添加 AI

konabot/plugins/handle_text/__init__.py

@@ -11,6 +11,7 @@ from nonebot.adapters.onebot.v11.message import Message as OB11Message
 from konabot.common.apis.ali_content_safety import AlibabaGreen
 from konabot.common.longtask import DepLongTaskTarget
 from konabot.plugins.handle_text.base import PipelineRunner, TextHandlerEnvironment, register_text_handlers
+from konabot.plugins.handle_text.handlers.ai_handlers import THQwen
 from konabot.plugins.handle_text.handlers.encoding_handlers import THAlign, THAlphaConv, THB64Hex, THBase64, THBaseConv, THCaesar, THMorse, THReverse
 from konabot.plugins.handle_text.handlers.random_handlers import THShuffle, THSorted
 from konabot.plugins.handle_text.handlers.unix_handlers import THCat, THEcho, THReplace, THRm
@@ -79,6 +80,7 @@ async def _():
         THAlign(),
         THSorted(),
         THMorse(),
+        THQwen(),
     )
     logger.info(f"注册了 TextHandler：{PipelineRunner.get_runner().handlers}")
 

konabot/plugins/handle_text/handlers/ai_handlers.py

@@ -0,0 +1,44 @@
+from typing import Any, cast
+from konabot.common.llm import get_llm
+from konabot.plugins.handle_text.base import TextHandler, TextHandlerEnvironment, TextHandleResult
+
+
+class THQwen(TextHandler):
+    name = "qwen"
+
+    async def handle(self, env: TextHandlerEnvironment, istream: str | None, args: list[str]) -> TextHandleResult:
+        llm = get_llm("qwen3-max")
+        messages = []
+
+        if istream is not None:
+            messages.append({
+                "role": "user",
+                "content": istream
+            })
+        if len(args) > 0:
+            message = ' '.join(args)
+            messages.append({
+                "role": "user",
+                "content": message,
+            })
+        if len(messages) == 0:
+            return TextHandleResult(
+                code=1,
+                ostream="使用方法：qwen <提示词>",
+            )
+
+        messages = [{
+            "role": "system",
+            "content": "除非用户要求，请尽可能短点回答。另外，当前环境不支持 Markdown 语法，如果可以，请使用纯文本回答"
+        }] + messages
+        result = await llm.chat(cast(Any, messages))
+        content = result.content
+        if content is None:
+            return TextHandleResult(
+                code=500,
+                ostream="问 AI 的时候发生了未知的错误",
+            )
+        return TextHandleResult(
+            code=0,
+            ostream=content,
+        )