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1476 1477 1478 1479 1480 1481 1482 1483 1484 1485 1486 1487 1488 1489 1490 1491 1492 1493 1494 1495 1496 1497 1498 1499 1500 1501 1502 1503 1504 1505 1506 1507 1508 1509 1510 1511 1512 1513 1514 1515 1516 1517 1518 1519 1520 1521 1522 1523 1524 1525 1526 1527 1528 1529 1530 1531 1532 1533 1534 1535 1536 1537 1538 1539 1540 1541 1542 1543 1544 1545 1546 1547 1548 1549 1550 1551 1552 1553 1554 1555 1556 1557 1558 1559 1560 1561 1562 1563 1564 1565 1566 1567 1568 1569 1570 1571 1572 1573 | """Conversation Service for managing active AI-to-AI conversations.
This service acts as the bridge between the WebSocket interface and the
ConversationManager. It handles the lifecycle of conversations, manages
active instances, and coordinates message streaming to connected clients.
Classes:
ConversationService: Main service for conversation management
ConversationInfo: Data class for conversation metadata
Example:
service = ConversationService(websocket_manager)
conversation_id = await service.start_conversation(
surveyor_id="surveyor_001",
patient_id="patient_001"
)
"""
import asyncio
import logging
from datetime import datetime
from typing import Dict, Optional, Any, List
from typing import Callable, Awaitable
from dataclasses import dataclass, field
from enum import Enum
import json
import sys
from pathlib import Path
import pysbd
# Add backend and project root to path for imports
BACKEND_DIR = Path(__file__).resolve().parents[2]
PROJECT_ROOT = Path(__file__).resolve().parents[3]
for path in (BACKEND_DIR, PROJECT_ROOT):
if str(path) not in sys.path:
sys.path.insert(0, str(path))
from config.settings import AppSettings, get_settings # noqa: E402
from backend.core.conversation_manager import ConversationManager # noqa: E402
from backend.core.llm_client import create_llm_client # noqa: E402
from backend.core.persona_system import get_persona_system # noqa: E402
from .conversation_ws import ConnectionManager # noqa: E402
from .storage_service import get_run_store # noqa: E402
from .storage_service import get_persona_store # noqa: E402
from backend.storage import RunRecord # noqa: E402
from backend.core.surveyor_knobs import compile_surveyor_attributes_overlay, compile_question_bank_overlay # noqa: E402
from backend.core.patient_knobs import compile_patient_attributes_overlay # noqa: E402
from backend.core.analysis_knobs import (
compile_analysis_rules_block,
DEFAULT_BOTTOM_UP_INSTRUCTIONS,
DEFAULT_BOTTOM_UP_ATTRIBUTES,
DEFAULT_RUBRIC_INSTRUCTIONS,
DEFAULT_RUBRIC_ATTRIBUTES,
DEFAULT_TOP_DOWN_INSTRUCTIONS,
DEFAULT_TOP_DOWN_ATTRIBUTES,
) # noqa: E402
from backend.core.universal_prompts import DEFAULT_PATIENT_SYSTEM_PROMPT, DEFAULT_SURVEYOR_SYSTEM_PROMPT # noqa: E402
# Setup logging
logger = logging.getLogger(__name__)
_SENTENCE_SEGMENTER = pysbd.Segmenter(language="en", clean=False)
SURVEYOR_MAX_TOKENS = 140
PATIENT_MAX_TOKENS = 220
def _split_sentences(text: str) -> List[str]:
normalized = " ".join((text or "").split())
if not normalized:
return []
try:
sentences = [s.strip() for s in _SENTENCE_SEGMENTER.segment(normalized) if s.strip()]
except Exception:
sentences = []
return sentences or [normalized]
def _normalize_confidence(value: Any) -> Optional[float]:
"""Normalize confidence values to a float in [0, 1]."""
try:
confidence = float(value)
except (TypeError, ValueError):
return None
if confidence < 0:
confidence = 0.0
if confidence > 1.0:
# Some models return percent-scale confidences.
if confidence <= 100.0:
confidence = confidence / 100.0
else:
confidence = 1.0
return max(0.0, min(1.0, confidence))
async def run_resource_agent_analysis(
*,
transcript: List[Dict[str, Any]],
llm_backend: str,
host: str,
model: str,
settings: AppSettings,
analysis_system_prompt: Optional[str] = None,
bottom_up_instructions: Optional[str] = None,
bottom_up_attributes: Optional[List[str]] = None,
rubric_instructions: Optional[str] = None,
rubric_attributes: Optional[List[str]] = None,
top_down_instructions: Optional[str] = None,
top_down_attributes: Optional[List[str]] = None,
top_down_template_id: Optional[str] = None,
top_down_template_version_id: Optional[str] = None,
top_down_template_categories: Optional[List[Dict[str, str]]] = None,
on_phase: Optional[Callable[[str, str], Awaitable[None]]] = None,
on_partial: Optional[Callable[[Dict[str, Any]], Awaitable[None]]] = None,
on_retry: Optional[Callable[[Dict[str, Any]], Awaitable[None]]] = None,
) -> Dict[str, Any]:
"""Run the resource agent analysis on an in-memory transcript and return parsed JSON.
This is the canonical analysis path. It performs three sequential passes:
1) Bottom-up findings
2) Care experience rubric
3) Top-down codebook (template-driven categories)
"""
llm_params: Dict[str, Any] = {
"timeout": settings.llm.timeout,
"max_retries": settings.llm.max_retries,
"retry_delay": settings.llm.retry_delay,
}
if llm_backend.lower() in ("openrouter", "open_router"):
llm_params["max_retries"] = 2
llm_params["retry_delay"] = 5.0
if settings.llm.api_key:
llm_params["api_key"] = settings.llm.api_key
if settings.llm.site_url:
llm_params["site_url"] = settings.llm.site_url
if settings.llm.app_name:
llm_params["app_name"] = settings.llm.app_name
if on_retry:
llm_params["on_retry"] = on_retry
client = create_llm_client(
llm_backend,
host=host,
model=model,
**llm_params,
)
schema_version = "9"
analysis_prompt_version = "v5-3pass"
evidence_catalog: Dict[str, Dict[str, Any]] = {}
for message in transcript:
message_index = message.get("index")
content = message.get("content", "") or ""
if not isinstance(message_index, int):
continue
for sentence_index, sentence in enumerate(_split_sentences(content)):
evidence_id = f"m{message_index}s{sentence_index}"
evidence_catalog[evidence_id] = {
"message_index": message_index,
"sentence_index": sentence_index,
"text": sentence,
}
base = (analysis_system_prompt or "").strip()
if not base:
base = (
"You are a clinical research 'resource agent'. You are given a transcript of a simulated "
"health survey conversation between a surveyor and a patient. Your task is to extract "
"post-hoc insights as strict JSON for a UI."
)
common_system_prompt = base.strip()
resolved_bottom_instructions = (bottom_up_instructions or "").strip() or DEFAULT_BOTTOM_UP_INSTRUCTIONS
bottom_system_prompt = (
common_system_prompt
+ "\n\nBottom-up analysis instructions:\n"
+ resolved_bottom_instructions.strip()
+ "\n\n"
+ compile_analysis_rules_block(bottom_up_attributes, defaults=DEFAULT_BOTTOM_UP_ATTRIBUTES)
).strip()
resolved_rubric_instructions = (rubric_instructions or "").strip() or DEFAULT_RUBRIC_INSTRUCTIONS
rubric_system_prompt = (
common_system_prompt
+ "\n\nCare experience rubric instructions:\n"
+ resolved_rubric_instructions.strip()
+ "\n\n"
+ compile_analysis_rules_block(rubric_attributes, defaults=DEFAULT_RUBRIC_ATTRIBUTES)
).strip()
resolved_top_down_instructions = (top_down_instructions or "").strip() or DEFAULT_TOP_DOWN_INSTRUCTIONS
top_down_system_prompt = (
common_system_prompt
+ "\n\nTop-down analysis instructions:\n"
+ resolved_top_down_instructions.strip()
+ "\n\n"
+ compile_analysis_rules_block(top_down_attributes, defaults=DEFAULT_TOP_DOWN_ATTRIBUTES)
).strip()
template_categories = top_down_template_categories or []
template_categories = [
{
"category_id": str(c.get("category_id") or "").strip(),
"label": str(c.get("label") or "").strip(),
"description": str(c.get("description") or "").strip(),
}
for c in template_categories
if isinstance(c, dict)
and isinstance(c.get("category_id"), str)
and str(c.get("category_id") or "").strip()
and isinstance(c.get("label"), str)
and str(c.get("label") or "").strip()
]
evidence_catalog_json = json.dumps(evidence_catalog, ensure_ascii=False)
def _base_prompt() -> str:
return (
"Evidence catalog (JSON object mapping evidence_id -> sentence):\n"
f"{evidence_catalog_json}\n\n"
"Output MUST be valid JSON only (no markdown, no backticks).\n"
)
def _bottom_up_prompt() -> str:
return (
_base_prompt()
+ "Return JSON matching this schema:\n"
"{\n"
f" \"schema_version\": \"{schema_version}\",\n"
f" \"analysis_prompt_version\": \"{analysis_prompt_version}\",\n"
" \"themes\": [\n"
" {\n"
" \"code\": string, // 1-3 word label\n"
" \"summary\": string,\n"
" \"evidence\": [ {\"evidence_id\": string} ],\n"
" \"confidence\": number // 0..1\n"
" }\n"
" ]\n"
"}\n"
)
def _rubric_prompt() -> str:
return (
_base_prompt()
+ "Return JSON matching this schema:\n"
"{\n"
f" \"schema_version\": \"{schema_version}\",\n"
f" \"analysis_prompt_version\": \"{analysis_prompt_version}\",\n"
" \"care_experience_breakdown\": {\n"
" \"positive\": number, // 0-100, sums to 100 with mixed + negative\n"
" \"mixed\": number,\n"
" \"negative\": number\n"
" },\n"
" \"care_experience\": {\n"
" \"positive\": {\n"
" \"summary\": string,\n"
" \"reasons\": [string],\n"
" \"evidence\": [ {\"evidence_id\": string} ],\n"
" \"confidence\": number // 0..1\n"
" },\n"
" \"mixed\": {\n"
" \"summary\": string,\n"
" \"reasons\": [string],\n"
" \"evidence\": [ {\"evidence_id\": string} ],\n"
" \"confidence\": number // 0..1\n"
" },\n"
" \"negative\": {\n"
" \"summary\": string,\n"
" \"reasons\": [string],\n"
" \"evidence\": [ {\"evidence_id\": string} ],\n"
" \"confidence\": number // 0..1\n"
" },\n"
" \"neutral\": {\n"
" \"summary\": string,\n"
" \"reasons\": [string],\n"
" \"evidence\": [ {\"evidence_id\": string} ],\n"
" \"confidence\": number // 0..1\n"
" }\n"
" }\n"
"}\n"
)
def _top_down_prompt() -> str:
prompt = (
_base_prompt()
+ "Return JSON matching this schema:\n"
"{\n"
f" \"schema_version\": \"{schema_version}\",\n"
f" \"analysis_prompt_version\": \"{analysis_prompt_version}\",\n"
" \"top_down_codebook\": {\n"
" \"template_id\": string,\n"
" \"template_version_id\": string,\n"
" \"categories\": [\n"
" {\n"
" \"category_id\": string,\n"
" \"label\": string,\n"
" \"items\": [ {\"code\": string, \"summary\": string, \"evidence\": [ {\"evidence_id\": string} ], \"confidence\": number // 0..1 } ]\n"
" }\n"
" ]\n"
" }\n"
"}\n"
)
if template_categories:
template_json = json.dumps(
{
"template_id": top_down_template_id or "",
"template_version_id": top_down_template_version_id or "",
"categories": template_categories,
},
ensure_ascii=False,
)
prompt = (
prompt
+ "\n\nTop-down codebook template (JSON):\n"
+ template_json
+ "\n\nFor top-down coding, you MUST populate top_down_codebook.categories in the same order as the template. "
"Use the exact category_id and label from the template. If no evidence supports a category, return an empty items array.\n"
)
return prompt
async def _phase(name: str, status: str):
if on_phase is None:
return
await on_phase(name, status)
async def _partial(data: Dict[str, Any]):
if on_partial is None:
return
await on_partial(data)
try:
await _phase("bottom_up", "pending")
await _phase("rubric", "pending")
await _phase("top_down", "pending")
await _phase("bottom_up", "running")
bottom_raw = await client.generate(prompt=_bottom_up_prompt(), system_prompt=bottom_system_prompt, temperature=0.2)
bottom = json.loads(bottom_raw) if isinstance(bottom_raw, str) else {}
themes = bottom.get("themes")
if not isinstance(themes, list):
themes = bottom.get("health_situations")
if not isinstance(themes, list):
themes = []
for item in themes:
normalized = _normalize_confidence(item.get("confidence"))
if normalized is not None:
item["confidence"] = normalized
await _partial({
"schema_version": schema_version,
"analysis_prompt_version": analysis_prompt_version,
"evidence_catalog": evidence_catalog,
"themes": themes,
})
await _phase("bottom_up", "complete")
await _phase("rubric", "running")
rubric_raw = await client.generate(prompt=_rubric_prompt(), system_prompt=rubric_system_prompt, temperature=0.2)
rubric = json.loads(rubric_raw) if isinstance(rubric_raw, str) else {}
breakdown = rubric.get("care_experience_breakdown") or {}
breakdown_normalized = {}
if isinstance(breakdown, dict):
for key in ("positive", "mixed", "negative"):
value = breakdown.get(key)
try:
num = float(value)
except (TypeError, ValueError):
num = None
if num is not None:
if num <= 1.0:
num = num * 100.0
breakdown_normalized[key] = max(0.0, min(100.0, num))
if breakdown_normalized:
total = sum(breakdown_normalized.values())
if total > 0:
for key in breakdown_normalized:
breakdown_normalized[key] = round((breakdown_normalized[key] / total) * 100.0, 1)
else:
breakdown_normalized = None
care_experience = rubric.get("care_experience") or {}
for key in ("positive", "mixed", "negative", "neutral"):
box = care_experience.get(key)
if isinstance(box, dict):
normalized = _normalize_confidence(box.get("confidence"))
if normalized is not None:
box["confidence"] = normalized
await _partial({
"care_experience_breakdown": breakdown_normalized,
"care_experience": care_experience,
})
await _phase("rubric", "complete")
await _phase("top_down", "running")
top_raw = await client.generate(prompt=_top_down_prompt(), system_prompt=top_down_system_prompt, temperature=0.2)
top = json.loads(top_raw) if isinstance(top_raw, str) else {}
td_book = top.get("top_down_codebook")
if not isinstance(td_book, dict):
td_book = None
if isinstance(td_book, dict):
categories = td_book.get("categories") or []
if isinstance(categories, list):
for cat in categories:
if not isinstance(cat, dict):
continue
items = cat.get("items") or []
if not isinstance(items, list):
continue
for item in items:
if not isinstance(item, dict):
continue
normalized = _normalize_confidence(item.get("confidence"))
if normalized is not None:
item["confidence"] = normalized
await _partial({
"top_down_codebook": td_book or {
"template_id": top_down_template_id or "",
"template_version_id": top_down_template_version_id or "",
"categories": [],
},
})
await _phase("top_down", "complete")
return {
"schema_version": schema_version,
"analysis_prompt_version": analysis_prompt_version,
"evidence_catalog": evidence_catalog,
"themes": themes,
"care_experience_breakdown": breakdown_normalized,
"care_experience": care_experience,
"top_down_codebook": td_book or {
"template_id": top_down_template_id or "",
"template_version_id": top_down_template_version_id or "",
"categories": [],
},
}
finally:
try:
await client.close()
except Exception:
pass
class ConversationStatus(Enum):
"""Status of managed conversations."""
STARTING = "starting"
RUNNING = "running"
PAUSED = "paused"
STOPPING = "stopping"
COMPLETED = "completed"
ERROR = "error"
@dataclass
class ConversationInfo:
"""Information about an active conversation."""
conversation_id: str
surveyor_persona_id: str
patient_persona_id: str
host: str
model: str
llm_backend: str
status: ConversationStatus
created_at: datetime
message_count: int = 0
task: Optional[asyncio.Task] = None
stop_requested: bool = False
surveyor_system_prompt: str = ""
patient_system_prompt: str = ""
analysis_system_prompt: str = ""
bottom_up_instructions: str = ""
bottom_up_attributes: List[str] = field(default_factory=list)
rubric_instructions: str = ""
rubric_attributes: List[str] = field(default_factory=list)
top_down_instructions: str = ""
top_down_attributes: List[str] = field(default_factory=list)
top_down_template_id: str = ""
top_down_template_version_id: str = ""
top_down_template_categories: List[Dict[str, str]] = field(default_factory=list)
patient_attributes: List[str] = field(default_factory=list)
surveyor_attributes: List[str] = field(default_factory=list)
surveyor_question_bank: Optional[str] = None
@dataclass
class HumanChatInfo:
"""Information about an active human-to-surveyor chat session."""
conversation_id: str
surveyor_persona_id: str
patient_persona_id: str
host: str
model: str
llm_backend: str
status: ConversationStatus
created_at: datetime
stop_requested: bool = False
surveyor_system_prompt: str = ""
patient_system_prompt: str = ""
analysis_system_prompt: str = ""
bottom_up_instructions: str = ""
bottom_up_attributes: List[str] = field(default_factory=list)
rubric_instructions: str = ""
rubric_attributes: List[str] = field(default_factory=list)
top_down_instructions: str = ""
top_down_attributes: List[str] = field(default_factory=list)
top_down_template_id: str = ""
top_down_template_version_id: str = ""
top_down_template_categories: List[Dict[str, str]] = field(default_factory=list)
patient_attributes: List[str] = field(default_factory=list)
surveyor_attributes: List[str] = field(default_factory=list)
surveyor_question_bank: Optional[str] = None
ai_role: str = "surveyor" # "surveyor" or "patient"
asked_question_ids: List[str] = field(default_factory=list)
lock: asyncio.Lock = field(default_factory=asyncio.Lock)
client: Any = None
class ConversationService:
"""Service for managing AI-to-AI conversation instances.
This service coordinates between the ConversationManager and WebSocket
infrastructure to provide real-time conversation streaming to web clients.
Attributes:
websocket_manager: WebSocket connection manager for broadcasting
persona_system: Persona system for loading personas
active_conversations: Dict of active conversation instances
settings: Shared application settings
"""
def __init__(self, websocket_manager: ConnectionManager, settings: Optional[AppSettings] = None):
"""Initialize conversation service.
Args:
websocket_manager: WebSocket manager for message broadcasting
settings: Shared application settings (optional)
"""
self.websocket_manager = websocket_manager
self.persona_system = get_persona_system()
self.active_conversations: Dict[str, ConversationInfo] = {}
self.active_human_chats: Dict[str, HumanChatInfo] = {}
self.transcripts: Dict[str, List[Dict[str, Any]]] = {}
self.settings = settings or get_settings()
def _persona_question_bank(self, persona: Dict[str, Any]) -> Optional[str]:
items = persona.get("question_bank_items")
lines: List[str] = []
if isinstance(items, list):
for item in items:
if isinstance(item, str) and item.strip():
lines.append(item.strip())
elif isinstance(item, dict):
text = item.get("text")
if isinstance(text, str) and text.strip():
lines.append(text.strip())
raw = "\n".join(lines).strip()
return raw or None
def _persona_attributes(self, persona: Dict[str, Any]) -> List[str]:
attrs = persona.get("attributes")
if not isinstance(attrs, list):
return []
return [s.strip() for s in attrs if isinstance(s, str) and s.strip()]
async def _resolve_top_down_template(self, template_id_override: Optional[str] = None) -> Dict[str, Any]:
store = get_persona_store()
override = template_id_override.strip() if isinstance(template_id_override, str) else ""
if override:
record = await store.get_analysis_template(override, include_deleted=False)
if record:
return {
"template_id": record.template_id,
"template_version_id": record.current_version_id,
"bottom_up_instructions": record.bottom_up_instructions,
"bottom_up_attributes": list(record.bottom_up_attributes),
"rubric_instructions": record.rubric_instructions,
"rubric_attributes": list(record.rubric_attributes),
"top_down_instructions": record.top_down_instructions,
"top_down_attributes": list(record.top_down_attributes),
"categories": record.categories,
}
template_id = await store.get_setting("top_down_codebook_template_id")
template_id_str = template_id.strip() if isinstance(template_id, str) else ""
record = await store.get_analysis_template(template_id_str, include_deleted=False) if template_id_str else None
if record:
return {
"template_id": record.template_id,
"template_version_id": record.current_version_id,
"bottom_up_instructions": record.bottom_up_instructions,
"bottom_up_attributes": list(record.bottom_up_attributes),
"rubric_instructions": record.rubric_instructions,
"rubric_attributes": list(record.rubric_attributes),
"top_down_instructions": record.top_down_instructions,
"top_down_attributes": list(record.top_down_attributes),
"categories": record.categories,
}
return {
"template_id": template_id_str,
"template_version_id": "",
"bottom_up_instructions": DEFAULT_BOTTOM_UP_INSTRUCTIONS,
"bottom_up_attributes": list(DEFAULT_BOTTOM_UP_ATTRIBUTES),
"rubric_instructions": DEFAULT_RUBRIC_INSTRUCTIONS,
"rubric_attributes": list(DEFAULT_RUBRIC_ATTRIBUTES),
"top_down_instructions": DEFAULT_TOP_DOWN_INSTRUCTIONS,
"top_down_attributes": list(DEFAULT_TOP_DOWN_ATTRIBUTES),
"categories": [],
}
async def start_human_chat(
self,
conversation_id: str,
surveyor_persona_id: str,
patient_persona_id: str,
host: Optional[str] = None,
model: Optional[str] = None,
patient_attributes: Optional[List[str]] = None, # deprecated (persona content is DB-canonical)
surveyor_system_prompt: Optional[str] = None, # deprecated (DB-canonical)
patient_system_prompt: Optional[str] = None, # deprecated (DB-canonical)
analysis_attributes: Optional[List[str]] = None, # deprecated legacy field (ignore)
surveyor_attributes: Optional[List[str]] = None,
surveyor_question_bank: Optional[str] = None,
ai_role: Optional[str] = None,
top_down_codebook_template_id: Optional[str] = None,
) -> bool:
"""Start a new human-to-surveyor chat session."""
if conversation_id in self.active_conversations or conversation_id in self.active_human_chats:
logger.warning(f"Conversation {conversation_id} already exists")
return False
surveyor_persona = self.persona_system.get_persona(surveyor_persona_id)
patient_persona = self.persona_system.get_persona(patient_persona_id)
if not surveyor_persona or not patient_persona:
await self._send_error(conversation_id, "Invalid persona IDs")
return False
resolved_host = host or self.settings.llm.host
resolved_model = model or self.settings.llm.model
resolved_backend = self.settings.llm.backend
resolved_ai_role = ai_role if ai_role in ("surveyor", "patient") else "surveyor"
# DB-canonical settings (shared on HF)
store = get_persona_store()
sp = await store.get_setting("surveyor_system_prompt")
pp = await store.get_setting("patient_system_prompt")
asp = await store.get_setting("analysis_system_prompt")
resolved_surveyor_prompt = sp if isinstance(sp, str) and sp.strip() else DEFAULT_SURVEYOR_SYSTEM_PROMPT
resolved_patient_prompt = pp if isinstance(pp, str) and pp.strip() else DEFAULT_PATIENT_SYSTEM_PROMPT
resolved_analysis_prompt = asp if isinstance(asp, str) and asp.strip() else ""
template = await self._resolve_top_down_template(top_down_codebook_template_id)
resolved_bottom_instructions = str(template.get("bottom_up_instructions") or "").strip() or DEFAULT_BOTTOM_UP_INSTRUCTIONS
resolved_top_down_instructions = str(template.get("top_down_instructions") or "").strip() or DEFAULT_TOP_DOWN_INSTRUCTIONS
resolved_rubric_instructions = str(template.get("rubric_instructions") or "").strip() or DEFAULT_RUBRIC_INSTRUCTIONS
bua = template.get("bottom_up_attributes")
ra = template.get("rubric_attributes")
tda = template.get("top_down_attributes")
resolved_bottom_attrs = [s.strip() for s in bua if isinstance(s, str) and s.strip()] if isinstance(bua, list) else []
resolved_rubric_attrs = [s.strip() for s in ra if isinstance(s, str) and s.strip()] if isinstance(ra, list) else []
resolved_top_down_attrs = [s.strip() for s in tda if isinstance(s, str) and s.strip()] if isinstance(tda, list) else []
if not resolved_bottom_attrs:
resolved_bottom_attrs = list(DEFAULT_BOTTOM_UP_ATTRIBUTES)
if not resolved_rubric_attrs:
resolved_rubric_attrs = list(DEFAULT_RUBRIC_ATTRIBUTES)
if not resolved_top_down_attrs:
resolved_top_down_attrs = list(DEFAULT_TOP_DOWN_ATTRIBUTES)
chat_info = HumanChatInfo(
conversation_id=conversation_id,
surveyor_persona_id=surveyor_persona_id,
patient_persona_id=patient_persona_id,
host=resolved_host,
model=resolved_model,
llm_backend=resolved_backend,
surveyor_system_prompt=resolved_surveyor_prompt,
patient_system_prompt=resolved_patient_prompt,
analysis_system_prompt=resolved_analysis_prompt,
bottom_up_instructions=resolved_bottom_instructions,
bottom_up_attributes=resolved_bottom_attrs,
rubric_instructions=resolved_rubric_instructions,
rubric_attributes=resolved_rubric_attrs,
top_down_instructions=resolved_top_down_instructions,
top_down_attributes=resolved_top_down_attrs,
top_down_template_id=str(template.get("template_id") or ""),
top_down_template_version_id=str(template.get("template_version_id") or ""),
top_down_template_categories=list(template.get("categories") or []),
patient_attributes=self._persona_attributes(patient_persona),
surveyor_attributes=self._persona_attributes(surveyor_persona),
surveyor_question_bank=self._persona_question_bank(surveyor_persona),
ai_role=resolved_ai_role,
status=ConversationStatus.STARTING,
created_at=datetime.now(),
)
llm_parameters = self._build_llm_parameters()
client_kwargs = {"host": resolved_host, "model": resolved_model}
client_kwargs.update(llm_parameters)
chat_info.client = create_llm_client(resolved_backend, **client_kwargs)
self.active_human_chats[conversation_id] = chat_info
self.transcripts[conversation_id] = []
await self._send_status_update(conversation_id, ConversationStatus.STARTING)
await self._send_status_update(conversation_id, ConversationStatus.RUNNING)
# If the AI is the surveyor, we can optionally generate an initial greeting + first question.
if chat_info.ai_role == "surveyor":
try:
greeting = await self._generate_human_chat_surveyor_message(
chat_info,
transcript=[],
user_prompt=(
"Please greet the patient briefly and ask your first survey question."
),
)
await self._append_and_broadcast_transcript(
conversation_id=conversation_id,
role="surveyor",
persona=surveyor_persona.get("name", "Surveyor"),
content=greeting,
)
except Exception as e:
logger.error(f"Failed to generate human-chat greeting: {e}")
# It's OK to proceed without a greeting.
else:
# If the AI is the patient, inject a short deterministic start so "Start" feels responsive
# without spending an LLM call on a trivial greeting.
try:
await self._append_and_broadcast_transcript(
conversation_id=conversation_id,
role="system",
persona="System",
content="You call the patient, and they picked up the phone.",
)
await self._append_and_broadcast_transcript(
conversation_id=conversation_id,
role="patient",
persona=patient_persona.get("name", "Patient"),
content="Hello?",
)
except Exception as e:
logger.error(f"Failed to inject human-chat AI-patient starter messages: {e}")
return True
async def human_chat_message(self, conversation_id: str, text: str) -> None:
"""Process a human patient message and generate a surveyor reply."""
chat_info = self.active_human_chats.get(conversation_id)
if not chat_info:
await self._send_error(conversation_id, "Human chat not found")
return
async with chat_info.lock:
if chat_info.stop_requested or chat_info.status in (ConversationStatus.COMPLETED, ConversationStatus.ERROR):
return
patient_persona = self.persona_system.get_persona(chat_info.patient_persona_id) or {}
surveyor_persona = self.persona_system.get_persona(chat_info.surveyor_persona_id) or {}
transcript = self.transcripts.get(conversation_id, [])
if chat_info.ai_role == "patient":
# Human is the surveyor; AI responds as the patient.
await self._append_and_broadcast_transcript(
conversation_id=conversation_id,
role="surveyor",
persona=f"{surveyor_persona.get('name', 'Surveyor')} (Human)",
content=text,
)
transcript = self.transcripts.get(conversation_id, [])
last_surveyor_msg = next((m for m in reversed(transcript) if m.get("role") == "surveyor"), None)
last_text = (last_surveyor_msg or {}).get("content", text)
reply = await self._generate_human_chat_patient_message(
chat_info,
transcript=transcript,
user_prompt=(
f"The interviewer just said: '{last_text}'. "
"Please respond naturally as your persona would."
),
)
await self._append_and_broadcast_transcript(
conversation_id=conversation_id,
role="patient",
persona=patient_persona.get("name", "Patient"),
content=reply,
)
return
# Default: Human is the patient; AI responds as the surveyor.
patient_label = patient_persona.get("name", "Patient")
await self._append_and_broadcast_transcript(
conversation_id=conversation_id,
role="patient",
persona=f"{patient_label} (Human)",
content=text,
)
transcript = self.transcripts.get(conversation_id, [])
reply = await self._generate_human_chat_surveyor_message(
chat_info,
transcript=transcript,
user_prompt=(
f"The patient just said: '{text}'. Respond with a brief acknowledgment and ask an appropriate follow-up question."
),
)
await self._append_and_broadcast_transcript(
conversation_id=conversation_id,
role="surveyor",
persona=surveyor_persona.get("name", "Surveyor"),
content=reply,
)
async def end_human_chat(self, conversation_id: str) -> bool:
"""End a human-to-surveyor chat session and run analysis."""
chat_info = self.active_human_chats.get(conversation_id)
if not chat_info:
return False
async with chat_info.lock:
if chat_info.status == ConversationStatus.COMPLETED:
return True
chat_info.status = ConversationStatus.COMPLETED
await self._send_status_update(conversation_id, ConversationStatus.COMPLETED)
asked_ids = None
try:
asked_ids = list(getattr(chat_info, "asked_question_ids", None) or [])
except Exception:
asked_ids = None
await self._run_resource_agent(conversation_id, asked_question_ids=asked_ids)
self.active_human_chats.pop(conversation_id, None)
self.transcripts.pop(conversation_id, None)
try:
if chat_info.client is not None:
await chat_info.client.close()
except Exception:
pass
return True
async def _append_and_broadcast_transcript(
self,
*,
conversation_id: str,
role: str,
persona: str,
content: str,
) -> None:
timestamp = datetime.now().isoformat()
idx = len(self.transcripts.setdefault(conversation_id, []))
self.transcripts[conversation_id].append(
{
"index": idx,
"role": role,
"persona": persona,
"content": content,
"timestamp": timestamp,
}
)
await self.websocket_manager.send_to_conversation(
conversation_id,
{
"type": "conversation_message",
"conversation_id": conversation_id,
"role": role,
"persona": persona,
"content": content,
"timestamp": timestamp,
},
)
async def _generate_human_chat_surveyor_message(
self,
chat_info: HumanChatInfo,
*,
transcript: List[Dict[str, Any]],
user_prompt: str,
) -> str:
conversation_history = [
{"role": "assistant" if msg.get("role") == "surveyor" else "user", "content": msg.get("content", "")}
for msg in (transcript or [])
]
system_prompt, prompt_with_history = self.persona_system.build_conversation_prompt(
persona_id=chat_info.surveyor_persona_id,
conversation_history=conversation_history,
user_prompt=user_prompt,
base_system_prompt=getattr(chat_info, "surveyor_system_prompt", None),
)
qb = compile_question_bank_overlay(chat_info.surveyor_question_bank)
if qb:
system_prompt = (system_prompt + "\n\n" + qb).strip()
attrs = compile_surveyor_attributes_overlay(chat_info.surveyor_attributes)
if attrs:
system_prompt = (system_prompt + "\n\n" + attrs).strip()
patient_persona = self.persona_system.get_persona(chat_info.patient_persona_id) or {}
try:
patient_context = self.persona_system.prompt_builder.build_system_prompt(patient_persona)
except Exception:
patient_context = patient_persona.get("system_prompt", "") or ""
patient_context = (patient_context or "").strip()
pat_lines = [s.strip() for s in (chat_info.patient_attributes or []) if isinstance(s, str) and s.strip()]
if pat_lines:
bullets = "\n".join(f"- {line}" for line in pat_lines)
patient_context = (patient_context + "\n\nPatient attributes (for context only):\n" + bullets).strip()
if patient_context:
system_prompt = (system_prompt + "\n\nPatient background (for context only):\n" + patient_context).strip()
final_prompt = prompt_with_history
if chat_info.surveyor_question_bank:
final_prompt = (
f"{prompt_with_history}\n\n"
"You must pick exactly ONE question from the question bank that has not been asked yet and fits the flow.\n"
f"Already asked question ids: {chat_info.asked_question_ids}\n\n"
"Return STRICT JSON only (no markdown):\n"
"{\n"
" \"selected_question_id\": string, // e.g. \"q01\"\n"
" \"message\": string\n"
"}\n"
)
response = await chat_info.client.generate(
prompt=final_prompt,
system_prompt=system_prompt,
max_tokens=SURVEYOR_MAX_TOKENS,
temperature=0.4,
)
cleaned = (response or "").strip()
if chat_info.surveyor_question_bank and cleaned:
import json
try:
parsed = json.loads(cleaned)
qid = parsed.get("selected_question_id")
msg = parsed.get("message")
if isinstance(qid, str) and qid and qid not in chat_info.asked_question_ids:
chat_info.asked_question_ids.append(qid)
if isinstance(msg, str) and msg.strip():
cleaned = msg.strip()
except Exception:
pass
return cleaned or "I apologize—I'm having trouble responding right now. Could you repeat that?"
async def _generate_human_chat_patient_message(
self,
chat_info: HumanChatInfo,
*,
transcript: List[Dict[str, Any]],
user_prompt: str,
) -> str:
conversation_history = [
{"role": "assistant" if msg.get("role") == "patient" else "user", "content": msg.get("content", "")}
for msg in (transcript or [])
]
system_prompt, prompt_with_history = self.persona_system.build_conversation_prompt(
persona_id=chat_info.patient_persona_id,
conversation_history=conversation_history,
user_prompt=user_prompt,
base_system_prompt=getattr(chat_info, "patient_system_prompt", None),
)
system_prompt = (system_prompt or "").strip()
pat_attrs = compile_patient_attributes_overlay(chat_info.patient_attributes)
if pat_attrs:
system_prompt = (system_prompt + "\n\n" + pat_attrs).strip()
response = await chat_info.client.generate(
prompt=prompt_with_history,
system_prompt=system_prompt,
max_tokens=PATIENT_MAX_TOKENS,
temperature=0.7,
)
return (response or "").strip() or "I'm sorry—I'm having trouble responding right now."
async def start_conversation(self,
conversation_id: str,
surveyor_persona_id: str,
patient_persona_id: str,
host: Optional[str] = None,
model: Optional[str] = None,
patient_attributes: Optional[List[str]] = None, # deprecated (DB-canonical)
surveyor_system_prompt: Optional[str] = None, # deprecated (DB-canonical)
patient_system_prompt: Optional[str] = None, # deprecated (DB-canonical)
analysis_attributes: Optional[List[str]] = None, # deprecated legacy field (ignore)
surveyor_attributes: Optional[List[str]] = None,
surveyor_question_bank: Optional[str] = None,
top_down_codebook_template_id: Optional[str] = None) -> bool:
"""Start a new AI-to-AI conversation.
Args:
conversation_id: Unique identifier for the conversation
surveyor_persona_id: ID of the surveyor persona
patient_persona_id: ID of the patient persona
host: Ollama server host
model: LLM model to use
Returns:
True if conversation started successfully
"""
if conversation_id in self.active_conversations:
logger.warning(f"Conversation {conversation_id} already exists")
return False
try:
# Load personas
surveyors = self.persona_system.list_personas("surveyor")
patients = self.persona_system.list_personas("patient")
surveyor_persona = next((p for p in surveyors if p.get("id") == surveyor_persona_id), None)
patient_persona = next((p for p in patients if p.get("id") == patient_persona_id), None)
if not surveyor_persona or not patient_persona:
await self._send_error(conversation_id, "Invalid persona IDs")
return False
# Resolve LLM configuration
resolved_host = host or self.settings.llm.host
resolved_model = model or self.settings.llm.model
resolved_backend = self.settings.llm.backend
store = get_persona_store()
sp = await store.get_setting("surveyor_system_prompt")
pp = await store.get_setting("patient_system_prompt")
asp = await store.get_setting("analysis_system_prompt")
resolved_surveyor_prompt = sp if isinstance(sp, str) and sp.strip() else DEFAULT_SURVEYOR_SYSTEM_PROMPT
resolved_patient_prompt = pp if isinstance(pp, str) and pp.strip() else DEFAULT_PATIENT_SYSTEM_PROMPT
resolved_analysis_prompt = asp if isinstance(asp, str) and asp.strip() else ""
template = await self._resolve_top_down_template(top_down_codebook_template_id)
resolved_bottom_instructions = str(template.get("bottom_up_instructions") or "").strip() or DEFAULT_BOTTOM_UP_INSTRUCTIONS
resolved_top_down_instructions = str(template.get("top_down_instructions") or "").strip() or DEFAULT_TOP_DOWN_INSTRUCTIONS
resolved_rubric_instructions = str(template.get("rubric_instructions") or "").strip() or DEFAULT_RUBRIC_INSTRUCTIONS
bua = template.get("bottom_up_attributes")
ra = template.get("rubric_attributes")
tda = template.get("top_down_attributes")
resolved_bottom_attrs = [s.strip() for s in bua if isinstance(s, str) and s.strip()] if isinstance(bua, list) else []
resolved_rubric_attrs = [s.strip() for s in ra if isinstance(s, str) and s.strip()] if isinstance(ra, list) else []
resolved_top_down_attrs = [s.strip() for s in tda if isinstance(s, str) and s.strip()] if isinstance(tda, list) else []
if not resolved_bottom_attrs:
resolved_bottom_attrs = list(DEFAULT_BOTTOM_UP_ATTRIBUTES)
if not resolved_rubric_attrs:
resolved_rubric_attrs = list(DEFAULT_RUBRIC_ATTRIBUTES)
if not resolved_top_down_attrs:
resolved_top_down_attrs = list(DEFAULT_TOP_DOWN_ATTRIBUTES)
# Create conversation info
conv_info = ConversationInfo(
conversation_id=conversation_id,
surveyor_persona_id=surveyor_persona_id,
patient_persona_id=patient_persona_id,
host=resolved_host,
model=resolved_model,
llm_backend=resolved_backend,
surveyor_system_prompt=resolved_surveyor_prompt,
patient_system_prompt=resolved_patient_prompt,
analysis_system_prompt=resolved_analysis_prompt,
bottom_up_instructions=resolved_bottom_instructions,
bottom_up_attributes=resolved_bottom_attrs,
rubric_instructions=resolved_rubric_instructions,
rubric_attributes=resolved_rubric_attrs,
top_down_instructions=resolved_top_down_instructions,
top_down_attributes=resolved_top_down_attrs,
top_down_template_id=str(template.get("template_id") or ""),
top_down_template_version_id=str(template.get("template_version_id") or ""),
top_down_template_categories=list(template.get("categories") or []),
patient_attributes=self._persona_attributes(patient_persona),
surveyor_attributes=self._persona_attributes(surveyor_persona),
surveyor_question_bank=self._persona_question_bank(surveyor_persona),
status=ConversationStatus.STARTING,
created_at=datetime.now()
)
self.active_conversations[conversation_id] = conv_info
self.transcripts[conversation_id] = []
# Send status update
await self._send_status_update(conversation_id, ConversationStatus.STARTING)
# Create and start conversation manager
llm_parameters = self._build_llm_parameters()
manager = ConversationManager(
surveyor_persona=surveyor_persona,
patient_persona=patient_persona,
host=resolved_host,
model=resolved_model,
llm_backend=self.settings.llm.backend,
llm_parameters=llm_parameters,
surveyor_system_prompt=conv_info.surveyor_system_prompt,
patient_system_prompt=conv_info.patient_system_prompt,
patient_attributes=conv_info.patient_attributes,
surveyor_attributes=conv_info.surveyor_attributes,
surveyor_question_bank=conv_info.surveyor_question_bank,
)
# Start conversation streaming task
conv_info.task = asyncio.create_task(
self._stream_conversation(conversation_id, manager)
)
conv_info.status = ConversationStatus.RUNNING
await self._send_status_update(conversation_id, ConversationStatus.RUNNING)
logger.info(f"Started conversation {conversation_id}")
return True
except Exception as e:
logger.error(f"Failed to start conversation {conversation_id}: {e}")
await self._send_error(conversation_id, f"Failed to start conversation: {str(e)}")
# Clean up
if conversation_id in self.active_conversations:
del self.active_conversations[conversation_id]
return False
async def stop_conversation(self, conversation_id: str) -> bool:
"""Stop an active conversation.
Args:
conversation_id: ID of conversation to stop
Returns:
True if conversation stopped successfully
"""
if conversation_id not in self.active_conversations and conversation_id not in self.active_human_chats:
logger.warning(f"Conversation {conversation_id} not found")
return False
if conversation_id in self.active_human_chats:
chat_info = self.active_human_chats[conversation_id]
try:
chat_info.stop_requested = True
chat_info.status = ConversationStatus.STOPPING
await self._send_status_update(conversation_id, ConversationStatus.STOPPING)
chat_info.status = ConversationStatus.COMPLETED
await self._send_status_update(conversation_id, ConversationStatus.COMPLETED)
self.active_human_chats.pop(conversation_id, None)
self.transcripts.pop(conversation_id, None)
try:
if chat_info.client is not None:
await chat_info.client.close()
except Exception:
pass
logger.info(f"Stopped human chat {conversation_id}")
return True
except Exception as e:
logger.error(f"Error stopping human chat {conversation_id}: {e}")
await self._send_error(conversation_id, f"Error stopping conversation: {str(e)}")
return False
conv_info = self.active_conversations[conversation_id]
try:
conv_info.stop_requested = True
conv_info.status = ConversationStatus.STOPPING
await self._send_status_update(conversation_id, ConversationStatus.STOPPING)
# Cancel the conversation task
if conv_info.task and not conv_info.task.done():
conv_info.task.cancel()
try:
await conv_info.task
except asyncio.CancelledError:
pass
# Update status and clean up
conv_info.status = ConversationStatus.COMPLETED
await self._send_status_update(conversation_id, ConversationStatus.COMPLETED)
del self.active_conversations[conversation_id]
self.transcripts.pop(conversation_id, None)
logger.info(f"Stopped conversation {conversation_id}")
return True
except Exception as e:
logger.error(f"Error stopping conversation {conversation_id}: {e}")
conv_info.status = ConversationStatus.ERROR
await self._send_error(conversation_id, f"Error stopping conversation: {str(e)}")
return False
async def get_conversation_status(self, conversation_id: str) -> Optional[Dict]:
"""Get status of a conversation.
Args:
conversation_id: ID of conversation
Returns:
Dict with conversation status or None if not found
"""
if conversation_id not in self.active_conversations:
return None
conv_info = self.active_conversations[conversation_id]
return {
"conversation_id": conversation_id,
"status": conv_info.status.value,
"surveyor_persona_id": conv_info.surveyor_persona_id,
"patient_persona_id": conv_info.patient_persona_id,
"created_at": conv_info.created_at.isoformat(),
"message_count": conv_info.message_count
}
async def list_active_conversations(self) -> Dict[str, Dict]:
"""List all active conversations.
Returns:
Dict mapping conversation_id to status info
"""
result = {}
for conv_id, conv_info in self.active_conversations.items():
result[conv_id] = {
"status": conv_info.status.value,
"surveyor_persona_id": conv_info.surveyor_persona_id,
"patient_persona_id": conv_info.patient_persona_id,
"created_at": conv_info.created_at.isoformat(),
"message_count": conv_info.message_count
}
return result
async def _stream_conversation(self, conversation_id: str, manager: ConversationManager):
"""Stream conversation messages to WebSocket clients.
Args:
conversation_id: ID of the conversation
manager: ConversationManager instance to stream from
"""
conv_info = self.active_conversations.get(conversation_id)
if not conv_info:
return
try:
async for message in manager.conduct_conversation():
# Check if conversation was stopped
if conversation_id not in self.active_conversations:
break
# Update message count
conv_info.message_count += 1
# Capture transcript utterance (MVP: utterances only)
try:
self.transcripts.setdefault(conversation_id, []).append({
"index": conv_info.message_count - 1,
"role": message.get("role", "unknown"),
"persona": message.get("persona"),
"content": message.get("content", ""),
"timestamp": message.get("timestamp"),
})
except Exception:
pass
# Add conversation metadata
websocket_message = {
"type": "conversation_message",
"conversation_id": conversation_id,
**message
}
# Send to all connected clients
await self.websocket_manager.send_to_conversation(
conversation_id, websocket_message
)
logger.info(f"Streamed message {conv_info.message_count} for conversation {conversation_id}: {message.get('role', 'unknown')} - {len(message.get('content', ''))} chars")
except asyncio.CancelledError:
logger.info(f"Conversation {conversation_id} streaming cancelled")
raise
except Exception as e:
logger.error(f"Error streaming conversation {conversation_id}: {e}")
conv_info.status = ConversationStatus.ERROR
await self._send_error(conversation_id, f"Streaming error: {str(e)}")
finally:
# Clean up conversation manager
try:
await manager.close()
except:
pass
# Mark as completed if not already in error state
if conv_info.status != ConversationStatus.ERROR:
conv_info.status = ConversationStatus.COMPLETED
await self._send_status_update(conversation_id, ConversationStatus.COMPLETED)
# Trigger resource-agent analysis only for natural completion (not user stop)
if not conv_info.stop_requested:
asked_ids = None
try:
asked_ids = list(getattr(manager, "asked_question_ids", None) or [])
except Exception:
asked_ids = None
await self._run_resource_agent(conversation_id, asked_question_ids=asked_ids)
# End of lifecycle (MVP): remove completed conversation state
self.active_conversations.pop(conversation_id, None)
self.transcripts.pop(conversation_id, None)
# Cleanup transcript if conversation is no longer active
if conversation_id not in self.active_conversations:
self.transcripts.pop(conversation_id, None)
async def _run_resource_agent(self, conversation_id: str, *, asked_question_ids: Optional[List[str]] = None):
"""Run post-conversation resource agent analysis and broadcast results."""
transcript = self.transcripts.get(conversation_id, [])
if not transcript:
return
async def on_phase(phase: str, status: str):
await self.websocket_manager.send_to_conversation(conversation_id, {
"type": "resource_agent_phase_status",
"conversation_id": conversation_id,
"phase": phase,
"status": status,
"timestamp": datetime.now().isoformat(),
})
async def on_partial(data: Dict[str, Any]):
await self.websocket_manager.send_to_conversation(conversation_id, {
"type": "resource_agent_partial_result",
"conversation_id": conversation_id,
"data": data,
"timestamp": datetime.now().isoformat(),
})
async def on_retry(info: Dict[str, Any]):
delay = info.get("delay")
await self.websocket_manager.send_to_conversation(conversation_id, {
"type": "resource_agent_status",
"conversation_id": conversation_id,
"status": "retrying",
"retry_in": delay,
"error": info.get("error"),
"provider": info.get("provider"),
"timestamp": datetime.now().isoformat(),
})
await self.websocket_manager.send_to_conversation(conversation_id, {
"type": "resource_agent_status",
"conversation_id": conversation_id,
"status": "running",
"timestamp": datetime.now().isoformat(),
})
conv_info = self.active_conversations.get(conversation_id) or self.active_human_chats.get(conversation_id)
if not conv_info:
return
try:
seal_timestamp = datetime.now().isoformat()
parsed = await run_resource_agent_analysis(
transcript=transcript,
llm_backend=conv_info.llm_backend,
host=conv_info.host,
model=conv_info.model,
settings=self.settings,
analysis_system_prompt=getattr(conv_info, "analysis_system_prompt", None),
bottom_up_instructions=getattr(conv_info, "bottom_up_instructions", None),
bottom_up_attributes=getattr(conv_info, "bottom_up_attributes", None),
rubric_instructions=getattr(conv_info, "rubric_instructions", None),
rubric_attributes=getattr(conv_info, "rubric_attributes", None),
top_down_instructions=getattr(conv_info, "top_down_instructions", None),
top_down_attributes=getattr(conv_info, "top_down_attributes", None),
top_down_template_id=getattr(conv_info, "top_down_template_id", None),
top_down_template_version_id=getattr(conv_info, "top_down_template_version_id", None),
top_down_template_categories=getattr(conv_info, "top_down_template_categories", None),
on_phase=on_phase,
on_partial=on_partial,
on_retry=on_retry,
)
persisted = False
run_id = None
try:
store = get_run_store()
mode = "human_to_ai" if conversation_id in self.active_human_chats else "ai_to_ai"
persona_snapshots: Dict[str, Dict[str, Any]] = {}
try:
surveyor_persona = self.persona_system.get_persona(conv_info.surveyor_persona_id) or {}
patient_persona = self.persona_system.get_persona(conv_info.patient_persona_id) or {}
persona_snapshots = {
"surveyor": {
"persona_id": conv_info.surveyor_persona_id,
"persona_version_id": surveyor_persona.get("version_id"),
"snapshot": surveyor_persona,
},
"patient": {
"persona_id": conv_info.patient_persona_id,
"persona_version_id": patient_persona.get("version_id"),
"snapshot": patient_persona,
},
}
except Exception:
persona_snapshots = {}
config_snapshot: Dict[str, Any] = {
"llm": {
"backend": conv_info.llm_backend,
"host": conv_info.host,
"model": conv_info.model,
"timeout": self.settings.llm.timeout,
"max_retries": self.settings.llm.max_retries,
"retry_delay": self.settings.llm.retry_delay,
},
"personas": {
"surveyor_persona_id": conv_info.surveyor_persona_id,
"patient_persona_id": conv_info.patient_persona_id,
"surveyor_system_prompt": getattr(conv_info, "surveyor_system_prompt", None),
"patient_system_prompt": getattr(conv_info, "patient_system_prompt", None),
"patient_attributes": getattr(conv_info, "patient_attributes", None),
"surveyor_attributes": getattr(conv_info, "surveyor_attributes", None),
"surveyor_question_bank": getattr(conv_info, "surveyor_question_bank", None),
"asked_question_ids": asked_question_ids,
},
"analysis": {
"analysis_system_prompt": getattr(conv_info, "analysis_system_prompt", None),
"bottom_up_instructions": getattr(conv_info, "bottom_up_instructions", None),
"bottom_up_attributes": getattr(conv_info, "bottom_up_attributes", None),
"rubric_instructions": getattr(conv_info, "rubric_instructions", None),
"rubric_attributes": getattr(conv_info, "rubric_attributes", None),
"top_down_instructions": getattr(conv_info, "top_down_instructions", None),
"top_down_attributes": getattr(conv_info, "top_down_attributes", None),
"top_down_codebook_template_id": getattr(conv_info, "top_down_template_id", None),
"top_down_codebook_template_version_id": getattr(conv_info, "top_down_template_version_id", None),
"top_down_codebook_template_snapshot": getattr(conv_info, "top_down_template_categories", None),
},
}
run_id = conversation_id
record = RunRecord(
run_id=run_id,
mode=mode,
status="completed",
created_at=getattr(conv_info, "created_at").isoformat(),
ended_at=seal_timestamp,
sealed_at=seal_timestamp,
title=None,
input_summary=None,
config=config_snapshot,
messages=transcript,
analyses={"resource_agent_v2": parsed},
persona_snapshots=persona_snapshots,
)
await store.save_sealed_run(record)
persisted = True
except Exception as e:
logger.error(f"Failed to persist sealed run {conversation_id}: {e}")
await self.websocket_manager.send_to_conversation(conversation_id, {
"type": "resource_agent_result",
"conversation_id": conversation_id,
"run_id": run_id if persisted else None,
"persisted": persisted,
"data": parsed,
"timestamp": datetime.now().isoformat(),
})
await self.websocket_manager.send_to_conversation(conversation_id, {
"type": "resource_agent_status",
"conversation_id": conversation_id,
"status": "complete",
"timestamp": datetime.now().isoformat(),
})
except Exception as e:
logger.error(f"Resource agent failed for {conversation_id}: {e}")
await self.websocket_manager.send_to_conversation(conversation_id, {
"type": "resource_agent_status",
"conversation_id": conversation_id,
"status": "error",
"error": str(e),
"timestamp": datetime.now().isoformat(),
})
def _build_llm_parameters(self) -> Dict[str, Any]:
"""Prepare keyword arguments for LLM client creation."""
params: Dict[str, Any] = {
"timeout": self.settings.llm.timeout,
"max_retries": self.settings.llm.max_retries,
"retry_delay": self.settings.llm.retry_delay,
}
if self.settings.llm.api_key:
params["api_key"] = self.settings.llm.api_key
if self.settings.llm.site_url:
params["site_url"] = self.settings.llm.site_url
if self.settings.llm.app_name:
params["app_name"] = self.settings.llm.app_name
return params
async def _send_status_update(self, conversation_id: str, status: ConversationStatus):
"""Send conversation status update to clients.
Args:
conversation_id: ID of the conversation
status: New conversation status
"""
message = {
"type": "conversation_status",
"conversation_id": conversation_id,
"status": status.value,
"timestamp": datetime.now().isoformat()
}
await self.websocket_manager.send_to_conversation(conversation_id, message)
async def _send_error(self, conversation_id: str, error_message: str):
"""Send error message to clients.
Args:
conversation_id: ID of the conversation
error_message: Error description
"""
message = {
"type": "conversation_error",
"conversation_id": conversation_id,
"error": error_message,
"timestamp": datetime.now().isoformat()
}
await self.websocket_manager.send_to_conversation(conversation_id, message)
async def cleanup(self):
"""Clean up all active conversations."""
for conversation_id in list(self.active_conversations.keys()):
await self.stop_conversation(conversation_id)
for conversation_id in list(self.active_human_chats.keys()):
await self.stop_conversation(conversation_id)
# Global service instance (initialized in main.py)
conversation_service: Optional[ConversationService] = None
def get_conversation_service() -> ConversationService:
"""Get the global conversation service instance.
Returns:
ConversationService instance
Raises:
RuntimeError: If service not initialized
"""
if conversation_service is None:
raise RuntimeError("ConversationService not initialized")
return conversation_service
def initialize_conversation_service(websocket_manager: ConnectionManager, settings: Optional[AppSettings] = None):
"""Initialize the global conversation service.
Args:
websocket_manager: WebSocket connection manager
settings: Shared application settings (optional)
"""
global conversation_service
conversation_service = ConversationService(websocket_manager, settings=settings)
logger.info("ConversationService initialized")
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