mirror of
https://github.com/MODSetter/SurfSense.git
synced 2026-05-09 07:42:39 +02:00
b14283e300
- Enhanced the new chat agent module to allow for configurable tools, enabling users to customize their experience with various functionalities. - Removed outdated tools including display image, knowledge base search, link preview, podcast generation, and web scraping, streamlining the codebase. - Updated the system prompt and agent factory to reflect these changes, ensuring a more cohesive and efficient architecture.
113 lines
4.3 KiB
Python
113 lines
4.3 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.
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"""
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from collections.abc import Sequence
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from deepagents import create_deep_agent
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from langchain_core.tools import BaseTool
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from langchain_litellm import ChatLiteLLM
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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.system_prompt import build_surfsense_system_prompt
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from app.agents.new_chat.tools import build_tools
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from app.services.connector_service import ConnectorService
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# =============================================================================
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# Deep Agent Factory
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# =============================================================================
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def create_surfsense_deep_agent(
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llm: ChatLiteLLM,
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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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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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):
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"""
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Create a SurfSense deep agent with configurable tools.
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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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- 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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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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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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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
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agent = create_surfsense_deep_agent(llm, search_space_id, db_session, ...)
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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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# Build dependencies dict for the tools registry
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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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}
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# Build tools using the registry
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tools = build_tools(
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dependencies=dependencies,
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enabled_tools=enabled_tools,
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disabled_tools=disabled_tools,
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additional_tools=list(additional_tools) if additional_tools else None,
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)
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# Create the deep agent with system prompt and checkpointer
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agent = create_deep_agent(
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model=llm,
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tools=tools,
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system_prompt=build_surfsense_system_prompt(),
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context_schema=SurfSenseContextSchema,
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checkpointer=checkpointer,
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)
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return agent
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