This notebook covers how to get started with embedding models provided by CLOVA Studio. For detailed documentation onClovaXEmbeddings features and configuration options, please refer to theAPI reference.

Overview​

Integration details​

Setup​

Before using embedding models provided by CLOVA Studio, you must go through the three steps below.

1. CreatingNAVER Cloud Platform account
2. Apply to useCLOVA Studio
3. Create a CLOVA Studio Test App or Service App of a model to use (Seehere.)
4. Issue a Test or Service API key (Seehere.)

Credentials​

Set theCLOVASTUDIO_API_KEY environment variable with your API key.

import getpass
import os

ifnot os.getenv("CLOVASTUDIO_API_KEY"):
    os.environ["CLOVASTUDIO_API_KEY"]= getpass.getpass("Enter CLOVA Studio API Key: ")

Installation​

ClovaXEmbeddings integration lives in thelangchain_naver package:

# install package
%pip install-qU langchain-naver

Instantiation​

Now we can instantiate our embeddings object and embed query or document:

• There are several embedding models available in CLOVA Studio. Please referhere for further details.
• Note that you might need to normalize the embeddings depending on your specific use case.

from langchain_naverimport ClovaXEmbeddings

embeddings= ClovaXEmbeddings(
    model="clir-emb-dolphin"# set with the model name of corresponding test/service app. Default is `clir-emb-dolphin`
)

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="CLOVA Studio is an AI development tool that allows you to customize your own HyperCLOVA X models."

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 CLOVA Studio?")

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

'CLOVA Studio is an AI development tool that allows you to customize your own HyperCLOVA X models.'

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

[-0.094717406, -0.4077411, -0.5513184, 1.6024436, -1.3235079, -1.0720996, -0.44471845, 1.3665184, 0.

Embed multiple texts​

You can embed multiple texts withembed_documents:

text2="LangChain is a framework for building context-aware reasoning applications"
two_vectors= embeddings.embed_documents([text, text2])
for vectorin two_vectors:
print(str(vector)[:100])# Show the first 100 characters of the vector

[-0.094717406, -0.4077411, -0.5513184, 1.6024436, -1.3235079, -1.0720996, -0.44471845, 1.3665184, 0.
[-0.25525448, -0.84877056, -0.6928286, 1.5867524, -1.2930486, -0.8166254, -0.17934391, 1.4236152, 0.

API Reference​

For detailed documentation onClovaXEmbeddings features and configuration options, please refer to theAPI reference.

Related​

• Embedding modelconceptual guide
• Embedding modelhow-to guides