mirror of
https://github.com/MODSetter/SurfSense.git
synced 2026-05-31 19:45:15 +02:00
refactor: remove search_surfsense_docs tool and related references
- Deleted the `search_surfsense_docs` tool and its associated files, streamlining the agent's toolset. - Updated various components and prompts to remove references to the now-removed tool, ensuring consistency across the codebase. - Adjusted documentation to direct users to the SurfSense documentation link for product-related queries instead.
This commit is contained in:
DESKTOP-RTLN3BA\$punk
parent
9b9e6828c7
commit
40ca9e6ed2
71 changed files with 232 additions and 1676 deletions
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@ -25,7 +25,6 @@ from uuid import UUID
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import anyio
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from langchain_core.messages import HumanMessage
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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.multi_agent_chat import create_multi_agent_chat_deep_agent
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from app.agents.new_chat.chat_deepagent import create_surfsense_deep_agent
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@ -55,7 +54,6 @@ from app.db import (
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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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shielded_async_session,
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)
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@ -77,7 +75,6 @@ from app.tasks.chat.streaming.helpers.interrupt_inspector import (
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)
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from app.utils.content_utils import bootstrap_history_from_db
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from app.utils.perf import get_perf_logger, log_system_snapshot, trim_native_heap
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from app.utils.surfsense_docs import surfsense_docs_public_url
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from app.utils.user_message_multimodal import build_human_message_content
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_background_tasks: set[asyncio.Task] = set()
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@ -198,58 +195,6 @@ def _extract_chunk_parts(chunk: Any) -> dict[str, Any]:
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return out
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def format_mentioned_surfsense_docs_as_context(
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documents: list[SurfsenseDocsDocument],
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) -> str:
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"""Format mentioned SurfSense documentation as context for the agent."""
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if not documents:
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return ""
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context_parts = ["<mentioned_surfsense_docs>"]
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context_parts.append(
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"The user has explicitly mentioned the following SurfSense documentation pages. "
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"These are official documentation about how to use SurfSense and should be used to answer questions about the application. "
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"Use [citation:CHUNK_ID] format for citations (e.g., [citation:doc-123])."
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)
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for doc in documents:
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public_url = surfsense_docs_public_url(doc.source)
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metadata_json = json.dumps(
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{"source": doc.source, "public_url": public_url}, ensure_ascii=False
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)
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context_parts.append("<document>")
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context_parts.append("<document_metadata>")
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context_parts.append(f" <document_id>doc-{doc.id}</document_id>")
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context_parts.append(" <document_type>SURFSENSE_DOCS</document_type>")
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context_parts.append(f" <title><![CDATA[{doc.title}]]></title>")
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context_parts.append(f" <url><![CDATA[{public_url}]]></url>")
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context_parts.append(
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f" <metadata_json><![CDATA[{metadata_json}]]></metadata_json>"
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)
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context_parts.append("</document_metadata>")
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context_parts.append("")
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context_parts.append("<document_content>")
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if hasattr(doc, "chunks") and doc.chunks:
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for chunk in doc.chunks:
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context_parts.append(
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f" <chunk id='doc-{chunk.id}'><![CDATA[{chunk.content}]]></chunk>"
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)
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else:
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context_parts.append(
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f" <chunk id='doc-0'><![CDATA[{doc.content}]]></chunk>"
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)
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context_parts.append("</document_content>")
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context_parts.append("</document>")
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context_parts.append("")
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context_parts.append("</mentioned_surfsense_docs>")
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return "\n".join(context_parts)
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def extract_todos_from_deepagents(command_output) -> dict:
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"""
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Extract todos from deepagents' TodoListMiddleware Command output.
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@ -837,7 +782,6 @@ async def stream_new_chat(
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user_id: str | None = None,
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llm_config_id: int = -1,
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mentioned_document_ids: list[int] | None = None,
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mentioned_surfsense_doc_ids: list[int] | None = None,
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mentioned_folder_ids: list[int] | None = None,
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mentioned_connector_ids: list[int] | None = None,
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mentioned_connectors: list[dict[str, Any]] | None = None,
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@ -869,7 +813,6 @@ async def stream_new_chat(
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llm_config_id: The LLM configuration ID (default: -1 for first global config)
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needs_history_bootstrap: If True, load message history from DB (for cloned chats)
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mentioned_document_ids: Optional list of document IDs mentioned with @ in the chat
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mentioned_surfsense_doc_ids: Optional list of SurfSense doc IDs mentioned with @ in the chat
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mentioned_folder_ids: Optional list of knowledge-base folder IDs mentioned with @ (cloud mode)
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checkpoint_id: Optional checkpoint ID to rewind/fork from (for edit/reload operations)
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@ -1295,19 +1238,7 @@ async def stream_new_chat(
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# Mentioned KB documents are now handled by KnowledgeBaseSearchMiddleware
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# which merges them into the scoped filesystem with full document
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# structure. Only SurfSense docs and report context are inlined here.
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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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result = await session.execute(
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select(SurfsenseDocsDocument)
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.options(selectinload(SurfsenseDocsDocument.chunks))
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.filter(
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SurfsenseDocsDocument.id.in_(mentioned_surfsense_doc_ids),
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)
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)
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mentioned_surfsense_docs = list(result.scalars().all())
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# structure. Only report context is inlined here.
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# Fetch the most recent report(s) in this thread so the LLM can
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# easily find report_id for versioning decisions, instead of
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@ -1341,10 +1272,7 @@ async def stream_new_chat(
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agent_user_query = user_query
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accepted_folder_ids: list[int] = []
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if fs_mode == FilesystemMode.CLOUD.value and (
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mentioned_document_ids
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or mentioned_surfsense_doc_ids
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or mentioned_folder_ids
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or mentioned_documents
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mentioned_document_ids or mentioned_folder_ids or mentioned_documents
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):
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from app.schemas.new_chat import (
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MentionedDocumentInfo as _MentionedDocumentInfo,
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@ -1370,23 +1298,17 @@ async def stream_new_chat(
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search_space_id=search_space_id,
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mentioned_documents=chip_objs,
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mentioned_document_ids=mentioned_document_ids,
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mentioned_surfsense_doc_ids=mentioned_surfsense_doc_ids,
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mentioned_folder_ids=mentioned_folder_ids,
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)
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agent_user_query = substitute_in_text(user_query, resolved.token_to_path)
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accepted_folder_ids = resolved.mentioned_folder_ids
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# Format the user query with context (SurfSense docs + reports only).
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# Format the user query with context (reports only).
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# Uses ``agent_user_query`` so the LLM sees backtick-wrapped paths
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# instead of bare ``@title`` tokens.
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final_query = agent_user_query
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context_parts = []
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if mentioned_surfsense_docs:
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context_parts.append(
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format_mentioned_surfsense_docs_as_context(mentioned_surfsense_docs)
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)
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if mentioned_connectors:
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connector_lines = []
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for connector in mentioned_connectors:
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@ -1617,12 +1539,8 @@ async def stream_new_chat(
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stream_result.content_builder = AssistantContentBuilder()
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# Initial thinking step - analyzing the request
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if mentioned_surfsense_docs:
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initial_title = "Analyzing referenced content"
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action_verb = "Analyzing"
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else:
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initial_title = "Understanding your request"
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action_verb = "Processing"
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initial_title = "Understanding your request"
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action_verb = "Processing"
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processing_parts = []
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if user_query.strip():
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@ -1633,18 +1551,6 @@ async def stream_new_chat(
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else:
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processing_parts.append("(message)")
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if mentioned_surfsense_docs:
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doc_names = []
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for doc in mentioned_surfsense_docs:
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title = doc.title
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if len(title) > 30:
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title = title[:27] + "..."
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doc_names.append(title)
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if len(doc_names) == 1:
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processing_parts.append(f"[{doc_names[0]}]")
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else:
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processing_parts.append(f"[{len(doc_names)} docs]")
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initial_items = [f"{action_verb}: {' '.join(processing_parts)}"]
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initial_step_id = "thinking-1"
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@ -1664,10 +1570,10 @@ async def stream_new_chat(
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items=initial_items,
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)
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# These ORM objects (with eagerly-loaded chunks) can be very large.
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# They're only needed to build context strings already copied into
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# final_query / langchain_messages — release them before streaming.
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del mentioned_surfsense_docs, recent_reports
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# These ORM objects can be large. They're only needed to build context
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# strings already copied into final_query / langchain_messages —
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# release them before streaming.
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del recent_reports
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del langchain_messages, final_query
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# Check if this is the first assistant response so we can generate
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