An integration package connecting Google VertexAI and LangChain
Project description
langchain-google-vertexai
This package contains the LangChain integrations for Google Cloud generative models.
Installation
pip install -U langchain-google-vertexai
Chat Models
ChatVertexAI class exposes models such as gemini-pro and chat-bison.
To use, you should have Google Cloud project with APIs enabled, and configured credentials. Initialize the model as:
from langchain_google_vertexai import ChatVertexAI
llm = ChatVertexAI(model_name="gemini-pro")
llm.invoke("Sing a ballad of LangChain.")
You can use other models, e.g. chat-bison:
from langchain_google_vertexai import ChatVertexAI
llm = ChatVertexAI(model_name="chat-bison", temperature=0.3)
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_vertexai import ChatVertexAI
llm = ChatVertexAI(model_name="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": {"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)
Embeddings
You can use Google Cloud's embeddings models as:
from langchain_google_vertexai import VertexAIEmbeddings
embeddings = VertexAIEmbeddings()
embeddings.embed_query("hello, world!")
LLMs
You can use Google Cloud's generative AI models as Langchain LLMs:
from langchain_core.prompts import PromptTemplate
from langchain_google_vertexai import ChatVertexAI
template = """Question: {question}
Answer: Let's think step by step."""
prompt = PromptTemplate.from_template(template)
llm = ChatVertexAI(model_name="gemini-pro")
chain = prompt | llm
question = "Who was the president of the USA in 1994?"
print(chain.invoke({"question": question}))
You can use Gemini and Palm models, including code-generations ones:
from langchain_google_vertexai import VertexAI
llm = VertexAI(model_name="code-bison", max_output_tokens=1000, temperature=0.3)
question = "Write a python function that checks if a string is a valid email address"
output = llm(question)
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