mirror of
https://github.com/GNS3/gns3-server.git
synced 2026-09-19 08:20:42 +03:00
6b73f00281
Update the AI chat API design documentation to provide comprehensive details about statistics collection during streaming conversations. The documentation now includes: 1. **Enhanced statistics collection logic**: - Clarified message_count increments for user messages, AI responses, and tool results - Added LLM call tracking via on_chat_model_start events - Detailed token counting methodology using LangGraph's usage_metadata 2. **Improved implementation details**: - Added specific event handlers for statistics collection - Explained LangGraph's cumulative token counting behavior - Provided real-world examples of token accumulation 3. **Updated data models**: - Enhanced ChatSession model documentation with field descriptions - Separated fields into categories (basic, statistics, timestamps, reserved) 4. **Refined architecture documentation**: - Added detailed flow for stream_chat method - Documented statistics collection mechanism during SSE streaming - Explained batch update strategy to reduce database writes The changes ensure developers understand how conversation statistics are collected, processed, and stored without impacting streaming performance.
511 lines
19 KiB
Python
511 lines
19 KiB
Python
"""
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GNS3 Copilot Agent Service
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Provides project-level Agent instances with SQLite checkpoint management.
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Each project has its own AgentService with a dedicated checkpoint database
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in the project directory.
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"""
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import asyncio
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import json
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import logging
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import os
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from datetime import datetime
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from typing import AsyncGenerator, Dict, Any, Optional, List
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from uuid import uuid4
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import aiosqlite
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from langchain_core.messages import HumanMessage, AIMessage, ToolMessage
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from langgraph.checkpoint.sqlite.aio import AsyncSqliteSaver
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from gns3server.agent.gns3_copilot.agent.gns3_copilot import agent_builder
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from gns3server.agent.gns3_copilot.chat_sessions_repository import ChatSessionsRepository
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log = logging.getLogger(__name__)
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class AgentService:
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"""
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Project-level Agent Service with async checkpoint management.
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Manages a LangGraph agent instance with SQLite-based state persistence
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for a single GNS3 project.
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"""
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def __init__(self, project_path: str):
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"""
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Initialize AgentService for a project.
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Args:
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project_path: Path to the GNS3 project directory
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"""
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self.project_path = project_path
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self._checkpointer: Optional[AsyncSqliteSaver] = None
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self._checkpointer_conn: Optional[aiosqlite.Connection] = None
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self._checkpointer_path: Optional[str] = None
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self._graph = None
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self._init_lock = asyncio.Lock()
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self._initialized = False
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def _get_checkpoint_dir(self) -> str:
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"""Get or create the checkpoint directory for this project."""
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checkpoint_dir = os.path.join(self.project_path, "gns3-copilot")
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os.makedirs(checkpoint_dir, exist_ok=True)
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return checkpoint_dir
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async def _get_checkpointer(self) -> AsyncSqliteSaver:
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"""
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Get or create the SQLite checkpointer for this project.
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Returns:
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AsyncSqliteSaver instance
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"""
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async with self._init_lock:
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if self._checkpointer is not None:
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return self._checkpointer
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checkpoint_dir = self._get_checkpoint_dir()
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checkpointer_path = os.path.join(checkpoint_dir, "copilot_checkpoints.db")
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log.debug("Creating checkpointer at: %s", checkpointer_path)
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# Close existing connection if switching projects
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if self._checkpointer_conn:
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try:
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await self._checkpointer_conn.close()
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log.debug("Closed previous checkpointer connection")
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except Exception as e:
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log.warning("Error closing old checkpointer connection: %s", e)
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# Create new connection
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conn = await aiosqlite.connect(checkpointer_path)
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# Enable WAL mode for better concurrent performance
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await conn.execute("PRAGMA journal_mode=WAL;")
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self._checkpointer_conn = conn # Save connection reference to prevent GC
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self._checkpointer = AsyncSqliteSaver(conn)
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# CRITICAL: Initialize database schema
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await self._checkpointer.setup()
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# Create chat_sessions table in the same database
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await self._create_chat_sessions_table(conn)
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self._checkpointer_path = checkpointer_path
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self._initialized = True
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log.info("Project checkpointer created at: %s", checkpointer_path)
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return self._checkpointer
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async def _create_chat_sessions_table(self, conn: aiosqlite.Connection):
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"""
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Create the chat_sessions table in the checkpoint database.
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Args:
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conn: aiosqlite connection
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"""
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await conn.execute("""
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CREATE TABLE IF NOT EXISTS chat_sessions (
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id INTEGER PRIMARY KEY AUTOINCREMENT,
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thread_id TEXT UNIQUE NOT NULL,
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user_id TEXT NOT NULL,
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project_id TEXT NOT NULL,
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title TEXT DEFAULT 'New Conversation',
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-- Statistics
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message_count INTEGER DEFAULT 0,
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llm_calls_count INTEGER DEFAULT 0,
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input_tokens INTEGER DEFAULT 0,
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output_tokens INTEGER DEFAULT 0,
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total_tokens INTEGER DEFAULT 0,
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-- Timestamps
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last_message_at TIMESTAMP,
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created_at TIMESTAMP DEFAULT CURRENT_TIMESTAMP,
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updated_at TIMESTAMP DEFAULT CURRENT_TIMESTAMP,
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-- Reserved fields (JSON strings)
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metadata TEXT DEFAULT '{}',
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stats TEXT DEFAULT '{}'
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)
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""")
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# Create indexes
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await conn.execute("CREATE INDEX IF NOT EXISTS idx_thread_id ON chat_sessions(thread_id)")
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await conn.execute("CREATE INDEX IF NOT EXISTS idx_user_project ON chat_sessions(user_id, project_id)")
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await conn.commit()
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log.debug("chat_sessions table created in checkpoint database")
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async def _get_graph(self):
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"""Get or compile the LangGraph agent."""
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if self._graph is None:
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checkpointer = await self._get_checkpointer()
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self._graph = agent_builder.compile(checkpointer=checkpointer)
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log.info("LangGraph agent compiled for project: %s", self.project_path)
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return self._graph
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async def stream_chat(
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self,
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message: str,
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session_id: str,
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project_id: Optional[str] = None,
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user_id: Optional[str] = None,
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jwt_token: Optional[str] = None,
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mode: str = "text",
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llm_config: Optional[Dict[str, Any]] = None
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) -> AsyncGenerator[Dict[str, Any], None]:
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"""
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Stream chat responses from the agent.
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Args:
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message: User message
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session_id: Session/thread ID for conversation continuity
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project_id: GNS3 project ID (optional, for context)
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user_id: User ID for metadata tracking
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jwt_token: JWT token for API authentication (optional)
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mode: Interaction mode (default: "text")
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llm_config: LLM configuration dict (provider, model, api_key, etc.)
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Yields:
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Dict containing SSE-compatible response chunks
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"""
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log.info(
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"Stream chat started: project_id=%s, user_id=%s, session_id=%s, mode=%s",
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project_id,
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user_id,
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session_id,
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mode,
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)
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# Ensure checkpointer is initialized
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if not self._checkpointer_conn:
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await self._get_checkpointer()
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# Get or create chat session
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repo = ChatSessionsRepository(self._checkpointer_conn)
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session = await repo.get_session_by_thread(session_id)
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is_new_session = session is None
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if is_new_session:
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# Create new session
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session = await repo.create_session(
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thread_id=session_id,
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user_id=user_id or "",
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project_id=project_id or "",
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title="New Conversation"
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)
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log.debug("Created new chat session: thread_id=%s", session_id)
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# Set request-scoped context variables (memory-only, not persisted)
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if jwt_token:
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from gns3server.agent.gns3_copilot.gns3_client import set_current_jwt_token
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set_current_jwt_token(jwt_token)
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log.debug("JWT token set in context")
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if llm_config:
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from gns3server.agent.gns3_copilot.gns3_client import set_current_llm_config
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set_current_llm_config(llm_config)
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log.debug("LLM config set in context: provider=%s, model=%s",
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llm_config.get("provider"), llm_config.get("model"))
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# Build config - only thread-safe identifiers
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config = {
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"configurable": {
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"thread_id": session_id,
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"project_id": project_id,
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},
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"metadata": {
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"user_id": user_id,
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}
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}
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# Build inputs
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inputs = {
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"messages": [HumanMessage(content=message)],
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"llm_calls": 0,
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"remaining_steps": 20,
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"mode": mode,
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}
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# Get the compiled graph
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graph = await self._get_graph()
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log.debug("LangGraph graph obtained, starting stream")
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# Track statistics for session update
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message_count = 1 # User message
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llm_calls_count = 0
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input_tokens = 0
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output_tokens = 0
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last_message_at = datetime.utcnow().isoformat()
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# Track if we've counted the AI response for this turn
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ai_response_counted = False
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tool_messages_counted = 0
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# Stream events
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try:
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async for event in graph.astream_events(inputs, config=config, version="v2"):
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event_type = event.get("event", "")
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data = event.get("data", {})
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# Track LLM calls and tokens
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if event_type == "on_chat_model_start":
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llm_calls_count += 1
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log.debug("LLM call started, count=%d", llm_calls_count)
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elif event_type == "on_chat_model_end":
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# Extract token usage from response metadata
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# Try multiple possible locations where token usage might be stored
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token_info_found = False
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# Method 1: response.usage_metadata
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response = data.get("response", {})
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if hasattr(response, "usage_metadata"):
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usage = response.usage_metadata
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if usage:
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input_tokens += usage.get("input_tokens", 0)
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output_tokens += usage.get("output_tokens", 0)
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token_info_found = True
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# Method 2: output.usage_metadata
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if not token_info_found:
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output_msg = data.get("output", {})
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if hasattr(output_msg, "usage_metadata"):
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usage = output_msg.usage_metadata
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if usage:
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input_tokens += usage.get("input_tokens", 0)
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output_tokens += usage.get("output_tokens", 0)
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token_info_found = True
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# Method 3: Check data directly for token usage fields
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if not token_info_found:
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if "input_tokens" in data:
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input_tokens += data.get("input_tokens", 0)
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if "output_tokens" in data:
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output_tokens += data.get("output_tokens", 0)
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if "input_tokens" in data or "output_tokens" in data:
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token_info_found = True
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# Count AI response as one message (only once per turn)
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if not ai_response_counted:
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message_count += 1
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ai_response_counted = True
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# Track tool messages
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elif event_type == "on_tool_end":
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message_count += 1 # Tool result message
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tool_messages_counted += 1
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log.debug("Tool message counted, message_count=%d", message_count)
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# Convert event to chunk for SSE streaming
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chunk = self._convert_event_to_chunk(event, session_id)
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if chunk:
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log.debug("Yielding chunk: type=%s", chunk.get("type"))
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yield chunk
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# Update session statistics after successful stream
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await repo.update_session(
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thread_id=session_id,
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message_count=message_count,
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llm_calls_count=llm_calls_count,
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input_tokens=input_tokens,
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output_tokens=output_tokens,
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total_tokens=input_tokens + output_tokens,
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last_message_at=last_message_at
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)
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log.info("Session statistics updated: thread_id=%s, messages=%d, llm_calls=%d, tokens=%d+%d=%d",
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session_id, message_count, llm_calls_count, input_tokens, output_tokens, input_tokens + output_tokens)
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# Sync auto-generated title from checkpoint state
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final_state = await graph.aget_state(config)
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if final_state and "conversation_title" in final_state.values:
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generated_title = final_state.values["conversation_title"]
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current_session = await repo.get_session_by_thread(session_id)
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if current_session and current_session.title != generated_title:
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await repo.update_session(thread_id=session_id, title=generated_title)
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log.info("Auto-generated title synced: thread_id=%s, title=%s",
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session_id, generated_title)
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except Exception as e:
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log.error("Error in stream_chat: %s", e, exc_info=True)
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yield {"type": "error", "error": str(e), "session_id": session_id}
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def _convert_event_to_chunk(self, event: Dict[str, Any], session_id: str) -> Optional[Dict[str, Any]]:
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"""
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Convert LangGraph event to API response chunk.
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Args:
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event: LangGraph event from astream_events
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session_id: Session ID for the response
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Returns:
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Dict for SSE response or None if event should be filtered
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"""
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event_type = event.get("event", "")
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data = event.get("data", {})
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if event_type == "on_chat_model_stream":
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# Streaming text content from LLM
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chunk = data.get("chunk", {})
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# chunk is AIMessageChunk object, access content directly
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content = getattr(chunk, "content", "")
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if content:
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return {"type": "content", "content": content}
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elif event_type == "on_tool_start":
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# Tool execution started
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return {
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"type": "tool_start",
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"tool_name": event.get("name", ""),
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"session_id": session_id
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}
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elif event_type == "on_tool_end":
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# Tool execution completed
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output = data.get("output", "")
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# Convert output to string if it's not already
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if not isinstance(output, str):
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output = str(output)
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return {
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"type": "tool_end",
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"tool_name": event.get("name", ""),
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"tool_output": output,
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"session_id": session_id
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}
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return None
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async def get_history(self, session_id: str, limit: int = 100) -> Dict[str, Any]:
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"""
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Get conversation history for a session.
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Args:
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session_id: Session/thread ID
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limit: Maximum number of messages to retrieve
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Returns:
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Dict containing thread_id, title, and messages
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"""
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config = {"configurable": {"thread_id": session_id}}
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try:
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graph = await self._get_graph()
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state = await graph.aget_state(config)
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if state and "messages" in state.values:
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messages = []
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for msg in state.values["messages"][-limit:]:
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messages.append(self._convert_message_to_dict(msg))
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title = state.values.get("conversation_title", "New Conversation")
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return {
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"thread_id": session_id,
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"title": title,
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"messages": messages
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}
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except Exception as e:
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log.error("Error getting history: %s", e, exc_info=True)
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return {
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"thread_id": session_id,
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"title": "New Conversation",
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"messages": []
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}
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def _convert_message_to_dict(self, msg) -> Dict[str, Any]:
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"""Convert a LangChain message to dict format."""
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from datetime import datetime
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msg_type = type(msg).__name__
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result = {
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"id": getattr(msg, "id", str(uuid4())),
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"role": "user",
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"content": getattr(msg, "content", str(msg)),
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"created_at": datetime.utcnow().isoformat() + "Z",
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}
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if msg_type == "HumanMessage":
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result["role"] = "user"
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elif msg_type == "AIMessage":
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result["role"] = "assistant"
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if hasattr(msg, "tool_calls") and msg.tool_calls:
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result["tool_calls"] = msg.tool_calls
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elif msg_type == "ToolMessage":
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result["role"] = "tool"
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result["tool_call_id"] = getattr(msg, "tool_call_id", None)
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result["name"] = getattr(msg, "name", None)
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elif msg_type == "SystemMessage":
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result["role"] = "system"
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return result
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async def list_sessions(self, user_id: Optional[str] = None, limit: int = 100) -> List[Dict[str, Any]]:
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"""
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List chat sessions for this project.
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Args:
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user_id: Filter by user ID (optional)
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limit: Maximum number of sessions to return
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Returns:
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List of session dictionaries
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"""
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if not self._checkpointer_conn:
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await self._get_checkpointer()
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repo = ChatSessionsRepository(self._checkpointer_conn)
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sessions = await repo.list_sessions(user_id=user_id, limit=limit)
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return [s.to_dict() for s in sessions]
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async def delete_session(self, session_id: str) -> bool:
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"""
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Delete a chat session and its checkpoints.
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Args:
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session_id: Thread ID to delete
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Returns:
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True if deleted, False if not found
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"""
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if not self._checkpointer_conn:
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await self._get_checkpointer()
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repo = ChatSessionsRepository(self._checkpointer_conn)
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return await repo.delete_session(session_id)
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async def rename_session(self, session_id: str, new_title: str) -> Optional[Dict[str, Any]]:
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"""
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Rename a chat session.
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Args:
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session_id: Thread ID
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new_title: New title
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Returns:
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Updated session dictionary or None
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"""
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if not self._checkpointer_conn:
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await self._get_checkpointer()
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repo = ChatSessionsRepository(self._checkpointer_conn)
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session = await repo.update_session(thread_id=session_id, title=new_title)
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return session.to_dict() if session else None
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async def close(self):
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"""
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Close the checkpointer connection and cleanup resources.
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"""
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async with self._init_lock:
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if self._checkpointer_conn:
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try:
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await self._checkpointer_conn.close()
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log.debug("Checkpointer connection closed for: %s", self.project_path)
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except Exception as e:
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log.warning("Error closing checkpointer connection: %s", e)
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finally:
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self._checkpointer_conn = None
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self._checkpointer = None
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self._graph = None
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self._initialized = False
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