Migration 144 -> 143. Matches the SQLAlchemy models added in commit 7
and the v1 data model in automation-design-plan.md §9.
Up:
- CREATE TYPE automation_status / automation_trigger_type /
automation_run_status (PostgreSQL ENUMs created first because the
tables reference them).
- CREATE TABLE automations with FK to searchspaces (CASCADE) and
user (SET NULL); five indexes matching the SQLAlchemy model.
- CREATE TABLE automation_triggers with FK to automations
(CASCADE); four indexes.
- CREATE TABLE automation_runs with FK to automations (CASCADE) and
automation_triggers (SET NULL — null trigger_id == manual via UI);
four indexes.
Down: drops every index, table, and ENUM in reverse-dependency order
so the migration is reversible without ON DELETE side effects.
Verified: `alembic history` resolves 143 -> 144 (head) cleanly.
domain_events (Phase 3) and mcp_connections / mcp_tools (Phase 4) ship
in their own migrations when the consuming feature lands; this
migration only covers the three v1 tables.
Three enums (one file each) plus three models (one file each), all
under app/automations/persistence/. The module imports from app.db
only (Base/BaseModel/TimestampMixin and FK targets searchspaces.id /
user.id); no business-logic imports.
Enums:
- AutomationStatus: active | paused | archived
- RunStatus: pending | running | succeeded | failed | cancelled
| timed_out
- TriggerType: schedule | manual (Phase-2/3 add webhook | event)
Models:
- Automation: search_space-scoped, created_by_user_id (SET NULL),
name + description, status enum, definition JSONB, version int,
updated_at with onupdate.
- AutomationTrigger: FK → automations (CASCADE), type enum, config
JSONB, enabled bool, last_fired_at. Webhook secret_hash is omitted
until Phase 2.
- AutomationRun: FK → automations (CASCADE), nullable trigger_id
(SET NULL — null = manual via UI), status enum,
definition_snapshot for immutable history, trigger_payload /
resolved_inputs / step_results / output / artifacts / error JSONB
columns, started_at / finished_at timestamps, agent_session_id for
linking to the LangGraph trace. cost_usd column omitted until at
least one v1 capability records token-level cost.
Verified: Base.metadata exposes all three table names; columns and
enums introspect as documented; no linter errors.
Create app/automations/ with the SRP-per-file / grouped-folders layout
that mirrors app/agents/multi_agent_chat/. Twelve __init__.py files,
each a thin re-export with a single-line docstring describing the
subpackage's role, no exports yet (filled in subsequent commits).
Tree:
app/automations/
├── persistence/
│ ├── enums/ (status / type enums; one per file)
│ └── models/ (SQLAlchemy tables; one per file)
├── schemas/
│ ├── definition/ (the JSON envelope, broken by concern)
│ ├── triggers/ (per-trigger config schemas)
│ └── actions/ (per-action config schemas)
└── registries/
├── capabilities/ (types.py + store.py)
├── actions/ (types.py + store.py)
└── triggers/ (types.py + store.py)
The persistence/ folder is named to avoid surfsense_backend/.gitignore's
data/ ignore rule, which silently masked the original data/ name and
its contents from version control.
Isolation invariant: the module imports only from app.db (foundational
Base + FK targets, unavoidable) and stdlib / SQLAlchemy / Pydantic.
No imports from app.agents.*, app.services.*, app.tasks.*, app.routes.*
or any other business-logic module. Confirmed importable with no side
effects.
Adds 34 tests under tests/unit/tasks/chat/streaming/ that cover the
new flows tree against the legacy stream_new_chat.py module to gate
the upcoming cutover. Coverage:
* Public entry points: stream_new_chat and stream_resume_chat are
async generator functions whose parameter signatures (name, kind,
annotation, default) match the legacy versions one-for-one. Uses a
normalized-annotation comparison so PEP-563 vs eager-annotation
representation differences are tolerated.
* Extracted helpers: image-capability gate, runtime-context builders
for new-chat and resume-chat, LLM-bundle dispatcher, premium-quota
needs check + reservation dataclass, rate-limit recovery truth
table, persistence-spawn registration/self-unregistration, await
helpers.
* SSE frame iterators: iter_initial_frames + iter_final_frames emit
the canonical sequence; iter_token_usage_frame skips on None.
* Initial thinking step: 4 parametrized branches (text, image-only,
empty, mentioned-docs), long-query truncation, many-docs collapse.
These tests are scaffolding for the cutover and will be removed once
the legacy module is deleted.
Slim composition root for the resume-chat streaming flow. Mirrors the
new_chat orchestrator but specialized for resumed turns:
* no fresh user turn, no title generation, no image-capability gate
* persists a fresh assistant shell for the resumed turn
* applies build_resume_routing to dispatch user decisions to the
correct paused subagent before invoking the agent
* shares the same stream_loop + flow-local _recover closure for in-
stream provider rate-limit recovery
Also lands flows/__init__.py, which becomes the public chat-flow API:
from app.tasks.chat.streaming.flows import stream_new_chat, stream_resume_chat
Existing wiring (routes, contract test) still imports from the legacy
app.tasks.chat.stream_new_chat module. Cutover is the next phase.
Three focused modules used by the upcoming resume-chat orchestrator:
* runtime_context: build_resume_chat_runtime_context assembles the
SurfSenseContextSchema for a resume turn (handles empty mention
lists, since resume requests do not carry fresh @-mentions).
* assistant_shell: persist_resume_assistant_shell writes a fresh
assistant row for the resumed turn so the post-stream finalize
has a target.
* resume_routing: build_resume_routing collects the pending
interrupts across paused subagents and slices the flat list of
ResumeDecision[] into the correct (thread, subagent) buckets so
LangGraph routes each decision back to the right paused tool call.
Add-only; no orchestrator yet (next commit).
Slim composition root for the new-chat streaming flow. Sequences:
1. validate inputs and load the LLM bundle (negative id => YAML)
2. open the OTEL chat_request span; set agent_mode tag
3. spawn the four pre-stream DB writes (set-ai-responding, persist
user turn, persist assistant shell, first-assistant probe)
4. reserve premium quota (with free-fallback retry on denial)
5. build connector + checkpointer + agent + input_state
6. emit first frames (message-start, step-start, initial thinking step)
7. spawn the background title generator
8. run the shared stream_loop with a flow-local _recover closure that
reroutes to the next auto-pin config on provider 429s
9. finalize: emit terminal title/token frames, shielded assistant
finalize, release-or-finalize premium quota, close session, GC,
record OTEL outcome
Public entry-point flows/new_chat/__init__ re-exports stream_new_chat.
Existing wiring (routes, tests) still imports the legacy function from
app.tasks.chat.stream_new_chat. Cutover is a later commit.
Seven focused modules that the upcoming new_chat orchestrator
composes:
* auto_pin: resolve_initial_auto_pin selects the initial config (with
vision-capable filtering and error classification).
* llm_capability: check_image_input_capability blocks routing an
image-bearing turn to a known text-only model.
* runtime_context: build_new_chat_runtime_context assembles the
SurfSenseContextSchema for a new-chat turn.
* persistence_spawn: spawn_set_ai_responding_bg, spawn_persist_user_task,
spawn_persist_assistant_shell_task, and await_persist_task background
the four pre-stream DB writes so they overlap with agent build.
* initial_thinking_step: build_initial_thinking_step +
iter_initial_thinking_step_frame produce the very first thinking-1 SSE
step ("Understanding your request" / "Analyzing referenced content").
* title_gen: spawn_title_task + maybe_emit_title_update +
await_pending_title_update background the thread-title generator and
interleave its update into the stream when ready.
* input_state: build_new_chat_input_state assembles the LangGraph
input_state (history bootstrap, mentions resolution, context blocks,
human-message construction). The heavy one.
Add-only; no orchestrator yet (next commit).
Extracts finalize_assistant_message: the post-stream server-side write
of the final assistant message (with content parts + token usage)
guarded by asyncio.shield + shielded_async_session so a client
disconnect cannot abort the persist.
Add-only; legacy stream_new_chat.py keeps its inline finalize block
until cutover.
Two cooperating modules that wrap stream_agent_events with in-stream
recovery from provider 429s:
* rate_limit_recovery: can_recover_provider_rate_limit truth-table
guard, reroute_to_next_auto_pin (selects the next eligible auto-pin
config and reloads the LLM bundle), log_rate_limit_recovered.
* stream_loop: run_stream_loop drives stream_agent_events in a
while-True loop, delegating recovery to a flow-supplied RecoverFn
callback so new_chat and resume_chat can share the same loop while
keeping their own nonlocal state.
Add-only; not yet wired into any orchestrator.
Extracts handle_terminal_exception: the shared except-branch behavior for
the chat orchestrators. Classifies the raised exception, logs the
structured chat_stream error event, and emits the terminal-error SSE
frame + done sentinel via the streaming service.
Add-only; nothing imports it yet.
Centralizes the premium-credits lifecycle for chat turns:
* needs_premium_quota: gate check (premium user + non-fallback config).
* PremiumReservation: dataclass capturing reservation state + token totals.
* reserve_premium / finalize_premium / release_premium: idempotent
reservation, commit, and rollback used by the orchestrators.
Add-only; legacy stream_new_chat.py keeps its inline quota handling
until cutover.
Six small, single-purpose modules shared by the upcoming new_chat and
resume_chat orchestrators:
* llm_bundle: dispatches negative config_id to the YAML loader and
non-negative config_id to the DB loader, returning (llm, AgentConfig).
* pre_stream_setup: builds the connector service, resolves the
Firecrawl API key, and returns the chat checkpointer.
* first_frames: iter_initial_frames + iter_final_frames emit the canonical
message-start / step-start / idle / finish / done SSE envelope.
* finalize_emit: iter_token_usage_frame emits the per-turn usage frame
from a TokenAccumulator summary.
* finally_cleanup: close_session_and_clear_ai_responding and run_gc_pass
centralize the finally-block bookkeeping.
* span: open_chat_request_span / set_agent_mode / close_chat_request_span /
record_outcome_attrs wrap the OpenTelemetry chat_request span.
Add-only; these are not yet wired into stream_new_chat.py.
Extracts the inner agent-streaming driver previously inlined as
_stream_agent_events in stream_new_chat.py.
stream_agent_events drives graph_stream.event_stream.stream_output and,
after the agent finishes, performs the post-stream safety-net work:
* commit any pending content the agent never explicitly finished
* evaluate file-operation contract outcomes and emit the appropriate
contract verdict for desktop_local_folder turns
This unit is what flows/shared/stream_loop.py wraps in the rate-limit
recovery while-loop. Add-only; no existing wiring uses it yet.
Extracts the agent-construction wrapper that the chat streamers call to
materialize the LangGraph agent for a given thread. Centralizes how we
pass the agent factory plus checkpointer, runtime context, and the
in-memory content builder.
Add-only; pre-existing inline equivalent in stream_new_chat.py stays
until cutover.
Extracts the desktop_local_folder file-operation contract helpers:
* contract_enforcement_active: gates the contract on filesystem mode.
* evaluate_file_contract_outcome: scores tool outputs as success/no-op.
* log_file_contract: structured logging of contract verdicts.
This is the unit responsible for catching agents that claim to have
written/edited a file without actually invoking the filesystem tool.
Add-only; stream_new_chat.py keeps its inline duplicates until cutover.
Extracts two pure context helpers used during input-state assembly:
* mentioned_docs.format_mentioned_surfsense_docs_as_context: renders the
user's @-mentioned SurfSense docs into the LLM context block.
* deepagents_todos.extract_todos_from_deepagents: pulls the in-progress
todo list from a deep-agents state snapshot for the title generator.
Add-only; existing call sites in stream_new_chat.py remain untouched
until cutover.
Foundation layer for the parallel refactor of stream_new_chat.py.
Extracts the StreamResult dataclass (tracks per-turn streaming state)
and a small set of shared utilities (resume_step_prefix, safe_float).
Add-only; no existing code imports from this package yet. Existing
stream_new_chat.py keeps its inline equivalents until cutover.
The citations fix (cacb27e0) added a "Chunk citations in your prose"
section to system_prompt_desktop.md telling the KB subagent to always
leave `evidence.chunk_ids` null and emit no `[citation:...]` markers in
desktop mode, but left the pre-existing line declaring that
`chunk_ids` apply to `<priority_documents>` hits. The two rules
contradicted each other; the model picked one per turn.
Strike the stale conditional clause and point at the dedicated section
as the single source of truth. Matches the parallel line in
system_prompt_cloud.md and the already-consistent
system_prompt_readonly_desktop.md.
Resolves: surfsense_backend/app/agents/new_chat/middleware/memory_injection.py
- Took both imports: upstream moved MEMORY_HARD_LIMIT/SOFT_LIMIT to
app.services.memory; kept our perf-logger import for timing.
Pulls in upstream changes:
- Memory document feature (services/memory refactor, removal of
app.agents.new_chat.memory_extraction and background extraction in
stream_new_chat — agent now drives memory via update_memory tool).
- BACKEND_URL env refactor across web tool-ui/editor/chat/dashboard/lib.
- GitHub Actions backend test workflow + pre-commit biome bump.
- Token-display polish in MessageInfoDropdown; save_memory no-update
sentinel.
Verified: 1723 unit tests pass, ruff clean. No semantic regression in
stream_new_chat (their memory-extraction deletion and our preflight
removal touch different functions).
