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---
title: OpenGradientToolkit
---
이 노트북은 OpenGradient toolkit을 사용하여 tool을 구축하는 방법을 보여줍니다. 이 toolkit은 사용자가 [OpenGradient network](https://www.opengradient.ai/)의 model과 workflow를 기반으로 커스텀 tool을 생성할 수 있는 기능을 제공합니다.
## Setup
OpenGradient network에 액세스하려면 OpenGradient API key가 있는지 확인하세요. 이미 API key가 있는 경우 환경 변수를 설정하기만 하면 됩니다:
```python
!export OPENGRADIENT_PRIVATE_KEY="your-api-key"
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!pip install opengradient
!opengradient config init
Installation
이 toolkit은langchain-opengradient package에 있습니다:
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pip install -qU langchain-opengradient
Instantiation
이제 이전의 API key로 toolkit을 인스턴스화할 수 있습니다.Copy
from langchain_opengradient import OpenGradientToolkit
toolkit = OpenGradientToolkit(
# Not required if you have already set the environment variable OPENGRADIENT_PRIVATE_KEY
private_key="your-api-key"
)
자체 tool 구축하기
OpenGradientToolkit은 커스텀 tool을 생성하기 위한 두 가지 주요 method를 제공합니다:1. ML model을 실행하는 tool 생성
OpenGradient model hub에 배포된 ML model을 활용하는 tool을 생성할 수 있습니다. 사용자가 생성한 model은 OpenGradient SDK를 통해 model hub에 업로드, 추론 및 공유할 수 있습니다.Copy
import opengradient as og
from pydantic import BaseModel, Field
# Example 1: Simple tool with no input schema
def price_data_provider():
"""Function that provides input data to the model."""
return {
"open_high_low_close": [
[2535.79, 2535.79, 2505.37, 2515.36],
[2515.37, 2516.37, 2497.27, 2506.94],
[2506.94, 2515, 2506.35, 2508.77],
[2508.77, 2519, 2507.55, 2518.79],
[2518.79, 2522.1, 2513.79, 2517.92],
[2517.92, 2521.4, 2514.65, 2518.13],
[2518.13, 2525.4, 2517.2, 2522.6],
[2522.59, 2528.81, 2519.49, 2526.12],
[2526.12, 2530, 2524.11, 2529.99],
[2529.99, 2530.66, 2525.29, 2526],
]
}
def format_volatility(inference_result):
"""Function that formats the model output."""
return format(float(inference_result.model_output["Y"].item()), ".3%")
# Create the tool
volatility_tool = toolkit.create_run_model_tool(
model_cid="QmRhcpDXfYCKsimTmJYrAVM4Bbvck59Zb2onj3MHv9Kw5N",
tool_name="eth_volatility",
model_input_provider=price_data_provider,
model_output_formatter=format_volatility,
tool_description="Generates volatility measurement for ETH/USDT trading pair",
inference_mode=og.InferenceMode.VANILLA,
)
# Example 2: Tool with input schema from the agent
class TokenInputSchema(BaseModel):
token: str = Field(description="Token name (ethereum or bitcoin)")
def token_data_provider(**inputs):
"""Dynamic function that changes behavior based on agent input."""
token = inputs.get("token")
if token == "bitcoin":
return {"price_series": [100001.1, 100013.2, 100149.2, 99998.1]}
else: # ethereum
return {"price_series": [2010.1, 2012.3, 2020.1, 2019.2]}
# Create the tool with schema
token_tool = toolkit.create_run_model_tool(
model_cid="QmZdSfHWGJyzBiB2K98egzu3MypPcv4R1ASypUxwZ1MFUG",
tool_name="token_volatility",
model_input_provider=token_data_provider,
model_output_formatter=lambda x: format(float(x.model_output["std"].item()), ".3%"),
tool_input_schema=TokenInputSchema,
tool_description="Measures return volatility for a specified token",
)
# Add tools to the toolkit
toolkit.add_tool(volatility_tool)
toolkit.add_tool(token_tool)
2. workflow 결과를 읽는 tool 생성
Read workflow는 라이브 oracle 데이터와 함께 smart-contract에 저장된 model을 정기적으로 실행하는 예약된 추론입니다. 이에 대한 자세한 내용은 여기에서 확인할 수 있습니다. workflow smart contract의 결과를 읽는 tool을 생성할 수 있습니다:Copy
# Create a tool to read from a workflow
forecast_tool = toolkit.create_read_workflow_tool(
workflow_contract_address="0x58826c6dc9A608238d9d57a65bDd50EcaE27FE99",
tool_name="ETH_Price_Forecast",
tool_description="Reads latest forecast for ETH price from deployed workflow",
output_formatter=lambda x: f"Price change forecast: {format(float(x.numbers['regression_output'].item()), '.2%')}",
)
# Add the tool to the toolkit
toolkit.add_tool(forecast_tool)
Tools
내장된get_tools() method를 사용하여 OpenGradient toolkit 내에서 사용 가능한 tool 목록을 확인하세요.
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tools = toolkit.get_tools()
# View tools
for tool in tools:
print(tool)
agent 내에서 사용하기
다음은 LangChain agent와 함께 OpenGradient tool을 사용하는 방법입니다:Copy
from langchain_openai import ChatOpenAI
from langchain.agents import create_agent
# Initialize LLM
model = ChatOpenAI(model="gpt-4o")
# Create tools from the toolkit
tools = toolkit.get_tools()
# Create agent
agent_executor = create_agent(model, tools)
# Example query for the agent
example_query = "What's the current volatility of ETH?"
# Execute the agent
events = agent_executor.stream(
{"messages": [("user", example_query)]},
stream_mode="values",
)
for event in events:
event["messages"][-1].pretty_print()
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================================ Human Message =================================
What's the current volatility of ETH?
================================== Ai Message ==================================
Tool Calls:
eth_volatility (chatcmpl-tool-d66ab9ee8f2c40e5a2634d90c7aeb17d)
Call ID: chatcmpl-tool-d66ab9ee8f2c40e5a2634d90c7aeb17d
Args:
================================= Tool Message =================================
Name: eth_volatility
0.038%
================================== Ai Message ==================================
The current volatility of the ETH/USDT trading pair is 0.038%.
API reference
자세한 내용은 Github page를 참조하세요.Copy
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