zltest01
/
huoyan-enterprise
1
0
Fork 0
Code Issues Pull Requests Packages Projects Releases Wiki Activity

update

This commit is contained in:
zhangqing 2026-07-01 12:01:48 +08:00
parent dc2d64421d
commit 86abc18c6f
6 changed files with 41 additions and 26 deletions
Show Stats Download Patch File Download Diff File Expand all files Collapse all files

6
backend/api/chat_router.py
View File

@ -119,8 +119,6 @@ async def chat_completion(
f"knowledge_graph_id: {request.knowledge_graph_id}, "
f"llm_provider: {request.llm_provider}, llm_model: {request.llm_model}, ip={client_ip}"
)
request.llm_model = "qwen3.7-plus"
request.llm_provider = "tongyi"
# ============ 内容审核前置处理 ============
# 在 AI 处理前对用户消息进行内容审核
try:
@ -156,7 +154,6 @@ async def chat_completion(
f"labels: {[label.label for label in moderation_result.labels]}"
)
# 使用统一的错误响应格式（与图片审核一致）
from fastapi.responses import JSONResponse
return JSONResponse(
status_code=status.HTTP_400_BAD_REQUEST,
content={
@ -185,7 +182,6 @@ async def chat_completion(
f"request_id: {moderation_request_id}"
)
# 使用统一的错误响应格式（与图片审核一致）
from fastapi.responses import JSONResponse
return JSONResponse(
status_code=status.HTTP_400_BAD_REQUEST,
content={
@ -260,7 +256,7 @@ async def chat_completion(
enable_thinking=user_is_reasoner,
logical_llm_id=llm_model_key,
)
logger.debug(
logger.info(
"chat_completion 模型: provider={} req_llm_model={} api_model={} user_is_reasoner={}",
llm_provider,
llm_model_key,

7
backend/core/config.py
View File

@ -162,6 +162,13 @@ class Settings(BaseSettings):
embedding_model: str = "text-embedding-v4" # 通义千问 Embedding 模型
embedding_dimension: int = 1536 # Embedding 维度
# 辅助任务模型（意图判断、摘要生成等），默认与主聊天模型一致
# 可在 .env 中通过 UTILITY_LLM_MODEL 覆盖（如 qwen-plus-latest、qwen3-max）
utility_llm_model: str = Field(
default="qwen3-max",
validation_alias=AliasChoices("UTILITY_LLM_MODEL", "utility_llm_model"),
)
# Neo4j 图数据库配置
neo4j_uri: str = "bolt://127.0.0.1:7687"
neo4j_user: str = "neo4j"

2
backend/core/llm_catalog.py
View File

@ -248,7 +248,6 @@ def build_chat_model(
p = normalize_provider(provider)
if p == "tongyi":
api_key = (os.getenv("DASHSCOPE_API_KEY") or "").strip()
print("-----------------------------------api_key: ", api_key)
if not api_key:
raise ValueError("缺少 DASHSCOPE_API_KEY")
base_url = llm_env.tongyi_openai_compatible_base_url().strip().rstrip("/")
@ -284,7 +283,6 @@ def build_chat_model(
else:
raise ValueError(f"未知提供方: {provider}")
print("-----------------------------------走到这里了-----------------------------------",extra_kwargs)
return ChatOpenAI(
model=api_model,
api_key=api_key,

3
backend/services/rag_intent_service.py
View File

@ -7,6 +7,7 @@ from typing import List, Dict, Optional
from pydantic import BaseModel, Field
from langchain_core.prompts import PromptTemplate
from core.config import get_settings
from core.llm_catalog import build_chat_model
from logger.logging import get_logger
@ -121,7 +122,7 @@ class RagIntentService:
def __init__(self):
self.model = build_chat_model(
provider="tongyi",
api_model="qwen-plus-latest",
api_model=get_settings().utility_llm_model,
streaming=False,
temperature=0.1, # 降低温度，让判断更稳定
)

11
backend/services/summary_service.py
View File

@ -80,11 +80,12 @@ class SummaryService:
async with cls._lock:
if cls._llm_cache is None:
from core.llm_catalog import _DEFAULT_MODEL_BY_PROVIDER
cls._llm_cache = build_chat_model(
provider="tongyi",
api_model="qwen-plus-latest",
api_model=_DEFAULT_MODEL_BY_PROVIDER["tongyi"],
streaming=False,
temperature=0.3, # 适度提高灵活性，更好地总结全文
temperature=0.3,
)
return cls._llm_cache
@ -194,9 +195,10 @@ sheet_summary： 对所有sheet表的描述进行总结，不超过20字；
async with cls._lock:
if cls._llm_cache is None:
from core.llm_catalog import _DEFAULT_MODEL_BY_PROVIDER
cls._llm_cache = build_chat_model(
provider="tongyi",
api_model="qwen-plus-latest",
api_model=_DEFAULT_MODEL_BY_PROVIDER["tongyi"],
streaming=False,
temperature=0.7,
model_kwargs={"response_format": {"type": "json_object"}},
@ -283,9 +285,10 @@ csv_description: 对csv表格的内容进行描述，不超过20字；
async with cls._lock:
if cls._llm_cache is None:
from core.llm_catalog import _DEFAULT_MODEL_BY_PROVIDER
cls._llm_cache = build_chat_model(
provider="tongyi",
api_model="qwen-plus-latest",
api_model=_DEFAULT_MODEL_BY_PROVIDER["tongyi"],
streaming=False,
temperature=0.7,
model_kwargs={"response_format": {"type": "json_object"}},

38
backend/tools/tools.py
View File

@ -189,13 +189,18 @@ def text_to_image(
f"开始生成图片（OpenAI 兼容 images/generations），model={model_image}, n={n_req}, size={size_norm}"
)
response = client.images.generate(
model=model_image,
prompt=prompt,
size=size_norm,
n=n_req,
extra_body=extra_body,
)
try:
response = client.images.generate(
model=model_image,
prompt=prompt,
size=size_norm,
n=n_req,
extra_body=extra_body,
)
except Exception as api_err:
err_str = str(api_err)
logger.error(f"文生图 API 调用失败: {err_str}")
return f"图片生成失败：{err_str}"
image_urls: list[str] = []
for item in response.data or []:
@ -475,13 +480,18 @@ def text_to_poster(
"negative_prompt": negative,
}
response = client.images.generate(
model=model_image,
prompt=prompt,
size=size_norm,
n=1,
extra_body=extra_body,
)
try:
response = client.images.generate(
model=model_image,
prompt=prompt,
size=size_norm,
n=1,
extra_body=extra_body,
)
except Exception as api_err:
err_str = str(api_err)
logger.error(f"海报生成 API 调用失败: {err_str}")
return f"海报生成失败：{err_str}"
image_urls: list[str] = []
for item in response.data or []: