mirror of
https://github.com/MODSetter/SurfSense.git
synced 2026-04-26 01:06:23 +02:00
feat: enhance performance logging and caching in various components
- Introduced slow callback logging in FastAPI to identify blocking calls. - Added performance logging for agent creation and tool loading processes. - Implemented caching for MCP tools to reduce redundant server calls. - Enhanced sandbox management with in-process caching for improved efficiency. - Refactored several functions for better readability and performance tracking. - Updated tests to ensure proper functionality of new features and optimizations.
This commit is contained in:
DESKTOP-RTLN3BA\$punk
parent
2e99f1e853
commit
aabc24f82c
22 changed files with 637 additions and 200 deletions
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@ -13,14 +13,17 @@ import asyncio
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import json
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import logging
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import re
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import time
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from collections.abc import AsyncGenerator
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from dataclasses import dataclass, field
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from typing import Any
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from uuid import UUID
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from langchain_core.messages import HumanMessage
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from sqlalchemy import func
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from sqlalchemy.ext.asyncio import AsyncSession
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from sqlalchemy.future import select
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from sqlalchemy.orm import selectinload
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from app.agents.new_chat.chat_deepagent import create_surfsense_deep_agent
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from app.agents.new_chat.checkpointer import get_checkpointer
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@ -31,10 +34,17 @@ from app.agents.new_chat.llm_config import (
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load_agent_config,
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load_llm_config_from_yaml,
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)
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from app.agents.new_chat.sandbox import (
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get_or_create_sandbox,
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is_sandbox_enabled,
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)
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from app.db import (
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ChatVisibility,
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Document,
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NewChatMessage,
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NewChatThread,
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Report,
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SearchSourceConnectorType,
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SurfsenseDocsDocument,
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async_session_maker,
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)
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@ -47,6 +57,16 @@ from app.services.connector_service import ConnectorService
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from app.services.new_streaming_service import VercelStreamingService
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from app.utils.content_utils import bootstrap_history_from_db
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_perf_log = logging.getLogger("surfsense.perf")
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_perf_log.setLevel(logging.DEBUG)
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if not _perf_log.handlers:
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_h = logging.StreamHandler()
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_h.setFormatter(logging.Formatter("%(asctime)s [PERF] %(message)s"))
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_perf_log.addHandler(_h)
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_perf_log.propagate = False
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_background_tasks: set[asyncio.Task] = set()
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def format_mentioned_documents_as_context(documents: list[Document]) -> str:
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"""
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@ -877,7 +897,9 @@ async def _stream_agent_events(
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output_text = om.group(1) if om else ""
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thread_id_str = config.get("configurable", {}).get("thread_id", "")
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for sf_match in re.finditer(r"^SANDBOX_FILE:\s*(.+)$", output_text, re.MULTILINE):
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for sf_match in re.finditer(
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r"^SANDBOX_FILE:\s*(.+)$", output_text, re.MULTILINE
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):
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fpath = sf_match.group(1).strip()
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if fpath and fpath not in result.sandbox_files:
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result.sandbox_files.append(fpath)
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@ -963,7 +985,10 @@ def _try_persist_and_delete_sandbox(
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sandbox_files: list[str],
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) -> None:
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"""Fire-and-forget: persist sandbox files locally then delete the sandbox."""
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from app.agents.new_chat.sandbox import is_sandbox_enabled, persist_and_delete_sandbox
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from app.agents.new_chat.sandbox import (
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is_sandbox_enabled,
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persist_and_delete_sandbox,
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)
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if not is_sandbox_enabled():
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return
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@ -980,7 +1005,9 @@ def _try_persist_and_delete_sandbox(
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try:
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loop = asyncio.get_running_loop()
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loop.create_task(_run())
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task = loop.create_task(_run())
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_background_tasks.add(task)
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task.add_done_callback(_background_tasks.discard)
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except RuntimeError:
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pass
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@ -1022,6 +1049,7 @@ async def stream_new_chat(
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"""
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streaming_service = VercelStreamingService()
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stream_result = StreamResult()
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_t_total = time.perf_counter()
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try:
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# Mark AI as responding to this user for live collaboration
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@ -1030,6 +1058,7 @@ async def stream_new_chat(
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# Load LLM config - supports both YAML (negative IDs) and database (positive IDs)
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agent_config: AgentConfig | None = None
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_t0 = time.perf_counter()
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if llm_config_id >= 0:
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# Positive ID: Load from NewLLMConfig database table
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agent_config = await load_agent_config(
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@ -1060,6 +1089,11 @@ async def stream_new_chat(
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llm = create_chat_litellm_from_config(llm_config)
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# Create AgentConfig from YAML for consistency (uses defaults for prompt settings)
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agent_config = AgentConfig.from_yaml_config(llm_config)
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_perf_log.info(
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"[stream_new_chat] LLM config loaded in %.3fs (config_id=%s)",
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time.perf_counter() - _t0,
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llm_config_id,
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)
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if not llm:
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yield streaming_service.format_error("Failed to create LLM instance")
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@ -1067,28 +1101,29 @@ async def stream_new_chat(
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return
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# Create connector service
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_t0 = time.perf_counter()
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connector_service = ConnectorService(session, search_space_id=search_space_id)
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# Get Firecrawl API key from webcrawler connector if configured
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from app.db import SearchSourceConnectorType
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firecrawl_api_key = None
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webcrawler_connector = await connector_service.get_connector_by_type(
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SearchSourceConnectorType.WEBCRAWLER_CONNECTOR, search_space_id
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)
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if webcrawler_connector and webcrawler_connector.config:
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firecrawl_api_key = webcrawler_connector.config.get("FIRECRAWL_API_KEY")
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# Get the PostgreSQL checkpointer for persistent conversation memory
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checkpointer = await get_checkpointer()
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# Optionally provision a sandboxed code execution environment
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sandbox_backend = None
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from app.agents.new_chat.sandbox import (
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get_or_create_sandbox,
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is_sandbox_enabled,
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_perf_log.info(
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"[stream_new_chat] Connector service + firecrawl key in %.3fs",
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time.perf_counter() - _t0,
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)
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# Get the PostgreSQL checkpointer for persistent conversation memory
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_t0 = time.perf_counter()
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checkpointer = await get_checkpointer()
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_perf_log.info(
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"[stream_new_chat] Checkpointer ready in %.3fs", time.perf_counter() - _t0
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)
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sandbox_backend = None
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_t0 = time.perf_counter()
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if is_sandbox_enabled():
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try:
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sandbox_backend = await get_or_create_sandbox(chat_id)
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@ -1097,8 +1132,14 @@ async def stream_new_chat(
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"Sandbox creation failed, continuing without execute tool: %s",
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sandbox_err,
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)
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_perf_log.info(
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"[stream_new_chat] Sandbox provisioning in %.3fs (enabled=%s)",
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time.perf_counter() - _t0,
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sandbox_backend is not None,
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)
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visibility = thread_visibility or ChatVisibility.PRIVATE
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_t0 = time.perf_counter()
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agent = await create_surfsense_deep_agent(
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llm=llm,
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search_space_id=search_space_id,
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@ -1112,19 +1153,20 @@ async def stream_new_chat(
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thread_visibility=visibility,
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sandbox_backend=sandbox_backend,
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)
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_perf_log.info(
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"[stream_new_chat] Agent created in %.3fs", time.perf_counter() - _t0
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)
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# Build input with message history
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langchain_messages = []
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_t0 = time.perf_counter()
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# Bootstrap history for cloned chats (no LangGraph checkpoint exists yet)
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if needs_history_bootstrap:
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langchain_messages = await bootstrap_history_from_db(
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session, chat_id, thread_visibility=visibility
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)
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# Clear the flag so we don't bootstrap again on next message
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from app.db import NewChatThread
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thread_result = await session.execute(
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select(NewChatThread).filter(NewChatThread.id == chat_id)
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)
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@ -1136,11 +1178,9 @@ async def stream_new_chat(
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# Fetch mentioned documents if any (with chunks for proper citations)
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mentioned_documents: list[Document] = []
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if mentioned_document_ids:
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from sqlalchemy.orm import selectinload as doc_selectinload
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result = await session.execute(
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select(Document)
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.options(doc_selectinload(Document.chunks))
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.options(selectinload(Document.chunks))
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.filter(
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Document.id.in_(mentioned_document_ids),
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Document.search_space_id == search_space_id,
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@ -1151,8 +1191,6 @@ async def stream_new_chat(
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# Fetch mentioned SurfSense docs if any
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mentioned_surfsense_docs: list[SurfsenseDocsDocument] = []
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if mentioned_surfsense_doc_ids:
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from sqlalchemy.orm import selectinload
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result = await session.execute(
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select(SurfsenseDocsDocument)
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.options(selectinload(SurfsenseDocsDocument.chunks))
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@ -1236,6 +1274,11 @@ async def stream_new_chat(
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"search_space_id": search_space_id,
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}
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_perf_log.info(
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"[stream_new_chat] History bootstrap + doc/report queries in %.3fs",
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time.perf_counter() - _t0,
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)
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# All pre-streaming DB reads are done. Commit to release the
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# transaction and its ACCESS SHARE locks so we don't block DDL
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# (e.g. migrations) for the entire duration of LLM streaming.
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@ -1243,6 +1286,12 @@ async def stream_new_chat(
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# short-lived transactions (or use isolated sessions).
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await session.commit()
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_perf_log.info(
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"[stream_new_chat] Total pre-stream setup in %.3fs (chat_id=%s)",
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time.perf_counter() - _t_total,
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chat_id,
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)
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# Configure LangGraph with thread_id for memory
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# If checkpoint_id is provided, fork from that checkpoint (for edit/reload)
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configurable = {"thread_id": str(chat_id)}
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@ -1304,6 +1353,8 @@ async def stream_new_chat(
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items=initial_items,
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)
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_t_stream_start = time.perf_counter()
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_first_event_logged = False
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async for sse in _stream_agent_events(
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agent=agent,
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config=config,
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@ -1315,8 +1366,23 @@ async def stream_new_chat(
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initial_step_title=initial_title,
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initial_step_items=initial_items,
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):
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if not _first_event_logged:
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_perf_log.info(
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"[stream_new_chat] First agent event in %.3fs (time since stream start), "
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"%.3fs (total since request start) (chat_id=%s)",
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time.perf_counter() - _t_stream_start,
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time.perf_counter() - _t_total,
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chat_id,
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)
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_first_event_logged = True
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yield sse
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_perf_log.info(
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"[stream_new_chat] Agent stream completed in %.3fs (chat_id=%s)",
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time.perf_counter() - _t_stream_start,
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chat_id,
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)
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if stream_result.is_interrupted:
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yield streaming_service.format_finish_step()
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yield streaming_service.format_finish()
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@ -1325,12 +1391,6 @@ async def stream_new_chat(
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accumulated_text = stream_result.accumulated_text
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# Generate LLM title for new chats after first response
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# Check if this is the first assistant response by counting existing assistant messages
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from sqlalchemy import func
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from app.db import NewChatMessage, NewChatThread
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assistant_count_result = await session.execute(
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select(func.count(NewChatMessage.id)).filter(
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NewChatMessage.thread_id == chat_id,
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@ -1431,12 +1491,14 @@ async def stream_resume_chat(
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) -> AsyncGenerator[str, None]:
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streaming_service = VercelStreamingService()
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stream_result = StreamResult()
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_t_total = time.perf_counter()
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try:
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if user_id:
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await set_ai_responding(session, chat_id, UUID(user_id))
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agent_config: AgentConfig | None = None
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_t0 = time.perf_counter()
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if llm_config_id >= 0:
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agent_config = await load_agent_config(
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session=session,
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@ -1460,31 +1522,37 @@ async def stream_resume_chat(
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return
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llm = create_chat_litellm_from_config(llm_config)
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agent_config = AgentConfig.from_yaml_config(llm_config)
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_perf_log.info(
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"[stream_resume] LLM config loaded in %.3fs", time.perf_counter() - _t0
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)
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if not llm:
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yield streaming_service.format_error("Failed to create LLM instance")
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yield streaming_service.format_done()
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return
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_t0 = time.perf_counter()
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connector_service = ConnectorService(session, search_space_id=search_space_id)
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from app.db import SearchSourceConnectorType
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firecrawl_api_key = None
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webcrawler_connector = await connector_service.get_connector_by_type(
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SearchSourceConnectorType.WEBCRAWLER_CONNECTOR, search_space_id
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)
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if webcrawler_connector and webcrawler_connector.config:
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firecrawl_api_key = webcrawler_connector.config.get("FIRECRAWL_API_KEY")
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checkpointer = await get_checkpointer()
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sandbox_backend = None
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from app.agents.new_chat.sandbox import (
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get_or_create_sandbox,
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is_sandbox_enabled,
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_perf_log.info(
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"[stream_resume] Connector service + firecrawl key in %.3fs",
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time.perf_counter() - _t0,
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)
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_t0 = time.perf_counter()
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checkpointer = await get_checkpointer()
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_perf_log.info(
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"[stream_resume] Checkpointer ready in %.3fs", time.perf_counter() - _t0
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)
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sandbox_backend = None
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_t0 = time.perf_counter()
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if is_sandbox_enabled():
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try:
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sandbox_backend = await get_or_create_sandbox(chat_id)
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@ -1493,9 +1561,15 @@ async def stream_resume_chat(
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"Sandbox creation failed, continuing without execute tool: %s",
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sandbox_err,
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)
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_perf_log.info(
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"[stream_resume] Sandbox provisioning in %.3fs (enabled=%s)",
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time.perf_counter() - _t0,
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sandbox_backend is not None,
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)
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visibility = thread_visibility or ChatVisibility.PRIVATE
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_t0 = time.perf_counter()
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agent = await create_surfsense_deep_agent(
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llm=llm,
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search_space_id=search_space_id,
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@ -1509,10 +1583,19 @@ async def stream_resume_chat(
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thread_visibility=visibility,
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sandbox_backend=sandbox_backend,
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)
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_perf_log.info(
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"[stream_resume] Agent created in %.3fs", time.perf_counter() - _t0
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)
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# Release the transaction before streaming (same rationale as stream_new_chat).
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await session.commit()
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_perf_log.info(
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"[stream_resume] Total pre-stream setup in %.3fs (chat_id=%s)",
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time.perf_counter() - _t_total,
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chat_id,
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)
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from langgraph.types import Command
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config = {
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@ -1523,6 +1606,8 @@ async def stream_resume_chat(
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yield streaming_service.format_message_start()
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yield streaming_service.format_start_step()
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_t_stream_start = time.perf_counter()
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_first_event_logged = False
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async for sse in _stream_agent_events(
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agent=agent,
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config=config,
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@ -1531,7 +1616,20 @@ async def stream_resume_chat(
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result=stream_result,
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step_prefix="thinking-resume",
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):
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if not _first_event_logged:
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_perf_log.info(
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"[stream_resume] First agent event in %.3fs (stream), %.3fs (total) (chat_id=%s)",
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time.perf_counter() - _t_stream_start,
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time.perf_counter() - _t_total,
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chat_id,
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)
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_first_event_logged = True
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yield sse
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_perf_log.info(
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"[stream_resume] Agent stream completed in %.3fs (chat_id=%s)",
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time.perf_counter() - _t_stream_start,
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chat_id,
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)
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if stream_result.is_interrupted:
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yield streaming_service.format_finish_step()
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yield streaming_service.format_finish()
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