b8c71b15c2
In-call (system prompt + per-call calendar injection): - Gather full name (prompt asks for last name if only first given). - Confirm the caller-ID number; if declined, use the number the caller gives. - Ask for and LOG insurance only — never promise/confirm/deny coverage or treatment based on it; staff verify on callback. - Validate the requested date against an injected 45-day calendar (recomputed per call since the server is long-running). Push back on impossible/mismatched dates, e.g. "Monday lands on the sixth — would you like that date?". - AGENT_NAME=AVA; 4s grace pause before hang-up (HANGUP_DELAY_SECS). Logging (post-call extraction -> Odoo): - Extract full name, phone_confirmed, chosen callback (caller-ID or alternate), insurance, reason, and preferred time annotated with a resolved YYYY-MM-DD date (today's date is fed to the extractor). - odoo_client: insurance row on the lead note (log only — staff verify). .gitignore: ignore rotated avc_run.log* files. Co-Authored-By: Claude Opus 4.8 <noreply@anthropic.com>
479 lines
23 KiB
Python
479 lines
23 KiB
Python
#!/usr/bin/env python3
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"""AVC optometry phone agent — the Pipecat pipeline for a single inbound call.
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Same VAD -> STT -> LLM -> TTS loop as pipecat-run/bot.py, but the ends are swapped
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for telephony: audio arrives/leaves as 8 kHz mu-law over a Twilio Media Stream
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(WebSocket), decoded by TwilioFrameSerializer. STT runs on the GPU; the LLM is the
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local `activeblue-avc` fine-tune via Ollama; TTS is local Kokoro.
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This module just builds + runs the pipeline for one connected call. server.py owns
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the FastAPI/TwiML/WebSocket side and calls run_call() once per call.
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"""
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import asyncio
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import os
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import re
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import time
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from datetime import datetime, timedelta
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from loguru import logger
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from pipecat.adapters.schemas.function_schema import FunctionSchema
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from pipecat.adapters.schemas.tools_schema import ToolsSchema
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from pipecat.audio.vad.silero import SileroVADAnalyzer
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from pipecat.audio.vad.vad_analyzer import VADParams
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from pipecat.frames.frames import (
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BotStoppedSpeakingFrame,
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EndFrame,
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EndTaskFrame,
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Frame,
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InputAudioRawFrame,
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LLMFullResponseEndFrame,
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LLMTextFrame,
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TTSSpeakFrame,
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)
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from pipecat.pipeline.pipeline import Pipeline
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from pipecat.pipeline.runner import PipelineRunner
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from pipecat.pipeline.task import PipelineParams, PipelineTask
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from pipecat.processors.aggregators.llm_context import LLMContext
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from pipecat.processors.aggregators.llm_response_universal import LLMContextAggregatorPair
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from pipecat.processors.audio.vad_processor import VADProcessor
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from pipecat.processors.frame_processor import FrameDirection, FrameProcessor
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from pipecat.serializers.twilio import TwilioFrameSerializer
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from pipecat.services.anthropic.llm import AnthropicLLMService
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from pipecat.services.kokoro.tts import KokoroTTSService
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from pipecat.services.ollama.llm import OLLamaLLMService
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from pipecat.services.whisper.stt import WhisperSTTService
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from pipecat.transports.websocket.fastapi import (
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FastAPIWebsocketParams,
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FastAPIWebsocketTransport,
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)
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from practice import practice_summary
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# ── Config (env-overridable) ─────────────────────────────────────────────────
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HERE = os.path.dirname(os.path.abspath(__file__))
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# Reuse the Kokoro model files already downloaded by the pipecat-run project.
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MODEL_DIR = os.environ.get("KOKORO_MODEL_DIR", "/home/tocmo0nlord/pipecat-run/models")
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OLLAMA_MODEL = os.environ.get("OLLAMA_MODEL", "activeblue-avc:latest")
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OLLAMA_URL = os.environ.get("OLLAMA_URL", "http://127.0.0.1:11434/v1")
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# Swappable LLM provider: "ollama" (local) or "anthropic" (Claude API). Same universal
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# LLMContext drives both — only the service construction differs (see build_llm_service).
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LLM_PROVIDER = os.environ.get("LLM_PROVIDER", "ollama").lower()
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ANTHROPIC_API_KEY = os.environ.get("ANTHROPIC_API_KEY", "")
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# Defaults to the most capable model. For low-latency PHONE voice, set ANTHROPIC_MODEL to
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# claude-haiku-4-5 (fastest) or claude-sonnet-4-6 (balance) — see notes in build_llm_service.
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ANTHROPIC_MODEL = os.environ.get("ANTHROPIC_MODEL", "claude-opus-4-8")
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# In-call function-calling: AUTO by provider — ON for Claude (reliable tool calls → real-time
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# Odoo booking), OFF for local Ollama (llama3.1:8b over-calls / leaks JSON). An explicit
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# ENABLE_TOOLS env overrides the auto choice either way.
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_enable_tools_env = os.environ.get("ENABLE_TOOLS")
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ENABLE_TOOLS = (
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_enable_tools_env.lower() in ("1", "true", "yes")
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if _enable_tools_env is not None
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else (LLM_PROVIDER == "anthropic")
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)
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LLM_TEMPERATURE = float(os.environ.get("LLM_TEMPERATURE", "0.3"))
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LLM_MAX_TOKENS = int(os.environ.get("LLM_MAX_TOKENS", "160"))
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KOKORO_VOICE = os.environ.get("KOKORO_VOICE", "af_heart")
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WHISPER_MODEL = os.environ.get("WHISPER_MODEL", "medium") # tiny|base|small|medium
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WHISPER_DEVICE = os.environ.get("WHISPER_DEVICE", "cuda") # cuda for the 5080
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WHISPER_COMPUTE = os.environ.get("WHISPER_COMPUTE", "float16")
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# Bias transcription toward our domain vocabulary (office cities + optometry terms) so
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# 8 kHz telephony audio doesn't turn "Hialeah" into "high allele" or "eye exam" into "hire".
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WHISPER_HOTWORDS = os.environ.get(
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"WHISPER_HOTWORDS",
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"Advanced Vision Care, eye exam, annual exam, appointment, optometry, contact lens, "
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"Hialeah, Kendall, Tamarac, Pembroke Pines, Lauderdale Lakes, Miami Gardens, Boca Raton",
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)
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# Twilio sends 8 kHz mu-law on the wire, but faster-whisper assumes any numpy array is
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# 16 kHz — so we run the PIPELINE at 16 kHz and let TwilioFrameSerializer resample to/from
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# the 8 kHz wire. Running the pipeline at 8 kHz makes Whisper hear 2x-speed audio and
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# transcribe nothing. (Silero VAD + Kokoro are happy at 16 kHz too.)
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WIRE_SAMPLE_RATE = 8000 # Twilio mu-law on the wire (serializer handles this)
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PIPELINE_SAMPLE_RATE = 16000 # internal rate Whisper/VAD actually need
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# VAD tuning. Defaults (confidence 0.7 / min_volume 0.6) are desktop-mic values that can
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# miss short/quiet 8 kHz telephony utterances like "yes" — loosen them for the phone.
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VAD_CONFIDENCE = float(os.environ.get("VAD_CONFIDENCE", "0.5"))
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VAD_MIN_VOLUME = float(os.environ.get("VAD_MIN_VOLUME", "0.3"))
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VAD_START_SECS = float(os.environ.get("VAD_START_SECS", "0.2"))
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VAD_STOP_SECS = float(os.environ.get("VAD_STOP_SECS", "0.5"))
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# Agent persona name — purely for warmth; change/remove freely.
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AGENT_NAME = os.environ.get("AGENT_NAME", "Sofia")
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# Grace period after the agent finishes the goodbye before we drop the carrier leg, so
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# the caller isn't cut off mid-word. The hang-up itself (EndTaskFrame -> auto_hang_up)
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# is unchanged — this only delays it.
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HANGUP_DELAY_SECS = float(os.environ.get("HANGUP_DELAY_SECS", "4.0"))
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SYSTEM_PROMPT = (
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f"You are {AGENT_NAME}, a warm, friendly receptionist for Advanced Vision Care, an "
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"optometry practice with eight offices in South Florida. You are on a real phone call, so "
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"talk like a helpful human being: natural, relaxed, and genuinely conversational — usually "
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"just one short sentence at a time. Speak in English. Say numbers, dates, and times as "
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"words a person would say.\n\n"
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"Your job is to answer callers' questions and to take appointment requests. For a "
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"booking, gather these SIX things naturally as the conversation flows — don't "
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"interrogate, and never ask for something the caller already told you:\n"
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" 1. Their FULL name (first and last). If they give only a first name, warmly ask for "
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"their last name too.\n"
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" 2. The phone number to reach them. Their caller-ID number is given to you below — read "
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"it back and ask if that is the best number. If they say no, ask for the right number and "
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"use that instead.\n"
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" 3. Which office or city is most convenient.\n"
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" 4. The reason for the visit.\n"
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" 5. Their insurance — ask what insurance they have and simply note it (see the insurance "
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"rule below).\n"
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" 6. The day and time they prefer (validate the date — see the date rule below).\n"
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"When you have the details, repeat them back in one warm sentence to confirm, and let them "
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"know a staff member will call to finalize the time.\n\n"
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"Stay truthful and within your limits:\n"
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"- Use ONLY the facts below for addresses, phone numbers, insurance, and services. Never "
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"make any of these up.\n"
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"- To find the right office, ask what CITY or AREA is most convenient for the caller. Do "
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"NOT suggest or name a specific office yourself — you don't know where they are. Only after "
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"they tell you their area, name the matching office; and only list locations if they ask "
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"what offices exist.\n"
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"- INSURANCE — log only, never promise: ask what insurance they have and note it for staff. "
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"Do NOT promise, confirm, or deny coverage or any treatment based on their insurance, even "
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"if the plan is one we list. Always say our staff will verify their coverage when they call "
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"back. Just capture the plan name.\n"
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"- DATES — always validate against the calendar provided below. Work out the real date the "
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"caller means and check it. If the weekday and the date they say do not match, or the date "
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"does not exist, gently correct them and offer the right one, then confirm before booking. "
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"For example, if they say 'Monday the fifth' but the Monday next month is the sixth, say: "
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"'Next month, Monday lands on the sixth — would you like to schedule that date?' Never accept "
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"an impossible or mismatched date silently.\n"
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"- You cannot see a calendar of openings, so never say a time slot is open or available — "
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"take the day/time as a request that staff will confirm.\n"
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"- Hours are not published — say they vary by office and staff will confirm; never give "
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"specific hours.\n"
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"- You don't give medical advice and can't transfer calls. If the caller mentions an eye "
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"problem, just note it as the reason and say a staff member or doctor will follow up.\n"
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"- If you're not sure you heard something, simply ask them to repeat it.\n"
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"- When the caller is all set, give a brief, warm closing that ends with the word "
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"'Goodbye' — that ends the call, so only say it when you truly mean to.\n\n"
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"PRACTICE FACTS:\n" + practice_summary()
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)
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def _date_context(now: datetime | None = None) -> str:
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"""Calendar grounding injected per call so the local model can resolve and VALIDATE
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the dates a caller mentions (e.g. catch 'Monday the 5th' when the Monday is the 6th).
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Recomputed each call because the server is long-running."""
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now = now or datetime.now()
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today = now.date()
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# 45 days covers 'next month' references for any call date.
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lines = []
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for i in range(45):
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d = today + timedelta(days=i)
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tag = " <- TODAY" if i == 0 else (" <- tomorrow" if i == 1 else "")
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lines.append(f" {d.strftime('%A, %B %d, %Y').replace(' 0', ' ')}{tag}")
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return (
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"CALENDAR — authoritative, use for EVERY date the caller mentions:\n"
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f"Today is {today.strftime('%A, %B %d, %Y').replace(' 0', ' ')}.\n"
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"Upcoming dates:\n" + "\n".join(lines) + "\n"
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)
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def _build_tools() -> ToolsSchema:
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# Only the booking action is a tool. Practice facts already live in the system prompt,
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# so no get_practice_info tool (avoids needless calls/latency). callback_number is NOT
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# required — we have the caller-ID and inject it in the handler.
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return ToolsSchema(
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standard_tools=[
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FunctionSchema(
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name="record_appointment_request",
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description=(
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"Record the caller's appointment request once you have their name and at "
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"least the office/city and reason. Call this when the caller wants to book "
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"a visit; staff will call back to confirm the exact time."
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),
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properties={
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"patient_name": {"type": "string", "description": "Caller's full name"},
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"location": {"type": "string", "description": "Which office/city the caller wants, e.g. Hialeah, Kendall, Tamarac"},
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"reason": {"type": "string", "description": "Reason for the visit, e.g. annual exam, broken glasses, eye pain"},
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"preferred_time": {"type": "string", "description": "Preferred day/time in the caller's words, if given"},
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},
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required=["patient_name"],
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),
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]
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)
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class EndCallProcessor(FrameProcessor):
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"""Lets the agent hang up. MUST sit between the LLM and the TTS: there it sees her reply
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text (LLMTextFrame, flowing downstream) AND the upstream copy of BotStoppedSpeakingFrame
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the output transport emits. It accumulates each reply; if the finished reply contains a
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closing ('goodbye'/'adiós'), it waits until she's done speaking, pauses HANGUP_DELAY_SECS
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so the caller isn't clipped, then pushes EndTaskFrame upstream — the task ends and
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TwilioFrameSerializer (auto_hang_up) drops the call."""
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_CLOSINGS = ("goodbye", "good-bye", "good bye", "adiós", "adios", "hasta luego")
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def __init__(self):
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super().__init__()
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self._buf = ""
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self._should_end = False
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self._end_task = None
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@classmethod
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def _is_closing(cls, text: str) -> bool:
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t = (text or "").lower()
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return any(c in t for c in cls._CLOSINGS)
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async def _hang_up_after_delay(self):
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await asyncio.sleep(HANGUP_DELAY_SECS)
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logger.info(f"{AGENT_NAME} ending task / hanging up")
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try:
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await self.push_frame(EndTaskFrame(), FrameDirection.UPSTREAM)
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except Exception:
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logger.exception("EndTaskFrame push failed (pipeline already ending?)")
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async def process_frame(self, frame: Frame, direction: FrameDirection):
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await super().process_frame(frame, direction)
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if isinstance(frame, LLMTextFrame):
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self._buf += frame.text
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elif isinstance(frame, LLMFullResponseEndFrame):
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if self._is_closing(self._buf):
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self._should_end = True
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logger.info(f"{AGENT_NAME} signalled closing -- will hang up "
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f"{HANGUP_DELAY_SECS:.0f}s after she finishes speaking")
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self._buf = ""
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elif isinstance(frame, BotStoppedSpeakingFrame) and self._should_end:
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self._should_end = False
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# Schedule the teardown so we don't block the pipeline during the grace pause.
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if self._end_task is None:
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self._end_task = asyncio.create_task(self._hang_up_after_delay())
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await self.push_frame(frame, direction)
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class AudioHeartbeat(FrameProcessor):
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"""Diagnostic: logs how many inbound audio frames arrive every ~5s. If this keeps
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ticking but VAD never fires, the issue is VAD/threshold; if it drops to 0 after a
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turn, inbound audio stalled at the transport. Cheap, leave it on while stabilizing."""
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def __init__(self):
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super().__init__()
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self._n = 0
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self._t = time.time()
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async def process_frame(self, frame: Frame, direction: FrameDirection):
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await super().process_frame(frame, direction)
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if isinstance(frame, InputAudioRawFrame):
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self._n += 1
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now = time.time()
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if now - self._t >= 5:
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logger.info(f"[audio-in] {self._n} frames in last {now - self._t:.0f}s")
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self._n = 0
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self._t = now
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await self.push_frame(frame, direction)
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class HintedWhisperSTTService(WhisperSTTService):
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"""WhisperSTTService that biases transcription toward domain vocabulary via
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faster-whisper `hotwords`. Pipecat's service doesn't expose hotwords, so we wrap
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the model's transcribe() for the duration of each call. Each call gets its own
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Whisper instance, so this per-instance patch is race-free."""
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def __init__(self, *args, hotwords: str | None = None, **kwargs):
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super().__init__(*args, **kwargs)
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self._hotwords = hotwords
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async def run_stt(self, audio):
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if self._hotwords and self._model is not None:
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real = self._model.transcribe
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def patched(audio_arg, **kw):
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kw.setdefault("hotwords", self._hotwords)
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return real(audio_arg, **kw)
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self._model.transcribe = patched
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try:
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async for frame in super().run_stt(audio):
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yield frame
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finally:
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self._model.transcribe = real
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else:
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async for frame in super().run_stt(audio):
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yield frame
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def build_llm_service():
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"""Build the LLM service for the selected provider. The universal LLMContext +
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aggregators work with either, so only this construction differs (true A/B swap)."""
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if LLM_PROVIDER == "anthropic":
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if not ANTHROPIC_API_KEY:
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raise RuntimeError("LLM_PROVIDER=anthropic but ANTHROPIC_API_KEY is not set")
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logger.info(f"LLM provider: anthropic ({ANTHROPIC_MODEL})")
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# NOTE: Opus 4.8/4.7 reject temperature/top_p/top_k (HTTP 400), so we omit them —
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# this keeps the default Opus model working. For low-latency phone voice, prefer
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# claude-haiku-4-5 (fastest) or claude-sonnet-4-6 over Opus. enable_prompt_caching
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# caches the system prompt + growing conversation prefix (helps multi-turn cost/latency).
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return AnthropicLLMService(
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api_key=ANTHROPIC_API_KEY,
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settings=AnthropicLLMService.Settings(
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model=ANTHROPIC_MODEL,
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enable_prompt_caching=True,
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max_tokens=LLM_MAX_TOKENS,
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),
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)
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logger.info(f"LLM provider: ollama ({OLLAMA_MODEL})")
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return OLLamaLLMService(
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settings=OLLamaLLMService.Settings(
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model=OLLAMA_MODEL,
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temperature=LLM_TEMPERATURE,
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max_tokens=LLM_MAX_TOKENS,
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),
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base_url=OLLAMA_URL,
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)
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async def run_agent(transport, caller_number=None, call_sid=None, do_capture=True):
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"""Build + run the AVC voice agent on a given transport. Shared by the phone path
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(Twilio Media Stream) and the browser path (WebRTC) — same prompt, model, voice, and
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booking/hang-up logic; only the transport differs. do_capture writes the post-call
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appointment to Odoo (on for phone; off for browser testing so it doesn't make cards)."""
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stt = HintedWhisperSTTService(
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settings=WhisperSTTService.Settings(model=WHISPER_MODEL),
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device=WHISPER_DEVICE,
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compute_type=WHISPER_COMPUTE,
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hotwords=WHISPER_HOTWORDS,
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)
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llm = build_llm_service()
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# In-call booking tool — only registered when ENABLE_TOOLS is on (auto: Claude yes,
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# local Ollama no, since llama3.1:8b over-calls/leaks). The handler is a closure so it
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# can stamp the verified caller-ID + call_sid onto the lead (the model never supplies a
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# phone number — we don't ask for one). With tools on, this writes the Odoo lead IN-CALL,
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# so the post-call extraction is skipped below to avoid a duplicate.
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if ENABLE_TOOLS:
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async def _record_appointment(params):
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args = params.arguments or {}
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if do_capture:
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from practice import persist_appointment
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persist_appointment({
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"call_sid": call_sid,
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"patient_name": args.get("patient_name"),
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"callback_number": caller_number, # verified caller-ID, not model-supplied
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"location": args.get("location"),
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"reason": args.get("reason"),
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"preferred_time": args.get("preferred_time"),
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"source": "in_call_tool",
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})
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else:
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logger.info(f"[capture off] would record appointment: {args.get('patient_name')} / {args.get('location')}")
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await params.result_callback(
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{"status": "recorded", "message": "Recorded — staff will call to confirm the time."}
|
|
)
|
|
|
|
llm.register_function("record_appointment_request", _record_appointment)
|
|
|
|
tts = KokoroTTSService(
|
|
model_path=os.path.join(MODEL_DIR, "kokoro-v1.0.onnx"),
|
|
voices_path=os.path.join(MODEL_DIR, "voices-v1.0.bin"),
|
|
settings=KokoroTTSService.Settings(voice=KOKORO_VOICE),
|
|
)
|
|
vad = VADProcessor(vad_analyzer=SileroVADAnalyzer(params=VADParams(
|
|
confidence=VAD_CONFIDENCE,
|
|
start_secs=VAD_START_SECS,
|
|
stop_secs=VAD_STOP_SECS,
|
|
min_volume=VAD_MIN_VOLUME,
|
|
)))
|
|
heartbeat = AudioHeartbeat()
|
|
|
|
# Per-call system message = static prompt + today's calendar + the caller-ID number to
|
|
# confirm. Built here (not at import) so the date is current on a long-running server.
|
|
if caller_number:
|
|
caller_line = (
|
|
f"\n\nCALLER ID: the caller's number on file is {caller_number}. Read it back and "
|
|
"ask if it's the best number to reach them; if they say no, use the number they give."
|
|
)
|
|
else:
|
|
caller_line = (
|
|
"\n\nCALLER ID: no number is available — ask the caller for the best phone number "
|
|
"to reach them."
|
|
)
|
|
system_content = SYSTEM_PROMPT + "\n\n" + _date_context() + caller_line
|
|
context_kwargs = {"messages": [{"role": "system", "content": system_content}]}
|
|
if ENABLE_TOOLS:
|
|
context_kwargs["tools"] = _build_tools()
|
|
context = LLMContext(**context_kwargs)
|
|
agg = LLMContextAggregatorPair(context)
|
|
endcall = EndCallProcessor()
|
|
|
|
pipeline = Pipeline(
|
|
[
|
|
transport.input(),
|
|
heartbeat,
|
|
vad,
|
|
stt,
|
|
agg.user(),
|
|
llm,
|
|
endcall,
|
|
tts,
|
|
transport.output(),
|
|
agg.assistant(),
|
|
]
|
|
)
|
|
|
|
task = PipelineTask(
|
|
pipeline,
|
|
params=PipelineParams(
|
|
audio_in_sample_rate=PIPELINE_SAMPLE_RATE,
|
|
audio_out_sample_rate=PIPELINE_SAMPLE_RATE,
|
|
allow_interruptions=True,
|
|
),
|
|
)
|
|
|
|
@transport.event_handler("on_client_connected")
|
|
async def on_client_connected(transport, client):
|
|
logger.info("Client connected -- greeting")
|
|
await task.queue_frames(
|
|
[TTSSpeakFrame(
|
|
f"Thank you for calling Advanced Vision Care, this is {AGENT_NAME}. "
|
|
"How can I help you today?"
|
|
)]
|
|
)
|
|
|
|
@transport.event_handler("on_client_disconnected")
|
|
async def on_client_disconnected(transport, client):
|
|
logger.info("Client disconnected -- ending task")
|
|
await task.queue_frame(EndFrame())
|
|
|
|
runner = PipelineRunner(handle_sigint=False)
|
|
await runner.run(task)
|
|
|
|
# Call is over. Post-call extraction is the capture path ONLY when in-call tools are
|
|
# off (local Ollama). With tools on (Claude), the booking was already written in-call,
|
|
# so skip extraction to avoid a duplicate lead.
|
|
if do_capture and not ENABLE_TOOLS:
|
|
try:
|
|
from extract import extract_and_record
|
|
|
|
await extract_and_record(
|
|
context.messages, OLLAMA_URL, OLLAMA_MODEL,
|
|
call_sid=call_sid, caller_number=caller_number,
|
|
)
|
|
except Exception:
|
|
logger.exception("Post-call appointment extraction failed")
|
|
|
|
|
|
async def run_call(websocket, serializer: TwilioFrameSerializer, caller_number=None, call_sid=None):
|
|
"""Phone entrypoint: wrap the Twilio Media Stream in a transport, run the shared agent."""
|
|
transport = FastAPIWebsocketTransport(
|
|
websocket=websocket,
|
|
params=FastAPIWebsocketParams(
|
|
audio_in_enabled=True,
|
|
audio_out_enabled=True,
|
|
audio_in_sample_rate=PIPELINE_SAMPLE_RATE,
|
|
audio_out_sample_rate=PIPELINE_SAMPLE_RATE,
|
|
add_wav_header=False,
|
|
serializer=serializer,
|
|
),
|
|
)
|
|
await run_agent(transport, caller_number=caller_number, call_sid=call_sid, do_capture=True)
|