An integration package connecting Google's genai package and LangChain
Project description
langchain-google-genai
This package contains the LangChain integrations for Gemini through their generative-ai SDK.
Installation
pip install -U langchain-google-genai
Image utilities
To use image utility methods, like loading images from GCS urls, install with extras group 'images':
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.
export GOOGLE_API_KEY=your-api-key
Then initialize
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!")
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