Merge branch 'main' into litellm_redis_cluster

.circleci/config.yml

@@ -282,7 +282,7 @@ jobs:
             pip install "pytest==7.3.1"
             pip install "pytest-asyncio==0.21.1"
             pip install aiohttp
-            pip install openai
+            pip install "openai==1.40.0"
             python -m pip install --upgrade pip
             pip install "pydantic==2.7.1"
             pip install "pytest==7.3.1"

deploy/charts/litellm-helm/templates/deployment.yaml

@@ -13,8 +13,9 @@ spec:
       {{- include "litellm.selectorLabels" . | nindent 6 }}
   template:
     metadata:
-      {{- with .Values.podAnnotations }}
       annotations:
+        checksum/config: {{ include (print $.Template.BasePath "/configmap-litellm.yaml") . | sha256sum }}
+        {{- with .Values.podAnnotations }}
         {{- toYaml . | nindent 8 }}
         {{- end }}
       labels:

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

@@ -81,6 +81,7 @@ Works for:
 ```python
 import os
 from litellm import completion 
+from pydantic import BaseModel
 
 # add to env var 
 os.environ["OPENAI_API_KEY"] = ""

docs/my-website/docs/observability/callbacks.md

@@ -8,6 +8,7 @@ liteLLM supports:
 
 - [Custom Callback Functions](https://docs.litellm.ai/docs/observability/custom_callback)
 - [Langfuse](https://langfuse.com/docs)
+- [LangSmith](https://www.langchain.com/langsmith)
 - [Helicone](https://docs.helicone.ai/introduction)
 - [Traceloop](https://traceloop.com/docs)
 - [Lunary](https://lunary.ai/docs)

docs/my-website/docs/observability/langsmith_integration.md

@@ -56,7 +56,7 @@ response = litellm.completion(
 ```
 
 ## Advanced
-### Set Langsmith fields - Custom Projec, Run names, tags
+### Set Langsmith fields
 
 ```python
 import litellm
@@ -77,7 +77,15 @@ response = litellm.completion(
     metadata={
         "run_name": "litellmRUN",                                   # langsmith run name
         "project_name": "litellm-completion",                       # langsmith project name
-        "tags": ["model1", "prod-2"]            # tags to log on langsmith
+        "run_id": "497f6eca-6276-4993-bfeb-53cbbbba6f08",           # langsmith run id
+        "parent_run_id": "f8faf8c1-9778-49a4-9004-628cdb0047e5",    # langsmith run parent run id
+        "trace_id": "df570c03-5a03-4cea-8df0-c162d05127ac",         # langsmith run trace id
+        "session_id": "1ffd059c-17ea-40a8-8aef-70fd0307db82",       # langsmith run session id
+        "tags": ["model1", "prod-2"],                               # langsmith run tags
+        "metadata": {                                               # langsmith run metadata
+            "key1": "value1"
+        },
+        "dotted_order": "20240429T004912090000Z497f6eca-6276-4993-bfeb-53cbbbba6f08"
     }
 )
 print(response)

docs/my-website/docs/pass_through/vertex_ai.md

@@ -1,6 +1,10 @@
+import Image from '@theme/IdealImage';
+import Tabs from '@theme/Tabs';
+import TabItem from '@theme/TabItem';
+
 # [BETA] Vertex AI Endpoints (Pass-Through)
 
-Pass-through endpoints for Vertex AI - call provider-specific endpoint, in native format (no translation).
+Use VertexAI SDK to call endpoints on LiteLLM Gateway (native provider format)
 
 :::tip
 
@@ -40,16 +44,119 @@ litellm --config /path/to/config.yaml
 
 #### 3. Test it 
 
-```shell
-curl http://localhost:4000/vertex-ai/publishers/google/models/textembedding-gecko@001:countTokens \
--H "Content-Type: application/json" \
--H "Authorization: Bearer sk-1234" \
--d '{"instances":[{"content": "gm"}]}'
+```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)
 ```
+
 ## Usage Examples
 
 ### Gemini API (Generate Content)
 
+<Tabs>
+<TabItem value="py" label="Vertex Python SDK">
+
+```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 \
   -H "Content-Type: application/json" \
@@ -57,8 +164,77 @@ 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="py" label="Vertex Python SDK">
+
+```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 \
   -H "Content-Type: application/json" \
@@ -66,8 +242,86 @@ curl http://localhost:4000/vertex-ai/publishers/google/models/textembedding-geck
   -d '{"instances":[{"content": "gm"}]}'
 ```
 
+</TabItem>
+
+</Tabs>
+
 ### Imagen API
 
+<Tabs>
+<TabItem value="py" label="Vertex Python SDK">
+
+```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" \
@@ -75,8 +329,86 @@ 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="py" label="Vertex Python SDK">
+
+```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" \
@@ -84,10 +416,83 @@ curl http://localhost:4000/vertex-ai/publishers/google/models/gemini-1.5-flash-0
   -d '{"contents":[{"role": "user", "parts":[{"text": "hi"}]}]}'
 ```
 
+</TabItem>
+</Tabs>
+
 ### Tuning API 
 
 Create Fine Tuning Job
 
+<Tabs>
+
+<TabItem value="py" label="Vertex Python SDK">
+
+```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 \
       -H "Content-Type: application/json" \
@@ -99,3 +504,7 @@ curl http://localhost:4000/vertex-ai/tuningJobs \
   }
 }'
 ```
+
+</TabItem>
+
+</Tabs>

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

@@ -131,6 +131,56 @@ Expected Response
 }
 ```
 
+## Add Streaming Support 
+
+Here's a simple example of returning unix epoch seconds for both completion + streaming use-cases. 
+
+s/o [@Eloy Lafuente](https://github.com/stronk7) for this code example.
+
+```python
+import time
+from typing import Iterator, AsyncIterator
+from litellm.types.utils import GenericStreamingChunk, ModelResponse
+from litellm import CustomLLM, completion, acompletion
+
+class UnixTimeLLM(CustomLLM):
+    def completion(self, *args, **kwargs) -> ModelResponse:
+        return completion(
+            model="test/unixtime",
+            mock_response=str(int(time.time())),
+        )  # type: ignore
+
+    async def acompletion(self, *args, **kwargs) -> ModelResponse:
+        return await acompletion(
+            model="test/unixtime",
+            mock_response=str(int(time.time())),
+        )  # type: ignore
+
+    def streaming(self, *args, **kwargs) -> Iterator[GenericStreamingChunk]:
+        generic_streaming_chunk: GenericStreamingChunk = {
+            "finish_reason": "stop",
+            "index": 0,
+            "is_finished": True,
+            "text": str(int(time.time())),
+            "tool_use": None,
+            "usage": {"completion_tokens": 0, "prompt_tokens": 0, "total_tokens": 0},
+        }
+        return generic_streaming_chunk # type: ignore
+
+    async def astreaming(self, *args, **kwargs) -> AsyncIterator[GenericStreamingChunk]:
+        generic_streaming_chunk: GenericStreamingChunk = {
+            "finish_reason": "stop",
+            "index": 0,
+            "is_finished": True,
+            "text": str(int(time.time())),
+            "tool_use": None,
+            "usage": {"completion_tokens": 0, "prompt_tokens": 0, "total_tokens": 0},
+        }
+        yield generic_streaming_chunk # type: ignore
+
+unixtime = UnixTimeLLM()
+```
+
 ## Custom Handler Spec
 
 ```python

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

@@ -661,6 +661,7 @@ Here's how to use Vertex AI with the LiteLLM Proxy Server
 ## Specifying Safety Settings 
 In certain use-cases you may need to make calls to the models and pass [safety settigns](https://ai.google.dev/docs/safety_setting_gemini) different from the defaults. To do so, simple pass the `safety_settings` argument to `completion` or `acompletion`. For example:
 
+### Set per model/request
 
 <Tabs>
 
@@ -752,6 +753,65 @@ response = client.chat.completions.create(
 </TabItem>
 </Tabs>
 
+### Set Globally
+
+<Tabs>
+
+<TabItem value="sdk" label="SDK">
+
+```python
+import litellm 
+
+litellm.set_verbose = True 👈 See RAW REQUEST/RESPONSE 
+
+litellm.vertex_ai_safety_settings = [
+        {
+            "category": "HARM_CATEGORY_HARASSMENT",
+            "threshold": "BLOCK_NONE",
+        },
+        {
+            "category": "HARM_CATEGORY_HATE_SPEECH",
+            "threshold": "BLOCK_NONE",
+        },
+        {
+            "category": "HARM_CATEGORY_SEXUALLY_EXPLICIT",
+            "threshold": "BLOCK_NONE",
+        },
+        {
+            "category": "HARM_CATEGORY_DANGEROUS_CONTENT",
+            "threshold": "BLOCK_NONE",
+        },
+    ]
+response = completion(
+    model="vertex_ai/gemini-pro", 
+    messages=[{"role": "user", "content": "write code for saying hi from LiteLLM"}]
+)
+```
+</TabItem>
+<TabItem value="proxy" label="Proxy">
+
+```yaml
+model_list:
+  - model_name: gemini-experimental
+    litellm_params:
+      model: vertex_ai/gemini-experimental
+      vertex_project: litellm-epic
+      vertex_location: us-central1
+
+litellm_settings:
+    vertex_ai_safety_settings:
+      - category: HARM_CATEGORY_HARASSMENT
+        threshold: BLOCK_NONE
+      - category: HARM_CATEGORY_HATE_SPEECH
+        threshold: BLOCK_NONE
+      - category: HARM_CATEGORY_SEXUALLY_EXPLICIT
+        threshold: BLOCK_NONE
+      - category: HARM_CATEGORY_DANGEROUS_CONTENT
+        threshold: BLOCK_NONE
+```
+</TabItem>
+</Tabs>
+
 ## Set Vertex Project & Vertex Location
 All calls using Vertex AI require the following parameters:
 * Your Project ID
@@ -1450,7 +1510,7 @@ curl http://0.0.0.0:4000/v1/chat/completions \
 | code-gecko@latest| `completion('code-gecko@latest', messages)` |
 
 
-## Embedding Models
+## **Embedding Models**
 
 #### Usage - Embedding
 ```python
@@ -1504,7 +1564,158 @@ response = litellm.embedding(
 )
 ```
 
-## Image Generation Models
+## **Multi-Modal Embeddings**
+
+Usage
+
+<Tabs>
+<TabItem value="sdk" label="SDK">
+
+```python
+response = await litellm.aembedding(
+    model="vertex_ai/multimodalembedding@001",
+    input=[
+        {
+            "image": {
+                "gcsUri": "gs://cloud-samples-data/vertex-ai/llm/prompts/landmark1.png"
+            },
+            "text": "this is a unicorn",
+        },
+    ],
+)
+```
+
+</TabItem>
+<TabItem value="proxy" label="LiteLLM PROXY (Unified Endpoint)">
+
+1. Add model to config.yaml
+```yaml
+model_list:
+  - model_name: multimodalembedding@001
+    litellm_params:
+      model: vertex_ai/multimodalembedding@001
+      vertex_project: "adroit-crow-413218"
+      vertex_location: "us-central1"
+      vertex_credentials: adroit-crow-413218-a956eef1a2a8.json 
+
+litellm_settings:
+  drop_params: True
+```
+
+2. Start Proxy 
+
+```
+$ litellm --config /path/to/config.yaml
+```
+
+3. Make Request use OpenAI Python SDK
+
+```python
+import openai
+
+client = openai.OpenAI(api_key="sk-1234", base_url="http://0.0.0.0:4000")
+
+# # request sent to model set on litellm proxy, `litellm --model`
+response = client.embeddings.create(
+    model="multimodalembedding@001", 
+    input = None,
+    extra_body = {
+        "instances": [
+        {
+            "image": {
+                "gcsUri": "gs://cloud-samples-data/vertex-ai/llm/prompts/landmark1.png"
+            },
+            "text": "this is a unicorn",
+        },
+    ],
+    }
+)
+
+print(response)
+```
+
+</TabItem>
+<TabItem value="proxy-vtx" label="LiteLLM PROXY (Vertex SDK)">
+
+1. Add model to config.yaml
+```yaml
+default_vertex_config:
+  vertex_project: "adroit-crow-413218"
+  vertex_location: "us-central1"
+  vertex_credentials: adroit-crow-413218-a956eef1a2a8.json 
+```
+
+2. Start Proxy 
+
+```
+$ litellm --config /path/to/config.yaml
+```
+
+3. Make Request use OpenAI Python SDK
+
+```python
+import vertexai
+
+from vertexai.vision_models import Image, MultiModalEmbeddingModel, Video
+from vertexai.vision_models import VideoSegmentConfig
+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 = MultiModalEmbeddingModel.from_pretrained("multimodalembedding")
+image = Image.load_from_file(
+    "gs://cloud-samples-data/vertex-ai/llm/prompts/landmark1.png"
+)
+
+embeddings = model.get_embeddings(
+    image=image,
+    contextual_text="Colosseum",
+    dimension=1408,
+)
+print(f"Image Embedding: {embeddings.image_embedding}")
+print(f"Text Embedding: {embeddings.text_embedding}")
+```
+
+</TabItem>
+</Tabs>
+
+
+## **Image Generation Models**
 
 Usage 
 

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

@@ -728,6 +728,7 @@ general_settings:
     "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_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": "list", # list of allowed proxy API routes - a user can access. (currently JWT-Auth only)

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

@@ -101,8 +101,38 @@ Requirements:
 
 
 <Tabs>
+<TabItem value="key" label="Set on Key">
 
+```bash
+curl -L -X POST 'http://0.0.0.0:4000/key/generate' \
+-H 'Authorization: Bearer sk-1234' \
+-H 'Content-Type: application/json' \
+-d '{
+    "metadata": {
+        "tags": ["tag1", "tag2", "tag3"]
+    }
+}
 
+'
+```
+
+</TabItem>
+<TabItem value="team" label="Set on Team">
+
+```bash
+curl -L -X POST 'http://0.0.0.0:4000/team/new' \
+-H 'Authorization: Bearer sk-1234' \
+-H 'Content-Type: application/json' \
+-d '{
+    "metadata": {
+        "tags": ["tag1", "tag2", "tag3"]
+    }
+}
+
+'
+```
+
+</TabItem>
 <TabItem value="openai" label="OpenAI Python v1.0.0+">
 
 Set `extra_body={"metadata": { }}` to `metadata` you want to pass
@@ -270,7 +300,42 @@ Requirements:
 
 
 <Tabs>
+<TabItem value="key" label="Set on Key">
 
+```bash
+curl -L -X POST 'http://0.0.0.0:4000/key/generate' \
+-H 'Authorization: Bearer sk-1234' \
+-H 'Content-Type: application/json' \
+-d '{
+    "metadata": {
+      "spend_logs_metadata": {
+          "hello": "world"
+      }
+    }
+}
+
+'
+```
+
+</TabItem>
+<TabItem value="team" label="Set on Team">
+
+```bash
+curl -L -X POST 'http://0.0.0.0:4000/team/new' \
+-H 'Authorization: Bearer sk-1234' \
+-H 'Content-Type: application/json' \
+-d '{
+    "metadata": {
+      "spend_logs_metadata": {
+          "hello": "world"
+      }
+    }
+}
+
+'
+```
+
+</TabItem>
 
 <TabItem value="openai" label="OpenAI Python v1.0.0+">
 

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

@@ -61,6 +61,51 @@ litellm_settings:
 
 Removes any field with `user_api_key_*` from metadata.
 
+## What gets logged?
+
+Found under `kwargs["standard_logging_payload"]`. This is a standard payload, logged for every response.
+
+```python
+class StandardLoggingPayload(TypedDict):
+    id: str
+    call_type: str
+    response_cost: float
+    total_tokens: int
+    prompt_tokens: int
+    completion_tokens: int
+    startTime: float
+    endTime: float
+    completionStartTime: float
+    model_map_information: StandardLoggingModelInformation
+    model: str
+    model_id: Optional[str]
+    model_group: Optional[str]
+    api_base: str
+    metadata: StandardLoggingMetadata
+    cache_hit: Optional[bool]
+    cache_key: Optional[str]
+    saved_cache_cost: Optional[float]
+    request_tags: list
+    end_user: Optional[str]
+    requester_ip_address: Optional[str]
+    messages: Optional[Union[str, list, dict]]
+    response: Optional[Union[str, list, dict]]
+    model_parameters: dict
+    hidden_params: StandardLoggingHiddenParams
+
+class StandardLoggingHiddenParams(TypedDict):
+    model_id: Optional[str]
+    cache_key: Optional[str]
+    api_base: Optional[str]
+    response_cost: Optional[str]
+    additional_headers: Optional[dict]
+
+
+class StandardLoggingModelInformation(TypedDict):
+    model_map_key: str
+    model_map_value: Optional[ModelInfo]
+```
+
 ## Logging Proxy Input/Output - Langfuse
 
 We will use the `--config` to set `litellm.success_callback = ["langfuse"]` this will log all successfull LLM calls to langfuse. Make sure to set `LANGFUSE_PUBLIC_KEY` and `LANGFUSE_SECRET_KEY` in your environment

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

@@ -334,3 +334,4 @@ curl -X POST 'http://0.0.0.0:4000/chat/completions' \
 ```
 Key=... over available RPM=0. Model RPM=100, Active keys=None
 ```
+

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

@@ -488,9 +488,34 @@ You can set:
 
 
 <Tabs>
+<TabItem value="per-team" label="Per Team">
+
+Use `/team/new` or `/team/update`, to persist rate limits across multiple keys for a team.
+
+
+```shell
+curl --location 'http://0.0.0.0:4000/team/new' \
+--header 'Authorization: Bearer sk-1234' \
+--header 'Content-Type: application/json' \
+--data '{"team_id": "my-prod-team", "max_parallel_requests": 10, "tpm_limit": 20, "rpm_limit": 4}' 
+```
+
+[**See Swagger**](https://litellm-api.up.railway.app/#/team%20management/new_team_team_new_post)
+
+**Expected Response**
+
+```json
+{
+    "key": "sk-sA7VDkyhlQ7m8Gt77Mbt3Q",
+    "expires": "2024-01-19T01:21:12.816168",
+    "team_id": "my-prod-team",
+}
+```
+
+</TabItem>
 <TabItem value="per-user" label="Per Internal User">
 
-Use `/user/new`, to persist rate limits across multiple keys.
+Use `/user/new` or `/user/update`, to persist rate limits across multiple keys for internal users.
 
 
 ```shell
@@ -653,6 +678,70 @@ curl --location 'http://localhost:4000/chat/completions' \
 </TabItem>
 </Tabs>
 
+## Set default budget for ALL internal users 
+
+Use this to set a default budget for users who you give keys to.
+
+This will apply when a user has [`user_role="internal_user"`](./self_serve.md#available-roles) (set this via `/user/new` or `/user/update`). 
+
+This will NOT apply if a key has a team_id (team budgets will apply then). [Tell us how we can improve this!](https://github.com/BerriAI/litellm/issues)
+
+1. Define max budget in your config.yaml
+
+```yaml
+model_list: 
+  - model_name: "gpt-3.5-turbo"
+    litellm_params:
+      model: gpt-3.5-turbo
+      api_key: os.environ/OPENAI_API_KEY
+
+litellm_settings:
+  max_internal_user_budget: 0 # amount in USD
+  internal_user_budget_duration: "1mo" # reset every month
+```
+
+2. Create key for user 
+
+```bash
+curl -L -X POST 'http://0.0.0.0:4000/key/generate' \
+-H 'Authorization: Bearer sk-1234' \
+-H 'Content-Type: application/json' \
+-d '{}'
+```
+
+Expected Response: 
+
+```bash
+{
+  ...
+  "key": "sk-X53RdxnDhzamRwjKXR4IHg"
+}
+```
+
+3. Test it! 
+
+```bash
+curl -L -X POST 'http://0.0.0.0:4000/chat/completions' \
+-H 'Content-Type: application/json' \
+-H 'Authorization: Bearer sk-X53RdxnDhzamRwjKXR4IHg' \
+-d '{
+    "model": "gpt-3.5-turbo",
+    "messages": [{"role": "user", "content": "Hey, how's it going?"}]
+}'
+```
+
+Expected Response: 
+
+```bash
+{
+    "error": {
+        "message": "ExceededBudget: User=<user_id> over budget. Spend=3.7e-05, Budget=0.0",
+        "type": "budget_exceeded",
+        "param": null,
+        "code": "400"
+    }
+}
+```
 ## Grant Access to new model 
 
 Use model access groups to give users access to select models, and add new ones to it over time (e.g. mistral, llama-2, etc.). 

litellm-js/spend-logs/package-lock.json

@@ -6,7 +6,7 @@
     "": {
       "dependencies": {
         "@hono/node-server": "^1.10.1",
-        "hono": "^4.2.7"
+        "hono": "^4.5.8"
       },
       "devDependencies": {
         "@types/node": "^20.11.17",
@@ -463,9 +463,9 @@
       }
     },
     "node_modules/hono": {
-      "version": "4.2.7",
-      "resolved": "https://registry.npmjs.org/hono/-/hono-4.2.7.tgz",
-      "integrity": "sha512-k1xHi86tJnRIVvqhFMBDGFKJ8r5O+bEsT4P59ZK59r0F300Xd910/r237inVfuT/VmE86RQQffX4OYNda6dLXw==",
+      "version": "4.5.8",
+      "resolved": "https://registry.npmjs.org/hono/-/hono-4.5.8.tgz",
+      "integrity": "sha512-pqpSlcdqGkpTTRpLYU1PnCz52gVr0zVR9H5GzMyJWuKQLLEBQxh96q45QizJ2PPX8NATtz2mu31/PKW/Jt+90Q==",
       "engines": {
         "node": ">=16.0.0"
       }

litellm-js/spend-logs/package.json

@@ -4,7 +4,7 @@
   },
   "dependencies": {
     "@hono/node-server": "^1.10.1",
-    "hono": "^4.2.7"
+    "hono": "^4.5.8"
   },
   "devDependencies": {
     "@types/node": "^20.11.17",

litellm/__init__.py

@@ -339,6 +339,7 @@ api_version = None
 organization = None
 project = None
 config_path = None
+vertex_ai_safety_settings: Optional[dict] = None
 ####### COMPLETION MODELS ###################
 open_ai_chat_completion_models: List = []
 open_ai_text_completion_models: List = []

litellm/integrations/langsmith.py

@@ -98,6 +98,10 @@ class LangsmithLogger(CustomLogger):
         project_name = metadata.get("project_name", self.langsmith_project)
         run_name = metadata.get("run_name", self.langsmith_default_run_name)
         run_id = metadata.get("id", None)
+        parent_run_id = metadata.get("parent_run_id", None)
+        trace_id = metadata.get("trace_id", None)
+        session_id = metadata.get("session_id", None)
+        dotted_order = metadata.get("dotted_order", None)
         tags = metadata.get("tags", []) or []
         verbose_logger.debug(
             f"Langsmith Logging - project_name: {project_name}, run_name {run_name}"
@@ -149,6 +153,18 @@ class LangsmithLogger(CustomLogger):
         if run_id:
             data["id"] = run_id
 
+        if parent_run_id:
+            data["parent_run_id"] = parent_run_id
+
+        if trace_id:
+            data["trace_id"] = trace_id
+
+        if session_id:
+            data["session_id"] = session_id
+
+        if dotted_order:
+            data["dotted_order"] = dotted_order
+
         verbose_logger.debug("Langsmith Logging data on langsmith: %s", data)
 
         return data

litellm/litellm_core_utils/litellm_logging.py

@@ -524,6 +524,7 @@ class Logging:
             TextCompletionResponse,
             HttpxBinaryResponseContent,
         ],
+        cache_hit: Optional[bool] = None,
     ):
         """
         Calculate response cost using result + logging object variables.
@@ -535,10 +536,13 @@ class Logging:
             litellm_params=self.litellm_params
         )
 
+        if cache_hit is None:
+            cache_hit = self.model_call_details.get("cache_hit", False)
+
         response_cost = litellm.response_cost_calculator(
             response_object=result,
             model=self.model,
-            cache_hit=self.model_call_details.get("cache_hit", False),
+            cache_hit=cache_hit,
             custom_llm_provider=self.model_call_details.get(
                 "custom_llm_provider", None
             ),
@@ -630,6 +634,7 @@ class Logging:
                     init_response_obj=result,
                     start_time=start_time,
                     end_time=end_time,
+                    logging_obj=self,
                 )
             )
             return start_time, end_time, result
@@ -2181,6 +2186,7 @@ def get_standard_logging_object_payload(
     init_response_obj: Any,
     start_time: dt_object,
     end_time: dt_object,
+    logging_obj: Logging,
 ) -> Optional[StandardLoggingPayload]:
     try:
         if kwargs is None:
@@ -2277,11 +2283,17 @@ def get_standard_logging_object_payload(
             cache_key = litellm.cache.get_cache_key(**kwargs)
         else:
             cache_key = None
+
+        saved_cache_cost: Optional[float] = None
         if cache_hit is True:
             import time
 
             id = f"{id}_cache_hit{time.time()}"  # do not duplicate the request id
 
+            saved_cache_cost = logging_obj._response_cost_calculator(
+                result=init_response_obj, cache_hit=False
+            )
+
         ## Get model cost information ##
         base_model = _get_base_model_from_metadata(model_call_details=kwargs)
         custom_pricing = use_custom_pricing_for_model(litellm_params=litellm_params)
@@ -2318,6 +2330,7 @@ def get_standard_logging_object_payload(
             id=str(id),
             call_type=call_type or "",
             cache_hit=cache_hit,
+            saved_cache_cost=saved_cache_cost,
             startTime=start_time_float,
             endTime=end_time_float,
             completionStartTime=completion_start_time_float,

litellm/llms/bedrock_httpx.py

@@ -84,6 +84,7 @@ BEDROCK_CONVERSE_MODELS = [
     "meta.llama3-1-8b-instruct-v1:0",
     "meta.llama3-1-70b-instruct-v1:0",
     "meta.llama3-1-405b-instruct-v1:0",
+    "meta.llama3-70b-instruct-v1:0",
     "mistral.mistral-large-2407-v1:0",
 ]
 
@@ -1480,7 +1481,7 @@ class BedrockConverseLLM(BaseAWSLLM):
         optional_params: dict,
         acompletion: bool,
         timeout: Optional[Union[float, httpx.Timeout]],
-        litellm_params=None,
+        litellm_params: dict,
         logger_fn=None,
         extra_headers: Optional[dict] = None,
         client: Optional[Union[AsyncHTTPHandler, HTTPHandler]] = None,
@@ -1596,6 +1597,14 @@ class BedrockConverseLLM(BaseAWSLLM):
         supported_tool_call_params = ["tools", "tool_choice"]
         supported_guardrail_params = ["guardrailConfig"]
         ## TRANSFORMATION ##
+
+        bedrock_messages: List[MessageBlock] = _bedrock_converse_messages_pt(
+            messages=messages,
+            model=model,
+            llm_provider="bedrock_converse",
+            user_continue_message=litellm_params.pop("user_continue_message", None),
+        )
+
         # send all model-specific params in 'additional_request_params'
         for k, v in inference_params.items():
             if (
@@ -1608,11 +1617,6 @@ class BedrockConverseLLM(BaseAWSLLM):
         for key in additional_request_keys:
             inference_params.pop(key, None)
 
-        bedrock_messages: List[MessageBlock] = _bedrock_converse_messages_pt(
-            messages=messages,
-            model=model,
-            llm_provider="bedrock_converse",
-        )
         bedrock_tools: List[ToolBlock] = _bedrock_tools_pt(
             inference_params.pop("tools", [])
         )

litellm/llms/cohere.py

@@ -124,12 +124,14 @@ class CohereConfig:
         }
 
 
-def validate_environment(api_key):
-    headers = {
+def validate_environment(api_key, headers: dict):
+    headers.update(
+        {
             "Request-Source": "unspecified:litellm",
             "accept": "application/json",
             "content-type": "application/json",
         }
+    )
     if api_key:
         headers["Authorization"] = f"Bearer {api_key}"
     return headers
@@ -144,11 +146,12 @@ def completion(
     encoding,
     api_key,
     logging_obj,
+    headers: dict,
     optional_params=None,
     litellm_params=None,
     logger_fn=None,
 ):
-    headers = validate_environment(api_key)
+    headers = validate_environment(api_key, headers=headers)
     completion_url = api_base
     model = model
     prompt = " ".join(message["content"] for message in messages)
@@ -338,13 +341,14 @@ def embedding(
     model_response: litellm.EmbeddingResponse,
     logging_obj: LiteLLMLoggingObj,
     optional_params: dict,
+    headers: dict,
     encoding: Any,
     api_key: Optional[str] = None,
     aembedding: Optional[bool] = None,
     timeout: Union[float, httpx.Timeout] = httpx.Timeout(None),
     client: Optional[Union[HTTPHandler, AsyncHTTPHandler]] = None,
 ):
-    headers = validate_environment(api_key)
+    headers = validate_environment(api_key, headers=headers)
     embed_url = "https://api.cohere.ai/v1/embed"
     model = model
     data = {"model": model, "texts": input, **optional_params}

litellm/llms/cohere_chat.py

@@ -116,12 +116,14 @@ class CohereChatConfig:
         }
 
 
-def validate_environment(api_key):
-    headers = {
+def validate_environment(api_key, headers: dict):
+    headers.update(
+        {
             "Request-Source": "unspecified:litellm",
             "accept": "application/json",
             "content-type": "application/json",
         }
+    )
     if api_key:
         headers["Authorization"] = f"Bearer {api_key}"
     return headers
@@ -203,13 +205,14 @@ def completion(
     model_response: ModelResponse,
     print_verbose: Callable,
     optional_params: dict,
+    headers: dict,
     encoding,
     api_key,
     logging_obj,
     litellm_params=None,
     logger_fn=None,
 ):
-    headers = validate_environment(api_key)
+    headers = validate_environment(api_key, headers=headers)
     completion_url = api_base
     model = model
     most_recent_message, chat_history = cohere_messages_pt_v2(

litellm/llms/ollama_chat.py

@@ -4,14 +4,17 @@ import traceback
 import types
 import uuid
 from itertools import chain
-from typing import Optional
+from typing import List, Optional
 
 import aiohttp
 import httpx
 import requests
+from pydantic import BaseModel
 
 import litellm
 from litellm import verbose_logger
+from litellm.types.llms.ollama import OllamaToolCall, OllamaToolCallFunction
+from litellm.types.llms.openai import ChatCompletionAssistantToolCall
 
 
 class OllamaError(Exception):
@@ -175,7 +178,7 @@ class OllamaChatConfig:
                 ## CHECK IF MODEL SUPPORTS TOOL CALLING ##
                 try:
                     model_info = litellm.get_model_info(
-                        model=model, custom_llm_provider="ollama_chat"
+                        model=model, custom_llm_provider="ollama"
                     )
                     if model_info.get("supports_function_calling") is True:
                         optional_params["tools"] = value
@@ -237,13 +240,30 @@ def get_ollama_response(
     function_name = optional_params.pop("function_name", None)
     tools = optional_params.pop("tools", None)
 
+    new_messages = []
     for m in messages:
-        if "role" in m and m["role"] == "tool":
-            m["role"] = "assistant"
+        if isinstance(
+            m, BaseModel
+        ):  # avoid message serialization issues - https://github.com/BerriAI/litellm/issues/5319
+            m = m.model_dump(exclude_none=True)
+        if m.get("tool_calls") is not None and isinstance(m["tool_calls"], list):
+            new_tools: List[OllamaToolCall] = []
+            for tool in m["tool_calls"]:
+                typed_tool = ChatCompletionAssistantToolCall(**tool)  # type: ignore
+                if typed_tool["type"] == "function":
+                    ollama_tool_call = OllamaToolCall(
+                        function=OllamaToolCallFunction(
+                            name=typed_tool["function"]["name"],
+                            arguments=json.loads(typed_tool["function"]["arguments"]),
+                        )
+                    )
+                    new_tools.append(ollama_tool_call)
+            m["tool_calls"] = new_tools
+        new_messages.append(m)
 
     data = {
         "model": model,
-        "messages": messages,
+        "messages": new_messages,
         "options": optional_params,
         "stream": stream,
     }
@@ -263,7 +283,7 @@ def get_ollama_response(
         },
     )
     if acompletion is True:
-        if stream == True:
+        if stream is True:
             response = ollama_async_streaming(
                 url=url,
                 api_key=api_key,
@@ -283,7 +303,7 @@ def get_ollama_response(
                 function_name=function_name,
             )
         return response
-    elif stream == True:
+    elif stream is True:
         return ollama_completion_stream(
             url=url, api_key=api_key, data=data, logging_obj=logging_obj
         )

litellm/llms/openai.py

@@ -84,6 +84,8 @@ class MistralConfig:
 
     - `tool_choice` (string - 'auto'/'any'/'none' or null): Specifies if/how functions are called. If set to none the model won't call a function and will generate a message instead. If set to auto the model can choose to either generate a message or call a function. If set to any the model is forced to call a function. Default - 'auto'.
 
+    - `stop` (string or array of strings): Stop generation if this token is detected. Or if one of these tokens is detected when providing an array
+
     - `random_seed` (integer or null): The seed to use for random sampling. If set, different calls will generate deterministic results.
 
     - `safe_prompt` (boolean): Whether to inject a safety prompt before all conversations. API Default - 'false'.
@@ -99,6 +101,7 @@ class MistralConfig:
     random_seed: Optional[int] = None
     safe_prompt: Optional[bool] = None
     response_format: Optional[dict] = None
+    stop: Optional[Union[str, list]] = None
 
     def __init__(
         self,
@@ -110,6 +113,7 @@ class MistralConfig:
         random_seed: Optional[int] = None,
         safe_prompt: Optional[bool] = None,
         response_format: Optional[dict] = None,
+        stop: Optional[Union[str, list]] = None
     ) -> None:
         locals_ = locals().copy()
         for key, value in locals_.items():
@@ -143,6 +147,7 @@ class MistralConfig:
             "tools",
             "tool_choice",
             "seed",
+            "stop",
             "response_format",
         ]
 
@@ -166,6 +171,8 @@ class MistralConfig:
                 optional_params["temperature"] = value
             if param == "top_p":
                 optional_params["top_p"] = value
+            if param == "stop":
+                optional_params["stop"] = value                
             if param == "tool_choice" and isinstance(value, str):
                 optional_params["tool_choice"] = self._map_tool_choice(
                     tool_choice=value

litellm/llms/prompt_templates/factory.py

@@ -38,6 +38,18 @@ def prompt_injection_detection_default_pt():
 
 BAD_MESSAGE_ERROR_STR = "Invalid Message "
 
+# used to interweave user messages, to ensure user/assistant alternating
+DEFAULT_USER_CONTINUE_MESSAGE = {
+    "role": "user",
+    "content": "Please continue.",
+}  # similar to autogen. Only used if `litellm.modify_params=True`.
+
+# used to interweave assistant messages, to ensure user/assistant alternating
+DEFAULT_ASSISTANT_CONTINUE_MESSAGE = {
+    "role": "assistant",
+    "content": "Please continue.",
+}  # similar to autogen. Only used if `litellm.modify_params=True`.
+
 
 def map_system_message_pt(messages: list) -> list:
     """
@@ -2254,6 +2266,7 @@ def _bedrock_converse_messages_pt(
     messages: List,
     model: str,
     llm_provider: str,
+    user_continue_message: Optional[dict] = None,
 ) -> List[BedrockMessageBlock]:
     """
     Converts given messages from OpenAI format to Bedrock format
@@ -2264,6 +2277,21 @@ def _bedrock_converse_messages_pt(
 
     contents: List[BedrockMessageBlock] = []
     msg_i = 0
+
+    # if initial message is assistant message
+    if messages[0].get("role") is not None and messages[0]["role"] == "assistant":
+        if user_continue_message is not None:
+            messages.insert(0, user_continue_message)
+        elif litellm.modify_params:
+            messages.insert(0, DEFAULT_USER_CONTINUE_MESSAGE)
+
+    # if final message is assistant message
+    if messages[-1].get("role") is not None and messages[-1]["role"] == "assistant":
+        if user_continue_message is not None:
+            messages.append(user_continue_message)
+        elif litellm.modify_params:
+            messages.append(DEFAULT_USER_CONTINUE_MESSAGE)
+
     while msg_i < len(messages):
         user_content: List[BedrockContentBlock] = []
         init_msg_i = msg_i
@@ -2344,6 +2372,7 @@ def _bedrock_converse_messages_pt(
                 model=model,
                 llm_provider=llm_provider,
             )
+
     return contents
 
 

litellm/llms/vertex_httpx.py

@@ -9,7 +9,7 @@ import types
 import uuid
 from enum import Enum
 from functools import partial
-from typing import Any, Callable, Dict, List, Literal, Optional, Tuple, Union
+from typing import Any, Callable, Coroutine, Dict, List, Literal, Optional, Tuple, Union
 
 import httpx  # type: ignore
 import requests  # type: ignore
@@ -38,12 +38,15 @@ from litellm.types.llms.vertex_ai import (
     FunctionDeclaration,
     GenerateContentResponseBody,
     GenerationConfig,
+    Instance,
+    InstanceVideo,
     PartType,
     RequestBody,
     SafetSettingsConfig,
     SystemInstructions,
     ToolConfig,
     Tools,
+    VertexMultimodalEmbeddingRequest,
 )
 from litellm.types.utils import GenericStreamingChunk
 from litellm.utils import CustomStreamWrapper, ModelResponse, Usage
@@ -188,9 +191,11 @@ class GoogleAIStudioGeminiConfig:  # key diff from VertexAI - 'frequency_penalty
                     elif value["type"] == "text":  # type: ignore
                         optional_params["response_mime_type"] = "text/plain"
                     if "response_schema" in value:  # type: ignore
+                        optional_params["response_mime_type"] = "application/json"
                         optional_params["response_schema"] = value["response_schema"]  # type: ignore
                 elif value["type"] == "json_schema":  # type: ignore
                     if "json_schema" in value and "schema" in value["json_schema"]:  # type: ignore
+                        optional_params["response_mime_type"] = "application/json"
                         optional_params["response_schema"] = value["json_schema"]["schema"]  # type: ignore
             if param == "tools" and isinstance(value, list):
                 gtool_func_declarations = []
@@ -400,9 +405,11 @@ class VertexGeminiConfig:
                 elif value["type"] == "text":
                     optional_params["response_mime_type"] = "text/plain"
                 if "response_schema" in value:
+                    optional_params["response_mime_type"] = "application/json"
                     optional_params["response_schema"] = value["response_schema"]
                 elif value["type"] == "json_schema":  # type: ignore
                     if "json_schema" in value and "schema" in value["json_schema"]:  # type: ignore
+                        optional_params["response_mime_type"] = "application/json"
                         optional_params["response_schema"] = value["json_schema"]["schema"]  # type: ignore
             if param == "frequency_penalty":
                 optional_params["frequency_penalty"] = value
@@ -594,6 +601,10 @@ class VertexLLM(BaseLLM):
         self._credentials: Optional[Any] = None
         self.project_id: Optional[str] = None
         self.async_handler: Optional[AsyncHTTPHandler] = None
+        self.SUPPORTED_MULTIMODAL_EMBEDDING_MODELS = [
+            "multimodalembedding",
+            "multimodalembedding@001",
+        ]
 
     def _process_response(
         self,
@@ -1537,6 +1548,160 @@ class VertexLLM(BaseLLM):
 
         return model_response
 
+    def multimodal_embedding(
+        self,
+        model: str,
+        input: Union[list, str],
+        print_verbose,
+        model_response: litellm.EmbeddingResponse,
+        optional_params: dict,
+        api_key: Optional[str] = None,
+        logging_obj=None,
+        encoding=None,
+        vertex_project=None,
+        vertex_location=None,
+        vertex_credentials=None,
+        aembedding=False,
+        timeout=300,
+        client=None,
+    ):
+
+        if client is None:
+            _params = {}
+            if timeout is not None:
+                if isinstance(timeout, float) or isinstance(timeout, int):
+                    _httpx_timeout = httpx.Timeout(timeout)
+                    _params["timeout"] = _httpx_timeout
+            else:
+                _params["timeout"] = httpx.Timeout(timeout=600.0, connect=5.0)
+
+            sync_handler: HTTPHandler = HTTPHandler(**_params)  # type: ignore
+        else:
+            sync_handler = client  # type: ignore
+
+        url = f"https://{vertex_location}-aiplatform.googleapis.com/v1/projects/{vertex_project}/locations/{vertex_location}/publishers/google/models/{model}:predict"
+
+        auth_header, _ = self._ensure_access_token(
+            credentials=vertex_credentials, project_id=vertex_project
+        )
+        optional_params = optional_params or {}
+
+        request_data = VertexMultimodalEmbeddingRequest()
+
+        if "instances" in optional_params:
+            request_data["instances"] = optional_params["instances"]
+        elif isinstance(input, list):
+            request_data["instances"] = input
+        else:
+            # construct instances
+            vertex_request_instance = Instance(**optional_params)
+
+            if isinstance(input, str):
+                vertex_request_instance["text"] = input
+
+            request_data["instances"] = [vertex_request_instance]
+
+        request_str = f"\n curl -X POST \\\n -H \"Authorization: Bearer {auth_header[:10] + 'XXXXXXXXXX'}\" \\\n -H \"Content-Type: application/json; charset=utf-8\" \\\n -d {request_data} \\\n \"{url}\""
+        logging_obj.pre_call(
+            input=[],
+            api_key=None,
+            additional_args={
+                "complete_input_dict": optional_params,
+                "request_str": request_str,
+            },
+        )
+
+        logging_obj.pre_call(
+            input=[],
+            api_key=None,
+            additional_args={
+                "complete_input_dict": optional_params,
+                "request_str": request_str,
+            },
+        )
+
+        headers = {
+            "Content-Type": "application/json; charset=utf-8",
+            "Authorization": f"Bearer {auth_header}",
+        }
+
+        if aembedding is True:
+            return self.async_multimodal_embedding(
+                model=model,
+                api_base=url,
+                data=request_data,
+                timeout=timeout,
+                headers=headers,
+                client=client,
+                model_response=model_response,
+            )
+
+        response = sync_handler.post(
+            url=url,
+            headers=headers,
+            data=json.dumps(request_data),
+        )
+
+        if response.status_code != 200:
+            raise Exception(f"Error: {response.status_code} {response.text}")
+
+        _json_response = response.json()
+        if "predictions" not in _json_response:
+            raise litellm.InternalServerError(
+                message=f"embedding response does not contain 'predictions', got {_json_response}",
+                llm_provider="vertex_ai",
+                model=model,
+            )
+        _predictions = _json_response["predictions"]
+
+        model_response.data = _predictions
+        model_response.model = model
+
+        return model_response
+
+    async def async_multimodal_embedding(
+        self,
+        model: str,
+        api_base: str,
+        data: VertexMultimodalEmbeddingRequest,
+        model_response: litellm.EmbeddingResponse,
+        timeout: Optional[Union[float, httpx.Timeout]],
+        headers={},
+        client: Optional[AsyncHTTPHandler] = None,
+    ) -> litellm.EmbeddingResponse:
+        if client is None:
+            _params = {}
+            if timeout is not None:
+                if isinstance(timeout, float) or isinstance(timeout, int):
+                    timeout = httpx.Timeout(timeout)
+                _params["timeout"] = timeout
+            client = AsyncHTTPHandler(**_params)  # type: ignore
+        else:
+            client = client  # type: ignore
+
+        try:
+            response = await client.post(api_base, headers=headers, json=data)  # type: ignore
+            response.raise_for_status()
+        except httpx.HTTPStatusError as err:
+            error_code = err.response.status_code
+            raise VertexAIError(status_code=error_code, message=err.response.text)
+        except httpx.TimeoutException:
+            raise VertexAIError(status_code=408, message="Timeout error occurred.")
+
+        _json_response = response.json()
+        if "predictions" not in _json_response:
+            raise litellm.InternalServerError(
+                message=f"embedding response does not contain 'predictions', got {_json_response}",
+                llm_provider="vertex_ai",
+                model=model,
+            )
+        _predictions = _json_response["predictions"]
+
+        model_response.data = _predictions
+        model_response.model = model
+
+        return model_response
+
 
 class ModelResponseIterator:
     def __init__(self, streaming_response, sync_stream: bool):

litellm/main.py

@@ -943,6 +943,7 @@ def completion(
             output_cost_per_token=output_cost_per_token,
             cooldown_time=cooldown_time,
             text_completion=kwargs.get("text_completion"),
+            user_continue_message=kwargs.get("user_continue_message"),
         )
         logging.update_environment_variables(
             model=model,
@@ -1634,6 +1635,13 @@ def completion(
                 or "https://api.cohere.ai/v1/generate"
             )
 
+            headers = headers or litellm.headers or {}
+            if headers is None:
+                headers = {}
+
+            if extra_headers is not None:
+                headers.update(extra_headers)
+
             model_response = cohere.completion(
                 model=model,
                 messages=messages,
@@ -1644,6 +1652,7 @@ def completion(
                 litellm_params=litellm_params,
                 logger_fn=logger_fn,
                 encoding=encoding,
+                headers=headers,
                 api_key=cohere_key,
                 logging_obj=logging,  # model call logging done inside the class as we make need to modify I/O to fit aleph alpha's requirements
             )
@@ -1674,6 +1683,13 @@ def completion(
                 or "https://api.cohere.ai/v1/chat"
             )
 
+            headers = headers or litellm.headers or {}
+            if headers is None:
+                headers = {}
+
+            if extra_headers is not None:
+                headers.update(extra_headers)
+
             model_response = cohere_chat.completion(
                 model=model,
                 messages=messages,
@@ -1682,6 +1698,7 @@ def completion(
                 print_verbose=print_verbose,
                 optional_params=optional_params,
                 litellm_params=litellm_params,
+                headers=headers,
                 logger_fn=logger_fn,
                 encoding=encoding,
                 api_key=cohere_key,
@@ -2288,7 +2305,7 @@ def completion(
                     model_response=model_response,
                     print_verbose=print_verbose,
                     optional_params=optional_params,
-                    litellm_params=litellm_params,
+                    litellm_params=litellm_params,  # type: ignore
                     logger_fn=logger_fn,
                     encoding=encoding,
                     logging_obj=logging,
@@ -2464,7 +2481,7 @@ def completion(
                 model_response=model_response,
                 encoding=encoding,
             )
-            if acompletion is True or optional_params.get("stream", False) == True:
+            if acompletion is True or optional_params.get("stream", False) is True:
                 return generator
 
             response = generator
@@ -3158,6 +3175,7 @@ def embedding(
     encoding_format = kwargs.get("encoding_format", None)
     proxy_server_request = kwargs.get("proxy_server_request", None)
     aembedding = kwargs.get("aembedding", None)
+    extra_headers = kwargs.get("extra_headers", None)
     ### CUSTOM MODEL COST ###
     input_cost_per_token = kwargs.get("input_cost_per_token", None)
     output_cost_per_token = kwargs.get("output_cost_per_token", None)
@@ -3229,6 +3247,7 @@ def embedding(
         "model_config",
         "cooldown_time",
         "tags",
+        "extra_headers",
     ]
     default_params = openai_params + litellm_params
     non_default_params = {
@@ -3292,7 +3311,7 @@ def embedding(
                 "cooldown_time": cooldown_time,
             },
         )
-        if azure == True or custom_llm_provider == "azure":
+        if azure is True or custom_llm_provider == "azure":
             # azure configs
             api_type = get_secret("AZURE_API_TYPE") or "azure"
 
@@ -3398,12 +3417,18 @@ def embedding(
                 or get_secret("CO_API_KEY")
                 or litellm.api_key
             )
+
+            if extra_headers is not None and isinstance(extra_headers, dict):
+                headers = extra_headers
+            else:
+                headers = {}
             response = cohere.embedding(
                 model=model,
                 input=input,
                 optional_params=optional_params,
                 encoding=encoding,
                 api_key=cohere_key,  # type: ignore
+                headers=headers,
                 logging_obj=logging,
                 model_response=EmbeddingResponse(),
                 aembedding=aembedding,
@@ -3477,6 +3502,26 @@ def embedding(
                 or get_secret("VERTEX_CREDENTIALS")
             )
 
+            if (
+                "image" in optional_params
+                or "video" in optional_params
+                or model in vertex_chat_completion.SUPPORTED_MULTIMODAL_EMBEDDING_MODELS
+            ):
+                # multimodal embedding is supported on vertex httpx
+                response = vertex_chat_completion.multimodal_embedding(
+                    model=model,
+                    input=input,
+                    encoding=encoding,
+                    logging_obj=logging,
+                    optional_params=optional_params,
+                    model_response=EmbeddingResponse(),
+                    vertex_project=vertex_ai_project,
+                    vertex_location=vertex_ai_location,
+                    vertex_credentials=vertex_credentials,
+                    aembedding=aembedding,
+                    print_verbose=print_verbose,
+                )
+            else:
                 response = vertex_ai.embedding(
                     model=model,
                     input=input,

litellm/proxy/_new_secret_config.yaml

@@ -3,9 +3,11 @@ model_list:
     litellm_params:
       model: "*"
 
-
 litellm_settings:
-  cache: True
-  cache_params:
-    type: redis
-    redis_startup_nodes: [{"host": "127.0.0.1", "port": "7001"}]
+  success_callback: ["s3"]
+  cache: true
+  s3_callback_params:
+    s3_bucket_name: mytestbucketlitellm   # 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

litellm/proxy/_types.py

@@ -21,6 +21,13 @@ else:
     Span = Any
 
 
+class LiteLLMTeamRoles(enum.Enum):
+    # team admin
+    TEAM_ADMIN = "admin"
+    # team member
+    TEAM_MEMBER = "user"
+
+
 class LitellmUserRoles(str, enum.Enum):
     """
     Admin Roles:
@@ -335,6 +342,11 @@ class LiteLLMRoutes(enum.Enum):
         + sso_only_routes
     )
 
+    self_managed_routes: List = [
+        "/team/member_add",
+        "/team/member_delete",
+    ]  # routes that manage their own allowed/disallowed logic
+
 
 # class LiteLLMAllowedRoutes(LiteLLMBase):
 #     """
@@ -1308,6 +1320,7 @@ class LiteLLM_VerificationTokenView(LiteLLM_VerificationToken):
     soft_budget: Optional[float] = None
     team_model_aliases: Optional[Dict] = None
     team_member_spend: Optional[float] = None
+    team_member: Optional[Member] = None
     team_metadata: Optional[Dict] = None
 
     # End User Params

litellm/proxy/auth/user_api_key_auth.py

@@ -975,8 +975,6 @@ async def user_api_key_auth(
             if not _is_user_proxy_admin(user_obj=user_obj):  # if non-admin
                 if is_llm_api_route(route=route):
                     pass
-                elif is_llm_api_route(route=request["route"].name):
-                    pass
                 elif (
                     route in LiteLLMRoutes.info_routes.value
                 ):  # check if user allowed to call an info route
@@ -1046,11 +1044,16 @@ async def user_api_key_auth(
                                 status_code=status.HTTP_403_FORBIDDEN,
                                 detail=f"user not allowed to access this route, role= {_user_role}. Trying to access: {route}",
                             )
+
                 elif (
                     _user_role == LitellmUserRoles.INTERNAL_USER.value
                     and route in LiteLLMRoutes.internal_user_routes.value
                 ):
                     pass
+                elif (
+                    route in LiteLLMRoutes.self_managed_routes.value
+                ):  # routes that manage their own allowed/disallowed logic
+                    pass
                 else:
                     user_role = "unknown"
                     user_id = "unknown"

litellm/proxy/hooks/parallel_request_limiter.py

@@ -120,6 +120,8 @@ class _PROXY_MaxParallelRequestsHandler(CustomLogger):
         max_parallel_requests = user_api_key_dict.max_parallel_requests
         if max_parallel_requests is None:
             max_parallel_requests = sys.maxsize
+        if data is None:
+            data = {}
         global_max_parallel_requests = data.get("metadata", {}).get(
             "global_max_parallel_requests", None
         )

litellm/proxy/management_endpoints/internal_user_endpoints.py

@@ -119,6 +119,7 @@ async def new_user(
             http_request=Request(
                 scope={"type": "http", "path": "/user/new"},
             ),
+            user_api_key_dict=user_api_key_dict,
         )
 
     if data.send_invite_email is True:
@@ -732,7 +733,7 @@ async def delete_user(
     delete user and associated user keys
 
     ```
-    curl --location 'http://0.0.0.0:8000/team/delete' \
+    curl --location 'http://0.0.0.0:8000/user/delete' \
 
     --header 'Authorization: Bearer sk-1234' \
 

litellm/proxy/management_endpoints/key_management_endpoints.py

@@ -849,7 +849,7 @@ async def generate_key_helper_fn(
         }
 
         if (
-            litellm.get_secret("DISABLE_KEY_NAME", False) == True
+            litellm.get_secret("DISABLE_KEY_NAME", False) is True
         ):  # allow user to disable storing abbreviated key name (shown in UI, to help figure out which key spent how much)
             pass
         else:

litellm/proxy/management_endpoints/team_endpoints.py

@@ -30,7 +30,7 @@ from litellm.proxy._types import (
     UpdateTeamRequest,
     UserAPIKeyAuth,
 )
-from litellm.proxy.auth.user_api_key_auth import user_api_key_auth
+from litellm.proxy.auth.user_api_key_auth import _is_user_proxy_admin, user_api_key_auth
 from litellm.proxy.management_helpers.utils import (
     add_new_member,
     management_endpoint_wrapper,
@@ -39,6 +39,16 @@ from litellm.proxy.management_helpers.utils import (
 router = APIRouter()
 
 
+def _is_user_team_admin(
+    user_api_key_dict: UserAPIKeyAuth, team_obj: LiteLLM_TeamTable
+) -> bool:
+    for member in team_obj.members_with_roles:
+        if member.user_id is not None and member.user_id == user_api_key_dict.user_id:
+            return True
+
+    return False
+
+
 #### TEAM MANAGEMENT ####
 @router.post(
     "/team/new",
@@ -417,6 +427,7 @@ async def team_member_add(
 
     If user doesn't exist, new user row will also be added to User Table
 
+    Only proxy_admin or admin of team, allowed to access this endpoint.
     ```
 
     curl -X POST 'http://0.0.0.0:4000/team/member_add' \
@@ -465,6 +476,25 @@ async def team_member_add(
 
     complete_team_data = LiteLLM_TeamTable(**existing_team_row.model_dump())
 
+    ## CHECK IF USER IS PROXY ADMIN OR TEAM ADMIN
+
+    if (
+        hasattr(user_api_key_dict, "user_role")
+        and user_api_key_dict.user_role != LitellmUserRoles.PROXY_ADMIN.value
+        and not _is_user_team_admin(
+            user_api_key_dict=user_api_key_dict, team_obj=complete_team_data
+        )
+    ):
+        raise HTTPException(
+            status_code=403,
+            detail={
+                "error": "Call not allowed. User not proxy admin OR team admin. route={}, team_id={}".format(
+                    "/team/member_add",
+                    complete_team_data.team_id,
+                )
+            },
+        )
+
     if isinstance(data.member, Member):
         # add to team db
         new_member = data.member
@@ -569,6 +599,23 @@ async def team_member_delete(
         )
     existing_team_row = LiteLLM_TeamTable(**_existing_team_row.model_dump())
 
+    ## CHECK IF USER IS PROXY ADMIN OR TEAM ADMIN
+
+    if (
+        user_api_key_dict.user_role != LitellmUserRoles.PROXY_ADMIN.value
+        and not _is_user_team_admin(
+            user_api_key_dict=user_api_key_dict, team_obj=existing_team_row
+        )
+    ):
+        raise HTTPException(
+            status_code=403,
+            detail={
+                "error": "Call not allowed. User not proxy admin OR team admin. route={}, team_id={}".format(
+                    "/team/member_delete", existing_team_row.team_id
+                )
+            },
+        )
+
     ## DELETE MEMBER FROM TEAM
     new_team_members: List[Member] = []
     for m in existing_team_row.members_with_roles:

litellm/proxy/management_helpers/utils.py

@@ -266,7 +266,7 @@ def management_endpoint_wrapper(func):
                 )
 
                 _http_request: Request = kwargs.get("http_request")
-                parent_otel_span = user_api_key_dict.parent_otel_span
+                parent_otel_span = getattr(user_api_key_dict, "parent_otel_span", None)
                 if parent_otel_span is not None:
                     from litellm.proxy.proxy_server import open_telemetry_logger
 
@@ -310,7 +310,7 @@ def management_endpoint_wrapper(func):
             user_api_key_dict: UserAPIKeyAuth = (
                 kwargs.get("user_api_key_dict") or UserAPIKeyAuth()
             )
-            parent_otel_span = user_api_key_dict.parent_otel_span
+            parent_otel_span = getattr(user_api_key_dict, "parent_otel_span", None)
             if parent_otel_span is not None:
                 from litellm.proxy.proxy_server import open_telemetry_logger
 

litellm/proxy/pass_through_endpoints/pass_through_endpoints.py

@@ -301,16 +301,19 @@ async def pass_through_request(
             request=request, headers=headers, forward_headers=forward_headers
         )
 
+        _parsed_body = None
         if custom_body:
             _parsed_body = custom_body
         else:
             request_body = await request.body()
+            if request_body == b"" or request_body is None:
+                _parsed_body = None
+            else:
                 body_str = request_body.decode()
                 try:
                     _parsed_body = ast.literal_eval(body_str)
                 except Exception:
                     _parsed_body = json.loads(body_str)
-
         verbose_proxy_logger.debug(
             "Pass through endpoint sending request to \nURL {}\nheaders: {}\nbody: {}\n".format(
                 url, headers, _parsed_body
@@ -320,7 +323,7 @@ async def pass_through_request(
         ### CALL HOOKS ### - modify incoming data / reject request before calling the model
         _parsed_body = await proxy_logging_obj.pre_call_hook(
             user_api_key_dict=user_api_key_dict,
-            data=_parsed_body,
+            data=_parsed_body or {},
             call_type="pass_through_endpoint",
         )
 
@@ -360,11 +363,20 @@ async def pass_through_request(
 
         # combine url with query params for logging
 
-        requested_query_params = query_params or request.query_params.__dict__
+        requested_query_params: Optional[dict] = (
+            query_params or request.query_params.__dict__
+        )
+        if requested_query_params == request.query_params.__dict__:
+            requested_query_params = None
+
+        requested_query_params_str = None
+        if requested_query_params:
             requested_query_params_str = "&".join(
                 f"{k}={v}" for k, v in requested_query_params.items()
             )
 
+        logging_url = str(url)
+        if requested_query_params_str:
             if "?" in str(url):
                 logging_url = str(url) + "&" + requested_query_params_str
             else:
@@ -409,6 +421,14 @@ async def pass_through_request(
                 status_code=response.status_code,
             )
 
+        verbose_proxy_logger.debug("request method: {}".format(request.method))
+        verbose_proxy_logger.debug("request url: {}".format(url))
+        verbose_proxy_logger.debug("request headers: {}".format(headers))
+        verbose_proxy_logger.debug(
+            "requested_query_params={}".format(requested_query_params)
+        )
+        verbose_proxy_logger.debug("request body: {}".format(_parsed_body))
+
         response = await async_client.request(
             method=request.method,
             url=url,

litellm/proxy/proxy_config.yaml

@@ -1,20 +1,18 @@
 model_list:
-  - model_name: fake-openai-endpoint
+  - model_name: gpt-4
     litellm_params:
       model: openai/fake
       api_key: fake-key
       api_base: https://exampleopenaiendpoint-production.up.railway.app/
-  - model_name: openai-embedding
-    litellm_params:
-      model: openai/text-embedding-3-small
-      api_key: os.environ/OPENAI_API_KEY
 
-litellm_settings:
-  set_verbose: True
-  cache: True          # set cache responses to True, litellm defaults to using a redis cache
-  cache_params:
-    type: qdrant-semantic
-    qdrant_semantic_cache_embedding_model: openai-embedding
-    qdrant_collection_name: test_collection
-    qdrant_quantization_config: binary
-    similarity_threshold: 0.8   # similarity threshold for semantic cache
+guardrails:
+  - guardrail_name: "lakera-pre-guard"
+    litellm_params:
+      guardrail: lakera  # supported values: "aporia", "bedrock", "lakera"
+      mode: "during_call"
+      api_key: os.environ/LAKERA_API_KEY
+      api_base: os.environ/LAKERA_API_BASE
+      category_thresholds:
+        prompt_injection: 0.1
+        jailbreak: 0.1
+  

litellm/proxy/proxy_server.py

@@ -1498,6 +1498,11 @@ class ProxyConfig:
                     litellm.get_secret(secret_name=key, default_value=value)
                 )
 
+            # check if litellm_license in general_settings
+            if "LITELLM_LICENSE" in environment_variables:
+                _license_check.license_str = os.getenv("LITELLM_LICENSE", None)
+                premium_user = _license_check.is_premium()
+
         ## LITELLM MODULE SETTINGS (e.g. litellm.drop_params=True,..)
         litellm_settings = config.get("litellm_settings", None)
         if litellm_settings is None:
@@ -1878,6 +1883,11 @@ class ProxyConfig:
                     + CommonProxyErrors.not_premium_user.value
                 )
 
+            # check if litellm_license in general_settings
+            if "litellm_license" in general_settings:
+                _license_check.license_str = general_settings["litellm_license"]
+                premium_user = _license_check.is_premium()
+
         router_params: dict = {
             "cache_responses": litellm.cache
             != None,  # cache if user passed in cache values
@@ -2784,11 +2794,14 @@ async def startup_event():
         await custom_db_client.connect()
 
     if prisma_client is not None and master_key is not None:
-        # add master key to db
         if os.getenv("PROXY_ADMIN_ID", None) is not None:
             litellm_proxy_admin_name = os.getenv(
                 "PROXY_ADMIN_ID", litellm_proxy_admin_name
             )
+        if general_settings.get("disable_adding_master_key_hash_to_db") is True:
+            verbose_proxy_logger.info("Skipping writing master key hash to db")
+        else:
+            # add master key to db
             asyncio.create_task(
                 generate_key_helper_fn(
                     request_type="user",
@@ -3011,6 +3024,29 @@ async def chat_completion(
     model: Optional[str] = None,
     user_api_key_dict: UserAPIKeyAuth = Depends(user_api_key_auth),
 ):
+    """
+
+    Follows the exact same API spec as `OpenAI's Chat API https://platform.openai.com/docs/api-reference/chat`
+
+    ```bash
+    curl -X POST http://localhost:4000/v1/chat/completions \
+
+    -H "Content-Type: application/json" \
+
+    -H "Authorization: Bearer sk-1234" \
+
+    -d '{
+        "model": "gpt-4o",
+        "messages": [
+            {
+                "role": "user",
+                "content": "Hello!"
+            }
+        ]
+    }'
+    ```
+
+    """
     global general_settings, user_debug, proxy_logging_obj, llm_model_list
 
     data = {}
@@ -3268,6 +3304,24 @@ async def completion(
     model: Optional[str] = None,
     user_api_key_dict: UserAPIKeyAuth = Depends(user_api_key_auth),
 ):
+    """
+    Follows the exact same API spec as `OpenAI's Completions API https://platform.openai.com/docs/api-reference/completions`
+
+    ```bash
+    curl -X POST http://localhost:4000/v1/completions \
+
+    -H "Content-Type: application/json" \
+
+    -H "Authorization: Bearer sk-1234" \
+
+    -d '{
+        "model": "gpt-3.5-turbo-instruct",
+        "prompt": "Once upon a time",
+        "max_tokens": 50,
+        "temperature": 0.7
+    }'
+    ```
+    """
     global user_temperature, user_request_timeout, user_max_tokens, user_api_base
     data = {}
     try:
@@ -3474,6 +3528,23 @@ async def embeddings(
     model: Optional[str] = None,
     user_api_key_dict: UserAPIKeyAuth = Depends(user_api_key_auth),
 ):
+    """
+    Follows the exact same API spec as `OpenAI's Embeddings API https://platform.openai.com/docs/api-reference/embeddings`
+
+    ```bash
+    curl -X POST http://localhost:4000/v1/embeddings \
+
+    -H "Content-Type: application/json" \
+
+    -H "Authorization: Bearer sk-1234" \
+
+    -d '{
+        "model": "text-embedding-ada-002",
+        "input": "The quick brown fox jumps over the lazy dog"
+    }'
+    ```
+
+"""
     global proxy_logging_obj
     data: Any = {}
     try:
@@ -3481,6 +3552,11 @@ async def embeddings(
         body = await request.body()
         data = orjson.loads(body)
 
+        verbose_proxy_logger.debug(
+            "Request received by LiteLLM:\n%s",
+            json.dumps(data, indent=4),
+        )
+
         # Include original request and headers in the data
         data = await add_litellm_data_to_request(
             data=data,

litellm/proxy/spend_tracking/spend_tracking_utils.py

@@ -1,4 +1,6 @@
 import json
+import os
+import secrets
 import traceback
 from typing import Optional
 
@@ -8,12 +10,30 @@ from litellm.proxy._types import SpendLogsMetadata, SpendLogsPayload
 from litellm.proxy.utils import hash_token
 
 
+def _is_master_key(api_key: str, _master_key: Optional[str]) -> bool:
+    if _master_key is None:
+        return False
+
+    ## string comparison
+    is_master_key = secrets.compare_digest(api_key, _master_key)
+    if is_master_key:
+        return True
+
+    ## hash comparison
+    is_master_key = secrets.compare_digest(api_key, hash_token(_master_key))
+    if is_master_key:
+        return True
+
+    return False
+
+
 def get_logging_payload(
     kwargs, response_obj, start_time, end_time, end_user_id: Optional[str]
 ) -> SpendLogsPayload:
     from pydantic import Json
 
     from litellm.proxy._types import LiteLLM_SpendLogs
+    from litellm.proxy.proxy_server import general_settings, master_key
 
     verbose_proxy_logger.debug(
         f"SpendTable: get_logging_payload - kwargs: {kwargs}\n\n"
@@ -36,9 +56,15 @@ def get_logging_payload(
         usage = dict(usage)
     id = response_obj.get("id", kwargs.get("litellm_call_id"))
     api_key = metadata.get("user_api_key", "")
-    if api_key is not None and isinstance(api_key, str) and api_key.startswith("sk-"):
+    if api_key is not None and isinstance(api_key, str):
+        if api_key.startswith("sk-"):
             # hash the api_key
             api_key = hash_token(api_key)
+        if (
+            _is_master_key(api_key=api_key, _master_key=master_key)
+            and general_settings.get("disable_adding_master_key_hash_to_db") is True
+        ):
+            api_key = "litellm_proxy_master_key"  # use a known alias, if the user disabled storing master key in db
 
     _model_id = metadata.get("model_info", {}).get("id", "")
     _model_group = metadata.get("model_group", "")

litellm/proxy/tests/test_vtx_embedding.py

@@ -0,0 +1,21 @@
+import openai
+
+client = openai.OpenAI(api_key="sk-1234", base_url="http://0.0.0.0:4000")
+
+# # request sent to model set on litellm proxy, `litellm --model`
+response = client.embeddings.create(
+    model="multimodalembedding@001",
+    input=[],
+    extra_body={
+        "instances": [
+            {
+                "image": {
+                    "gcsUri": "gs://cloud-samples-data/vertex-ai/llm/prompts/landmark1.png"
+                },
+                "text": "this is a unicorn",
+            },
+        ],
+    },
+)
+
+print(response)

litellm/proxy/tests/test_vtx_sdk_embedding.py

@@ -0,0 +1,58 @@
+import vertexai
+from google.auth.credentials import Credentials
+from vertexai.vision_models import (
+    Image,
+    MultiModalEmbeddingModel,
+    Video,
+    VideoSegmentConfig,
+)
+
+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 = MultiModalEmbeddingModel.from_pretrained("multimodalembedding")
+image = Image.load_from_file(
+    "gs://cloud-samples-data/vertex-ai/llm/prompts/landmark1.png"
+)
+
+embeddings = model.get_embeddings(
+    image=image,
+    contextual_text="Colosseum",
+    dimension=1408,
+)
+print(f"Image Embedding: {embeddings.image_embedding}")
+print(f"Text Embedding: {embeddings.text_embedding}")

litellm/proxy/utils.py

@@ -44,6 +44,7 @@ from litellm.proxy._types import (
     DynamoDBArgs,
     LiteLLM_VerificationTokenView,
     LitellmUserRoles,
+    Member,
     ResetTeamBudgetRequest,
     SpendLogsMetadata,
     SpendLogsPayload,
@@ -1395,6 +1396,7 @@ class PrismaClient:
                     t.blocked AS team_blocked,
                     t.team_alias AS team_alias,
                     t.metadata AS team_metadata,
+                    t.members_with_roles AS team_members_with_roles,
                     tm.spend AS team_member_spend,
                     m.aliases as team_model_aliases
                     FROM "LiteLLM_VerificationToken" AS v
@@ -1412,6 +1414,33 @@ class PrismaClient:
                             response["team_models"] = []
                         if response["team_blocked"] is None:
                             response["team_blocked"] = False
+
+                        team_member: Optional[Member] = None
+                        if (
+                            response["team_members_with_roles"] is not None
+                            and response["user_id"] is not None
+                        ):
+                            ## find the team member corresponding to user id
+                            """
+                            [
+                                {
+                                    "role": "admin",
+                                    "user_id": "default_user_id",
+                                    "user_email": null
+                                },
+                                {
+                                    "role": "user",
+                                    "user_id": null,
+                                    "user_email": "test@email.com"
+                                }
+                            ]
+                            """
+                            for tm in response["team_members_with_roles"]:
+                                if tm.get("user_id") is not None and response[
+                                    "user_id"
+                                ] == tm.get("user_id"):
+                                    team_member = Member(**tm)
+                        response["team_member"] = team_member
                         response = LiteLLM_VerificationTokenView(
                             **response, last_refreshed_at=time.time()
                         )

litellm/proxy/vertex_ai_endpoints/vertex_endpoints.py

@@ -25,6 +25,9 @@ from litellm.batches.main import FileObject
 from litellm.fine_tuning.main import vertex_fine_tuning_apis_instance
 from litellm.proxy._types import *
 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,
+)
 
 router = APIRouter()
 default_vertex_config = None
@@ -70,10 +73,17 @@ def exception_handler(e: Exception):
         )
 
 
-async def execute_post_vertex_ai_request(
+@router.api_route(
+    "/vertex-ai/{endpoint:path}", methods=["GET", "POST", "PUT", "DELETE"]
+)
+async def vertex_proxy_route(
+    endpoint: str,
     request: Request,
-    route: str,
+    fastapi_response: Response,
+    user_api_key_dict: UserAPIKeyAuth = Depends(user_api_key_auth),
 ):
+    encoded_endpoint = httpx.URL(endpoint).path
+
     from litellm.fine_tuning.main import vertex_fine_tuning_apis_instance
 
     if default_vertex_config is None:
@@ -83,250 +93,52 @@ async def execute_post_vertex_ai_request(
     vertex_project = default_vertex_config.get("vertex_project", None)
     vertex_location = default_vertex_config.get("vertex_location", None)
     vertex_credentials = default_vertex_config.get("vertex_credentials", None)
+    base_target_url = f"https://{vertex_location}-aiplatform.googleapis.com/"
 
-    request_data_json = {}
-    body = await request.body()
-    body_str = body.decode()
-    if len(body_str) > 0:
-        try:
-            request_data_json = ast.literal_eval(body_str)
-        except:
-            request_data_json = json.loads(body_str)
-
-    verbose_proxy_logger.debug(
-        "Request received by LiteLLM:\n{}".format(
-            json.dumps(request_data_json, indent=4)
-        ),
-    )
-
-    response = (
-        await vertex_fine_tuning_apis_instance.pass_through_vertex_ai_POST_request(
-            request_data=request_data_json,
+    auth_header, _ = vertex_fine_tuning_apis_instance._get_token_and_url(
+        model="",
+        gemini_api_key=None,
+        vertex_credentials=vertex_credentials,
         vertex_project=vertex_project,
         vertex_location=vertex_location,
-            vertex_credentials=vertex_credentials,
-            request_route=route,
-        )
+        stream=False,
+        custom_llm_provider="vertex_ai_beta",
+        api_base="",
     )
 
-    return response
+    headers = {
+        "Authorization": f"Bearer {auth_header}",
+    }
 
+    request_route = encoded_endpoint
+    verbose_proxy_logger.debug("request_route %s", request_route)
 
-@router.post(
-    "/vertex-ai/publishers/google/models/{model_id:path}:generateContent",
-    dependencies=[Depends(user_api_key_auth)],
-    tags=["Vertex AI endpoints"],
-)
-async def vertex_generate_content(
-    request: Request,
-    fastapi_response: Response,
-    model_id: str,
-    user_api_key_dict: UserAPIKeyAuth = Depends(user_api_key_auth),
-):
-    """
-    this is a pass through endpoint for the Vertex AI API. /generateContent endpoint
+    # Ensure endpoint starts with '/' for proper URL construction
+    if not encoded_endpoint.startswith("/"):
+        encoded_endpoint = "/" + encoded_endpoint
 
-    Example Curl:
-    ```
-    curl http://localhost:4000/vertex-ai/publishers/google/models/gemini-1.5-flash-001:generateContent \
-      -H "Content-Type: application/json" \
-      -H "Authorization: Bearer sk-1234" \
-      -d '{"contents":[{"role": "user", "parts":[{"text": "hi"}]}]}'
-    ```
+    # Construct the full target URL using httpx
+    base_url = httpx.URL(base_target_url)
+    updated_url = base_url.copy_with(path=encoded_endpoint)
 
-    Vertex API Reference: https://cloud.google.com/vertex-ai/generative-ai/docs/model-reference/inference#rest
-    it uses the vertex ai credentials on the proxy and forwards to vertex ai api
-    """
-    try:
-        response = await execute_post_vertex_ai_request(
-            request=request,
-            route=f"/publishers/google/models/{model_id}:generateContent",
+    verbose_proxy_logger.debug("updated url %s", updated_url)
+
+    ## check for streaming
+    is_streaming_request = False
+    if "stream" in str(updated_url):
+        is_streaming_request = True
+
+    ## CREATE PASS-THROUGH
+    endpoint_func = create_pass_through_route(
+        endpoint=endpoint,
+        target=str(updated_url),
+        custom_headers=headers,
+    )  # dynamically construct pass-through endpoint based on incoming path
+    received_value = await endpoint_func(
+        request,
+        fastapi_response,
+        user_api_key_dict,
+        stream=is_streaming_request,
     )
-        return response
-    except Exception as e:
-        raise exception_handler(e) from e
 
-
-@router.post(
-    "/vertex-ai/publishers/google/models/{model_id:path}:predict",
-    dependencies=[Depends(user_api_key_auth)],
-    tags=["Vertex AI endpoints"],
-)
-async def vertex_predict_endpoint(
-    request: Request,
-    fastapi_response: Response,
-    model_id: str,
-    user_api_key_dict: UserAPIKeyAuth = Depends(user_api_key_auth),
-):
-    """
-    this is a pass through endpoint for the Vertex AI API. /predict endpoint
-    Use this for:
-    - Embeddings API - Text Embedding, Multi Modal Embedding
-    - Imagen API
-    - Code Completion API
-
-    Example Curl:
-    ```
-    curl http://localhost:4000/vertex-ai/publishers/google/models/textembedding-gecko@001:predict \
-      -H "Content-Type: application/json" \
-      -H "Authorization: Bearer sk-1234" \
-      -d '{"instances":[{"content": "gm"}]}'
-    ```
-
-    Vertex API Reference: https://cloud.google.com/vertex-ai/generative-ai/docs/model-reference/text-embeddings-api#generative-ai-get-text-embedding-drest
-    it uses the vertex ai credentials on the proxy and forwards to vertex ai api
-    """
-    try:
-        response = await execute_post_vertex_ai_request(
-            request=request,
-            route=f"/publishers/google/models/{model_id}:predict",
-        )
-        return response
-    except Exception as e:
-        raise exception_handler(e) from e
-
-
-@router.post(
-    "/vertex-ai/publishers/google/models/{model_id:path}:countTokens",
-    dependencies=[Depends(user_api_key_auth)],
-    tags=["Vertex AI endpoints"],
-)
-async def vertex_countTokens_endpoint(
-    request: Request,
-    fastapi_response: Response,
-    model_id: str,
-    user_api_key_dict: UserAPIKeyAuth = Depends(user_api_key_auth),
-):
-    """
-    this is a pass through endpoint for the Vertex AI API. /countTokens endpoint
-    https://cloud.google.com/vertex-ai/generative-ai/docs/model-reference/count-tokens#curl
-
-
-    Example Curl:
-    ```
-    curl http://localhost:4000/vertex-ai/publishers/google/models/gemini-1.5-flash-001:countTokens \
-      -H "Content-Type: application/json" \
-      -H "Authorization: Bearer sk-1234" \
-      -d '{"contents":[{"role": "user", "parts":[{"text": "hi"}]}]}'
-    ```
-
-    it uses the vertex ai credentials on the proxy and forwards to vertex ai api
-    """
-    try:
-        response = await execute_post_vertex_ai_request(
-            request=request,
-            route=f"/publishers/google/models/{model_id}:countTokens",
-        )
-        return response
-    except Exception as e:
-        raise exception_handler(e) from e
-
-
-@router.post(
-    "/vertex-ai/batchPredictionJobs",
-    dependencies=[Depends(user_api_key_auth)],
-    tags=["Vertex AI endpoints"],
-)
-async def vertex_create_batch_prediction_job(
-    request: Request,
-    fastapi_response: Response,
-    user_api_key_dict: UserAPIKeyAuth = Depends(user_api_key_auth),
-):
-    """
-    this is a pass through endpoint for the Vertex AI API. /batchPredictionJobs endpoint
-
-    Vertex API Reference: https://cloud.google.com/vertex-ai/generative-ai/docs/model-reference/batch-prediction-api#syntax
-
-    it uses the vertex ai credentials on the proxy and forwards to vertex ai api
-    """
-    try:
-        response = await execute_post_vertex_ai_request(
-            request=request,
-            route="/batchPredictionJobs",
-        )
-        return response
-    except Exception as e:
-        raise exception_handler(e) from e
-
-
-@router.post(
-    "/vertex-ai/tuningJobs",
-    dependencies=[Depends(user_api_key_auth)],
-    tags=["Vertex AI endpoints"],
-)
-async def vertex_create_fine_tuning_job(
-    request: Request,
-    fastapi_response: Response,
-    user_api_key_dict: UserAPIKeyAuth = Depends(user_api_key_auth),
-):
-    """
-    this is a pass through endpoint for the Vertex AI API. /tuningJobs endpoint
-
-    Vertex API Reference: https://cloud.google.com/vertex-ai/generative-ai/docs/model-reference/tuning
-
-    it uses the vertex ai credentials on the proxy and forwards to vertex ai api
-    """
-    try:
-        response = await execute_post_vertex_ai_request(
-            request=request,
-            route="/tuningJobs",
-        )
-        return response
-    except Exception as e:
-        raise exception_handler(e) from e
-
-
-@router.post(
-    "/vertex-ai/tuningJobs/{job_id:path}:cancel",
-    dependencies=[Depends(user_api_key_auth)],
-    tags=["Vertex AI endpoints"],
-)
-async def vertex_cancel_fine_tuning_job(
-    request: Request,
-    job_id: str,
-    fastapi_response: Response,
-    user_api_key_dict: UserAPIKeyAuth = Depends(user_api_key_auth),
-):
-    """
-    this is a pass through endpoint for the Vertex AI API. tuningJobs/{job_id:path}:cancel
-
-    Vertex API Reference: https://cloud.google.com/vertex-ai/generative-ai/docs/model-reference/tuning#cancel_a_tuning_job
-
-    it uses the vertex ai credentials on the proxy and forwards to vertex ai api
-    """
-    try:
-
-        response = await execute_post_vertex_ai_request(
-            request=request,
-            route=f"/tuningJobs/{job_id}:cancel",
-        )
-        return response
-    except Exception as e:
-        raise exception_handler(e) from e
-
-
-@router.post(
-    "/vertex-ai/cachedContents",
-    dependencies=[Depends(user_api_key_auth)],
-    tags=["Vertex AI endpoints"],
-)
-async def vertex_create_add_cached_content(
-    request: Request,
-    fastapi_response: Response,
-    user_api_key_dict: UserAPIKeyAuth = Depends(user_api_key_auth),
-):
-    """
-    this is a pass through endpoint for the Vertex AI API. /cachedContents endpoint
-
-    Vertex API Reference: https://cloud.google.com/vertex-ai/generative-ai/docs/context-cache/context-cache-create#create-context-cache-sample-drest
-
-    it uses the vertex ai credentials on the proxy and forwards to vertex ai api
-    """
-    try:
-        response = await execute_post_vertex_ai_request(
-            request=request,
-            route="/cachedContents",
-        )
-        return response
-    except Exception as e:
-        raise exception_handler(e) from e
+    return received_value

litellm/tests/test_amazing_vertex_completion.py

@@ -15,7 +15,7 @@ import asyncio
 import json
 import os
 import tempfile
-from unittest.mock import MagicMock, patch
+from unittest.mock import AsyncMock, MagicMock, patch
 
 import pytest
 
@@ -501,6 +501,8 @@ async def test_async_vertexai_streaming_response():
             assert len(complete_response) > 0
         except litellm.RateLimitError as e:
             pass
+        except litellm.APIConnectionError:
+            pass
         except litellm.Timeout as e:
             pass
         except litellm.InternalServerError as e:
@@ -955,6 +957,8 @@ async def test_partner_models_httpx(model, sync_mode):
         assert isinstance(response._hidden_params["response_cost"], float)
     except litellm.RateLimitError as e:
         pass
+    except litellm.InternalServerError as e:
+        pass
     except Exception as e:
         if "429 Quota exceeded" in str(e):
             pass
@@ -1004,7 +1008,9 @@ async def test_partner_models_httpx_streaming(model, sync_mode):
                 idx += 1
 
         print(f"response: {response}")
-    except litellm.RateLimitError:
+    except litellm.RateLimitError as e:
+        pass
+    except litellm.InternalServerError as e:
         pass
     except Exception as e:
         if "429 Quota exceeded" in str(e):
@@ -1558,6 +1564,16 @@ async def test_gemini_pro_json_schema_args_sent_httpx_openai_schema(
                     "response_schema"
                     in mock_call.call_args.kwargs["json"]["generationConfig"]
                 )
+                assert (
+                    "response_mime_type"
+                    in mock_call.call_args.kwargs["json"]["generationConfig"]
+                )
+                assert (
+                    mock_call.call_args.kwargs["json"]["generationConfig"][
+                        "response_mime_type"
+                    ]
+                    == "application/json"
+                )
             else:
                 assert (
                     "response_schema"
@@ -1826,6 +1842,71 @@ def test_vertexai_embedding():
         pytest.fail(f"Error occurred: {e}")
 
 
+@pytest.mark.asyncio
+async def test_vertexai_multimodal_embedding():
+    load_vertex_ai_credentials()
+    mock_response = AsyncMock()
+
+    def return_val():
+        return {
+            "predictions": [
+                {
+                    "imageEmbedding": [0.1, 0.2, 0.3],  # Simplified example
+                    "textEmbedding": [0.4, 0.5, 0.6],  # Simplified example
+                }
+            ]
+        }
+
+    mock_response.json = return_val
+    mock_response.status_code = 200
+
+    expected_payload = {
+        "instances": [
+            {
+                "image": {
+                    "gcsUri": "gs://cloud-samples-data/vertex-ai/llm/prompts/landmark1.png"
+                },
+                "text": "this is a unicorn",
+            }
+        ]
+    }
+
+    with patch(
+        "litellm.llms.custom_httpx.http_handler.AsyncHTTPHandler.post",
+        return_value=mock_response,
+    ) as mock_post:
+        # Act: Call the litellm.aembedding function
+        response = await litellm.aembedding(
+            model="vertex_ai/multimodalembedding@001",
+            input=[
+                {
+                    "image": {
+                        "gcsUri": "gs://cloud-samples-data/vertex-ai/llm/prompts/landmark1.png"
+                    },
+                    "text": "this is a unicorn",
+                },
+            ],
+        )
+
+        # Assert
+        mock_post.assert_called_once()
+        _, kwargs = mock_post.call_args
+        args_to_vertexai = kwargs["json"]
+
+        print("args to vertex ai call:", args_to_vertexai)
+
+        assert args_to_vertexai == expected_payload
+        assert response.model == "multimodalembedding@001"
+        assert len(response.data) == 1
+        response_data = response.data[0]
+        assert "imageEmbedding" in response_data
+        assert "textEmbedding" in response_data
+
+        # Optional: Print for debugging
+        print("Arguments passed to Vertex AI:", args_to_vertexai)
+        print("Response:", response)
+
+
 @pytest.mark.skip(
     reason="new test - works locally running into vertex version issues on ci/cd"
 )

litellm/tests/test_bedrock_completion.py

@@ -738,8 +738,9 @@ def test_bedrock_system_prompt(system, model):
             "temperature": 0.3,
             "messages": [
                 {"role": "system", "content": system},
-                {"role": "user", "content": "hey, how's it going?"},
+                {"role": "assistant", "content": "hey, how's it going?"},
             ],
+            "user_continue_message": {"role": "user", "content": "Be a good bot!"},
         }
         response: ModelResponse = completion(
             model="bedrock/{}".format(model),

litellm/tests/test_completion.py

@@ -3653,6 +3653,7 @@ def test_completion_cohere():
         response = completion(
             model="command-r",
             messages=messages,
+            extra_headers={"Helicone-Property-Locale": "ko"},
         )
         print(response)
     except Exception as e:

litellm/tests/test_custom_callback_input.py

@@ -1252,3 +1252,48 @@ def test_standard_logging_payload(model, turn_off_message_logging):
         ]
         if turn_off_message_logging:
             assert "redacted-by-litellm" == slobject["messages"][0]["content"]
+
+
+@pytest.mark.skip(reason="Works locally. Flaky on ci/cd")
+def test_aaastandard_logging_payload_cache_hit():
+    from litellm.types.utils import StandardLoggingPayload
+
+    # sync completion
+
+    litellm.cache = Cache()
+
+    _ = litellm.completion(
+        model="gpt-3.5-turbo",
+        messages=[{"role": "user", "content": "Hey, how's it going?"}],
+        caching=True,
+    )
+
+    customHandler = CompletionCustomHandler()
+    litellm.callbacks = [customHandler]
+    litellm.success_callback = []
+
+    with patch.object(
+        customHandler, "log_success_event", new=MagicMock()
+    ) as mock_client:
+        _ = litellm.completion(
+            model="gpt-3.5-turbo",
+            messages=[{"role": "user", "content": "Hey, how's it going?"}],
+            caching=True,
+        )
+
+        time.sleep(2)
+        mock_client.assert_called_once()
+
+        assert "standard_logging_object" in mock_client.call_args.kwargs["kwargs"]
+        assert (
+            mock_client.call_args.kwargs["kwargs"]["standard_logging_object"]
+            is not None
+        )
+
+        standard_logging_object: StandardLoggingPayload = mock_client.call_args.kwargs[
+            "kwargs"
+        ]["standard_logging_object"]
+
+        assert standard_logging_object["cache_hit"] is True
+        assert standard_logging_object["response_cost"] == 0
+        assert standard_logging_object["saved_cache_cost"] > 0

litellm/tests/test_function_calling.py

@@ -54,6 +54,7 @@ def get_current_weather(location, unit="fahrenheit"):
 )
 def test_parallel_function_call(model):
     try:
+        litellm.set_verbose = True
         # Step 1: send the conversation and available functions to the model
         messages = [
             {
@@ -141,6 +142,8 @@ def test_parallel_function_call(model):
                 drop_params=True,
             )  # get a new response from the model where it can see the function response
             print("second response\n", second_response)
+    except litellm.RateLimitError:
+        pass
     except Exception as e:
         pytest.fail(f"Error occurred: {e}")
 
@@ -322,6 +325,7 @@ def test_groq_parallel_function_call():
                         location=function_args.get("location"),
                         unit=function_args.get("unit"),
                     )
+
                     messages.append(
                         {
                             "tool_call_id": tool_call.id,
@@ -337,27 +341,3 @@ def test_groq_parallel_function_call():
                 print("second response\n", second_response)
     except Exception as e:
         pytest.fail(f"Error occurred: {e}")
-
-
-@pytest.mark.parametrize("model", ["gemini/gemini-1.5-pro"])
-def test_simple_function_call_function_param(model):
-    try:
-        litellm.set_verbose = True
-        messages = [{"role": "user", "content": "What is the weather like in Boston?"}]
-        response = completion(
-            model=model,
-            messages=messages,
-            tools=[
-                {
-                    "type": "function",
-                    "function": {
-                        "name": "plot",
-                        "description": "Generate plots",
-                    },
-                }
-            ],
-            tool_choice="auto",
-        )
-        print(f"response: {response}")
-    except Exception as e:
-        raise e

litellm/tests/test_image_generation.py

@@ -116,6 +116,8 @@ async def test_async_image_generation_openai():
         )
         print(f"response: {response}")
         assert len(response.data) > 0
+    except litellm.APIError:
+        pass
     except litellm.RateLimitError as e:
         pass
     except litellm.ContentPolicyViolationError:

litellm/tests/test_key_generate_prisma.py

@@ -2328,6 +2328,11 @@ async def test_master_key_hashing(prisma_client):
         from litellm.proxy.proxy_server import user_api_key_cache
 
         _team_id = "ishaans-special-team_{}".format(uuid.uuid4())
+        user_api_key_dict = UserAPIKeyAuth(
+            user_role=LitellmUserRoles.PROXY_ADMIN,
+            api_key="sk-1234",
+            user_id="1234",
+        )
         await new_team(
             NewTeamRequest(team_id=_team_id),
             user_api_key_dict=UserAPIKeyAuth(
@@ -2343,7 +2348,8 @@ async def test_master_key_hashing(prisma_client):
                 models=["azure-gpt-3.5"],
                 team_id=_team_id,
                 tpm_limit=20,
-            )
+            ),
+            user_api_key_dict=user_api_key_dict,
         )
         print(_response)
         assert _response.models == ["azure-gpt-3.5"]

litellm/tests/test_optional_params.py

@@ -19,7 +19,11 @@ from litellm.types.completion import (
     ChatCompletionSystemMessageParam,
     ChatCompletionUserMessageParam,
 )
-from litellm.utils import get_optional_params, get_optional_params_embeddings
+from litellm.utils import (
+    get_optional_params,
+    get_optional_params_embeddings,
+    get_optional_params_image_gen,
+)
 
 ## get_optional_params_embeddings
 ### Models: OpenAI, Azure, Bedrock
@@ -430,7 +434,6 @@ def test_get_optional_params_image_gen():
     print(response)
 
     assert "aws_region_name" not in response
-
     response = litellm.utils.get_optional_params_image_gen(
         aws_region_name="us-east-1", custom_llm_provider="bedrock"
     )
@@ -463,3 +466,36 @@ def test_get_optional_params_num_retries():
 
         print(f"mock_client.call_args: {mock_client.call_args}")
         assert mock_client.call_args.kwargs["max_retries"] == 10
+
+
+@pytest.mark.parametrize(
+    "provider",
+    [
+        "vertex_ai",
+        "vertex_ai_beta",
+    ],
+)
+def test_vertex_safety_settings(provider):
+    litellm.vertex_ai_safety_settings = [
+        {
+            "category": "HARM_CATEGORY_HARASSMENT",
+            "threshold": "BLOCK_NONE",
+        },
+        {
+            "category": "HARM_CATEGORY_HATE_SPEECH",
+            "threshold": "BLOCK_NONE",
+        },
+        {
+            "category": "HARM_CATEGORY_SEXUALLY_EXPLICIT",
+            "threshold": "BLOCK_NONE",
+        },
+        {
+            "category": "HARM_CATEGORY_DANGEROUS_CONTENT",
+            "threshold": "BLOCK_NONE",
+        },
+    ]
+
+    optional_params = get_optional_params(
+        model="gemini-1.5-pro", custom_llm_provider=provider
+    )
+    assert len(optional_params) == 1

litellm/tests/test_proxy_server.py

@@ -909,7 +909,7 @@ async def test_create_team_member_add(prisma_client, new_member_method):
 
         await team_member_add(
             data=team_member_add_request,
-            user_api_key_dict=UserAPIKeyAuth(),
+            user_api_key_dict=UserAPIKeyAuth(user_role="proxy_admin"),
             http_request=Request(
                 scope={"type": "http", "path": "/user/new"},
             ),
@@ -930,6 +930,172 @@ async def test_create_team_member_add(prisma_client, new_member_method):
         )
 
 
+@pytest.mark.parametrize("team_member_role", ["admin", "user"])
+@pytest.mark.parametrize("team_route", ["/team/member_add", "/team/member_delete"])
+@pytest.mark.asyncio
+async def test_create_team_member_add_team_admin_user_api_key_auth(
+    prisma_client, team_member_role, team_route
+):
+    import time
+
+    from fastapi import Request
+
+    from litellm.proxy._types import LiteLLM_TeamTableCachedObj, Member
+    from litellm.proxy.proxy_server import (
+        ProxyException,
+        hash_token,
+        user_api_key_auth,
+        user_api_key_cache,
+    )
+
+    setattr(litellm.proxy.proxy_server, "prisma_client", prisma_client)
+    setattr(litellm.proxy.proxy_server, "master_key", "sk-1234")
+    setattr(litellm, "max_internal_user_budget", 10)
+    setattr(litellm, "internal_user_budget_duration", "5m")
+    await litellm.proxy.proxy_server.prisma_client.connect()
+    user = f"ishaan {uuid.uuid4().hex}"
+    _team_id = "litellm-test-client-id-new"
+    user_key = "sk-12345678"
+
+    valid_token = UserAPIKeyAuth(
+        team_id=_team_id,
+        token=hash_token(user_key),
+        team_member=Member(role=team_member_role, user_id=user),
+        last_refreshed_at=time.time(),
+    )
+    user_api_key_cache.set_cache(key=hash_token(user_key), value=valid_token)
+
+    team_obj = LiteLLM_TeamTableCachedObj(
+        team_id=_team_id,
+        blocked=False,
+        last_refreshed_at=time.time(),
+        metadata={"guardrails": {"modify_guardrails": False}},
+    )
+
+    user_api_key_cache.set_cache(key="team_id:{}".format(_team_id), value=team_obj)
+
+    setattr(litellm.proxy.proxy_server, "user_api_key_cache", user_api_key_cache)
+
+    ## TEST IF TEAM ADMIN ALLOWED TO CALL /MEMBER_ADD ENDPOINT
+    import json
+
+    from starlette.datastructures import URL
+
+    request = Request(scope={"type": "http"})
+    request._url = URL(url=team_route)
+
+    body = {}
+    json_bytes = json.dumps(body).encode("utf-8")
+
+    request._body = json_bytes
+
+    ## ALLOWED BY USER_API_KEY_AUTH
+    await user_api_key_auth(request=request, api_key="Bearer " + user_key)
+
+
+@pytest.mark.parametrize("new_member_method", ["user_id", "user_email"])
+@pytest.mark.parametrize("user_role", ["admin", "user"])
+@pytest.mark.asyncio
+async def test_create_team_member_add_team_admin(
+    prisma_client, new_member_method, user_role
+):
+    """
+    Relevant issue - https://github.com/BerriAI/litellm/issues/5300
+
+    Allow team admins to:
+        - Add and remove team members
+        - raise error if team member not an existing 'internal_user'
+    """
+    import time
+
+    from fastapi import Request
+
+    from litellm.proxy._types import LiteLLM_TeamTableCachedObj, Member
+    from litellm.proxy.proxy_server import (
+        HTTPException,
+        ProxyException,
+        hash_token,
+        user_api_key_auth,
+        user_api_key_cache,
+    )
+
+    setattr(litellm.proxy.proxy_server, "prisma_client", prisma_client)
+    setattr(litellm.proxy.proxy_server, "master_key", "sk-1234")
+    setattr(litellm, "max_internal_user_budget", 10)
+    setattr(litellm, "internal_user_budget_duration", "5m")
+    await litellm.proxy.proxy_server.prisma_client.connect()
+    user = f"ishaan {uuid.uuid4().hex}"
+    _team_id = "litellm-test-client-id-new"
+    user_key = "sk-12345678"
+
+    valid_token = UserAPIKeyAuth(
+        team_id=_team_id,
+        user_id=user,
+        token=hash_token(user_key),
+        last_refreshed_at=time.time(),
+    )
+    user_api_key_cache.set_cache(key=hash_token(user_key), value=valid_token)
+
+    team_obj = LiteLLM_TeamTableCachedObj(
+        team_id=_team_id,
+        blocked=False,
+        last_refreshed_at=time.time(),
+        members_with_roles=[Member(role=user_role, user_id=user)],
+        metadata={"guardrails": {"modify_guardrails": False}},
+    )
+
+    user_api_key_cache.set_cache(key="team_id:{}".format(_team_id), value=team_obj)
+
+    setattr(litellm.proxy.proxy_server, "user_api_key_cache", user_api_key_cache)
+    if new_member_method == "user_id":
+        data = {
+            "team_id": _team_id,
+            "member": [{"role": "user", "user_id": user}],
+        }
+    elif new_member_method == "user_email":
+        data = {
+            "team_id": _team_id,
+            "member": [{"role": "user", "user_email": user}],
+        }
+    team_member_add_request = TeamMemberAddRequest(**data)
+
+    with patch(
+        "litellm.proxy.proxy_server.prisma_client.db.litellm_usertable",
+        new_callable=AsyncMock,
+    ) as mock_litellm_usertable:
+        mock_client = AsyncMock()
+        mock_litellm_usertable.upsert = mock_client
+        mock_litellm_usertable.find_many = AsyncMock(return_value=None)
+
+        try:
+            await team_member_add(
+                data=team_member_add_request,
+                user_api_key_dict=valid_token,
+                http_request=Request(
+                    scope={"type": "http", "path": "/user/new"},
+                ),
+            )
+        except HTTPException as e:
+            if user_role == "user":
+                assert e.status_code == 403
+            else:
+                raise e
+
+        mock_client.assert_called()
+
+        print(f"mock_client.call_args: {mock_client.call_args}")
+        print("mock_client.call_args.kwargs: {}".format(mock_client.call_args.kwargs))
+
+        assert (
+            mock_client.call_args.kwargs["data"]["create"]["max_budget"]
+            == litellm.max_internal_user_budget
+        )
+        assert (
+            mock_client.call_args.kwargs["data"]["create"]["budget_duration"]
+            == litellm.internal_user_budget_duration
+        )
+
+
 @pytest.mark.asyncio
 async def test_user_info_team_list(prisma_client):
     """Assert user_info for admin calls team_list function"""

litellm/types/llms/ollama.py

@@ -0,0 +1,24 @@
+import json
+from typing import Any, Optional, TypedDict, Union
+
+from pydantic import BaseModel
+from typing_extensions import (
+    Protocol,
+    Required,
+    Self,
+    TypeGuard,
+    get_origin,
+    override,
+    runtime_checkable,
+)
+
+
+class OllamaToolCallFunction(
+    TypedDict
+):  # follows - https://github.com/ollama/ollama/blob/6bd8a4b0a1ac15d5718f52bbe1cd56f827beb694/api/types.go#L148
+    name: str
+    arguments: dict
+
+
+class OllamaToolCall(TypedDict):
+    function: OllamaToolCallFunction

litellm/types/llms/vertex_ai.py

@@ -1,6 +1,6 @@
 import json
 from enum import Enum
-from typing import Any, Dict, List, Literal, Optional, TypedDict, Union
+from typing import Any, Dict, List, Literal, Optional, Tuple, TypedDict, Union
 
 from typing_extensions import (
     Protocol,
@@ -305,3 +305,18 @@ class ResponseTuningJob(TypedDict):
     ]
     createTime: Optional[str]
     updateTime: Optional[str]
+
+
+class InstanceVideo(TypedDict, total=False):
+    gcsUri: str
+    videoSegmentConfig: Tuple[float, float, float]
+
+
+class Instance(TypedDict, total=False):
+    text: str
+    image: Dict[str, str]
+    video: InstanceVideo
+
+
+class VertexMultimodalEmbeddingRequest(TypedDict, total=False):
+    instances: List[Instance]

litellm/types/utils.py

@@ -1116,6 +1116,7 @@ all_litellm_params = [
     "cooldown_time",
     "cache_key",
     "max_retries",
+    "user_continue_message",
 ]
 
 
@@ -1218,6 +1219,7 @@ class StandardLoggingPayload(TypedDict):
     metadata: StandardLoggingMetadata
     cache_hit: Optional[bool]
     cache_key: Optional[str]
+    saved_cache_cost: Optional[float]
     request_tags: list
     end_user: Optional[str]
     requester_ip_address: Optional[str]

litellm/utils.py

@@ -541,7 +541,7 @@ def function_setup(
             call_type == CallTypes.embedding.value
             or call_type == CallTypes.aembedding.value
         ):
-            messages = args[1] if len(args) > 1 else kwargs["input"]
+            messages = args[1] if len(args) > 1 else kwargs.get("input", None)
         elif (
             call_type == CallTypes.image_generation.value
             or call_type == CallTypes.aimage_generation.value
@@ -2323,6 +2323,7 @@ def get_litellm_params(
     output_cost_per_second=None,
     cooldown_time=None,
     text_completion=None,
+    user_continue_message=None,
 ):
     litellm_params = {
         "acompletion": acompletion,
@@ -2347,6 +2348,7 @@ def get_litellm_params(
         "output_cost_per_second": output_cost_per_second,
         "cooldown_time": cooldown_time,
         "text_completion": text_completion,
+        "user_continue_message": user_continue_message,
     }
 
     return litellm_params
@@ -3145,7 +3147,6 @@ def get_optional_params(
         or model in litellm.vertex_embedding_models
         or model in litellm.vertex_vision_models
     ):
-        print_verbose(f"(start) INSIDE THE VERTEX AI OPTIONAL PARAM BLOCK")
         ## check if unsupported param passed in
         supported_params = get_supported_openai_params(
             model=model, custom_llm_provider=custom_llm_provider
@@ -3157,9 +3158,8 @@ def get_optional_params(
             optional_params=optional_params,
         )
 
-        print_verbose(
-            f"(end) INSIDE THE VERTEX AI OPTIONAL PARAM BLOCK - optional_params: {optional_params}"
-        )
+        if litellm.vertex_ai_safety_settings is not None:
+            optional_params["safety_settings"] = litellm.vertex_ai_safety_settings
     elif custom_llm_provider == "gemini":
         supported_params = get_supported_openai_params(
             model=model, custom_llm_provider=custom_llm_provider
@@ -3170,7 +3170,7 @@ def get_optional_params(
             optional_params=optional_params,
             model=model,
         )
-    elif custom_llm_provider == "vertex_ai_beta" or custom_llm_provider == "gemini":
+    elif custom_llm_provider == "vertex_ai_beta":
         supported_params = get_supported_openai_params(
             model=model, custom_llm_provider=custom_llm_provider
         )
@@ -3185,6 +3185,8 @@ def get_optional_params(
                 else False
             ),
         )
+        if litellm.vertex_ai_safety_settings is not None:
+            optional_params["safety_settings"] = litellm.vertex_ai_safety_settings
     elif (
         custom_llm_provider == "vertex_ai" and model in litellm.vertex_anthropic_models
     ):
@@ -4219,6 +4221,7 @@ def get_supported_openai_params(
             "presence_penalty",
             "stop",
             "n",
+            "extra_headers",
         ]
     elif custom_llm_provider == "cohere_chat":
         return [
@@ -4233,6 +4236,7 @@ def get_supported_openai_params(
             "tools",
             "tool_choice",
             "seed",
+            "extra_headers",
         ]
     elif custom_llm_provider == "maritalk":
         return [
@@ -7121,6 +7125,14 @@ def exception_type(
                         llm_provider="bedrock",
                         response=original_exception.response,
                     )
+                elif "A conversation must start with a user message." in error_str:
+                    exception_mapping_worked = True
+                    raise BadRequestError(
+                        message=f"BedrockException - {error_str}\n. Pass in default user message via `completion(..,user_continue_message=)` or enable `litellm.modify_params=True`.\nFor Proxy: do via `litellm_settings::modify_params: True` or user_continue_message under `litellm_params`",
+                        model=model,
+                        llm_provider="bedrock",
+                        response=original_exception.response,
+                    )
                 elif (
                     "Unable to locate credentials" in error_str
                     or "The security token included in the request is invalid"

pyproject.toml

@@ -1,6 +1,6 @@
 [tool.poetry]
 name = "litellm"
-version = "1.44.1"
+version = "1.44.2"
 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 = "1.44.1"
+version = "1.44.2"
 version_files = [
     "pyproject.toml:^version"
 ]