或指定版本

AI小龙虾OPENCLAW与Python的集成主要有以下几种方式：

安装OPENCLAW SDK

通过pip安装

pip install openclaw-sdkpip install openclaw-sdk==1.2.0

从源码安装

git clone https://github.com/openclaw/ai-xiaolongxia.git
cd ai-xiaolongxia
pip install -e .

基础集成示例

基础调用

import openclaw
# 初始化客户端
client = openclaw.OpenClawClient(
    api_key="your-api-key",
    endpoint="https://api.openclaw.ai/v1"
)
# 调用AI服务
response = client.process(
    input_text="你的问题或指令",
    model="claw-7b",  # 指定模型
    temperature=0.7
)
print(response.result)

异步调用

import asyncio
from openclaw import AsyncOpenClawClient
async def main():
    client = AsyncOpenClawClient(api_key="your-api-key")
    # 异步处理
    response = await client.async_process(
        input_text="分析以下数据...",
        stream=True  # 启用流式输出
    )
    async for chunk in response:
        print(chunk, end="", flush=True)
asyncio.run(main())

高级集成功能

多模态处理

from openclaw import OpenClawMultiModal
# 初始化多模态客户端
mm_client = OpenClawMultiModal(api_key="your-api-key")
# 图像理解
image_result = mm_client.analyze_image(
    image_path="path/to/image.jpg",
    prompt="描述这张图片的内容"
)
# 文档处理
doc_result = mm_client.process_document(
    file_path="document.pdf",
    task="extract_tables"  # 提取表格、总结等
)

批量处理

# 批量文本处理
batch_inputs = [
    {"text": "文本1", "task": "分类"},
    {"text": "文本2", "task": "quot;},
    {"text": "文本3", "task": "情感分析"}
]
batch_results = client.batch_process(
    inputs=batch_inputs,
    max_concurrent=5  # 最大并发数
)

自定义模型微调

from openclaw.finetuning import FineTuningClient
# 初始化微调客户端
ft_client = FineTuningClient(api_key="your-api-key")
# 准备训练数据
training_data = [
    {"input": "问题1", "output": "答案1"},
    {"input": "问题2", "output": "答案2"}
]
# 开始微调
job = ft_client.create_finetuning_job(
    model="claw-base",
    training_data=training_data,
    hyperparameters={
        "epochs": 3,
        "learning_rate": 2e-5
    }
)
# 监控训练进度
while job.status != "completed":
    job.refresh()
    print(f"进度: {job.progress}%")
    time.sleep(60)

集成框架支持

FastAPI集成

from fastapi import FastAPI
from openclaw import OpenClawClient
app = FastAPI()
client = OpenClawClient(api_key="your-api-key")
@app.post("/analyze")
async def analyze_text(text: str):
    response = client.process(
        input_text=text,
        task="sentiment_analysis"
    )
    return {"result": response.result}
@app.post("/chat")
async def chat_endpoint(message: str):
    response = client.chat(
        messages=[{"role": "user", "content": message}],
        stream=False
    )
    return {"reply": response.choices[0].message.content}

Django集成

# views.py
from django.http import JsonResponse
from openclaw import OpenClawClient
client = OpenClawClient(api_key="your-api-key")
def process_request(request):
    if request.method == 'POST':
        text = request.POST.get('text', '')
        result = client.process(input_text=text)
        return JsonResponse({'result': result.result})

Streamlit应用

import streamlit as st
from openclaw import OpenClawClient
"OPENCLAW AI应用")
# 初始化客户端
if 'client' not in st.session_state:
    st.session_state.client = OpenClawClient(
        api_key=st.secrets["OPENCLAW_API_KEY"]
    )
# 用户输入
user_input = st.text_area("输入文本:")
if st.button("分析"):
    with st.spinner("处理中..."):
        result = st.session_state.client.process(
            input_text=user_input
        )
        st.write("结果:", result.result)

最佳实践

配置管理

# config.py
import os
from dataclasses import dataclass
from dotenv import load_dotenv
load_dotenv()
@dataclass
class OpenClawConfig:
    api_key: str = os.getenv("OPENCLAW_API_KEY")
    endpoint: str = os.getenv("OPENCLAW_ENDPOINT", "https://api.openclaw.ai/v1")
    timeout: int = 30
    max_retries: int = 3
# 使用配置
config = OpenClawConfig()
client = OpenClawClient(**config.__dict__)

错误处理

from openclaw.exceptions import (
    OpenClawAPIError,
    RateLimitError,
    AuthenticationError
)
try:
    response = client.process(input_text="测试")
except AuthenticationError as e:
    print("认证失败，请检查API Key")
except RateLimitError as e:
    print("超出速率限制，请稍后重试")
except OpenClawAPIError as e:
    print(f"API错误: {e}")
except Exception as e:
    print(f"未知错误: {e}")

性能优化

# 使用连接池
from openclaw import OpenClawClient
import httpx
client = OpenClawClient(
    api_key="your-api-key",
    http_client=httpx.Client(
        timeout=30.0,
        limits=httpx.Limits(max_connections=100, max_keepalive_connections=20)
    )
)
# 缓存结果
from functools import lru_cache
@lru_cache(maxsize=128)
def cached_process(text: str, task: str = "default"):
    return client.process(input_text=text, task=task)

监控和日志

import logging
from openclaw import OpenClawClient
# 设置日志
logging.basicConfig(level=logging.INFO)
logger = logging.getLogger(__name__)
# 自定义回调
def logging_callback(response, metadata):
    logger.info(f"请求ID: {metadata.request_id}")
    logger.info(f"处理时间: {metadata.latency}ms")
    logger.info(f"使用token数: {metadata.usage.total_tokens}")
client = OpenClawClient(
    api_key="your-api-key",
    callbacks=[logging_callback]
)

这些集成方法涵盖了从基础调用到高级应用的各种场景,根据具体需求选择合适的集成方式。

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