(feat) use @google-cloud/vertexai js sdk with litellm (#6873)

* stash gemini JS test

* add vertex js sdj example

* handle vertex pass through separately

* tes vertex JS sdk

* fix vertex_proxy_route

* use PassThroughStreamingHandler

* fix PassThroughStreamingHandler

* use common _create_vertex_response_logging_payload_for_generate_content

* test vertex js

* add working vertex jest tests

* move basic bass through test

* use good name for test

* test vertex

* test_chunk_processor_yields_raw_bytes

* unit tests for streaming

* test_convert_raw_bytes_to_str_lines

* run unit tests 1st

* simplify local

* docs add usage example for js

* use get_litellm_virtual_key

* add unit tests for vertex pass through

.circleci/config.yml

@@ -1179,6 +1179,15 @@ jobs:
             pip install "PyGithub==1.59.1"
             pip install "google-cloud-aiplatform==1.59.0"
             pip install anthropic
+            python -m pip install -r requirements.txt
+      # Run pytest and generate JUnit XML report
+      - run:
+          name: Run tests
+          command: |
+            pwd
+            ls
+            python -m pytest -vv tests/pass_through_unit_tests --cov=litellm --cov-report=xml -x -s -v --junitxml=test-results/junit.xml --durations=5
+          no_output_timeout: 120m
       - run:
           name: Build Docker image
           command: docker build -t my-app:latest -f ./docker/Dockerfile.database .
@@ -1214,6 +1223,26 @@ jobs:
       - run:
           name: Wait for app to be ready
           command: dockerize -wait http://localhost:4000 -timeout 5m
+      # New steps to run Node.js test
+      - run:
+          name: Install Node.js
+          command: |
+            curl -fsSL https://deb.nodesource.com/setup_18.x | sudo -E bash -
+            sudo apt-get install -y nodejs
+            node --version
+            npm --version
+
+      - run:
+          name: Install Node.js dependencies
+          command: |
+            npm install @google-cloud/vertexai
+            npm install --save-dev jest
+
+      - run:
+          name: Run Vertex AI tests
+          command: |
+            npx jest tests/pass_through_tests/test_vertex.test.js --verbose
+          no_output_timeout: 30m
       - run:
           name: Run tests
           command: |
@@ -1221,7 +1250,6 @@ jobs:
             ls
             python -m pytest -vv tests/pass_through_tests/ -x --junitxml=test-results/junit.xml --durations=5
           no_output_timeout: 120m
-
       # Store test results
       - store_test_results:
           path: test-results

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

@@ -12,6 +12,71 @@ Looking for the Unified API (OpenAI format) for VertexAI ? [Go here - using vert
 
 :::
 
+Pass-through endpoints for Vertex AI - call provider-specific endpoint, in native format (no translation).
+
+Just replace `https://REGION-aiplatform.googleapis.com` with `LITELLM_PROXY_BASE_URL/vertex-ai`
+
+
+#### **Example Usage**
+
+<Tabs>
+<TabItem value="curl" label="curl">
+
+```bash
+curl http://localhost:4000/vertex-ai/publishers/google/models/gemini-1.0-pro:generateContent \
+  -H "Content-Type: application/json" \
+  -H "Authorization: Bearer sk-1234" \
+  -d '{
+    "contents":[{
+      "role": "user", 
+      "parts":[{"text": "How are you doing today?"}]
+    }]
+  }'
+```
+
+</TabItem>
+<TabItem value="js" label="Vertex Node.js SDK">
+
+```javascript
+const { VertexAI } = require('@google-cloud/vertexai');
+
+const vertexAI = new VertexAI({
+    project: 'your-project-id', // enter your vertex project id
+    location: 'us-central1', // enter your vertex region
+    apiEndpoint: "localhost:4000/vertex-ai" // <proxy-server-url>/vertex-ai # note, do not include 'https://' in the url
+});
+
+const model = vertexAI.getGenerativeModel({
+    model: 'gemini-1.0-pro'
+}, {
+    customHeaders: {
+        "x-litellm-api-key": "sk-1234" // Your litellm Virtual Key
+    }
+});
+
+async function generateContent() {
+    try {
+        const prompt = {
+            contents: [{
+                role: 'user',
+                parts: [{ text: 'How are you doing today?' }]
+            }]
+        };
+
+        const response = await model.generateContent(prompt);
+        console.log('Response:', response);
+    } catch (error) {
+        console.error('Error:', error);
+    }
+}
+
+generateContent();
+```
+
+</TabItem>
+</Tabs>
+
+
 ## Supported API Endpoints
 
 - Gemini API

litellm/proxy/pass_through_endpoints/llm_provider_handlers/anthropic_passthrough_logging_handler.py

@@ -100,7 +100,7 @@ class AnthropicPassthroughLoggingHandler:
         kwargs["response_cost"] = response_cost
         kwargs["model"] = model
 
-        # Make standard logging object for Vertex AI
+        # Make standard logging object for Anthropic
         standard_logging_object = get_standard_logging_object_payload(
             kwargs=kwargs,
             init_response_obj=litellm_model_response,

litellm/proxy/pass_through_endpoints/llm_provider_handlers/vertex_passthrough_logging_handler.py

@@ -56,8 +56,14 @@ class VertexPassthroughLoggingHandler:
                     encoding=None,
                 )
             )
-            logging_obj.model = litellm_model_response.model or model
+            kwargs = VertexPassthroughLoggingHandler._create_vertex_response_logging_payload_for_generate_content(
-            logging_obj.model_call_details["model"] = logging_obj.model
+                litellm_model_response=litellm_model_response,
+                model=model,
+                kwargs=kwargs,
+                start_time=start_time,
+                end_time=end_time,
+                logging_obj=logging_obj,
+            )
 
             await logging_obj.async_success_handler(
                 result=litellm_model_response,
@@ -147,6 +153,14 @@ class VertexPassthroughLoggingHandler:
                 "Unable to build complete streaming response for Vertex passthrough endpoint, not logging..."
             )
             return
+        kwargs = VertexPassthroughLoggingHandler._create_vertex_response_logging_payload_for_generate_content(
+            litellm_model_response=complete_streaming_response,
+            model=model,
+            kwargs=kwargs,
+            start_time=start_time,
+            end_time=end_time,
+            logging_obj=litellm_logging_obj,
+        )
         await litellm_logging_obj.async_success_handler(
             result=complete_streaming_response,
             start_time=start_time,
@@ -193,3 +207,47 @@ class VertexPassthroughLoggingHandler:
         if match:
             return match.group(1)
         return "unknown"
+
+    @staticmethod
+    def _create_vertex_response_logging_payload_for_generate_content(
+        litellm_model_response: Union[
+            litellm.ModelResponse, litellm.TextCompletionResponse
+        ],
+        model: str,
+        kwargs: dict,
+        start_time: datetime,
+        end_time: datetime,
+        logging_obj: LiteLLMLoggingObj,
+    ):
+        """
+        Create the standard logging object for Vertex passthrough generateContent (streaming and non-streaming)
+
+        """
+        response_cost = litellm.completion_cost(
+            completion_response=litellm_model_response,
+            model=model,
+        )
+        kwargs["response_cost"] = response_cost
+        kwargs["model"] = model
+
+        # Make standard logging object for Vertex AI
+        standard_logging_object = get_standard_logging_object_payload(
+            kwargs=kwargs,
+            init_response_obj=litellm_model_response,
+            start_time=start_time,
+            end_time=end_time,
+            logging_obj=logging_obj,
+            status="success",
+        )
+
+        # pretty print standard logging object
+        verbose_proxy_logger.debug(
+            "standard_logging_object= %s", json.dumps(standard_logging_object, indent=4)
+        )
+        kwargs["standard_logging_object"] = standard_logging_object
+
+        # set litellm_call_id to logging response object
+        litellm_model_response.id = logging_obj.litellm_call_id
+        logging_obj.model = litellm_model_response.model or model
+        logging_obj.model_call_details["model"] = logging_obj.model
+        return kwargs

litellm/proxy/pass_through_endpoints/pass_through_endpoints.py

@@ -36,7 +36,7 @@ from litellm.proxy._types import (
 from litellm.proxy.auth.user_api_key_auth import user_api_key_auth
 from litellm.secret_managers.main import get_secret_str
 
-from .streaming_handler import chunk_processor
+from .streaming_handler import PassThroughStreamingHandler
 from .success_handler import PassThroughEndpointLogging
 from .types import EndpointType, PassthroughStandardLoggingPayload
 
@@ -448,7 +448,7 @@ async def pass_through_request(  # noqa: PLR0915
                 )
 
             return StreamingResponse(
-                chunk_processor(
+                PassThroughStreamingHandler.chunk_processor(
                     response=response,
                     request_body=_parsed_body,
                     litellm_logging_obj=logging_obj,
@@ -491,7 +491,7 @@ async def pass_through_request(  # noqa: PLR0915
                 )
 
             return StreamingResponse(
-                chunk_processor(
+                PassThroughStreamingHandler.chunk_processor(
                     response=response,
                     request_body=_parsed_body,
                     litellm_logging_obj=logging_obj,

litellm/proxy/pass_through_endpoints/streaming_handler.py

@@ -27,6 +27,9 @@ from .success_handler import PassThroughEndpointLogging
 from .types import EndpointType
 
 
+class PassThroughStreamingHandler:
+
+    @staticmethod
     async def chunk_processor(
         response: httpx.Response,
         request_body: Optional[dict],
@@ -40,41 +43,30 @@ async def chunk_processor(
         - Yields chunks from the response
         - Collect non-empty chunks for post-processing (logging)
         """
-    collected_chunks: List[str] = []  # List to store all chunks
         try:
-        async for chunk in response.aiter_lines():
+            raw_bytes: List[bytes] = []
-            verbose_proxy_logger.debug(f"Processing chunk: {chunk}")
+            async for chunk in response.aiter_bytes():
-            if not chunk:
+                raw_bytes.append(chunk)
-                continue
+                yield chunk
-
-            # Handle SSE format - pass through the raw SSE format
-            if isinstance(chunk, bytes):
-                chunk = chunk.decode("utf-8")
-
-            # Store the chunk for post-processing
-            if chunk.strip():  # Only store non-empty chunks
-                collected_chunks.append(chunk)
-                yield f"{chunk}\n"
 
             # After all chunks are processed, handle post-processing
             end_time = datetime.now()
 
-        await _route_streaming_logging_to_handler(
+            await PassThroughStreamingHandler._route_streaming_logging_to_handler(
                 litellm_logging_obj=litellm_logging_obj,
                 passthrough_success_handler_obj=passthrough_success_handler_obj,
                 url_route=url_route,
                 request_body=request_body or {},
                 endpoint_type=endpoint_type,
                 start_time=start_time,
-            all_chunks=collected_chunks,
+                raw_bytes=raw_bytes,
                 end_time=end_time,
             )
-
         except Exception as e:
             verbose_proxy_logger.error(f"Error in chunk_processor: {str(e)}")
             raise
 
-
+    @staticmethod
     async def _route_streaming_logging_to_handler(
         litellm_logging_obj: LiteLLMLoggingObj,
         passthrough_success_handler_obj: PassThroughEndpointLogging,
@@ -82,7 +74,7 @@ async def _route_streaming_logging_to_handler(
         request_body: dict,
         endpoint_type: EndpointType,
         start_time: datetime,
-    all_chunks: List[str],
+        raw_bytes: List[bytes],
         end_time: datetime,
     ):
         """
@@ -92,6 +84,9 @@ async def _route_streaming_logging_to_handler(
         - Anthropic
         - Vertex AI
         """
+        all_chunks = PassThroughStreamingHandler._convert_raw_bytes_to_str_lines(
+            raw_bytes
+        )
         if endpoint_type == EndpointType.ANTHROPIC:
             await AnthropicPassthroughLoggingHandler._handle_logging_anthropic_collected_chunks(
                 litellm_logging_obj=litellm_logging_obj,
@@ -117,3 +112,22 @@ async def _route_streaming_logging_to_handler(
         elif endpoint_type == EndpointType.GENERIC:
             # No logging is supported for generic streaming endpoints
             pass
+
+    @staticmethod
+    def _convert_raw_bytes_to_str_lines(raw_bytes: List[bytes]) -> List[str]:
+        """
+        Converts a list of raw bytes into a list of string lines, similar to aiter_lines()
+
+        Args:
+            raw_bytes: List of bytes chunks from aiter.bytes()
+
+        Returns:
+            List of string lines, with each line being a complete data: {} chunk
+        """
+        # Combine all bytes and decode to string
+        combined_str = b"".join(raw_bytes).decode("utf-8")
+
+        # Split by newlines and filter out empty lines
+        lines = [line.strip() for line in combined_str.split("\n") if line.strip()]
+
+        return lines

litellm/proxy/vertex_ai_endpoints/vertex_endpoints.py

@@ -119,7 +119,6 @@ async def vertex_proxy_route(
     endpoint: str,
     request: Request,
     fastapi_response: Response,
-    user_api_key_dict: UserAPIKeyAuth = Depends(user_api_key_auth),
 ):
     encoded_endpoint = httpx.URL(endpoint).path
 
@@ -127,6 +126,11 @@ async def vertex_proxy_route(
 
     verbose_proxy_logger.debug("requested endpoint %s", endpoint)
     headers: dict = {}
+    api_key_to_use = get_litellm_virtual_key(request=request)
+    user_api_key_dict = await user_api_key_auth(
+        request=request,
+        api_key=api_key_to_use,
+    )
 
     vertex_project = None
     vertex_location = None
@@ -214,3 +218,18 @@ async def vertex_proxy_route(
     )
 
     return received_value
+
+
+def get_litellm_virtual_key(request: Request) -> str:
+    """
+    Extract and format API key from request headers.
+    Prioritizes x-litellm-api-key over Authorization header.
+
+
+    Vertex JS SDK uses `Authorization` header, we use `x-litellm-api-key` to pass litellm virtual key
+
+    """
+    litellm_api_key = request.headers.get("x-litellm-api-key")
+    if litellm_api_key:
+        return f"Bearer {litellm_api_key}"
+    return request.headers.get("Authorization", "")

tests/pass_through_tests/test_gemini.js

@@ -0,0 +1,23 @@
+// const { GoogleGenerativeAI } = require("@google/generative-ai");
+
+// const genAI = new GoogleGenerativeAI("sk-1234");
+// const model = genAI.getGenerativeModel({ model: "gemini-1.5-flash" });
+
+// const prompt = "Explain how AI works in 2 pages";
+
+// async function run() {
+//     try {
+//         const result = await model.generateContentStream(prompt, { baseUrl: "http://localhost:4000/gemini" });
+//         const response = await result.response;
+//         console.log(response.text());
+//         for await (const chunk of result.stream) {
+//             const chunkText = chunk.text();
+//             console.log(chunkText);
+//             process.stdout.write(chunkText);
+//         }
+//     } catch (error) {
+//         console.error("Error:", error);
+//     }
+// }
+
+// run();

tests/pass_through_tests/test_local_vertex.js

@@ -0,0 +1,68 @@
+const { VertexAI, RequestOptions } = require('@google-cloud/vertexai');
+
+
+// Import fetch if the SDK uses it
+const originalFetch = global.fetch || require('node-fetch');
+
+// Monkey-patch the fetch used internally
+global.fetch = async function patchedFetch(url, options) {
+    // Modify the URL to use HTTP instead of HTTPS
+    if (url.startsWith('https://localhost:4000')) {
+        url = url.replace('https://', 'http://');
+    }
+    console.log('Patched fetch sending request to:', url);
+    return originalFetch(url, options);
+};
+
+const vertexAI = new VertexAI({
+    project: 'adroit-crow-413218',
+    location: 'us-central1',
+    apiEndpoint: "localhost:4000/vertex-ai"
+});
+
+
+// Use customHeaders in RequestOptions
+const requestOptions = {
+    customHeaders: new Headers({
+        "x-litellm-api-key": "sk-1234"
+    })
+};
+
+const generativeModel = vertexAI.getGenerativeModel(
+    { model: 'gemini-1.0-pro' },
+    requestOptions
+);
+
+async function streamingResponse() {
+    try {
+        const request = {
+            contents: [{role: 'user', parts: [{text: 'How are you doing today tell me your name?'}]}],
+          };
+        const streamingResult = await generativeModel.generateContentStream(request);
+        for await (const item of streamingResult.stream) {
+            console.log('stream chunk: ', JSON.stringify(item));
+        }
+        const aggregatedResponse = await streamingResult.response;
+        console.log('aggregated response: ', JSON.stringify(aggregatedResponse));
+    } catch (error) {
+        console.error('Error:', error);
+    }
+}
+
+
+async function nonStreamingResponse() {
+    try {
+        const request = {
+            contents: [{role: 'user', parts: [{text: 'How are you doing today tell me your name?'}]}],
+          };
+        const response = await generativeModel.generateContent(request);
+        console.log('non streaming response: ', JSON.stringify(response));
+    } catch (error) {
+        console.error('Error:', error);
+    }
+}
+
+
+
+streamingResponse();
+nonStreamingResponse();

tests/pass_through_tests/test_vertex.test.js

@@ -0,0 +1,114 @@
+const { VertexAI, RequestOptions } = require('@google-cloud/vertexai');
+const fs = require('fs');
+const path = require('path');
+const os = require('os');
+const { writeFileSync } = require('fs');
+
+
+// Import fetch if the SDK uses it
+const originalFetch = global.fetch || require('node-fetch');
+
+// Monkey-patch the fetch used internally
+global.fetch = async function patchedFetch(url, options) {
+    // Modify the URL to use HTTP instead of HTTPS
+    if (url.startsWith('https://localhost:4000')) {
+        url = url.replace('https://', 'http://');
+    }
+    console.log('Patched fetch sending request to:', url);
+    return originalFetch(url, options);
+};
+
+function loadVertexAiCredentials() {
+    console.log("loading vertex ai credentials");
+    const filepath = path.dirname(__filename);
+    const vertexKeyPath = path.join(filepath, "vertex_key.json");
+
+    // Initialize default empty service account data
+    let serviceAccountKeyData = {};
+
+    // Try to read existing vertex_key.json
+    try {
+        const content = fs.readFileSync(vertexKeyPath, 'utf8');
+        if (content && content.trim()) {
+            serviceAccountKeyData = JSON.parse(content);
+        }
+    } catch (error) {
+        // File doesn't exist or is invalid, continue with empty object
+    }
+
+    // Update with environment variables
+    const privateKeyId = process.env.VERTEX_AI_PRIVATE_KEY_ID || "";
+    const privateKey = (process.env.VERTEX_AI_PRIVATE_KEY || "").replace(/\\n/g, "\n");
+    
+    serviceAccountKeyData.private_key_id = privateKeyId;
+    serviceAccountKeyData.private_key = privateKey;
+
+    // Create temporary file
+    const tempFilePath = path.join(os.tmpdir(), `vertex-credentials-${Date.now()}.json`);
+    writeFileSync(tempFilePath, JSON.stringify(serviceAccountKeyData, null, 2));
+    
+    // Set environment variable
+    process.env.GOOGLE_APPLICATION_CREDENTIALS = tempFilePath;
+}
+
+// Run credential loading before tests
+beforeAll(() => {
+    loadVertexAiCredentials();
+});
+
+
+
+describe('Vertex AI Tests', () => {
+    test('should successfully generate content from Vertex AI', async () => {
+        const vertexAI = new VertexAI({
+            project: 'adroit-crow-413218',
+            location: 'us-central1',
+            apiEndpoint: "localhost:4000/vertex-ai"
+        });
+
+        const customHeaders = new Headers({
+            "x-litellm-api-key": "sk-1234"
+        });
+
+        const requestOptions = {
+            customHeaders: customHeaders
+        };
+
+        const generativeModel = vertexAI.getGenerativeModel(
+            { model: 'gemini-1.0-pro' },
+            requestOptions
+        );
+
+        const request = {
+            contents: [{role: 'user', parts: [{text: 'How are you doing today tell me your name?'}]}],
+        };
+
+        const streamingResult = await generativeModel.generateContentStream(request);
+        
+        // Add some assertions
+        expect(streamingResult).toBeDefined();
+        
+        for await (const item of streamingResult.stream) {
+            console.log('stream chunk:', JSON.stringify(item));
+            expect(item).toBeDefined();
+        }
+
+        const aggregatedResponse = await streamingResult.response;
+        console.log('aggregated response:', JSON.stringify(aggregatedResponse));
+        expect(aggregatedResponse).toBeDefined();
+    });
+
+
+    test('should successfully generate non-streaming content from Vertex AI', async () => {
+        const vertexAI = new VertexAI({project: 'adroit-crow-413218', location: 'us-central1', apiEndpoint: "localhost:4000/vertex-ai"});
+        const customHeaders = new Headers({"x-litellm-api-key": "sk-1234"});
+        const requestOptions = {customHeaders: customHeaders};
+        const generativeModel = vertexAI.getGenerativeModel({model: 'gemini-1.0-pro'}, requestOptions);
+        const request = {contents: [{role: 'user', parts: [{text: 'What is 2+2?'}]}]};
+
+        const result = await generativeModel.generateContent(request);
+        expect(result).toBeDefined();
+        expect(result.response).toBeDefined();
+        console.log('non-streaming response:', JSON.stringify(result.response));
+    });
+});

tests/pass_through_unit_tests/test_unit_test_streaming.py

@@ -0,0 +1,118 @@
+import json
+import os
+import sys
+from datetime import datetime
+from unittest.mock import AsyncMock, Mock, patch, MagicMock
+
+sys.path.insert(
+    0, os.path.abspath("../..")
+)  # Adds the parent directory to the system path
+
+import httpx
+import pytest
+import litellm
+from typing import AsyncGenerator
+from litellm.litellm_core_utils.litellm_logging import Logging as LiteLLMLoggingObj
+from litellm.proxy.pass_through_endpoints.types import EndpointType
+from litellm.proxy.pass_through_endpoints.success_handler import (
+    PassThroughEndpointLogging,
+)
+from litellm.proxy.pass_through_endpoints.streaming_handler import (
+    PassThroughStreamingHandler,
+)
+
+
+# Helper function to mock async iteration
+async def aiter_mock(iterable):
+    for item in iterable:
+        yield item
+
+
+@pytest.mark.asyncio
+@pytest.mark.parametrize(
+    "endpoint_type,url_route",
+    [
+        (
+            EndpointType.VERTEX_AI,
+            "v1/projects/adroit-crow-413218/locations/us-central1/publishers/google/models/gemini-1.0-pro:generateContent",
+        ),
+        (EndpointType.ANTHROPIC, "/v1/messages"),
+    ],
+)
+async def test_chunk_processor_yields_raw_bytes(endpoint_type, url_route):
+    """
+    Test that the chunk_processor yields raw bytes
+
+    This is CRITICAL for pass throughs streaming with Vertex AI and Anthropic
+    """
+    # Mock inputs
+    response = AsyncMock(spec=httpx.Response)
+    raw_chunks = [
+        b'{"id": "1", "content": "Hello"}',
+        b'{"id": "2", "content": "World"}',
+        b'\n\ndata: {"id": "3"}',  # Testing different byte formats
+    ]
+
+    # Mock aiter_bytes to return an async generator
+    async def mock_aiter_bytes():
+        for chunk in raw_chunks:
+            yield chunk
+
+    response.aiter_bytes = mock_aiter_bytes
+
+    request_body = {"key": "value"}
+    litellm_logging_obj = MagicMock()
+    start_time = datetime.now()
+    passthrough_success_handler_obj = MagicMock()
+
+    # Capture yielded chunks and perform detailed assertions
+    received_chunks = []
+    async for chunk in PassThroughStreamingHandler.chunk_processor(
+        response=response,
+        request_body=request_body,
+        litellm_logging_obj=litellm_logging_obj,
+        endpoint_type=endpoint_type,
+        start_time=start_time,
+        passthrough_success_handler_obj=passthrough_success_handler_obj,
+        url_route=url_route,
+    ):
+        # Assert each chunk is bytes
+        assert isinstance(chunk, bytes), f"Chunk should be bytes, got {type(chunk)}"
+        # Assert no decoding/encoding occurred (chunk should be exactly as input)
+        assert (
+            chunk in raw_chunks
+        ), f"Chunk {chunk} was modified during processing. For pass throughs streaming, chunks should be raw bytes"
+        received_chunks.append(chunk)
+
+    # Assert all chunks were processed
+    assert len(received_chunks) == len(raw_chunks), "Not all chunks were processed"
+
+    # collected chunks all together
+    assert b"".join(received_chunks) == b"".join(
+        raw_chunks
+    ), "Collected chunks do not match raw chunks"
+
+
+def test_convert_raw_bytes_to_str_lines():
+    """
+    Test that the _convert_raw_bytes_to_str_lines method correctly converts raw bytes to a list of strings
+    """
+    # Test case 1: Single chunk
+    raw_bytes = [b'data: {"content": "Hello"}\n']
+    result = PassThroughStreamingHandler._convert_raw_bytes_to_str_lines(raw_bytes)
+    assert result == ['data: {"content": "Hello"}']
+
+    # Test case 2: Multiple chunks
+    raw_bytes = [b'data: {"content": "Hello"}\n', b'data: {"content": "World"}\n']
+    result = PassThroughStreamingHandler._convert_raw_bytes_to_str_lines(raw_bytes)
+    assert result == ['data: {"content": "Hello"}', 'data: {"content": "World"}']
+
+    # Test case 3: Empty input
+    raw_bytes = []
+    result = PassThroughStreamingHandler._convert_raw_bytes_to_str_lines(raw_bytes)
+    assert result == []
+
+    # Test case 4: Chunks with empty lines
+    raw_bytes = [b'data: {"content": "Hello"}\n\n', b'\ndata: {"content": "World"}\n']
+    result = PassThroughStreamingHandler._convert_raw_bytes_to_str_lines(raw_bytes)
+    assert result == ['data: {"content": "Hello"}', 'data: {"content": "World"}']

tests/pass_through_unit_tests/test_unit_test_vertex_pass_through.py

@@ -0,0 +1,84 @@
+import json
+import os
+import sys
+from datetime import datetime
+from unittest.mock import AsyncMock, Mock, patch
+
+sys.path.insert(
+    0, os.path.abspath("../..")
+)  # Adds the parent directory to the system path
+
+
+import httpx
+import pytest
+import litellm
+from litellm.litellm_core_utils.litellm_logging import Logging as LiteLLMLoggingObj
+
+
+from litellm.proxy.vertex_ai_endpoints.vertex_endpoints import (
+    get_litellm_virtual_key,
+    vertex_proxy_route,
+)
+
+
+@pytest.mark.asyncio
+async def test_get_litellm_virtual_key():
+    """
+    Test that the get_litellm_virtual_key function correctly handles the API key authentication
+    """
+    # Test with x-litellm-api-key
+    mock_request = Mock()
+    mock_request.headers = {"x-litellm-api-key": "test-key-123"}
+    result = get_litellm_virtual_key(mock_request)
+    assert result == "Bearer test-key-123"
+
+    # Test with Authorization header
+    mock_request.headers = {"Authorization": "Bearer auth-key-456"}
+    result = get_litellm_virtual_key(mock_request)
+    assert result == "Bearer auth-key-456"
+
+    # Test with both headers (x-litellm-api-key should take precedence)
+    mock_request.headers = {
+        "x-litellm-api-key": "test-key-123",
+        "Authorization": "Bearer auth-key-456",
+    }
+    result = get_litellm_virtual_key(mock_request)
+    assert result == "Bearer test-key-123"
+
+
+@pytest.mark.asyncio
+async def test_vertex_proxy_route_api_key_auth():
+    """
+    Critical
+
+    This is how Vertex AI JS SDK will Auth to Litellm Proxy
+    """
+    # Mock dependencies
+    mock_request = Mock()
+    mock_request.headers = {"x-litellm-api-key": "test-key-123"}
+    mock_request.method = "POST"
+    mock_response = Mock()
+
+    with patch(
+        "litellm.proxy.vertex_ai_endpoints.vertex_endpoints.user_api_key_auth"
+    ) as mock_auth:
+        mock_auth.return_value = {"api_key": "test-key-123"}
+
+        with patch(
+            "litellm.proxy.vertex_ai_endpoints.vertex_endpoints.create_pass_through_route"
+        ) as mock_pass_through:
+            mock_pass_through.return_value = AsyncMock(
+                return_value={"status": "success"}
+            )
+
+            # Call the function
+            result = await vertex_proxy_route(
+                endpoint="v1/projects/test-project/locations/us-central1/publishers/google/models/gemini-1.5-pro:generateContent",
+                request=mock_request,
+                fastapi_response=mock_response,
+            )
+
+            # Verify user_api_key_auth was called with the correct Bearer token
+            mock_auth.assert_called_once()
+            call_args = mock_auth.call_args[1]
+            assert call_args["api_key"] == "Bearer test-key-123"