"""LlamaIndex adapter stub for RAG pipeline orchestration.

Implements OrchestratorAdapter interface for LlamaIndex integration.
This is a placeholder stub - full implementation exists in
mahavishnu/engines/llamaindex_adapter.py.

TODO: Future implementation should integrate with the complete engine adapter
or replace this stub with the full implementation.

LlamaIndex: https://www.llamaindex.ai/
"""

from __future__ import annotations

import asyncio
import logging
from typing import Any

try:
    from oneiric.core.ulid import generate_config_id
except ImportError:

    def generate_config_id() -> str:
        import uuid

        return uuid.uuid4().hex


from mahavishnu.core.adapters.base import (
    OrchestratorAdapter,
    AdapterType,
    AdapterCapabilities,
)
from mahavishnu.core.errors import AdapterInitializationError, WorkflowExecutionError

logger = logging.getLogger(__name__)


class LlamaIndexAdapter(OrchestratorAdapter):
    """LlamaIndex RAG pipeline orchestration adapter (STUB).

    Features (TODO - not yet implemented):
    - RAG query execution with vector indexes
    - Document ingestion and embedding
    - Semantic search capabilities
    - Knowledge base management

    Architecture:
    ┌──────────────────────────────────┐
    │   Mahavishnu                 │
    │  • Adapter Stub              │
    │  • Placeholder Execution      │
    └──────────────┬─────────────────┘
                   │
                   ↓
         ┌──────────────────────┐
         │  LlamaIndex Engine  │
         │  • RAG Queries        │
         │  • Vector Indexes     │
         │  • Document Stores    │
         │  • Embedding Models   │
         └──────────────────────┘
                   │
                   ↓
         ┌────────────────────┐
         │  Knowledge Base     │
         │  • Vector Search    │
         │  • Semantic Similarity│
         └────────────────────┘

    NOTE: Full implementation exists at:
    mahavishnu/engines/llamaindex_adapter.py

    This stub provides the interface shape and will be replaced
    or integrated with the full implementation when ready.
    """

    def __init__(
        self,
        api_url: str = "http://localhost:11434",
        timeout_seconds: int = 300,
    ):
        """Initialize LlamaIndex adapter stub.

        Args:
            api_url: LlamaIndex server URL (default: localhost:11434)
            timeout_seconds: Request timeout (default: 300)
        """
        self.api_url = api_url.rstrip("/")
        self.timeout = timeout_seconds

        logger.info(f"LlamaIndexAdapter stub initialized (API: {self.api_url})")

    @property
    def adapter_type(self) -> AdapterType:
        return AdapterType.LLAMAINDEX

    @property
    def name(self) -> str:
        return "llamaindex"

    @property
    def capabilities(self) -> AdapterCapabilities:
        """Return supported capabilities."""
        return AdapterCapabilities(
            can_deploy_flows=True,  # RAG pipelines
            can_monitor_execution=True,  # Query tracking
            can_cancel_workflows=True,  # Can cancel queries
            can_sync_state=False,  # TODO: State sync not implemented
            supports_batch_execution=True,  # Multiple queries
            supports_multi_agent=False,  # Single query engine
            has_cloud_ui=False,  # Local engine
        )

    async def initialize(self) -> None:
        """Initialize LlamaIndex adapter (stub).

        Raises:
            AdapterInitializationError: If full implementation is needed

        TODO: Implement actual LlamaIndex client initialization
        - Connect to LlamaIndex server
        - Load or create vector indexes
        - Initialize embedding models
        - Configure document stores
        """
        logger.info("LlamaIndexAdapter stub initialized (no-op)")

        # Stub implementation - no actual initialization
        # Full implementation should initialize httpx.AsyncClient here

    async def execute(
        self,
        task: dict[str, Any],
        repos: list[str],
    ) -> dict[str, Any]:
        """Execute RAG query task (stub).

        Args:
            task: Task specification with 'type' and 'params' keys
                  Types: 'query', 'ingest', 'create_index'
            repos: List of repository paths to operate on

        Returns:
            Execution result with ULID execution ID

        Raises:
            WorkflowExecutionError: If execution fails

        TODO: Implement actual RAG query execution
        - Parse task type (query vs ingest vs create_index)
        - Execute vector similarity search
        - Return relevant documents with scores
        - Handle LlamaIndex server communication
        """
        execution_id = generate_config_id()

        logger.warning(
            f"LlamaIndexAdapter.execute() called (stub mode) - returning mock ULID: {execution_id}"
        )

        # Stub implementation - return mock response
        return {
            "status": "completed",
            "result": {
                "execution_id": execution_id,
                "message": "Stub execution - implement full RAG query",
                "task": task,
                "repos_processed": len(repos),
            },
            "task_id": execution_id,
        }

    async def get_health(self) -> dict[str, Any]:
        """Get LlamaIndex adapter health (stub).

        Returns:
            Dict with 'status' key ('healthy', 'degraded', 'unhealthy')
                 and adapter-specific health details

        TODO: Implement actual health checks
        - Check LlamaIndex server connectivity
        - Verify embedding model availability
        - Check vector store status
        - Validate index integrity
        """
        logger.info("LlamaIndexAdapter.get_health() called (stub mode)")

        # Stub implementation - always return healthy
        return {
            "status": "healthy",
            "details": {
                "implementation": "stub",
                "message": "Full implementation at mahavishnu/engines/llamaindex_adapter.py",
                "api_url": self.api_url,
                "note": "Replace with full implementation or integrate existing engine",
            },
        }

    async def shutdown(self) -> None:
        """Shutdown LlamaIndex adapter stub.

        TODO: Implement resource cleanup
        - Close vector store connections
        - Release embedding model resources
        - Flush any pending operations
        """
        logger.info("LlamaIndexAdapter stub shutdown (no-op)")
        # Stub implementation - no resources to cleanup
