Google Vertex AI Embeddings
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.
Overview
Integration details
Provider | Package |
---|---|
langchain-google-vertexai |
Setup
To access Google Vertex AI Embeddings models 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:
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]" # @param {type:"string"}
LOCATION = "us-central1" # @param {type:"string"}
import vertexai
vertexai.init(project=PROJECT_ID, location=LOCATION)
If you want to get automated tracing of your model calls you can also set your LangSmith API key by uncommenting below:
# os.environ["LANGCHAIN_TRACING_V2"] = "true"
# os.environ["LANGCHAIN_API_KEY"] = getpass.getpass("Enter your LangSmith API key: ")
Installation
The LangChain Google Vertex AI 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="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 our RAG tutorials under the working with external knowledge 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 Google Vertex AI Embeddings
features and configuration options, please refer to the API reference.
Related
- Embedding model conceptual guide
- Embedding model how-to guides