Gemini Enterprise Agent Platform integration

This will help you get started with embedding models on Gemini Enterprise Agent Platform using LangChain. For detailed documentation on VertexAIEmbeddings features and configuration options, please refer to the API reference.

Overview

Integration details

Setup

To access embedding models on Gemini Enterprise Agent Platform you'll need to

• Create a Google Cloud account
• Install the langchain-google-vertexai integration package.

Credentials

Head to Google Cloud to sign up to create an account. Once you've done this set the GOOGLE_APPLICATION_CREDENTIALS environment variable:

For more information, see:

cloud.google.com/docs/authentication/application-default-credentials#GAC googleapis.dev/python/google-auth/latest/reference/google.auth.html#module-google.auth

OPTIONAL : Authenticate your notebook environment (Colab only)

If you're running this notebook on Google Colab, run the cell below to authenticate your environment.

import sys

if "google.colab" in sys.modules:
    from google.colab import auth

    auth.authenticate_user()

Set Google Cloud project information and initialize Gemini Enterprise Agent Platform SDK

To get started using Gemini Enterprise Agent Platform, you must have an existing Google Cloud project and enable the Gemini Enterprise Agent Platform API.

Learn more about setting up a project and a development environment.

PROJECT_ID = "[your-project-id]"  # @param {type:"string"}
LOCATION = "us-central1"  # @param {type:"string"}

import vertexai

vertexai.init(project=PROJECT_ID, location=LOCATION)

To enable automated tracing of your model calls, set your LangSmith API key:

os.environ["LANGSMITH_TRACING"] = "true"
os.environ["LANGSMITH_API_KEY"] = getpass.getpass("Enter your LangSmith API key: ")

Installation

The LangChain Gemini Enterprise Agent Platform embeddings integration lives in the langchain-google-vertexai package:

pip install -qU langchain-google-vertexai

Instantiation

Now we can instantiate our model object and generate embeddings:

Check the list of Supported Models

from langchain_google_vertexai import VertexAIEmbeddings

# Initialize the a specific Embeddings Model version
embeddings = VertexAIEmbeddings(model_name="gemini-embedding-001")

Indexing and retrieval

Embedding models are often used in retrieval-augmented generation (RAG) flows, both as part of indexing data as well as later retrieving it. For more detailed instructions, please see our RAG tutorials.

Below, see how to index and retrieve data using the embeddings object we initialized above. In this example, we will index and retrieve a sample document in the InMemoryVectorStore.

# Create a vector store with a sample text
from langchain_core.vectorstores import InMemoryVectorStore

text = "LangChain is the framework for building context-aware reasoning applications"

vectorstore = InMemoryVectorStore.from_texts(
    [text],
    embedding=embeddings,
)

# Use the vectorstore as a retriever
retriever = vectorstore.as_retriever()

# Retrieve the most similar text
retrieved_documents = retriever.invoke("What is LangChain?")

# show the retrieved document's content
retrieved_documents[0].page_content

'LangChain is the framework for building context-aware reasoning applications'

Direct usage

Under the hood, the vectorstore and retriever implementations are calling embeddings.embed_documents(...) and embeddings.embed_query(...) to create embeddings for the text(s) used in from_texts and retrieval invoke operations, respectively.

You can directly call these methods to get embeddings for your own use cases.

Embed single texts

You can embed single texts or documents with embed_query:

single_vector = embeddings.embed_query(text)
print(str(single_vector)[:100])  # Show the first 100 characters of the vector

[-0.02831101417541504, 0.022063178941607475, -0.07454229146242142, 0.006448323838412762, 0.001955120

Embed multiple texts

You can embed multiple texts with embed_documents:

text2 = (
    "LangGraph is a library for building stateful, multi-actor applications with LLMs"
)
two_vectors = embeddings.embed_documents([text, text2])
for vector in two_vectors:
    print(str(vector)[:100])  # Show the first 100 characters of the vector

[-0.01092718355357647, 0.01213780976831913, -0.05650627985596657, 0.006737854331731796, 0.0085973171
[0.010135706514120102, 0.01234869472682476, -0.07284046709537506, 0.00027134662377648056, 0.01546290

API reference

For detailed documentation on VertexAIEmbeddings features and configuration options, please refer to the API reference.