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.
