feat(pilot): Phase 3 — Suggested fix tracking + Resolve preview with state_version cache

Adds the AI-proposed resolution path and the inline preview of the
markdown that will be posted to the customer ticket on Resolve. The
preview is keyed on (session_id, ai_sessions.state_version) so back-to-
back fetches against unchanged state hit an in-process cache instead
of paying for a Sonnet call.

Backend:
- preview_cache: in-process LRU keyed on (kind, session_id, state_version).
  No TTL — state_version is the source of truth. Soft-cap 5000 entries.
- unified_chat_service: [SUGGEST_FIX] parser (last-block-wins, JSON
  payload, confidence clamped 0-100), supersession persistence (sets
  superseded_at on prior active row), atomic state_version bump.
- ResolutionNoteGeneratorService: pulls session, facts, active fix, and
  redacted script_generations into a structured input bundle for Sonnet;
  produces the four-section markdown (Problem / What we confirmed /
  Root cause / Resolution). Sensitive script parameters redacted via
  ScriptTemplateEngine.redact_sensitive driven by the template's
  parameters_schema.
- /api/v1/ai-sessions/{id}/suggested-fixes/active — 200 with the active
  fix or 404.
- /api/v1/ai-sessions/{id}/suggested-fixes/{fix_id}/decision — records
  one_off / draft_template / build_template / dismissed; dismiss
  supersedes; bumps state_version. 409 on dismissing an already-
  superseded fix.
- /api/v1/ai-sessions/{id}/resolution-note/preview — generates or returns
  cached markdown; from_cache flag in payload signals cache hit.
- scripts.py POST /generate now bumps state_version on the linked
  ai_session_id when present (third source of preview-cache invalidation
  per Section 5.5).
- ASSISTANT_SYSTEM_PROMPT documents [SUGGEST_FIX] (when to/not to emit,
  format, supersession semantics).
- 12 tests covering the parser (well-formed, last-wins, malformed,
  confidence clamping), supersession + state_version invariant, all
  decision branches, preview cache hit-on-no-change + miss-after-write.

Frontend:
- src/components/pilot/sections/SuggestedFix.tsx — amber-accented card
  with confidence badge; dismiss action wired to the decision endpoint.
- src/components/pilot/ResolutionNotePreview.tsx — popover with refresh,
  loading state, cached/fresh indicator, ticket-ref display.
- src/api/sessionSuggestedFixes.ts — typed client; getActive normalizes
  404 to null so callers don't have to special-case.
- TaskLane gains suggestedFixSlot + bottomSlot props (rendered after
  Diagnostic Checks; bottomSlot anchors the Resolve action).
- AssistantChatPage: refreshSessionDerived helper batches fact + fix
  refresh; fact mutations and chat sends both schedule a 500ms-debounced
  preview refresh per the Section 5.5 spec.

Verified end-to-end against the dev stack with a real Sonnet call:
- /active 404 → fact create → preview generates four-section markdown
  grounded only in provided facts → second preview call hits cache
  (from_cache=true, no LLM call) → fact write 2 → cache miss, regenerates.

Co-Authored-By: Claude Opus 4.7 (1M context) <noreply@anthropic.com>
This commit is contained in:
2026-04-21 21:45:52 -04:00
parent 625dba7548
commit 66e592096c
16 changed files with 1617 additions and 22 deletions

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@@ -159,6 +159,44 @@ words, no period.
the engineer's message confirms the fact, do not emit a [PROMOTE]. Hallucinated \
facts get posted to customer tickets and will erode trust in the system.
## Proposing a fix with [SUGGEST_FIX]
When you have a concrete proposed resolution path with reasonable confidence, \
emit a `[SUGGEST_FIX]` marker. This populates the "Suggested fix" card the \
engineer can act on (run a script, build a template, etc.). A new \
[SUGGEST_FIX] supersedes any prior suggested fix on the session — emit a fresh \
one whenever your top hypothesis changes meaningfully.
**When to emit [SUGGEST_FIX]:**
- You have a concrete resolution path (not just "investigate further")
- Confidence is at least ~50% — below that, keep diagnosing
- Either a known Script Library template applies, OR you can draft a script \
that resolves the issue end-to-end
**When NOT to emit [SUGGEST_FIX]:**
- You're still narrowing causes and the fix depends on the next answer
- The "fix" is just running another diagnostic — that goes in [ACTIONS]
- Two paths are equally likely — fork or ask first, suggest later
**[SUGGEST_FIX] marker format (one block per response, last one wins):**
[SUGGEST_FIX]
{"title": "Clear cached credentials + rebuild Outlook profile", "description": "Stale cached credential in Credential Manager is holding the pre-reset token. Clearing it and recreating the profile completes the password change.", "confidence": 94, "script_template_slug": "clear-outlook-credentials"}
[/SUGGEST_FIX]
- `title`: short imperative summary, ≤ 200 chars
- `description`: one short paragraph explaining the root cause and the fix
- `confidence`: integer 0-100 (what you'd bet this resolves the ticket)
- `script_template_slug`: slug of an existing Script Library template if one \
applies; OMIT or set null otherwise
- `ai_drafted_script`: full script body if no template matches (only when \
`script_template_slug` is null/omitted)
- `ai_drafted_parameters`: optional JSON object of suggested parameter values \
for the drafted script
The marker is stripped from display — the engineer sees the suggested fix as \
an interactive card with confidence badge, not raw JSON.
## Using the Team's Flow Library
Your team has built troubleshooting flows in ResolutionFlow. When relevant flows \
appear in the context below, reference them by name so the engineer can launch them \
@@ -232,6 +270,9 @@ in your markers unless you are ≥75% confident that information is no longer re
[PROMOTE] markers are OPTIONAL and IN ADDITION to the required ones — emit them only \
when the engineer's most recent message confirmed something worth recording, and copy \
the originating item's `id` into `source_ref` verbatim.
[SUGGEST_FIX] is OPTIONAL — emit one at most per response, only when you have a \
concrete proposed resolution at ~50%+ confidence. A new [SUGGEST_FIX] supersedes \
any prior suggested fix.
"""

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@@ -0,0 +1,52 @@
"""In-process preview cache for FlowPilot resolution-note / escalation-package previews.
Phase 3 implementation per FLOWPILOT-MIGRATION.md Section 5.5:
- Cache key: `(kind, session_id, state_version)` — no TTL needed, state_version
is the source of truth.
- Invalidation: any write to session_facts, session_suggested_fixes, or
script_generations bumps `ai_sessions.state_version`. Old entries simply
stop being looked up and leak harmlessly until process restart.
- Storage: plain dict, single-process. When Session Sharing brings Redis,
swap the storage without changing the call sites.
Bound: best-effort soft cap of 5000 entries. When exceeded we drop the
oldest insertion. Not a TTL — at current scale, the cap is more about
resident-memory hygiene than correctness.
"""
from __future__ import annotations
from collections import OrderedDict
from typing import Any
from uuid import UUID
_MAX_ENTRIES = 5000
class _PreviewCache:
def __init__(self) -> None:
self._store: OrderedDict[tuple[str, UUID, int], Any] = OrderedDict()
def get(self, kind: str, session_id: UUID, state_version: int) -> Any | None:
key = (kind, session_id, state_version)
if key not in self._store:
return None
# Touch on access so LRU eviction is meaningful.
self._store.move_to_end(key)
return self._store[key]
def set(self, kind: str, session_id: UUID, state_version: int, value: Any) -> None:
key = (kind, session_id, state_version)
self._store[key] = value
self._store.move_to_end(key)
# Evict oldest if over cap. OrderedDict.popitem(last=False) is O(1).
while len(self._store) > _MAX_ENTRIES:
self._store.popitem(last=False)
def invalidate_session(self, session_id: UUID) -> None:
"""Drop all entries for a session — used when the session is deleted."""
keys = [k for k in self._store if k[1] == session_id]
for k in keys:
del self._store[k]
preview_cache = _PreviewCache()

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@@ -0,0 +1,320 @@
"""ResolutionNoteGeneratorService — drafts the structured Resolve note for a session.
Produces the four-section markdown that ships to the customer ticket (per
FLOWPILOT-MIGRATION.md Section 6.2):
## Problem
## What we confirmed
## Root cause
## Resolution
The output is the *draft* — engineers review and edit in the preview popover
before clicking Confirm & post (Phase 4). Caching is keyed on
`(session_id, ai_sessions.state_version)` per Section 5.5; the cache lives in
`preview_cache` and invalidates automatically when any fact / suggested fix /
script generation bumps the session's state_version.
Model: Sonnet (`resolution_note` action tier — quality matters because the
output is customer-facing). MCP intentionally disabled — this is a summary
of existing state, not a research task.
Sensitive parameter values in script_generations are redacted using the
script template's `parameters_schema` (`field_type: "password"`). Existing
ScriptTemplateEngine.redact_sensitive handles the substitution.
"""
from __future__ import annotations
import logging
from typing import Any
from uuid import UUID
from sqlalchemy import select
from sqlalchemy.ext.asyncio import AsyncSession
from app.core.ai_provider import get_ai_provider
from app.core.config import settings
from app.models.ai_session import AISession
from app.models.script_template import ScriptGeneration, ScriptTemplate
from app.models.session_fact import SessionFact
from app.models.session_suggested_fix import SessionSuggestedFix
from app.services.preview_cache import preview_cache
from app.services.script_template_engine import ScriptTemplateEngine
logger = logging.getLogger(__name__)
_RESOLUTION_NOTE_SYSTEM_PROMPT = """\
You produce structured resolution notes for an MSP troubleshooting platform. \
The notes are posted as ticket notes in the customer's PSA, so they must read \
like a competent senior engineer summarized the work — not like an AI \
narration. Your output goes in front of paying customers.
Output exactly this markdown structure, no preamble, no closing remarks, no \
extra headings:
## Problem
<one short paragraph stating the issue the engineer worked on, derived from the \
session's intake/title and the incident header. Past tense. No "user reported" \
hedging — state the problem directly.>
## What we confirmed
<bulleted list of facts from the "What we know" section, each one a short line. \
Group similar facts together; do not invent connecting prose. If there are no \
facts, write "Nothing was confirmed." and skip to Root cause.>
## Root cause
<one short paragraph naming the root cause based on the active suggested fix \
and confirmed facts. If the suggested fix is low-confidence (<60%) or absent, \
say "Root cause not definitively isolated." and explain what is suspected based \
on facts.>
## Resolution
<one short paragraph describing the resolution applied. If a script ran during \
the session, mention it (e.g. "Cleared cached credentials via the \
clear-outlook-credentials script."). If no resolution has been performed yet, \
write "Resolution not yet applied — fix proposed: <fix title>." Pull verbatim \
script names and template references when available.>
Strict rules:
- Use ONLY the facts and state I provide. Never invent specifics that are not \
in the input.
- Do not include placeholder text like "TBD", "TODO", or empty bullets.
- Do not include the engineer's name, the AI's name, internal session IDs, or \
the session's chat transcript.
- Markdown headings exactly as shown (## level), no bolding the headings.
- No trailing whitespace, no double-blank lines, no horizontal rules.
"""
class ResolutionNoteGeneratorService:
"""Generates and caches the four-section Resolve note markdown."""
KIND = "resolution_note"
def __init__(self, db: AsyncSession) -> None:
self.db = db
async def generate_or_get_cached(
self, session_id: UUID, *, force: bool = False,
) -> dict[str, Any]:
"""Return the preview for the session.
Reads `(KIND, session_id, state_version)` from the in-process cache;
on miss, generates fresh markdown and stores under the same key.
`force=True` bypasses the cache and refreshes the cached entry.
Returns `{"markdown": str, "target_ticket_ref": str | None,
"state_version": int, "from_cache": bool}`.
"""
session = await self._load_session(session_id)
cached = preview_cache.get(self.KIND, session.id, session.state_version) if not force else None
if cached is not None:
return {**cached, "from_cache": True}
markdown = await self._render(session)
target = self._target_ticket_ref(session)
payload = {
"markdown": markdown,
"target_ticket_ref": target,
"state_version": session.state_version,
}
preview_cache.set(self.KIND, session.id, session.state_version, payload)
return {**payload, "from_cache": False}
# ── Internals ─────────────────────────────────────────────────────────
async def _load_session(self, session_id: UUID) -> AISession:
result = await self.db.execute(
select(AISession).where(AISession.id == session_id)
)
session = result.scalar_one_or_none()
if session is None:
raise ValueError(f"Session {session_id} not found")
return session
async def _render(self, session: AISession) -> str:
"""Build the prompt input bundle, call the model, return markdown."""
facts = await self._load_facts(session.id)
active_fix = await self._load_active_fix(session.id)
gens = await self._load_redacted_generations(session.id)
bundle = self._build_input_bundle(session, facts, active_fix, gens)
model = settings.get_model_for_action("resolution_note")
provider = get_ai_provider(model=model)
# Cache the system prompt — identical across every preview call for
# every session. Per-session bundle is in the user message, uncached.
system_blocks: list[dict[str, Any]] = [
{
"type": "text",
"text": _RESOLUTION_NOTE_SYSTEM_PROMPT,
"cache_control": {"type": "ephemeral"},
# cacheable: identical across every resolution-note preview call
},
]
try:
text, _in, _out = await provider.generate_text(
system_prompt=system_blocks,
messages=[{"role": "user", "content": bundle}],
max_tokens=1200,
)
except Exception:
logger.exception("Resolution note generation failed for session %s", session.id)
raise
return text.strip()
async def _load_facts(self, session_id: UUID) -> list[SessionFact]:
result = await self.db.execute(
select(SessionFact)
.where(
SessionFact.session_id == session_id,
SessionFact.deleted_at.is_(None),
)
.order_by(SessionFact.created_at.asc())
)
return list(result.scalars().all())
async def _load_active_fix(self, session_id: UUID) -> SessionSuggestedFix | None:
result = await self.db.execute(
select(SessionSuggestedFix)
.where(
SessionSuggestedFix.session_id == session_id,
SessionSuggestedFix.superseded_at.is_(None),
)
.order_by(SessionSuggestedFix.created_at.desc())
)
return result.scalars().first()
async def _load_redacted_generations(
self, session_id: UUID
) -> list[dict[str, Any]]:
"""Pull script_generations for the session, redacting password params.
Password fields are inferred from the linked template's
`parameters_schema` (`field_type: "password"`). The existing
ScriptTemplateEngine.redact_sensitive handles the substitution.
"""
result = await self.db.execute(
select(ScriptGeneration)
.where(ScriptGeneration.ai_session_id == session_id)
.order_by(ScriptGeneration.created_at.asc())
)
gens = list(result.scalars().all())
if not gens:
return []
template_ids = {g.template_id for g in gens}
tpl_result = await self.db.execute(
select(ScriptTemplate).where(ScriptTemplate.id.in_(template_ids))
)
templates_by_id = {t.id: t for t in tpl_result.scalars().all()}
engine = ScriptTemplateEngine()
out: list[dict[str, Any]] = []
for g in gens:
tpl = templates_by_id.get(g.template_id)
sensitive_keys = self._sensitive_keys_from_schema(
(tpl.parameters_schema if tpl else {}) or {}
)
redacted_params = engine.redact_sensitive(g.parameters_used or {}, sensitive_keys)
out.append({
"template_name": tpl.name if tpl else "(unknown template)",
"template_slug": tpl.slug if tpl else None,
"parameters_used": redacted_params,
"created_at": g.created_at.isoformat(),
})
return out
@staticmethod
def _sensitive_keys_from_schema(schema: dict[str, Any]) -> set[str]:
"""Extract password-typed parameter keys from a template's schema.
The schema shape is `{"parameters": [{"key": "...", "field_type": "password", ...}]}`
per the existing Script Generator convention. Tolerate both that shape
and the simpler `{"key": {"field_type": "password"}}` form.
"""
keys: set[str] = set()
params = schema.get("parameters") if isinstance(schema, dict) else None
if isinstance(params, list):
for p in params:
if isinstance(p, dict) and p.get("field_type") == "password":
k = p.get("key") or p.get("variable_name")
if isinstance(k, str):
keys.add(k)
elif isinstance(schema, dict):
for k, v in schema.items():
if isinstance(v, dict) and v.get("field_type") == "password":
keys.add(k)
return keys
@staticmethod
def _target_ticket_ref(session: AISession) -> str | None:
"""Display ref for the linked PSA ticket, e.g. 'CW #48291'.
ConnectWise is the only PSA wired today (per the Phase 1 constraint),
so a CW prefix is reasonable. Other PSAs will need provider-aware
formatting in Phase 4.
"""
if not session.psa_ticket_id:
return None
return f"CW #{session.psa_ticket_id}"
@staticmethod
def _build_input_bundle(
session: AISession,
facts: list[SessionFact],
active_fix: SessionSuggestedFix | None,
generations: list[dict[str, Any]],
) -> str:
"""Compose the structured input the LLM sees for one preview call."""
lines: list[str] = []
lines.append("# Session context")
lines.append(f"Title: {session.title or '(untitled)'}")
if session.problem_summary:
lines.append(f"Problem summary: {session.problem_summary}")
if session.problem_domain:
lines.append(f"Domain: {session.problem_domain}")
intake_text = (session.intake_content or {}).get("text") if isinstance(session.intake_content, dict) else None
if intake_text:
lines.append(f"Intake message: {intake_text}")
if session.psa_ticket_id:
lines.append(f"Linked PSA ticket: CW #{session.psa_ticket_id}")
lines.append("")
lines.append("# Confirmed facts (What we know)")
if not facts:
lines.append("(none)")
else:
for f in facts:
tag = f.source_type
summary = f"{f.source_summary}" if f.source_summary else ""
lines.append(f"- [{tag}] {f.text}{summary}")
lines.append("")
lines.append("# Active suggested fix")
if active_fix is None:
lines.append("(no active suggested fix)")
else:
lines.append(f"Title: {active_fix.title}")
lines.append(f"Confidence: {active_fix.confidence_pct}%")
lines.append(f"Description: {active_fix.description}")
if active_fix.user_decision:
lines.append(f"Engineer decision: {active_fix.user_decision}")
lines.append("")
lines.append("# Scripts run during the session (passwords redacted)")
if not generations:
lines.append("(none)")
else:
for g in generations:
lines.append(f"- {g['template_name']} (slug={g['template_slug']})")
if g["parameters_used"]:
lines.append(f" parameters: {g['parameters_used']}")
lines.append("")
lines.append(
"Produce the four-section resolution note now. Use only the input above."
)
return "\n".join(lines)

View File

@@ -11,6 +11,9 @@ infrastructure and system prompt from assistant_chat_service.
Items in pending_task_lane carry stable UUIDs (assigned here) so PROMOTE
source_refs survive across turns even when the model re-emits the same
question/action.
- `[SUGGEST_FIX]` (Phase 3) — proposes a resolution path for the session.
Each new emission supersedes the previous active row (sets superseded_at)
so there's exactly one active fix at a time.
"""
import json
import logging
@@ -22,7 +25,13 @@ from uuid import UUID
from sqlalchemy import select
from sqlalchemy.ext.asyncio import AsyncSession
from datetime import datetime, timezone
from sqlalchemy import update
from app.models.ai_session import AISession
from app.models.script_template import ScriptTemplate
from app.models.session_suggested_fix import SessionSuggestedFix
from app.services.assistant_chat_service import (
ASSISTANT_SYSTEM_PROMPT,
_call_ai,
@@ -287,6 +296,125 @@ def _assign_stable_task_lane_ids(
return out_questions, out_actions
def _parse_suggest_fix_marker(
ai_content: str,
) -> tuple[str, dict[str, Any] | None]:
"""Extract a single [SUGGEST_FIX]...[/SUGGEST_FIX] JSON block from AI response.
The block contains:
{"title": "...", "description": "...", "confidence": 0..100,
"script_template_slug": "..." | null,
"ai_drafted_script": "..." | null,
"ai_drafted_parameters": {...} | null}
Per FLOWPILOT-MIGRATION.md Section 8.2. Only the LAST block in the response
is honored — if the model emits multiple, only its final view of the fix
matters; earlier ones in the same turn are stale even before persistence.
Returns (cleaned_content, fix_dict_or_None). Marker stripped from display.
"""
blocks = list(re.finditer(r"\[SUGGEST_FIX\]\s*([\s\S]*?)\s*\[/SUGGEST_FIX\]", ai_content))
if not blocks:
return ai_content, None
# Take the last block — most-recent intent wins within a single turn.
last = blocks[-1]
raw = last.group(1).strip()
if raw.startswith("```"):
raw = re.sub(r"^```(?:json)?\s*", "", raw)
raw = re.sub(r"\s*```$", "", raw)
try:
data = json.loads(raw)
except (json.JSONDecodeError, ValueError) as e:
logger.warning("Failed to parse [SUGGEST_FIX] block: %s", e)
return re.sub(r"\[SUGGEST_FIX\]\s*[\s\S]*?\s*\[/SUGGEST_FIX\]", "", ai_content).strip(), None
if not isinstance(data, dict):
return re.sub(r"\[SUGGEST_FIX\]\s*[\s\S]*?\s*\[/SUGGEST_FIX\]", "", ai_content).strip(), None
title = (data.get("title") or "").strip()
description = (data.get("description") or "").strip()
confidence = data.get("confidence")
if not title or not description or not isinstance(confidence, (int, float)):
logger.warning("[SUGGEST_FIX] missing required fields, dropping")
return re.sub(r"\[SUGGEST_FIX\]\s*[\s\S]*?\s*\[/SUGGEST_FIX\]", "", ai_content).strip(), None
confidence_int = max(0, min(100, int(round(float(confidence)))))
parsed = {
"title": title[:200],
"description": description,
"confidence_pct": confidence_int,
"script_template_slug": (data.get("script_template_slug") or None),
"ai_drafted_script": (data.get("ai_drafted_script") or None),
"ai_drafted_parameters": data.get("ai_drafted_parameters") if isinstance(data.get("ai_drafted_parameters"), dict) else None,
}
cleaned = re.sub(r"\[SUGGEST_FIX\]\s*[\s\S]*?\s*\[/SUGGEST_FIX\]", "", ai_content).strip()
return cleaned, parsed
async def _persist_suggested_fix(
*,
db: AsyncSession,
session: AISession,
fix: dict[str, Any],
) -> None:
"""Supersede the prior active fix and insert the new one. Bumps state_version.
A session has at most one active suggested fix (`superseded_at IS NULL`).
Emitting [SUGGEST_FIX] is the only way to introduce a new one; the
engineer's user_decision is recorded via the decision endpoint.
"""
now = datetime.now(timezone.utc)
# Mark any prior active rows for this session as superseded.
await db.execute(
update(SessionSuggestedFix)
.where(
SessionSuggestedFix.session_id == session.id,
SessionSuggestedFix.superseded_at.is_(None),
)
.values(superseded_at=now)
)
# Resolve script_template_slug → script_template_id if provided.
script_template_id = None
slug = fix.get("script_template_slug")
if slug:
result = await db.execute(
select(ScriptTemplate).where(ScriptTemplate.slug == slug)
)
tpl = result.scalar_one_or_none()
if tpl is not None:
script_template_id = tpl.id
else:
logger.warning(
"SUGGEST_FIX referenced unknown script_template_slug=%r"
"treating as no template match", slug,
)
new_fix = SessionSuggestedFix(
session_id=session.id,
account_id=session.account_id,
title=fix["title"],
description=fix["description"],
confidence_pct=fix["confidence_pct"],
script_template_id=script_template_id,
ai_drafted_script=fix.get("ai_drafted_script"),
ai_drafted_parameters=fix.get("ai_drafted_parameters"),
)
db.add(new_fix)
# Bump preview-cache version atomically with the supersession+insert.
await db.execute(
update(AISession)
.where(AISession.id == session.id)
.values(state_version=AISession.state_version + 1)
)
await db.flush()
async def _persist_promote_items(
*,
db: AsyncSession,
@@ -431,11 +559,13 @@ async def send_chat_message(
if session.status == "paused":
session.status = "active"
# Check for fork, actions, questions, and promote markers in branch response too
# Check for fork, actions, questions, promote, and suggest_fix markers
# in branch response too
branch_display, branch_fork_data = _parse_fork_marker(ai_content)
branch_display, branch_actions_data = _parse_actions_marker(branch_display)
branch_display, branch_questions_data = _parse_questions_marker(branch_display)
branch_display, branch_promote_items = _parse_promote_marker(branch_display)
branch_display, branch_suggest_fix = _parse_suggest_fix_marker(branch_display)
if branch_display != ai_content:
# Store stripped content in branch history
msgs[-1] = {"role": "assistant", "content": branch_display}
@@ -493,6 +623,12 @@ async def send_chat_message(
db=db, session=session, user_id=user_id, items=branch_promote_items,
)
# Persist a [SUGGEST_FIX] if the branch turn included one.
if branch_suggest_fix:
await _persist_suggested_fix(
db=db, session=session, fix=branch_suggest_fix,
)
suggested_flows = extract_suggested_flows(
await rag_search(query=message, account_id=account_id, db=db, limit=8)
)
@@ -542,10 +678,14 @@ async def send_chat_message(
# Check for promote markers — facts the AI is surfacing to What we know.
display_content, promote_items = _parse_promote_marker(display_content)
# Check for a [SUGGEST_FIX] marker — supersedes the prior active fix.
display_content, suggest_fix_data = _parse_suggest_fix_marker(display_content)
logger.info(
"Marker parsing results — actions: %s, questions: %s, fork: %s, promote: %d, raw_length: %d, display_length: %d",
"Marker parsing results — actions: %s, questions: %s, fork: %s, "
"promote: %d, suggest_fix: %s, raw_length: %d, display_length: %d",
bool(actions_data), bool(questions_data), bool(fork_data),
len(promote_items or []),
len(promote_items or []), bool(suggest_fix_data),
len(ai_content), len(display_content),
)
@@ -630,6 +770,10 @@ async def send_chat_message(
db=db, session=session, user_id=user_id, items=promote_items,
)
# Persist a [SUGGEST_FIX] if this turn included one — supersedes prior fix.
if suggest_fix_data:
await _persist_suggested_fix(db=db, session=session, fix=suggest_fix_data)
suggested_flows = extract_suggested_flows(rag_results)
return display_content, suggested_flows, session, fork_metadata, actions_data, questions_data