Merge branch 'dev' of https://github.com/MoFox-Studio/MoFox-Core into dev
This commit is contained in:
minecraft1024a
@@ -12,8 +12,8 @@ class APIProvider(ValidatedConfigBase):
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name: str = Field(..., min_length=1, description="API提供商名称")
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base_url: str = Field(..., description="API基础URL")
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api_key: str | list[str] = Field(..., min_length=1, description="API密钥，支持单个密钥或密钥列表轮询")
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client_type: Literal["openai", "gemini", "aiohttp_gemini"] = Field(
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default="openai", description="客户端类型（如openai/google等，默认为openai）"
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client_type: Literal["openai", "gemini", "aiohttp_gemini", "bedrock"] = Field(
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default="openai", description="客户端类型（如openai/google/bedrock等，默认为openai）"
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)
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max_retry: int = Field(default=2, ge=0, description="最大重试次数（单个模型API调用失败，最多重试的次数）")
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timeout: int = Field(
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@@ -6,3 +6,5 @@ if "openai" in used_client_types:
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from . import openai_client # noqa: F401
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if "aiohttp_gemini" in used_client_types:
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from . import aiohttp_gemini_client # noqa: F401
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if "bedrock" in used_client_types:
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from . import bedrock_client # noqa: F401
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495
src/llm_models/model_client/bedrock_client.py
Normal file
495
src/llm_models/model_client/bedrock_client.py
Normal file
@@ -0,0 +1,495 @@
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import asyncio
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import base64
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import io
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import json
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from collections.abc import Callable, Coroutine
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from typing import Any
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import aioboto3
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import orjson
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from botocore.config import Config
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from json_repair import repair_json
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from src.common.logger import get_logger
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from src.config.api_ada_configs import APIProvider, ModelInfo
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from ..exceptions import (
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NetworkConnectionError,
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ReqAbortException,
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RespNotOkException,
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RespParseException,
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)
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from ..payload_content.message import Message, RoleType
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from ..payload_content.resp_format import RespFormat
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from ..payload_content.tool_option import ToolCall, ToolOption, ToolParam
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from .base_client import APIResponse, BaseClient, UsageRecord, client_registry
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logger = get_logger("Bedrock客户端")
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def _convert_messages_to_converse(messages: list[Message]) -> list[dict[str, Any]]:
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"""
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转换消息格式 - 将消息转换为 Bedrock Converse API 所需的格式
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:param messages: 消息列表
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:return: 转换后的消息列表
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"""
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def _convert_message_item(message: Message) -> dict[str, Any]:
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"""
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转换单个消息格式
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:param message: 消息对象
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:return: 转换后的消息字典
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"""
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# Bedrock Converse API 格式
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content: list[dict[str, Any]] = []
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if isinstance(message.content, str):
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content.append({"text": message.content})
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elif isinstance(message.content, list):
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for item in message.content:
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if isinstance(item, tuple):
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# 图片格式：(format, base64_data)
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image_format = item[0].lower()
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image_bytes = base64.b64decode(item[1])
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content.append(
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{
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"image": {
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"format": image_format if image_format in ["png", "jpeg", "gif", "webp"] else "jpeg",
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"source": {"bytes": image_bytes},
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}
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}
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)
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elif isinstance(item, str):
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content.append({"text": item})
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else:
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raise RuntimeError("无法触及的代码：请使用MessageBuilder类构建消息对象")
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ret = {
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"role": "user" if message.role == RoleType.User else "assistant",
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"content": content,
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}
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return ret
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# Bedrock 不支持 system 和 tool 角色，需要过滤
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converted = []
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for msg in messages:
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if msg.role in [RoleType.User, RoleType.Assistant]:
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converted.append(_convert_message_item(msg))
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return converted
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def _convert_tool_options_to_bedrock(tool_options: list[ToolOption]) -> list[dict[str, Any]]:
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"""
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转换工具选项格式 - 将工具选项转换为 Bedrock Converse API 所需的格式
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:param tool_options: 工具选项列表
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:return: 转换后的工具选项列表
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"""
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def _convert_tool_param(tool_param: ToolParam) -> dict[str, Any]:
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"""转换单个工具参数"""
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param_dict: dict[str, Any] = {
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"type": tool_param.param_type.value,
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"description": tool_param.description,
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}
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if tool_param.enum_values:
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param_dict["enum"] = tool_param.enum_values
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return param_dict
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def _convert_tool_option_item(tool_option: ToolOption) -> dict[str, Any]:
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"""转换单个工具项"""
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tool_spec: dict[str, Any] = {
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"name": tool_option.name,
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"description": tool_option.description,
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}
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if tool_option.params:
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tool_spec["inputSchema"] = {
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"json": {
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"type": "object",
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"properties": {param.name: _convert_tool_param(param) for param in tool_option.params},
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"required": [param.name for param in tool_option.params if param.required],
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}
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}
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return {"toolSpec": tool_spec}
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return [_convert_tool_option_item(opt) for opt in tool_options]
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async def _default_stream_response_handler(
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resp_stream: Any,
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interrupt_flag: asyncio.Event | None,
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) -> tuple[APIResponse, tuple[int, int, int] | None]:
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"""
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流式响应处理函数 - 处理 Bedrock Converse Stream API 的响应
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:param resp_stream: 流式响应对象
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:param interrupt_flag: 中断标志
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:return: (APIResponse对象, usage元组)
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"""
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_fc_delta_buffer = io.StringIO() # 正式内容缓冲区
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_tool_calls_buffer: list[tuple[str, str, io.StringIO]] = [] # 工具调用缓冲区
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_usage_record = None
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def _insure_buffer_closed():
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if _fc_delta_buffer and not _fc_delta_buffer.closed:
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_fc_delta_buffer.close()
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for _, _, buffer in _tool_calls_buffer:
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if buffer and not buffer.closed:
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buffer.close()
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try:
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async for event in resp_stream["stream"]:
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if interrupt_flag and interrupt_flag.is_set():
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_insure_buffer_closed()
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raise ReqAbortException("请求被外部信号中断")
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# 处理内容块
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if "contentBlockDelta" in event:
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delta = event["contentBlockDelta"]["delta"]
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if "text" in delta:
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_fc_delta_buffer.write(delta["text"])
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elif "toolUse" in delta:
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# 工具调用
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tool_use = delta["toolUse"]
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if "input" in tool_use:
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# 追加工具调用参数
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if tool_use.get("toolUseId"):
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# 新的工具调用
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_tool_calls_buffer.append(
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(
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tool_use["toolUseId"],
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tool_use.get("name", ""),
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io.StringIO(json.dumps(tool_use["input"])),
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)
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)
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# 处理元数据（包含 usage）
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if "metadata" in event:
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metadata = event["metadata"]
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if "usage" in metadata:
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usage = metadata["usage"]
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_usage_record = (
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usage.get("inputTokens", 0),
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usage.get("outputTokens", 0),
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usage.get("totalTokens", 0),
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)
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# 构建响应
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resp = APIResponse()
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if _fc_delta_buffer.tell() > 0:
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resp.content = _fc_delta_buffer.getvalue()
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_fc_delta_buffer.close()
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if _tool_calls_buffer:
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resp.tool_calls = []
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for call_id, function_name, arguments_buffer in _tool_calls_buffer:
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if arguments_buffer.tell() > 0:
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raw_arg_data = arguments_buffer.getvalue()
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arguments_buffer.close()
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try:
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arguments = orjson.loads(repair_json(raw_arg_data))
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if not isinstance(arguments, dict):
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raise RespParseException(
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None,
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f"响应解析失败，工具调用参数无法解析为字典类型。原始响应：\n{raw_arg_data}",
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)
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except orjson.JSONDecodeError as e:
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raise RespParseException(
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None,
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f"响应解析失败，无法解析工具调用参数。原始响应：{raw_arg_data}",
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) from e
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else:
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arguments_buffer.close()
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arguments = None
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resp.tool_calls.append(ToolCall(call_id, function_name, args=arguments))
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return resp, _usage_record
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except Exception as e:
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_insure_buffer_closed()
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raise
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async def _default_async_response_parser(
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resp_data: dict[str, Any],
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) -> tuple[APIResponse, tuple[int, int, int] | None]:
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"""
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默认异步响应解析函数 - 解析 Bedrock Converse API 的响应
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:param resp_data: 响应数据
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:return: (APIResponse对象, usage元组)
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"""
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resp = APIResponse()
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# 解析输出内容
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if "output" in resp_data and "message" in resp_data["output"]:
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message = resp_data["output"]["message"]
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content_blocks = message.get("content", [])
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text_parts = []
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tool_calls = []
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for block in content_blocks:
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if "text" in block:
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text_parts.append(block["text"])
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elif "toolUse" in block:
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tool_use = block["toolUse"]
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tool_calls.append(
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ToolCall(
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call_id=tool_use.get("toolUseId", ""),
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func_name=tool_use.get("name", ""),
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args=tool_use.get("input", {}),
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||||
)
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)
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if text_parts:
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resp.content = "".join(text_parts)
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if tool_calls:
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resp.tool_calls = tool_calls
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# 解析 usage
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usage_record = None
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if "usage" in resp_data:
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usage = resp_data["usage"]
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usage_record = (
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||||
usage.get("inputTokens", 0),
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||||
usage.get("outputTokens", 0),
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||||
usage.get("totalTokens", 0),
|
||||
)
|
||||
|
||||
resp.raw_data = resp_data
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||||
return resp, usage_record
|
||||
|
||||
|
||||
@client_registry.register_client_class("bedrock")
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||||
class BedrockClient(BaseClient):
|
||||
"""AWS Bedrock 客户端"""
|
||||
|
||||
def __init__(self, api_provider: APIProvider):
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||||
super().__init__(api_provider)
|
||||
|
||||
# 从 extra_params 获取 AWS 配置
|
||||
# 支持两种认证方式：
|
||||
# 方式1（显式凭证）：api_key + extra_params.aws_secret_key
|
||||
# 方式2（IAM角色）：只配置 region，自动从环境/实例角色获取凭证
|
||||
region = api_provider.extra_params.get("region", "us-east-1")
|
||||
aws_secret_key = api_provider.extra_params.get("aws_secret_key")
|
||||
|
||||
# 配置 boto3
|
||||
self.region = region
|
||||
self.boto_config = Config(
|
||||
region_name=self.region,
|
||||
connect_timeout=api_provider.timeout,
|
||||
read_timeout=api_provider.timeout,
|
||||
retries={"max_attempts": api_provider.max_retry, "mode": "adaptive"},
|
||||
)
|
||||
|
||||
# 判断认证方式
|
||||
if aws_secret_key:
|
||||
# 方式1：显式 IAM 凭证
|
||||
self.aws_access_key_id = api_provider.get_api_key()
|
||||
self.aws_secret_access_key = aws_secret_key
|
||||
self.session = aioboto3.Session(
|
||||
aws_access_key_id=self.aws_access_key_id,
|
||||
aws_secret_access_key=self.aws_secret_access_key,
|
||||
region_name=self.region,
|
||||
)
|
||||
logger.info(f"初始化 Bedrock 客户端（IAM 凭证模式），区域: {self.region}")
|
||||
else:
|
||||
# 方式2：IAM 角色自动认证（从环境变量、EC2/ECS 实例角色获取）
|
||||
self.session = aioboto3.Session(region_name=self.region)
|
||||
logger.info(f"初始化 Bedrock 客户端（IAM 角色模式），区域: {self.region}")
|
||||
logger.info("将使用环境变量或实例角色自动获取 AWS 凭证")
|
||||
|
||||
async def get_response(
|
||||
self,
|
||||
model_info: ModelInfo,
|
||||
message_list: list[Message],
|
||||
tool_options: list[ToolOption] | None = None,
|
||||
max_tokens: int = 1024,
|
||||
temperature: float = 0.7,
|
||||
response_format: RespFormat | None = None,
|
||||
stream_response_handler: Callable[[Any, asyncio.Event | None], tuple[APIResponse, tuple[int, int, int]]]
|
||||
| None = None,
|
||||
async_response_parser: Callable[[Any], tuple[APIResponse, tuple[int, int, int]]] | None = None,
|
||||
interrupt_flag: asyncio.Event | None = None,
|
||||
extra_params: dict[str, Any] | None = None,
|
||||
) -> APIResponse:
|
||||
"""
|
||||
获取对话响应
|
||||
"""
|
||||
try:
|
||||
# 提取 system prompt
|
||||
system_prompts = []
|
||||
filtered_messages = []
|
||||
for msg in message_list:
|
||||
if msg.role == RoleType.System:
|
||||
if isinstance(msg.content, str):
|
||||
system_prompts.append({"text": msg.content})
|
||||
else:
|
||||
filtered_messages.append(msg)
|
||||
|
||||
# 转换消息格式
|
||||
messages = _convert_messages_to_converse(filtered_messages)
|
||||
|
||||
# 构建请求参数
|
||||
request_params: dict[str, Any] = {
|
||||
"modelId": model_info.model_identifier,
|
||||
"messages": messages,
|
||||
"inferenceConfig": {
|
||||
"maxTokens": max_tokens,
|
||||
"temperature": temperature,
|
||||
},
|
||||
}
|
||||
|
||||
# 添加 system prompt
|
||||
if system_prompts:
|
||||
request_params["system"] = system_prompts
|
||||
|
||||
# 添加工具配置
|
||||
if tool_options:
|
||||
request_params["toolConfig"] = {"tools": _convert_tool_options_to_bedrock(tool_options)}
|
||||
|
||||
# 合并额外参数
|
||||
if extra_params:
|
||||
request_params.update(extra_params)
|
||||
|
||||
# 合并模型配置的额外参数
|
||||
if model_info.extra_params:
|
||||
request_params.update(model_info.extra_params)
|
||||
|
||||
# 创建 Bedrock Runtime 客户端
|
||||
async with self.session.client("bedrock-runtime", config=self.boto_config) as bedrock_client:
|
||||
# 判断是否使用流式模式
|
||||
use_stream = model_info.force_stream_mode or stream_response_handler is not None
|
||||
|
||||
if use_stream:
|
||||
# 流式调用
|
||||
response = await bedrock_client.converse_stream(**request_params)
|
||||
if stream_response_handler:
|
||||
# 用户提供的处理器（可能是同步的）
|
||||
result = stream_response_handler(response, interrupt_flag)
|
||||
if asyncio.iscoroutine(result):
|
||||
api_resp, usage_tuple = await result
|
||||
else:
|
||||
api_resp, usage_tuple = result # type: ignore
|
||||
else:
|
||||
# 默认异步处理器
|
||||
api_resp, usage_tuple = await _default_stream_response_handler(response, interrupt_flag)
|
||||
else:
|
||||
# 非流式调用
|
||||
response = await bedrock_client.converse(**request_params)
|
||||
if async_response_parser:
|
||||
# 用户提供的解析器（可能是同步的）
|
||||
result = async_response_parser(response)
|
||||
if asyncio.iscoroutine(result):
|
||||
api_resp, usage_tuple = await result
|
||||
else:
|
||||
api_resp, usage_tuple = result # type: ignore
|
||||
else:
|
||||
# 默认异步解析器
|
||||
api_resp, usage_tuple = await _default_async_response_parser(response)
|
||||
|
||||
# 设置 usage
|
||||
if usage_tuple:
|
||||
api_resp.usage = UsageRecord(
|
||||
model_name=model_info.model_identifier,
|
||||
provider_name=self.api_provider.name,
|
||||
prompt_tokens=usage_tuple[0],
|
||||
completion_tokens=usage_tuple[1],
|
||||
total_tokens=usage_tuple[2],
|
||||
)
|
||||
|
||||
return api_resp
|
||||
|
||||
except Exception as e:
|
||||
error_type = type(e).__name__
|
||||
logger.error(f"Bedrock API 调用失败 ({error_type}): {e!s}")
|
||||
|
||||
# 处理特定错误类型
|
||||
if "ThrottlingException" in error_type or "ServiceQuota" in error_type:
|
||||
raise RespNotOkException(429, f"请求限流: {e!s}") from e
|
||||
elif "ValidationException" in error_type:
|
||||
raise RespParseException(400, f"请求参数错误: {e!s}") from e
|
||||
elif "AccessDeniedException" in error_type:
|
||||
raise RespNotOkException(403, f"访问被拒绝: {e!s}") from e
|
||||
elif "ResourceNotFoundException" in error_type:
|
||||
raise RespNotOkException(404, f"模型不存在: {e!s}") from e
|
||||
elif "timeout" in str(e).lower() or "timed out" in str(e).lower():
|
||||
logger.error(f"请求超时: {e!s}")
|
||||
raise NetworkConnectionError() from e
|
||||
else:
|
||||
logger.error(f"网络连接错误: {e!s}")
|
||||
raise NetworkConnectionError() from e
|
||||
|
||||
async def get_embedding(
|
||||
self,
|
||||
model_info: ModelInfo,
|
||||
embedding_input: str | list[str],
|
||||
extra_params: dict[str, Any] | None = None,
|
||||
) -> APIResponse:
|
||||
"""
|
||||
获取文本嵌入（Bedrock 支持 Titan Embeddings 等模型）
|
||||
"""
|
||||
try:
|
||||
async with self.session.client("bedrock-runtime", config=self.boto_config) as bedrock_client:
|
||||
# Bedrock Embeddings 使用 InvokeModel API
|
||||
is_batch = isinstance(embedding_input, list)
|
||||
input_text = embedding_input if is_batch else [embedding_input]
|
||||
|
||||
results = []
|
||||
total_tokens = 0
|
||||
|
||||
for text in input_text:
|
||||
# 构建请求体（Titan Embeddings 格式）
|
||||
body = json.dumps({"inputText": text})
|
||||
|
||||
response = await bedrock_client.invoke_model(
|
||||
modelId=model_info.model_identifier,
|
||||
contentType="application/json",
|
||||
accept="application/json",
|
||||
body=body,
|
||||
)
|
||||
|
||||
# 解析响应
|
||||
response_body = json.loads(await response["body"].read())
|
||||
embedding = response_body.get("embedding", [])
|
||||
results.append(embedding)
|
||||
|
||||
# 累计 token 使用
|
||||
if "inputTokenCount" in response_body:
|
||||
total_tokens += response_body["inputTokenCount"]
|
||||
|
||||
api_resp = APIResponse()
|
||||
api_resp.embedding = results if is_batch else results[0]
|
||||
api_resp.usage = UsageRecord(
|
||||
model_name=model_info.model_identifier,
|
||||
provider_name=self.api_provider.name,
|
||||
prompt_tokens=total_tokens,
|
||||
completion_tokens=0,
|
||||
total_tokens=total_tokens,
|
||||
)
|
||||
|
||||
return api_resp
|
||||
|
||||
except Exception as e:
|
||||
logger.error(f"Bedrock Embedding 调用失败: {e!s}")
|
||||
raise NetworkConnectionError() from e
|
||||
|
||||
async def get_audio_transcriptions(
|
||||
self,
|
||||
model_info: ModelInfo,
|
||||
audio_base64: str,
|
||||
extra_params: dict[str, Any] | None = None,
|
||||
) -> APIResponse:
|
||||
"""
|
||||
获取音频转录（Bedrock 暂不直接支持，抛出未实现异常）
|
||||
"""
|
||||
raise NotImplementedError("AWS Bedrock 暂不支持音频转录功能，建议使用 AWS Transcribe 服务")
|
||||
|
||||
def get_support_image_formats(self) -> list[str]:
|
||||
"""
|
||||
获取支持的图片格式
|
||||
:return: 支持的图片格式列表
|
||||
"""
|
||||
return ["png", "jpeg", "jpg", "gif", "webp"]
|
||||
Reference in New Issue
Block a user