ARIA-AGENT/aria-brain/main.py

"""
ARIA Brain — FastAPI-Einstieg.

Phase B Punkt 1: nur Skeleton.
- /health                        → Liveness
- /memory/list                   → alle Punkte (gefiltert)
- /memory/pinned                 → Hot Memory
- /memory/search?q=...&k=5       → semantische Suche
- /memory/save                   → neuen Punkt anlegen
- /memory/update/{id}            → Punkt aendern (re-embed wenn content geaendert)
- /memory/delete/{id}            → Punkt loeschen
- /memory/stats                  → Anzahl Punkte pro Type

/chat (Conversation-Loop) und /skills/* kommen in spaeteren Phasen.
"""

from __future__ import annotations

import logging
import os
from typing import List, Optional

from fastapi import FastAPI, HTTPException, BackgroundTasks, Request
from fastapi.responses import Response
from pydantic import BaseModel, Field

from memory import Embedder, VectorStore, MemoryPoint
from conversation import Conversation
from proxy_client import ProxyClient
from agent import Agent
import skills as skills_mod
import metrics as metrics_mod

logging.basicConfig(level=logging.INFO, format="%(asctime)s [%(levelname)s] %(name)s: %(message)s")
logger = logging.getLogger("aria-brain")

QDRANT_HOST = os.environ.get("QDRANT_HOST", "aria-qdrant")
QDRANT_PORT = int(os.environ.get("QDRANT_PORT", "6333"))

app = FastAPI(title="ARIA Brain", version="0.1.0")

_embedder: Optional[Embedder] = None
_store: Optional[VectorStore] = None
_conversation: Optional[Conversation] = None
_proxy: Optional[ProxyClient] = None
_agent: Optional[Agent] = None


def embedder() -> Embedder:
    global _embedder
    if _embedder is None:
        _embedder = Embedder()
    return _embedder


def store() -> VectorStore:
    global _store
    if _store is None:
        _store = VectorStore(host=QDRANT_HOST, port=QDRANT_PORT)
    return _store


def conversation() -> Conversation:
    global _conversation
    if _conversation is None:
        _conversation = Conversation()
    return _conversation


def proxy_client() -> ProxyClient:
    global _proxy
    if _proxy is None:
        _proxy = ProxyClient()
    return _proxy


def agent() -> Agent:
    global _agent
    if _agent is None:
        _agent = Agent(store(), embedder(), conversation(), proxy_client())
    return _agent


# ─── Pydantic-Schemas ─────────────────────────────────────────────────

class MemoryIn(BaseModel):
    type: str = Field(..., description="identity|rule|preference|tool|skill|fact|conversation|reminder")
    title: str
    content: str
    pinned: bool = False
    category: str = ""
    source: str = "manual"
    tags: List[str] = Field(default_factory=list)
    conversation_id: Optional[str] = None


class MemoryUpdate(BaseModel):
    title: Optional[str] = None
    content: Optional[str] = None
    pinned: Optional[bool] = None
    category: Optional[str] = None
    tags: Optional[List[str]] = None


class MemoryOut(BaseModel):
    id: str
    type: str
    title: str
    content: str
    pinned: bool
    category: str
    source: str
    tags: List[str]
    created_at: str
    updated_at: str
    conversation_id: Optional[str] = None
    score: Optional[float] = None

    @classmethod
    def from_point(cls, p: MemoryPoint) -> "MemoryOut":
        return cls(**p.__dict__)


# ─── Health ───────────────────────────────────────────────────────────

@app.get("/health")
def health():
    try:
        n = store().count()
        return {"status": "ok", "memory_count": n, "qdrant": f"{QDRANT_HOST}:{QDRANT_PORT}"}
    except Exception as exc:
        return {"status": "degraded", "error": str(exc), "qdrant": f"{QDRANT_HOST}:{QDRANT_PORT}"}


# ─── Memory-Endpoints ─────────────────────────────────────────────────

@app.get("/memory/stats")
def memory_stats():
    s = store()
    points = s.list_all()
    by_type = {}
    pinned = 0
    for p in points:
        by_type[p.type] = by_type.get(p.type, 0) + 1
        if p.pinned:
            pinned += 1
    return {"total": len(points), "pinned": pinned, "by_type": by_type}


@app.get("/memory/list", response_model=List[MemoryOut])
def memory_list(type: Optional[str] = None, limit: int = 200):
    s = store()
    points = s.list_by_type(type, limit=limit) if type else s.list_all(limit=limit)
    return [MemoryOut.from_point(p) for p in points]


@app.get("/memory/pinned", response_model=List[MemoryOut])
def memory_pinned():
    return [MemoryOut.from_point(p) for p in store().list_pinned()]


@app.get("/memory/search", response_model=List[MemoryOut])
def memory_search(q: str, k: int = 5, type: Optional[str] = None, include_pinned: bool = False):
    vec = embedder().embed(q)
    points = store().search(vec, k=k, type_filter=type, exclude_pinned=not include_pinned)
    return [MemoryOut.from_point(p) for p in points]


@app.post("/memory/save", response_model=MemoryOut)
def memory_save(body: MemoryIn):
    s = store()
    vec = embedder().embed(body.content)
    point = MemoryPoint(
        id="",
        type=body.type,
        title=body.title,
        content=body.content,
        pinned=body.pinned,
        category=body.category,
        source=body.source,
        tags=body.tags,
        conversation_id=body.conversation_id,
    )
    pid = s.upsert(point, vec)
    saved = s.get(pid)
    return MemoryOut.from_point(saved)


@app.patch("/memory/update/{point_id}", response_model=MemoryOut)
def memory_update(point_id: str, body: MemoryUpdate):
    s = store()
    existing = s.get(point_id)
    if not existing:
        raise HTTPException(404, f"Memory {point_id} nicht gefunden")

    content_changed = body.content is not None and body.content != existing.content
    if body.title is not None:
        existing.title = body.title
    if body.content is not None:
        existing.content = body.content
    if body.pinned is not None:
        existing.pinned = body.pinned
    if body.category is not None:
        existing.category = body.category
    if body.tags is not None:
        existing.tags = body.tags

    vec = embedder().embed(existing.content) if content_changed else None
    if vec is None:
        # Vektor unveraendert lassen — nur Payload neu schreiben
        from qdrant_client.http import models as qm
        from memory.vector_store import COLLECTION
        s.client.set_payload(
            collection_name=COLLECTION,
            payload=existing.to_payload() | {"updated_at": __import__("datetime").datetime.now(__import__("datetime").timezone.utc).isoformat()},
            points=[point_id],
        )
        saved = s.get(point_id)
    else:
        s.upsert(existing, vec)
        saved = s.get(point_id)
    return MemoryOut.from_point(saved)


@app.delete("/memory/delete/{point_id}")
def memory_delete(point_id: str):
    s = store()
    if not s.get(point_id):
        raise HTTPException(404, f"Memory {point_id} nicht gefunden")
    s.delete(point_id)
    return {"deleted": point_id}


# ─── Migration aus brain-import/ ──────────────────────────────────────

IMPORT_DIR = os.environ.get("IMPORT_DIR", "/import")


@app.post("/memory/migrate")
def memory_migrate():
    """Liest /import/*.md und schreibt atomare Memory-Punkte in die DB.
    Idempotent: bei Re-Run werden Punkte mit gleicher migration_key ersetzt."""
    from pathlib import Path
    from migration import run_migration
    s = store()
    e = embedder()
    result = run_migration(Path(IMPORT_DIR), s, e)
    return result


@app.get("/memory/import-files")
def memory_import_files():
    """Listet was unter /import/ liegt — fuer die Diagnostic-UI."""
    from pathlib import Path
    d = Path(IMPORT_DIR)
    if not d.exists():
        return {"import_dir": str(d), "exists": False, "files": []}
    out = []
    for p in sorted(d.iterdir()):
        if p.is_file():
            try:
                out.append({"name": p.name, "size": p.stat().st_size})
            except Exception:
                pass
    return {"import_dir": str(d), "exists": True, "files": out}


# ─── Bootstrap-Snapshot ───────────────────────────────────────────────
# "Bootstrap" = alle pinned Memories. Export/Import zum schnellen
# Wiederherstellen einer schlanken ARIA nach Wipe.

@app.get("/memory/export-bootstrap")
def memory_export_bootstrap():
    """Gibt alle pinned Memories als JSON zurueck — fuer Browser-Download."""
    s = store()
    pinned = s.list_pinned()
    return {
        "version": 1,
        "exported_at": __import__("datetime").datetime.now(
            __import__("datetime").timezone.utc
        ).isoformat(),
        "count": len(pinned),
        "memories": [
            {
                "type": p.type,
                "title": p.title,
                "content": p.content,
                "pinned": True,
                "category": p.category,
                "source": p.source,
                "tags": p.tags,
            }
            for p in pinned
        ],
    }


class BootstrapBundle(BaseModel):
    version: int = 1
    memories: List[dict]


@app.post("/memory/import-bootstrap")
def memory_import_bootstrap(body: BootstrapBundle):
    """Loescht alle pinned Memories und importiert die im Bundle.
    Cold Memory (unpinned) bleibt unangetastet.

    Wenn keine Memories im Bundle: nur loeschen ist NICHT erlaubt — der
    Caller soll erst exportieren und dann importieren.
    """
    if not body.memories:
        raise HTTPException(400, "Bundle hat keine memories — Abbruch zur Sicherheit")

    s = store()
    e = embedder()

    # Alle aktuell pinned Punkte loeschen
    from qdrant_client.http import models as qm
    from memory.vector_store import COLLECTION
    s.client.delete(
        collection_name=COLLECTION,
        points_selector=qm.FilterSelector(filter=qm.Filter(must=[
            qm.FieldCondition(key="pinned", match=qm.MatchValue(value=True))
        ])),
    )

    # Neue Punkte einspeisen
    created = 0
    for m in body.memories:
        content = (m.get("content") or "").strip()
        if not content:
            continue
        point = MemoryPoint(
            id="",
            type=m.get("type", "fact"),
            title=m.get("title", "(ohne Titel)"),
            content=content,
            pinned=True,
            category=m.get("category", ""),
            source=m.get("source", "bootstrap-import"),
            tags=list(m.get("tags", [])),
        )
        vec = e.embed(content)
        s.upsert(point, vec)
        created += 1

    return {"created": created, "deleted_previous_pinned": True}


# ─── Conversation-Loop ──────────────────────────────────────────────

class ChatIn(BaseModel):
    message: str
    source: str = ""  # "app" / "diagnostic" / "stt" — optional


class ChatOut(BaseModel):
    reply: str
    turns: int
    distilling: bool
    events: list = Field(default_factory=list)


@app.post("/chat", response_model=ChatOut)
def chat(body: ChatIn, background: BackgroundTasks):
    """Hauptpfad. Antwort kommt synchron. Memory-Destillat laeuft
    im Hintergrund nachdem die Response rausging."""
    a = agent()
    try:
        reply = a.chat(body.message, source=body.source)
    except ValueError as exc:
        raise HTTPException(400, str(exc))
    except RuntimeError as exc:
        logger.error("chat fehlgeschlagen: %s", exc)
        raise HTTPException(502, str(exc))

    needs_distill = a.conversation.needs_distill()
    if needs_distill:
        background.add_task(a.distill_old_turns)
    return ChatOut(
        reply=reply,
        turns=len(a.conversation.turns),
        distilling=needs_distill,
        events=a.pop_events(),
    )


@app.get("/conversation/stats")
def conversation_stats():
    return conversation().stats()


@app.post("/conversation/reset")
def conversation_reset():
    """Hardes Reset — der Rolling-Window-Verlauf wird komplett geleert.
    Destillierte facts bleiben in der DB."""
    conversation().reset()
    return {"ok": True, "turns": 0}


@app.post("/conversation/distill")
def conversation_distill_now():
    """Manueller Trigger fuer Destillat — fuer Tests oder vor einem
    bewussten Reset."""
    return agent().distill_old_turns()


# ─── Call-Metrics (Token / Quota-Monitoring) ────────────────────────

@app.get("/metrics/calls")
def metrics_calls():
    """Liefert Aggregate fuer 1h / 5h / 24h / 30d.
    Jedes Window: {window_seconds, calls, tokens_in, tokens_out, by_model}."""
    return metrics_mod.stats()


# ─── Skills ─────────────────────────────────────────────────────────

class SkillCreate(BaseModel):
    name: str
    description: str
    execution: str  # local-venv | local-bin | bash
    entry_code: str
    readme: str = ""
    args: list = Field(default_factory=list)
    requires: dict = Field(default_factory=dict)
    pip_packages: list = Field(default_factory=list)
    author: str = "stefan"


class SkillRun(BaseModel):
    name: str
    args: dict = Field(default_factory=dict)
    timeout_sec: int = 300


class SkillPatch(BaseModel):
    description: str | None = None
    active: bool | None = None
    args: list | None = None


@app.get("/skills/list")
def skills_list(active_only: bool = False):
    return {"skills": skills_mod.list_skills(active_only=active_only)}


@app.get("/skills/{name}")
def skills_get(name: str):
    m = skills_mod.read_manifest(name)
    if m is None:
        raise HTTPException(404, f"Skill '{name}' nicht gefunden")
    readme = skills_mod.read_readme(name)
    return {"manifest": m, "readme": readme}


@app.post("/skills/create")
def skills_create(body: SkillCreate):
    try:
        return skills_mod.create_skill(
            name=body.name,
            description=body.description,
            execution=body.execution,
            entry_code=body.entry_code,
            readme=body.readme,
            args=body.args,
            requires=body.requires,
            pip_packages=body.pip_packages,
            author=body.author,
        )
    except ValueError as exc:
        raise HTTPException(400, str(exc))


@app.post("/skills/run")
def skills_run(body: SkillRun):
    try:
        return skills_mod.run_skill(body.name, args=body.args, timeout_sec=body.timeout_sec)
    except ValueError as exc:
        raise HTTPException(400, str(exc))


@app.patch("/skills/{name}")
def skills_patch(name: str, body: SkillPatch):
    patch = {k: v for k, v in body.model_dump().items() if v is not None}
    try:
        return skills_mod.update_skill(name, patch)
    except ValueError as exc:
        raise HTTPException(404, str(exc))


@app.delete("/skills/{name}")
def skills_delete(name: str):
    try:
        skills_mod.delete_skill(name)
    except ValueError as exc:
        raise HTTPException(404, str(exc))
    return {"deleted": name}


@app.get("/skills/{name}/logs")
def skills_logs(name: str, limit: int = 50):
    return {"logs": skills_mod.list_logs(name, limit=limit)}


@app.get("/skills/{name}/export")
def skills_export(name: str):
    try:
        data = skills_mod.export_skill(name)
    except ValueError as exc:
        raise HTTPException(404, str(exc))
    return Response(
        content=data,
        media_type="application/gzip",
        headers={"Content-Disposition": f'attachment; filename="skill-{name}.tar.gz"'},
    )


@app.post("/skills/import")
async def skills_import(request: Request, overwrite: bool = False):
    data = await request.body()
    if not data:
        raise HTTPException(400, "Leerer Body")
    try:
        manifest = skills_mod.import_skill(data, overwrite=overwrite)
    except ValueError as exc:
        raise HTTPException(400, str(exc))
    return {"imported": manifest}