Phase 5B commit 6: delete CallerBackground dataclass, relationship_context, update docs
Final cleanup pass for the caller generation redesign: - Delete CallerBackground dataclass + all isinstance() checks - Delete orphaned _build_relationship_context and session.relationship_context - Delete Session.get_caller_model (caller_dialog category routes to haiku-4.5) - Delete dead _get_show_energy (CallRecord no longer tracks energy_level) - Delete unused tone_streak field - Drop topic_category/emotional_state/energy_level from CallRecord - Simplify caller property, _find_thematic_match, enrich, promotion paths to slim dict - Drop broken emotional_state=/energy_level= kwargs from generate_speech calls - _load_checkpoint drops pre-slim schema backgrounds; startup re-pregens if empty - Rewrite CLAUDE.md "Caller Generation System" section for the new two-stage architecture 35 tests passing. backend/main.py: 4981 -> 4810 lines.
This commit is contained in:
@@ -58,13 +58,14 @@ Required in `.env`:
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- `generate_with_tools()` in llm.py supports OpenRouter function calling for the intern feature
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## Caller Generation System
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- **CallerBackground dataclass**: Structured output from LLM background generation (JSON mode). Fields: name, age, gender, job, location, reason_for_calling, pool_name, communication_style, energy_level, emotional_state, signature_detail, situation_summary, natural_description, seeds, verbal_fluency, calling_from.
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- **Voice-personality matching**: `_match_voices_to_styles()` runs after background generation. 68 voice profiles in `VOICE_PROFILES` (tts.py), 18 style-to-voice mappings in `STYLE_VOICE_PREFERENCES` (main.py). Soft matching — scores voices against style preferences.
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- **Adaptive call shapes**: `SHAPE_STYLE_AFFINITIES` maps communication styles to shape weight multipliers. Consecutive shape repeats are dampened.
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- **Inter-caller awareness**: Thematic matching in `get_show_history()` scores previous callers by keyword/category overlap. Adaptive reaction frequency (60%/35%/15%). Show energy tracking via `_get_show_energy()`.
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- **Caller memory**: Returning callers store structured backgrounds, key moments, arc status, and relationships with other regulars. `RegularCallerService` has `add_relationship()` and expanded `update_after_call()`.
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- **Show pacing**: `_sort_caller_queue()` sorts presentation order by energy alternation, topic variety, shape variety.
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- **Call quality signals**: `_assess_call_quality()` captures exchange count, response length, host engagement, shape target hit, natural ending.
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- **Two-stage pipeline**: (1) batch identity pregen via Sonnet 4.6 at session start, (2) live dialog via Haiku 4.5 per turn. Cost ~$1/show.
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- **Slim caller dict**: Populated once at `Session._pregenerate_backgrounds()` via `caller_gen.generate_batch()`. Keys: `name`, `age`, `voice`, `location`, `identity`, `situation`, `reason_calling`, `opening_line`, `secret_want`, `specific_details`, `emotional_register`. Stored in `session.caller_backgrounds[caller_key]`.
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- **Dialog model**: Always Haiku 4.5 via the `caller_dialog` category in `config.category_models`. No per-caller model routing — deleted in Phase 5B.
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- **Prompt builder**: `get_caller_prompt(caller)` in main.py builds the slim system prompt from the dict; see `tests/test_caller_prompt.py` for the contract.
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- **Regulars**: `backend/services/regulars_v2.py` loads lore from Obsidian markdown files for named recurring callers (e.g. Silas). The batch prompt optionally includes 2-3 active regulars per session.
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- **Inter-caller awareness**: `get_show_history()` scores previous callers by keyword overlap with the current caller's `situation`/`reason_calling`. Reaction frequency scales with match strength (60%/35%/15%).
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- **Caller memory**: Returning callers auto-promote from first-timers at ~5% probability after 8+ exchanges. `RegularCallerService` tracks summaries, relationships, arc state.
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- **Call quality signals**: `_assess_call_quality()` captures exchange count, response length, host engagement, caller depth, natural ending.
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## Devon (Intern Character)
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- **Service**: `backend/services/intern.py` — persistent show character, not a caller
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@@ -78,8 +79,8 @@ Required in `.env`:
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## Frontend Control Panel
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- **Keyboard shortcuts**: 1-0 (callers), H (hangup), W (wrap up), M (music toggle), D (ask Devon), Escape (close modals)
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- **Wrap It Up**: Amber button that signals callers to wind down gracefully. Reduces response budget, injects wrap-up signals, forces goodbye after 2 exchanges.
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- **Caller info panel**: Shows call shape, energy level, emotional state, signature detail, situation summary during active calls
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- **Caller buttons**: Energy dots (colored by level) and shape badges on each button
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- **Caller info panel**: Shows identity, situation, signature detail, secret want during active calls
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- **Caller buttons**: Populated from the slim caller background dicts
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- **Pinned SFX**: Cheer/Applause/Boo always visible, rest collapsible
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- **Visual polish**: Thinking pulse, call glow, compact media row, smoother transitions
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+61
-232
@@ -35,31 +35,6 @@ from .services.intern import intern_service
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from .services.avatars import avatar_service
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# --- Structured Caller Background (must be defined before functions that use it) ---
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@dataclass
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class CallerBackground:
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name: str
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age: int
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gender: str
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job: str
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location: str | None
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reason_for_calling: str
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pool_name: str
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communication_style: str
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energy_level: str # low / medium / high / very_high
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emotional_state: str # nervous, excited, angry, vulnerable, calm, etc.
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signature_detail: str # The memorable thing about them
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situation_summary: str # 1-sentence summary for other callers to reference
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natural_description: str # 3-5 sentence prose for the prompt
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seeds: list[str] = field(default_factory=list)
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verbal_fluency: str = "medium"
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calling_from: str = ""
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hidden_layers: list[str] = field(default_factory=list) # 3 details they haven't mentioned yet
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burning_opinion: str = "" # Something they're dying to say — will bring up even without being asked
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stakes: str = "" # What's at risk for them — why this matters, what happens if nothing changes
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theme_connected: bool = False # True if this caller's story was generated around the show theme
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app = FastAPI(title="AI Radio Show")
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app.add_middleware(
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@@ -276,42 +251,6 @@ async def _regenerate_backgrounds_for_keys(keys: list[str]):
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print(f"[Background] Regen failed: {e}")
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def _build_relationship_context():
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"""Find regulars with existing relationships who are both in the current session.
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Inject mutual awareness into both callers' prompts."""
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regulars = regular_caller_service.get_regulars()
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if not regulars:
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return
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# Map regular names to their caller keys in this session
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name_to_key = {}
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key_to_regular = {}
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for key, base in CALLER_BASES.items():
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if base.get("returning") and base.get("regular_id"):
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for reg in regulars:
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if reg["id"] == base["regular_id"]:
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name_to_key[reg["name"]] = key
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key_to_regular[key] = reg
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break
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if len(name_to_key) < 2:
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return # Need at least 2 regulars to have relationships
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# Check for mutual relationships
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for key, regular in key_to_regular.items():
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relationships = regular.get("relationships", {})
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for other_name, rel_info in relationships.items():
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if other_name in name_to_key:
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other_key = name_to_key[other_name]
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rel_type = rel_info.get("type", "knows")
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context = rel_info.get("context", "")
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# Inject awareness into this caller's prompt
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line = f"\nSOMEONE YOU KNOW IS ON THE SHOW TONIGHT: {other_name} is also calling in. You know them — {rel_type}. {context} You might hear them on air. If Luke mentions them or you hear them, react naturally. Don't force it — if it comes up, it comes up."
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existing = session.relationship_context.get(key, "")
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session.relationship_context[key] = existing + line
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print(f"[Relationships] {regular['name']} knows {other_name} ({rel_type})")
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# Known topics for smarter search queries — maps keywords in backgrounds to search terms
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_TOPIC_SEARCH_MAP = [
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# TV shows
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@@ -572,12 +511,9 @@ class CallRecord:
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started_at: float = 0.0
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ended_at: float = 0.0
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quality_signals: dict = field(default_factory=dict) # Per-call quality heuristics
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# Inter-caller awareness fields (populated from CallerBackground)
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topic_category: str = "" # Pool name: PROBLEMS, STORIES, etc.
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# Inter-caller awareness fields (populated from slim caller background dicts)
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situation_summary: str = "" # 1-sentence summary for other callers
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emotional_state: str = "" # How the caller was feeling
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energy_level: str = "" # low/medium/high/very_high
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communication_style: str = "" # Style key
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communication_style: str = "" # Emotional register of the caller
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key_details: list[str] = field(default_factory=list) # Specific memorable details
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@@ -590,10 +526,7 @@ def _serialize_call_record(record: CallRecord) -> dict:
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"started_at": record.started_at,
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"ended_at": record.ended_at,
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"quality_signals": record.quality_signals,
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"topic_category": record.topic_category,
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"situation_summary": record.situation_summary,
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"emotional_state": record.emotional_state,
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"energy_level": record.energy_level,
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"communication_style": record.communication_style,
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"key_details": record.key_details,
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}
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@@ -608,10 +541,7 @@ def _deserialize_call_record(data: dict) -> CallRecord:
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started_at=data.get("started_at", 0.0),
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ended_at=data.get("ended_at", 0.0),
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quality_signals=data.get("quality_signals", {}),
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topic_category=data.get("topic_category", ""),
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situation_summary=data.get("situation_summary", ""),
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emotional_state=data.get("emotional_state", ""),
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energy_level=data.get("energy_level", ""),
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communication_style=data.get("communication_style", ""),
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key_details=data.get("key_details", []),
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)
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@@ -656,7 +586,7 @@ class Session:
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self.id = str(uuid.uuid4())[:8]
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self.current_caller_key: str = None
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self.conversation: list[dict] = []
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self.caller_backgrounds: dict[str, CallerBackground | str] = {} # Generated backgrounds
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self.caller_backgrounds: dict[str, dict] = {} # Slim caller identity dicts, keyed by caller_key
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self.call_history: list[CallRecord] = []
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self._call_started_at: float = 0.0
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self.active_real_caller: dict | None = None
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@@ -666,13 +596,11 @@ class Session:
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self.research_notes: dict[str, list] = {}
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self._research_task: asyncio.Task | None = None
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self.used_reasons: set[str] = set() # Track used caller reasons to prevent repeats
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self.tone_streak: list[str] = [] # Track tone per call for variety balancing
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self.call_quality_signals: list[dict] = [] # Per-call quality heuristics for tuning
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self._caller_hangup: bool = False # Set when [HANGUP] sentinel detected in current call
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self._wrapping_up: bool = False # Set via /api/wrap-up to gracefully wind down calls
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self._wrapup_exchanges: int = 0 # Track how many exchanges since wrap-up started
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self.caller_queue: list[str] = [] # Sorted presentation order of caller keys
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self.relationship_context: dict[str, str] = {} # caller_key → relationship prompt injection
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self.intern_monitoring: bool = True # Devon monitors conversations by default
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self.show_theme: str = "" # Current show theme (e.g. "St. Patrick's Day")
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@@ -691,18 +619,11 @@ class Session:
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def add_message(self, role: str, content: str):
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self.conversation.append({"role": role, "content": content, "timestamp": time.time()})
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def get_caller_model(self, caller_key: str) -> str | None:
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"""All callers run through the single haiku-4.5 dialog model."""
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return "anthropic/claude-haiku-4.5"
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def get_caller_background(self, caller_key: str) -> str:
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"""Get background for a caller in this session.
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Returns the natural_description string for prompt injection.
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"""Return the caller's situation string for UI display.
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Backgrounds are populated by _pregenerate_backgrounds at session start."""
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bg = self.caller_backgrounds.get(caller_key, "")
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if isinstance(bg, dict):
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return bg.get("situation", "")
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return bg.natural_description if isinstance(bg, CallerBackground) else bg
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bg = self.caller_backgrounds.get(caller_key) or {}
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return bg.get("situation", "") if isinstance(bg, dict) else ""
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def get_show_history(self) -> str:
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"""Get formatted show history for AI caller prompts.
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@@ -743,11 +664,6 @@ class Session:
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else:
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lines.append("You're aware of these but you're calling about YOUR thing, not theirs. Don't bring them up unless the host does.")
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# Show energy tracking
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energy_note = self._get_show_energy()
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if energy_note:
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lines.append(f"\n{energy_note}")
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return "\n".join(lines)
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def _find_thematic_match(self, current_bg) -> tuple:
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@@ -759,16 +675,16 @@ class Session:
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best_target = None
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best_score = 0
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current_pool = current_bg.pool_name if isinstance(current_bg, CallerBackground) else ""
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current_reason = current_bg.reason_for_calling if isinstance(current_bg, CallerBackground) else ""
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current_summary = current_bg.situation_summary if isinstance(current_bg, CallerBackground) else ""
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if isinstance(current_bg, dict):
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current_reason = current_bg.get("reason_calling", "")
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current_summary = current_bg.get("situation", "")
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else:
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current_reason = ""
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current_summary = ""
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current_words = set((current_reason + " " + current_summary).lower().split())
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for record in self.call_history:
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score = 0
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# Same topic pool = strong match
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if current_pool and record.topic_category == current_pool:
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score += 2
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# Keyword overlap in situation summaries
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if record.situation_summary:
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record_words = set(record.situation_summary.lower().split())
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@@ -777,11 +693,6 @@ class Session:
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score += 2
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elif len(overlap) >= 1:
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score += 1
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# Emotional contrast bonus (opposite energies are interesting)
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if record.energy_level and isinstance(current_bg, CallerBackground):
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if (record.energy_level in ("low", "medium") and current_bg.energy_level in ("high", "very_high")) or \
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(record.energy_level in ("high", "very_high") and current_bg.energy_level in ("low", "medium")):
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score += 1
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if score > best_score:
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best_score = score
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@@ -820,20 +731,6 @@ class Session:
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# Fallback to generic reactions
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return random.choice(SHOW_HISTORY_REACTIONS)
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def _get_show_energy(self) -> str:
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"""Summarize the energy arc of the show for caller awareness."""
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if len(self.call_history) < 3:
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return ""
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recent = self.call_history[-3:]
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energies = [r.energy_level for r in recent if r.energy_level]
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if not energies:
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return ""
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if all(e in ("high", "very_high") for e in energies):
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return "SHOW ENERGY: The last few calls have been high-energy — the show could use a breather."
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if all(e in ("low", "medium") for e in energies):
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return "SHOW ENERGY: The last few calls have been mellow — some energy would shake things up."
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return ""
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def get_conversation_summary(self) -> str:
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"""Get a brief summary of conversation so far for context"""
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if len(self.conversation) <= 2:
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@@ -863,29 +760,11 @@ class Session:
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if self.current_caller_key:
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base = CALLER_BASES.get(self.current_caller_key)
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if base:
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bg = self.caller_backgrounds.get(self.current_caller_key)
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emotional_state = ""
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energy_level = ""
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hidden_layers = []
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burning_opinion = ""
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stakes = ""
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if hasattr(bg, "emotional_state"):
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emotional_state = bg.emotional_state
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energy_level = bg.energy_level
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if hasattr(bg, "hidden_layers"):
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hidden_layers = bg.hidden_layers
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burning_opinion = bg.burning_opinion
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stakes = bg.stakes
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return {
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"name": base["name"],
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"voice": base["voice"],
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"vibe": self.get_caller_background(self.current_caller_key),
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"tts_provider": base.get("tts_provider"),
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"emotional_state": emotional_state,
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"energy_level": energy_level,
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"hidden_layers": hidden_layers,
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"burning_opinion": burning_opinion,
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"stakes": stakes,
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}
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return None
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@@ -955,12 +834,10 @@ class Session:
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if self._research_task and not self._research_task.done():
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self._research_task.cancel()
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self._research_task = None
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self.tone_streak = []
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self.call_quality_signals = []
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self._wrapping_up = False
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self._wrapup_exchanges = 0
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self.caller_queue = []
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self.relationship_context = {}
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self.used_reasons = set()
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self.intern_monitoring = True
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intern_service.stop_monitoring()
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@@ -1045,17 +922,15 @@ def _save_checkpoint():
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data = {
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"session_id": session.id,
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"call_history": [_serialize_call_record(r) for r in session.call_history],
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"caller_backgrounds": {k: asdict(v) if isinstance(v, CallerBackground) else v for k, v in session.caller_backgrounds.items()},
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"caller_backgrounds": session.caller_backgrounds,
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"used_reasons": list(session.used_reasons),
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"ai_respond_mode": session.ai_respond_mode,
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"auto_followup": session.auto_followup,
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"news_headlines": session.news_headlines,
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"research_notes": session.research_notes,
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"caller_bases": caller_bases_snapshot,
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"tone_streak": session.tone_streak,
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"call_quality_signals": session.call_quality_signals,
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"caller_queue": session.caller_queue,
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"relationship_context": session.relationship_context,
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"intern_monitoring": session.intern_monitoring,
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"costs": cost_tracker.get_live_summary(),
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"cost_records": {
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@@ -1083,22 +958,20 @@ def _load_checkpoint() -> bool:
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return False
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session.id = data["session_id"]
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session.call_history = [_deserialize_call_record(r) for r in data.get("call_history", [])]
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# Drop any legacy background dicts (pre-slim schema). They'll be regenerated
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# fresh on next Session.reset or when startup sees no restored backgrounds.
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raw_bgs = data.get("caller_backgrounds", {})
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session.caller_backgrounds = {}
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for k, v in raw_bgs.items():
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if isinstance(v, dict) and "natural_description" in v:
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session.caller_backgrounds[k] = CallerBackground(**v)
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else:
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session.caller_backgrounds[k] = v
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session.caller_backgrounds = {
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k: v for k, v in raw_bgs.items()
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if isinstance(v, dict) and "identity" in v and "situation" in v
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}
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session.used_reasons = set(data.get("used_reasons", []))
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session.ai_respond_mode = data.get("ai_respond_mode", "manual")
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session.auto_followup = data.get("auto_followup", False)
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session.news_headlines = data.get("news_headlines", [])
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session.research_notes = data.get("research_notes", {})
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session.tone_streak = data.get("tone_streak", [])
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session.call_quality_signals = data.get("call_quality_signals", [])
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session.caller_queue = data.get("caller_queue", [])
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session.relationship_context = data.get("relationship_context", {})
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session.intern_monitoring = data.get("intern_monitoring", True)
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for key, snapshot in data.get("caller_bases", {}).items():
|
||||
if key in CALLER_BASES:
|
||||
@@ -1351,7 +1224,7 @@ async def startup():
|
||||
asyncio.create_task(_sync_signalwire_voicemails())
|
||||
asyncio.create_task(_poll_imap_emails())
|
||||
restored = _load_checkpoint()
|
||||
if not restored:
|
||||
if not restored or not session.caller_backgrounds:
|
||||
asyncio.create_task(session._pregenerate_backgrounds())
|
||||
asyncio.create_task(avatar_service.ensure_devon())
|
||||
threading.Thread(target=_update_on_air_cdn, args=(False,), daemon=True).start()
|
||||
@@ -2090,10 +1963,8 @@ def _get_all_caller_names() -> list[str]:
|
||||
names = []
|
||||
for key in CALLER_BASES:
|
||||
bg = session.caller_backgrounds.get(key)
|
||||
if bg and hasattr(bg, "name"):
|
||||
names.append(bg.name)
|
||||
elif isinstance(bg, str):
|
||||
pass # raw string background, no structured name
|
||||
if isinstance(bg, dict) and bg.get("name"):
|
||||
names.append(bg["name"])
|
||||
elif "name" in CALLER_BASES[key]:
|
||||
names.append(CALLER_BASES[key]["name"])
|
||||
# Always include Devon (the intern)
|
||||
@@ -2311,13 +2182,11 @@ async def start_call(caller_key: str):
|
||||
if not base.get("returning"):
|
||||
callback = _maybe_generate_callback()
|
||||
if callback:
|
||||
existing_bg = session.caller_backgrounds.get(caller_key, "")
|
||||
callback_ctx = f"\n\nPREVIOUS CALLS:\n- (earlier tonight) {callback['original_summary']}\nYou're calling back with an update — {callback['callback_reason']}. Reference your earlier call naturally."
|
||||
if isinstance(existing_bg, CallerBackground):
|
||||
existing_bg.natural_description += callback_ctx
|
||||
else:
|
||||
session.caller_backgrounds[caller_key] = existing_bg + callback_ctx
|
||||
print(f"[Callback] Injected callback context for {base.get('name', caller_key)}")
|
||||
existing_bg = session.caller_backgrounds.get(caller_key)
|
||||
if isinstance(existing_bg, dict):
|
||||
callback_ctx = f"\n\nPREVIOUS CALLS:\n- (earlier tonight) {callback['original_summary']}\nYou're calling back with an update — {callback['callback_reason']}. Reference your earlier call naturally."
|
||||
existing_bg["situation"] = existing_bg.get("situation", "") + callback_ctx
|
||||
print(f"[Callback] Injected callback context for {base.get('name', caller_key)}")
|
||||
|
||||
caller = session.caller # This generates the background if needed
|
||||
|
||||
@@ -2358,13 +2227,11 @@ async def start_call(caller_key: str):
|
||||
async def _enrich_background_async(caller_key: str):
|
||||
"""Enrich caller background with news/weather without blocking the call"""
|
||||
try:
|
||||
bg = session.caller_backgrounds[caller_key]
|
||||
bg_text = bg.natural_description if isinstance(bg, CallerBackground) else bg
|
||||
enriched = await enrich_caller_background(bg_text)
|
||||
if isinstance(bg, CallerBackground):
|
||||
bg.natural_description = enriched
|
||||
else:
|
||||
session.caller_backgrounds[caller_key] = enriched
|
||||
bg = session.caller_backgrounds.get(caller_key)
|
||||
if not isinstance(bg, dict):
|
||||
return
|
||||
enriched = await enrich_caller_background(bg.get("situation", ""))
|
||||
bg["situation"] = enriched
|
||||
except Exception as e:
|
||||
print(f"[Research] Background enrichment failed: {e}")
|
||||
|
||||
@@ -2444,19 +2311,9 @@ async def _summarize_ai_call(caller_key: str, caller_name: str, conversation: li
|
||||
|
||||
# Populate from slim caller background dict
|
||||
bg = session.caller_backgrounds.get(caller_key) or {}
|
||||
if isinstance(bg, dict):
|
||||
comm_style = bg.get("emotional_register", "")
|
||||
sit_summary = bg.get("situation", "")
|
||||
key_dets = list(bg.get("specific_details") or [])
|
||||
else:
|
||||
# Legacy CallerBackground object from stale checkpoint — removed in later commit
|
||||
comm_style = getattr(bg, "communication_style", "")
|
||||
sit_summary = getattr(bg, "situation_summary", "")
|
||||
sig = getattr(bg, "signature_detail", "")
|
||||
key_dets = [sig] if sig else []
|
||||
topic_cat = ""
|
||||
emo_state = ""
|
||||
energy = ""
|
||||
comm_style = bg.get("emotional_register", "") if isinstance(bg, dict) else ""
|
||||
sit_summary = bg.get("situation", "") if isinstance(bg, dict) else ""
|
||||
key_dets = list(bg.get("specific_details") or []) if isinstance(bg, dict) else []
|
||||
|
||||
quality_signals = _assess_call_quality(
|
||||
conversation,
|
||||
@@ -2472,10 +2329,7 @@ async def _summarize_ai_call(caller_key: str, caller_name: str, conversation: li
|
||||
started_at=started_at,
|
||||
ended_at=ended_at,
|
||||
quality_signals=quality_signals,
|
||||
topic_category=topic_cat,
|
||||
situation_summary=sit_summary,
|
||||
emotional_state=emo_state,
|
||||
energy_level=energy,
|
||||
communication_style=comm_style,
|
||||
key_details=key_dets,
|
||||
))
|
||||
@@ -2491,47 +2345,28 @@ async def _summarize_ai_call(caller_key: str, caller_name: str, conversation: li
|
||||
regular_caller_service.update_after_call(base["regular_id"], summary)
|
||||
elif len(conversation) >= 8 and random.random() < 0.05:
|
||||
# 5% chance to promote first-timer with 8+ messages
|
||||
bg = session.caller_backgrounds.get(caller_key, "")
|
||||
|
||||
if isinstance(bg, CallerBackground):
|
||||
# Clean extraction from structured data
|
||||
traits = [bg.signature_detail] + bg.seeds[:3] if bg.signature_detail else bg.seeds[:4]
|
||||
promo_job = bg.job
|
||||
promo_location = bg.location or "unknown"
|
||||
promo_age = bg.age
|
||||
promo_gender = bg.gender
|
||||
else:
|
||||
# Legacy fallback — fragile string parsing
|
||||
traits = []
|
||||
for label in ["QUIRK", "STRONG OPINION", "SECRET SIDE", "FOOD OPINION"]:
|
||||
for line in bg.split("\n"):
|
||||
if label in line:
|
||||
traits.append(line.split(":", 1)[-1].strip()[:80])
|
||||
break
|
||||
first_line = bg.split(".")[0] if bg else ""
|
||||
parts = first_line.split(",", 1)
|
||||
job_loc = parts[1].strip() if len(parts) > 1 else ""
|
||||
job_parts = job_loc.rsplit(" in ", 1) if " in " in job_loc else (job_loc, "unknown")
|
||||
promo_job = job_parts[0].strip() if isinstance(job_parts, tuple) else job_parts[0]
|
||||
promo_location = "in " + job_parts[1].strip() if isinstance(job_parts, tuple) and len(job_parts) > 1 else "unknown"
|
||||
promo_age = random.randint(*base.get("age_range", (30, 50)))
|
||||
bg = session.caller_backgrounds.get(caller_key) or {}
|
||||
if isinstance(bg, dict):
|
||||
traits = list(bg.get("specific_details") or [])[:4]
|
||||
promo_job = bg.get("identity", "") or ""
|
||||
promo_location = bg.get("location") or "unknown"
|
||||
promo_age = bg.get("age") or random.randint(*base.get("age_range", (30, 50)))
|
||||
promo_gender = base.get("gender", "male")
|
||||
|
||||
structured_bg = asdict(bg) if isinstance(bg, CallerBackground) else None
|
||||
avatar_path = avatar_service.get_path(caller_name)
|
||||
regular_caller_service.add_regular(
|
||||
name=caller_name,
|
||||
gender=promo_gender,
|
||||
age=promo_age,
|
||||
job=promo_job,
|
||||
location=promo_location,
|
||||
personality_traits=traits[:4],
|
||||
first_call_summary=summary,
|
||||
voice=base.get("voice"),
|
||||
stable_seeds={},
|
||||
structured_background=structured_bg,
|
||||
avatar=avatar_path.name if avatar_path else None,
|
||||
)
|
||||
structured_bg = dict(bg)
|
||||
avatar_path = avatar_service.get_path(caller_name)
|
||||
regular_caller_service.add_regular(
|
||||
name=caller_name,
|
||||
gender=promo_gender,
|
||||
age=promo_age,
|
||||
job=promo_job,
|
||||
location=promo_location,
|
||||
personality_traits=traits,
|
||||
first_call_summary=summary,
|
||||
voice=base.get("voice"),
|
||||
stable_seeds={},
|
||||
structured_background=structured_bg,
|
||||
avatar=avatar_path.name if avatar_path else None,
|
||||
)
|
||||
except Exception as e:
|
||||
print(f"[Regulars] Promotion logic error: {e}")
|
||||
|
||||
@@ -3289,7 +3124,7 @@ async def chat(request: ChatRequest):
|
||||
max_tokens, max_sentences = _pick_response_budget(wrapping_up=is_wrapping)
|
||||
messages = _normalize_messages_for_llm(session.conversation[-_dynamic_context_window():])
|
||||
_caller_name = session.caller.get("name", "") if session.caller else ""
|
||||
_model_override = session.get_caller_model(session.current_caller_key) if session.current_caller_key else None
|
||||
_model_override = None # caller_dialog category routes to haiku-4.5
|
||||
response = await llm_service.generate(
|
||||
messages=messages,
|
||||
system_prompt=system_prompt,
|
||||
@@ -4250,7 +4085,7 @@ async def _trigger_ai_auto_respond(accumulated_text: str):
|
||||
max_tokens, max_sentences = _pick_response_budget(wrapping_up=is_wrapping)
|
||||
messages = _normalize_messages_for_llm(session.conversation[-_dynamic_context_window():])
|
||||
_caller_name = session.caller.get("name", "") if session.caller else ""
|
||||
_model_override = session.get_caller_model(session.current_caller_key) if session.current_caller_key else None
|
||||
_model_override = None # caller_dialog category routes to haiku-4.5
|
||||
response = await llm_service.generate(
|
||||
messages=messages,
|
||||
system_prompt=system_prompt,
|
||||
@@ -4308,9 +4143,7 @@ async def _trigger_ai_auto_respond(accumulated_text: str):
|
||||
broadcast_event("ai_status", {"text": f"{ai_name} is speaking..."})
|
||||
try:
|
||||
audio_bytes = await generate_speech(response, session.caller["voice"], "none",
|
||||
provider_override=session.caller.get("tts_provider"),
|
||||
emotional_state=session.caller.get("emotional_state", ""),
|
||||
energy_level=session.caller.get("energy_level", ""))
|
||||
provider_override=session.caller.get("tts_provider"))
|
||||
except Exception as e:
|
||||
print(f"[Auto-Respond] TTS failed: {e}")
|
||||
broadcast_event("ai_done")
|
||||
@@ -4370,7 +4203,7 @@ async def ai_respond():
|
||||
max_tokens, max_sentences = _pick_response_budget(wrapping_up=is_wrapping)
|
||||
messages = _normalize_messages_for_llm(session.conversation[-_dynamic_context_window():])
|
||||
_caller_name = session.caller.get("name", "") if session.caller else ""
|
||||
_model_override = session.get_caller_model(session.current_caller_key) if session.current_caller_key else None
|
||||
_model_override = None # caller_dialog category routes to haiku-4.5
|
||||
response = await llm_service.generate(
|
||||
messages=messages,
|
||||
system_prompt=system_prompt,
|
||||
@@ -4420,15 +4253,11 @@ async def ai_respond():
|
||||
ai_name = caller["name"]
|
||||
ai_voice = caller["voice"]
|
||||
ai_tts_provider = caller.get("tts_provider")
|
||||
ai_emotional_state = caller.get("emotional_state", "")
|
||||
ai_energy_level = caller.get("energy_level", "")
|
||||
|
||||
# TTS — outside the lock so other requests aren't blocked
|
||||
try:
|
||||
audio_bytes = await generate_speech(response, ai_voice, "none",
|
||||
provider_override=ai_tts_provider,
|
||||
emotional_state=ai_emotional_state,
|
||||
energy_level=ai_energy_level)
|
||||
provider_override=ai_tts_provider)
|
||||
except Exception as e:
|
||||
print(f"[AI-Respond] TTS failed: {e}")
|
||||
broadcast_event("ai_done")
|
||||
|
||||
Reference in New Issue
Block a user