If `EventsCompactionConfig` is provided without a `compactor`, a `SlidingWindowCompactor` is now automatically instantiated using the `root_agent`'s LLM. This simplifies configuration by providing a sensible default.
PiperOrigin-RevId: 816038579
The class is now named `LlmEventSummarizer` to better reflect that its primary function is to use an LLM to summarize events. The docstring has been updated to clarify that this class is responsible *only* for the LLM-based summarization of a given set of events, while the logic for determining *when* and *which* events form the sliding window is handled by an external component, such as an ADK Runner.
PiperOrigin-RevId: 815976264
This change introduces a new `analyze_contribution` function in `query_tool.py` which uses BigQuery ML's `CREATE MODEL` with `CONTRIBUTION_ANALYSIS` type and `ML.GET_INSIGHTS` to analyze the contribution of different dimensions to a given metric. The new function is also added to the `bigquery_toolset`.
PiperOrigin-RevId: 815849281
before this change, we estimate the token count of the contents to cache and use it to compare with the threshold user set. but that's not precise , so we use the actual prompt token count of previous llm request.
We won't create cache for the very initial request
PiperOrigin-RevId: 814484840
We updated the one of the public methods on AgentEvaluator to take in eval metric configurations using a more formal EvalConfig data model.
We also mark "criteria" field on the method as deprecated.
Updated some integration test cases.
PiperOrigin-RevId: 814314134
