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="kimi-k2.5",
    messages=[
        {"role": "system", "content": "You are an expert software engineer and technical architect."},
        {"role": "user", "content": "Design the database schema for a multi-tenant SaaS billing system."}
    ]
)
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: "kimi-k2.5",
  messages: [
    { role: "user", content: "Analyse this React component and suggest performance improvements." }
  ],
});
console.log(response.choices[0].message.content);

stream = client.chat.completions.create(
    model="kimi-k2.5",
    messages=[{"role": "user", "content": "Walk me through building an agentic coding assistant step by step."}],
    stream=True
)

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

tools = [
    {
        "type": "function",
        "function": {
            "name": "search_codebase",
            "description": "Search the codebase for files matching a pattern",
            "parameters": {
                "type": "object",
                "properties": {
                    "pattern": {"type": "string"},
                    "file_type": {"type": "string", "enum": ["py", "ts", "js", "go", "rust"]}
                },
                "required": ["pattern"]
            }
        }
    },
    {
        "type": "function",
        "function": {
            "name": "read_file",
            "description": "Read the contents of a file",
            "parameters": {
                "type": "object",
                "properties": {
                    "path": {"type": "string"}
                },
                "required": ["path"]
            }
        }
    },
    {
        "type": "function",
        "function": {
            "name": "write_file",
            "description": "Write content to a file",
            "parameters": {
                "type": "object",
                "properties": {
                    "path": {"type": "string"},
                    "content": {"type": "string"}
                },
                "required": ["path", "content"]
            }
        }
    }
]

messages = [
    {"role": "user", "content": "Find all authentication-related files in the codebase and add rate limiting to each login endpoint."}
]

# Agentic loop
while True:
    response = client.chat.completions.create(
        model="kimi-k2.5",
        messages=messages,
        tools=tools,
        tool_choice="auto"
    )

    message = response.choices[0].message
    messages.append(message)

    if not message.tool_calls:
        print("Task complete:", message.content)
        break

    # Execute tool calls and feed results back
    for tool_call in message.tool_calls:
        result = execute_tool(tool_call.function.name, tool_call.function.arguments)
        messages.append({
            "role": "tool",
            "tool_call_id": tool_call.id,
            "content": str(result)
        })

import json

response = client.chat.completions.create(
    model="kimi-k2.5",
    messages=[
        {"role": "user", "content": "Extract all API endpoints from this codebase description and return as JSON with: method, path, description, and auth_required fields."}
    ],
    response_format={"type": "json_object"}
)

endpoints = json.loads(response.choices[0].message.content)

# Load a large codebase into context
import os

codebase_contents = ""
for root, dirs, files in os.walk("./my-project"):
    # Skip node_modules, .git, etc.
    dirs[:] = [d for d in dirs if d not in ['.git', 'node_modules', '__pycache__']]
    for file in files:
        if file.endswith(('.py', '.ts', '.js')):
            path = os.path.join(root, file)
            with open(path) as f:
                codebase_contents += f"\n\n--- {path} ---\n{f.read()}"

response = client.chat.completions.create(
    model="kimi-k2.5",
    messages=[
        {"role": "system", "content": "You are a code reviewer. Analyse the entire codebase provided."},
        {"role": "user", "content": f"Here is the codebase:\n{codebase_contents}\n\nIdentify all security vulnerabilities and provide remediation steps."}
    ]
)

# First call — cold (full rate)
response1 = client.chat.completions.create(
    model="kimi-k2.5",
    messages=[
        {"role": "system", "content": large_system_prompt},  # This prefix is cached
        {"role": "user", "content": "Question 1"}
    ]
)

# Second call — cache hit (90% cheaper on the cached prefix)
response2 = client.chat.completions.create(
    model="kimi-k2.5",
    messages=[
        {"role": "system", "content": large_system_prompt},  # Cached — $0.10/M
        {"role": "user", "content": "Question 2"}            # New — full rate
    ]
)