mirror of
https://github.com/NousResearch/hermes-agent.git
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feat: add BedrockTransport + wire all Bedrock transport paths
Add BedrockTransport wrapping agent/bedrock_adapter.py behind the ProviderTransport ABC. Fourth and final transport. Wire ALL transport methods to production paths in run_agent.py: - build_kwargs: _build_api_kwargs bedrock branch (L6713) - normalize_response: main normalize loop, new bedrock_converse branch (handles both raw boto3 dicts and already-normalized SimpleNamespace) - validate_response: response validation, new bedrock_converse branch - finish_reason: new bedrock_converse branch in finish_reason extraction The truncation path (L9588) intentionally groups bedrock with chat_completions — both have the same response.choices shape because normalize_converse_response runs at the dispatch site. 17 new tests. 231 bedrock/converse/transport tests pass (0 failures). PR 6 of the provider transport refactor.
This commit is contained in:
kshitijk4poor
parent
d1cfe53d85
commit
f56c373a27
4 changed files with 383 additions and 13 deletions
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@ -37,3 +37,11 @@ def _discover_transports() -> None:
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import agent.transports.anthropic # noqa: F401
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except ImportError:
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pass
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try:
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import agent.transports.codex # noqa: F401
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except ImportError:
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pass
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try:
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import agent.transports.bedrock # noqa: F401
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except ImportError:
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pass
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154
agent/transports/bedrock.py
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154
agent/transports/bedrock.py
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@ -0,0 +1,154 @@
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"""AWS Bedrock Converse API transport.
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Delegates to the existing adapter functions in agent/bedrock_adapter.py.
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Bedrock uses its own boto3 client (not the OpenAI SDK), so the transport
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owns format conversion and normalization, while client construction and
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boto3 calls stay on AIAgent.
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"""
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from typing import Any, Dict, List, Optional
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from agent.transports.base import ProviderTransport
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from agent.transports.types import NormalizedResponse, ToolCall, Usage
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class BedrockTransport(ProviderTransport):
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"""Transport for api_mode='bedrock_converse'."""
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@property
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def api_mode(self) -> str:
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return "bedrock_converse"
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def convert_messages(self, messages: List[Dict[str, Any]], **kwargs) -> Any:
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"""Convert OpenAI messages to Bedrock Converse format."""
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from agent.bedrock_adapter import convert_messages_to_converse
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return convert_messages_to_converse(messages)
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def convert_tools(self, tools: List[Dict[str, Any]]) -> Any:
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"""Convert OpenAI tool schemas to Bedrock Converse toolConfig."""
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from agent.bedrock_adapter import convert_tools_to_converse
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return convert_tools_to_converse(tools)
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def build_kwargs(
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self,
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model: str,
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messages: List[Dict[str, Any]],
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tools: Optional[List[Dict[str, Any]]] = None,
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**params,
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) -> Dict[str, Any]:
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"""Build Bedrock converse() kwargs.
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Calls convert_messages and convert_tools internally.
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params:
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max_tokens: int — output token limit (default 4096)
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temperature: float | None
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guardrail_config: dict | None — Bedrock guardrails
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region: str — AWS region (default 'us-east-1')
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"""
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from agent.bedrock_adapter import build_converse_kwargs
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region = params.get("region", "us-east-1")
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guardrail = params.get("guardrail_config")
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kwargs = build_converse_kwargs(
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model=model,
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messages=messages,
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tools=tools,
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max_tokens=params.get("max_tokens", 4096),
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temperature=params.get("temperature"),
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guardrail_config=guardrail,
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)
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# Sentinel keys for dispatch — agent pops these before the boto3 call
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kwargs["__bedrock_converse__"] = True
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kwargs["__bedrock_region__"] = region
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return kwargs
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def normalize_response(self, response: Any, **kwargs) -> NormalizedResponse:
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"""Normalize Bedrock response to NormalizedResponse.
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Handles two shapes:
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1. Raw boto3 dict (from direct converse() calls)
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2. Already-normalized SimpleNamespace with .choices (from dispatch site)
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"""
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from agent.bedrock_adapter import normalize_converse_response
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# Normalize to OpenAI-compatible SimpleNamespace
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if hasattr(response, "choices") and response.choices:
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# Already normalized at dispatch site
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ns = response
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else:
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# Raw boto3 dict
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ns = normalize_converse_response(response)
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choice = ns.choices[0]
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msg = choice.message
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finish_reason = choice.finish_reason or "stop"
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tool_calls = None
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if msg.tool_calls:
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tool_calls = [
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ToolCall(
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id=tc.id,
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name=tc.function.name,
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arguments=tc.function.arguments,
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)
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for tc in msg.tool_calls
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]
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usage = None
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if hasattr(ns, "usage") and ns.usage:
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u = ns.usage
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usage = Usage(
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prompt_tokens=getattr(u, "prompt_tokens", 0) or 0,
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completion_tokens=getattr(u, "completion_tokens", 0) or 0,
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total_tokens=getattr(u, "total_tokens", 0) or 0,
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)
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reasoning = getattr(msg, "reasoning", None) or getattr(msg, "reasoning_content", None)
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return NormalizedResponse(
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content=msg.content,
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tool_calls=tool_calls,
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finish_reason=finish_reason,
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reasoning=reasoning,
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usage=usage,
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)
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def validate_response(self, response: Any) -> bool:
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"""Check Bedrock response structure.
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After normalize_converse_response, the response has OpenAI-compatible
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.choices — same check as chat_completions.
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"""
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if response is None:
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return False
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# Raw Bedrock dict response — check for 'output' key
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if isinstance(response, dict):
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return "output" in response
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# Already-normalized SimpleNamespace
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if hasattr(response, "choices"):
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return bool(response.choices)
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return False
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def map_finish_reason(self, raw_reason: str) -> str:
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"""Map Bedrock stop reason to OpenAI finish_reason.
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The adapter already does this mapping inside normalize_converse_response,
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so this is only used for direct access to raw responses.
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"""
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_MAP = {
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"end_turn": "stop",
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"tool_use": "tool_calls",
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"max_tokens": "length",
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"stop_sequence": "stop",
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"guardrail_intervened": "content_filter",
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"content_filtered": "content_filter",
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}
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return _MAP.get(raw_reason, "stop")
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# Auto-register on import
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from agent.transports import register_transport # noqa: E402
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register_transport("bedrock_converse", BedrockTransport)
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70
run_agent.py
70
run_agent.py
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@ -6554,6 +6554,24 @@ class AIAgent:
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self._anthropic_transport = t
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return t
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def _get_codex_transport(self):
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"""Return the cached ResponsesApiTransport instance (lazy singleton)."""
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t = getattr(self, "_codex_transport", None)
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if t is None:
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from agent.transports import get_transport
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t = get_transport("codex_responses")
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self._codex_transport = t
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return t
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def _get_bedrock_transport(self):
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"""Return the cached BedrockTransport instance (lazy singleton)."""
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t = getattr(self, "_bedrock_transport", None)
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if t is None:
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from agent.transports import get_transport
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t = get_transport("bedrock_converse")
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self._bedrock_transport = t
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return t
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def _prepare_anthropic_messages_for_api(self, api_messages: list) -> list:
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if not any(
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isinstance(msg, dict) and self._content_has_image_parts(msg.get("content"))
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@ -6693,21 +6711,17 @@ class AIAgent:
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# AWS Bedrock native Converse API — bypasses the OpenAI client entirely.
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# The adapter handles message/tool conversion and boto3 calls directly.
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if self.api_mode == "bedrock_converse":
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from agent.bedrock_adapter import build_converse_kwargs
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_bt = self._get_bedrock_transport()
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region = getattr(self, "_bedrock_region", None) or "us-east-1"
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guardrail = getattr(self, "_bedrock_guardrail_config", None)
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return {
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"__bedrock_converse__": True,
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"__bedrock_region__": region,
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**build_converse_kwargs(
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model=self.model,
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messages=api_messages,
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tools=self.tools,
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max_tokens=self.max_tokens or 4096,
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temperature=None, # Let the model use its default
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guardrail_config=guardrail,
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),
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}
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return _bt.build_kwargs(
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model=self.model,
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messages=api_messages,
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tools=self.tools,
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max_tokens=self.max_tokens or 4096,
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region=region,
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guardrail_config=guardrail,
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)
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if self.api_mode == "codex_responses":
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instructions = ""
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@ -9372,6 +9386,14 @@ class AIAgent:
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error_details.append("response is None")
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else:
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error_details.append("response.content invalid (not a non-empty list)")
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elif self.api_mode == "bedrock_converse":
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_btv = self._get_bedrock_transport()
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if not _btv.validate_response(response):
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response_invalid = True
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if response is None:
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error_details.append("response is None")
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else:
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error_details.append("Bedrock response invalid (no output or choices)")
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else:
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if response is None or not hasattr(response, 'choices') or response.choices is None or not response.choices:
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response_invalid = True
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@ -9534,6 +9556,10 @@ class AIAgent:
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elif self.api_mode == "anthropic_messages":
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_tfr = self._get_anthropic_transport()
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finish_reason = _tfr.map_finish_reason(response.stop_reason)
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elif self.api_mode == "bedrock_converse":
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# Bedrock response is already normalized at dispatch — finish_reason
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# is already in OpenAI format via normalize_converse_response()
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finish_reason = response.choices[0].finish_reason if hasattr(response, "choices") and response.choices else "stop"
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else:
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finish_reason = response.choices[0].finish_reason
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assistant_message = response.choices[0].message
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@ -10811,6 +10837,24 @@ class AIAgent:
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),
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)
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finish_reason = _nr.finish_reason
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elif self.api_mode == "bedrock_converse":
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_bt = self._get_bedrock_transport()
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_bnr = _bt.normalize_response(response)
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assistant_message = SimpleNamespace(
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content=_bnr.content,
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tool_calls=[
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SimpleNamespace(
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id=tc.id,
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type="function",
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function=SimpleNamespace(name=tc.name, arguments=tc.arguments),
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)
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for tc in (_bnr.tool_calls or [])
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] or None,
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reasoning=_bnr.reasoning,
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reasoning_content=None,
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reasoning_details=None,
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)
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finish_reason = _bnr.finish_reason
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else:
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assistant_message = response.choices[0].message
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164
tests/agent/transports/test_bedrock_transport.py
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164
tests/agent/transports/test_bedrock_transport.py
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@ -0,0 +1,164 @@
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"""Tests for the BedrockTransport."""
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import json
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import pytest
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from types import SimpleNamespace
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from agent.transports import get_transport
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from agent.transports.types import NormalizedResponse, ToolCall
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@pytest.fixture
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def transport():
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import agent.transports.bedrock # noqa: F401
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return get_transport("bedrock_converse")
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class TestBedrockBasic:
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def test_api_mode(self, transport):
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assert transport.api_mode == "bedrock_converse"
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def test_registered(self, transport):
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assert transport is not None
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class TestBedrockBuildKwargs:
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def test_basic_kwargs(self, transport):
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msgs = [{"role": "user", "content": "Hello"}]
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kw = transport.build_kwargs(model="anthropic.claude-3-5-sonnet-20241022-v2:0", messages=msgs)
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assert kw["modelId"] == "anthropic.claude-3-5-sonnet-20241022-v2:0"
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assert kw["__bedrock_converse__"] is True
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assert kw["__bedrock_region__"] == "us-east-1"
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assert "messages" in kw
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def test_custom_region(self, transport):
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msgs = [{"role": "user", "content": "Hi"}]
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kw = transport.build_kwargs(
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model="anthropic.claude-3-5-sonnet-20241022-v2:0",
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messages=msgs,
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region="eu-west-1",
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)
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assert kw["__bedrock_region__"] == "eu-west-1"
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def test_max_tokens(self, transport):
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msgs = [{"role": "user", "content": "Hi"}]
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kw = transport.build_kwargs(
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model="anthropic.claude-3-5-sonnet-20241022-v2:0",
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messages=msgs,
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max_tokens=8192,
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)
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assert kw["inferenceConfig"]["maxTokens"] == 8192
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class TestBedrockConvertTools:
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def test_convert_tools(self, transport):
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tools = [{
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"type": "function",
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"function": {
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"name": "terminal",
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"description": "Run commands",
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"parameters": {"type": "object", "properties": {"command": {"type": "string"}}},
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}
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}]
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result = transport.convert_tools(tools)
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assert len(result) == 1
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assert result[0]["toolSpec"]["name"] == "terminal"
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class TestBedrockValidate:
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def test_none(self, transport):
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assert transport.validate_response(None) is False
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def test_raw_dict_valid(self, transport):
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assert transport.validate_response({"output": {"message": {}}}) is True
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def test_raw_dict_invalid(self, transport):
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assert transport.validate_response({"error": "fail"}) is False
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def test_normalized_valid(self, transport):
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r = SimpleNamespace(choices=[SimpleNamespace(message=SimpleNamespace(content="hi"))])
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assert transport.validate_response(r) is True
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class TestBedrockMapFinishReason:
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def test_end_turn(self, transport):
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assert transport.map_finish_reason("end_turn") == "stop"
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def test_tool_use(self, transport):
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assert transport.map_finish_reason("tool_use") == "tool_calls"
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def test_max_tokens(self, transport):
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assert transport.map_finish_reason("max_tokens") == "length"
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def test_guardrail(self, transport):
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assert transport.map_finish_reason("guardrail_intervened") == "content_filter"
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def test_unknown(self, transport):
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assert transport.map_finish_reason("unknown") == "stop"
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class TestBedrockNormalize:
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def _make_bedrock_response(self, text="Hello", tool_calls=None, stop_reason="end_turn"):
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"""Build a raw Bedrock converse response dict."""
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content = []
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if text:
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content.append({"text": text})
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if tool_calls:
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for tc in tool_calls:
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content.append({
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"toolUse": {
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"toolUseId": tc["id"],
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"name": tc["name"],
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"input": tc["input"],
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}
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})
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return {
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"output": {"message": {"role": "assistant", "content": content}},
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"stopReason": stop_reason,
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"usage": {"inputTokens": 10, "outputTokens": 5, "totalTokens": 15},
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}
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def test_text_response(self, transport):
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raw = self._make_bedrock_response(text="Hello world")
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nr = transport.normalize_response(raw)
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assert isinstance(nr, NormalizedResponse)
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assert nr.content == "Hello world"
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assert nr.finish_reason == "stop"
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def test_tool_call_response(self, transport):
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raw = self._make_bedrock_response(
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text=None,
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tool_calls=[{"id": "tool_1", "name": "terminal", "input": {"command": "ls"}}],
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stop_reason="tool_use",
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)
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nr = transport.normalize_response(raw)
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assert nr.finish_reason == "tool_calls"
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assert len(nr.tool_calls) == 1
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assert nr.tool_calls[0].name == "terminal"
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def test_already_normalized_response(self, transport):
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"""Test normalize_response handles already-normalized SimpleNamespace (from dispatch site)."""
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pre_normalized = SimpleNamespace(
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choices=[SimpleNamespace(
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message=SimpleNamespace(
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content="Hello from Bedrock",
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tool_calls=None,
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reasoning=None,
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reasoning_content=None,
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),
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finish_reason="stop",
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)],
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usage=SimpleNamespace(prompt_tokens=10, completion_tokens=5, total_tokens=15),
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)
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nr = transport.normalize_response(pre_normalized)
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assert isinstance(nr, NormalizedResponse)
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assert nr.content == "Hello from Bedrock"
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assert nr.finish_reason == "stop"
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assert nr.usage is not None
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assert nr.usage.prompt_tokens == 10
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