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feat(llm): 添加响应格式转换功能，支持JSON_SCHEMA输出

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DrSmoothl
2026-03-04 21:11:10 +08:00
parent 81bc25dba8
commit 5cccdf6715
2 changed files with 131 additions and 5 deletions
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29
src/llm_models/model_client/openai_client.py
View File

@@ -33,12 +33,34 @@ from ..exceptions import (
EmptyResponseException,
)
from ..payload_content.message import Message, RoleType
from ..payload_content.resp_format import RespFormat
from ..payload_content.resp_format import RespFormat, RespFormatType
from ..payload_content.tool_option import ToolOption, ToolParam, ToolCall
logger = get_logger("llm_models")
def _convert_response_format(response_format: RespFormat | None) -> Any:
"""
转换响应格式 - 将内部RespFormat转换为OpenAI API所需格式
"""
if response_format is None:
return NOT_GIVEN
if response_format.format_type == RespFormatType.TEXT:
return NOT_GIVEN
if response_format.format_type == RespFormatType.JSON_OBJ:
return {"type": "json_object"}
if response_format.format_type == RespFormatType.JSON_SCHEMA:
return {
"type": "json_schema",
"json_schema": response_format.schema,
}
return NOT_GIVEN
def _convert_messages(messages: list[Message]) -> list[ChatCompletionMessageParam]:
"""
转换消息格式 - 将消息转换为OpenAI API所需的格式
@@ -553,6 +575,7 @@ class OpenaiClient(BaseClient):
messages: Iterable[ChatCompletionMessageParam] = _convert_messages(message_list)
# 将tool_options转换为OpenAI API所需的格式
tools: Iterable[ChatCompletionToolParam] = _convert_tool_options(tool_options) if tool_options else NOT_GIVEN # type: ignore
openai_response_format = _convert_response_format(response_format)
try:
if model_info.force_stream_mode:
@@ -564,7 +587,7 @@ class OpenaiClient(BaseClient):
temperature=temperature,
max_tokens=max_tokens,
stream=True,
response_format=NOT_GIVEN,
response_format=openai_response_format,
extra_body=extra_params,
)
)
@@ -587,7 +610,7 @@ class OpenaiClient(BaseClient):
temperature=temperature,
max_tokens=max_tokens,
stream=False,
response_format=NOT_GIVEN,
response_format=openai_response_format,
extra_body=extra_params,
)
)

107
src/llm_models/utils_model.py
View File

@@ -2,6 +2,7 @@ import re
import asyncio
import time
import random
import json
from enum import Enum
from rich.traceback import install
@@ -12,7 +13,7 @@ from src.common.logger import get_logger
from src.config.config import config_manager
from src.config.model_configs import APIProvider, ModelInfo, TaskConfig
from .payload_content.message import MessageBuilder, Message
from .payload_content.resp_format import RespFormat
from .payload_content.resp_format import RespFormat, RespFormatType
from .payload_content.tool_option import ToolOption, ToolCall, ToolOptionBuilder, ToolParamType
from .model_client.base_client import BaseClient, APIResponse, client_registry
from .utils import compress_messages, llm_usage_recorder
@@ -170,6 +171,7 @@ class LLMRequest:
temperature: Optional[float] = None,
max_tokens: Optional[int] = None,
tools: Optional[List[Dict[str, Any]]] = None,
response_format: RespFormat | None = None,
raise_when_empty: bool = True,
) -> Tuple[str, Tuple[str, str, Optional[List[ToolCall]]]]:
"""
@@ -179,6 +181,7 @@ class LLMRequest:
temperature (float, optional): 温度参数
max_tokens (int, optional): 最大token数
tools (Optional[List[Dict[str, Any]]]): 工具列表
response_format (RespFormat | None): 响应格式
raise_when_empty (bool): 当响应为空时是否抛出异常
Returns:
(Tuple[str, str, str, Optional[List[ToolCall]]]): 响应内容、推理内容、模型名称、工具调用列表
@@ -199,6 +202,7 @@ class LLMRequest:
temperature=temperature,
max_tokens=max_tokens,
tool_options=tool_built,
response_format=response_format,
)
logger.debug(f"LLM请求总耗时: {time.time() - start_time}")
@@ -227,6 +231,7 @@ class LLMRequest:
temperature: Optional[float] = None,
max_tokens: Optional[int] = None,
tools: Optional[List[Dict[str, Any]]] = None,
response_format: RespFormat | None = None,
raise_when_empty: bool = True,
) -> Tuple[str, Tuple[str, str, Optional[List[ToolCall]]]]:
"""
@@ -236,6 +241,7 @@ class LLMRequest:
temperature (float, optional): 温度参数
max_tokens (int, optional): 最大token数
tools (Optional[List[Dict[str, Any]]]): 工具列表
response_format (RespFormat | None): 响应格式
raise_when_empty (bool): 当响应为空时是否抛出异常
Returns:
(Tuple[str, str, str, Optional[List[ToolCall]]]): 响应内容、推理内容、模型名称、工具调用列表
@@ -251,6 +257,7 @@ class LLMRequest:
temperature=temperature,
max_tokens=max_tokens,
tool_options=tool_built,
response_format=response_format,
)
time_cost = time.time() - start_time
@@ -275,6 +282,100 @@ class LLMRequest:
)
return content or "", (reasoning_content, model_info.name, tool_calls)
async def generate_structured_response_async(
self,
prompt: str,
schema: type | dict[str, Any],
fallback_result: dict[str, Any] | None = None,
temperature: Optional[float] = 0.0,
max_tokens: Optional[int] = None,
) -> Tuple[dict[str, Any], Tuple[str, str, Optional[List[ToolCall]]], bool]:
"""
结构化输出快速接口：
- 默认启用 JSON_SCHEMA 严格模式
- 单模型单次尝试（不重试、不切换模型）
- 失败时立即返回 fallback_result
Returns:
(结构化结果, (推理内容, 模型名, 工具调用), 是否成功)
"""
self._refresh_task_config()
start_time = time.time()
message_builder = MessageBuilder()
message_builder.add_text_content(prompt)
message_list = [message_builder.build()]
response_format = RespFormat(schema=schema, format_type=RespFormatType.JSON_SCHEMA)
if response_format.schema:
response_format.schema["strict"] = True
model_info, api_provider, client = self._select_model()
fallback_data = fallback_result or {}
try:
response = await self._attempt_request_on_model(
model_info=model_info,
api_provider=api_provider,
client=client,
request_type=RequestType.RESPONSE,
message_list=message_list,
tool_options=None,
response_format=response_format,
stream_response_handler=None,
async_response_parser=None,
temperature=temperature,
max_tokens=max_tokens,
embedding_input=None,
audio_base64=None,
retry_limit=1,
)
time_cost = time.time() - start_time
self._check_slow_request(time_cost, model_info.name)
reasoning_content = response.reasoning_content or ""
tool_calls = response.tool_calls
parsed_result: dict[str, Any] | None = None
if response.content:
try:
parsed = json.loads(response.content)
if isinstance(parsed, dict):
parsed_result = parsed
except json.JSONDecodeError:
parsed_result = None
if parsed_result is None:
logger.warning(f"结构化输出解析失败，使用降级结果。模型: {model_info.name}")
total_tokens, penalty, usage_penalty = self.model_usage[model_info.name]
self.model_usage[model_info.name] = (total_tokens, penalty + 1, max(usage_penalty - 1, 0))
return fallback_data, (reasoning_content, model_info.name, tool_calls), False
total_tokens, penalty, usage_penalty = self.model_usage[model_info.name]
if response_usage := response.usage:
total_tokens += response_usage.total_tokens
llm_usage_recorder.record_usage_to_database(
model_info=model_info,
model_usage=response_usage,
user_id="system",
request_type=self.request_type,
endpoint="/chat/completions",
time_cost=time_cost,
)
self.model_usage[model_info.name] = (total_tokens, penalty, max(usage_penalty - 1, 0))
return parsed_result, (reasoning_content, model_info.name, tool_calls), True
except Exception as e:
time_cost = time.time() - start_time
self._check_slow_request(time_cost, model_info.name)
logger.warning(f"结构化输出请求失败，直接降级。模型: {model_info.name}, 错误: {e}")
total_tokens, penalty, usage_penalty = self.model_usage[model_info.name]
self.model_usage[model_info.name] = (total_tokens, penalty + 1, max(usage_penalty - 1, 0))
return fallback_data, ("", model_info.name, None), False
async def get_embedding(self, embedding_input: str) -> Tuple[List[float], str]:
"""
获取嵌入向量
@@ -359,12 +460,14 @@ class LLMRequest:
max_tokens: Optional[int],
embedding_input: str | None,
audio_base64: str | None,
retry_limit: Optional[int] = None,
) -> APIResponse:
"""
在单个模型上执行请求，包含针对临时错误的重试逻辑。
如果成功，返回APIResponse。如果失败（重试耗尽或硬错误），则抛出ModelAttemptFailed异常。
"""
retry_remain = api_provider.max_retry
retry_remain = retry_limit if retry_limit is not None else api_provider.max_retry
retry_remain = max(1, retry_remain)
compressed_messages: Optional[List[Message]] = None
while retry_remain > 0: