agenticmem-commons 0.1.4.3

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/user_profiler/agenticmem/agenticmem_commons

Description: Shared data schemas and configurations used across client and server

Purpose

Central repository for Pydantic models that define the contract between client and server. All API requests/responses and configuration schemas are defined here to ensure consistency.

API Schemas

Directory: agenticmem_commons/api_schema Purpose: Pydantic models for API requests and responses

Key files:

• service_schemas.py: Core data models

  • Interaction: User interaction data (content, actions, images)
  • UserProfile: Generated user profile with TTL and embeddings
  • Request: Tracks individual requests with metadata (request_id, user_id, source, agent_version)
  • PublishUserInteractionRequest/Response: Publishing new interactions
  • DeleteUserProfileRequest/Response: Profile deletion
  • DeleteUserInteractionRequest/Response: Interaction deletion
  • InteractionData: Client-provided interaction data
  • ProfileChangeLog: History of profile changes
  • RawFeedback: Raw developer feedback from interactions
  • Feedback: Aggregated feedback with status (pending/approved/rejected)
  • AgentSuccessEvaluationResult: Agent success evaluation results
  • RunFeedbackAggregationRequest/Response: Run feedback aggregation
  • Enums: UserActionType, ProfileTimeToLive, FeedbackStatus

• retriever_schema.py: Search and retrieval models

  • SearchUserProfileRequest/Response: Profile search with query and filters
  • SearchInteractionRequest/Response: Interaction search
  • GetUserProfilesRequest/Response: Get profiles by user_id
  • GetInteractionsRequest/Response: Get interactions by user_id
  • GetRequestsRequest/Response: Get requests with filters (user_id, request_id, request_group)
  • RequestData: Request with associated interactions
  • RequestGroup: Group of requests sharing a request_group
  • GetRawFeedbacksRequest/Response: Get raw feedbacks
  • GetFeedbacksRequest/Response: Get aggregated feedbacks

• login_schema.py: Authentication models

  • Token: JWT token for authentication
  • User login/registration schemas

• data_schema.py: Additional data structures

Configuration Schema

File: config_schema.py Purpose: YAML configuration file schemas

Key models:

• Config: Root configuration object

  • storage_config: Storage backend configuration (Local/S3/Supabase)
  • agent_context_prompt: Agent environment description
  • profile_extractor_configs: List of profile extraction configurations
  • agent_feedback_configs: List of feedback extraction configurations
  • agent_success_configs: List of success evaluation configurations
  • extraction_window_size: Max interactions to process per batch (optional)
  • extraction_window_stride: Min new interactions needed to trigger processing (optional)

• StorageConfig: Storage backend options

  • StorageConfigLocal: Local file storage (dir_path)
  • StorageConfigS3: S3 storage (region, bucket, credentials)
  • StorageConfigSupabase: Supabase storage (url, key, db_url)

• ProfileExtractorConfig: Profile extraction configuration

  • profile_content_definition_prompt: What to extract as profiles
  • context_prompt: Additional context
  • metadata_definition_prompt: Custom metadata fields

• AgentFeedbackConfig: Feedback extraction configuration

  • feedback_name: Unique identifier for feedback type
  • feedback_definition_prompt: What constitutes this feedback
  • metadata_definition_prompt: Custom metadata fields
  • feedback_aggregator_config: Aggregation settings

• AgentSuccessConfig: Success evaluation configuration

  • feedback_name: Unique identifier
  • success_definition_prompt: What constitutes success
  • tool_can_use: List of available tools (ToolUseConfig)
  • action_space: List of possible actions
  • metadata_definition_prompt: Custom metadata fields