This will help you get started with Google Vertex AI Embeddings models using LangChain. For detailed documentation onGoogle Vertex AI Embeddings features and configuration options, please refer to theAPI reference.

Overview​

Integration details​

Setup​

To access Google Vertex AI Embeddings models you'll need to

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

Credentials​

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

For more information, see:

https://cloud.google.com/docs/authentication/application-default-credentials#GAChttps://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.colabimport auth

    auth.authenticate_user()

Set Google Cloud project information and initialize Vertex AI SDK

To get started using Vertex AI, you must have an existing Google Cloud project andenable the Vertex AI API.

Learn more aboutsetting 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 yourLangSmith API key:

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

Installation​

The LangChain Google Vertex AI Embeddings integration lives in thelangchain-google-vertexai package:

%pip install-qU langchain-google-vertexai

Instantiation​

Now we can instantiate our model object and generate embeddings:

Check the list ofSupported Models

from langchain_google_vertexaiimport VertexAIEmbeddings

# Initialize the a specific Embeddings Model version
embeddings= VertexAIEmbeddings(model_name="text-embedding-004")

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 ourRAG tutorials.

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

# Create a vector store with a sample text
from langchain_core.vectorstoresimport 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 callingembeddings.embed_documents(...) andembeddings.embed_query(...) to create embeddings for the text(s) used infrom_texts and retrievalinvoke 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 withembed_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 withembed_documents:

text2=(
"LangGraph is a library for building stateful, multi-actor applications with LLMs"
)
two_vectors= embeddings.embed_documents([text, text2])
for vectorin 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 onGoogle Vertex AI Embeddings features and configuration options, please refer to theAPI reference.

Related​

• Embedding modelconceptual guide
• Embedding modelhow-to guides