# ===== 模型初始化 =====
from langchain_deepseek import ChatDeepSeek

model = ChatDeepSeek(model="deepseek-chat")

# ===== 1. 流式调用 =====
for chunk in model.stream("帮我解释一下什么是人工智能"):
    print(chunk.content, end="", flush=True)

# ===== 2. 批量调用 - 一次性接收所有响应 =====
messages = [
    "你是谁？",
    "请介绍一下北京",
    "中国的首都在哪里？"
]
results = model.batch(messages)
for res in results:
    print(res.content)

# ===== 3. 批量调用 - 按完成顺序接收 =====
for idx, result in model.batch_as_completed(messages):
    print(f"[索引{idx}] {result.content}")

# ===== 4. 性能对比：batch vs 顺序invoke =====
import time

start = time.time()
results = model.batch(messages)
batch_time = time.time() - start
print(f"batch耗时: {batch_time:.2f}秒")

start = time.time()
for msg in messages:
    model.invoke(msg)
invoke_time = time.time() - start
print(f"顺序invoke耗时: {invoke_time:.2f}秒")
print(f"节省: {(1 - batch_time / invoke_time) * 100:.1f}%")

# ===== 5. 异步调用示例 =====
import asyncio

async def main():
    task = asyncio.create_task(model.ainvoke("你好"))
    for i in range(5):
        print(f"主线程循环 {i}")
        await asyncio.sleep(1)
    response = await task
    print(response.content)

asyncio.run(main())

# ===== 6. 异步流式调用 =====
async def stream_demo():
    async for chunk in model.astream("用一句话介绍Python"):
        print(chunk.content, end="", flush=True)

asyncio.run(stream_demo())

# ===== 7. 异步批量调用 =====
async def batch_demo():
    task = asyncio.create_task(model.abatch(messages))
    for i in range(3):
        print(f"主线程循环 {i}")
        await asyncio.sleep(1)
    results = await task
    for res in results:
        print(res.content)

asyncio.run(batch_demo())

# ===== 8. 异常处理 =====
try:
    response = model.invoke("你好")
    print(response.content)
except Exception as e:
    print(f"调用出错: {e}")