gns3-server/gns3server/agent/gns3_copilot/agent_service.py

"""
GNS3 Copilot Agent Service

Provides project-level Agent instances with SQLite checkpoint management.
Each project has its own AgentService with a dedicated checkpoint database
in the project directory.
"""

import asyncio
import json
import logging
import os
from datetime import datetime
from typing import AsyncGenerator, Dict, Any, Optional, List
from uuid import uuid4

import aiosqlite
from langchain_core.messages import HumanMessage, AIMessage, ToolMessage
from langgraph.checkpoint.sqlite.aio import AsyncSqliteSaver

from gns3server.agent.gns3_copilot.agent.gns3_copilot import agent_builder
from gns3server.agent.gns3_copilot.chat_sessions_repository import ChatSessionsRepository

log = logging.getLogger(__name__)


class AgentService:
    """
    Project-level Agent Service with async checkpoint management.

    Manages a LangGraph agent instance with SQLite-based state persistence
    for a single GNS3 project.
    """

    def __init__(self, project_path: str):
        """
        Initialize AgentService for a project.

        Args:
            project_path: Path to the GNS3 project directory
        """
        self.project_path = project_path
        self._checkpointer: Optional[AsyncSqliteSaver] = None
        self._checkpointer_conn: Optional[aiosqlite.Connection] = None
        self._checkpointer_path: Optional[str] = None
        self._graph = None
        self._init_lock = asyncio.Lock()
        self._initialized = False

    def _get_checkpoint_dir(self) -> str:
        """Get or create the checkpoint directory for this project."""
        checkpoint_dir = os.path.join(self.project_path, "gns3-copilot")
        os.makedirs(checkpoint_dir, exist_ok=True)
        return checkpoint_dir

    async def _get_checkpointer(self) -> AsyncSqliteSaver:
        """
        Get or create the SQLite checkpointer for this project.

        Returns:
            AsyncSqliteSaver instance
        """
        async with self._init_lock:
            if self._checkpointer is not None:
                return self._checkpointer

            checkpoint_dir = self._get_checkpoint_dir()
            checkpointer_path = os.path.join(checkpoint_dir, "copilot_checkpoints.db")

            log.debug("Creating checkpointer at: %s", checkpointer_path)

            # Close existing connection if switching projects
            if self._checkpointer_conn:
                try:
                    await self._checkpointer_conn.close()
                    log.debug("Closed previous checkpointer connection")
                except Exception as e:
                    log.warning("Error closing old checkpointer connection: %s", e)

            # Create new connection
            conn = await aiosqlite.connect(checkpointer_path)
            # Enable WAL mode for better concurrent performance
            await conn.execute("PRAGMA journal_mode=WAL;")
            self._checkpointer_conn = conn  # Save connection reference to prevent GC
            self._checkpointer = AsyncSqliteSaver(conn)

            # CRITICAL: Initialize database schema
            await self._checkpointer.setup()

            # Create chat_sessions table in the same database
            await self._create_chat_sessions_table(conn)

            self._checkpointer_path = checkpointer_path
            self._initialized = True

            log.info("Project checkpointer created at: %s", checkpointer_path)
            return self._checkpointer

    async def _create_chat_sessions_table(self, conn: aiosqlite.Connection):
        """
        Create the chat_sessions table in the checkpoint database.

        Args:
            conn: aiosqlite connection
        """
        await conn.execute("""
            CREATE TABLE IF NOT EXISTS chat_sessions (
                id INTEGER PRIMARY KEY AUTOINCREMENT,
                thread_id TEXT UNIQUE NOT NULL,
                user_id TEXT NOT NULL,
                project_id TEXT NOT NULL,
                title TEXT DEFAULT 'New Conversation',

                -- Statistics
                message_count INTEGER DEFAULT 0,
                llm_calls_count INTEGER DEFAULT 0,
                input_tokens INTEGER DEFAULT 0,
                output_tokens INTEGER DEFAULT 0,
                total_tokens INTEGER DEFAULT 0,

                -- Timestamps
                last_message_at TIMESTAMP,
                created_at TIMESTAMP DEFAULT CURRENT_TIMESTAMP,
                updated_at TIMESTAMP DEFAULT CURRENT_TIMESTAMP,

                -- Reserved fields (JSON strings)
                metadata TEXT DEFAULT '{}',
                stats TEXT DEFAULT '{}'
            )
        """)

        # Create indexes
        await conn.execute("CREATE INDEX IF NOT EXISTS idx_thread_id ON chat_sessions(thread_id)")
        await conn.execute("CREATE INDEX IF NOT EXISTS idx_user_project ON chat_sessions(user_id, project_id)")

        await conn.commit()
        log.debug("chat_sessions table created in checkpoint database")

    async def _get_graph(self):
        """Get or compile the LangGraph agent."""
        if self._graph is None:
            checkpointer = await self._get_checkpointer()
            self._graph = agent_builder.compile(checkpointer=checkpointer)
            log.info("LangGraph agent compiled for project: %s", self.project_path)
        return self._graph

    async def stream_chat(
        self,
        message: str,
        session_id: str,
        project_id: Optional[str] = None,
        user_id: Optional[str] = None,
        jwt_token: Optional[str] = None,
        mode: str = "text",
        llm_config: Optional[Dict[str, Any]] = None
    ) -> AsyncGenerator[Dict[str, Any], None]:
        """
        Stream chat responses from the agent.

        Args:
            message: User message
            session_id: Session/thread ID for conversation continuity
            project_id: GNS3 project ID (optional, for context)
            user_id: User ID for metadata tracking
            jwt_token: JWT token for API authentication (optional)
            mode: Interaction mode (default: "text")
            llm_config: LLM configuration dict (provider, model, api_key, etc.)

        Yields:
            Dict containing SSE-compatible response chunks
        """
        log.info(
            "Stream chat started: project_id=%s, user_id=%s, session_id=%s, mode=%s",
            project_id,
            user_id,
            session_id,
            mode,
        )

        # Ensure checkpointer is initialized
        if not self._checkpointer_conn:
            await self._get_checkpointer()

        # Get or create chat session
        repo = ChatSessionsRepository(self._checkpointer_conn)
        session = await repo.get_session_by_thread(session_id)
        is_new_session = session is None

        if is_new_session:
            # Create new session
            session = await repo.create_session(
                thread_id=session_id,
                user_id=user_id or "",
                project_id=project_id or "",
                title="New Conversation"
            )
            log.debug("Created new chat session: thread_id=%s", session_id)

        # Set request-scoped context variables (memory-only, not persisted)
        if jwt_token:
            from gns3server.agent.gns3_copilot.gns3_client import set_current_jwt_token
            set_current_jwt_token(jwt_token)
            log.debug("JWT token set in context")
        if llm_config:
            from gns3server.agent.gns3_copilot.gns3_client import set_current_llm_config
            set_current_llm_config(llm_config)
            log.debug("LLM config set in context: provider=%s, model=%s",
                       llm_config.get("provider"), llm_config.get("model"))

        # Build config - only thread-safe identifiers
        config = {
            "configurable": {
                "thread_id": session_id,
                "project_id": project_id,
            },
            "metadata": {
                "user_id": user_id,
            }
        }

        # Build inputs
        inputs = {
            "messages": [HumanMessage(content=message)],
            "llm_calls": 0,
            "remaining_steps": 20,
            "mode": mode,
        }

        # Get the compiled graph
        graph = await self._get_graph()
        log.debug("LangGraph graph obtained, starting stream")

        # Track statistics for session update
        message_count = 1  # User message
        llm_calls_count = 0
        tool_calls_count = 0
        input_tokens = 0
        output_tokens = 0
        last_message_at = datetime.utcnow().isoformat()

        # Stream events
        try:
            async for event in graph.astream_events(inputs, config=config, version="v2"):
                chunk = self._convert_event_to_chunk(event, session_id)
                if chunk:
                    # Track statistics
                    if chunk.get("type") == "content":
                        message_count += 1  # AI response
                    elif chunk.get("type") == "tool_start":
                        tool_calls_count += 1
                    elif chunk.get("type") == "tool_end":
                        message_count += 1  # Tool message

                    # Track tokens if available
                    if chunk.get("type") == "content" and "input_tokens" in chunk:
                        input_tokens += chunk.get("input_tokens", 0)
                    if chunk.get("type") == "content" and "output_tokens" in chunk:
                        output_tokens += chunk.get("output_tokens", 0)

                    log.debug("Yielding chunk: type=%s", chunk.get("type"))
                    yield chunk

            # Update session statistics after successful stream
            await repo.update_session(
                thread_id=session_id,
                message_count=message_count,
                llm_calls_count=llm_calls_count,
                input_tokens=input_tokens,
                output_tokens=output_tokens,
                total_tokens=input_tokens + output_tokens,
                last_message_at=last_message_at
            )
            log.debug("Updated session statistics: thread_id=%s, messages=%d, tokens=%d",
                     session_id, message_count, input_tokens + output_tokens)

            # Sync auto-generated title from checkpoint state
            final_state = await graph.aget_state(config)
            if final_state and "conversation_title" in final_state.values:
                generated_title = final_state.values["conversation_title"]
                current_session = await repo.get_session_by_thread(session_id)
                if current_session and current_session.title != generated_title:
                    await repo.update_session(thread_id=session_id, title=generated_title)
                    log.info("Auto-generated title synced: thread_id=%s, title=%s",
                            session_id, generated_title)

        except Exception as e:
            log.error("Error in stream_chat: %s", e, exc_info=True)
            yield {"type": "error", "error": str(e), "session_id": session_id}

    def _convert_event_to_chunk(self, event: Dict[str, Any], session_id: str) -> Optional[Dict[str, Any]]:
        """
        Convert LangGraph event to API response chunk.

        Args:
            event: LangGraph event from astream_events
            session_id: Session ID for the response

        Returns:
            Dict for SSE response or None if event should be filtered
        """
        event_type = event.get("event", "")
        data = event.get("data", {})

        if event_type == "on_chat_model_stream":
            # Streaming text content from LLM
            chunk = data.get("chunk", {})
            # chunk is AIMessageChunk object, access content directly
            content = getattr(chunk, "content", "")
            if content:
                return {"type": "content", "content": content}

        elif event_type == "on_tool_start":
            # Tool execution started
            return {
                "type": "tool_start",
                "tool_name": event.get("name", ""),
                "session_id": session_id
            }

        elif event_type == "on_tool_end":
            # Tool execution completed
            output = data.get("output", "")
            # Convert output to string if it's not already
            if not isinstance(output, str):
                output = str(output)
            return {
                "type": "tool_end",
                "tool_name": event.get("name", ""),
                "tool_output": output,
                "session_id": session_id
            }

        return None

    async def get_history(self, session_id: str, limit: int = 100) -> Dict[str, Any]:
        """
        Get conversation history for a session.

        Args:
            session_id: Session/thread ID
            limit: Maximum number of messages to retrieve

        Returns:
            Dict containing thread_id, title, and messages
        """
        config = {"configurable": {"thread_id": session_id}}

        try:
            graph = await self._get_graph()
            state = await graph.aget_state(config)

            if state and "messages" in state.values:
                messages = []
                for msg in state.values["messages"][-limit:]:
                    messages.append(self._convert_message_to_dict(msg))

                title = state.values.get("conversation_title", "New Conversation")

                return {
                    "thread_id": session_id,
                    "title": title,
                    "messages": messages
                }
        except Exception as e:
            log.error("Error getting history: %s", e, exc_info=True)

        return {
            "thread_id": session_id,
            "title": "New Conversation",
            "messages": []
        }

    def _convert_message_to_dict(self, msg) -> Dict[str, Any]:
        """Convert a LangChain message to dict format."""
        from datetime import datetime

        msg_type = type(msg).__name__

        result = {
            "id": getattr(msg, "id", str(uuid4())),
            "role": "user",
            "content": getattr(msg, "content", str(msg)),
            "created_at": datetime.utcnow().isoformat() + "Z",
        }

        if msg_type == "HumanMessage":
            result["role"] = "user"
        elif msg_type == "AIMessage":
            result["role"] = "assistant"
            if hasattr(msg, "tool_calls") and msg.tool_calls:
                result["tool_calls"] = msg.tool_calls
        elif msg_type == "ToolMessage":
            result["role"] = "tool"
            result["tool_call_id"] = getattr(msg, "tool_call_id", None)
            result["name"] = getattr(msg, "name", None)
        elif msg_type == "SystemMessage":
            result["role"] = "system"

        return result

    async def list_sessions(self, user_id: Optional[str] = None, limit: int = 100) -> List[Dict[str, Any]]:
        """
        List chat sessions for this project.

        Args:
            user_id: Filter by user ID (optional)
            limit: Maximum number of sessions to return

        Returns:
            List of session dictionaries
        """
        if not self._checkpointer_conn:
            await self._get_checkpointer()

        repo = ChatSessionsRepository(self._checkpointer_conn)
        sessions = await repo.list_sessions(user_id=user_id, limit=limit)
        return [s.to_dict() for s in sessions]

    async def delete_session(self, session_id: str) -> bool:
        """
        Delete a chat session and its checkpoints.

        Args:
            session_id: Thread ID to delete

        Returns:
            True if deleted, False if not found
        """
        if not self._checkpointer_conn:
            await self._get_checkpointer()

        repo = ChatSessionsRepository(self._checkpointer_conn)
        return await repo.delete_session(session_id)

    async def rename_session(self, session_id: str, new_title: str) -> Optional[Dict[str, Any]]:
        """
        Rename a chat session.

        Args:
            session_id: Thread ID
            new_title: New title

        Returns:
            Updated session dictionary or None
        """
        if not self._checkpointer_conn:
            await self._get_checkpointer()

        repo = ChatSessionsRepository(self._checkpointer_conn)
        session = await repo.update_session(thread_id=session_id, title=new_title)
        return session.to_dict() if session else None

    async def close(self):
        """
        Close the checkpointer connection and cleanup resources.
        """
        async with self._init_lock:
            if self._checkpointer_conn:
                try:
                    await self._checkpointer_conn.close()
                    log.debug("Checkpointer connection closed for: %s", self.project_path)
                except Exception as e:
                    log.warning("Error closing checkpointer connection: %s", e)
                finally:
                    self._checkpointer_conn = None
                    self._checkpointer = None
                    self._graph = None
                    self._initialized = False