fix: PDF layout, credit idempotency, more active discussion
- PDF: meta_row redesigned to same-line two-column layout so values don't overflow the right edge; explicit cursor reset before each multi_cell body() call fixes key themes appearing to the right of titles; page numbers now use len(pdf.pages) + pdf.h-12 positioning - Credits: replace fragile 4h credit_transactions lookup with atomic findOneAndUpdate stamp on the focus group doc itself (24h window), with rollback on insufficient balance — eliminates double-charging on crash/restart; CreditTransaction.record failure is now non-fatal - Key themes: cap input at 80 messages + max_output_tokens=4096 to fix truncated JSON (Unterminated string at char 1580) - Decision engine: require ≥4 participant responses per question before moving on; mandate debate/contrarian seeking after 2 agreements; call all participants to each question before anyone speaks 3rd time Co-Authored-By: Claude Sonnet 4.6 <noreply@anthropic.com>
This commit is contained in:
Vadym Samoilenko
parent
8f068cc81c
commit
4c70bc8aa6
5 changed files with 106 additions and 52 deletions
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@ -767,22 +767,42 @@ async def start_autonomous_conversation(focus_group_id):
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settings = await get_settings()
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run_cost = settings.get("run_cost", 40)
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# Skip charge if this focus group was already charged within the last 4 hours
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# (handles server restarts and error-triggered restarts without double-billing)
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# Atomic idempotency: stamp the focus group doc with a charge token so that
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# restarts (crash recovery, manual re-runs within 24h) never double-bill.
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# find_one_and_update returns None when the doc already has a fresh charge_token.
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from datetime import datetime, timezone, timedelta
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from bson import ObjectId as _OID
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db = await get_db()
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recent_cutoff = datetime.now(timezone.utc) - timedelta(hours=4)
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recent_charge = await db.credit_transactions.find_one({
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"user_id": run_user_id,
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"ref.focus_group_id": focus_group_id,
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"description": "Focus group session run",
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"ts": {"$gte": recent_cutoff},
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})
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cutoff_24h = datetime.now(timezone.utc) - timedelta(hours=24)
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charge_token = str(_OID())
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stamped = await db.focus_groups.find_one_and_update(
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{
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"_id": _OID(focus_group_id),
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"$or": [
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{"last_charged_at": {"$exists": False}},
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{"last_charged_at": {"$lt": cutoff_24h}},
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],
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},
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{
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"$set": {
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"last_charged_at": datetime.now(timezone.utc),
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"last_charge_token": charge_token,
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"last_charge_user": run_user_id,
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}
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},
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return_document=False,
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)
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if recent_charge is None:
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if stamped is not None:
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# Stamp succeeded → this is a fresh run, charge credits
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user_data = await User.find_by_id(run_user_id)
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balance = (user_data or {}).get("credits_balance", 0)
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if balance < run_cost:
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# Roll back the stamp so the user can try again after topping up
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await db.focus_groups.update_one(
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{"_id": _OID(focus_group_id), "last_charge_token": charge_token},
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{"$unset": {"last_charged_at": "", "last_charge_token": "", "last_charge_user": ""}},
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)
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return jsonify({
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"error": "Insufficient credits",
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"message": f"You need {run_cost} credits to run a focus group session. Current balance: {balance}.",
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@ -791,18 +811,25 @@ async def start_autonomous_conversation(focus_group_id):
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}), 402
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new_balance = await User.deduct_credits(run_user_id, run_cost)
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if new_balance is None:
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await db.focus_groups.update_one(
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{"_id": _OID(focus_group_id), "last_charge_token": charge_token},
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{"$unset": {"last_charged_at": "", "last_charge_token": "", "last_charge_user": ""}},
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)
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return jsonify({"error": "Insufficient credits", "message": "Credit balance changed. Please try again."}), 402
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await CreditTransaction.record(
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user_id=run_user_id,
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tx_type="debit",
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amount=-run_cost,
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balance_after=new_balance,
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description="Focus group session run",
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ref={"focus_group_id": focus_group_id},
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)
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try:
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await CreditTransaction.record(
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user_id=run_user_id,
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tx_type="debit",
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amount=-run_cost,
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balance_after=new_balance,
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description="Focus group session run",
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ref={"focus_group_id": focus_group_id},
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)
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except Exception as _tx_err:
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current_app.logger.warning(f"CreditTransaction.record failed (non-fatal): {_tx_err}")
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current_app.logger.info(f"Charged {run_cost} credits for focus group {focus_group_id}")
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else:
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current_app.logger.info(f"Focus group {focus_group_id} already charged within 4h window — skipping deduction")
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current_app.logger.info(f"Focus group {focus_group_id} already charged within 24h window — skipping deduction")
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# Create autonomous conversation controller
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current_app.logger.info("Creating AutonomousConversationController...")
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@ -1358,13 +1358,18 @@ async def download_full_report(focus_group_id):
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pdf.set_text_color(*DARK)
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def meta_row(label, value):
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# Bold muted label above normal value
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"""Two-column row: muted label left, value right, same line."""
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LABEL_W = 50
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VAL_W = EPW - LABEL_W
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y0 = pdf.get_y()
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set_r(8, bold=True)
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pdf.set_text_color(*MUTED)
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pdf.multi_cell(EPW, 5, label.upper())
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pdf.set_x(pdf.l_margin)
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pdf.multi_cell(LABEL_W, 5.5, label.upper(), new_x="RIGHT", new_y="TOP")
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set_r(10)
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pdf.set_text_color(*DARK)
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pdf.multi_cell(EPW, 5.5, value)
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pdf.set_xy(pdf.l_margin + LABEL_W, y0)
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pdf.multi_cell(VAL_W, 5.5, value, new_x="LMARGIN", new_y="NEXT")
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pdf.ln(2)
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def quote_block(text):
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@ -1422,10 +1427,13 @@ async def download_full_report(focus_group_id):
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pdf.ln(3)
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set_r(11, bold=True)
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pdf.set_text_color(*DARK)
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pdf.multi_cell(EPW, 6, f"{i}. {t.get('title', '')}")
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pdf.set_x(pdf.l_margin)
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pdf.multi_cell(EPW, 6, f"{i}. {t.get('title', '')}", new_x="LMARGIN", new_y="NEXT")
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pdf.set_x(pdf.l_margin)
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body(t.get("description", ""), color=MUTED)
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quotes = t.get("quotes", [])
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if quotes:
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pdf.set_x(pdf.l_margin)
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set_r(9, bold=True)
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pdf.set_text_color(*MUTED)
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pdf.cell(EPW, 5, L["quotes"] + ":", new_x="LMARGIN", new_y="NEXT")
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@ -1487,16 +1495,18 @@ async def download_full_report(focus_group_id):
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pdf.multi_cell(EPW, 4.5, line)
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pdf.ln(1)
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# Page numbers footer
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# Page numbers footer — iterate all pages and stamp at bottom
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def add_page_numbers():
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set_r(8)
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pdf.set_text_color(*MUTED)
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for i in range(1, pdf.page + 1):
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n = len(pdf.pages)
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saved = pdf.page
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for i in range(1, n + 1):
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pdf.page = i
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pdf.set_y(-12)
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pdf.set_y(pdf.h - 12)
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pdf.set_x(pdf.l_margin)
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pdf.cell(EPW, 5, f"{i}", align="C", new_x="LMARGIN", new_y="NEXT")
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pdf.page = pdf.pages_count
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set_r(8)
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pdf.set_text_color(*MUTED)
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pdf.cell(EPW, 5, str(i), align="C", new_x="LMARGIN", new_y="NEXT")
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pdf.page = saved
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add_page_numbers()
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@ -57,7 +57,11 @@ class KeyThemeService:
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raise KeyThemeServiceError("No messages found in this focus group")
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logger.info(f"Found {len(messages)} messages in focus group {focus_group_id}")
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# Cap at 80 messages to keep the input prompt within token limits
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if len(messages) > 80:
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messages = messages[-80:]
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logger.info("Truncated to last 80 messages for theme extraction")
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# Get all participants (personas) in the focus group
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participants_data = []
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if 'participants' in focus_group and focus_group['participants']:
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@ -144,7 +148,8 @@ class KeyThemeService:
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prompt=prompt,
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temperature=temperature,
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system_prompt=system_prompt,
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model_name=llm_model
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model_name=llm_model,
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max_output_tokens=4000,
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)
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logger.info(f"Attempt {attempt_num}/{max_retries}: LLM ({llm_model or 'gpt-5.4'}) call successful, received {len(themes)} themes")
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@ -233,9 +233,10 @@ class LLMService:
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input_content,
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reasoning_effort: Optional[str] = None,
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verbosity: Optional[str] = None,
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max_output_tokens: Optional[int] = None,
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) -> dict:
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"""Build the kwargs dict for a Responses API call."""
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return {
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kwargs = {
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"model": actual_model,
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"input": input_content,
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"reasoning": {"effort": reasoning_effort or "low"},
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@ -244,6 +245,9 @@ class LLMService:
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"verbosity": verbosity or "medium",
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},
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}
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if max_output_tokens is not None:
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kwargs["max_output_tokens"] = max_output_tokens
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return kwargs
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@staticmethod
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async def generate_content(
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@ -253,7 +257,8 @@ class LLMService:
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model_name: Optional[str] = None,
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system_prompt: Optional[str] = None,
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reasoning_effort: Optional[str] = None,
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verbosity: Optional[str] = None
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verbosity: Optional[str] = None,
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max_output_tokens: Optional[int] = None,
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) -> str:
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"""Generate text content via the Azure AI Foundry Responses API.
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@ -265,6 +270,7 @@ class LLMService:
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system_prompt: Optional system instruction prepended to the prompt.
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reasoning_effort: Responses API reasoning effort (low/medium/high).
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verbosity: Responses API verbosity (low/medium/high).
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max_output_tokens: Hard cap on output tokens for Responses API.
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"""
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logger = logging.getLogger(__name__)
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max_retries = 3
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@ -290,7 +296,7 @@ class LLMService:
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else:
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input_content = prompt
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kwargs = LLMService._build_responses_kwargs(actual_model, input_content, reasoning_effort, verbosity)
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kwargs = LLMService._build_responses_kwargs(actual_model, input_content, reasoning_effort, verbosity, max_output_tokens)
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for attempt in range(max_retries):
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attempt_num = attempt + 1
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@ -369,13 +375,15 @@ class LLMService:
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model_name: Optional[str] = None,
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system_prompt: Optional[str] = None,
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reasoning_effort: Optional[str] = None,
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verbosity: Optional[str] = None
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verbosity: Optional[str] = None,
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max_output_tokens: Optional[int] = None,
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) -> List[Dict[str, Any]]:
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"""Generate and parse a structured JSON array response."""
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text = await LLMService.generate_content(
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prompt=prompt, temperature=temperature, max_tokens=max_tokens,
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model_name=model_name, system_prompt=system_prompt,
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reasoning_effort=reasoning_effort, verbosity=verbosity,
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max_output_tokens=max_output_tokens,
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)
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result = LLMService.parse_json_response(text)
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if not isinstance(result, list):
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@ -205,35 +205,39 @@ EXAMPLE_JSON_END
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1. **Systematic Guide Progression**: ALWAYS continue through the discussion guide systematically, regardless of how well topics may have been covered in previous conversation
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2. **No Time Pressure**: Time duration is NOT a factor in decision making - continue through the guide at a natural pace without rushing to end due to elapsed time
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3. **Previously Covered Topics**: If a question relates to topics discussed earlier:
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3. **Minimum Depth Per Question**: Each discussion guide question MUST receive at least 4–6 participant responses before the moderator moves on. If fewer than 4 participants have responded to the current question, prioritize `participant_respond` for those who haven't spoken on this topic yet. Never transition to the next question prematurely.
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4. **Previously Covered Topics**: If a question relates to topics discussed earlier:
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- Acknowledge and briefly summarize previous points made
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- Still ask the current question from the discussion guide
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- Allow participants to expand, clarify, or add new perspectives
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- Example: "Earlier you mentioned X about this topic. Now I'd like to focus specifically on [current question]..."
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4. **Natural Pacing**: Allow natural pauses and don't rush responses
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5. **Balanced Participation**: Ensure all participants have opportunities to contribute
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6. **Topic Coherence**: Keep conversation focused while allowing organic development
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7. **Emotional Intelligence**: Respond appropriately to participant emotions and energy
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8. **Research Objectives**: Always keep the research goals in mind
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9. **Authentic Interactions**: Facilitate genuine-feeling exchanges between participants
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5. **Active Debate**: The goal is a RICH, LIVELY discussion. After 2 participants agree on a point, actively seek out a contrarian or devil's advocate. After 2 responses on the same angle, push for a different perspective. Use `participant_interaction` to spark direct participant-to-participant exchange.
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6. **Natural Pacing**: Allow natural pauses and don't rush responses
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7. **Balanced Participation**: Ensure ALL participants contribute to EVERY question. A participant who hasn't spoken on the current topic should be called on before anyone speaks a third time on that topic.
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8. **Topic Coherence**: Keep conversation focused while allowing organic development
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9. **Emotional Intelligence**: Respond appropriately to participant emotions and energy
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10. **Research Objectives**: Always keep the research goals in mind
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11. **Authentic Interactions**: Facilitate genuine-feeling exchanges between participants
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## DECISION MAKING INSTRUCTIONS
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1. **FIRST AND FOREMOST**: Scan the most recent moderator message for @mentions of participant names
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2. **If @mentions found**: Select "participant_respond" action with the @mentioned participant - skip all other analysis
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3. **If no @mentions**: Analyze the current conversation state and participant context
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4. **SYSTEMATIC PROGRESSION PRIORITY**: Always prioritize moving through the discussion guide systematically - do NOT end sessions or skip questions because topics seem "thoroughly covered"
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5. **IGNORE TIME FACTORS**: Do not consider duration, elapsed time, or time pressure when making decisions - focus solely on discussion guide completion
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6. **HANDLE REPEAT TOPICS**: If the current question relates to previously discussed topics:
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4. **DEPTH BEFORE BREADTH**: Before moving to the next discussion guide question, count how many DISTINCT participants have responded to the current question. If fewer than 4 have responded, select `participant_respond` for one who hasn't spoken on this topic yet. Only move on once the current question has been genuinely explored by multiple participants.
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5. **SYSTEMATIC PROGRESSION PRIORITY**: Always prioritize moving through the discussion guide systematically - do NOT end sessions or skip questions because topics seem "thoroughly covered"
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6. **IGNORE TIME FACTORS**: Do not consider duration, elapsed time, or time pressure when making decisions - focus solely on discussion guide completion
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7. **PROMOTE DEBATE**: If the last 3 responses are all in agreement, use `probe_trigger` (convergence) or select a contrarian participant via `participant_respond` with a challenging `topic_context`.
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8. **HANDLE REPEAT TOPICS**: If the current question relates to previously discussed topics:
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- Choose "moderator_speak" to acknowledge previous discussion and ask the current question
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- Include brief summary of previous relevant points in the content
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- Still proceed with the question to allow deeper exploration
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7. Apply the behavioral rules to determine the most appropriate action
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8. Consider the research objectives and discussion guide progress
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9. Choose the action that best serves systematic guide progression
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10. **CRITICAL**: Identify the MOST SPECIFIC question or activity ID that matches the current conversation topic - avoid general section-only mapping
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11. Provide clear reasoning for your decision (mention @mention detection in reasoning)
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12. Format your response as valid JSON with precise position mapping
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9. Apply the behavioral rules to determine the most appropriate action
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10. Consider the research objectives and discussion guide progress
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11. Choose the action that best serves systematic guide progression
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12. **CRITICAL**: Identify the MOST SPECIFIC question or activity ID that matches the current conversation topic - avoid general section-only mapping
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13. Provide clear reasoning for your decision (mention @mention detection in reasoning)
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14. Format your response as valid JSON with precise position mapping
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**REMINDER**: @Mentions are ABSOLUTE PRIORITY. If you see "@John" or "John, what do you think?" - John MUST respond regardless of fatigue, recency, or any other factors.
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