use litellm proxy with vertex ai sdk

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

@@ -84,7 +84,6 @@ vertexai.init(
     api_endpoint=LITELLM_PROXY_BASE,
     credentials=credentials,
     api_transport="rest",
-    request_metadata=[("Authorization", f"Bearer {LITELLM_PROXY_API_KEY}")],
 )
 
 model = GenerativeModel("gemini-1.5-flash-001")
@@ -143,7 +142,7 @@ vertexai.init(
     api_endpoint=LITELLM_PROXY_BASE,
     credentials=credentials,
     api_transport="rest",
-    request_metadata=[("Authorization", f"Bearer {LITELLM_PROXY_API_KEY}")],
+   
 )
 
 model = GenerativeModel("gemini-1.5-flash-001")
@@ -216,7 +215,7 @@ vertexai.init(
     api_endpoint=LITELLM_PROXY_BASE,
     credentials=credentials,
     api_transport="rest",
-    request_metadata=[("Authorization", f"Bearer {LITELLM_PROXY_API_KEY}")],
+)
 
 
 def embed_text(
@@ -249,6 +248,80 @@ curl http://localhost:4000/vertex-ai/publishers/google/models/textembedding-geck
 
 ### 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" \
@@ -256,8 +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" \
@@ -265,10 +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" \
@@ -280,3 +504,7 @@ curl http://localhost:4000/vertex-ai/tuningJobs \
   }
 }'
 ```
+
+</TabItem>
+
+</Tabs>

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

@@ -1694,7 +1694,7 @@ vertexai.init(
     api_endpoint=LITELLM_PROXY_BASE,
     credentials = credentials,
     api_transport="rest",
-    request_metadata=[("Authorization", f"Bearer {LITELLM_PROXY_API_KEY}")],
+   
 )
 
 model = MultiModalEmbeddingModel.from_pretrained("multimodalembedding")

litellm/proxy/tests/test_vtx_sdk_embedding.py

@@ -42,7 +42,6 @@ vertexai.init(
     api_endpoint=LITELLM_PROXY_BASE,
     credentials=credentials,
     api_transport="rest",
-    request_metadata=[("Authorization", f"Bearer {LITELLM_PROXY_API_KEY}")],
 )
 
 model = MultiModalEmbeddingModel.from_pretrained("multimodalembedding")