src/cache.py

"""Redis cache layer (Tier 2) — sub-millisecond reads for session and entity data."""

import json
import logging
import os

import redis


def _default_redis_url() -> str:
    return os.getenv("REDIS_URL", "redis://localhost:6379")


class BodhiCache:
    """Cache-aside wrapper around Redis.

    Key patterns:
        entity:{company}     — company context (TTL 24h)
        session:{session_id} — live session scores/phase (TTL 2h)
    """

    def __init__(self, redis_url: str | None = None):
        url = redis_url or _default_redis_url()
        try:
            self.r = redis.from_url(
                url, 
                decode_responses=True,
                socket_connect_timeout=10,  # 10 second connection timeout
                socket_timeout=10,  # 10 second operation timeout
                retry_on_timeout=True,
                retry_on_error=[redis.exceptions.ConnectionError, redis.exceptions.TimeoutError],
                health_check_interval=30,  # Check connection health every 30s
                max_connections=50,  # Connection pool size
            )
            logging.getLogger("bodhi.cache").info(f"Redis client initialized with URL: {url}")
            # Force an immediate connection test
            self.r.ping()
            logging.getLogger("bodhi.cache").info(f"✓ Redis connection verified successfully")
        except redis.ConnectionError as e:
            logging.getLogger("bodhi.cache").error(f"✗ Redis connection failed: {e}")
            logging.getLogger("bodhi.cache").error(f"  URL: {url}")
            logging.getLogger("bodhi.cache").error(f"  Ensure Redis server is running and accessible")
            raise
        except Exception as e:
            logging.getLogger("bodhi.cache").error(f"✗ Failed to initialize Redis client: {type(e).__name__}: {e}")
            raise

    def ping(self) -> bool:
        try:
            result = self.r.ping()
            logging.getLogger("bodhi.cache").info(f"Redis ping successful: {result}")
            return result
        except redis.ConnectionError as e:
            logging.getLogger("bodhi.cache").error(f"Redis connection error during ping: {e}")
            return False
        except Exception as e:
            logging.getLogger("bodhi.cache").error(f"Unexpected error during Redis ping: {type(e).__name__}: {e}")
            return False

    # ── Entity cache ──────────────────────────────────────────────

    def get_entity(self, company: str) -> str | None:
        """Return cached company context or None on miss."""
        return self.r.get(f"entity:{company.lower().strip()}")

    def set_entity(self, company: str, context: str, ttl: int = 86400) -> None:
        self.r.setex(f"entity:{company.lower().strip()}", ttl, context)

    # ── Session cache ─────────────────────────────────────────────

    def save_session_state(
        self, session_id: str, data: dict, ttl: int = 7200,
    ) -> None:
        """Persist session snapshot (scores, phase, difficulty) in Redis."""
        self.r.setex(f"session:{session_id}", ttl, json.dumps(data))

    def get_session_state(self, session_id: str) -> dict | None:
        raw = self.r.get(f"session:{session_id}")
        if raw is None:
            return None
        return json.loads(raw)

    def save_initial_state(self, session_id: str, state: dict, ttl: int = 3600) -> None:
        key = f"initial:{session_id}"
        try:
            payload = json.dumps(state)
            self.r.setex(key, ttl, payload)
            logging.getLogger("bodhi.cache").info(f"Saved initial state | key={key} | size={len(payload)} bytes")
        except Exception as e:
            logging.getLogger("bodhi.cache").error(f"Failed to save initial state | key={key} | error={e}")

    def get_initial_state(self, session_id: str) -> dict | None:
        key = f"initial:{session_id}"
        raw = self.r.get(key)
        if raw is None:
            logging.getLogger("bodhi.cache").warning(f"Initial state NOT found | key={key}")
            return None
        logging.getLogger("bodhi.cache").info(f"Retrieved initial state | key={key} | size={len(raw)} bytes")
        return json.loads(raw)

    def delete_session(self, session_id: str) -> None:
        self.r.delete(f"session:{session_id}")
        self.r.delete(f"initial:{session_id}")

    # ── RAG context cache ─────────────────────────────────────────

    def get_rag_context(self, company: str, role: str) -> str | None:
        """Return cached RAG context for a company+role, or None on miss."""
        key = f"rag:{company.lower().strip()}:{role.lower().strip()}"
        return self.r.get(key)

    def set_rag_context(
        self, company: str, role: str, context: str, ttl: int = 3600,
    ) -> None:
        """Cache assembled RAG context (1-hour TTL by default)."""
        key = f"rag:{company.lower().strip()}:{role.lower().strip()}"
        self.r.setex(key, ttl, context)

    # ── Suggested topics cache ─────────────────────────────────────

    def get_topics(self, company: str, role: str) -> list[str] | None:
        """Return cached suggested interview topics, or None on miss."""
        key = f"topics:{company.lower().strip()}:{role.lower().strip()}"
        raw = self.r.get(key)
        if raw is None:
            return None
        return json.loads(raw)

    def set_topics(
        self, company: str, role: str, topics: list[str], ttl: int = 86400,
    ) -> None:
        """Cache suggested topics extracted from uploaded documents (24h TTL)."""
        key = f"topics:{company.lower().strip()}:{role.lower().strip()}"
        self.r.setex(key, ttl, json.dumps(topics))

    # ── Pre-generated Question Queues ─────────────────────────────

    def get_question_queue(self, session_id: str, phase: str) -> list[str] | None:
        """Return the pre-generated question queue for a session phase."""
        key = f"interview:{session_id}:queue:{phase}"
        raw = self.r.get(key)
        if raw is None:
            return None
        return json.loads(raw)

    def set_question_queue(self, session_id: str, phase: str, questions: list[str], ttl: int = 7200) -> None:
        """Store the pre-generated question queue for a session phase (2h TTL)."""
        key = f"interview:{session_id}:queue:{phase}"
        self.r.setex(key, ttl, json.dumps(questions))

    # ── Phase Memory (context memory per phase) ───────────────────

    def save_phase_memory(self, session_id: str, phase: str, memory: dict, ttl: int = 7200) -> None:
        """Store compacted phase memory summary (2h TTL)."""
        key = f"memory:{session_id}:{phase}"
        self.r.setex(key, ttl, json.dumps(memory))

    def get_phase_memory(self, session_id: str, phase: str) -> dict | None:
        """Retrieve compacted memory for a single phase."""
        key = f"memory:{session_id}:{phase}"
        raw = self.r.get(key)
        if raw is None:
            return None
        return json.loads(raw)

    def get_all_phase_memories(self, session_id: str) -> dict:
        """Retrieve all compacted phase memories for cross-section context."""
        from src.state import PHASES
        result = {}
        for phase in PHASES:
            mem = self.get_phase_memory(session_id, phase)
            if mem:
                result[phase] = mem
        return result