[Feat] Add Support for calling Gemini/Vertex models in their native format

litellm/google_genai/Readme.md

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+# LiteLLM Google GenAI Interface
+
+Interface to interact with Google GenAI Functions in the native Google interface format.
+
+## Overview
+
+This module provides a native interface to Google's Generative AI API, allowing you to use Google's content generation capabilities with both streaming and non-streaming modes, in both synchronous and asynchronous contexts.
+
+## Available Functions
+
+### Non-Streaming Functions
+
+- `generate_content()` - Synchronous content generation
+- `agenerate_content()` - Asynchronous content generation
+
+### Streaming Functions
+
+- `generate_content_stream()` - Synchronous streaming content generation
+- `agenerate_content_stream()` - Asynchronous streaming content generation
+
+## Usage Examples
+
+### Basic Non-Streaming Usage
+
+```python
+from litellm.google_genai import generate_content, agenerate_content
+from google.genai.types import ContentDict, PartDict
+
+# Synchronous usage
+contents = ContentDict(
+    parts=[
+        PartDict(text="Hello, can you tell me a short joke?")
+    ],
+)
+
+response = generate_content(
+    contents=contents,
+    model="gemini-pro",  # or your preferred model
+    # Add other model-specific parameters as needed
+)
+
+print(response)
+```
+
+### Async Non-Streaming Usage
+
+```python
+import asyncio
+from litellm.google_genai import agenerate_content
+from google.genai.types import ContentDict, PartDict
+
+async def main():
+    contents = ContentDict(
+        parts=[
+            PartDict(text="Hello, can you tell me a short joke?")
+        ],
+    )
+
+    response = await agenerate_content(
+        contents=contents,
+        model="gemini-pro",
+        # Add other model-specific parameters as needed
+    )
+
+    print(response)
+
+# Run the async function
+asyncio.run(main())
+```
+
+### Streaming Usage
+
+```python
+from litellm.google_genai import generate_content_stream
+from google.genai.types import ContentDict, PartDict
+
+# Synchronous streaming
+contents = ContentDict(
+    parts=[
+        PartDict(text="Tell me a story about space exploration")
+    ],
+)
+
+for chunk in generate_content_stream(
+    contents=contents,
+    model="gemini-pro",
+):
+    print(f"Chunk: {chunk}")
+```
+
+### Async Streaming Usage
+
+```python
+import asyncio
+from litellm.google_genai import agenerate_content_stream
+from google.genai.types import ContentDict, PartDict
+
+async def main():
+    contents = ContentDict(
+        parts=[
+            PartDict(text="Tell me a story about space exploration")
+        ],
+    )
+
+    async for chunk in agenerate_content_stream(
+        contents=contents,
+        model="gemini-pro",
+    ):
+        print(f"Async chunk: {chunk}")
+
+asyncio.run(main())
+```
+
+
+## Testing
+
+This module includes comprehensive tests covering:
+- Sync and async non-streaming requests
+- Sync and async streaming requests
+- Response validation
+- Error handling scenarios
+
+See `tests/unified_google_tests/base_google_test.py` for test implementation examples.

litellm/google_genai/__init__.py

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+"""
+This allows using Google GenAI model in their native interface.
+
+This module provides generate_content functionality for Google GenAI models.
+"""
+
+from .main import (
+    agenerate_content,
+    agenerate_content_stream,
+    generate_content,
+    generate_content_stream,
+)
+
+__all__ = [
+    "generate_content",
+    "agenerate_content", 
+    "generate_content_stream",
+    "agenerate_content_stream",
+]