Skip to main content

An integration package connecting GoogleVertexAI 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.prompts import PromptTemplate
from langchain_google_vertexai import VertexAI

template = """Question: {question}

Answer: Let's think step by step."""
prompt = PromptTemplate.from_template(template)

chain = prompt | llm

question = "Who was the president in the year Justin Beiber was born?"
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)

Project details


Download files

Download the file for your platform. If you're not sure which to choose, learn more about installing packages.

Source Distribution

langchain_google_vertexai-0.0.5.tar.gz (20.2 kB view details)

Uploaded Source

Built Distribution

File details

Details for the file langchain_google_vertexai-0.0.5.tar.gz.

File metadata

File hashes

Hashes for langchain_google_vertexai-0.0.5.tar.gz
Algorithm Hash digest
SHA256 40cd9d5238722af2ba3c072b489d2dc95b8d4a03267bd64a8edff49c2d2f82c2
MD5 cba427e7ee6877418900b64a20103a8e
BLAKE2b-256 e1ce8cf881c9696d54d2bb446a4b845f8a472630793a6c71a6f7fb0fcce49d3b

See more details on using hashes here.

File details

Details for the file langchain_google_vertexai-0.0.5-py3-none-any.whl.

File metadata

File hashes

Hashes for langchain_google_vertexai-0.0.5-py3-none-any.whl
Algorithm Hash digest
SHA256 7d20250a19262d8e7fc21ad63d891dde0b7b6362a117f182df428552289626fc
MD5 e96846e365da5b6dcef18d130aba46fc
BLAKE2b-256 317e73c22b413910a680ec5f92931209506182c9cfeb534bf118c32dc494c547

See more details on using hashes here.

Supported by

AWS AWS Cloud computing and Security Sponsor Datadog Datadog Monitoring Fastly Fastly CDN Google Google Download Analytics Microsoft Microsoft PSF Sponsor Pingdom Pingdom Monitoring Sentry Sentry Error logging StatusPage StatusPage Status page