mirror of
https://github.com/MODSetter/SurfSense.git
synced 2026-04-25 00:36:31 +02:00
664c43ca13
- Introduced RequestPerfMiddleware to log request performance metrics, including slow request thresholds. - Updated various services and retrievers to utilize the new performance logging utility for better tracking of execution times. - Enhanced existing methods with detailed performance logs for operations such as embedding, searching, and indexing. - Removed deprecated logging setup in stream_new_chat and replaced it with the new performance logger.
350 lines
14 KiB
Python
350 lines
14 KiB
Python
"""
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SurfSense deep agent implementation.
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This module provides the factory function for creating SurfSense deep agents
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with configurable tools via the tools registry and configurable prompts
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via NewLLMConfig.
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"""
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import asyncio
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import logging
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import time
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from collections.abc import Sequence
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from typing import Any
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from deepagents import create_deep_agent
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from deepagents.backends.protocol import SandboxBackendProtocol
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from langchain_core.language_models import BaseChatModel
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from langchain_core.tools import BaseTool
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from langgraph.types import Checkpointer
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from sqlalchemy.ext.asyncio import AsyncSession
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from app.agents.new_chat.context import SurfSenseContextSchema
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from app.agents.new_chat.llm_config import AgentConfig
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from app.agents.new_chat.system_prompt import (
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build_configurable_system_prompt,
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build_surfsense_system_prompt,
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)
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from app.agents.new_chat.tools.registry import build_tools_async
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from app.db import ChatVisibility
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from app.services.connector_service import ConnectorService
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from app.utils.perf import get_perf_logger
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_perf_log = get_perf_logger()
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# =============================================================================
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# Connector Type Mapping
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# =============================================================================
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# Maps SearchSourceConnectorType enum values to the searchable document/connector types
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# used by the knowledge_base tool. Some connectors map to different document types.
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_CONNECTOR_TYPE_TO_SEARCHABLE: dict[str, str] = {
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# Direct mappings (connector type == searchable type)
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"TAVILY_API": "TAVILY_API",
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"SEARXNG_API": "SEARXNG_API",
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"LINKUP_API": "LINKUP_API",
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"BAIDU_SEARCH_API": "BAIDU_SEARCH_API",
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"SLACK_CONNECTOR": "SLACK_CONNECTOR",
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"TEAMS_CONNECTOR": "TEAMS_CONNECTOR",
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"NOTION_CONNECTOR": "NOTION_CONNECTOR",
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"GITHUB_CONNECTOR": "GITHUB_CONNECTOR",
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"LINEAR_CONNECTOR": "LINEAR_CONNECTOR",
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"DISCORD_CONNECTOR": "DISCORD_CONNECTOR",
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"JIRA_CONNECTOR": "JIRA_CONNECTOR",
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"CONFLUENCE_CONNECTOR": "CONFLUENCE_CONNECTOR",
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"CLICKUP_CONNECTOR": "CLICKUP_CONNECTOR",
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"GOOGLE_CALENDAR_CONNECTOR": "GOOGLE_CALENDAR_CONNECTOR",
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"GOOGLE_GMAIL_CONNECTOR": "GOOGLE_GMAIL_CONNECTOR",
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"GOOGLE_DRIVE_CONNECTOR": "GOOGLE_DRIVE_FILE", # Connector type differs from document type
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"AIRTABLE_CONNECTOR": "AIRTABLE_CONNECTOR",
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"LUMA_CONNECTOR": "LUMA_CONNECTOR",
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"ELASTICSEARCH_CONNECTOR": "ELASTICSEARCH_CONNECTOR",
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"WEBCRAWLER_CONNECTOR": "CRAWLED_URL", # Maps to document type
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"BOOKSTACK_CONNECTOR": "BOOKSTACK_CONNECTOR",
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"CIRCLEBACK_CONNECTOR": "CIRCLEBACK", # Connector type differs from document type
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"OBSIDIAN_CONNECTOR": "OBSIDIAN_CONNECTOR",
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# Composio connectors
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"COMPOSIO_GOOGLE_DRIVE_CONNECTOR": "COMPOSIO_GOOGLE_DRIVE_CONNECTOR",
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"COMPOSIO_GMAIL_CONNECTOR": "COMPOSIO_GMAIL_CONNECTOR",
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"COMPOSIO_GOOGLE_CALENDAR_CONNECTOR": "COMPOSIO_GOOGLE_CALENDAR_CONNECTOR",
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}
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# Document types that don't come from SearchSourceConnector but should always be searchable
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_ALWAYS_AVAILABLE_DOC_TYPES: list[str] = [
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"EXTENSION", # Browser extension data
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"FILE", # Uploaded files
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"NOTE", # User notes
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"YOUTUBE_VIDEO", # YouTube videos
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]
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def _map_connectors_to_searchable_types(
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connector_types: list[Any],
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) -> list[str]:
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"""
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Map SearchSourceConnectorType enums to searchable document/connector types.
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This function:
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1. Converts connector type enums to their searchable counterparts
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2. Includes always-available document types (EXTENSION, FILE, NOTE, YOUTUBE_VIDEO)
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3. Deduplicates while preserving order
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Args:
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connector_types: List of SearchSourceConnectorType enum values
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Returns:
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List of searchable connector/document type strings
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"""
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result_set: set[str] = set()
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result_list: list[str] = []
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# Add always-available document types first
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for doc_type in _ALWAYS_AVAILABLE_DOC_TYPES:
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if doc_type not in result_set:
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result_set.add(doc_type)
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result_list.append(doc_type)
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# Map each connector type to its searchable equivalent
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for ct in connector_types:
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# Handle both enum and string types
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ct_str = ct.value if hasattr(ct, "value") else str(ct)
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searchable = _CONNECTOR_TYPE_TO_SEARCHABLE.get(ct_str)
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if searchable and searchable not in result_set:
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result_set.add(searchable)
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result_list.append(searchable)
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return result_list
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# =============================================================================
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# Deep Agent Factory
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# =============================================================================
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async def create_surfsense_deep_agent(
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llm: BaseChatModel,
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search_space_id: int,
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db_session: AsyncSession,
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connector_service: ConnectorService,
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checkpointer: Checkpointer,
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user_id: str | None = None,
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thread_id: int | None = None,
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agent_config: AgentConfig | None = None,
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enabled_tools: list[str] | None = None,
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disabled_tools: list[str] | None = None,
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additional_tools: Sequence[BaseTool] | None = None,
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firecrawl_api_key: str | None = None,
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thread_visibility: ChatVisibility | None = None,
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sandbox_backend: SandboxBackendProtocol | None = None,
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):
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"""
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Create a SurfSense deep agent with configurable tools and prompts.
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The agent comes with built-in tools that can be configured:
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- search_knowledge_base: Search the user's personal knowledge base
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- generate_podcast: Generate audio podcasts from content
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- generate_image: Generate images from text descriptions using AI models
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- link_preview: Fetch rich previews for URLs
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- display_image: Display images in chat
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- scrape_webpage: Extract content from webpages
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- save_memory: Store facts/preferences about the user
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- recall_memory: Retrieve relevant user memories
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The agent also includes TodoListMiddleware by default (via create_deep_agent) which provides:
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- write_todos: Create and update planning/todo lists for complex tasks
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The system prompt can be configured via agent_config:
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- Custom system instructions (or use defaults)
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- Citation toggle (enable/disable citation requirements)
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Args:
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llm: ChatLiteLLM instance for the agent's language model
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search_space_id: The user's search space ID
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db_session: Database session for tools that need DB access
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connector_service: Initialized connector service for knowledge base search
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checkpointer: LangGraph checkpointer for conversation state persistence.
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Use AsyncPostgresSaver for production or MemorySaver for testing.
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user_id: The current user's UUID string (required for memory tools)
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agent_config: Optional AgentConfig from NewLLMConfig for prompt configuration.
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If None, uses default system prompt with citations enabled.
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enabled_tools: Explicit list of tool names to enable. If None, all default tools
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are enabled. Use this to limit which tools are available.
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disabled_tools: List of tool names to disable. Applied after enabled_tools.
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Use this to exclude specific tools from the defaults.
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additional_tools: Extra custom tools to add beyond the built-in ones.
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These are always added regardless of enabled/disabled settings.
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firecrawl_api_key: Optional Firecrawl API key for premium web scraping.
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Falls back to Chromium/Trafilatura if not provided.
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sandbox_backend: Optional sandbox backend (e.g. DaytonaSandbox) for
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secure code execution. When provided, the agent gets an
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isolated ``execute`` tool for running shell commands.
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Returns:
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CompiledStateGraph: The configured deep agent
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Examples:
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# Create agent with all default tools and default prompt
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agent = create_surfsense_deep_agent(llm, search_space_id, db_session, ...)
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# Create agent with custom prompt configuration
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agent = create_surfsense_deep_agent(
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llm, search_space_id, db_session, ...,
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agent_config=AgentConfig(
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provider="OPENAI",
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model_name="gpt-4",
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api_key="...",
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system_instructions="Custom instructions...",
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citations_enabled=False,
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)
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)
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# Create agent with only specific tools
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agent = create_surfsense_deep_agent(
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llm, search_space_id, db_session, ...,
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enabled_tools=["search_knowledge_base", "link_preview"]
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)
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# Create agent without podcast generation
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agent = create_surfsense_deep_agent(
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llm, search_space_id, db_session, ...,
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disabled_tools=["generate_podcast"]
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)
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# Add custom tools
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agent = create_surfsense_deep_agent(
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llm, search_space_id, db_session, ...,
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additional_tools=[my_custom_tool]
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)
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"""
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_t_agent_total = time.perf_counter()
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# Discover available connectors and document types for this search space
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available_connectors: list[str] | None = None
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available_document_types: list[str] | None = None
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_t0 = time.perf_counter()
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try:
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connector_types = await connector_service.get_available_connectors(
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search_space_id
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)
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if connector_types:
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available_connectors = _map_connectors_to_searchable_types(connector_types)
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available_document_types = await connector_service.get_available_document_types(
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search_space_id
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)
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except Exception as e:
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logging.warning(f"Failed to discover available connectors/document types: {e}")
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_perf_log.info(
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"[create_agent] Connector/doc-type discovery in %.3fs",
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time.perf_counter() - _t0,
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)
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# Build dependencies dict for the tools registry
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visibility = thread_visibility or ChatVisibility.PRIVATE
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# Extract the model's context window so tools can size their output.
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_model_profile = getattr(llm, "profile", None)
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_max_input_tokens: int | None = (
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_model_profile.get("max_input_tokens")
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if isinstance(_model_profile, dict)
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else None
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)
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dependencies = {
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"search_space_id": search_space_id,
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"db_session": db_session,
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"connector_service": connector_service,
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"firecrawl_api_key": firecrawl_api_key,
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"user_id": user_id,
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"thread_id": thread_id,
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"thread_visibility": visibility,
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"available_connectors": available_connectors,
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"available_document_types": available_document_types,
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"max_input_tokens": _max_input_tokens,
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}
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# Disable Notion action tools if no Notion connector is configured
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modified_disabled_tools = list(disabled_tools) if disabled_tools else []
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has_notion_connector = (
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available_connectors is not None and "NOTION_CONNECTOR" in available_connectors
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)
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if not has_notion_connector:
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notion_tools = [
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"create_notion_page",
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"update_notion_page",
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"delete_notion_page",
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]
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modified_disabled_tools.extend(notion_tools)
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# Disable Linear action tools if no Linear connector is configured
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has_linear_connector = (
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available_connectors is not None and "LINEAR_CONNECTOR" in available_connectors
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)
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if not has_linear_connector:
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linear_tools = [
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"create_linear_issue",
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"update_linear_issue",
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"delete_linear_issue",
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]
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modified_disabled_tools.extend(linear_tools)
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# Build tools using the async registry (includes MCP tools)
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_t0 = time.perf_counter()
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tools = await build_tools_async(
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dependencies=dependencies,
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enabled_tools=enabled_tools,
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disabled_tools=modified_disabled_tools,
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additional_tools=list(additional_tools) if additional_tools else None,
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)
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_perf_log.info(
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"[create_agent] build_tools_async in %.3fs (%d tools)",
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time.perf_counter() - _t0,
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len(tools),
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)
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# Build system prompt based on agent_config
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_t0 = time.perf_counter()
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_sandbox_enabled = sandbox_backend is not None
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if agent_config is not None:
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system_prompt = build_configurable_system_prompt(
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custom_system_instructions=agent_config.system_instructions,
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use_default_system_instructions=agent_config.use_default_system_instructions,
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citations_enabled=agent_config.citations_enabled,
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thread_visibility=thread_visibility,
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sandbox_enabled=_sandbox_enabled,
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)
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else:
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system_prompt = build_surfsense_system_prompt(
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thread_visibility=thread_visibility,
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sandbox_enabled=_sandbox_enabled,
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)
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_perf_log.info(
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"[create_agent] System prompt built in %.3fs", time.perf_counter() - _t0
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)
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# Build optional kwargs for the deep agent
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deep_agent_kwargs: dict[str, Any] = {}
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if sandbox_backend is not None:
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deep_agent_kwargs["backend"] = sandbox_backend
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_t0 = time.perf_counter()
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agent = await asyncio.to_thread(
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create_deep_agent,
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model=llm,
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tools=tools,
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system_prompt=system_prompt,
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context_schema=SurfSenseContextSchema,
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checkpointer=checkpointer,
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**deep_agent_kwargs,
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)
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_perf_log.info(
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"[create_agent] Graph compiled (create_deep_agent) in %.3fs",
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time.perf_counter() - _t0,
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)
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_perf_log.info(
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"[create_agent] Total agent creation in %.3fs",
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time.perf_counter() - _t_agent_total,
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)
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return agent
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