Add external service APIs for memory detail queries
Provides memory data access endpoints for external service integration
Add utility functions for API key user resolution and end_user validation
Modified files:
- api/app/controllers/service/user_memory_api_controller.py
- api/app/core/api_key_utils.py
- api/app/controllers/service/__init__.py
- Refactor quota management logic to support usage checks scoped by workspace.
- Update quota statistics API to return granular quota details for each workspace.
- Revise default configuration settings for terminal user and model limits.
- Remove quota check decorators from the model controller.
- Refactor quota management logic to support usage checks scoped by workspace.
- Update quota statistics API to return granular quota details for each workspace.
- Revise default configuration settings for terminal user and model limits.
- Remove quota check decorators from the model controller.
- Refactor quota management logic to support usage checks scoped by workspace.
- Update quota statistics API to return granular quota details for each workspace.
- Revise default configuration settings for terminal user and model limits.
- Remove quota check decorators from the model controller.
* 'release/v0.3.1' of github.com:SuanmoSuanyangTechnology/MemoryBear:
fix(web): stream add default error message
fix(quota): restrict quota check to new terminal user creation only
fix(api): fix API Key rate limiting and terminal user quota checks
feat(exception): enhance I18nException response format and add error code mapping
feat(quota): add quota checks during app duplication and import operations
fix(知识服务): 添加工作空间模型配置的校验
refactor(knowledge_service): 简化模型绑定逻辑，直接使用工作区配置
fix(知识服务): 修复创建知识库时未检查视觉模型存在的错误
refactor(knowledge_service): 优化模型绑定逻辑，使用ID查询并简化回退机制
- Standardize error response format to include business error codes, timestamps, and other fields.
- Add ERROR_CODE_TO_BIZ_CODE mapping table for error code conversion.
- Introduce QUOTA_EXCEEDED and RATE_LIMIT_EXCEEDED business error codes.
- Add logic to detect tenant plan QPS limits and return a specific error message when triggered.
- Simplify boolean check in model activation quota validation.
- Fix issue where tenant ID lookup from shared records failed to query the workspace correctly.
- Switch QPS counting from sliding window to simple counter to improve performance and simplify logic.
- Remove unnecessary `time` module import.
- Replace direct memory agent service calls with unified MemoryService in read endpoint
- Update query preprocessor to use new prompt format and return structured queries
- Enhance MemorySearchResult model with filtering, merging, and ID tracking capabilities
- Add intermediate outputs display for problem split, perceptual retrieval, and search results
- Fix parameter alignment and remove unused history parameter in memory agent service
- Streamline rate limit check flow by removing redundant tenant-level QPS checks.
- Restrict checks to API Key QPS and plan degradation protection only.
- Update constant naming and error message handling for consistency.
- Refactor rate limiting mechanism to limit per API Key instead of per tenant (workspace).
- Update error code logic and Redis key naming conventions.
- Adjust quota usage statistics to display the QPS of the API Key closest to its limit.
- Add `API_KEY_RATE_LIMIT_EXCEEDED` error code.
- Refactor `QuotaExceededError` to support resource type localization.
- Optimize rate limiting service by implementing the sliding window algorithm.
- Add rate limit validation for tenant plans.
- Unify quota check decorator to support both synchronous and asynchronous operations.
- Enhance quota usage statistics endpoints.
The changes remove the `ConditionGroup` abstraction and flatten condition expressions directly under `ConditionBranchConfig.expressions`. This simplifies the data model and evaluation logic, eliminating redundant grouping layers while preserving all functionality. The migration logic and group-level operators are removed as they are no longer needed.
BREAKING CHANGE: `ConditionBranchConfig.expressions` now expects a flat list of `ConditionDetail` instead of `ConditionGroup`; existing configurations must be updated to use direct condition lists.
Added support for evaluating conditions on individual fields of file objects within array[file] variables. Extended variable pool to extract fields from array elements, introduced new condition models (SubVariableConditionItem, SubVariableCondition, ConditionGroup), and added ArrayFileContainsOperator to handle contains/not_contains logic with nested sub-conditions. Includes backward compatibility migration for legacy flat expressions.
- Remove redundant `response_format` injection for VOLCANO provider since it's unsupported; rely on system prompt injection instead
- Extend system prompt JSON injection logic to cover VOLCANO and tool-enabled cases universally
- Simplify model parameter construction by removing redundant `params["model_kwargs"] = model_kwargs` assignments
- Refactor `CompatibleChatOpenAI._get_request_payload` to strip `response_format` when tools are present, avoiding strict validation errors in langchain_openai
- Fix timestamp calculation order in `datetime_tool.py` to avoid integer truncation before multiplication
- Consolidate memory search services by removing separate content_search.py and perceptual_search.py
- Update model client handling in base_pipeline.py to use ModelApiKeyService for LLM client initialization
- Add new prompt files and modify existing services to support consolidated search architecture
- Refactor memory read pipeline and related services to use updated model client approach
- Use Literal['set', 'remove'] for MetadataFieldChange.action instead of str
- Simplify field_path description to reflect current schema
- Remove redundant isinstance check in extract_user_metadata_task
- Replace UserMetadata full-object overwrite with incremental MetadataFieldChange
operations (set/remove per field path)
- Convert profile.role and profile.domain from scalar strings to lists
- Remove UserMetadataBehavioralHints and knowledge_tags fields
- Update Jinja2 prompt to instruct LLM to output incremental changes
- Update extract_user_metadata_task to apply changes via deep-copy and
per-field mutation for proper SQLAlchemy change detection
- Minor lint: remove unnecessary f-string prefixes in tasks.py
