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langchain-google-genai
Advanced tools
An integration package connecting Google's genai package and LangChain
This package contains the LangChain integrations for Gemini through their generative-ai SDK.
pip install -U langchain-google-genai
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.")
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:
data:image/png;base64,abcd124
)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!")
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)
FAQs
An integration package connecting Google's genai package and LangChain
We found that langchain-google-genai demonstrated a healthy version release cadence and project activity because the last version was released less than a year ago. It has 4 open source maintainers collaborating on the project.
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