feat: Populate AppDetails to each Invocation

AppDetails require two pieces of information:
1) Instructions
2) Tools

Both these pieces of information are gathered using the llm_request that was passed to the model. This approach, slightly invasive, ensures that we capture the "exact" instructions and tools that were given to the model.

PiperOrigin-RevId: 811180648

src/google/adk/evaluation/request_intercepter_plugin.py

@@ -0,0 +1,94 @@
+# Copyright 2025 Google LLC
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+#     http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+
+from __future__ import annotations
+
+import logging
+from typing import Optional
+import uuid
+
+from typing_extensions import override
+
+from ..agents.callback_context import CallbackContext
+from ..models.llm_request import LlmRequest
+from ..models.llm_response import LlmResponse
+from ..plugins.base_plugin import BasePlugin
+
+logger = logging.getLogger("google_adk." + __name__)
+
+_LLM_REQUEST_ID_KEY = "__llm_request_key__"
+
+
+class _RequestIntercepterPlugin(BasePlugin):
+  """A plugin that intercepts requests that are made to the model and couples them with the model response.
+
+  NOTE: This implementation is intended for eval systems internal usage. Do not
+  take direct depdency on it.
+
+  Context behind the creation of this intercepter:
+  Some of the newer AutoRater backed metrics need access the pieces of
+  information that were presented to the model like instructions and the list
+  of available tools.
+
+  We intercept the llm_request using this intercepter and make it available to
+  eval system.
+
+  How is it done?
+  The class maintains a cache of llm_requests that pass through it. Each request
+  is given a unique id. The id is put in custom_metadata field of the response.
+  Eval systems have access to the response and can use the request id to
+  get the llm_request.
+  """
+
+  def __init__(self, name: str):
+    super().__init__(name=name)
+    self._llm_requests_cache: dict[str, LlmRequest] = {}
+
+  @override
+  async def before_model_callback(
+      self, *, callback_context: CallbackContext, llm_request: LlmRequest
+  ) -> Optional[LlmResponse]:
+    # We add the llm_request to the call back context so that we can fetch
+    # it later.
+    request_id = str(uuid.uuid4())
+    self._llm_requests_cache[request_id] = llm_request
+    callback_context.state[_LLM_REQUEST_ID_KEY] = request_id
+
+  @override
+  async def after_model_callback(
+      self, *, callback_context: CallbackContext, llm_response: LlmResponse
+  ) -> Optional[LlmResponse]:
+    # Fetch the request_id from the callback_context
+    if callback_context and _LLM_REQUEST_ID_KEY in callback_context.state:
+      if llm_response.custom_metadata is None:
+        llm_response.custom_metadata = {}
+
+      llm_response.custom_metadata[_LLM_REQUEST_ID_KEY] = (
+          callback_context.state[_LLM_REQUEST_ID_KEY]
+      )
+
+  def get_model_request(
+      self, llm_response: LlmResponse
+  ) -> Optional[LlmRequest]:
+    """Fetches the request object, if found."""
+    if (
+        llm_response.custom_metadata
+        and _LLM_REQUEST_ID_KEY in llm_response.custom_metadata
+    ):
+      request_id = llm_response.custom_metadata[_LLM_REQUEST_ID_KEY]
+
+      if request_id in self._llm_requests_cache:
+        return self._llm_requests_cache[request_id]
+      else:
+        logger.warning("`%s` not found in llm_request_cache.", request_id)