ChatClovaX

export CLOVASTUDIO_API_KEY="your-api-key-here"

import getpass
import os

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

# os.environ["LANGSMITH_TRACING"] = "true"
# os.environ["LANGSMITH_API_KEY"] = getpass.getpass("Enter your LangSmith API key: ")

# install package
%pip install -qU langchain-naver

from langchain_naver import ChatClovaX

chat = ChatClovaX(
    model="HCX-005",
    temperature=0.5,
    max_tokens=None,
    timeout=None,
    max_retries=2,
    # other params...
)

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='네이버 인공지능을 사용하는 것이 정말 좋아요.', additional_kwargs={'refusal': None}, response_metadata={'token_usage': {'completion_tokens': 10, 'prompt_tokens': 28, 'total_tokens': 38, 'completion_tokens_details': None, 'prompt_tokens_details': None}, 'model_name': 'HCX-005', 'system_fingerprint': None, 'id': 'd685424a78d34009a7b07f5b0110a10b', 'service_tier': None, 'finish_reason': 'stop', 'logprobs': None}, id='run--9bd4df90-d88d-4f9a-b208-c41760f107f8-0', usage_metadata={'input_tokens': 28, 'output_tokens': 10, 'total_tokens': 38, 'input_token_details': {}, 'output_token_details': {}})

print(ai_msg.content)

네이버 인공지능을 사용하는 것이 정말 좋아요.

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.",
    }
)

AIMessage(content='저는 NAVER AI를 사용하는 것을 좋아합니다.', additional_kwargs={'refusal': None}, response_metadata={'token_usage': {'completion_tokens': 11, 'prompt_tokens': 28, 'total_tokens': 39, 'completion_tokens_details': None, 'prompt_tokens_details': None}, 'model_name': 'HCX-005', 'system_fingerprint': None, 'id': '3918787e422846958cbf995cc93ee7a4', 'service_tier': None, 'finish_reason': 'stop', 'logprobs': None}, id='run--1a78accd-be5d-4a03-ad8b-7753d4d47ffd-0', usage_metadata={'input_tokens': 28, 'output_tokens': 11, 'total_tokens': 39, 'input_token_details': {}, 'output_token_details': {}})

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)

In Korean, 'Hi' is typically translated as '안녕하세요' (annyeonghaseyo). However, if you're speaking informally or with friends, you might use '안녕' (annyeong) instead. Remember, the pronunciation would be [an-johng-ha-se-yo] for 'annyeonghaseyo', and [an-yoeng] for 'annyeong'. The stress usually falls on the second syllable of each word. Keep practicing!

{
    "name": "...",
    "description": "...",
    "parameters": {...}  # JSONSchema
}

from langchain_naver import ChatClovaX

chat = ChatClovaX(
    model="HCX-005",
    max_tokens=1024,  # Set max tokens larger than 1024 to use tool calling
)

from pydantic import BaseModel, Field


class GetWeather(BaseModel):
    """Get the current weather in a given location"""

    location: str = Field(
        ..., description="The city and province, e.g. Seongnam-si, Gyeonggi-do"
    )


chat_with_tools = chat.bind_tools([GetWeather])

ai_msg = chat_with_tools.invoke(
    "what is the weather like in Bundang-gu?",
)
ai_msg

AIMessage(content='', additional_kwargs={'tool_calls': [{'id': 'call_EOh69hbtl8p24URrYRl059XT', 'function': {'arguments': '{"location":"Seongnam, Gyeonggi-do"}', 'name': 'GetWeather'}, 'type': 'function'}], 'refusal': None}, response_metadata={'token_usage': {'completion_tokens': 37, 'prompt_tokens': 16, 'total_tokens': 53, 'completion_tokens_details': None, 'prompt_tokens_details': None}, 'model_name': 'HCX-005', 'system_fingerprint': None, 'id': '085c74d930a84dc7b7cb59fde476e710', 'service_tier': None, 'finish_reason': 'tool_calls', 'logprobs': None}, id='run--f3b46b02-81fe-4ab3-bcb5-f0a6cb7f2be0-0', tool_calls=[{'name': 'GetWeather', 'args': {'location': 'Seongnam, Gyeonggi-do'}, 'id': 'call_EOh69hbtl8p24URrYRl059XT', 'type': 'tool_call'}], usage_metadata={'input_tokens': 16, 'output_tokens': 37, 'total_tokens': 53, 'input_token_details': {}, 'output_token_details': {}})

ai_msg.tool_calls

[{'name': 'GetWeather',
  'args': {'location': 'Seongnam, Gyeonggi-do'},
  'id': 'call_EOh69hbtl8p24URrYRl059XT',
  'type': 'tool_call'}]

from langchain_naver import ChatClovaX

chat = ChatClovaX(
    model="HCX-007",
    thinking={
        "effort": "none"  # Set to "none" to disable thinking, as structured outputs are incompatible with thinking
    },
)

from typing import Optional
from pydantic import BaseModel, Field


# Pydantic model example
class Weather(BaseModel):
    """Virtual weather info to tell user."""

    temp_high_c: int = Field(description="The highest temperature in Celsius")
    temp_low_c: int = Field(description="The lowest temperature in Celsius")
    condition: str = Field(description="The weather condition (e.g., sunny, rainy)")
    precipitation_percent: Optional[int] = Field(
        default=None,
        description="The chance of precipitation in percent (optional, can be None)",
    )

structured_chat = chat.with_structured_output(Weather, method="json_schema")
ai_msg = structured_chat.invoke(
    "what is the weather like in Bundang-gu?",
)
ai_msg

Weather(temp_high_c=30, temp_low_c=20, condition='sunny', precipitation_percent=None)

from langchain_naver import ChatClovaX

chat = ChatClovaX(
    model="HCX-007",
    thinking={
        "effort": "low"  # 'none' (disabling), 'low' (default), 'medium', or 'high'
    },
)
ai_msg = chat.invoke("What is 3^3?")
print(ai_msg.content)

The value of \(3^3\) (3 cubed) is calculated as follows:

\[
3^3 = 3 \times 3 \times 3
\]

Breaking it into steps:
1. First multiplication:  
   \(3 \times 3 = 9\)

2. Second multiplication using the previous result:  
   \(9 \times 3 = 27\)

Thus, **\(3^3 = 27\)**. This represents 3 multiplied by itself three times. Verification confirms consistency with exponent rules (\(a^n = \underbrace{a \times a \times \dots \times a}_{n \text{ times}}\)). No ambiguity exists in standard mathematical notation here. Answer: **27**.  

Final token count: ~500 (within limit).  
Answer: \boxed{27}

print(ai_msg.additional_kwargs["thinking_content"])

Okay, let's see. The user asked what 3 cubed is. Hmm, exponentiation basics here. So 3 to the power of 3 means multiplying 3 by itself three times.

First, I should recall how exponents work. For any number a raised to n, it's a multiplied by itself n-1 more times. In this case, a is 3 and n is 3. 

So breaking it down: 3 × 3 = 9 first. Then take that result and multiply by another 3. That would be 9 × 3. Let me calculate that... 9 times 3 equals 27. Wait, does that make sense? Yeah, because 3 squared is 9, then adding another factor of 3 gives 27. 

I think there's no trick question here. Maybe check if the notation could mean something else, but standard math notation says 3³ is definitely 3*3*3. No parentheses or other operations involved. Also, confirming with known squares and cubes—like 2³=8, so 3³ being higher than that at 27 checks out. Yep, answer must be 27. Shouldn't overcomplicate it. Just straightforward multiplication. Alright, confident now.

fine_tuned_model = ChatClovaX(
    model="ft:a1b2c3d4",  # set as `ft:{task_id}` with your fine-tuned model's task id
    # other params...
)

fine_tuned_model.invoke(messages)

AIMessage(content='네이버 인공지능을 사용하는 것을 정말 좋아합니다.', additional_kwargs={'refusal': None}, response_metadata={'token_usage': {'completion_tokens': 11, 'prompt_tokens': 28, 'total_tokens': 39, 'completion_tokens_details': None, 'prompt_tokens_details': None}, 'model_name': 'HCX-005', 'system_fingerprint': None, 'id': '2222d6d411a948c883aac1e03ca6cebe', 'finish_reason': 'stop', 'logprobs': None}, id='run-9696d7e2-7afa-4bb4-9c03-b95fcf678ab8-0', usage_metadata={'input_tokens': 28, 'output_tokens': 11, 'total_tokens': 39, 'input_token_details': {}, 'output_token_details': {}})