somew1nd
a45daf23f8
Report device actions as dispatched and preserve full failure details. Route Flash through McpActionExecutor and use ToolFailure across helper tools and agent loops instead of inferring failure from response text. Require model-provided descriptions for coordinate targets. Keep observed element metadata separate from those descriptions in safety checks, visual summaries, history, and frontend labels. Add Gemini Interactions API video analysis with configurable processing mode, chunk size, and timeouts. Centralize Google provider capabilities and token usage normalization, and update google-genai to 2.22.0. Preserve system instructions on follow-up interactions, replan pending chunks when falling back to static processing, and reject unanswered native video and audio segments instead of caching placeholder results. Keep helper-tool failure status intact through history tools and committee calls. Validation: 1,762 Python unit tests passed (3 skipped), 40 frontend formatter tests passed, and the production UI build completed. Ruff lint, formatting, the quality ratchet, and git diff checks passed.
107 lines
3.4 KiB
Python
107 lines
3.4 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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"""Token-usage normalization across the LangChain, generate_content and Interactions shapes."""
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from types import SimpleNamespace
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from artemis.llm.google import normalize_usage, usage_from_message
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from langchain_core.messages import AIMessage
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def test_langchain_usage_metadata_shape():
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usage = normalize_usage(
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{
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"input_tokens": 1200,
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"output_tokens": 80,
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"total_tokens": 1280,
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"input_token_details": {"cache_read": 900},
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"output_token_details": {"reasoning": 30},
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}
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)
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assert usage == {
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"prompt_tokens": 1200,
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"completion_tokens": 80,
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"total_tokens": 1280,
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"cached_tokens": 900,
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"thought_tokens": 30,
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"tool_use_tokens": 0,
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}
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def test_generate_content_usage_metadata_object_shape():
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raw = SimpleNamespace(
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prompt_token_count=500,
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candidates_token_count=25,
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total_token_count=560,
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cached_content_token_count=400,
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thoughts_token_count=35,
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tool_use_prompt_token_count=None,
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)
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assert normalize_usage(raw) == {
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"prompt_tokens": 500,
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"completion_tokens": 25,
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"total_tokens": 560,
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"cached_tokens": 400,
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"thought_tokens": 35,
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"tool_use_tokens": 0,
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}
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def test_interactions_usage_shape_counts_tool_use_and_thoughts():
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raw = SimpleNamespace(
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total_input_tokens=191,
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total_output_tokens=655,
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total_tokens=10571,
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total_cached_tokens=0,
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total_thought_tokens=12,
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total_tool_use_tokens=9725,
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)
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assert normalize_usage(raw) == {
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"prompt_tokens": 191,
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"completion_tokens": 655,
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"total_tokens": 10571,
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"cached_tokens": 0,
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"thought_tokens": 12,
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"tool_use_tokens": 9725,
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}
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def test_total_is_derived_when_missing_and_empty_payloads_return_none():
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assert normalize_usage({"prompt_tokens": 3, "completion_tokens": 4})["total_tokens"] == 7
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assert normalize_usage(None) is None
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assert normalize_usage({}) is None
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assert normalize_usage({"prompt_tokens": "not-a-number"}) is None
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assert normalize_usage(SimpleNamespace()) is None
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def test_usage_from_message_prefers_langchain_then_response_metadata():
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msg = AIMessage(content="ok")
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msg.usage_metadata = {"input_tokens": 10, "output_tokens": 2, "total_tokens": 12}
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assert usage_from_message(msg)["prompt_tokens"] == 10
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fallback = AIMessage(content="ok")
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fallback.response_metadata = {"token_usage": {"prompt_tokens": 7, "completion_tokens": 1}}
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assert usage_from_message(fallback) == {
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"prompt_tokens": 7,
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"completion_tokens": 1,
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"total_tokens": 8,
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"cached_tokens": 0,
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"thought_tokens": 0,
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"tool_use_tokens": 0,
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}
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assert usage_from_message(AIMessage(content="none")) is None
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assert usage_from_message(object()) is None
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