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ChatClovaX

This notebook provides a quick overview for getting started with Naver’s HyperCLOVA X chat models via CLOVA Studio. For detailed documentation of all ChatClovaX features and configurations head to the API reference.

CLOVA Studio has several chat models. You can find information about latest models and their costs, context windows, and supported input types in the CLOVA Studio API Guide documentation.

Overview​

Integration details​

ClassPackageLocalSerializableJS supportPackage downloadsPackage latest
ChatClovaXlangchain-community❌❌❌PyPI - DownloadsPyPI - Version

Model features​

Tool callingStructured outputJSON modeImage inputAudio inputVideo inputToken-level streamingNative asyncToken usageLogprobs
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Setup​

Before using the chat model, you must go through the three steps below.

  1. Creating NAVER Cloud Platform account
  2. Apply to use CLOVA Studio
  3. Find API Keys after creating CLOVA Studio Test App or Service App (See here.)

Credentials​

CLOVA Studio requires 2 keys (NCP_CLOVASTUDIO_API_KEY and NCP_APIGW_API_KEY).

  • NCP_CLOVASTUDIO_API_KEY is issued per Test App or Service App
  • NCP_APIGW_API_KEY is issued per account, could be optional depending on the region you are using

The two API Keys could be found by clicking App Request Status > Service App, Test App List > ‘Details’ button for each app in CLOVA Studio

You can add them to your environment variables as below:

export NCP_CLOVASTUDIO_API_KEY="your-api-key-here"
export NCP_APIGW_API_KEY="your-api-key-here"
import getpass
import os

if not os.getenv("NCP_CLOVASTUDIO_API_KEY"):
    os.environ["NCP_CLOVASTUDIO_API_KEY"] = getpass.getpass(
        "Enter your NCP CLOVA Studio API Key: "
    )
if not os.getenv("NCP_APIGW_API_KEY"):
    os.environ["NCP_APIGW_API_KEY"] = getpass.getpass(
        "Enter your NCP API Gateway API key: "
    )

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 Naver integration lives in the langchain-community package:

# install package
!pip install -qU langchain-community

Instantiation​

Now we can instantiate our model object and generate chat completions:

from langchain_community.chat_models import ChatClovaX

chat = ChatClovaX(
    model="HCX-003",
    max_tokens=100,
    temperature=0.5,
    # clovastudio_api_key="..."    # set if you prefer to pass api key directly instead of using environment variables
    # task_id="..."    # set if you want to use fine-tuned model
    # service_app=False    # set True if using Service App. Default value is False (means using Test App)
    # include_ai_filters=False     # set True if you want to detect inappropriate content. Default value is False
    # other params...
)
API Reference:ChatClovaX

Invocation​

In addition to invoke, we also support batch and stream functionalities.

messages = [
    (
        "system",
        "You are a helpful assistant that translates English to Korean. Translate the user sentence.",
    ),
    ("human", "I love using NAVER AI."),
]

ai_msg = chat.invoke(messages)
ai_msg
AIMessage(content='저는 네이버 AI를 사용하는 것이 좋아요.', additional_kwargs={}, response_metadata={'stop_reason': 'stop_before', 'input_length': 25, 'output_length': 14, 'seed': 1112164354, 'ai_filter': None}, id='run-b57bc356-1148-4007-837d-cc409dbd57cc-0', usage_metadata={'input_tokens': 25, 'output_tokens': 14, 'total_tokens': 39})
print(ai_msg.content)
저는 네이버 AI를 사용하는 것이 좋아요.

Chaining​

We can chain our model with a prompt template like so:

from langchain_core.prompts import ChatPromptTemplate

prompt = ChatPromptTemplate.from_messages(
    [
        (
            "system",
            "You are a helpful assistant that translates {input_language} to {output_language}. Translate the user sentence.",
        ),
        ("human", "{input}"),
    ]
)

chain = prompt | chat
chain.invoke(
    {
        "input_language": "English",
        "output_language": "Korean",
        "input": "I love using NAVER AI.",
    }
)
API Reference:ChatPromptTemplate
AIMessage(content='저는 네이버 AI를 사용하는 것이 좋아요.', additional_kwargs={}, response_metadata={'stop_reason': 'stop_before', 'input_length': 25, 'output_length': 14, 'seed': 2575184681, 'ai_filter': None}, id='run-7014b330-eba3-4701-bb62-df73ce39b854-0', usage_metadata={'input_tokens': 25, 'output_tokens': 14, 'total_tokens': 39})

Streaming​

system = "You are a helpful assistant that can teach Korean pronunciation."
human = "Could you let me know how to say '{phrase}' in Korean?"
prompt = ChatPromptTemplate.from_messages([("system", system), ("human", human)])

chain = prompt | chat

for chunk in chain.stream({"phrase": "Hi"}):
    print(chunk.content, end="", flush=True)
Certainly! In Korean, "Hi" is pronounced as "안녕" (annyeong). The first syllable, "안," sounds like the "ahh" sound in "apple," while the second syllable, "녕," sounds like the "yuh" sound in "you." So when you put them together, it's like saying "ahhyuh-nyuhng." Remember to pronounce each syllable clearly and separately for accurate pronunciation.

Additional functionalities​

Using fine-tuned models​

You can call fine-tuned models by passing in your corresponding task_id parameter. (You don’t need to specify the model_name parameter when calling fine-tuned model.)

You can check task_id from corresponding Test App or Service App details.

fine_tuned_model = ChatClovaX(
    task_id="5s8egt3a",  # set if you want to use fine-tuned model
    # other params...
)

fine_tuned_model.invoke(messages)
AIMessage(content='저는 네이버 AI를 사용하는 것이 너무 좋아요.', additional_kwargs={}, response_metadata={'stop_reason': 'stop_before', 'input_length': 25, 'output_length': 15, 'seed': 52559061, 'ai_filter': None}, id='run-5bea8d4a-48f3-4c34-ae70-66e60dca5344-0', usage_metadata={'input_tokens': 25, 'output_tokens': 15, 'total_tokens': 40})

Service App​

When going live with production-level application using CLOVA Studio, you should apply for and use Service App. (See here.)

For a Service App, a corresponding NCP_CLOVASTUDIO_API_KEY is issued and can only be called with it.

# Update environment variables

os.environ["NCP_CLOVASTUDIO_API_KEY"] = getpass.getpass(
    "Enter NCP CLOVA Studio API Key for Service App: "
)
chat = ChatClovaX(
    service_app=True,  # True if you want to use your service app, default value is False.
    # clovastudio_api_key="..."  # if you prefer to pass api key in directly instead of using env vars
    model="HCX-003",
    # other params...
)
ai_msg = chat.invoke(messages)

AI Filter​

AI Filter detects inappropriate output such as profanity from the test app (or service app included) created in Playground and informs the user. See here for details.

chat = ChatClovaX(
    model="HCX-003",
    include_ai_filters=True,  # True if you want to enable ai filter
    # other params...
)

ai_msg = chat.invoke(messages)
print(ai_msg.response_metadata["ai_filter"])

API reference​

For detailed documentation of all ChatNaver features and configurations head to the API reference: https://python.langchain.com/api_reference/community/chat_models/langchain_community.chat_models.naver.ChatClovaX.html

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