Merge branch 'develop' of codeup.aliyun.com:redbearai/python/redbear-mem-open into develop
# Conflicts: # api/app/services/workspace_service.py
This commit is contained in:
Mark
@@ -18,7 +18,6 @@ class EndUser(BaseModel):
|
||||
updated_at: datetime.datetime = Field(description="更新时间", default_factory=datetime.datetime.now)
|
||||
|
||||
# 用户基本信息字段
|
||||
name: Optional[str] = Field(description="姓名", default=None)
|
||||
position: Optional[str] = Field(description="职位", default=None)
|
||||
department: Optional[str] = Field(description="部门", default=None)
|
||||
contact: Optional[str] = Field(description="联系方式", default=None)
|
||||
@@ -32,7 +31,7 @@ class EndUserProfileResponse(BaseModel):
|
||||
model_config = ConfigDict(from_attributes=True)
|
||||
|
||||
id: uuid.UUID = Field(description="终端用户ID")
|
||||
name: Optional[str] = Field(description="姓名", default=None)
|
||||
other_name: Optional[str] = Field(description="其他名称", default="")
|
||||
position: Optional[str] = Field(description="职位", default=None)
|
||||
department: Optional[str] = Field(description="部门", default=None)
|
||||
contact: Optional[str] = Field(description="联系方式", default=None)
|
||||
@@ -44,7 +43,7 @@ class EndUserProfileResponse(BaseModel):
|
||||
class EndUserProfileUpdate(BaseModel):
|
||||
"""终端用户基本信息更新请求模型"""
|
||||
end_user_id: str = Field(description="终端用户ID")
|
||||
name: Optional[str] = Field(description="姓名", default=None)
|
||||
other_name: Optional[str] = Field(description="其他名称", default="")
|
||||
position: Optional[str] = Field(description="职位", default=None)
|
||||
department: Optional[str] = Field(description="部门", default=None)
|
||||
contact: Optional[str] = Field(description="联系方式", default=None)
|
||||
|
||||
474
api/app/schemas/memory_config_schema.py
Normal file
474
api/app/schemas/memory_config_schema.py
Normal file
@@ -0,0 +1,474 @@
|
||||
# -*- coding: utf-8 -*-
|
||||
"""Memory Configuration Schemas
|
||||
|
||||
This module provides schema definitions for memory configuration.
|
||||
|
||||
Classes:
|
||||
MemoryConfig: Immutable memory configuration loaded from database
|
||||
MemoryConfigValidation: Pydantic model for configuration validation
|
||||
WorkspaceValidation: Pydantic model for workspace validation
|
||||
ModelValidation: Pydantic model for model configuration validation
|
||||
ConfigurationError: Base exception for configuration-related errors
|
||||
WorkspaceNotFoundError: Raised when workspace does not exist
|
||||
ModelNotFoundError: Raised when a required model does not exist
|
||||
ModelInactiveError: Raised when a required model exists but is inactive
|
||||
InvalidConfigError: Raised when configuration validation fails
|
||||
"""
|
||||
|
||||
from dataclasses import dataclass, field
|
||||
from datetime import datetime
|
||||
from typing import Any, Dict, Literal, Optional, Union
|
||||
from uuid import UUID
|
||||
|
||||
from pydantic import BaseModel, ConfigDict, Field, ValidationError, field_validator
|
||||
|
||||
# ==================== Configuration Exception Classes ====================
|
||||
|
||||
|
||||
class ConfigurationError(Exception):
|
||||
"""Base exception for configuration-related errors.
|
||||
|
||||
This exception includes context information to help with debugging
|
||||
and provides detailed error messages for different failure scenarios.
|
||||
"""
|
||||
|
||||
def __init__(
|
||||
self,
|
||||
message: str,
|
||||
config_id: Optional[int] = None,
|
||||
workspace_id: Optional[UUID] = None,
|
||||
context: Optional[Dict[str, Any]] = None,
|
||||
):
|
||||
"""Initialize configuration error with context.
|
||||
|
||||
Args:
|
||||
message: Error message describing the failure
|
||||
config_id: Optional configuration ID for context
|
||||
workspace_id: Optional workspace ID for context
|
||||
context: Optional additional context information
|
||||
"""
|
||||
self.config_id = config_id
|
||||
self.workspace_id = workspace_id
|
||||
self.context = context or {}
|
||||
|
||||
# Build detailed error message with context
|
||||
detailed_message = message
|
||||
if config_id is not None:
|
||||
detailed_message = f"Configuration {config_id}: {message}"
|
||||
if workspace_id is not None:
|
||||
detailed_message = f"{detailed_message} (workspace: {workspace_id})"
|
||||
|
||||
# Add context information if available
|
||||
if self.context:
|
||||
context_str = ", ".join(f"{k}={v}" for k, v in self.context.items())
|
||||
detailed_message = f"{detailed_message} [Context: {context_str}]"
|
||||
|
||||
super().__init__(detailed_message)
|
||||
|
||||
|
||||
class WorkspaceNotFoundError(ConfigurationError):
|
||||
"""Raised when workspace does not exist."""
|
||||
|
||||
def __init__(
|
||||
self,
|
||||
workspace_id: UUID,
|
||||
config_id: Optional[int] = None,
|
||||
message: Optional[str] = None,
|
||||
):
|
||||
if message is None:
|
||||
message = f"Workspace {workspace_id} not found in database"
|
||||
|
||||
context = {"workspace_id": str(workspace_id)}
|
||||
super().__init__(message, config_id=config_id, workspace_id=workspace_id, context=context)
|
||||
|
||||
|
||||
class ModelNotFoundError(ConfigurationError):
|
||||
"""Raised when a required model does not exist."""
|
||||
|
||||
def __init__(
|
||||
self,
|
||||
model_id: Union[str, UUID],
|
||||
model_type: str,
|
||||
config_id: Optional[int] = None,
|
||||
workspace_id: Optional[UUID] = None,
|
||||
message: Optional[str] = None,
|
||||
):
|
||||
if message is None:
|
||||
message = f"{model_type.title()} model {model_id} not found in database"
|
||||
|
||||
context = {
|
||||
"model_id": str(model_id),
|
||||
"model_type": model_type,
|
||||
"failure_type": "not_found",
|
||||
}
|
||||
super().__init__(message, config_id=config_id, workspace_id=workspace_id, context=context)
|
||||
|
||||
|
||||
class ModelInactiveError(ConfigurationError):
|
||||
"""Raised when a required model exists but is inactive."""
|
||||
|
||||
def __init__(
|
||||
self,
|
||||
model_id: Union[str, UUID],
|
||||
model_name: str,
|
||||
model_type: str,
|
||||
config_id: Optional[int] = None,
|
||||
workspace_id: Optional[UUID] = None,
|
||||
message: Optional[str] = None,
|
||||
):
|
||||
if message is None:
|
||||
message = f"{model_type.title()} model {model_id} ({model_name}) is inactive"
|
||||
|
||||
context = {
|
||||
"model_id": str(model_id),
|
||||
"model_name": model_name,
|
||||
"model_type": model_type,
|
||||
"failure_type": "inactive",
|
||||
}
|
||||
super().__init__(message, config_id=config_id, workspace_id=workspace_id, context=context)
|
||||
|
||||
|
||||
class InvalidConfigError(ConfigurationError):
|
||||
"""Raised when configuration validation fails."""
|
||||
|
||||
def __init__(
|
||||
self,
|
||||
message: str,
|
||||
field_name: Optional[str] = None,
|
||||
invalid_value: Optional[Any] = None,
|
||||
config_id: Optional[int] = None,
|
||||
workspace_id: Optional[UUID] = None,
|
||||
):
|
||||
context = {}
|
||||
if field_name is not None:
|
||||
context["field_name"] = field_name
|
||||
if invalid_value is not None:
|
||||
context["invalid_value"] = str(invalid_value)
|
||||
context["invalid_value_type"] = type(invalid_value).__name__
|
||||
|
||||
super().__init__(message, config_id=config_id, workspace_id=workspace_id, context=context)
|
||||
|
||||
|
||||
# ==================== Pydantic Validation Models ====================
|
||||
|
||||
|
||||
class MemoryConfigValidation(BaseModel):
|
||||
"""Pydantic model for validating memory configuration data from database."""
|
||||
|
||||
config_id: int = Field(..., gt=0, description="Configuration ID must be positive")
|
||||
config_name: str = Field(..., min_length=1, max_length=255)
|
||||
workspace_id: UUID = Field(..., description="Workspace UUID")
|
||||
workspace_name: str = Field(..., min_length=1, max_length=255)
|
||||
tenant_id: UUID = Field(..., description="Tenant UUID")
|
||||
|
||||
embedding_model_id: UUID = Field(..., description="Embedding model UUID (required)")
|
||||
embedding_model_name: str = Field(..., min_length=1, max_length=255)
|
||||
llm_model_id: UUID = Field(..., description="LLM model UUID (required)")
|
||||
llm_model_name: str = Field(..., min_length=1, max_length=255)
|
||||
rerank_model_id: Optional[UUID] = Field(None, description="Rerank model UUID (optional)")
|
||||
rerank_model_name: Optional[str] = Field(None, max_length=255)
|
||||
|
||||
storage_type: str = Field(..., min_length=1, max_length=50)
|
||||
|
||||
chunker_strategy: str = Field(default="RecursiveChunker", min_length=1, max_length=100)
|
||||
reflexion_enabled: bool = Field(default=False)
|
||||
reflexion_iteration_period: int = Field(default=3, ge=1, le=100)
|
||||
reflexion_range: Literal["retrieval", "all"] = Field(default="retrieval")
|
||||
reflexion_baseline: Literal["time", "fact", "time_and_fact"] = Field(default="time")
|
||||
|
||||
llm_params: Dict[str, Any] = Field(default_factory=dict)
|
||||
embedding_params: Dict[str, Any] = Field(default_factory=dict)
|
||||
config_version: str = Field(default="2.0", min_length=1, max_length=10)
|
||||
|
||||
@field_validator("config_name", "workspace_name", "embedding_model_name", "llm_model_name")
|
||||
@classmethod
|
||||
def validate_non_empty_strings(cls, v):
|
||||
if not v or not v.strip():
|
||||
raise ValueError("Field cannot be empty or whitespace-only")
|
||||
return v.strip()
|
||||
|
||||
@field_validator("storage_type")
|
||||
@classmethod
|
||||
def validate_storage_type(cls, v):
|
||||
valid_types = ["neo4j", "elasticsearch", "qdrant", "milvus", "chroma"]
|
||||
if v.lower() not in valid_types:
|
||||
raise ValueError(f"Storage type must be one of: {valid_types}")
|
||||
return v.lower()
|
||||
|
||||
@field_validator("llm_params", "embedding_params")
|
||||
@classmethod
|
||||
def validate_model_params(cls, v):
|
||||
if not isinstance(v, dict):
|
||||
raise ValueError("Model parameters must be a dictionary")
|
||||
reserved_keys = ["model_id", "model_name", "api_key", "base_url"]
|
||||
for key in v.keys():
|
||||
if key in reserved_keys:
|
||||
raise ValueError(f"Model parameters cannot contain reserved parameter '{key}'")
|
||||
return v
|
||||
|
||||
model_config = ConfigDict(validate_assignment=True, extra="forbid")
|
||||
|
||||
|
||||
class WorkspaceValidation(BaseModel):
|
||||
"""Pydantic model for validating workspace data from database."""
|
||||
|
||||
id: UUID = Field(..., description="Workspace UUID")
|
||||
name: str = Field(..., min_length=1, max_length=255)
|
||||
tenant_id: UUID = Field(..., description="Tenant UUID")
|
||||
storage_type: Optional[str] = Field(None, max_length=50)
|
||||
llm: Optional[str] = Field(None)
|
||||
embedding: Optional[str] = Field(None)
|
||||
rerank: Optional[str] = Field(None)
|
||||
is_active: bool = Field(default=True)
|
||||
|
||||
@field_validator("llm", "embedding", "rerank")
|
||||
@classmethod
|
||||
def validate_model_ids(cls, v):
|
||||
if v is None or v == "":
|
||||
return None
|
||||
try:
|
||||
UUID(v.strip())
|
||||
except ValueError:
|
||||
raise ValueError("Model ID must be a valid UUID string")
|
||||
return v.strip()
|
||||
|
||||
@field_validator("is_active")
|
||||
@classmethod
|
||||
def validate_active_status(cls, v):
|
||||
if not v:
|
||||
raise ValueError("Workspace must be active for configuration loading")
|
||||
return v
|
||||
|
||||
model_config = ConfigDict(validate_assignment=True, extra="forbid")
|
||||
|
||||
|
||||
class ModelValidation(BaseModel):
|
||||
"""Pydantic model for validating model configuration data."""
|
||||
|
||||
id: UUID = Field(..., description="Model UUID")
|
||||
name: str = Field(..., min_length=1, max_length=255)
|
||||
type: str = Field(..., description="Model type (llm, embedding, rerank)")
|
||||
tenant_id: UUID = Field(..., description="Tenant UUID")
|
||||
is_active: bool = Field(..., description="Whether model is active")
|
||||
is_public: bool = Field(default=False)
|
||||
|
||||
@field_validator("type")
|
||||
@classmethod
|
||||
def validate_type(cls, v):
|
||||
valid_types = ["llm", "embedding", "rerank"]
|
||||
if v.lower() not in valid_types:
|
||||
raise ValueError(f"Model type must be one of: {valid_types}")
|
||||
return v.lower()
|
||||
|
||||
@field_validator("is_active")
|
||||
@classmethod
|
||||
def validate_active_status(cls, v):
|
||||
if not v:
|
||||
raise ValueError("Model must be active for configuration use")
|
||||
return v
|
||||
|
||||
model_config = ConfigDict(validate_assignment=True, extra="forbid")
|
||||
|
||||
|
||||
# ==================== Validation Helper Functions ====================
|
||||
|
||||
|
||||
def validate_memory_config_data(
|
||||
config_data: Dict[str, Any], config_id: Optional[int] = None
|
||||
) -> MemoryConfigValidation:
|
||||
"""Validate memory configuration data using Pydantic model."""
|
||||
try:
|
||||
return MemoryConfigValidation(**config_data)
|
||||
except ValidationError as e:
|
||||
error_messages = []
|
||||
for error in e.errors():
|
||||
field_path = " -> ".join(str(loc) for loc in error["loc"])
|
||||
error_messages.append(f"Field '{field_path}': {error['msg']}")
|
||||
|
||||
detailed_message = "Configuration validation failed:\n" + "\n".join(
|
||||
f" - {msg}" for msg in error_messages
|
||||
)
|
||||
|
||||
first_error = e.errors()[0] if e.errors() else {}
|
||||
first_field = " -> ".join(str(loc) for loc in first_error.get("loc", []))
|
||||
|
||||
raise InvalidConfigError(
|
||||
detailed_message,
|
||||
field_name=first_field or None,
|
||||
invalid_value=first_error.get("input"),
|
||||
config_id=config_id,
|
||||
)
|
||||
|
||||
|
||||
def validate_workspace_data(
|
||||
workspace_data: Dict[str, Any], config_id: Optional[int] = None
|
||||
) -> WorkspaceValidation:
|
||||
"""Validate workspace data using Pydantic model."""
|
||||
try:
|
||||
return WorkspaceValidation(**workspace_data)
|
||||
except ValidationError as e:
|
||||
error_messages = []
|
||||
for error in e.errors():
|
||||
field_path = " -> ".join(str(loc) for loc in error["loc"])
|
||||
error_messages.append(f"Field '{field_path}': {error['msg']}")
|
||||
|
||||
detailed_message = "Workspace validation failed:\n" + "\n".join(
|
||||
f" - {msg}" for msg in error_messages
|
||||
)
|
||||
|
||||
first_error = e.errors()[0] if e.errors() else {}
|
||||
first_field = " -> ".join(str(loc) for loc in first_error.get("loc", []))
|
||||
workspace_id = workspace_data.get("id") if isinstance(workspace_data, dict) else None
|
||||
|
||||
raise InvalidConfigError(
|
||||
detailed_message,
|
||||
field_name=first_field or None,
|
||||
invalid_value=first_error.get("input"),
|
||||
config_id=config_id,
|
||||
workspace_id=workspace_id,
|
||||
)
|
||||
|
||||
|
||||
def validate_model_data(
|
||||
model_data: Dict[str, Any], config_id: Optional[int] = None
|
||||
) -> ModelValidation:
|
||||
"""Validate model data using Pydantic model."""
|
||||
try:
|
||||
return ModelValidation(**model_data)
|
||||
except ValidationError as e:
|
||||
error_messages = []
|
||||
for error in e.errors():
|
||||
field_path = " -> ".join(str(loc) for loc in error["loc"])
|
||||
error_messages.append(f"Field '{field_path}': {error['msg']}")
|
||||
|
||||
detailed_message = "Model validation failed:\n" + "\n".join(
|
||||
f" - {msg}" for msg in error_messages
|
||||
)
|
||||
|
||||
first_error = e.errors()[0] if e.errors() else {}
|
||||
first_field = " -> ".join(str(loc) for loc in first_error.get("loc", []))
|
||||
|
||||
raise InvalidConfigError(
|
||||
detailed_message,
|
||||
field_name=first_field or None,
|
||||
invalid_value=first_error.get("input"),
|
||||
config_id=config_id,
|
||||
)
|
||||
|
||||
|
||||
# ==================== Immutable Configuration Data Structure ====================
|
||||
|
||||
|
||||
@dataclass(frozen=True)
|
||||
class MemoryConfig:
|
||||
"""Immutable memory configuration loaded from database."""
|
||||
|
||||
config_id: int
|
||||
config_name: str
|
||||
workspace_id: UUID
|
||||
workspace_name: str
|
||||
tenant_id: UUID
|
||||
|
||||
embedding_model_id: UUID
|
||||
embedding_model_name: str
|
||||
llm_model_id: UUID
|
||||
llm_model_name: str
|
||||
|
||||
storage_type: str
|
||||
|
||||
chunker_strategy: str
|
||||
reflexion_enabled: bool
|
||||
reflexion_iteration_period: int
|
||||
reflexion_range: str
|
||||
reflexion_baseline: str
|
||||
|
||||
loaded_at: datetime
|
||||
|
||||
rerank_model_id: Optional[UUID] = None
|
||||
rerank_model_name: Optional[str] = None
|
||||
|
||||
llm_params: Dict[str, Any] = field(default_factory=dict)
|
||||
embedding_params: Dict[str, Any] = field(default_factory=dict)
|
||||
config_version: str = "2.0"
|
||||
|
||||
# Pipeline config: Deduplication
|
||||
enable_llm_dedup_blockwise: bool = False
|
||||
enable_llm_disambiguation: bool = False
|
||||
deep_retrieval: bool = True
|
||||
t_type_strict: float = 0.8
|
||||
t_name_strict: float = 0.8
|
||||
t_overall: float = 0.8
|
||||
|
||||
# Pipeline config: Statement extraction
|
||||
statement_granularity: int = 2
|
||||
include_dialogue_context: bool = False
|
||||
max_dialogue_context_chars: int = 1000
|
||||
|
||||
# Pipeline config: Forgetting engine
|
||||
lambda_time: float = 0.5
|
||||
lambda_mem: float = 0.5
|
||||
offset: float = 0.0
|
||||
|
||||
# Pipeline config: Pruning
|
||||
pruning_enabled: bool = False
|
||||
pruning_scene: Optional[str] = "education"
|
||||
pruning_threshold: float = 0.5
|
||||
|
||||
def __post_init__(self):
|
||||
"""Validate configuration after initialization."""
|
||||
if not self.config_name or not self.config_name.strip():
|
||||
raise InvalidConfigError("Configuration name cannot be empty")
|
||||
|
||||
if not self.embedding_model_id:
|
||||
raise InvalidConfigError("Embedding model ID is required")
|
||||
|
||||
if not self.llm_model_id:
|
||||
raise InvalidConfigError("LLM model ID is required")
|
||||
|
||||
@classmethod
|
||||
def from_validated_data(
|
||||
cls, validated_config: MemoryConfigValidation, loaded_at: datetime
|
||||
) -> "MemoryConfig":
|
||||
"""Create MemoryConfig from validated Pydantic data."""
|
||||
return cls(
|
||||
config_id=validated_config.config_id,
|
||||
config_name=validated_config.config_name,
|
||||
workspace_id=validated_config.workspace_id,
|
||||
workspace_name=validated_config.workspace_name,
|
||||
tenant_id=validated_config.tenant_id,
|
||||
embedding_model_id=validated_config.embedding_model_id,
|
||||
embedding_model_name=validated_config.embedding_model_name,
|
||||
storage_type=validated_config.storage_type,
|
||||
chunker_strategy=validated_config.chunker_strategy,
|
||||
reflexion_enabled=validated_config.reflexion_enabled,
|
||||
reflexion_iteration_period=validated_config.reflexion_iteration_period,
|
||||
reflexion_range=validated_config.reflexion_range,
|
||||
reflexion_baseline=validated_config.reflexion_baseline,
|
||||
loaded_at=loaded_at,
|
||||
llm_model_id=validated_config.llm_model_id,
|
||||
llm_model_name=validated_config.llm_model_name,
|
||||
rerank_model_id=validated_config.rerank_model_id,
|
||||
rerank_model_name=validated_config.rerank_model_name,
|
||||
llm_params=validated_config.llm_params,
|
||||
embedding_params=validated_config.embedding_params,
|
||||
config_version=validated_config.config_version,
|
||||
)
|
||||
|
||||
def get_model_summary(self) -> Dict[str, Optional[str]]:
|
||||
"""Get a summary of configured models."""
|
||||
return {
|
||||
"llm": self.llm_model_name,
|
||||
"embedding": self.embedding_model_name,
|
||||
"rerank": self.rerank_model_name,
|
||||
}
|
||||
|
||||
def is_model_configured(self, model_type: str) -> bool:
|
||||
"""Check if a specific model type is configured."""
|
||||
if model_type == "llm":
|
||||
return True
|
||||
elif model_type == "embedding":
|
||||
return True
|
||||
elif model_type == "rerank":
|
||||
return self.rerank_model_id is not None
|
||||
else:
|
||||
raise ValueError(f"Unknown model type: {model_type}")
|
||||
@@ -31,7 +31,7 @@ class BaseDataSchema(BaseModel):
|
||||
# 保持原有必需字段为可选，以兼容不同数据源
|
||||
id: Optional[str] = Field(None, description="The unique identifier for the data entry.")
|
||||
statement: Optional[str] = Field(None, description="The statement text.")
|
||||
created_at: str = Field(..., description="The creation timestamp in ISO 8601 format.")
|
||||
created_at: Optional[str] = Field(None, description="The creation timestamp in ISO 8601 format.")
|
||||
expired_at: Optional[str] = Field(None, description="The expiration timestamp in ISO 8601 format.")
|
||||
description: Optional[str] = Field(None, description="The description of the data entry.")
|
||||
|
||||
@@ -46,6 +46,14 @@ class BaseDataSchema(BaseModel):
|
||||
relationship: Optional[Union[str, Dict[str, Any]]] = Field(None, description="The relationship object or string.")
|
||||
entity2: Optional[Dict[str, Any]] = Field(None, description="The second entity object.")
|
||||
|
||||
@model_validator(mode="before")
|
||||
def _set_default_created_at(cls, v):
|
||||
"""Set default created_at if missing"""
|
||||
if isinstance(v, dict) and v.get("created_at") is None:
|
||||
from datetime import datetime
|
||||
v["created_at"] = datetime.now().isoformat()
|
||||
return v
|
||||
|
||||
|
||||
class QualityAssessmentSchema(BaseModel):
|
||||
"""Schema for memory quality assessment results."""
|
||||
|
||||
@@ -1,9 +1,17 @@
|
||||
import email
|
||||
from pydantic import BaseModel, Field, EmailStr, field_serializer, computed_field, ConfigDict
|
||||
import datetime
|
||||
import email
|
||||
import uuid
|
||||
from typing import Literal
|
||||
from app.models.workspace_model import WorkspaceRole, InviteStatus
|
||||
from typing import Literal, Optional
|
||||
|
||||
from app.models.workspace_model import InviteStatus, WorkspaceRole
|
||||
from pydantic import (
|
||||
BaseModel,
|
||||
ConfigDict,
|
||||
EmailStr,
|
||||
Field,
|
||||
computed_field,
|
||||
field_serializer,
|
||||
)
|
||||
|
||||
|
||||
class WorkspaceBase(BaseModel):
|
||||
@@ -170,3 +178,19 @@ class InviteValidateResponse(BaseModel):
|
||||
|
||||
class InviteAcceptRequest(BaseModel):
|
||||
token: str = Field(..., description="邀请令牌")
|
||||
|
||||
|
||||
class WorkspaceModelsUpdate(BaseModel):
|
||||
"""工作空间模型配置更新请求"""
|
||||
llm: Optional[uuid.UUID] = Field(default=None, description="LLM模型ID")
|
||||
embedding: Optional[uuid.UUID] = Field(default=None, description="嵌入模型ID")
|
||||
rerank: Optional[uuid.UUID] = Field(default=None, description="重排序模型ID")
|
||||
|
||||
|
||||
class WorkspaceModelsConfig(BaseModel):
|
||||
"""工作空间模型配置响应"""
|
||||
model_config = ConfigDict(from_attributes=True)
|
||||
|
||||
llm: Optional[str] = Field(default=None, description="LLM模型ID")
|
||||
embedding: Optional[str] = Field(default=None, description="嵌入模型ID")
|
||||
rerank: Optional[str] = Field(default=None, description="重排序模型ID")
|
||||
|
||||
Reference in New Issue
Block a user