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)
Release history Release notifications | RSS feed
Download files
Download the file for your platform. If you're not sure which to choose, learn more about installing packages.
Source Distribution
Built Distribution
File details
Details for the file langchain_google_vertexai-0.1.1.tar.gz.
File metadata
- Download URL: langchain_google_vertexai-0.1.1.tar.gz
- Upload date:
- Size: 39.7 kB
- Tags: Source
- Uploaded using Trusted Publishing? Yes
- Uploaded via: twine/5.0.0 CPython/3.12.2
File hashes
| Algorithm | Hash digest | |
|---|---|---|
| SHA256 | 6f872d9e3fb188a100ee9348cd6867483e9416544b4fe031a1b26543ec547776 |
|
| MD5 | ce5f9432b58de676d857b0c20429bfd8 |
|
| BLAKE2b-256 | 74ed2a555d2931129c7a736178004b6665cf3972e8a2431dd1d0f4d4c4b120dc |
File details
Details for the file langchain_google_vertexai-0.1.1-py3-none-any.whl.
File metadata
- Download URL: langchain_google_vertexai-0.1.1-py3-none-any.whl
- Upload date:
- Size: 48.9 kB
- Tags: Python 3
- Uploaded using Trusted Publishing? Yes
- Uploaded via: twine/5.0.0 CPython/3.12.2
File hashes
| Algorithm | Hash digest | |
|---|---|---|
| SHA256 | 704c2487a6250d3a85839e5e791469773f62c5da2b4bfef3e63069657408d2a8 |
|
| MD5 | 49b61e28b9d3cbe884e0b51e505c29f5 |
|
| BLAKE2b-256 | ec25ba150547a9f03fdc75793934236a6a61addc54abc9adbe3304a43daf58d9 |
