176,7 @@ from litellm.proxy.health_endpoints._health_endpoints import router as health_ro
 from litellm.proxy.hooks.prompt_injection_detection import (
    _OPTIONAL_PromptInjectionDetection,
 )
 from litellm.proxy.hooks.proxy_failure_handler import _PROXY_failure_handler
 from litellm.proxy.litellm_pre_call_utils import add_litellm_data_to_request
 from litellm.proxy.management_endpoints.customer_endpoints import (
    router as customer_router,
@ -526,14 +530,6 @@ db_writer_client: Optional[HTTPHandler] = None
 ### logger ###
 def _get_pydantic_json_dict(pydantic_obj: BaseModel) -> dict:
    try:
        return pydantic_obj.model_dump()  # type: ignore
    except Exception:
        # if using pydantic v1
        return pydantic_obj.dict()
 def get_custom_headers(
    *,
    user_api_key_dict: UserAPIKeyAuth,
@ -687,68 +683,6 @@ def cost_tracking():
                litellm._async_success_callback.append(_PROXY_track_cost_callback)  # type: ignore
 async def _PROXY_failure_handler(
    kwargs,  # kwargs to completion
    completion_response: litellm.ModelResponse,  # response from completion
    start_time=None,
    end_time=None,  # start/end time for completion
 ):
    global prisma_client
    if prisma_client is not None:
        verbose_proxy_logger.debug(
            "inside _PROXY_failure_handler kwargs=", extra=kwargs
        )
        _exception = kwargs.get("exception")
        _exception_type = _exception.__class__.__name__
        _model = kwargs.get("model", None)
        _optional_params = kwargs.get("optional_params", {})
        _optional_params = copy.deepcopy(_optional_params)
        for k, v in _optional_params.items():
            v = str(v)
            v = v[:100]
        _status_code = "500"
        try:
            _status_code = str(_exception.status_code)
        except Exception:
            # Don't let this fail logging the exception to the dB
            pass
        _litellm_params = kwargs.get("litellm_params", {}) or {}
        _metadata = _litellm_params.get("metadata", {}) or {}
        _model_id = _metadata.get("model_info", {}).get("id", "")
        _model_group = _metadata.get("model_group", "")
        api_base = litellm.get_api_base(model=_model, optional_params=_litellm_params)
        _exception_string = str(_exception)
        error_log = LiteLLM_ErrorLogs(
            request_id=str(uuid.uuid4()),
            model_group=_model_group,
            model_id=_model_id,
            litellm_model_name=kwargs.get("model"),
            request_kwargs=_optional_params,
            api_base=api_base,
            exception_type=_exception_type,
            status_code=_status_code,
            exception_string=_exception_string,
            startTime=kwargs.get("start_time"),
            endTime=kwargs.get("end_time"),
        )
        # helper function to convert to dict on pydantic v2 & v1
        error_log_dict = _get_pydantic_json_dict(error_log)
        error_log_dict["request_kwargs"] = json.dumps(error_log_dict["request_kwargs"])
        await prisma_client.db.litellm_errorlogs.create(
            data=error_log_dict  # type: ignore
        )
    pass
@log_db_metrics
 async def _PROXY_track_cost_callback(
    kwargs,  # kwargs to completion
@ -1377,6 +1311,16 @@ class ProxyConfig:
        _, file_extension = os.path.splitext(config_file_path)
        return file_extension.lower() == ".yaml" or file_extension.lower() == ".yml"
    def _load_yaml_file(self, file_path: str) -> dict:
        """
        Load and parse a YAML file
        """
        try:
            with open(file_path, "r") as file:
                return yaml.safe_load(file) or {}
        except Exception as e:
            raise Exception(f"Error loading yaml file {file_path}: {str(e)}")
    async def _get_config_from_file(
        self, config_file_path: Optional[str] = None
    ) -> dict:
@ -1407,6 +1351,51 @@ class ProxyConfig:
                "litellm_settings": {},
            }
        # Process includes
        config = self._process_includes(
            config=config, base_dir=os.path.dirname(os.path.abspath(file_path or ""))
        )
        verbose_proxy_logger.debug(f"loaded config={json.dumps(config, indent=4)}")
        return config
    def _process_includes(self, config: dict, base_dir: str) -> dict:
        """
        Process includes by appending their contents to the main config
        Handles nested config.yamls with `include` section
        Example config: This will get the contents from files in `include` and append it
        ```yaml
        include:
            - model_config.yaml
        litellm_settings:
            callbacks: ["prometheus"]
        ```
        """
        if "include" not in config:
            return config
        if not isinstance(config["include"], list):
            raise ValueError("'include' must be a list of file paths")
        # Load and append all included files
        for include_file in config["include"]:
            file_path = os.path.join(base_dir, include_file)
            if not os.path.exists(file_path):
                raise FileNotFoundError(f"Included file not found: {file_path}")
            included_config = self._load_yaml_file(file_path)
            # Simply update/extend the main config with included config
            for key, value in included_config.items():
                if isinstance(value, list) and key in config:
                    config[key].extend(value)
                else:
                    config[key] = value
        # Remove the include directive
        del config["include"]
        return config
    async def save_config(self, new_config: dict):
@ -4339,7 +4328,11 @@ from litellm import _arealtime
@app.websocket("/v1/realtime")
-async def websocket_endpoint(websocket: WebSocket, model: str):
+async def websocket_endpoint(
    websocket: WebSocket,
    model: str,
    user_api_key_dict=Depends(user_api_key_auth_websocket),
 ):
    import websockets
    await websocket.accept()
@ -5663,11 +5656,11 @@ async def anthropic_response(  # noqa: PLR0915
    user_api_key_dict: UserAPIKeyAuth = Depends(user_api_key_auth),
 ):
    """
-    This is a BETA endpoint that calls 100+ LLMs in the anthropic format.
+    🚨 DEPRECATED ENDPOINT🚨
-    To do a simple pass-through for anthropic, do `{PROXY_BASE_URL}/anthropic/v1/messages`
+    Use `{PROXY_BASE_URL}/anthropic/v1/messages` instead - [Docs](https://docs.litellm.ai/docs/anthropic_completion).
-    Docs - https://docs.litellm.ai/docs/anthropic_completion
+    This was a BETA endpoint that calls 100+ LLMs in the anthropic format.
    """
    from litellm import adapter_completion
    from litellm.adapters.anthropic_adapter import anthropic_adapter
--- a/litellm/proxy/route_llm_request.py
+++ b/litellm/proxy/route_llm_request.py
@ -86,7 +86,6 @@ async def route_request(
        else:
            models = [model.strip() for model in data.pop("model").split(",")]
            return llm_router.abatch_completion(models=models, **data)
    elif llm_router is not None:
        if (
            data["model"] in router_model_names
@ -113,6 +112,9 @@ async def route_request(
                or len(llm_router.pattern_router.patterns) > 0
            ):
                return getattr(llm_router, f"{route_type}")(**data)
            elif route_type == "amoderation":
                # moderation endpoint does not require `model` parameter
                return getattr(llm_router, f"{route_type}")(**data)
    elif user_model is not None:
        return getattr(litellm, f"{route_type}")(**data)
--- a/litellm/proxy/utils.py
+++ b/litellm/proxy/utils.py
@ -854,6 +854,20 @@ class ProxyLogging:
                    ),
                ).start()
        await self._run_post_call_failure_hook_custom_loggers(
            original_exception=original_exception,
            request_data=request_data,
            user_api_key_dict=user_api_key_dict,
        )
        return
    async def _run_post_call_failure_hook_custom_loggers(
        self,
        original_exception: Exception,
        request_data: dict,
        user_api_key_dict: UserAPIKeyAuth,
    ):
        for callback in litellm.callbacks:
            try:
                _callback: Optional[CustomLogger] = None
@ -872,7 +886,38 @@ class ProxyLogging:
            except Exception as e:
                raise e
-        return
+    async def async_log_proxy_authentication_errors(
        self,
        original_exception: Exception,
        request: Request,
        parent_otel_span: Optional[Any],
        api_key: Optional[str],
    ):
        """
        Handler for Logging Authentication Errors on LiteLLM Proxy
        Why not use post_call_failure_hook?
        - `post_call_failure_hook` calls `litellm_logging_obj.async_failure_handler`. This led to the Exception being logged twice
        What does this handler do?
        - Logs Authentication Errors (like invalid API Key passed) to CustomLogger compatible classes (OTEL, Datadog etc)
            - calls CustomLogger.async_post_call_failure_hook
        """
        user_api_key_dict = UserAPIKeyAuth(
            parent_otel_span=parent_otel_span,
            token=_hash_token_if_needed(token=api_key or ""),
        )
        try:
            request_data = await request.json()
        except json.JSONDecodeError:
            # For GET requests or requests without a JSON body
            request_data = {}
        await self._run_post_call_failure_hook_custom_loggers(
            original_exception=original_exception,
            request_data=request_data,
            user_api_key_dict=user_api_key_dict,
        )
        pass
    async def post_call_success_hook(
        self,
--- a/litellm/proxy/vertex_ai_endpoints/langfuse_endpoints.py
+++ b/litellm/proxy/vertex_ai_endpoints/langfuse_endpoints.py
@ -58,12 +58,21 @@ def create_request_copy(request: Request):
    }
-@router.api_route("/langfuse/{endpoint:path}", methods=["GET", "POST", "PUT", "DELETE"])
+@router.api_route(
    "/langfuse/{endpoint:path}",
    methods=["GET", "POST", "PUT", "DELETE", "PATCH"],
    tags=["Langfuse Pass-through", "pass-through"],
 )
 async def langfuse_proxy_route(
    endpoint: str,
    request: Request,
    fastapi_response: Response,
 ):
    """
    Call Langfuse via LiteLLM proxy. Works with Langfuse SDK.
    [Docs](https://docs.litellm.ai/docs/pass_through/langfuse)
    """
    ## CHECK FOR LITELLM API KEY IN THE QUERY PARAMS - ?..key=LITELLM_API_KEY
    api_key = request.headers.get("Authorization") or ""
--- a/litellm/proxy/vertex_ai_endpoints/vertex_endpoints.py
+++ b/litellm/proxy/vertex_ai_endpoints/vertex_endpoints.py
@ -28,25 +28,54 @@ from litellm.proxy.auth.user_api_key_auth import user_api_key_auth
 from litellm.proxy.pass_through_endpoints.pass_through_endpoints import (
    create_pass_through_route,
 )
 from litellm.secret_managers.main import get_secret_str
 from litellm.types.passthrough_endpoints.vertex_ai import *
 router = APIRouter()
-default_vertex_config = None
+
 default_vertex_config: VertexPassThroughCredentials = VertexPassThroughCredentials()
-def set_default_vertex_config(config):
+def _get_vertex_env_vars() -> VertexPassThroughCredentials:
    """
    Helper to get vertex pass through config from environment variables
    The following environment variables are used:
    - DEFAULT_VERTEXAI_PROJECT (project id)
    - DEFAULT_VERTEXAI_LOCATION (location)
    - DEFAULT_GOOGLE_APPLICATION_CREDENTIALS (path to credentials file)
    """
    return VertexPassThroughCredentials(
        vertex_project=get_secret_str("DEFAULT_VERTEXAI_PROJECT"),
        vertex_location=get_secret_str("DEFAULT_VERTEXAI_LOCATION"),
        vertex_credentials=get_secret_str("DEFAULT_GOOGLE_APPLICATION_CREDENTIALS"),
    )
 def set_default_vertex_config(config: Optional[dict] = None):
    """Sets vertex configuration from provided config and/or environment variables
    Args:
        config (Optional[dict]): Configuration dictionary
        Example: {
            "vertex_project": "my-project-123",
            "vertex_location": "us-central1",
            "vertex_credentials": "os.environ/GOOGLE_CREDS"
        }
    """
    global default_vertex_config
    if config is None:
        return
-    if not isinstance(config, dict):
+    # Initialize config dictionary if None
-        raise ValueError("invalid config, vertex default config must be a dictionary")
+    if config is None:
        default_vertex_config = _get_vertex_env_vars()
        return
    if isinstance(config, dict):
        for key, value in config.items():
            if isinstance(value, str) and value.startswith("os.environ/"):
                config[key] = litellm.get_secret(value)
-    default_vertex_config = config
+    default_vertex_config = VertexPassThroughCredentials(**config)
 def exception_handler(e: Exception):
@ -114,17 +143,25 @@ def construct_target_url(
@router.api_route(
    "/vertex-ai/{endpoint:path}",
-    methods=["GET", "POST", "PUT", "DELETE"],
+    methods=["GET", "POST", "PUT", "DELETE", "PATCH"],
    tags=["Vertex AI Pass-through", "pass-through"],
    include_in_schema=False,
 )
@router.api_route(
-    "/vertex_ai/{endpoint:path}", methods=["GET", "POST", "PUT", "DELETE"]
+    "/vertex_ai/{endpoint:path}",
    methods=["GET", "POST", "PUT", "DELETE", "PATCH"],
    tags=["Vertex AI Pass-through", "pass-through"],
 )
 async def vertex_proxy_route(
    endpoint: str,
    request: Request,
    fastapi_response: Response,
 ):
    """
    Call LiteLLM proxy via Vertex AI SDK.
    [Docs](https://docs.litellm.ai/docs/pass_through/vertex_ai)
    """
    encoded_endpoint = httpx.URL(endpoint).path
    import re
@ -140,7 +177,7 @@ async def vertex_proxy_route(
    vertex_project = None
    vertex_location = None
    # Use headers from the incoming request if default_vertex_config is not set
-    if default_vertex_config is None:
+    if default_vertex_config.vertex_project is None:
        headers = dict(request.headers) or {}
        verbose_proxy_logger.debug(
            "default_vertex_config  not set, incoming request headers %s", headers
@ -153,9 +190,9 @@ async def vertex_proxy_route(
        headers.pop("content-length", None)
        headers.pop("host", None)
    else:
-        vertex_project = default_vertex_config.get("vertex_project")
+        vertex_project = default_vertex_config.vertex_project
-        vertex_location = default_vertex_config.get("vertex_location")
+        vertex_location = default_vertex_config.vertex_location
-        vertex_credentials = default_vertex_config.get("vertex_credentials")
+        vertex_credentials = default_vertex_config.vertex_credentials
        base_target_url = f"https://{vertex_location}-aiplatform.googleapis.com/"
--- a/litellm/router.py
+++ b/litellm/router.py
@ -41,6 +41,7 @@ from typing import (
 import httpx
 import openai
 from openai import AsyncOpenAI
 from pydantic import BaseModel
 from typing_extensions import overload
 import litellm
@ -122,6 +123,7 @@ from litellm.types.router import (
    ModelInfo,
    ProviderBudgetConfigType,
    RetryPolicy,
    RouterCacheEnum,
    RouterErrors,
    RouterGeneralSettings,
    RouterModelGroupAliasItem,
@ -239,7 +241,6 @@ class Router:
        ] = "simple-shuffle",
        routing_strategy_args: dict = {},  # just for latency-based
        provider_budget_config: Optional[ProviderBudgetConfigType] = None,
        semaphore: Optional[asyncio.Semaphore] = None,
        alerting_config: Optional[AlertingConfig] = None,
        router_general_settings: Optional[
            RouterGeneralSettings
@ -315,8 +316,6 @@ class Router:
        from litellm._service_logger import ServiceLogging
        if semaphore:
            self.semaphore = semaphore
        self.set_verbose = set_verbose
        self.debug_level = debug_level
        self.enable_pre_call_checks = enable_pre_call_checks
@ -506,6 +505,14 @@ class Router:
            litellm.success_callback.append(self.sync_deployment_callback_on_success)
        else:
            litellm.success_callback = [self.sync_deployment_callback_on_success]
        if isinstance(litellm._async_failure_callback, list):
            litellm._async_failure_callback.append(
                self.async_deployment_callback_on_failure
            )
        else:
            litellm._async_failure_callback = [
                self.async_deployment_callback_on_failure
            ]
        ## COOLDOWNS ##
        if isinstance(litellm.failure_callback, list):
            litellm.failure_callback.append(self.deployment_callback_on_failure)
@ -2556,10 +2563,7 @@ class Router:
        original_function: Callable,
        **kwargs,
    ):
-        if (
+        if kwargs.get("model") and self.get_model_list(model_name=kwargs["model"]):
            "model" in kwargs
            and self.get_model_list(model_name=kwargs["model"]) is not None
        ):
            deployment = await self.async_get_available_deployment(
                model=kwargs["model"]
            )
@ -3291,13 +3295,14 @@ class Router:
    ):
        """
        Track remaining tpm/rpm quota for model in model_list
        Currently, only updates TPM usage.
        """
        try:
            if kwargs["litellm_params"].get("metadata") is None:
                pass
            else:
                deployment_name = kwargs["litellm_params"]["metadata"].get(
                    "deployment", None
                )  # stable name - works for wildcard routes as well
                model_group = kwargs["litellm_params"]["metadata"].get(
                    "model_group", None
                )
@ -3308,6 +3313,8 @@ class Router:
                elif isinstance(id, int):
                    id = str(id)
                parent_otel_span = _get_parent_otel_span_from_kwargs(kwargs)
                _usage_obj = completion_response.get("usage")
                total_tokens = _usage_obj.get("total_tokens", 0) if _usage_obj else 0
@ -3319,13 +3326,14 @@ class Router:
                    "%H-%M"
                )  # use the same timezone regardless of system clock
-                tpm_key = f"global_router:{id}:tpm:{current_minute}"
+                tpm_key = RouterCacheEnum.TPM.value.format(
                    id=id, current_minute=current_minute, model=deployment_name
                )
                # ------------
                # Update usage
                # ------------
                # update cache
                parent_otel_span = _get_parent_otel_span_from_kwargs(kwargs)
                ## TPM
                await self.cache.async_increment_cache(
                    key=tpm_key,
@ -3334,6 +3342,17 @@ class Router:
                    ttl=RoutingArgs.ttl.value,
                )
                ## RPM
                rpm_key = RouterCacheEnum.RPM.value.format(
                    id=id, current_minute=current_minute, model=deployment_name
                )
                await self.cache.async_increment_cache(
                    key=rpm_key,
                    value=1,
                    parent_otel_span=parent_otel_span,
                    ttl=RoutingArgs.ttl.value,
                )
                increment_deployment_successes_for_current_minute(
                    litellm_router_instance=self,
                    deployment_id=id,
@ -3446,6 +3465,40 @@ class Router:
        except Exception as e:
            raise e
    async def async_deployment_callback_on_failure(
        self, kwargs, completion_response: Optional[Any], start_time, end_time
    ):
        """
        Update RPM usage for a deployment
        """
        deployment_name = kwargs["litellm_params"]["metadata"].get(
            "deployment", None
        )  # handles wildcard routes - by giving the original name sent to `litellm.completion`
        model_group = kwargs["litellm_params"]["metadata"].get("model_group", None)
        model_info = kwargs["litellm_params"].get("model_info", {}) or {}
        id = model_info.get("id", None)
        if model_group is None or id is None:
            return
        elif isinstance(id, int):
            id = str(id)
        parent_otel_span = _get_parent_otel_span_from_kwargs(kwargs)
        dt = get_utc_datetime()
        current_minute = dt.strftime(
            "%H-%M"
        )  # use the same timezone regardless of system clock
        ## RPM
        rpm_key = RouterCacheEnum.RPM.value.format(
            id=id, current_minute=current_minute, model=deployment_name
        )
        await self.cache.async_increment_cache(
            key=rpm_key,
            value=1,
            parent_otel_span=parent_otel_span,
            ttl=RoutingArgs.ttl.value,
        )
    def log_retry(self, kwargs: dict, e: Exception) -> dict:
        """
        When a retry or fallback happens, log the details of the just failed model call - similar to Sentry breadcrumbing
@ -4123,7 +4176,24 @@ class Router:
                    raise Exception("Model Name invalid - {}".format(type(model)))
        return None
-    def get_router_model_info(self, deployment: dict) -> ModelMapInfo:
+    @overload
    def get_router_model_info(
        self, deployment: dict, received_model_name: str, id: None = None
    ) -> ModelMapInfo:
        pass
    @overload
    def get_router_model_info(
        self, deployment: None, received_model_name: str, id: str
    ) -> ModelMapInfo:
        pass
    def get_router_model_info(
        self,
        deployment: Optional[dict],
        received_model_name: str,
        id: Optional[str] = None,
    ) -> ModelMapInfo:
        """
        For a given model id, return the model info (max tokens, input cost, output cost, etc.).
@ -4137,6 +4207,14 @@ class Router:
        Raises:
        - ValueError -> If model is not mapped yet
        """
        if id is not None:
            _deployment = self.get_deployment(model_id=id)
            if _deployment is not None:
                deployment = _deployment.model_dump(exclude_none=True)
        if deployment is None:
            raise ValueError("Deployment not found")
        ## GET BASE MODEL
        base_model = deployment.get("model_info", {}).get("base_model", None)
        if base_model is None:
@ -4158,10 +4236,27 @@ class Router:
        elif custom_llm_provider != "azure":
            model = _model
            potential_models = self.pattern_router.route(received_model_name)
            if "*" in model and potential_models is not None:  # if wildcard route
                for potential_model in potential_models:
                    try:
                        if potential_model.get("model_info", {}).get(
                            "id"
                        ) == deployment.get("model_info", {}).get("id"):
                            model = potential_model.get("litellm_params", {}).get(
                                "model"
                            )
                            break
                    except Exception:
                        pass
        ## GET LITELLM MODEL INFO - raises exception, if model is not mapped
-        model_info = litellm.get_model_info(
+        if not model.startswith(custom_llm_provider):
-            model="{}/{}".format(custom_llm_provider, model)
+            model_info_name = "{}/{}".format(custom_llm_provider, model)
-        )
+        else:
            model_info_name = model
        model_info = litellm.get_model_info(model=model_info_name)
        ## CHECK USER SET MODEL INFO
        user_model_info = deployment.get("model_info", {})
@ -4211,8 +4306,10 @@ class Router:
        total_tpm: Optional[int] = None
        total_rpm: Optional[int] = None
        configurable_clientside_auth_params: CONFIGURABLE_CLIENTSIDE_AUTH_PARAMS = None
-
+        model_list = self.get_model_list(model_name=model_group)
-        for model in self.model_list:
+        if model_list is None:
            return None
        for model in model_list:
            is_match = False
            if (
                "model_name" in model and model["model_name"] == model_group
@ -4227,7 +4324,7 @@ class Router:
            if not is_match:
                continue
            # model in model group found #
-            litellm_params = LiteLLM_Params(**model["litellm_params"])
+            litellm_params = LiteLLM_Params(**model["litellm_params"])  # type: ignore
            # get configurable clientside auth params
            configurable_clientside_auth_params = (
                litellm_params.configurable_clientside_auth_params
@ -4235,38 +4332,30 @@ class Router:
            # get model tpm
            _deployment_tpm: Optional[int] = None
            if _deployment_tpm is None:
-                _deployment_tpm = model.get("tpm", None)
+                _deployment_tpm = model.get("tpm", None)  # type: ignore
            if _deployment_tpm is None:
-                _deployment_tpm = model.get("litellm_params", {}).get("tpm", None)
+                _deployment_tpm = model.get("litellm_params", {}).get("tpm", None)  # type: ignore
            if _deployment_tpm is None:
-                _deployment_tpm = model.get("model_info", {}).get("tpm", None)
+                _deployment_tpm = model.get("model_info", {}).get("tpm", None)  # type: ignore
            if _deployment_tpm is not None:
                if total_tpm is None:
                    total_tpm = 0
                total_tpm += _deployment_tpm  # type: ignore
            # get model rpm
            _deployment_rpm: Optional[int] = None
            if _deployment_rpm is None:
-                _deployment_rpm = model.get("rpm", None)
+                _deployment_rpm = model.get("rpm", None)  # type: ignore
            if _deployment_rpm is None:
-                _deployment_rpm = model.get("litellm_params", {}).get("rpm", None)
+                _deployment_rpm = model.get("litellm_params", {}).get("rpm", None)  # type: ignore
            if _deployment_rpm is None:
-                _deployment_rpm = model.get("model_info", {}).get("rpm", None)
+                _deployment_rpm = model.get("model_info", {}).get("rpm", None)  # type: ignore
            if _deployment_rpm is not None:
                if total_rpm is None:
                    total_rpm = 0
                total_rpm += _deployment_rpm  # type: ignore
            # get model info
            try:
                model_info = litellm.get_model_info(model=litellm_params.model)
            except Exception:
                model_info = None
            # get llm provider
-            model, llm_provider = "", ""
+            litellm_model, llm_provider = "", ""
            try:
-                model, llm_provider, _, _ = litellm.get_llm_provider(
+                litellm_model, llm_provider, _, _ = litellm.get_llm_provider(
                    model=litellm_params.model,
                    custom_llm_provider=litellm_params.custom_llm_provider,
                )
@ -4277,7 +4366,7 @@ class Router:
            if model_info is None:
                supported_openai_params = litellm.get_supported_openai_params(
-                    model=model, custom_llm_provider=llm_provider
+                    model=litellm_model, custom_llm_provider=llm_provider
                )
                if supported_openai_params is None:
                    supported_openai_params = []
@ -4367,7 +4456,20 @@ class Router:
                    model_group_info.supported_openai_params = model_info[
                        "supported_openai_params"
                    ]
                if model_info.get("tpm", None) is not None and _deployment_tpm is None:
                    _deployment_tpm = model_info.get("tpm")
                if model_info.get("rpm", None) is not None and _deployment_rpm is None:
                    _deployment_rpm = model_info.get("rpm")
            if _deployment_tpm is not None:
                if total_tpm is None:
                    total_tpm = 0
                total_tpm += _deployment_tpm  # type: ignore
            if _deployment_rpm is not None:
                if total_rpm is None:
                    total_rpm = 0
                total_rpm += _deployment_rpm  # type: ignore
        if model_group_info is not None:
            ## UPDATE WITH TOTAL TPM/RPM FOR MODEL GROUP
            if total_tpm is not None:
@ -4419,7 +4521,10 @@ class Router:
        self, model_group: str
    ) -> Tuple[Optional[int], Optional[int]]:
        """
-        Returns remaining tpm/rpm quota for model group
+        Returns current tpm/rpm usage for model group
        Parameters:
        - model_group: str - the received model name from the user (can be a wildcard route).
        Returns:
        - usage: Tuple[tpm, rpm]
@ -4430,20 +4535,37 @@ class Router:
        )  # use the same timezone regardless of system clock
        tpm_keys: List[str] = []
        rpm_keys: List[str] = []
-        for model in self.model_list:
+
-            if "model_name" in model and model["model_name"] == model_group:
+        model_list = self.get_model_list(model_name=model_group)
-                tpm_keys.append(
+        if model_list is None:  # no matching deployments
-                    f"global_router:{model['model_info']['id']}:tpm:{current_minute}"
+            return None, None
        for model in model_list:
            id: Optional[str] = model.get("model_info", {}).get("id")  # type: ignore
            litellm_model: Optional[str] = model["litellm_params"].get(
                "model"
            )  # USE THE MODEL SENT TO litellm.completion() - consistent with how global_router cache is written.
            if id is None or litellm_model is None:
                continue
            tpm_keys.append(
                RouterCacheEnum.TPM.value.format(
                    id=id,
                    model=litellm_model,
                    current_minute=current_minute,
                )
-                rpm_keys.append(
+            )
-                    f"global_router:{model['model_info']['id']}:rpm:{current_minute}"
+            rpm_keys.append(
                RouterCacheEnum.RPM.value.format(
                    id=id,
                    model=litellm_model,
                    current_minute=current_minute,
                )
            )
        combined_tpm_rpm_keys = tpm_keys + rpm_keys
        combined_tpm_rpm_values = await self.cache.async_batch_get_cache(
            keys=combined_tpm_rpm_keys
        )
        if combined_tpm_rpm_values is None:
            return None, None
@ -4468,6 +4590,32 @@ class Router:
                    rpm_usage += t
        return tpm_usage, rpm_usage
    async def get_remaining_model_group_usage(self, model_group: str) -> Dict[str, int]:
        current_tpm, current_rpm = await self.get_model_group_usage(model_group)
        model_group_info = self.get_model_group_info(model_group)
        if model_group_info is not None and model_group_info.tpm is not None:
            tpm_limit = model_group_info.tpm
        else:
            tpm_limit = None
        if model_group_info is not None and model_group_info.rpm is not None:
            rpm_limit = model_group_info.rpm
        else:
            rpm_limit = None
        returned_dict = {}
        if tpm_limit is not None and current_tpm is not None:
            returned_dict["x-ratelimit-remaining-tokens"] = tpm_limit - current_tpm
            returned_dict["x-ratelimit-limit-tokens"] = tpm_limit
        if rpm_limit is not None and current_rpm is not None:
            returned_dict["x-ratelimit-remaining-requests"] = rpm_limit - current_rpm
            returned_dict["x-ratelimit-limit-requests"] = rpm_limit
        return returned_dict
    async def set_response_headers(
        self, response: Any, model_group: Optional[str] = None
    ) -> Any:
@ -4478,6 +4626,30 @@ class Router:
        # - if healthy_deployments > 1, return model group rate limit headers
        # - else return the model's rate limit headers
        """
        if (
            isinstance(response, BaseModel)
            and hasattr(response, "_hidden_params")
            and isinstance(response._hidden_params, dict)  # type: ignore
        ):
            response._hidden_params.setdefault("additional_headers", {})  # type: ignore
            response._hidden_params["additional_headers"][  # type: ignore
                "x-litellm-model-group"
            ] = model_group
            additional_headers = response._hidden_params["additional_headers"]  # type: ignore
            if (
                "x-ratelimit-remaining-tokens" not in additional_headers
                and "x-ratelimit-remaining-requests" not in additional_headers
                and model_group is not None
            ):
                remaining_usage = await self.get_remaining_model_group_usage(
                    model_group
                )
                for header, value in remaining_usage.items():
                    if value is not None:
                        additional_headers[header] = value
        return response
    def get_model_ids(self, model_name: Optional[str] = None) -> List[str]:
@ -4540,6 +4712,9 @@ class Router:
        if hasattr(self, "model_list"):
            returned_models: List[DeploymentTypedDict] = []
            if model_name is not None:
                returned_models.extend(self._get_all_deployments(model_name=model_name))
            if hasattr(self, "model_group_alias"):
                for model_alias, model_value in self.model_group_alias.items():
@ -4560,21 +4735,32 @@ class Router:
                        )
                    )
            if len(returned_models) == 0:  # check if wildcard route
                potential_wildcard_models = self.pattern_router.route(model_name)
                if potential_wildcard_models is not None:
                    returned_models.extend(
                        [DeploymentTypedDict(**m) for m in potential_wildcard_models]  # type: ignore
                    )
            if model_name is None:
                returned_models += self.model_list
                return returned_models
-            returned_models.extend(self._get_all_deployments(model_name=model_name))
+
            return returned_models
        return None
-    def get_model_access_groups(self):
+    def get_model_access_groups(self, model_name: Optional[str] = None):
        """
        If model_name is provided, only return access groups for that model.
        """
        from collections import defaultdict
        access_groups = defaultdict(list)
-        if self.model_list:
+        model_list = self.get_model_list(model_name=model_name)
-            for m in self.model_list:
+        if model_list:
            for m in model_list:
                for group in m.get("model_info", {}).get("access_groups", []):
                    model_name = m["model_name"]
                    access_groups[group].append(model_name)
@ -4810,10 +4996,12 @@ class Router:
                    base_model = deployment.get("litellm_params", {}).get(
                        "base_model", None
                    )
                model_info = self.get_router_model_info(
                    deployment=deployment, received_model_name=model
                )
                model = base_model or deployment.get("litellm_params", {}).get(
                    "model", None
                )
                model_info = self.get_router_model_info(deployment=deployment)
                if (
                    isinstance(model_info, dict)
--- a/litellm/router_utils/pattern_match_deployments.py
+++ b/litellm/router_utils/pattern_match_deployments.py
@ -79,7 +79,9 @@ class PatternMatchRouter:
        return new_deployments
-    def route(self, request: Optional[str]) -> Optional[List[Dict]]:
+    def route(
        self, request: Optional[str], filtered_model_names: Optional[List[str]] = None
    ) -> Optional[List[Dict]]:
        """
        Route a requested model to the corresponding llm deployments based on the regex pattern
@ -89,14 +91,26 @@ class PatternMatchRouter:
        Args:
            request: Optional[str]
-
+            filtered_model_names: Optional[List[str]] - if provided, only return deployments that match the filtered_model_names
        Returns:
            Optional[List[Deployment]]: llm deployments
        """
        try:
            if request is None:
                return None
            regex_filtered_model_names = (
                [self._pattern_to_regex(m) for m in filtered_model_names]
                if filtered_model_names is not None
                else []
            )
            for pattern, llm_deployments in self.patterns.items():
                if (
                    filtered_model_names is not None
                    and pattern not in regex_filtered_model_names
                ):
                    continue
                pattern_match = re.match(pattern, request)
                if pattern_match:
                    return self._return_pattern_matched_deployments(
--- a/litellm/router_utils/response_headers.py
+++ b/litellm/router_utils/response_headers.py
--- a/litellm/tests/test_mlflow.py
+++ b/litellm/tests/test_mlflow.py
@ -1,29 +0,0 @@
 import pytest
 import litellm
 def test_mlflow_logging():
    litellm.success_callback = ["mlflow"]
    litellm.failure_callback = ["mlflow"]
    litellm.completion(
        model="gpt-4o-mini",
        messages=[{"role": "user", "content": "what llm are u"}],
        max_tokens=10,
        temperature=0.2,
        user="test-user",
    )
@pytest.mark.asyncio()
 async def test_async_mlflow_logging():
    litellm.success_callback = ["mlflow"]
    litellm.failure_callback = ["mlflow"]
    await litellm.acompletion(
        model="gpt-4o-mini",
        messages=[{"role": "user", "content": "hi test from local arize"}],
        mock_response="hello",
        temperature=0.1,
        user="OTEL_USER",
    )
--- a/litellm/types/integrations/datadog.py
+++ b/litellm/types/integrations/datadog.py
@ -1,5 +1,5 @@
 from enum import Enum
-from typing import TypedDict
+from typing import Optional, TypedDict
 class DataDogStatus(str, Enum):
@ -19,3 +19,11 @@ class DatadogPayload(TypedDict, total=False):
 class DD_ERRORS(Enum):
    DATADOG_413_ERROR = "Datadog API Error - Payload too large (batch is above 5MB uncompressed). If you want this logged either disable request/response logging or set `DD_BATCH_SIZE=50`"
 class DatadogProxyFailureHookJsonMessage(TypedDict, total=False):
    exception: str
    error_class: str
    status_code: Optional[int]
    traceback: str
    user_api_key_dict: dict
--- a/litellm/types/passthrough_endpoints/vertex_ai.py
+++ b/litellm/types/passthrough_endpoints/vertex_ai.py
@ -0,0 +1,18 @@
 """
 Used for /vertex_ai/ pass through endpoints
 """
 from typing import Optional
 from pydantic import BaseModel
 class VertexPassThroughCredentials(BaseModel):
    # Example: vertex_project = "my-project-123"
    vertex_project: Optional[str] = None
    # Example: vertex_location = "us-central1"
    vertex_location: Optional[str] = None
    # Example: vertex_credentials = "/path/to/credentials.json" or "os.environ/GOOGLE_CREDS"
    vertex_credentials: Optional[str] = None
--- a/litellm/types/router.py
+++ b/litellm/types/router.py
@ -9,7 +9,7 @@ from typing import Any, Dict, List, Literal, Optional, Tuple, Union
 import httpx
 from pydantic import BaseModel, ConfigDict, Field
-from typing_extensions import TypedDict
+from typing_extensions import Required, TypedDict
 from ..exceptions import RateLimitError
 from .completion import CompletionRequest
@ -352,9 +352,10 @@ class LiteLLMParamsTypedDict(TypedDict, total=False):
    tags: Optional[List[str]]
-class DeploymentTypedDict(TypedDict):
+class DeploymentTypedDict(TypedDict, total=False):
-    model_name: str
+    model_name: Required[str]
-    litellm_params: LiteLLMParamsTypedDict
+    litellm_params: Required[LiteLLMParamsTypedDict]
    model_info: dict
 SPECIAL_MODEL_INFO_PARAMS = [
@ -640,3 +641,8 @@ class ProviderBudgetInfo(BaseModel):
 ProviderBudgetConfigType = Dict[str, ProviderBudgetInfo]
 class RouterCacheEnum(enum.Enum):
    TPM = "global_router:{id}:{model}:tpm:{current_minute}"
    RPM = "global_router:{id}:{model}:rpm:{current_minute}"
--- a/litellm/types/utils.py
+++ b/litellm/types/utils.py
@ -106,6 +106,8 @@ class ModelInfo(TypedDict, total=False):
    supports_prompt_caching: Optional[bool]
    supports_audio_input: Optional[bool]
    supports_audio_output: Optional[bool]
    tpm: Optional[int]
    rpm: Optional[int]
 class GenericStreamingChunk(TypedDict, total=False):
--- a/litellm/utils.py
+++ b/litellm/utils.py
@ -4656,6 +4656,8 @@ def get_model_info(  # noqa: PLR0915
                ),
                supports_audio_input=_model_info.get("supports_audio_input", False),
                supports_audio_output=_model_info.get("supports_audio_output", False),
                tpm=_model_info.get("tpm", None),
                rpm=_model_info.get("rpm", None),
            )
    except Exception as e:
        if "OllamaError" in str(e):
--- a/model_prices_and_context_window.json
+++ b/model_prices_and_context_window.json
@ -2032,7 +2032,6 @@
        "tool_use_system_prompt_tokens": 264,
        "supports_assistant_prefill": true,
        "supports_prompt_caching": true,
        "supports_pdf_input": true,
        "supports_response_schema": true
    },
    "claude-3-opus-20240229": {
@ -2098,6 +2097,7 @@
        "supports_vision": true,
        "tool_use_system_prompt_tokens": 159,
        "supports_assistant_prefill": true,
        "supports_pdf_input": true,
        "supports_prompt_caching": true,
        "supports_response_schema": true
    },
@ -3383,6 +3383,8 @@
        "supports_vision": true,
        "supports_response_schema": true,
        "supports_prompt_caching": true,
        "tpm": 4000000,
        "rpm": 2000,
        "source": "https://ai.google.dev/pricing"
    },
    "gemini/gemini-1.5-flash-001": {
@ -3406,6 +3408,8 @@
        "supports_vision": true,
        "supports_response_schema": true,
        "supports_prompt_caching": true,
        "tpm": 4000000,
        "rpm": 2000,
        "source": "https://ai.google.dev/pricing"
    },
    "gemini/gemini-1.5-flash": {
@ -3428,6 +3432,8 @@
        "supports_function_calling": true,
        "supports_vision": true,
        "supports_response_schema": true, 
        "tpm": 4000000,
        "rpm": 2000,
        "source": "https://ai.google.dev/pricing"
    },
    "gemini/gemini-1.5-flash-latest": {
@ -3450,6 +3456,32 @@
        "supports_function_calling": true,
        "supports_vision": true,
        "supports_response_schema": true,
        "tpm": 4000000,
        "rpm": 2000,
        "source": "https://ai.google.dev/pricing"
    },
    "gemini/gemini-1.5-flash-8b": {
        "max_tokens": 8192,
        "max_input_tokens": 1048576,
        "max_output_tokens": 8192,
        "max_images_per_prompt": 3000,
        "max_videos_per_prompt": 10,
        "max_video_length": 1,
        "max_audio_length_hours": 8.4,
        "max_audio_per_prompt": 1,
        "max_pdf_size_mb": 30, 
        "input_cost_per_token": 0,
        "input_cost_per_token_above_128k_tokens": 0,
        "output_cost_per_token": 0,
        "output_cost_per_token_above_128k_tokens": 0,
        "litellm_provider": "gemini",
        "mode": "chat",
        "supports_system_messages": true,
        "supports_function_calling": true,
        "supports_vision": true,
        "supports_response_schema": true,
        "tpm": 4000000,
        "rpm": 4000,
        "source": "https://ai.google.dev/pricing"
    },
    "gemini/gemini-1.5-flash-8b-exp-0924": {
@ -3472,6 +3504,8 @@
        "supports_function_calling": true,
        "supports_vision": true,
        "supports_response_schema": true,
        "tpm": 4000000,
        "rpm": 4000,
        "source": "https://ai.google.dev/pricing"
    },
    "gemini/gemini-exp-1114": {
@ -3494,7 +3528,12 @@
        "supports_function_calling": true,
        "supports_vision": true,
        "supports_response_schema": true,
-        "source": "https://ai.google.dev/pricing"
+        "tpm": 4000000,
        "rpm": 1000,
        "source": "https://ai.google.dev/pricing",
        "metadata": {
            "notes": "Rate limits not documented for gemini-exp-1114. Assuming same as gemini-1.5-pro."
        }
    },
    "gemini/gemini-1.5-flash-exp-0827": {
        "max_tokens": 8192,
@ -3516,6 +3555,8 @@
        "supports_function_calling": true,
        "supports_vision": true,
        "supports_response_schema": true,
        "tpm": 4000000,
        "rpm": 2000,
        "source": "https://ai.google.dev/pricing"
    },
    "gemini/gemini-1.5-flash-8b-exp-0827": {
@ -3537,6 +3578,9 @@
        "supports_system_messages": true,
        "supports_function_calling": true,
        "supports_vision": true,
        "supports_response_schema": true,
        "tpm": 4000000,
        "rpm": 4000,
        "source": "https://ai.google.dev/pricing"
    },
    "gemini/gemini-pro": {
@ -3550,7 +3594,10 @@
        "litellm_provider": "gemini",
        "mode": "chat",
        "supports_function_calling": true,
-        "source": "https://cloud.google.com/vertex-ai/generative-ai/docs/learn/models#foundation_models"
+        "rpd": 30000,
        "tpm": 120000,
        "rpm": 360,
        "source": "https://ai.google.dev/gemini-api/docs/models/gemini"
    },
    "gemini/gemini-1.5-pro": {
        "max_tokens": 8192,
@ -3567,6 +3614,8 @@
        "supports_vision": true,
        "supports_tool_choice": true, 
        "supports_response_schema": true, 
        "tpm": 4000000,
        "rpm": 1000,
        "source": "https://ai.google.dev/pricing"
    },
    "gemini/gemini-1.5-pro-002": {
@ -3585,6 +3634,8 @@
        "supports_tool_choice": true, 
        "supports_response_schema": true, 
        "supports_prompt_caching": true,
        "tpm": 4000000,
        "rpm": 1000,
        "source": "https://ai.google.dev/pricing"
    },
    "gemini/gemini-1.5-pro-001": {
@ -3603,6 +3654,8 @@
        "supports_tool_choice": true, 
        "supports_response_schema": true, 
        "supports_prompt_caching": true,
        "tpm": 4000000,
        "rpm": 1000,
        "source": "https://ai.google.dev/pricing"
    },
    "gemini/gemini-1.5-pro-exp-0801": {
@ -3620,6 +3673,8 @@
        "supports_vision": true,
        "supports_tool_choice": true,
        "supports_response_schema": true,
        "tpm": 4000000,
        "rpm": 1000,
        "source": "https://ai.google.dev/pricing"
    },
    "gemini/gemini-1.5-pro-exp-0827": {
@ -3637,6 +3692,8 @@
        "supports_vision": true,
        "supports_tool_choice": true,
        "supports_response_schema": true,
        "tpm": 4000000,
        "rpm": 1000,
        "source": "https://ai.google.dev/pricing"
    },
    "gemini/gemini-1.5-pro-latest": {
@ -3654,6 +3711,8 @@
        "supports_vision": true,
        "supports_tool_choice": true, 
        "supports_response_schema": true, 
        "tpm": 4000000,
        "rpm": 1000,
        "source": "https://ai.google.dev/pricing"
    },
    "gemini/gemini-pro-vision": {
@ -3668,6 +3727,9 @@
        "mode": "chat",
        "supports_function_calling": true,
        "supports_vision": true,
        "rpd": 30000,
        "tpm": 120000,
        "rpm": 360,
        "source": "https://cloud.google.com/vertex-ai/generative-ai/docs/learn/models#foundation_models"
    },
    "gemini/gemini-gemma-2-27b-it": {
--- a/pyproject.toml
+++ b/pyproject.toml
@ -1,6 +1,6 @@
 [tool.poetry]
 name = "litellm"
-version = "1.52.15"
+version = "1.53.2"
 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 = "1.52.15"
+version = "1.53.2"
 version_files = [
    "pyproject.toml:^version"
 ]
--- a/requirements.txt
+++ b/requirements.txt
@ -1,6 +1,6 @@
 # LITELLM PROXY DEPENDENCIES #
 anyio==4.4.0 # openai + http req.
-openai==1.54.0  # openai req. 
+openai==1.55.3  # openai req. 
 fastapi==0.111.0 # server dep
 backoff==2.2.1 # server dep
 pyyaml==6.0.0 # server dep
--- a/tests/documentation_tests/test_env_keys.py
+++ b/tests/documentation_tests/test_env_keys.py
@ -45,16 +45,23 @@ print(env_keys)
 # Parse the documentation to extract documented keys
 repo_base = "./"
 print(os.listdir(repo_base))
-docs_path = "./docs/my-website/docs/proxy/configs.md"  # Path to the documentation
+docs_path = (
    "./docs/my-website/docs/proxy/config_settings.md"  # Path to the documentation
 )
 documented_keys = set()
 try:
    with open(docs_path, "r", encoding="utf-8") as docs_file:
        content = docs_file.read()
        print(f"content: {content}")
        # Find the section titled "general_settings - Reference"
        general_settings_section = re.search(
-            r"### environment variables - Reference(.*?)###", content, re.DOTALL
+            r"### environment variables - Reference(.*?)(?=\n###|\Z)",
            content,
            re.DOTALL | re.MULTILINE,
        )
        print(f"general_settings_section: {general_settings_section}")
        if general_settings_section:
            # Extract the table rows, which contain the documented keys
            table_content = general_settings_section.group(1)
@ -68,6 +75,7 @@ except Exception as e:
    )
 print(f"documented_keys: {documented_keys}")
 # Compare and find undocumented keys
 undocumented_keys = env_keys - documented_keys
--- a/tests/documentation_tests/test_general_setting_keys.py
+++ b/tests/documentation_tests/test_general_setting_keys.py
@ -34,7 +34,9 @@ for root, dirs, files in os.walk(repo_base):
 # Parse the documentation to extract documented keys
 repo_base = "./"
 print(os.listdir(repo_base))
-docs_path = "./docs/my-website/docs/proxy/configs.md"  # Path to the documentation
+docs_path = (
    "./docs/my-website/docs/proxy/config_settings.md"  # Path to the documentation
 )
 documented_keys = set()
 try:
    with open(docs_path, "r", encoding="utf-8") as docs_file:
--- a/tests/documentation_tests/test_router_settings.py
+++ b/tests/documentation_tests/test_router_settings.py
@ -0,0 +1,87 @@
 import os
 import re
 import inspect
 from typing import Type
 import sys
 sys.path.insert(
    0, os.path.abspath("../..")
 )  # Adds the parent directory to the system path
 import litellm
 def get_init_params(cls: Type) -> list[str]:
    """
    Retrieve all parameters supported by the `__init__` method of a given class.
    Args:
        cls: The class to inspect.
    Returns:
        A list of parameter names.
    """
    if not hasattr(cls, "__init__"):
        raise ValueError(
            f"The provided class {cls.__name__} does not have an __init__ method."
        )
    init_method = cls.__init__
    argspec = inspect.getfullargspec(init_method)
    # The first argument is usually 'self', so we exclude it
    return argspec.args[1:]  # Exclude 'self'
 router_init_params = set(get_init_params(litellm.router.Router))
 print(router_init_params)
 router_init_params.remove("model_list")
 # Parse the documentation to extract documented keys
 repo_base = "./"
 print(os.listdir(repo_base))
 docs_path = (
    "./docs/my-website/docs/proxy/config_settings.md"  # Path to the documentation
 )
 # docs_path = (
 #     "../../docs/my-website/docs/proxy/config_settings.md"  # Path to the documentation
 # )
 documented_keys = set()
 try:
    with open(docs_path, "r", encoding="utf-8") as docs_file:
        content = docs_file.read()
        # Find the section titled "general_settings - Reference"
        general_settings_section = re.search(
            r"### router_settings - Reference(.*?)###", content, re.DOTALL
        )
        if general_settings_section:
            # Extract the table rows, which contain the documented keys
            table_content = general_settings_section.group(1)
            doc_key_pattern = re.compile(
                r"\|\s*([^\|]+?)\s*\|"
            )  # Capture the key from each row of the table
            documented_keys.update(doc_key_pattern.findall(table_content))
 except Exception as e:
    raise Exception(
        f"Error reading documentation: {e}, \n repo base - {os.listdir(repo_base)}"
    )
 # Compare and find undocumented keys
 undocumented_keys = router_init_params - documented_keys
 # Print results
 print("Keys expected in 'router settings' (found in code):")
 for key in sorted(router_init_params):
    print(key)
 if undocumented_keys:
    raise Exception(
        f"\nKeys not documented in 'router settings - Reference': {undocumented_keys}"
    )
 else:
    print(
        "\nAll keys are documented in 'router settings - Reference'. - {}".format(
            router_init_params
        )
    )
--- a/tests/llm_translation/Readme.md
+++ b/tests/llm_translation/Readme.md
@ -1 +1,3 @@
-More tests under `litellm/litellm/tests/*`.
+Unit tests for individual LLM providers. 
 Name of the test file is the name of the LLM provider - e.g. `test_openai.py` is for OpenAI. 
--- a/tests/llm_translation/base_llm_unit_tests.py
+++ b/tests/llm_translation/base_llm_unit_tests.py
@ -62,7 +62,14 @@ class BaseLLMChatTest(ABC):
        response = litellm.completion(**base_completion_call_args, messages=messages)
        assert response is not None
-    def test_json_response_format(self):
+    @pytest.mark.parametrize(
        "response_format",
        [
            {"type": "json_object"},
            {"type": "text"},
        ],
    )
    def test_json_response_format(self, response_format):
        """
        Test that the JSON response format is supported by the LLM API
        """
@ -83,7 +90,7 @@ class BaseLLMChatTest(ABC):
        response = litellm.completion(
            **base_completion_call_args,
            messages=messages,
-            response_format={"type": "json_object"},
+            response_format=response_format,
        )
        print(response)
@ -190,6 +197,35 @@ class BaseLLMChatTest(ABC):
        """Test that tool calls with no arguments is translated correctly. Relevant issue: https://github.com/BerriAI/litellm/issues/6833"""
        pass
    def test_image_url(self):
        litellm.set_verbose = True
        from litellm.utils import supports_vision
        os.environ["LITELLM_LOCAL_MODEL_COST_MAP"] = "True"
        litellm.model_cost = litellm.get_model_cost_map(url="")
        base_completion_call_args = self.get_base_completion_call_args()
        if not supports_vision(base_completion_call_args["model"], None):
            pytest.skip("Model does not support image input")
        messages = [
            {
                "role": "user",
                "content": [
                    {"type": "text", "text": "What's in this image?"},
                    {
                        "type": "image_url",
                        "image_url": {
                            "url": "https://i.pinimg.com/736x/b4/b1/be/b4b1becad04d03a9071db2817fc9fe77.jpg"
                        },
                    },
                ],
            }
        ]
        response = litellm.completion(**base_completion_call_args, messages=messages)
        assert response is not None
    @pytest.fixture
    def pdf_messages(self):
        import base64
--- a/tests/llm_translation/test_anthropic_completion.py
+++ b/tests/llm_translation/test_anthropic_completion.py
--- a/tests/llm_translation/test_azure_ai.py
+++ b/tests/llm_translation/test_azure_ai.py
@ -45,81 +45,59 @@ def test_map_azure_model_group(model_group_header, expected_model):
@pytest.mark.asyncio
-@pytest.mark.respx
+async def test_azure_ai_with_image_url():
 async def test_azure_ai_with_image_url(respx_mock: MockRouter):
    """
    Important test:
    Test that Azure AI studio can handle image_url passed when content is a list containing both text and image_url
    """
    from openai import AsyncOpenAI
    litellm.set_verbose = True
-    # Mock response based on the actual API response
+    client = AsyncOpenAI(
    mock_response = {
        "id": "cmpl-53860ea1efa24d2883555bfec13d2254",
        "choices": [
            {
                "finish_reason": "stop",
                "index": 0,
                "logprobs": None,
                "message": {
                    "content": "The image displays a graphic with the text 'LiteLLM' in black",
                    "role": "assistant",
                    "refusal": None,
                    "audio": None,
                    "function_call": None,
                    "tool_calls": None,
                },
            }
        ],
        "created": 1731801937,
        "model": "phi35-vision-instruct",
        "object": "chat.completion",
        "usage": {
            "completion_tokens": 69,
            "prompt_tokens": 617,
            "total_tokens": 686,
            "completion_tokens_details": None,
            "prompt_tokens_details": None,
        },
    }
    # Mock the API request
    mock_request = respx_mock.post(
        "https://Phi-3-5-vision-instruct-dcvov.eastus2.models.ai.azure.com"
    ).mock(return_value=httpx.Response(200, json=mock_response))
    response = await litellm.acompletion(
        model="azure_ai/Phi-3-5-vision-instruct-dcvov",
        api_base="https://Phi-3-5-vision-instruct-dcvov.eastus2.models.ai.azure.com",
        messages=[
            {
                "role": "user",
                "content": [
                    {
                        "type": "text",
                        "text": "What is in this image?",
                    },
                    {
                        "type": "image_url",
                        "image_url": {
                            "url": "https://litellm-listing.s3.amazonaws.com/litellm_logo.png"
                        },
                    },
                ],
            },
        ],
        api_key="fake-api-key",
        base_url="https://Phi-3-5-vision-instruct-dcvov.eastus2.models.ai.azure.com",
    )
-    # Verify the request was made
+    with patch.object(
-    assert mock_request.called
+        client.chat.completions.with_raw_response, "create"
    ) as mock_client:
        try:
            await litellm.acompletion(
                model="azure_ai/Phi-3-5-vision-instruct-dcvov",
                api_base="https://Phi-3-5-vision-instruct-dcvov.eastus2.models.ai.azure.com",
                messages=[
                    {
                        "role": "user",
                        "content": [
                            {
                                "type": "text",
                                "text": "What is in this image?",
                            },
                            {
                                "type": "image_url",
                                "image_url": {
                                    "url": "https://litellm-listing.s3.amazonaws.com/litellm_logo.png"
                                },
                            },
                        ],
                    },
                ],
                api_key="fake-api-key",
                client=client,
            )
        except Exception as e:
            traceback.print_exc()
            print(f"Error: {e}")
-    # Check the request body
+        # Verify the request was made
-    request_body = json.loads(mock_request.calls[0].request.content)
+        mock_client.assert_called_once()
-    assert request_body == {
+
-        "model": "Phi-3-5-vision-instruct-dcvov",
+        # Check the request body
-        "messages": [
+        request_body = mock_client.call_args.kwargs
        assert request_body["model"] == "Phi-3-5-vision-instruct-dcvov"
        assert request_body["messages"] == [
            {
                "role": "user",
                "content": [
@ -132,7 +110,4 @@ async def test_azure_ai_with_image_url(respx_mock: MockRouter):
                    },
                ],
            }
-        ],
+        ]
    }
    print(f"response: {response}")
--- a/tests/llm_translation/test_bedrock_completion.py
+++ b/tests/llm_translation/test_bedrock_completion.py
@ -1243,6 +1243,19 @@ def test_bedrock_cross_region_inference(model):
    )
@pytest.mark.parametrize(
    "model, expected_base_model",
    [
        (
            "apac.anthropic.claude-3-5-sonnet-20240620-v1:0",
            "anthropic.claude-3-5-sonnet-20240620-v1:0",
        ),
    ],
 )
 def test_bedrock_get_base_model(model, expected_base_model):
    assert litellm.AmazonConverseConfig()._get_base_model(model) == expected_base_model
 from litellm.llms.prompt_templates.factory import _bedrock_converse_messages_pt
--- a/tests/llm_translation/test_gemini.py
+++ b/tests/llm_translation/test_gemini.py
@ -0,0 +1,15 @@
 from base_llm_unit_tests import BaseLLMChatTest
 class TestGoogleAIStudioGemini(BaseLLMChatTest):
    def get_base_completion_call_args(self) -> dict:
        return {"model": "gemini/gemini-1.5-flash"}
    def test_tool_call_no_arguments(self, tool_call_no_arguments):
        """Test that tool calls with no arguments is translated correctly. Relevant issue: https://github.com/BerriAI/litellm/issues/6833"""
        from litellm.llms.prompt_templates.factory import (
            convert_to_gemini_tool_call_invoke,
        )
        result = convert_to_gemini_tool_call_invoke(tool_call_no_arguments)
        print(result)
--- a/tests/llm_translation/test_max_completion_tokens.py
+++ b/tests/llm_translation/test_max_completion_tokens.py
@ -13,6 +13,7 @@ load_dotenv()
 import httpx
 import pytest
 from respx import MockRouter
 from unittest.mock import patch, MagicMock, AsyncMock
 import litellm
 from litellm import Choices, Message, ModelResponse
@ -41,56 +42,58 @@ def return_mocked_response(model: str):
        "bedrock/mistral.mistral-large-2407-v1:0",
    ],
 )
@pytest.mark.respx
@pytest.mark.asyncio()
-async def test_bedrock_max_completion_tokens(model: str, respx_mock: MockRouter):
+async def test_bedrock_max_completion_tokens(model: str):
    """
    Tests that:
    - max_completion_tokens is passed as max_tokens to bedrock models
    """
    from litellm.llms.custom_httpx.http_handler import AsyncHTTPHandler
    litellm.set_verbose = True
    client = AsyncHTTPHandler()
    mock_response = return_mocked_response(model)
    _model = model.split("/")[1]
    print("\n\nmock_response: ", mock_response)
    url = f"https://bedrock-runtime.us-west-2.amazonaws.com/model/{_model}/converse"
    mock_request = respx_mock.post(url).mock(
        return_value=httpx.Response(200, json=mock_response)
    )
-    response = await litellm.acompletion(
+    with patch.object(client, "post") as mock_client:
-        model=model,
+        try:
-        max_completion_tokens=10,
+            response = await litellm.acompletion(
-        messages=[{"role": "user", "content": "Hello!"}],
+                model=model,
-    )
+                max_completion_tokens=10,
                messages=[{"role": "user", "content": "Hello!"}],
                client=client,
            )
        except Exception as e:
            print(f"Error: {e}")
-    assert mock_request.called
+        mock_client.assert_called_once()
-    request_body = json.loads(mock_request.calls[0].request.content)
+        request_body = json.loads(mock_client.call_args.kwargs["data"])
-    print("request_body: ", request_body)
+        print("request_body: ", request_body)
-    assert request_body == {
+        assert request_body == {
-        "messages": [{"role": "user", "content": [{"text": "Hello!"}]}],
+            "messages": [{"role": "user", "content": [{"text": "Hello!"}]}],
-        "additionalModelRequestFields": {},
+            "additionalModelRequestFields": {},
-        "system": [],
+            "system": [],
-        "inferenceConfig": {"maxTokens": 10},
+            "inferenceConfig": {"maxTokens": 10},
-    }
+        }
    print(f"response: {response}")
    assert isinstance(response, ModelResponse)
@pytest.mark.parametrize(
    "model",
-    ["anthropic/claude-3-sonnet-20240229", "anthropic/claude-3-opus-20240229,"],
+    ["anthropic/claude-3-sonnet-20240229", "anthropic/claude-3-opus-20240229"],
 )
@pytest.mark.respx
@pytest.mark.asyncio()
-async def test_anthropic_api_max_completion_tokens(model: str, respx_mock: MockRouter):
+async def test_anthropic_api_max_completion_tokens(model: str):
    """
    Tests that:
    - max_completion_tokens is passed as max_tokens to anthropic models
    """
    litellm.set_verbose = True
    from litellm.llms.custom_httpx.http_handler import HTTPHandler
    mock_response = {
        "content": [{"text": "Hi! My name is Claude.", "type": "text"}],
@ -103,30 +106,32 @@ async def test_anthropic_api_max_completion_tokens(model: str, respx_mock: MockR
        "usage": {"input_tokens": 2095, "output_tokens": 503},
    }
    client = HTTPHandler()
    print("\n\nmock_response: ", mock_response)
    url = f"https://api.anthropic.com/v1/messages"
    mock_request = respx_mock.post(url).mock(
        return_value=httpx.Response(200, json=mock_response)
    )
-    response = await litellm.acompletion(
+    with patch.object(client, "post") as mock_client:
-        model=model,
+        try:
-        max_completion_tokens=10,
+            response = await litellm.acompletion(
-        messages=[{"role": "user", "content": "Hello!"}],
+                model=model,
-    )
+                max_completion_tokens=10,
                messages=[{"role": "user", "content": "Hello!"}],
                client=client,
            )
        except Exception as e:
            print(f"Error: {e}")
        mock_client.assert_called_once()
        request_body = mock_client.call_args.kwargs["json"]
-    assert mock_request.called
+        print("request_body: ", request_body)
    request_body = json.loads(mock_request.calls[0].request.content)
-    print("request_body: ", request_body)
+        assert request_body == {
-
+            "messages": [
-    assert request_body == {
+                {"role": "user", "content": [{"type": "text", "text": "Hello!"}]}
-        "messages": [{"role": "user", "content": [{"type": "text", "text": "Hello!"}]}],
+            ],
-        "max_tokens": 10,
+            "max_tokens": 10,
-        "model": model.split("/")[-1],
+            "model": model.split("/")[-1],
-    }
+        }
    print(f"response: {response}")
    assert isinstance(response, ModelResponse)
 def test_all_model_configs():
--- a/tests/llm_translation/test_nvidia_nim.py
+++ b/tests/llm_translation/test_nvidia_nim.py
@ -12,95 +12,78 @@ sys.path.insert(
 import httpx
 import pytest
 from respx import MockRouter
 from unittest.mock import patch, MagicMock, AsyncMock
 import litellm
 from litellm import Choices, Message, ModelResponse, EmbeddingResponse, Usage
 from litellm import completion
-@pytest.mark.respx
+def test_completion_nvidia_nim():
-def test_completion_nvidia_nim(respx_mock: MockRouter):
+    from openai import OpenAI
    litellm.set_verbose = True
    mock_response = ModelResponse(
        id="cmpl-mock",
        choices=[Choices(message=Message(content="Mocked response", role="assistant"))],
        created=int(datetime.now().timestamp()),
        model="databricks/dbrx-instruct",
    )
    model_name = "nvidia_nim/databricks/dbrx-instruct"
    client = OpenAI(
        api_key="fake-api-key",
    )
-    mock_request = respx_mock.post(
+    with patch.object(
-        "https://integrate.api.nvidia.com/v1/chat/completions"
+        client.chat.completions.with_raw_response, "create"
-    ).mock(return_value=httpx.Response(200, json=mock_response.dict()))
+    ) as mock_client:
-    try:
+        try:
-        response = completion(
+            completion(
-            model=model_name,
+                model=model_name,
-            messages=[
+                messages=[
-                {
+                    {
-                    "role": "user",
+                        "role": "user",
-                    "content": "What's the weather like in Boston today in Fahrenheit?",
+                        "content": "What's the weather like in Boston today in Fahrenheit?",
-                }
+                    }
-            ],
+                ],
-            presence_penalty=0.5,
+                presence_penalty=0.5,
-            frequency_penalty=0.1,
+                frequency_penalty=0.1,
-        )
+                client=client,
            )
        except Exception as e:
            print(e)
        # Add any assertions here to check the response
        print(response)
        assert response.choices[0].message.content is not None
        assert len(response.choices[0].message.content) > 0
-        assert mock_request.called
+        mock_client.assert_called_once()
-        request_body = json.loads(mock_request.calls[0].request.content)
+        request_body = mock_client.call_args.kwargs
        print("request_body: ", request_body)
-        assert request_body == {
+        assert request_body["messages"] == [
            "messages": [
                {
                    "role": "user",
                    "content": "What's the weather like in Boston today in Fahrenheit?",
                }
            ],
            "model": "databricks/dbrx-instruct",
            "frequency_penalty": 0.1,
            "presence_penalty": 0.5,
        }
    except litellm.exceptions.Timeout as e:
        pass
    except Exception as e:
        pytest.fail(f"Error occurred: {e}")
 def test_embedding_nvidia_nim(respx_mock: MockRouter):
    litellm.set_verbose = True
    mock_response = EmbeddingResponse(
        model="nvidia_nim/databricks/dbrx-instruct",
        data=[
            {
-                "embedding": [0.1, 0.2, 0.3],
+                "role": "user",
-                "index": 0,
+                "content": "What's the weather like in Boston today in Fahrenheit?",
-            }
+            },
-        ],
+        ]
-        usage=Usage(
+        assert request_body["model"] == "databricks/dbrx-instruct"
-            prompt_tokens=10,
+        assert request_body["frequency_penalty"] == 0.1
-            completion_tokens=0,
+        assert request_body["presence_penalty"] == 0.5
-            total_tokens=10,
+
-        ),
+
 def test_embedding_nvidia_nim():
    litellm.set_verbose = True
    from openai import OpenAI
    client = OpenAI(
        api_key="fake-api-key",
    )
-    mock_request = respx_mock.post(
+    with patch.object(client.embeddings.with_raw_response, "create") as mock_client:
-        "https://integrate.api.nvidia.com/v1/embeddings"
+        try:
-    ).mock(return_value=httpx.Response(200, json=mock_response.dict()))
+            litellm.embedding(
-    response = litellm.embedding(
+                model="nvidia_nim/nvidia/nv-embedqa-e5-v5",
-        model="nvidia_nim/nvidia/nv-embedqa-e5-v5",
+                input="What is the meaning of life?",
-        input="What is the meaning of life?",
+                input_type="passage",
-        input_type="passage",
+                client=client,
-    )
+            )
-    assert mock_request.called
+        except Exception as e:
-    request_body = json.loads(mock_request.calls[0].request.content)
+            print(e)
-    print("request_body: ", request_body)
+        mock_client.assert_called_once()
-    assert request_body == {
+        request_body = mock_client.call_args.kwargs
-        "input": "What is the meaning of life?",
+        print("request_body: ", request_body)
-        "model": "nvidia/nv-embedqa-e5-v5",
+        assert request_body["input"] == "What is the meaning of life?"
-        "input_type": "passage",
+        assert request_body["model"] == "nvidia/nv-embedqa-e5-v5"
-        "encoding_format": "base64",
+        assert request_body["extra_body"]["input_type"] == "passage"
    }
--- a/tests/llm_translation/test_openai_prediction_param.py
+++ b/tests/llm_translation/test_openai_prediction_param.py
@ -2,7 +2,7 @@ import json
 import os
 import sys
 from datetime import datetime
-from unittest.mock import AsyncMock
+from unittest.mock import AsyncMock, patch
 sys.path.insert(
    0, os.path.abspath("../..")
@ -63,8 +63,7 @@ def test_openai_prediction_param():
@pytest.mark.asyncio
-@pytest.mark.respx
+async def test_openai_prediction_param_mock():
 async def test_openai_prediction_param_mock(respx_mock: MockRouter):
    """
    Tests that prediction parameter is correctly passed to the API
    """
@ -92,60 +91,36 @@ async def test_openai_prediction_param_mock(respx_mock: MockRouter):
        public string Username { get; set; }
    }
    """
    from openai import AsyncOpenAI
-    mock_response = ModelResponse(
+    client = AsyncOpenAI(api_key="fake-api-key")
-        id="chatcmpl-AQ5RmV8GvVSRxEcDxnuXlQnsibiY9",
+
-        choices=[
+    with patch.object(
-            Choices(
+        client.chat.completions.with_raw_response, "create"
-                message=Message(
+    ) as mock_client:
-                    content=code.replace("Username", "Email").replace(
+        try:
-                        "username", "email"
+            await litellm.acompletion(
-                    ),
+                model="gpt-4o-mini",
-                    role="assistant",
+                messages=[
-                )
+                    {
                        "role": "user",
                        "content": "Replace the Username property with an Email property. Respond only with code, and with no markdown formatting.",
                    },
                    {"role": "user", "content": code},
                ],
                prediction={"type": "content", "content": code},
                client=client,
            )
-        ],
+        except Exception as e:
-        created=int(datetime.now().timestamp()),
+            print(f"Error: {e}")
        model="gpt-4o-mini-2024-07-18",
        usage={
            "completion_tokens": 207,
            "prompt_tokens": 175,
            "total_tokens": 382,
            "completion_tokens_details": {
                "accepted_prediction_tokens": 0,
                "reasoning_tokens": 0,
                "rejected_prediction_tokens": 80,
            },
        },
    )
-    mock_request = respx_mock.post("https://api.openai.com/v1/chat/completions").mock(
+        mock_client.assert_called_once()
-        return_value=httpx.Response(200, json=mock_response.dict())
+        request_body = mock_client.call_args.kwargs
    )
-    completion = await litellm.acompletion(
+        # Verify the request contains the prediction parameter
-        model="gpt-4o-mini",
+        assert "prediction" in request_body
-        messages=[
+        # verify prediction is correctly sent to the API
-            {
+        assert request_body["prediction"] == {"type": "content", "content": code}
                "role": "user",
                "content": "Replace the Username property with an Email property. Respond only with code, and with no markdown formatting.",
            },
            {"role": "user", "content": code},
        ],
        prediction={"type": "content", "content": code},
    )
    assert mock_request.called
    request_body = json.loads(mock_request.calls[0].request.content)
    # Verify the request contains the prediction parameter
    assert "prediction" in request_body
    # verify prediction is correctly sent to the API
    assert request_body["prediction"] == {"type": "content", "content": code}
    # Verify the completion tokens details
    assert completion.usage.completion_tokens_details.accepted_prediction_tokens == 0
    assert completion.usage.completion_tokens_details.rejected_prediction_tokens == 80
@pytest.mark.asyncio
@ -223,3 +198,73 @@ async def test_openai_prediction_param_with_caching():
    )
    assert completion_response_3.id != completion_response_1.id
@pytest.mark.asyncio()
 async def test_vision_with_custom_model():
    """
    Tests that an OpenAI compatible endpoint when sent an image will receive the image in the request
    """
    import base64
    import requests
    from openai import AsyncOpenAI
    client = AsyncOpenAI(api_key="fake-api-key")
    litellm.set_verbose = True
    api_base = "https://my-custom.api.openai.com"
    # Fetch and encode a test image
    url = "https://dummyimage.com/100/100/fff&text=Test+image"
    response = requests.get(url)
    file_data = response.content
    encoded_file = base64.b64encode(file_data).decode("utf-8")
    base64_image = f"data:image/png;base64,{encoded_file}"
    with patch.object(
        client.chat.completions.with_raw_response, "create"
    ) as mock_client:
        try:
            response = await litellm.acompletion(
                model="openai/my-custom-model",
                max_tokens=10,
                api_base=api_base,  # use the mock api
                messages=[
                    {
                        "role": "user",
                        "content": [
                            {"type": "text", "text": "What's in this image?"},
                            {
                                "type": "image_url",
                                "image_url": {"url": base64_image},
                            },
                        ],
                    }
                ],
                client=client,
            )
        except Exception as e:
            print(f"Error: {e}")
        mock_client.assert_called_once()
        request_body = mock_client.call_args.kwargs
        print("request_body: ", request_body)
        assert request_body["messages"] == [
            {
                "role": "user",
                "content": [
                    {"type": "text", "text": "What's in this image?"},
                    {
                        "type": "image_url",
                        "image_url": {
                            "url": "data:image/png;base64,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"
                        },
                    },
                ],
            },
        ]
        assert request_body["model"] == "my-custom-model"
        assert request_body["max_tokens"] == 10
--- a/tests/llm_translation/test_openai_o1.py
+++ b/tests/llm_translation/test_openai_o1.py
@ -2,7 +2,7 @@ import json
 import os
 import sys
 from datetime import datetime
-from unittest.mock import AsyncMock
+from unittest.mock import AsyncMock, patch, MagicMock
 sys.path.insert(
    0, os.path.abspath("../..")
@ -18,87 +18,75 @@ from litellm import Choices, Message, ModelResponse
@pytest.mark.asyncio
-@pytest.mark.respx
+async def test_o1_handle_system_role():
 async def test_o1_handle_system_role(respx_mock: MockRouter):
    """
    Tests that:
    - max_tokens is translated to 'max_completion_tokens'
    - role 'system' is translated to 'user'
    """
    from openai import AsyncOpenAI
    litellm.set_verbose = True
-    mock_response = ModelResponse(
+    client = AsyncOpenAI(api_key="fake-api-key")
        id="cmpl-mock",
        choices=[Choices(message=Message(content="Mocked response", role="assistant"))],
        created=int(datetime.now().timestamp()),
        model="o1-preview",
    )
-    mock_request = respx_mock.post("https://api.openai.com/v1/chat/completions").mock(
+    with patch.object(
-        return_value=httpx.Response(200, json=mock_response.dict())
+        client.chat.completions.with_raw_response, "create"
-    )
+    ) as mock_client:
        try:
            await litellm.acompletion(
                model="o1-preview",
                max_tokens=10,
                messages=[{"role": "system", "content": "Hello!"}],
                client=client,
            )
        except Exception as e:
            print(f"Error: {e}")
-    response = await litellm.acompletion(
+        mock_client.assert_called_once()
-        model="o1-preview",
+        request_body = mock_client.call_args.kwargs
        max_tokens=10,
        messages=[{"role": "system", "content": "Hello!"}],
    )
-    assert mock_request.called
+        print("request_body: ", request_body)
    request_body = json.loads(mock_request.calls[0].request.content)
-    print("request_body: ", request_body)
+        assert request_body["model"] == "o1-preview"
-
+        assert request_body["max_completion_tokens"] == 10
-    assert request_body == {
+        assert request_body["messages"] == [{"role": "user", "content": "Hello!"}]
        "model": "o1-preview",
        "max_completion_tokens": 10,
        "messages": [{"role": "user", "content": "Hello!"}],
    }
    print(f"response: {response}")
    assert isinstance(response, ModelResponse)
@pytest.mark.asyncio
@pytest.mark.respx
@pytest.mark.parametrize("model", ["gpt-4", "gpt-4-0314", "gpt-4-32k", "o1-preview"])
-async def test_o1_max_completion_tokens(respx_mock: MockRouter, model: str):
+async def test_o1_max_completion_tokens(model: str):
    """
    Tests that:
    - max_completion_tokens is passed directly to OpenAI chat completion models
    """
    from openai import AsyncOpenAI
    litellm.set_verbose = True
-    mock_response = ModelResponse(
+    client = AsyncOpenAI(api_key="fake-api-key")
        id="cmpl-mock",
        choices=[Choices(message=Message(content="Mocked response", role="assistant"))],
        created=int(datetime.now().timestamp()),
        model=model,
    )
-    mock_request = respx_mock.post("https://api.openai.com/v1/chat/completions").mock(
+    with patch.object(
-        return_value=httpx.Response(200, json=mock_response.dict())
+        client.chat.completions.with_raw_response, "create"
-    )
+    ) as mock_client:
        try:
            await litellm.acompletion(
                model=model,
                max_completion_tokens=10,
                messages=[{"role": "user", "content": "Hello!"}],
                client=client,
            )
        except Exception as e:
            print(f"Error: {e}")
-    response = await litellm.acompletion(
+        mock_client.assert_called_once()
-        model=model,
+        request_body = mock_client.call_args.kwargs
        max_completion_tokens=10,
        messages=[{"role": "user", "content": "Hello!"}],
    )
-    assert mock_request.called
+        print("request_body: ", request_body)
    request_body = json.loads(mock_request.calls[0].request.content)
-    print("request_body: ", request_body)
+        assert request_body["model"] == model
-
+        assert request_body["max_completion_tokens"] == 10
-    assert request_body == {
+        assert request_body["messages"] == [{"role": "user", "content": "Hello!"}]
        "model": model,
        "max_completion_tokens": 10,
        "messages": [{"role": "user", "content": "Hello!"}],
    }
    print(f"response: {response}")
    assert isinstance(response, ModelResponse)
 def test_litellm_responses():
--- a/tests/llm_translation/test_prompt_factory.py
+++ b/tests/llm_translation/test_prompt_factory.py
@ -687,3 +687,16 @@ def test_just_system_message():
            llm_provider="bedrock",
        )
        assert "bedrock requires at least one non-system message" in str(e.value)
 def test_convert_generic_image_chunk_to_openai_image_obj():
    from litellm.llms.prompt_templates.factory import (
        convert_generic_image_chunk_to_openai_image_obj,
        convert_to_anthropic_image_obj,
    )
    url = "https://i.pinimg.com/736x/b4/b1/be/b4b1becad04d03a9071db2817fc9fe77.jpg"
    image_obj = convert_to_anthropic_image_obj(url)
    url_str = convert_generic_image_chunk_to_openai_image_obj(image_obj)
    image_obj = convert_to_anthropic_image_obj(url_str)
    print(image_obj)
--- a/tests/llm_translation/test_supports_vision.py
+++ b/tests/llm_translation/test_supports_vision.py
@ -1,94 +0,0 @@
 import json
 import os
 import sys
 from datetime import datetime
 from unittest.mock import AsyncMock
 sys.path.insert(
    0, os.path.abspath("../..")
 )  # Adds the parent directory to the system path
 import httpx
 import pytest
 from respx import MockRouter
 import litellm
 from litellm import Choices, Message, ModelResponse
@pytest.mark.asyncio()
@pytest.mark.respx
 async def test_vision_with_custom_model(respx_mock: MockRouter):
    """
    Tests that an OpenAI compatible endpoint when sent an image will receive the image in the request
    """
    import base64
    import requests
    litellm.set_verbose = True
    api_base = "https://my-custom.api.openai.com"
    # Fetch and encode a test image
    url = "https://dummyimage.com/100/100/fff&text=Test+image"
    response = requests.get(url)
    file_data = response.content
    encoded_file = base64.b64encode(file_data).decode("utf-8")
    base64_image = f"data:image/png;base64,{encoded_file}"
    mock_response = ModelResponse(
        id="cmpl-mock",
        choices=[Choices(message=Message(content="Mocked response", role="assistant"))],
        created=int(datetime.now().timestamp()),
        model="my-custom-model",
    )
    mock_request = respx_mock.post(f"{api_base}/chat/completions").mock(
        return_value=httpx.Response(200, json=mock_response.dict())
    )
    response = await litellm.acompletion(
        model="openai/my-custom-model",
        max_tokens=10,
        api_base=api_base,  # use the mock api
        messages=[
            {
                "role": "user",
                "content": [
                    {"type": "text", "text": "What's in this image?"},
                    {
                        "type": "image_url",
                        "image_url": {"url": base64_image},
                    },
                ],
            }
        ],
    )
    assert mock_request.called
    request_body = json.loads(mock_request.calls[0].request.content)
    print("request_body: ", request_body)
    assert request_body == {
        "messages": [
            {
                "role": "user",
                "content": [
                    {"type": "text", "text": "What's in this image?"},
                    {
                        "type": "image_url",
                        "image_url": {
                            "url": "data:image/png;base64,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"
                        },
                    },
                ],
            }
        ],
        "model": "my-custom-model",
        "max_tokens": 10,
    }
    print(f"response: {response}")
    assert isinstance(response, ModelResponse)
--- a/tests/llm_translation/test_text_completion_unit_tests.py
+++ b/tests/llm_translation/test_text_completion_unit_tests.py
@ -6,6 +6,7 @@ from unittest.mock import AsyncMock
 import pytest
 import httpx
 from respx import MockRouter
 from unittest.mock import patch, MagicMock, AsyncMock
 sys.path.insert(
    0, os.path.abspath("../..")
@ -68,13 +69,16 @@ def test_convert_dict_to_text_completion_response():
    assert response.choices[0].logprobs.top_logprobs == [None, {",": -2.1568563}]
@pytest.mark.skip(
    reason="need to migrate huggingface to support httpx client being passed in"
 )
@pytest.mark.asyncio
@pytest.mark.respx
-async def test_huggingface_text_completion_logprobs(respx_mock: MockRouter):
+async def test_huggingface_text_completion_logprobs():
    """Test text completion with Hugging Face, focusing on logprobs structure"""
    litellm.set_verbose = True
    from litellm.llms.custom_httpx.http_handler import HTTPHandler, AsyncHTTPHandler
    # Mock the raw response from Hugging Face
    mock_response = [
        {
            "generated_text": ",\n\nI have a question...",  # truncated for brevity
@ -91,46 +95,48 @@ async def test_huggingface_text_completion_logprobs(respx_mock: MockRouter):
        }
    ]
-    # Mock the API request
+    return_val = AsyncMock()
    mock_request = respx_mock.post(
        "https://api-inference.huggingface.co/models/mistralai/Mistral-7B-v0.1"
    ).mock(return_value=httpx.Response(200, json=mock_response))
-    response = await litellm.atext_completion(
+    return_val.json.return_value = mock_response
        model="huggingface/mistralai/Mistral-7B-v0.1",
        prompt="good morning",
    )
-    # Verify the request
+    client = AsyncHTTPHandler()
-    assert mock_request.called
+    with patch.object(client, "post", return_value=return_val) as mock_post:
-    request_body = json.loads(mock_request.calls[0].request.content)
+        response = await litellm.atext_completion(
-    assert request_body == {
+            model="huggingface/mistralai/Mistral-7B-v0.1",
-        "inputs": "good morning",
+            prompt="good morning",
-        "parameters": {"details": True, "return_full_text": False},
+            client=client,
-        "stream": False,
+        )
    }
-    print("response=", response)
+        # Verify the request
        mock_post.assert_called_once()
        request_body = json.loads(mock_post.call_args.kwargs["data"])
        assert request_body == {
            "inputs": "good morning",
            "parameters": {"details": True, "return_full_text": False},
            "stream": False,
        }
-    # Verify response structure
+        print("response=", response)
    assert isinstance(response, TextCompletionResponse)
    assert response.object == "text_completion"
    assert response.model == "mistralai/Mistral-7B-v0.1"
-    # Verify logprobs structure
+        # Verify response structure
-    choice = response.choices[0]
+        assert isinstance(response, TextCompletionResponse)
-    assert choice.finish_reason == "length"
+        assert response.object == "text_completion"
-    assert choice.index == 0
+        assert response.model == "mistralai/Mistral-7B-v0.1"
    assert isinstance(choice.logprobs.tokens, list)
    assert isinstance(choice.logprobs.token_logprobs, list)
    assert isinstance(choice.logprobs.text_offset, list)
    assert isinstance(choice.logprobs.top_logprobs, list)
    assert choice.logprobs.tokens == [",", "\n"]
    assert choice.logprobs.token_logprobs == [-1.7626953, -1.7314453]
    assert choice.logprobs.text_offset == [0, 1]
    assert choice.logprobs.top_logprobs == [{}, {}]
-    # Verify usage
+        # Verify logprobs structure
-    assert response.usage["completion_tokens"] > 0
+        choice = response.choices[0]
-    assert response.usage["prompt_tokens"] > 0
+        assert choice.finish_reason == "length"
-    assert response.usage["total_tokens"] > 0
+        assert choice.index == 0
        assert isinstance(choice.logprobs.tokens, list)
        assert isinstance(choice.logprobs.token_logprobs, list)
        assert isinstance(choice.logprobs.text_offset, list)
        assert isinstance(choice.logprobs.top_logprobs, list)
        assert choice.logprobs.tokens == [",", "\n"]
        assert choice.logprobs.token_logprobs == [-1.7626953, -1.7314453]
        assert choice.logprobs.text_offset == [0, 1]
        assert choice.logprobs.top_logprobs == [{}, {}]
        # Verify usage
        assert response.usage["completion_tokens"] > 0
        assert response.usage["prompt_tokens"] > 0
        assert response.usage["total_tokens"] > 0
--- a/tests/llm_translation/test_vertex.py
+++ b/tests/llm_translation/test_vertex.py
@ -1146,6 +1146,21 @@ def test_process_gemini_image():
        mime_type="image/png", file_uri="https://example.com/image.png"
    )
    # Test HTTPS VIDEO URL
    https_result = _process_gemini_image("https://cloud-samples-data/video/animals.mp4")
    print("https_result PNG", https_result)
    assert https_result["file_data"] == FileDataType(
        mime_type="video/mp4", file_uri="https://cloud-samples-data/video/animals.mp4"
    )
    # Test HTTPS PDF URL
    https_result = _process_gemini_image("https://cloud-samples-data/pdf/animals.pdf")
    print("https_result PDF", https_result)
    assert https_result["file_data"] == FileDataType(
        mime_type="application/pdf",
        file_uri="https://cloud-samples-data/pdf/animals.pdf",
    )
    # Test base64 image
    base64_image = "data:image/jpeg;base64,/9j/4AAQSkZJRg..."
    base64_result = _process_gemini_image(base64_image)
@ -1190,80 +1205,6 @@ def test_get_image_mime_type_from_url():
    assert _get_image_mime_type_from_url("invalid_url") is None
@pytest.mark.parametrize(
    "image_url", ["https://example.com/image.jpg", "https://example.com/image.png"]
 )
 def test_image_completion_request(image_url):
    """https:// .jpg, .png images are passed directly to the model"""
    from unittest.mock import patch, Mock
    import litellm
    from litellm.llms.vertex_ai_and_google_ai_studio.gemini.transformation import (
        _get_image_mime_type_from_url,
    )
    # Mock response data
    mock_response = Mock()
    mock_response.json.return_value = {
        "candidates": [{"content": {"parts": [{"text": "This is a sunflower"}]}}],
        "usageMetadata": {
            "promptTokenCount": 11,
            "candidatesTokenCount": 50,
            "totalTokenCount": 61,
        },
        "modelVersion": "gemini-1.5-pro",
    }
    mock_response.raise_for_status = MagicMock()
    mock_response.status_code = 200
    # Expected request body
    expected_request_body = {
        "contents": [
            {
                "role": "user",
                "parts": [
                    {"text": "Whats in this image?"},
                    {
                        "file_data": {
                            "file_uri": image_url,
                            "mime_type": _get_image_mime_type_from_url(image_url),
                        }
                    },
                ],
            }
        ],
        "system_instruction": {"parts": [{"text": "Be a good bot"}]},
        "generationConfig": {},
    }
    messages = [
        {"role": "system", "content": "Be a good bot"},
        {
            "role": "user",
            "content": [
                {"type": "text", "text": "Whats in this image?"},
                {"type": "image_url", "image_url": {"url": image_url}},
            ],
        },
    ]
    client = HTTPHandler()
    with patch.object(client, "post", new=MagicMock()) as mock_post:
        mock_post.return_value = mock_response
        try:
            litellm.completion(
                model="gemini/gemini-1.5-pro",
                messages=messages,
                client=client,
            )
        except Exception as e:
            print(e)
        # Assert the request body matches expected
        mock_post.assert_called_once()
        print("mock_post.call_args.kwargs['json']", mock_post.call_args.kwargs["json"])
        assert mock_post.call_args.kwargs["json"] == expected_request_body
@pytest.mark.parametrize(
    "model, expected_url",
    [
@ -1298,20 +1239,3 @@ def test_vertex_embedding_url(model, expected_url):
    assert url == expected_url
    assert endpoint == "predict"
 from base_llm_unit_tests import BaseLLMChatTest
 class TestVertexGemini(BaseLLMChatTest):
    def get_base_completion_call_args(self) -> dict:
        return {"model": "gemini/gemini-1.5-flash"}
    def test_tool_call_no_arguments(self, tool_call_no_arguments):
        """Test that tool calls with no arguments is translated correctly. Relevant issue: https://github.com/BerriAI/litellm/issues/6833"""
        from litellm.llms.prompt_templates.factory import (
            convert_to_gemini_tool_call_invoke,
        )
        result = convert_to_gemini_tool_call_invoke(tool_call_no_arguments)
        print(result)
--- a/tests/local_testing/test_auth_checks.py
+++ b/tests/local_testing/test_auth_checks.py
@ -95,3 +95,107 @@ async def test_handle_failed_db_connection():
    print("_handle_failed_db_connection_for_get_key_object got exception", exc_info)
    assert str(exc_info.value) == "Failed to connect to DB"
@pytest.mark.parametrize(
    "model, expect_to_work",
    [("openai/gpt-4o-mini", True), ("openai/gpt-4o", False)],
 )
@pytest.mark.asyncio
 async def test_can_key_call_model(model, expect_to_work):
    """
    If wildcard model + specific model is used, choose the specific model settings
    """
    from litellm.proxy.auth.auth_checks import can_key_call_model
    from fastapi import HTTPException
    llm_model_list = [
        {
            "model_name": "openai/*",
            "litellm_params": {
                "model": "openai/*",
                "api_key": "test-api-key",
            },
            "model_info": {
                "id": "e6e7006f83029df40ebc02ddd068890253f4cd3092bcb203d3d8e6f6f606f30f",
                "db_model": False,
                "access_groups": ["public-openai-models"],
            },
        },
        {
            "model_name": "openai/gpt-4o",
            "litellm_params": {
                "model": "openai/gpt-4o",
                "api_key": "test-api-key",
            },
            "model_info": {
                "id": "0cfcd87f2cb12a783a466888d05c6c89df66db23e01cecd75ec0b83aed73c9ad",
                "db_model": False,
                "access_groups": ["private-openai-models"],
            },
        },
    ]
    router = litellm.Router(model_list=llm_model_list)
    args = {
        "model": model,
        "llm_model_list": llm_model_list,
        "valid_token": UserAPIKeyAuth(
            models=["public-openai-models"],
        ),
        "llm_router": router,
    }
    if expect_to_work:
        await can_key_call_model(**args)
    else:
        with pytest.raises(Exception) as e:
            await can_key_call_model(**args)
        print(e)
@pytest.mark.parametrize(
    "model, expect_to_work",
    [("openai/gpt-4o", False), ("openai/gpt-4o-mini", True)],
 )
@pytest.mark.asyncio
 async def test_can_team_call_model(model, expect_to_work):
    from litellm.proxy.auth.auth_checks import model_in_access_group
    from fastapi import HTTPException
    llm_model_list = [
        {
            "model_name": "openai/*",
            "litellm_params": {
                "model": "openai/*",
                "api_key": "test-api-key",
            },
            "model_info": {
                "id": "e6e7006f83029df40ebc02ddd068890253f4cd3092bcb203d3d8e6f6f606f30f",
                "db_model": False,
                "access_groups": ["public-openai-models"],
            },
        },
        {
            "model_name": "openai/gpt-4o",
            "litellm_params": {
                "model": "openai/gpt-4o",
                "api_key": "test-api-key",
            },
            "model_info": {
                "id": "0cfcd87f2cb12a783a466888d05c6c89df66db23e01cecd75ec0b83aed73c9ad",
                "db_model": False,
                "access_groups": ["private-openai-models"],
            },
        },
    ]
    router = litellm.Router(model_list=llm_model_list)
    args = {
        "model": model,
        "team_models": ["public-openai-models"],
        "llm_router": router,
    }
    if expect_to_work:
        assert model_in_access_group(**args)
    else:
        assert not model_in_access_group(**args)
--- a/tests/local_testing/test_azure_openai.py
+++ b/tests/local_testing/test_azure_openai.py
@ -33,7 +33,7 @@ from litellm.router import Router
@pytest.mark.asyncio()
@pytest.mark.respx()
-async def test_azure_tenant_id_auth(respx_mock: MockRouter):
+async def test_aaaaazure_tenant_id_auth(respx_mock: MockRouter):
    """
    Tests when we set  tenant_id, client_id, client_secret they don't get sent with the request
--- a/tests/local_testing/test_azure_perf.py
+++ b/tests/local_testing/test_azure_perf.py
@ -1,128 +1,128 @@
-#### What this tests ####
+# #### What this tests ####
-#    This adds perf testing to the router, to ensure it's never > 50ms slower than the azure-openai sdk.
+# #    This adds perf testing to the router, to ensure it's never > 50ms slower than the azure-openai sdk.
-import sys, os, time, inspect, asyncio, traceback
+# import sys, os, time, inspect, asyncio, traceback
-from datetime import datetime
+# from datetime import datetime
-import pytest
+# import pytest
-sys.path.insert(0, os.path.abspath("../.."))
+# sys.path.insert(0, os.path.abspath("../.."))
-import openai, litellm, uuid
+# import openai, litellm, uuid
-from openai import AsyncAzureOpenAI
+# from openai import AsyncAzureOpenAI
-client = AsyncAzureOpenAI(
+# client = AsyncAzureOpenAI(
-    api_key=os.getenv("AZURE_API_KEY"),
+#     api_key=os.getenv("AZURE_API_KEY"),
-    azure_endpoint=os.getenv("AZURE_API_BASE"),  # type: ignore
+#     azure_endpoint=os.getenv("AZURE_API_BASE"),  # type: ignore
-    api_version=os.getenv("AZURE_API_VERSION"),
+#     api_version=os.getenv("AZURE_API_VERSION"),
-)
+# )
-model_list = [
+# model_list = [
-    {
+#     {
-        "model_name": "azure-test",
+#         "model_name": "azure-test",
-        "litellm_params": {
+#         "litellm_params": {
-            "model": "azure/chatgpt-v-2",
+#             "model": "azure/chatgpt-v-2",
-            "api_key": os.getenv("AZURE_API_KEY"),
+#             "api_key": os.getenv("AZURE_API_KEY"),
-            "api_base": os.getenv("AZURE_API_BASE"),
+#             "api_base": os.getenv("AZURE_API_BASE"),
-            "api_version": os.getenv("AZURE_API_VERSION"),
+#             "api_version": os.getenv("AZURE_API_VERSION"),
-        },
+#         },
-    }
+#     }
-]
+# ]
-router = litellm.Router(model_list=model_list)  # type: ignore
+# router = litellm.Router(model_list=model_list)  # type: ignore
-async def _openai_completion():
+# async def _openai_completion():
-    try:
+#     try:
-        start_time = time.time()
+#         start_time = time.time()
-        response = await client.chat.completions.create(
+#         response = await client.chat.completions.create(
-            model="chatgpt-v-2",
+#             model="chatgpt-v-2",
-            messages=[{"role": "user", "content": f"This is a test: {uuid.uuid4()}"}],
+#             messages=[{"role": "user", "content": f"This is a test: {uuid.uuid4()}"}],
-            stream=True,
+#             stream=True,
-        )
+#         )
-        time_to_first_token = None
+#         time_to_first_token = None
-        first_token_ts = None
+#         first_token_ts = None
-        init_chunk = None
+#         init_chunk = None
-        async for chunk in response:
+#         async for chunk in response:
-            if (
+#             if (
-                time_to_first_token is None
+#                 time_to_first_token is None
-                and len(chunk.choices) > 0
+#                 and len(chunk.choices) > 0
-                and chunk.choices[0].delta.content is not None
+#                 and chunk.choices[0].delta.content is not None
-            ):
+#             ):
-                first_token_ts = time.time()
+#                 first_token_ts = time.time()
-                time_to_first_token = first_token_ts - start_time
+#                 time_to_first_token = first_token_ts - start_time
-                init_chunk = chunk
+#                 init_chunk = chunk
-        end_time = time.time()
+#         end_time = time.time()
-        print(
+#         print(
-            "OpenAI Call: ",
+#             "OpenAI Call: ",
-            init_chunk,
+#             init_chunk,
-            start_time,
+#             start_time,
-            first_token_ts,
+#             first_token_ts,
-            time_to_first_token,
+#             time_to_first_token,
-            end_time,
+#             end_time,
-        )
+#         )
-        return time_to_first_token
+#         return time_to_first_token
-    except Exception as e:
+#     except Exception as e:
-        print(e)
+#         print(e)
-        return None
+#         return None
-async def _router_completion():
+# async def _router_completion():
-    try:
+#     try:
-        start_time = time.time()
+#         start_time = time.time()
-        response = await router.acompletion(
+#         response = await router.acompletion(
-            model="azure-test",
+#             model="azure-test",
-            messages=[{"role": "user", "content": f"This is a test: {uuid.uuid4()}"}],
+#             messages=[{"role": "user", "content": f"This is a test: {uuid.uuid4()}"}],
-            stream=True,
+#             stream=True,
-        )
+#         )
-        time_to_first_token = None
+#         time_to_first_token = None
-        first_token_ts = None
+#         first_token_ts = None
-        init_chunk = None
+#         init_chunk = None
-        async for chunk in response:
+#         async for chunk in response:
-            if (
+#             if (
-                time_to_first_token is None
+#                 time_to_first_token is None
-                and len(chunk.choices) > 0
+#                 and len(chunk.choices) > 0
-                and chunk.choices[0].delta.content is not None
+#                 and chunk.choices[0].delta.content is not None
-            ):
+#             ):
-                first_token_ts = time.time()
+#                 first_token_ts = time.time()
-                time_to_first_token = first_token_ts - start_time
+#                 time_to_first_token = first_token_ts - start_time
-                init_chunk = chunk
+#                 init_chunk = chunk
-        end_time = time.time()
+#         end_time = time.time()
-        print(
+#         print(
-            "Router Call: ",
+#             "Router Call: ",
-            init_chunk,
+#             init_chunk,
-            start_time,
+#             start_time,
-            first_token_ts,
+#             first_token_ts,
-            time_to_first_token,
+#             time_to_first_token,
-            end_time - first_token_ts,
+#             end_time - first_token_ts,
-        )
+#         )
-        return time_to_first_token
+#         return time_to_first_token
-    except Exception as e:
+#     except Exception as e:
-        print(e)
+#         print(e)
-        return None
+#         return None
-async def test_azure_completion_streaming():
+# async def test_azure_completion_streaming():
-    """
+#     """
-    Test azure streaming call - measure on time to first (non-null) token.
+#     Test azure streaming call - measure on time to first (non-null) token.
-    """
+#     """
-    n = 3  # Number of concurrent tasks
+#     n = 3  # Number of concurrent tasks
-    ## OPENAI AVG. TIME
+#     ## OPENAI AVG. TIME
-    tasks = [_openai_completion() for _ in range(n)]
+#     tasks = [_openai_completion() for _ in range(n)]
-    chat_completions = await asyncio.gather(*tasks)
+#     chat_completions = await asyncio.gather(*tasks)
-    successful_completions = [c for c in chat_completions if c is not None]
+#     successful_completions = [c for c in chat_completions if c is not None]
-    total_time = 0
+#     total_time = 0
-    for item in successful_completions:
+#     for item in successful_completions:
-        total_time += item
+#         total_time += item
-    avg_openai_time = total_time / 3
+#     avg_openai_time = total_time / 3
-    ## ROUTER AVG. TIME
+#     ## ROUTER AVG. TIME
-    tasks = [_router_completion() for _ in range(n)]
+#     tasks = [_router_completion() for _ in range(n)]
-    chat_completions = await asyncio.gather(*tasks)
+#     chat_completions = await asyncio.gather(*tasks)
-    successful_completions = [c for c in chat_completions if c is not None]
+#     successful_completions = [c for c in chat_completions if c is not None]
-    total_time = 0
+#     total_time = 0
-    for item in successful_completions:
+#     for item in successful_completions:
-        total_time += item
+#         total_time += item
-    avg_router_time = total_time / 3
+#     avg_router_time = total_time / 3
-    ## COMPARE
+#     ## COMPARE
-    print(f"avg_router_time: {avg_router_time}; avg_openai_time: {avg_openai_time}")
+#     print(f"avg_router_time: {avg_router_time}; avg_openai_time: {avg_openai_time}")
-    assert avg_router_time < avg_openai_time + 0.5
+#     assert avg_router_time < avg_openai_time + 0.5
-# asyncio.run(test_azure_completion_streaming())
+# # asyncio.run(test_azure_completion_streaming())
--- a/tests/local_testing/test_caching_ssl.py
+++ b/tests/local_testing/test_caching_ssl.py
@ -99,3 +99,29 @@ def test_caching_router():
 # test_caching_router()
@pytest.mark.asyncio
 async def test_redis_with_ssl():
    """
    Test connecting to redis connection pool when ssl=None
    Relevant issue:
        User was seeing this error: `TypeError: AbstractConnection.__init__() got an unexpected keyword argument 'ssl'`
    """
    from litellm._redis import get_redis_connection_pool, get_redis_async_client
    # Get the connection pool with SSL
    # REDIS_HOST_WITH_SSL is just a redis cloud instance with Transport layer security (TLS) enabled
    pool = get_redis_connection_pool(
        host=os.environ.get("REDIS_HOST_WITH_SSL"),
        port=os.environ.get("REDIS_PORT_WITH_SSL"),
        password=os.environ.get("REDIS_PASSWORD_WITH_SSL"),
        ssl=None,
    )
    # Create Redis client with the pool
    redis_client = get_redis_async_client(connection_pool=pool)
    print("pinging redis")
    print(await redis_client.ping())
    print("pinged redis")
--- a/tests/local_testing/test_datadog.py
+++ b/tests/local_testing/test_datadog.py
@ -1,246 +0,0 @@
 import io
 import os
 import sys
 sys.path.insert(0, os.path.abspath("../.."))
 import asyncio
 import gzip
 import json
 import logging
 import time
 from unittest.mock import AsyncMock, patch
 import pytest
 import litellm
 from litellm import completion
 from litellm._logging import verbose_logger
 from litellm.integrations.datadog.types import DatadogPayload
 verbose_logger.setLevel(logging.DEBUG)
@pytest.mark.asyncio
 async def test_datadog_logging_http_request():
    """
    - Test that the HTTP request is made to Datadog
    - sent to the /api/v2/logs endpoint
    - the payload is batched
    - each element in the payload is a DatadogPayload
    - each element in a DatadogPayload.message contains all the valid fields
    """
    try:
        from litellm.integrations.datadog.datadog import DataDogLogger
        os.environ["DD_SITE"] = "https://fake.datadoghq.com"
        os.environ["DD_API_KEY"] = "anything"
        dd_logger = DataDogLogger()
        litellm.callbacks = [dd_logger]
        litellm.set_verbose = True
        # Create a mock for the async_client's post method
        mock_post = AsyncMock()
        mock_post.return_value.status_code = 202
        mock_post.return_value.text = "Accepted"
        dd_logger.async_client.post = mock_post
        # Make the completion call
        for _ in range(5):
            response = await litellm.acompletion(
                model="gpt-3.5-turbo",
                messages=[{"role": "user", "content": "what llm are u"}],
                max_tokens=10,
                temperature=0.2,
                mock_response="Accepted",
            )
            print(response)
        # Wait for 5 seconds
        await asyncio.sleep(6)
        # Assert that the mock was called
        assert mock_post.called, "HTTP request was not made"
        # Get the arguments of the last call
        args, kwargs = mock_post.call_args
        print("CAll args and kwargs", args, kwargs)
        # Print the request body
        # You can add more specific assertions here if needed
        # For example, checking if the URL is correct
        assert kwargs["url"].endswith("/api/v2/logs"), "Incorrect DataDog endpoint"
        body = kwargs["data"]
        # use gzip to unzip the body
        with gzip.open(io.BytesIO(body), "rb") as f:
            body = f.read().decode("utf-8")
        print(body)
        # body is string parse it to dict
        body = json.loads(body)
        print(body)
        assert len(body) == 5  # 5 logs should be sent to DataDog
        # Assert that the first element in body has the expected fields and shape
        assert isinstance(body[0], dict), "First element in body should be a dictionary"
        # Get the expected fields and their types from DatadogPayload
        expected_fields = DatadogPayload.__annotations__
        # Assert that all elements in body have the fields of DatadogPayload with correct types
        for log in body:
            assert isinstance(log, dict), "Each log should be a dictionary"
            for field, expected_type in expected_fields.items():
                assert field in log, f"Field '{field}' is missing from the log"
                assert isinstance(
                    log[field], expected_type
                ), f"Field '{field}' has incorrect type. Expected {expected_type}, got {type(log[field])}"
        # Additional assertion to ensure no extra fields are present
        for log in body:
            assert set(log.keys()) == set(
                expected_fields.keys()
            ), f"Log contains unexpected fields: {set(log.keys()) - set(expected_fields.keys())}"
        # Parse the 'message' field as JSON and check its structure
        message = json.loads(body[0]["message"])
        expected_message_fields = [
            "id",
            "call_type",
            "cache_hit",
            "start_time",
            "end_time",
            "response_time",
            "model",
            "user",
            "model_parameters",
            "spend",
            "messages",
            "response",
            "usage",
            "metadata",
        ]
        for field in expected_message_fields:
            assert field in message, f"Field '{field}' is missing from the message"
        # Check specific fields
        assert message["call_type"] == "acompletion"
        assert message["model"] == "gpt-3.5-turbo"
        assert isinstance(message["model_parameters"], dict)
        assert "temperature" in message["model_parameters"]
        assert "max_tokens" in message["model_parameters"]
        assert isinstance(message["response"], dict)
        assert isinstance(message["usage"], dict)
        assert isinstance(message["metadata"], dict)
    except Exception as e:
        pytest.fail(f"Test failed with exception: {str(e)}")
@pytest.mark.asyncio
 async def test_datadog_log_redis_failures():
    """
    Test that poorly configured Redis is logged as Warning on DataDog
    """
    try:
        from litellm.caching.caching import Cache
        from litellm.integrations.datadog.datadog import DataDogLogger
        litellm.cache = Cache(
            type="redis", host="badhost", port="6379", password="badpassword"
        )
        os.environ["DD_SITE"] = "https://fake.datadoghq.com"
        os.environ["DD_API_KEY"] = "anything"
        dd_logger = DataDogLogger()
        litellm.callbacks = [dd_logger]
        litellm.service_callback = ["datadog"]
        litellm.set_verbose = True
        # Create a mock for the async_client's post method
        mock_post = AsyncMock()
        mock_post.return_value.status_code = 202
        mock_post.return_value.text = "Accepted"
        dd_logger.async_client.post = mock_post
        # Make the completion call
        for _ in range(3):
            response = await litellm.acompletion(
                model="gpt-3.5-turbo",
                messages=[{"role": "user", "content": "what llm are u"}],
                max_tokens=10,
                temperature=0.2,
                mock_response="Accepted",
            )
            print(response)
        # Wait for 5 seconds
        await asyncio.sleep(6)
        # Assert that the mock was called
        assert mock_post.called, "HTTP request was not made"
        # Get the arguments of the last call
        args, kwargs = mock_post.call_args
        print("CAll args and kwargs", args, kwargs)
        # For example, checking if the URL is correct
        assert kwargs["url"].endswith("/api/v2/logs"), "Incorrect DataDog endpoint"
        body = kwargs["data"]
        # use gzip to unzip the body
        with gzip.open(io.BytesIO(body), "rb") as f:
            body = f.read().decode("utf-8")
        print(body)
        # body is string parse it to dict
        body = json.loads(body)
        print(body)
        failure_events = [log for log in body if log["status"] == "warning"]
        assert len(failure_events) > 0, "No failure events logged"
        print("ALL FAILURE/WARN EVENTS", failure_events)
        for event in failure_events:
            message = json.loads(event["message"])
            assert (
                event["status"] == "warning"
            ), f"Event status is not 'warning': {event['status']}"
            assert (
                message["service"] == "redis"
            ), f"Service is not 'redis': {message['service']}"
            assert "error" in message, "No 'error' field in the message"
            assert message["error"], "Error field is empty"
    except Exception as e:
        pytest.fail(f"Test failed with exception: {str(e)}")
@pytest.mark.asyncio
@pytest.mark.skip(reason="local-only test, to test if everything works fine.")
 async def test_datadog_logging():
    try:
        litellm.success_callback = ["datadog"]
        litellm.set_verbose = True
        response = await litellm.acompletion(
            model="gpt-3.5-turbo",
            messages=[{"role": "user", "content": "what llm are u"}],
            max_tokens=10,
            temperature=0.2,
        )
        print(response)
        await asyncio.sleep(5)
    except Exception as e:
        print(e)
--- a/tests/local_testing/test_exceptions.py
+++ b/tests/local_testing/test_exceptions.py
@ -1146,7 +1146,9 @@ async def test_exception_with_headers_httpx(
        except litellm.RateLimitError as e:
            exception_raised = True
-            assert e.litellm_response_headers is not None
+            assert (
                e.litellm_response_headers is not None
            ), "litellm_response_headers is None"
            print("e.litellm_response_headers", e.litellm_response_headers)
            assert int(e.litellm_response_headers["retry-after"]) == cooldown_time
--- a/tests/local_testing/test_get_model_info.py
+++ b/tests/local_testing/test_get_model_info.py
@ -102,3 +102,17 @@ def test_get_model_info_ollama_chat():
        print(mock_client.call_args.kwargs)
        assert mock_client.call_args.kwargs["json"]["name"] == "mistral"
 def test_get_model_info_gemini():
    """
    Tests if ALL gemini models have 'tpm' and 'rpm' in the model info
    """
    os.environ["LITELLM_LOCAL_MODEL_COST_MAP"] = "True"
    litellm.model_cost = litellm.get_model_cost_map(url="")
    model_map = litellm.model_cost
    for model, info in model_map.items():
        if model.startswith("gemini/") and not "gemma" in model:
            assert info.get("tpm") is not None, f"{model} does not have tpm"
            assert info.get("rpm") is not None, f"{model} does not have rpm"
--- a/tests/local_testing/test_http_parsing_utils.py
+++ b/tests/local_testing/test_http_parsing_utils.py
@ -0,0 +1,79 @@
 import pytest
 from fastapi import Request
 from fastapi.testclient import TestClient
 from starlette.datastructures import Headers
 from starlette.requests import HTTPConnection
 import os
 import sys
 sys.path.insert(
    0, os.path.abspath("../..")
 )  # Adds the parent directory to the system path
 from litellm.proxy.common_utils.http_parsing_utils import _read_request_body
@pytest.mark.asyncio
 async def test_read_request_body_valid_json():
    """Test the function with a valid JSON payload."""
    class MockRequest:
        async def body(self):
            return b'{"key": "value"}'
    request = MockRequest()
    result = await _read_request_body(request)
    assert result == {"key": "value"}
@pytest.mark.asyncio
 async def test_read_request_body_empty_body():
    """Test the function with an empty body."""
    class MockRequest:
        async def body(self):
            return b""
    request = MockRequest()
    result = await _read_request_body(request)
    assert result == {}
@pytest.mark.asyncio
 async def test_read_request_body_invalid_json():
    """Test the function with an invalid JSON payload."""
    class MockRequest:
        async def body(self):
            return b'{"key": value}'  # Missing quotes around `value`
    request = MockRequest()
    result = await _read_request_body(request)
    assert result == {}  # Should return an empty dict on failure
@pytest.mark.asyncio
 async def test_read_request_body_large_payload():
    """Test the function with a very large payload."""
    large_payload = '{"key":' + '"a"' * 10**6 + "}"  # Large payload
    class MockRequest:
        async def body(self):
            return large_payload.encode()
    request = MockRequest()
    result = await _read_request_body(request)
    assert result == {}  # Large payloads could trigger errors, so validate behavior
@pytest.mark.asyncio
 async def test_read_request_body_unexpected_error():
    """Test the function when an unexpected error occurs."""
    class MockRequest:
        async def body(self):
            raise ValueError("Unexpected error")
    request = MockRequest()
    result = await _read_request_body(request)
    assert result == {}  # Ensure fallback behavior
--- a/tests/local_testing/test_router.py
+++ b/tests/local_testing/test_router.py
@ -2115,10 +2115,14 @@ def test_router_get_model_info(model, base_model, llm_provider):
    assert deployment is not None
    if llm_provider == "openai" or (base_model is not None and llm_provider == "azure"):
-        router.get_router_model_info(deployment=deployment.to_json())
+        router.get_router_model_info(
            deployment=deployment.to_json(), received_model_name=model
        )
    else:
        try:
-            router.get_router_model_info(deployment=deployment.to_json())
+            router.get_router_model_info(
                deployment=deployment.to_json(), received_model_name=model
            )
            pytest.fail("Expected this to raise model not mapped error")
        except Exception as e:
            if "This model isn't mapped yet" in str(e):
--- a/tests/local_testing/test_router_init.py
+++ b/tests/local_testing/test_router_init.py
@ -536,7 +536,7 @@ def test_init_clients_azure_command_r_plus():
@pytest.mark.asyncio
-async def test_text_completion_with_organization():
+async def test_aaaaatext_completion_with_organization():
    try:
        print("Testing Text OpenAI with organization")
        model_list = [
--- a/tests/local_testing/test_router_utils.py
+++ b/tests/local_testing/test_router_utils.py
@ -174,3 +174,185 @@ async def test_update_kwargs_before_fallbacks(call_type):
            print(mock_client.call_args.kwargs)
            assert mock_client.call_args.kwargs["litellm_trace_id"] is not None
 def test_router_get_model_info_wildcard_routes():
    os.environ["LITELLM_LOCAL_MODEL_COST_MAP"] = "True"
    litellm.model_cost = litellm.get_model_cost_map(url="")
    router = Router(
        model_list=[
            {
                "model_name": "gemini/*",
                "litellm_params": {"model": "gemini/*"},
                "model_info": {"id": 1},
            },
        ]
    )
    model_info = router.get_router_model_info(
        deployment=None, received_model_name="gemini/gemini-1.5-flash", id="1"
    )
    print(model_info)
    assert model_info is not None
    assert model_info["tpm"] is not None
    assert model_info["rpm"] is not None
@pytest.mark.asyncio
 async def test_router_get_model_group_usage_wildcard_routes():
    os.environ["LITELLM_LOCAL_MODEL_COST_MAP"] = "True"
    litellm.model_cost = litellm.get_model_cost_map(url="")
    router = Router(
        model_list=[
            {
                "model_name": "gemini/*",
                "litellm_params": {"model": "gemini/*"},
                "model_info": {"id": 1},
            },
        ]
    )
    resp = await router.acompletion(
        model="gemini/gemini-1.5-flash",
        messages=[{"role": "user", "content": "Hello, how are you?"}],
        mock_response="Hello, I'm good.",
    )
    print(resp)
    await asyncio.sleep(1)
    tpm, rpm = await router.get_model_group_usage(model_group="gemini/gemini-1.5-flash")
    assert tpm is not None, "tpm is None"
    assert rpm is not None, "rpm is None"
@pytest.mark.asyncio
 async def test_call_router_callbacks_on_success():
    router = Router(
        model_list=[
            {
                "model_name": "gemini/*",
                "litellm_params": {"model": "gemini/*"},
                "model_info": {"id": 1},
            },
        ]
    )
    with patch.object(
        router.cache, "async_increment_cache", new=AsyncMock()
    ) as mock_callback:
        await router.acompletion(
            model="gemini/gemini-1.5-flash",
            messages=[{"role": "user", "content": "Hello, how are you?"}],
            mock_response="Hello, I'm good.",
        )
        await asyncio.sleep(1)
        assert mock_callback.call_count == 2
        assert (
            mock_callback.call_args_list[0]
            .kwargs["key"]
            .startswith("global_router:1:gemini/gemini-1.5-flash:tpm")
        )
        assert (
            mock_callback.call_args_list[1]
            .kwargs["key"]
            .startswith("global_router:1:gemini/gemini-1.5-flash:rpm")
        )
@pytest.mark.asyncio
 async def test_call_router_callbacks_on_failure():
    router = Router(
        model_list=[
            {
                "model_name": "gemini/*",
                "litellm_params": {"model": "gemini/*"},
                "model_info": {"id": 1},
            },
        ]
    )
    with patch.object(
        router.cache, "async_increment_cache", new=AsyncMock()
    ) as mock_callback:
        with pytest.raises(litellm.RateLimitError):
            await router.acompletion(
                model="gemini/gemini-1.5-flash",
                messages=[{"role": "user", "content": "Hello, how are you?"}],
                mock_response="litellm.RateLimitError",
                num_retries=0,
            )
        await asyncio.sleep(1)
        print(mock_callback.call_args_list)
        assert mock_callback.call_count == 1
        assert (
            mock_callback.call_args_list[0]
            .kwargs["key"]
            .startswith("global_router:1:gemini/gemini-1.5-flash:rpm")
        )
@pytest.mark.asyncio
 async def test_router_model_group_headers():
    os.environ["LITELLM_LOCAL_MODEL_COST_MAP"] = "True"
    litellm.model_cost = litellm.get_model_cost_map(url="")
    from litellm.types.utils import OPENAI_RESPONSE_HEADERS
    router = Router(
        model_list=[
            {
                "model_name": "gemini/*",
                "litellm_params": {"model": "gemini/*"},
                "model_info": {"id": 1},
            }
        ]
    )
    for _ in range(2):
        resp = await router.acompletion(
            model="gemini/gemini-1.5-flash",
            messages=[{"role": "user", "content": "Hello, how are you?"}],
            mock_response="Hello, I'm good.",
        )
        await asyncio.sleep(1)
    assert (
        resp._hidden_params["additional_headers"]["x-litellm-model-group"]
        == "gemini/gemini-1.5-flash"
    )
    assert "x-ratelimit-remaining-requests" in resp._hidden_params["additional_headers"]
    assert "x-ratelimit-remaining-tokens" in resp._hidden_params["additional_headers"]
@pytest.mark.asyncio
 async def test_get_remaining_model_group_usage():
    os.environ["LITELLM_LOCAL_MODEL_COST_MAP"] = "True"
    litellm.model_cost = litellm.get_model_cost_map(url="")
    from litellm.types.utils import OPENAI_RESPONSE_HEADERS
    router = Router(
        model_list=[
            {
                "model_name": "gemini/*",
                "litellm_params": {"model": "gemini/*"},
                "model_info": {"id": 1},
            }
        ]
    )
    for _ in range(2):
        await router.acompletion(
            model="gemini/gemini-1.5-flash",
            messages=[{"role": "user", "content": "Hello, how are you?"}],
            mock_response="Hello, I'm good.",
        )
        await asyncio.sleep(1)
    remaining_usage = await router.get_remaining_model_group_usage(
        model_group="gemini/gemini-1.5-flash"
    )
    assert remaining_usage is not None
    assert "x-ratelimit-remaining-requests" in remaining_usage
    assert "x-ratelimit-remaining-tokens" in remaining_usage
--- a/tests/local_testing/test_tpm_rpm_routing_v2.py
+++ b/tests/local_testing/test_tpm_rpm_routing_v2.py
@ -506,7 +506,7 @@ async def test_router_caching_ttl():
    ) as mock_client:
        await router.acompletion(model=model, messages=messages)
-        mock_client.assert_called_once()
+        # mock_client.assert_called_once()
        print(f"mock_client.call_args.kwargs: {mock_client.call_args.kwargs}")
        print(f"mock_client.call_args.args: {mock_client.call_args.args}")
--- a/Show more
+++ b/Show more
Author	SHA1	Message	Date
Krrish Dholakia	d2b123eef7	bump: version 1.53.1 → 1.53.2	2024-12-01 06:55:33 -08:00
Krish Dholakia	859b47f08b	LiteLLM Minor Fixes & Improvements (11/29/2024) (#6965 ) * fix(factory.py): ensure tool call converts image url Fixes https://github.com/BerriAI/litellm/issues/6953 * fix(transformation.py): support mp4 + pdf url's for vertex ai Fixes https://github.com/BerriAI/litellm/issues/6936 * fix(http_handler.py): mask gemini api key in error logs Fixes https://github.com/BerriAI/litellm/issues/6963 * docs(prometheus.md): update prometheus FAQs * feat(auth_checks.py): ensure specific model access > wildcard model access if wildcard model is in access group, but specific model is not - deny access * fix(auth_checks.py): handle auth checks for team based model access groups handles scenario where model access group used for wildcard models * fix(internal_user_endpoints.py): support adding guardrails on `/user/update` Fixes https://github.com/BerriAI/litellm/issues/6942 * fix(key_management_endpoints.py): fix prepare_metadata_fields helper * fix: fix tests * build(requirements.txt): bump openai dep version fixes proxies argument * test: fix tests * fix(http_handler.py): fix error message masking * fix(bedrock_guardrails.py): pass in prepped data * test: fix test * test: fix nvidia nim test * fix(http_handler.py): return original response headers * fix: revert maskedhttpstatuserror * test: update tests * test: cleanup test * fix(key_management_endpoints.py): fix metadata field update logic * fix(key_management_endpoints.py): maintain initial order of guardrails in key update * fix(key_management_endpoints.py): handle prepare metadata * fix: fix linting errors * fix: fix linting errors * fix: fix linting errors * fix: fix key management errors * fix(key_management_endpoints.py): update metadata * test: update test * refactor: add more debug statements * test: skip flaky test * test: fix test * fix: fix test * fix: fix update metadata logic * fix: fix test * ci(config.yml): change db url for e2e ui testing	2024-12-01 05:24:11 -08:00
Krish Dholakia	bd59f18809	fix(key_management_endpoints.py): support 'tags' param on `/key/update` (#6945 )	2024-11-29 02:02:54 -08:00
Ishaan Jaff	05f810922c	(feat) Allow disabling ErrorLogs written to the DB (#6940 ) * fix - allow disabling logging error logs * docs on disabling error logs * doc string for _PROXY_failure_handler * test_disable_error_logs * rename file * fix rename file * increase test coverage for test_enable_error_logs	2024-11-27 19:34:51 -08:00
Ishaan Jaff	0ac2d8b256	fix doc string	2024-11-27 18:55:06 -08:00
Ishaan Jaff	9393434d01	(fix) tag merging / aggregation logic (#6932 ) * use 1 helper to merge tags + ensure unique ness * test_add_litellm_data_to_request_duplicate_tags * fix _merge_tags * fix proxy utils test	2024-11-27 18:40:33 -08:00
Ishaan Jaff	d6181b2c9f	(feat) add enforcement for unique key aliases on /key/update and /key/generate (#6944 ) * add enforcement for unique key aliases * fix _enforce_unique_key_alias * fix _enforce_unique_key_alias * fix _enforce_unique_key_alias * test_enforce_unique_key_alias	2024-11-27 18:40:21 -08:00
Ishaan Jaff	4ebb7c8a7f	(docs + fix) Add docs on Moderations endpoint, Text Completion (#6947 ) * fix _pass_through_moderation_endpoint_factory * fix route_llm_request * doc moderations api * docs on /moderations * add e2e tests for moderations api * docs moderations api * test_pass_through_moderation_endpoint_factory * docs text completion	2024-11-27 16:30:48 -08:00
Ishaan Jaff	eba700a491	Revert "Revert "(feat) Allow using include to include external YAML files in a config.yaml (#6922 )"" This reverts commit `5d13302e6b`.	2024-11-27 16:08:59 -08:00
Ishaan Jaff	a8b8deb793	(fix) handle json decode errors for DD exception logging (#6934 ) * fix JSONDecodeError * handle async_log_proxy_authentication_errors * fix test_async_log_proxy_authentication_errors_get_request	2024-11-27 14:48:54 -08:00
Ishaan Jaff	77f714dc51	(bug fix) /key/update was not storing `budget_duration` in the DB (#6941 ) * fix - store budget_duration for keys * test_generate_and_update_key * test_update_user_unit_test * fix user update	2024-11-27 14:48:01 -08:00
Sara Han	8af5b11f54	docs: update the docs (#6923 )	2024-11-28 03:43:20 +05:30
Krish Dholakia	21156ff5d0	LiteLLM Minor Fixes & Improvements (11/27/2024) (#6943 ) * fix(http_parsing_utils.py): remove `ast.literal_eval()` from http utils Security fix - https://huntr.com/bounties/96a32812-213c-4819-ba4e-36143d35e95b?token=bf414bbd77f8b346556e 64ab2dd9301ea44339910877ea50401c76f977e36cdd78272f5fb4ca852a88a7e832828aae1192df98680544ee24aa98f3cf6980d8 bab641a66b7ccbc02c0e7d4ddba2db4dbe7318889dc0098d8db2d639f345f574159814627bb084563bad472e2f990f825bff0878a9 e281e72c88b4bc5884d637d186c0d67c9987c57c3f0caf395aff07b89ad2b7220d1dd7d1b427fd2260b5f01090efce5250f8b56ea2 c0ec19916c24b23825d85ce119911275944c840a1340d69e23ca6a462da610 * fix(converse/transformation.py): support bedrock apac cross region inference Fixes https://github.com/BerriAI/litellm/issues/6905 * fix(user_api_key_auth.py): add auth check for websocket endpoint Fixes https://github.com/BerriAI/litellm/issues/6926 * fix(user_api_key_auth.py): use `model` from query param * fix: fix linting error * test: run flaky tests first	2024-11-28 00:32:46 +05:30
Krish Dholakia	2d2931a215	LiteLLM Minor Fixes & Improvements (11/26/2024) (#6913 ) * docs(config_settings.md): document all router_settings * ci(config.yml): add router_settings doc test to ci/cd * test: debug test on ci/cd * test: debug ci/cd test * test: fix test * fix(team_endpoints.py): skip invalid team object. don't fail `/team/list` call Causes downstream errors if ui just fails to load team list * test(base_llm_unit_tests.py): add 'response_format={"type": "text"}' test to base_llm_unit_tests adds complete coverage for all 'response_format' values to ci/cd * feat(router.py): support wildcard routes in `get_router_model_info()` Addresses https://github.com/BerriAI/litellm/issues/6914 * build(model_prices_and_context_window.json): add tpm/rpm limits for all gemini models Allows for ratelimit tracking for gemini models even with wildcard routing enabled Addresses https://github.com/BerriAI/litellm/issues/6914 * feat(router.py): add tpm/rpm tracking on success/failure to global_router Addresses https://github.com/BerriAI/litellm/issues/6914 * feat(router.py): support wildcard routes on router.get_model_group_usage() * fix(router.py): fix linting error * fix(router.py): implement get_remaining_tokens_and_requests Addresses https://github.com/BerriAI/litellm/issues/6914 * fix(router.py): fix linting errors * test: fix test * test: fix tests * docs(config_settings.md): add missing dd env vars to docs * fix(router.py): check if hidden params is dict	2024-11-28 00:01:38 +05:30
Ishaan Jaff	5d13302e6b	Revert "(feat) Allow using include to include external YAML files in a config.yaml (#6922 )" This reverts commit `68e59824a3`.	2024-11-27 10:17:09 -08:00
Krrish Dholakia	07223bdedf	bump: version 1.53.0 → 1.53.1	2024-11-27 12:53:32 +05:30
Krrish Dholakia	562e7defe6	build(ui/): update ui build	2024-11-27 12:53:19 +05:30
Ishaan Jaff	a6da3dea03	(feat) dd logger - set tags according to the values set by those env vars (#6933 ) * dd logger, inherit from .envs * test_datadog_payload_environment_variables * fix _get_datadog_service	2024-11-26 22:08:04 -08:00
Ishaan Jaff	fe151db27c	bump: version 1.52.16 → 1.53.	2024-11-26 20:27:58 -08:00
Ishaan Jaff	68e59824a3	(feat) Allow using include to include external YAML files in a config.yaml (#6922 ) * add helper to process inlcudes directive on yaml * add doc on config management * unit tests for `include` on config.yaml	2024-11-26 20:27:12 -08:00
Ishaan Jaff	4bc06392db	(feat) log proxy auth errors on datadog (#6931 ) * add new dd type for auth errors * add async_log_proxy_authentication_errors * fix comment * use async_log_proxy_authentication_errors * test_datadog_post_call_failure_hook * test_async_log_proxy_authentication_errors	2024-11-26 20:26:57 -08:00
Ishaan Jaff	aea68cbeb6	(feat) DataDog Logger - Add Failure logging + use Standard Logging payload (#6929 ) * add async_log_failure_event for dd * use standard logging payload for DD logging * use standard logging payload for DD * fix use SLP status * allow opting into _create_v0_logging_payload * add unit tests for DD logging payload * fix dd logging tests	2024-11-26 19:27:06 -08:00
paul-gauthier	d84e355eab	sonnet supports pdf, haiku does not (#6928 )	2024-11-26 19:06:17 -08:00
Ishaan Jaff	8fd3bf34d8	(feat) pass through llm endpoints - add `PATCH` support (vertex context caching requires for update ops) (#6924 ) * add PATCH for pass through endpoints * test_pass_through_routes_support_all_methods	2024-11-26 14:39:13 -08:00
Krish Dholakia	8673f2541e	fix(key_management_endpoints.py): fix user-membership check when creating team key (#6890 ) * fix(key_management_endpoints.py): fix user-membership check when creating team key * docs: add deprecation notice on original `/v1/messages` endpoint + add better swagger tags on pass-through endpoints * fix(gemini/): fix image_url handling for gemini Fixes https://github.com/BerriAI/litellm/issues/6897 * fix(teams.tsx): fix member add when role is 'user' * fix(team_endpoints.py): /team/member_add fix adding several new members to team * test(test_vertex.py): remove redundant test * test(test_proxy_server.py): fix team member add tests	2024-11-26 14:19:24 +05:30
Ishaan Jaff	dcea31e50a	run ci/cd again for new release	2024-11-26 00:26:27 -08:00
Krrish Dholakia	0b15662c6e	test: temporarily comment out doc test - fix ci/cd issue in separate pr	2024-11-26 13:52:40 +05:30
Krrish Dholakia	fd288c5081	test: fix test	2024-11-26 13:48:08 +05:30
Krrish Dholakia	195112565d	test: fix documentation tests	2024-11-26 13:45:00 +05:30
Ishaan Jaff	8ec0e8cbc4	bump: version 1.52.15 → 1.52.16	2024-11-25 23:58:21 -08:00
Ishaan Jaff	c285132ad6	(docs) Simplify `/vertex_ai/` pass through docs (#6910 ) * simplify vertex pass through docs * allow using known path for setting up pass throughs * add unit testing for vtx pass through auth	2024-11-25 23:57:50 -08:00
Krrish Dholakia	d26ad42f86	docs(router_architecture.md): add router architecture docs	2024-11-26 12:54:38 +05:30
Ishaan Jaff	5c854650c2	(redis fix) - fix `AbstractConnection.__init__() got an unexpected keyword argument 'ssl'` (#6908 ) * add better debugging for get_redis_connection_pool + allow passing ssl=None * test_redis_with_ssl * test_redis_with_ssl * test_redis_with_ssl	2024-11-25 22:52:44 -08:00
Ishaan Jaff	552c0dd7a4	(fix) pass through endpoints - run logging async + use thread pool executor for sync logging callbacks (#6907 ) * run pass through logging async * fix use thread_pool_executor for pass through logging * test_pass_through_request_logging_failure_with_stream * fix anthropic pt logging test * test_pass_through_request_logging_failure	2024-11-25 22:52:05 -08:00
Ishaan Jaff	d52aae4e82	ui new build	2024-11-25 22:42:59 -08:00
Ishaan Jaff	e952c666f3	(UI fix) UI does not reload when you login / open a new tab (#6909 ) * store current page on url * update menu history	2024-11-25 22:41:45 -08:00
		`@ -1 +1 @@`
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`@ -1 +1,3 @@`
	More tests under `litellm/litellm/tests/*`.	`Unit tests for individual LLM providers.`

		Name of the test file is the name of the LLM provider - e.g. `test_openai.py` is for OpenAI.