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https://github.com/encounter/adk-python.git
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Ankur SharmaandCopybara-Service
d48679582d
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
95 lines
3.3 KiB
Python
95 lines
3.3 KiB
Python
# Copyright 2025 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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from __future__ import annotations
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import logging
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from typing import Optional
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import uuid
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from typing_extensions import override
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from ..agents.callback_context import CallbackContext
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from ..models.llm_request import LlmRequest
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from ..models.llm_response import LlmResponse
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from ..plugins.base_plugin import BasePlugin
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logger = logging.getLogger("google_adk." + __name__)
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_LLM_REQUEST_ID_KEY = "__llm_request_key__"
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class _RequestIntercepterPlugin(BasePlugin):
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"""A plugin that intercepts requests that are made to the model and couples them with the model response.
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NOTE: This implementation is intended for eval systems internal usage. Do not
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take direct depdency on it.
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Context behind the creation of this intercepter:
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Some of the newer AutoRater backed metrics need access the pieces of
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information that were presented to the model like instructions and the list
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of available tools.
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We intercept the llm_request using this intercepter and make it available to
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eval system.
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How is it done?
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The class maintains a cache of llm_requests that pass through it. Each request
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is given a unique id. The id is put in custom_metadata field of the response.
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Eval systems have access to the response and can use the request id to
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get the llm_request.
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"""
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def __init__(self, name: str):
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super().__init__(name=name)
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self._llm_requests_cache: dict[str, LlmRequest] = {}
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@override
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async def before_model_callback(
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self, *, callback_context: CallbackContext, llm_request: LlmRequest
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) -> Optional[LlmResponse]:
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# We add the llm_request to the call back context so that we can fetch
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# it later.
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request_id = str(uuid.uuid4())
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self._llm_requests_cache[request_id] = llm_request
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callback_context.state[_LLM_REQUEST_ID_KEY] = request_id
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@override
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async def after_model_callback(
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self, *, callback_context: CallbackContext, llm_response: LlmResponse
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) -> Optional[LlmResponse]:
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# Fetch the request_id from the callback_context
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if callback_context and _LLM_REQUEST_ID_KEY in callback_context.state:
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if llm_response.custom_metadata is None:
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llm_response.custom_metadata = {}
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llm_response.custom_metadata[_LLM_REQUEST_ID_KEY] = (
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callback_context.state[_LLM_REQUEST_ID_KEY]
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)
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def get_model_request(
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self, llm_response: LlmResponse
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) -> Optional[LlmRequest]:
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"""Fetches the request object, if found."""
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if (
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llm_response.custom_metadata
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and _LLM_REQUEST_ID_KEY in llm_response.custom_metadata
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):
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request_id = llm_response.custom_metadata[_LLM_REQUEST_ID_KEY]
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if request_id in self._llm_requests_cache:
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return self._llm_requests_cache[request_id]
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else:
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logger.warning("`%s` not found in llm_request_cache.", request_id)
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