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The pull-based KB design (on-demand search_knowledge_base tool + pre-injected workspace tree) fully replaced the old eager retrieval path. Remove its last remnants: - Delete KnowledgePriorityMiddleware (knowledge_search.py) and its tests. - Drop the kb_priority state field + reducer default; trim KbContextProjectionMiddleware to project only workspace_tree_text. - Remove the now-dead feature flags enable_kb_priority_preinjection and enable_kb_planner_runnable across backend (flags, route schema, tests, env examples) and frontend (settings toggle, zod schema). - Scrub <priority_documents> and stale KnowledgePriorityMiddleware references from prompts, docstrings, and the ADR. No functional change: nothing wrote kb_priority and neither flag gated live behavior after the cutover. Full backend suite green (pre-existing unrelated failures aside).
70 lines
3 KiB
Python
70 lines
3 KiB
Python
"""
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Context schema definitions for SurfSense agents.
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This module defines the per-invocation context object passed to the SurfSense
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deep agent via ``agent.astream_events(..., context=ctx)`` (LangGraph >= 0.6).
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The agent's compiled graph is the same across invocations (and cached by
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``agent_cache``), so anything that varies per turn — the user mentions a
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specific document, the front-end issues a unique ``request_id``, etc. —
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MUST live on this context object instead of being captured into a
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middleware ``__init__`` closure. Middlewares read fields back via
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``runtime.context.<field>``; tools read them via ``runtime.context``.
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This object is read by the ``search_knowledge_base`` tool (for
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``mentioned_document_ids``) and any middleware that needs per-request
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state without invalidating the compiled-agent cache.
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"""
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from __future__ import annotations
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from dataclasses import dataclass, field
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from typing import TypedDict
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class FileOperationContractState(TypedDict):
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intent: str
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confidence: float
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suggested_path: str
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timestamp: str
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turn_id: str
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@dataclass
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class SurfSenseContextSchema:
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"""
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Per-invocation context for the SurfSense deep agent.
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Defaults are chosen so the dataclass can be safely default-constructed
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(LangGraph's ``Runtime.context`` itself defaults to ``None`` if no
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context is supplied — see ``langgraph.runtime.Runtime``). All fields
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are optional; consumers must None-check before reading.
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Phase 1.5 fields:
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search_space_id: Search space the request is scoped to.
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mentioned_document_ids: KB documents the user @-mentioned this turn.
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Read by the ``search_knowledge_base`` tool to pin these docs
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into the retrieval scope. Stays out of the compiled-agent cache
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key — that's the whole point of putting it here.
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mentioned_folder_ids: KB folders the user @-mentioned this turn
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(cloud filesystem mode). Pinned into the ``search_knowledge_base``
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retrieval scope so matches from those folders are prioritised.
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file_operation_contract: One-shot file operation contract for the
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upcoming turn (reserved; not currently populated).
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turn_id / request_id: Correlation IDs surfaced by the streaming
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task; populated for telemetry.
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Phase 2 will extend with: thread_id, user_id, visibility,
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filesystem_mode, anon_session_id, available_connectors,
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available_document_types, created_by_id (everything currently captured
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by middleware ``__init__`` closures).
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"""
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search_space_id: int | None = None
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mentioned_document_ids: list[int] = field(default_factory=list)
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mentioned_folder_ids: list[int] = field(default_factory=list)
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mentioned_connector_ids: list[int] = field(default_factory=list)
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mentioned_connectors: list[dict[str, object]] = field(default_factory=list)
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file_operation_contract: FileOperationContractState | None = None
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turn_id: str | None = None
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request_id: str | None = None
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