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Merge pull request #724 from SuanmoSuanyangTechnology/feature/memory-agent-perceptual

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feat(agent, memory): add agent-perceived memory writing
This commit is contained in:
Ke Sun
2026-03-31 12:03:47 +08:00
committed by GitHub
parent 4a71484151 7acb7045f0
commit 3ea57d1cb0
12 changed files with 304 additions and 530 deletions
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65
api/app/services/app_chat_service.py
View File

@@ -10,6 +10,7 @@ from sqlalchemy.orm import Session
from app.core.agent.langchain_agent import LangChainAgent
from app.core.logging_config import get_business_logger
from app.core.memory.agent.langgraph_graph.write_graph import write_long_term
from app.db import get_db
from app.models import MultiAgentConfig, AgentConfig, ModelType
from app.models import WorkflowConfig
@@ -20,11 +21,11 @@ from app.schemas.model_schema import ModelInfo
from app.schemas.prompt_schema import render_prompt_message, PromptMessageRole
from app.services.conversation_service import ConversationService
from app.services.draft_run_service import AgentRunService
from app.services.memory_agent_service import get_end_user_connected_config
from app.services.model_service import ModelApiKeyService
from app.services.multi_agent_orchestrator import MultiAgentOrchestrator
from app.services.multimodal_service import MultimodalService
from app.services.workflow_service import WorkflowService
from app.schemas import FileType
logger = get_business_logger()
@@ -43,18 +44,17 @@ class AppChatService:
message: str,
conversation_id: uuid.UUID,
config: AgentConfig,
user_id: Optional[str] = None,
files: list[FileInput],
user_id: str,
variables: Optional[Dict[str, Any]] = None,
web_search: bool = False,
memory: bool = True,
storage_type: Optional[str] = None,
user_rag_memory_id: Optional[str] = None,
workspace_id: Optional[str] = None,
files: Optional[List[FileInput]] = None
workspace_id: Optional[str] = None
) -> Dict[str, Any]:
"""聊天（非流式）"""
start_time = time.time()
config_id = None
# 应用 features 配置
features_config: dict = config.features or {}
@@ -93,7 +93,8 @@ class AppChatService:
tools.extend(skill_tools)
if skill_prompts:
system_prompt = f"{system_prompt}\n\n{skill_prompts}"
kb_tools, citations_collector = self.agent_service.load_knowledge_retrieval_config(config.knowledge_retrieval, user_id)
kb_tools, citations_collector = self.agent_service.load_knowledge_retrieval_config(config.knowledge_retrieval,
user_id)
tools.extend(kb_tools)
memory_flag = False
if memory:
@@ -168,11 +169,6 @@ class AppChatService:
message=message,
history=history,
context=None,
end_user_id=user_id,
storage_type=storage_type,
user_rag_memory_id=user_rag_memory_id,
config_id=config_id,
memory_flag=memory_flag,
files=processed_files # 传递处理后的文件
)
@@ -229,6 +225,21 @@ class AppChatService:
# 保存消息
if audio_url:
assistant_meta["audio_url"] = audio_url
if memory_flag:
connected_config = get_end_user_connected_config(user_id, self.db)
memory_config_id: str = connected_config.get("memory_config_id")
messages = [
{"role": "user", "content": message, "files": [file.model_dump() for file in files]},
{"role": "assistant", "content": result["content"]}
]
if memory_config_id:
await write_long_term(
storage_type,
user_id,
messages,
user_rag_memory_id,
memory_config_id
)
self.conversation_service.add_message(
conversation_id=conversation_id,
role="user",
@@ -264,20 +275,19 @@ class AppChatService:
message: str,
conversation_id: uuid.UUID,
config: AgentConfig,
files: list[FileInput],
user_id: Optional[str] = None,
variables: Optional[Dict[str, Any]] = None,
web_search: bool = False,
memory: bool = True,
storage_type: Optional[str] = None,
user_rag_memory_id: Optional[str] = None,
workspace_id: Optional[str] = None,
files: Optional[List[FileInput]] = None
workspace_id: Optional[str] = None
) -> AsyncGenerator[str, None]:
"""聊天（流式）"""
try:
start_time = time.time()
config_id = None
message_id = uuid.uuid4()
# 应用 features 配置
@@ -319,7 +329,8 @@ class AppChatService:
tools.extend(skill_tools)
if skill_prompts:
system_prompt = f"{system_prompt}\n\n{skill_prompts}"
kb_tools, citations_collector = self.agent_service.load_knowledge_retrieval_config(config.knowledge_retrieval, user_id)
kb_tools, citations_collector = self.agent_service.load_knowledge_retrieval_config(
config.knowledge_retrieval, user_id)
tools.extend(kb_tools)
# 添加长期记忆工具
memory_flag = False
@@ -411,11 +422,6 @@ class AppChatService:
message=message,
history=history,
context=None,
end_user_id=user_id,
storage_type=storage_type,
user_rag_memory_id=user_rag_memory_id,
config_id=config_id,
memory_flag=memory_flag,
files=processed_files
):
if isinstance(chunk, int):
@@ -459,7 +465,7 @@ class AppChatService:
# 保存消息
human_meta = {
"files":[],
"files": [],
"history_files": {}
}
assistant_meta = {
@@ -484,6 +490,22 @@ class AppChatService:
if stream_audio_url:
assistant_meta["audio_url"] = stream_audio_url
if memory_flag:
connected_config = get_end_user_connected_config(user_id, self.db)
memory_config_id: str = connected_config.get("memory_config_id")
messages = [
{"role": "user", "content": message, "files": [file.model_dump() for file in files]},
{"role": "assistant", "content": full_content}
]
if memory_config_id:
await write_long_term(
storage_type,
user_id,
messages,
user_rag_memory_id,
memory_config_id
)
self.conversation_service.add_message(
conversation_id=conversation_id,
role="user",
@@ -618,7 +640,6 @@ class AppChatService:
# 2. 创建编排器
orchestrator = MultiAgentOrchestrator(self.db, config)
# 3. 流式执行任务
async for event in orchestrator.execute_stream(
message=message,

13
api/app/services/draft_run_service.py
View File

@@ -24,7 +24,7 @@ from app.core.exceptions import BusinessException
from app.core.logging_config import get_business_logger
from app.core.rag.nlp.search import knowledge_retrieval
from app.db import get_db_context
from app.models import AgentConfig, ModelConfig, ModelType
from app.models import AgentConfig, ModelConfig
from app.repositories.tool_repository import ToolRepository
from app.schemas.app_schema import FileInput, Citation
from app.schemas.model_schema import ModelInfo
@@ -37,7 +37,6 @@ from app.services.model_parameter_merger import ModelParameterMerger
from app.services.model_service import ModelApiKeyService
from app.services.multimodal_service import MultimodalService
from app.services.tool_service import ToolService
from app.schemas import FileType
logger = get_business_logger()
@@ -657,11 +656,6 @@ class AgentRunService:
message=message,
history=history,
context=context,
end_user_id=user_id,
config_id=config_id,
storage_type=storage_type,
user_rag_memory_id=user_rag_memory_id,
memory_flag=memory_flag,
files=processed_files # 传递处理后的文件
)
@@ -911,11 +905,6 @@ class AgentRunService:
message=message,
history=history,
context=context,
end_user_id=user_id,
config_id=config_id,
storage_type=storage_type,
user_rag_memory_id=user_rag_memory_id,
memory_flag=memory_flag,
files=processed_files
):
if isinstance(chunk, int):

21
api/app/services/memory_perceptual_service.py
View File

@@ -243,27 +243,6 @@ class MemoryPerceptualService:
memory_config: MemoryConfig,
file: FileInput
):
memories = self.repository.get_by_url(file.url)
if memories:
business_logger.info(f"Perceptual memory already exists: {file.url}")
if end_user_id not in [memory.end_user_id for memory in memories]:
business_logger.info(f"Copy perceptual memory end_user_id: {end_user_id}")
memory_cache = memories[0]
memory = self.repository.create_perceptual_memory(
end_user_id=uuid.UUID(end_user_id),
perceptual_type=PerceptualType(memory_cache.perceptual_type),
file_path=memory_cache.file_path,
file_name=memory_cache.file_name,
file_ext=memory_cache.file_ext,
summary=memory_cache.summary,
meta_data=memory_cache.meta_data
)
self.db.commit()
return memory
else:
for memory in memories:
if memory.end_user_id == uuid.UUID(end_user_id):
return memory
llm, model_config = self._get_mutlimodal_client(file.type, memory_config)
multimodel_service = MultimodalService(self.db, ModelInfo(
model_name=model_config.model_name,

138
api/app/services/model_service.py
View File

@@ -69,7 +69,8 @@ class ModelConfigService:
return items
@staticmethod
def get_model_by_name(db: Session, name: str, provider: str | None = None, tenant_id: uuid.UUID | None = None) -> ModelConfig:
def get_model_by_name(db: Session, name: str, provider: str | None = None,
tenant_id: uuid.UUID | None = None) -> ModelConfig:
"""根据名称获取模型配置"""
model = ModelConfigRepository.get_by_name(db, name, provider=provider, tenant_id=tenant_id)
if not model:
@@ -77,21 +78,22 @@ class ModelConfigService:
return model
@staticmethod
def search_models_by_name(db: Session, name: str, tenant_id: uuid.UUID | None = None, limit: int = 10) -> List[ModelConfig]:
def search_models_by_name(db: Session, name: str, tenant_id: uuid.UUID | None = None, limit: int = 10) -> List[
ModelConfig]:
"""按名称模糊匹配获取模型配置列表"""
return ModelConfigRepository.search_by_name(db, name, tenant_id=tenant_id, limit=limit)
@staticmethod
async def validate_model_config(
db: Session,
*,
model_name: str,
provider: str,
api_key: str,
api_base: Optional[str] = None,
model_type: str = "llm",
test_message: str = "Hello",
is_omni: bool = False
db: Session,
*,
model_name: str,
provider: str,
api_key: str,
api_base: Optional[str] = None,
model_type: str = "llm",
test_message: str = "Hello",
is_omni: bool = False
) -> Dict[str, Any]:
"""验证模型配置是否有效
@@ -158,13 +160,13 @@ class ModelConfigService:
# 统一使用 RedBearEmbeddings（自动支持火山引擎多模态）
embedding = RedBearEmbeddings(model_config)
test_texts = [test_message, "测试文本"]
# 火山引擎使用 embed_batch，其他使用 embed_documents
if provider.lower() == "volcano":
vectors = await asyncio.to_thread(embedding.embed_batch, test_texts)
else:
vectors = await asyncio.to_thread(embedding.embed_documents, test_texts)
elapsed_time = time.time() - start_time
return {
@@ -200,11 +202,11 @@ class ModelConfigService:
},
"error": None
}
elif model_type_lower == "image":
# 图片生成模型验证
from app.core.models.generation import RedBearImageGenerator
generator = RedBearImageGenerator(model_config)
result = await generator.agenerate(
prompt="a cute panda",
@@ -212,7 +214,7 @@ class ModelConfigService:
)
elapsed_time = time.time() - start_time
logger.info(f"成功生成图片，结果: {result}")
return {
"valid": True,
"message": "图片生成模型配置验证成功",
@@ -224,21 +226,21 @@ class ModelConfigService:
},
"error": None
}
elif model_type_lower == "video":
# 视频生成模型验证
from app.core.models.generation import RedBearVideoGenerator
generator = RedBearVideoGenerator(model_config)
result = await generator.agenerate(
prompt="a cute panda playing in bamboo forest",
duration=5
)
elapsed_time = time.time() - start_time
# 视频生成是异步任务，返回任务ID
task_id = result.get("task_id") if isinstance(result, dict) else None
return {
"valid": True,
"message": "视频生成模型配置验证成功",
@@ -265,7 +267,6 @@ class ModelConfigService:
# 提取详细的错误信息
error_message = str(e)
error_type = type(e).__name__
print("=========error_message:",error_message.lower())
# 特殊处理常见的错误类型
if "unsupported countries" in error_message.lower() or "unsupported region" in error_message.lower():
# 区域/国家限制（适用于所有提供商）
@@ -354,14 +355,16 @@ class ModelConfigService:
return model
@staticmethod
def update_model(db: Session, model_id: uuid.UUID, model_data: ModelConfigUpdate, tenant_id: uuid.UUID | None = None) -> ModelConfig:
def update_model(db: Session, model_id: uuid.UUID, model_data: ModelConfigUpdate,
tenant_id: uuid.UUID | None = None) -> ModelConfig:
"""更新模型配置"""
existing_model = ModelConfigRepository.get_by_id(db, model_id, tenant_id=tenant_id)
if not existing_model:
raise BusinessException("模型配置不存在", BizCode.MODEL_NOT_FOUND)
if model_data.name and model_data.name != existing_model.name:
if ModelConfigRepository.get_by_name(db, model_data.name, provider=existing_model.provider, tenant_id=tenant_id):
if ModelConfigRepository.get_by_name(db, model_data.name, provider=existing_model.provider,
tenant_id=tenant_id):
raise BusinessException("模型名称已存在", BizCode.DUPLICATE_NAME)
model = ModelConfigRepository.update(db, model_id, model_data, tenant_id=tenant_id)
@@ -370,25 +373,27 @@ class ModelConfigService:
return model
@staticmethod
async def create_composite_model(db: Session, model_data: model_schema.CompositeModelCreate, tenant_id: uuid.UUID) -> ModelConfig:
async def create_composite_model(db: Session, model_data: model_schema.CompositeModelCreate,
tenant_id: uuid.UUID) -> ModelConfig:
"""创建组合模型"""
if ModelConfigRepository.get_by_name(db, model_data.name, provider=ModelProvider.COMPOSITE, tenant_id=tenant_id):
if ModelConfigRepository.get_by_name(db, model_data.name, provider=ModelProvider.COMPOSITE,
tenant_id=tenant_id):
raise BusinessException("模型名称已存在", BizCode.DUPLICATE_NAME)
# 验证所有 API Key 存在且类型匹配
for api_key_id in model_data.api_key_ids:
api_key = ModelApiKeyRepository.get_by_id(db, api_key_id)
if not api_key:
raise BusinessException(f"API Key {api_key_id} 不存在", BizCode.NOT_FOUND)
# 检查 API Key 关联的模型配置类型
for model_config in api_key.model_configs:
# chat 和 llm 类型可以兼容
compatible_types = {ModelType.LLM, ModelType.CHAT}
config_type = model_config.type
request_type = model_data.type
if not (config_type == request_type or
if not (config_type == request_type or
(config_type in compatible_types and request_type in compatible_types)):
raise BusinessException(
f"API Key {api_key_id} 关联的模型类型 ({model_config.type}) 与组合模型类型 ({model_data.type}) 不匹配",
@@ -399,7 +404,7 @@ class ModelConfigService:
# f"API Key {api_key_id} 关联的模型是组合模型，不能用于创建新的组合模型",
# BizCode.INVALID_PARAMETER
# )
# 创建组合模型
model_config_data = {
"tenant_id": tenant_id,
@@ -418,49 +423,51 @@ class ModelConfigService:
model = ModelConfigRepository.create(db, model_config_data)
db.flush()
# 关联 API Keys
for api_key_id in model_data.api_key_ids:
api_key = ModelApiKeyRepository.get_by_id(db, api_key_id)
if api_key:
model.api_keys.append(api_key)
db.commit()
db.refresh(model)
return model
@staticmethod
async def update_composite_model(db: Session, model_id: uuid.UUID, model_data: model_schema.CompositeModelCreate, tenant_id: uuid.UUID) -> ModelConfig:
async def update_composite_model(db: Session, model_id: uuid.UUID, model_data: model_schema.CompositeModelCreate,
tenant_id: uuid.UUID) -> ModelConfig:
"""更新组合模型"""
existing_model = ModelConfigRepository.get_by_id(db, model_id, tenant_id=tenant_id)
if not existing_model:
raise BusinessException("模型配置不存在", BizCode.MODEL_NOT_FOUND)
if model_data.name and model_data.name != existing_model.name:
if ModelConfigRepository.get_by_name(db, model_data.name, provider=existing_model.provider, tenant_id=tenant_id):
if ModelConfigRepository.get_by_name(db, model_data.name, provider=existing_model.provider,
tenant_id=tenant_id):
raise BusinessException("模型名称已存在", BizCode.DUPLICATE_NAME)
if not existing_model.is_composite:
raise BusinessException("该模型不是组合模型", BizCode.INVALID_PARAMETER)
# 验证所有 API Key 存在且类型匹配
for api_key_id in model_data.api_key_ids:
api_key = ModelApiKeyRepository.get_by_id(db, api_key_id)
if not api_key:
raise BusinessException(f"API Key {api_key_id} 不存在", BizCode.NOT_FOUND)
for model_config in api_key.model_configs:
compatible_types = {ModelType.LLM, ModelType.CHAT}
config_type = model_config.type
request_type = existing_model.type
if not (config_type == request_type or
if not (config_type == request_type or
(config_type in compatible_types and request_type in compatible_types)):
raise BusinessException(
f"API Key {api_key_id} 关联的模型类型 ({model_config.type}) 与组合模型类型 ({model_data.type}) 不匹配",
BizCode.INVALID_PARAMETER
)
# 更新基本信息
existing_model.name = model_data.name
# existing_model.type = model_data.type
@@ -471,14 +478,14 @@ class ModelConfigService:
existing_model.is_public = model_data.is_public
if "load_balance_strategy" in model_data.model_fields_set:
existing_model.load_balance_strategy = model_data.load_balance_strategy
# 更新 API Keys 关联
existing_model.api_keys.clear()
for api_key_id in model_data.api_key_ids:
api_key = ModelApiKeyRepository.get_by_id(db, api_key_id)
if api_key:
existing_model.api_keys.append(api_key)
db.commit()
db.refresh(existing_model)
return existing_model
@@ -532,7 +539,7 @@ class ModelApiKeyService:
"""根据provider为多个ModelConfig创建API Key"""
created_keys = []
failed_models = [] # 记录验证失败的模型
for model_config_id in data.model_config_ids:
model_config = ModelConfigRepository.get_by_id(db, model_config_id)
if not model_config:
@@ -540,10 +547,10 @@ class ModelApiKeyService:
data.is_omni = model_config.is_omni
data.capability = model_config.capability
# 从ModelBase获取model_name
model_name = model_config.model_base.name if model_config.model_base else model_config.name
# 检查是否存在API Key（包括软删除），需要考虑tenant_id
existing_key = db.query(ModelApiKey).join(
ModelApiKey.model_configs
@@ -553,7 +560,7 @@ class ModelApiKeyService:
ModelApiKey.model_name == model_name,
ModelConfig.tenant_id == model_config.tenant_id
).first()
if existing_key:
# 如果已存在，重新激活并更新
if existing_key.is_active:
@@ -566,14 +573,14 @@ class ModelApiKeyService:
existing_key.model_name = model_name
existing_key.capability = data.capability
existing_key.is_omni = data.is_omni
# 检查是否已关联该模型配置
if model_config not in existing_key.model_configs:
existing_key.model_configs.append(model_config)
created_keys.append(existing_key)
continue
# 验证配置
validation_result = await ModelConfigService.validate_model_config(
db=db,
@@ -589,7 +596,7 @@ class ModelApiKeyService:
# 记录验证失败的模型，但不抛出异常
failed_models.append(model_name)
continue
# 创建API Key
api_key_data = ModelApiKeyCreate(
model_config_ids=[model_config_id],
@@ -606,12 +613,12 @@ class ModelApiKeyService:
)
api_key_obj = ModelApiKeyRepository.create(db, api_key_data)
created_keys.append(api_key_obj)
if created_keys:
db.commit()
for key in created_keys:
db.refresh(key)
return created_keys, failed_models
@staticmethod
@@ -626,7 +633,7 @@ class ModelApiKeyService:
api_key_data.is_omni = model_config.is_omni
if api_key_data.capability is None:
api_key_data.capability = model_config.capability
# 检查API Key是否已存在(包括软删除)，需要考虑tenant_id
existing_key = db.query(ModelApiKey).join(
ModelApiKey.model_configs
@@ -650,15 +657,15 @@ class ModelApiKeyService:
existing_key.model_name = api_key_data.model_name
existing_key.capability = api_key_data.capability
existing_key.is_omni = api_key_data.is_omni
# 检查是否已关联该模型配置
if model_config not in existing_key.model_configs:
existing_key.model_configs.append(model_config)
db.commit()
db.refresh(existing_key)
return existing_key
# 验证配置
validation_result = await ModelConfigService.validate_model_config(
db=db,
@@ -691,7 +698,7 @@ class ModelApiKeyService:
# 获取关联的模型配置以获取模型类型
if existing_api_key.model_configs:
model_config = existing_api_key.model_configs[0]
validation_result = await ModelConfigService.validate_model_config(
db=db,
model_name=api_key_data.model_name or existing_api_key.model_name,
@@ -729,15 +736,15 @@ class ModelApiKeyService:
model_config = ModelConfigRepository.get_by_id(db, model_config_id)
if not model_config:
return None
api_keys = [key for key in model_config.api_keys if key.is_active]
if not api_keys:
return None
# 如果是轮询策略，按使用次数最少，次数相同则选最早使用的
if model_config.load_balance_strategy == LoadBalanceStrategy.ROUND_ROBIN:
return min(api_keys, key=lambda x: (int(x.usage_count or "0"), x.last_used_at or datetime.min))
# 否则返回第一个
return api_keys[0]
@@ -760,20 +767,19 @@ class ModelApiKeyService:
raise BusinessException("没有可用的 API Key", BizCode.AGENT_CONFIG_MISSING)
class ModelBaseService:
"""基础模型服务"""
@staticmethod
def get_model_base_list(db: Session, query: model_schema.ModelBaseQuery, tenant_id: uuid.UUID = None) -> List:
models = ModelBaseRepository.get_list(db, query)
provider_groups = {}
for m in models:
model_dict = model_schema.ModelBase.model_validate(m).model_dump()
if tenant_id:
model_dict['is_added'] = ModelBaseRepository.check_added_by_tenant(db, m.id, tenant_id)
provider = m.provider
if provider not in provider_groups:
provider_groups[provider] = {
@@ -781,7 +787,7 @@ class ModelBaseService:
"models": []
}
provider_groups[provider]["models"].append(model_dict)
return list(provider_groups.values())
@staticmethod
@@ -823,10 +829,10 @@ class ModelBaseService:
model_base = ModelBaseRepository.get_by_id(db, model_base_id)
if not model_base:
raise BusinessException("基础模型不存在", BizCode.MODEL_NOT_FOUND)
if ModelBaseRepository.check_added_by_tenant(db, model_base_id, tenant_id):
raise BusinessException("模型已添加", BizCode.DUPLICATE_NAME)
model_config_data = {
"model_id": model_base_id,
"tenant_id": tenant_id,

43
api/app/services/shared_chat_service.py
View File

@@ -1,26 +1,24 @@
"""基于分享链接的聊天服务"""
import uuid
import time
import asyncio
import json
import time
import uuid
from typing import Optional, Dict, Any, AsyncGenerator
from deprecated import deprecated
from sqlalchemy.orm import Session
from app.repositories.model_repository import ModelApiKeyRepository
from app.services.memory_konwledges_server import write_rag
from app.core.error_codes import BizCode
from app.core.exceptions import BusinessException, ResourceNotFoundException
from app.core.logging_config import get_business_logger
from app.models import MultiAgentConfig
from app.models import ReleaseShare, AppRelease, Conversation
from app.repositories import knowledge_repository
from app.services.conversation_service import ConversationService
from app.services.draft_run_service import create_web_search_tool
from app.services.model_service import ModelApiKeyService
from app.services.release_share_service import ReleaseShareService
from app.core.exceptions import BusinessException, ResourceNotFoundException
from app.core.error_codes import BizCode
from app.core.logging_config import get_business_logger
from app.services.multi_agent_service import MultiAgentService
from app.models import MultiAgentConfig
from app.repositories import knowledge_repository
import json
from app.services.task_service import get_task_memory_write_result
from app.tasks import write_message_task
from app.services.release_share_service import ReleaseShareService
logger = get_business_logger()
@@ -118,6 +116,7 @@ class SharedChatService:
return conversation
@deprecated("Use the chat method under app_chat_service instead.")
async def chat(
self,
share_token: str,
@@ -136,10 +135,7 @@ class SharedChatService:
config_id = actual_config_id
from app.core.agent.langchain_agent import LangChainAgent
from app.services.draft_run_service import create_knowledge_retrieval_tool, create_long_term_memory_tool
from app.services.model_parameter_merger import ModelParameterMerger
from app.schemas.prompt_schema import render_prompt_message, PromptMessageRole
from sqlalchemy import select
from app.models import ModelApiKey
start_time = time.time()
actual_config_id = None
@@ -273,11 +269,6 @@ class SharedChatService:
message=message,
history=history,
context=None,
end_user_id=user_id,
storage_type=storage_type,
user_rag_memory_id=user_rag_memory_id,
config_id=config_id,
memory_flag=memory_flag
)
# 保存消息
@@ -324,6 +315,7 @@ class SharedChatService:
"elapsed_time": elapsed_time
}
@deprecated("Use the chat method under app_chat_service instead.")
async def chat_stream(
self,
share_token: str,
@@ -341,8 +333,6 @@ class SharedChatService:
from app.core.agent.langchain_agent import LangChainAgent
from app.services.draft_run_service import create_knowledge_retrieval_tool, create_long_term_memory_tool
from app.schemas.prompt_schema import render_prompt_message, PromptMessageRole
from sqlalchemy import select
from app.models import ModelApiKey
import json
start_time = time.time()
@@ -486,11 +476,6 @@ class SharedChatService:
message=message,
history=history,
context=None,
end_user_id=user_id,
storage_type=storage_type,
user_rag_memory_id=user_rag_memory_id,
config_id=config_id,
memory_flag=memory_flag
):
if isinstance(chunk, int):
total_tokens = chunk
@@ -585,6 +570,7 @@ class SharedChatService:
return conversations, total
@deprecated("Use the chat method under app_chat_service instead.")
async def multi_agent_chat(
self,
share_token: str,
@@ -680,6 +666,7 @@ class SharedChatService:
"elapsed_time": elapsed_time
}
@deprecated("Use the chat method under app_chat_service instead.")
async def multi_agent_chat_stream(
self,
share_token: str,