mindnet/app/models/dto.py

"""
FILE: app/models/dto.py
DESCRIPTION: Pydantic-Modelle (DTOs) für Request/Response Bodies. Definiert das API-Schema.
VERSION: 0.7.1 (WP-25: Stream-Tracing Support)
STATUS: Active
DEPENDENCIES: pydantic, typing, uuid
"""

from __future__ import annotations
from pydantic import BaseModel, Field
from typing import List, Literal, Optional, Dict, Any
import uuid

# WP-25: Erweiterte Kanten-Typen gemäß neuer decision_engine.yaml
EdgeKind = Literal[
    "references", "references_at", "backlink", "next", "prev", 
    "belongs_to", "depends_on", "related_to", "similar_to", 
    "caused_by", "derived_from", "based_on", "solves", "blocks", 
    "uses", "guides", "enforced_by", "implemented_in", "part_of",
    "experienced_in", "impacts", "risk_of"
]


# --- Basis-DTOs ---

class NodeDTO(BaseModel):
    """Darstellung eines Knotens (Note oder Chunk) im API-Graph."""
    id: str
    type: Literal["note", "chunk"]
    title: Optional[str] = None
    note_id: Optional[str] = None
    tags: Optional[List[str]] = None
    in_degree: Optional[int] = None
    out_degree: Optional[int] = None
    score: Optional[float] = None
    section_title: Optional[str] = None
    section_path: Optional[str] = None
    path: Optional[str] = None


class EdgeDTO(BaseModel):
    """Darstellung einer Kante im API-Graph."""
    id: str
    kind: str
    source: str
    target: str
    weight: float
    direction: Literal["out", "in", "undirected"] = "out"
    # WP-24c v4.5.3: Erweiterte Provenance-Werte für Chunk-Aware Edges
    # Unterstützt alle tatsächlich verwendeten Provenance-Typen im System
    provenance: Optional[Literal[
        "explicit", "rule", "smart", "structure",
        "explicit:callout", "explicit:wikilink", "explicit:note_zone", "explicit:note_scope",
        "inline:rel", "callout:edge", "semantic_ai", "structure:belongs_to", "structure:order",
        "derived:backlink", "edge_defaults", "global_pool"
    ]] = "explicit"
    confidence: float = 1.0
    target_section: Optional[str] = None 


# --- Request Models ---

class QueryRequest(BaseModel):
    """
    Request für /query. Unterstützt Multi-Stream Isolation via filters.
    WP-24c v4.1.0: Erweitert um Section-Filtering und Scope-Awareness.
    """
    mode: Literal["semantic", "edge", "hybrid"] = "hybrid"
    query: Optional[str] = None
    query_vector: Optional[List[float]] = None
    top_k: int = 10
    expand: Dict = {"depth": 1, "edge_types": ["references", "belongs_to", "prev", "next", "depends_on", "related_to"]}
    filters: Optional[Dict] = None
    ret: Dict = {"with_paths": True, "with_notes": True, "with_chunks": True}
    explain: bool = False
    
    # WP-22/25: Dynamische Gewichtung der Graphen-Highways
    boost_edges: Optional[Dict[str, float]] = None
    
    # WP-24c v4.1.0: Section-Filtering für präzise Section-Links
    target_section: Optional[str] = None 


class FeedbackRequest(BaseModel):
    """User-Feedback zu einem spezifischen Treffer oder der Gesamtantwort."""
    query_id: str = Field(..., description="ID der ursprünglichen Suche")
    node_id: str = Field(..., description="ID des bewerteten Treffers oder 'generated_answer'")
    score: int = Field(..., ge=1, le=5, description="1 (Irrelevant) bis 5 (Perfekt)")
    comment: Optional[str] = None


class ChatRequest(BaseModel):
    """Request für /chat (WP-25 Einstieg)."""
    message: str = Field(..., description="Die Nachricht des Users")
    conversation_id: Optional[str] = Field(None, description="ID für Chat-Verlauf")
    top_k: int = 5
    explain: bool = False


# --- Explanation Models ---

class ScoreBreakdown(BaseModel):
    """Aufschlüsselung der Score-Komponenten nach der WP-22 Formel."""
    semantic_contribution: float
    edge_contribution: float
    centrality_contribution: float
    raw_semantic: float
    raw_edge_bonus: float
    raw_centrality: float
    node_weight: float
    status_multiplier: float = 1.0
    graph_boost_factor: float = 1.0


class Reason(BaseModel):
    """Ein semantischer Grund für das Ranking."""
    # WP-25: 'status' hinzugefügt für Synchronität mit retriever.py
    kind: Literal["semantic", "edge", "type", "centrality", "lifecycle", "status"]
    message: str
    score_impact: Optional[float] = None
    details: Optional[Dict[str, Any]] = None


class Explanation(BaseModel):
    """Container für alle Erklärungsdaten eines Treffers."""
    breakdown: ScoreBreakdown
    reasons: List[Reason]
    related_edges: Optional[List[EdgeDTO]] = None
    applied_intent: Optional[str] = None
    applied_boosts: Optional[Dict[str, float]] = None


# --- Response Models ---

class QueryHit(BaseModel):
    """
    Einzelnes Trefferobjekt.
    WP-25: stream_origin hinzugefügt für Tracing und Feedback-Optimierung.
    WP-24c v4.1.0: source_chunk_id für RAG-Kontext hinzugefügt.
    """
    node_id: str
    note_id: str 
    semantic_score: float
    edge_bonus: float
    centrality_bonus: float
    total_score: float
    paths: Optional[List[List[Dict]]] = None
    source: Optional[Dict] = None
    payload: Optional[Dict] = None 
    explanation: Optional[Explanation] = None
    stream_origin: Optional[str] = Field(None, description="Name des Ursprungs-Streams")
    source_chunk_id: Optional[str] = Field(None, description="Chunk-ID der Quelle (für RAG-Kontext)")


class QueryResponse(BaseModel):
    """Antwortstruktur für /query (wird von DecisionEngine Streams genutzt)."""
    query_id: str = Field(default_factory=lambda: str(uuid.uuid4()))
    results: List[QueryHit]
    used_mode: str
    latency_ms: int


class GraphResponse(BaseModel):
    """Antwortstruktur für /graph/{note_id}."""
    center_note_id: str
    nodes: List[NodeDTO]
    edges: List[EdgeDTO]
    stats: Dict[str, int]


class ChatResponse(BaseModel):
    """
    Antwortstruktur für /chat. 
    WP-25: 'intent' spiegelt nun die gewählte Strategie wider.
    """
    query_id: str = Field(..., description="Traceability ID")
    answer: str = Field(..., description="Generierte Antwort vom LLM")
    sources: List[QueryHit] = Field(..., description="Die genutzten Quellen (alle Streams)")
    latency_ms: int
    intent: Optional[str] = Field("FACT", description="Die gewählte WP-25 Strategie")
    intent_source: Optional[str] = Field("LLM_Router", description="Quelle der Intent-Erkennung")