在 REPL 中使用 lmstudio-python | LM Studio 文档

为了简化交互式使用，lmstudio-python 提供了一个便利 API，通过 atexit 钩子管理其资源，允许在多个交互式命令中使用默认的同步客户端会话。

此便利 API 在整个文档的示例中以 Python（便利 API） 标签（以及使用 with 语句确保网络通信资源确定性清理的 Python（范围资源 API） 示例）显示。

便利 API 允许使用标准 Python REPL 或更灵活的替代方案（如 Juypter Notebooks）与加载到 LM Studio 中的 AI 模型进行交互。例如：

Python REPL

>>> import lmstudio as lms
>>> loaded_models = lms.list_loaded_models()
>>> for idx, model in enumerate(loaded_models):
...     print(f"{idx:>3} {model}")
...
  0 LLM(identifier='qwen2.5-7b-instruct')
>>> model = loaded_models[0]
>>> chat = lms.Chat("You answer questions concisely")
>>> chat = lms.Chat("You answer questions concisely")
>>> chat.add_user_message("Tell me three fruits")
UserMessage(content=[TextData(text='Tell me three fruits')])
>>> print(model.respond(chat, on_message=chat.append))
Banana, apple, orange.
>>> chat.add_user_message("Tell me three more fruits")
UserMessage(content=[TextData(text='Tell me three more fruits')])
>>> print(model.respond(chat, on_message=chat.append))
Mango, strawberry, avocado.
>>> chat.add_user_message("How many fruits have you told me?")
UserMessage(content=[TextData(text='How many fruits have you told me?')])
>>> print(model.respond(chat, on_message=chat.append))
You asked for three initial fruits and three more, so I've listed a total of six fruits.

虽然并非主要用于此目的，但 SDK 的异步结构化并发 API 与由 python -m asyncio 启动的异步 Python REPL 兼容。例如：

Python REPL

# Note: assumes use of the "python -m asyncio" asynchronous REPL (or equivalent)
# Requires Python SDK version 1.5.0 or later
>>> from contextlib import AsyncExitStack
>>> import lmstudio as lms
>>> resources = AsyncExitStack()
>>> client = await resources.enter_async_context(lms.AsyncClient())
>>> loaded_models = await client.llm.list_loaded()
>>> for idx, model in enumerate(loaded_models):
...     print(f"{idx:>3} {model}")
...
  0 AsyncLLM(identifier='qwen2.5-7b-instruct-1m')
>>> model = loaded_models[0]
>>> chat = lms.Chat("You answer questions concisely")
>>> chat.add_user_message("Tell me three fruits")
UserMessage(content=[TextData(text='Tell me three fruits')])
>>> print(await model.respond(chat, on_message=chat.append))
Apple, banana, and orange.
>>> chat.add_user_message("Tell me three more fruits")
UserMessage(content=[TextData(text='Tell me three more fruits')])
>>> print(await model.respond(chat, on_message=chat.append))
Mango, strawberry, and pineapple.
>>> chat.add_user_message("How many fruits have you told me?")
UserMessage(content=[TextData(text='How many fruits have you told me?')])
>>> print(await model.respond(chat, on_message=chat.append))
You asked for three fruits initially, then three more, so I’ve listed six fruits in total.

入门在 REPL 中使用 lmstudio-python

入门
在 REPL 中使用 `lmstudio-python`