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Showing content from https://python.langchain.com/docs/integrations/text_embedding/google_vertex_ai_palm below:

Google Vertex AI Embeddings | 🦜️🔗 LangChain

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

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

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

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:

https://cloud.google.com/docs/authentication/application-default-credentials#GAC https://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 Vertex AI SDK

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

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

PROJECT_ID = "[your-project-id]"  
LOCATION = "us-central1"  

import vertexai

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

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

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

%pip install -qU langchain-google-vertexai

Now we can instantiate our model object and generate embeddings:

Check the list of Supported Models

from langchain_google_vertexai import VertexAIEmbeddings


embeddings = VertexAIEmbeddings(model_name="text-embedding-004")

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.

from langchain_core.vectorstores import InMemoryVectorStore

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

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


retriever = vectorstore.as_retriever()


retrieved_documents = retriever.invoke("What is LangChain?")


retrieved_documents[0].page_content

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

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.

You can embed single texts or documents with embed_query:

single_vector = embeddings.embed_query(text)
print(str(single_vector)[:100])  

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

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])  

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

For detailed documentation on Google Vertex AI Embeddings features and configuration options, please refer to the API reference.

• Embedding model conceptual guide
• Embedding model how-to guides

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