mime

rootfs/agents/TextSummary/agent.py

@@ -0,0 +1,48 @@
+import copy
+
+from aios_kernel import CustomAIAgent, AgentMsg, AgentMsgType, AgentPrompt
+from aios_kernel.compute_task import ComputeTaskResultCode
+from knowledge.data.writer import split_text
+
+
+class TextSummaryAgent(CustomAIAgent):
+    def __init__(self):
+        super().__init__("TextSummary", "Text Summary", 128000)
+
+    async def _process_msg(self, msg: AgentMsg, workspace=None) -> AgentMsg:
+        if msg.msg_type is not AgentMsgType.TYPE_MSG:
+            return AgentMsg.create_error_resp(msg, "only support msg type")
+
+        if msg.body_mime is not None and msg.body_mime != "text/plain":
+            return AgentMsg.create_error_resp(msg, "only support text/plain mime type")
+
+        chunks = split_text(msg.body, separators=["\n\n", "\n"], chunk_size=4000, chunk_overlap=200, length_function=len)
+
+        prompt = AgentPrompt()
+        prompt.system_message = "Your job is to generate a summary based on the input."
+        if len(chunks) == 1:
+            prompt.append(AgentPrompt(chunks[0]))
+            resp = await self.do_llm_complection(prompt)
+            if resp.result_code != ComputeTaskResultCode.OK:
+                return msg.create_error_resp(resp.error_str)
+            return msg.create_resp_msg(resp.result_str)
+
+        segments = []
+        for i, chunk in enumerate(chunks):
+            seg_prompt = copy.deepcopy(prompt)
+            seg_prompt.append(AgentPrompt(chunk))
+            resp = await self.do_llm_complection(seg_prompt)
+            if resp.result_code != ComputeTaskResultCode.OK:
+                return msg.create_error_resp(resp.error_str)
+            segments.append(resp.result_str)
+
+        segments_str = "\n".join(segments)
+        prompt.append(AgentPrompt(f"以下文本分段之后的各段摘要，请合并生成一个完整摘要：\n{segments_str}"))
+        resp = await self.do_llm_complection(prompt)
+        if resp.result_code != ComputeTaskResultCode.OK:
+            return msg.create_error_resp(resp.error_str)
+        return msg.create_resp_msg(resp.result_str)
+
+
+def init():
+    return TextSummaryAgent()

rootfs/agents/Vision/agent.toml

@@ -0,0 +1,7 @@
+instance_id = "Vision"
+fullname = "Vision"
+llm_model_name = "gpt-4-vision-preview"
+
+[[prompt]]
+role = "system"
+content = """你的工作对用户输入的图片和视频做分析，并根据用户的意图做出回应。"""