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Merge branch 'refs/heads/develop' into feature/20251219_xjn

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谢俊男
2026-01-04 15:35:57 +08:00
parent 0475d80472 aca5be2d1b
commit b731389c81
13 changed files with 200 additions and 138 deletions
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100
api/app/controllers/knowledge_controller.py
View File

@@ -1,26 +1,28 @@
from typing import Optional
import datetime
import json
from typing import Optional
import uuid
from fastapi import APIRouter, Depends, HTTPException, status, Query
from sqlalchemy import or_
from sqlalchemy.orm import Session
from app.celery_app import celery_app
from app.core.logging_config import get_api_logger
from app.core.rag.common import settings
from app.core.rag.llm.chat_model import Base
from app.core.rag.nlp import rag_tokenizer, search
from app.core.rag.prompts.generator import graph_entity_types
from app.core.rag.vdb.elasticsearch.elasticsearch_vector import ElasticSearchVectorFactory
from app.core.response_utils import success
from app.db import get_db
from app.dependencies import get_current_user
from app.models.user_model import User
from app.models import knowledge_model, document_model, file_model
from app.schemas import knowledge_schema
from app.schemas.response_schema import ApiResponse
from app.core.response_utils import success
from app.services import knowledge_service, document_service
from app.core.rag.llm.chat_model import Base
from app.core.rag.prompts.generator import graph_entity_types
from app.core.rag.vdb.elasticsearch.elasticsearch_vector import ElasticSearchVectorFactory
from app.core.logging_config import get_api_logger
from app.core.rag.nlp import rag_tokenizer, search
from app.core.rag.common import settings
from app.celery_app import celery_app
from app.services.model_service import ModelConfigService
# Obtain a dedicated API logger
api_logger = get_api_logger()
@@ -47,6 +49,45 @@ def get_parser_types():
return success(msg="Successfully obtained the knowledge parser type", data=list(knowledge_model.ParserType))
@router.get("/knowledge_graph_entity_types", response_model=ApiResponse)
async def get_knowledge_graph_entity_types(
llm_id: uuid.UUID,
scenario: str,
db: Session = Depends(get_db),
current_user: User = Depends(get_current_user)
):
"""
get knowledge graph entity types based on llm_id
"""
api_logger.info(f"Obtain details of the knowledge graph: llm_id={llm_id}, username: {current_user.username}")
try:
# 1. Check whether the model exists
api_logger.debug(f"Check whether the model exists: {llm_id}")
config = ModelConfigService.get_model_by_id(db=db, model_id=llm_id)
if not config:
api_logger.warning(
f"The model does not exist or you do not have permission to access it: llm_id={llm_id}")
raise HTTPException(
status_code=status.HTTP_404_NOT_FOUND,
detail="The model does not exist or you do not have permission to access it"
)
# 2. Prepare to configure chat_mdl information
chat_model = Base(
key=config.api_keys[0].api_key,
model_name=config.api_keys[0].model_name,
base_url=config.api_keys[0].api_base
)
response = graph_entity_types(chat_model, scenario)
return success(data=response, msg="Successfully obtained knowledge graph entity types")
except HTTPException:
raise
except Exception as e:
api_logger.error(f"get knowledge graph entity types failed: llm_id={llm_id} - {str(e)}")
raise
@router.get("/knowledges", response_model=ApiResponse)
async def get_knowledges(
parent_id: Optional[uuid.UUID] = Query(None, description="parent folder id"),
@@ -379,7 +420,7 @@ async def delete_knowledge_graph(
current_user: User = Depends(get_current_user)
):
"""
Soft-delete knowledge graph
delete knowledge graph
"""
api_logger.info(f"Request to delete knowledge graph: knowledge_id={knowledge_id}, username: {current_user.username}")
@@ -442,42 +483,3 @@ async def rebuild_knowledge_graph(
except Exception as e:
api_logger.error(f"Failed to rebuild knowledge graph: knowledge_id={knowledge_id} - {str(e)}")
raise
@router.get("/{knowledge_id}/knowledge_graph_entity_types", response_model=ApiResponse)
async def get_knowledge_graph_entity_types(
knowledge_id: uuid.UUID,
scenario: str,
db: Session = Depends(get_db),
current_user: User = Depends(get_current_user)
):
"""
get knowledge graph entity types based on knowledge_id
"""
api_logger.info(f"Obtain details of the knowledge graph: knowledge_id={knowledge_id}, username: {current_user.username}")
try:
# 1. Check whether the knowledge base exists
api_logger.debug(f"Check whether the knowledge base exists: {knowledge_id}")
db_knowledge = knowledge_service.get_knowledge_by_id(db, knowledge_id=knowledge_id, current_user=current_user)
if not db_knowledge:
api_logger.warning(
f"The knowledge base does not exist or you do not have permission to access it: knowledge_id={knowledge_id}")
raise HTTPException(
status_code=status.HTTP_404_NOT_FOUND,
detail="The knowledge base does not exist or you do not have permission to access it"
)
# 2. Prepare to configure chat_mdl information
chat_model = Base(
key=db_knowledge.llm.api_keys[0].api_key,
model_name=db_knowledge.llm.api_keys[0].model_name,
base_url=db_knowledge.llm.api_keys[0].api_base
)
response = graph_entity_types(chat_model, scenario)
return success(data=response, msg="Successfully obtained knowledge graph entity types")
except HTTPException:
raise
except Exception as e:
api_logger.error(f"get knowledge graph entity types failed: knowledge_id={knowledge_id} - {str(e)}")
raise

48
api/app/core/memory/storage_services/reflection_engine/example/example.json
View File

@@ -2,52 +2,39 @@
"memory_verify": {
"source_data": [
{
"statement_name": "用户是2023年春天去北京工作的。",
"statement_id": "62beac695b1346f4871740a45db88782"
"statement_name": "我是 2023 年春天去北京工作的，后来基本一直都在北京上班，也没怎么换过城市。不过后来公司调整，2024 年上半年我被调到上海待了差不多半年，那段时间每天都是在上海办公室打卡。当时入职资料用的还是我之前的身份信息，身份证号是 11010119950308123X，银行卡是 6222023847595898，这些一直没变。对了，其实我 从 2023 年开始就一直在北京生活，从来没有长期离开过北京，上海那段更多算是远程配合。"
},
{
"statement_name": "用户后来基本一直都在北京上班。",
"statement_id": "4cba5ac08b674d7fb1e2ae634d2b8f0b"
"statement_name": "用户后来基本一直都在北京上班。"
},
{
"statement_name": "用户从2023年开始就一直在北京生活。",
"statement_id": "e612a44da4db483993c350df7c97a1a1"
"statement_name": "用户从2023年开始就一直在北京生活。"
},
{
"statement_name": "用户从来没有长期离开过北京。",
"statement_id": "b3c787a2e33c49f7981accabbbb4538a"
"statement_name": "用户从来没有长期离开过北京。"
},
{
"statement_name": "由于公司调整，用户在2024年上半年被调到上海待了差不多半年。",
"statement_id": "64cde4230cb24a4da726e7db9e7aa616"
"statement_name": "由于公司调整，用户在2024年上半年被调到上海待了差不多半年。"
},
{
"statement_name": "用户在被调到上海期间每天都是在上海办公室打卡。",
"statement_id": "8b1b12e23b844b8088dfeb67da6ad669"
"statement_name": "用户在被调到上海期间每天都是在上海办公室打卡。"
},
{
"statement_name": "用户在入职时使用的身份信息是之前的，身份证号为11010119950308123X。",
"statement_id": "030afd362e9b4110b139e68e5d3e7143"
"statement_name": "用户在入职时使用的身份信息是之前的，身份证号为11010119950308123X。"
},
{
"statement_name": "用户的银行卡号是6222023847595898。",
"statement_id": "6c7567cd1f3c478bb42d1b65383e6f2f"
"statement_name": "用户的银行卡号是6222023847595898。"
},
{
"statement_name": "用户的身份信息和银行卡信息一直没变。",
"statement_id": "b3ca618e1e204b83bebd70e75cf2073f"
"statement_name": "用户的身份信息和银行卡信息一直没变。"
},
{
"statement_name": "用户认为在上海的那段时间更多算是远程配合。",
"statement_id": "150af89d2c154e6eb41ff1a91e37f962"
"statement_name": "用户认为在上海的那段时间更多算是远程配合。"
}
],
"databasets": [
{
"entity1_name": "Person",
"description": "表示人类个体的通用类型",
"statement_id": "62beac695b1346f4871740a45db88782",
"entity2_name": "用户",
"entity2": {
"description": "叙述者，讲述个人工作与生活经历的个体",
"name": "用户"
@@ -55,9 +42,6 @@
},
{
"entity1_name": "用户",
"description": "叙述者，讲述个人工作与生活经历的个体",
"statement_id": "62beac695b1346f4871740a45db88782",
"entity2_name": "身份信息",
"entity2": {
"description": "用于个人身份识别的数据",
"name": "身份信息"
@@ -65,9 +49,6 @@
},
{
"entity1_name": "用户",
"description": "叙述者，讲述个人工作与生活经历的个体",
"statement_id": "62beac695b1346f4871740a45db88782",
"entity2_name": "6222023847595898",
"entity2": {
"description": "用户的银行卡号码",
"name": "6222023847595898"
@@ -76,33 +57,24 @@
{
"entity1_name": "用户",
"description": "叙述者，讲述个人工作与生活经历的个体",
"statement_id": "62beac695b1346f4871740a45db88782",
"entity2_name": "上海办公室",
"entity2": {
"entity_idx": 1,
"aliases": ["上海办"],
"description": "位于上海的工作办公场所",
"name": "上海办公室"
}
},
{
"entity1_name": "用户",
"description": "叙述者，讲述个人工作与生活经历的个体",
"statement_id": "62beac695b1346f4871740a45db88782",
"entity2_name": "北京",
"entity2": {
"aliases": ["京", "京城", "北平"],
"description": "中国的首都城市，用户主要工作和生活所在地",
"name": "北京"
}
},
{
"entity1_name": "11010119950308123X",
"description": "具体的身份证号码值",
"statement_id": "030afd362e9b4110b139e68e5d3e7143",
"entity2_name": "身份证号",
"entity2": {
"description": "中华人民共和国公民的身份号码",
"name": "身份证号"
}
}

20
api/app/core/memory/storage_services/reflection_engine/self_reflexion.py
View File

@@ -387,7 +387,7 @@ class ReflectionEngine:
result_data['memory_verifies'] = memory_verifies
result_data['quality_assessments'] = quality_assessments
conflicts_found=''
REMOVE_KEYS = {"created_at", "expired_at","relationship","predicate","statement_id","id","statement_id","relationship_statement_id"}
# Clearn conflict_data，And memory_verify和quality_assessment
cleaned_conflict_data = []
for item in conflict_data:
@@ -396,7 +396,23 @@ class ReflectionEngine:
'conflict': item['conflict']
}
cleaned_conflict_data.append(cleaned_item)
cleaned_conflict_data_=[]
for item in conflict_data:
cleaned_data = []
for row in item.get("data", []):
# 删除 created_at / expired_at
cleaned_row = {
k: v
for k, v in row.items()
if k not in REMOVE_KEYS
}
cleaned_data.append(cleaned_row)
cleaned_item = {
"data": cleaned_data,
"conflict": item.get("conflict"),
}
cleaned_conflict_data_.append(cleaned_item)
print(cleaned_conflict_data_)
# 3. 解决冲突
solved_data = await self._resolve_conflicts(cleaned_conflict_data, source_data)
if not solved_data:

13
api/app/core/memory/utils/prompt/prompts/reflexion.jinja2
View File

@@ -9,9 +9,7 @@
## 任务目标
作为数据冲突解决专家，分析冲突原因，按类型分组处理，为每种冲突生成独立解决方案。
**数据关系**: statement_databasets中的statement_id对应data中的记录，statement_created_at为用户输入时间。
**处理模式**:
- memory_verify=false: 仅处理数据冲突
- memory_verify=true: 处理数据冲突 + 隐私脱敏
@@ -111,7 +109,8 @@
- 隐私保护优先: 所有输出记录必须完成隐私脱敏
- 脱敏变更记录: 隐私脱敏变更也必须在change字段中记录{% endif %}
- 不可修改数据: 数据被判定为正确时不可修改，无数据可输出时为空
- 输出的结果reflexion字段中的reason字段和solution不允许含有（expired_at设为2024-01-01T00:00:00Z、memory_verify=true)等原数据字段以及涉及需要修改的字段以及内容
- 输出的结果reflexion字段中的reason字段和solution不允许含有（expired_at设为2024-01-01T00:00:00Z、memory_verify=true、memory_verify=false)等原数据字段以及涉及需要修改的字段以及内容，
，如果是FACT，只记录事实冲突相关的数据；如果是TIME，只记录时间冲突相关的数据；如果是HYBRID，则记录所有冲突相关的数据
**变更记录格式**:
```json
@@ -158,8 +157,9 @@
"conflict": true
},
"reflexion": {
"reason": "该冲突类型的原因分析",
"solution": "该冲突类型的解决方案"
"reason": "该冲突类型的原因分析，如果是FACT就是存在事实冲突，分析该冲突原因，如果是TIME就是存在时间冲突，分析该冲突原因，如果是HYBRID，可以输出存在时间与事实的混合冲突再添加上原因分析，
不可以随意分配冲突类型以及原因，不允许输出字段比如（statement、description、entity1_name、entity2_name、name、memory_verify、expired_at、conflict）等类似这种",
"solution": "该冲突类型的解决方案（不允许输出字段比如（statement、description、entity1_name、entity2_name、name、memory_verify、expired_at、conflict）等类似这种）"
},
"resolved": {
"original_memory_id": "被设为失效的记忆id",
@@ -182,4 +182,5 @@
- **resolved.change**: 包含详细变更信息
- 无需修改的冲突类型resolved为null
- 与baseline不匹配的冲突类型不包含在results中
模式参考: {{ json_schema }}
模式参考: {{ json_schema }}

1
api/app/core/rag/app/naive.py
View File

@@ -672,6 +672,7 @@ def chunk(filename, binary=None, from_page=0, to_page=100000,
excel_parser = ExcelParser()
if parser_config.get("html4excel"):
sections = [(_, "") for _ in excel_parser.html(binary, 12) if _]
parser_config["chunk_token_num"] = 0
else:
sections = [(_, "") for _ in excel_parser(binary) if _]
parser_config["chunk_token_num"] = 12800

116
api/app/core/rag/deepdoc/parser/excel_parser.py
View File

@@ -5,6 +5,7 @@ from io import BytesIO
import pandas as pd
from openpyxl import Workbook, load_workbook
from PIL import Image
from app.core.rag.nlp import find_codec
@@ -28,7 +29,7 @@ class RAGExcelParser:
try:
file_like_object.seek(0)
df = pd.read_csv(file_like_object)
df = pd.read_csv(file_like_object, on_bad_lines='skip')
return RAGExcelParser._dataframe_to_workbook(df)
except Exception as e_csv:
@@ -42,14 +43,11 @@ class RAGExcelParser:
file_like_object.seek(0)
try:
dfs = pd.read_excel(file_like_object, sheet_name=None)
if isinstance(dfs, dict):
dfs = next(iter(dfs.values()))
return RAGExcelParser._dataframe_to_workbook(dfs)
except Exception as ex:
logging.info(f"pandas with default engine load error: {ex}, try calamine instead")
file_like_object.seek(0)
df = pd.read_excel(file_like_object, engine="calamine")
print(df)
return RAGExcelParser._dataframe_to_workbook(df)
except Exception as e_pandas:
raise Exception(f"pandas.read_excel error: {e_pandas}, original openpyxl error: {e}")
@@ -68,7 +66,6 @@ class RAGExcelParser:
# if contains multiple sheets use _dataframes_to_workbook
if isinstance(df, dict) and len(df) > 1:
return RAGExcelParser._dataframes_to_workbook(df)
df = RAGExcelParser._clean_dataframe(df)
wb = Workbook()
ws = wb.active
@@ -80,15 +77,14 @@ class RAGExcelParser:
for row_num, row in enumerate(df.values, 2):
for col_num, value in enumerate(row, 1):
ws.cell(row=row_num, column=col_num, value=value)
return wb
@staticmethod
def _dataframes_to_workbook(dfs: dict):
wb = Workbook()
default_sheet = wb.active
wb.remove(default_sheet)
for sheet_name, df in dfs.items():
df = RAGExcelParser._clean_dataframe(df)
ws = wb.create_sheet(title=sheet_name)
@@ -99,6 +95,52 @@ class RAGExcelParser:
ws.cell(row=row_num, column=col_num, value=value)
return wb
@staticmethod
def _extract_images_from_worksheet(ws, sheetname=None):
"""
Extract images from a worksheet and enrich them with vision-based descriptions.
Returns: List[dict]
"""
images = getattr(ws, "_images", [])
if not images:
return []
raw_items = []
for img in images:
try:
img_bytes = img._data()
pil_img = Image.open(BytesIO(img_bytes)).convert("RGB")
anchor = img.anchor
if hasattr(anchor, "_from") and hasattr(anchor, "_to"):
r1, c1 = anchor._from.row + 1, anchor._from.col + 1
r2, c2 = anchor._to.row + 1, anchor._to.col + 1
if r1 == r2 and c1 == c2:
span = "single_cell"
else:
span = "multi_cell"
else:
r1, c1 = anchor._from.row + 1, anchor._from.col + 1
r2, c2 = r1, c1
span = "single_cell"
item = {
"sheet": sheetname or ws.title,
"image": pil_img,
"image_description": "",
"row_from": r1,
"col_from": c1,
"row_to": r2,
"col_to": c2,
"span_type": span,
}
raw_items.append(item)
except Exception:
continue
return raw_items
def html(self, fnm, chunk_rows=256):
from html import escape
@@ -131,7 +173,7 @@ class RAGExcelParser:
tb = ""
tb += f"<table><caption>{sheetname}</caption>"
tb += tb_rows_0
for r in list(rows[1 + chunk_i * chunk_rows : min(1 + (chunk_i + 1) * chunk_rows, len(rows))]):
for r in list(rows[1 + chunk_i * chunk_rows: min(1 + (chunk_i + 1) * chunk_rows, len(rows))]):
tb += "<tr>"
for i, c in enumerate(r):
if c.value is None:
@@ -154,7 +196,7 @@ class RAGExcelParser:
except Exception as e:
logging.warning(f"Parse spreadsheet error: {e}, trying to interpret as CSV file")
file_like_object.seek(0)
df = pd.read_csv(file_like_object)
df = pd.read_csv(file_like_object, on_bad_lines='skip')
df = df.replace(r"^\s*$", "", regex=True)
return df.to_markdown(index=False)
@@ -172,19 +214,35 @@ class RAGExcelParser:
continue
if not rows:
continue
# 获取表头
ti = list(rows[0])
for r in list(rows[1:]):
fields = []
for i, c in enumerate(r):
if not c.value:
continue
t = str(ti[i].value) if i < len(ti) else ""
t += ("：" if t else "") + str(c.value)
fields.append(t)
line = "; ".join(fields)
if sheetname.lower().find("sheet") < 0:
line += " ——" + sheetname
res.append(line)
header_fields = []
for cell in ti:
if cell.value: # 只添加有值的表头
header_fields.append(str(cell.value))
# 如果有数据行，处理数据行；否则只处理表头
data_rows = rows[1:]
if data_rows:
for r in data_rows:
fields = []
for i, c in enumerate(r):
if not c.value:
continue
t = str(ti[i].value) if i < len(ti) else ""
t += ("：" if t else "") + str(c.value)
fields.append(t)
line = "; ".join(fields)
if sheetname.lower().find("sheet") < 0:
line += " ——" + sheetname
res.append(line)
else:
# 只有表头的情况
if header_fields:
line = "; ".join(header_fields)
if sheetname.lower().find("sheet") < 0:
line += " ——" + sheetname
res.append(line)
return res
@staticmethod
@@ -192,14 +250,14 @@ class RAGExcelParser:
if fnm.split(".")[-1].lower().find("xls") >= 0:
wb = RAGExcelParser._load_excel_to_workbook(BytesIO(binary))
total = 0
for sheetname in wb.sheetnames:
try:
ws = wb[sheetname]
total += len(list(ws.rows))
except Exception as e:
logging.warning(f"Skip sheet '{sheetname}' due to rows access error: {e}")
continue
try:
ws = wb[sheetname]
total += len(list(ws.rows))
except Exception as e:
logging.warning(f"Skip sheet '{sheetname}' due to rows access error: {e}")
continue
return total
if fnm.split(".")[-1].lower() in ["csv", "txt"]:

2
api/app/core/rag/graphrag/entity_resolution.py
View File

@@ -196,7 +196,7 @@ class EntityResolution(Extractor):
ans_list = []
records = [r.strip() for r in results.split(record_delimiter)]
for record in records:
pattern_int = f"{re.escape(entity_index_delimiter)}(\d+){re.escape(entity_index_delimiter)}"
pattern_int = fr"{re.escape(entity_index_delimiter)}(\d+){re.escape(entity_index_delimiter)}"
match_int = re.search(pattern_int, record)
res_int = int(str(match_int.group(1) if match_int else '0'))
if res_int > records_length:

4
api/app/core/validators/memory_config_validators.py
View File

@@ -197,6 +197,10 @@ def validate_embedding_model(
embedding_id, "embedding", db, tenant_id, required=True,
config_id=config_id, workspace_id=workspace_id
)
print(100*'-')
print(embedding_uuid)
print(_)
print(100*'-')
if embedding_uuid is None:
raise InvalidConfigError(

2
api/app/core/workflow/nodes/end/node.py
View File

@@ -61,7 +61,7 @@ class EndNode(BaseNode):
引用的节点 ID 列表
"""
# 匹配 {{node_id.xxx}} 格式
pattern = r'\{\{([a-zA-Z0-9_]+)\.[a-zA-Z0-9_]+\}\}'
pattern = r'\{\{([a-zA-Z0-9_-]+)\.[a-zA-Z0-9_]+\}\}'
matches = re.findall(pattern, template)
return list(set(matches)) # 去重

4
api/app/models/data_config_model.py
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@@ -51,8 +51,8 @@ class DataConfig(Base):
# 自我反思配置
enable_self_reflexion = Column(Boolean, default=False, comment="是否启用自我反思")
iteration_period = Column(String, default="3", comment="反思迭代周期")
reflexion_range = Column(String, default="retrieval", comment="反思范围：部分/全部")
baseline = Column(String, default="time", comment="基线：时间/事实/时间和事实")
reflexion_range = Column(String, default="partial", comment="反思范围：部分/全部")
baseline = Column(String, default="TIME", comment="基线：时间/事实/时间和事实")
reflection_model_id = Column(String, nullable=True, comment="反思模型ID")
memory_verify = Column(Boolean, default=True, comment="记忆验证")
quality_assessment = Column(Boolean, default=True, comment="质量评估")

14
api/app/services/master_agent_router.py
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@@ -361,14 +361,14 @@ class MasterAgentRouter:
"model_name": api_key_config.model_name
}
)
temperature = 0.3 # 决策任务使用较低温度
max_tokens = 1000
if self.model_parameters:
temperature = self.model_parameters.temperature
max_tokens = self.model_parameters.max_tokens
# temperature = 0.3 # 决策任务使用较低温度
# max_tokens = 1000
# if self.model_parameters:
# temperature = self.model_parameters["temperature"]
# max_tokens = self.model_parameters["max_tokens"]
extra_params = {"temperature": temperature,
"max_tokens":max_tokens
extra_params = {"temperature": self.model_parameters.get("temperature", 0.3),
"max_tokens":self.model_parameters.get("max_tokens", 1000)
}
# 创建 RedBearModelConfig
model_config = RedBearModelConfig(

12
api/app/services/multi_agent_service.py
View File

@@ -290,12 +290,22 @@ class MultiAgentService:
else:
execution_config_data = convert_uuids_to_str(data.execution_config.model_dump())
# 处理 model_parameters（可能是 None、字典或 Pydantic 模型）
if data.model_parameters is None:
model_parameters_data = None
elif isinstance(data.model_parameters, dict):
# 过滤掉值为 None 的字段
model_parameters_data = {k: v for k, v in data.model_parameters.items() if v is not None}
else:
# 过滤掉值为 None 的字段
model_parameters_data = {k: v for k, v in data.model_parameters.model_dump().items() if v is not None}
config = MultiAgentConfig(
app_id=app_id,
master_agent_id=data.master_agent_id,
master_agent_name=data.master_agent_name,
default_model_config_id=data.default_model_config_id,
model_parameters=data.model_parameters,
model_parameters=model_parameters_data,
orchestration_mode=data.orchestration_mode,
sub_agents=sub_agents_data,
# routing_rules=routing_rules_data,

2
api/app/templates/workflows/simple_qa/template.yml
View File

@@ -41,8 +41,6 @@ nodes:
- 使用友好、礼貌的语气
- 适当使用格式化（如列表、段落）提高可读性
- role: user
content: "{{sys.message}}"
model_id: null
temperature: 0.7