use schemars::JsonSchema;
use serde::Deserialize;
#[derive(Debug, Deserialize, JsonSchema)]
pub struct StoreMemoryRequest {
#[schemars(description = "The text content of the memory to store")]
pub content: String,
#[schemars(
description = "Memory type: episodic, semantic, procedural, anti_pattern, reasoning, or correction"
)]
pub memory_type: String,
#[schemars(description = "Optional agent UUID that owns this memory (defaults to nil UUID)")]
pub agent_id: Option<String>,
#[schemars(description = "Optional tags for categorization")]
pub tags: Option<Vec<String>>,
#[schemars(
description = "Memory scope: 'contextual' (default) = retrieved by semantic similarity. 'always' = returned on every process_turn call, regardless of conversation topic."
)]
pub scope: Option<String>,
#[schemars(description = "Optional key-value metadata")]
pub metadata: Option<serde_json::Map<String, serde_json::Value>>,
}
#[derive(Debug, Deserialize, JsonSchema)]
pub struct RecallMemoryRequest {
#[schemars(description = "The UUID of the memory to recall")]
pub id: String,
}
#[derive(Debug, Deserialize, JsonSchema)]
pub struct SearchMemoriesRequest {
#[schemars(
description = "Search query text for semantic search, OR a memory UUID to get full content by ID"
)]
pub query: String,
#[schemars(description = "Maximum number of results to return (default: 10)")]
pub limit: Option<usize>,
#[schemars(
description = "Optional memory type filter: episodic, semantic, procedural, anti_pattern, reasoning, correction"
)]
pub memory_type: Option<String>,
}
#[derive(Debug, Deserialize, JsonSchema)]
pub struct RelateMemoriesRequest {
#[schemars(description = "UUID of the source memory")]
pub from_id: String,
#[schemars(description = "UUID of the target memory")]
pub to_id: String,
#[schemars(
description = "Relationship type: caused, before, related, contradicts, supports, supersedes, derived, part_of"
)]
pub edge_type: String,
#[schemars(description = "Optional edge weight (default: 1.0)")]
pub weight: Option<f32>,
}
#[derive(Debug, Deserialize, JsonSchema)]
pub struct ForgetMemoryRequest {
#[schemars(description = "UUID of the memory to delete")]
pub id: String,
#[schemars(description = "Optional reason for deletion")]
pub reason: Option<String>,
}
#[derive(Debug, Deserialize, JsonSchema)]
pub struct ForgetAllRequest {
#[schemars(
description = "Safety confirmation. Must be exactly 'CONFIRM' to proceed. This deletes ALL memories permanently."
)]
pub confirm: String,
#[schemars(description = "Optional reason for resetting the database")]
pub reason: Option<String>,
}
#[derive(Debug, Deserialize, JsonSchema)]
pub struct AssembleContextRequest {
#[schemars(description = "The query to assemble context for")]
pub query: String,
#[schemars(description = "Maximum token budget for the assembled context")]
pub token_budget: usize,
#[schemars(description = "Output format: structured, compact, or delta")]
pub format: Option<String>,
}
#[derive(Debug, Deserialize, JsonSchema)]
pub struct RegisterEntityRequest {
#[schemars(description = "Name of the entity to register")]
pub name: String,
#[schemars(description = "Type classification of the entity (e.g. person, tool, concept)")]
pub entity_type: String,
}
#[derive(Debug, Deserialize, JsonSchema)]
pub struct IngestConversationRequest {
#[schemars(description = "The raw conversation text to extract memories from")]
pub conversation: String,
#[schemars(
description = "LLM provider to use: openai, anthropic, ollama, or mock (default: mock)"
)]
pub provider: Option<String>,
#[schemars(description = "API key for the LLM provider (uses env var if not provided)")]
pub api_key: Option<String>,
#[schemars(
description = "Optional agent UUID that owns extracted memories (defaults to nil UUID)"
)]
pub agent_id: Option<String>,
}
#[derive(Debug, Deserialize, JsonSchema)]
pub struct GetMemoryRequest {
#[schemars(description = "The UUID of the memory to retrieve")]
pub id: String,
}
#[derive(Debug, Deserialize, JsonSchema)]
pub struct ConsolidateMemoriesRequest {
#[schemars(description = "Minimum cluster size for consolidation (default: 2)")]
pub min_cluster_size: Option<usize>,
#[schemars(description = "Similarity threshold for clustering (default: 0.85)")]
pub similarity_threshold: Option<f32>,
}
#[derive(Debug, Deserialize, JsonSchema)]
pub struct CompressMemoryRequest {
#[schemars(description = "UUID of the memory to compress")]
pub id: String,
}
#[derive(Debug, Deserialize, JsonSchema)]
pub struct ExtractFactsRequest {
#[schemars(description = "UUID of the memory to extract facts from")]
pub id: String,
}
#[derive(Debug, Deserialize, JsonSchema)]
pub struct GdprForgetRequest {
#[schemars(description = "Subject identifier (agent UUID) whose memories should be forgotten")]
pub subject: String,
#[schemars(description = "Reason for the GDPR forget request")]
pub reason: String,
}
#[derive(Debug, Deserialize, JsonSchema)]
pub struct ProcessTurnRequest {
#[schemars(
description = "The user's message or question from this turn. Used for context retrieval and memory extraction."
)]
pub user_message: String,
#[schemars(
description = "The assistant's response from this turn. Can be empty if calling before drafting a response."
)]
pub assistant_response: Option<String>,
#[schemars(
description = "Conversation turn number (monotonically increasing). Used for trajectory tracking."
)]
pub turn_id: u64,
#[schemars(
description = "Current project or workspace name for scoping memories, e.g. 'mentedb-mcp' or 'my-app'."
)]
pub project_context: Option<String>,
#[schemars(description = "Optional agent UUID. Defaults to nil UUID if not provided.")]
pub agent_id: Option<String>,
#[schemars(
description = "Originating session ID. Stored turns are tagged with it so injection recall can exclude them for that session."
)]
pub session_id: Option<String>,
}
#[derive(Debug, Deserialize, JsonSchema)]
pub struct RecordPainRequest {
#[schemars(description = "UUID of the memory associated with this pain signal")]
pub memory_id: String,
#[schemars(description = "Pain intensity from 0.0 to 1.0")]
pub intensity: f32,
#[schemars(description = "Keywords that should trigger this pain warning")]
pub trigger_keywords: Vec<String>,
#[schemars(description = "Human-readable description of the negative experience")]
pub description: String,
}
#[derive(Debug, Deserialize, JsonSchema)]
pub struct DetectPhantomsRequest {
#[schemars(description = "Content text to scan for knowledge gaps")]
pub content: String,
#[schemars(description = "Optional list of entities already known to the caller")]
pub known_entities: Option<Vec<String>>,
#[schemars(description = "Optional conversation turn ID for tracking")]
pub turn_id: Option<u64>,
}
#[derive(Debug, Deserialize, JsonSchema)]
pub struct ResolvePhantomRequest {
#[schemars(description = "UUID of the phantom memory to mark as resolved")]
pub phantom_id: String,
}
#[derive(Debug, Deserialize, JsonSchema)]
pub struct RecordTrajectoryRequest {
#[schemars(description = "Conversation turn identifier")]
pub turn_id: u64,
#[schemars(description = "Summary of the topic discussed in this turn")]
pub topic_summary: String,
#[schemars(
description = "Decision state: investigating, narrowed_to:<choice>, decided:<decision>, interrupted, or completed"
)]
pub decision_state: String,
#[schemars(description = "Optional list of open questions remaining")]
pub open_questions: Option<Vec<String>>,
}
#[derive(Debug, Deserialize, JsonSchema)]
pub struct DetectInterferenceRequest {
#[schemars(description = "List of memory UUIDs to check for interference")]
pub memory_ids: Vec<String>,
#[schemars(description = "Similarity threshold (reserved for future use)")]
#[allow(dead_code)]
pub similarity_threshold: Option<f32>,
}
#[derive(Debug, Deserialize, JsonSchema)]
pub struct KnownFact {
#[schemars(description = "UUID of the memory this fact came from")]
pub memory_id: String,
#[schemars(description = "Summary text of the known fact")]
pub summary: String,
}
#[derive(Debug, Deserialize, JsonSchema)]
pub struct CheckStreamRequest {
#[schemars(description = "LLM output text to check against known facts")]
pub text: String,
#[schemars(description = "List of known facts to check for contradictions")]
pub known_facts: Vec<KnownFact>,
}
#[derive(Debug, Deserialize, JsonSchema)]
pub struct WriteInferenceRequest {
#[schemars(description = "UUID of the memory to run write-time inference on")]
pub memory_id: String,
}
#[derive(Debug, Deserialize, JsonSchema)]
pub struct GetRelatedRequest {
#[schemars(description = "UUID of the memory to find relations for")]
pub id: String,
#[schemars(
description = "Optional edge type filter: caused, before, related, contradicts, supports, supersedes, derived, part_of"
)]
pub edge_type: Option<String>,
#[schemars(description = "Maximum traversal depth (default: 1)")]
pub depth: Option<usize>,
}
#[derive(Debug, Deserialize, JsonSchema)]
pub struct FindPathRequest {
#[schemars(description = "UUID of the source memory")]
pub from_id: String,
#[schemars(description = "UUID of the target memory")]
pub to_id: String,
}
#[derive(Debug, Deserialize, JsonSchema)]
pub struct GetSubgraphRequest {
#[schemars(description = "UUID of the center memory")]
pub center_id: String,
#[schemars(description = "Maximum hop radius from center (default: 2)")]
pub radius: Option<usize>,
}
#[derive(Debug, Deserialize, JsonSchema)]
pub struct FindContradictionsRequest {
#[schemars(description = "UUID of the memory to find contradictions for")]
pub id: String,
}
#[derive(Debug, Deserialize, JsonSchema)]
pub struct PropagateBeliefRequest {
#[schemars(description = "UUID of the memory whose confidence changed")]
pub id: String,
#[schemars(description = "New confidence value (0.0 to 1.0)")]
pub new_confidence: f32,
}