docs: update import paths for Gemini README

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google3/third_party/py/google/cloud/aiplatform/gemini_docs/README.rst

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+# Vertex Generative AI SDK for Python
+The Vertex Generative AI SDK helps developers use Google's generative AI
+[Gemini models](http://cloud.google.com/vertex-ai/docs/generative-ai/multimodal/overview)
+and [PaLM language models](http://cloud.google.com/vertex-ai/docs/generative-ai/language-model-overview)
+to build AI-powered features and applications.
+The SDKs support use cases like the following:
+
+- Generate text from texts, images and videos (multimodal generation)
+- Build stateful multi-turn conversations (chat)
+- Function calling
+
+## Installation
+
+To install the
+[google-cloud-aiplatform](https://pypi.org/project/google-cloud-aiplatform/)
+Python package, run the following command:
+
+```shell
+pip3 install --upgrade --user "google-cloud-aiplatform>=1.38"
+```
+
+## Usage
+
+For detailed instructions, see [quickstart](http://cloud.google.com/vertex-ai/docs/generative-ai/start/quickstarts/quickstart-multimodal) and [Introduction to multimodal classes in the Vertex AI SDK](http://cloud.google.com/vertex-ai/docs/generative-ai/multimodal/sdk-for-gemini/gemini-sdk-overview-reference).
+
+#### Imports:
+```python
+from vertexai.generative_models import GenerativeModel, Image, Content, Part, Tool, FunctionDeclaration, GenerationConfig
+```
+
+#### Basic generation:
+```python
+from vertexai.generative_models import GenerativeModel
+model = GenerativeModel("gemini-pro")
+print(model.generate_content("Why is sky blue?"))
+```
+
+#### Using images and videos
+```python
+from vertexai.generative_models import GenerativeModel, Image
+vision_model = GenerativeModel("gemini-pro-vision")
+
+# Local image
+image = Image.load_from_file("image.jpg")
+print(vision_model.generate_content(["What is shown in this image?", image]))
+
+# Image from Cloud Storage
+image_part = generative_models.Part.from_uri("gs://download.tensorflow.org/example_images/320px-Felis_catus-cat_on_snow.jpg", mime_type="image/jpeg")
+print(vision_model.generate_content([image_part, "Describe this image?"]))
+
+# Text and video
+video_part = Part.from_uri("gs://cloud-samples-data/video/animals.mp4", mime_type="video/mp4")
+print(vision_model.generate_content(["What is in the video? ", video_part]))
+```
+
+#### Chat
+```
+from vertexai.generative_models import GenerativeModel, Image
+vision_model = GenerativeModel("gemini-ultra-vision")
+vision_chat = vision_model.start_chat()
+image = Image.load_from_file("image.jpg")
+print(vision_chat.send_message(["I like this image.", image]))
+print(vision_chat.send_message("What things do I like?."))
+```
+
+#### System instructions
+```
+from vertexai.generative_models import GenerativeModel
+model = GenerativeModel(
+    "gemini-1.0-pro",
+    system_instruction=[
+        "Talk like a pirate.",
+        "Don't use rude words.",
+    ],
+)
+print(model.generate_content("Why is sky blue?"))
+```
+
+#### Function calling
+
+```
+# First, create tools that the model is can use to answer your questions.
+# Describe a function by specifying it's schema (JsonSchema format)
+get_current_weather_func = generative_models.FunctionDeclaration(
+    name="get_current_weather",
+    description="Get the current weather in a given location",
+    parameters={
+        "type": "object",
+        "properties": {
+            "location": {
+                "type": "string",
+                "description": "The city and state, e.g. San Francisco, CA"
+            },
+            "unit": {
+                "type": "string",
+                "enum": [
+                    "celsius",
+                    "fahrenheit",
+                ]
+            }
+        },
+        "required": [
+            "location"
+        ]
+    },
+)
+# Tool is a collection of related functions
+weather_tool = generative_models.Tool(
+    function_declarations=[get_current_weather_func],
+)
+
+# Use tools in chat:
+model = GenerativeModel(
+    "gemini-pro",
+    # You can specify tools when creating a model to avoid having to send them with every request.
+    tools=[weather_tool],
+)
+chat = model.start_chat()
+# Send a message to the model. The model will respond with a function call.
+print(chat.send_message("What is the weather like in Boston?"))
+# Then send a function response to the model. The model will use it to answer.
+print(chat.send_message(
+    Part.from_function_response(
+        name="get_current_weather",
+        response={
+            "content": {"weather": "super nice"},
+        }
+    ),
+))
+```
+
+
+#### Automatic Function calling
+
+```
+from vertexai.preview.generative_models import GenerativeModel, Tool, FunctionDeclaration, AutomaticFunctionCallingResponder
+
+# First, create functions that the model can use to answer your questions.
+def get_current_weather(location: str, unit: str = "centigrade"):
+    """Gets weather in the specified location.
+
+    Args:
+        location: The location for which to get the weather.
+        unit: Optional. Temperature unit. Can be Centigrade or Fahrenheit. Defaults to Centigrade.
+    """
+    return dict(
+        location=location,
+        unit=unit,
+        weather="Super nice, but maybe a bit hot.",
+    )
+
+# Infer function schema
+get_current_weather_func = FunctionDeclaration.from_func(get_current_weather)
+# Tool is a collection of related functions
+weather_tool = Tool(
+    function_declarations=[get_current_weather_func],
+)
+
+# Use tools in chat:
+model = GenerativeModel(
+    "gemini-pro",
+    # You can specify tools when creating a model to avoid having to send them with every request.
+    tools=[weather_tool],
+)
+
+# Activate automatic function calling:
+afc_responder = AutomaticFunctionCallingResponder(
+    # Optional:
+    max_automatic_function_calls=5,
+)
+chat = model.start_chat(responder=afc_responder)
+# Send a message to the model. The model will respond with a function call.
+# The SDK will automatically call the requested function and respond to the model.
+# The model will use the function call response to answer the original question.
+print(chat.send_message("What is the weather like in Boston?"))
+```
+
+## Documentation
+
+You can find complete documentation for the Vertex AI SDKs and the Gemini model in the Google Cloud [documentation](https://cloud.google.com/vertex-ai/docs/generative-ai/learn/overview)
+
+## Contributing
+
+See [Contributing](https://github.com/googleapis/python-aiplatform/blob/main/CONTRIBUTING.rst) for more information on contributing to the Vertex AI Python SDK.
+
+## License
+
+The contents of this repository are licensed under the [Apache License, version 2.0](http://www.apache.org/licenses/LICENSE-2.0).