Soulter
286 lines
9.7 KiB
Python
286 lines
9.7 KiB
Python
import abc
|
|
import asyncio
|
|
from typing import List
|
|
from typing import AsyncGenerator
|
|
from astrbot.core.agent.tool import ToolSet
|
|
from astrbot.core.provider.entities import (
|
|
LLMResponse,
|
|
ToolCallsResult,
|
|
ProviderType,
|
|
RerankResult,
|
|
)
|
|
from astrbot.core.provider.register import provider_cls_map
|
|
from astrbot.core.db.po import Personality
|
|
from dataclasses import dataclass
|
|
|
|
|
|
@dataclass
|
|
class ProviderMeta:
|
|
id: str
|
|
model: str
|
|
type: str
|
|
provider_type: ProviderType
|
|
|
|
|
|
class AbstractProvider(abc.ABC):
|
|
def __init__(self, provider_config: dict) -> None:
|
|
super().__init__()
|
|
self.model_name = ""
|
|
self.provider_config = provider_config
|
|
|
|
def set_model(self, model_name: str):
|
|
"""设置当前使用的模型名称"""
|
|
self.model_name = model_name
|
|
|
|
def get_model(self) -> str:
|
|
"""获得当前使用的模型名称"""
|
|
return self.model_name
|
|
|
|
def meta(self) -> ProviderMeta:
|
|
"""获取 Provider 的元数据"""
|
|
provider_type_name = self.provider_config["type"]
|
|
meta_data = provider_cls_map.get(provider_type_name)
|
|
provider_type = meta_data.provider_type if meta_data else None
|
|
return ProviderMeta(
|
|
id=self.provider_config["id"],
|
|
model=self.get_model(),
|
|
type=provider_type_name,
|
|
provider_type=provider_type,
|
|
)
|
|
|
|
|
|
class Provider(AbstractProvider):
|
|
def __init__(
|
|
self,
|
|
provider_config: dict,
|
|
provider_settings: dict,
|
|
default_persona: Personality | None = None,
|
|
) -> None:
|
|
super().__init__(provider_config)
|
|
|
|
self.provider_settings = provider_settings
|
|
|
|
self.curr_personality = default_persona
|
|
"""维护了当前的使用的 persona，即人格。可能为 None"""
|
|
|
|
@abc.abstractmethod
|
|
def get_current_key(self) -> str:
|
|
raise NotImplementedError()
|
|
|
|
def get_keys(self) -> List[str]:
|
|
"""获得提供商 Key"""
|
|
keys = self.provider_config.get("key", [""])
|
|
return keys or [""]
|
|
|
|
@abc.abstractmethod
|
|
def set_key(self, key: str):
|
|
raise NotImplementedError()
|
|
|
|
@abc.abstractmethod
|
|
async def get_models(self) -> List[str]:
|
|
"""获得支持的模型列表"""
|
|
raise NotImplementedError()
|
|
|
|
@abc.abstractmethod
|
|
async def text_chat(
|
|
self,
|
|
prompt: str,
|
|
session_id: str = None,
|
|
image_urls: list[str] = None,
|
|
func_tool: ToolSet = None,
|
|
contexts: list = None,
|
|
system_prompt: str = None,
|
|
tool_calls_result: ToolCallsResult | list[ToolCallsResult] = None,
|
|
model: str | None = None,
|
|
**kwargs,
|
|
) -> LLMResponse:
|
|
"""获得 LLM 的文本对话结果。会使用当前的模型进行对话。
|
|
|
|
Args:
|
|
prompt: 提示词
|
|
session_id: 会话 ID(此属性已经被废弃)
|
|
image_urls: 图片 URL 列表
|
|
tools: Function-calling 工具
|
|
contexts: 上下文
|
|
tool_calls_result: 回传给 LLM 的工具调用结果。参考: https://platform.openai.com/docs/guides/function-calling
|
|
kwargs: 其他参数
|
|
|
|
Notes:
|
|
- 如果传入了 image_urls，将会在对话时附上图片。如果模型不支持图片输入，将会抛出错误。
|
|
- 如果传入了 tools，将会使用 tools 进行 Function-calling。如果模型不支持 Function-calling，将会抛出错误。
|
|
"""
|
|
...
|
|
|
|
async def text_chat_stream(
|
|
self,
|
|
prompt: str,
|
|
session_id: str = None,
|
|
image_urls: list[str] = None,
|
|
func_tool: ToolSet = None,
|
|
contexts: list = None,
|
|
system_prompt: str = None,
|
|
tool_calls_result: ToolCallsResult | list[ToolCallsResult] = None,
|
|
model: str | None = None,
|
|
**kwargs,
|
|
) -> AsyncGenerator[LLMResponse, None]:
|
|
"""获得 LLM 的流式文本对话结果。会使用当前的模型进行对话。在生成的最后会返回一次完整的结果。
|
|
|
|
Args:
|
|
prompt: 提示词
|
|
session_id: 会话 ID(此属性已经被废弃)
|
|
image_urls: 图片 URL 列表
|
|
tools: Function-calling 工具
|
|
contexts: 上下文
|
|
tool_calls_result: 回传给 LLM 的工具调用结果。参考: https://platform.openai.com/docs/guides/function-calling
|
|
kwargs: 其他参数
|
|
|
|
Notes:
|
|
- 如果传入了 image_urls，将会在对话时附上图片。如果模型不支持图片输入，将会抛出错误。
|
|
- 如果传入了 tools，将会使用 tools 进行 Function-calling。如果模型不支持 Function-calling，将会抛出错误。
|
|
"""
|
|
...
|
|
|
|
async def pop_record(self, context: List):
|
|
"""
|
|
弹出 context 第一条非系统提示词对话记录
|
|
"""
|
|
poped = 0
|
|
indexs_to_pop = []
|
|
for idx, record in enumerate(context):
|
|
if record["role"] == "system":
|
|
continue
|
|
else:
|
|
indexs_to_pop.append(idx)
|
|
poped += 1
|
|
if poped == 2:
|
|
break
|
|
|
|
for idx in reversed(indexs_to_pop):
|
|
context.pop(idx)
|
|
|
|
|
|
class STTProvider(AbstractProvider):
|
|
def __init__(self, provider_config: dict, provider_settings: dict) -> None:
|
|
super().__init__(provider_config)
|
|
self.provider_config = provider_config
|
|
self.provider_settings = provider_settings
|
|
|
|
@abc.abstractmethod
|
|
async def get_text(self, audio_url: str) -> str:
|
|
"""获取音频的文本"""
|
|
raise NotImplementedError()
|
|
|
|
|
|
class TTSProvider(AbstractProvider):
|
|
def __init__(self, provider_config: dict, provider_settings: dict) -> None:
|
|
super().__init__(provider_config)
|
|
self.provider_config = provider_config
|
|
self.provider_settings = provider_settings
|
|
|
|
@abc.abstractmethod
|
|
async def get_audio(self, text: str) -> str:
|
|
"""获取文本的音频，返回音频文件路径"""
|
|
raise NotImplementedError()
|
|
|
|
|
|
class EmbeddingProvider(AbstractProvider):
|
|
def __init__(self, provider_config: dict, provider_settings: dict) -> None:
|
|
super().__init__(provider_config)
|
|
self.provider_config = provider_config
|
|
self.provider_settings = provider_settings
|
|
|
|
@abc.abstractmethod
|
|
async def get_embedding(self, text: str) -> list[float]:
|
|
"""获取文本的向量"""
|
|
...
|
|
|
|
@abc.abstractmethod
|
|
async def get_embeddings(self, text: list[str]) -> list[list[float]]:
|
|
"""批量获取文本的向量"""
|
|
...
|
|
|
|
@abc.abstractmethod
|
|
def get_dim(self) -> int:
|
|
"""获取向量的维度"""
|
|
...
|
|
|
|
async def get_embeddings_batch(
|
|
self,
|
|
texts: list[str],
|
|
batch_size: int = 16,
|
|
tasks_limit: int = 3,
|
|
max_retries: int = 3,
|
|
progress_callback=None,
|
|
) -> list[list[float]]:
|
|
"""批量获取文本的向量，分批处理以节省内存
|
|
|
|
Args:
|
|
texts: 文本列表
|
|
batch_size: 每批处理的文本数量
|
|
tasks_limit: 并发任务数量限制
|
|
max_retries: 失败时的最大重试次数
|
|
progress_callback: 进度回调函数，接收参数 (current, total)
|
|
|
|
Returns:
|
|
向量列表
|
|
"""
|
|
semaphore = asyncio.Semaphore(tasks_limit)
|
|
all_embeddings: list[list[float]] = []
|
|
failed_batches: list[tuple[int, list[str]]] = []
|
|
completed_count = 0
|
|
total_count = len(texts)
|
|
|
|
async def process_batch(batch_idx: int, batch_texts: list[str]):
|
|
nonlocal completed_count
|
|
async with semaphore:
|
|
for attempt in range(max_retries):
|
|
try:
|
|
batch_embeddings = await self.get_embeddings(batch_texts)
|
|
all_embeddings.extend(batch_embeddings)
|
|
completed_count += len(batch_texts)
|
|
if progress_callback:
|
|
await progress_callback(completed_count, total_count)
|
|
return
|
|
except Exception as e:
|
|
if attempt == max_retries - 1:
|
|
# 最后一次重试失败，记录失败的批次
|
|
failed_batches.append((batch_idx, batch_texts))
|
|
raise Exception(
|
|
f"批次 {batch_idx} 处理失败，已重试 {max_retries} 次: {str(e)}"
|
|
)
|
|
# 等待一段时间后重试，使用指数退避
|
|
await asyncio.sleep(2**attempt)
|
|
|
|
tasks = []
|
|
for i in range(0, len(texts), batch_size):
|
|
batch_texts = texts[i : i + batch_size]
|
|
batch_idx = i // batch_size
|
|
tasks.append(process_batch(batch_idx, batch_texts))
|
|
|
|
# 收集所有任务的结果，包括失败的任务
|
|
results = await asyncio.gather(*tasks, return_exceptions=True)
|
|
|
|
# 检查是否有失败的任务
|
|
errors = [r for r in results if isinstance(r, Exception)]
|
|
if errors:
|
|
error_msg = (
|
|
f"有 {len(errors)} 个批次处理失败: {'; '.join(str(e) for e in errors)}"
|
|
)
|
|
raise Exception(error_msg)
|
|
|
|
return all_embeddings
|
|
|
|
|
|
class RerankProvider(AbstractProvider):
|
|
def __init__(self, provider_config: dict, provider_settings: dict) -> None:
|
|
super().__init__(provider_config)
|
|
self.provider_config = provider_config
|
|
self.provider_settings = provider_settings
|
|
|
|
@abc.abstractmethod
|
|
async def rerank(
|
|
self, query: str, documents: list[str], top_n: int | None = None
|
|
) -> list[RerankResult]:
|
|
"""获取查询和文档的重排序分数"""
|
|
...
|