langchain-google-genai 2.1.4

An integration package connecting Google's genai package and LangChain

langchain-google-genai

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

Installation

pip install -U langchain-google-genai

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 base64 encoded image (e.g., data:image/png;base64,abcd124)

Multimodal outputs

Gemini 2.0 Flash Experimental model supports text output with inline images

from langchain_google_genai import ChatGoogleGenerativeAI

llm = ChatGoogleGenerativeAI(model="models/gemini-2.0-flash-exp-image-generation")
# example
response = llm.invoke(
    "Generate an image of a cat and say meow",
    generation_config=dict(response_modalities=["TEXT", "IMAGE"]),
)

# Base64 encoded binary data of the image
image_base64 = response.content[0].get("image_url").get("url").split(",")[-1]
meow_str = response.content[1]

Multimodal Outputs in Chains

from langchain_core.runnables import RunnablePassthrough
from langchain_core.prompts import ChatPromptTemplate

from langchain_google_genai import ChatGoogleGenerativeAI, Modality

llm = ChatGoogleGenerativeAI(
    model="models/gemini-2.0-flash-exp-image-generation",
    response_modalities=[Modality.TEXT, Modality.IMAGE],
)

prompt = ChatPromptTemplate(
    [("human", "Generate an image of {animal} and tell me the sound of the animal")]
)
chain = {"animal": RunnablePassthrough()} | prompt | llm
res = chain.invoke("cat")

Thinking support

Gemini 2.5 Flash model supports reasoning through their thoughts

from langchain_google_genai import ChatGoogleGenerativeAI

llm = ChatGoogleGenerativeAI(model="models/gemini-2.5-flash-preview-04-17", thinking_budget=1024)

response = llm.invoke(
    "How many O's are in Google? Please tell me how you double checked the result"
)

assert response.usage_metadata["output_token_details"]["reasoning"] > 0

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!")

Semantic Retrieval

Enables retrieval augmented generation (RAG) in your application.

# Create a new store for housing your documents.
corpus_store = GoogleVectorStore.create_corpus(display_name="My Corpus")

# Create a new document under the above corpus.
document_store = GoogleVectorStore.create_document(
    corpus_id=corpus_store.corpus_id, display_name="My Document"
)

# Upload some texts to the document.
text_splitter = CharacterTextSplitter(chunk_size=500, chunk_overlap=0)
for file in DirectoryLoader(path="data/").load():
    documents = text_splitter.split_documents([file])
    document_store.add_documents(documents)

# Talk to your entire corpus with possibly many documents. 
aqa = corpus_store.as_aqa()
answer = aqa.invoke("What is the meaning of life?")

# Read the response along with the attributed passages and answerability.
print(response.answer)
print(response.attributed_passages)
print(response.answerable_probability)