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Conversation API support (dapr#787)
* Conversation API support (sync) Signed-off-by: Elena Kolevska <[email protected]> * async conversation api support Signed-off-by: Elena Kolevska <[email protected]> * Adds docs Signed-off-by: Elena Kolevska <[email protected]> * Ignore validation of x.com link that needs authentication Signed-off-by: Elena Kolevska <[email protected]> * Registers e2e test. Clean up. Signed-off-by: Elena Kolevska <[email protected]> * Apply suggestions from code review Co-authored-by: Marc Duiker <[email protected]> Signed-off-by: Elena Kolevska <[email protected]> * Linter Signed-off-by: Elena Kolevska <[email protected]> * Updates protos and renames parameter Signed-off-by: Elena Kolevska <[email protected]> * Improves error handling Signed-off-by: Elena Kolevska <[email protected]> --------- Signed-off-by: Elena Kolevska <[email protected]> Signed-off-by: Elena Kolevska <[email protected]> Co-authored-by: Marc Duiker <[email protected]>
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‎README.md‎

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[![GitHub issue custom search in repo](https://img.shields.io/github/issues-search/dapr/python-sdk?query=type%3Aissue%20is%3Aopen%20label%3A%22good%20first%20issue%22&label=Good%20first%20issues&style=flat&logo=github)](https://github.com/dapr/python-sdk/issues?q=is%3Aissue+is%3Aopen+label%3A%22good+first+issue%22)
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[![Discord](https://img.shields.io/discord/778680217417809931?label=Discord&style=flat&logo=discord)](http://bit.ly/dapr-discord)
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[![YouTube Channel Views](https://img.shields.io/youtube/channel/views/UCtpSQ9BLB_3EXdWAUQYwnRA?style=flat&label=YouTube%20views&logo=youtube)](https://youtube.com/@daprdev)
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<!-- IGNORE_LINKS -->
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[![X (formerly Twitter) Follow](https://img.shields.io/twitter/follow/daprdev?logo=x&style=flat)](https://twitter.com/daprdev)
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<!-- END_IGNORE -->
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[Dapr](https://docs.dapr.io/concepts/overview/) is a portable, event-driven, serverless runtime for building distributed applications across cloud and edge.
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‎dapr/aio/clients/grpc/client.py‎

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from google.protobuf.message import Message as GrpcMessage
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from google.protobuf.empty_pb2 import Empty as GrpcEmpty
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from google.protobuf.any_pb2 import Any as GrpcAny
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import grpc.aio # type: ignore
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from grpc.aio import ( # type: ignore
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InvokeMethodRequest,
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BindingRequest,
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TransactionalStateOperation,
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ConversationInput,
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)
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from dapr.clients.grpc._response import (
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BindingResponse,
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ConversationResponse,
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ConversationResult,
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DaprResponse,
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GetSecretResponse,
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GetBulkSecretResponse,
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except grpc.aio.AioRpcError as err:
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raise DaprInternalError(err.details())
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async def converse_alpha1(
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self,
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name: str,
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inputs: List[ConversationInput],
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*,
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context_id: Optional[str] = None,
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parameters: Optional[Dict[str, GrpcAny]] = None,
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metadata: Optional[Dict[str, str]] = None,
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scrub_pii: Optional[bool] = None,
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temperature: Optional[float] = None,
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) -> ConversationResponse:
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"""Invoke an LLM using the conversation API (Alpha).
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Args:
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name: Name of the LLM component to invoke
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inputs: List of conversation inputs
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context_id: Optional ID for continuing an existing chat
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parameters: Optional custom parameters for the request
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metadata: Optional metadata for the component
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scrub_pii: Optional flag to scrub PII from inputs and outputs
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temperature: Optional temperature setting for the LLM to optimize for creativity or predictability
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Returns:
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ConversationResponse containing the conversation results
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Raises:
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DaprGrpcError: If the Dapr runtime returns an error
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"""
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inputs_pb = [
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api_v1.ConversationInput(content=inp.content, role=inp.role, scrubPII=inp.scrub_pii)
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for inp in inputs
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]
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request = api_v1.ConversationRequest(
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name=name,
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inputs=inputs_pb,
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contextID=context_id,
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parameters=parameters or {},
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metadata=metadata or {},
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scrubPII=scrub_pii,
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temperature=temperature,
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)
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try:
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response = await self._stub.ConverseAlpha1(request)
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outputs = [
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ConversationResult(result=output.result, parameters=output.parameters)
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for output in response.outputs
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]
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return ConversationResponse(context_id=response.contextID, outputs=outputs)
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except grpc.aio.AioRpcError as err:
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raise DaprGrpcError(err) from err
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async def wait(self, timeout_s: float):
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"""Waits for sidecar to be available within the timeout.
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‎dapr/clients/grpc/_request.py‎

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import io
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from enum import Enum
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from dataclasses import dataclass
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from typing import Dict, Optional, Union
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from google.protobuf.any_pb2 import Any as GrpcAny
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self.seq += 1
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return request_proto
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@dataclass
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class ConversationInput:
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"""A single input message for the conversation."""
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content: str
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role: Optional[str] = None
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scrub_pii: Optional[bool] = None

‎dapr/clients/grpc/_response.py‎

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import contextlib
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import json
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import threading
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from dataclasses import dataclass, field
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from datetime import datetime
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from enum import Enum
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from typing import (
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class DecryptResponse(CryptoResponse[TCryptoResponse]):
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...
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@dataclass
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class ConversationResult:
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"""Result from a single conversation input."""
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result: str
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parameters: Dict[str, GrpcAny] = field(default_factory=dict)
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@dataclass
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class ConversationResponse:
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"""Response from the conversation API."""
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context_id: Optional[str]
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outputs: List[ConversationResult]

‎dapr/clients/grpc/client.py‎

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from datetime import datetime
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from google.protobuf.message import Message as GrpcMessage
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from google.protobuf.empty_pb2 import Empty as GrpcEmpty
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from google.protobuf.any_pb2 import Any as GrpcAny
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import grpc # type: ignore
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from grpc import ( # type: ignore
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TransactionalStateOperation,
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EncryptRequestIterator,
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DecryptRequestIterator,
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ConversationInput,
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)
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from dapr.clients.grpc._response import (
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BindingResponse,
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EncryptResponse,
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DecryptResponse,
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TopicEventResponse,
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ConversationResponse,
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ConversationResult,
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)
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except RpcError as err:
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raise DaprInternalError(err.details())
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def converse_alpha1(
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self,
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name: str,
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inputs: List[ConversationInput],
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*,
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context_id: Optional[str] = None,
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parameters: Optional[Dict[str, GrpcAny]] = None,
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metadata: Optional[Dict[str, str]] = None,
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scrub_pii: Optional[bool] = None,
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temperature: Optional[float] = None,
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) -> ConversationResponse:
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"""Invoke an LLM using the conversation API (Alpha).
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Args:
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name: Name of the LLM component to invoke
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inputs: List of conversation inputs
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context_id: Optional ID for continuing an existing chat
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parameters: Optional custom parameters for the request
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metadata: Optional metadata for the component
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scrub_pii: Optional flag to scrub PII from inputs and outputs
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temperature: Optional temperature setting for the LLM to optimize for creativity or predictability
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Returns:
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ConversationResponse containing the conversation results
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Raises:
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DaprGrpcError: If the Dapr runtime returns an error
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"""
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inputs_pb = [
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api_v1.ConversationInput(content=inp.content, role=inp.role, scrubPII=inp.scrub_pii)
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for inp in inputs
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]
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request = api_v1.ConversationRequest(
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name=name,
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inputs=inputs_pb,
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contextID=context_id,
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parameters=parameters or {},
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metadata=metadata or {},
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scrubPII=scrub_pii,
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temperature=temperature,
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)
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try:
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response, call = self.retry_policy.run_rpc(self._stub.ConverseAlpha1.with_call, request)
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outputs = [
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ConversationResult(result=output.result, parameters=output.parameters)
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for output in response.outputs
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]
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return ConversationResponse(context_id=response.contextID, outputs=outputs)
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except RpcError as err:
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raise DaprGrpcError(err) from err
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def wait(self, timeout_s: float):
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"""Waits for sidecar to be available within the timeout.
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