Yijia-Xiao
895ed130f9
Bedrock uses the Converse API (langchain-aws) and the AWS credential chain, so it has its own client like Anthropic/Google rather than the OpenAI-compatible registry. langchain-aws is an optional dependency (pip install ".[bedrock]"), lazy-imported with a clear install hint; importing the package never requires it. The model name is a Bedrock model ID / inference profile ID.
70 lines
2.4 KiB
Python
70 lines
2.4 KiB
Python
import os
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from typing import Any
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from .base_client import BaseLLMClient, normalize_content
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from .validators import validate_model
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# Bedrock has no global default region; us-west-2 hosts the broadest model set.
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_DEFAULT_REGION = "us-west-2"
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_BEDROCK_CLASS = None
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def _bedrock_class():
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"""Lazily import langchain-aws (the optional ``[bedrock]`` extra) and return a
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ChatBedrockConverse subclass with normalized content output.
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Imported on demand so the optional dependency (and boto3) isn't required by
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the rest of the package; cached after the first call.
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"""
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global _BEDROCK_CLASS
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if _BEDROCK_CLASS is not None:
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return _BEDROCK_CLASS
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try:
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from langchain_aws import ChatBedrockConverse
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except ImportError as exc:
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raise ImportError(
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"AWS Bedrock support requires the optional 'langchain-aws' dependency. "
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'Install it with: pip install "tradingagents[bedrock]"'
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) from exc
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class NormalizedChatBedrockConverse(ChatBedrockConverse):
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"""ChatBedrockConverse with normalized (string) content output."""
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def invoke(self, input, config=None, **kwargs):
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return normalize_content(super().invoke(input, config, **kwargs))
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_BEDROCK_CLASS = NormalizedChatBedrockConverse
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return _BEDROCK_CLASS
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class BedrockClient(BaseLLMClient):
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"""Client for Amazon Bedrock via the Converse API (langchain-aws).
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Authentication uses the standard AWS credential chain (env vars,
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``~/.aws/credentials``, or an IAM role); set ``AWS_REGION`` /
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``AWS_DEFAULT_REGION`` and optionally ``AWS_PROFILE``. The model name is a
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Bedrock model ID or cross-region inference profile ID, e.g.
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``us.anthropic.claude-opus-4-8-v1:0``.
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"""
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def get_llm(self) -> Any:
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"""Return a configured ChatBedrockConverse instance."""
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self.warn_if_unknown_model()
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chat_cls = _bedrock_class()
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region = (
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os.environ.get("AWS_REGION")
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or os.environ.get("AWS_DEFAULT_REGION")
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or _DEFAULT_REGION
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)
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llm_kwargs = {"model": self.model, "region_name": region}
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for key in ("temperature", "max_tokens", "max_retries", "callbacks"):
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if key in self.kwargs:
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llm_kwargs[key] = self.kwargs[key]
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return chat_cls(**llm_kwargs)
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def validate_model(self) -> bool:
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"""Validate model for Bedrock (any model ID accepted)."""
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return validate_model("bedrock", self.model)
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