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google-genai, google-vertexai: move to langchain-google (#17899)
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These packages have moved to
https://github.com/langchain-ai/langchain-google

Left tombstone readmes incase anyone ends up at the "Source Code" link
from old pypi releases. Can keep these around for a few months.
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efriis authored Feb 26, 2024
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# langchain-google-genai
This package has moved!

This package contains the LangChain integrations for Gemini through their generative-ai SDK.

## Installation

```bash
pip install -U langchain-google-genai
```

### Image utilities
To use image utility methods, like loading images from GCS urls, install with extras group 'images':

```bash
pip install -e "langchain-google-genai[images]"
```

## Chat Models

This package contains the `ChatGoogleGenerativeAI` class, which is the recommended way to interface with the Google Gemini series of models.

To use, install the requirements, and configure your environment.

```bash
export GOOGLE_API_KEY=your-api-key
```

Then initialize

```python
from langchain_google_genai import ChatGoogleGenerativeAI

llm = ChatGoogleGenerativeAI(model="gemini-pro")
llm.invoke("Sing a ballad of LangChain.")
```

#### Multimodal inputs

Gemini vision model supports image inputs when providing a single chat message. Example:

```
from langchain_core.messages import HumanMessage
from langchain_google_genai import ChatGoogleGenerativeAI
llm = ChatGoogleGenerativeAI(model="gemini-pro-vision")
# example
message = HumanMessage(
content=[
{
"type": "text",
"text": "What's in this image?",
}, # You can optionally provide text parts
{"type": "image_url", "image_url": "https://picsum.photos/seed/picsum/200/300"},
]
)
llm.invoke([message])
```

The value of `image_url` can be any of the following:

- A public image URL
- An accessible gcs file (e.g., "gcs://path/to/file.png")
- A local file path
- A base64 encoded image (e.g., `data:image/png;base64,abcd124`)
- A PIL image



## Embeddings

This package also adds support for google's embeddings models.

```
from langchain_google_genai import GoogleGenerativeAIEmbeddings
embeddings = GoogleGenerativeAIEmbeddings(model="models/embedding-001")
embeddings.embed_query("hello, world!")
```
https://github.com/langchain-ai/langchain-google/tree/main/libs/genai
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