Deploy v1 — single-Docker FastAPI + Next.js + RAG + voice + faithfulness

backend/main.py

@@ -219,7 +219,16 @@ async def chat(req: ChatRequest):
     audio_b64 = None
     if req.return_audio and turn.reply_text:
         try:
-            audio = await get_tts().synthesize(turn.reply_text, language_code=req.tts_language_code)
+            from backend.voice_format import tts_preprocess
+            # Send a CLEANED version of the reply to TTS — strip markdown,
+            # citations, expand acronyms, truncate. The text in the chat
+            # bubble remains the full structured reply.
+            spoken = tts_preprocess(
+                turn.reply_text,
+                language="indic" if req.tts_language_code.startswith("hi") else "en",
+                max_words=55,
+            )
+            audio = await get_tts().synthesize(spoken, language_code=req.tts_language_code)
             audio_b64 = base64.b64encode(audio).decode("utf-8")
         except Exception as e:
             # Don't fail the whole turn if TTS hiccups — log + return text only

backend/persona.py

@@ -29,7 +29,12 @@ ABSOLUTE RULES (these are non-negotiable)
    When a regulation OVERRIDES a policy clause (e.g., IRDAI mandates 30-day initial waiting period as a minimum), surface both. Regulatory citations are STRONGER signals than policy text — flag them when relevant.
    For multi-policy compares, cite each policy separately.
 
-3. CONCISE FOR VOICE. Default reply length: under 60 words. Buyers hear this over voice — long replies are unusable. Use bullet points sparingly; prefer short complete sentences.
+3. CONCISE FOR VOICE — DEFAULT IS SHORT. Most replies should be 2-3 sentences (≤60 words). Buyers hear this over voice — long replies are unusable.
+   - Do NOT use markdown bold (`**text**`), italics, or numbered lists in your reply
+   - Do NOT use multi-section structures like "Direct answer / Key details / Important note"
+   - Use prose sentences, not bullets, unless the user explicitly asks for a list
+   - Only go longer (up to 100 words) if the user explicitly asks for "more detail", "full breakdown", or "exclusions list"
+   - The text in your reply will be both displayed in chat AND read aloud by TTS — write as if speaking to the user
 
 4. NEVER GIVE MEDICAL ADVICE. "Will this be covered if I have X condition?" → answer the COVERAGE question, never the medical one.
 

backend/voice_format.py

@@ -0,0 +1,130 @@
+"""Convert an LLM reply (markdown, citations, lists, acronyms) into clean
+spoken-language text for Sarvam Bulbul TTS.
+
+Why this exists: an unprocessed LLM reply with markdown bold, inline
+[Source: ...] tags, and acronyms reads like a screenshot when spoken. Users
+hear "asterisk asterisk bold asterisk asterisk A-Y-U-S-H pp dot 1 dash 2".
+That's a UX-killing bug — not a Sarvam limitation, a *us* bug.
+
+The function turns text like:
+
+  "**Direct answer:**
+   Yes, HDFC ERGO Optima Secure covers Ayurveda... [Source: my:Optima
+   Secure (older variant) (hdfc-ergo), pp.1-2]."
+
+Into spoken-ready:
+
+  "Yes, HDFC ERGO Optima Secure covers Ayurveda treatment at recognized
+  Ayush hospitals under specific conditions. For full coverage details
+  and exclusions, see the source link below this message."
+
+Rules applied (in order):
+  1. Strip [Source: ...] and [Regulation: ...] inline citations
+  2. Strip markdown formatting (** bold, * italic, # headings, > quote, - bullet, 1. number)
+  3. Expand acronyms common in insurance to pronounceable forms
+  4. Compress whitespace
+  5. Truncate to first ~60 spoken words; append "More details on screen." if cut
+"""
+
+from __future__ import annotations
+
+import re
+
+# ---- markdown / formatting strippers ----
+
+CITATION_INLINE = re.compile(r"\s*\[(?:Source|Regulation):[^\]]+\]", flags=re.IGNORECASE)
+MD_BOLD = re.compile(r"\*\*(.+?)\*\*", flags=re.DOTALL)
+MD_ITALIC = re.compile(r"(?<!\*)\*(?!\*)([^*]+?)\*(?!\*)")
+MD_HEADING = re.compile(r"^#{1,6}\s+", flags=re.MULTILINE)
+MD_BLOCKQUOTE = re.compile(r"^>\s+", flags=re.MULTILINE)
+MD_BULLET = re.compile(r"^[\s]*[-•*]\s+", flags=re.MULTILINE)
+MD_NUMBERED = re.compile(r"^\s*\d+\.\s+", flags=re.MULTILINE)
+MD_INLINE_CODE = re.compile(r"`([^`]+)`")
+MD_LINK = re.compile(r"\[([^\]]+)\]\(([^)]+)\)")  # keep the text, drop the URL
+
+# Acronym expansions — domain-specific so they read naturally aloud.
+# Rule of thumb: if the acronym is normally PRONOUNCED AS A WORD in spoken
+# Indian English (AYUSH, IRDAI, HDFC ERGO), leave it alone; TTS pronounces
+# it fine. If it's normally said as letters (CIS, PED), expand to plain words.
+ACRONYMS = {
+    r"\bPED\b": "pre-existing disease",
+    r"\bOPD\b": "out-patient",
+    r"\bICU\b": "I-C-U",
+    r"\bTAT\b": "turnaround time",
+    r"\bCSR\b": "claim settlement ratio",
+    r"\bNCB\b": "no-claim bonus",
+    r"\bSI\b": "sum insured",
+    r"\bCIS\b": "Customer Information Sheet",
+    r"\bKFD\b": "Key Feature Document",
+    r"\bUIN\b": "U-I-N",
+    r"\bp\.(\d+)": r"page \1",
+    r"\bpp\.(\d+)-(\d+)": r"pages \1 to \2",
+    r"\bpp\.(\d+)": r"page \1",
+}
+
+# Strip section labels that LLMs love but ruin voice flow.
+# Require the trailing colon so we only catch actual labels, not normal prose
+# that happens to start with "Coverage applies..." etc.
+SECTION_LABELS = re.compile(
+    r"^\s*(?:Direct answer|Key details?|Important notes?|Summary|TL;DR|Exclusions? apply|Caveat|Note|Disclaimer)\s*:\s*",
+    flags=re.IGNORECASE | re.MULTILINE,
+)
+
+
+def _strip_markdown(text: str) -> str:
+    text = CITATION_INLINE.sub("", text)
+    text = MD_BOLD.sub(r"\1", text)
+    text = MD_ITALIC.sub(r"\1", text)
+    text = MD_HEADING.sub("", text)
+    text = MD_BLOCKQUOTE.sub("", text)
+    text = MD_BULLET.sub("", text)
+    text = MD_NUMBERED.sub("", text)
+    text = MD_INLINE_CODE.sub(r"\1", text)
+    text = MD_LINK.sub(r"\1", text)
+    text = SECTION_LABELS.sub("", text)
+    return text
+
+
+def _expand_acronyms(text: str, language: str = "en") -> str:
+    if language == "indic":
+        # In Indic mode, keep acronyms — Indic TTS handles them OK
+        return text
+    for pat, repl in ACRONYMS.items():
+        text = re.sub(pat, repl, text, flags=re.IGNORECASE if "ayush" in pat.lower() else 0)
+    return text
+
+
+def _compress_whitespace(text: str) -> str:
+    text = re.sub(r"\n{2,}", ". ", text)  # paragraph break → sentence break
+    text = re.sub(r"\n", " ", text)
+    text = re.sub(r"[ \t]{2,}", " ", text)
+    # collapse repeated punctuation: ".. ." → "."
+    text = re.sub(r"\s*\.\s*\.+", ".", text)
+    text = re.sub(r"\s+([.,;:!?])", r"\1", text)
+    return text.strip()
+
+
+def _truncate_for_voice(text: str, max_words: int = 60) -> str:
+    """Keep first N words, then append a cutoff cue if we cut anything."""
+    words = text.split()
+    if len(words) <= max_words:
+        return text
+    truncated = " ".join(words[:max_words])
+    # End on a sentence boundary near the cut
+    last_period = max(truncated.rfind("."), truncated.rfind("!"), truncated.rfind("?"))
+    if last_period > 0 and last_period > len(truncated) - 80:
+        truncated = truncated[: last_period + 1]
+    else:
+        truncated = truncated + "."
+    return truncated + " More details are on screen."
+
+
+def tts_preprocess(text: str, language: str = "en", max_words: int = 60) -> str:
+    """Public entry — turn an LLM reply into spoken-language text for TTS."""
+    if not text:
+        return ""
+    cleaned = _strip_markdown(text)
+    cleaned = _expand_acronyms(cleaned, language=language)
+    cleaned = _compress_whitespace(cleaned)
+    cleaned = _truncate_for_voice(cleaned, max_words=max_words)
+    return cleaned