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

Class	Package	Local	Serializable	JS support	Package downloads	Package latest
ChatClovaX	langchain-community	❌	❌	❌

Model features

Tool calling	Structured output	JSON mode	Image input	Audio input	Video input	Token-level streaming	Native async	Token usage	Logprobs
❌	❌	❌	❌	❌	❌	✅	✅	✅	❌

Setup

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

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

Credentials

Set the NCP_CLOVASTUDIO_API_KEY environment variable with your API key.

Note that if you are using a legacy API Key (that doesn't start with nv-* prefix), you might need to get an additional API Key by clicking App Request Status > Service App, Test App List > ‘Details’ button for each app in CLOVA Studio and set it as NCP_APIGW_API_KEY.

You can add them to your environment variables as below:

export NCP_CLOVASTUDIO_API_KEY="your-api-key-here"
# Uncomment below to use a legacy API key
# 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: "
    )
# Uncomment below to use a legacy 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["LANGSMITH_TRACING"] = "true"
# os.environ["LANGSMITH_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, you should use a corresponding Service API key and can only be called with it.

# Update environment variables

os.environ["NCP_CLOVASTUDIO_API_KEY"] = getpass.getpass(
    "Enter NCP CLOVA Studio Service API Key: "
)

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

Chat model conceptual guide
Chat model how-to guides

Overview​

Integration details​

Model features​

Setup​

Credentials​

Installation​

Instantiation​

Invocation​

Chaining​

Streaming​

Additional functionalities​

Using fine-tuned models​

Service App​

AI Filter​

API reference​

Related​

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