langchain-google-genai 2.1.6

An integration package connecting Google's genai package and LangChain

langchain-google-genai

LangChain integration for Google Gemini models using the generative-ai SDK

This package enables seamless access to Google Gemini's chat, vision, embeddings, and retrieval-augmented generation (RAG) features within the LangChain ecosystem.

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Table of Contents

• Overview
• Installation
• Quickstart
• Chat Models
  • Multimodal Inputs
  • Multimodal Outputs
  • Multimodal Outputs in Chains
  • Thinking Support
• Embeddings
• Semantic Retrieval (RAG)

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Overview

This package provides LangChain support for Google Gemini models (via the official Google Generative AI SDK). It supports:

• Text and vision-based chat models
• Embeddings for semantic search
• Multimodal inputs and outputs
• Retrieval-Augmented Generation (RAG)
• Thought tracing with reasoning tokens

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Installation

pip install -U langchain-google-genai

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Quickstart

Set up your environment variable with your Gemini API key:

export GOOGLE_API_KEY=your-api-key

Then use the ChatGoogleGenerativeAI interface:

from langchain_google_genai import ChatGoogleGenerativeAI

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

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Chat Models

The main interface for Gemini chat models is ChatGoogleGenerativeAI.

Multimodal Inputs

Gemini vision models support image inputs in single messages.

from langchain_core.messages import HumanMessage
from langchain_google_genai import ChatGoogleGenerativeAI

llm = ChatGoogleGenerativeAI(model="gemini-pro-vision")

message = HumanMessage(
    content=[
        {"type": "text", "text": "What's in this image?"},
        {"type": "image_url", "image_url": "https://picsum.photos/seed/picsum/200/300"},
    ]
)

response = llm.invoke([message])
print(response.content)

✅ image_url can be:

• A public image URL
• A Google Cloud Storage path (gcs://...)
• A base64-encoded image (e.g., data:image/png;base64,...)

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Multimodal Outputs

The Gemini 2.0 Flash Experimental model supports both text and inline image outputs.

from langchain_google_genai import ChatGoogleGenerativeAI

llm = ChatGoogleGenerativeAI(model="models/gemini-2.0-flash-exp-image-generation")

response = llm.invoke(
    "Generate an image of a cat and say meow",
    generation_config=dict(response_modalities=["TEXT", "IMAGE"]),
)

image_base64 = response.content[0].get("image_url").get("url").split(",")[-1]
meow_text = response.content[1]
print(meow_text)

---

Audio Output

from langchain_google_genai import ChatGoogleGenerativeAI

llm = ChatGoogleGenerativeAI(model="models/gemini-2.5-flash-preview-tts")
# example
response = llm.invoke(
    "Please say The quick brown fox jumps over the lazy dog",
    generation_config=dict(response_modalities=["AUDIO"]),
)

# Base64 encoded binary data of the image
wav_data = response.additional_kwargs.get("audio")
with open("output.wav", "wb") as f:
    f.write(wav_data)

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Multimodal Outputs in Chains

You can use Gemini models in a LangChain chain:

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.from_messages([
    ("human", "Generate an image of {animal} and tell me the sound it makes.")
])

chain = {"animal": RunnablePassthrough()} | prompt | llm
response = chain.invoke("cat")

---

Thinking Support

Gemini 2.5 Flash Preview supports internal reasoning ("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? How did you verify your answer?")
reasoning_score = response.usage_metadata["output_token_details"]["reasoning"]

print("Response:", response.content)
print("Reasoning tokens used:", reasoning_score)

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Embeddings

You can use Gemini embeddings in LangChain:

from langchain_google_genai import GoogleGenerativeAIEmbeddings

embeddings = GoogleGenerativeAIEmbeddings(model="models/embedding-001")
vector = embeddings.embed_query("hello, world!")
print(vector)

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Semantic Retrieval (RAG)

Use Gemini with RAG to retrieve relevant documents from your knowledge base.

from langchain_google_genai.vectorstores import GoogleVectorStore
from langchain_text_splitters import CharacterTextSplitter
from langchain_community.document_loaders import DirectoryLoader

# Create a corpus (collection of documents)
corpus_store = GoogleVectorStore.create_corpus(display_name="My Corpus")

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

# Load and upload documents
text_splitter = CharacterTextSplitter(chunk_size=500, chunk_overlap=0)
for file in DirectoryLoader(path="data/").load():
    chunks = text_splitter.split_documents([file])
    document_store.add_documents(chunks)

# Query the document corpus
aqa = corpus_store.as_aqa()
response = aqa.invoke("What is the meaning of life?")

print("Answer:", response.answer)
print("Passages:", response.attributed_passages)
print("Answerable probability:", response.answerable_probability)

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Resources

• LangChain Documentation
• Google Generative AI SDK
• Gemini Model Documentation