langchain-google-genai

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)

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)