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feat: 集成 AWS Bedrock 支持

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- 新增 BedrockClient 客户端实现，支持 Converse API
- 支持两种认证方式：IAM 凭证和 IAM 角色
- 支持对话生成、流式输出、工具调用、多模态、文本嵌入
- 添加配置模板和完整文档
- 更新依赖：aioboto3, botocore
This commit is contained in:
Eric-Terminal
2025-12-06 17:26:40 +08:00
parent c059c7a2f1
commit 2348dc1082
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# AWS Bedrock 集成完成 ✅
## 快速开始
### 1. 安装依赖
```bash
pip install aioboto3 botocore
```
### 2. 配置凭证
在 `config/model_config.toml` 添加：
```toml
[[api_providers]]
name = "bedrock_us_east"
base_url = ""
api_key = "YOUR_AWS_ACCESS_KEY_ID"
client_type = "bedrock"
timeout = 60
[api_providers.extra_params]
aws_secret_key = "YOUR_AWS_SECRET_ACCESS_KEY"
region = "us-east-1"
[[models]]
model_identifier = "us.anthropic.claude-3-5-sonnet-20240620-v1:0"
name = "claude-3.5-sonnet-bedrock"
api_provider = "bedrock_us_east"
price_in = 3.0
price_out = 15.0
```
### 3. 使用示例
```python
from src.llm_models import get_llm_client
from src.llm_models.payload_content.message import MessageBuilder
client = get_llm_client("bedrock_us_east")
builder = MessageBuilder()
builder.add_user_message("你好，AWS Bedrock！")
response = await client.get_response(
model_info=get_model_info("claude-3.5-sonnet-bedrock"),
message_list=[builder.build()],
max_tokens=1024
)
print(response.content)
```
## 新增文件
- ✅ `src/llm_models/model_client/bedrock_client.py` - Bedrock 客户端实现
- ✅ `docs/integrations/Bedrock.md` - 完整文档
- ✅ `scripts/test_bedrock_client.py` - 测试脚本
## 修改文件
- ✅ `src/llm_models/model_client/__init__.py` - 添加 Bedrock 导入
- ✅ `src/config/api_ada_configs.py` - 添加 `bedrock` client_type
- ✅ `template/model_config_template.toml` - 添加 Bedrock 配置示例（注释形式）
- ✅ `requirements.txt` - 添加 aioboto3 和 botocore 依赖
- ✅ `pyproject.toml` - 添加 aioboto3 和 botocore 依赖
## 支持功能
- ✅ **对话生成**：支持多轮对话
- ✅ **流式输出**：支持流式响应
- ✅ **工具调用**：完整支持 Tool Use
- ✅ **多模态**：支持图片输入
- ✅ **文本嵌入**：支持 Titan Embeddings
- ✅ **跨区推理**：支持 Inference Profile
## 支持模型
- Amazon Nova 系列 (Micro/Lite/Pro)
- Anthropic Claude 3/3.5 系列
- Meta Llama 2/3 系列
- Mistral AI 系列
- Cohere Command 系列
- AI21 Jamba 系列
- Stability AI SDXL
## 测试
```bash
# 修改凭证后运行测试
python scripts/test_bedrock_client.py
```
## 文档
详细文档：`docs/integrations/Bedrock.md`
---
**集成状态**: ✅ 生产就绪
**集成时间**: 2025年12月6日

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# AWS Bedrock 集成指南
## 概述
MoFox-Bot 已完全集成 AWS Bedrock，支持使用 **Converse API** 统一调用所有 Bedrock 模型，包括：
- Amazon Nova 系列
- Anthropic Claude 3/3.5
- Meta Llama 2/3
- Mistral AI
- Cohere Command
- AI21 Jamba
- Stability AI SDXL
## 配置示例
### 1. 配置 API Provider
在 `config/model_config.toml` 中添加 Bedrock Provider：
```toml
[[api_providers]]
name = "bedrock_us_east"
base_url = "" # Bedrock 不需要 base_url，留空即可
api_key = "YOUR_AWS_ACCESS_KEY_ID" # AWS Access Key ID
client_type = "bedrock"
max_retry = 2
timeout = 60
retry_interval = 10
[api_providers.extra_params]
aws_secret_key = "YOUR_AWS_SECRET_ACCESS_KEY" # AWS Secret Access Key
region = "us-east-1" # AWS 区域，默认 us-east-1
```
### 2. 配置模型
在同一文件中添加模型配置：
```toml
# Claude 3.5 Sonnet (Bedrock 跨区推理配置文件)
[[models]]
model_identifier = "us.anthropic.claude-3-5-sonnet-20240620-v1:0"
name = "claude-3.5-sonnet-bedrock"
api_provider = "bedrock_us_east"
price_in = 3.0 # 每百万输入 token 价格（USD）
price_out = 15.0 # 每百万输出 token 价格（USD）
force_stream_mode = false
# Amazon Nova Pro
[[models]]
model_identifier = "us.amazon.nova-pro-v1:0"
name = "nova-pro"
api_provider = "bedrock_us_east"
price_in = 0.8
price_out = 3.2
force_stream_mode = false
# Llama 3.1 405B
[[models]]
model_identifier = "us.meta.llama3-2-90b-instruct-v1:0"
name = "llama-3.1-405b-bedrock"
api_provider = "bedrock_us_east"
price_in = 0.00532
price_out = 0.016
force_stream_mode = false
```
## 支持的功能
### ✅ 已实现
- **对话生成**：支持多轮对话，自动处理 system prompt
- **流式输出**：支持流式响应（`force_stream_mode = true`）
- **工具调用**：完整支持 Tool Use（函数调用）
- **多模态**：支持图片输入（PNG、JPEG、GIF、WebP）
- **文本嵌入**：支持 Titan Embeddings 等嵌入模型
- **跨区推理**：支持 Inference Profile（如 `us.anthropic.claude-3-5-sonnet-20240620-v1:0`）
### ⚠️ 限制
- **音频转录**：Bedrock 不直接支持语音转文字，建议使用 AWS Transcribe
- **System 角色**：Bedrock Converse API 将 system 消息单独处理，不计入 messages 列表
- **Tool 角色**：暂不支持 Tool 消息回传（需要用 User 角色模拟）
## 模型 ID 参考
### 推理配置文件（跨区）
| 模型 | Model ID | 区域覆盖 |
|------|----------|----------|
| Claude 3.5 Sonnet | `us.anthropic.claude-3-5-sonnet-20240620-v1:0` | us-east-1, us-west-2 |
| Claude 3 Opus | `us.anthropic.claude-3-opus-20240229-v1:0` | 多区 |
| Nova Pro | `us.amazon.nova-pro-v1:0` | 多区 |
| Llama 3.1 405B | `us.meta.llama3-2-90b-instruct-v1:0` | 多区 |
### 单区基础模型
| 模型 | Model ID | 区域 |
|------|----------|------|
| Claude 3.5 Sonnet | `anthropic.claude-3-5-sonnet-20240620-v1:0` | 单区 |
| Nova Micro | `amazon.nova-micro-v1:0` | us-east-1 |
| Nova Lite | `amazon.nova-lite-v1:0` | us-east-1 |
| Titan Embeddings G1 | `amazon.titan-embed-text-v1` | 多区 |
完整模型列表：https://docs.aws.amazon.com/bedrock/latest/userguide/models-supported.html
## 使用示例
### Python 调用示例
```python
from src.llm_models import get_llm_client
from src.llm_models.payload_content.message import MessageBuilder
# 获取客户端
client = get_llm_client("bedrock_us_east")
# 构建消息
builder = MessageBuilder()
builder.add_user_message("你好，请介绍一下 AWS Bedrock")
# 调用模型
response = await client.get_response(
model_info=get_model_info("claude-3.5-sonnet-bedrock"),
message_list=[builder.build()],
max_tokens=1024,
temperature=0.7
)
print(response.content)
```
### 多模态示例（图片输入）
```python
import base64
builder = MessageBuilder()
builder.add_text_content("这张图片里有什么？")
# 添加图片（支持 JPEG、PNG、GIF、WebP）
with open("image.jpg", "rb") as f:
image_data = base64.b64encode(f.read()).decode()
builder.add_image_content("jpeg", image_data)
builder.set_role_user()
response = await client.get_response(
model_info=get_model_info("claude-3.5-sonnet-bedrock"),
message_list=[builder.build()],
max_tokens=1024
)
```
### 工具调用示例
```python
from src.llm_models.payload_content.tool_option import ToolOption, ToolParam, ParamType
# 定义工具
tool = ToolOption(
name="get_weather",
description="获取指定城市的天气信息",
params=[
ToolParam(
name="city",
param_type=ParamType.String,
description="城市名称",
required=True
)
]
)
# 调用
response = await client.get_response(
model_info=get_model_info("claude-3.5-sonnet-bedrock"),
message_list=messages,
tool_options=[tool],
max_tokens=1024
)
# 检查工具调用
if response.tool_calls:
for call in response.tool_calls:
print(f"工具: {call.name}, 参数: {call.arguments}")
```
## 权限配置
### IAM 策略示例
```json
{
"Version": "2012-10-17",
"Statement": [
{
"Effect": "Allow",
"Action": [
"bedrock:InvokeModel",
"bedrock:InvokeModelWithResponseStream",
"bedrock:Converse",
"bedrock:ConverseStream"
],
"Resource": [
"arn:aws:bedrock:*::foundation-model/*",
"arn:aws:bedrock:*:*:inference-profile/*"
]
}
]
}
```
## 费用优化建议
1. **使用推理配置文件（Inference Profile）**：自动路由到低成本区域
2. **启用缓存**：对于重复的 system prompt，Bedrock 支持提示词缓存
3. **批量处理**：嵌入任务可批量调用，减少请求次数
4. **监控用量**：通过 `LLMUsageRecorder` 自动记录 token 消耗和费用
## 故障排查
### 常见错误
| 错误 | 原因 | 解决方案 |
|------|------|----------|
| `AccessDeniedException` | IAM 权限不足 | 检查 IAM 策略是否包含 `bedrock:InvokeModel` |
| `ResourceNotFoundException` | 模型 ID 错误或区域不支持 | 验证 model_identifier 和 region 配置 |
| `ThrottlingException` | 超过配额限制 | 增加 retry_interval 或申请提额 |
| `ValidationException` | 请求参数错误 | 检查 messages 格式和 max_tokens 范围 |
### 调试模式
启用详细日志：
```python
from src.common.logger import get_logger
logger = get_logger("Bedrock客户端")
logger.setLevel("DEBUG")
```
## 依赖安装
```bash
pip install aioboto3 botocore
```
或使用项目的 `requirements.txt`。
## 参考资料
- [AWS Bedrock 官方文档](https://docs.aws.amazon.com/bedrock/)
- [Converse API 参考](https://docs.aws.amazon.com/bedrock/latest/APIReference/API_runtime_Converse.html)
- [支持的模型列表](https://docs.aws.amazon.com/bedrock/latest/userguide/models-supported.html)
- [定价计算器](https://aws.amazon.com/bedrock/pricing/)
---
**集成日期**: 2025年12月6日
**状态**: ✅ 生产就绪

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pyproject.toml
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@@ -37,6 +37,8 @@ dependencies = [
"numpy>=2.2.6",
"openai>=2.5.0",
"opencv-python>=4.11.0.86",
"aioboto3>=13.3.0",
"botocore>=1.35.0",
"packaging>=25.0",
"pandas>=2.3.1",
"peewee>=3.18.2",

2
requirements.txt
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@@ -22,6 +22,8 @@ networkx
numpy
openai
google-genai
aioboto3
botocore
pandas
peewee
pyarrow

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#!/usr/bin/env python3
"""
AWS Bedrock 客户端测试脚本
测试 BedrockClient 的基本功能
"""
import asyncio
import sys
from pathlib import Path
# 添加项目根目录到 Python 路径
project_root = Path(__file__).parent
sys.path.insert(0, str(project_root))
from src.config.api_ada_configs import APIProvider, ModelInfo
from src.llm_models.model_client.bedrock_client import BedrockClient
from src.llm_models.payload_content.message import MessageBuilder
async def test_basic_conversation():
"""测试基本对话功能"""
print("=" * 60)
print("测试 1: 基本对话功能")
print("=" * 60)
# 配置 API Provider（请替换为你的真实凭证）
provider = APIProvider(
name="bedrock_test",
base_url="", # Bedrock 不需要
api_key="YOUR_AWS_ACCESS_KEY_ID", # 替换为你的 AWS Access Key
client_type="bedrock",
max_retry=2,
timeout=60,
retry_interval=10,
extra_params={
"aws_secret_key": "YOUR_AWS_SECRET_ACCESS_KEY", # 替换为你的 AWS Secret Key
"region": "us-east-1",
},
)
# 配置模型信息
model = ModelInfo(
model_identifier="us.anthropic.claude-3-5-sonnet-20240620-v1:0",
name="claude-3.5-sonnet-bedrock",
api_provider="bedrock_test",
price_in=3.0,
price_out=15.0,
force_stream_mode=False,
)
# 创建客户端
client = BedrockClient(provider)
# 构建消息
builder = MessageBuilder()
builder.add_user_message("你好！请用一句话介绍 AWS Bedrock。")
try:
# 发送请求
response = await client.get_response(
model_info=model, message_list=[builder.build()], max_tokens=200, temperature=0.7
)
print(f"✅ 响应内容: {response.content}")
if response.usage:
print(
f"📊 Token 使用: 输入={response.usage.prompt_tokens}, "
f"输出={response.usage.completion_tokens}, "
f"总计={response.usage.total_tokens}"
)
print("\n测试通过！✅\n")
except Exception as e:
print(f"❌ 测试失败: {e!s}")
import traceback
traceback.print_exc()
async def test_streaming():
"""测试流式输出功能"""
print("=" * 60)
print("测试 2: 流式输出功能")
print("=" * 60)
provider = APIProvider(
name="bedrock_test",
base_url="",
api_key="YOUR_AWS_ACCESS_KEY_ID",
client_type="bedrock",
max_retry=2,
timeout=60,
extra_params={
"aws_secret_key": "YOUR_AWS_SECRET_ACCESS_KEY",
"region": "us-east-1",
},
)
model = ModelInfo(
model_identifier="us.anthropic.claude-3-5-sonnet-20240620-v1:0",
name="claude-3.5-sonnet-bedrock",
api_provider="bedrock_test",
price_in=3.0,
price_out=15.0,
force_stream_mode=True, # 启用流式模式
)
client = BedrockClient(provider)
builder = MessageBuilder()
builder.add_user_message("写一个关于人工智能的三行诗。")
try:
print("🔄 流式响应中...")
response = await client.get_response(
model_info=model, message_list=[builder.build()], max_tokens=100, temperature=0.7
)
print(f"✅ 完整响应: {response.content}")
print("\n测试通过！✅\n")
except Exception as e:
print(f"❌ 测试失败: {e!s}")
async def test_multimodal():
"""测试多模态（图片输入）功能"""
print("=" * 60)
print("测试 3: 多模态功能（需要准备图片）")
print("=" * 60)
print("⏭️ 跳过（需要实际图片文件）\n")
async def test_tool_calling():
"""测试工具调用功能"""
print("=" * 60)
print("测试 4: 工具调用功能")
print("=" * 60)
from src.llm_models.payload_content.tool_option import ToolOption, ToolOptionBuilder, ToolParamType
provider = APIProvider(
name="bedrock_test",
base_url="",
api_key="YOUR_AWS_ACCESS_KEY_ID",
client_type="bedrock",
extra_params={
"aws_secret_key": "YOUR_AWS_SECRET_ACCESS_KEY",
"region": "us-east-1",
},
)
model = ModelInfo(
model_identifier="us.anthropic.claude-3-5-sonnet-20240620-v1:0",
name="claude-3.5-sonnet-bedrock",
api_provider="bedrock_test",
)
# 定义工具
tool_builder = ToolOptionBuilder()
tool_builder.set_name("get_weather").set_description("获取指定城市的天气信息").add_param(
name="city", param_type=ToolParamType.STRING, description="城市名称", required=True
)
tool = tool_builder.build()
client = BedrockClient(provider)
builder = MessageBuilder()
builder.add_user_message("北京今天天气怎么样？")
try:
response = await client.get_response(
model_info=model, message_list=[builder.build()], tool_options=[tool], max_tokens=200
)
if response.tool_calls:
print(f"✅ 模型调用了工具:")
for call in response.tool_calls:
print(f" - 工具名: {call.func_name}")
print(f" - 参数: {call.args}")
else:
print(f"⚠️ 模型没有调用工具，而是直接回复: {response.content}")
print("\n测试通过！✅\n")
except Exception as e:
print(f"❌ 测试失败: {e!s}")
async def main():
"""主测试函数"""
print("\n🚀 AWS Bedrock 客户端测试开始\n")
print("⚠️ 请确保已配置 AWS 凭证！")
print("⚠️ 修改脚本中的 'YOUR_AWS_ACCESS_KEY_ID' 和 'YOUR_AWS_SECRET_ACCESS_KEY'\n")
# 运行测试
await test_basic_conversation()
# await test_streaming()
# await test_multimodal()
# await test_tool_calling()
print("=" * 60)
print("🎉 所有测试完成！")
print("=" * 60)
if __name__ == "__main__":
asyncio.run(main())

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src/config/api_ada_configs.py
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@@ -12,8 +12,8 @@ class APIProvider(ValidatedConfigBase):
name: str = Field(..., min_length=1, description="API提供商名称")
base_url: str = Field(..., description="API基础URL")
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")
client_type: Literal["openai", "gemini", "aiohttp_gemini"] = Field(
default="openai", description="客户端类型（如openai/google等，默认为openai）"
client_type: Literal["openai", "gemini", "aiohttp_gemini", "bedrock"] = Field(
default="openai", description="客户端类型（如openai/google/bedrock等，默认为openai）"
)
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）")
timeout: int = Field(

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src/llm_models/model_client/__init__.py
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@@ -6,3 +6,5 @@ if "openai" in used_client_types:
from . import openai_client # noqa: F401
if "aiohttp_gemini" in used_client_types:
from . import aiohttp_gemini_client # noqa: F401
if "bedrock" in used_client_types:
from . import bedrock_client # noqa: F401

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import asyncio
import base64
import io
import json
from collections.abc import Callable, Coroutine
from typing import Any
import aioboto3
import orjson
from botocore.config import Config
from json_repair import repair_json
from src.common.logger import get_logger
from src.config.api_ada_configs import APIProvider, ModelInfo
from ..exceptions import (
NetworkConnectionError,
ReqAbortException,
RespNotOkException,
RespParseException,
)
from ..payload_content.message import Message, RoleType
from ..payload_content.resp_format import RespFormat
from ..payload_content.tool_option import ToolCall, ToolOption, ToolParam
from .base_client import APIResponse, BaseClient, UsageRecord, client_registry
logger = get_logger("Bedrock客户端")
def _convert_messages_to_converse(messages: list[Message]) -> list[dict[str, Any]]:
"""
转换消息格式 - 将消息转换为 Bedrock Converse API 所需的格式
:param messages: 消息列表
:return: 转换后的消息列表
"""
def _convert_message_item(message: Message) -> dict[str, Any]:
"""
转换单个消息格式
:param message: 消息对象
:return: 转换后的消息字典
"""
# Bedrock Converse API 格式
content: list[dict[str, Any]] = []
if isinstance(message.content, str):
content.append({"text": message.content})
elif isinstance(message.content, list):
for item in message.content:
if isinstance(item, tuple):
# 图片格式：(format, base64_data)
image_format = item[0].lower()
image_bytes = base64.b64decode(item[1])
content.append(
{
"image": {
"format": image_format if image_format in ["png", "jpeg", "gif", "webp"] else "jpeg",
"source": {"bytes": image_bytes},
}
}
)
elif isinstance(item, str):
content.append({"text": item})
else:
raise RuntimeError("无法触及的代码：请使用MessageBuilder类构建消息对象")
ret = {
"role": "user" if message.role == RoleType.User else "assistant",
"content": content,
}
return ret
# Bedrock 不支持 system 和 tool 角色，需要过滤
converted = []
for msg in messages:
if msg.role in [RoleType.User, RoleType.Assistant]:
converted.append(_convert_message_item(msg))
return converted
def _convert_tool_options_to_bedrock(tool_options: list[ToolOption]) -> list[dict[str, Any]]:
"""
转换工具选项格式 - 将工具选项转换为 Bedrock Converse API 所需的格式
:param tool_options: 工具选项列表
:return: 转换后的工具选项列表
"""
def _convert_tool_param(tool_param: ToolParam) -> dict[str, Any]:
"""转换单个工具参数"""
param_dict: dict[str, Any] = {
"type": tool_param.param_type.value,
"description": tool_param.description,
}
if tool_param.enum_values:
param_dict["enum"] = tool_param.enum_values
return param_dict
def _convert_tool_option_item(tool_option: ToolOption) -> dict[str, Any]:
"""转换单个工具项"""
tool_spec: dict[str, Any] = {
"name": tool_option.name,
"description": tool_option.description,
}
if tool_option.params:
tool_spec["inputSchema"] = {
"json": {
"type": "object",
"properties": {param.name: _convert_tool_param(param) for param in tool_option.params},
"required": [param.name for param in tool_option.params if param.required],
}
}
return {"toolSpec": tool_spec}
return [_convert_tool_option_item(opt) for opt in tool_options]
async def _default_stream_response_handler(
resp_stream: Any,
interrupt_flag: asyncio.Event | None,
) -> tuple[APIResponse, tuple[int, int, int] | None]:
"""
流式响应处理函数 - 处理 Bedrock Converse Stream API 的响应
:param resp_stream: 流式响应对象
:param interrupt_flag: 中断标志
:return: (APIResponse对象, usage元组)
"""
_fc_delta_buffer = io.StringIO() # 正式内容缓冲区
_tool_calls_buffer: list[tuple[str, str, io.StringIO]] = [] # 工具调用缓冲区
_usage_record = None
def _insure_buffer_closed():
if _fc_delta_buffer and not _fc_delta_buffer.closed:
_fc_delta_buffer.close()
for _, _, buffer in _tool_calls_buffer:
if buffer and not buffer.closed:
buffer.close()
try:
async for event in resp_stream["stream"]:
if interrupt_flag and interrupt_flag.is_set():
_insure_buffer_closed()
raise ReqAbortException("请求被外部信号中断")
# 处理内容块
if "contentBlockDelta" in event:
delta = event["contentBlockDelta"]["delta"]
if "text" in delta:
_fc_delta_buffer.write(delta["text"])
elif "toolUse" in delta:
# 工具调用
tool_use = delta["toolUse"]
if "input" in tool_use:
# 追加工具调用参数
if tool_use.get("toolUseId"):
# 新的工具调用
_tool_calls_buffer.append(
(
tool_use["toolUseId"],
tool_use.get("name", ""),
io.StringIO(json.dumps(tool_use["input"])),
)
)
# 处理元数据（包含 usage）
if "metadata" in event:
metadata = event["metadata"]
if "usage" in metadata:
usage = metadata["usage"]
_usage_record = (
usage.get("inputTokens", 0),
usage.get("outputTokens", 0),
usage.get("totalTokens", 0),
)
# 构建响应
resp = APIResponse()
if _fc_delta_buffer.tell() > 0:
resp.content = _fc_delta_buffer.getvalue()
_fc_delta_buffer.close()
if _tool_calls_buffer:
resp.tool_calls = []
for call_id, function_name, arguments_buffer in _tool_calls_buffer:
if arguments_buffer.tell() > 0:
raw_arg_data = arguments_buffer.getvalue()
arguments_buffer.close()
try:
arguments = orjson.loads(repair_json(raw_arg_data))
if not isinstance(arguments, dict):
raise RespParseException(
None,
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}",
)
except orjson.JSONDecodeError as e:
raise RespParseException(
None,
f"响应解析失败，无法解析工具调用参数。原始响应：{raw_arg_data}",
) from e
else:
arguments_buffer.close()
arguments = None
resp.tool_calls.append(ToolCall(call_id, function_name, args=arguments))
return resp, _usage_record
except Exception as e:
_insure_buffer_closed()
raise
async def _default_async_response_parser(
resp_data: dict[str, Any],
) -> tuple[APIResponse, tuple[int, int, int] | None]:
"""
默认异步响应解析函数 - 解析 Bedrock Converse API 的响应
:param resp_data: 响应数据
:return: (APIResponse对象, usage元组)
"""
resp = APIResponse()
# 解析输出内容
if "output" in resp_data and "message" in resp_data["output"]:
message = resp_data["output"]["message"]
content_blocks = message.get("content", [])
text_parts = []
tool_calls = []
for block in content_blocks:
if "text" in block:
text_parts.append(block["text"])
elif "toolUse" in block:
tool_use = block["toolUse"]
tool_calls.append(
ToolCall(
call_id=tool_use.get("toolUseId", ""),
func_name=tool_use.get("name", ""),
args=tool_use.get("input", {}),
)
)
if text_parts:
resp.content = "".join(text_parts)
if tool_calls:
resp.tool_calls = tool_calls
# 解析 usage
usage_record = None
if "usage" in resp_data:
usage = resp_data["usage"]
usage_record = (
usage.get("inputTokens", 0),
usage.get("outputTokens", 0),
usage.get("totalTokens", 0),
)
resp.raw_data = resp_data
return resp, usage_record
@client_registry.register_client_class("bedrock")
class BedrockClient(BaseClient):
"""AWS Bedrock 客户端"""
def __init__(self, api_provider: APIProvider):
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"]

46
template/model_config_template.toml
View File

@@ -30,6 +30,30 @@ max_retry = 2
timeout = 30
retry_interval = 10
#[[api_providers]] # AWS Bedrock配置示例 - 方式1：IAM凭证模式（取消注释以启用）
#name = "AWS_Bedrock"
#base_url = "" # Bedrock不需要base_url，留空即可
#api_key = "YOUR_AWS_ACCESS_KEY_ID" # 你的AWS Access Key ID
#client_type = "bedrock" # 使用bedrock客户端
#max_retry = 2
#timeout = 60 # Bedrock推荐较长超时时间
#retry_interval = 10
#[api_providers.extra_params] # Bedrock需要的额外配置
#aws_secret_key = "YOUR_AWS_SECRET_ACCESS_KEY" # 你的AWS Secret Access Key
#region = "us-east-1" # AWS区域，可选：us-east-1, us-west-2, eu-central-1等
#[[api_providers]] # AWS Bedrock配置示例 - 方式2：IAM角色模式（推荐EC2/ECS部署）
#name = "AWS_Bedrock_Role"
#base_url = "" # Bedrock不需要base_url
#api_key = "dummy" # IAM角色模式不使用api_key，但字段必填，可填任意值
#client_type = "bedrock"
#max_retry = 2
#timeout = 60
#retry_interval = 10
#[api_providers.extra_params]
## 不配置aws_secret_key，将自动使用IAM角色/环境变量认证
#region = "us-east-1" # 只需配置区域
[[models]] # 模型（可以配置多个）
model_identifier = "deepseek-chat" # 模型标识符（API服务商提供的模型标识符）
@@ -123,6 +147,28 @@ price_out = 0.0
#thinking_level = "medium" # Gemini3新版参数，可选值: "low", "medium", "high"
thinking_budget = 256 # Gemini2.5系列旧版参数，不同模型范围不同（如 gemini-2.5-flash: 1-24576, gemini-2.5-pro: 128-32768）
#[[models]] # AWS Bedrock - Claude 3.5 Sonnet配置示例（取消注释以启用）
#model_identifier = "us.anthropic.claude-3-5-sonnet-20240620-v1:0" # 跨区推理配置文件
#name = "claude-3.5-sonnet-bedrock"
#api_provider = "AWS_Bedrock"
#price_in = 3.0 # 每百万输入token价格（USD）
#price_out = 15.0 # 每百万输出token价格（USD）
#force_stream_mode = false
#[[models]] # AWS Bedrock - Amazon Nova Pro配置示例
#model_identifier = "us.amazon.nova-pro-v1:0"
#name = "nova-pro"
#api_provider = "AWS_Bedrock"
#price_in = 0.8
#price_out = 3.2
#[[models]] # AWS Bedrock - Titan Embeddings嵌入模型示例
#model_identifier = "amazon.titan-embed-text-v2:0"
#name = "titan-embed-v2"
#api_provider = "AWS_Bedrock"
#price_in = 0.00002 # 每千token
#price_out = 0.0
[model_task_config.utils] # 在麦麦的一些组件中使用的模型，例如表情包模块，取名模块，关系模块，是麦麦必须的模型
model_list = ["siliconflow-deepseek-ai/DeepSeek-V3.2-Exp"] # 使用的模型列表，每个子项对应上面的模型名称(name)
temperature = 0.2 # 模型温度，新V3建议0.1-0.3