from openai import OpenAI

client = OpenAI(
    api_key="YOUR_AISA_API_KEY",
    base_url="https://api.aisa.one/v1"
)

response = client.chat.completions.create(
    model="MiniMax-M2.5",
    messages=[
        {"role": "user", "content": "Summarise the key arguments in this research paper."}
    ]
)
print(response.choices[0].message.content)

import OpenAI from "openai";

const client = new OpenAI({
  apiKey: process.env.AISA_API_KEY,
  baseURL: "https://api.aisa.one/v1",
});

const response = await client.chat.completions.create({
  model: "MiniMax-M2.5",
  messages: [
    { role: "user", content: "Draft a detailed competitive analysis of the EV market in Southeast Asia." }
  ],
});
console.log(response.choices[0].message.content);

stream = client.chat.completions.create(
    model="MiniMax-M2.5",
    messages=[{"role": "user", "content": "Write a thorough technical breakdown of retrieval-augmented generation."}],
    stream=True
)

for chunk in stream:
    if chunk.choices[0].delta.content:
        print(chunk.choices[0].delta.content, end="", flush=True)

# Long document processing
with open("annual_report.txt") as f:
    document = f.read()

response = client.chat.completions.create(
    model="MiniMax-M2.5",
    messages=[
        {"role": "system", "content": "You are a financial analyst. Extract key risks, opportunities, and financial highlights."},
        {"role": "user", "content": f"Analyse this annual report:\n\n{document}"}
    ]
)

# Multilingual tasks
response = client.chat.completions.create(
    model="MiniMax-M2.5",
    messages=[
        {"role": "user", "content": "请将以下英文合同条款翻译成中文，并标注任何可能存在法律风险的部分。\n\n[contract text here]"}
    ]
)

# Extended conversation
messages = [{"role": "system", "content": "You are a senior product strategist."}]
messages.append({"role": "user", "content": "Let's build a go-to-market plan for our new B2B SaaS product..."})

response = client.chat.completions.create(
    model="MiniMax-M2.5",
    messages=messages
)

tools = [
    {
        "type": "function",
        "function": {
            "name": "search_documents",
            "description": "Search an internal document database and return relevant passages",
            "parameters": {
                "type": "object",
                "properties": {
                    "query": {"type": "string", "description": "Search query"},
                    "max_results": {"type": "integer", "description": "Maximum number of results to return", "default": 5}
                },
                "required": ["query"]
            }
        }
    }
]

response = client.chat.completions.create(
    model="MiniMax-M2.5",
    messages=[{"role": "user", "content": "Find all documents related to our Q3 revenue projections."}],
    tools=tools,
    tool_choice="auto"
)