Release/v0.2.3 (#355)
* feat(web): add PageEmpty component
* feat(web): add PageTabs component
* feat(web): add PageEmpty component
* feat(web): add PageTabs component
* feat(prompt): add history tracking for prompt releases
* feat(web): add prompt menu
* refactor: The PageScrollList component supports two generic parameters
* feat(web): BodyWrapper compoent update PageLoading
* feat(web): add Ontology menu
* feat(web): memory management add scene
* feat(tasks): add celery task configuration for periodic jobs
- Add ignore_result=True to prevent storing results for periodic tasks
- Set max_retries=0 to skip failed periodic tasks without retry attempts
- Configure acks_late=False for immediate acknowledgment in beat tasks
- Add time_limit and soft_time_limit to regenerate_memory_cache task (3600s/3300s)
- Add time_limit and soft_time_limit to workspace_reflection_task (300s/240s)
- Add time_limit and soft_time_limit to run_forgetting_cycle_task (7200s/7000s)
- Improve task reliability and resource management for scheduled jobs
* feat(sandbox): add Node.js code execution support to sandbox
* Release/v0.2.2 (#260)
* [modify] migration script
* [add] migration script
* fix(web): change form message
* fix(web): the memoryContent field is compatible with numbers and strings
* feat(web): code node hidden
* fix(model):
1. create a basic model to check if the name and provider are duplicated.
2. The result shows error models because the provider created API Keys for all matching models.
---------
Co-authored-by: Mark <zhuwenhui5566@163.com>
Co-authored-by: zhaoying <yzhao96@best-inc.com>
Co-authored-by: yingzhao <zhaoyingyz@126.com>
Co-authored-by: Timebomb2018 <18868801967@163.com>
* Feature/ontology class clean (#249)
* [add] Complete ontology engineering feature implementation
* [add] Add ontology feature integration and validation utilities
* [add] Add OWL validator and validation utilities
* [fix] Add missing render_ontology_extraction_prompt function
* [fix]Add dependencies, fix functionality
* [add] migration script
* feat(celery): add dedicated periodic tasks worker and queue (#261)
* fix(web): conflict resolve
* Fix/v022 bug (#263)
* [fix]Fix the issue of inconsistent language in explicit and episodic memory.
* [fix]Fix the issue of inconsistent language in explicit and episodic memory.
* [add]Add scene_id
* [fix]Based on the AI review to fix the code
* Fix/develop memory reflex (#265)
* 遗漏的历史映射
* 遗漏的历史映射
* 反思后台报错处理
* [add] migration script
* fix: chat conversation_id add node_start
* feat(web): show code node
* fix(web): Restructure the CustomSelect component, repair the interface that is called multiple times when the form is updated
* feat(web): RadioGroupCard support block mode
* feat(web): create space add icon
* feat(app and model): token consumption statistics
* Add/develop memory (#264)
* 遗漏的历史映射
* 遗漏的历史映射
* 遗漏的历史映射
* 遗漏的历史映射
* 遗漏的历史映射
* 遗漏的历史映射
* 遗漏的历史映射
* 遗漏的历史映射
* 遗漏的历史映射
* 新增长期记忆功能
* 新增长期记忆功能
* 新增长期记忆功能
* 知识库检索多余字段
* 长期
* feat(app and model): token consumption statistics of the cluster
* memory_BUG_fix
* fix(web): prompt history remove pageLoading
* fix(prompt): remove hard-coded import of prompt file paths (#279)
* Fix/develop memory bug (#274)
* 遗漏的历史映射
* 遗漏的历史映射
* fix_timeline_memories
* fix(web): update retrieve_type key
* Fix/develop memory bug (#276)
* 遗漏的历史映射
* 遗漏的历史映射
* fix_timeline_memories
* fix_timeline_memories
* write_gragp/bug_fix
* write_gragp/bug_fix
* write_gragp/bug_fix
* chore(celery): disable periodic task scheduling
* fix(prompt): remove hard-coded import of prompt file paths
---------
Co-authored-by: lixinyue11 <94037597+lixinyue11@users.noreply.github.com>
Co-authored-by: zhaoying <yzhao96@best-inc.com>
Co-authored-by: yingzhao <zhaoyingyz@126.com>
Co-authored-by: Ke Sun <kesun5@illinois.edu>
* fix(web): remove delete confirm content
* refactor(workflow): relocate template directory into workflow
* feat(memory): add long-term storage task routing and batching
* fix(web): PageScrollList loading update
* fix(web): PageScrollList loading update
* Ontology v1 bug (#291)
* [changes]Add 'id' as the secondary sorting key, and 'scene_id' now returns a UUID object
* [fix]Fix the "end_user" return to be sorted by update time.
* [fix]Set the default values of the memory configuration model based on the spatial model.
* [fix]Remove the entity extraction check combination model, read the configuration list, and add the return of scene_id
* [fix]Fix the "end_user" return to be sorted by update time.
* [fix]
* fix(memory): add Redis session validation
- Add macOS fork() safety configuration in celery_app.py to prevent initialization issues
- Add null/False checks for Redis session queries in term_memory_save to handle missing sessions gracefully
- Add null/False checks in memory_long_term_storage to prevent processing empty Redis results
- Add null/False checks in aggregate_judgment before format_parsing to avoid errors on missing data
- Initialize redis_messages variable in window_dialogue for consistency
- Add debug logging when no existing session found in Redis for better troubleshooting
- Add TODO comments for magic numbers (scope=6, time=5) to be extracted as constants
- Improve error handling when Redis returns False or empty results instead of crashing
* fix(web): PageScrollList style update
* fix(workflow): fix argument passing in code execution nodes
* fix(web): prompt add disabled
* fix(web): space icon required
* feat(app): modify the key of the token
* fix(fix the key of the app's token):
* fix(workflow): switch code input encoding to base64+URL encoding
* [add]The main project adds multi-API Key load balancing.
* [changes]Attribute security access, secure numerical conversion, unified use of local variables
* fix(web): save add session update
* fix(web): language editor support paste
* [changes]Active status filtering logic, API Key selection strategy
* memory_BUG
* memory_BUG_long_term
* [changes]
* memory_BUG_long_term
* memory_BUG_long_term
* Fix/release memory bug (#306)
* memory_BUG_fix
* memory_BUG
* memory_BUG_long_term
* memory_BUG_long_term
* memory_BUG_long_term
* knowledge_retrieval/bug/fix
* knowledge_retrieval/bug/fix
* knowledge_retrieval/bug/fix
* [fix]1.The "read_all_config" interface returns "scene_name";2.Memory configuration for lightweight query ontology scenarios
* fix(web): replace code editor
* [changes]Modify the description of the time for the recent event
* [changes]Modify the code based on the AI review
* feat(web): update memory config ontology api
* fix(web): ui update
* knowledge_retrieval/bug/fix
* knowledge_retrieval/bug/fix
* knowledge_retrieval/bug/fix
* feat(workflow): add token usage statistics for question classifier and parameter extraction
* feat(web): move prompt menu
* Multiple independent transactions - single transaction
* Multiple independent transactions - single transaction
* Multiple independent transactions - single transaction
* Multiple independent transactions - single transaction
* Write Missing None (#321)
* Write Missing None
* Write Missing None
* Write Missing None
* Apply suggestion from @sourcery-ai[bot]
Co-authored-by: sourcery-ai[bot] <58596630+sourcery-ai[bot]@users.noreply.github.com>
* Write Missing None
---------
Co-authored-by: sourcery-ai[bot] <58596630+sourcery-ai[bot]@users.noreply.github.com>
* Fix/release memory bug (#324)
* Write Missing None
* Write Missing None
* Write Missing None
* Apply suggestion from @sourcery-ai[bot]
Co-authored-by: sourcery-ai[bot] <58596630+sourcery-ai[bot]@users.noreply.github.com>
* Write Missing None
* redis update
* redis update
* redis update
* redis update
---------
Co-authored-by: sourcery-ai[bot] <58596630+sourcery-ai[bot]@users.noreply.github.com>
* Fix/writer memory bug (#326)
* [fix]Fix the bug
* [fix]Fix the bug
* [fix]Correct the direction indication.
* fix(web): markdown table ui update
* Fix/release memory bug (#332)
* Write Missing None
* Write Missing None
* Write Missing None
* Apply suggestion from @sourcery-ai[bot]
Co-authored-by: sourcery-ai[bot] <58596630+sourcery-ai[bot]@users.noreply.github.com>
* Write Missing None
* redis update
* redis update
* redis update
* redis update
* writer_dup_bug/fix
---------
Co-authored-by: sourcery-ai[bot] <58596630+sourcery-ai[bot]@users.noreply.github.com>
* Fix/fact summary (#333)
* [fix]Disable the contents related to fact_summary
* [fix]Disable the contents related to fact_summary
* [fix]Modify the code based on the AI review
* Fix/release memory bug (#335)
* Write Missing None
* Write Missing None
* Write Missing None
* Apply suggestion from @sourcery-ai[bot]
Co-authored-by: sourcery-ai[bot] <58596630+sourcery-ai[bot]@users.noreply.github.com>
* Write Missing None
* redis update
* redis update
* redis update
* redis update
* writer_dup_bug/fix
* writer_graph_bug/fix
* writer_graph_bug/fix
---------
Co-authored-by: sourcery-ai[bot] <58596630+sourcery-ai[bot]@users.noreply.github.com>
* Revert "feat(web): move prompt menu"
This reverts commit 9e6e8f50f8.
* fix(web): ui update
* fix(web): update text
* fix(web): ui update
* fix(model): change the "vl" model type of dashscope to "chat"
* fix(model): change the "vl" model type of dashscope to "chat"
---------
Co-authored-by: zhaoying <yzhao96@best-inc.com>
Co-authored-by: Eternity <1533512157@qq.com>
Co-authored-by: Mark <zhuwenhui5566@163.com>
Co-authored-by: yingzhao <zhaoyingyz@126.com>
Co-authored-by: Timebomb2018 <18868801967@163.com>
Co-authored-by: 乐力齐 <162269739+lanceyq@users.noreply.github.com>
Co-authored-by: lixinyue11 <94037597+lixinyue11@users.noreply.github.com>
Co-authored-by: lixinyue <2569494688@qq.com>
Co-authored-by: Eternity <61316157+myhMARS@users.noreply.github.com>
Co-authored-by: lanceyq <1982376970@qq.com>
Co-authored-by: sourcery-ai[bot] <58596630+sourcery-ai[bot]@users.noreply.github.com>
This commit is contained in:
Ke Sun
@@ -12,8 +12,8 @@ class KnowledgeBaseConfig(BaseModel):
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kb_id: str = Field(..., description="知识库ID")
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top_k: int = Field(default=3, ge=1, le=20, description="检索返回的文档数量")
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similarity_threshold: float = Field(default=0.7, ge=0.0, le=1.0, description="相似度阈值")
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strategy: str = Field(default="hybrid", description="检索策略: hybrid | bm25 | dense")
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weight: float = Field(default=1.0, ge=0.0, le=1.0, description="知识库权重（用于多知识库融合）")
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# strategy: str = Field(default="hybrid", description="检索策略: hybrid | bm25 | dense")
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# weight: float = Field(default=1.0, ge=0.0, le=1.0, description="知识库权重（用于多知识库融合）")
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vector_similarity_weight: float = Field(default=0.5, ge=0.0, le=1.0, description="向量相似度权重")
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retrieve_type: str = Field(default="hybrid", description="检索方式participle｜ semantic｜hybrid")
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@@ -1,3 +1,4 @@
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from abc import ABC
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from typing import Optional
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from pydantic import BaseModel
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@@ -14,4 +15,15 @@ class UserInput(BaseModel):
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class Write_UserInput(BaseModel):
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messages: list[dict]
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end_user_id: str
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config_id: Optional[str] = None
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config_id: Optional[str] = None
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class AgentMemory_Long_Term(ABC):
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"""长期记忆配置常量"""
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STORAGE_NEO4J = "neo4j"
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STORAGE_RAG = "rag"
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STRATEGY_AGGREGATE = "aggregate"
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STRATEGY_CHUNK = "chunk"
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STRATEGY_TIME = "time"
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DEFAULT_SCOPE = 6
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@@ -229,10 +229,15 @@ class ConfigParamsCreate(BaseModel): # 创建配置参数模型（仅 body，
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config_desc: str = Field("配置描述", description="配置描述（字符串）")
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workspace_id: Optional[uuid.UUID] = Field(None, description="工作空间ID（UUID）")
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# 本体场景关联（可选）
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scene_id: Optional[uuid.UUID] = Field(None, description="本体场景ID（UUID），关联ontology_scene表")
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# 模型配置字段（可选，用于手动指定或自动填充）
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llm_id: Optional[str] = Field(None, description="LLM模型配置ID")
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embedding_id: Optional[str] = Field(None, description="嵌入模型配置ID")
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rerank_id: Optional[str] = Field(None, description="重排序模型配置ID")
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reflection_model_id: Optional[str] = Field(None, description="反思模型ID，默认与llm_id一致")
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emotion_model_id: Optional[str] = Field(None, description="情绪分析模型ID，默认与llm_id一致")
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class ConfigParamsDelete(BaseModel): # 删除配置参数模型（请求体）
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@@ -243,8 +248,9 @@ class ConfigParamsDelete(BaseModel): # 删除配置参数模型（请求体）
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class ConfigUpdate(BaseModel): # 更新记忆萃取引擎配置参数时使用的模型
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config_id: Union[uuid.UUID, int, str] = None
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config_name: str = Field("配置名称", description="配置名称（字符串）")
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config_desc: str = Field("配置描述", description="配置描述（字符串）")
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config_name: Optional[str] = Field(None, description="配置名称（字符串）")
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config_desc: Optional[str] = Field(None, description="配置描述（字符串）")
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scene_id: Optional[uuid.UUID] = Field(None, description="本体场景ID")
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class ConfigUpdateExtracted(BaseModel): # 更新记忆萃取引擎配置参数时使用的模型
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461
api/app/schemas/ontology_schemas.py
Normal file
461
api/app/schemas/ontology_schemas.py
Normal file
@@ -0,0 +1,461 @@
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"""本体提取API的请求和响应模型
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本模块定义了本体提取系统的所有API请求和响应的Pydantic模型。
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Classes:
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ExtractionRequest: 本体提取请求模型
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ExtractionResponse: 本体提取响应模型
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ExportRequest: OWL文件导出请求模型
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ExportResponse: OWL文件导出响应模型
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OntologyResultResponse: 本体提取结果响应模型(带毫秒时间戳)
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SceneCreateRequest: 场景创建请求模型
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SceneUpdateRequest: 场景更新请求模型
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SceneResponse: 场景响应模型
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SceneListResponse: 场景列表响应模型
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ClassCreateRequest: 类型创建请求模型
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ClassUpdateRequest: 类型更新请求模型
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ClassResponse: 类型响应模型
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ClassListResponse: 类型列表响应模型
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"""
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from typing import List, Optional
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import datetime
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from uuid import UUID
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from pydantic import BaseModel, Field, field_serializer, ConfigDict
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from app.core.memory.models.ontology_models import OntologyClass
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class ExtractionRequest(BaseModel):
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"""本体提取请求模型
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用于POST /api/ontology/extract端点的请求体。
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Attributes:
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scenario: 场景描述文本,不能为空
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domain: 可选的领域提示(如Healthcare, Education等)
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llm_id: LLM模型ID,必须提供
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scene_id: 场景ID,必须提供,用于将提取的类保存到指定场景
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Examples:
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>>> request = ExtractionRequest(
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... scenario="医院管理患者记录...",
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... domain="Healthcare",
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... llm_id="550e8400-e29b-41d4-a716-446655440000",
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... scene_id="660e8400-e29b-41d4-a716-446655440000"
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... )
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"""
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scenario: str = Field(..., description="场景描述文本", min_length=1)
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domain: Optional[str] = Field(None, description="可选的领域提示")
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llm_id: str = Field(..., description="LLM模型ID")
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scene_id: UUID = Field(..., description="场景ID,用于将提取的类保存到指定场景")
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class ExtractionResponse(BaseModel):
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"""本体提取响应模型
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用于POST /api/ontology/extract端点的响应体。
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Attributes:
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classes: 提取的本体类列表
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domain: 识别的领域
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extracted_count: 提取的类数量
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Examples:
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>>> response = ExtractionResponse(
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... classes=[...],
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... domain="Healthcare",
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... extracted_count=7
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... )
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"""
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classes: List[OntologyClass] = Field(default_factory=list, description="提取的本体类列表")
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domain: str = Field(..., description="识别的领域")
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extracted_count: int = Field(..., description="提取的类数量")
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class ExportRequest(BaseModel):
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"""OWL文件导出请求模型
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用于POST /api/ontology/export端点的请求体。
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Attributes:
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classes: 要导出的本体类列表
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format: 导出格式,可选值: rdfxml, turtle, ntriples, json
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include_metadata: 是否包含完整的OWL元数据(命名空间等),默认True
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Examples:
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>>> request = ExportRequest(
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... classes=[...],
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... format="rdfxml",
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... include_metadata=True
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... )
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"""
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classes: List[OntologyClass] = Field(..., description="要导出的本体类列表", min_length=1)
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format: str = Field("rdfxml", description="导出格式: rdfxml, turtle, ntriples, json")
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include_metadata: bool = Field(True, description="是否包含完整的OWL元数据")
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class ExportResponse(BaseModel):
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"""OWL文件导出响应模型
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用于POST /api/ontology/export端点的响应体。
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Attributes:
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owl_content: OWL文件内容
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format: 导出格式
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classes_count: 导出的类数量
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Examples:
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>>> response = ExportResponse(
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... owl_content="<?xml version='1.0'?>...",
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... format="rdfxml",
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... classes_count=7
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... )
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"""
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owl_content: str = Field(..., description="OWL文件内容")
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format: str = Field(..., description="导出格式")
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classes_count: int = Field(..., description="导出的类数量")
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class OntologyResultResponse(BaseModel):
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"""本体提取结果响应模型
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用于返回数据库中存储的提取结果,时间戳为毫秒级。
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Attributes:
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id: 结果ID (UUID)
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scenario: 场景描述文本
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domain: 领域
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classes_json: 提取的本体类数据(JSON格式)
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extracted_count: 提取的类数量
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user_id: 用户ID
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created_at: 创建时间(毫秒时间戳)
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Examples:
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>>> response = OntologyResultResponse(
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... id=uuid.uuid4(),
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... scenario="医院管理患者记录...",
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... domain="Healthcare",
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... classes_json={"classes": [...]},
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... extracted_count=7,
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... user_id=123,
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... created_at=datetime.now()
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... )
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"""
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id: UUID = Field(..., description="结果ID")
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scenario: str = Field(..., description="场景描述文本")
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domain: Optional[str] = Field(None, description="领域")
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classes_json: dict = Field(..., description="提取的本体类数据(JSON格式)")
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extracted_count: int = Field(..., description="提取的类数量")
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user_id: Optional[int] = Field(None, description="用户ID")
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created_at: datetime.datetime = Field(..., description="创建时间")
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@field_serializer("created_at", when_used="json")
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def _serialize_created_at(self, dt: datetime.datetime):
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"""将创建时间序列化为毫秒时间戳"""
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return int(dt.timestamp() * 1000) if dt else None
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class Config:
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from_attributes = True
|
||||
|
||||
|
||||
|
||||
# ==================== 本体场景相关 Schema ====================
|
||||
|
||||
class SceneCreateRequest(BaseModel):
|
||||
"""场景创建请求模型
|
||||
|
||||
用于创建新的本体场景。
|
||||
|
||||
Attributes:
|
||||
scene_name: 场景名称，必填，1-200字符
|
||||
scene_description: 场景描述，可选
|
||||
|
||||
Examples:
|
||||
>>> request = SceneCreateRequest(
|
||||
... scene_name="医疗场景",
|
||||
... scene_description="用于医疗领域的本体建模"
|
||||
... )
|
||||
"""
|
||||
scene_name: str = Field(..., min_length=1, max_length=200, description="场景名称")
|
||||
scene_description: Optional[str] = Field(None, description="场景描述")
|
||||
|
||||
|
||||
class SceneUpdateRequest(BaseModel):
|
||||
"""场景更新请求模型
|
||||
|
||||
用于更新已有本体场景信息。
|
||||
|
||||
Attributes:
|
||||
scene_name: 场景名称，可选，1-200字符
|
||||
scene_description: 场景描述，可选
|
||||
|
||||
Examples:
|
||||
>>> request = SceneUpdateRequest(
|
||||
... scene_name="更新后的场景名称",
|
||||
... scene_description="更新后的描述"
|
||||
... )
|
||||
"""
|
||||
scene_name: Optional[str] = Field(None, min_length=1, max_length=200, description="场景名称")
|
||||
scene_description: Optional[str] = Field(None, description="场景描述")
|
||||
|
||||
|
||||
class SceneResponse(BaseModel):
|
||||
"""场景响应模型
|
||||
|
||||
用于返回本体场景信息。
|
||||
|
||||
Attributes:
|
||||
scene_id: 场景ID
|
||||
scene_name: 场景名称
|
||||
scene_description: 场景描述
|
||||
type_num: 类型数量
|
||||
workspace_id: 所属工作空间ID
|
||||
created_at: 创建时间（毫秒时间戳）
|
||||
updated_at: 更新时间（毫秒时间戳）
|
||||
classes_count: 类型数量
|
||||
|
||||
Examples:
|
||||
>>> response = SceneResponse(
|
||||
... scene_id=uuid.uuid4(),
|
||||
... scene_name="医疗场景",
|
||||
... scene_description="用于医疗领域的本体建模",
|
||||
... type_num=0,
|
||||
... workspace_id=uuid.uuid4(),
|
||||
... created_at=datetime.now(),
|
||||
... updated_at=datetime.now(),
|
||||
... classes_count=5
|
||||
... )
|
||||
"""
|
||||
scene_id: UUID = Field(..., description="场景ID")
|
||||
scene_name: str = Field(..., description="场景名称")
|
||||
scene_description: Optional[str] = Field(None, description="场景描述")
|
||||
type_num: int = Field(..., description="类型数量")
|
||||
entity_type: Optional[List[str]] = Field(None, description="实体类型列表（最多3个class_name）")
|
||||
workspace_id: UUID = Field(..., description="所属工作空间ID")
|
||||
created_at: datetime.datetime = Field(..., description="创建时间（毫秒时间戳）")
|
||||
updated_at: datetime.datetime = Field(..., description="更新时间（毫秒时间戳）")
|
||||
classes_count: int = Field(0, description="类型数量")
|
||||
|
||||
@field_serializer("created_at", when_used="json")
|
||||
def _serialize_created_at(self, dt: datetime.datetime):
|
||||
"""将创建时间序列化为毫秒时间戳"""
|
||||
return int(dt.timestamp() * 1000) if dt else None
|
||||
|
||||
@field_serializer("updated_at", when_used="json")
|
||||
def _serialize_updated_at(self, dt: datetime.datetime):
|
||||
"""将更新时间序列化为毫秒时间戳"""
|
||||
return int(dt.timestamp() * 1000) if dt else None
|
||||
|
||||
model_config = ConfigDict(from_attributes=True)
|
||||
|
||||
|
||||
class PaginationInfo(BaseModel):
|
||||
"""分页信息模型
|
||||
|
||||
Attributes:
|
||||
page: 当前页码
|
||||
pagesize: 每页数量
|
||||
total: 总数量
|
||||
hasnext: 是否有下一页
|
||||
"""
|
||||
page: int = Field(..., description="当前页码")
|
||||
pagesize: int = Field(..., description="每页数量")
|
||||
total: int = Field(..., description="总数量")
|
||||
hasnext: bool = Field(..., description="是否有下一页")
|
||||
|
||||
|
||||
class SceneListResponse(BaseModel):
|
||||
"""场景列表响应模型（支持分页）
|
||||
|
||||
用于返回本体场景列表。
|
||||
|
||||
Attributes:
|
||||
items: 场景列表
|
||||
page: 分页信息（可选，分页时返回）
|
||||
|
||||
Examples:
|
||||
>>> # 不分页
|
||||
>>> response = SceneListResponse(
|
||||
... items=[scene1, scene2]
|
||||
... )
|
||||
>>> # 分页
|
||||
>>> response = SceneListResponse(
|
||||
... items=[scene1, scene2, ...],
|
||||
... page=PaginationInfo(page=1, pagesize=100, total=150, hasnext=True)
|
||||
... )
|
||||
"""
|
||||
items: List[SceneResponse] = Field(..., description="场景列表")
|
||||
page: Optional[PaginationInfo] = Field(None, description="分页信息")
|
||||
|
||||
|
||||
# ==================== 本体类型相关 Schema ====================
|
||||
|
||||
class ClassItem(BaseModel):
|
||||
"""单个类型信息模型
|
||||
|
||||
Attributes:
|
||||
class_name: 类型名称，必填，1-200字符
|
||||
class_description: 类型描述，可选
|
||||
|
||||
Examples:
|
||||
>>> item = ClassItem(
|
||||
... class_name="患者",
|
||||
... class_description="医院患者信息"
|
||||
... )
|
||||
"""
|
||||
class_name: str = Field(..., min_length=1, max_length=200, description="类型名称")
|
||||
class_description: Optional[str] = Field(None, description="类型描述")
|
||||
|
||||
|
||||
class ClassCreateRequest(BaseModel):
|
||||
"""类型创建请求模型（统一使用列表形式）
|
||||
|
||||
通过列表中元素数量决定创建模式：
|
||||
- 列表包含 1 个元素：单个创建
|
||||
- 列表包含多个元素：批量创建
|
||||
|
||||
Attributes:
|
||||
scene_id: 所属场景ID，必填
|
||||
classes: 类型列表，必填，至少包含 1 个元素
|
||||
|
||||
Examples:
|
||||
# 单个创建（列表中 1 个元素）
|
||||
>>> request = ClassCreateRequest(
|
||||
... scene_id=uuid.uuid4(),
|
||||
... classes=[
|
||||
... ClassItem(class_name="患者", class_description="医院患者信息")
|
||||
... ]
|
||||
... )
|
||||
|
||||
# 批量创建（列表中多个元素）
|
||||
>>> request = ClassCreateRequest(
|
||||
... scene_id=uuid.uuid4(),
|
||||
... classes=[
|
||||
... ClassItem(class_name="患者", class_description="医院患者信息"),
|
||||
... ClassItem(class_name="医生", class_description="医院医生信息"),
|
||||
... ClassItem(class_name="药品", class_description="医院药品信息")
|
||||
... ]
|
||||
... )
|
||||
"""
|
||||
scene_id: UUID = Field(..., description="所属场景ID")
|
||||
classes: List[ClassItem] = Field(..., min_length=1, description="类型列表，至少包含 1 个元素")
|
||||
|
||||
|
||||
class ClassUpdateRequest(BaseModel):
|
||||
"""类型更新请求模型
|
||||
|
||||
用于更新已有本体类型信息。
|
||||
|
||||
Attributes:
|
||||
class_name: 类型名称，可选，1-200字符
|
||||
class_description: 类型描述，可选
|
||||
|
||||
Examples:
|
||||
>>> request = ClassUpdateRequest(
|
||||
... class_name="更新后的类型名称",
|
||||
... class_description="更新后的描述"
|
||||
... )
|
||||
"""
|
||||
class_name: Optional[str] = Field(None, min_length=1, max_length=200, description="类型名称")
|
||||
class_description: Optional[str] = Field(None, description="类型描述")
|
||||
|
||||
|
||||
class ClassResponse(BaseModel):
|
||||
"""类型响应模型
|
||||
|
||||
用于返回本体类型信息。
|
||||
|
||||
Attributes:
|
||||
class_id: 类型ID
|
||||
class_name: 类型名称
|
||||
class_description: 类型描述
|
||||
scene_id: 所属场景ID
|
||||
created_at: 创建时间（毫秒时间戳）
|
||||
updated_at: 更新时间（毫秒时间戳）
|
||||
|
||||
Examples:
|
||||
>>> response = ClassResponse(
|
||||
... class_id=uuid.uuid4(),
|
||||
... class_name="患者",
|
||||
... class_description="医院患者信息",
|
||||
... scene_id=uuid.uuid4(),
|
||||
... created_at=datetime.now(),
|
||||
... updated_at=datetime.now()
|
||||
... )
|
||||
"""
|
||||
class_id: UUID = Field(..., description="类型ID")
|
||||
class_name: str = Field(..., description="类型名称")
|
||||
class_description: Optional[str] = Field(None, description="类型描述")
|
||||
scene_id: UUID = Field(..., description="所属场景ID")
|
||||
created_at: datetime.datetime = Field(..., description="创建时间（毫秒时间戳）")
|
||||
updated_at: datetime.datetime = Field(..., description="更新时间（毫秒时间戳）")
|
||||
|
||||
@field_serializer("created_at", when_used="json")
|
||||
def _serialize_created_at(self, dt: datetime.datetime):
|
||||
"""将创建时间序列化为毫秒时间戳"""
|
||||
return int(dt.timestamp() * 1000) if dt else None
|
||||
|
||||
@field_serializer("updated_at", when_used="json")
|
||||
def _serialize_updated_at(self, dt: datetime.datetime):
|
||||
"""将更新时间序列化为毫秒时间戳"""
|
||||
return int(dt.timestamp() * 1000) if dt else None
|
||||
|
||||
model_config = ConfigDict(from_attributes=True)
|
||||
|
||||
|
||||
class ClassBatchCreateResponse(BaseModel):
|
||||
"""批量创建类型响应模型
|
||||
|
||||
用于返回批量创建的结果统计和详情。
|
||||
|
||||
Attributes:
|
||||
total: 总共尝试创建的数量
|
||||
success_count: 成功创建的数量
|
||||
failed_count: 失败的数量
|
||||
items: 成功创建的类型列表
|
||||
errors: 失败的错误信息列表（可选）
|
||||
|
||||
Examples:
|
||||
>>> response = ClassBatchCreateResponse(
|
||||
... total=3,
|
||||
... success_count=2,
|
||||
... failed_count=1,
|
||||
... items=[class1, class2],
|
||||
... errors=["创建类型 '药品' 失败: 类型名称已存在"]
|
||||
... )
|
||||
"""
|
||||
total: int = Field(..., description="总共尝试创建的数量")
|
||||
success_count: int = Field(..., description="成功创建的数量")
|
||||
failed_count: int = Field(0, description="失败的数量")
|
||||
items: List[ClassResponse] = Field(..., description="成功创建的类型列表")
|
||||
errors: Optional[List[str]] = Field(None, description="失败的错误信息列表")
|
||||
|
||||
|
||||
class ClassListResponse(BaseModel):
|
||||
"""类型列表响应模型
|
||||
|
||||
用于返回本体类型列表。
|
||||
|
||||
Attributes:
|
||||
total: 总数量
|
||||
scene_id: 所属场景ID
|
||||
scene_name: 场景名称
|
||||
scene_description: 场景描述
|
||||
items: 类型列表
|
||||
|
||||
Examples:
|
||||
>>> response = ClassListResponse(
|
||||
... total=3,
|
||||
... scene_id=uuid.uuid4(),
|
||||
... scene_name="医疗场景",
|
||||
... scene_description="用于医疗领域的本体建模",
|
||||
... items=[class1, class2, class3]
|
||||
... )
|
||||
"""
|
||||
total: int = Field(..., description="总数量")
|
||||
scene_id: UUID = Field(..., description="所属场景ID")
|
||||
scene_name: str = Field(..., description="场景名称")
|
||||
scene_description: Optional[str] = Field(None, description="场景描述")
|
||||
items: List[ClassResponse] = Field(..., description="类型列表")
|
||||
@@ -22,6 +22,23 @@ class PromptOptMessage(BaseModel):
|
||||
)
|
||||
|
||||
|
||||
class PromptSaveRequest(BaseModel):
|
||||
session_id: UUID = Field(
|
||||
...,
|
||||
description="Session ID"
|
||||
)
|
||||
|
||||
title: str = Field(
|
||||
...,
|
||||
description="Prompt Title"
|
||||
)
|
||||
|
||||
prompt: str = Field(
|
||||
...,
|
||||
description="Optimized prompt content"
|
||||
)
|
||||
|
||||
|
||||
class PromptOptModelSet(BaseModel):
|
||||
id: UUID | None = Field(
|
||||
default=None,
|
||||
|
||||
Reference in New Issue
Block a user