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

ClovaXEmbeddings | 🦜️🔗 LangChain

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

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

1. Creating NAVER Cloud Platform account
2. Apply to use CLOVA Studio
3. Create a CLOVA Studio Test App or Service App of a model to use (See here.)
4. Issue a Test or Service API key (See here.)

Set the CLOVASTUDIO_API_KEY environment variable with your API key.

import getpass
import os

if not os.getenv("CLOVASTUDIO_API_KEY"):
    os.environ["CLOVASTUDIO_API_KEY"] = getpass.getpass("Enter CLOVA Studio API Key: ")

ClovaXEmbeddings integration lives in the langchain_naver package:

%pip install -qU langchain-naver

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

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

from langchain_naver import ClovaXEmbeddings

embeddings = ClovaXEmbeddings(
    model="clir-emb-dolphin"  
)

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

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


retriever = vectorstore.as_retriever()


retrieved_documents = retriever.invoke("What is CLOVA Studio?")


retrieved_documents[0].page_content

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

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])  

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

You can embed multiple texts with embed_documents:

text2 = "LangChain is a framework for building context-aware reasoning applications"
two_vectors = embeddings.embed_documents([text, text2])
for vector in two_vectors:
    print(str(vector)[:100])  

[-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.

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

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

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