Python OpenAI SDK

pip install openai

from openai import OpenAI

client = OpenAI(
    api_key="sk-xxx",
    base_url="https://api.crazyrouter.com/v1"
)

# 동기 호출
response = client.chat.completions.create(
    model="gpt-5.5",
    messages=[{"role": "user", "content": "안녕하세요"}],
    temperature=0.7,
    max_tokens=1000
)
print(response.choices[0].message.content)

from openai import AsyncOpenAI
import asyncio

client = AsyncOpenAI(
    api_key="sk-xxx",
    base_url="https://api.crazyrouter.com/v1"
)

async def main():
    response = await client.chat.completions.create(
        model="gpt-5.5",
        messages=[{"role": "user", "content": "안녕하세요"}]
    )
    print(response.choices[0].message.content)

asyncio.run(main())

pip install langchain-openai

from langchain_openai import ChatOpenAI

llm = ChatOpenAI(
    model="gpt-5.5",
    api_key="sk-xxx",
    base_url="https://api.crazyrouter.com/v1",
    temperature=0.7
)

# 간단한 호출
response = llm.invoke("Python으로 퀵 정렬을 작성해줘")
print(response.content)

from langchain_openai import ChatOpenAI
from langchain_core.prompts import ChatPromptTemplate

llm = ChatOpenAI(
    model="gpt-5.5",
    api_key="sk-xxx",
    base_url="https://api.crazyrouter.com/v1"
)

prompt = ChatPromptTemplate.from_messages([
    ("system", "당신은 {role}입니다."),
    ("user", "{input}")
])

chain = prompt | llm
response = chain.invoke({"role": "Python 전문가", "input": "데코레이터를 설명해줘"})
print(response.content)

from langchain_openai import OpenAIEmbeddings

embeddings = OpenAIEmbeddings(
    model="text-embedding-3-large",
    api_key="sk-xxx",
    base_url="https://api.crazyrouter.com/v1"
)

vectors = embeddings.embed_documents(["텍스트 1", "텍스트 2"])
print(f"벡터 차원: {len(vectors[0])}")

pip install llama-index-llms-openai llama-index-embeddings-openai

from llama_index.llms.openai import OpenAI
from llama_index.embeddings.openai import OpenAIEmbedding

# LLM 설정
llm = OpenAI(
    model="gpt-5.5",
    api_key="sk-xxx",
    api_base="https://api.crazyrouter.com/v1"
)

response = llm.complete("RAG란 무엇인가요?")
print(response.text)

# Embedding 설정
embed_model = OpenAIEmbedding(
    model="text-embedding-3-large",
    api_key="sk-xxx",
    api_base="https://api.crazyrouter.com/v1"
)

vector = embed_model.get_text_embedding("테스트 텍스트")
print(f"차원: {len(vector)}")