Python OpenAI SDK

pip install openai

from openai import OpenAI

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

# Synchronous call
response = client.chat.completions.create(
    model="gpt-5.4",
    messages=[{"role": "user", "content": "Hello"}],
    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://crazyrouter.com/v1"
)

async def main():
    response = await client.chat.completions.create(
        model="gpt-5.4",
        messages=[{"role": "user", "content": "Hello"}]
    )
    print(response.choices[0].message.content)

asyncio.run(main())

pip install langchain-openai

from langchain_openai import ChatOpenAI

llm = ChatOpenAI(
    model="gpt-5.4",
    api_key="sk-xxx",
    base_url="https://crazyrouter.com/v1",
    temperature=0.7
)

# Simple call
response = llm.invoke("Write a quicksort in Python")
print(response.content)

from langchain_openai import ChatOpenAI
from langchain_core.prompts import ChatPromptTemplate

llm = ChatOpenAI(
    model="gpt-5.4",
    api_key="sk-xxx",
    base_url="https://crazyrouter.com/v1"
)

prompt = ChatPromptTemplate.from_messages([
    ("system", "You are a {role}."),
    ("user", "{input}")
])

chain = prompt | llm
response = chain.invoke({"role": "Python expert", "input": "Explain decorators"})
print(response.content)

from langchain_openai import OpenAIEmbeddings

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

vectors = embeddings.embed_documents(["Text one", "Text two"])
print(f"Vector dimensions: {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 configuration
llm = OpenAI(
    model="gpt-5.4",
    api_key="sk-xxx",
    api_base="https://crazyrouter.com/v1"
)

response = llm.complete("What is RAG?")
print(response.text)

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

vector = embed_model.get_text_embedding("Test text")
print(f"Dimensions: {len(vector)}")