diff --git a/surfsense_backend/alembic/versions/121_add_memory_md_columns.py b/surfsense_backend/alembic/versions/121_add_memory_md_columns.py new file mode 100644 index 000000000..d5ff967fd --- /dev/null +++ b/surfsense_backend/alembic/versions/121_add_memory_md_columns.py @@ -0,0 +1,38 @@ +"""Add memory_md columns to user and searchspaces tables + +Revision ID: 121 +Revises: 120 + +Changes: +1. Add memory_md TEXT column to user table (personal memory) +2. Add shared_memory_md TEXT column to searchspaces table (team memory) +""" + +from __future__ import annotations + +from collections.abc import Sequence + +import sqlalchemy as sa + +from alembic import op + +revision: str = "121" +down_revision: str | None = "120" +branch_labels: str | Sequence[str] | None = None +depends_on: str | Sequence[str] | None = None + + +def upgrade() -> None: + op.add_column( + "user", + sa.Column("memory_md", sa.Text(), nullable=True, server_default=""), + ) + op.add_column( + "searchspaces", + sa.Column("shared_memory_md", sa.Text(), nullable=True, server_default=""), + ) + + +def downgrade() -> None: + op.drop_column("searchspaces", "shared_memory_md") + op.drop_column("user", "memory_md") diff --git a/surfsense_backend/alembic/versions/122_migrate_and_drop_old_memory_tables.py b/surfsense_backend/alembic/versions/122_migrate_and_drop_old_memory_tables.py new file mode 100644 index 000000000..b8ddddb9c --- /dev/null +++ b/surfsense_backend/alembic/versions/122_migrate_and_drop_old_memory_tables.py @@ -0,0 +1,247 @@ +"""Migrate row-per-fact memories to markdown, then drop legacy tables + +Revision ID: 122 +Revises: 121 + +Converts user_memories rows into per-user markdown documents stored in +user.memory_md, and shared_memories rows into per-search-space markdown +stored in searchspaces.shared_memory_md. Then drops the old tables and +the memorycategory enum. + +The markdown format matches the new memory system: + ## Heading + - (YYYY-MM-DD) [fact|pref|instr] memory text +""" + +from __future__ import annotations + +import logging +from collections import defaultdict +from collections.abc import Sequence +from uuid import UUID + +import sqlalchemy as sa +from sqlalchemy import inspect as sa_inspect + +from alembic import op +from app.config import config + +logger = logging.getLogger(__name__) + +revision: str = "122" +down_revision: str | None = "121" +branch_labels: str | Sequence[str] | None = None +depends_on: str | Sequence[str] | None = None + +EMBEDDING_DIM = config.embedding_model_instance.dimension + +_CATEGORY_TO_MARKER = { + "fact": "fact", + "context": "fact", + "preference": "pref", + "instruction": "instr", +} + +_CATEGORY_HEADING = { + "fact": "Facts", + "preference": "Preferences", + "instruction": "Instructions", + "context": "Context", +} + +_HEADING_ORDER = ["fact", "preference", "instruction", "context"] + + +def _build_markdown(rows: list[tuple]) -> str: + """Build a markdown document from (memory_text, category, created_at) rows.""" + by_category: dict[str, list[str]] = defaultdict(list) + + for memory_text, category, created_at in rows: + cat = str(category) + marker = _CATEGORY_TO_MARKER.get(cat, "fact") + date_str = created_at.strftime("%Y-%m-%d") + clean_text = str(memory_text).replace("\n", " ").strip() + bullet = f"- ({date_str}) [{marker}] {clean_text}" + by_category[cat].append(bullet) + + sections: list[str] = [] + for cat in _HEADING_ORDER: + if cat in by_category: + heading = _CATEGORY_HEADING[cat] + sections.append(f"## {heading}") + sections.extend(by_category[cat]) + sections.append("") + + return "\n".join(sections).strip() + "\n" + + +def _migrate_user_memories(conn: sa.engine.Connection) -> None: + """Convert user_memories rows → user.memory_md grouped by user_id.""" + rows = conn.execute( + sa.text( + "SELECT user_id, memory_text, category::text, created_at " + "FROM user_memories ORDER BY created_at" + ) + ).fetchall() + + if not rows: + logger.info("user_memories is empty, skipping data migration.") + return + + by_user: dict[UUID, list[tuple]] = defaultdict(list) + for user_id, memory_text, category, created_at in rows: + by_user[user_id].append((memory_text, category, created_at)) + + migrated = 0 + for uid, user_rows in by_user.items(): + existing = conn.execute( + sa.text('SELECT memory_md FROM "user" WHERE id = :uid'), + {"uid": uid}, + ).scalar() + + if existing and existing.strip(): + logger.info("User %s already has memory_md, skipping.", uid) + continue + + markdown = _build_markdown(user_rows) + conn.execute( + sa.text('UPDATE "user" SET memory_md = :md WHERE id = :uid'), + {"md": markdown, "uid": uid}, + ) + migrated += 1 + + logger.info("Migrated user_memories for %d user(s).", migrated) + + +def _migrate_shared_memories(conn: sa.engine.Connection) -> None: + """Convert shared_memories rows → searchspaces.shared_memory_md.""" + rows = conn.execute( + sa.text( + "SELECT search_space_id, memory_text, category::text, created_at " + "FROM shared_memories ORDER BY created_at" + ) + ).fetchall() + + if not rows: + logger.info("shared_memories is empty, skipping data migration.") + return + + by_space: dict[int, list[tuple]] = defaultdict(list) + for search_space_id, memory_text, category, created_at in rows: + by_space[search_space_id].append((memory_text, category, created_at)) + + migrated = 0 + for space_id, space_rows in by_space.items(): + existing = conn.execute( + sa.text("SELECT shared_memory_md FROM searchspaces WHERE id = :sid"), + {"sid": space_id}, + ).scalar() + + if existing and existing.strip(): + logger.info( + "Search space %s already has shared_memory_md, skipping.", space_id + ) + continue + + markdown = _build_markdown(space_rows) + conn.execute( + sa.text("UPDATE searchspaces SET shared_memory_md = :md WHERE id = :sid"), + {"md": markdown, "sid": space_id}, + ) + migrated += 1 + + logger.info("Migrated shared_memories for %d search space(s).", migrated) + + +def upgrade() -> None: + conn = op.get_bind() + inspector = sa_inspect(conn) + tables = inspector.get_table_names() + + if "user_memories" in tables: + _migrate_user_memories(conn) + + if "shared_memories" in tables: + _migrate_shared_memories(conn) + + op.execute("DROP TABLE IF EXISTS shared_memories CASCADE;") + op.execute("DROP TABLE IF EXISTS user_memories CASCADE;") + op.execute("DROP TYPE IF EXISTS memorycategory;") + + +def downgrade() -> None: + op.execute( + """ + DO $$ + BEGIN + IF NOT EXISTS (SELECT 1 FROM pg_type WHERE typname = 'memorycategory') THEN + CREATE TYPE memorycategory AS ENUM ( + 'preference', + 'fact', + 'instruction', + 'context' + ); + END IF; + END$$; + """ + ) + + op.execute( + f""" + CREATE TABLE IF NOT EXISTS user_memories ( + id SERIAL PRIMARY KEY, + created_at TIMESTAMP WITH TIME ZONE NOT NULL DEFAULT NOW(), + user_id UUID NOT NULL REFERENCES "user"(id) ON DELETE CASCADE, + search_space_id INTEGER REFERENCES searchspaces(id) ON DELETE CASCADE, + memory_text TEXT NOT NULL, + category memorycategory NOT NULL DEFAULT 'fact', + embedding vector({EMBEDDING_DIM}), + updated_at TIMESTAMP WITH TIME ZONE NOT NULL DEFAULT NOW() + ); + """ + ) + op.execute( + "CREATE INDEX IF NOT EXISTS ix_user_memories_user_id ON user_memories(user_id);" + ) + op.execute( + "CREATE INDEX IF NOT EXISTS ix_user_memories_search_space_id ON user_memories(search_space_id);" + ) + op.execute( + "CREATE INDEX IF NOT EXISTS ix_user_memories_updated_at ON user_memories(updated_at);" + ) + op.execute( + "CREATE INDEX IF NOT EXISTS ix_user_memories_category ON user_memories(category);" + ) + op.execute( + "CREATE INDEX IF NOT EXISTS ix_user_memories_user_search_space ON user_memories(user_id, search_space_id);" + ) + op.execute( + "CREATE INDEX IF NOT EXISTS user_memories_vector_index ON user_memories USING hnsw (embedding public.vector_cosine_ops);" + ) + + op.execute( + f""" + CREATE TABLE IF NOT EXISTS shared_memories ( + id SERIAL PRIMARY KEY, + created_at TIMESTAMP WITH TIME ZONE NOT NULL DEFAULT NOW(), + updated_at TIMESTAMP WITH TIME ZONE NOT NULL DEFAULT NOW(), + search_space_id INTEGER NOT NULL REFERENCES searchspaces(id) ON DELETE CASCADE, + created_by_id UUID NOT NULL REFERENCES "user"(id) ON DELETE CASCADE, + memory_text TEXT NOT NULL, + category memorycategory NOT NULL DEFAULT 'fact', + embedding vector({EMBEDDING_DIM}) + ); + """ + ) + op.execute( + "CREATE INDEX IF NOT EXISTS ix_shared_memories_search_space_id ON shared_memories(search_space_id);" + ) + op.execute( + "CREATE INDEX IF NOT EXISTS ix_shared_memories_updated_at ON shared_memories(updated_at);" + ) + op.execute( + "CREATE INDEX IF NOT EXISTS ix_shared_memories_created_by_id ON shared_memories(created_by_id);" + ) + op.execute( + "CREATE INDEX IF NOT EXISTS shared_memories_vector_index ON shared_memories USING hnsw (embedding public.vector_cosine_ops);" + ) diff --git a/surfsense_backend/app/agents/new_chat/chat_deepagent.py b/surfsense_backend/app/agents/new_chat/chat_deepagent.py index fc1e80d28..6ff98badf 100644 --- a/surfsense_backend/app/agents/new_chat/chat_deepagent.py +++ b/surfsense_backend/app/agents/new_chat/chat_deepagent.py @@ -38,6 +38,7 @@ from app.agents.new_chat.llm_config import AgentConfig from app.agents.new_chat.middleware import ( DedupHITLToolCallsMiddleware, KnowledgeBaseSearchMiddleware, + MemoryInjectionMiddleware, SurfSenseFilesystemMiddleware, ) from app.agents.new_chat.system_prompt import ( @@ -168,8 +169,7 @@ async def create_surfsense_deep_agent( - generate_podcast: Generate audio podcasts from content - generate_image: Generate images from text descriptions using AI models - scrape_webpage: Extract content from webpages - - save_memory: Store facts/preferences about the user - - recall_memory: Retrieve relevant user memories + - update_memory: Update the user's personal or team memory document The agent also includes TodoListMiddleware by default (via create_deep_agent) which provides: - write_todos: Create and update planning/todo lists for complex tasks @@ -281,6 +281,7 @@ async def create_surfsense_deep_agent( "available_connectors": available_connectors, "available_document_types": available_document_types, "max_input_tokens": _max_input_tokens, + "llm": llm, } # Disable Notion action tools if no Notion connector is configured @@ -425,9 +426,16 @@ async def create_surfsense_deep_agent( ) # -- Build the middleware stack (mirrors create_deep_agent internals) ------ + _memory_middleware = MemoryInjectionMiddleware( + user_id=user_id, + search_space_id=search_space_id, + thread_visibility=visibility, + ) + # General-purpose subagent middleware gp_middleware = [ TodoListMiddleware(), + _memory_middleware, SurfSenseFilesystemMiddleware( search_space_id=search_space_id, created_by_id=user_id, @@ -447,6 +455,7 @@ async def create_surfsense_deep_agent( # Main agent middleware deepagent_middleware = [ TodoListMiddleware(), + _memory_middleware, KnowledgeBaseSearchMiddleware( llm=llm, search_space_id=search_space_id, diff --git a/surfsense_backend/app/agents/new_chat/memory_extraction.py b/surfsense_backend/app/agents/new_chat/memory_extraction.py new file mode 100644 index 000000000..221c4c75a --- /dev/null +++ b/surfsense_backend/app/agents/new_chat/memory_extraction.py @@ -0,0 +1,239 @@ +"""Background memory extraction for the SurfSense agent. + +After each agent response, if the agent did not call ``update_memory`` during +the turn, this module can run a lightweight LLM call to decide whether the +latest message contains long-term information worth persisting. +""" + +from __future__ import annotations + +import logging +from typing import Any +from uuid import UUID + +from langchain_core.messages import HumanMessage +from sqlalchemy import select + +from app.agents.new_chat.tools.update_memory import _save_memory +from app.db import SearchSpace, User, shielded_async_session + +logger = logging.getLogger(__name__) + +_MEMORY_EXTRACT_PROMPT = """\ +You are a memory extraction assistant. Analyze the user's message and decide \ +if it contains any long-term information worth persisting to memory. + +Worth remembering: preferences, background/identity, goals, projects, \ +instructions, tools/languages they use, decisions, expertise, workplace — \ +durable facts that will matter in future conversations. + +NOT worth remembering: greetings, one-off factual questions, session \ +logistics, ephemeral requests, follow-up clarifications with no new personal \ +info, things that only matter for the current task. + +If the message contains memorizable information, output the FULL updated \ +memory document with the new facts merged into the existing content. Follow \ +these rules: +- Every entry MUST be under a ## heading. Preserve existing headings; create new ones + freely. Keep heading names short (2-3 words) and natural. Do NOT include the user's + name in headings. +- Keep entries as single bullet points. Be descriptive but concise — include relevant + details and context rather than just a few words. +- Every bullet MUST use format: - (YYYY-MM-DD) [fact|pref|instr] text + [fact] = durable facts, [pref] = preferences, [instr] = standing instructions. +- Use the user's first name (from ) in entry text, not "the user". +- If a new fact contradicts an existing entry, update the existing entry. +- Do not duplicate information that is already present. + +If nothing is worth remembering, output exactly: NO_UPDATE + +{user_name} + + +{current_memory} + + + +{user_message} +""" + +_TEAM_MEMORY_EXTRACT_PROMPT = """\ +You are a team-memory extraction assistant. Analyze the latest message and \ +decide if it contains durable TEAM-level information worth persisting. + +Decision policy: +- Prioritize recall for durable team context, while avoiding personal-only facts. +- Do NOT require explicit consensus language. A direct team-level statement can + be stored if it is stable and broadly useful for future team chats. +- If evidence is weak or clearly tentative, output NO_UPDATE. + +Worth remembering (team-level only): +- Decisions and defaults that guide future team work +- Team conventions/standards (naming, review policy, coding norms) +- Stable org/project facts (locations, ownership, constraints) +- Long-lived architecture/process facts +- Ongoing priorities that are likely relevant beyond this turn + +NOT worth remembering: +- Personal preferences or biography of one person +- Questions, brainstorming, tentative ideas, or speculation +- One-off requests, status updates, TODOs, logistics for this session +- Information scoped only to a single ephemeral task + +If the message contains memorizable team information, output the FULL updated \ +team memory document with new facts merged into existing content. Follow rules: +- Every entry MUST be under a ## heading. Preserve existing headings; create new ones + freely. Keep heading names short (2-3 words) and natural. +- Keep entries as single bullet points. Be descriptive but concise — include relevant + details and context rather than just a few words. +- Every bullet MUST use format: - (YYYY-MM-DD) [fact] text + Team memory uses ONLY the [fact] marker. Never use [pref] or [instr]. +- If a new fact contradicts an existing entry, update the existing entry. +- Do not duplicate existing information. +- Preserve neutral team phrasing; avoid person-specific memory unless role-anchored. + +If nothing is worth remembering, output exactly: NO_UPDATE + + +{current_memory} + + + +{author} + + + +{user_message} +""" + + +async def extract_and_save_memory( + *, + user_message: str, + user_id: str | None, + llm: Any, +) -> None: + """Background task: extract memorizable info and persist it. + + Designed to be fire-and-forget — catches all exceptions internally. + """ + if not user_id: + return + + try: + uid = UUID(user_id) if isinstance(user_id, str) else user_id + + async with shielded_async_session() as session: + result = await session.execute(select(User).where(User.id == uid)) + user = result.scalars().first() + if not user: + return + + old_memory = user.memory_md + first_name = ( + user.display_name.strip().split()[0] + if user.display_name and user.display_name.strip() + else "The user" + ) + prompt = _MEMORY_EXTRACT_PROMPT.format( + current_memory=old_memory or "(empty)", + user_message=user_message, + user_name=first_name, + ) + response = await llm.ainvoke( + [HumanMessage(content=prompt)], + config={"tags": ["surfsense:internal", "memory-extraction"]}, + ) + text = ( + response.content + if isinstance(response.content, str) + else str(response.content) + ).strip() + + if text == "NO_UPDATE" or not text: + logger.debug("Memory extraction: no update needed (user %s)", uid) + return + + save_result = await _save_memory( + updated_memory=text, + old_memory=old_memory, + llm=llm, + apply_fn=lambda content: setattr(user, "memory_md", content), + commit_fn=session.commit, + rollback_fn=session.rollback, + label="memory", + scope="user", + ) + logger.info( + "Background memory extraction for user %s: %s", + uid, + save_result.get("status"), + ) + except Exception: + logger.exception("Background user memory extraction failed") + + +async def extract_and_save_team_memory( + *, + user_message: str, + search_space_id: int | None, + llm: Any, + author_display_name: str | None = None, +) -> None: + """Background task: extract team-level memory and persist it. + + Runs only for shared threads. Designed to be fire-and-forget and catches + exceptions internally. + """ + if not search_space_id: + return + + try: + async with shielded_async_session() as session: + result = await session.execute( + select(SearchSpace).where(SearchSpace.id == search_space_id) + ) + space = result.scalars().first() + if not space: + return + + old_memory = space.shared_memory_md + prompt = _TEAM_MEMORY_EXTRACT_PROMPT.format( + current_memory=old_memory or "(empty)", + author=author_display_name or "Unknown team member", + user_message=user_message, + ) + response = await llm.ainvoke( + [HumanMessage(content=prompt)], + config={"tags": ["surfsense:internal", "team-memory-extraction"]}, + ) + text = ( + response.content + if isinstance(response.content, str) + else str(response.content) + ).strip() + + if text == "NO_UPDATE" or not text: + logger.debug( + "Team memory extraction: no update needed (space %s)", + search_space_id, + ) + return + + save_result = await _save_memory( + updated_memory=text, + old_memory=old_memory, + llm=llm, + apply_fn=lambda content: setattr(space, "shared_memory_md", content), + commit_fn=session.commit, + rollback_fn=session.rollback, + label="team memory", + scope="team", + ) + logger.info( + "Background team memory extraction for space %s: %s", + search_space_id, + save_result.get("status"), + ) + except Exception: + logger.exception("Background team memory extraction failed") diff --git a/surfsense_backend/app/agents/new_chat/middleware/__init__.py b/surfsense_backend/app/agents/new_chat/middleware/__init__.py index 91996f702..1f6b12852 100644 --- a/surfsense_backend/app/agents/new_chat/middleware/__init__.py +++ b/surfsense_backend/app/agents/new_chat/middleware/__init__.py @@ -9,9 +9,13 @@ from app.agents.new_chat.middleware.filesystem import ( from app.agents.new_chat.middleware.knowledge_search import ( KnowledgeBaseSearchMiddleware, ) +from app.agents.new_chat.middleware.memory_injection import ( + MemoryInjectionMiddleware, +) __all__ = [ "DedupHITLToolCallsMiddleware", "KnowledgeBaseSearchMiddleware", + "MemoryInjectionMiddleware", "SurfSenseFilesystemMiddleware", ] diff --git a/surfsense_backend/app/agents/new_chat/middleware/memory_injection.py b/surfsense_backend/app/agents/new_chat/middleware/memory_injection.py new file mode 100644 index 000000000..6179adccd --- /dev/null +++ b/surfsense_backend/app/agents/new_chat/middleware/memory_injection.py @@ -0,0 +1,138 @@ +"""Memory injection middleware for the SurfSense agent. + +Injects memory markdown into the system prompt on every turn: +- Private threads: only personal memory () +- Shared threads: only team memory () +""" + +from __future__ import annotations + +import logging +from typing import Any +from uuid import UUID + +from langchain.agents.middleware import AgentMiddleware, AgentState +from langchain_core.messages import HumanMessage, SystemMessage +from langgraph.runtime import Runtime +from sqlalchemy import select +from sqlalchemy.ext.asyncio import AsyncSession + +from app.agents.new_chat.tools.update_memory import MEMORY_HARD_LIMIT, MEMORY_SOFT_LIMIT +from app.db import ChatVisibility, SearchSpace, User, shielded_async_session + +logger = logging.getLogger(__name__) + + +class MemoryInjectionMiddleware(AgentMiddleware): # type: ignore[type-arg] + """Injects memory markdown into the conversation on every turn.""" + + tools = () + + def __init__( + self, + *, + user_id: str | UUID | None, + search_space_id: int, + thread_visibility: ChatVisibility | None = None, + ) -> None: + self.user_id = UUID(user_id) if isinstance(user_id, str) else user_id + self.search_space_id = search_space_id + self.visibility = thread_visibility or ChatVisibility.PRIVATE + + async def abefore_agent( # type: ignore[override] + self, + state: AgentState, + runtime: Runtime[Any], + ) -> dict[str, Any] | None: + del runtime + messages = state.get("messages") or [] + if not messages: + return None + + last_message = messages[-1] + if not isinstance(last_message, HumanMessage): + return None + + memory_blocks: list[str] = [] + + async with shielded_async_session() as session: + if self.visibility == ChatVisibility.SEARCH_SPACE: + team_memory = await self._load_team_memory(session) + if team_memory: + chars = len(team_memory) + memory_blocks.append( + f'\n' + f"{team_memory}\n" + f"" + ) + if chars > MEMORY_SOFT_LIMIT: + memory_blocks.append( + f"Team memory is at " + f"{chars:,}/{MEMORY_HARD_LIMIT:,} characters and approaching " + f"the hard limit. On your next update_memory call, consolidate " + f"by merging duplicates, removing outdated entries, and " + f"shortening descriptions before adding anything new." + f"" + ) + elif self.user_id is not None: + user_memory, display_name = await self._load_user_memory(session) + if display_name and display_name.strip(): + first_name = display_name.strip().split()[0] + memory_blocks.append(f"{first_name}") + if user_memory: + chars = len(user_memory) + memory_blocks.append( + f'\n' + f"{user_memory}\n" + f"" + ) + if chars > MEMORY_SOFT_LIMIT: + memory_blocks.append( + f"Your personal memory is at " + f"{chars:,}/{MEMORY_HARD_LIMIT:,} characters and approaching " + f"the hard limit. On your next update_memory call, consolidate " + f"by merging duplicates, removing outdated entries, and " + f"shortening descriptions before adding anything new." + f"" + ) + + if not memory_blocks: + return None + + memory_text = "\n\n".join(memory_blocks) + memory_msg = SystemMessage(content=memory_text) + + new_messages = list(messages) + insert_idx = 1 if len(new_messages) > 1 else 0 + new_messages.insert(insert_idx, memory_msg) + + return {"messages": new_messages} + + async def _load_user_memory( + self, session: AsyncSession + ) -> tuple[str | None, str | None]: + """Return (memory_content, display_name).""" + try: + result = await session.execute( + select(User.memory_md, User.display_name).where(User.id == self.user_id) + ) + row = result.one_or_none() + if row is None: + return None, None + return row.memory_md or None, row.display_name + except Exception: + logger.exception("Failed to load user memory") + return None, None + + async def _load_team_memory(self, session: AsyncSession) -> str | None: + try: + result = await session.execute( + select(SearchSpace.shared_memory_md).where( + SearchSpace.id == self.search_space_id + ) + ) + row = result.scalar_one_or_none() + return row if row else None + except Exception: + logger.exception("Failed to load team memory") + return None diff --git a/surfsense_backend/app/agents/new_chat/system_prompt.py b/surfsense_backend/app/agents/new_chat/system_prompt.py index 3d2442be8..dc1dd19b7 100644 --- a/surfsense_backend/app/agents/new_chat/system_prompt.py +++ b/surfsense_backend/app/agents/new_chat/system_prompt.py @@ -40,6 +40,13 @@ CRITICAL RULE — KNOWLEDGE BASE FIRST, NEVER DEFAULT TO GENERAL KNOWLEDGE: * Tool-usage actions like generating reports, podcasts, images, or scraping webpages + +IMPORTANT — After understanding each user message, ALWAYS check: does this message +reveal durable facts about the user (role, interests, preferences, projects, +background, or standing instructions)? If yes, you MUST call update_memory +alongside your normal response — do not defer this to a later turn. + + """ @@ -71,6 +78,13 @@ CRITICAL RULE — KNOWLEDGE BASE FIRST, NEVER DEFAULT TO GENERAL KNOWLEDGE: * Tool-usage actions like generating reports, podcasts, images, or scraping webpages + +IMPORTANT — After understanding each user message, ALWAYS check: does this message +reveal durable facts about the team (decisions, conventions, architecture, processes, +or key facts)? If yes, you MUST call update_memory alongside your normal response — +do not defer this to a later turn. + + """ @@ -248,115 +262,97 @@ _TOOL_INSTRUCTIONS["web_search"] = """ """ # Memory tool instructions have private and shared variants. -# We store them keyed as "save_memory" / "recall_memory" with sub-keys. +# We store them keyed as "update_memory" with sub-keys. _MEMORY_TOOL_INSTRUCTIONS: dict[str, dict[str, str]] = { - "save_memory": { + "update_memory": { "private": """ -- save_memory: Save facts, preferences, or context for personalized responses. - - Use this when the user explicitly or implicitly shares information worth remembering. - - Trigger scenarios: - * User says "remember this", "keep this in mind", "note that", or similar - * User shares personal preferences (e.g., "I prefer Python over JavaScript") - * User shares facts about themselves (e.g., "I'm a senior developer at Company X") - * User gives standing instructions (e.g., "always respond in bullet points") - * User shares project context (e.g., "I'm working on migrating our codebase to TypeScript") +- update_memory: Update your personal memory document about the user. + - Your current memory is already in in your context. The `chars` and + `limit` attributes show your current usage and the maximum allowed size. + - This is your curated long-term memory — the distilled essence of what you know about + the user, not raw conversation logs. + - Call update_memory when: + * The user explicitly asks to remember or forget something + * The user shares durable facts or preferences that will matter in future conversations + - The user's first name is provided in . Use it in memory entries + instead of "the user" (e.g. "{name} works at..." not "The user works at..."). + Do not store the name itself as a separate memory entry. + - Do not store short-lived or ephemeral info: one-off questions, greetings, + session logistics, or things that only matter for the current task. - Args: - - content: The fact/preference to remember. Phrase it clearly: - * "User prefers dark mode for all interfaces" - * "User is a senior Python developer" - * "User wants responses in bullet point format" - * "User is working on project called ProjectX" - - category: Type of memory: - * "preference": User preferences (coding style, tools, formats) - * "fact": Facts about the user (role, expertise, background) - * "instruction": Standing instructions (response format, communication style) - * "context": Current context (ongoing projects, goals, challenges) - - Returns: Confirmation of saved memory - - IMPORTANT: Only save information that would be genuinely useful for future conversations. - Don't save trivial or temporary information. + - updated_memory: The FULL updated markdown document (not a diff). + Merge new facts with existing ones, update contradictions, remove outdated entries. + Treat every update as a curation pass — consolidate, don't just append. + - Every bullet MUST use this format: - (YYYY-MM-DD) [marker] text + Markers: + [fact] — durable facts (role, background, projects, tools, expertise) + [pref] — preferences (response style, languages, formats, tools) + [instr] — standing instructions (always/never do, response rules) + - Keep it concise and well under the character limit shown in . + - Every entry MUST be under a `##` heading. Keep heading names short (2-3 words) and + natural. Do NOT include the user's name in headings. Organize by context — e.g. + who they are, what they're focused on, how they prefer things. Create, split, or + merge headings freely as the memory grows. + - Each entry MUST be a single bullet point. Be descriptive but concise — include relevant + details and context rather than just a few words. + - During consolidation, prioritize keeping: [instr] > [pref] > [fact]. """, "shared": """ -- save_memory: Save a fact, preference, or context to the team's shared memory for future reference. - - Use this when the user or a team member says "remember this", "keep this in mind", or similar in this shared chat. - - Use when the team agrees on something to remember (e.g., decisions, conventions). - - Someone shares a preference or fact that should be visible to the whole team. - - The saved information will be available in future shared conversations in this space. +- update_memory: Update the team's shared memory document for this search space. + - Your current team memory is already in in your context. The `chars` + and `limit` attributes show current usage and the maximum allowed size. + - This is the team's curated long-term memory — decisions, conventions, key facts. + - NEVER store personal memory in team memory (e.g. personal bio, individual + preferences, or user-only standing instructions). + - Call update_memory when: + * A team member explicitly asks to remember or forget something + * The conversation surfaces durable team decisions, conventions, or facts + that will matter in future conversations + - Do not store short-lived or ephemeral info: one-off questions, greetings, + session logistics, or things that only matter for the current task. - Args: - - content: The fact/preference/context to remember. Phrase it clearly, e.g. "API keys are stored in Vault", "The team prefers weekly demos on Fridays" - - category: Type of memory. One of: - * "preference": Team or workspace preferences - * "fact": Facts the team agreed on (e.g., processes, locations) - * "instruction": Standing instructions for the team - * "context": Current context (e.g., ongoing projects, goals) - - Returns: Confirmation of saved memory; returned context may include who added it (added_by). - - IMPORTANT: Only save information that would be genuinely useful for future team conversations in this space. -""", - }, - "recall_memory": { - "private": """ -- recall_memory: Retrieve relevant memories about the user for personalized responses. - - Use this to access stored information about the user. - - Trigger scenarios: - * You need user context to give a better, more personalized answer - * User references something they mentioned before - * User asks "what do you know about me?" or similar - * Personalization would significantly improve response quality - * Before making recommendations that should consider user preferences - - Args: - - query: Optional search query to find specific memories (e.g., "programming preferences") - - category: Optional filter by category ("preference", "fact", "instruction", "context") - - top_k: Number of memories to retrieve (default: 5) - - Returns: Relevant memories formatted as context - - IMPORTANT: Use the recalled memories naturally in your response without explicitly - stating "Based on your memory..." - integrate the context seamlessly. -""", - "shared": """ -- recall_memory: Recall relevant team memories for this space to provide contextual responses. - - Use when you need team context to answer (e.g., "where do we store X?", "what did we decide about Y?"). - - Use when someone asks about something the team agreed to remember. - - Use when team preferences or conventions would improve the response. - - Args: - - query: Optional search query to find specific memories. If not provided, returns the most recent memories. - - category: Optional filter by category ("preference", "fact", "instruction", "context") - - top_k: Number of memories to retrieve (default: 5, max: 20) - - Returns: Relevant team memories and formatted context (may include added_by). Integrate naturally without saying "Based on team memory...". + - updated_memory: The FULL updated markdown document (not a diff). + Merge new facts with existing ones, update contradictions, remove outdated entries. + Treat every update as a curation pass — consolidate, don't just append. + - Every bullet MUST use this format: - (YYYY-MM-DD) [fact] text + Team memory uses ONLY the [fact] marker. Never use [pref] or [instr] in team memory. + - Keep it concise and well under the character limit shown in . + - Every entry MUST be under a `##` heading. Keep heading names short (2-3 words) and + natural. Organize by context — e.g. what the team decided, current architecture, + active processes. Create, split, or merge headings freely as the memory grows. + - Each entry MUST be a single bullet point. Be descriptive but concise — include relevant + details and context rather than just a few words. + - During consolidation, prioritize keeping: decisions/conventions > key facts > current priorities. """, }, } _MEMORY_TOOL_EXAMPLES: dict[str, dict[str, str]] = { - "save_memory": { + "update_memory": { "private": """ -- User: "Remember that I prefer TypeScript over JavaScript" - - Call: `save_memory(content="User prefers TypeScript over JavaScript for development", category="preference")` -- User: "I'm a data scientist working on ML pipelines" - - Call: `save_memory(content="User is a data scientist working on ML pipelines", category="fact")` -- User: "Always give me code examples in Python" - - Call: `save_memory(content="User wants code examples to be written in Python", category="instruction")` +- Alex, is empty. User: "I'm a space enthusiast, explain astrophage to me" + - The user casually shared a durable fact. Use their first name in the entry, short neutral heading: + update_memory(updated_memory="## Interests & background\\n- (2025-03-15) [fact] Alex is a space enthusiast\\n") +- User: "Remember that I prefer concise answers over detailed explanations" + - Durable preference. Merge with existing memory, add a new heading: + update_memory(updated_memory="## Interests & background\\n- (2025-03-15) [fact] Alex is a space enthusiast\\n\\n## Response style\\n- (2025-03-15) [pref] Alex prefers concise answers over detailed explanations\\n") +- User: "I actually moved to Tokyo last month" + - Updated fact, date prefix reflects when recorded: + update_memory(updated_memory="## Interests & background\\n...\\n\\n## Personal context\\n- (2025-03-15) [fact] Alex lives in Tokyo (previously London)\\n...") +- User: "I'm a freelance photographer working on a nature documentary" + - Durable background info under a fitting heading: + update_memory(updated_memory="...\\n\\n## Current focus\\n- (2025-03-15) [fact] Alex is a freelance photographer\\n- (2025-03-15) [fact] Alex is working on a nature documentary\\n") +- User: "Always respond in bullet points" + - Standing instruction: + update_memory(updated_memory="...\\n\\n## Response style\\n- (2025-03-15) [instr] Always respond to Alex in bullet points\\n") """, "shared": """ -- User: "Remember that API keys are stored in Vault" - - Call: `save_memory(content="API keys are stored in Vault", category="fact")` -- User: "Let's remember that the team prefers weekly demos on Fridays" - - Call: `save_memory(content="The team prefers weekly demos on Fridays", category="preference")` -""", - }, - "recall_memory": { - "private": """ -- User: "What programming language should I use for this project?" - - First recall: `recall_memory(query="programming language preferences")` - - Then provide a personalized recommendation based on their preferences -- User: "What do you know about me?" - - Call: `recall_memory(top_k=10)` - - Then summarize the stored memories -""", - "shared": """ -- User: "What did we decide about the release date?" - - First recall: `recall_memory(query="release date decision")` - - Then answer based on the team memories -- User: "Where do we document onboarding?" - - Call: `recall_memory(query="onboarding documentation")` - - Then answer using the recalled team context +- User: "Let's remember that we decided to do weekly standup meetings on Mondays" + - Durable team decision: + update_memory(updated_memory="- (2025-03-15) [fact] Weekly standup meetings on Mondays\\n...") +- User: "Our office is in downtown Seattle, 5th floor" + - Durable team fact: + update_memory(updated_memory="- (2025-03-15) [fact] Office location: downtown Seattle, 5th floor\\n...") """, }, } @@ -456,8 +452,7 @@ _ALL_TOOL_NAMES_ORDERED = [ "generate_report", "generate_image", "scrape_webpage", - "save_memory", - "recall_memory", + "update_memory", ] diff --git a/surfsense_backend/app/agents/new_chat/tools/__init__.py b/surfsense_backend/app/agents/new_chat/tools/__init__.py index a46fadf11..bc444b0c0 100644 --- a/surfsense_backend/app/agents/new_chat/tools/__init__.py +++ b/surfsense_backend/app/agents/new_chat/tools/__init__.py @@ -10,8 +10,7 @@ Available tools: - generate_video_presentation: Generate video presentations with slides and narration - generate_image: Generate images from text descriptions using AI models - scrape_webpage: Extract content from webpages -- save_memory: Store facts/preferences about the user -- recall_memory: Retrieve relevant user memories +- update_memory: Update the user's / team's memory document """ # Registry exports @@ -33,7 +32,7 @@ from .registry import ( ) from .scrape_webpage import create_scrape_webpage_tool from .search_surfsense_docs import create_search_surfsense_docs_tool -from .user_memory import create_recall_memory_tool, create_save_memory_tool +from .update_memory import create_update_memory_tool, create_update_team_memory_tool from .video_presentation import create_generate_video_presentation_tool __all__ = [ @@ -47,10 +46,10 @@ __all__ = [ "create_generate_image_tool", "create_generate_podcast_tool", "create_generate_video_presentation_tool", - "create_recall_memory_tool", - "create_save_memory_tool", "create_scrape_webpage_tool", "create_search_surfsense_docs_tool", + "create_update_memory_tool", + "create_update_team_memory_tool", "format_documents_for_context", "get_all_tool_names", "get_default_enabled_tools", diff --git a/surfsense_backend/app/agents/new_chat/tools/registry.py b/surfsense_backend/app/agents/new_chat/tools/registry.py index 442f3ba35..af00cc44d 100644 --- a/surfsense_backend/app/agents/new_chat/tools/registry.py +++ b/surfsense_backend/app/agents/new_chat/tools/registry.py @@ -94,11 +94,7 @@ from .podcast import create_generate_podcast_tool from .report import create_generate_report_tool from .scrape_webpage import create_scrape_webpage_tool from .search_surfsense_docs import create_search_surfsense_docs_tool -from .shared_memory import ( - create_recall_shared_memory_tool, - create_save_shared_memory_tool, -) -from .user_memory import create_recall_memory_tool, create_save_memory_tool +from .update_memory import create_update_memory_tool, create_update_team_memory_tool from .video_presentation import create_generate_video_presentation_tool from .web_search import create_web_search_tool @@ -214,42 +210,31 @@ BUILTIN_TOOLS: list[ToolDefinition] = [ requires=["db_session"], ), # ========================================================================= - # USER MEMORY TOOLS - private or team store by thread_visibility + # MEMORY TOOL - single update_memory, private or team by thread_visibility # ========================================================================= ToolDefinition( - name="save_memory", - description="Save facts, preferences, or context for personalized or team responses", + name="update_memory", + description="Save important long-term facts, preferences, and instructions to the (personal or team) memory", factory=lambda deps: ( - create_save_shared_memory_tool( + create_update_team_memory_tool( search_space_id=deps["search_space_id"], - created_by_id=deps["user_id"], db_session=deps["db_session"], + llm=deps.get("llm"), ) if deps["thread_visibility"] == ChatVisibility.SEARCH_SPACE - else create_save_memory_tool( + else create_update_memory_tool( user_id=deps["user_id"], - search_space_id=deps["search_space_id"], db_session=deps["db_session"], + llm=deps.get("llm"), ) ), - requires=["user_id", "search_space_id", "db_session", "thread_visibility"], - ), - ToolDefinition( - name="recall_memory", - description="Recall relevant memories (personal or team) for context", - factory=lambda deps: ( - create_recall_shared_memory_tool( - search_space_id=deps["search_space_id"], - db_session=deps["db_session"], - ) - if deps["thread_visibility"] == ChatVisibility.SEARCH_SPACE - else create_recall_memory_tool( - user_id=deps["user_id"], - search_space_id=deps["search_space_id"], - db_session=deps["db_session"], - ) - ), - requires=["user_id", "search_space_id", "db_session", "thread_visibility"], + requires=[ + "user_id", + "search_space_id", + "db_session", + "thread_visibility", + "llm", + ], ), # ========================================================================= # LINEAR TOOLS - create, update, delete issues diff --git a/surfsense_backend/app/agents/new_chat/tools/shared_memory.py b/surfsense_backend/app/agents/new_chat/tools/shared_memory.py deleted file mode 100644 index c826d808f..000000000 --- a/surfsense_backend/app/agents/new_chat/tools/shared_memory.py +++ /dev/null @@ -1,281 +0,0 @@ -"""Shared (team) memory backend for search-space-scoped AI context.""" - -import asyncio -import logging -from typing import Any -from uuid import UUID - -from langchain_core.tools import tool -from sqlalchemy import select -from sqlalchemy.ext.asyncio import AsyncSession - -from app.db import MemoryCategory, SharedMemory, User -from app.utils.document_converters import embed_text - -logger = logging.getLogger(__name__) - -DEFAULT_RECALL_TOP_K = 5 -MAX_MEMORIES_PER_SEARCH_SPACE = 250 - - -async def get_shared_memory_count( - db_session: AsyncSession, - search_space_id: int, -) -> int: - result = await db_session.execute( - select(SharedMemory).where(SharedMemory.search_space_id == search_space_id) - ) - return len(result.scalars().all()) - - -async def delete_oldest_shared_memory( - db_session: AsyncSession, - search_space_id: int, -) -> None: - result = await db_session.execute( - select(SharedMemory) - .where(SharedMemory.search_space_id == search_space_id) - .order_by(SharedMemory.updated_at.asc()) - .limit(1) - ) - oldest = result.scalars().first() - if oldest: - await db_session.delete(oldest) - await db_session.commit() - - -def _to_uuid(value: str | UUID) -> UUID: - if isinstance(value, UUID): - return value - return UUID(value) - - -async def save_shared_memory( - db_session: AsyncSession, - search_space_id: int, - created_by_id: str | UUID, - content: str, - category: str = "fact", -) -> dict[str, Any]: - category = category.lower() if category else "fact" - valid = ["preference", "fact", "instruction", "context"] - if category not in valid: - category = "fact" - try: - count = await get_shared_memory_count(db_session, search_space_id) - if count >= MAX_MEMORIES_PER_SEARCH_SPACE: - await delete_oldest_shared_memory(db_session, search_space_id) - embedding = await asyncio.to_thread(embed_text, content) - row = SharedMemory( - search_space_id=search_space_id, - created_by_id=_to_uuid(created_by_id), - memory_text=content, - category=MemoryCategory(category), - embedding=embedding, - ) - db_session.add(row) - await db_session.commit() - await db_session.refresh(row) - return { - "status": "saved", - "memory_id": row.id, - "memory_text": content, - "category": category, - "message": f"I'll remember: {content}", - } - except Exception as e: - logger.exception("Failed to save shared memory: %s", e) - await db_session.rollback() - return { - "status": "error", - "error": str(e), - "message": "Failed to save memory. Please try again.", - } - - -async def recall_shared_memory( - db_session: AsyncSession, - search_space_id: int, - query: str | None = None, - category: str | None = None, - top_k: int = DEFAULT_RECALL_TOP_K, -) -> dict[str, Any]: - top_k = min(max(top_k, 1), 20) - try: - valid_categories = ["preference", "fact", "instruction", "context"] - stmt = select(SharedMemory).where( - SharedMemory.search_space_id == search_space_id - ) - if category and category in valid_categories: - stmt = stmt.where(SharedMemory.category == MemoryCategory(category)) - if query: - query_embedding = await asyncio.to_thread(embed_text, query) - stmt = stmt.order_by( - SharedMemory.embedding.op("<=>")(query_embedding) - ).limit(top_k) - else: - stmt = stmt.order_by(SharedMemory.updated_at.desc()).limit(top_k) - result = await db_session.execute(stmt) - rows = result.scalars().all() - memory_list = [ - { - "id": m.id, - "memory_text": m.memory_text, - "category": m.category.value if m.category else "unknown", - "updated_at": m.updated_at.isoformat() if m.updated_at else None, - "created_by_id": str(m.created_by_id) if m.created_by_id else None, - } - for m in rows - ] - created_by_ids = list( - {m["created_by_id"] for m in memory_list if m["created_by_id"]} - ) - created_by_map: dict[str, str] = {} - if created_by_ids: - uuids = [UUID(uid) for uid in created_by_ids] - users_result = await db_session.execute( - select(User).where(User.id.in_(uuids)) - ) - for u in users_result.scalars().all(): - created_by_map[str(u.id)] = u.display_name or "A team member" - formatted_context = format_shared_memories_for_context( - memory_list, created_by_map - ) - return { - "status": "success", - "count": len(memory_list), - "memories": memory_list, - "formatted_context": formatted_context, - } - except Exception as e: - logger.exception("Failed to recall shared memory: %s", e) - await db_session.rollback() - return { - "status": "error", - "error": str(e), - "memories": [], - "formatted_context": "Failed to recall memories.", - } - - -def format_shared_memories_for_context( - memories: list[dict[str, Any]], - created_by_map: dict[str, str] | None = None, -) -> str: - if not memories: - return "No relevant team memories found." - created_by_map = created_by_map or {} - parts = [""] - for memory in memories: - category = memory.get("category", "unknown") - text = memory.get("memory_text", "") - updated = memory.get("updated_at", "") - created_by_id = memory.get("created_by_id") - added_by = ( - created_by_map.get(str(created_by_id), "A team member") - if created_by_id is not None - else "A team member" - ) - parts.append( - f" {text}" - ) - parts.append("") - return "\n".join(parts) - - -def create_save_shared_memory_tool( - search_space_id: int, - created_by_id: str | UUID, - db_session: AsyncSession, -): - """ - Factory function to create the save_memory tool for shared (team) chats. - - Args: - search_space_id: The search space ID - created_by_id: The user ID of the person adding the memory - db_session: Database session for executing queries - - Returns: - A configured tool function for saving team memories - """ - - @tool - async def save_memory( - content: str, - category: str = "fact", - ) -> dict[str, Any]: - """ - Save a fact, preference, or context to the team's shared memory for future reference. - - Use this tool when: - - User or a team member says "remember this", "keep this in mind", or similar in this shared chat - - The team agrees on something to remember (e.g., decisions, conventions, where things live) - - Someone shares a preference or fact that should be visible to the whole team - - The saved information will be available in future shared conversations in this space. - - Args: - content: The fact/preference/context to remember. - Phrase it clearly, e.g., "API keys are stored in Vault", - "The team prefers weekly demos on Fridays" - category: Type of memory. One of: - - "preference": Team or workspace preferences - - "fact": Facts the team agreed on (e.g., processes, locations) - - "instruction": Standing instructions for the team - - "context": Current context (e.g., ongoing projects, goals) - - Returns: - A dictionary with the save status and memory details - """ - return await save_shared_memory( - db_session, search_space_id, created_by_id, content, category - ) - - return save_memory - - -def create_recall_shared_memory_tool( - search_space_id: int, - db_session: AsyncSession, -): - """ - Factory function to create the recall_memory tool for shared (team) chats. - - Args: - search_space_id: The search space ID - db_session: Database session for executing queries - - Returns: - A configured tool function for recalling team memories - """ - - @tool - async def recall_memory( - query: str | None = None, - category: str | None = None, - top_k: int = DEFAULT_RECALL_TOP_K, - ) -> dict[str, Any]: - """ - Recall relevant team memories for this space to provide contextual responses. - - Use this tool when: - - You need team context to answer (e.g., "where do we store X?", "what did we decide about Y?") - - Someone asks about something the team agreed to remember - - Team preferences or conventions would improve the response - - Args: - query: Optional search query to find specific memories. - If not provided, returns the most recent memories. - category: Optional category filter. One of: - "preference", "fact", "instruction", "context" - top_k: Number of memories to retrieve (default: 5, max: 20) - - Returns: - A dictionary containing relevant memories and formatted context - """ - return await recall_shared_memory( - db_session, search_space_id, query, category, top_k - ) - - return recall_memory diff --git a/surfsense_backend/app/agents/new_chat/tools/update_memory.py b/surfsense_backend/app/agents/new_chat/tools/update_memory.py new file mode 100644 index 000000000..4128ac0dc --- /dev/null +++ b/surfsense_backend/app/agents/new_chat/tools/update_memory.py @@ -0,0 +1,389 @@ +"""Markdown-document memory tool for the SurfSense agent. + +Replaces the old row-per-fact save_memory / recall_memory tools with a single +update_memory tool that overwrites a freeform markdown TEXT column. The LLM +always sees the current memory in / tags injected +by MemoryInjectionMiddleware, so it passes the FULL updated document each time. + +Overflow handling: + - Soft limit (18K chars): a warning is returned telling the agent to + consolidate on the next update. + - Hard limit (25K chars): a forced LLM-driven rewrite compresses the document. + If it still exceeds the limit after rewriting, the save is rejected. + - Diff validation: warns when entire ``##`` sections are dropped or when the + document shrinks by more than 60%. +""" + +from __future__ import annotations + +import logging +import re +from typing import Any, Literal +from uuid import UUID + +from langchain_core.messages import HumanMessage +from langchain_core.tools import tool +from sqlalchemy import select +from sqlalchemy.ext.asyncio import AsyncSession + +from app.db import SearchSpace, User + +logger = logging.getLogger(__name__) + +MEMORY_SOFT_LIMIT = 18_000 +MEMORY_HARD_LIMIT = 25_000 + +_SECTION_HEADING_RE = re.compile(r"^##\s+(.+)$", re.MULTILINE) +_HEADING_NORMALIZE_RE = re.compile(r"\s+") + +_MARKER_RE = re.compile(r"\[(fact|pref|instr)\]") +_BULLET_FORMAT_RE = re.compile(r"^- \(\d{4}-\d{2}-\d{2}\) \[(fact|pref|instr)\] .+$") +_PERSONAL_ONLY_MARKERS = {"pref", "instr"} + + +# --------------------------------------------------------------------------- +# Diff validation +# --------------------------------------------------------------------------- + + +def _extract_headings(memory: str) -> set[str]: + """Return all ``## …`` heading texts (without the ``## `` prefix).""" + return set(_SECTION_HEADING_RE.findall(memory)) + + +def _normalize_heading(heading: str) -> str: + """Normalize heading text for robust scope checks.""" + return _HEADING_NORMALIZE_RE.sub(" ", heading.strip().lower()) + + +def _validate_memory_scope( + content: str, scope: Literal["user", "team"] +) -> dict[str, Any] | None: + """Reject personal-only markers ([pref], [instr]) in team memory.""" + if scope != "team": + return None + + markers = set(_MARKER_RE.findall(content)) + leaked = sorted(markers & _PERSONAL_ONLY_MARKERS) + if leaked: + tags = ", ".join(f"[{m}]" for m in leaked) + return { + "status": "error", + "message": ( + f"Team memory cannot include personal markers: {tags}. " + "Use [fact] only in team memory." + ), + } + return None + + +def _validate_bullet_format(content: str) -> list[str]: + """Return warnings for bullet lines that don't match the required format. + + Expected: ``- (YYYY-MM-DD) [fact|pref|instr] text`` + """ + warnings: list[str] = [] + for line in content.splitlines(): + stripped = line.strip() + if not stripped.startswith("- "): + continue + if not _BULLET_FORMAT_RE.match(stripped): + short = stripped[:80] + ("..." if len(stripped) > 80 else "") + warnings.append(f"Malformed bullet: {short}") + return warnings + + +def _validate_diff(old_memory: str | None, new_memory: str) -> list[str]: + """Return a list of warning strings about suspicious changes.""" + if not old_memory: + return [] + + warnings: list[str] = [] + old_headings = _extract_headings(old_memory) + new_headings = _extract_headings(new_memory) + dropped = old_headings - new_headings + if dropped: + names = ", ".join(sorted(dropped)) + warnings.append( + f"Sections removed: {names}. " + "If unintentional, the user can restore from the settings page." + ) + + old_len = len(old_memory) + new_len = len(new_memory) + if old_len > 0 and new_len < old_len * 0.4: + warnings.append( + f"Memory shrank significantly ({old_len:,} -> {new_len:,} chars). " + "Possible data loss." + ) + return warnings + + +# --------------------------------------------------------------------------- +# Size validation & soft warning +# --------------------------------------------------------------------------- + + +def _validate_memory_size(content: str) -> dict[str, Any] | None: + """Return an error/warning dict if *content* is too large, else None.""" + length = len(content) + if length > MEMORY_HARD_LIMIT: + return { + "status": "error", + "message": ( + f"Memory exceeds {MEMORY_HARD_LIMIT:,} character limit " + f"({length:,} chars). Consolidate by merging related items, " + "removing outdated entries, and shortening descriptions. " + "Then call update_memory again." + ), + } + return None + + +def _soft_warning(content: str) -> str | None: + """Return a warning string if content exceeds the soft limit.""" + length = len(content) + if length > MEMORY_SOFT_LIMIT: + return ( + f"Memory is at {length:,}/{MEMORY_HARD_LIMIT:,} characters. " + "Consolidate by merging related items and removing less important " + "entries on your next update." + ) + return None + + +# --------------------------------------------------------------------------- +# Forced rewrite when memory exceeds the hard limit +# --------------------------------------------------------------------------- + +_FORCED_REWRITE_PROMPT = """\ +You are a memory curator. The following memory document exceeds the character \ +limit and must be shortened. + +RULES: +1. Rewrite the document to be under {target} characters. +2. Preserve existing ## headings. Every entry must remain under a heading. You may merge + or rename headings to consolidate, but keep names personal and descriptive. +3. Priority for keeping content: [instr] > [pref] > [fact]. +4. Merge duplicate entries, remove outdated entries, shorten verbose descriptions. +5. Every bullet MUST have format: - (YYYY-MM-DD) [fact|pref|instr] text +6. Preserve the user's first name in entries — do not replace it with "the user". +7. Output ONLY the consolidated markdown — no explanations, no wrapping. + + +{content} +""" + + +async def _forced_rewrite(content: str, llm: Any) -> str | None: + """Use a focused LLM call to compress *content* under the hard limit. + + Returns the rewritten string, or ``None`` if the call fails. + """ + try: + prompt = _FORCED_REWRITE_PROMPT.format( + target=MEMORY_HARD_LIMIT, content=content + ) + response = await llm.ainvoke( + [HumanMessage(content=prompt)], + config={"tags": ["surfsense:internal"]}, + ) + text = ( + response.content + if isinstance(response.content, str) + else str(response.content) + ) + return text.strip() + except Exception: + logger.exception("Forced rewrite LLM call failed") + return None + + +# --------------------------------------------------------------------------- +# Shared save-and-respond logic +# --------------------------------------------------------------------------- + + +async def _save_memory( + *, + updated_memory: str, + old_memory: str | None, + llm: Any | None, + apply_fn, + commit_fn, + rollback_fn, + label: str, + scope: Literal["user", "team"], +) -> dict[str, Any]: + """Validate, optionally force-rewrite if over the hard limit, save, and + return a response dict. + + Parameters + ---------- + updated_memory : str + The new document the agent submitted. + old_memory : str | None + The previously persisted document (for diff checks). + llm : Any | None + LLM instance for forced rewrite (may be ``None``). + apply_fn : callable(str) -> None + Callback that sets the new memory on the ORM object. + commit_fn : coroutine + ``session.commit``. + rollback_fn : coroutine + ``session.rollback``. + label : str + Human label for log messages (e.g. "user memory", "team memory"). + """ + content = updated_memory + + # --- forced rewrite if over the hard limit --- + if len(content) > MEMORY_HARD_LIMIT and llm is not None: + rewritten = await _forced_rewrite(content, llm) + if rewritten is not None and len(rewritten) < len(content): + content = rewritten + + # --- hard-limit gate (reject if still too large after rewrite) --- + size_err = _validate_memory_size(content) + if size_err: + return size_err + + scope_err = _validate_memory_scope(content, scope) + if scope_err: + return scope_err + + # --- persist --- + try: + apply_fn(content) + await commit_fn() + except Exception as e: + logger.exception("Failed to update %s: %s", label, e) + await rollback_fn() + return {"status": "error", "message": f"Failed to update {label}: {e}"} + + # --- build response --- + resp: dict[str, Any] = { + "status": "saved", + "message": f"{label.capitalize()} updated.", + } + + if content is not updated_memory: + resp["notice"] = "Memory was automatically rewritten to fit within limits." + + diff_warnings = _validate_diff(old_memory, content) + if diff_warnings: + resp["diff_warnings"] = diff_warnings + + format_warnings = _validate_bullet_format(content) + if format_warnings: + resp["format_warnings"] = format_warnings + + warning = _soft_warning(content) + if warning: + resp["warning"] = warning + + return resp + + +# --------------------------------------------------------------------------- +# Tool factories +# --------------------------------------------------------------------------- + + +def create_update_memory_tool( + user_id: str | UUID, + db_session: AsyncSession, + llm: Any | None = None, +): + uid = UUID(user_id) if isinstance(user_id, str) else user_id + + @tool + async def update_memory(updated_memory: str) -> dict[str, Any]: + """Update the user's personal memory document. + + Your current memory is shown in in the system prompt. + When the user shares important long-term information (preferences, + facts, instructions, context), rewrite the memory document to include + the new information. Merge new facts with existing ones, update + contradictions, remove outdated entries, and keep it concise. + + Args: + updated_memory: The FULL updated markdown document (not a diff). + """ + try: + result = await db_session.execute(select(User).where(User.id == uid)) + user = result.scalars().first() + if not user: + return {"status": "error", "message": "User not found."} + + old_memory = user.memory_md + + return await _save_memory( + updated_memory=updated_memory, + old_memory=old_memory, + llm=llm, + apply_fn=lambda content: setattr(user, "memory_md", content), + commit_fn=db_session.commit, + rollback_fn=db_session.rollback, + label="memory", + scope="user", + ) + except Exception as e: + logger.exception("Failed to update user memory: %s", e) + await db_session.rollback() + return { + "status": "error", + "message": f"Failed to update memory: {e}", + } + + return update_memory + + +def create_update_team_memory_tool( + search_space_id: int, + db_session: AsyncSession, + llm: Any | None = None, +): + @tool + async def update_memory(updated_memory: str) -> dict[str, Any]: + """Update the team's shared memory document for this search space. + + Your current team memory is shown in in the system + prompt. When the team shares important long-term information + (decisions, conventions, key facts, priorities), rewrite the memory + document to include the new information. Merge new facts with + existing ones, update contradictions, remove outdated entries, and + keep it concise. + + Args: + updated_memory: The FULL updated markdown document (not a diff). + """ + try: + result = await db_session.execute( + select(SearchSpace).where(SearchSpace.id == search_space_id) + ) + space = result.scalars().first() + if not space: + return {"status": "error", "message": "Search space not found."} + + old_memory = space.shared_memory_md + + return await _save_memory( + updated_memory=updated_memory, + old_memory=old_memory, + llm=llm, + apply_fn=lambda content: setattr(space, "shared_memory_md", content), + commit_fn=db_session.commit, + rollback_fn=db_session.rollback, + label="team memory", + scope="team", + ) + except Exception as e: + logger.exception("Failed to update team memory: %s", e) + await db_session.rollback() + return { + "status": "error", + "message": f"Failed to update team memory: {e}", + } + + return update_memory diff --git a/surfsense_backend/app/agents/new_chat/tools/user_memory.py b/surfsense_backend/app/agents/new_chat/tools/user_memory.py deleted file mode 100644 index 81e849856..000000000 --- a/surfsense_backend/app/agents/new_chat/tools/user_memory.py +++ /dev/null @@ -1,351 +0,0 @@ -""" -User memory tools for the SurfSense agent. - -This module provides tools for storing and retrieving user memories, -enabling personalized AI responses similar to Claude's memory feature. - -Features: -- save_memory: Store facts, preferences, and context about the user -- recall_memory: Retrieve relevant memories using semantic search -""" - -import asyncio -import logging -from typing import Any -from uuid import UUID - -from langchain_core.tools import tool -from sqlalchemy import select -from sqlalchemy.ext.asyncio import AsyncSession - -from app.db import MemoryCategory, UserMemory -from app.utils.document_converters import embed_text - -logger = logging.getLogger(__name__) - - -# ============================================================================= -# Constants -# ============================================================================= - -# Default number of memories to retrieve -DEFAULT_RECALL_TOP_K = 5 - -# Maximum number of memories per user (to prevent unbounded growth) -MAX_MEMORIES_PER_USER = 100 - - -# ============================================================================= -# Helper Functions -# ============================================================================= - - -def _to_uuid(user_id: str) -> UUID: - """Convert a string user_id to a UUID object.""" - if isinstance(user_id, UUID): - return user_id - return UUID(user_id) - - -async def get_user_memory_count( - db_session: AsyncSession, - user_id: str, - search_space_id: int | None = None, -) -> int: - """Get the count of memories for a user.""" - uuid_user_id = _to_uuid(user_id) - query = select(UserMemory).where(UserMemory.user_id == uuid_user_id) - if search_space_id is not None: - query = query.where( - (UserMemory.search_space_id == search_space_id) - | (UserMemory.search_space_id.is_(None)) - ) - result = await db_session.execute(query) - return len(result.scalars().all()) - - -async def delete_oldest_memory( - db_session: AsyncSession, - user_id: str, - search_space_id: int | None = None, -) -> None: - """Delete the oldest memory for a user to make room for new ones.""" - uuid_user_id = _to_uuid(user_id) - query = ( - select(UserMemory) - .where(UserMemory.user_id == uuid_user_id) - .order_by(UserMemory.updated_at.asc()) - .limit(1) - ) - if search_space_id is not None: - query = query.where( - (UserMemory.search_space_id == search_space_id) - | (UserMemory.search_space_id.is_(None)) - ) - result = await db_session.execute(query) - oldest_memory = result.scalars().first() - if oldest_memory: - await db_session.delete(oldest_memory) - await db_session.commit() - - -def format_memories_for_context(memories: list[dict[str, Any]]) -> str: - """Format retrieved memories into a readable context string for the LLM.""" - if not memories: - return "No relevant memories found for this user." - - parts = [""] - for memory in memories: - category = memory.get("category", "unknown") - text = memory.get("memory_text", "") - updated = memory.get("updated_at", "") - parts.append( - f" {text}" - ) - parts.append("") - - return "\n".join(parts) - - -# ============================================================================= -# Tool Factory Functions -# ============================================================================= - - -def create_save_memory_tool( - user_id: str, - search_space_id: int, - db_session: AsyncSession, -): - """ - Factory function to create the save_memory tool. - - Args: - user_id: The user's UUID - search_space_id: The search space ID (for space-specific memories) - db_session: Database session for executing queries - - Returns: - A configured tool function for saving user memories - """ - - @tool - async def save_memory( - content: str, - category: str = "fact", - ) -> dict[str, Any]: - """ - Save a fact, preference, or context about the user for future reference. - - Use this tool when: - - User explicitly says "remember this", "keep this in mind", or similar - - User shares personal preferences (e.g., "I prefer Python over JavaScript") - - User shares important facts about themselves (name, role, interests, projects) - - User gives standing instructions (e.g., "always respond in bullet points") - - User shares relevant context (e.g., "I'm working on project X") - - The saved information will be available in future conversations to provide - more personalized and contextual responses. - - Args: - content: The fact/preference/context to remember. - Phrase it clearly, e.g., "User prefers dark mode", - "User is a senior Python developer", "User is working on an AI project" - category: Type of memory. One of: - - "preference": User preferences (e.g., coding style, tools, formats) - - "fact": Facts about the user (e.g., name, role, expertise) - - "instruction": Standing instructions (e.g., response format preferences) - - "context": Current context (e.g., ongoing projects, goals) - - Returns: - A dictionary with the save status and memory details - """ - # Normalize and validate category (LLMs may send uppercase) - category = category.lower() if category else "fact" - valid_categories = ["preference", "fact", "instruction", "context"] - if category not in valid_categories: - category = "fact" - - try: - # Convert user_id to UUID - uuid_user_id = _to_uuid(user_id) - - # Check if we've hit the memory limit - memory_count = await get_user_memory_count( - db_session, user_id, search_space_id - ) - if memory_count >= MAX_MEMORIES_PER_USER: - # Delete oldest memory to make room - await delete_oldest_memory(db_session, user_id, search_space_id) - - embedding = await asyncio.to_thread(embed_text, content) - - # Create new memory using ORM - # The pgvector Vector column type handles embedding conversion automatically - new_memory = UserMemory( - user_id=uuid_user_id, - search_space_id=search_space_id, - memory_text=content, - category=MemoryCategory(category), # Convert string to enum - embedding=embedding, # Pass embedding directly (list or numpy array) - ) - - db_session.add(new_memory) - await db_session.commit() - await db_session.refresh(new_memory) - - return { - "status": "saved", - "memory_id": new_memory.id, - "memory_text": content, - "category": category, - "message": f"I'll remember: {content}", - } - - except Exception as e: - logger.exception(f"Failed to save memory for user {user_id}: {e}") - # Rollback the session to clear any failed transaction state - await db_session.rollback() - return { - "status": "error", - "error": str(e), - "message": "Failed to save memory. Please try again.", - } - - return save_memory - - -def create_recall_memory_tool( - user_id: str, - search_space_id: int, - db_session: AsyncSession, -): - """ - Factory function to create the recall_memory tool. - - Args: - user_id: The user's UUID - search_space_id: The search space ID - db_session: Database session for executing queries - - Returns: - A configured tool function for recalling user memories - """ - - @tool - async def recall_memory( - query: str | None = None, - category: str | None = None, - top_k: int = DEFAULT_RECALL_TOP_K, - ) -> dict[str, Any]: - """ - Recall relevant memories about the user to provide personalized responses. - - Use this tool when: - - You need user context to give a better, more personalized answer - - User asks about their preferences or past information they shared - - User references something they told you before - - Personalization would significantly improve the response quality - - User asks "what do you know about me?" or similar - - Args: - query: Optional search query to find specific memories. - If not provided, returns the most recent memories. - Example: "programming preferences", "current projects" - category: Optional category filter. One of: - "preference", "fact", "instruction", "context" - If not provided, searches all categories. - top_k: Number of memories to retrieve (default: 5, max: 20) - - Returns: - A dictionary containing relevant memories and formatted context - """ - top_k = min(max(top_k, 1), 20) # Clamp between 1 and 20 - - try: - # Convert user_id to UUID - uuid_user_id = _to_uuid(user_id) - - if query: - query_embedding = await asyncio.to_thread(embed_text, query) - - # Build query with vector similarity - stmt = ( - select(UserMemory) - .where(UserMemory.user_id == uuid_user_id) - .where( - (UserMemory.search_space_id == search_space_id) - | (UserMemory.search_space_id.is_(None)) - ) - ) - - # Add category filter if specified - if category and category in [ - "preference", - "fact", - "instruction", - "context", - ]: - stmt = stmt.where(UserMemory.category == MemoryCategory(category)) - - # Order by vector similarity - stmt = stmt.order_by( - UserMemory.embedding.op("<=>")(query_embedding) - ).limit(top_k) - - else: - # No query - return most recent memories - stmt = ( - select(UserMemory) - .where(UserMemory.user_id == uuid_user_id) - .where( - (UserMemory.search_space_id == search_space_id) - | (UserMemory.search_space_id.is_(None)) - ) - ) - - # Add category filter if specified - if category and category in [ - "preference", - "fact", - "instruction", - "context", - ]: - stmt = stmt.where(UserMemory.category == MemoryCategory(category)) - - stmt = stmt.order_by(UserMemory.updated_at.desc()).limit(top_k) - - result = await db_session.execute(stmt) - memories = result.scalars().all() - - # Format memories for response - memory_list = [ - { - "id": m.id, - "memory_text": m.memory_text, - "category": m.category.value if m.category else "unknown", - "updated_at": m.updated_at.isoformat() if m.updated_at else None, - } - for m in memories - ] - - formatted_context = format_memories_for_context(memory_list) - - return { - "status": "success", - "count": len(memory_list), - "memories": memory_list, - "formatted_context": formatted_context, - } - - except Exception as e: - logger.exception(f"Failed to recall memories for user {user_id}: {e}") - await db_session.rollback() - return { - "status": "error", - "error": str(e), - "memories": [], - "formatted_context": "Failed to recall memories.", - } - - return recall_memory diff --git a/surfsense_backend/app/db.py b/surfsense_backend/app/db.py index 01a6bbda0..2de6ab572 100644 --- a/surfsense_backend/app/db.py +++ b/surfsense_backend/app/db.py @@ -861,99 +861,6 @@ class ChatSessionState(BaseModel): ai_responding_to_user = relationship("User") -class MemoryCategory(StrEnum): - """Categories for user memories.""" - - # Using lowercase keys to match PostgreSQL enum values - preference = "preference" # User preferences (e.g., "prefers dark mode") - fact = "fact" # Facts about the user (e.g., "is a Python developer") - instruction = ( - "instruction" # Standing instructions (e.g., "always respond in bullet points") - ) - context = "context" # Contextual information (e.g., "working on project X") - - -class UserMemory(BaseModel, TimestampMixin): - """ - Private memory: facts, preferences, context per user per search space. - Used only for private chats (not shared/team chats). - """ - - __tablename__ = "user_memories" - - user_id = Column( - UUID(as_uuid=True), - ForeignKey("user.id", ondelete="CASCADE"), - nullable=False, - index=True, - ) - # Optional association with a search space (if memory is space-specific) - search_space_id = Column( - Integer, - ForeignKey("searchspaces.id", ondelete="CASCADE"), - nullable=True, - index=True, - ) - - # The actual memory content - memory_text = Column(Text, nullable=False) - # Category for organization and filtering - category = Column( - SQLAlchemyEnum(MemoryCategory), - nullable=False, - default=MemoryCategory.fact, - ) - # Vector embedding for semantic search - embedding = Column(Vector(config.embedding_model_instance.dimension)) - - # Track when memory was last updated - updated_at = Column( - TIMESTAMP(timezone=True), - nullable=False, - default=lambda: datetime.now(UTC), - onupdate=lambda: datetime.now(UTC), - index=True, - ) - - # Relationships - user = relationship("User", back_populates="memories") - search_space = relationship("SearchSpace", back_populates="user_memories") - - -class SharedMemory(BaseModel, TimestampMixin): - __tablename__ = "shared_memories" - - search_space_id = Column( - Integer, - ForeignKey("searchspaces.id", ondelete="CASCADE"), - nullable=False, - index=True, - ) - created_by_id = Column( - UUID(as_uuid=True), - ForeignKey("user.id", ondelete="CASCADE"), - nullable=False, - index=True, - ) - memory_text = Column(Text, nullable=False) - category = Column( - SQLAlchemyEnum(MemoryCategory), - nullable=False, - default=MemoryCategory.fact, - ) - embedding = Column(Vector(config.embedding_model_instance.dimension)) - updated_at = Column( - TIMESTAMP(timezone=True), - nullable=False, - default=lambda: datetime.now(UTC), - onupdate=lambda: datetime.now(UTC), - index=True, - ) - - search_space = relationship("SearchSpace", back_populates="shared_memories") - created_by = relationship("User") - - class Folder(BaseModel, TimestampMixin): __tablename__ = "folders" @@ -1394,6 +1301,8 @@ class SearchSpace(BaseModel, TimestampMixin): Text, nullable=True, default="" ) # User's custom instructions + shared_memory_md = Column(Text, nullable=True, server_default="") + # Search space-level LLM preferences (shared by all members) # Note: ID values: # - 0: Auto mode (uses LiteLLM Router for load balancing) - default for new search spaces @@ -1516,20 +1425,6 @@ class SearchSpace(BaseModel, TimestampMixin): cascade="all, delete-orphan", ) - # User memories associated with this search space - user_memories = relationship( - "UserMemory", - back_populates="search_space", - order_by="UserMemory.updated_at.desc()", - cascade="all, delete-orphan", - ) - shared_memories = relationship( - "SharedMemory", - back_populates="search_space", - order_by="SharedMemory.updated_at.desc()", - cascade="all, delete-orphan", - ) - class SearchSourceConnector(BaseModel, TimestampMixin): __tablename__ = "search_source_connectors" @@ -2030,14 +1925,6 @@ if config.AUTH_TYPE == "GOOGLE": passive_deletes=True, ) - # User memories for personalized AI responses - memories = relationship( - "UserMemory", - back_populates="user", - order_by="UserMemory.updated_at.desc()", - cascade="all, delete-orphan", - ) - # Incentive tasks completed by this user incentive_tasks = relationship( "UserIncentiveTask", @@ -2065,6 +1952,8 @@ if config.AUTH_TYPE == "GOOGLE": last_login = Column(TIMESTAMP(timezone=True), nullable=True) + memory_md = Column(Text, nullable=True, server_default="") + # Refresh tokens for this user refresh_tokens = relationship( "RefreshToken", @@ -2150,14 +2039,6 @@ else: passive_deletes=True, ) - # User memories for personalized AI responses - memories = relationship( - "UserMemory", - back_populates="user", - order_by="UserMemory.updated_at.desc()", - cascade="all, delete-orphan", - ) - # Incentive tasks completed by this user incentive_tasks = relationship( "UserIncentiveTask", @@ -2185,6 +2066,8 @@ else: last_login = Column(TIMESTAMP(timezone=True), nullable=True) + memory_md = Column(Text, nullable=True, server_default="") + # Refresh tokens for this user refresh_tokens = relationship( "RefreshToken", diff --git a/surfsense_backend/app/routes/__init__.py b/surfsense_backend/app/routes/__init__.py index 02367606b..5e3c84c8c 100644 --- a/surfsense_backend/app/routes/__init__.py +++ b/surfsense_backend/app/routes/__init__.py @@ -29,6 +29,7 @@ from .jira_add_connector_route import router as jira_add_connector_router from .linear_add_connector_route import router as linear_add_connector_router from .logs_routes import router as logs_router from .luma_add_connector_route import router as luma_add_connector_router +from .memory_routes import router as memory_router from .model_list_routes import router as model_list_router from .new_chat_routes import router as new_chat_router from .new_llm_config_routes import router as new_llm_config_router @@ -98,4 +99,5 @@ router.include_router(incentive_tasks_router) # Incentive tasks for earning fre router.include_router(stripe_router) # Stripe checkout for additional page packs router.include_router(youtube_router) # YouTube playlist resolution router.include_router(prompts_router) +router.include_router(memory_router) # User personal memory (memory.md style) router.include_router(autocomplete_router) # Lightweight autocomplete with KB context diff --git a/surfsense_backend/app/routes/memory_routes.py b/surfsense_backend/app/routes/memory_routes.py new file mode 100644 index 000000000..f5df45cf1 --- /dev/null +++ b/surfsense_backend/app/routes/memory_routes.py @@ -0,0 +1,153 @@ +"""Routes for user memory management (personal memory.md).""" + +from __future__ import annotations + +import logging + +from fastapi import APIRouter, Depends, HTTPException +from langchain_core.messages import HumanMessage +from pydantic import BaseModel +from sqlalchemy.ext.asyncio import AsyncSession + +from app.agents.new_chat.llm_config import ( + create_chat_litellm_from_agent_config, + load_agent_llm_config_for_search_space, +) +from app.agents.new_chat.tools.update_memory import MEMORY_HARD_LIMIT, _save_memory +from app.db import User, get_async_session +from app.users import current_active_user + +logger = logging.getLogger(__name__) + +router = APIRouter() + + +class MemoryRead(BaseModel): + memory_md: str + + +class MemoryUpdate(BaseModel): + memory_md: str + + +class MemoryEditRequest(BaseModel): + query: str + search_space_id: int + + +_MEMORY_EDIT_PROMPT = """\ +You are a memory editor. The user wants to modify their memory document. \ +Apply the user's instruction to the existing memory document and output the \ +FULL updated document. + +RULES: +1. If the instruction asks to add something, add it with format: \ +- (YYYY-MM-DD) [fact|pref|instr] text, under an existing or new ## heading. \ +Heading names should be personal and descriptive, not generic categories. +2. If the instruction asks to remove something, remove the matching entry. +3. If the instruction asks to change something, update the matching entry. +4. Preserve existing ## headings and all other entries. +5. Every bullet must include a marker: [fact], [pref], or [instr]. +6. Use the user's first name (from ) in entries instead of "the user". +7. Output ONLY the updated markdown — no explanations, no wrapping. + +{user_name} + + +{current_memory} + + + +{instruction} +""" + + +@router.get("/users/me/memory", response_model=MemoryRead) +async def get_user_memory( + user: User = Depends(current_active_user), + session: AsyncSession = Depends(get_async_session), +): + await session.refresh(user, ["memory_md"]) + return MemoryRead(memory_md=user.memory_md or "") + + +@router.put("/users/me/memory", response_model=MemoryRead) +async def update_user_memory( + body: MemoryUpdate, + user: User = Depends(current_active_user), + session: AsyncSession = Depends(get_async_session), +): + if len(body.memory_md) > MEMORY_HARD_LIMIT: + raise HTTPException( + status_code=400, + detail=f"Memory exceeds {MEMORY_HARD_LIMIT:,} character limit ({len(body.memory_md):,} chars).", + ) + user.memory_md = body.memory_md + session.add(user) + await session.commit() + await session.refresh(user, ["memory_md"]) + return MemoryRead(memory_md=user.memory_md or "") + + +@router.post("/users/me/memory/edit", response_model=MemoryRead) +async def edit_user_memory( + body: MemoryEditRequest, + user: User = Depends(current_active_user), + session: AsyncSession = Depends(get_async_session), +): + """Apply a natural language edit to the user's personal memory via LLM.""" + agent_config = await load_agent_llm_config_for_search_space( + session, body.search_space_id + ) + if not agent_config: + raise HTTPException(status_code=500, detail="No LLM configuration available.") + llm = create_chat_litellm_from_agent_config(agent_config) + if not llm: + raise HTTPException(status_code=500, detail="Failed to create LLM instance.") + + await session.refresh(user, ["memory_md", "display_name"]) + current_memory = user.memory_md or "" + first_name = ( + user.display_name.strip().split()[0] + if user.display_name and user.display_name.strip() + else "The user" + ) + + prompt = _MEMORY_EDIT_PROMPT.format( + current_memory=current_memory or "(empty)", + instruction=body.query, + user_name=first_name, + ) + try: + response = await llm.ainvoke( + [HumanMessage(content=prompt)], + config={"tags": ["surfsense:internal", "memory-edit"]}, + ) + updated = ( + response.content + if isinstance(response.content, str) + else str(response.content) + ).strip() + except Exception as e: + logger.exception("Memory edit LLM call failed: %s", e) + raise HTTPException(status_code=500, detail="Memory edit failed.") from e + + if not updated: + raise HTTPException(status_code=400, detail="LLM returned empty result.") + + result = await _save_memory( + updated_memory=updated, + old_memory=current_memory, + llm=llm, + apply_fn=lambda content: setattr(user, "memory_md", content), + commit_fn=session.commit, + rollback_fn=session.rollback, + label="memory", + scope="user", + ) + + if result.get("status") == "error": + raise HTTPException(status_code=400, detail=result["message"]) + + await session.refresh(user, ["memory_md"]) + return MemoryRead(memory_md=user.memory_md or "") diff --git a/surfsense_backend/app/routes/search_spaces_routes.py b/surfsense_backend/app/routes/search_spaces_routes.py index 78be97aa1..7fa33ba1c 100644 --- a/surfsense_backend/app/routes/search_spaces_routes.py +++ b/surfsense_backend/app/routes/search_spaces_routes.py @@ -1,10 +1,17 @@ import logging from fastapi import APIRouter, Depends, HTTPException +from langchain_core.messages import HumanMessage +from pydantic import BaseModel as PydanticBaseModel from sqlalchemy import func from sqlalchemy.ext.asyncio import AsyncSession from sqlalchemy.future import select +from app.agents.new_chat.llm_config import ( + create_chat_litellm_from_agent_config, + load_agent_llm_config_for_search_space, +) +from app.agents.new_chat.tools.update_memory import MEMORY_HARD_LIMIT, _save_memory from app.config import config from app.db import ( ImageGenerationConfig, @@ -34,6 +41,34 @@ logger = logging.getLogger(__name__) router = APIRouter() +class _TeamMemoryEditRequest(PydanticBaseModel): + query: str + + +_TEAM_MEMORY_EDIT_PROMPT = """\ +You are a memory editor for a team workspace. The user wants to modify the \ +team's shared memory document. Apply the user's instruction to the existing \ +memory document and output the FULL updated document. + +RULES: +1. If the instruction asks to add something, add it with format: \ +- (YYYY-MM-DD) [fact] text, under an existing or new ## heading. \ +Heading names should be descriptive, not generic categories. +2. If the instruction asks to remove something, remove the matching entry. +3. If the instruction asks to change something, update the matching entry. +4. Preserve existing ## headings and all other entries. +5. NEVER use [pref] or [instr] markers. Team memory uses [fact] only. +6. Output ONLY the updated markdown — no explanations, no wrapping. + + +{current_memory} + + + +{instruction} +""" + + async def create_default_roles_and_membership( session: AsyncSession, search_space_id: int, @@ -255,6 +290,16 @@ async def update_search_space( raise HTTPException(status_code=404, detail="Search space not found") update_data = search_space_update.model_dump(exclude_unset=True) + + if ( + "shared_memory_md" in update_data + and len(update_data["shared_memory_md"] or "") > MEMORY_HARD_LIMIT + ): + raise HTTPException( + status_code=400, + detail=f"Team memory exceeds {MEMORY_HARD_LIMIT:,} character limit.", + ) + for key, value in update_data.items(): setattr(db_search_space, key, value) await session.commit() @@ -269,6 +314,76 @@ async def update_search_space( ) from e +@router.post( + "/searchspaces/{search_space_id}/memory/edit", + response_model=SearchSpaceRead, +) +async def edit_team_memory( + search_space_id: int, + body: _TeamMemoryEditRequest, + session: AsyncSession = Depends(get_async_session), + user: User = Depends(current_active_user), +): + """Apply a natural language edit to the team memory via LLM.""" + await check_search_space_access(session, user, search_space_id) + + agent_config = await load_agent_llm_config_for_search_space( + session, search_space_id + ) + if not agent_config: + raise HTTPException(status_code=500, detail="No LLM configuration available.") + llm = create_chat_litellm_from_agent_config(agent_config) + if not llm: + raise HTTPException(status_code=500, detail="Failed to create LLM instance.") + + result = await session.execute( + select(SearchSpace).filter(SearchSpace.id == search_space_id) + ) + db_search_space = result.scalars().first() + if not db_search_space: + raise HTTPException(status_code=404, detail="Search space not found") + + current_memory = db_search_space.shared_memory_md or "" + + prompt = _TEAM_MEMORY_EDIT_PROMPT.format( + current_memory=current_memory or "(empty)", + instruction=body.query, + ) + try: + response = await llm.ainvoke( + [HumanMessage(content=prompt)], + config={"tags": ["surfsense:internal", "memory-edit"]}, + ) + updated = ( + response.content + if isinstance(response.content, str) + else str(response.content) + ).strip() + except Exception as e: + logger.exception("Team memory edit LLM call failed: %s", e) + raise HTTPException(status_code=500, detail="Team memory edit failed.") from e + + if not updated: + raise HTTPException(status_code=400, detail="LLM returned empty result.") + + save_result = await _save_memory( + updated_memory=updated, + old_memory=current_memory, + llm=llm, + apply_fn=lambda content: setattr(db_search_space, "shared_memory_md", content), + commit_fn=session.commit, + rollback_fn=session.rollback, + label="team memory", + scope="team", + ) + + if save_result.get("status") == "error": + raise HTTPException(status_code=400, detail=save_result["message"]) + + await session.refresh(db_search_space) + return db_search_space + + @router.delete("/searchspaces/{search_space_id}", response_model=dict) async def delete_search_space( search_space_id: int, diff --git a/surfsense_backend/app/schemas/search_space.py b/surfsense_backend/app/schemas/search_space.py index 054fe1465..e3b50be65 100644 --- a/surfsense_backend/app/schemas/search_space.py +++ b/surfsense_backend/app/schemas/search_space.py @@ -21,6 +21,7 @@ class SearchSpaceUpdate(BaseModel): description: str | None = None citations_enabled: bool | None = None qna_custom_instructions: str | None = None + shared_memory_md: str | None = None class SearchSpaceRead(SearchSpaceBase, IDModel, TimestampModel): @@ -29,6 +30,7 @@ class SearchSpaceRead(SearchSpaceBase, IDModel, TimestampModel): user_id: uuid.UUID citations_enabled: bool qna_custom_instructions: str | None = None + shared_memory_md: str | None = None model_config = ConfigDict(from_attributes=True) diff --git a/surfsense_backend/app/tasks/chat/stream_new_chat.py b/surfsense_backend/app/tasks/chat/stream_new_chat.py index 5ff907459..fd118528e 100644 --- a/surfsense_backend/app/tasks/chat/stream_new_chat.py +++ b/surfsense_backend/app/tasks/chat/stream_new_chat.py @@ -37,6 +37,10 @@ from app.agents.new_chat.llm_config import ( load_agent_config, load_llm_config_from_yaml, ) +from app.agents.new_chat.memory_extraction import ( + extract_and_save_memory, + extract_and_save_team_memory, +) from app.db import ( ChatVisibility, NewChatMessage, @@ -59,8 +63,6 @@ from app.utils.perf import get_perf_logger, log_system_snapshot, trim_native_hea _perf_log = get_perf_logger() -_background_tasks: set[asyncio.Task] = set() - def format_mentioned_surfsense_docs_as_context( documents: list[SurfsenseDocsDocument], @@ -141,6 +143,7 @@ class StreamResult: is_interrupted: bool = False interrupt_value: dict[str, Any] | None = None sandbox_files: list[str] = field(default_factory=list) # unused, kept for compat + agent_called_update_memory: bool = False async def _stream_agent_events( @@ -183,6 +186,7 @@ async def _stream_agent_events( last_active_step_items: list[str] = initial_step_items or [] just_finished_tool: bool = False active_tool_depth: int = 0 # Track nesting: >0 means we're inside a tool + called_update_memory: bool = False def next_thinking_step_id() -> str: nonlocal thinking_step_counter @@ -490,6 +494,9 @@ async def _stream_agent_events( tool_name = event.get("name", "unknown_tool") raw_output = event.get("data", {}).get("output", "") + if tool_name == "update_memory": + called_update_memory = True + if hasattr(raw_output, "content"): content = raw_output.content if isinstance(content, str): @@ -1111,6 +1118,7 @@ async def _stream_agent_events( yield completion_event result.accumulated_text = accumulated_text + result.agent_called_update_memory = called_update_memory state = await agent.aget_state(config) is_interrupted = state.tasks and any(task.interrupts for task in state.tasks) @@ -1540,6 +1548,27 @@ async def stream_new_chat( chat_id, generated_title ) + # Fire background memory extraction if the agent didn't handle it. + # Shared threads write to team memory; private threads write to user memory. + if not stream_result.agent_called_update_memory: + if visibility == ChatVisibility.SEARCH_SPACE: + asyncio.create_task( + extract_and_save_team_memory( + user_message=user_query, + search_space_id=search_space_id, + llm=llm, + author_display_name=current_user_display_name, + ) + ) + elif user_id: + asyncio.create_task( + extract_and_save_memory( + user_message=user_query, + user_id=user_id, + llm=llm, + ) + ) + # Finish the step and message yield streaming_service.format_finish_step() yield streaming_service.format_finish() diff --git a/surfsense_backend/tests/unit/agents/new_chat/tools/test_update_memory_scope.py b/surfsense_backend/tests/unit/agents/new_chat/tools/test_update_memory_scope.py new file mode 100644 index 000000000..f7fbacf50 --- /dev/null +++ b/surfsense_backend/tests/unit/agents/new_chat/tools/test_update_memory_scope.py @@ -0,0 +1,198 @@ +"""Unit tests for memory scope validation and bullet format validation.""" + +import pytest + +from app.agents.new_chat.tools.update_memory import ( + _save_memory, + _validate_bullet_format, + _validate_memory_scope, +) + +pytestmark = pytest.mark.unit + + +class _Recorder: + def __init__(self) -> None: + self.applied_content: str | None = None + self.commit_calls = 0 + self.rollback_calls = 0 + + def apply(self, content: str) -> None: + self.applied_content = content + + async def commit(self) -> None: + self.commit_calls += 1 + + async def rollback(self) -> None: + self.rollback_calls += 1 + + +# --------------------------------------------------------------------------- +# _validate_memory_scope — marker-based +# --------------------------------------------------------------------------- + + +def test_validate_memory_scope_rejects_pref_marker_in_team_scope() -> None: + content = "- (2026-04-10) [pref] Prefers dark mode\n" + result = _validate_memory_scope(content, "team") + assert result is not None + assert result["status"] == "error" + assert "[pref]" in result["message"] + + +def test_validate_memory_scope_rejects_instr_marker_in_team_scope() -> None: + content = "- (2026-04-10) [instr] Always respond in Spanish\n" + result = _validate_memory_scope(content, "team") + assert result is not None + assert result["status"] == "error" + assert "[instr]" in result["message"] + + +def test_validate_memory_scope_rejects_both_personal_markers_in_team() -> None: + content = ( + "- (2026-04-10) [pref] Prefers dark mode\n" + "- (2026-04-10) [instr] Always respond in Spanish\n" + ) + result = _validate_memory_scope(content, "team") + assert result is not None + assert result["status"] == "error" + assert "[instr]" in result["message"] + assert "[pref]" in result["message"] + + +def test_validate_memory_scope_allows_fact_in_team_scope() -> None: + content = "- (2026-04-10) [fact] Office is in downtown Seattle\n" + result = _validate_memory_scope(content, "team") + assert result is None + + +def test_validate_memory_scope_allows_all_markers_in_user_scope() -> None: + content = ( + "- (2026-04-10) [fact] Python developer\n" + "- (2026-04-10) [pref] Prefers concise answers\n" + "- (2026-04-10) [instr] Always use bullet points\n" + ) + result = _validate_memory_scope(content, "user") + assert result is None + + +def test_validate_memory_scope_allows_any_heading_in_team() -> None: + content = "## Architecture\n- (2026-04-10) [fact] Uses PostgreSQL for persistence\n" + result = _validate_memory_scope(content, "team") + assert result is None + + +def test_validate_memory_scope_allows_any_heading_in_user() -> None: + content = "## My Projects\n- (2026-04-10) [fact] Working on SurfSense\n" + result = _validate_memory_scope(content, "user") + assert result is None + + +# --------------------------------------------------------------------------- +# _validate_bullet_format +# --------------------------------------------------------------------------- + + +def test_validate_bullet_format_passes_valid_bullets() -> None: + content = ( + "## Work\n" + "- (2026-04-10) [fact] Senior Python developer\n" + "- (2026-04-10) [pref] Prefers dark mode\n" + "- (2026-04-10) [instr] Always respond in bullet points\n" + ) + warnings = _validate_bullet_format(content) + assert warnings == [] + + +def test_validate_bullet_format_warns_on_missing_marker() -> None: + content = "- (2026-04-10) Senior Python developer\n" + warnings = _validate_bullet_format(content) + assert len(warnings) == 1 + assert "Malformed bullet" in warnings[0] + + +def test_validate_bullet_format_warns_on_missing_date() -> None: + content = "- [fact] Senior Python developer\n" + warnings = _validate_bullet_format(content) + assert len(warnings) == 1 + assert "Malformed bullet" in warnings[0] + + +def test_validate_bullet_format_warns_on_unknown_marker() -> None: + content = "- (2026-04-10) [context] Working on project X\n" + warnings = _validate_bullet_format(content) + assert len(warnings) == 1 + assert "Malformed bullet" in warnings[0] + + +def test_validate_bullet_format_ignores_non_bullet_lines() -> None: + content = "## Some Heading\nSome paragraph text\n" + warnings = _validate_bullet_format(content) + assert warnings == [] + + +def test_validate_bullet_format_warns_on_old_format_without_marker() -> None: + content = "## About the user\n- (2026-04-10) Likes cats\n" + warnings = _validate_bullet_format(content) + assert len(warnings) == 1 + + +# --------------------------------------------------------------------------- +# _save_memory — end-to-end with marker scope check +# --------------------------------------------------------------------------- + + +@pytest.mark.asyncio +async def test_save_memory_blocks_pref_in_team_before_commit() -> None: + recorder = _Recorder() + result = await _save_memory( + updated_memory="- (2026-04-10) [pref] Prefers dark mode\n", + old_memory=None, + llm=None, + apply_fn=recorder.apply, + commit_fn=recorder.commit, + rollback_fn=recorder.rollback, + label="team memory", + scope="team", + ) + assert result["status"] == "error" + assert recorder.commit_calls == 0 + assert recorder.applied_content is None + + +@pytest.mark.asyncio +async def test_save_memory_allows_fact_in_team_and_commits() -> None: + recorder = _Recorder() + content = "- (2026-04-10) [fact] Weekly standup on Mondays\n" + result = await _save_memory( + updated_memory=content, + old_memory=None, + llm=None, + apply_fn=recorder.apply, + commit_fn=recorder.commit, + rollback_fn=recorder.rollback, + label="team memory", + scope="team", + ) + assert result["status"] == "saved" + assert recorder.commit_calls == 1 + assert recorder.applied_content == content + + +@pytest.mark.asyncio +async def test_save_memory_includes_format_warnings() -> None: + recorder = _Recorder() + content = "- (2026-04-10) Missing marker text\n" + result = await _save_memory( + updated_memory=content, + old_memory=None, + llm=None, + apply_fn=recorder.apply, + commit_fn=recorder.commit, + rollback_fn=recorder.rollback, + label="memory", + scope="user", + ) + assert result["status"] == "saved" + assert "format_warnings" in result + assert len(result["format_warnings"]) == 1 diff --git a/surfsense_web/app/dashboard/[search_space_id]/user-settings/components/MemoryContent.tsx b/surfsense_web/app/dashboard/[search_space_id]/user-settings/components/MemoryContent.tsx new file mode 100644 index 000000000..ef17e5a89 --- /dev/null +++ b/surfsense_web/app/dashboard/[search_space_id]/user-settings/components/MemoryContent.tsx @@ -0,0 +1,291 @@ +"use client"; + +import { useAtomValue } from "jotai"; +import { ArrowUp, ChevronDown, ClipboardCopy, Download, Info, Pen } from "lucide-react"; +import { useCallback, useEffect, useRef, useState } from "react"; +import { toast } from "sonner"; +import { z } from "zod"; +import { activeSearchSpaceIdAtom } from "@/atoms/search-spaces/search-space-query.atoms"; +import { PlateEditor } from "@/components/editor/plate-editor"; +import { Alert, AlertDescription } from "@/components/ui/alert"; +import { Button } from "@/components/ui/button"; +import { + DropdownMenu, + DropdownMenuContent, + DropdownMenuItem, + DropdownMenuTrigger, +} from "@/components/ui/dropdown-menu"; +import { Spinner } from "@/components/ui/spinner"; + +import { baseApiService } from "@/lib/apis/base-api.service"; + +const MEMORY_HARD_LIMIT = 25_000; + +const MemoryReadSchema = z.object({ + memory_md: z.string(), +}); + +export function MemoryContent() { + const activeSearchSpaceId = useAtomValue(activeSearchSpaceIdAtom); + const [memory, setMemory] = useState(""); + const [loading, setLoading] = useState(true); + const [saving, setSaving] = useState(false); + const [editQuery, setEditQuery] = useState(""); + const [editing, setEditing] = useState(false); + const [showInput, setShowInput] = useState(false); + const textareaRef = useRef(null); + const inputContainerRef = useRef(null); + + const fetchMemory = useCallback(async () => { + try { + setLoading(true); + const data = await baseApiService.get("/api/v1/users/me/memory", MemoryReadSchema); + setMemory(data.memory_md); + } catch { + toast.error("Failed to load memory"); + } finally { + setLoading(false); + } + }, []); + + useEffect(() => { + fetchMemory(); + }, [fetchMemory]); + + useEffect(() => { + if (!showInput) return; + + const handlePointerDownOutside = (event: MouseEvent | TouchEvent) => { + const target = event.target; + if (!(target instanceof Node)) return; + if (inputContainerRef.current?.contains(target)) return; + + setShowInput(false); + }; + + document.addEventListener("mousedown", handlePointerDownOutside); + document.addEventListener("touchstart", handlePointerDownOutside, { passive: true }); + + return () => { + document.removeEventListener("mousedown", handlePointerDownOutside); + document.removeEventListener("touchstart", handlePointerDownOutside); + }; + }, [showInput]); + + const handleClear = async () => { + try { + setSaving(true); + const data = await baseApiService.put("/api/v1/users/me/memory", MemoryReadSchema, { + body: { memory_md: "" }, + }); + setMemory(data.memory_md); + toast.success("Memory cleared"); + } catch { + toast.error("Failed to clear memory"); + } finally { + setSaving(false); + } + }; + + const handleEdit = async () => { + const query = editQuery.trim(); + if (!query) return; + + try { + setEditing(true); + const data = await baseApiService.post("/api/v1/users/me/memory/edit", MemoryReadSchema, { + body: { query, search_space_id: Number(activeSearchSpaceId) }, + }); + setMemory(data.memory_md); + setEditQuery(""); + setShowInput(false); + toast.success("Memory updated"); + } catch { + toast.error("Failed to edit memory"); + } finally { + setEditing(false); + } + }; + + const openInput = () => { + setShowInput(true); + requestAnimationFrame(() => textareaRef.current?.focus()); + }; + + const handleDownload = () => { + if (!memory) return; + try { + const blob = new Blob([memory], { type: "text/markdown;charset=utf-8" }); + const url = URL.createObjectURL(blob); + const a = document.createElement("a"); + a.href = url; + a.download = "personal-memory.md"; + document.body.appendChild(a); + a.click(); + document.body.removeChild(a); + URL.revokeObjectURL(url); + } catch { + toast.error("Failed to download memory"); + } + }; + + const handleCopyMarkdown = async () => { + if (!memory) return; + try { + await navigator.clipboard.writeText(memory); + toast.success("Copied to clipboard"); + } catch { + toast.error("Failed to copy memory"); + } + }; + + const handleKeyDown = (e: React.KeyboardEvent) => { + if (e.key === "Enter" && !e.shiftKey) { + e.preventDefault(); + handleEdit(); + } + }; + + const displayMemory = memory.replace(/\(\d{4}-\d{2}-\d{2}\)\s*\[(fact|pref|instr)\]\s*/g, ""); + const charCount = memory.length; + + const getCounterColor = () => { + if (charCount > MEMORY_HARD_LIMIT) return "text-red-500"; + if (charCount > 15_000) return "text-orange-500"; + if (charCount > 10_000) return "text-yellow-500"; + return "text-muted-foreground"; + }; + + if (loading) { + return ( +
+ +
+ ); + } + + if (!memory) { + return ( +
+

What does SurfSense remember?

+

+ Nothing yet. SurfSense picks up on your preferences and context as you chat. +

+
+ ); + } + + return ( +
+ + + +

+ SurfSense uses this personal memory to personalize your responses across all + conversations. +

+ + + +
+
+ +
+ + {showInput ? ( +
+
+ setEditQuery(e.target.value)} + onKeyDown={handleKeyDown} + placeholder="Tell SurfSense what to remember or forget" + disabled={editing} + className="flex-1 bg-transparent text-sm outline-none placeholder:text-muted-foreground/70" + /> + +
+
+ ) : ( + + )} +
+ +
+ + {charCount.toLocaleString()} / {MEMORY_HARD_LIMIT.toLocaleString()} + characters + chars + {charCount > 15_000 && charCount <= MEMORY_HARD_LIMIT && " - Approaching limit"} + {charCount > MEMORY_HARD_LIMIT && " - Exceeds limit"} + +
+ + + + + + + + + Copy as Markdown + + + + Download as Markdown + + + +
+
+
+ ); +} diff --git a/surfsense_web/app/dashboard/[search_space_id]/user-settings/components/PurchaseHistoryContent.tsx b/surfsense_web/app/dashboard/[search_space_id]/user-settings/components/PurchaseHistoryContent.tsx index 2eb56520b..9bc77edff 100644 --- a/surfsense_web/app/dashboard/[search_space_id]/user-settings/components/PurchaseHistoryContent.tsx +++ b/surfsense_web/app/dashboard/[search_space_id]/user-settings/components/PurchaseHistoryContent.tsx @@ -1,7 +1,7 @@ "use client"; import { useQuery } from "@tanstack/react-query"; -import { Receipt } from "lucide-react"; +import { ReceiptText } from "lucide-react"; import { Badge } from "@/components/ui/badge"; import { Spinner } from "@/components/ui/spinner"; import { @@ -65,7 +65,7 @@ export function PurchaseHistoryContent() { if (purchases.length === 0) { return (
- +

No purchases yet

Your page-pack purchases will appear here after checkout. diff --git a/surfsense_web/components/assistant-ui/assistant-message.tsx b/surfsense_web/components/assistant-ui/assistant-message.tsx index 097197107..764acabba 100644 --- a/surfsense_web/components/assistant-ui/assistant-message.tsx +++ b/surfsense_web/components/assistant-ui/assistant-message.tsx @@ -76,12 +76,8 @@ const GenerateImageToolUI = dynamic( import("@/components/tool-ui/generate-image").then((m) => ({ default: m.GenerateImageToolUI })), { ssr: false } ); -const SaveMemoryToolUI = dynamic( - () => import("@/components/tool-ui/user-memory").then((m) => ({ default: m.SaveMemoryToolUI })), - { ssr: false } -); -const RecallMemoryToolUI = dynamic( - () => import("@/components/tool-ui/user-memory").then((m) => ({ default: m.RecallMemoryToolUI })), +const UpdateMemoryToolUI = dynamic( + () => import("@/components/tool-ui/user-memory").then((m) => ({ default: m.UpdateMemoryToolUI })), { ssr: false } ); const SandboxExecuteToolUI = dynamic( @@ -386,8 +382,7 @@ const AssistantMessageInner: FC = () => { generate_video_presentation: GenerateVideoPresentationToolUI, display_image: GenerateImageToolUI, generate_image: GenerateImageToolUI, - save_memory: SaveMemoryToolUI, - recall_memory: RecallMemoryToolUI, + update_memory: UpdateMemoryToolUI, execute: SandboxExecuteToolUI, create_notion_page: CreateNotionPageToolUI, update_notion_page: UpdateNotionPageToolUI, diff --git a/surfsense_web/components/assistant-ui/thread.tsx b/surfsense_web/components/assistant-ui/thread.tsx index e1351cb18..7fb01401f 100644 --- a/surfsense_web/components/assistant-ui/thread.tsx +++ b/surfsense_web/components/assistant-ui/thread.tsx @@ -77,6 +77,7 @@ import { DropdownMenuTrigger, } from "@/components/ui/dropdown-menu"; import { Popover, PopoverContent, PopoverTrigger } from "@/components/ui/popover"; +import { Skeleton } from "@/components/ui/skeleton"; import { Switch } from "@/components/ui/switch"; import { Tooltip, TooltipContent, TooltipTrigger } from "@/components/ui/tooltip"; import { getConnectorIcon } from "@/contracts/enums/connectorIcons"; @@ -92,7 +93,7 @@ import { useMediaQuery } from "@/hooks/use-media-query"; import { useElectronAPI } from "@/hooks/use-platform"; import { cn } from "@/lib/utils"; -const COMPOSER_PLACEHOLDER = "Ask anything · Type / for prompts · Type @ to mention docs"; +const COMPOSER_PLACEHOLDER = "Ask anything, type / for prompts, type @ to mention docs"; export const Thread: FC = () => { return ; @@ -804,7 +805,7 @@ const ComposerAction: FC = ({ isBlockedByOtherUser = false const isDesktop = useMediaQuery("(min-width: 640px)"); const { openDialog: openUploadDialog } = useDocumentUploadDialog(); const [toolsScrollPos, setToolsScrollPos] = useState<"top" | "middle" | "bottom">("top"); - const toolsRafRef = useRef(); + const toolsRafRef = useRef(undefined); const handleToolsScroll = useCallback((e: React.UIEvent) => { const el = e.currentTarget; if (toolsRafRef.current) return; @@ -1021,8 +1022,23 @@ const ComposerAction: FC = ({ isBlockedByOtherUser = false

)} {!filteredTools?.length && ( -
- Loading tools... +
+ + {["t1", "t2", "t3", "t4"].map((k) => ( +
+ + + +
+ ))} + + {["c1", "c2", "c3"].map((k) => ( +
+ + + +
+ ))}
)}
@@ -1058,12 +1074,12 @@ const ComposerAction: FC = ({ isBlockedByOtherUser = false side="bottom" align="start" sideOffset={12} - className="w-[calc(100vw-2rem)] max-w-56 sm:max-w-72 sm:w-72 p-0 select-none" + className="w-[calc(100vw-2rem)] max-w-48 sm:max-w-56 sm:w-56 p-0 select-none" onOpenAutoFocus={(e) => e.preventDefault()} >
Manage Tools
= ({ isBlockedByOtherUser = false .filter((g) => !g.connectorIcon) .map((group) => (
-
+
{group.label}
{group.tools.map((tool) => { const isDisabled = disabledToolsSet.has(tool.name); const ToolIcon = getToolIcon(tool.name); const row = ( -
- - +
+ + {formatToolName(tool.name)} toggleTool(tool.name)} - className="shrink-0 scale-[0.6] sm:scale-75" + className="shrink-0 scale-50 sm:scale-[0.6]" />
); @@ -1106,7 +1122,7 @@ const ComposerAction: FC = ({ isBlockedByOtherUser = false ))} {groupedTools.some((g) => g.connectorIcon) && (
-
+
Connector Actions
{groupedTools @@ -1118,26 +1134,26 @@ const ComposerAction: FC = ({ isBlockedByOtherUser = false const allDisabled = toolNames.every((n) => disabledToolsSet.has(n)); const groupDef = TOOL_GROUPS.find((g) => g.label === group.label); const row = ( -
+
{iconInfo ? ( {iconInfo.alt} ) : ( - + )} - + {group.label} toggleToolGroup(toolNames)} - className="shrink-0 scale-[0.6] sm:scale-75" + className="shrink-0 scale-50 sm:scale-[0.6]" />
); @@ -1158,8 +1174,23 @@ const ComposerAction: FC = ({ isBlockedByOtherUser = false
)} {!filteredTools?.length && ( -
- Loading tools... +
+ + {["dt1", "dt2", "dt3", "dt4"].map((k) => ( +
+ + + +
+ ))} + + {["dc1", "dc2", "dc3"].map((k) => ( +
+ + + +
+ ))}
)}
@@ -1297,7 +1328,7 @@ const TOOL_GROUPS: ToolGroup[] = [ }, { label: "Memory", - tools: ["save_memory", "recall_memory"], + tools: ["update_memory"], }, { label: "Gmail", diff --git a/surfsense_web/components/editor/plate-editor.tsx b/surfsense_web/components/editor/plate-editor.tsx index 66e9a0e5e..61f84126c 100644 --- a/surfsense_web/components/editor/plate-editor.tsx +++ b/surfsense_web/components/editor/plate-editor.tsx @@ -3,7 +3,7 @@ import { MarkdownPlugin, remarkMdx } from "@platejs/markdown"; import { slateToHtml } from "@slate-serializers/html"; import type { AnyPluginConfig, Descendant, Value } from "platejs"; -import { createPlatePlugin, Key, Plate, usePlateEditor } from "platejs/react"; +import { createPlatePlugin, Key, Plate, useEditorReadOnly, usePlateEditor } from "platejs/react"; import { useEffect, useMemo, useRef } from "react"; import remarkGfm from "remark-gfm"; import remarkMath from "remark-math"; @@ -60,6 +60,24 @@ export interface PlateEditorProps { extraPlugins?: AnyPluginConfig[]; } +function PlateEditorContent({ + editorVariant, + placeholder, +}: { + editorVariant: PlateEditorProps["editorVariant"]; + placeholder?: string; +}) { + const isReadOnly = useEditorReadOnly(); + + return ( + + ); +} + export function PlateEditor({ markdown, html, @@ -188,7 +206,7 @@ export function PlateEditor({ }} > - + diff --git a/surfsense_web/components/editor/presets.ts b/surfsense_web/components/editor/presets.ts index 7800f7c7d..c207b5e56 100644 --- a/surfsense_web/components/editor/presets.ts +++ b/surfsense_web/components/editor/presets.ts @@ -1,6 +1,7 @@ "use client"; import type { AnyPluginConfig } from "platejs"; +import { TrailingBlockPlugin } from "platejs"; import { AutoformatKit } from "@/components/editor/plugins/autoformat-kit"; import { BasicNodesKit } from "@/components/editor/plugins/basic-nodes-kit"; @@ -36,6 +37,7 @@ export const fullPreset: AnyPluginConfig[] = [ ...FloatingToolbarKit, ...AutoformatKit, ...DndKit, + TrailingBlockPlugin, ]; /** @@ -48,8 +50,8 @@ export const minimalPreset: AnyPluginConfig[] = [ ...ListKit, ...CodeBlockKit, ...LinkKit, - ...FloatingToolbarKit, ...AutoformatKit, + TrailingBlockPlugin, ]; /** diff --git a/surfsense_web/components/homepage/hero-section.tsx b/surfsense_web/components/homepage/hero-section.tsx index ba5a9d9ac..936facef7 100644 --- a/surfsense_web/components/homepage/hero-section.tsx +++ b/surfsense_web/components/homepage/hero-section.tsx @@ -2,7 +2,7 @@ import { ChevronDown, Download, Monitor } from "lucide-react"; import { AnimatePresence, motion } from "motion/react"; import Link from "next/link"; -import React, { memo, useCallback, useEffect, useMemo, useRef, useState } from "react"; +import React, { memo, useCallback, useEffect, useRef, useState } from "react"; import Balancer from "react-wrap-balancer"; import { DropdownMenu, @@ -12,6 +12,11 @@ import { } from "@/components/ui/dropdown-menu"; import { ExpandedMediaOverlay, useExpandedMedia } from "@/components/ui/expanded-gif-overlay"; import { Tooltip, TooltipContent, TooltipTrigger } from "@/components/ui/tooltip"; +import { + GITHUB_RELEASES_URL, + getAssetLabel, + usePrimaryDownload, +} from "@/lib/desktop-download-utils"; import { AUTH_TYPE, BACKEND_URL } from "@/lib/env-config"; import { trackLoginAttempt } from "@/lib/posthog/events"; import { cn } from "@/lib/utils"; @@ -200,107 +205,8 @@ function GetStartedButton() { ); } -type OSInfo = { - os: "macOS" | "Windows" | "Linux"; - arch: "arm64" | "x64"; -}; - -function useUserOS(): OSInfo { - const [info, setInfo] = useState({ os: "macOS", arch: "arm64" }); - useEffect(() => { - const ua = navigator.userAgent; - let os: OSInfo["os"] = "macOS"; - let arch: OSInfo["arch"] = "x64"; - - if (/Windows/i.test(ua)) { - os = "Windows"; - arch = "x64"; - } else if (/Linux/i.test(ua)) { - os = "Linux"; - arch = "x64"; - } else { - os = "macOS"; - arch = /Mac/.test(ua) && !/Intel/.test(ua) ? "arm64" : "arm64"; - } - - const uaData = (navigator as Navigator & { userAgentData?: { architecture?: string } }) - .userAgentData; - if (uaData?.architecture === "arm") arch = "arm64"; - else if (uaData?.architecture === "x86") arch = "x64"; - - setInfo({ os, arch }); - }, []); - return info; -} - -interface ReleaseAsset { - name: string; - url: string; -} - -function useLatestRelease() { - const [assets, setAssets] = useState([]); - - useEffect(() => { - const controller = new AbortController(); - fetch("https://api.github.com/repos/MODSetter/SurfSense/releases/latest", { - signal: controller.signal, - }) - .then((r) => r.json()) - .then((data) => { - if (data?.assets) { - setAssets( - data.assets - .filter((a: { name: string }) => /\.(exe|dmg|AppImage|deb)$/.test(a.name)) - .map((a: { name: string; browser_download_url: string }) => ({ - name: a.name, - url: a.browser_download_url, - })) - ); - } - }) - .catch(() => {}); - return () => controller.abort(); - }, []); - - return assets; -} - -const ASSET_LABELS: Record = { - ".exe": "Windows (exe)", - "-arm64.dmg": "macOS Apple Silicon (dmg)", - "-x64.dmg": "macOS Intel (dmg)", - "-arm64.zip": "macOS Apple Silicon (zip)", - "-x64.zip": "macOS Intel (zip)", - ".AppImage": "Linux (AppImage)", - ".deb": "Linux (deb)", -}; - -function getAssetLabel(name: string): string { - for (const [suffix, label] of Object.entries(ASSET_LABELS)) { - if (name.endsWith(suffix)) return label; - } - return name; -} - function DownloadButton() { - const { os, arch } = useUserOS(); - const assets = useLatestRelease(); - - const { primary, alternatives } = useMemo(() => { - if (assets.length === 0) return { primary: null, alternatives: [] }; - - const matchers: Record boolean> = { - Windows: (n) => n.endsWith(".exe"), - macOS: (n) => n.endsWith(`-${arch}.dmg`), - Linux: (n) => n.endsWith(".AppImage"), - }; - - const match = matchers[os]; - const primary = assets.find((a) => match(a.name)) ?? null; - const alternatives = assets.filter((a) => a !== primary); - return { primary, alternatives }; - }, [assets, os, arch]); + const { os, primary, alternatives } = usePrimaryDownload(); const fallbackUrl = GITHUB_RELEASES_URL; @@ -504,5 +410,3 @@ const TabVideo = memo(function TabVideo({ src }: { src: string }) {
); }); - -const GITHUB_RELEASES_URL = "https://github.com/MODSetter/SurfSense/releases/latest"; diff --git a/surfsense_web/components/layout/ui/sidebar/SidebarSlideOutPanel.tsx b/surfsense_web/components/layout/ui/sidebar/SidebarSlideOutPanel.tsx index ed54e65aa..5195082cd 100644 --- a/surfsense_web/components/layout/ui/sidebar/SidebarSlideOutPanel.tsx +++ b/surfsense_web/components/layout/ui/sidebar/SidebarSlideOutPanel.tsx @@ -91,12 +91,12 @@ export function SidebarSlideOutPanel({ {/* Panel extending from sidebar's right edge, flush with the wrapper border */}
{ setLocale(newLocale); @@ -294,6 +300,15 @@ export function SidebarUserProfile({ + {!isDesktop && ( + + + + {t("download_for_os", { os })} + + + )} + @@ -439,6 +454,15 @@ export function SidebarUserProfile({ + {!isDesktop && ( + + + + {t("download_for_os", { os })} + + + )} + diff --git a/surfsense_web/components/report-panel/report-panel.tsx b/surfsense_web/components/report-panel/report-panel.tsx index c602a3f04..6ec2a08eb 100644 --- a/surfsense_web/components/report-panel/report-panel.tsx +++ b/surfsense_web/components/report-panel/report-panel.tsx @@ -18,14 +18,39 @@ import { DropdownMenuItem, DropdownMenuTrigger, } from "@/components/ui/dropdown-menu"; -import { Skeleton } from "@/components/ui/skeleton"; import { useMediaQuery } from "@/hooks/use-media-query"; import { baseApiService } from "@/lib/apis/base-api.service"; import { authenticatedFetch } from "@/lib/auth-utils"; +function ReportPanelSkeleton() { + return ( +
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+ ); +} + const PlateEditor = dynamic( () => import("@/components/editor/plate-editor").then((m) => ({ default: m.PlateEditor })), - { ssr: false, loading: () => } + { ssr: false, loading: () => } ); /** @@ -59,46 +84,6 @@ const ReportContentResponseSchema = z.object({ type ReportContentResponse = z.infer; type VersionInfo = z.infer; -/** - * Shimmer loading skeleton for report panel - */ -function ReportPanelSkeleton() { - return ( -
- {/* Title skeleton */} -
- - {/* Paragraph 1 */} -
-
-
-
-
-
- - {/* Heading */} -
- - {/* Paragraph 2 */} -
-
-
-
-
- - {/* Heading */} -
- - {/* Paragraph 3 */} -
-
-
-
-
-
- ); -} - /** * Inner content component used by desktop panel, mobile drawer, and the layout right panel */ diff --git a/surfsense_web/components/settings/image-model-manager.tsx b/surfsense_web/components/settings/image-model-manager.tsx index 55dfe3d31..831519aa2 100644 --- a/surfsense_web/components/settings/image-model-manager.tsx +++ b/surfsense_web/components/settings/image-model-manager.tsx @@ -190,7 +190,7 @@ export function ImageModelManager({ searchSpaceId }: ImageModelManagerProps) { ? "model" : "models"} {" "} - available from your administrator. Use the model selector to view and select them. + available from your administrator.

diff --git a/surfsense_web/components/settings/llm-role-manager.tsx b/surfsense_web/components/settings/llm-role-manager.tsx index 995159d58..e280db493 100644 --- a/surfsense_web/components/settings/llm-role-manager.tsx +++ b/surfsense_web/components/settings/llm-role-manager.tsx @@ -10,7 +10,6 @@ import { FileText, ImageIcon, RefreshCw, - Shuffle, } from "lucide-react"; import { useCallback, useEffect, useRef, useState } from "react"; import { toast } from "sonner"; @@ -44,7 +43,6 @@ import { } from "@/components/ui/select"; import { Skeleton } from "@/components/ui/skeleton"; import { Spinner } from "@/components/ui/spinner"; -import { getProviderIcon } from "@/lib/provider-icons"; import { cn } from "@/lib/utils"; const ROLE_DESCRIPTIONS = { @@ -79,8 +77,8 @@ const ROLE_DESCRIPTIONS = { icon: Eye, title: "Vision LLM", description: "Vision-capable model for screenshot analysis and context extraction", - color: "text-amber-600 dark:text-amber-400", - bgColor: "bg-amber-500/10", + color: "text-muted-foreground", + bgColor: "bg-muted", prefKey: "vision_llm_config_id" as const, configType: "vision" as const, }, @@ -205,11 +203,6 @@ export function LLMRoleManager({ searchSpaceId }: LLMRoleManagerProps) { ), ]; - const isAssignmentComplete = - allLLMConfigs.some((c) => c.id === assignments.agent_llm_id) && - allLLMConfigs.some((c) => c.id === assignments.document_summary_llm_id) && - allImageConfigs.some((c) => c.id === assignments.image_generation_config_id); - const isLoading = configsLoading || preferencesLoading || @@ -231,7 +224,7 @@ export function LLMRoleManager({ searchSpaceId }: LLMRoleManagerProps) { return (
{/* Header actions */} -
+
- {isAssignmentComplete && !isLoading && !hasError && ( - - - All roles assigned - - )}
{/* Error Alert */} @@ -343,8 +330,6 @@ export function LLMRoleManager({ searchSpaceId }: LLMRoleManagerProps) { const assignedConfig = roleAllConfigs.find((config) => config.id === currentAssignment); const isAssigned = !!assignedConfig; - const isAutoMode = - assignedConfig && "is_auto_mode" in assignedConfig && assignedConfig.is_auto_mode; return (
@@ -389,7 +374,7 @@ export function LLMRoleManager({ searchSpaceId }: LLMRoleManagerProps) { - +
- {isAuto ? ( - - ) : ( - getProviderIcon(config.provider, { - className: "size-3 md:size-3.5 shrink-0", - }) - )} {config.name} - {!isAuto && ( - - ({config.model_name}) - - )} {isAuto && (
- {getProviderIcon(config.provider, { - className: "size-3 md:size-3.5 shrink-0", - })} {config.name} - - ({config.model_name}) -
))} @@ -472,63 +439,6 @@ export function LLMRoleManager({ searchSpaceId }: LLMRoleManagerProps) {
- - {/* Assigned Config Summary */} - {assignedConfig && ( -
- {isAutoMode ? ( -
- -
-

- Auto Mode -

-

- Routes across all available providers -

-
-
- ) : ( -
- -
-
- {assignedConfig.name} - {"is_global" in assignedConfig && assignedConfig.is_global && ( - - 🌐 Global - - )} -
-
- {getProviderIcon(assignedConfig.provider, { - className: "size-3 shrink-0", - })} - - {assignedConfig.model_name} - -
- {assignedConfig.api_base && ( -

- {assignedConfig.api_base} -

- )} -
-
- )} -
- )}
diff --git a/surfsense_web/components/settings/model-config-manager.tsx b/surfsense_web/components/settings/model-config-manager.tsx index 0b89379be..f83251426 100644 --- a/surfsense_web/components/settings/model-config-manager.tsx +++ b/surfsense_web/components/settings/model-config-manager.tsx @@ -196,7 +196,7 @@ export function ModelConfigManager({ searchSpaceId }: ModelConfigManagerProps) { {globalConfigs.length} global {globalConfigs.length === 1 ? "model" : "models"} {" "} - available from your administrator. Use the model selector to view and select them. + available from your administrator.

diff --git a/surfsense_web/components/settings/more-pages-content.tsx b/surfsense_web/components/settings/more-pages-content.tsx index 29245775c..944f7418f 100644 --- a/surfsense_web/components/settings/more-pages-content.tsx +++ b/surfsense_web/components/settings/more-pages-content.tsx @@ -113,11 +113,11 @@ export function MorePagesContent() { {isLoading ? ( - +
- +
- + ) : ( diff --git a/surfsense_web/components/settings/search-space-settings-dialog.tsx b/surfsense_web/components/settings/search-space-settings-dialog.tsx index 34d28eb2a..56ad0ab8f 100644 --- a/surfsense_web/components/settings/search-space-settings-dialog.tsx +++ b/surfsense_web/components/settings/search-space-settings-dialog.tsx @@ -1,7 +1,17 @@ "use client"; import { useAtom } from "jotai"; -import { Bot, Brain, Eye, FileText, Globe, ImageIcon, MessageSquare, Shield } from "lucide-react"; +import { + BookText, + Bot, + Brain, + CircleUser, + Earth, + Eye, + ImageIcon, + ListChecks, + UserKey, +} from "lucide-react"; import dynamic from "next/dynamic"; import { useTranslations } from "next-intl"; import type React from "react"; @@ -59,6 +69,13 @@ const PublicChatSnapshotsManager = dynamic( })), { ssr: false } ); +const TeamMemoryManager = dynamic( + () => + import("@/components/settings/team-memory-manager").then((m) => ({ + default: m.TeamMemoryManager, + })), + { ssr: false } +); interface SearchSpaceSettingsDialogProps { searchSpaceId: number; @@ -69,9 +86,9 @@ export function SearchSpaceSettingsDialog({ searchSpaceId }: SearchSpaceSettings const [state, setState] = useAtom(searchSpaceSettingsDialogAtom); const navItems = [ - { value: "general", label: t("nav_general"), icon: }, + { value: "general", label: t("nav_general"), icon: }, + { value: "roles", label: t("nav_role_assignments"), icon: }, { value: "models", label: t("nav_agent_configs"), icon: }, - { value: "roles", label: t("nav_role_assignments"), icon: }, { value: "image-models", label: t("nav_image_models"), @@ -82,13 +99,18 @@ export function SearchSpaceSettingsDialog({ searchSpaceId }: SearchSpaceSettings label: t("nav_vision_models"), icon: , }, - { value: "team-roles", label: t("nav_team_roles"), icon: }, + { value: "team-roles", label: t("nav_team_roles"), icon: }, { value: "prompts", label: t("nav_system_instructions"), - icon: , + icon: , }, - { value: "public-links", label: t("nav_public_links"), icon: }, + { + value: "team-memory", + label: "Team Memory", + icon: , + }, + { value: "public-links", label: t("nav_public_links"), icon: }, ]; const content: Record = { @@ -99,6 +121,7 @@ export function SearchSpaceSettingsDialog({ searchSpaceId }: SearchSpaceSettings "vision-models": , "team-roles": , prompts: , + "team-memory": , "public-links": , }; diff --git a/surfsense_web/components/settings/team-memory-manager.tsx b/surfsense_web/components/settings/team-memory-manager.tsx new file mode 100644 index 000000000..67369879b --- /dev/null +++ b/surfsense_web/components/settings/team-memory-manager.tsx @@ -0,0 +1,297 @@ +"use client"; + +import { useQuery, useQueryClient } from "@tanstack/react-query"; +import { useAtomValue } from "jotai"; +import { ArrowUp, ChevronDown, ClipboardCopy, Download, Info, Pen } from "lucide-react"; +import { useEffect, useRef, useState } from "react"; +import { toast } from "sonner"; +import { z } from "zod"; +import { updateSearchSpaceMutationAtom } from "@/atoms/search-spaces/search-space-mutation.atoms"; +import { PlateEditor } from "@/components/editor/plate-editor"; +import { Alert, AlertDescription } from "@/components/ui/alert"; +import { Button } from "@/components/ui/button"; +import { + DropdownMenu, + DropdownMenuContent, + DropdownMenuItem, + DropdownMenuTrigger, +} from "@/components/ui/dropdown-menu"; +import { Spinner } from "@/components/ui/spinner"; +import { baseApiService } from "@/lib/apis/base-api.service"; +import { searchSpacesApiService } from "@/lib/apis/search-spaces-api.service"; +import { cacheKeys } from "@/lib/query-client/cache-keys"; + +const MEMORY_HARD_LIMIT = 25_000; + +const SearchSpaceSchema = z + .object({ + shared_memory_md: z.string().optional().default(""), + }) + .passthrough(); + +interface TeamMemoryManagerProps { + searchSpaceId: number; +} + +export function TeamMemoryManager({ searchSpaceId }: TeamMemoryManagerProps) { + const queryClient = useQueryClient(); + const { data: searchSpace, isLoading: loading } = useQuery({ + queryKey: cacheKeys.searchSpaces.detail(searchSpaceId.toString()), + queryFn: () => searchSpacesApiService.getSearchSpace({ id: searchSpaceId }), + enabled: !!searchSpaceId, + }); + + const { mutateAsync: updateSearchSpace } = useAtomValue(updateSearchSpaceMutationAtom); + + const [saving, setSaving] = useState(false); + const [editQuery, setEditQuery] = useState(""); + const [editing, setEditing] = useState(false); + const [showInput, setShowInput] = useState(false); + const textareaRef = useRef(null); + const inputContainerRef = useRef(null); + + const memory = searchSpace?.shared_memory_md || ""; + + useEffect(() => { + if (!showInput) return; + + const handlePointerDownOutside = (event: MouseEvent | TouchEvent) => { + const target = event.target; + if (!(target instanceof Node)) return; + if (inputContainerRef.current?.contains(target)) return; + + setShowInput(false); + }; + + document.addEventListener("mousedown", handlePointerDownOutside); + document.addEventListener("touchstart", handlePointerDownOutside, { passive: true }); + + return () => { + document.removeEventListener("mousedown", handlePointerDownOutside); + document.removeEventListener("touchstart", handlePointerDownOutside); + }; + }, [showInput]); + + const handleClear = async () => { + try { + setSaving(true); + await updateSearchSpace({ + id: searchSpaceId, + data: { shared_memory_md: "" }, + }); + toast.success("Team memory cleared"); + } catch { + toast.error("Failed to clear team memory"); + } finally { + setSaving(false); + } + }; + + const handleEdit = async () => { + const query = editQuery.trim(); + if (!query) return; + + try { + setEditing(true); + await baseApiService.post( + `/api/v1/searchspaces/${searchSpaceId}/memory/edit`, + SearchSpaceSchema, + { body: { query } } + ); + setEditQuery(""); + setShowInput(false); + await queryClient.invalidateQueries({ + queryKey: cacheKeys.searchSpaces.detail(searchSpaceId.toString()), + }); + toast.success("Team memory updated"); + } catch { + toast.error("Failed to edit team memory"); + } finally { + setEditing(false); + } + }; + + const openInput = () => { + setShowInput(true); + requestAnimationFrame(() => textareaRef.current?.focus()); + }; + + const handleDownload = () => { + if (!memory) return; + try { + const blob = new Blob([memory], { type: "text/markdown;charset=utf-8" }); + const url = URL.createObjectURL(blob); + const a = document.createElement("a"); + a.href = url; + a.download = "team-memory.md"; + document.body.appendChild(a); + a.click(); + document.body.removeChild(a); + URL.revokeObjectURL(url); + } catch { + toast.error("Failed to download team memory"); + } + }; + + const handleCopyMarkdown = async () => { + if (!memory) return; + try { + await navigator.clipboard.writeText(memory); + toast.success("Copied to clipboard"); + } catch { + toast.error("Failed to copy team memory"); + } + }; + + const handleKeyDown = (e: React.KeyboardEvent) => { + if (e.key === "Enter" && !e.shiftKey) { + e.preventDefault(); + handleEdit(); + } + }; + + const displayMemory = memory.replace(/\(\d{4}-\d{2}-\d{2}\)\s*\[(fact|pref|instr)\]\s*/g, ""); + const charCount = memory.length; + + const getCounterColor = () => { + if (charCount > MEMORY_HARD_LIMIT) return "text-red-500"; + if (charCount > 15_000) return "text-orange-500"; + if (charCount > 10_000) return "text-yellow-500"; + return "text-muted-foreground"; + }; + + if (loading) { + return ( +
+ +
+ ); + } + + if (!memory) { + return ( +
+

+ What does SurfSense remember about your team? +

+

+ Nothing yet. SurfSense picks up on team decisions and conventions as your team chats. +

+
+ ); + } + + return ( +
+ + + +

+ SurfSense uses this shared memory to provide team-wide context across all conversations + in this search space. +

+ + + +
+
+ +
+ + {showInput ? ( +
+
+ setEditQuery(e.target.value)} + onKeyDown={handleKeyDown} + placeholder="Tell SurfSense what to remember or forget about your team" + disabled={editing} + className="flex-1 bg-transparent text-sm outline-none placeholder:text-muted-foreground/70" + /> + +
+
+ ) : ( + + )} +
+ +
+ + {charCount.toLocaleString()} / {MEMORY_HARD_LIMIT.toLocaleString()} + characters + chars + {charCount > 15_000 && charCount <= MEMORY_HARD_LIMIT && " - Approaching limit"} + {charCount > MEMORY_HARD_LIMIT && " - Exceeds limit"} + +
+ + + + + + + + + Copy as Markdown + + + + Download as Markdown + + + +
+
+
+ ); +} diff --git a/surfsense_web/components/settings/user-settings-dialog.tsx b/surfsense_web/components/settings/user-settings-dialog.tsx index e755da197..0732b63b9 100644 --- a/surfsense_web/components/settings/user-settings-dialog.tsx +++ b/surfsense_web/components/settings/user-settings-dialog.tsx @@ -1,7 +1,7 @@ "use client"; import { useAtom } from "jotai"; -import { Globe, KeyRound, Monitor, Receipt, Sparkles, User } from "lucide-react"; +import { Brain, CircleUser, Globe, KeyRound, Monitor, ReceiptText, Sparkles } from "lucide-react"; import dynamic from "next/dynamic"; import { useTranslations } from "next-intl"; import { useMemo } from "react"; @@ -51,6 +51,13 @@ const DesktopContent = dynamic( ), { ssr: false } ); +const MemoryContent = dynamic( + () => + import("@/app/dashboard/[search_space_id]/user-settings/components/MemoryContent").then( + (m) => ({ default: m.MemoryContent }) + ), + { ssr: false } +); export function UserSettingsDialog() { const t = useTranslations("userSettings"); @@ -59,7 +66,7 @@ export function UserSettingsDialog() { const navItems = useMemo( () => [ - { value: "profile", label: t("profile_nav_label"), icon: }, + { value: "profile", label: t("profile_nav_label"), icon: }, { value: "api-key", label: t("api_key_nav_label"), @@ -75,10 +82,15 @@ export function UserSettingsDialog() { label: "Community Prompts", icon: , }, + { + value: "memory", + label: "Memory", + icon: , + }, { value: "purchases", label: "Purchase History", - icon: , + icon: , }, ...(isDesktop ? [{ value: "desktop", label: "Desktop", icon: }] @@ -101,6 +113,7 @@ export function UserSettingsDialog() { {state.initialTab === "api-key" && } {state.initialTab === "prompts" && } {state.initialTab === "community-prompts" && } + {state.initialTab === "memory" && } {state.initialTab === "purchases" && } {state.initialTab === "desktop" && }
diff --git a/surfsense_web/components/settings/vision-model-manager.tsx b/surfsense_web/components/settings/vision-model-manager.tsx index 31e6655cb..57ea8c205 100644 --- a/surfsense_web/components/settings/vision-model-manager.tsx +++ b/surfsense_web/components/settings/vision-model-manager.tsx @@ -191,7 +191,7 @@ export function VisionModelManager({ searchSpaceId }: VisionModelManagerProps) { ? "model" : "models"} {" "} - available from your administrator. Use the model selector to view and select them. + available from your administrator.

diff --git a/surfsense_web/components/tool-ui/index.ts b/surfsense_web/components/tool-ui/index.ts index 6371554ae..4d885a38c 100644 --- a/surfsense_web/components/tool-ui/index.ts +++ b/surfsense_web/components/tool-ui/index.ts @@ -51,17 +51,11 @@ export { SandboxExecuteToolUI, } from "./sandbox-execute"; export { - type MemoryItem, - type RecallMemoryArgs, - RecallMemoryArgsSchema, - type RecallMemoryResult, - RecallMemoryResultSchema, - RecallMemoryToolUI, - type SaveMemoryArgs, - SaveMemoryArgsSchema, - type SaveMemoryResult, - SaveMemoryResultSchema, - SaveMemoryToolUI, + type UpdateMemoryArgs, + UpdateMemoryArgsSchema, + type UpdateMemoryResult, + UpdateMemoryResultSchema, + UpdateMemoryToolUI, } from "./user-memory"; export { GenerateVideoPresentationToolUI } from "./video-presentation"; export { type WriteTodosData, WriteTodosSchema, WriteTodosToolUI } from "./write-todos"; diff --git a/surfsense_web/components/tool-ui/user-memory.tsx b/surfsense_web/components/tool-ui/user-memory.tsx index e232bdcc7..f7c446806 100644 --- a/surfsense_web/components/tool-ui/user-memory.tsx +++ b/surfsense_web/components/tool-ui/user-memory.tsx @@ -1,100 +1,38 @@ "use client"; import type { ToolCallMessagePartProps } from "@assistant-ui/react"; -import { BrainIcon, CheckIcon, Loader2Icon, SearchIcon, XIcon } from "lucide-react"; +import { AlertTriangleIcon, BrainIcon, CheckIcon, Loader2Icon, XIcon } from "lucide-react"; import { z } from "zod"; // ============================================================================ -// Zod Schemas for save_memory tool +// Zod Schemas for update_memory tool // ============================================================================ -const SaveMemoryArgsSchema = z.object({ - content: z.string(), - category: z.string().default("fact"), +const UpdateMemoryArgsSchema = z.object({ + updated_memory: z.string(), }); -const SaveMemoryResultSchema = z.object({ +const UpdateMemoryResultSchema = z.object({ status: z.enum(["saved", "error"]), - memory_id: z.number().nullish(), - memory_text: z.string().nullish(), - category: z.string().nullish(), message: z.string().nullish(), - error: z.string().nullish(), + warning: z.string().nullish(), }); -type SaveMemoryArgs = z.infer; -type SaveMemoryResult = z.infer; +type UpdateMemoryArgs = z.infer; +type UpdateMemoryResult = z.infer; // ============================================================================ -// Zod Schemas for recall_memory tool +// Update Memory Tool UI // ============================================================================ -const RecallMemoryArgsSchema = z.object({ - query: z.string().nullish(), - category: z.string().nullish(), - top_k: z.number().default(5), -}); - -const MemoryItemSchema = z.object({ - id: z.number(), - memory_text: z.string(), - category: z.string(), - updated_at: z.string().nullish(), -}); - -const RecallMemoryResultSchema = z.object({ - status: z.enum(["success", "error"]), - count: z.number().nullish(), - memories: z.array(MemoryItemSchema).nullish(), - formatted_context: z.string().nullish(), - error: z.string().nullish(), -}); - -type RecallMemoryArgs = z.infer; -type RecallMemoryResult = z.infer; -type MemoryItem = z.infer; - -// ============================================================================ -// Category badge colors -// ============================================================================ - -const categoryColors: Record = { - preference: "bg-blue-500/10 text-blue-600 dark:text-blue-400", - fact: "bg-green-500/10 text-green-600 dark:text-green-400", - instruction: "bg-purple-500/10 text-purple-600 dark:text-purple-400", - context: "bg-orange-500/10 text-orange-600 dark:text-orange-400", -}; - -function CategoryBadge({ category }: { category: string }) { - const colorClass = categoryColors[category] || "bg-gray-500/10 text-gray-600 dark:text-gray-400"; - return ( - - {category} - - ); -} - -// ============================================================================ -// Save Memory Tool UI -// ============================================================================ - -export const SaveMemoryToolUI = ({ - args, +export const UpdateMemoryToolUI = ({ result, status, -}: ToolCallMessagePartProps) => { +}: ToolCallMessagePartProps) => { const isRunning = status.type === "running" || status.type === "requires-action"; const isComplete = status.type === "complete"; const isError = result?.status === "error"; - // Parse args safely - const parsedArgs = SaveMemoryArgsSchema.safeParse(args); - const content = parsedArgs.success ? parsedArgs.data.content : ""; - const category = parsedArgs.success ? parsedArgs.data.category : "fact"; - - // Loading state if (isRunning) { return (
@@ -102,13 +40,12 @@ export const SaveMemoryToolUI = ({
- Saving to memory... + Updating memory...
); } - // Error state if (isError) { return (
@@ -116,14 +53,13 @@ export const SaveMemoryToolUI = ({
- Failed to save memory - {result?.error &&

{result.error}

} + Failed to update memory + {result?.message &&

{result.message}

}
); } - // Success state if (isComplete && result?.status === "saved") { return (
@@ -133,138 +69,19 @@ export const SaveMemoryToolUI = ({
- Memory saved - + Memory updated
-

{content}

-
-
- ); - } - - // Default/incomplete state - show what's being saved - if (content) { - return ( -
-
- -
-
-
- Saving memory - -
-

{content}

-
-
- ); - } - - return null; -}; - -// ============================================================================ -// Recall Memory Tool UI -// ============================================================================ - -export const RecallMemoryToolUI = ({ - args, - result, - status, -}: ToolCallMessagePartProps) => { - const isRunning = status.type === "running" || status.type === "requires-action"; - const isComplete = status.type === "complete"; - const isError = result?.status === "error"; - - // Parse args safely - const parsedArgs = RecallMemoryArgsSchema.safeParse(args); - const query = parsedArgs.success ? parsedArgs.data.query : null; - - // Loading state - if (isRunning) { - return ( -
-
- -
-
- - {query ? `Searching memories for "${query}"...` : "Recalling memories..."} - -
-
- ); - } - - // Error state - if (isError) { - return ( -
-
- -
-
- Failed to recall memories - {result?.error &&

{result.error}

} -
-
- ); - } - - // Success state with memories - if (isComplete && result?.status === "success") { - const memories = result.memories || []; - const count = result.count || 0; - - if (count === 0) { - return ( -
-
- -
- No memories found -
- ); - } - - return ( -
-
- - - Recalled {count} {count === 1 ? "memory" : "memories"} - -
-
- {memories.slice(0, 5).map((memory: MemoryItem) => ( -
- - {memory.memory_text} + {result.warning && ( +
+ +

{result.warning}

- ))} - {memories.length > 5 && ( -

...and {memories.length - 5} more

)}
); } - // Default/incomplete state - if (query) { - return ( -
-
- -
- Searching memories for "{query}" -
- ); - } - return null; }; @@ -273,13 +90,8 @@ export const RecallMemoryToolUI = ({ // ============================================================================ export { - SaveMemoryArgsSchema, - SaveMemoryResultSchema, - RecallMemoryArgsSchema, - RecallMemoryResultSchema, - type SaveMemoryArgs, - type SaveMemoryResult, - type RecallMemoryArgs, - type RecallMemoryResult, - type MemoryItem, + UpdateMemoryArgsSchema, + UpdateMemoryResultSchema, + type UpdateMemoryArgs, + type UpdateMemoryResult, }; diff --git a/surfsense_web/components/ui/alert-dialog.tsx b/surfsense_web/components/ui/alert-dialog.tsx index ec0d3cbd5..21632ca18 100644 --- a/surfsense_web/components/ui/alert-dialog.tsx +++ b/surfsense_web/components/ui/alert-dialog.tsx @@ -45,7 +45,7 @@ function AlertDialogContent({ diff --git a/surfsense_web/components/ui/heading-node.tsx b/surfsense_web/components/ui/heading-node.tsx index 11bdcf0af..047471811 100644 --- a/surfsense_web/components/ui/heading-node.tsx +++ b/surfsense_web/components/ui/heading-node.tsx @@ -6,7 +6,7 @@ import type { PlateElementProps } from "platejs/react"; import { PlateElement } from "platejs/react"; import * as React from "react"; -const headingVariants = cva("relative mb-1", { +const headingVariants = cva("relative mb-1 first:mt-0", { variants: { variant: { h1: "mt-[1.6em] pb-1 font-bold font-heading text-4xl", diff --git a/surfsense_web/contracts/enums/toolIcons.tsx b/surfsense_web/contracts/enums/toolIcons.tsx index 8e5d1e452..fd12aaa9c 100644 --- a/surfsense_web/contracts/enums/toolIcons.tsx +++ b/surfsense_web/contracts/enums/toolIcons.tsx @@ -19,8 +19,7 @@ const TOOL_ICONS: Record = { scrape_webpage: ScanLine, web_search: Globe, search_surfsense_docs: BookOpen, - save_memory: Brain, - recall_memory: Brain, + update_memory: Brain, }; export function getToolIcon(name: string): LucideIcon { diff --git a/surfsense_web/contracts/types/search-space.types.ts b/surfsense_web/contracts/types/search-space.types.ts index 8a0a2fb4c..7b4fefb62 100644 --- a/surfsense_web/contracts/types/search-space.types.ts +++ b/surfsense_web/contracts/types/search-space.types.ts @@ -9,6 +9,7 @@ export const searchSpace = z.object({ user_id: z.string(), citations_enabled: z.boolean(), qna_custom_instructions: z.string().nullable(), + shared_memory_md: z.string().nullable().optional(), member_count: z.number(), is_owner: z.boolean(), }); @@ -54,6 +55,7 @@ export const updateSearchSpaceRequest = z.object({ description: true, citations_enabled: true, qna_custom_instructions: true, + shared_memory_md: true, }) .partial(), }); diff --git a/surfsense_web/lib/desktop-download-utils.ts b/surfsense_web/lib/desktop-download-utils.ts new file mode 100644 index 000000000..d4e2d4e68 --- /dev/null +++ b/surfsense_web/lib/desktop-download-utils.ts @@ -0,0 +1,108 @@ +import { useEffect, useMemo, useState } from "react"; + +export type OSInfo = { + os: "macOS" | "Windows" | "Linux"; + arch: "arm64" | "x64"; +}; + +export function useUserOS(): OSInfo { + const [info, setInfo] = useState({ os: "macOS", arch: "arm64" }); + useEffect(() => { + const ua = navigator.userAgent; + let os: OSInfo["os"] = "macOS"; + let arch: OSInfo["arch"] = "x64"; + + if (/Windows/i.test(ua)) { + os = "Windows"; + arch = "x64"; + } else if (/Linux/i.test(ua)) { + os = "Linux"; + arch = "x64"; + } else { + os = "macOS"; + arch = /Mac/.test(ua) && !/Intel/.test(ua) ? "arm64" : "arm64"; + } + + const uaData = (navigator as Navigator & { userAgentData?: { architecture?: string } }) + .userAgentData; + if (uaData?.architecture === "arm") arch = "arm64"; + else if (uaData?.architecture === "x86") arch = "x64"; + + setInfo({ os, arch }); + }, []); + return info; +} + +export interface ReleaseAsset { + name: string; + url: string; +} + +export function useLatestRelease() { + const [assets, setAssets] = useState([]); + + useEffect(() => { + const controller = new AbortController(); + fetch("https://api.github.com/repos/MODSetter/SurfSense/releases/latest", { + signal: controller.signal, + }) + .then((r) => r.json()) + .then((data) => { + if (data?.assets) { + setAssets( + data.assets + .filter((a: { name: string }) => /\.(exe|dmg|AppImage|deb)$/.test(a.name)) + .map((a: { name: string; browser_download_url: string }) => ({ + name: a.name, + url: a.browser_download_url, + })) + ); + } + }) + .catch(() => {}); + return () => controller.abort(); + }, []); + + return assets; +} + +export const ASSET_LABELS: Record = { + ".exe": "Windows (exe)", + "-arm64.dmg": "macOS Apple Silicon (dmg)", + "-x64.dmg": "macOS Intel (dmg)", + "-arm64.zip": "macOS Apple Silicon (zip)", + "-x64.zip": "macOS Intel (zip)", + ".AppImage": "Linux (AppImage)", + ".deb": "Linux (deb)", +}; + +export function getAssetLabel(name: string): string { + for (const [suffix, label] of Object.entries(ASSET_LABELS)) { + if (name.endsWith(suffix)) return label; + } + return name; +} + +export const GITHUB_RELEASES_URL = "https://github.com/MODSetter/SurfSense/releases/latest"; + +export function usePrimaryDownload() { + const { os, arch } = useUserOS(); + const assets = useLatestRelease(); + + const { primary, alternatives } = useMemo(() => { + if (assets.length === 0) return { primary: null, alternatives: [] }; + + const matchers: Record boolean> = { + Windows: (n) => n.endsWith(".exe"), + macOS: (n) => n.endsWith(`-${arch}.dmg`), + Linux: (n) => n.endsWith(".AppImage"), + }; + + const match = matchers[os]; + const primary = assets.find((a) => match(a.name)) ?? null; + const alternatives = assets.filter((a) => a !== primary); + return { primary, alternatives }; + }, [assets, os, arch]); + + return { os, arch, assets, primary, alternatives }; +} diff --git a/surfsense_web/messages/en.json b/surfsense_web/messages/en.json index e93e9ceaa..4672fc5d4 100644 --- a/surfsense_web/messages/en.json +++ b/surfsense_web/messages/en.json @@ -693,6 +693,7 @@ "learn_more": "Learn more", "documentation": "Documentation", "github": "GitHub", + "download_for_os": "Download for {os}", "inbox": "Inbox", "search_inbox": "Search inbox", "mark_all_read": "Mark all as read", diff --git a/surfsense_web/messages/es.json b/surfsense_web/messages/es.json index fa620e271..f41aba2cc 100644 --- a/surfsense_web/messages/es.json +++ b/surfsense_web/messages/es.json @@ -693,6 +693,7 @@ "learn_more": "Más información", "documentation": "Documentación", "github": "GitHub", + "download_for_os": "Descargar para {os}", "inbox": "Bandeja de entrada", "search_inbox": "Buscar en bandeja de entrada", "mark_all_read": "Marcar todo como leído", diff --git a/surfsense_web/messages/hi.json b/surfsense_web/messages/hi.json index faeb4cb94..514acc06f 100644 --- a/surfsense_web/messages/hi.json +++ b/surfsense_web/messages/hi.json @@ -693,6 +693,7 @@ "learn_more": "और जानें", "documentation": "दस्तावेज़ीकरण", "github": "GitHub", + "download_for_os": "{os} के लिए डाउनलोड करें", "inbox": "इनबॉक्स", "search_inbox": "इनबॉक्स में खोजें", "mark_all_read": "सभी पढ़ा हुआ चिह्नित करें", diff --git a/surfsense_web/messages/pt.json b/surfsense_web/messages/pt.json index 0bed7c6cc..421033810 100644 --- a/surfsense_web/messages/pt.json +++ b/surfsense_web/messages/pt.json @@ -693,6 +693,7 @@ "learn_more": "Saiba mais", "documentation": "Documentação", "github": "GitHub", + "download_for_os": "Baixar para {os}", "inbox": "Caixa de entrada", "search_inbox": "Pesquisar caixa de entrada", "mark_all_read": "Marcar tudo como lido", diff --git a/surfsense_web/messages/zh.json b/surfsense_web/messages/zh.json index 0d4f7e1c9..1e931726b 100644 --- a/surfsense_web/messages/zh.json +++ b/surfsense_web/messages/zh.json @@ -677,6 +677,7 @@ "learn_more": "了解更多", "documentation": "文档", "github": "GitHub", + "download_for_os": "下载 {os} 版本", "inbox": "收件箱", "search_inbox": "搜索收件箱", "mark_all_read": "全部标记为已读",