LiteRT-LM Python API

LiteRT-LM 的 Python API 適用於 Linux 和 macOS (Windows 支援即將推出)。支援多模態和工具使用等功能，GPU 加速功能即將推出。

簡介

以下是使用 Python API 建構的終端機即時通訊應用程式範例：

import litert_lm

litert_lm.set_min_log_severity(litert_lm.LogSeverity.ERROR) # Hide log for TUI app

with litert_lm.Engine("path/to/model.litertlm") as engine:
  with engine.create_conversation() as conversation:
    while True:
      user_input = input("\n>>> ")
      for chunk in conversation.send_message_async(user_input):
        print(chunk["content"][0]["text"], end="", flush=True)

開始使用

LiteRT-LM 以 Python 程式庫的形式提供，您可以從 PyPI 安裝每夜版：

# Using pip
pip install litert-lm-api-nightly

# Using uv
uv pip install litert-lm-api-nightly

1. 初始化引擎

Engine 是 API 的進入點。這個類別會處理模型載入和資源管理作業。將其做為內容管理員 (使用 with 陳述式) 可確保系統立即釋出原生資源。

注意：初始化引擎可能需要幾秒鐘才能載入模型。

import litert_lm

# Initialize with the model path and optionally specify the backend.
# backend can be Backend.CPU (default). GPU support is upcoming.
with litert_lm.Engine(
    "path/to/your/model.litertlm",
    backend=litert_lm.Backend.CPU,
    # Optional: Pick a writable dir for caching compiled artifacts.
    # cache_dir="/tmp/litert-lm-cache"
) as engine:
    # ... Use the engine to create a conversation ...
    pass

2. 建立對話

Conversation會管理你與模型互動的狀態和記錄。

# Optional: Configure system instruction and initial messages
messages = [
    {"role": "system", "content": [{"type": "text", "text": "You are a helpful assistant."}]},
]

# Create the conversation
with engine.create_conversation(messages=messages) as conversation:
    # ... Interact with the conversation ...
    pass

3. 傳送訊息

您可以同步或非同步 (串流) 傳送訊息。

同步範例：

# Simple string input
response = conversation.send_message("What is the capital of France?")
print(response["content"][0]["text"])

# Or with full message structure
# response = conversation.send_message({"role": "user", "content": "..."})

非同步 (串流) 範例：

# sendMessageAsync returns an iterator of response chunks
stream = conversation.send_message_async("Tell me a long story.")
for chunk in stream:
    # Chunks are dictionaries containing pieces of the response
    for item in chunk.get("content", []):
      if item.get("type") == "text":
        print(item["text"], end="", flush=True)
print()

4. 多模態

# Initialize with vision and/or audio backends if needed
with litert_lm.Engine(
    "path/to/multimodal_model.litertlm",
    audio_backend=litert_lm.Backend.CPU,
    # vision_backend=litert_lm.Backend.CPU, (GPU support is upcoming)
) as engine:
    with engine.create_conversation() as conversation:
        user_message = {
            "role": "user",
            "content": [
                {"type": "audio", "path": "/path/to/audio.wav"},
                {"type": "text", "text": "Describe this audio."},
            ],
        }
        response = conversation.send_message(user_message)
        print(response["content"][0]["text"])

5. 定義及使用工具

您可以將 Python 函式定義為工具，供模型自動呼叫。

def add_numbers(a: float, b: float) -> float:
    """Adds two numbers.

    Args:
        a: The first number.
        b: The second number.
    """
    return a + b

# Register the tool in the conversation
tools = [add_numbers]
with engine.create_conversation(tools=tools) as conversation:
    # The model will call add_numbers automatically if it needs to sum values
    response = conversation.send_message("What is 123 + 456?")
    print(response["content"][0]["text"])

LiteRT-LM 會使用函式的 docstring 和型別提示，為模型產生工具結構定義。