somew1nd
ea3708a98f
- Android Text Input & IME Normalization:
- Implement progressive 3-tier input architecture in AndroidAdbDriver and AndroidDeviceController:
- Tier 1 (Primary): Zero-dependency clipboard injection + KEYCODE_PASTE (immune to IME Pinyin interception, full Unicode/Emoji support, preserves multiline formatting).
- Tier 2 (Broadcast): Base64 broadcast injection when ADBKeyboard is active.
- Tier 3 (Fallback): Native ADB physical keyevent fallback.
- Add literal newline normalization (\r\n, \n, \r) to prevent JSON escape string corruption (e.g. '10086\n' -> '10086、n').
- Add set_clipboard method to UIAutomatorClient without auxiliary APK requirements.
- Diagnostics & System Probes:
- Add adb_keys diagnostics probe for ADB key auth checks.
- Integrate diagnostics into CLI doctor command.
- Admin Console & Showcase UI:
- Improve model service and system router in admin console.
- Enhance workspace stream view and session state in showcase UI.
125 lines
4.6 KiB
Python
125 lines
4.6 KiB
Python
# Copyright 2026 Google LLC
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#
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# Licensed under the Apache License, Version 2.0 (the "License");
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# you may not use this file except in compliance with the License.
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# You may obtain a copy of the License at
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#
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# http://www.apache.org/licenses/LICENSE-2.0
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#
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# Unless required by applicable law or agreed to in writing, software
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# distributed under the License is distributed on an "AS IS" BASIS,
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# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
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# See the License for the specific language governing permissions and
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# limitations under the License.
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import json
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from typing import Any
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class ModelService:
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"""Service handling agent architecture profile detection and metadata formatting."""
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@staticmethod
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def get_active_model_info(profile: str | None = None) -> dict[str, str]:
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"""Return active architecture and underlying LLM model configuration."""
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# 1. Determine underlying LLM model and provider from config
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model_id = "gemini-3.7-flash"
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provider = "google"
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try:
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from artemis.config import parse_llm_config
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llm_cfg = parse_llm_config()
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if llm_cfg and llm_cfg.operator:
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provider = str(llm_cfg.operator.provider)
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model_id = str(llm_cfg.operator.model)
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elif llm_cfg and llm_cfg.default:
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provider = str(llm_cfg.default.provider)
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model_id = str(llm_cfg.default.model)
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except Exception:
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pass
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# 2. Determine agent architecture name (Flash vs Pro)
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arch_name = "Flash"
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if profile:
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p_lower = str(profile).lower()
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if "pro" in p_lower:
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arch_name = "Pro"
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elif "flash" in p_lower:
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arch_name = "Flash"
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else:
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arch_name = str(profile).capitalize()
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return {
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"name": arch_name,
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"id": model_id,
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"provider": provider,
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"architecture": f"ARTEMIS {arch_name}",
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}
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@staticmethod
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def resolve_session_profile(
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row_dict: dict[str, Any],
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llm_trace_payloads: list[str] | None = None,
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running_profile: str | None = None,
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agent_names: list[str] | None = None,
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) -> str | None:
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"""Resolve the Artemis agent architecture profile ('flash' or 'pro') for a session."""
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sess_profile = None
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d_info_raw = row_dict.get("device_info")
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if d_info_raw:
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try:
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d_info = json.loads(d_info_raw) if isinstance(d_info_raw, str) else d_info_raw
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if isinstance(d_info, dict):
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p = d_info.get("profile")
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if p:
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p_str = str(p).lower()
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if "pro" in p_str:
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return "pro"
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elif "flash" in p_str:
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return "flash"
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except Exception:
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pass
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if (row_dict.get("status") == "running") and running_profile:
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p_str = str(running_profile).lower()
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if "pro" in p_str:
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return "pro"
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elif "flash" in p_str:
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return "flash"
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# Check Agent/Trace names - the most definitive indicator of architecture
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if agent_names:
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agent_names_lower = [str(a).lower() for a in agent_names]
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if any(
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name in ("planner", "validator", "summarizer", "operator", "checker", "diagnoser")
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for name in agent_names_lower
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):
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return "pro"
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if any("flashrunner" in name for name in agent_names_lower):
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return "flash"
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# Check LLM traces for agent name
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if llm_trace_payloads:
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for tr_payload in llm_trace_payloads:
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if tr_payload:
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try:
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p_obj = (
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json.loads(tr_payload) if isinstance(tr_payload, str) else tr_payload
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)
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if isinstance(p_obj, dict):
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agent_name = str(p_obj.get("agent") or p_obj.get("name") or "").lower()
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if "flashrunner" in agent_name:
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return "flash"
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if any(
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x in agent_name
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for x in ("planner", "validator", "operator", "checker")
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):
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return "pro"
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except Exception:
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pass
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return None
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model_service = ModelService()
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